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# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. 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
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from typing import Optional

import torch
import torch.distributed as dist

from vllm.distributed.device_communicators.base_device_communicator import (
    DeviceCommunicatorBase)
from vllm.logger import logger
from vllm_br import envs


class SUPACommunicator(DeviceCommunicatorBase):

    def __init__(self,
                 cpu_group: dist.ProcessGroup,
                 device: Optional[torch.device] = None,
                 device_group: Optional[dist.ProcessGroup] = None,
                 unique_name: str = ""):
        super().__init__(cpu_group, device, device_group, unique_name)
        self.device = torch.supa.current_device()

    # TODO: Deprecate this method in the future if torch_br support gather
    def gather(self,
               input_: torch.Tensor,
               dst: int = 0,
               dim: int = -1) -> torch.Tensor:
        """All gather as gather"""

        output_tensor = self.all_gather(input_, dim)
        if self.rank_in_group == dst:
            return output_tensor
        return None

    def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
        if envs.VLLM_BR_USE_FP32_ALL_REDUCE and input_ is not None and input_.dtype == torch.bfloat16:
            logger.debug(
                '[Patch] patch all_reduce: use fp32 all_reduce when env VLLM_BR_USE_FP32_ALL_REDUCE is set'
            )
            input_ = input_.to(torch.float32)
            dist.all_reduce(input_, group=self.device_group)
            input_ = input_.to(torch.bfloat16)
        else:
            dist.all_reduce(input_, group=self.device_group)
        return input_