# SPDX-License-Identifier: Apache-2.0
# Adapted from: https://github.com/vllm-project/vllm/blob/main/vllm/distributed/device_communicators/cpu_communicator.py
# CPU-based communicator with optional shared memory optimization

import os

import torch
from torch.distributed import ProcessGroup

from platforms import current_platform
from platforms.interface import CpuArchEnum

from .base_device_communicator import DeviceCommunicatorBase


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)
        # Default to standard PyTorch distributed module
        self.dist_module = torch.distributed

        # Use shared memory optimization for x86 CPUs with tensor parallel
        if (
            (current_platform.get_cpu_architecture() == CpuArchEnum.X86)
            and hasattr(torch.ops._C, "init_shm_manager")
            and unique_name.startswith("tp")
        ):
            # Enable shared memory for faster communication
            self.dist_module = _CPUSHMDistributed(self)

    def all_reduce(
        self,
        input_: torch.Tensor,
        op: torch.distributed.ReduceOp | None = torch.distributed.ReduceOp.SUM,
    ) -> torch.Tensor:
        # Perform all-reduce using distributed or shared memory module
        self.dist_module.all_reduce(input_, group=self.device_group, op=op)
        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()}"
        # Normalize negative dimension to positive
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()

        # Allocate output tensor.
        if self.rank_in_group == dst:
            # Destination rank prepares list to receive from all ranks
            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:
            # Concatenate gathered tensors along specified dimension
            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:
        # Normalize negative dimension to positive
        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: collect tensors from all ranks
        self.dist_module.all_gather_into_tensor(
            output_tensor, input_, group=self.device_group
        )

        # Reshape: move world_size dimension to target dim position
        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


class _CPUSHMDistributed:
    """Shared memory distributed operations for CPU tensor parallel"""

    def __init__(self, communicator: CpuCommunicator):
        # Get unique instance identifier from environment
        instance_identifier = os.environ["VLLM_DIST_IDENT"]
        unique_name = communicator.unique_name
        instance_identifier = f"{instance_identifier}-{unique_name}"
        self.communicator = communicator

        # Create group identifier from rank list
        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"

        # Initialize shared memory manager
        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 int(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)