[transformer] UCC async test (NVIDIA#1417)

* add test

* update batch sizes

* update batch sizes

* small updates

* delete comment

* add async comm

* add sync if needed

* update tests

* remove redundant imports

* code cleanup

* minor updates

* update dtype for comparison

* fix dtypes

* fix typo

* modify sizes and use common_utils.find_free_port

* fix typo and use double precision

* revert some changes, create test for profiling on L1

* remove redundant line

* revert UCC_TLS and add sync to fwd_bwd

* code clean up

* code clean up

* modify BERT test

* add comment

apex/transformer/pipeline_parallel/p2p_communication.py

@@ -53,35 +53,47 @@ def _run_p2pops(
     async_comm: bool = False
 ):
     ops = []
+    p2p_group = parallel_state.get_pipeline_model_parallel_group()
+    default_group = parallel_state.get_model_parallel_group()
+
+    need_to_sync = p2p_group.name() != default_group.name()
+
     if tensor_send_prev is not None:
         send_prev_op = torch.distributed.P2POp(
-            torch.distributed.isend,
-            tensor_send_prev,
-            parallel_state.get_pipeline_model_parallel_prev_rank(),
+            op=torch.distributed.isend,
+            tensor=tensor_send_prev,
+            peer=parallel_state.get_pipeline_model_parallel_prev_rank(),
+            group=p2p_group,
         )
         ops.append(send_prev_op)
     if tensor_recv_prev is not None:
         recv_prev_op = torch.distributed.P2POp(
-            torch.distributed.irecv,
-            tensor_recv_prev,
-            parallel_state.get_pipeline_model_parallel_prev_rank(),
+            op=torch.distributed.irecv,
+            tensor=tensor_recv_prev,
+            peer=parallel_state.get_pipeline_model_parallel_prev_rank(),
+            group=p2p_group,
         )
         ops.append(recv_prev_op)
     if tensor_send_next is not None:
         send_next_op = torch.distributed.P2POp(
-            torch.distributed.isend,
-            tensor_send_next,
-            parallel_state.get_pipeline_model_parallel_next_rank(),
+            op=torch.distributed.isend,
+            tensor=tensor_send_next,
+            peer=parallel_state.get_pipeline_model_parallel_next_rank(),
+            group=p2p_group,
         )
         ops.append(send_next_op)
     if tensor_recv_next is not None:
         recv_next_op = torch.distributed.P2POp(
-            torch.distributed.irecv,
-            tensor_recv_next,
-            parallel_state.get_pipeline_model_parallel_next_rank(),
+            op=torch.distributed.irecv,
+            tensor=tensor_recv_next,
+            peer=parallel_state.get_pipeline_model_parallel_next_rank(),
+            group=p2p_group,
         )
         ops.append(recv_next_op)
     if len(ops) > 0:
+        if need_to_sync:
+            torch.cuda.synchronize()
+
         reqs = torch.distributed.batch_isend_irecv(ops)
         if async_comm:
             assert len(reqs) == len(ops)

apex/transformer/pipeline_parallel/schedules/fwd_bwd_pipelining_with_interleaving.py

