| import torch |
| import numpy as np |
| import apex |
| import syncbn |
| import os |
| import argparse |
| import torch.optim as optim |
|
|
| def compare(desc, inp1, inp2, error): |
| a = inp1.clone().detach().cpu().numpy() |
| b = inp2.clone().detach().cpu().numpy() |
| close = np.allclose(a,b, error, error) |
| if not close: |
| print(desc, close) |
| z = a - b |
| index = (np.abs(z) >= error + error * np.abs(b)).nonzero() |
| print("dif : ", z[index]) |
| print("inp1 : ", a[index]) |
| print("inp2 : ", b[index]) |
| return close |
|
|
| feature_size = 10 |
| space_size = 40 |
| batch_size = 32 |
|
|
|
|
| from apex.parallel import DistributedDataParallel as DDP |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--local_rank", default=0, type=int) |
| parser.add_argument("--fp16", action='store_true', default=False) |
| parser.add_argument("--fp64", action='store_true', default=False) |
| args = parser.parse_args() |
| args.world_size = int(os.environ['WORLD_SIZE']) |
| torch.cuda.set_device(args.local_rank) |
| torch.distributed.init_process_group(backend='nccl', init_method='env://') |
| start = args.local_rank * batch_size//args.world_size |
| finish = (args.local_rank + 1) * batch_size//args.world_size |
|
|
| error = 1e-5 |
| dtype = np.float32 |
| if args.fp16: |
| error = 1e-3 |
| dtype = np.float16 |
| elif args.fp64: |
| error = 1e-8 |
| dtype = np.float64 |
|
|
| np.random.seed(18) |
| inp = np.random.randn(batch_size, feature_size, space_size, space_size).astype(dtype) |
| grad = np.random.randn(batch_size, feature_size, space_size, space_size).astype(dtype) |
| weight = np.random.randn(feature_size).astype(dtype) |
| bias = np.random.randn(feature_size).astype(dtype) |
|
|
|
|
| type_tensor = torch.cuda.FloatTensor |
| if args.fp16: |
| type_tensor = torch.cuda.HalfTensor |
| if args.fp64: |
| type_tensor = torch.cuda.DoubleTensor |
|
|
| ref_tensor = torch.cuda.DoubleTensor |
|
|
| inp_t = type_tensor(inp) |
| weight_t = type_tensor(weight) |
| bias_t = type_tensor(bias) |
|
|
| inp_r = ref_tensor(inp.transpose(1, 0, 2, 3).reshape(feature_size, -1)) |
| inp2_r = ref_tensor(inp) |
| weight_r = ref_tensor(weight).view(-1, 1, 1) |
| bias_r = ref_tensor(bias).view(-1, 1, 1) |
|
|
| grad_output_t = type_tensor(grad) |
|
|
| m = inp_r.mean(1) |
| b_v = inp_r.var(1, unbiased=False) |
| unb_v = inp_r.var(1, unbiased=True) |
|
|
| eps = 1e-5 |
|
|
| mean, var_biased = syncbn.welford_mean_var(inp_t) |
| inv_std = 1.0 / torch.sqrt(var_biased + eps) |
|
|
| bn = torch.nn.BatchNorm2d(feature_size).cuda() |
| bn.momentum = 1.0 |
| bn.weight.data = weight_t.clone() |
| bn.bias.data = bias_t.clone() |
| if args.fp16: |
| bn.half() |
| if args.fp64: |
| bn.double() |
| inp_bn = inp_t.clone().requires_grad_() |
| grad_bn = grad_output_t.clone().detach() |
| out_bn = bn(inp_bn) |
| out_bn.backward(grad_bn) |
| |
| |
| |
| for param in bn.parameters(): |
| param.grad = param.grad / args.world_size |
| bn_opt = optim.SGD(bn.parameters(), lr=1.0) |
|
|
| sbn = apex.parallel.SyncBatchNorm(feature_size).cuda() |
| sbn.momentum = 1.0 |
| sbn.weight.data = weight_t.clone() |
| sbn.bias.data = bias_t.clone() |
| if args.fp16: |
| sbn.half() |
| if args.fp64: |
| sbn.double() |
| sbn = DDP(sbn) |
| sbn_opt = optim.SGD(sbn.parameters(), lr=1.0) |
| inp_sbn = inp_t.clone().requires_grad_() |
| grad_sbn = grad_output_t.clone().detach() |
| out_sbn = sbn(inp_sbn[start:finish]) |
| out_sbn.backward(grad_sbn[start:finish]) |
|
|
| sbn_result = True |
| bn_result = True |
|
|
| if args.local_rank == 0: |
| sbn_result = compare("comparing mean: ", mean, m, error) and sbn_result |
| sbn_result = compare("comparing biased variance: ", var_biased, b_v, error) and sbn_result |
|
|
| out = syncbn.batchnorm_forward(inp_t, mean, inv_std, weight_t, bias_t) |
| out_r = weight_r * (inp2_r - m.view(-1, 1, 1)) * torch.rsqrt(b_v.view(-1,1,1) + eps) + bias_r |
|
|
| if args.local_rank == 0: |
| sbn_result = compare("comparing output: ", out, out_r, error) and sbn_result |
| 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 |
|
|
| |
| compare("comparing layers output: ", out_bn[start:finish], out_sbn, error) and sbn_result |
| compare("comparing layers grad_input: ", inp_bn.grad[start:finish], inp_sbn.grad[start:finish], error) and sbn_result |
|
|
| bn_opt.step() |
| sbn_opt.step() |
|
|
| if args.local_rank == 0: |
| compare("comparing bn vs sbn bias: ", bn.bias, sbn.module.bias, error) |
| compare("comparing bn vs sbn weight: ", bn.weight, sbn.module.weight, error) |
|
|
|
|
| if sbn_result: |
| print("====SBN two gpu passed tests") |
| else: |
| print("*SBN two gpu failed*") |
|
|
