Fix hvd.barrier() tensor queue management and torch test failures fro…

…m op name mismatches (#3300)

Signed-off-by: Max H. Gerlach <git@maxgerlach.de>

horovod/common/controller.cc

@@ -273,12 +273,6 @@ ResponseList Controller::ComputeResponseList(bool this_process_requested_shutdow
 
         bool reduce = IncrementTensorCount(message, process_set.joined_size);
 
-        // For barrier request, if not ready to reduce, we add it back to tensor queue
-        // to process in the next cycle.
-        if(!reduce && message.request_type() == Request::BARRIER) {
-          tensor_queue_.PushMessageToQueue(message);
-        }
-
         stall_inspector_.RecordUncachedTensorStart(
             message.tensor_name(), message.request_rank(), size_);
         if (reduce) {

test/parallel/test_torch.py

@@ -607,6 +607,7 @@ def test_horovod_allreduce_duplicate_name_error(self):
         two concurrent operations with the same name."""
         hvd.init()
         size = hvd.size()
+        rank = hvd.rank()
 
         # This test does not apply if there is only one worker.
         if size == 1:
@@ -615,13 +616,22 @@ def test_horovod_allreduce_duplicate_name_error(self):
         dims = [17] * 3
         tensor = torch.FloatTensor(*dims)
 
-        hvd.allreduce_async(tensor, name='duplicate_name')
-        try:
-            for i in range(10):
+        if rank == 0:
+            hvd.allreduce_async(tensor, name='duplicate_name')
+            try:
                 hvd.allreduce_async(tensor, name='duplicate_name')
-            assert False, 'hvd.allreduce_async did not throw error'
-        except (torch.FatalError, ValueError):
-            pass
+                assert False, 'hvd.allreduce_async did not throw error'
+            except (torch.FatalError, ValueError):
+                pass
+        hvd.allreduce(torch.FloatTensor([1]), name="synch1")
+        if rank > 0:
+            hvd.allreduce_async(tensor, name='duplicate_name')
+            try:
+                hvd.allreduce_async(tensor, name='duplicate_name')
+                assert False, 'hvd.allreduce_async did not throw error'
+            except (torch.FatalError, ValueError):
+                pass
+        hvd.allreduce(torch.FloatTensor([2]), name="synch2")
 
     def test_horovod_allreduce_grad(self):
         """Test the correctness of the allreduce gradient."""
@@ -1213,6 +1223,7 @@ def test_horovod_allgather_duplicate_name_error(self):
         two concurrent operations with the same name."""
         hvd.init()
         size = hvd.size()
+        rank = hvd.rank()
 
         # This test does not apply if there is only one worker.
         if size == 1:
@@ -1221,13 +1232,22 @@ def test_horovod_allgather_duplicate_name_error(self):
         dims = [17] * 3
         tensor = torch.FloatTensor(*dims)
 
-        hvd.allgather_async(tensor, name='duplicate_name')
-        try:
-            for i in range(10):
+        if rank == 0:
+            hvd.allgather_async(tensor, name='duplicate_name')
+            try:
                 hvd.allgather_async(tensor, name='duplicate_name')
-            assert False, 'hvd.allgather_async did not throw error'
-        except (torch.FatalError, ValueError):
-            pass
+                assert False, 'hvd.allgather_async did not throw error'
+            except (torch.FatalError, ValueError):
+                pass
+        hvd.allreduce(torch.FloatTensor([1]), name="synch1")
+        if rank > 0:
+            hvd.allgather_async(tensor, name='duplicate_name')
+            try:
+                hvd.allgather_async(tensor, name='duplicate_name')
+                assert False, 'hvd.allgather_async did not throw error'
+            except (torch.FatalError, ValueError):
+                pass
+        hvd.allreduce(torch.FloatTensor([2]), name="synch2")
 
     def test_horovod_allgather_grad(self):
         """Test the correctness of the allgather gradient."""
@@ -1523,6 +1543,7 @@ def test_horovod_broadcast_duplicate_name_error(self):
         two concurrent operations with the same name."""
         hvd.init()
         size = hvd.size()
+        rank = hvd.rank()
 
         # This test does not apply if there is only one worker.
         if size == 1:
@@ -1531,13 +1552,22 @@ def test_horovod_broadcast_duplicate_name_error(self):
         dims = [17] * 3
         tensor = torch.FloatTensor(*dims)
 
