utils.py 47.2 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import contextlib
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import os
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import threading
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import weakref
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from collections.abc import Callable, Iterator
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from dataclasses import dataclass
from enum import Enum, auto
from multiprocessing import Process, connection
from multiprocessing.process import BaseProcess
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from multiprocessing.queues import Queue
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from typing import TYPE_CHECKING, cast
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from unittest.mock import patch
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import msgspec
import zmq

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from vllm import envs
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from vllm.config import CacheConfig, ParallelConfig, VllmConfig
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.utils import numa_utils
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from vllm.utils.network_utils import get_open_zmq_ipc_path, zmq_socket_ctx
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from vllm.utils.system_utils import get_mp_context
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from vllm.v1.engine.coordinator import DPCoordinator
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from vllm.v1.executor import Executor
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from vllm.v1.utils import get_engine_client_zmq_addr, shutdown

if TYPE_CHECKING:
    from ray.util.placement_group import PlacementGroup

logger = init_logger(__name__)

STARTUP_POLL_PERIOD_MS = 10000


class CoreEngineState(Enum):
    NEW = auto()
    CONNECTED = auto()
    READY = auto()


class CoreEngine:
    """One per data parallel rank, used to track state during handshaking."""

    def __init__(self, index: int = 0, local: bool = True):
        self.local = local
        self.identity = index.to_bytes(2, "little")

        self.state = CoreEngineState.NEW


@dataclass
class EngineZmqAddresses:
    # ZMQ input socket addresses for each front-end client (requests)
    inputs: list[str]
    # ZMQ output socket addresses for each front-end client (responses)
    outputs: list[str]
    # ZMQ input socket address of DP coordinator if applicable
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    coordinator_input: str | None = None
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    # ZMQ output socket address of DP coordinator if applicable
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    coordinator_output: str | None = None
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    # ZMQ socket for front-end to connect to DP coordinator.
    # Not used by engine, just relayed to front-end in handshake response.
    # Only required for external DP LB case.
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    frontend_stats_publish_address: str | None = None
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@dataclass
class EngineHandshakeMetadata:
    """Metadata sent to each engine process during startup handshake,
    including addresses of the front-end ZMQ queues that they should
    connect to.
    """
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    addresses: EngineZmqAddresses
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    parallel_config: dict[str, int | str | list[int]]
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class CoreEngineProcManager:
    """
    Utility class to handle creation, readiness, and shutdown
    of background processes used by the AsyncLLM and LLMEngine.
    """

    def __init__(
        self,
        local_engine_count: int,
        start_index: int,
        local_start_index: int,
        vllm_config: VllmConfig,
        local_client: bool,
        handshake_address: str,
        executor_class: type[Executor],
        log_stats: bool,
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        client_handshake_address: str | None = None,
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        tensor_queue: Queue | None = None,
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    ):
        context = get_mp_context()
        common_kwargs = {
            "vllm_config": vllm_config,
            "local_client": local_client,
            "handshake_address": handshake_address,
            "executor_class": executor_class,
            "log_stats": log_stats,
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            "tensor_queue": tensor_queue,
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        }

        if client_handshake_address:
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            common_kwargs["client_handshake_address"] = client_handshake_address
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        is_dp = vllm_config.parallel_config.data_parallel_size > 1

        from vllm.v1.engine.core import EngineCoreProc

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        self.processes: list[BaseProcess] = []
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        local_dp_ranks = []
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        for index in range(local_engine_count):
            local_index = local_start_index + index
            global_index = start_index + index
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            # Start EngineCore in background process.
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            local_dp_ranks.append(local_index)
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            self.processes.append(
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                context.Process(
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                    target=EngineCoreProc.run_engine_core,
                    name=f"EngineCore_DP{global_index}" if is_dp else "EngineCore",
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                    kwargs=common_kwargs
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                    | {"dp_rank": global_index, "local_dp_rank": local_index},
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                )
            )
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        self._finalizer = weakref.finalize(self, shutdown, self.processes)
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        self.manager_stopped = threading.Event()
        self.failed_proc_name: str | None = None
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        try:
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            for proc, local_dp_rank in zip(self.processes, local_dp_ranks):
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                # Adjust device control in DP for non-CUDA platforms
                # as well as external and ray launchers
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                # For CUDA platforms, we use torch.accelerator.set_device_index()()
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                device_control_context: contextlib.AbstractContextManager[None] = (
                    contextlib.nullcontext()
                )
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                if is_dp and (
                    not current_platform.is_cuda_alike()
                    or vllm_config.parallel_config.use_ray
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                ):
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                    device_control_context = set_device_control_env_var(
                        vllm_config, local_dp_rank
                    )

                with (
                    device_control_context,
                    numa_utils.configure_subprocess(
                        # EngineCore itself does not have a TP/PP-local rank.
                        # When DP is enabled, set_device_control_env_var()
                        # narrows visible devices to this DP shard first, so
                        # local_rank=0 means "the first local GPU in this
                        # shard". The actual TP/PP worker processes spawned by
                        # the executor are bound separately with their own
                        # local_rank values.
                        vllm_config,
                        local_rank=0,
                        dp_local_rank=local_dp_rank,
                        process_kind="EngineCore",
                    ),
                ):
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                    proc.start()
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        finally:
            # Kill other procs if not all are running.
            if self.finished_procs():
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                self.shutdown()
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    def shutdown(self, timeout: float | None = None) -> None:
        """Shutdown engine core processes with configurable timeout."""
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        self.manager_stopped.set()
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        if self._finalizer.detach() is not None:
            shutdown(self.processes, timeout=timeout)
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    def monitor_engine_liveness(self) -> None:
        """Monitor engine core process liveness."""

