uniproc_executor.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from collections.abc import Callable
from concurrent.futures import Future, ThreadPoolExecutor
from functools import cached_property
from multiprocessing import Lock
from typing import Any

import torch
import torch.distributed as dist

import vllm.envs as envs
from vllm.logger import init_logger
from vllm.utils.network_utils import get_distributed_init_method, get_ip, get_open_port
from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
from vllm.v1.executor.abstract import Executor
from vllm.v1.outputs import AsyncModelRunnerOutput, DraftTokenIds, ModelRunnerOutput
from vllm.v1.serial_utils import run_method
from vllm.v1.worker.worker_base import WorkerWrapperBase

logger = init_logger(__name__)


class UniProcExecutor(Executor):
    def _init_executor(self) -> None:
        """Initialize the worker and load the model."""
        self.driver_worker = WorkerWrapperBase(rpc_rank=0)
        distributed_init_method, rank, local_rank = self._distributed_args()
        kwargs = dict(
            vllm_config=self.vllm_config,
            local_rank=local_rank,
            rank=rank,
            distributed_init_method=distributed_init_method,
            is_driver_worker=True,
            shared_worker_lock=Lock(),
        )

        self.async_output_thread: ThreadPoolExecutor | None = None
        if self.max_concurrent_batches > 1:
            self.async_output_thread = ThreadPoolExecutor(
                max_workers=1, thread_name_prefix="WorkerAsyncOutput"
            )

        is_eep_new_worker = envs.VLLM_ELASTIC_EP_SCALE_UP_LAUNCH
        self.driver_worker.init_worker(all_kwargs=[kwargs])
        if not is_eep_new_worker:
            self.driver_worker.init_device()
            self.driver_worker.load_model()

    def _distributed_args(self) -> tuple[str, int, int]:
        """Return (distributed_init_method, rank, local_rank)."""
        distributed_init_method = get_distributed_init_method(get_ip(), get_open_port())
        # set local rank as the device index if specified
        device_info = self.vllm_config.device_config.device.__str__().split(":")
        local_rank = int(device_info[1]) if len(device_info) > 1 else 0
        return distributed_init_method, 0, local_rank

    @cached_property
    def max_concurrent_batches(self) -> int:
        return 2 if self.scheduler_config.async_scheduling else 1

    def collective_rpc(  # type: ignore[override]
        self,
        method: str | Callable,
        timeout: float | None = None,
        args: tuple = (),
        kwargs: dict | None = None,
        non_block: bool = False,
        single_value: bool = False,
    ) -> Any:
        if kwargs is None:
            kwargs = {}

        if not non_block:
            result = run_method(self.driver_worker, method, args, kwargs)
            return result if single_value else [result]

        try:
            result = run_method(self.driver_worker, method, args, kwargs)
            if isinstance(result, AsyncModelRunnerOutput):
                if (async_thread := self.async_output_thread) is not None:
                    if single_value:
                        return async_thread.submit(result.get_output)

                    def get_output_list() -> list[Any]:
                        return [result.get_output()]

                    return async_thread.submit(get_output_list)
                result = result.get_output()
            future = Future[Any]()
            future.set_result(result if single_value else [result])
        except Exception as e:
            future = Future[Any]()
            future.set_exception(e)
        return future

    def execute_model(  # type: ignore[override]
        self, scheduler_output: SchedulerOutput, non_block: bool = False
    ) -> ModelRunnerOutput | None | Future[ModelRunnerOutput | None]:
        return self.collective_rpc(
            "execute_model",
            args=(scheduler_output,),
            non_block=non_block,
            single_value=True,
        )

    def sample_tokens(  # type: ignore[override]
        self, grammar_output: GrammarOutput | None, non_block: bool = False
    ) -> ModelRunnerOutput | None | Future[ModelRunnerOutput | None]:
        return self.collective_rpc(
            "sample_tokens",
            args=(grammar_output,),
            non_block=non_block,
            single_value=True,
        )

    def take_draft_token_ids(self) -> DraftTokenIds | None:
        return self.collective_rpc("take_draft_token_ids", single_value=True)

    def check_health(self) -> None:
        # UniProcExecutor will always be healthy as long as
        # it's running.
        return

    def shutdown(self) -> None:
        if worker := self.driver_worker:
            worker.shutdown()


class ExecutorWithExternalLauncher(UniProcExecutor):
    """An executor that uses external launchers to launch engines,
    specially designed for torchrun-compatible launchers, for
    offline inference with tensor parallelism.

    see https://github.com/vllm-project/vllm/issues/11400 for
    the motivation, and examples/offline_inference/torchrun_example.py
    for the usage example.

    The key idea: although it is tensor-parallel inference, we only
    create one worker per executor, users will launch multiple
    engines with torchrun-compatible launchers, and all these engines
    work together to process the same prompts. When scheduling is
    deterministic, all the engines will generate the same outputs,
    and they don't need to synchronize the states with each other.
    """

    def _init_executor(self) -> None:
        """Initialize the worker and load the model."""
        assert not envs.VLLM_ENABLE_V1_MULTIPROCESSING, (
            "To get deterministic execution, "
            "please set VLLM_ENABLE_V1_MULTIPROCESSING=0"
        )
        super()._init_executor()

    def _distributed_args(self) -> tuple[str, int, int]:
        # engines are launched in torchrun-compatible launchers
        # so we can use the env:// method.
        # required env vars:
        # - RANK
        # - LOCAL_RANK
        # - MASTER_ADDR
        # - MASTER_PORT
        distributed_init_method = "env://"
        rank = int(os.environ["RANK"])
        local_rank = int(os.environ["LOCAL_RANK"])
        return distributed_init_method, rank, local_rank

    def determine_available_memory(self) -> list[int]:  # in bytes
        # we need to get the min across all ranks.
        memory = super().determine_available_memory()
        from vllm.distributed.parallel_state import get_world_group

        cpu_group = get_world_group().cpu_group
        memory_tensor = torch.tensor([memory], device="cpu", dtype=torch.int64)
        dist.all_reduce(memory_tensor, group=cpu_group, op=dist.ReduceOp.MIN)
        return [memory_tensor.item()]