Enable symmetric memory all reduce by default only enabling for TP

.buildkite/test-pipeline.yaml

@@ -164,6 +164,7 @@ steps:
   - tests/v1/test_internal_lb_dp.py
   - tests/v1/test_hybrid_lb_dp.py
   - tests/v1/engine/test_engine_core_client.py
+  - tests/distributed/test_symm_mem_allreduce.py
   commands:
   # test with torchrun tp=2 and external_dp=2
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
@@ -188,6 +189,7 @@ steps:
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s distributed/test_events.py
+  - pytest -v -s distributed/test_symm_mem_allreduce.py
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - pushd ../examples/offline_inference

tests/distributed/test_symm_mem_allreduce.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import queue
 import random
 import typing
 
@@ -10,26 +11,31 @@
 import torch.multiprocessing as mp
 
 import vllm.envs as envs
+from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.distributed.communication_op import tensor_model_parallel_all_reduce
 from vllm.distributed.device_communicators.cuda_communicator import (
     CudaCommunicator)
-from vllm.distributed.parallel_state import (get_tensor_model_parallel_group,
-                                             get_tp_group,
+from vllm.distributed.parallel_state import (get_tp_group,
                                              init_distributed_environment,
                                              initialize_model_parallel)
+from vllm.engine.arg_utils import EngineArgs
+from vllm.engine.llm_engine import LLMEngine
 from vllm.platforms import current_platform
 from vllm.utils import update_environment_variables
 
 torch.manual_seed(42)
 random.seed(44)
 
-test_size_elements = 4 * 1024 * 1024
+test_size_elements = 1024 * 1024
 
 
-def symm_mem_allreduce_worker(local_rank: int, world_size: int):
+def symm_mem_allreduce_worker(local_rank: int, world_size: int, q: mp.Queue):
     monkeypatch = pytest.MonkeyPatch()
-    with monkeypatch.context() as m:
+    config = VllmConfig(parallel_config=ParallelConfig(
+        tensor_parallel_size=world_size))
+
+    with monkeypatch.context() as m, set_current_vllm_config(config):
         m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
         dtype = torch.bfloat16
         device = torch.device(f"cuda:{local_rank}")
@@ -51,22 +57,26 @@ def symm_mem_allreduce_worker(local_rank: int, world_size: int):
                                         get_tp_group().device_communicator)
         symm_mem_comm = cuda_communicator.symm_mem_comm
         if symm_mem_comm is None or symm_mem_comm.disabled:
-            pytest.skip("SymmMemCommunicator is not available or disabled.")
+            # can't use skip under multiprocessing
+            q.put("SymmMemCommunicator is not available or disabled.")
+            return
 
         inp_direct_symm_mem = torch.randint(1,
                                             23, (test_size_elements, ),
                                             dtype=dtype,
                                             device=device)
         if not symm_mem_comm.should_use_symm_mem(inp_direct_symm_mem):
-            pytest.skip(
+            # can't use skip under multiprocessing
+            q.put(
                 "SymmMemCommunicator isn't used for this world and input size."
             )
+            return
 
         original_inp_direct_symm_mem = inp_direct_symm_mem.clone()
         out_direct_symm_mem = symm_mem_comm.all_reduce(inp_direct_symm_mem)
         assert out_direct_symm_mem is not None
 
-        group = get_tensor_model_parallel_group().device_group
+        group = get_tp_group().device_group
         dist.all_reduce(original_inp_direct_symm_mem, group=group)
         torch.testing.assert_close(out_direct_symm_mem,
                                    original_inp_direct_symm_mem,
@@ -100,9 +110,34 @@ def test_symm_mem_allreduce(monkeypatch: pytest.MonkeyPatch, tp_size,
     world_size = tp_size * pipeline_parallel_size
     if world_size > torch.cuda.device_count():
         pytest.skip("Not enough GPUs to run the test.")
+    q = mp.get_context('spawn').Queue()
+    mp.spawn(symm_mem_allreduce_worker,
+             args=(world_size, q),
+             nprocs=world_size)
+    try:
+        val = q.get(timeout=1)
+    except queue.Empty:
+        val = None
+    finally:
+        cleanup_dist_env_and_memory()
+        if val is not None:
+            pytest.skip(val)
 
