# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules.utils import _pair, _single, _triple
[docs]class StdConv1d(nn.Conv1d):
[docs] def forward(self, inputs: Tensor):
w = self.weight
std, mean = torch.std_mean(w, dim=[1, 2], keepdim=True, unbiased=False)
w = (w - mean) / (std + 1e-6)
if self.padding_mode != "zeros":
return F.conv1d(
F.pad(
inputs,
self._reversed_padding_repeated_twice,
mode=self.padding_mode,
),
w,
self.bias,
self.stride,
_single(0),
self.dilation,
self.groups,
)
return F.conv1d(
inputs,
w,
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
[docs]class StdConv2d(nn.Conv2d):
[docs] def forward(self, inputs: Tensor):
w = self.weight
std, mean = torch.std_mean(
w, dim=[1, 2, 3], keepdim=True, unbiased=False
)
w = (w - mean) / (std + 1e-6)
if self.padding_mode != "zeros":
return F.conv2d(
F.pad(
inputs,
self._reversed_padding_repeated_twice,
mode=self.padding_mode,
),
w,
self.bias,
self.stride,
_pair(0),
self.dilation,
self.groups,
)
return F.conv2d(
inputs,
w,
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
[docs]class StdConv3d(nn.Conv3d):
[docs] def forward(self, inputs: Tensor):
w = self.weight
std, mean = torch.std_mean(
w, dim=[1, 2, 3, 4], keepdim=True, unbiased=False
)
w = (w - mean) / (std + 1e-6)
if self.padding_mode != "zeros":
return F.conv3d(
F.pad(
inputs,
self._reversed_padding_repeated_twice,
mode=self.padding_mode,
),
w,
self.bias,
self.stride,
_triple(0),
self.dilation,
self.groups,
)
return F.conv3d(
inputs,
w,
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)