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, running_mean, running_variance = ctx.saved_tensors
eps = ctx.eps
process_group = ctx.process_group
world_size = ctx.world_size
grad_input = grad_weight = grad_bias = None
# TODO(jie): why do I have to clone here? life time of grad_output?
mean_dy, mean_dy_xmu, grad_weight, grad_bias = syncbn.reduce_bn(grad_output, saved_input, running_mean, running_variance, weight, eps)
# calculate grad_input
if ctx.needs_input_grad[0]:
if torch.distributed.is_initialized():
torch.distributed.all_reduce(
mean_dy, torch.distributed.reduce_op.SUM, process_group)
mean_dy = mean_dy / world_size
torch.distributed.all_reduce(
mean_dy_xmu, torch.distributed.reduce_op.SUM, process_group)
mean_dy_xmu = mean_dy_xmu / world_size
grad_input = syncbn.batchnorm_backward(grad_output, saved_input, running_mean, running_variance, weight, mean_dy, mean_dy_xmu, eps)
if weight is None or not ctx.needs_input_grad[1]:
grad_weight = None
if weight is None or not ctx.needs_input_grad[2]:
grad_bias = None
torch.cuda.nvtx.range_pop()
return grad_input, grad_weight, grad_bias, None, None, None, None, None, None
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
compare("comparing bn output: ", out_bn, out_r, error)
grad_output_t = type_tensor(grad)
grad_output_r = ref_tensor(grad.transpose(1, 0, 2, 3).reshape(feature_size, -1))
grad_output2_r = ref_tensor(grad)
grad_bias_r = grad_output_r.sum(1)
grad_weight_r = ((inp2_r - m.view(-1, 1, 1)) * torch.rsqrt(b_v.view(-1,1,1) + eps) * grad_output2_r).transpose(1,0).contiguous().view(feature_size, -1).sum(1)
mean_dy_r = grad_output_r.mean(1)
mean_dy_xmu_r = ((inp2_r - m.view(-1, 1, 1)) * grad_output2_r).transpose(1,0).contiguous().view(feature_size, -1).mean(1)
grad_input_r = (grad_output2_r - mean_dy_r.view(-1, 1, 1) - (inp2_r - m.view(-1, 1, 1)) / (b_v.view(-1,1,1) + eps) * mean_dy_xmu_r.view(-1, 1, 1) ) * torch.rsqrt(b_v.view(-1,1,1) + eps) * weight_r.view(-1,1,1)
mean_dy, mean_dy_xmu, grad_weight, grad_bias = syncbn.reduce_bn(grad_output_t, inp_t, mean, inv_std, weight_t)
grad_input = syncbn.batchnorm_backward(grad_output_t, inp_t, mean, inv_std, weight_t, mean_dy, mean_dy_xmu)
if args.local_rank == 0:
sbn_result = compare("comparing bias grad: ", grad_bias, grad_bias_r, error) and sbn_result
sbn_result = compare("comparing weight grad: ", grad_weight, grad_weight_r, error) and sbn_result
sbn_result = compare("comparing mean_dy grad: ", mean_dy, mean_dy_r, error) and sbn_result
sbn_result = compare("comparing mean_dy_xmu grad: ", mean_dy_xmu, mean_dy_xmu_r, error) and sbn_result
sbn_result = compare("comparing input grad: ", grad_input, grad_input_r, error) and sbn_result
compare("comparing bn input grad: ", inp_bn.grad, grad_input_r, error)
if args.local_rank == 0:
sbn_result = compare("comparing running_mean: ", bn.running_mean.data, sbn.module.running_mean.data, error) and sbn_result
sbn_result = compare("comparing running_variance: ", bn.running_var.data, sbn.module.running_var.data, error) and sbn_result
# execute by both
compare("comparing layers output: ", out_bn[start:finish], out_sbn, error) and sbn_result
grad_weight_r = ((inp2_r - m.view(-1, 1, 1)) *
torch.rsqrt(b_v.view(-1, 1, 1) + eps) *
grad_output2_r).transpose(1, 0).contiguous().view(
feature_size, -1).sum(1)
mean_dy_r = grad_output_r.mean(1)
mean_dy_xmu_r = ((inp2_r - m.view(-1, 1, 1)) * grad_output2_r).transpose(
1, 0).contiguous().view(feature_size, -1).mean(1)
grad_input_r = (grad_output2_r - mean_dy_r.view(-1, 1, 1) -
(inp2_r - m.view(-1, 1, 1)) /
(b_v.view(-1, 1, 1) + eps) * mean_dy_xmu_r.view(-1, 1, 1)
) * torch.rsqrt(b_v.view(-1, 1, 1) + eps) * weight_r.view(
-1, 1, 1)
mean_dy, mean_dy_xmu, grad_weight, grad_bias = syncbn.reduce_bn(
grad_output_t, inp_t, mean, var_biased, weight_t, eps)
grad_input = syncbn.batchnorm_backward(grad_output_t, inp_t, mean, var_biased,
weight_t, mean_dy, mean_dy_xmu, eps)
sbn_result = compare("comparing bias grad: ", grad_bias, grad_bias_r,
error) and sbn_result
sbn_result = compare("comparing weight grad: ", grad_weight, grad_weight_r,
error) and sbn_result
sbn_result = compare("comparing mean_dy grad: ", mean_dy, mean_dy_r,
error) and sbn_result
sbn_result = compare("comparing mean_dy_xmu grad: ", mean_dy_xmu,
mean_dy_xmu_r, error) and sbn_result
sbn_result = compare("comparing input grad: ", grad_input, grad_input_r,
error) and sbn_result
compare("comparing bn input grad: ", inp_bn.grad, grad_input_r, error)
sbn_result = compare("comparing sbn input grad: ", inp_sbn.grad, grad_input_r,
error) and sbn_result
