# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import os
from typing import Any

import torch
from torch.distributed import ProcessGroup

from vllm.distributed.utils import pickle
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.platforms.interface import CpuArchEnum

from .base_device_communicator import DeviceCommunicatorBase

logger = init_logger(__name__)


class CpuCommunicator(DeviceCommunicatorBase):
    def __init__(
        self,
        cpu_group: ProcessGroup,
        device: torch.device | None = None,
        device_group: ProcessGroup | None = None,
        unique_name: str = "",
    ):
        super().__init__(cpu_group, device, device_group, unique_name)
        self.dist_module = torch.distributed

        if (
            (
                current_platform.get_cpu_architecture() == CpuArchEnum.X86
                or current_platform.get_cpu_architecture() == CpuArchEnum.ARM
            )
            and hasattr(torch.ops._C, "init_shm_manager")
            and (unique_name.startswith("tp") or unique_name.startswith("pp"))
        ):
            self.dist_module = _CPUSHMDistributed(self)

        if self.use_all2all:
            if self.all2all_backend != "naive":  # type: ignore[has-type]
                logger.warning(
                    "`%s` all2all manager is not supported on CPU. "
                    "Falling back to `naive` all2all manager for CPU.",
                    self.all2all_backend,  # type: ignore[has-type]
                )
                self.all2all_backend = "naive"
            if self.all2all_backend == "naive":
                from .all2all import NaiveAll2AllManager

                self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
                logger.info("Using naive all2all manager.")

    def all_reduce(self, input_):
        self.dist_module.all_reduce(input_, group=self.device_group)
        return input_

    def gather(
        self, input_: torch.Tensor, dst: int = 0, dim: int = -1
    ) -> torch.Tensor | None:
        """
        NOTE: We assume that the input tensor is on the same device across
        all the ranks.
        NOTE: `dst` is the local rank of the destination rank.
        """
        world_size = self.world_size
        assert -input_.dim() <= dim < input_.dim(), (
            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        )
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()

        # Allocate output tensor.
        if self.rank_in_group == dst:
            gather_list = [torch.empty_like(input_) for _ in range(world_size)]
        else:
            gather_list = None

        # Gather.
        self.dist_module.gather(
            input_, gather_list, dst=self.ranks[dst], group=self.device_group
        )

        if self.rank_in_group == dst:
            output_tensor = torch.cat(gather_list, dim=dim)
        else:
            output_tensor = None
        return output_tensor

    def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()
        input_size = input_.size()
        # NOTE: we have to use concat-style all-gather here,
        # stack-style all-gather has compatibility issues with
        # torch.compile . see https://github.com/pytorch/pytorch/issues/138795
        output_size = (input_size[0] * self.world_size,) + input_size[1:]
        # Allocate output tensor.
        output_tensor = torch.empty(
            output_size, dtype=input_.dtype, device=input_.device
        )
        # All-gather.
        self.dist_module.all_gather_into_tensor(
            output_tensor, input_, group=self.device_group
        )

        # Reshape
        output_tensor = output_tensor.reshape((self.world_size,) + input_size)
        output_tensor = output_tensor.movedim(0, dim)
        output_tensor = output_tensor.reshape(
            input_size[:dim]
            + (self.world_size * input_size[dim],)
            + input_size[dim + 1 :]
        )
        return output_tensor

    def send_tensor_dict(
        self,
        tensor_dict: dict[str, torch.Tensor | Any],
        dst: int,
    ) -> None:
        return self.dist_module.send_tensor_dict(tensor_dict, dst)

    def recv_tensor_dict(
        self,
        src: int,
    ) -> dict[str, torch.Tensor | Any]:
        return self.dist_module.recv_tensor_dict(src)

    def dispatch(  # type: ignore[override]
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
        is_sequence_parallel: bool = False,
        extra_tensors: list[torch.Tensor] | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        assert self.all2all_manager is not None
        return self.all2all_manager.dispatch(
            hidden_states,
            router_logits,
            is_sequence_parallel,
            extra_tensors,  # type: ignore[call-arg]
        )

    def combine(
        self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
    ) -> torch.Tensor:
        assert self.all2all_manager is not None
        hidden_states = self.all2all_manager.combine(
            hidden_states, is_sequence_parallel
        )
        return hidden_states


class _CPUSHMDistributed:
    def __init__(self, communicator: CpuCommunicator):
        instance_identifier = os.environ["VLLM_DIST_IDENT"]
        unique_name = communicator.unique_name
        instance_identifier = f"{instance_identifier}-{unique_name}"
        self.communicator = communicator

        group_ranks = [str(rank) for rank in self.communicator.ranks]
        shm_group_identifier = f"[{'-'.join(group_ranks)}]"
        self.group_name = f"{instance_identifier}-{shm_group_identifier}-cpushm"

        self.handle = self._init_cpu_shm()

    def _init_cpu_shm(self) -> int:
        handle = torch.ops._C.init_shm_manager(
            self.group_name,
            self.communicator.world_size,
            self.communicator.rank,
        )
        torch.distributed.barrier(self.communicator.device_group)
        torch.ops._C.join_shm_manager(
            handle,
            self.group_name,
        )
        torch.distributed.barrier(self.communicator.device_group)

        return handle

    def all_reduce(
        self, input: torch.Tensor, group: ProcessGroup | None = None
    ) -> None:
        torch.ops._C.shm_allreduce(self.handle, input)

    def gather(
        self,
        input: torch.Tensor,
        gather_list: list[torch.Tensor] | None,
        dst: int = -1,
        group: ProcessGroup | None = None,
    ) -> None:
        # Note: different from the torch gather, here we use local dst rank.
        torch.ops._C.shm_gather(
            self.handle,
            input,
            gather_list,
            torch.distributed.get_group_rank(group, dst),
        )

    def all_gather_into_tensor(
        self,
        output: torch.Tensor,
        input: torch.Tensor,
        group: ProcessGroup | None = None,
    ) -> None:
        torch.ops._C.shm_all_gather(self.handle, input, output)

    def send_tensor_dict(
        self,
        tensor_dict: dict[str, torch.Tensor | Any],
        dst: int,
    ) -> None:
        key_list = list(tensor_dict.keys())
        value_list = list(tensor_dict.values())
        size_list = []
        for v in value_list:
            if not isinstance(v, torch.Tensor):
                raise RuntimeError("CpuCommunicator only supports sending tensors.")
            size_list.append(v.size())
        key_size_tensor = torch.frombuffer(
            pickle.dumps([key_list, size_list]), dtype=torch.uint8
        )
        value_list.append(key_size_tensor)

        torch.ops._C.shm_send_tensor_list(self.handle, value_list, dst)

        return None

    def recv_tensor_dict(
        self,
        src: int,
    ) -> dict[str, torch.Tensor | Any]:
        tensor_list = torch.ops._C.shm_recv_tensor_list(self.handle, src)

        value_list: list[torch.Tensor] = tensor_list[:-1]
        key_size_tensor = tensor_list[-1]

        key_size = pickle.loads(key_size_tensor.numpy().tobytes())
        key_list = key_size[0]
        size_list = key_size[1]
        assert len(key_list) == len(size_list)
        assert len(key_list) == len(value_list)

        tensor_dict: dict[str, torch.Tensor] = {}
        for key, size, t in zip(key_list, size_list, value_list):
            tensor_dict[key] = t.view(size)
        return tensor_dict