[misc] remove engine_use_ray (#8126)

tests/async_engine/test_api_server.py

-import os
 import subprocess
 import sys
 import time
@@ -26,8 +25,7 @@ def _query_server_long(prompt: str) -> dict:
 
 
 @pytest.fixture
-def api_server(tokenizer_pool_size: int, engine_use_ray: bool,
-               worker_use_ray: bool):
+def api_server(tokenizer_pool_size: int, worker_use_ray: bool):
     script_path = Path(__file__).parent.joinpath(
         "api_server_async_engine.py").absolute()
     commands = [
@@ -37,25 +35,17 @@ def api_server(tokenizer_pool_size: int, engine_use_ray: bool,
         str(tokenizer_pool_size)
     ]
 
-    # Copy the environment variables and append `VLLM_ALLOW_ENGINE_USE_RAY=1`
-    # to prevent `--engine-use-ray` raises an exception due to it deprecation
-    env_vars = os.environ.copy()
-    env_vars["VLLM_ALLOW_ENGINE_USE_RAY"] = "1"
-
-    if engine_use_ray:
-        commands.append("--engine-use-ray")
     if worker_use_ray:
         commands.append("--worker-use-ray")
-    uvicorn_process = subprocess.Popen(commands, env=env_vars)
+    uvicorn_process = subprocess.Popen(commands)
     yield
     uvicorn_process.terminate()
 
 
 @pytest.mark.parametrize("tokenizer_pool_size", [0, 2])
 @pytest.mark.parametrize("worker_use_ray", [False, True])
-@pytest.mark.parametrize("engine_use_ray", [False, True])
-def test_api_server(api_server, tokenizer_pool_size: int, worker_use_ray: bool,
-                    engine_use_ray: bool):
+def test_api_server(api_server, tokenizer_pool_size: int,
+                    worker_use_ray: bool):
     """
     Run the API server and test it.
 

tests/async_engine/test_async_llm_engine.py

 import asyncio
-import os
 from asyncio import CancelledError
 from dataclasses import dataclass
 from typing import Optional
@@ -72,14 +71,12 @@ class MockEngine:
 
 
 class MockAsyncLLMEngine(AsyncLLMEngine):
-
-    def _init_engine(self, *args, **kwargs):
-        return MockEngine()
+    _engine_class = MockEngine
 
 
 @pytest.mark.asyncio
 async def test_new_requests_event():
-    engine = MockAsyncLLMEngine(worker_use_ray=False, engine_use_ray=False)
+    engine = MockAsyncLLMEngine(worker_use_ray=False)
     engine.start_background_loop()
     await asyncio.sleep(0.01)
     assert engine.engine.step_calls == 0
@@ -112,16 +109,11 @@ async def test_new_requests_event():
     assert engine.engine.add_request_calls == 3
     assert engine.engine.step_calls == old_step_calls + 1
 
-    # Allow deprecated engine_use_ray to not raise exception
-    os.environ["VLLM_ALLOW_ENGINE_USE_RAY"] = "1"
-
-    engine = MockAsyncLLMEngine(worker_use_ray=True, engine_use_ray=True)
+    engine = MockAsyncLLMEngine(worker_use_ray=True)
     assert engine.get_model_config() is not None
     assert engine.get_tokenizer() is not None
     assert engine.get_decoding_config() is not None
 
-    os.environ.pop("VLLM_ALLOW_ENGINE_USE_RAY")
-
 
 def start_engine():
     wait_for_gpu_memory_to_clear(

tests/async_engine/test_openapi_server_ray.py → tests/async_engine/test_openapi_server.py

@@ -19,16 +19,11 @@ def server():
         "--max-model-len",
         "2048",
         "--enforce-eager",
-        "--engine-use-ray",
         "--chat-template",
         str(chatml_jinja_path),
     ]
 
-    # Allow `--engine-use-ray`, otherwise the launch of the server throw
-    # an error due to try to use a deprecated feature
-    env_dict = {"VLLM_ALLOW_ENGINE_USE_RAY": "1"}
-    with RemoteOpenAIServer(MODEL_NAME, args,
-                            env_dict=env_dict) as remote_server:
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
         yield remote_server
 
 

vllm/engine/arg_utils.py

@@ -1035,7 +1035,6 @@ class EngineArgs:
 @dataclass
 class AsyncEngineArgs(EngineArgs):
     """Arguments for asynchronous vLLM engine."""
-    engine_use_ray: bool = False
     disable_log_requests: bool = False
 