@@ -34,6 +34,7 @@ def _forward_backward_pipelining_with_interleaving(
     grad_scaler: Optional[torch.cuda.amp.GradScaler] = None,
     disable_autocast: bool = False,
     deallocate_pipeline_outputs: bool = False,
+    async_comm: bool = False,
     sequence_parallel_enabled: bool = False,
     **kwargs,
 ) -> List[Union[torch.Tensor, Sequence[torch.Tensor]]]:
@@ -218,6 +219,7 @@ def backward_step_helper(microbatch_id: int) -> torch.Tensor:
         p2p_communication.recv_forward(
             tensor_shape=tensor_shape,
             dtype=dtype,
+            async_comm=async_comm,
             sequence_parallel_enabled=sequence_parallel_enabled,
         )
     )
@@ -265,6 +267,7 @@ def backward_step_helper(microbatch_id: int) -> torch.Tensor:
                 recv_next=recv_next,
                 tensor_shape=tensor_shape,
                 dtype=dtype,
+                async_comm=async_comm,
                 sequence_parallel_enabled=sequence_parallel_enabled,
             )
             output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
@@ -275,6 +278,7 @@ def backward_step_helper(microbatch_id: int) -> torch.Tensor:
                 recv_prev=recv_prev,
                 tensor_shape=tensor_shape,
                 dtype=dtype,
+                async_comm=async_comm,
                 sequence_parallel_enabled=sequence_parallel_enabled,
             )
         input_tensors[next_forward_model_chunk_id].append(input_tensor)
@@ -359,6 +363,7 @@ def backward_step_helper(microbatch_id: int) -> torch.Tensor:
             recv_next=recv_next,
             tensor_shape=tensor_shape,
             dtype=dtype,
+            async_comm=async_comm,
             sequence_parallel_enabled=sequence_parallel_enabled,
         )
         free_output_tensor(output_tensor, deallocate_pipeline_outputs)
@@ -380,6 +385,7 @@ def backward_step_helper(microbatch_id: int) -> torch.Tensor:
                 p2p_communication.recv_backward(
                     tensor_shape=tensor_shape,
                     dtype=dtype,
+                    async_comm=async_comm,
                     sequence_parallel_enabled=sequence_parallel_enabled,
                 )
             )
@@ -401,6 +407,7 @@ def backward_step_helper(microbatch_id: int) -> torch.Tensor:
                     recv_next=recv_next,
                     tensor_shape=tensor_shape,
                     dtype=dtype,
+                    async_comm=async_comm,
                     sequence_parallel_enabled=sequence_parallel_enabled,
                 )
             )

apex/transformer/testing/distributed_test_base.py

@@ -115,7 +115,7 @@ def _setup_pre_spawn(self) -> None:
 
         self._has_ucx_tls = "UCX_TLS" in os.environ
         if not self._has_ucx_tls:
-            os.environ["UCX_TLS"] = "tcp,cuda_copy"
+            os.environ["UCX_TLS"] = "tcp,cuda"
         print('os.environ[\"UCX_TLS\"] = {}'.format(os.environ["UCX_TLS"]))
 
     def tearDown(self) -> None:

tests/L0/run_transformer/run_bert_minimal_test.py

@@ -186,7 +186,12 @@ def train(
     failure = None
     init = True
     try:
-        for virtual_pipeline_model_parallel_size in (2, None):
+        virtual_pipeline_model_parallel_sizes = (None, 2,)
+        if HAS_TORCH_UCC:
+            # Deliberately skipping test with interleaved schedule for BERT model.
+            # It deadlocks on hybrid UCC/NCCL backend.
+            virtual_pipeline_model_parallel_sizes = (None,)
+        for virtual_pipeline_model_parallel_size in virtual_pipeline_model_parallel_sizes:
             args = global_vars.get_args()
             async_comm = not args.sequence_parallel and virtual_pipeline_model_parallel_size is None
             data_idx = 0

tests/L0/run_transformer/test_pipeline_parallel_fwd_bwd.py

@@ -49,12 +49,19 @@ def init_weights(m):
     return init_weights
 
 
+def get_dtype_for_comparison():
+    if(torch.cuda.get_device_capability() >= (8, 0)):
+        return torch.float64
+    return torch.float32
+
+
 def get_target_loss_and_model(global_batch_shape: tuple, hidden_size: int, total_layers: int) -> Tuple[torch.Tensor, List[torch.Tensor]]:
     model = []
-    data = torch.ones(global_batch_shape, dtype=torch.double)
+    dtype = get_dtype_for_comparison()
+    data = torch.ones(global_batch_shape, dtype=dtype)
     for i in range(total_layers):
-        w = torch.ones((hidden_size, hidden_size), dtype=torch.double) * (i + 1.0) / weight_coeff
-        b = torch.ones(hidden_size, dtype=torch.double)
+        w = torch.ones((hidden_size, hidden_size), dtype=dtype) * (i + 1.0) / weight_coeff
+        b = torch.ones(hidden_size, dtype=dtype)
 
         w.requires_grad_()
         b.requires_grad_()
@@ -187,7 +194,8 @@ def _forward_backward_test_impl(
                 deallocate_pipeline_output=deallocate_pipeline_outputs,
             )
 
