def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
torch.cuda.nvtx.range_push("sync_BN_bw")
# mini batch mean & var are calculated by forward path.
# mu = 1./N*np.sum(h, axis = 0)
# var = 1./N*np.sum((h-mu)**2, axis = 0)
saved_input, weight, mean, inv_std, z, bias = ctx.saved_tensors
process_group = ctx.process_group
channel_last = ctx.channel_last
world_size = ctx.world_size
fuse_relu = ctx.fuse_relu
grad_input = grad_z = grad_weight = grad_bias = None
if fuse_relu:
grad_output = syncbn.relu_bw_c_last(grad_output, saved_input, z,
mean, inv_std, weight, bias)
if isinstance(z, torch.Tensor) and ctx.needs_input_grad[1]:
grad_z = grad_output.clone()
# TODO(jie): why do I have to clone here? life time of grad_output?
if channel_last:
mean_dy, mean_dy_xmu, grad_weight, grad_bias = syncbn.reduce_bn_c_last(
grad_output, saved_input, mean, inv_std, weight)
else:
mean_dy, mean_dy_xmu, grad_weight, grad_bias = syncbn.reduce_bn(
grad_output, saved_input, mean, inv_std, weight)
# calculate grad_input
if ctx.needs_input_grad[0]:
if torch.distributed.is_initialized():
torch.distributed.all_reduce(mean_dy, ReduceOp.SUM,
process_group)
mean_dy = mean_dy / world_size
torch.distributed.all_reduce(mean_dy_xmu, ReduceOp.SUM,
process_group)
mean_dy_xmu = mean_dy_xmu / world_size
if channel_last:
grad_input = syncbn.batchnorm_backward_c_last(
grad_output, saved_input, mean, inv_std, weight, mean_dy,
mean_dy_xmu)
else:
grad_input = syncbn.batchnorm_backward(grad_output,
saved_input, mean,
inv_std, weight,
mean_dy, mean_dy_xmu)
if weight is None or not ctx.needs_input_grad[2]:
grad_weight = None
if weight is None or not ctx.needs_input_grad[3]:
grad_bias = None
torch.cuda.nvtx.range_pop()
return grad_input, grad_z, grad_weight, grad_bias, None, None, None, None, None, None, None, None
def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
# mini batch mean & var are calculated by forward path.
# mu = 1./N*np.sum(h, axis = 0)
# var = 1./N*np.sum((h-mu)**2, axis = 0)
saved_input, weight, mean, inv_std, z, bias, count = ctx.saved_tensors
process_group = ctx.process_group
channel_last = ctx.channel_last
world_size = ctx.world_size
fuse_relu = ctx.fuse_relu
grad_input = grad_z = grad_weight = grad_bias = None
if fuse_relu:
grad_output = syncbn.relu_bw_c_last(grad_output, saved_input, z,
mean, inv_std, weight, bias)
if isinstance(z, torch.Tensor) and ctx.needs_input_grad[1]:
grad_z = grad_output.clone()
# TODO: update kernel to not pre_divide by item_num
if channel_last:
sum_dy, sum_dy_xmu, grad_weight, grad_bias = syncbn.reduce_bn_c_last(
grad_output, saved_input, mean, inv_std, weight)
else:
sum_dy, sum_dy_xmu, grad_weight, grad_bias = syncbn.reduce_bn(
grad_output, saved_input, mean, inv_std, weight)
# calculate grad_input
if ctx.needs_input_grad[0]:
if torch.distributed.is_initialized():
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)
if channel_last:
grad_input = syncbn.batchnorm_backward_c_last(
grad_output, saved_input, mean, inv_std, weight, sum_dy,
sum_dy_xmu, count)
else:
grad_input = syncbn.batchnorm_backward(grad_output,
saved_input, mean,
inv_std, weight, sum_dy,
sum_dy_xmu, count)
if weight is None or not ctx.needs_input_grad[2]:
grad_weight = None
if weight is None or not ctx.needs_input_grad[3]:
grad_bias = None
return grad_input, grad_z, grad_weight, grad_bias, None, None, None, None, None, None, None, None