751 lines
30 KiB
Python
751 lines
30 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 asyncio
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import os
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from collections import defaultdict
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from collections.abc import Callable
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any
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import cloudpickle
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import msgspec
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import vllm.envs as envs
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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, ray
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from vllm.logger import init_logger
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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 (
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_run_task_with_lock,
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get_distributed_init_method,
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get_ip,
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get_open_port,
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make_async,
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)
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from vllm.v1.outputs import SamplerOutput
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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 RayDistributedExecutor(DistributedExecutorBase):
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"""Ray-based distributed executor"""
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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",
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"VLLM_HOST_PORT",
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"LOCAL_RANK",
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"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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def _init_executor(self) -> None:
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self.forward_dag: ray.dag.CompiledDAG | None = None
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if envs.VLLM_USE_V1:
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# V1 uses SPMD worker and compiled DAG
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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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# If the env var is set, it uses the Ray's compiled DAG API
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# which optimizes the control plane overhead.
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# Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
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# Currently, this requires USE_RAY_SPMD_WORKER=True.
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self.use_ray_compiled_dag = envs.VLLM_USE_RAY_COMPILED_DAG
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# If the env var is set, then we do not distinguish between the
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# "driver worker" vs other workers. Also, the rank 0 worker will
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# be executed in a remote Ray worker. Currently this requires
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# USE_RAY_COMPILED_DAG=True.
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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 VLLM_USE_RAY_SPMD_WORKER=1"
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)
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if self.use_ray_spmd_worker:
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# TODO: Support SPMD worker for non-DAG Ray executor.
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assert self.use_ray_compiled_dag, (
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"VLLM_USE_RAY_SPMD_WORKER=1 requires VLLM_USE_RAY_COMPILED_DAG=1"
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)
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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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self.input_encoder = msgspec.msgpack.Encoder(enc_hook=encode_hook)
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self.output_decoder = msgspec.msgpack.Decoder(list[SamplerOutput] | None)
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self.use_v1 = envs.VLLM_USE_V1
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self.pp_locks: list[asyncio.Lock] | None = None
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if not self.use_ray_compiled_dag:
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self.driver_exec_method = make_async(self.driver_worker.execute_method)
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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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)
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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
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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, 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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{
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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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)
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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", **ray_remote_kwargs):
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num_gpus = envs.VLLM_RAY_PER_WORKER_GPUS
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# The driver dummy worker does not actually use any resources.
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# It holds the resource for the driver worker.
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self.driver_dummy_worker: RayWorkerWrapper | None = None
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# The remaining workers are the actual ray actors.
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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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)
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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(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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)
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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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)
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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( # type: ignore[attr-defined]
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vllm_config=self.vllm_config, rpc_rank=rank
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)
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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( # type: ignore[attr-defined]
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vllm_config=self.vllm_config, rpc_rank=rank
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)
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worker_metadata.append(RayWorkerMetaData(worker=worker, created_rank=rank))
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worker_ips = ray.get(
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[
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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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)
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for each, ip in zip(worker_metadata, worker_ips):
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each.ip = ip
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if not self.use_ray_spmd_worker:
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for i, each in enumerate(worker_metadata):
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# find and remove the dummy worker from the list
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worker = each.worker
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worker_ip = each.ip
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if self.driver_dummy_worker is None and worker_ip == driver_ip:
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# If the worker is on the same node as the driver, we use it
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# as the resource holder for the driver process.
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self.driver_dummy_worker = worker
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self.driver_worker = RayWorkerWrapper(
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vllm_config=self.vllm_config, rpc_rank=0
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)
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worker_metadata.pop(i)
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break
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logger.debug("workers: %s", worker_metadata)
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logger.debug("driver_dummy_worker: %s", self.driver_dummy_worker)
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if not self.use_ray_spmd_worker and self.driver_dummy_worker is None:
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raise ValueError(
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"Ray does not allocate any GPUs on the driver node."
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f"Driver IP: {driver_ip}, worker IPs: {worker_ips}."
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"Consider adjusting the Ray placement group or running "
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"the driver on a GPU node."
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)
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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(
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worker_metadata, key=sort_by_driver_then_worker_ip
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)
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start_rank = 0 if self.use_ray_spmd_worker else 1
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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 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.driver_dummy_worker] + self.workers:
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if worker is None:
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# driver_dummy_worker can be None when using ray spmd worker.
