[sbn update] (#384)

fixing empty return from python implementation
  adding proper test to verify functional correctness for python implementation

apex/parallel/sync_batchnorm.py

@@ -53,7 +53,10 @@ class SyncBatchNorm(_BatchNorm):
             raise AttributeError("channel_last is not supported by primitive SyncBatchNorm implementation. Try install apex with `--cuda_ext` if channel_last is desired.")
 
         if not SyncBatchNorm.warned:
+            if hasattr(self, "syncbn_import_error"):
                 print("Warning:  using Python fallback for SyncBatchNorm, possibly because apex was installed without --cuda_ext.  The exception raised when attempting to import the cuda backend was: ", self.syncbn_import_error)
+            else:
+                print("Warning:  using Python fallback for SyncBatchNorm")
             SyncBatchNorm.warned = True
 
         super(SyncBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats)
@@ -128,4 +131,4 @@ class SyncBatchNorm(_BatchNorm):
                         (1 - self.momentum) * self.running_var
             torch.cuda.nvtx.range_pop()
             out = SyncBatchnormFunction.apply(input, self.weight, self.bias, mean, var, self.eps, process_group, world_size)
-        out = out.to(cast)
+        return out.to(cast)

tests/distributed/synced_batchnorm/python_single_gpu_unit_test.py

@@ -0,0 +1,111 @@
+import torch
+import numpy as np
+import apex
+
+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)
+
+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
+
+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)
+
+from apex.parallel.sync_batchnorm import SyncBatchNorm
+
+sbn = 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_result = True
+sbn_result_c_last = True
+bn_result = True
+
+out_r = weight_r * (inp2_r - m.view(-1, 1, 1)) * torch.rsqrt(b_v.view(-1,1,1) + eps) + bias_r
+
+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)
+
+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
+
+if sbn_result:
+    print("====SBN single gpu passed tests")
+else:
+    print("*SBN single gpu failed*")
+

tests/distributed/synced_batchnorm/unit_test.sh

@@ -1,5 +1,6 @@
+python python_single_gpu_unit_test.py
 python single_gpu_unit_test.py
 python -m torch.distributed.launch --nproc_per_node=2 two_gpu_unit_test.py
-python -m torch.distributed.launch --nproc_per_node=2 two_gpu_unit_test.py --fp64
+python -m torch.distributed.launch --nproc_per_node=2 two_gpu_unit_test.py --fp16
 #beware, you need a system with at least 4 gpus to test group_size<world_size
-python -m torch.distributed.launch --nproc_per_node=4 test_groups.py --group_size=2
+#python -m torch.distributed.launch --nproc_per_node=4 test_groups.py --group_size=2