        sentinel_to_proc = {proc.sentinel: proc for proc in self.processes}
        sentinels = set(sentinel_to_proc.keys())

        while sentinels and not self.manager_stopped.is_set():
            died_sentinels = connection.wait(sentinels, timeout=1)

            for sentinel in died_sentinels:
                proc = sentinel_to_proc.pop(cast(int, sentinel))
                exitcode = proc.exitcode
                if exitcode != 0 and not self.manager_stopped.is_set():
                    self.failed_proc_name = proc.name
            if died_sentinels:
                # Any engine exit currently triggers a shutdown. Future
                # work (e.g., Elastic and fault-tolerant EP) will add finer-grained
                # handling for different exit scenarios.
                break

        self.shutdown()
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    def sentinels(self) -> list:
        return [proc.sentinel for proc in self.processes]

    def finished_procs(self) -> dict[str, int]:
        """Returns dict of proc name -> exit code for any finished procs."""
        return {
            proc.name: proc.exitcode
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            for proc in self.processes
            if proc.exitcode is not None
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        }


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class SignalCallback:
    """Safely trigger a callback from signal handler context via a dedicated thread."""

    def __init__(self, callback: Callable[[], None]):
        self._callback = callback
        self._event = threading.Event()
        self._stopped = False
        self._thread = threading.Thread(
            target=self._run,
            daemon=True,
            name="signal-callback",
        )
        self._thread.start()

    def _run(self):
        self._event.wait()
        if not self._stopped:
            self._callback()

    def trigger(self):
        self._event.set()

    def stop(self):
        self._stopped = True
        self._event.set()


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@contextlib.contextmanager
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def set_device_control_env_var(
    vllm_config: VllmConfig, local_dp_rank: int
) -> Iterator[None]:
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    """
    Temporarily set CUDA_VISIBLE_DEVICES or equivalent
    for engine subprocess.
    """
    world_size = vllm_config.parallel_config.world_size
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    local_world_size = vllm_config.parallel_config.local_world_size
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    evar = current_platform.device_control_env_var
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    value = get_device_indices(evar, local_dp_rank, world_size, local_world_size)
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    with patch.dict(os.environ, values=((evar, value),)):
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        yield


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def get_device_indices(
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    device_control_env_var: str,
    local_dp_rank: int,
    world_size: int,
    local_world_size: int | None = None,
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):
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    """
    Returns a comma-separated string of device indices for the specified
    data parallel rank.

    For example, if world_size=2 and local_dp_rank=1, and there are 4 devices,
    this will select devices 2 and 3 for local_dp_rank=1.
    """
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    if local_world_size is None:
        local_world_size = world_size
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    try:
        value = ",".join(
            str(current_platform.device_id_to_physical_device_id(i))
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            for i in range(
                local_dp_rank * world_size,
                local_dp_rank * world_size + local_world_size,
            )
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        )
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    except IndexError as e:
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        raise Exception(
            f"Error setting {device_control_env_var}: "
            f"local range: [{local_dp_rank * world_size}, "
            f"{(local_dp_rank + 1) * world_size}) "
            "base value: "
            f'"{os.getenv(device_control_env_var)}"'
        ) from e
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    return value
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class CoreEngineActorManager:
    """
    Utility class to handle creation, readiness, and shutdown
    of core engine Ray actors used by the AsyncLLM and LLMEngine.

    Different from CoreEngineProcManager, this class manages
    core engines for both local and remote nodes.
    """

    def __init__(
        self,
        vllm_config: VllmConfig,
        addresses: EngineZmqAddresses,
        executor_class: type[Executor],
        log_stats: bool,
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        placement_groups: list["PlacementGroup"] | None = None,
        local_dp_ranks: list[int] | None = None,
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    ):
        import copy

        import ray
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        from ray.runtime_env import RuntimeEnv
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        from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
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        from vllm.v1.engine.core import DPMoEEngineCoreActor, EngineCoreActor

        dp_size = vllm_config.parallel_config.data_parallel_size
        actor_class = (
            DPMoEEngineCoreActor
            if dp_size > 1 and vllm_config.model_config.is_moe
            else EngineCoreActor
        )
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        self.local_engine_actors: list[ray.ActorHandle] = []
        self.remote_engine_actors: list[ray.ActorHandle] = []
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        env_vars_list = get_env_vars_to_copy(destination=actor_class.__name__)
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        self.env_vars_dict = {
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            name: os.environ[name] for name in env_vars_list if name in os.environ
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        }
        runtime_env = RuntimeEnv(env_vars=self.env_vars_dict)

        self.addresses = addresses
        self.executor_class = executor_class
        self.log_stats = log_stats
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        local_engine_count = vllm_config.parallel_config.data_parallel_size_local
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        world_size = vllm_config.parallel_config.world_size
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        self.manager_stopped = threading.Event()
        self.failed_proc_name: str | None = None
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        if ray.is_initialized():
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            logger.info("Ray is already initialized. Skipping Ray initialization.")
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        else:
            ray.init()

