# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import multiprocessing
import os
import pickle
import signal
import sys
import threading
import time
import traceback
import weakref
from concurrent.futures import Future, ThreadPoolExecutor
from dataclasses import dataclass
from enum import Enum, auto
from functools import partial
from multiprocessing.connection import Connection
from multiprocessing.process import BaseProcess
from threading import Thread
from typing import Any, Callable, Optional, Union, cast

import cloudpickle

import vllm.envs as envs
from vllm.config import VllmConfig
from vllm.distributed import (destroy_distributed_environment,
                              destroy_model_parallel)
from vllm.distributed.device_communicators.shm_broadcast import (Handle,
                                                                 MessageQueue)
from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
from vllm.executor.multiproc_worker_utils import (
    _add_prefix, set_multiprocessing_worker_envs)
from vllm.logger import init_logger
from vllm.utils import (bind_process_name, get_distributed_init_method,
                        get_loopback_ip, get_mp_context, get_open_port)
from vllm.v1.executor.abstract import Executor, FailureCallback
from vllm.v1.outputs import ModelRunnerOutput
from vllm.worker.worker_base import WorkerWrapperBase

logger = init_logger(__name__)


class MultiprocExecutor(Executor):

    def _init_executor(self) -> None:
        # Call self.shutdown at exit to clean up
        # and ensure workers will be terminated.
        self._finalizer = weakref.finalize(self, self.shutdown)
        self.is_failed = False
        self.shutdown_event = threading.Event()
        self.failure_callback: Optional[FailureCallback] = None
        self.io_thread_pool: Optional[ThreadPoolExecutor] = None

        self.world_size = self.parallel_config.world_size
        tensor_parallel_size = self.parallel_config.tensor_parallel_size
        pp_parallel_size = self.parallel_config.pipeline_parallel_size
        assert self.world_size == tensor_parallel_size * pp_parallel_size, (
            f"world_size ({self.world_size}) must be equal to the "
            f"tensor_parallel_size ({tensor_parallel_size}) x pipeline"
            f"_parallel_size ({pp_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
        # get_loopback_ip() for communication.
        distributed_init_method = get_distributed_init_method(
            get_loopback_ip(), get_open_port())

        # Initialize worker and set up message queues for SchedulerOutputs
        # and ModelRunnerOutputs
        max_chunk_bytes = envs.VLLM_MQ_MAX_CHUNK_BYTES_MB * 1024 * 1024
        self.rpc_broadcast_mq = MessageQueue(self.world_size,
                                             self.world_size,
                                             max_chunk_bytes=max_chunk_bytes)
        scheduler_output_handle = self.rpc_broadcast_mq.export_handle()

        # Create workers
        unready_workers: list[UnreadyWorkerProcHandle] = []
        success = False
        try:
            for rank in range(self.world_size):
                unready_workers.append(
                    WorkerProc.make_worker_process(
                        vllm_config=self.vllm_config,
                        local_rank=rank,
                        rank=rank,
                        distributed_init_method=distributed_init_method,
                        input_shm_handle=scheduler_output_handle,
                    ))

            # Workers must be created before wait_for_ready to avoid
            # deadlock, since worker.init_device() does a device sync.
            self.workers = WorkerProc.wait_for_ready(unready_workers)

            # Ensure message queues are ready. Will deadlock if re-ordered
            # Must be kept consistent with the WorkerProc.
            self.rpc_broadcast_mq.wait_until_ready()
            for w in self.workers:
                w.worker_response_mq.wait_until_ready()

            self.start_worker_monitor()
            success = True
        finally:
            if not success:
                # Clean up the worker procs if there was a failure.
                self._ensure_worker_termination(
                    [w.proc for w in unready_workers])

        # For pipeline parallel, we use a thread pool for asynchronous
        # execute_model.
        if self.max_concurrent_batches > 1:
            # Note: must use only 1 IO thread to keep dequeue sequence
            # from the response queue
            # _async_aggregate_workers_output also assumes a single IO thread
            self.io_thread_pool = ThreadPoolExecutor(
                max_workers=1, thread_name_prefix="mp_exec_io")

        self.output_rank = self._get_output_rank()
        self.has_connector = self.vllm_config.kv_transfer_config is not None
        self.kv_output_aggregator = KVOutputAggregator(
            self.parallel_config.world_size)

    def start_worker_monitor(self):
        workers = self.workers
        self_ref = weakref.ref(self)

