[distributed] remove pynccl's redundant change_state

tests/distributed/test_pynccl.py

@@ -59,8 +59,7 @@ def worker_fn():
                                      device=get_world_group().device)
     tensor = torch.ones(16, 1024, 1024,
                         dtype=torch.float32).cuda(pynccl_comm.rank)
-    with pynccl_comm.change_state(enable=True):
-        tensor = pynccl_comm.all_reduce(tensor)
+    tensor = pynccl_comm.all_reduce(tensor)
     torch.cuda.synchronize()
     assert torch.all(tensor == pynccl_comm.world_size).cpu().item()
 
@@ -81,17 +80,16 @@ def multiple_allreduce_worker_fn():
     group = groups[0] if torch.distributed.get_rank() in [0, 1] else groups[1]
     pynccl_comm = PyNcclCommunicator(group=group, device=device)
     tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
-    with pynccl_comm.change_state(enable=True):
-        # two groups can communicate independently
-        if torch.distributed.get_rank() in [0, 1]:
-            tensor = pynccl_comm.all_reduce(tensor)
-            tensor = pynccl_comm.all_reduce(tensor)
-            torch.cuda.synchronize()
-            assert torch.all(tensor == 4).cpu().item()
-        else:
-            tensor = pynccl_comm.all_reduce(tensor)
-            torch.cuda.synchronize()
-            assert torch.all(tensor == 2).cpu().item()
+    # two groups can communicate independently
+    if torch.distributed.get_rank() in [0, 1]:
+        tensor = pynccl_comm.all_reduce(tensor)
+        tensor = pynccl_comm.all_reduce(tensor)
+        torch.cuda.synchronize()
+        assert torch.all(tensor == 4).cpu().item()
+    else:
+        tensor = pynccl_comm.all_reduce(tensor)
+        torch.cuda.synchronize()
+        assert torch.all(tensor == 2).cpu().item()
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 4,
@@ -137,8 +135,7 @@ def worker_fn_with_cudagraph():
         # run something in the default stream to initialize torch engine
         a = torch.ones((4, 4), device=f'cuda:{pynccl_comm.rank}')
         torch.cuda.synchronize()
-        with torch.cuda.graph(graph), \
-            pynccl_comm.change_state(enable=True):
+        with torch.cuda.graph(graph):
             a_out = pynccl_comm.all_reduce(a)
         torch.cuda.synchronize()
         graph.replay()
@@ -167,8 +164,7 @@ def all_gather_worker_fn():
         for r in range(world_size)
     ]).to(device)
 
-    with pynccl_comm.change_state(enable=True):
-        pynccl_comm.all_gather(result, tensor)
+    pynccl_comm.all_gather(result, tensor)
     torch.cuda.synchronize()
     torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
 
@@ -205,8 +201,7 @@ def reduce_scatter_worker_fn():
     expected = sum(tensor[rank * scattered_size:(rank + 1) * scattered_size]
                    for tensor in all_tensors).to(device)
 
-    with pynccl_comm.change_state(enable=True):
-        pynccl_comm.reduce_scatter(result, tensor)
+    pynccl_comm.reduce_scatter(result, tensor)
     torch.cuda.synchronize()
     torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
 
@@ -233,15 +228,13 @@ def send_recv_worker_fn():
     else:
         tensor = torch.empty(16, 1024, 1024,
                              dtype=torch.float32).cuda(pynccl_comm.rank)
-    with pynccl_comm.change_state(enable=True):
-        if pynccl_comm.rank == 0:
-            pynccl_comm.send(tensor,
-                             dst=(pynccl_comm.rank + 1) %
-                             pynccl_comm.world_size)
-        else:
-            pynccl_comm.recv(tensor,
-                             src=(pynccl_comm.rank - 1) %
-                             pynccl_comm.world_size)
+
+    if pynccl_comm.rank == 0:
+        pynccl_comm.send(tensor,
+                         dst=(pynccl_comm.rank + 1) % pynccl_comm.world_size)
+    else:
+        pynccl_comm.recv(tensor,
+                         src=(pynccl_comm.rank - 1) % pynccl_comm.world_size)
     torch.cuda.synchronize()
     assert torch.all(tensor == 1).cpu().item()
 
@@ -272,15 +265,12 @@ def multiple_send_recv_worker_fn():
                              1024,
                              dtype=torch.float32,
                              device=device)
-    with pynccl_comm.change_state(enable=True):
-        if torch.distributed.get_rank() in [0, 1]:
-            pynccl_comm.send(tensor,
-                             dst=(pynccl_comm.rank + 1) %
-                             pynccl_comm.world_size)
-        else:
-            pynccl_comm.recv(tensor,
-                             src=(pynccl_comm.rank - 1) %
-                             pynccl_comm.world_size)
+    if torch.distributed.get_rank() in [0, 1]:
+        pynccl_comm.send(tensor,
+                         dst=(pynccl_comm.rank + 1) % pynccl_comm.world_size)
+    else:
+        pynccl_comm.recv(tensor,
+                         src=(pynccl_comm.rank - 1) % pynccl_comm.world_size)
     torch.cuda.synchronize()
     if torch.distributed.get_rank() in [0, 2]:
         assert torch.all(tensor == 1).cpu().item()

vllm/distributed/device_communicators/pynccl.py

@@ -1,4 +1,3 @@
-from contextlib import contextmanager
 from typing import Optional, Union
 
 # ===================== import region =====================
@@ -213,19 +212,3 @@ def broadcast(self, tensor: torch.Tensor, src: int, stream=None):
         self.nccl.ncclBroadcast(sendbuff, recvbuff, tensor.numel(),
                                 ncclDataTypeEnum.from_torch(tensor.dtype), src,
                                 self.comm, cudaStream_t(stream.cuda_stream))
-
-    @contextmanager
-    def change_state(self, enable: Optional[bool] = None):
-        """
-        A context manager to change the state of the communicator.
-        """
-        if enable is None:
-            # guess a default value when not specified
-            enable = self.available
-
-        old_disable = self.disabled
-
-        self.disabled = not enable
-        yield
-
-        self.disabled = old_disable

vllm/distributed/parallel_state.py

@@ -305,14 +305,7 @@ def graph_capture(
             stream.wait_stream(curr_stream)
 
         with torch.cuda.stream(stream), maybe_ca_context:
-            pynccl_comm = self.pynccl_comm
-            maybe_pynccl_context: Any
-            if not pynccl_comm:
-                maybe_pynccl_context = nullcontext()
-            else:
-                maybe_pynccl_context = pynccl_comm.change_state()
-            with maybe_pynccl_context:
-                yield graph_capture_context
+            yield graph_capture_context
 
     def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
         """