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612 lines
25 KiB
Python
612 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import threading
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from collections import defaultdict
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from concurrent.futures import Future
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
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import cloudpickle
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import vllm.envs as envs
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from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
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from vllm.executor.executor_base import DistributedExecutorBase
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from vllm.executor.msgspec_utils import encode_hook
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from vllm.executor.ray_utils import (RayWorkerWrapper, initialize_ray_cluster,
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ray)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.sampler import SamplerOutput
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from vllm.platforms import current_platform
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from vllm.ray.ray_env import get_env_vars_to_copy
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from vllm.sequence import ExecuteModelRequest
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from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
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make_async)
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
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from vllm.v1.executor.abstract import Executor
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from vllm.v1.outputs import ModelRunnerOutput
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try: # msgspec is optional at runtime but required for serialization.
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import msgspec
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except ImportError: # pragma: no cover - msgspec is an optional dependency.
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msgspec = None # type: ignore
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if ray is not None:
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from ray.actor import ActorHandle
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from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
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else:
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ActorHandle = None
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if TYPE_CHECKING:
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from ray.util.placement_group import PlacementGroup
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logger = init_logger(__name__)
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@dataclass
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class RayWorkerMetaData:
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"""
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Metadata for a Ray worker.
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The order of ray worker creation can be random,
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and we need to reset the rank after creating all workers.
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"""
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worker: ActorHandle
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created_rank: int
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adjusted_rank: int = -1
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ip: str = ""
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class FutureWrapper(Future):
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"""Future compatible wrapper around Ray object references."""
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def __init__(self,
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refs,
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aggregator: Optional[KVOutputAggregator] = None) -> None:
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super().__init__()
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self._refs = refs
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self._aggregator = aggregator
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# Resolve the Ray object references off-thread so that the driver event
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# loop is not blocked and Future callbacks fire when the result is
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# ready.
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threading.Thread(target=self._resolve, daemon=True).start()
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def cancel(self) -> bool: # pragma: no cover - cancellation unsupported.
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return False
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def _resolve(self) -> None:
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try:
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if ray is None:
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raise RuntimeError("Ray is required to resolve distributed "
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"results.")
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outputs = ray.get(self._refs)
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if self._aggregator is None:
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result = outputs[0]
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else:
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result = self._aggregator.aggregate(outputs, output_rank=0)
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self.set_result(result)
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except BaseException as exc: # pragma: no cover - Ray errors propagated.
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self.set_exception(exc)
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finally:
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self._refs = None
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self._aggregator = None
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class RayDistributedExecutor(DistributedExecutorBase, Executor):
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"""Ray-based distributed executor for the v1 engine."""
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# These env vars are worker-specific, therefore are NOT copied
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# from the driver to the workers
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WORKER_SPECIFIC_ENV_VARS = {
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"VLLM_HOST_IP", "VLLM_HOST_PORT", "LOCAL_RANK", "CUDA_VISIBLE_DEVICES"
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}
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# These non-vLLM env vars are copied from the driver to workers
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ADDITIONAL_ENV_VARS = {"HF_TOKEN", "HUGGING_FACE_HUB_TOKEN"}
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uses_ray: bool = True
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supports_pp: bool = True
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def _init_executor(self) -> None:
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self.forward_dag: Optional[ray.dag.CompiledDAG] = None # type: ignore
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# V1 executor always relies on the SPMD worker implementation which in
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# turn requires the compiled DAG API.
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os.environ["VLLM_USE_RAY_SPMD_WORKER"] = "1"
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os.environ["VLLM_USE_RAY_COMPILED_DAG"] = "1"
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# For TPU or XPU, avoid compiling NVIDIA's NCCL
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if current_platform.is_tpu() or current_platform.is_xpu():
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os.environ["VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE"] = "shm"
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# These flags configure the worker setup.