        # Monitors worker process liveness. If any die unexpectedly,
        # logs an error, shuts down the executor and invokes the failure
        # callback to inform the engine.
        def monitor_workers():
            sentinels = [h.proc.sentinel for h in workers]
            died = multiprocessing.connection.wait(sentinels)
            _self = self_ref()
            if not _self or getattr(_self, 'shutting_down', False):
                return
            _self.is_failed = True
            proc_name = next(h.proc.name for h in workers
                             if h.proc.sentinel == died[0])
            logger.error(
                "Worker proc %s died unexpectedly, "
                "shutting down executor.", proc_name)
            _self.shutdown()
            callback = _self.failure_callback
            if callback is not None:
                _self.failure_callback = None
                callback()

        Thread(target=monitor_workers,
               daemon=True,
               name="MultiprocWorkerMonitor").start()

    def register_failure_callback(self, callback: FailureCallback):
        if self.is_failed:
            callback()
        else:
            self.failure_callback = callback

    def execute_model(
        self,
        scheduler_output,
    ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
        non_block = self.max_concurrent_batches > 1

        if not self.has_connector:
            # get output only from a single worker (output_rank)
            (output, ) = self.collective_rpc(
                "execute_model",
                args=(scheduler_output, ),
                unique_reply_rank=self.output_rank,
                non_block=non_block,
                timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS)
            return output

        # get output from all workers
        outputs = self.collective_rpc(
            "execute_model",
            args=(scheduler_output, ),
            non_block=non_block,
            timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS)

        # aggregate all workers output to a single output
        if non_block:
            return self.kv_output_aggregator.async_aggregate(
                outputs, self.output_rank)
        return self.kv_output_aggregator.aggregate(outputs, self.output_rank)

    def collective_rpc(self,
                       method: Union[str, Callable],
                       timeout: Optional[float] = None,
                       args: tuple = (),
                       kwargs: Optional[dict] = None,
                       non_block: bool = False,
                       unique_reply_rank: Optional[int] = None) -> list[Any]:
        if self.is_failed:
            raise RuntimeError("Executor failed.")

        deadline = None if timeout is None else time.monotonic() + timeout
        kwargs = kwargs or {}

        # NOTE: If the args are heterogeneous, then we pack them into a list,
        # and unpack them in the method of every worker, because every worker
        # knows their own rank.
        try:
            if isinstance(method, str):
                send_method = method
            else:
                send_method = cloudpickle.dumps(
                    method, protocol=pickle.HIGHEST_PROTOCOL)
            self.rpc_broadcast_mq.enqueue(
                (send_method, args, kwargs, unique_reply_rank))

            workers = (self.workers[unique_reply_rank],
                       ) if unique_reply_rank is not None else self.workers
            responses = []

            def get_response(w: WorkerProcHandle,
                             dequeue_timeout: Optional[float] = None,
                             cancel_event: Optional[threading.Event] = None):
                status, result = w.worker_response_mq.dequeue(
                    timeout=dequeue_timeout, cancel=cancel_event)

                if status != WorkerProc.ResponseStatus.SUCCESS:
                    raise RuntimeError(
                        f"Worker failed with error '{result}', please check the"
                        " stack trace above for the root cause")
                return result

            for w in workers:
                dequeue_timeout = None if deadline is None else (
                    deadline - time.monotonic())

                if non_block:
                    result = self.io_thread_pool.submit(  # type: ignore
                        get_response, w, dequeue_timeout, self.shutdown_event)
                else:
                    result = get_response(w, dequeue_timeout)

                responses.append(result)

            return responses
        except TimeoutError as e:
            raise TimeoutError(f"RPC call to {method} timed out.") from e

    @staticmethod
    def _ensure_worker_termination(worker_procs: list[BaseProcess]):
        """Ensure that all worker processes are terminated. Assumes workers have
        received termination requests. Waits for processing, then sends
        termination and kill signals if needed."""

        def wait_for_termination(procs, timeout):
            if not time:
                # If we are in late stage shutdown, the interpreter may replace
                # `time` with `None`.
                return all(not proc.is_alive() for proc in procs)
            start_time = time.time()
            while time.time() - start_time < timeout:
                if all(not proc.is_alive() for proc in procs):
                    return True
                time.sleep(0.1)
            return False

        # Send SIGTERM if still running
        active_procs = [proc for proc in worker_procs if proc.is_alive()]
        for p in active_procs:
            p.terminate()
        if not wait_for_termination(active_procs, 4):
            # Send SIGKILL if still running
            active_procs = [p for p in active_procs if p.is_alive()]
            for p in active_procs:
                p.kill()

    def shutdown(self):
        """Properly shut down the executor and its workers"""
        if not getattr(self, 'shutting_down', False):
            self.shutting_down = True
            self.shutdown_event.set()

            if self.io_thread_pool is not None:
                self.io_thread_pool.shutdown(wait=False, cancel_futures=True)
                self.io_thread_pool = None

            if workers := getattr(self, 'workers', None):
                for w in workers:
                    w.worker_response_mq = None
                self._ensure_worker_termination([w.proc for w in workers])

        self.rpc_broadcast_mq = None

    def check_health(self) -> None:
        self.collective_rpc("check_health", timeout=10)
        return

