[Frontend] Kill the server on engine death (#6594)

Signed-off-by: Joe Runde <[email protected]>
Signed-off-by: Joe Runde <[email protected]>

tests/entrypoints/openai/test_shutdown.py

@@ -0,0 +1,47 @@
+import json
+import os
+
+import openai
+import pytest
+
+from ...utils import RemoteOpenAIServer
+
+MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+
+
+@pytest.mark.asyncio
+async def test_shutdown_on_engine_failure(tmp_path):
+    # Use a bad adapter to crash the engine
+    # (This test will fail when that bug is fixed)
+    adapter_path = tmp_path / "bad_adapter"
+    os.mkdir(adapter_path)
+    with open(adapter_path / "adapter_model_config.json", "w") as f:
+        json.dump({"not": "real"}, f)
+    with open(adapter_path / "adapter_model.safetensors", "wb") as f:
+        f.write(b"this is fake")
+
+    # dtype, max-len etc set so that this can run in CI
+    args = [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+        "--max-num-seqs",
+        "128",
+        "--enable-lora",
+        "--lora-modules",
+        f"bad-adapter={tmp_path / 'bad_adapter'}",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        client = remote_server.get_async_client()
+
+        with pytest.raises(openai.APIConnectionError):
+            # This crashes the engine
+            await client.completions.create(model="bad-adapter",
+                                            prompt="Hello, my name is")
+
+        # Now the server should shut down
+        return_code = remote_server.proc.wait(timeout=1)
+        assert return_code is not None

vllm/engine/async_llm_engine.py

@@ -58,7 +58,7 @@ def _log_task_completion(task: asyncio.Task,
         error_callback(exception)
         raise AsyncEngineDeadError(
             "Task finished unexpectedly. This should never happen! "
-            "Please open an issue on Github. See stack trace above for the"
+            "Please open an issue on Github. See stack trace above for the "
             "actual cause.") from e
 
 
@@ -132,7 +132,9 @@ def propagate_exception(self,
             self._request_streams[request_id].put(exc)
             self.abort_request(request_id)
         else:
-            for rid, stream in self._request_streams.items():
+            # NB: list() used here because self.abort_request pops the stream
+            # out of self._request_streams, so we can't iterate on it directly
+            for rid, stream in list(self._request_streams.items()):
                 stream.put(exc)
                 self.abort_request(rid)
 

vllm/entrypoints/api_server.py

@@ -118,6 +118,7 @@ async def run_server(args: Namespace,
 
     shutdown_task = await serve_http(
         app,
+        engine=engine,
         host=args.host,
         port=args.port,
         log_level=args.log_level,

vllm/entrypoints/launcher.py

@@ -1,16 +1,21 @@
 import asyncio
 import signal
+from http import HTTPStatus
 from typing import Any
 
 import uvicorn
-from fastapi import FastAPI
+from fastapi import FastAPI, 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
 
 logger = init_logger(__name__)
 
 
-async def serve_http(app: FastAPI, **uvicorn_kwargs: Any):
+async def serve_http(app: FastAPI, engine: AsyncEngineClient,
+                     **uvicorn_kwargs: Any):
     logger.info("Available routes are:")
     for route in app.routes:
         methods = getattr(route, "methods", None)
@@ -23,6 +28,7 @@ async def serve_http(app: FastAPI, **uvicorn_kwargs: Any):
 
     config = uvicorn.Config(app, **uvicorn_kwargs)
     server = uvicorn.Server(config)
+    _add_shutdown_handlers(app, server, engine)
 
     loop = asyncio.get_running_loop()
 
@@ -44,3 +50,37 @@ async def dummy_shutdown() -> None:
     except asyncio.CancelledError:
         logger.info("Gracefully stopping http server")
         return server.shutdown()
+
+
+def _add_shutdown_handlers(app: FastAPI, server: uvicorn.Server,
+                           engine: AsyncEngineClient) -> None:
+    """Adds handlers for fatal errors that should crash the server"""
+
+    @app.exception_handler(RuntimeError)
+    async def runtime_error_handler(_, __):
+        """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."""
+        if (not envs.VLLM_KEEP_ALIVE_ON_ENGINE_DEATH and engine.errored
+                and not engine.is_running):
+            logger.fatal("AsyncLLMEngine has failed, terminating server "
+                         "process")
+            # See discussions here on shutting down a uvicorn server
+            # https://github.com/encode/uvicorn/discussions/1103
+            # In this case we cannot await the server shutdown here because
+            # this handler must first return to close the connection for
+            # this request.
+            server.should_exit = True
+
+        return Response(status_code=HTTPStatus.INTERNAL_SERVER_ERROR)
+
+    @app.exception_handler(AsyncEngineDeadError)
+    async def engine_dead_handler(_, __):
+        """Kill the server if the async engine is already dead. It will
+        not handle any further requests."""
+        if not envs.VLLM_KEEP_ALIVE_ON_ENGINE_DEATH:
+            logger.fatal("AsyncLLMEngine is already dead, terminating server "
+                         "process")
+            server.should_exit = True
+
+        return Response(status_code=HTTPStatus.INTERNAL_SERVER_ERROR)

