# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import multiprocessing import os import pickle import queue import signal import threading import time import traceback import weakref from concurrent.futures import Future, ThreadPoolExecutor from dataclasses import dataclass from enum import Enum, auto from functools import cached_property, partial from multiprocessing.connection import Connection from multiprocessing.process import BaseProcess from multiprocessing.synchronize import Lock as LockType from threading import Thread from typing import Any, Callable, Optional, Union, cast import cloudpickle import torch import vllm.envs as envs from vllm.config import VllmConfig from vllm.distributed import (destroy_distributed_environment, destroy_model_parallel) from vllm.distributed.device_communicators.shm_broadcast import (Handle, MessageQueue) from vllm.distributed.parallel_state import (get_dp_group, get_ep_group, get_pp_group, get_tp_group) from vllm.logger import init_logger from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.cache import worker_receiver_cache_from_config from vllm.utils import (_maybe_force_spawn, decorate_logs, get_distributed_init_method, get_loopback_ip, get_mp_context, get_open_port, set_process_title) from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.executor.abstract import Executor, FailureCallback from vllm.v1.executor.utils import get_and_update_mm_cache from vllm.v1.outputs import (AsyncModelRunnerOutput, DraftTokenIds, ModelRunnerOutput) from vllm.worker.worker_base import WorkerWrapperBase logger = init_logger(__name__) class MultiprocExecutor(Executor): supports_pp: bool = True def _init_executor(self) -> None: # Call self.shutdown at exit to clean up # and ensure workers will be terminated. self._finalizer = weakref.finalize(self, self.shutdown) self.is_failed = False self.shutdown_event = threading.Event() self.failure_callback: Optional[FailureCallback] = None self.io_thread_pool: Optional[ThreadPoolExecutor] = None self.world_size = self.parallel_config.world_size tensor_parallel_size = self.parallel_config.tensor_parallel_size pp_parallel_size = self.parallel_config.pipeline_parallel_size assert self.world_size == tensor_parallel_size * pp_parallel_size, ( f"world_size ({self.world_size}) must be equal to the " f"tensor_parallel_size ({tensor_parallel_size}) x pipeline" f"_parallel_size ({pp_parallel_size}). ") # Set multiprocessing envs set_multiprocessing_worker_envs() # Multiprocessing-based executor does not support multi-node setting. # Since it only works for single node, we can use the loopback address # get_loopback_ip() for communication. distributed_init_method = get_distributed_init_method( get_loopback_ip(), get_open_port()) # Initialize worker and set up message queues for SchedulerOutputs # and ModelRunnerOutputs max_chunk_bytes = envs.VLLM_MQ_MAX_CHUNK_BYTES_MB * 1024 * 1024 self.rpc_broadcast_mq = MessageQueue(self.world_size, self.world_size, max_chunk_bytes=max_chunk_bytes) scheduler_output_handle = self.rpc_broadcast_mq.export_handle() # Create workers context = get_mp_context() shared_worker_lock = context.Lock() unready_workers: list[UnreadyWorkerProcHandle] = [] success = False try: for rank in range(self.world_size): unready_workers.append( WorkerProc.make_worker_process( vllm_config=self.vllm_config, local_rank=rank, rank=rank, distributed_init_method=distributed_init_method, input_shm_handle=scheduler_output_handle, shared_worker_lock=shared_worker_lock, )) # Workers must be created before wait_for_ready to avoid # deadlock, since worker.init_device() does a device sync. self.workers = WorkerProc.wait_for_ready(unready_workers) # Ensure message queues are ready. Will deadlock if re-ordered # Must be kept consistent with the WorkerProc. self.rpc_broadcast_mq.wait_until_ready() for w in self.workers: w.worker_response_mq.wait_until_ready() self.start_worker_monitor() success = True finally: if not success: # Clean up the worker procs if there was a failure. # Close death_writers first to signal workers to exit for uw in unready_workers: if uw.death_writer is not None: uw.death_writer.close() self._ensure_worker_termination( [uw.proc for uw in unready_workers]) # For pipeline parallel, we use a thread pool for asynchronous # execute_model. if self.max_concurrent_batches > 1: # Note: must use only 1 IO thread to keep dequeue sequence # from the response queue # _async_aggregate_workers_output also assumes a single IO thread self.io_thread_pool = ThreadPoolExecutor( max_workers=1, thread_name_prefix="mp_exec_io") self.output_rank = self._get_output_rank() self.has_connector = self.vllm_config.kv_transfer_config is not None def start_worker_monitor(self): workers = self.workers self_ref = weakref.ref(self) # Monitors worker process liveness. If any die unexpectedly, # logs an error, shuts down the executor and invokes the failure # callback to inform the engine. def monitor_workers(): sentinels = [h.proc.sentinel for h in workers] died = multiprocessing.connection.wait(sentinels) _self = self_ref() if not _self or getattr(_self, 'shutting_down', False): return _self.is_failed = True proc_name = next(h.proc.name for h in workers if h.proc.sentinel == died[0]) logger.error( "Worker proc %s died unexpectedly, " "shutting down executor.", proc_name) _self.shutdown() callback = _self.failure_callback if callback is not None: _self.failure_callback = None callback() Thread(target=monitor_workers, daemon=True, name="MultiprocWorkerMonitor").start() def register_failure_callback(self, callback: FailureCallback): if self.is_failed: callback() else: self.failure_callback = callback def execute_model( self, scheduler_output: SchedulerOutput, non_block: bool = False, ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]: if not self.has_connector: # get output only from a single worker (output_rank) (output, ) = self.collective_rpc( "execute_model", args=(scheduler_output, ), unique_reply_rank=self.output_rank, non_block=non_block, timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS) return output # get output from all workers outputs = self.collective_rpc( "execute_model", args=(scheduler_output, ), non_block=non_block, timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS) # aggregate all workers output to a single output if non_block: return self.kv_output_aggregator.async_aggregate( outputs, self.output_rank) return self.kv_output_aggregator.aggregate(outputs, self.output_rank) def execute_dummy_batch(self) -> None: self.collective_rpc("execute_dummy_batch", unique_reply_rank=self.output_rank) def take_draft_token_ids(self) -> Optional[DraftTokenIds]: # OPTIMIZATION: Get output only from a single worker (output_rank) outputs = self.collective_rpc("take_draft_token_ids", unique_reply_rank=self.output_rank) return outputs[0] def collective_rpc(self, method: Union[str, Callable], timeout: Optional[float] = None, args: tuple = (), kwargs: Optional[dict] = None, non_block: bool = False, unique_reply_rank: Optional[int] = None) -> list[Any]: if self.is_failed: raise RuntimeError("Executor failed.") deadline = None if timeout is None else time.monotonic() + timeout kwargs = kwargs or {} # NOTE: If the args are heterogeneous, then we pack them into a list, # and unpack them in the method of every worker, because every worker # knows their own rank. try: if isinstance(method, str): send_method = method else: send_method = cloudpickle.dumps( method, protocol=pickle.HIGHEST_PROTOCOL) self.rpc_broadcast_mq.enqueue( (send_method, args, kwargs, unique_reply_rank)) workers = (self.workers[unique_reply_rank], ) if unique_reply_rank is not None else self.workers responses = [] def get_response(w: WorkerProcHandle, dequeue_timeout: Optional[float] = None, cancel_event: Optional[threading.Event] = None): status, result = w.worker_response_mq.dequeue( timeout=dequeue_timeout, cancel=cancel_event) if status != WorkerProc.ResponseStatus.SUCCESS: raise RuntimeError( f"Worker failed with error '{result}', please check the" " stack trace above for the root cause") return result for w in workers: dequeue_timeout = None if deadline is None else ( deadline - time.monotonic()) if self.io_thread_pool is not None: # We must consume worker_response_mq from a single thread. result = self.io_thread_pool.submit( # type: ignore get_response, w, dequeue_timeout, self.shutdown_event) if not non_block: result = result.result() elif not non_block: result = get_response(w, dequeue_timeout, self.shutdown_event) else: raise RuntimeError("non_block can only be used when" " max_concurrent_batches > 1") responses.append(result) return responses except TimeoutError as e: raise TimeoutError(f"RPC call to {method} timed out.") from e @staticmethod def _ensure_worker_termination(worker_procs: list[BaseProcess]): """Ensure that all worker processes are