    @property
    def max_concurrent_batches(self) -> int:
        if self.scheduler_config.async_scheduling:
            return 2
        return self.parallel_config.pipeline_parallel_size

    def _get_output_rank(self) -> int:
        # Only returns ModelRunnerOutput from TP rank=0 and PP rank=-1
        # (the first TP worker of the last PP stage).
        # Example:
        # Assuming TP=8, PP=4, then the world_size=32
        # 0-7, PP rank 0
        # 8-15, PP rank 1
        # 16-23, PP rank 2
        # 24-31, PP rank 3
        # so world_size - tp_size = 32 - 8 = 24 should be PP rank = -1 (i.e. 3)
        return self.world_size - self.parallel_config.tensor_parallel_size


@dataclass
class UnreadyWorkerProcHandle:
    """WorkerProcess handle before READY."""
    proc: BaseProcess
    rank: int
    ready_pipe: Connection


@dataclass
class WorkerProcHandle:
    proc: BaseProcess
    rank: int
    worker_response_mq: MessageQueue  # The worker process writes to this MQ

    @classmethod
    def from_unready_handle(
            cls, unready_handle: UnreadyWorkerProcHandle,
            worker_response_mq: MessageQueue) -> "WorkerProcHandle":
        return cls(
            proc=unready_handle.proc,
            rank=unready_handle.rank,
            worker_response_mq=worker_response_mq,
        )


class WorkerProc:
    """Wrapper that runs one Worker in a separate process."""

    READY_STR = "READY"

    def __init__(
        self,
        vllm_config: VllmConfig,
        local_rank: int,
        rank: int,
        distributed_init_method: str,
        input_shm_handle: Handle,
    ):
        self.rank = rank
        wrapper = WorkerWrapperBase(vllm_config=vllm_config, rpc_rank=rank)
        # TODO: move `init_worker` to executor level as a collective rpc call
        all_kwargs: list[dict] = [
            {} for _ in range(vllm_config.parallel_config.world_size)
        ]
        is_driver_worker = (
            rank % vllm_config.parallel_config.tensor_parallel_size == 0)
        all_kwargs[rank] = {
            "vllm_config": vllm_config,
            "local_rank": local_rank,
            "rank": rank,
            "distributed_init_method": distributed_init_method,
            "is_driver_worker": is_driver_worker,
        }
        wrapper.init_worker(all_kwargs)
        self.worker = wrapper
        bind_process_name(
            self.worker.worker.__class__.__name__,
            f"TP{self.rank}_DP{vllm_config.parallel_config.data_parallel_rank}"
        )
        pid = os.getpid()
        _add_prefix(sys.stdout, f"VllmWorker rank={rank}", pid)
        _add_prefix(sys.stderr, f"VllmWorker rank={rank}", pid)

        # Initialize MessageQueue for receiving SchedulerOutput
        self.rpc_broadcast_mq = MessageQueue.create_from_handle(
            input_shm_handle, self.worker.rank)

        # Initializes a message queue for sending the model output
        self.worker_response_mq = MessageQueue(1, 1)

        # Initialize device and loads weights
        self.worker.init_device()
        self.worker.load_model()

    @staticmethod
    def make_worker_process(
            vllm_config: VllmConfig,
            local_rank: int,
            rank: int,
            distributed_init_method: str,
            input_shm_handle,  # Receive SchedulerOutput
    ) -> UnreadyWorkerProcHandle:
        context = get_mp_context()
        # (reader, writer)
        reader, writer = context.Pipe(duplex=False)

        process_kwargs = {
            "vllm_config": vllm_config,
            "local_rank": local_rank,
            "rank": rank,
            "distributed_init_method": distributed_init_method,
            "input_shm_handle": input_shm_handle,
            "ready_pipe": (reader, writer),
        }
        # Run EngineCore busy loop in background process.
        proc = context.Process(target=WorkerProc.worker_main,
                               kwargs=process_kwargs,
                               name=f"VllmWorker-{rank}",
                               daemon=True)

        proc.start()
        writer.close()
        return UnreadyWorkerProcHandle(proc, rank, reader)

