#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
#
# 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.
# Adapted from vllm/model_executor/models/qwen2_vl.py
# This file is a part of the vllm-ascend project.

import torch

from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type


class NullHandle:

    def __init__(self):
        pass

    def wait(self):
        pass


def communication_adaptation_310p():

    def broadcast310p_wrapper(fn):

        def broadcast310p(tensor, src, group=None, async_op=False):
            if tensor.device == torch.device('cpu'):
                return fn(tensor, src, group, async_op)
            rank = torch.distributed.get_rank(group)
            world_size = torch.distributed.get_world_size(group)
            tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
            tensor_list[rank] = tensor
            torch.distributed.all_gather(tensor_list, tensor, group=group)
            tensor[...] = tensor_list[src]
            if async_op:
                return NullHandle()
            else:
                return None

        return broadcast310p

    torch.distributed.broadcast = broadcast310p_wrapper(
        torch.distributed.broadcast)
    torch.distributed.distributed_c10d.broadcast = broadcast310p_wrapper(
        torch.distributed.distributed_c10d.broadcast)

    def all_reduce_wrapper_310p(fn):

        def all_reduce(
            tensor,
            op=torch.distributed.ReduceOp.SUM,
            group=None,
            async_op=False,
        ):
            if tensor.dtype != torch.int64:
                return fn(tensor, op, group, async_op)
            rank = torch.distributed.get_rank(group)
            world_size = torch.distributed.get_world_size(group)
            tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
            tensor_list[rank] = tensor
            torch.distributed.all_gather(tensor_list, tensor, group=group)
            if op == torch.distributed.ReduceOp.SUM:
                return torch.stack(tensor_list).sum(0)
            elif op == torch.distributed.ReduceOp.MAX:
                return torch.tensor(
                    torch.stack(tensor_list).cpu().numpy().max(0),
                    device=tensor.device,
                )
            else:
                raise RuntimeError(f"not implement op {op}")

        return all_reduce

    torch.distributed.all_reduce = all_reduce_wrapper_310p(
        torch.distributed.all_reduce)
    torch.distributed.distributed_c10d.all_reduce = all_reduce_wrapper_310p(
        torch.distributed.distributed_c10d.all_reduce)


if get_ascend_device_type() == AscendDeviceType._310P:
    communication_adaptation_310p()