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        parallel_config = vllm_config.parallel_config
        if parallel_config.enable_elastic_ep:
            from vllm.distributed.utils import create_tcp_store

            ip = parallel_config.data_parallel_master_ip
            store = create_tcp_store(
                ip,
                0,
                is_master=True,
                world_size=-1,
                wait_for_workers=False,
            )
            parallel_config._coord_store_port = store.port
            self._coord_store = store
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        if placement_groups is not None:
            assert local_dp_ranks is not None, (
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                "local_dp_ranks must be provided if placement_groups is provided"
            )
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            assert len(placement_groups) == len(local_dp_ranks), (
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                "placement_groups and local_dp_ranks must have the same length"
            )
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            logger.info("Using provided placement groups")
            # TODO(rui): validate passed-in placement groups
            self.created_placement_groups = []
        else:
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            placement_groups, local_dp_ranks = (
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                CoreEngineActorManager.create_dp_placement_groups(vllm_config)
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            )
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            self.created_placement_groups = placement_groups
        assert len(placement_groups) == dp_size, (
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            "Number of placement groups must match data parallel size"
        )
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        self.placement_group_is_local = []
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        refs = []
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        for index, local_index, pg in zip(
            range(dp_size), local_dp_ranks, placement_groups
        ):
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            dp_vllm_config = copy.deepcopy(vllm_config)
            dp_vllm_config.parallel_config.placement_group = pg
            local_client = index < local_engine_count
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            if dp_size > 1 and dp_vllm_config.kv_transfer_config is not None:
                # modify the engine_id and append the local_dp_rank to it to ensure
                # that the kv_transfer_config is unique for each DP rank.
                dp_vllm_config.kv_transfer_config.engine_id = (
                    f"{dp_vllm_config.kv_transfer_config.engine_id}_dp{local_index}"
                )

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            # Ray XPU known issue: dpctl initializes the GPU runtime early, so
            # setting device env vars in Ray actor's initialization method
            # will not affect device selection. See:
            # https://github.com/ray-project/ray/blob/master/python/ray/_private/accelerators/intel_gpu.py#L56 # noqa: E501
            if current_platform.is_xpu():
                device_evar = current_platform.device_control_env_var
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                device_indices = get_device_indices(
                    device_evar, local_index, world_size
                )
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                actor_env_vars = self.env_vars_dict.copy()
                actor_env_vars[device_evar] = device_indices
                runtime_env = RuntimeEnv(env_vars=actor_env_vars)

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            actor = (
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                ray.remote(actor_class)
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                .options(
                    scheduling_strategy=PlacementGroupSchedulingStrategy(
                        placement_group=pg,
                        placement_group_bundle_index=world_size,
                    ),
                    runtime_env=runtime_env,
                )
                .remote(
                    vllm_config=dp_vllm_config,
                    executor_class=executor_class,
                    log_stats=log_stats,
                    local_client=local_client,
                    addresses=addresses,
                    dp_rank=index,
                    local_dp_rank=local_index,
                )
            )
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            if local_client:
                self.local_engine_actors.append(actor)
            else:
                self.remote_engine_actors.append(actor)
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            self.placement_group_is_local.append(local_client)
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            refs.append(actor.wait_for_init.remote())

        ray.get(refs)
        self.run_refs = []
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        self.actor_run_ref_dict = dict()
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        for actor in self.local_engine_actors + self.remote_engine_actors:
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            ref = actor.run.remote()
            self.run_refs.append(ref)
            self.actor_run_ref_dict[actor] = ref
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    @staticmethod
    def create_dp_placement_groups(
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        vllm_config: VllmConfig,
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    ) -> tuple[list["PlacementGroup"], list[int]]:
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        """
        Create placement groups for data parallel.
        """
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        import ray
        from ray._private.state import available_resources_per_node

        logger.info("Creating placement groups for data parallel")
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        dp_master_ip = vllm_config.parallel_config.data_parallel_master_ip
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        dp_size = vllm_config.parallel_config.data_parallel_size
        dp_size_local = vllm_config.parallel_config.data_parallel_size_local
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        available_resources = available_resources_per_node()
        world_size = vllm_config.parallel_config.world_size
        placement_groups: list[PlacementGroup] = []
        local_dp_ranks: list[int] = []
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        dp_master_ip_key = f"node:{dp_master_ip}"
        nodes = sorted(
            available_resources.values(), key=lambda x: dp_master_ip_key not in x
        )
        assert len(nodes) > 0, "No nodes with resources found in Ray cluster."
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        assert dp_master_ip_key in nodes[0], (
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            f"The DP master node (ip: {dp_master_ip}) is missing or dead"
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        )
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        device_str = current_platform.ray_device_key
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        n_node_devices: list[int] = [
            int(node_resources[device_str])
            for node_resources in nodes
            if device_str in node_resources
        ]
        assert n_node_devices, f"No {device_str} found in Ray cluster."
        max_device_per_node = max(n_node_devices)

        pack_strategy = envs.VLLM_RAY_DP_PACK_STRATEGY
        _supported_pack_strategies = ("strict", "fill", "span")
        if pack_strategy not in _supported_pack_strategies:
            raise ValueError(
                f"{envs.VLLM_RAY_DP_PACK_STRATEGY} is not supported. "
                "Make sure to set `VLLM_RAY_DP_PACK_STRATEGY` "
                f"to one of {_supported_pack_strategies}"
            )
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        all2all_backend = vllm_config.parallel_config.all2all_backend
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        if pack_strategy == "fill" and (
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            all2all_backend == "deepep_high_throughput"
            or all2all_backend == "deepep_low_latency"
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        ):
            raise ValueError(
                "DeepEP kernels require EP ranks [0,7] (same for [8,15], ...) "
                "to be on the same node, but VLLM_RAY_DP_PACK_STRATEGY=fill "
                "does not guarantee that. "
                "Please use VLLM_RAY_DP_PACK_STRATEGY=strict instead."
            )