     @staticmethod
@@ -1043,16 +1042,6 @@ class AsyncEngineArgs(EngineArgs):
                      async_args_only: bool = False) -> FlexibleArgumentParser:
         if not async_args_only:
             parser = EngineArgs.add_cli_args(parser)
-        parser.add_argument('--engine-use-ray',
-                            action='store_true',
-                            help='Use Ray to start the LLM engine in a '
-                            'separate process as the server process.'
-                            '(DEPRECATED. This argument is deprecated '
-                            'and will be removed in a future update. '
-                            'Set `VLLM_ALLOW_ENGINE_USE_RAY=1` to force '
-                            'use it. See '
-                            'https://github.com/vllm-project/vllm/issues/7045.'
-                            ')')
         parser.add_argument('--disable-log-requests',
                             action='store_true',
                             help='Disable logging requests.')

vllm/engine/async_llm_engine.py

@@ -16,7 +16,7 @@ from vllm.engine.llm_engine import (DecoderPromptComponents, LLMEngine,
                                     PromptComponents, SchedulerOutputState)
 from vllm.engine.metrics_types import StatLoggerBase
 from vllm.executor.executor_base import ExecutorAsyncBase
-from vllm.executor.ray_utils import initialize_ray_cluster, ray
+from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import (EncoderDecoderLLMInputs, LLMInputs, PromptInputs,
                          SingletonPromptInputs)
 from vllm.inputs.parse import is_explicit_encoder_decoder_prompt
@@ -30,7 +30,6 @@ from vllm.sampling_params import SamplingParams
 from vllm.sequence import ExecuteModelRequest
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import print_warning_once
 
 logger = init_logger(__name__)
 ENGINE_ITERATION_TIMEOUT_S = envs.VLLM_ENGINE_ITERATION_TIMEOUT_S
@@ -590,9 +589,6 @@ class AsyncLLMEngine:
         worker_use_ray: Whether to use Ray for model workers. Required for
             distributed execution. Should be the same as
             `parallel_config.worker_use_ray`.
-        engine_use_ray: Whether to make LLMEngine a Ray actor. If so, the
-            async frontend will be executed in a separate process as the
-            model workers.
         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.
@@ -604,41 +600,23 @@ class AsyncLLMEngine:
 
     def __init__(self,
                  worker_use_ray: bool,
-                 engine_use_ray: bool,
                  *args,
                  log_requests: bool = True,
                  start_engine_loop: bool = True,
                  **kwargs) -> None:
         self.worker_use_ray = worker_use_ray
-        self.engine_use_ray = engine_use_ray
         self.log_requests = log_requests
-        self.engine = self._init_engine(*args, **kwargs)
+        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.
         #
-        # TODO: Currently, disabled for engine_use_ray, ask
-        # Cody/Will/Woosuk about this case.
-        self.use_process_request_outputs_callback = not self.engine_use_ray
+        self.use_process_request_outputs_callback = True
         if self.use_process_request_outputs_callback:
             self.engine.process_request_outputs_callback = \
                 self.process_request_outputs
 
-        if self.engine_use_ray:
-            print_warning_once(
-                "DEPRECATED. `--engine-use-ray` is deprecated and will "
-                "be removed in a future update. "
-                "See https://github.com/vllm-project/vllm/issues/7045.")
-
-            if envs.VLLM_ALLOW_ENGINE_USE_RAY:
-                print_warning_once(
-                    "VLLM_ALLOW_ENGINE_USE_RAY is set, force engine use Ray")
-            else:
-                raise ValueError("`--engine-use-ray` is deprecated. "
-                                 "Set `VLLM_ALLOW_ENGINE_USE_RAY=1` to "
-                                 "force use it")
-
         self.background_loop: Optional[asyncio.Future] = None
         # We need to keep a reference to unshielded
         # task as well to prevent it from being garbage
@@ -725,16 +703,11 @@ class AsyncLLMEngine:
         # Create the engine configs.
         engine_config = engine_args.create_engine_config()
 
-        if engine_args.engine_use_ray:
-            from vllm.executor import ray_utils
-            ray_utils.assert_ray_available()
-
         executor_class = cls._get_executor_cls(engine_config)
 
         # Create the async LLM engine.
         engine = cls(
             executor_class.uses_ray,
-            engine_args.engine_use_ray,
             **engine_config.to_dict(),
             executor_class=executor_class,
             log_requests=not engine_args.disable_log_requests,
@@ -777,10 +750,6 @@ class AsyncLLMEngine:
         self,
         lora_request: Optional[LoRARequest] = None,
     ) -> AnyTokenizer:
-        if self.engine_use_ray:
-            return await self.engine.get_tokenizer.remote(  # type: ignore
-                lora_request)
-
         return await (self.engine.get_tokenizer_group().
                       get_lora_tokenizer_async(lora_request))
 
@@ -814,26 +783,6 @@ class AsyncLLMEngine:
             self._background_loop_unshielded = None
         self.background_loop = None
 
