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import torch
from torch.types import Number
from tile_kernels.quant.types import QuantTensor
def swiglu_forward(
x: torch.Tensor,
pos_to_token_topk: Optional[torch.Tensor] = None,
topk_weights: Optional[torch.Tensor] = None,
swiglu_clamp_value: Optional[float] = None,
clamped_count: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
PyTorch implementation of the SwiGLU forward pass.
Computes ``silu(x_left) * x_right`` where ``x_left`` and ``x_right`` are
the two halves of the last dimension of ``x``, then optionally scales each
row by its corresponding top-k routing weight.
Args:
x: Input 2D contiguous tensor of shape ``(num_expanded_tokens, hidden * 2)``
in BF16 or FP32.
pos_to_token_topk: Optional 1-D int32 tensor of shape
``(num_expanded_tokens,)`` mapping each expanded position to a
flat ``(token, topk)`` index into ``topk_weights``. Entries that
are ``< 0`` indicate padding and the corresponding output rows are
left as zero.
topk_weights: Optional 2-D float32 tensor of shape
``(num_tokens, num_topk)`` containing routing weights. Required
when ``pos_to_token_topk`` is provided.
swiglu_clamp_value: Optional clamp threshold applied before the
activation. ``x_left`` is clamped to
``(-inf, swiglu_clamp_value]`` and ``x_right`` is clamped to
``[-swiglu_clamp_value, swiglu_clamp_value]``.
clamped_count: Optional 1-D int64 tensor of length 3. When provided
alongside ``swiglu_clamp_value``, the counts of clamped elements
are added in-place: index 0 counts ``x_left > swiglu_clamp_value``,
index 1 counts ``x_right > swiglu_clamp_value``, index 2 counts
``x_right < -swiglu_clamp_value``.
Returns:
FP32 output tensor of shape ``(num_expanded_tokens, hidden)``.
"""
assert x.dim() == 2 and x.is_contiguous()
assert x.dtype in (torch.bfloat16, torch.float32)
num_expanded_tokens, hidden2 = x.shape
assert hidden2 % 2 == 0
hidden = hidden2 // 2
if pos_to_token_topk is not None:
assert pos_to_token_topk.dim() == 1
assert pos_to_token_topk.shape[0] == num_expanded_tokens
assert topk_weights is not None
assert topk_weights.dim() == 2
# Split into left (gate) and right (value) halves
x_fp32 = x.float()
x_left = x_fp32[:, :hidden]
x_right = x_fp32[:, hidden:]
# Optional clamp before activation
if swiglu_clamp_value is not None:
if clamped_count is not None:
clamped_count[0] += (x_left > swiglu_clamp_value).sum()
clamped_count[1] += (x_right > swiglu_clamp_value).sum()
clamped_count[2] += (x_right < -swiglu_clamp_value).sum()
x_left = torch.clamp(x_left, max=swiglu_clamp_value)
x_right = torch.clamp(x_right, min=-swiglu_clamp_value, max=swiglu_clamp_value)
# SwiGLU: silu(x_left) * x_right where silu(x) = x * sigmoid(x)
out = x_left / (1.0 + torch.exp(-x_left)) * x_right
# Optional per-row weight scaling
if pos_to_token_topk is not None:
num_tokens, num_topk = topk_weights.shape
pos_mask = pos_to_token_topk >= 0
token_indices = torch.div(pos_to_token_topk[pos_mask], num_topk, rounding_mode='floor')
topk_indices = pos_to_token_topk[pos_mask] % num_topk
w_expanded = torch.zeros(num_expanded_tokens, device=x.device, dtype=torch.float32)
w_expanded[pos_mask] = topk_weights[token_indices, topk_indices].float()
out = out * w_expanded.unsqueeze(1)
return out
# Explicit extract fma pattern for torch.compile to capture.
# This is for precision issue.
@torch.compile
def elementwise_fma(a: torch.Tensor, b: Number | torch.Tensor, c: Number | torch.Tensor) -> torch.Tensor:
return a * b + c
def swiglu_backward(
x: QuantTensor,
grad_out: torch.Tensor,
weight: torch.Tensor,
pos_to_token_topk: torch.Tensor,
token_topk_to_pos: torch.Tensor,
num_per_channels: int,
swiglu_clamp_value: Optional[float] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
PyTorch implementation of the SwiGLU backward pass.
