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import torch
import torch.distributed as dist
from torch.autograd.function import Function
class SyncBatchNorm(Function):
@staticmethod
def forward(self, input, weight, bias, running_mean, running_var, eps, momentum, process_group, world_size):
input = input.contiguous()
count = torch.empty(1,
dtype=running_mean.dtype,
device=input.device).fill_(input.numel() // input.size(1))
# calculate mean/invstd for input.
mean, invstd = torch.batch_norm_stats(input, eps)
num_channels = input.shape[1]
# C, C, 1 -> (2C + 1)
combined = torch.cat([mean, invstd, count], dim=0)
# world_size * (2C + 1)
combined_list = [
torch.empty_like(combined) for k in range(world_size)
]
# Use allgather instead of allreduce since I don't trust in-place operations ..
dist.all_gather(combined_list, combined, process_group, async_op=False)
combined = torch.stack(combined_list, dim=0)
# world_size * (2C + 1) -> world_size * C, world_size * C, world_size * 1
mean_all, invstd_all, count_all = torch.split(combined, num_channels, dim=1)
size = count_all.view(-1).long().sum()
if size == 1:
raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
# calculate global mean & invstd
mean, invstd = torch.batch_norm_gather_stats_with_counts(
input,
mean_all,
invstd_all,
running_mean,
running_var,
momentum,
eps,
count_all.view(-1)
)
self.save_for_backward(input, weight, mean, invstd, count_all)
self.process_group = process_group
# apply element-wise normalization
out = torch.batch_norm_elemt(input, weight, bias, mean, invstd, eps)
return out
@staticmethod
def backward(self, grad_output):
grad_output = grad_output.contiguous()
saved_input, weight, mean, invstd, count_tensor = self.saved_tensors
grad_input = grad_weight = grad_bias = None
process_group = self.process_group
# calculate local stats as well as grad_weight / grad_bias
sum_dy, sum_dy_xmu, grad_weight, grad_bias = torch.batch_norm_backward_reduce(
grad_output,
saved_input,
mean,
invstd,
weight,
self.needs_input_grad[0],
self.needs_input_grad[1],
self.needs_input_grad[2]
)
if self.needs_input_grad[0]:
# synchronizing stats used to calculate input gradient.
# TODO: move div_ into batch_norm_backward_elemt kernel
num_channels = sum_dy.shape[0]
combined = torch.cat([sum_dy, sum_dy_xmu], dim=0)
torch.distributed.all_reduce(
combined, torch.distributed.ReduceOp.SUM, process_group, async_op=False)
sum_dy, sum_dy_xmu = torch.split(combined, num_channels)
divisor = count_tensor.sum()
mean_dy = sum_dy / divisor
mean_dy_xmu = sum_dy_xmu / divisor
# backward pass for gradient calculation
grad_input = torch.batch_norm_backward_elemt(
grad_output,
saved_input,
mean,
invstd,
weight,
mean_dy,
mean_dy_xmu
)
# synchronizing of grad_weight / grad_bias is not needed as distributed
# training would handle all reduce.
if weight is None or not self.needs_input_grad[1]:
grad_weight = None
if weight is None or not self.needs_input_grad[2]:
grad_bias = None
return grad_input, grad_weight, grad_bias, None, None, None, None, None, None
class CrossMapLRN2d(Function):
@staticmethod
def forward(ctx, input, size, alpha=1e-4, beta=0.75, k=1):
ctx.size = size
ctx.alpha = alpha
ctx.beta = beta
ctx.k = k
ctx.scale = None
assert input.dim() == 4
ctx.scale = ctx.scale or input.new()
output = input.new()
batch_size = input.size(0)
channels = input.size(1)
input_height = input.size(2)
input_width = input.size(3)
output.resize_as_(input)
ctx.scale.resize_as_(input)
# use output storage as temporary buffer
input_square = output
torch.pow(input, 2, out=input_square)
pre_pad = int((ctx.size - 1) / 2 + 1)
pre_pad_crop = channels if pre_pad > channels else pre_pad
scale_first = ctx.scale.select(1, 0)
scale_first.zero_()
# compute first feature map normalization
for c in range(pre_pad_crop):
scale_first.add_(input_square.select(1, c))
# reuse computations for next feature maps normalization
# by adding the next feature map and removing the previous
for c in range(1, channels):
scale_previous = ctx.scale.select(1, c - 1)
scale_current = ctx.scale.select(1, c)
scale_current.copy_(scale_previous)
if c < channels - pre_pad + 1:
square_next = input_square.select(1, c + pre_pad - 1)
scale_current.add_(square_next, alpha=1)
if c > pre_pad:
square_previous = input_square.select(1, c - pre_pad)
scale_current.add_(square_previous, alpha=-1)
ctx.scale.mul_(ctx.alpha / ctx.size).add_(ctx.k)
torch.pow(ctx.scale, -ctx.beta, out=output)
output.mul_(input)
ctx.save_for_backward(input, output)
return output
@staticmethod
def backward(ctx, grad_output):
input, output = ctx.saved_tensors
grad_input = grad_output.new()
batch_size = input.size(0)
channels = input.size(1)
input_height = input.size(2)
input_width = input.size(3)
paddded_ratio = input.new(channels + ctx.size - 1, input_height,
input_width)
accum_ratio = input.new(input_height, input_width)
cache_ratio_value = 2 * ctx.alpha * ctx.beta / ctx.size
inversePrePad = int(ctx.size - (ctx.size - 1) / 2)
grad_input.resize_as_(input)
torch.pow(ctx.scale, -ctx.beta, out=grad_input).mul_(grad_output)
paddded_ratio.zero_()
padded_ratio_center = paddded_ratio.narrow(0, inversePrePad,
channels)
for n in range(batch_size):
torch.mul(grad_output[n], output[n], out=padded_ratio_center)
padded_ratio_center.div_(ctx.scale[n])
torch.sum(
paddded_ratio.narrow(0, 0, ctx.size - 1), 0, keepdim=False, out=accum_ratio)
for c in range(channels):
accum_ratio.add_(paddded_ratio[c + ctx.size - 1])
grad_input[n][c].addcmul_(input[n][c], accum_ratio, value=-cache_ratio_value)
accum_ratio.add_(paddded_ratio[c], alpha=-1)
return grad_input, None, None, None, None
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