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[V0 Deprecation] Remove V0 MP executor (#25329)

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
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Woosuk Kwon 2025-09-20 21:26:35 -07:00 committed by GitHub
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3 changed files with 33 additions and 530 deletions
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244
vllm/executor/mp_distributed_executor.py
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@ -1,244 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import os
from typing import Any, Callable, List, Optional, Union
import cloudpickle
from vllm.executor.executor_base import DistributedExecutorBase
from vllm.executor.multiproc_worker_utils import (
ProcessWorkerWrapper, ResultHandler, WorkerMonitor,
set_multiprocessing_worker_envs)
from vllm.logger import init_logger
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.sequence import ExecuteModelRequest
from vllm.utils import (_run_task_with_lock, cuda_device_count_stateless,
get_distributed_init_method, get_ip, get_open_port,
make_async, run_method, update_environment_variables)
from vllm.worker.worker_base import WorkerWrapperBase
logger = init_logger(__name__)
class MultiprocessingDistributedExecutor(DistributedExecutorBase):
"""Python multiprocessing-based distributed executor"""
uses_ray: bool = False
def _check_cuda(self) -> None:
"""Check that the number of GPUs is sufficient for the parallel
configuration. Separate from _init_executor to reduce the number of
indented blocks.
"""
parallel_config = self.parallel_config
world_size = parallel_config.world_size
tensor_parallel_size = parallel_config.tensor_parallel_size
cuda_device_count = cuda_device_count_stateless()
# Use confusing message for more common TP-only case.
if tensor_parallel_size > cuda_device_count:
raise RuntimeError(
f"please set tensor_parallel_size ({tensor_parallel_size}) "
f"to less than max local gpu count ({cuda_device_count})")
if world_size > cuda_device_count:
raise RuntimeError(
f"please ensure that world_size ({world_size}) "
f"is less than than max local gpu count ({cuda_device_count})")
# Set CUDA_VISIBLE_DEVICES for the driver, inherited by workers
if "CUDA_VISIBLE_DEVICES" not in os.environ:
update_environment_variables({
"CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
})
def _init_executor(self) -> None:
from vllm.platforms import current_platform
if current_platform.is_cuda_alike():
self._check_cuda()
# Create the parallel GPU workers.
world_size = self.parallel_config.world_size
tensor_parallel_size = self.parallel_config.tensor_parallel_size
# Set multiprocessing envs that are common to V0 and V1
set_multiprocessing_worker_envs(self.parallel_config)
# Multiprocessing-based executor does not support multi-node setting.
# Since it only works for single node, we can use the loopback address
# 127.0.0.1 for communication.
distributed_init_method = get_distributed_init_method(
"127.0.0.1", get_open_port())
self.workers: List[ProcessWorkerWrapper] = []
# This is the list of workers that are rank 0 of each TP group EXCEPT
# global rank 0. These are the workers that will broadcast to the
# rest of the workers.
self.tp_driver_workers: List[ProcessWorkerWrapper] = []
# This is the list of workers that are not drivers and not the first
# worker in a TP group. These are the workers that will be
# broadcasted to.
self.non_driver_workers: List[ProcessWorkerWrapper] = []
if world_size == 1:
self.worker_monitor = None
else:
result_handler = ResultHandler()
for rank in range(1, world_size):
worker = ProcessWorkerWrapper(result_handler,
WorkerWrapperBase,
self.vllm_config, rank)
self.workers.append(worker)
if rank % tensor_parallel_size == 0:
self.tp_driver_workers.append(worker)
else:
self.non_driver_workers.append(worker)
self.worker_monitor = WorkerMonitor(self.workers, result_handler)
result_handler.start()
self.worker_monitor.start()
# Set up signal handlers to shut down the executor cleanly
# sometimes gc does not work well
self.driver_worker = WorkerWrapperBase(self.vllm_config, 0)
all_kwargs = []
distributed_init_method = get_distributed_init_method(
get_ip(), get_open_port())
for i in range(world_size):
local_rank = i
rank = i
kwargs = dict(
vllm_config=self.vllm_config,
local_rank=local_rank,
rank=rank,
distributed_init_method=distributed_init_method,
is_driver_worker=(not self.parallel_config)
or (rank % self.parallel_config.tensor_parallel_size == 0),
)
all_kwargs.append(kwargs)
self._run_workers("init_worker", all_kwargs)
self._run_workers("init_device")
self._run_workers("load_model",
max_concurrent_workers=self.parallel_config.