vllm/entrypoints/openai/api_server.py

@@ -357,6 +357,7 @@ async def run_server(args, **uvicorn_kwargs) -> None:
 
         shutdown_task = await serve_http(
             app,
+            engine=async_engine_client,
             host=args.host,
             port=args.port,
             log_level=args.uvicorn_log_level,

vllm/entrypoints/openai/rpc/client.py

@@ -33,6 +33,7 @@ async def setup(self):
 
         # Wait until server is ready.
         await self.wait_for_server()
+        self._errored = False
 
         # Get the configs.
         self.model_config = await self._get_model_config_rpc()
@@ -169,15 +170,15 @@ async def _get_scheduler_config_rpc(self) -> SchedulerConfig:
             expected_type=SchedulerConfig,
             error_message="Could not get SchedulerConfig from RPC Server")
 
-    async def _get_lora_config_rpc(self):
+    async def _get_lora_config_rpc(self) -> LoRAConfig:
         """Get LoRAConfig from the RPCServer"""
 
         return await self._send_get_data_rpc_request(
             RPCUtilityRequest.GET_LORA_CONFIG,
             expected_type=LoRAConfig,
             error_message="Could not get LoRAConfig from RPC Server")
 
-    async def _is_tracing_enabled_rpc(self) -> ParallelConfig:
+    async def _is_tracing_enabled_rpc(self) -> bool:
         """Get is_tracing_enabled flag from the RPCServer"""
 
         return await self._send_get_data_rpc_request(
@@ -200,6 +201,18 @@ async def do_log_stats(self):
             request=RPCUtilityRequest.DO_LOG_STATS,
             error_message="RPCRequest DO_LOG_STATS failed.")
 
+    @property
+    def is_running(self) -> bool:
+        return not self._errored
+
+    @property
+    def is_stopped(self) -> bool:
+        return self._errored
+
+    @property
+    def errored(self) -> bool:
+        return self._errored
+
     async def generate(
         self,
         inputs: PromptInputs,
@@ -233,6 +246,15 @@ async def generate(
                     request_output = cloudpickle.loads(message)
 
                     if isinstance(request_output, Exception):
+                        # On exception, check if the server is still healthy.
+                        # Use this to set the sync `is_running` and `errored`
+                        # properties.
+                        try:
+                            await self.check_health()
+                        except Exception:
+                            self._errored = True
+                        # NB: do before raising here so that the flag is set
+                        # by the time the caller receives this exception
                         raise request_output
 
                     finished = request_output.finished

vllm/entrypoints/openai/rpc/server.py

@@ -96,14 +96,17 @@ async def is_server_ready(self, identity):
 
     async def abort(self, identity, request: RPCAbortRequest):
         """Abort request and notify the client of success."""
-        # Abort the request in the llm engine.
-        await self.engine.abort(request.request_id)
-
-        # Send confirmation to the client.
-        await self.socket.send_multipart([
-            identity,
-            cloudpickle.dumps(VLLM_RPC_SUCCESS_STR),
-        ])
+        try:
+            # Abort the request in the llm engine.
+            await self.engine.abort(request.request_id)
+        except Exception:
+            logger.warning("Failed to abort request %s", request.request_id)
+        finally:
+            # Send confirmation to the client.
+            await self.socket.send_multipart([
+                identity,
+                cloudpickle.dumps(VLLM_RPC_SUCCESS_STR),
+            ])
 
     async def generate(self, identity, generate_request: RPCGenerateRequest):
         try:

vllm/envs.py

@@ -49,6 +49,7 @@
     NVCC_THREADS: Optional[str] = None
     VLLM_USE_PRECOMPILED: bool = False
     VLLM_NO_DEPRECATION_WARNING: bool = False
+    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
     CMAKE_BUILD_TYPE: Optional[str] = None
     VERBOSE: bool = False
     VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
@@ -335,6 +336,11 @@ def get_default_config_root():
     "VLLM_NO_DEPRECATION_WARNING":
     lambda: bool(int(os.getenv("VLLM_NO_DEPRECATION_WARNING", "0"))),
 
+    # If set, the OpenAI API server will stay alive even after the underlying
+    # AsyncLLMEngine errors and stops serving requests
+    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH":
+    lambda: bool(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", 0)),
+
     # If the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN is set, it allows
     # the user to specify a max sequence length greater than
     # the max length derived from the model's config.json.