-            if dtype == torch.double:
+            if dtype == get_dtype_for_comparison():
+                torch.cuda.synchronize()
                 hidden_size = self.HIDDEN_SIZE
                 microbatch_size = self.MICRO_BATCH_SIZE
                 total_layers = pipeline_model_parallel_world_size
@@ -221,44 +229,56 @@ def _forward_backward_test_impl(
 
             parallel_state.destroy_model_parallel()
 
-    def test_no_pipelining(self):
+    def test_learning_no_pipelining(self):
         self._forward_backward_test_impl(False, forward_backward_no_pipelining, 1, None)
 
-    def test_no_pipelining_inference(self):
+    def test_inference_no_pipelining(self):
         self._forward_backward_test_impl(True, forward_backward_no_pipelining, 1, None)
 
-    def test_pipelining_without_interleaving(self):
+    def test_learning_pipelining_without_interleaving(self):
         self._forward_backward_test_impl(
             False, forward_backward_pipelining_without_interleaving, None, None
         )
 
-    def test_pipelining_async(self):
+    def test_inference_pipelining_without_interleaving(self):
         self._forward_backward_test_impl(
-            False, forward_backward_pipelining_without_interleaving, None, None, async_comm=True
+            True, forward_backward_pipelining_without_interleaving, None, None
         )
 
-    def test_pipelining_without_interleaving_inference(self):
+    def test_learning_async_pipelining_without_interleaving(self):
         self._forward_backward_test_impl(
-            True, forward_backward_pipelining_without_interleaving, None, None
+            False, forward_backward_pipelining_without_interleaving, None, None, async_comm=True
         )
 
-    def test_pipelining_inference_async(self):
+    def test_inference_async_pipelining_without_interleaving(self):
         self._forward_backward_test_impl(
             True, forward_backward_pipelining_without_interleaving, None, None, async_comm=True
         )
 
-    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Megatron-LM voodoo")
-    def test_pipelining_with_interleaving(self):
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_learning_pipelining_with_interleaving(self):
         self._forward_backward_test_impl(
             False, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2
         )
 
-    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Megatron-LM voodoo")
-    def test_pipelining_with_interleaving_inference(self):
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_inference_pipelining_with_interleaving(self):
         self._forward_backward_test_impl(
             True, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2
         )
 
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_learning_async_pipelining_with_interleaving(self):
+        self._forward_backward_test_impl(
+            False, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2, async_comm=True
+        )
+
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_inference_async_pipelining_with_interleaving(self):
+        self._forward_backward_test_impl(
+            True, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2, async_comm=True
+        )
+
 
 class NcclPipelineParallelForwardBackwardTest(NcclDistributedTestBase, PipelineParallelForwardBackwardTestBase):
 
@@ -283,10 +303,10 @@ def _test_hybrid_backends(self, forward_only: bool) -> None:
             ):
                 self._run_hybrid_distributed_backend(forward_only)
 
-    def test_pipelining_without_interleaving_ucc_for_p2p(self):
+    def test_learning_pipelining_without_interleaving_ucc_for_p2p(self):
         self._test_hybrid_backends(False)
 
-    def test_pipelining_without_interleaving_inference_ucc_for_p2p(self):
+    def test_inference_pipelining_without_interleaving_ucc_for_p2p(self):
         self._test_hybrid_backends(True)