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self.use_ray_compiled_dag = envs.VLLM_USE_RAY_COMPILED_DAG
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self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
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if self.use_ray_compiled_dag:
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assert self.use_ray_spmd_worker, (
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"VLLM_USE_RAY_COMPILED_DAG=1 requires "
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"VLLM_USE_RAY_SPMD_WORKER=1")
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if self.use_ray_spmd_worker:
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assert self.use_ray_compiled_dag, (
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"VLLM_USE_RAY_SPMD_WORKER=1 requires "
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"VLLM_USE_RAY_COMPILED_DAG=1")
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assert self.uses_ray
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initialize_ray_cluster(self.parallel_config)
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placement_group = self.parallel_config.placement_group
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# Disable Ray usage stats collection.
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ray_usage = os.environ.get("RAY_USAGE_STATS_ENABLED", "0")
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if ray_usage != "1":
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os.environ["RAY_USAGE_STATS_ENABLED"] = "0"
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# Create the parallel GPU workers.
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self._init_workers_ray(placement_group)
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# msgspec is only required when compiled DAG is disabled which is not
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# expected for V1, but initialize the codec for completeness.
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if msgspec is not None:
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self.input_encoder = msgspec.msgpack.Encoder(enc_hook=encode_hook)
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self.output_decoder = msgspec.msgpack.Decoder(
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Optional[List[SamplerOutput]])
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else: # pragma: no cover - msgspec should normally be available.
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self.input_encoder = None
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self.output_decoder = None
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# KV connector setup
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self.has_connector = self.vllm_config.kv_transfer_config is not None
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@property
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def max_concurrent_batches(self) -> int:
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"""Ray distributed executor supports pipeline parallelism."""
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if self.scheduler_config.async_scheduling:
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return 2
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return self.parallel_config.pipeline_parallel_size
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def execute_model(
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self,
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scheduler_output: SchedulerOutput,
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non_block: bool = False,
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) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
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"""Execute the model on the Ray workers."""
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# Build the compiled DAG for the first time.
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if self.forward_dag is None:
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self.forward_dag = self._compiled_ray_dag(enable_asyncio=False)
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refs = self.forward_dag.execute(scheduler_output)
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if not self.has_connector:
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if not non_block:
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return refs[0].get()
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return FutureWrapper(refs)
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assert self.kv_output_aggregator is not None, (
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"KVOutputAggregator must be initialized when kv transfer is "
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"configured")
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if not non_block:
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outputs = [ref.get() for ref in refs]
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return self.kv_output_aggregator.aggregate(outputs)
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return FutureWrapper(refs, self.kv_output_aggregator)
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def reinitialize_distributed(
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self, reconfig_request: ReconfigureDistributedRequest) -> None:
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self._run_workers("reinitialize_distributed", reconfig_request)
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if reconfig_request.new_data_parallel_rank == \
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ReconfigureRankType.SHUTDOWN_CURRENT_RANK:
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self.shutdown()
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def shutdown(self) -> None:
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if logger:
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# Somehow logger can be None here.
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logger.info(
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"Shutting down Ray distributed executor. If you see error log "
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"from logging.cc regarding SIGTERM received, please ignore "
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"because this is the expected termination process in Ray.")
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if hasattr(self, "forward_dag") and self.forward_dag is not None:
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self.forward_dag.teardown()
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import ray as _ray
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for worker in self.workers:
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_ray.kill(worker)
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self.forward_dag = None
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def _configure_ray_workers_use_nsight(self,
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ray_remote_kwargs) -> Dict[str, Any]:
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# If nsight profiling is enabled, we need to set the profiling
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# configuration for the ray workers as runtime env.
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runtime_env = ray_remote_kwargs.setdefault("runtime_env", {})
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runtime_env.update({
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"nsight": {
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"t": "cuda,cudnn,cublas",
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"o": "'worker_process_%p'",
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"cuda-graph-trace": "node",
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}
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})
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return ray_remote_kwargs
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# child class could overwrite this to return actual env vars.