terminated. Assumes workers have received termination requests. Waits for processing, then sends termination and kill signals if needed.""" def wait_for_termination(procs, timeout): if not time: # If we are in late stage shutdown, the interpreter may replace # `time` with `None`. return all(not proc.is_alive() for proc in procs) start_time = time.time() while time.time() - start_time < timeout: if all(not proc.is_alive() for proc in procs): return True time.sleep(0.1) return False # Send SIGTERM if still running active_procs = [proc for proc in worker_procs if proc.is_alive()] for p in active_procs: p.terminate() if not wait_for_termination(active_procs, 4): # Send SIGKILL if still running active_procs = [p for p in active_procs if p.is_alive()] for p in active_procs: p.kill() def shutdown(self): """Properly shut down the executor and its workers""" if not getattr(self, 'shutting_down', False): self.shutting_down = True # Make sure all the worker processes are terminated first. if workers := getattr(self, 'workers', None): for w in workers: # Close death_writer to signal child processes to exit if w.death_writer is not None: w.death_writer.close() w.death_writer = None w.worker_response_mq = None self._ensure_worker_termination([w.proc for w in workers]) self.shutdown_event.set() if self.io_thread_pool is not None: self.io_thread_pool.shutdown(wait=False, cancel_futures=True) del self.io_thread_pool self.rpc_broadcast_mq = None def check_health(self) -> None: self.collective_rpc("check_health", timeout=10) return @cached_property def max_concurrent_batches(self) -> int: if self.scheduler_config.async_scheduling: return 2 return self.parallel_config.pipeline_parallel_size def _get_output_rank(self) -> int: # Only returns ModelRunnerOutput from TP rank=0 and PP rank=-1 # (the first TP worker of the last PP stage). # Example: # Assuming TP=8, PP=4, then the world_size=32 # 0-7, PP rank 0 # 8-15, PP rank 1 # 16-23, PP rank 2 # 24-31, PP rank 3 # so world_size - tp_size = 32 - 8 = 24 should be PP rank = -1 (i.e. 3) return self.world_size - self.parallel_config.tensor_parallel_size @dataclass class UnreadyWorkerProcHandle: """WorkerProcess handle before READY.""" proc: BaseProcess rank: int ready_pipe: Connection death_writer: Optional[Connection] = None @dataclass class WorkerProcHandle: proc: BaseProcess rank: int worker_response_mq: MessageQueue # The worker process writes to this MQ death_writer: Optional[Connection] = None @classmethod def from_unready_handle( cls, unready_handle: UnreadyWorkerProcHandle, worker_response_mq: MessageQueue) -> "WorkerProcHandle": return cls( proc=unready_handle.proc, rank=unready_handle.rank, worker_response_mq=worker_response_mq, death_writer=unready_handle.death_writer, ) class WorkerProc: """Wrapper that runs one Worker in a separate process.""" READY_STR = "READY" def __init__( self, vllm_config: VllmConfig, local_rank: int, rank: int, distributed_init_method: str, input_shm_handle: Handle, shared_worker_lock: LockType, ): self.rank = rank wrapper = WorkerWrapperBase(vllm_config=vllm_config, rpc_rank=rank) # TODO: move `init_worker` to executor level as a collective rpc call all_kwargs: list[dict] = [ {} for _ in range(vllm_config.parallel_config.world_size) ] is_driver_worker = ( rank % vllm_config.parallel_config.tensor_parallel_size == 0) all_kwargs[rank] = { "vllm_config": vllm_config, "local_rank": local_rank, "rank": rank, "distributed_init_method": distributed_init_method, "is_driver_worker": is_driver_worker, } wrapper.init_worker(all_kwargs) self.worker = wrapper # Initialize MessageQueue for receiving SchedulerOutput self.rpc_broadcast_mq = MessageQueue.create_from_handle( input_shm_handle, self.worker.rank) # Initializes a message queue for sending the model output self.worker_response_mq = MessageQueue(1, 1) scheduler_config = vllm_config.scheduler_config self.use_async_scheduling = scheduler_config.async_scheduling if self.use_async_scheduling: self.async_output_queue: queue.Queue = queue.Queue() self.async_output_copy_thread = Thread( target=self.async_output_busy_loop, daemon=True, name="WorkerAsyncOutputCopy") self.async_output_copy_thread.start() # Initialize multimodal receiver cache if needed self.mm_receiver_cache = worker_receiver_cache_from_config( vllm_config, MULTIMODAL_REGISTRY, shared_worker_lock) # Initialize device self.worker.init_device() # Set process title and log prefix