fixing batchnorm 1d input (#590)

apex/parallel/optimized_sync_batchnorm.py

@@ -71,7 +71,7 @@ class SyncBatchNorm(_BatchNorm):
         # if input.dim() == 2, we switch to channel_last for efficient memory accessing
         channel_last = self.channel_last if input.dim() != 2 else True
 
-        if not self.training and self.track_running_stats and not self.channel_last and not self.fuse_relu and z == None:
+        if not self.training and self.track_running_stats and not channel_last and not self.fuse_relu and z == None:
             # fall back to pytorch implementation for inference
             return F.batch_norm(input, self.running_mean, self.running_var, self.weight, self.bias, False, 0.0, self.eps)
         else:
@@ -82,4 +82,4 @@ class SyncBatchNorm(_BatchNorm):
                     exponential_average_factor = 1.0 / float(self.num_batches_tracked)
                 else:
                     exponential_average_factor = self.momentum
-            return SyncBatchnormFunction.apply(input, z, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.training or not self.track_running_stats, exponential_average_factor, self.process_group, self.channel_last, self.fuse_relu)
+            return SyncBatchnormFunction.apply(input, z, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.training or not self.track_running_stats, exponential_average_factor, self.process_group, channel_last, self.fuse_relu)

tests/distributed/synced_batchnorm/test_batchnorm1d.py

+import torch
+import apex
+
+model = apex.parallel.SyncBatchNorm(4).cuda()
+model.weight.data.uniform_()
+model.bias.data.uniform_()
+data = torch.rand((8,4)).cuda()
+
+model_ref = torch.nn.BatchNorm1d(4).cuda()
+model_ref.load_state_dict(model.state_dict())
+data_ref = data.clone()
+
+output = model(data)
+output_ref = model_ref(data_ref)
+
+assert(output.allclose(output_ref))
+assert(model.running_mean.allclose(model_ref.running_mean))
+assert(model.running_var.allclose(model_ref.running_var))

tests/distributed/synced_batchnorm/unit_test.sh

 python python_single_gpu_unit_test.py
 python single_gpu_unit_test.py
+python test_batchnorm1d.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 --fp16
 #beware, you need a system with at least 4 gpus to test group_size<world_size