preload heavy modules when mp method is forkserver (#22214)

Signed-off-by: Lionel Villard <villard@us.ibm.com>

vllm/benchmarks/latency.py

@@ -13,7 +13,6 @@ import numpy as np
 from tqdm import tqdm
 
 import vllm.envs as envs
-from vllm import LLM, SamplingParams
 from vllm.benchmarks.lib.utils import (convert_to_pytorch_benchmark_format,
                                        write_to_json)
 from vllm.engine.arg_utils import EngineArgs
@@ -85,6 +84,9 @@ def main(args: argparse.Namespace):
             "Please set it to a valid path to use torch profiler.")
     engine_args = EngineArgs.from_cli_args(args)
 
+    # Lazy import to avoid importing LLM when the bench command is not selected.
+    from vllm import LLM, SamplingParams
+
     # NOTE(woosuk): If the request cannot be processed in a single batch,
     # the engine will automatically process the request in multiple batches.
     llm = LLM(**dataclasses.asdict(engine_args))

vllm/entrypoints/openai/api_server.py

@@ -8,6 +8,7 @@ import importlib
 import inspect
 import json
 import multiprocessing
+import multiprocessing.forkserver as forkserver
 import os
 import signal
 import socket
@@ -155,6 +156,15 @@ async def build_async_engine_client(
     client_config: Optional[dict[str, Any]] = None,
 ) -> AsyncIterator[EngineClient]:
 
+    if os.getenv("VLLM_WORKER_MULTIPROC_METHOD") == "forkserver":
+        # The executor is expected to be mp.
+        # Pre-import heavy modules in the forkserver process
+        logger.debug("Setup forkserver with pre-imports")
+        multiprocessing.set_start_method('forkserver')
+        multiprocessing.set_forkserver_preload(["vllm.v1.engine.async_llm"])
+        forkserver.ensure_running()
+        logger.debug("Forkserver setup complete!")
+
     # Context manager to handle engine_client lifecycle
     # Ensures everything is shutdown and cleaned up on error/exit
     engine_args = AsyncEngineArgs.from_cli_args(args)