Source code for apex.parallel.optimized_sync_batchnorm

import torch
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn import functional as F

import syncbn
from .optimized_sync_batchnorm_kernel import SyncBatchnormFunction

[docs]class SyncBatchNorm(_BatchNorm): """ synchronized batch normalization module extented from `torch.nn.BatchNormNd` with the added stats reduction across multiple processes. :class:`apex.parallel.SyncBatchNorm` is designed to work with `DistributedDataParallel`. When running in training mode, the layer reduces stats across all processes to increase the effective batchsize for normalization layer. This is useful in applications where batch size is small on a given process that would diminish converged accuracy of the model. The model uses collective communication package from `torch.distributed`. When running in evaluation mode, the layer falls back to `torch.nn.functional.batch_norm` Args: num_features: :math:`C` from an expected input of size :math:`(N, C, L)` or :math:`L` from input of size :math:`(N, L)` eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Can be set to ``None`` for cumulative moving average (i.e. simple average). Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters. Default: ``True`` track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default: ``True`` process_group: pass in a process group within which the stats of the mini-batch is being synchronized. ``None`` for using default process group channel_last: a boolean value that when set to ``True``, this module take the last dimension of the input tensor to be the channel dimension. Default: False Examples:: >>> # channel first tensor >>> sbn = apex.parallel.SyncBatchNorm(100).cuda() >>> inp = torch.randn(10, 100, 14, 14).cuda() >>> out = sbn(inp) >>> inp = torch.randn(3, 100, 20).cuda() >>> out = sbn(inp) >>> # channel last tensor >>> sbn = apex.parallel.SyncBatchNorm(100, channel_last=True).cuda() >>> inp = torch.randn(10, 14, 14, 100).cuda() """ def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True, process_group=None, channel_last=False, fuse_relu=False): super(SyncBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats) self.process_group = process_group self.channel_last = channel_last self.fuse_relu = fuse_relu def _specify_process_group(self, process_group): self.process_group = process_group def _specify_channel_last(self, channel_last): self.channel_last = channel_last
[docs] def forward(self, input, z = None): # if input.dim() == 2, we switch to channel_last for efficient memory accessing channel_last = self.channel_last if input.dim() != 2 else True if not and self.track_running_stats and not self.channel_last and not self.fuse_relu and z == None: # fall back to pytorch implementation for inference return F.batch_norm(input, self.running_mean, self.running_var, self.weight, self.bias, False, 0.0, self.eps) else: exponential_average_factor = 0.0 if and self.track_running_stats: self.num_batches_tracked += 1 if self.momentum is None: exponential_average_factor = 1.0 / float(self.num_batches_tracked) else: exponential_average_factor = self.momentum return SyncBatchnormFunction.apply(input, z, self.weight, self.bias, self.running_mean, self.running_var, self.eps, or not self.track_running_stats, exponential_average_factor, self.process_group, self.channel_last, self.fuse_relu)