# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations

import contextlib
from typing import TYPE_CHECKING

import paddle.distributed as dist
from paddle import framework
from paddle.distributed.communication.group import (
    _get_global_group,
    _warn_cur_rank_not_in_group,
)

if TYPE_CHECKING:
    from collections.abc import Callable, Generator, Sequence

    from paddle import Tensor
    from paddle.base.core import task
    from paddle.distributed import Group

    _P2POpType = Callable[[Tensor, int, Group], task]


class P2POp:
    """
    A class that makes point-to-point operations for "batch_isend_irecv".

    This class creates the type of P2P operation, communication buffer, peer rank,
    Group. Instances of this class will be passed to
    ``paddle.distributed.batch_isend_irecv`` for point-to-point communication.

    Args:
        op (callable): A function to send data to or receive data from a peer process.
            The type of ``op`` is either ``paddle.distributed.isend`` or ``paddle.distributed.irecv``.
        tensor (Tensor): Tensor to send or receive.
        peer (int): The destination or source rank.
        group (Group, optional): The group instance return by new_group or None for global
            default group. Default: None.

    Examples:
        .. code-block:: pycon

            >>> # doctest: +REQUIRES(env: DISTRIBUTED)

            >>> import paddle
            >>> import paddle.distributed as dist

            >>> dist.init_parallel_env()
            >>> rank = dist.get_rank()
            >>> world_size = dist.get_world_size()

            >>> send_t = paddle.arange(2) + rank
            >>> # paddle.tensor([0, 1])  # Rank-0
            >>> # paddle.tensor([1, 2])  # Rank-1

            >>> recv_t = paddle.empty(shape=[2], dtype=send_t.dtype)

            >>> send_op = dist.P2POp(dist.isend, send_t, (rank + 1) % world_size)
            >>> recv_op = dist.P2POp(dist.irecv, recv_t, (rank - 1 + world_size) % world_size)

    """

    op: _P2POpType
    tensor: Tensor
    peer: int
    group: Group | None

    def __init__(
        self,
        op: _P2POpType,
        tensor: Tensor,
        peer: int,
        group: Group | None = None,
    ) -> None:
        if op not in [dist.isend, dist.irecv]:
            raise RuntimeError(
                "Invalid ``op`` function. Expected ``op`` "
                "to be of type ``paddle.distributed.isend`` or "
                "``paddle.distributed.irecv``."
            )

        self.op = op
        self.tensor = tensor
        self.peer = peer
        self.group = _get_global_group() if group is None else group


@contextlib.contextmanager
def _coalescing_manager(
    group: Group, tasks: task | None = None
) -> Generator[None, None, None]:
    group = _get_global_group() if group is None else group
    pg = group.process_group
    pg._start_coalescing()
    try:
        yield
    finally:
        if tasks is None or len(tasks) == 0:
            pg._end_coalescing()
        else:
            pg._end_coalescing(tasks)


def _check_p2p_op_list(p2p_op_list: Sequence[P2POp]) -> None:
    """
    Helper to check that the ``p2p_op_list`` is a list of P2POp instances and
    all ops use the same backend.
    """
    if not isinstance(p2p_op_list, list) or not all(
        isinstance(p2p_op, P2POp) for p2p_op in p2p_op_list
    ):
        raise RuntimeError(
            "Invalid ``p2p_op_list``. Each op is expected to "
            "to be of type ``paddle.distributed.P2POp``."
        )

    backend = p2p_op_list[0].group.backend
    if not all(backend == p2p_op.group.backend for p2p_op in p2p_op_list):
        raise RuntimeError("All groups need to use the same backend.")


def batch_isend_irecv(p2p_op_list: list[P2POp]) -> list[task]:
    """
    Send or Receive a batch of tensors asynchronously and return a list of requests.

    Process each of the point-to-point operations in ``p2p_op_list`` and return the
    corresponding tasks. NCCL are currently supported.

    Args:
        p2p_op_list (List[P2POp]): A list of point-to-point operations(type of each operator is
            ``paddle.distributed.P2POp``). The order of the isend/irecv in the list
            matters and it needs to match with corresponding isend/irecv on the
            remote end.

    Returns:
        A list of distributed tasks returned by calling the corresponding
        op in the op_list.

    Warning:
        This API only supports the dygraph mode.

    Examples:
        .. code-block:: pycon

            >>> # doctest: +REQUIRES(env: DISTRIBUTED)

            >>> import paddle
            >>> import paddle.distributed as dist

            >>> dist.init_parallel_env()
            >>> rank = dist.get_rank()
            >>> world_size = dist.get_world_size()

            >>> send_t = paddle.arange(2) + rank
            >>> # paddle.tensor([0, 1])  # Rank-0
            >>> # paddle.tensor([1, 2])  # Rank-1

            >>> recv_t = paddle.empty(shape=[2], dtype=send_t.dtype)

            >>> send_op = dist.P2POp(dist.isend, send_t, (rank + 1) % world_size)
            >>> recv_op = dist.P2POp(dist.irecv, recv_t, (rank - 1 + world_size) % world_size)

            >>> tasks = dist.batch_isend_irecv([send_op, recv_op])

            >>> for task in tasks:
            ...     task.wait()

            >>> print(recv_t)
            >>> # paddle.tensor([1, 2])     # Rank-0
            >>> # paddle.tensor([0, 1])     # Rank-1
    """
    _check_p2p_op_list(p2p_op_list)
    group = p2p_op_list[0].group
    if _warn_cur_rank_not_in_group(group):
        return

    if framework.in_dynamic_mode():
        group = _get_global_group() if group is None else group
        backend = group.backend
        tasks = []
        with _coalescing_manager(group, tasks):
            for p2p_op in p2p_op_list:
                op = p2p_op.op
                tensor = p2p_op.tensor
                peer = p2p_op.peer
                comm_group = p2p_op.group
                task = op(tensor, peer, comm_group)
                if task is not None:
                    tasks.append(task)
        return tasks
    else:
        raise RuntimeError("Don't support static graph mode currently.")