Source code for torch.nn.utils.fusion
from __future__ import annotations
import copy
from typing import Optional, Tuple, 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: Optional[torch.Tensor],
bn_rm: torch.Tensor,
bn_rv: torch.Tensor,
bn_eps: float,
bn_w: Optional[torch.Tensor],
bn_b: Optional[torch.Tensor],
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: Optional[torch.Tensor],
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.
transpose (bool, optional): If True, transpose the conv weight. Defaults to False.
Returns:
Tuple[torch.nn.Parameter, torch.nn.Parameter]: Fused linear weight and bias.
"""
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)
fused_b = (linear_b - bn_rm) * bn_scale + bn_b
return torch.nn.Parameter(fused_w, linear_w.requires_grad), torch.nn.Parameter(fused_b, linear_b.requires_grad)