self.setup_proc_title_and_log_prefix( enable_ep=vllm_config.parallel_config.enable_expert_parallel) # Load model self.worker.load_model() @staticmethod def make_worker_process( vllm_config: VllmConfig, local_rank: int, rank: int, distributed_init_method: str, input_shm_handle, # Receive SchedulerOutput shared_worker_lock: LockType, ) -> UnreadyWorkerProcHandle: context = get_mp_context() # (reader, writer) reader, writer = context.Pipe(duplex=False) # Create death pipe to detect parent process exit death_reader, death_writer = context.Pipe(duplex=False) process_kwargs = { "vllm_config": vllm_config, "local_rank": local_rank, "rank": rank, "distributed_init_method": distributed_init_method, "input_shm_handle": input_shm_handle, "ready_pipe": (reader, writer), "death_pipe": death_reader, "shared_worker_lock": shared_worker_lock, } # Run EngineCore busy loop in background process. proc = context.Process(target=WorkerProc.worker_main, kwargs=process_kwargs, name=f"VllmWorker-{rank}", daemon=True) proc.start() writer.close() # Keep death_writer open in parent - when parent exits, # death_reader in child will get EOFError return UnreadyWorkerProcHandle(proc, rank, reader, death_writer) @staticmethod def wait_for_ready( unready_proc_handles: list[UnreadyWorkerProcHandle] ) -> list[WorkerProcHandle]: e = Exception("WorkerProc initialization failed due to " "an exception in a background process. " "See stack trace for root cause.") pipes = {handle.ready_pipe: handle for handle in unready_proc_handles} ready_proc_handles: list[Optional[WorkerProcHandle]] = ( [None] * len(unready_proc_handles)) while pipes: ready = multiprocessing.connection.wait(pipes.keys()) for pipe in ready: assert isinstance(pipe, Connection) try: # Wait until the WorkerProc is ready. unready_proc_handle = pipes.pop(pipe) response: dict[str, Any] = pipe.recv() if response["status"] != "READY": raise e # Extract the message queue handle. worker_response_mq = MessageQueue.create_from_handle( response["handle"], 0) ready_proc_handles[unready_proc_handle.rank] = ( WorkerProcHandle.from_unready_handle( unready_proc_handle, worker_response_mq)) except EOFError: e.__suppress_context__ = True raise e from None finally: # Close connection. pipe.close() return cast(list[WorkerProcHandle], ready_proc_handles) def shutdown(self): self.worker.shutdown() self.rpc_broadcast_mq = None self.worker_response_mq = None destroy_model_parallel() destroy_distributed_environment() @staticmethod def worker_main(*args, **kwargs): """ Worker initialization and execution loops. This runs a background process """ # Signal handler used for graceful termination. # SystemExit exception is only raised once to allow this and worker # processes to terminate without error shutdown_requested = False def signal_handler(signum, frame): nonlocal shutdown_requested if not shutdown_requested: shutdown_requested = True raise SystemExit() # Either SIGTERM or SIGINT will terminate the worker signal.signal(signal.SIGTERM, signal_handler) signal.signal(signal.SIGINT, signal_handler) worker = None # tuple[Connection, Connection] reader, ready_writer = kwargs.pop("ready_pipe") death_pipe = kwargs.pop("death_pipe", None) shutdown_event = threading.Event() # Start death monitoring thread if death_pipe is provided if death_pipe is not None: def monitor_parent_death(): try: # This will block until parent process exits (pipe closes) death_pipe.recv() except EOFError: # Parent process has exited, terminate this worker logger.info("Parent process exited, terminating worker") # Send signal to self to trigger clean shutdown shutdown_event.set() except Exception as e: logger.warning("Death monitoring error: %s", e) death_monitor = Thread(target=monitor_parent_death, daemon=True, name="WorkerDeathMonitor") death_monitor.start() try: reader.close() worker = WorkerProc(*args, **kwargs) # Send READY once we know everything is loaded ready_writer.send({ "status": WorkerProc.READY_STR, "handle": worker.worker_response_mq.export_handle(), }) # Ensure message queues are ready. Will deadlock if re-ordered. # Must be kept consistent with the Executor worker.rpc_broadcast_mq.wait_until_ready() worker.worker_response_mq.wait_until_ready() ready_writer.close() ready_writer = None worker.worker_busy_loop(cancel=shutdown_event) except Exception: # NOTE: if an Exception arises in busy_loop, we send # a FAILURE message over the MQ RPC to notify the Executor, # which