-    def _init_engine(self, *args,
-                     **kwargs) -> Union[_AsyncLLMEngine, "ray.ObjectRef"]:
-        if not self.engine_use_ray:
-            engine_class = self._engine_class
-        elif self.worker_use_ray:
-            engine_class = ray.remote(num_cpus=0)(self._engine_class).remote
-        else:
-            # FIXME(woosuk): This is a bit hacky. Be careful when changing the
-            # order of the arguments.
-            cache_config = kwargs["cache_config"]
-            parallel_config = kwargs["parallel_config"]
-            if (parallel_config.tensor_parallel_size == 1
-                    and parallel_config.pipeline_parallel_size == 1):
-                num_gpus = cache_config.gpu_memory_utilization
-            else:
-                num_gpus = 1
-            engine_class = ray.remote(num_gpus=num_gpus)(
-                self._engine_class).remote
-        return engine_class(*args, **kwargs)
-
     async def engine_step(self, virtual_engine: int) -> bool:
         """Kick the engine to process the waiting requests.
 
@@ -844,12 +793,7 @@ class AsyncLLMEngine:
 
         for new_request in new_requests:
             # Add the request into the vLLM engine's waiting queue.
-            # TODO: Maybe add add_request_batch to reduce Ray overhead
             try:
-                if self.engine_use_ray:
-                    await self.engine.add_request.remote(  # type: ignore
-                        **new_request)
-                else:
                 await self.engine.add_request_async(**new_request)
             except ValueError as e:
                 # TODO: use a vLLM specific error for failed validation
@@ -862,9 +806,6 @@ class AsyncLLMEngine:
         if aborted_requests:
             await self._engine_abort(aborted_requests)
 
-        if self.engine_use_ray:
-            request_outputs = await self.engine.step.remote()  # type: ignore
-        else:
         request_outputs = await self.engine.step_async(virtual_engine)
 
         # Put the outputs into the corresponding streams.
@@ -891,15 +832,9 @@ class AsyncLLMEngine:
         return all_finished
 
     async def _engine_abort(self, request_ids: Iterable[str]):
-        if self.engine_use_ray:
-            await self.engine.abort_request.remote(request_ids)  # type: ignore
-        else:
         self.engine.abort_request(request_ids)
 
     async def run_engine_loop(self):
-        if self.engine_use_ray:
-            pipeline_parallel_size = 1  # type: ignore
-        else:
         pipeline_parallel_size = \
                 self.engine.parallel_config.pipeline_parallel_size
         has_requests_in_progress = [False] * pipeline_parallel_size
@@ -912,11 +847,6 @@ class AsyncLLMEngine:
                 # 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.
-                if self.engine_use_ray:
-                    await (self.engine.stop_remote_worker_execution_loop.
-                           remote()  # type: ignore
-                           )
-                else:
                 await self.engine.stop_remote_worker_execution_loop_async()
                 await self._request_tracker.wait_for_new_requests()
                 logger.debug("Got new requests!")
@@ -938,16 +868,8 @@ class AsyncLLMEngine:
                 for task in done:
                     result = task.result()
                     virtual_engine = requests_in_progress.index(task)
-                    if self.engine_use_ray:
-                        has_unfinished_requests = (
-                            await (self.engine.
-                                   has_unfinished_requests_for_virtual_engine.
-                                   remote(  # type: ignore
-                                       virtual_engine)))
-                    else:
                     has_unfinished_requests = (
-                            self.engine.
-                            has_unfinished_requests_for_virtual_engine(
+                        self.engine.has_unfinished_requests_for_virtual_engine(
                             virtual_engine))
                     if result or has_unfinished_requests:
                         requests_in_progress[virtual_engine] = (
@@ -1190,51 +1112,28 @@ class AsyncLLMEngine:
 
     async def get_model_config(self) -> ModelConfig:
         """Get the model configuration of the vLLM engine."""
-        if self.engine_use_ray:
-            return await self.engine.get_model_config.remote()  # type: ignore
-        else:
         return self.engine.get_model_config()
 
     async def get_parallel_config(self) -> ParallelConfig:
         """Get the parallel configuration of the vLLM engine."""
-        if self.engine_use_ray:
-            return await self.engine.get_parallel_config.remote(  # type: ignore
-            )
-        else:
         return self.engine.get_parallel_config()
 
     async def get_decoding_config(self) -> DecodingConfig:
         """Get the decoding configuration of the vLLM engine."""
-        if self.engine_use_ray:
-            return await self.engine.get_decoding_config.remote(  # type: ignore
-            )
-        else:
         return self.engine.get_decoding_config()
 