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continue
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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[attr-defined]
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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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)
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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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{
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current_platform.device_control_env_var: ",".join(
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map(str, node_gpus[node_id])
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),
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}
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for (node_id, _) in worker_node_and_gpu_ids
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]
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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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),
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destination="workers",
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)
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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 = all_args_to_update_environment_variables
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self._run_workers(
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"update_environment_variables", self._get_env_vars_to_be_updated()
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)
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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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)
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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(
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"load_model",
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max_concurrent_workers=self.parallel_config.max_parallel_loading_workers,
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)
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if self.use_ray_spmd_worker:
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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 = (
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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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# This is the list of workers that are rank 0 of each TP group EXCEPT
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# global rank 0. These are the workers that will broadcast to the
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# rest of the workers.
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self.tp_driver_workers: list[RayWorkerWrapper] = []
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# This is the list of workers that are not drivers and not the first
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# worker in a TP group. These are the workers that will be
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# broadcasted to.
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self.non_driver_workers: list[RayWorkerWrapper] = []
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# Enforce rank order for correct rank to return final output.
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for index, worker in enumerate(self.workers):
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# The driver worker is rank 0 and not in self.workers.
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rank = index + 1
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if rank % self.parallel_config.tensor_parallel_size == 0:
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self.tp_driver_workers.append(worker)
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else:
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self.non_driver_workers.append(worker)
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def _driver_execute_model(
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self, execute_model_req: ExecuteModelRequest | None
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) -> list[SamplerOutput] | None:
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"""Run execute_model in the driver worker.
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Passing None will cause the driver to stop the model execution
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loop running in each of the remote workers.
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"""
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assert not self.use_ray_spmd_worker, (
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"driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1"
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)
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return self.driver_worker.execute_method("execute_model", execute_model_req)
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def execute_model(
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self, execute_model_req: ExecuteModelRequest
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) -> list[SamplerOutput]:
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if not self.use_ray_spmd_worker:
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return super().execute_model(execute_model_req)
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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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if self.use_v1:
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serialized_data = execute_model_req
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else:
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serialized_data = self.input_encoder.encode(execute_model_req)
|
|
outputs = ray.get(self.forward_dag.execute(serialized_data))
|
|
output = outputs[0] if self.use_v1 else self.output_decoder.decode(outputs[0])
|
|
return output
|
|
|
|
def _run_workers(
|
|
self,
|
|
method: str | Callable,
|
|
*args,
|
|
async_run_tensor_parallel_workers_only: bool = False,
|
|
max_concurrent_workers: int | None = None,
|
|
**kwargs,
|
|
) -> Any:
|
|
"""Runs the given method on all workers. Can be used in the following
|
|
ways:
|
|
|
|
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.
|
|
- args/kwargs: All workers share the same args/kwargs
|
|
"""
|
|
sent_method = method if isinstance(method, str) else 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_workers = self.workers
|
|
if async_run_tensor_parallel_workers_only:
|
|
ray_workers = self.non_driver_workers
|
|
ray_worker_outputs = [
|
|
worker.execute_method.remote( # type: ignore[attr-defined]
|
|
sent_method, *args, **kwargs
|
|
)
|
|
for worker in ray_workers
|
|
]
|
|
|
|
if async_run_tensor_parallel_workers_only:
|
|
# Just return futures
|
|
return ray_worker_outputs
|
|
|
|
driver_worker_output = []
|
|
# In SPMD mode, the driver worker is the same as any other worker,
|
|
# so we only explicitly execute on the driver worker if using a
|
|
# non-SPMD worker class.
|
|
if not self.use_ray_spmd_worker:
|
|
# Start the driver worker after all the ray workers.
|
|
driver_worker_output = [
|
|
self.driver_worker.execute_method(sent_method, *args, **kwargs)
|
|
]
|
|
|
|
# Get the results of the ray workers.
|
|
if self.workers:
|
|
ray_worker_outputs = ray.get(ray_worker_outputs)
|
|
|
|
return driver_worker_output + ray_worker_outputs
|
|
|
|
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."""
