Upload apex-master/tests/distributed/synced_batchnorm/single_gpu_unit_test.py with huggingface_hub

apex-master/tests/distributed/synced_batchnorm/single_gpu_unit_test.py

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+import torch
+import numpy as np
+import apex
+if True:
+    print("using setup tools")
+    import syncbn
+else:
+    print("using jit")
+    from torch.utils.cpp_extension import load
+    syncbn = load(name='syncbn', sources=['../../csrc/syncbn.cpp', '../../csrc/welford.cu'])
+
+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 = 16
+batch_size = 5
+
+
+error = 1e-5
+
+np.random.seed(1)
+dtype = np.float32
+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)
+count = torch.cuda.IntTensor([batch_size*space_size**2])
+
+type_tensor = torch.cuda.FloatTensor
+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, var_biased = syncbn.welford_mean_var(inp_t)
+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()
+inp_bn = inp_t.clone().requires_grad_()
+grad_bn = grad_output_t.clone().detach()
+out_bn = bn(inp_bn)
+out_bn.backward(grad_bn)
+
+sbn = apex.parallel.SyncBatchNorm(feature_size).cuda()
+sbn.momentum = 1.0
+sbn.weight.data = weight_t.clone()
+sbn.bias.data = bias_t.clone()
+inp_sbn = inp_t.clone().requires_grad_()
+grad_sbn = grad_output_t.clone().detach()
+out_sbn = sbn(inp_sbn)
+out_sbn.backward(grad_sbn)
+
+sbn_c_last = apex.parallel.SyncBatchNorm(feature_size, channel_last=True).cuda()
+sbn_c_last.momentum = 1.0
+sbn_c_last.weight.data = weight_t.clone()
+sbn_c_last.bias.data = bias_t.clone()
+inp_sbn_c_last = inp_t.clone().transpose(-1, 1).contiguous().requires_grad_()
+grad_sbn_c_last = grad_output_t.clone().transpose(-1, 1).contiguous().detach()
+out_sbn_c_last = sbn_c_last(inp_sbn_c_last)
+out_sbn_c_last.backward(grad_sbn_c_last)
+
+sbn_result = True
+sbn_result_c_last = True
+bn_result = True
+
+sbn_result = compare("comparing mean: ", mean, m, error) and sbn_result
+#sbn_result = compare("comparing variance: ", var, unb_v, 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
+
+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)
+
+sum_dy_r = grad_output_r.sum(1)
+mean_dy_r = grad_output_r.mean(1)
+sum_dy_xmu_r = ((inp2_r - m.view(-1, 1, 1)) * grad_output2_r).transpose(1,0).contiguous().view(feature_size, -1).sum(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)
+
+sum_dy, sum_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, sum_dy, sum_dy_xmu, count)
+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 sum_dy grad: ", sum_dy, sum_dy_r, error) and sbn_result
+sbn_result = compare("comparing sum_dy_xmu grad: ", sum_dy_xmu, sum_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
+
+compare("comparing bn/sbn output: ", out_bn, out_sbn, error)
+sbn_result = compare("comparing running_mean: ", bn.running_mean.data, sbn.running_mean.data, error) and sbn_result
+sbn_result = compare("comparing running_variance: ", bn.running_var.data, sbn.running_var.data, error) and sbn_result
+compare("comparing grad_input: ", inp_bn.grad, inp_sbn.grad, error)
+compare("comparing grad_bias: ", bn.bias.grad, sbn.bias.grad, error)
+compare("comparing grad_bias bn to ref: ", bn.bias.grad, grad_bias_r, error)
+sbn_result = compare("comparing grad_bias sbn to ref: ", sbn.bias.grad, grad_bias_r, error) and sbn_result
+compare("comparing grad_weight: ", bn.weight.grad, sbn.weight.grad, error)
+compare("comparing grad_weight bn to ref: ", bn.weight.grad, grad_weight_r, error)
+sbn_result = compare("comparing grad_weight sbn to ref: ", sbn.weight.grad, grad_weight_r, error) and sbn_result
+
+compare("comparing channel last bn/sbn output: ", out_bn, out_sbn_c_last.transpose(-1, 1).contiguous(), error)
+sbn_result_c_last = compare("comparing channel last running_mean: ", bn.running_mean.data, sbn_c_last.running_mean.data, error) and sbn_result_c_last
+sbn_result_c_last = compare("comparing channel last running_variance: ", bn.running_var.data, sbn_c_last.running_var.data, error) and sbn_result_c_last
+compare("comparing channel last grad_input: ", inp_bn.grad, inp_sbn_c_last.grad.transpose(-1, 1).contiguous(), error)
+compare("comparing channel last grad_bias: ", bn.bias.grad, sbn_c_last.bias.grad, error)
+sbn_result_c_last = compare("comparing channel last grad_bias sbn to ref: ", sbn_c_last.bias.grad, grad_bias_r, error) and sbn_result_c_last
+compare("comparing channel last grad_weight: ", bn.weight.grad, sbn_c_last.weight.grad, error)
+sbn_result_c_last = compare("comparing channel last grad_weight sbn to ref: ", sbn_c_last.weight.grad, grad_weight_r, error) and sbn_result_c_last
+
+if sbn_result:
+    print("====SBN single gpu passed tests")
+else:
+    print("*SBN single gpu failed*")
+
+if sbn_result_c_last:
+    print("====SBN channel last single gpu passed tests")
+else:
+    print("*SBN channel last single gpu failed*")