mp_distributed_executor.py

# SPDX-License-Identifier: Apache-2.0

import asyncio
import os
from typing import Any, Callable, List, Optional, Union

import cloudpickle

from vllm.executor.executor_base import DistributedExecutorBase
from vllm.executor.multiproc_worker_utils import (
    ProcessWorkerWrapper, ResultHandler, WorkerMonitor,
    set_multiprocessing_worker_envs)
from vllm.logger import init_logger
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.sequence import ExecuteModelRequest
from vllm.utils import (_run_task_with_lock, cuda_device_count_stateless,
                        get_distributed_init_method, get_ip, get_open_port,
                        make_async, run_method, update_environment_variables)
from vllm.worker.worker_base import WorkerWrapperBase

logger = init_logger(__name__)


class MultiprocessingDistributedExecutor(DistributedExecutorBase):
    """Python multiprocessing-based distributed executor"""

    uses_ray: bool = False

    def _check_cuda(self) -> None:
        """Check that the number of GPUs is sufficient for the parallel
        configuration. Separate from _init_executor to reduce the number of
        indented blocks.
        """
        parallel_config = self.parallel_config
        world_size = parallel_config.world_size
        tensor_parallel_size = parallel_config.tensor_parallel_size

        cuda_device_count = cuda_device_count_stateless()
        # Use confusing message for more common TP-only case.
        if tensor_parallel_size > cuda_device_count:
            raise RuntimeError(
                f"please set tensor_parallel_size ({tensor_parallel_size}) "
                f"to less than max local gpu count ({cuda_device_count})")

        if world_size > cuda_device_count:
            raise RuntimeError(
                f"please ensure that world_size ({world_size}) "
                f"is less than than max local gpu count ({cuda_device_count})")

        # Set CUDA_VISIBLE_DEVICES for the driver, inherited by workers
        if "CUDA_VISIBLE_DEVICES" or "HIP_VISIBLE_DEVICES" not in os.environ:
            update_environment_variables({
                "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
            })
            update_environment_variables({
                "HIP_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
            })

    def _init_executor(self) -> None:

        from vllm.platforms import current_platform
        if current_platform.is_cuda_alike():
            self._check_cuda()

        # Create the parallel GPU workers.
        world_size = self.parallel_config.world_size
        tensor_parallel_size = self.parallel_config.tensor_parallel_size

        # Set multiprocessing envs that are common to V0 and V1
        set_multiprocessing_worker_envs(self.parallel_config)

        # Multiprocessing-based executor does not support multi-node setting.
        # Since it only works for single node, we can use the loopback address
        # 127.0.0.1 for communication.
        distributed_init_method = get_distributed_init_method(
            "127.0.0.1", get_open_port())

        self.workers: List[ProcessWorkerWrapper] = []
        # This is the list of workers that are rank 0 of each TP group EXCEPT
        # global rank 0. These are the workers that will broadcast to the
        # rest of the workers.
        self.tp_driver_workers: List[ProcessWorkerWrapper] = []
        # This is the list of workers that are not drivers and not the first
        # worker in a TP group. These are the workers that will be
        # broadcasted to.
        self.non_driver_workers: List[ProcessWorkerWrapper] = []

        if world_size == 1:
            self.worker_monitor = None
        else:
            result_handler = ResultHandler()
            for rank in range(1, world_size):
                worker = ProcessWorkerWrapper(result_handler,
                                              WorkerWrapperBase,
                                              self.vllm_config, rank)
                self.workers.append(worker)
                if rank % tensor_parallel_size == 0:
                    self.tp_driver_workers.append(worker)
                else:
                    self.non_driver_workers.append(worker)

            self.worker_monitor = WorkerMonitor(self.workers, result_handler)
            result_handler.start()
            self.worker_monitor.start()