     async def get_scheduler_config(self) -> SchedulerConfig:
         """Get the scheduling configuration of the vLLM engine."""
-        if self.engine_use_ray:
-            return await self.engine.get_scheduler_config.remote(  # type: ignore
-            )
-        else:
         return self.engine.get_scheduler_config()
 
     async def get_lora_config(self) -> LoRAConfig:
         """Get the lora configuration of the vLLM engine."""
-        if self.engine_use_ray:
-            return await self.engine.get_lora_config.remote(  # type: ignore
-            )
-        else:
         return self.engine.get_lora_config()
 
     async def do_log_stats(
             self,
             scheduler_outputs: Optional[SchedulerOutputs] = None,
             model_output: Optional[List[SamplerOutput]] = None) -> None:
-        if self.engine_use_ray:
-            await self.engine.do_log_stats.remote(  # type: ignore
-                scheduler_outputs, model_output)
-        else:
         self.engine.do_log_stats()
 
     async def check_health(self) -> None:
@@ -1244,36 +1143,16 @@ class AsyncLLMEngine:
         if self.is_stopped:
             raise AsyncEngineDeadError("Background loop is stopped.")
 
-        if self.engine_use_ray:
-            try:
-                await self.engine.check_health.remote()  # type: ignore
-            except ray.exceptions.RayActorError as e:
-                raise RuntimeError("Engine is dead.") from e
-        else:
         await self.engine.check_health_async()
         logger.debug("Health check took %fs", time.perf_counter() - t)
 
     async def is_tracing_enabled(self) -> bool:
-        if self.engine_use_ray:
-            return await self.engine.is_tracing_enabled.remote(  # type: ignore
-            )
-        else:
         return self.engine.is_tracing_enabled()
 
     def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
-        if self.engine_use_ray:
-            ray.get(
-                self.engine.add_logger.remote(  # type: ignore
-                    logger_name=logger_name, logger=logger))
-        else:
         self.engine.add_logger(logger_name=logger_name, logger=logger)
 
     def remove_logger(self, logger_name: str) -> None:
-        if self.engine_use_ray:
-            ray.get(
-                self.engine.remove_logger.remote(  # type: ignore
-                    logger_name=logger_name))
-        else:
         self.engine.remove_logger(logger_name=logger_name)
 
     async def start_profile(self) -> None:

vllm/engine/llm_engine.py

@@ -3,8 +3,8 @@ import time
 from collections import deque
 from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import (TYPE_CHECKING, Any, ClassVar, Deque, Dict, Iterable, List,
-                    Mapping, NamedTuple, Optional)
+from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict,
+                    Iterable, List, Mapping, NamedTuple, Optional)
 from typing import Sequence as GenericSequence
 from typing import Set, Tuple, Type, Union
 
@@ -397,7 +397,7 @@ class LLMEngine:
 
         # Currently used by AsyncLLMEngine to ensure quick append
         # of request outputs to asyncio queues
-        self.process_request_outputs_callback = None
+        self.process_request_outputs_callback: Optional[Callable] = None
 
         # Create the scheduler.
         # NOTE: the cache_config here have been updated with the numbers of

vllm/entrypoints/openai/run_batch.py

@@ -195,7 +195,6 @@ async def main(args):
     engine = AsyncLLMEngine.from_engine_args(
         engine_args, usage_context=UsageContext.OPENAI_BATCH_RUNNER)
 
-    # When using single vLLM without engine_use_ray
     model_config = await engine.get_model_config()
 
     if args.disable_log_requests:

vllm/envs.py

@@ -58,7 +58,6 @@ if TYPE_CHECKING:
     VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
     VLLM_TEST_FORCE_FP8_MARLIN: bool = False
     VLLM_RPC_GET_DATA_TIMEOUT_MS: int = 5000
-    VLLM_ALLOW_ENGINE_USE_RAY: bool = False
     VLLM_PLUGINS: Optional[List[str]] = None
     VLLM_TORCH_PROFILER_DIR: Optional[str] = None
     VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
@@ -391,14 +390,6 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     "VLLM_RPC_GET_DATA_TIMEOUT_MS":
     lambda: int(os.getenv("VLLM_RPC_GET_DATA_TIMEOUT_MS", "5000")),
 
-    # If set, allow running the engine as a separate ray actor,
-    # which is a deprecated feature soon to be removed.
-    # See https://github.com/vllm-project/vllm/issues/7045
-    "VLLM_ALLOW_ENGINE_USE_RAY":
-    lambda:
-    (os.environ.get("VLLM_ALLOW_ENGINE_USE_RAY", "0").strip().lower() in
-     ("1", "true")),
-
     # a list of plugin names to load, separated by commas.
     # if this is not set, it means all plugins will be loaded
     # if this is set to an empty string, no plugins will be loaded