[BugFix] Fix clean shutdown issues (vllm-project#8492)

tests/async_engine/test_async_llm_engine.py

@@ -26,6 +26,11 @@ class RequestOutput:
     finished: bool = False
 
 
+@dataclass
+class MockModelConfig:
+    use_async_output_proc = True
+
+
 class MockEngine:
 
     def __init__(self):
@@ -35,6 +40,7 @@ def __init__(self):
         self.request_id = None
         # Ugly, remove dependency when possible
         self.parallel_config = ParallelConfig(1, 1, False)
+        self.model_config = MockModelConfig()
 
     async def step_async(self, virtual_engine):
         # PP size is 1, ignore virtual engine
@@ -80,7 +86,7 @@ class MockAsyncLLMEngine(AsyncLLMEngine):
 
 @pytest.mark.asyncio
 async def test_new_requests_event():
-    engine = MockAsyncLLMEngine(worker_use_ray=False)
+    engine = MockAsyncLLMEngine()
     engine.start_background_loop()
     await asyncio.sleep(0.01)
     assert engine.engine.step_calls == 0
@@ -113,7 +119,7 @@ async def test_new_requests_event():
     assert engine.engine.add_request_calls == 3
     assert engine.engine.step_calls == old_step_calls + 1
 
-    engine = MockAsyncLLMEngine(worker_use_ray=True)
+    engine = MockAsyncLLMEngine()
     assert engine.get_model_config() is not None
     assert engine.get_tokenizer() is not None
     assert engine.get_decoding_config() is not None

vllm/engine/async_llm_engine.py

@@ -1,8 +1,10 @@
 import asyncio
 import time
+import weakref
 from functools import partial
 from typing import (Any, AsyncGenerator, Callable, Dict, Iterable, List,
                     Mapping, Optional, Set, Tuple, Type, Union)
+from weakref import ReferenceType
 
 import vllm.envs as envs
 from vllm.config import (DecodingConfig, EngineConfig, LoRAConfig, ModelConfig,
@@ -26,6 +28,7 @@
 from vllm.sequence import ExecuteModelRequest
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.usage.usage_lib import UsageContext
+from vllm.utils import weak_bind
 
 logger = init_logger(__name__)
 ENGINE_ITERATION_TIMEOUT_S = envs.VLLM_ENGINE_ITERATION_TIMEOUT_S
@@ -450,9 +453,6 @@ class AsyncLLMEngine:
     method yields the outputs from the :class:`LLMEngine` to the caller.
 
     Args:
-        worker_use_ray: Whether to use Ray for model workers. Required for
-            distributed execution. Should be the same as
-            `parallel_config.worker_use_ray`.
         log_requests: Whether to log the requests.
         start_engine_loop: If True, the background task to run the engine
             will be automatically started in the generate call.
@@ -463,23 +463,22 @@ class AsyncLLMEngine:
     _engine_class: Type[_AsyncLLMEngine] = _AsyncLLMEngine
 
     def __init__(self,
-                 worker_use_ray: bool,
                  *args,
                  log_requests: bool = True,
                  start_engine_loop: bool = True,
                  **kwargs) -> None:
-        self.worker_use_ray = worker_use_ray
         self.log_requests = log_requests
         self.engine = self._engine_class(*args, **kwargs)
 
         # This ensures quick processing of request outputs
         # so the append to asyncio queues is not delayed,
         # especially for multi-step.
-        #
-        self.use_process_request_outputs_callback = True
+        self.use_process_request_outputs_callback = (
+            self.engine.model_config.use_async_output_proc)
+
         if self.use_process_request_outputs_callback:
             self.engine.process_request_outputs_callback = \
-                self.process_request_outputs
+                weak_bind(self.process_request_outputs)
 
         self.background_loop: Optional[asyncio.Future] = None
         # We need to keep a reference to unshielded
@@ -492,6 +491,11 @@ def __init__(self,
         # Lazy initialized fields
         self._request_tracker: RequestTracker
 