        # Set up signal handlers to shutdown the executor cleanly
        # sometimes gc does not work well

        self.driver_worker = WorkerWrapperBase(self.vllm_config, 0)

        all_kwargs = []
        distributed_init_method = get_distributed_init_method(
            get_ip(), get_open_port())
        for i in range(world_size):
            local_rank = i
            rank = i
            kwargs = dict(
                vllm_config=self.vllm_config,
                local_rank=local_rank,
                rank=rank,
                distributed_init_method=distributed_init_method,
                is_driver_worker=(not self.parallel_config)
                or (rank % self.parallel_config.tensor_parallel_size == 0),
            )
            all_kwargs.append(kwargs)
        self._run_workers("init_worker", all_kwargs)
        self._run_workers("init_device")
        self._run_workers("load_model",
                          max_concurrent_workers=self.parallel_config.
                          max_parallel_loading_workers)
        self.driver_exec_model = make_async(self.driver_worker.execute_model)
        self.pp_locks: Optional[List[asyncio.Lock]] = None

    def shutdown(self):
        if (worker_monitor := getattr(self, "worker_monitor",
                                      None)) is not None:
            worker_monitor.close()

    def _driver_execute_model(
        self, execute_model_req: Optional[ExecuteModelRequest]
    ) -> Optional[List[SamplerOutput]]:
        """Run execute_model in the driver worker.

        Passing None will cause the driver to stop the model execution
        loop running in each of the remote workers.
        """
        return self.driver_worker.execute_model(execute_model_req)

    def _run_workers(
        self,
        method: Union[str, Callable],
        *args,
        async_run_tensor_parallel_workers_only: bool = False,
        max_concurrent_workers: Optional[int] = None,
        **kwargs,
    ) -> List[Any]:
        """Runs the given method on all workers.

        Args:
            async_run_tensor_parallel_workers_only: If True the method will be
                run only in the remote TP workers, not the driver worker.
                It will also be run asynchronously and return a list of futures
                rather than blocking on the results.
        """
        if isinstance(method, str):
            sent_method = method
        else:
            sent_method = cloudpickle.dumps(method)
        del method

        if max_concurrent_workers:
            raise NotImplementedError(
                "max_concurrent_workers is not supported yet.")

        if async_run_tensor_parallel_workers_only:
            # Run only non-driver workers and just return futures.
            return [
                worker.execute_method(sent_method, *args, **kwargs)
                for worker in self.non_driver_workers
            ]

        # Start all remote workers first.
        worker_outputs = [
            worker.execute_method(sent_method, *args, **kwargs)
            for worker in self.workers
        ]

        driver_worker_output = run_method(self.driver_worker, sent_method,
                                          args, kwargs)

        # Get the results of the workers.
        return [driver_worker_output
                ] + [output.get() for output in worker_outputs]

    def check_health(self) -> None:
        """Raises an error if engine is unhealthy."""
        if self.worker_monitor is not None and not self.worker_monitor.is_alive(
        ):
            raise RuntimeError("Worker processes are not running")

    def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
        """Wait for futures returned from _run_workers() with
        async_run_remote_workers_only to complete."""
        for result in parallel_worker_tasks:
            result.get()

    async def _driver_execute_model_async(
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> List[SamplerOutput]:
        if not self.tp_driver_workers:
            return await self.driver_exec_model(execute_model_req)

        if self.pp_locks is None:
            # This locks each pipeline parallel stage so multiple virtual
            # engines can't execute on the same stage at the same time
            # We create the locks here to avoid creating them in the constructor
            # which uses a different asyncio loop.
            self.pp_locks = [
                asyncio.Lock()
                for _ in range(self.parallel_config.pipeline_parallel_size)
            ]

        tasks = [
            asyncio.create_task(
                _run_task_with_lock(self.driver_exec_model, self.pp_locks[0],
                                    execute_model_req))
        ]
        for pp_rank, driver_worker in enumerate(self.tp_driver_workers,
                                                start=1):
            tasks.append(
                asyncio.create_task(
                    _run_task_with_lock(driver_worker.execute_method_async,
                                        self.pp_locks[pp_rank],
                                        "execute_model", execute_model_req)))
        results = await asyncio.gather(*tasks)

        # Only the last PP stage has the final results.
        return results[-1]

    async def _start_worker_execution_loop(self):
        coros = [
            worker.execute_method_async("start_worker_execution_loop")
            for worker in self.non_driver_workers
        ]
        return await asyncio.gather(*coros)