Args:
x: Quantized input as a QuantTensor ``(data, sf)`` where data has shape
``(num_expand_tokens, hidden * 2)`` in FP8 format and sf has shape
``(num_expand_tokens, hidden * 2 // num_per_channels)``.
grad_out: Gradient of output of shape (num_expand_tokens, hidden)
weight: Weight tensor of shape (num_tokens, num_topk)
pos_to_token_topk: Mapping from expanded token position to token-topk index
token_topk_to_pos: Mapping from token-topk index to expanded token position
num_per_channels: Number of channels per sf factor (32 or 128)
swiglu_clamp_value: Clamp value for SwiGLU activation
Returns:
out: FP32 output tensor of shape (num_expand_tokens, hidden)
x_grad: FP32 gradient of x, shape (num_expand_tokens, hidden * 2)
weight_grad: Gradient of weight
"""
x_data, x_sf = x
# Only support num_per_channels in (32, 128)
assert num_per_channels in (32, 128)
assert (x_data.dim() == 2 or x_data.dim() == 3) and x_data.is_contiguous()
assert x_sf.dim() == 2 and x_sf.is_contiguous()
assert weight.dim() == 2 and weight.is_contiguous()
assert pos_to_token_topk.dim() == 1
assert token_topk_to_pos.dim() == 2 and token_topk_to_pos.is_contiguous()
# Assert `hidden % num_per_channels == 0`
assert x_data.size(-1) % (2 * num_per_channels) == 0
hidden = x_data.size(-1) // 2
x_data = x_data.view(-1, hidden * 2)
grad_out = grad_out.view(-1, hidden)
num_expand_tokens = x_data.size(0)
num_tokens, num_topk = token_topk_to_pos.shape
assert x_sf.shape == (num_expand_tokens, 2 * hidden // num_per_channels)
assert grad_out.shape == (num_expand_tokens, hidden)
assert weight.shape == (num_tokens, num_topk)
assert pos_to_token_topk.shape == (num_expand_tokens,)
assert token_topk_to_pos.shape == (num_tokens, num_topk)
# Dequantize x from FP8 to FP32
# Expand sf to match x shape
x_sf_expanded = x_sf.repeat_interleave(num_per_channels, dim=1)
x_fp32 = x_data.float() * x_sf_expanded
# Split x into x and y parts
x_part = x_fp32[:, :hidden]
y_part = x_fp32[:, hidden:]
# Apply SwiGLU clamp if needed
use_clamp = swiglu_clamp_value is not None
clamp_value = swiglu_clamp_value
x_clamped = None
y_clamped = None
# Apply clamp
if use_clamp:
# For x: clamp when x > clamp_value
x_clamped = x_part > clamp_value
x_part[x_clamped] = clamp_value
# For y: clamp when y > clamp_value or y < -clamp_value
y_clamped_upper = y_part > clamp_value
y_clamped_lower = y_part < -clamp_value
y_clamped = y_clamped_upper | y_clamped_lower
y_part[y_clamped_upper] = clamp_value
y_part[y_clamped_lower] = -clamp_value
# Compute SwiGLU activation: x * sigmoid(x) * y
tmp_x = 1.0 + torch.exp(-x_part)
sigmoid_x = torch.ones_like(x_part) / tmp_x
# Compute output with weight scaling
# Get weight for each expanded token
pos_mask = pos_to_token_topk >= 0
token_indices = torch.div(pos_to_token_topk[pos_mask], num_topk, rounding_mode='floor')
topk_indices = pos_to_token_topk[pos_mask] % num_topk
# Initialize weight tensor for expanded tokens
w_expanded = torch.zeros(num_expand_tokens, device=x_data.device, dtype=torch.float32)
w_expanded[pos_mask] = weight[token_indices, topk_indices]
# Convert grad_out to FP32
grad_out_fp32 = grad_out.float()
# grad_out_ws = grad_out * w * s
grad_out_ws = grad_out_fp32 * w_expanded.unsqueeze(1) * sigmoid_x
# x_grad = grad_out_ws * y * (1 + x * (1 - s)) if not clamped
x_grad = grad_out_ws * y_part * elementwise_fma(x_part, 1.0 - sigmoid_x, 1.0)
# y_grad = grad_out_ws * x if not clamped
y_grad = grad_out_ws * x_part
# Apply clamp gradients
if use_clamp:
x_grad[x_clamped] = 0.0
y_grad[y_clamped] = 0.0
# Output
act_out = x_part / tmp_x * y_part
out = act_out * w_expanded.unsqueeze(1)
# Combine x_grad and y_grad
x_grad_full = torch.cat([x_grad, y_grad], dim=1)
# Compute weight gradient: sum(grad_out * act_out) for each token-topk
weight_grad = torch.zeros_like(weight)
# Compute dot product for each expanded token
dot_products = (grad_out_fp32 * act_out).sum(dim=1)
# Accumulate to weight_grad based on pos_to_token_topk mapping
weight_grad[token_indices, topk_indices] = dot_products[pos_mask]
return out, x_grad_full, weight_grad
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