max_parallel_loading_workers)
self.driver_exec_model = make_async(self.driver_worker.execute_model)
self.pp_locks: Optional[List[asyncio.Lock]] = None
def shutdown(self):
if (worker_monitor := getattr(self, "worker_monitor",
None)) is not None:
worker_monitor.close()
def _driver_execute_model(
self, execute_model_req: Optional[ExecuteModelRequest]
) -> Optional[List[SamplerOutput]]:
"""Run execute_model in the driver worker.
Passing None will cause the driver to stop the model execution
loop running in each of the remote workers.
"""
return self.driver_worker.execute_model(execute_model_req)
def _run_workers(
self,
method: Union[str, Callable],
*args,
async_run_tensor_parallel_workers_only: bool = False,
max_concurrent_workers: Optional[int] = None,
**kwargs,
) -> List[Any]:
"""Runs the given method on all workers.
Args:
async_run_tensor_parallel_workers_only: If True the method will be
run only in the remote TP workers, not the driver worker.
It will also be run asynchronously and return a list of futures
rather than blocking on the results.
"""
if isinstance(method, str):
sent_method = method
else:
sent_method = cloudpickle.dumps(method)
del method
if max_concurrent_workers:
raise NotImplementedError(
"max_concurrent_workers is not supported yet.")
if async_run_tensor_parallel_workers_only:
# Run only non-driver workers and just return futures.
return [
worker.execute_method(sent_method, *args, **kwargs)
for worker in self.non_driver_workers
]
# Start all remote workers first.
worker_outputs = [
worker.execute_method(sent_method, *args, **kwargs)
for worker in self.workers
]
driver_worker_output = run_method(self.driver_worker, sent_method,
args, kwargs)
# Get the results of the workers.
return [driver_worker_output
] + [output.get() for output in worker_outputs]
def check_health(self) -> None:
"""Raises an error if engine is unhealthy."""
if self.worker_monitor is not None and not self.worker_monitor.is_alive(
):
raise RuntimeError("Worker processes are not running")
def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
"""Wait for futures returned from _run_workers() with
async_run_remote_workers_only to complete."""
for result in parallel_worker_tasks:
result.get()
async def _driver_execute_model_async(
self,
execute_model_req: Optional[ExecuteModelRequest] = None
) -> List[SamplerOutput]:
if not self.tp_driver_workers:
return await self.driver_exec_model(execute_model_req)
if self.pp_locks is None:
# This locks each pipeline parallel stage so multiple virtual
# engines can't execute on the same stage at the same time
# We create the locks here to avoid creating them in the constructor
# which uses a different asyncio loop.
self.pp_locks = [
asyncio.Lock()
for _ in range(self.parallel_config.pipeline_parallel_size)
]
tasks = [
asyncio.create_task(
_run_task_with_lock(self.driver_exec_model, self.pp_locks[0],
execute_model_req))
]
for pp_rank, driver_worker in enumerate(self.tp_driver_workers,
start=1):
tasks.append(
asyncio.create_task(
_run_task_with_lock(driver_worker.execute_method_async,
self.pp_locks[pp_rank],
"execute_model", execute_model_req)))
results = await asyncio.gather(*tasks)