+    def __del__(self):
+        if rt := getattr(self, "request_tracker", None):
+            # Wake up engine loop so that it will exit cleanly
+            rt.new_requests_event.set()
+
     @classmethod
     def _get_executor_cls(
             cls, engine_config: EngineConfig) -> Type[ExecutorAsyncBase]:
@@ -502,15 +506,12 @@ def _get_executor_cls(
                 raise TypeError(
                     "distributed_executor_backend must be a subclass of "
                     f"ExecutorAsyncBase. Got {distributed_executor_backend}.")
-            if distributed_executor_backend.uses_ray:  # type: ignore
-                initialize_ray_cluster(engine_config.parallel_config)
             executor_class = distributed_executor_backend
         elif engine_config.device_config.device_type == "neuron":
             from vllm.executor.neuron_executor import NeuronExecutorAsync
             executor_class = NeuronExecutorAsync
         elif engine_config.device_config.device_type == "tpu":
             if distributed_executor_backend == "ray":
-                initialize_ray_cluster(engine_config.parallel_config)
                 from vllm.executor.ray_tpu_executor import RayTPUExecutorAsync
                 executor_class = RayTPUExecutorAsync
             else:
@@ -531,11 +532,9 @@ def _get_executor_cls(
                 from vllm.executor.xpu_executor import XPUExecutorAsync
                 executor_class = XPUExecutorAsync
             elif distributed_executor_backend == "ray":
-                initialize_ray_cluster(engine_config.parallel_config)
                 from vllm.executor.ray_xpu_executor import RayXPUExecutorAsync
                 executor_class = RayXPUExecutorAsync
             elif distributed_executor_backend == "mp":
-                initialize_ray_cluster(engine_config.parallel_config)
                 from vllm.executor.multiproc_xpu_executor import (
                     MultiprocessingXPUExecutorAsync)
                 executor_class = MultiprocessingXPUExecutorAsync
@@ -552,7 +551,6 @@ def _get_executor_cls(
                 from vllm.executor.npu_executor import NPUExecutorAsync
                 executor_class = NPUExecutorAsync
         elif distributed_executor_backend == "ray":
-            initialize_ray_cluster(engine_config.parallel_config)
             from vllm.executor.ray_gpu_executor import RayGPUExecutorAsync
             executor_class = RayGPUExecutorAsync
         elif distributed_executor_backend == "mp":
@@ -568,19 +566,23 @@ def _get_executor_cls(
     def from_engine_args(
         cls,
         engine_args: AsyncEngineArgs,
+        engine_config: Optional[EngineConfig] = None,
         start_engine_loop: bool = True,
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
         stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
     ) -> "AsyncLLMEngine":
         """Creates an async LLM engine from the engine arguments."""
         # Create the engine configs.
-        engine_config = engine_args.create_engine_config()
+        if engine_config is None:
+            engine_config = engine_args.create_engine_config()
 
         executor_class = cls._get_executor_cls(engine_config)
 
+        if executor_class.uses_ray:
+            initialize_ray_cluster(engine_config.parallel_config)
+
         # Create the async LLM engine.
         engine = cls(
-            executor_class.uses_ray,
             **engine_config.to_dict(),
             executor_class=executor_class,
             log_requests=not engine_args.disable_log_requests,
@@ -637,7 +639,7 @@ def start_background_loop(self) -> None:
         self._request_tracker = RequestTracker()
 
         self._background_loop_unshielded = asyncio.get_event_loop(
-        ).create_task(self.run_engine_loop())
+        ).create_task(self.run_engine_loop(weakref.ref(self)))
         self._background_loop_unshielded.add_done_callback(
             partial(_log_task_completion, error_callback=self._error_callback))
         self.background_loop = asyncio.shield(self._background_loop_unshielded)
@@ -707,9 +709,16 @@ def process_request_outputs(self, request_outputs) -> bool:
     async def _engine_abort(self, request_ids: Iterable[str]):
         self.engine.abort_request(request_ids)
 
-    async def run_engine_loop(self):
+    @staticmethod
+    async def run_engine_loop(engine_ref: ReferenceType):
+        """We use a weakref to the engine so that the running loop
+        doesn't prevent the engine being garbage collected."""
+        engine: Optional["AsyncLLMEngine"] = engine_ref()
+        if not engine:
+            return
+
         pipeline_parallel_size = \
-                self.engine.parallel_config.pipeline_parallel_size
+                engine.engine.parallel_config.pipeline_parallel_size
         has_requests_in_progress = [False] * pipeline_parallel_size
         while True:
             if not any(has_requests_in_progress):
@@ -720,11 +729,21 @@ async def run_engine_loop(self):
                 # timeout, and unblocks the RPC thread in the workers so that
                 # they can process any other queued control plane messages,
                 # such as add/remove lora adapters.
-                await self.engine.stop_remote_worker_execution_loop_async()
-                await self._request_tracker.wait_for_new_requests()
+                await engine.engine.stop_remote_worker_execution_loop_async()
+                request_tracker = engine._request_tracker
+                # Allow engine to be garbage collected while
+                # waiting for new requests
+                del engine
+                await asyncio.sleep(0)
+                if engine_ref() is None:
+                    return
+                await request_tracker.wait_for_new_requests()
+                engine = engine_ref()
+                if not engine:
+                    return
                 logger.debug("Got new requests!")
                 requests_in_progress = [
-                    asyncio.create_task(self.engine_step(ve))
+                    asyncio.create_task(engine.engine_step(ve))
                     for ve in range(pipeline_parallel_size)
                 ]
                 has_requests_in_progress = [True] * pipeline_parallel_size
@@ -742,19 +761,20 @@ async def run_engine_loop(self):
                     result = task.result()
                     virtual_engine = requests_in_progress.index(task)
                     has_unfinished_requests = (
-                        self.engine.has_unfinished_requests_for_virtual_engine(
+                        engine.engine.
+                        has_unfinished_requests_for_virtual_engine(
                             virtual_engine))
                     if result or has_unfinished_requests:
                         requests_in_progress[virtual_engine] = (
                             asyncio.create_task(
-                                self.engine_step(virtual_engine)))
+                                engine.engine_step(virtual_engine)))
                         has_requests_in_progress[virtual_engine] = True
                     else:
                         has_requests_in_progress[virtual_engine] = False
             except asyncio.TimeoutError as exc:
                 logger.error(
                     "Engine iteration timed out. This should never happen!")
-                self.set_errored(exc)
+                engine.set_errored(exc)
                 raise
             await asyncio.sleep(0)
 