|
|
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
|
|
# (it is 10 seconds by default). This is a Ray environment variable to
|
|
# control the timeout of getting result from a compiled graph execution,
|
|
# i.e., the distributed execution that includes model forward runs and
|
|
# intermediate tensor communications, in the case of vllm.
|
|
# Note: we should set this env var before importing
|
|
# ray.dag, otherwise it will not take effect.
|
|
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
|
|
#
|
|
# For V0:
|
|
# ExecuteModelRequest -> 0 -> (ExecuteModelReq, IntermediateTensors) -> 4 -> SamplerOutput # noqa: E501
|
|
# ExecuteModelRequest -> 1 -> (ExecuteModelReq, IntermediateTensors) -> 5 -> SamplerOutput # noqa: E501
|
|
# ExecuteModelRequest -> 2 -> (ExecuteModelReq, IntermediateTensors) -> 6 -> SamplerOutput # noqa: E501
|
|
# ExecuteModelRequest -> 3 -> (ExecuteModelReq, IntermediateTensors) -> 7 -> SamplerOutput # noqa: E501
|
|
#
|
|
# For V1:
|
|
# SchedulerOutput -> 0 -> (SchedulerOutput, IntermediateTensors) -> 4 -> ModelRunnerOutput # noqa: E501
|
|
# SchedulerOutput -> 1 -> (SchedulerOutput, IntermediateTensors) -> 5 -> ModelRunnerOutput # noqa: E501
|
|
# SchedulerOutput -> 2 -> (SchedulerOutput, IntermediateTensors) -> 6 -> ModelRunnerOutput # noqa: E501
|
|
# SchedulerOutput -> 3 -> (SchedulerOutput, IntermediateTensors) -> 7 -> ModelRunnerOutput # noqa: E501
|
|
|
|
# All workers in the first TP group will take in the
|
|
# ExecuteModelRequest as input.
|
|
outputs = [input_data for _ in self.pp_tp_workers[0]]
|
|
for pp_rank, tp_group in enumerate(self.pp_tp_workers):
|
|
# Each PP worker takes in the output of the previous PP worker,
|
|
# and the TP group executes in SPMD fashion.
|
|
if self.use_v1:
|
|
outputs = [
|
|
worker.execute_model_ray.bind( # type: ignore[attr-defined]
|
|
outputs[i]
|
|
)
|
|
for i, worker in enumerate(tp_group)
|
|
]
|
|
else:
|
|
outputs = [
|
|
worker.execute_model_spmd.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"
|
|
):
|
|
# Specify how intermediate tensors should be passed
|
|
# between pp stages, no need to specify for the last
|
|
# pp stage or when using shared memory (the default).
|
|
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, execute_model_req: ExecuteModelRequest
|
|
) -> list[SamplerOutput]:
|
|
if not self.use_ray_spmd_worker:
|
|
return await super().execute_model_async(execute_model_req)
|
|
|
|
if self.forward_dag is None:
|
|
self.forward_dag = self._compiled_ray_dag(enable_asyncio=True)
|
|
|
|
serialized_data = self.input_encoder.encode(execute_model_req)
|
|
dag_future = await self.forward_dag.execute_async(serialized_data)
|
|
output = await dag_future[0]
|
|
return self.output_decoder.decode(output)
|
|
|
|
async def _driver_execute_model_async(
|
|
self, execute_model_req: ExecuteModelRequest | None = None
|
|
) -> list[SamplerOutput]:
|
|
assert not self.use_ray_spmd_worker, (
|
|
"driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1"
|
|
)
|
|
if not self.tp_driver_workers:
|
|
return await self.driver_exec_method("execute_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_method,
|
|
self.pp_locks[0],
|
|
"execute_model",
|
|
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.remote, # type: ignore[attr-defined]
|
|
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):
|
|
assert not self.use_ray_spmd_worker, (
|
|
"worker loop is disabled for VLLM_USE_RAY_SPMD_WORKER=1"
|
|
)
|
|
coros = [
|
|
worker.execute_method.remote("start_worker_execution_loop") # type: ignore[attr-defined]
|
|
for worker in self.non_driver_workers
|
|
]
|
|
return await asyncio.gather(*coros)
|
|
|
|
def check_health(self) -> None:
|
|
# Assume that the Ray workers are healthy.
|
|
# TODO: check the health of the Ray workers
|
|
return
|