[bugfix] respect distributed_executor_backend in world_size=1

tests/engine/test_custom_executor.py → tests/engine/test_executor.py

@@ -55,6 +55,7 @@ def test_custom_executor(model, tmp_path):
         engine_args = EngineArgs(
             model=model,
             distributed_executor_backend=CustomUniExecutor,
+            enforce_eager=True,  # reduce test time
         )
         engine = LLMEngine.from_engine_args(engine_args)
         sampling_params = SamplingParams(max_tokens=1)
@@ -75,7 +76,10 @@ def test_custom_executor_async(model, tmp_path):
         assert not os.path.exists(".marker")
 
         engine_args = AsyncEngineArgs(
-            model=model, distributed_executor_backend=CustomUniExecutorAsync)
+            model=model,
+            distributed_executor_backend=CustomUniExecutorAsync,
+            enforce_eager=True,  # reduce test time
+        )
         engine = AsyncLLMEngine.from_engine_args(engine_args)
         sampling_params = SamplingParams(max_tokens=1)
 
@@ -89,3 +93,18 @@ async def t():
         assert os.path.exists(".marker")
     finally:
         os.chdir(cwd)
+
+
+@pytest.mark.parametrize("model", ["facebook/opt-125m"])
+def test_respect_ray(model):
+    # even for TP=1 and PP=1,
+    # if users specify ray, we should use ray.
+    # users might do this if they want to manage the
+    # resources using ray.
+    engine_args = EngineArgs(
+        model=model,
+        distributed_executor_backend="ray",
+        enforce_eager=True,  # reduce test time
+    )
+    engine = LLMEngine.from_engine_args(engine_args)
+    assert engine.model_executor.uses_ray

vllm/config.py

@@ -1401,6 +1401,9 @@ def __post_init__(self) -> None:
             logger.info("Defaulting to use %s for distributed inference",
                         backend)
 
+        if self.distributed_executor_backend is None and self.world_size == 1:
+            self.distributed_executor_backend = "uni"
+
         self._verify_args()
 
     @property

vllm/engine/llm_engine.py

@@ -434,6 +434,7 @@ def _initialize_kv_caches(self) -> None:
     @classmethod
     def _get_executor_cls(cls,
                           engine_config: VllmConfig) -> Type[ExecutorBase]:
+        # distributed_executor_backend must be set in VllmConfig.__post_init__
         distributed_executor_backend = (
             engine_config.parallel_config.distributed_executor_backend)
         # Initialize the cluster and specify the executor class.
@@ -443,30 +444,29 @@ def _get_executor_cls(cls,
                     "distributed_executor_backend must be a subclass of "
                     f"ExecutorBase. Got {distributed_executor_backend}.")
             executor_class = distributed_executor_backend
-        elif engine_config.parallel_config.world_size > 1:
-            if distributed_executor_backend == "ray":
-                from vllm.executor.ray_distributed_executor import (
-                    RayDistributedExecutor)
-                executor_class = RayDistributedExecutor
-            elif distributed_executor_backend == "mp":
-                from vllm.executor.mp_distributed_executor import (
-                    MultiprocessingDistributedExecutor)
-                assert not envs.VLLM_USE_RAY_SPMD_WORKER, (
-                    "multiprocessing distributed executor backend does not "
-                    "support VLLM_USE_RAY_SPMD_WORKER=1")
-                executor_class = MultiprocessingDistributedExecutor
-            elif distributed_executor_backend == "uni":
-                # JAX-style, single-process, multi-device executor.
-                from vllm.executor.uniproc_executor import UniProcExecutor
-                executor_class = UniProcExecutor
-            elif distributed_executor_backend == "external_launcher":
-                # executor with external launcher
-                from vllm.executor.uniproc_executor import (  # noqa
-                    ExecutorWithExternalLauncher)
-                executor_class = ExecutorWithExternalLauncher
-        else:
+        elif distributed_executor_backend == "ray":
+            from vllm.executor.ray_distributed_executor import (
+                RayDistributedExecutor)
+            executor_class = RayDistributedExecutor
+        elif distributed_executor_backend == "mp":
+            from vllm.executor.mp_distributed_executor import (
+                MultiprocessingDistributedExecutor)
+            assert not envs.VLLM_USE_RAY_SPMD_WORKER, (
+                "multiprocessing distributed executor backend does not "
+                "support VLLM_USE_RAY_SPMD_WORKER=1")
+            executor_class = MultiprocessingDistributedExecutor
+        elif distributed_executor_backend == "uni":
+            # JAX-style, single-process, multi-device executor.
             from vllm.executor.uniproc_executor import UniProcExecutor
             executor_class = UniProcExecutor
+        elif distributed_executor_backend == "external_launcher":
+            # executor with external launcher
+            from vllm.executor.uniproc_executor import (  # noqa
+                ExecutorWithExternalLauncher)
+            executor_class = ExecutorWithExternalLauncher
+        else:
+            raise ValueError("unrecognized distributed_executor_backend: "
+                             f"{distributed_executor_backend}")
         return executor_class
 
     @classmethod

vllm/v1/executor/abstract.py

@@ -25,15 +25,14 @@ def get_class(vllm_config: VllmConfig) -> Type["Executor"]:
         parallel_config = vllm_config.parallel_config
         distributed_executor_backend = (
             parallel_config.distributed_executor_backend)
-        if distributed_executor_backend is None:
-            # If the user does not specify the distributed executor backend,
-            # we will choose the backend based on the world size.
-            if parallel_config.world_size > 1:
-                distributed_executor_backend = "mp"
-            else:
-                distributed_executor_backend = "uni"
-
-        if distributed_executor_backend == "ray":
+        # distributed_executor_backend must be set in VllmConfig.__post_init__
+        if isinstance(distributed_executor_backend, type):
+            if not issubclass(distributed_executor_backend, ExecutorBase):
+                raise TypeError(
+                    "distributed_executor_backend must be a subclass of "
+                    f"ExecutorBase. Got {distributed_executor_backend}.")
+            executor_class = distributed_executor_backend
+        elif distributed_executor_backend == "ray":
             executor_class = RayDistributedExecutor
         elif distributed_executor_backend == "mp":
             from vllm.v1.executor.multiproc_executor import MultiprocExecutor