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        if pack_strategy in ("strict", "fill"):
            placement_strategy = "STRICT_PACK"
        else:
            placement_strategy = "PACK"
            assert world_size > max_device_per_node, (
                f"World size {world_size} is smaller than the "
                "maximum number of devices per node "
                f"{max_device_per_node}. Make sure to set "
                "`VLLM_RAY_DP_PACK_STRATEGY` to `strict` or `fill`"
            )

            # if we need multiple nodes per dp group, we require for now that
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            # available nodes are homogeneous
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            assert set(n_node_devices) == {max_device_per_node}, (
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                f"Nodes are not homogeneous, {nodes}"
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            )
            assert world_size % max_device_per_node == 0, (
                f"For multi-node data parallel groups, world_size ({world_size}) must "
                f"be a multiple of number of devices per node ({max_device_per_node})."
            )
            assert len(n_node_devices) * max_device_per_node >= world_size * dp_size, (
                f"Not enough total available nodes ({len(n_node_devices)}) "
                f"and devices per node ({max_device_per_node}) "
                f"to satisfy required world size {world_size} and data parallel size "
                f"{dp_size}"
            )
            assert dp_size_local == 1, (
                f"data-parallel-size-local {dp_size_local} should be set as the "
                "default (1) for VLLM_RAY_DP_PACK_STRATEGY=span. "
                "The actual data-parallel-size-local will be auto determined."
            )

        # bundles collected for a single DP rank from multiple nodes,
        # for "span" pack strategy
        collected_bundles = []
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        for node_resources in nodes:
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            node_ip_keys = [
                key
                for key in node_resources
                if key != "node:__internal_head__" and key.startswith("node:")
            ]
            assert len(node_ip_keys) == 1, (
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                f"Zero or multiple node IP keys found in node resources: {node_ip_keys}"
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            )
            node_ip_key = node_ip_keys[0]
            node_ip = node_ip_key.split(":")[1]

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            n_device_on_node = int(node_resources.get(device_str, 0))
            if pack_strategy == "span" and n_device_on_node != 0:
                # Strictly speaking,
                # dp_size_available = n_device_on_node / world_size
                # and is a fraction, but we use 1 for easier processing
                dp_size_available = 1
            else:
                dp_size_available = n_device_on_node // world_size
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            if node_ip == dp_master_ip:
                if dp_size_available < dp_size_local:
                    raise ValueError(
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                        f"Not enough resources to allocate {dp_size_local} DP ranks "
                        f"on DP master node {dp_master_ip}, possible to fit "
                        f"{dp_size_available} DP ranks."
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                    )
570
                dp_size_to_allocate = dp_size_local
571
            elif pack_strategy == "strict":
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                if dp_size_available < dp_size_local:
                    logger.info(
                        "Skipping node %s as %s DP ranks could not fit, "
                        "possible to fit %s DP ranks",
                        node_ip,
                        dp_size_local,
                        dp_size_available,
579
                    )
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                    continue
                dp_size_to_allocate = dp_size_local
            else:
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                # for "pack_strategy" in "fill" and "span"
                # we always take everything that's available
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                dp_size_to_allocate = dp_size_available

            for i in range(dp_size_to_allocate):
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                device_bundle = [{device_str: 1.0, "node:" + node_ip: 0.001}]
                if pack_strategy == "span":
                    collected_bundles += device_bundle * n_device_on_node
                    assert len(collected_bundles) <= world_size, (
                        "collected_bundles should be <= world_size, "
                        f"but got {len(collected_bundles)=} and {world_size=}"
                    )

                    # we only create a placement group if we collected enough devices
                    if len(collected_bundles) < world_size:
                        continue

                    bundles = collected_bundles + [{"CPU": 1.0}]
                    collected_bundles = []
                else:
                    bundles = device_bundle * world_size + [{"CPU": 1.0}]

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                pg = ray.util.placement_group(
                    name=f"dp_rank_{len(placement_groups)}",
607
                    strategy=placement_strategy,
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                    bundles=bundles,
                )
                placement_groups.append(pg)
                local_dp_ranks.append(i)
612
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                if len(placement_groups) == dp_size:
                    break
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615

        if len(placement_groups) < dp_size:
616
            raise ValueError(
617
                f"Not enough resources to allocate {dp_size} "
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                "placement groups, only created "
                f"{len(placement_groups)} placement groups. "
                "Available resources: "
621
622
                f"{available_resources}"
            )
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        assert len(placement_groups) == dp_size, (
            f"Created {len(placement_groups)} DP placement groups, expected {dp_size}"
        )
        assert len(local_dp_ranks) == dp_size, (
            f"local_dp_ranks length {len(local_dp_ranks)} does not match "
            f"expected {dp_size}"
        )
630
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        return placement_groups, local_dp_ranks

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637
638
639
    @staticmethod
    def add_dp_placement_groups(
        old_vllm_config: VllmConfig, new_data_parallel_size: int
    ) -> tuple[list["PlacementGroup"], list[int]]:
        """
        Add placement groups for new data parallel size.
        """
        import ray
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        from ray._private.state import (
            available_resources_per_node,
            total_resources_per_node,
        )
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655
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        from ray.util.state import list_nodes

        old_dp_size = old_vllm_config.parallel_config.data_parallel_size
        num_pg_to_create = new_data_parallel_size - old_dp_size

        if num_pg_to_create <= 0:
            return [], []

        dp_master_ip = old_vllm_config.parallel_config.data_parallel_master_ip
        world_size = old_vllm_config.parallel_config.world_size

        nodes = list_nodes()
        nodes = sorted(nodes, key=lambda node: node.node_ip != dp_master_ip)
657
        assert nodes[0].node_ip == dp_master_ip, "The first node must be the head node"
658
        assert len(nodes) == 1 or nodes[1].node_ip != dp_master_ip, (
659
660
            "There can only be one head node"
        )
661
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667
668

        available_resources = available_resources_per_node()
        total_resources = total_resources_per_node()

        placement_groups = []
        local_dp_ranks = []
        num_pg_created = 0

669
        device_str = current_platform.ray_device_key
670
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        for node in nodes:
            if num_pg_created >= num_pg_to_create:
                break

            node_ip = node.node_ip
            node_id = node.node_id
676
677
            if device_str not in available_resources[node_id]:
                continue
678
            available_gpus = int(available_resources[node_id][device_str])
679
680
681

