[Core] Improve choice of Python multiprocessing method (vllm-project#8823)

Signed-off-by: Russell Bryant <[email protected]>
Co-authored-by: youkaichao <[email protected]>
Signed-off-by: Amit Garg <[email protected]>

Author: 2 people

vllm/executor/multiproc_gpu_executor.py

@@ -15,8 +15,8 @@
 from vllm.sequence import ExecuteModelRequest
 from vllm.triton_utils import maybe_set_triton_cache_manager
 from vllm.utils import (_run_task_with_lock, cuda_device_count_stateless,
-                        get_distributed_init_method, get_open_port,
-                        get_vllm_instance_id, make_async,
+                        cuda_is_initialized, get_distributed_init_method,
+                        get_open_port, get_vllm_instance_id, make_async,
                         update_environment_variables)
 
 logger = init_logger(__name__)
@@ -122,6 +122,13 @@ def _check_executor_parameters(self):
                 "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
             })
 
+        if (cuda_is_initialized()
+                and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") != "spawn"):
+            logger.warning("CUDA was previously initialized. We must use "
+                           "the `spawn` multiprocessing start method. Setting "
+                           "VLLM_WORKER_MULTIPROC_METHOD to 'spawn'.")
+            os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
         cuda_device_count = cuda_device_count_stateless()
         # Use confusing message for more common TP-only case.
         assert tensor_parallel_size <= cuda_device_count, (

vllm/executor/multiproc_worker_utils.py

@@ -27,9 +27,6 @@
 
 JOIN_TIMEOUT_S = 2
 
-mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD
-mp = multiprocessing.get_context(mp_method)
-
 
 @dataclass
 class Result(Generic[T]):
@@ -77,7 +74,7 @@ class ResultHandler(threading.Thread):
 
     def __init__(self) -> None:
         super().__init__(daemon=True)
-        self.result_queue = mp.Queue()
+        self.result_queue = get_mp_context().Queue()
         self.tasks: Dict[uuid.UUID, Union[ResultFuture, asyncio.Future]] = {}
 
     def run(self):
@@ -147,10 +144,11 @@ class ProcessWorkerWrapper:
 
     def __init__(self, result_handler: ResultHandler,
                  worker_factory: Callable[[], Any]) -> None:
-        self._task_queue = mp.Queue()
+        self.mp = get_mp_context()
+        self._task_queue = self.mp.Queue()
         self.result_queue = result_handler.result_queue
         self.tasks = result_handler.tasks
-        self.process: BaseProcess = mp.Process(  # type: ignore[attr-defined]
+        self.process: BaseProcess = self.mp.Process(  # type: ignore[attr-defined]
             target=_run_worker_process,
             name="VllmWorkerProcess",
             kwargs=dict(
@@ -204,7 +202,7 @@ def _run_worker_process(
     """Worker process event loop"""
 
     # Add process-specific prefix to stdout and stderr
-    process_name = mp.current_process().name
+    process_name = get_mp_context().current_process().name
     pid = os.getpid()
     _add_prefix(sys.stdout, process_name, pid)
     _add_prefix(sys.stderr, process_name, pid)
@@ -269,3 +267,8 @@ def write_with_prefix(s: str):
 
     file.start_new_line = True  # type: ignore[attr-defined]
     file.write = write_with_prefix  # type: ignore[method-assign]
+
+
+def get_mp_context():
+    mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD
+    return multiprocessing.get_context(mp_method)

vllm/scripts.py

@@ -12,8 +12,11 @@
 from vllm.engine.arg_utils import EngineArgs
 from vllm.entrypoints.openai.api_server import run_server
 from vllm.entrypoints.openai.cli_args import make_arg_parser
+from vllm.logger import init_logger
 from vllm.utils import FlexibleArgumentParser
 
+logger = init_logger(__name__)
+
 
 def register_signal_handlers():
 
@@ -114,7 +117,30 @@ def _add_query_options(
     return parser
 
 
+def env_setup():
+    # The safest multiprocessing method is `spawn`, as the default `fork` method
+    # is not compatible with some accelerators. The default method will be
+    # changing in future versions of Python, so we should use it explicitly when
+    # possible.
+    #
+    # We only set it here in the CLI entrypoint, because changing to `spawn`
+    # could break some existing code using vLLM as a library. `spawn` will cause
+    # unexpected behavior if the code is not protected by
+    # `if __name__ == "__main__":`.
+    #
+    # References:
+    # - https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
+    # - https://pytorch.org/docs/stable/notes/multiprocessing.html#cuda-in-multiprocessing
+    # - https://pytorch.org/docs/stable/multiprocessing.html#sharing-cuda-tensors
+    # - https://docs.habana.ai/en/latest/PyTorch/Getting_Started_with_PyTorch_and_Gaudi/Getting_Started_with_PyTorch.html?highlight=multiprocessing#torch-multiprocessing-for-dataloaders
+    if "VLLM_WORKER_MULTIPROC_METHOD" not in os.environ:
+        logger.debug("Setting VLLM_WORKER_MULTIPROC_METHOD to 'spawn'")
+        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+
 def main():
+    env_setup()
+
     parser = FlexibleArgumentParser(description="vLLM CLI")
     subparsers = parser.add_subparsers(required=True)
 

vllm/utils.py

@@ -1091,6 +1091,13 @@ def cuda_device_count_stateless() -> int:
     return _cuda_device_count_stateless(envs.CUDA_VISIBLE_DEVICES)
 
 
+def cuda_is_initialized() -> bool:
+    """Check if CUDA is initialized."""
+    if not torch.cuda._is_compiled():
+        return False
+    return torch.cuda.is_initialized()
+
+
 def weak_bind(bound_method: Callable[..., Any], ) -> Callable[..., None]:
     """Make an instance method that weakly references
     its associated instance and no-ops once that