    @staticmethod
    def wait_for_ready(
        unready_proc_handles: list[UnreadyWorkerProcHandle]
    ) -> list[WorkerProcHandle]:

        e = Exception("WorkerProc initialization failed due to "
                      "an exception in a background process. "
                      "See stack trace for root cause.")

        pipes = {handle.ready_pipe: handle for handle in unready_proc_handles}
        ready_proc_handles: list[Optional[WorkerProcHandle]] = (
            [None] * len(unready_proc_handles))
        while pipes:
            ready = multiprocessing.connection.wait(pipes.keys())
            for pipe in ready:
                assert isinstance(pipe, Connection)
                try:
                    # Wait until the WorkerProc is ready.
                    unready_proc_handle = pipes.pop(pipe)
                    response: dict[str, Any] = pipe.recv()
                    if response["status"] != "READY":
                        raise e

                    # Extract the message queue handle.
                    worker_response_mq = MessageQueue.create_from_handle(
                        response["handle"], 0)
                    ready_proc_handles[unready_proc_handle.rank] = (
                        WorkerProcHandle.from_unready_handle(
                            unready_proc_handle, worker_response_mq))

                except EOFError:
                    e.__suppress_context__ = True
                    raise e from None

                finally:
                    # Close connection.
                    pipe.close()

        return cast(list[WorkerProcHandle], ready_proc_handles)

    def shutdown(self):
        self.rpc_broadcast_mq = None
        self.worker_response_mq = None
        destroy_model_parallel()
        destroy_distributed_environment()

    @staticmethod
    def worker_main(*args, **kwargs):
        """ Worker initialization and execution loops.
        This runs a background process """

        # Signal handler used for graceful termination.
        # SystemExit exception is only raised once to allow this and worker
        # processes to terminate without error
        shutdown_requested = False

        def signal_handler(signum, frame):
            nonlocal shutdown_requested
            if not shutdown_requested:
                shutdown_requested = True
                raise SystemExit()

        # Either SIGTERM or SIGINT will terminate the worker
        signal.signal(signal.SIGTERM, signal_handler)
        signal.signal(signal.SIGINT, signal_handler)

        worker = None
        # tuple[Connection, Connection]
        reader, ready_writer = kwargs.pop("ready_pipe")
        try:
            reader.close()
            worker = WorkerProc(*args, **kwargs)

            # Send READY once we know everything is loaded
            ready_writer.send({
                "status":
                WorkerProc.READY_STR,
                "handle":
                worker.worker_response_mq.export_handle(),
            })

            # Ensure message queues are ready. Will deadlock if re-ordered.
            # Must be kept consistent with the Executor
            worker.rpc_broadcast_mq.wait_until_ready()
            worker.worker_response_mq.wait_until_ready()
            ready_writer.close()
            ready_writer = None

            worker.worker_busy_loop()

        except Exception:
            # NOTE: if an Exception arises in busy_loop, we send
            # a FAILURE message over the MQ RPC to notify the Executor,
            # which triggers system shutdown.
            # TODO(rob): handle case where the MQ itself breaks.

            if ready_writer is not None:
                logger.exception("WorkerProc failed to start.")
            else:
                logger.exception("WorkerProc failed.")

            # The parent sends a SIGTERM to all worker processes if
            # any worker dies. Set this value so we don't re-throw
            # SystemExit() to avoid zmq exceptions in __del__.
            shutdown_requested = True

        finally:
            if ready_writer is not None:
                ready_writer.close()
            # Clean up once worker exits busy loop
            if worker is not None:
                worker.shutdown()

    class ResponseStatus(Enum):
        SUCCESS = auto()
        FAILURE = auto()

    def worker_busy_loop(self):
        """Main busy loop for Multiprocessing Workers"""
        while True:
            method, args, kwargs, output_rank = self.rpc_broadcast_mq.dequeue()

            try:
                if isinstance(method, str):
                    func = getattr(self.worker, method)
                elif isinstance(method, bytes):
                    func = partial(cloudpickle.loads(method), self.worker)
                output = func(*args, **kwargs)
            except Exception as e:
                # Notes have been introduced in python 3.11
                if hasattr(e, "add_note"):
                    e.add_note(traceback.format_exc())
                logger.exception("WorkerProc hit an exception.")
                # exception might not be serializable, so we convert it to
                # string, only for logging purpose.
                if output_rank is None or self.rank == output_rank:
                    self.worker_response_mq.enqueue(
                        (WorkerProc.ResponseStatus.FAILURE, str(e)))
                continue

            if output_rank is None or self.rank == output_rank:
                self.worker_response_mq.enqueue(
                    (WorkerProc.ResponseStatus.SUCCESS, output))
