Spaces:
Running
Running
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") | |
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 | |
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) | |
) | |
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 | |
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) | |