# Only the last PP stage has the final results.
return results[-1]
async def _start_worker_execution_loop(self):
coros = [
worker.execute_method_async("start_worker_execution_loop")
for worker in self.non_driver_workers
]
return await asyncio.gather(*coros)

279
vllm/executor/multiproc_worker_utils.py
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@ -1,279 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import os
import threading
import uuid
from dataclasses import dataclass
from multiprocessing import Queue
from multiprocessing.connection import wait
from multiprocessing.process import BaseProcess
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
import torch
from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.utils import (_maybe_force_spawn, decorate_logs, get_mp_context,
run_method)
logger = init_logger(__name__)
T = TypeVar('T')
_TERMINATE = "TERMINATE" # sentinel
JOIN_TIMEOUT_S = 2
@dataclass
class Result(Generic[T]):
"""Result of task dispatched to worker"""
task_id: uuid.UUID
value: Optional[T] = None
exception: Optional[BaseException] = None
class ResultFuture(threading.Event, Generic[T]):
"""Synchronous future for non-async case"""
def __init__(self):
super().__init__()
self.result: Optional[Result[T]] = None
def set_result(self, result: Result[T]):
self.result = result
self.set()
def get(self) -> T:
self.wait()
assert self.result is not None
if self.result.exception is not None:
raise self.result.exception
return self.result.value # type: ignore[return-value]
def _set_future_result(future: Union[ResultFuture, asyncio.Future],
result: Result):
if isinstance(future, ResultFuture):
future.set_result(result)
return
loop = future.get_loop()
if not loop.is_closed():
if result.exception is not None:
loop.call_soon_threadsafe(future.set_exception, result.exception)
else:
loop.call_soon_threadsafe(future.set_result, result.value)
class ResultHandler(threading.Thread):
"""Handle results from all workers (in background thread)"""
def __init__(self) -> None:
super().__init__(daemon=True)
self.result_queue = get_mp_context().Queue()
self.tasks: Dict[uuid.UUID, Union[ResultFuture, asyncio.Future]] = {}
def run(self):
for result in iter(self.result_queue.get, _TERMINATE):
future = self.tasks.pop(result.task_id)
_set_future_result(future, result)
# Ensure that all waiters will receive an exception
for task_id, future in self.tasks.items():
_set_future_result(
future,
Result(task_id=task_id,
exception=ChildProcessError("worker died")))
def close(self):
self.result_queue.put(_TERMINATE)
class WorkerMonitor(threading.Thread):
"""Monitor worker status (in background thread)"""
def __init__(self, workers: List['ProcessWorkerWrapper'],
result_handler: ResultHandler):
super().__init__(daemon=True)
self.workers = workers
self.result_handler = result_handler
self._close = False
def run(self) -> None:
# Blocks until any worker exits
dead_sentinels = wait([w.process.sentinel for w in self.workers])
if not self._close:
self._close = True
# Kill / cleanup all workers
for worker in self.workers:
process = worker.process
if process.sentinel in dead_sentinels:
process.join(JOIN_TIMEOUT_S)
if process.exitcode is not None and process.exitcode != 0:
logger.error("Worker %s pid %s died, exit code: %s",
process.name, process.pid, process.exitcode)
# Cleanup any remaining workers
if logger:
logger.info("Killing local vLLM worker processes")
for worker in self.workers:
worker.kill_worker()
# Must be done after worker task queues are all closed
self.result_handler.close()
for worker in self.workers:
worker.process.join(JOIN_TIMEOUT_S)
def close(self):
if self._close:
return
self._close = True
logger.info("Terminating local vLLM worker processes")
for worker in self.workers:
worker.terminate_worker()
# Must be done after worker task queues are all closed
self.result_handler.close()
class ProcessWorkerWrapper:
"""Local process wrapper for vllm.worker.Worker,
for handling single-node multi-GPU tensor parallel."""