            # Get total GPUs on this node from the node's resources
            # Ray stores node resources with node ID as key
682
            total_gpus = int(total_resources[node_id][device_str])
683
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691
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695
696
697
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            # Calculate used GPUs and used engines on this node
            used_gpus = max(0, total_gpus - available_gpus)
            used_engines_on_node = used_gpus // world_size

            # Calculate how many new engines this node can accommodate
            available_engine_count = available_gpus // world_size

            # Create placement groups for new engines on this node
            for i in range(available_engine_count):
                if num_pg_created >= num_pg_to_create:
                    break

                rank = old_dp_size + num_pg_created

                # Create bundles with node constraint for master node
                if node_ip == dp_master_ip:
700
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702
                    bundles = [
                        {device_str: 1.0, "node:" + dp_master_ip: 0.001}
                    ] * world_size + [{"CPU": 1.0}]
703
                else:
704
                    bundles = [{device_str: 1.0}] * world_size + [{"CPU": 1.0}]
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                pg = ray.util.placement_group(
                    name=f"dp_rank_{rank}",
                    strategy="STRICT_PACK",
                    bundles=bundles,
                )
                placement_groups.append(pg)

                # Local rank starts from the number of engines already used
                # on this node
                local_rank = used_engines_on_node + i
                local_dp_ranks.append(local_rank)
                num_pg_created += 1

        return placement_groups, local_dp_ranks

721
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723
    def scale_up_elastic_ep(
        self, cur_vllm_config: VllmConfig, new_data_parallel_size: int
    ) -> None:
724
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        import copy

        import ray
        from ray.runtime_env import RuntimeEnv
728
        from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
729

730
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732
733
734
735
736
        from vllm.v1.engine.core import DPMoEEngineCoreActor, EngineCoreActor

        actor_class = (
            DPMoEEngineCoreActor
            if cur_vllm_config.model_config.is_moe
            else EngineCoreActor
        )
737

738
739
740
        cur_data_parallel_size = len(self.local_engine_actors) + len(
            self.remote_engine_actors
        )
741
742
743
744

        assert new_data_parallel_size > cur_data_parallel_size, (
            f"New data parallel size {new_data_parallel_size} must be greater "
            f"than current data parallel size {cur_data_parallel_size} "
745
746
            "for scale up"
        )
747

748
749
750
        placement_groups, local_dp_ranks = self.add_dp_placement_groups(
            cur_vllm_config, new_data_parallel_size
        )
751
752
753
754
755

        world_size = cur_vllm_config.parallel_config.world_size
        dp_master_ip = cur_vllm_config.parallel_config.data_parallel_master_ip
        new_local_engines = 0

756
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758
759
        runtime_env = RuntimeEnv(
            env_vars=self.env_vars_dict | {"VLLM_ELASTIC_EP_SCALE_UP_LAUNCH": "1"}
        )
        for i, (pg, local_rank) in enumerate(zip(placement_groups, local_dp_ranks)):
760
761
            rank = cur_data_parallel_size + i
            dp_vllm_config = copy.deepcopy(cur_vllm_config)
762
            dp_vllm_config.parallel_config.data_parallel_size = new_data_parallel_size
763
764
765
766
            dp_vllm_config.parallel_config.placement_group = pg

            # Check if this placement group is on the head node
            local_client = any(
767
768
                bundle.get("node:" + dp_master_ip, 0) > 0 for bundle in pg.bundle_specs
            )
769
770
771
772
773

            if local_client:
                new_local_engines += 1
                # Update data_parallel_size_local
                dp_vllm_config.parallel_config.data_parallel_size_local = (
774
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776
777
778
                    cur_vllm_config.parallel_config.data_parallel_size_local
                    + new_local_engines
                )

            actor = (
779
                ray.remote(actor_class)
780
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782
783
784
785
786
787
                .options(
                    scheduling_strategy=PlacementGroupSchedulingStrategy(
                        placement_group=pg,
                        placement_group_bundle_index=world_size,
                    ),
                    runtime_env=runtime_env,
                )
                .remote(
788
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793
                    vllm_config=dp_vllm_config,
                    executor_class=self.executor_class,
                    log_stats=self.log_stats,
                    local_client=local_client,
                    addresses=self.addresses,
                    dp_rank=rank,
794
795
796
                    local_dp_rank=local_rank,
                )
            )
797
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799
800
801
802
803
804

            if local_client:
                self.local_engine_actors.append(actor)
            else:
                self.remote_engine_actors.append(actor)
            self.created_placement_groups.append(pg)
            self.placement_group_is_local.append(local_client)

805
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811
812
813
814
815
816
817
        ray.get(
            [
                actor.wait_for_init.remote()
                for actor in (
                    self.local_engine_actors[-new_local_engines:]
                    if new_local_engines > 0
                    else []
                )
                + self.remote_engine_actors[
                    -(len(placement_groups) - new_local_engines) :
                ]
            ]
        )
818