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def _get_env_vars_to_be_updated(self):
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return self._env_vars_for_all_workers
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def _init_workers_ray(self, placement_group: "PlacementGroup",
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**ray_remote_kwargs):
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num_gpus = envs.VLLM_RAY_PER_WORKER_GPUS
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# Ray actors that perform all model execution.
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self.workers: List[RayWorkerWrapper] = []
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# Used in ray compiled DAG: indexed first by PP rank,
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# and then TP rank. In other words, the inner list is
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# the TP group of workers for a PP rank.
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self.pp_tp_workers: List[List[RayWorkerWrapper]] = []
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if self.parallel_config.ray_workers_use_nsight:
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ray_remote_kwargs = self._configure_ray_workers_use_nsight(
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ray_remote_kwargs)
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logger.info("use_ray_spmd_worker: %s", self.use_ray_spmd_worker)
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# Create the workers.
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bundle_indices: List[int]
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if envs.VLLM_RAY_BUNDLE_INDICES:
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# Use the bundle indices specified by the user.
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bundle_indices = list(
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map(int, envs.VLLM_RAY_BUNDLE_INDICES.split(",")))
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assert len(bundle_indices) == self.parallel_config.world_size, \
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("VLLM_RAY_BUNDLE_INDICES must have the same size"
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f" as the world size, but got {bundle_indices=} "
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f"and {self.parallel_config.world_size=}")
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assert len(set(bundle_indices)) == len(bundle_indices), \
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("VLLM_RAY_BUNDLE_INDICES cannot have duplicate values,"
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f" but got {bundle_indices=}")
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else:
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# use the first N bundles that have GPU resources.
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bundle_indices = []
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for bundle_id, bundle in enumerate(placement_group.bundle_specs):
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if bundle.get(current_platform.ray_device_key, 0):
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bundle_indices.append(bundle_id)
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bundle_indices = bundle_indices[:self.parallel_config.world_size]
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worker_metadata: List[RayWorkerMetaData] = []
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driver_ip = get_ip()
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for rank, bundle_id in enumerate(bundle_indices):
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scheduling_strategy = PlacementGroupSchedulingStrategy(
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placement_group=placement_group,
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placement_group_capture_child_tasks=True,
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placement_group_bundle_index=bundle_id,
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)
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if current_platform.ray_device_key == "GPU":
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# NV+AMD GPUs, and Intel XPUs
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worker = ray.remote(
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num_cpus=0,
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num_gpus=num_gpus,
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scheduling_strategy=scheduling_strategy,
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**ray_remote_kwargs,
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)(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
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rpc_rank=rank)
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else:
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worker = ray.remote(
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num_cpus=0,
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num_gpus=0,
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resources={current_platform.ray_device_key: num_gpus},
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scheduling_strategy=scheduling_strategy,
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**ray_remote_kwargs,
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)(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
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rpc_rank=rank)
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worker_metadata.append(
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RayWorkerMetaData(worker=worker, created_rank=rank))
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worker_ips = ray.get([
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each.worker.get_node_ip.remote() # type: ignore[attr-defined]
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for each in worker_metadata
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])
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for each, ip in zip(worker_metadata, worker_ips):
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each.ip = ip
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logger.debug("workers: %s", worker_metadata)
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ip_counts: Dict[str, int] = {}
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for ip in worker_ips:
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ip_counts[ip] = ip_counts.get(ip, 0) + 1
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def sort_by_driver_then_worker_ip(item: RayWorkerMetaData):
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"""
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Sort the workers based on 3 properties:
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1. If the worker is on the same node as the driver (vllm engine),
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it should be placed first.
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2. Then, if the worker is on a node with fewer workers, it should
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be placed first.
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3. Finally, if the work is on a node with smaller IP address, it
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should be placed first.
