[V1] Refactor get_executor_cls (#11754)

tests/v1/engine/test_engine_core.py

@@ -8,8 +8,8 @@ from vllm import SamplingParams
 from vllm.engine.arg_utils import EngineArgs
 from vllm.platforms import current_platform
 from vllm.v1.engine import EngineCoreRequest
-from vllm.v1.engine.async_llm import AsyncLLM
 from vllm.v1.engine.core import EngineCore
+from vllm.v1.executor.abstract import Executor
 
 if not current_platform.is_cuda():
     pytest.skip(reason="V1 currently only supported on CUDA.",
@@ -43,7 +43,7 @@ def test_engine_core(monkeypatch):
         """Setup the EngineCore."""
         engine_args = EngineArgs(model=MODEL_NAME)
         vllm_config = engine_args.create_engine_config()
-        executor_class = AsyncLLM._get_executor_cls(vllm_config)
+        executor_class = Executor.get_class(vllm_config)
 
         engine_core = EngineCore(vllm_config=vllm_config,
                                  executor_class=executor_class)
@@ -149,7 +149,7 @@ def test_engine_core_advanced_sampling(monkeypatch):
         """Setup the EngineCore."""
         engine_args = EngineArgs(model=MODEL_NAME)
         vllm_config = engine_args.create_engine_config()
-        executor_class = AsyncLLM._get_executor_cls(vllm_config)
+        executor_class = Executor.get_class(vllm_config)
 
         engine_core = EngineCore(vllm_config=vllm_config,
                                  executor_class=executor_class)

tests/v1/engine/test_engine_core_client.py

@@ -11,8 +11,8 @@ from vllm.engine.arg_utils import EngineArgs
 from vllm.platforms import current_platform
 from vllm.usage.usage_lib import UsageContext
 from vllm.v1.engine import EngineCoreRequest
-from vllm.v1.engine.async_llm import AsyncLLM
 from vllm.v1.engine.core_client import EngineCoreClient
+from vllm.v1.executor.abstract import Executor
 
 if not current_platform.is_cuda():
     pytest.skip(reason="V1 currently only supported on CUDA.",
@@ -84,7 +84,7 @@ def test_engine_core_client(monkeypatch, multiprocessing_mode: bool):
         engine_args = EngineArgs(model=MODEL_NAME, compilation_config=3)
         vllm_config = engine_args.create_engine_config(
             UsageContext.UNKNOWN_CONTEXT)
-        executor_class = AsyncLLM._get_executor_cls(vllm_config)
+        executor_class = Executor.get_class(vllm_config)
         client = EngineCoreClient.make_client(
             multiprocess_mode=multiprocessing_mode,
             asyncio_mode=False,
@@ -152,7 +152,7 @@ async def test_engine_core_client_asyncio(monkeypatch):
         engine_args = EngineArgs(model=MODEL_NAME)
         vllm_config = engine_args.create_engine_config(
             usage_context=UsageContext.UNKNOWN_CONTEXT)
-        executor_class = AsyncLLM._get_executor_cls(vllm_config)
+        executor_class = Executor.get_class(vllm_config)
         client = EngineCoreClient.make_client(
             multiprocess_mode=True,
             asyncio_mode=True,

vllm/v1/engine/async_llm.py

@@ -22,7 +22,6 @@ from vllm.v1.engine.core_client import EngineCoreClient
 from vllm.v1.engine.detokenizer import Detokenizer
 from vllm.v1.engine.processor import Processor
 from vllm.v1.executor.abstract import Executor
-from vllm.v1.executor.ray_utils import initialize_ray_cluster
 
 logger = init_logger(__name__)
 
@@ -105,7 +104,7 @@ class AsyncLLM(EngineClient):
         else:
             vllm_config = engine_config
 
-        executor_class = cls._get_executor_cls(vllm_config)
+        executor_class = Executor.get_class(vllm_config)
 