819
820
821
822
823
        actors = (
            self.local_engine_actors[-new_local_engines:]
            if new_local_engines > 0
            else []
        ) + self.remote_engine_actors[-(len(placement_groups) - new_local_engines) :]
824
825

        for actor in actors:
826
827
828
            ref = actor.run.remote()
            self.run_refs.append(ref)
            self.actor_run_ref_dict[actor] = ref
829

830
        cur_vllm_config.parallel_config.data_parallel_size = new_data_parallel_size
831
832
833
        # Update old_vllm_config with new data_parallel_size_local if any new
        # local engines were added
        if new_local_engines > 0:
834
            cur_vllm_config.parallel_config.data_parallel_size_local += (
835
                new_local_engines
836
            )
837

838
839
840
    def scale_down_elastic_ep(
        self, cur_data_parallel_size: int, new_data_parallel_size: int
    ) -> None:
841
        import ray
842

843
844
845
        assert cur_data_parallel_size > new_data_parallel_size, (
            f"cur_data_parallel_size {cur_data_parallel_size} must be greater "
            f"than new_data_parallel_size {new_data_parallel_size} "
846
847
            "for scale down"
        )
848
849
850
851
852
853
854
855
856
        for _ in range(cur_data_parallel_size - new_data_parallel_size):
            pg = self.created_placement_groups.pop()
            is_local = self.placement_group_is_local.pop()
            if is_local:
                self.local_engine_actors.pop()
            else:
                self.remote_engine_actors.pop()
            ray.util.remove_placement_group(pg)

857
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864
865
866
867
868
869
870
871
872
    def remove_run_refs_for_scale_down(self, removed_dp_size: int) -> None:
        if removed_dp_size <= 0:
            return
        flags = self.placement_group_is_local[-removed_dp_size:]
        li = len(self.local_engine_actors) - 1
        ri = len(self.remote_engine_actors) - 1
        for is_local in reversed(flags):
            if is_local:
                actor = self.local_engine_actors[li]
                li -= 1
            else:
                actor = self.remote_engine_actors[ri]
                ri -= 1
            ref = self.actor_run_ref_dict.pop(actor)
            self.run_refs.remove(ref)

873
874
875
    def get_run_refs(self):
        return self.run_refs

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901
902
903
904
905
    def monitor_engine_liveness(self) -> None:
        import ray

        while not self.manager_stopped.is_set():
            actor_run_refs = list(self.get_run_refs())
            if not actor_run_refs:
                logger.info(
                    "There are no actors to monitor currently. "
                    "The monitoring function is about to terminate."
                )
                break
            actor_done_refs, _ = ray.wait(actor_run_refs, timeout=5)
            unexpected_failure = False
            for actor_ref in actor_done_refs:
                if self.manager_stopped.is_set():
                    break
                if actor_ref not in self.get_run_refs():
                    # The run refs may have been updated by elastic scale-down.
                    continue
                try:
                    ray.get(actor_ref)
                except ray.exceptions.RayActorError:
                    self.failed_proc_name = f"Actor {actor_ref}"
                    unexpected_failure = True

            if unexpected_failure:
                break

        self.shutdown()

906
    def shutdown(self, timeout: float | None = None) -> None:
907
        import ray
908

909
        self.manager_stopped.set()
910
911
912
913
914
915
        for actor in self.local_engine_actors + self.remote_engine_actors:
            ray.kill(actor)
        for pg in self.created_placement_groups:
            ray.util.remove_placement_group(pg)


916
def get_engine_zmq_addresses(
917
918
    vllm_config: VllmConfig,
    num_api_servers: int = 1,
919
920
) -> EngineZmqAddresses:
    """Allocate ZMQ addresses for engine-client communication."""
921
922
923
    parallel_config = vllm_config.parallel_config
    local_engine_count = parallel_config.data_parallel_size_local
    local_start_index = parallel_config.data_parallel_rank_local
924
    dp_size = parallel_config.data_parallel_size
925
    host = parallel_config.data_parallel_master_ip
926
    local_engines_only = parallel_config.local_engines_only
927
928
929
930
931
932
933
934

    # In offline mode there is an LLM instance per DP rank and
    # one core engine per LLM, see
    # examples/offline_inference/data_parallel.py.
    offline_mode = local_start_index is not None

    # client_local_only = True for cases where this front-end
    # sends requests only to colocated engines.
935
936
937
    client_local_only = (
        offline_mode or local_engines_only or (local_engine_count == dp_size)
    )
938
939
940
    # NOTE(yongji): handling scaling from intra-node to inter-node
    if parallel_config.enable_elastic_ep:
        client_local_only = False
941

942
    return EngineZmqAddresses(
943
944
945
946
947
948
949
950
951
952
        inputs=[
            get_engine_client_zmq_addr(client_local_only, host)
            for _ in range(num_api_servers)
        ],
        outputs=[
            get_engine_client_zmq_addr(client_local_only, host)
            for _ in range(num_api_servers)
        ],
    )

953
954
955
956
957
958
959
960
961
962
963
964
965

@contextlib.contextmanager
def launch_core_engines(
    vllm_config: VllmConfig,
    executor_class: type[Executor],
    log_stats: bool,
    addresses: EngineZmqAddresses,
    num_api_servers: int = 1,
) -> Iterator[
    tuple[
        CoreEngineProcManager | CoreEngineActorManager | None,
        DPCoordinator | None,
        EngineZmqAddresses,
966
        Queue | None,
967
968
969
970
971
972
973
974
975
976
977
978
979
980
    ]
]:
    """Launch engine and DP coordinator processes as needed."""