vllm/engine/llm_engine.py

@@ -1,8 +1,8 @@
-import functools
 import time
 from collections import deque
 from contextlib import contextmanager
 from dataclasses import dataclass
+from functools import partial
 from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict,
                     Iterable, List, Mapping, NamedTuple, Optional)
 from typing import Sequence as GenericSequence
@@ -51,7 +51,7 @@
     BaseTokenizerGroup, init_tokenizer_from_configs)
 from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
                                   usage_message)
-from vllm.utils import Counter, Device
+from vllm.utils import Counter, Device, weak_bind
 from vllm.version import __version__ as VLLM_VERSION
 
 logger = init_logger(__name__)
@@ -382,11 +382,16 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer:
             for _ in range(self.parallel_config.pipeline_parallel_size)
         ]
 
-        self.async_callbacks = [
-            functools.partial(self._process_model_outputs,
-                              ctx=self.scheduler_contexts[v_id])
-            for v_id in range(self.parallel_config.pipeline_parallel_size)
-        ]
+        if model_config.use_async_output_proc:
+            process_model_outputs = weak_bind(self._process_model_outputs)
+
+            self.async_callbacks = [
+                partial(process_model_outputs,
+                        ctx=self.scheduler_contexts[v_id])
+                for v_id in range(self.parallel_config.pipeline_parallel_size)
+            ]
+        else:
+            self.async_callbacks = []
 
         # Currently used by AsyncLLMEngine to ensure quick append
         # of request outputs to asyncio queues
@@ -879,8 +884,8 @@ def has_unfinished_requests_for_virtual_engine(
         """
         return self.scheduler[virtual_engine].has_unfinished_seqs()
 
+    @staticmethod
     def _process_sequence_group_outputs(
-        self,
         seq_group: SequenceGroup,
         outputs: List[EmbeddingSequenceGroupOutput],
     ) -> None:

vllm/entrypoints/launcher.py

@@ -1,21 +1,20 @@
 import asyncio
 import signal
 from http import HTTPStatus
-from typing import Any
+from typing import Any, Optional
 
 import uvicorn
-from fastapi import FastAPI, Response
+from fastapi import FastAPI, Request, Response
 
 from vllm import envs
 from vllm.engine.async_llm_engine import AsyncEngineDeadError
-from vllm.engine.protocol import AsyncEngineClient
 from vllm.logger import init_logger
 from vllm.utils import find_process_using_port
 
 logger = init_logger(__name__)
 
 
-async def serve_http(app: FastAPI, engine: AsyncEngineClient,
+async def serve_http(app: FastAPI, limit_concurrency: Optional[int],
                      **uvicorn_kwargs: Any):
     logger.info("Available routes are:")
     for route in app.routes:
@@ -29,16 +28,16 @@ async def serve_http(app: FastAPI, engine: AsyncEngineClient,
 
     # Set concurrency limits in uvicorn if running in multiprocessing mode
     # since zmq has maximum socket limit of zmq.constants.SOCKET_LIMIT (65536).
-    if engine.limit_concurrency is not None:
+    if limit_concurrency is not None:
         logger.info(
             "Launching Uvicorn with --limit_concurrency %s. To avoid this "
             "limit at the expense of performance run with "
-            "--disable-frontend-multiprocessing", engine.limit_concurrency)
-        uvicorn_kwargs["limit_concurrency"] = engine.limit_concurrency
+            "--disable-frontend-multiprocessing", limit_concurrency)
+        uvicorn_kwargs["limit_concurrency"] = limit_concurrency
 
     config = uvicorn.Config(app, **uvicorn_kwargs)
     server = uvicorn.Server(config)
-    _add_shutdown_handlers(app, server, engine)
+    _add_shutdown_handlers(app, server)
 
     loop = asyncio.get_running_loop()
 
@@ -68,15 +67,15 @@ async def dummy_shutdown() -> None:
         return server.shutdown()
 
 
-def _add_shutdown_handlers(app: FastAPI, server: uvicorn.Server,
-                           engine: AsyncEngineClient) -> None:
+def _add_shutdown_handlers(app: FastAPI, server: uvicorn.Server) -> None:
     """Adds handlers for fatal errors that should crash the server"""
 
     @app.exception_handler(RuntimeError)
-    async def runtime_error_handler(_, __):
+    async def runtime_error_handler(request: Request, __):
         """On generic runtime error, check to see if the engine has died.
         It probably has, in which case the server will no longer be able to
         handle requests. Trigger a graceful shutdown with a SIGTERM."""
+        engine = request.app.state.engine_client
         if (not envs.VLLM_KEEP_ALIVE_ON_ENGINE_DEATH and engine.errored
                 and not engine.is_running):
             logger.fatal("AsyncLLMEngine has failed, terminating server "