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"""
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ip = item.ip
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return (0 if ip == driver_ip else 1, ip_counts[ip], ip)
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# After sorting, the workers on the same node will be
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# close to each other, and the workers on the driver
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# node will be placed first.
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sorted_worker_metadata = sorted(worker_metadata,
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key=sort_by_driver_then_worker_ip)
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start_rank = 0
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for i, item in enumerate(sorted_worker_metadata):
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item.adjusted_rank = i + start_rank
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self.workers = [item.worker for item in sorted_worker_metadata]
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rerank_mapping = {
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item.created_rank: item.adjusted_rank
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for item in sorted_worker_metadata
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}
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self._run_workers("adjust_rank", rerank_mapping)
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# Get the set of GPU IDs used on each node.
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worker_node_and_gpu_ids = []
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for worker in self.workers:
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worker_node_and_gpu_ids.append(
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ray.get(worker.get_node_and_gpu_ids.remote())
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) # type: ignore
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node_workers = defaultdict(list) # node id -> list of worker ranks
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node_gpus = defaultdict(list) # node id -> list of gpu ids
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for i, (node_id, gpu_ids) in enumerate(worker_node_and_gpu_ids):
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node_workers[node_id].append(i)
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# `gpu_ids` can be a list of strings or integers.
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# convert them to integers for consistency.
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# NOTE: gpu_ids can be larger than 9 (e.g. 16 GPUs),
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# string sorting is not sufficient.
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# see https://github.com/vllm-project/vllm/issues/5590
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gpu_ids = [int(x) for x in gpu_ids]
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node_gpus[node_id].extend(gpu_ids)
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for node_id, gpu_ids in node_gpus.items():
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node_gpus[node_id] = sorted(gpu_ids)
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all_ips = set(worker_ips + [driver_ip])
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n_ips = len(all_ips)
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n_nodes = len(node_workers)
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if n_nodes != n_ips:
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raise RuntimeError(
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f"Every node should have a unique IP address. Got {n_nodes}"
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f" nodes with node ids {list(node_workers.keys())} and "
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f"{n_ips} unique IP addresses {all_ips}. Please check your"
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" network configuration. If you set `VLLM_HOST_IP`"
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" environment variable, make sure it is unique for"
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" each node.")
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# Set environment variables for the driver and workers.
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all_args_to_update_environment_variables = [{
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current_platform.device_control_env_var:
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",".join(map(str, node_gpus[node_id])),
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} for (node_id, _) in worker_node_and_gpu_ids]
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# Environment variables to copy from driver to workers
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env_vars_to_copy = get_env_vars_to_copy(
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exclude_vars=self.WORKER_SPECIFIC_ENV_VARS,
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additional_vars=set(current_platform.additional_env_vars).union(
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self.ADDITIONAL_ENV_VARS),
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destination="workers")
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# Copy existing env vars to each worker's args
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for args in all_args_to_update_environment_variables:
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# TODO: refactor platform-specific env vars
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for name in env_vars_to_copy:
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if name in os.environ:
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args[name] = os.environ[name]
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self._env_vars_for_all_workers = (
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all_args_to_update_environment_variables)
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self._run_workers("update_environment_variables",
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self._get_env_vars_to_be_updated())
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if len(node_gpus) == 1:
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# in single node case, we don't need to get the IP address.
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# the loopback address is sufficient
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# NOTE: a node may have several IP addresses, one for each
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# network interface. `get_ip()` might return any of them,
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# while they might not work for communication inside the node
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# if the network setup is complicated. Using the loopback address
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# solves this issue, as it always works for communication inside
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# the node.
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driver_ip = "127.0.0.1"
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distributed_init_method = get_distributed_init_method(
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driver_ip, get_open_port())
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# Initialize the actual workers inside worker wrapper.