    parallel_config = vllm_config.parallel_config
    dp_size = parallel_config.data_parallel_size
    local_engine_count = parallel_config.data_parallel_size_local
    local_start_index = parallel_config.data_parallel_rank_local
    dp_rank = parallel_config.data_parallel_rank
    host = parallel_config.data_parallel_master_ip
    local_engines_only = parallel_config.local_engines_only

    offline_mode = local_start_index is not None

981
982
983
984
985
986
987
988
    # Create a single tensor IPC queue for sharing multimodal tensors between
    # API servers and engine core. Returns a single queue since we only support
    # DP=1 for this data flow.
    tensor_queue: Queue | None = None
    multimodal_config = vllm_config.model_config.multimodal_config
    if multimodal_config is not None and multimodal_config.mm_tensor_ipc == "torch_shm":
        tensor_queue = get_mp_context().Queue()

989
990
991
992
993
994
995
    # Run the DP Coordinator process with rank 0 when in online DP mode.
    # The coordinator is needed for:
    # 1. Internal/hybrid LB: collecting and publishing queue stats for load balancing
    # 2. MoE models: wave coordination in addition to stats
    run_coordinator = (
        vllm_config.needs_dp_coordinator and not offline_mode and dp_rank == 0
    )
996
997

    if run_coordinator:
998
999
1000
1001
        coordinator = DPCoordinator(
            parallel_config,
            enable_wave_coordination=vllm_config.model_config.is_moe,
        )
1002
1003

        addresses.coordinator_input, addresses.coordinator_output = (
1004
1005
            coordinator.get_engine_socket_addresses()
        )
1006
        addresses.frontend_stats_publish_address = (
1007
1008
            coordinator.get_stats_publish_address()
        )
1009

1010
        logger.info("Started DP Coordinator process (PID: %d)", coordinator.proc.pid)
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
    else:
        coordinator = None

    if parallel_config.data_parallel_backend == "ray":
        logger.info("Starting ray-based data parallel backend")

        engine_actor_manager = CoreEngineActorManager(
            vllm_config=vllm_config,
            addresses=addresses,
            executor_class=executor_class,
            log_stats=log_stats,
        )

1024
        yield engine_actor_manager, coordinator, addresses, tensor_queue
1025
1026
        return

1027
    if offline_mode:
1028
1029
        assert local_engine_count == 1
        engines_to_handshake = [CoreEngine(index=dp_rank, local=True)]
1030
1031
1032
1033
1034
    elif dp_rank == 0:
        # Rank 0 holds Coordinator, so it handshakes with all Cores
        # in both external dplb and internal dplb mode.
        # Note this also covers the case where we have zero local engines
        # and rank 0 is headless.
1035
        engines_to_handshake = [
1036
            CoreEngine(index=i, local=(i < local_engine_count)) for i in range(dp_size)
1037
        ]
1038
1039
1040
1041
    else:
        # Rank > 0 handshakes with just the local cores it is managing.
        assert local_engines_only, (
            "Attempting to launch core_engines from dp_rank > 0, but "
1042
1043
            "found internal DPLB, which is incompatible."
        )
1044
1045
1046
1047
        engines_to_handshake = [
            CoreEngine(index=i, local=True)
            for i in range(dp_rank, dp_rank + local_engine_count)
        ]
1048
1049
1050
1051
1052
1053
1054

    # Whether the started engines will handshake only with co-located
    # front-end processes. In external_dp_lb mode, ranks > 0 handshake with
    # their co-located frontend and also the rank 0 front-end, and hence this
    # will be False.
    handshake_local_only = offline_mode or local_engine_count == dp_size

1055
1056
1057
1058
    # NOTE(yongji): handling scaling from intra-node to inter-node
    if parallel_config.enable_elastic_ep:
        handshake_local_only = False

1059
    handshake_address = get_engine_client_zmq_addr(
1060
1061
        handshake_local_only, host, parallel_config.data_parallel_rpc_port
    )
1062

1063
    if local_engines_only and dp_rank > 0:
1064
1065
1066
1067
1068
1069
1070
        assert not handshake_local_only
        local_handshake_address = get_open_zmq_ipc_path()
        client_handshake_address = local_handshake_address
    else:
        local_handshake_address = handshake_address
        client_handshake_address = None

1071
1072
1073
    with zmq_socket_ctx(
        local_handshake_address, zmq.ROUTER, bind=True
    ) as handshake_socket:
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
        # Start local engines.
        if local_engine_count:
            local_engine_manager = CoreEngineProcManager(
                vllm_config=vllm_config,
                executor_class=executor_class,
                log_stats=log_stats,
                handshake_address=handshake_address,
                client_handshake_address=client_handshake_address,
                local_client=True,
                local_engine_count=local_engine_count,
                start_index=dp_rank,
1085
                local_start_index=local_start_index or 0,
1086
                tensor_queue=tensor_queue,
1087
            )
1088
1089
1090
        else:
            local_engine_manager = None

1091
        yield local_engine_manager, coordinator, addresses, tensor_queue
1092
1093
1094
1095
1096
1097
1098

        # Now wait for engines to start.
        wait_for_engine_startup(
            handshake_socket,
            addresses,
            engines_to_handshake,
            parallel_config,
1099
            dp_size > 1 and vllm_config.model_config.is_moe,
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
            vllm_config.cache_config,
            local_engine_manager,
            coordinator.proc if coordinator else None,
        )


def wait_for_engine_startup(
    handshake_socket: zmq.Socket,
    addresses: EngineZmqAddresses,
    core_engines: list[CoreEngine],
    parallel_config: ParallelConfig,
1111
    coordinated_dp: bool,
1112
    cache_config: CacheConfig,
1113
1114
    proc_manager: CoreEngineProcManager | None,
    coord_process: Process | None,
1115
1116
1117
1118
1119
1120
1121
1122
1123
):
    # Wait for engine core process(es) to send ready messages.
    local_count = parallel_config.data_parallel_size_local
    remote_count = len(core_engines) - local_count
    # [local, remote] counts
    conn_pending, start_pending = [local_count, remote_count], [0, 0]
    poller = zmq.Poller()
    poller.register(handshake_socket, zmq.POLLIN)