-        hvd.broadcast_async(tensor, root_rank=0, name='duplicate_name')
-        try:
-            for i in range(10):
-                hvd.broadcast_async(tensor, root_rank=0, name='duplicate_name')
-            assert False, 'hvd.broadcast_async did not throw error'
-        except (torch.FatalError, ValueError):
-            pass
+        if rank == 0:
+            hvd.broadcast_async(tensor, name='duplicate_name', root_rank=0)
+            try:
+                hvd.broadcast_async(tensor, name='duplicate_name', root_rank=0)
+                assert False, 'hvd.broadcast_async did not throw error'
+            except (torch.FatalError, ValueError):
+                pass
+        hvd.allreduce(torch.FloatTensor([1]), name="synch1")
+        if rank > 0:
+            hvd.broadcast_async(tensor, name='duplicate_name', root_rank=0)
+            try:
+                hvd.broadcast_async(tensor, name='duplicate_name', root_rank=0)
+                assert False, 'hvd.broadcast_async did not throw error'
+            except (torch.FatalError, ValueError):
+                pass
+        hvd.allreduce(torch.FloatTensor([2]), name="synch2")
 
     def test_horovod_broadcast_grad(self):
         """Test the correctness of the broadcast gradient."""
@@ -2743,7 +2773,7 @@ def test_horovod_join_allreduce(self):
         integral_types = [torch.IntTensor, torch.LongTensor, torch.cuda.IntTensor, torch.cuda.LongTensor]
 
         dims = [1, 2, 3]
-        first_join_ranks = [0, 1]
+        first_join_ranks = list(range(size))
         cachings = [False, True]
         for dtype, dim, first_join_rank, caching in itertools.product(dtypes, dims, first_join_ranks, cachings):
             torch.manual_seed(1234)
@@ -2814,25 +2844,31 @@ def test_horovod_join_allgather(self):
         dims = [17] * 3
         tensor = torch.FloatTensor(*dims)
 
-        if rank == 0:
-            if torch.cuda.is_available():
-                ret = hvd.join(hvd.local_rank())
+        first_join_ranks = list(range(size))
+
+        for first_join_rank in first_join_ranks:
+            if rank == first_join_rank:
+                if torch.cuda.is_available():
+                    ret = hvd.join(hvd.local_rank())
+                else:
+                    ret = hvd.join()
             else:
-                ret = hvd.join()
-        else:
-            try:
-                hvd.allgather(tensor)
-                assert False, 'hvd.allgather did not throw error'
-            except (torch.FatalError, RuntimeError):
-                pass
+                try:
+                    hvd.allgather(tensor)
+                    assert False, 'hvd.allgather did not throw error'
+                except (torch.FatalError, RuntimeError):
+                    pass
 
-            ret = hvd.join(hvd.local_rank())
+                if torch.cuda.is_available():
+                    ret = hvd.join(hvd.local_rank())
+                else:
+                    ret = hvd.join()
 
-        self.assertNotEqual(ret, 0,
-                            msg="The return value of hvd.join() may not be equal to 0 because that would be the first rank to join")
-        ret_values = hvd.allgather_object(ret)
-        self.assertSequenceEqual(ret_values, [ret] * size,
-                                 msg="hvd.join() did not return the same value on each rank")
+            self.assertNotEqual(ret, first_join_rank,
+                                msg="The return value of hvd.join() may not be equal to first_join_rank")
+            ret_values = hvd.allgather_object(ret)
+            self.assertSequenceEqual(ret_values, [ret] * size,
+                                     msg="hvd.join() did not return the same value on each rank")
 
     def test_horovod_join_broadcast(self):
         """Test Join op with broadcast."""
@@ -2847,25 +2883,28 @@ def test_horovod_join_broadcast(self):
         dims = [17] * 3
         tensor = torch.FloatTensor(*dims)
 
-        if rank == 0:
-            ret = hvd.join(hvd.local_rank())
-        else:
-            try:
-                broadcasted_tensor = hvd.broadcast(tensor, 1, name="test_horovod_join_broadcast")
-                assert False, 'hvd.broadcast did not throw error'
-            except (torch.FatalError, RuntimeError):
-                pass
+        first_join_ranks = list(range(size))
 
-            if torch.cuda.is_available():
+        for first_join_rank in first_join_ranks:
+            if rank == first_join_rank:
                 ret = hvd.join(hvd.local_rank())
             else:
-                ret = hvd.join()
+                try:
+                    broadcasted_tensor = hvd.broadcast(tensor, rank, name="test_horovod_join_broadcast")
+                    assert False, 'hvd.broadcast did not throw error'
+                except (torch.FatalError, RuntimeError):
+                    pass
 
-        self.assertNotEqual(ret, 0,
-                            msg="The return value of hvd.join() may not be equal to 0 because that would be the first rank to join")
-        ret_values = hvd.allgather_object(ret)
-        self.assertSequenceEqual(ret_values, [ret] * size,
-                                 msg="hvd.join() did not return the same value on each rank")
+                if torch.cuda.is_available():
+                    ret = hvd.join(hvd.local_rank())
+                else:
+                    ret = hvd.join()
+
+            self.assertNotEqual(ret, first_join_rank,
+                                msg="The return value of hvd.join() may not be equal to first_join_rank")
+            ret_values = hvd.allgather_object(ret)
+            self.assertSequenceEqual(ret_values, [ret] * size,
+                                     msg="hvd.join() did not return the same value on each rank")
 
     def test_horovod_sync_batch_norm(self):
         """Tests Horovod version of SyncBatchNorm."""