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all_kwargs = []
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for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids):
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local_rank = node_workers[node_id].index(rank)
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kwargs = dict(
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vllm_config=self.vllm_config,
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local_rank=local_rank,
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rank=rank,
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distributed_init_method=distributed_init_method,
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is_driver_worker=(not self.parallel_config)
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or (rank % self.parallel_config.tensor_parallel_size == 0),
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)
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all_kwargs.append(kwargs)
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self._run_workers("init_worker", all_kwargs)
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self._run_workers("init_device")
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self._run_workers("load_model",
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max_concurrent_workers=self.parallel_config.
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max_parallel_loading_workers)
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for pp_rank in range(self.parallel_config.pipeline_parallel_size):
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self.pp_tp_workers.append([])
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for tp_rank in range(self.parallel_config.tensor_parallel_size):
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# PP=2, TP=4
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# pp_tp_workers = [[0, 1, 2, 3], [4, 5, 6, 7]]
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rank = (pp_rank * self.parallel_config.tensor_parallel_size
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) + tp_rank
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assert len(self.pp_tp_workers[pp_rank]) == tp_rank
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assert pp_rank < len(self.pp_tp_workers)
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self.pp_tp_workers[pp_rank].append(self.workers[rank])
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def _driver_execute_model(
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self, execute_model_req: Optional[ExecuteModelRequest]
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) -> Optional[List[SamplerOutput]]:
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raise RuntimeError(
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"RayDistributedExecutor only supports compiled DAG execution "
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"and does not expose a separate driver worker loop.")
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def _run_workers(
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self,
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method: Union[str, Callable],
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*args,
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async_run_tensor_parallel_workers_only: bool = False,
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max_concurrent_workers: Optional[int] = None,
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**kwargs,
|
|
) -> Any:
|
|
"""Runs the given method on all workers."""
|
|
|
|
if isinstance(method, str):
|
|
sent_method = method
|
|
else:
|
|
sent_method = cloudpickle.dumps(method)
|
|
del method
|
|
if self.use_ray_spmd_worker:
|
|
assert not async_run_tensor_parallel_workers_only, (
|
|
"async_run_tensor_parallel_workers_only is not supported for "
|
|
"spmd mode.")
|
|
|
|
if max_concurrent_workers:
|
|
raise NotImplementedError(
|
|
"max_concurrent_workers is not supported yet.")
|
|
|
|
# Start the ray workers first.
|
|
ray_worker_outputs = [
|
|
worker.execute_method.remote(sent_method, *args, **kwargs)
|
|
for worker in self.workers
|
|
]
|
|
|
|
if not self.workers:
|
|
return []
|
|
|
|
# Get the results of the ray workers.
|
|
return ray.get(ray_worker_outputs)
|
|
|
|
def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
|
|
"""Wait for futures returned from _run_workers()."""
|
|
ray.get(parallel_worker_tasks)
|
|
|
|
def _check_ray_cgraph_installation(self):
|
|
import importlib.metadata
|
|
|
|
from packaging import version
|
|
|
|
required_version = version.parse("2.43.0")
|
|
current_version = version.parse(importlib.metadata.version("ray"))
|
|
if current_version < required_version:
|
|
raise ValueError(f"Ray version {required_version} is "
|
|
f"required, but found {current_version}")
|
|
|
|
import importlib.util
|
|
cgraph_spec = importlib.util.find_spec(
|
|
"ray.experimental.compiled_dag_ref")
|
|
if cgraph_spec is None:
|
|
raise ValueError("Ray Compiled Graph is not installed. "
|
|
"Run `pip install ray[cgraph]` to install it.")
|
|
|
|
cupy_spec = importlib.util.find_spec("cupy")
|
|
if (cupy_spec is None
|
|
and envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE == "nccl"):
|
|
raise ValueError(
|
|
"cupy is not installed but required since "
|
|
"VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE is set to 'nccl'. "
|
|
"Run `pip install ray[cgraph]` and check cupy installation.")