def __init__(self, result_handler: ResultHandler,
worker_factory: Callable[[VllmConfig, int], Any],
vllm_config: VllmConfig, rank: int) -> None:
self.mp = get_mp_context()
self._task_queue = self.mp.Queue()
self.result_queue = result_handler.result_queue
self.tasks = result_handler.tasks
self.process: BaseProcess = self.mp.Process( # type: ignore[attr-defined]
target=_run_worker_process,
name="VllmWorkerProcess",
kwargs=dict(
worker_factory=worker_factory,
task_queue=self._task_queue,
result_queue=self.result_queue,
vllm_config=vllm_config,
rank=rank,
),
daemon=True)
self.process.start()
def _enqueue_task(self, future: Union[ResultFuture, asyncio.Future],
method: Union[str, bytes], args, kwargs):
task_id = uuid.uuid4()
self.tasks[task_id] = future
try:
self._task_queue.put((task_id, method, args, kwargs))
except SystemExit:
raise
except BaseException as e:
del self.tasks[task_id]
raise ChildProcessError("worker died") from e
def execute_method(self, method: Union[str, bytes], *args, **kwargs):
future: ResultFuture = ResultFuture()
self._enqueue_task(future, method, args, kwargs)
return future
async def execute_method_async(self, method: Union[str, bytes], *args,
**kwargs):
future = asyncio.get_running_loop().create_future()
self._enqueue_task(future, method, args, kwargs)
return await future
def terminate_worker(self):
try:
self._task_queue.put(_TERMINATE)
except ValueError:
self.process.kill()
self._task_queue.close()
def kill_worker(self):
self._task_queue.close()
self.process.kill()
def _run_worker_process(
worker_factory: Callable[[VllmConfig, int], Any],
task_queue: Queue,
result_queue: Queue,
vllm_config: VllmConfig,
rank: int,
) -> None:
"""Worker process event loop"""
# Add process-specific prefix to stdout and stderr
process_name = get_mp_context().current_process().name
decorate_logs(process_name)
# Initialize worker
worker = worker_factory(vllm_config, rank)
del worker_factory
# Accept tasks from the engine in task_queue
# and return task output in result_queue
logger.info("Worker ready; awaiting tasks")
try:
for items in iter(task_queue.get, _TERMINATE):
output = None
exception = None
task_id, method, args, kwargs = items
try:
output = run_method(worker, method, args, kwargs)
except SystemExit:
raise
except KeyboardInterrupt:
break
except BaseException as e:
logger.exception(
"Exception in worker %s while processing method %s.",
process_name, method)
exception = e
result_queue.put(
Result(task_id=task_id, value=output, exception=exception))
except KeyboardInterrupt:
pass
except Exception:
logger.exception("Worker failed")
# Flush TunableOp results when TunableOp is enabled and
# online (in situ) tuning is enabled.
# Offline tuning API (record_untuned_is_enabled()) only
# available in PyTorch 2.6 or later.
if torch.cuda.is_available():
import torch.cuda.tunable as tunable
if (tunable.is_enabled() and tunable.tuning_is_enabled()
and not tunable.record_untuned_is_enabled()):
tunable.write_file()
logger.info("Worker exiting")
def set_multiprocessing_worker_envs(parallel_config):
""" 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)

40
vllm/v1/executor/multiproc_executor.py
View File

@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import multiprocessing
import os
import pickle
import queue
import signal
@ -19,6 +20,7 @@ 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
@ -28,14 +30,12 @@ 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.executor.multiproc_worker_utils import (
set_multiprocessing_worker_envs)
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 (decorate_logs, get_distributed_init_method,
get_loopback_ip, get_mp_context, get_open_port,
set_process_title)
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
@ -67,8 +67,8 @@ class MultiprocExecutor(Executor):
f"tensor_parallel_size ({tensor_parallel_size}) x pipeline"
f"_parallel_size ({pp_parallel_size}). ")
# Set multiprocessing envs that are common to V0 and V1
set_multiprocessing_worker_envs(self.parallel_config)
# 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
@ -698,3 +698,29 @@ class WorkerProc:
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)