# 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.
from functools import partial
import torch
import torch.nn as nn
from modelzoo.common.pytorch.layers.utils import ModuleWrapperClass
[docs]class BatchChannelNorm2D(nn.Module):
"""
Implements Batch Channel Normalization
proposed in `Micro-Batch Training with Batch-Channel
Normalization and Weight Standardization`
<https://arxiv.org/abs/1903.10520>
Args:
num_groups (int): number of groups to separate the channels into.
num_channels (int): number of channels. `C` from an expected input of size (N, C, H, W).
eps (float): a value added to the denominator for numerical stability. Default: 1e-5.
momentum (float): The `Update rate` value used for the
`running_mean` and `running_var` computation. Default: 0.1.
device (torch.device): Device to place the learnable parameters.
dtype (torch.dtype): Data type of learnable parameters.
Shape:
input: `(N, C, H, W)`
output: `(N, C, H, W)` (same shape as input)
"""
[docs] def __init__(
self,
num_groups,
num_channels,
eps=1.0e-5,
momentum=0.1,
device=None,
dtype=None,
):
factory_kwargs = {'device': device, 'dtype': dtype}
super(BatchChannelNorm2D, self).__init__()
if num_channels % num_groups != 0:
raise ValueError('num_channels must be divisible by num_groups')
self.num_channels = num_channels
self.num_groups = num_groups
self.eps = eps
self.momentum = momentum
self.weight = nn.Parameter(
torch.empty(1, num_groups, 1, **factory_kwargs)
)
self.bias = nn.Parameter(
torch.empty(1, num_groups, 1, **factory_kwargs)
)
self.batchnorm = nn.BatchNorm2d(
num_channels,
eps=self.eps,
momentum=self.momentum,
affine=True,
**factory_kwargs,
)
cn = partial(
nn.functional.batch_norm,
running_mean=None,
running_var=None,
weight=None,
bias=None,
training=self.training,
momentum=0,
eps=self.eps,
)
self.channelnorm = ModuleWrapperClass(
fcn=cn,
name='ChannelNorm',
kwargs={
"momentum": 0,
"eps": self.eps,
"affine": False,
"track_running_stats": False,
},
)
self.reset_parameters()
def forward(self, input):
out = self.batchnorm(input)
out = out.reshape(1, input.shape[0] * self.num_groups, -1)
out = self.channelnorm(out)
out = out.reshape(input.shape[0], self.num_groups, -1)
out = self.weight * out + self.bias
out = out.reshape(input.shape)
return out
def reset_parameters(self) -> None:
nn.init.ones_(self.batchnorm.weight.data)
nn.init.zeros_(self.batchnorm.bias.data)
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)