|
|
|
|
def _compiled_ray_dag(self, enable_asyncio: bool):
|
|
assert self.parallel_config.use_ray
|
|
self._check_ray_cgraph_installation()
|
|
# Enlarge the default value of "RAY_CGRAPH_get_timeout" to 300 seconds
|
|
os.environ.setdefault("RAY_CGRAPH_get_timeout", "300") # noqa: SIM112
|
|
from ray.dag import InputNode, MultiOutputNode
|
|
logger.info("RAY_CGRAPH_get_timeout is set to %s",
|
|
os.environ["RAY_CGRAPH_get_timeout"]) # noqa: SIM112
|
|
logger.info("VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE = %s",
|
|
envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE)
|
|
logger.info("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM = %s",
|
|
envs.VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
|
|
|
|
channel_type = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
|
|
if channel_type not in ("auto", "nccl", "shm"):
|
|
raise ValueError(
|
|
"Invalid value for VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: "
|
|
f"{channel_type}. Valid values are: 'auto', 'nccl', or 'shm'.")
|
|
|
|
with InputNode() as input_data:
|
|
# Example DAG: PP=2, TP=4
|
|
# SchedulerOutput -> 0 -> (SchedulerOutput, IntermediateTensors) ->
|
|
# 4 -> ModelRunnerOutput, etc.
|
|
outputs = [input_data for _ in self.pp_tp_workers[0]]
|
|
for pp_rank, tp_group in enumerate(self.pp_tp_workers):
|
|
outputs = [
|
|
worker.execute_model_ray.
|
|
bind( # type: ignore[attr-defined]
|
|
outputs[i]) for i, worker in enumerate(tp_group)
|
|
]
|
|
|
|
last_pp_rank = len(self.pp_tp_workers) - 1
|
|
if (pp_rank < last_pp_rank and
|
|
envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE != "shm"):
|
|
transport = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
|
|
outputs = [
|
|
output.with_tensor_transport(transport=transport)
|
|
for output in outputs
|
|
]
|
|
|
|
forward_dag = MultiOutputNode(outputs)
|
|
|
|
if envs.VLLM_USE_RAY_WRAPPED_PP_COMM:
|
|
from ray.experimental.channel.accelerator_context import (
|
|
register_accelerator_context)
|
|
|
|
from vllm.distributed.device_communicators.ray_communicator import (
|
|
RayPPCommunicator)
|
|
register_accelerator_context(torch_module_name="cuda",
|
|
communicator_cls=RayPPCommunicator)
|
|
logger.info("Using RayPPCommunicator "
|
|
"(which wraps vLLM _PP GroupCoordinator) "
|
|
"for Ray Compiled Graph communication.")
|
|
else:
|
|
logger.info("Using Ray's NCCL communicator for "
|
|
"Ray Compiled Graph communication.")
|
|
|
|
return forward_dag.experimental_compile(
|
|
enable_asyncio=enable_asyncio,
|
|
_overlap_gpu_communication=envs.
|
|
VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
|
|
|
|
def __del__(self):
|
|
self.shutdown()
|
|
|
|
async def execute_model_async(
|
|
self,
|
|
scheduler_output: SchedulerOutput) -> ModelRunnerOutput:
|
|
return await make_async(self.execute_model)(scheduler_output)
|
|
|
|
async def _driver_execute_model_async(
|
|
self,
|
|
execute_model_req: Optional[ExecuteModelRequest] = None
|
|
) -> List[SamplerOutput]:
|
|
raise RuntimeError(
|
|
"RayDistributedExecutor only supports compiled DAG execution "
|
|
"and does not expose a separate driver worker loop.")
|
|
|
|
async def _start_worker_execution_loop(self):
|
|
raise RuntimeError(
|
|
"RayDistributedExecutor only supports compiled DAG execution "
|
|
"and does not expose a separate driver worker loop.")
|
|
|
|
def check_health(self) -> None:
|
|
# Assume that the Ray workers are healthy.
|
|
# TODO: check the health of the Ray workers
|
|
return
|