Spaces:
Runtime error
Runtime error
File size: 6,690 Bytes
8a42f8f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
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)
parser.add_argument("--group_size", default=0, type=int)
args = parser.parse_args()
try:
args.world_size = int(os.environ['WORLD_SIZE'])
except:
print("This is a multi-gpu test. To run it please use 'python -m torch.distributed.launch --nproc_per_node=<num gpus> test_groups.py <more options>'")
exit(1)
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
start = (args.local_rank%args.group_size) * batch_size//args.group_size
finish = (args.local_rank%args.group_size + 1) * batch_size//args.group_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 + args.local_rank//args.group_size)
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()
bn = DDP(bn)
inp_bn = inp_t.clone().requires_grad_()
grad_bn = grad_output_t.clone().detach()
out_bn = bn(inp_bn)
out_bn.backward(grad_bn)
# compensating the averaging over processes done by DDP
# in order to produce mathematically equivalent result
# https://github.com/NVIDIA/apex/issues/134#issuecomment-458307368
for param in bn.parameters():
param.grad = param.grad / args.group_size
bn_opt = optim.SGD(bn.parameters(), lr=1.0)
sbn = apex.parallel.SyncBatchNorm(feature_size, process_group=apex.parallel.create_syncbn_process_group(args.group_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.module.running_mean.data, sbn.module.running_mean.data, error) and sbn_result
sbn_result = compare("comparing running_variance: ", bn.module.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
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.module.bias, sbn.module.bias, error)
compare("comparing bn vs sbn weight: ", bn.module.weight, sbn.module.weight, error)
if sbn_result:
print("====SBN group test passed")
else:
print("*SBN group test failed*")
|