# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Multithreading in pipeline parallelism."""
from contextlib import contextmanager
from queue import Queue
import sys
from threading import Thread
from types import TracebackType
from typing import TYPE_CHECKING, Callable, Dict, Generator, List, Optional, Tuple, Type, Union, cast

import torch

from .microbatch import Batch
from .stream import AbstractStream, use_device, use_stream

__all__: List[str] = ["Task", "worker", "create_workers", "spawn_workers"]


ExcInfo = Tuple[Type[BaseException], BaseException, TracebackType]

# Queue is generic only in stubs.
# https://mypy.readthedocs.io/en/latest/common_issues.html#using-classes-that-are-generic-in-stubs-but-not-at-runtime
if TYPE_CHECKING:
    InQueue = Queue[Optional["Task"]]
    OutQueue = Queue[Tuple[bool, Union[Tuple["Task", Batch], ExcInfo, None]]]
else:
    InQueue = Queue
    OutQueue = Queue


class Task:
    """A task represents how to compute a micro-batch on a partition.

    It consists of two parts: :meth:`compute` and :meth:`finalize`.
    :meth:`compute` should be executed in worker threads concurrently.
    :meth:`finalize` should be executed after when worker threads complete to
    execute :meth:`compute`.

    :meth:`compute` might be boosted by worker threads. Because it produces
    several CUDA API calls by user code. In PyTorch, parallel CUDA API calls
    are not serialized through GIL. So more than one CUDA API call can be
    produced at the same time.

    """

    def __init__(
        self, stream: AbstractStream, *, compute: Callable[[], Batch], finalize: Optional[Callable[[Batch], None]],
    ) -> None:
        self.stream = stream
        self._compute = compute
        self._finalize = finalize
        self._grad_enabled = torch.is_grad_enabled()

    def compute(self) -> Batch:
        with use_stream(self.stream), torch.set_grad_enabled(self._grad_enabled):
            return self._compute()

    def finalize(self, batch: Batch) -> None:
        if self._finalize is None:
            return
        with use_stream(self.stream), torch.set_grad_enabled(self._grad_enabled):
            self._finalize(batch)


def worker(in_queue: InQueue, out_queue: OutQueue, device: torch.device) -> None:
    """Main loop of a worker thread."""
    with use_device(device):
        while True:
            task = in_queue.get()

            if task is None:
                break

            try:
                batch = task.compute()
            except Exception:
                exc_info = cast(ExcInfo, sys.exc_info())
                out_queue.put((False, exc_info))
                continue

            out_queue.put((True, (task, batch)))

    done = (False, None)
    out_queue.put(done)


def create_workers(devices: List[torch.device],) -> Tuple[List[InQueue], List[OutQueue]]:
    """Spawns worker threads. A worker thread is bound to a device."""
    in_queues: List[InQueue] = []
    out_queues: List[OutQueue] = []

    # Spawn workers.
    workers: Dict[torch.device, Tuple[InQueue, OutQueue]] = {}

    def normalize_device(device: torch.device) -> torch.device:
        if device.type == "cuda" and device.index is None:
            return torch.device("cuda", index=torch.cuda.current_device())

        if device.type == "cpu" and device.index is not None:
            return torch.device("cpu")

        return device

    for device in devices:
        device = normalize_device(device)

        try:
            in_queue, out_queue = workers[device]
        except KeyError:
            in_queue = Queue()
            out_queue = Queue()
            workers[device] = (in_queue, out_queue)

            t = Thread(target=worker, args=(in_queue, out_queue, device), daemon=True,)
            t.start()

        in_queues.append(in_queue)
        out_queues.append(out_queue)

    return (in_queues, out_queues)

@contextmanager
def spawn_workers(devices: List[torch.device],) -> Generator[Tuple[List[InQueue], List[OutQueue]], None, None]:
    try:
        (in_queues, out_queues) = create_workers(devices)
        yield (in_queues, out_queues)
    finally:
        pass