1124
1125
    remote_should_be_headless = (
        not parallel_config.data_parallel_hybrid_lb
1126
        and not parallel_config.data_parallel_external_lb
1127
    )
1128

1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
    if proc_manager is not None:
        for sentinel in proc_manager.sentinels():
            poller.register(sentinel, zmq.POLLIN)
    if coord_process is not None:
        poller.register(coord_process.sentinel, zmq.POLLIN)
    while any(conn_pending) or any(start_pending):
        events = poller.poll(STARTUP_POLL_PERIOD_MS)
        if not events:
            if any(conn_pending):
                logger.debug(
1139
1140
1141
                    "Waiting for %d local, %d remote core engine proc(s) to connect.",
                    *conn_pending,
                )
1142
1143
            if any(start_pending):
                logger.debug(
1144
1145
1146
                    "Waiting for %d local, %d remote core engine proc(s) to start.",
                    *start_pending,
                )
1147
1148
1149
1150
1151
1152
            continue
        if len(events) > 1 or events[0][0] != handshake_socket:
            # One of the local core processes exited.
            finished = proc_manager.finished_procs() if proc_manager else {}
            if coord_process is not None and coord_process.exitcode is not None:
                finished[coord_process.name] = coord_process.exitcode
1153
1154
1155
1156
1157
            raise RuntimeError(
                "Engine core initialization failed. "
                "See root cause above. "
                f"Failed core proc(s): {finished}"
            )
1158
1159
1160
1161

        # Receive HELLO and READY messages from the input socket.
        eng_identity, ready_msg_bytes = handshake_socket.recv_multipart()
        eng_index = int.from_bytes(eng_identity, "little")
1162
        engine = next((e for e in core_engines if e.identity == eng_identity), None)
1163
        if engine is None:
1164
1165
1166
            raise RuntimeError(
                f"Message from engine with unexpected data parallel rank: {eng_index}"
            )
1167
        msg = msgspec.msgpack.decode(ready_msg_bytes)
1168
        status, local, headless = msg["status"], msg["local"], msg["headless"]
1169
        if local != engine.local:
1170
1171
1172
1173
1174
1175
            raise RuntimeError(
                f"{status} message from "
                f"{'local' if local else 'remote'} "
                f"engine {eng_index}, expected it to be "
                f"{'local' if engine.local else 'remote'}"
            )
1176

1177
1178
1179
        # Remote engines must be headless iff we aren't in hybrid dp lb mode.
        if not local and headless != remote_should_be_headless:
            if headless:
1180
1181
1182
1183
1184
                raise RuntimeError(
                    f"Remote engine {eng_index} must not use "
                    f"--headless in external or hybrid dp lb "
                    f"mode"
                )
1185
            else:
1186
1187
1188
1189
1190
                raise RuntimeError(
                    f"Remote engine {eng_index} must use "
                    f"--headless unless in external or hybrid "
                    f"dp lb mode"
                )
1191

1192
        if status == "HELLO" and engine.state == CoreEngineState.NEW:
1193
            # Send init message with DP config info.
1194
1195
1196
1197
            init_message = msgspec.msgpack.encode(
                EngineHandshakeMetadata(
                    addresses=addresses,
                    parallel_config={
1198
1199
1200
1201
1202
1203
1204
                        k: getattr(parallel_config, k)
                        for k in (
                            "data_parallel_master_ip",
                            "data_parallel_master_port",
                            "_data_parallel_master_port_list",
                            "data_parallel_size",
                        )
1205
1206
1207
                    }
                    if coordinated_dp
                    else {},
1208
1209
1210
                )
            )
            handshake_socket.send_multipart((eng_identity, init_message), copy=False)
1211
1212
1213
1214
            conn_pending[0 if local else 1] -= 1
            start_pending[0 if local else 1] += 1
            engine.state = CoreEngineState.CONNECTED
        elif status == "READY" and engine.state == CoreEngineState.CONNECTED:
1215
1216
            # Validate config hash consistency across DP workers for MoE models.
            if coordinated_dp:
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
                worker_config_hash = msg.get("parallel_config_hash")
                expected_hash = parallel_config.compute_hash()
                if worker_config_hash != expected_hash:
                    raise RuntimeError(
                        f"Configuration mismatch detected for engine "
                        f"{eng_index}. All DP workers must have identical "
                        f"configurations for parameters that affect collective "
                        f"communication (e.g., enable_eplb, "
                        f"eplb_config.log_balancedness). "
                        f"Worker hash: {worker_config_hash}, "
                        f"Expected hash: {expected_hash}. "
                        f"Please ensure all workers are started with the same "
                        f"command-line arguments."
                    )

1232
1233
1234
            start_pending[0 if local else 1] -= 1
            engine.state = CoreEngineState.READY
        else:
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
            raise RuntimeError(
                f"Unexpected {status} message for "
                f"{'local' if local else 'remote'} engine "
                f"{eng_index} in {engine.state} state."
            )

        logger.debug(
            "%s from %s core engine process %s.",
            status,
            "local" if local else "remote",
            eng_index,
        )