triggers system shutdown. # TODO(rob): handle case where the MQ itself breaks. if ready_writer is not None: logger.exception("WorkerProc failed to start.") elif shutdown_event.is_set(): logger.info("WorkerProc shutting down.") else: logger.exception("WorkerProc failed.") # The parent sends a SIGTERM to all worker processes if # any worker dies. Set this value so we don't re-throw # SystemExit() to avoid zmq exceptions in __del__. shutdown_requested = True finally: if ready_writer is not None: ready_writer.close() if death_pipe is not None: death_pipe.close() # Clean up once worker exits busy loop if worker is not None: worker.shutdown() class ResponseStatus(Enum): SUCCESS = auto() FAILURE = auto() def enqueue_output(self, output: Any): """Prepares output from the worker and enqueues it to the worker_response_mq. If the output is an Exception, it is converted to a FAILURE response. """ if isinstance(output, AsyncModelRunnerOutput): output = output.get_output() if isinstance(output, Exception): result = (WorkerProc.ResponseStatus.FAILURE, str(output)) else: result = (WorkerProc.ResponseStatus.SUCCESS, output) if (response_mq := self.worker_response_mq) is not None: response_mq.enqueue(result) def handle_output(self, output: Any): """Handles output from the worker. If async scheduling is enabled, it is passed to the async_output_busy_loop thread. Otherwise, it is enqueued directly to the worker_response_mq. """ if self.use_async_scheduling: self.async_output_queue.put(output) else: self.enqueue_output(output) def async_output_busy_loop(self): """Entrypoint for the thread which handles outputs asynchronously.""" while True: output = self.async_output_queue.get() self.enqueue_output(output) def worker_busy_loop(self, cancel: Optional[threading.Event] = None): """Main busy loop for Multiprocessing Workers""" while True: method, args, kwargs, output_rank = self.rpc_broadcast_mq.dequeue( cancel=cancel, indefinite=True) try: if isinstance(method, str): func = getattr(self.worker, method) elif isinstance(method, bytes): func = partial(cloudpickle.loads(method), self.worker) # retrieve from shm cache if available if self.mm_receiver_cache is not None \ and func.__name__ == "execute_model": get_and_update_mm_cache(self.mm_receiver_cache, args) output = func(*args, **kwargs) except Exception as e: # Notes have been introduced in python 3.11 if hasattr(e, "add_note"): e.add_note(traceback.format_exc()) logger.exception("WorkerProc hit an exception.") # exception might not be serializable, so we convert it to # string, only for logging purpose. if output_rank is None or self.rank == output_rank: self.handle_output(e) continue if output_rank is None or self.rank == output_rank: self.handle_output(output) @staticmethod def setup_proc_title_and_log_prefix(enable_ep: bool) -> None: dp_size = get_dp_group().world_size dp_rank = get_dp_group().rank_in_group pp_size = get_pp_group().world_size pp_rank = get_pp_group().rank_in_group tp_size = get_tp_group().world_size tp_rank = get_tp_group().rank_in_group process_name = "Worker" if dp_size > 1: process_name += f"_DP{dp_rank}" if pp_size > 1: process_name += f"_PP{pp_rank}" if tp_size > 1: process_name += f"_TP{tp_rank}" if enable_ep: ep_rank = get_ep_group().rank_in_group process_name += f"_EP{ep_rank}" set_process_title(name=process_name) decorate_logs(process_name) def set_multiprocessing_worker_envs(): """ Set up environment variables that should be used when there are workers in a multiprocessing environment. This should be called by the parent process before worker processes are created""" _maybe_force_spawn() # Configure thread parallelism if OMP_NUM_THREADS isn't set # # Helps to avoid CPU contention. The default of spawning a thread per # core combined with multiprocessing for each GPU can have a negative # impact on performance. The contention is amplified when running in a # container where CPU limits can cause throttling. default_omp_num_threads = 1 if "OMP_NUM_THREADS" not in os.environ and ( current_parallelism := torch.get_num_threads()) > default_omp_num_threads: logger.warning( "Reducing Torch parallelism from %d threads to %d to avoid " "unnecessary CPU contention. Set OMP_NUM_THREADS in the " "external environment to tune this value as needed.", current_parallelism, default_omp_num_threads) os.environ["OMP_NUM_THREADS"] = str(default_omp_num_threads) torch.set_num_threads(default_omp_num_threads)