         # Create the AsyncLLM.
         return cls(
@@ -127,24 +126,6 @@ class AsyncLLM(EngineClient):
         if handler := getattr(self, "output_handler", None):
             handler.cancel()
 
-    @classmethod
-    def _get_executor_cls(cls, vllm_config: VllmConfig) -> Type[Executor]:
-        executor_class: Type[Executor]
-        distributed_executor_backend = (
-            vllm_config.parallel_config.distributed_executor_backend)
-        if distributed_executor_backend == "ray":
-            initialize_ray_cluster(vllm_config.parallel_config)
-            from vllm.v1.executor.ray_executor import RayExecutor
-            executor_class = RayExecutor
-        elif distributed_executor_backend == "mp":
-            from vllm.v1.executor.multiproc_executor import MultiprocExecutor
-            executor_class = MultiprocExecutor
-        else:
-            assert (distributed_executor_backend is None)
-            from vllm.v1.executor.uniproc_executor import UniprocExecutor
-            executor_class = UniprocExecutor
-        return executor_class
-
     async def add_request(
         self,
         request_id: str,

vllm/v1/engine/llm_engine.py

@@ -89,7 +89,7 @@ class LLMEngine:
 
         # Create the engine configs.
         vllm_config = engine_args.create_engine_config(usage_context)
-        executor_class = cls._get_executor_cls(vllm_config)
+        executor_class = Executor.get_class(vllm_config)
 
         if VLLM_ENABLE_V1_MULTIPROCESSING:
             logger.debug("Enabling multiprocessing for LLMEngine.")
@@ -103,24 +103,6 @@ class LLMEngine:
                    stat_loggers=stat_loggers,
                    multiprocess_mode=enable_multiprocessing)
 
-    @classmethod
-    def _get_executor_cls(cls, vllm_config: VllmConfig) -> Type[Executor]:
-        executor_class: Type[Executor]
-        distributed_executor_backend = (
-            vllm_config.parallel_config.distributed_executor_backend)
-        if distributed_executor_backend == "ray":
-            from vllm.v1.executor.ray_executor import RayExecutor
-            executor_class = RayExecutor
-        elif distributed_executor_backend == "mp":
-            from vllm.v1.executor.multiproc_executor import MultiprocExecutor
-            executor_class = MultiprocExecutor
-        else:
-            assert (distributed_executor_backend is None)
-            from vllm.v1.executor.uniproc_executor import UniprocExecutor
-            executor_class = UniprocExecutor
-
-        return executor_class
-
     def get_num_unfinished_requests(self) -> int:
         return self.detokenizer.get_num_unfinished_requests()
 

vllm/v1/executor/abstract.py

 from abc import ABC, abstractmethod
-from typing import Tuple
+from typing import Tuple, Type
 
 from vllm.config import VllmConfig
 from vllm.v1.outputs import ModelRunnerOutput
@@ -8,6 +8,23 @@ from vllm.v1.outputs import ModelRunnerOutput
 class Executor(ABC):
     """Abstract class for executors."""
 
+    @staticmethod
+    def get_class(vllm_config: VllmConfig) -> Type["Executor"]:
+        executor_class: Type[Executor]
+        distributed_executor_backend = (
+            vllm_config.parallel_config.distributed_executor_backend)
+        if distributed_executor_backend == "ray":
+            from vllm.v1.executor.ray_executor import RayExecutor
+            executor_class = RayExecutor
+        elif distributed_executor_backend == "mp":
+            from vllm.v1.executor.multiproc_executor import MultiprocExecutor
+            executor_class = MultiprocExecutor
+        else:
+            assert (distributed_executor_backend is None)
+            from vllm.v1.executor.uniproc_executor import UniprocExecutor
+            executor_class = UniprocExecutor
+        return executor_class
+
     @abstractmethod
     def __init__(self, vllm_config: VllmConfig) -> None:
         raise NotImplementedError