torch.nn.utils.fusion — PyTorch 2.7 documentation (original) (raw)

from future import annotations

import copy from typing import TypeVar

import torch

all = [ "fuse_conv_bn_eval", "fuse_conv_bn_weights", "fuse_linear_bn_eval", "fuse_linear_bn_weights", ]

ConvT = TypeVar("ConvT", bound="torch.nn.modules.conv._ConvNd") LinearT = TypeVar("LinearT", bound="torch.nn.Linear")

[docs]def fuse_conv_bn_eval( conv: ConvT, bn: torch.nn.modules.batchnorm._BatchNorm, transpose: bool = False, ) -> ConvT: r"""Fuse a convolutional module and a BatchNorm module into a single, new convolutional module.

Args:
    conv (torch.nn.modules.conv._ConvNd): A convolutional module.
    bn (torch.nn.modules.batchnorm._BatchNorm): A BatchNorm module.
    transpose (bool, optional): If True, transpose the convolutional weight. Defaults to False.

Returns:
    torch.nn.modules.conv._ConvNd: The fused convolutional module.

.. note::
    Both ``conv`` and ``bn`` must be in eval mode, and ``bn`` must have its running buffers computed.
"""
assert not (conv.training or bn.training), "Fusion only for eval!"
fused_conv = copy.deepcopy(conv)

assert bn.running_mean is not None and bn.running_var is not None
fused_conv.weight, fused_conv.bias = fuse_conv_bn_weights(
    fused_conv.weight,
    fused_conv.bias,
    bn.running_mean,
    bn.running_var,
    bn.eps,
    bn.weight,
    bn.bias,
    transpose,
)

return fused_conv

[docs]def fuse_conv_bn_weights( conv_w: torch.Tensor, conv_b: torch.Tensor | None, bn_rm: torch.Tensor, bn_rv: torch.Tensor, bn_eps: float, bn_w: torch.Tensor | None, bn_b: torch.Tensor | None, transpose: bool = False, ) -> tuple[torch.nn.Parameter, torch.nn.Parameter]: r"""Fuse convolutional module parameters and BatchNorm module parameters into new convolutional module parameters.

Args:
    conv_w (torch.Tensor): Convolutional weight.
    conv_b (Optional[torch.Tensor]): Convolutional bias.
    bn_rm (torch.Tensor): BatchNorm running mean.
    bn_rv (torch.Tensor): BatchNorm running variance.
    bn_eps (float): BatchNorm epsilon.
    bn_w (Optional[torch.Tensor]): BatchNorm weight.
    bn_b (Optional[torch.Tensor]): BatchNorm bias.
    transpose (bool, optional): If True, transpose the conv weight. Defaults to False.

Returns:
    Tuple[torch.nn.Parameter, torch.nn.Parameter]: Fused convolutional weight and bias.
"""
conv_weight_dtype = conv_w.dtype
conv_bias_dtype = conv_b.dtype if conv_b is not None else conv_weight_dtype
if conv_b is None:
    conv_b = torch.zeros_like(bn_rm)
if bn_w is None:
    bn_w = torch.ones_like(bn_rm)
if bn_b is None:
    bn_b = torch.zeros_like(bn_rm)
bn_var_rsqrt = torch.rsqrt(bn_rv + bn_eps)

if transpose:
    shape = [1, -1] + [1] * (len(conv_w.shape) - 2)
else:
    shape = [-1, 1] + [1] * (len(conv_w.shape) - 2)

fused_conv_w = (conv_w * (bn_w * bn_var_rsqrt).reshape(shape)).to(
    dtype=conv_weight_dtype
)
fused_conv_b = ((conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b).to(
    dtype=conv_bias_dtype
)

return (
    torch.nn.Parameter(fused_conv_w, conv_w.requires_grad),
    torch.nn.Parameter(fused_conv_b, conv_b.requires_grad),
)

[docs]def fuse_linear_bn_eval( linear: LinearT, bn: torch.nn.modules.batchnorm._BatchNorm, ) -> LinearT: r"""Fuse a linear module and a BatchNorm module into a single, new linear module.

Args:
    linear (torch.nn.Linear): A Linear module.
    bn (torch.nn.modules.batchnorm._BatchNorm): A BatchNorm module.

Returns:
    torch.nn.Linear: The fused linear module.

.. note::
    Both ``linear`` and ``bn`` must be in eval mode, and ``bn`` must have its running buffers computed.
"""
assert not (linear.training or bn.training), "Fusion only for eval!"
fused_linear = copy.deepcopy(linear)

"""
Linear-BN needs to be fused while preserving the shapes of linear weight/bias.
To preserve the shapes of linear weight/bias, the channel dim of bn needs to be broadcastable with the last dim of linear,
because bn operates over the channel dim, (N, C_in, H, W) while linear operates over the last dim, (*, H_in).
To be broadcastable, the number of features in bn and
the number of output features from linear must satisfy the following condition:
1. they are equal, or
2. the number of features in bn is 1
Otherwise, skip the folding path
"""
assert (
    linear.out_features == bn.num_features or bn.num_features == 1
), "To fuse, linear.out_features == bn.num_features or bn.num_features == 1"

assert bn.running_mean is not None and bn.running_var is not None
fused_linear.weight, fused_linear.bias = fuse_linear_bn_weights(
    fused_linear.weight,
    fused_linear.bias,
    bn.running_mean,
    bn.running_var,
    bn.eps,
    bn.weight,
    bn.bias,
)

return fused_linear

[docs]def fuse_linear_bn_weights( linear_w: torch.Tensor, linear_b: torch.Tensor | None, bn_rm: torch.Tensor, bn_rv: torch.Tensor, bn_eps: float, bn_w: torch.Tensor, bn_b: torch.Tensor, ) -> tuple[torch.nn.Parameter, torch.nn.Parameter]: r"""Fuse linear module parameters and BatchNorm module parameters into new linear module parameters.

Args:
    linear_w (torch.Tensor): Linear weight.
    linear_b (Optional[torch.Tensor]): Linear bias.
    bn_rm (torch.Tensor): BatchNorm running mean.
    bn_rv (torch.Tensor): BatchNorm running variance.
    bn_eps (float): BatchNorm epsilon.
    bn_w (torch.Tensor): BatchNorm weight.
    bn_b (torch.Tensor): BatchNorm bias.

Returns:
    Tuple[torch.nn.Parameter, torch.nn.Parameter]: Fused linear weight and bias.
"""
linear_weight_dtype = linear_w.dtype
linear_bias_dtype = linear_b.dtype if linear_b is not None else linear_weight_dtype
if linear_b is None:
    linear_b = torch.zeros_like(bn_rm)
bn_scale = bn_w * torch.rsqrt(bn_rv + bn_eps)

fused_w = linear_w * bn_scale.unsqueeze(-1).to(dtype=linear_weight_dtype)
fused_b = ((linear_b - bn_rm) * bn_scale + bn_b).to(dtype=linear_bias_dtype)

return torch.nn.Parameter(fused_w, linear_w.requires_grad), torch.nn.Parameter(
    fused_b, linear_b.requires_grad
)