-    # Enable SymmMemCommunicator
-    monkeypatch.setenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "1")
 
-    mp.spawn(symm_mem_allreduce_worker, args=(world_size, ), nprocs=world_size)
-    cleanup_dist_env_and_memory()
+@pytest.mark.skipif(
+    not current_platform.is_cuda(),
+    reason="SymmMemAllreduce is only available for CUDA platforms.")
+@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"],
+                    reason="Only test on CUDA")
+def test_dp_with_symm_mem_allreduce(monkeypatch: pytest.MonkeyPatch):
+    world_size = 4
+    if world_size > torch.cuda.device_count():
+        pytest.skip("Not enough GPUs to run the test.")
+    # Verify that the DataParallel runs without error
+    engine_args = EngineArgs(model="distilbert/distilgpt2",
+                             enforce_eager=True,
+                             enable_prefix_caching=True,
+                             data_parallel_size=2,
+                             tensor_parallel_size=2,
+                             data_parallel_backend="mp")
+    LLMEngine.from_engine_args(engine_args)

vllm/distributed/device_communicators/cuda_communicator.py

@@ -24,18 +24,21 @@ def __init__(self,
                  unique_name: str = ""):
         super().__init__(cpu_group, device, device_group, unique_name)
         if "tp" not in unique_name:
-            # only tp uses custom allreduce
+            # custom allreduce or torch symm mem can be used only by tp
             use_custom_allreduce = False
+            use_torch_symm_mem = False
         else:
             from vllm.distributed.parallel_state import (
                 _ENABLE_CUSTOM_ALL_REDUCE)
             use_custom_allreduce = _ENABLE_CUSTOM_ALL_REDUCE
+            use_torch_symm_mem = envs.VLLM_ALLREDUCE_USE_SYMM_MEM
 
         # ep does not use pynccl
         use_pynccl = "ep" not in unique_name
 
         self.use_pynccl = use_pynccl
         self.use_custom_allreduce = use_custom_allreduce
+        self.use_torch_symm_mem = use_torch_symm_mem
 
         # lazy import to avoid documentation build error
         from vllm.distributed.device_communicators.custom_all_reduce import (
@@ -57,7 +60,7 @@ def __init__(self,
         self.ca_comm: Optional[CustomAllreduce] = None
         self.qr_comm: Optional[QuickAllReduce] = None
         self.symm_mem_comm: Optional[SymmMemCommunicator] = None
-        if envs.VLLM_ALLREDUCE_USE_SYMM_MEM and current_platform.is_cuda():
+        if use_torch_symm_mem and current_platform.is_cuda():
             self.symm_mem_comm = SymmMemCommunicator(
                 group=self.cpu_group,
                 device=self.device,

vllm/envs.py

@@ -182,7 +182,7 @@
     VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
     VLLM_ROCM_FP8_MFMA_PAGE_ATTN: bool = False
     VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS: bool = False
-    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = False
+    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = True
     VLLM_TUNED_CONFIG_FOLDER: Optional[str] = None
     VLLM_DISABLE_PAD_FOR_CUDAGRAPH: bool = False
     VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
@@ -1368,7 +1368,7 @@ def get_vllm_port() -> Optional[int]:
 
     # Whether to use pytorch symmetric memory for allreduce
     "VLLM_ALLREDUCE_USE_SYMM_MEM":
-    lambda: bool(int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "0"))),
+    lambda: bool(int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "1"))),
 
     # Allows vllm to find tuned config under customized folder
     "VLLM_TUNED_CONFIG_FOLDER":