import threading
import weakref
from collections import deque
from collections.abc import Callable
from multiprocessing.synchronize import Lock as LockType

import vllm.v1.executor.multiproc_executor
from vllm import envs
from vllm.config import VllmConfig
from vllm.distributed.device_communicators.shm_broadcast import (Handle,
                                                                 MessageQueue)
from vllm.utils.network_utils import (get_distributed_init_method,
                                      get_loopback_ip, get_open_port)
from vllm.utils.system_utils import get_mp_context
from vllm.v1.executor.abstract import FailureCallback
from vllm.v1.executor.multiproc_executor import (
    FutureWrapper, MultiprocExecutor, UnreadyWorkerProcHandle, WorkerProc,
    set_multiprocessing_worker_envs)


class AscendMultiprocExecutor(MultiprocExecutor):

    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: FailureCallback | None = None

        self.world_size = self.parallel_config.world_size
        assert self.world_size % self.parallel_config.nnodes_within_dp == 0, (
            f"global world_size ({self.parallel_config.world_size}) must be "
            f"divisible by nnodes_within_dp "
            f"({self.parallel_config.nnodes_within_dp}). ")
        self.local_world_size = self.parallel_config.local_world_size
        tensor_parallel_size = self.parallel_config.tensor_parallel_size
        pp_parallel_size = self.parallel_config.pipeline_parallel_size
        pcp_parallel_size = self.parallel_config.prefill_context_parallel_size
        assert self.world_size == tensor_parallel_size * pp_parallel_size * pcp_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}) x prefill_context"
            f"_parallel_size ({pcp_parallel_size}). ")

        # Set multiprocessing envs
        set_multiprocessing_worker_envs()

        # 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())
        self.rpc_broadcast_mq: MessageQueue | None = None
        scheduler_output_handle: Handle | None = None
        # Initialize worker and set up message queues for SchedulerOutputs
        # and ModelRunnerOutputs
        if self.parallel_config.node_rank_within_dp == 0:
            # For leader node within each dp rank,
            # each dp will have its own leader multiproc executor.
            max_chunk_bytes = envs.VLLM_MQ_MAX_CHUNK_BYTES_MB * 1024 * 1024
            self.rpc_broadcast_mq = MessageQueue(
                self.world_size,
                self.local_world_size,
                max_chunk_bytes=max_chunk_bytes,
                connect_ip=self.parallel_config.master_addr,
            )
            scheduler_output_handle = self.rpc_broadcast_mq.export_handle()
        # Create workers
        context = get_mp_context()
        shared_worker_lock = context.Lock()
        unready_workers: list[UnreadyWorkerProcHandle] = []
        success = False
        try:
            global_start_rank = (self.local_world_size *
                                 self.parallel_config.node_rank_within_dp)
            for local_rank in range(self.local_world_size):
                global_rank = global_start_rank + local_rank
                unready_workers.append(
                    AscendWorkerProc.make_worker_process(
                        vllm_config=self.vllm_config,
                        local_rank=local_rank,
                        rank=global_rank,
                        distributed_init_method=distributed_init_method,
                        input_shm_handle=scheduler_output_handle,
                        shared_worker_lock=shared_worker_lock,
                    ))

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

            # Wait for all local workers to be ready.
            self.workers = AscendWorkerProc.wait_for_ready(unready_workers)

            # Start background thread to monitor worker health if not in headless mode.
            if self.monitor_workers:
                self.start_worker_monitor()

            self.response_mqs = []
            # Only leader node have remote response mqs
            if self.parallel_config.node_rank_within_dp == 0:
                for rank in range(self.world_size):
                    if rank < self.local_world_size:
                        local_message_queue = self.workers[
                            rank].worker_response_mq
                        assert local_message_queue is not None
                        self.response_mqs.append(local_message_queue)
                    else:
                        remote_message_queue = self.workers[
                            0].peer_worker_response_mqs[rank]
                        assert remote_message_queue is not None
                        self.response_mqs.append(remote_message_queue)

            # Ensure message queues are ready. Will deadlock if re-ordered
            # Must be kept consistent with the WorkerProc.

            # Wait for all input mqs to be ready.
            if self.rpc_broadcast_mq is not None:
                self.rpc_broadcast_mq.wait_until_ready()
            # Wait for all remote response mqs to be ready.
            for response_mq in self.response_mqs:
                response_mq.wait_until_ready()
            success = True
        finally:
            if not success:
                # Clean up the worker procs if there was a failure.
                # Close death_writers first to signal workers to exit
                for uw in unready_workers:
                    if uw.death_writer is not None:
                        uw.death_writer.close()
                self._ensure_worker_termination(
                    [uw.proc for uw in unready_workers])

        self.futures_queue = deque[tuple[FutureWrapper, Callable]]()

        self.output_rank = self._get_output_rank()


class AscendWorkerProc(WorkerProc):

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

        # Create death pipe to detect parent process exit
        death_reader, death_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),
            "death_pipe": death_reader,
            "shared_worker_lock": shared_worker_lock,
        }
        # Run EngineCore busy loop in background process.
        proc = context.Process(
            target=WorkerProc.worker_main,
            kwargs=process_kwargs,
            name=f"VllmWorker-{rank}",
            daemon=False,
        )

        proc.start()
        writer.close()
        # Keep death_writer open in parent - when parent exits,
        # death_reader in child will get EOFError
        return UnreadyWorkerProcHandle(proc, rank, reader, death_writer)


vllm.v1.executor.multiproc_executor.MultiprocExecutor = AscendMultiprocExecutor
