[Serve.llm][P/D] Fix health check in prefill disagg

python/ray/llm/_internal/serve/deployments/llm/llm_server.py

@@ -437,10 +437,14 @@ async def __init__(
         """
         await super().__init__(llm_config)
 
-        self._engine_cls = engine_cls or self._default_engine_cls
-        self.engine = self._get_engine_class(self._llm_config)
-        await asyncio.wait_for(self._start_engine(), timeout=ENGINE_START_TIMEOUT_S)
-
+        self._engine_cls = engine_cls or self._get_default_engine_class()
+        self.engine: Optional[LLMEngine] = None
+        if self._engine_cls is not None:
+            self.engine = self._engine_cls(self._llm_config)
+            await asyncio.wait_for(self._start_engine(), timeout=ENGINE_START_TIMEOUT_S)
+
+        # TODO (Kourosh): I think we can completely remove image retriever.
+        # It was missed to get removed.
         self.image_retriever = (
             image_retriever_cls()
             if image_retriever_cls
@@ -466,25 +470,20 @@ async def __init__(
 
         self.response_postprocessor = ResponsePostprocessor()
 
-    @property
-    def _get_engine_class(self) -> Type[LLMEngine]:
+    def _get_default_engine_class(self) -> Type[LLMEngine]:
         """Helper to load the engine class from the environment variable.
-
         This is used for testing or escape-hatch for patching purposes.
         If env variable is not set, it will fallback to the default engine class.
         """
         engine_cls_path = os.environ.get(RAYLLM_VLLM_ENGINE_CLS_ENV)
         if engine_cls_path:
-            try:
-                return import_attr(engine_cls_path)
-            except AttributeError:
-                logger.warning(
-                    f"Failed to import engine class {engine_cls_path}. "
-                    f"Using the default engine class {self._engine_cls}."
-                )
-        return self._engine_cls
+            return import_attr(engine_cls_path)
+        return self._default_engine_cls
 
     async def _start_engine(self):
+        if self.engine is None:
+            raise ValueError("Engine is not set")
+
         await self.engine.start()
 
         # Push telemetry reports for the model in the current deployment.
@@ -616,7 +615,13 @@ async def check_health(self) -> None:
         Check the health of the replica. Does not return anything. Raise error when
         the engine is dead and needs to be restarted.
         """
-        return await self.engine.check_health()
+        if self.engine is None:
+            return
+        try:
+            return await self.engine.check_health()
+        except Exception as e:
+            logger.error("Engine health check failed in LLMServer.check_health: %s", e)
+            raise e
 
     async def embeddings(self, request: EmbeddingRequest) -> LLMEmbeddingsResponse:
         """Runs an embeddings request to the vllm engine, and return the response.

python/ray/llm/_internal/serve/deployments/llm/vllm/vllm_engine.py

@@ -1,4 +1,3 @@
-import asyncio
 import os
 import re
 import time
@@ -816,9 +815,9 @@ async def check_health(self) -> None:
             raise RuntimeError(f"{type(self.engine)} does not support health check.")
 
         try:
-            return await asyncio.wait_for(self.engine.check_health(), timeout=15)
+            await self.engine.check_health()
         except BaseException as e:
-            logger.exception("Healthcheck failed. The replica will be restarted")
+            logger.error("Healthcheck failed. The replica will be restarted")
             raise e from None
 
     @staticmethod

python/ray/llm/_internal/serve/deployments/prefill_decode_disagg/prefill_decode_disagg.py

@@ -1,6 +1,5 @@
 """Using Ray Serve to deploy LLM models with P/D disaggregation.
 """
-import asyncio
 import logging
 import uuid
 from typing import Any, AsyncGenerator, Dict, Union
@@ -63,6 +62,7 @@ def parse_configs_and_cast_type(config: Union[str, LLMConfig]) -> LLMConfig:
 
 
 class PDProxyServer(LLMServer):
+    _default_engine_cls = None
     """
     Proxy between P/D LLM servers.
 
@@ -83,22 +83,13 @@ async def __init__(
         prefill_server: DeploymentHandle,
         decode_server: DeploymentHandle,
     ):
-        class FakeEngine:
-            """Provide a fake engine such that proxy don't really start any engine."""
-
-            def __init__(self, *args, **kwargs):
-                pass
-
-            async def start(self, *args, **kwargs):
-                pass
 
         # We pass `llm_config` here to let super() extract the model_id, such that /v1/models
         # endpoint can work correctly.
         # TODO(lk-chen): refactor LLMRouter <-> LLMServer such that router query model_id through
         # API, instead of passing it in as an argument.
         await super().__init__(
             llm_config,
-            engine_cls=FakeEngine,
         )
 
         self.prefill_server = prefill_server
@@ -160,13 +151,6 @@ async def _predict(
         ):
             yield chunk
 
-    async def check_health(self) -> None:
-        """Check the health of the llm engine."""
-        await asyncio.gather(
-            self.prefill_server.check_health.remote(),
-            self.decode_server.check_health.remote(),
-        )
-
     @classmethod
     def as_deployment(cls) -> serve.Deployment:
         """Turns PDProxyServer into a Ray Serve deployment."""

