Buckets:
| """ | |
| 2026.5.1 | |
| 2026.5.2 | |
| 5.5.0 | |
| 0.24.0 | |
| __UNSLOTH_VERSIONING__ | |
| """ | |
| # Unsloth auto generated code | |
| # Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. | |
| # | |
| # This program is free software: you can redistribute it and/or modify | |
| # it under the terms of the GNU Lesser General Public License as published by | |
| # the Free Software Foundation, either version 3 of the License, or | |
| # (at your option) any later version. | |
| # | |
| # This program is distributed in the hope that it will be useful, | |
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| # GNU General Public License for more details. | |
| # | |
| # You should have received a copy of the GNU Lesser General Public License | |
| # along with this program. If not, see <https://www.gnu.org/licenses/>. | |
| import os | |
| import sys | |
| import torch | |
| import importlib.util | |
| import math | |
| if importlib.util.find_spec("unsloth_studio") is None: | |
| UNSLOTH_STUDIO_ENABLED = False | |
| else: | |
| UNSLOTH_STUDIO_ENABLED = os.environ.get("UNSLOTH_STUDIO_DISABLED", "0") == "0" | |
| pass | |
| from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable | |
| import math | |
| UNSLOTH_ENABLE_LOGGING = os.environ.get("UNSLOTH_ENABLE_LOGGING", "0") == "1" | |
| UNSLOTH_ENABLE_CCE = os.environ.get("UNSLOTH_ENABLE_CCE", "1") == "1" | |
| UNSLOTH_COMPILE_DISABLE = os.environ.get("UNSLOTH_COMPILE_DISABLE", "0") in ("1", "partial",) | |
| UNSLOTH_COMPILE_LOCATION = os.environ.get("UNSLOTH_COMPILE_LOCATION", "unsloth_compiled_cache") | |
| if UNSLOTH_COMPILE_LOCATION not in sys.path: | |
| sys.path.insert(0, UNSLOTH_COMPILE_LOCATION) | |
| import logging | |
| logger_compiler = logging.getLogger(__name__) | |
| if UNSLOTH_ENABLE_LOGGING: | |
| logger_compiler.setLevel(logging.DEBUG) | |
| global INFERENCE_RUNS | |
| INFERENCE_RUNS = 0 | |
| try: | |
| import torch._dynamo.eval_frame as torch_dynamo_eval_frame | |
| torch_dynamo_eval_frame._stance.stance | |
| torch_compiler_set_stance = torch.compiler.set_stance | |
| except: | |
| torch_dynamo_eval_frame = None | |
| torch_compiler_set_stance = None | |
| pass | |
| from unsloth_zoo import DEVICE_TYPE_TORCH, DEVICE_COUNT | |
| torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False, 'debug': False, 'dce': True, 'memory_planning': True, 'coordinate_descent_tuning': False, 'trace.graph_diagram': False, 'compile_threads': 32, 'group_fusion': True, 'disable_progress': True, 'verbose_progress': False, 'triton.multi_kernel': 0, 'triton.use_block_ptr': False, 'triton.enable_persistent_tma_matmul': True, 'triton.autotune_at_compile_time': False, 'triton.cooperative_reductions': False, 'cuda.compile_opt_level': '-O2', 'cuda.enable_cuda_lto': True, 'combo_kernels': False, 'benchmark_combo_kernel': True, 'combo_kernel_foreach_dynamic_shapes': True} | |
| from torch import Tensor | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable | |
| def forward(self, input: Tensor) -> Tensor: | |
| original_dtype = input.dtype | |
| input = input.to(self.weight.dtype) | |
| original_dtype = input.dtype | |
| input = input.to(self.weight.dtype) | |
| return self._conv_forward(input, self.weight, self.bias).to(original_dtype).to(original_dtype) | |
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