python/ray/llm/_internal/serve/deployments/routers/router.py

@@ -232,12 +232,6 @@ async def _setup_handle_and_config_maps(
 
     async def check_health(self):
         await self._init_completed.wait()
-        await asyncio.gather(
-            *[
-                handle.check_health.remote()
-                for handle in self._default_serve_handles.values()
-            ]
-        )
 
     def _get_configured_serve_handle(self, model_id: str):
         """Gets a ServeHandle to a model deployment.

python/ray/llm/tests/serve/cpu/deployments/routers/test_router.py

@@ -170,8 +170,6 @@ async def test_check_health(self, llm_config: LLMConfig):
 
         await router.check_health()
 
-        assert server.check_health.remote.call_count == 1
-
 
 if __name__ == "__main__":
     sys.exit(pytest.main(["-v", __file__]))

release/llm_tests/serve/test_llm_serve_fault_tolerance.py

@@ -0,0 +1,95 @@
+import time
+from typing import Literal, List, Generator
+
+import pytest
+import ray
+from ray import serve
+from ray.serve.llm import LLMConfig, ModelLoadingConfig, build_llm_deployment
+
+MODEL_ID = "Qwen/Qwen2.5-0.5B-Instruct"
+RAY_MODEL_ID = "qwen-0.5b"
+
+
+def get_llm_config(
+    tensor_parallel_size: int = 1,
+) -> LLMConfig:
+    """Create LLMConfig with specified parallelism parameters."""
+    return LLMConfig(
+        model_loading_config=ModelLoadingConfig(
+            model_id=RAY_MODEL_ID,
+            model_source=MODEL_ID,
+        ),
+        deployment_config=dict(
+            name="test",
+            num_replicas=2,
+        ),
+        engine_kwargs=dict(
+            tensor_parallel_size=tensor_parallel_size,
+            enforce_eager=True,
+        ),
+        runtime_env={"env_vars": {"VLLM_USE_V1": "1"}},
+    )
+
+
+def find_replica_ids(deployment_name: str) -> List[str]:
+    actors = ray.util.list_named_actors("serve")
+    found_replica_ids = []
+    for actor in actors:
+        if deployment_name in actor["name"]:
+            found_replica_ids.append(actor["name"])
+    return found_replica_ids
+
+
+def kill_replica(replica_id: str) -> None:
+    actor = ray.get_actor(replica_id, namespace="serve")
+    ray.kill(actor)
+
+
+@pytest.fixture(name="app", scope="function")
+def start_ray_serve(
+    tensor_parallel_size: int = 1,
+) -> Generator:
+    """Start Ray Serve with specified parallelism parameters."""
+    llm_config: LLMConfig = get_llm_config(tensor_parallel_size)
+    app = build_llm_deployment(llm_config, name_prefix="LLM:")
+    serve.run(app, blocking=False)
+    yield app
+    serve.shutdown()
+
+
+def wait_for_deployment_status(
+    deployment_name: str, status: Literal["HEALTHY", "UNHEALTHY"], timeout_s: int = 120
+) -> None:
+    s = time.time()
+    while time.time() - s < timeout_s:
+        print(f"Waiting for deployment {deployment_name} to become {status}")
+        state = serve.status()
+        if state.applications["default"].deployments[deployment_name].status == status:
+            return
+        time.sleep(1)
+    raise TimeoutError(
+        f"Deployment {deployment_name} did not become "
+        f"{status} within {timeout_s} seconds"
+    )
+
+
+def test_recovery_from_replica_failure(app) -> None:
+    """Tests that the deployment recovers from replica failure."""
+    dname = "LLM:test"
+    wait_for_deployment_status(dname, "HEALTHY", timeout_s=60)
+
+    # Kill both replicas
+    replica_ids = find_replica_ids(dname)
+    for replica_id in replica_ids:
+        print(f"Killing replica {replica_id}")
+        kill_replica(replica_id)
+
+    # wait for deployment to get unhealthy
+    wait_for_deployment_status(dname, "UNHEALTHY", timeout_s=60)
+
+    # Wait again for deployment to get healthy
+    wait_for_deployment_status(dname, "HEALTHY", timeout_s=60)
+
+
+if __name__ == "__main__":
+    pytest.main(["-xvs", __file__])

release/llm_tests/serve/test_llm_serve_integration.py

@@ -27,6 +27,7 @@ async def test_engine_metrics():
         model="Qwen/Qwen2.5-0.5B-Instruct",
         dtype="auto",
         disable_log_stats=False,
+        enforce_eager=True,
     )
 
     engine = AsyncLLM.from_engine_args(
@@ -75,6 +76,7 @@ def remote_model_app(request):
             enable_chunked_prefill=True,
             enable_prefix_caching=True,
             trust_remote_code=remote_code,
+            enforce_eager=True,
         ),
     }
 

release/release_tests.yaml

@@ -4288,7 +4288,7 @@
     long_running: false
     script: pytest -vs test_llm_serve_correctness.py
 
-- name: llm_serve_integration
+- name: llm_serve_vllm_integration_tests
   frequency: nightly
   python: "3.11"
   group: llm-serve
@@ -4307,7 +4307,7 @@
   run:
     timeout: 3600
     long_running: false
-    script: pytest -vs test_llm_serve_integration.py
+    script: pytest -vs test_llm_serve_integration.py test_llm_serve_fault_tolerance.py
 
 - name: llm_serve_llama_3dot1_8B_quantized_tp1_1p1d
   frequency: nightly