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


import torch
from torch.distributed import ProcessGroup

import vllm.envs as envs
from vllm.distributed.device_communicators.all_reduce_utils import (
    should_nccl_symm_mem_allreduce,
)
from vllm.distributed.device_communicators.pynccl import register_nccl_symmetric_ops
from vllm.distributed.device_communicators.pynccl_allocator import (
    is_symmetric_memory_enabled,
)
from vllm.distributed.parallel_state import is_global_first_rank
from vllm.logger import init_logger
from vllm.platforms import current_platform

from .base_device_communicator import DeviceCommunicatorBase

logger = init_logger(__name__)


class CudaCommunicator(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)
        if "tp" not in unique_name:
            # custom allreduce or torch symm mem can be used only by tp
            use_custom_allreduce = False
            use_torch_symm_mem = False
        else:
            from vllm.distributed.parallel_state import _ENABLE_CUSTOM_ALL_REDUCE

            use_custom_allreduce = _ENABLE_CUSTOM_ALL_REDUCE
            use_torch_symm_mem = envs.VLLM_ALLREDUCE_USE_SYMM_MEM

        self.use_custom_allreduce = use_custom_allreduce
        self.use_torch_symm_mem = use_torch_symm_mem

        # lazy import to avoid documentation build error
        from vllm.distributed.device_communicators.custom_all_reduce import (
            CustomAllreduce,
        )
        from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
        from vllm.distributed.device_communicators.quick_all_reduce import (
            QuickAllReduce,
        )
        from vllm.distributed.device_communicators.symm_mem import SymmMemCommunicator

        self.pynccl_comm: PyNcclCommunicator | None = None
        if self.world_size > 1:
            self.pynccl_comm = PyNcclCommunicator(
                group=self.cpu_group,
                device=self.device,
            )
            if is_symmetric_memory_enabled():
                register_nccl_symmetric_ops(self.pynccl_comm)

        self.ca_comm: CustomAllreduce | None = None
        self.qr_comm: QuickAllReduce | None = None
        self.symm_mem_comm: SymmMemCommunicator | None = None
        if use_torch_symm_mem and current_platform.is_cuda():
            self.symm_mem_comm = SymmMemCommunicator(
                group=self.cpu_group,
                device=self.device,
            )

        if use_custom_allreduce and self.world_size > 1:
            # Initialize a custom fast all-reduce implementation.
            self.ca_comm = CustomAllreduce(
                group=self.cpu_group,
                device=self.device,
                symm_mem_enabled=(
                    self.symm_mem_comm is not None and not self.symm_mem_comm.disabled
                ),
            )

            if current_platform.is_rocm():
                # Initialize a custom quick all-reduce implementation for AMD.
                # Quick reduce is designed as a complement to custom allreduce.
                # Based on quickreduce (https://github.com/mk1-project/quickreduce).
                # If it's a rocm, 'use_custom_allreduce==True' means it must
                # currently be an MI300 series.
                self.qr_comm = QuickAllReduce(group=self.cpu_group, device=self.device)

        if self.use_all2all:
            if self.all2all_backend == "naive":
                from .all2all import NaiveAll2AllManager

                self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
            elif self.all2all_backend == "allgather_reducescatter":
                from .all2all import AgRsAll2AllManager

                self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
            elif self.all2all_backend == "pplx":
                from .all2all import PPLXAll2AllManager

                self.all2all_manager = PPLXAll2AllManager(self.cpu_group)
            elif self.all2all_backend == "deepep_high_throughput":
                from .all2all import DeepEPHTAll2AllManager

                self.all2all_manager = DeepEPHTAll2AllManager(self.cpu_group)
            elif self.all2all_backend == "deepep_low_latency":
                from .all2all import DeepEPLLAll2AllManager

                self.all2all_manager = DeepEPLLAll2AllManager(self.cpu_group)
            elif self.all2all_backend == "flashinfer_all2allv":
                from .all2all import FlashInferAllToAllManager

                self.all2all_manager = FlashInferAllToAllManager(self.cpu_group)
            else:
                raise ValueError(f"Unknown all2all backend: {self.all2all_backend}")

            if is_global_first_rank():
                logger.info(
                    "Using %s all2all manager.",
                    self.all2all_manager.__class__.__name__,
                )

    def all_reduce(self, input_):
        # since currently we perform copy input -> symm_input -> out-of-place AR
        # return symm_output, we don't need to check if input is symmetric
        if self.pynccl_comm is not None and should_nccl_symm_mem_allreduce(
            self.pynccl_comm.world_size, input_
        ):
            out = torch.ops.vllm.all_reduce_symmetric_with_copy(input_)
            if out is not None:
                return out
        # always try quick reduce first, then custom allreduce,
        # and then pynccl. (quick reduce just for ROCM MI3*)
        qr_comm = self.qr_comm
        if (
            qr_comm is not None
            and not qr_comm.disabled
            and qr_comm.should_quick_allreduce(input_)
        ):
            out = qr_comm.quick_all_reduce(input_)
            assert out is not None
            return out
        ca_comm = self.ca_comm
        if (
            ca_comm is not None
            and not ca_comm.disabled
            and ca_comm.should_custom_ar(input_)
        ):
            out = ca_comm.custom_all_reduce(input_)
            assert out is not None
            return out
        symm_mem_comm = self.symm_mem_comm
        if symm_mem_comm is not None and symm_mem_comm.should_use_symm_mem(input_):
            out = symm_mem_comm.all_reduce(input_)
            assert out is not None
            return out
        pynccl_comm = self.pynccl_comm
        if pynccl_comm is None or pynccl_comm.disabled:
            out = input_.clone()
            torch.distributed.all_reduce(out, group=self.device_group)
            return out
        assert pynccl_comm is not None
        out = pynccl_comm.all_reduce(input_)
        if out is None:
            # fall back to the default all-reduce using PyTorch.
            # this usually happens during testing.
            # when we run the model, allreduce only happens for the TP
            # group, where we always have either custom allreduce or pynccl.
            out = input_.clone()
            torch.distributed.all_reduce(out, group=self.device_group)
        return out

    def reduce_scatter(self, input_: torch.Tensor, dim: int = -1):
        world_size = self.world_size
        pynccl_comm = self.pynccl_comm
        assert pynccl_comm is not None
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()

        # Note: This will produce an incorrect answer if we don't make
        # the input_tensor contiguous. Possible bug in reduce_scatter_tensor?
        input_tensor = input_.movedim(0, dim).contiguous()

        assert input_tensor.shape[0] % world_size == 0
        chunk_size = input_tensor.shape[0] // world_size
        output_shape = (chunk_size,) + input_tensor.shape[1:]

        output = torch.empty(
            output_shape, dtype=input_tensor.dtype, device=input_tensor.device
        )

        pynccl_comm.reduce_scatter(output, input_tensor)

        # Reshape before returning
        return output.movedim(0, dim).contiguous()

    def reduce_scatterv(
        self, input_: torch.Tensor, dim: int = -1, sizes: list[int] | None = None
    ):
        world_size = self.world_size
        pynccl_comm = self.pynccl_comm
        assert pynccl_comm is not None
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()

        # Note: This will produce an incorrect answer if we don't make
        # the input_tensor contiguous. Possible bug in reduce_scatter_tensor?
        input_tensor = input_.movedim(0, dim).contiguous()

        if sizes is not None:
            assert len(sizes) == world_size
            assert input_tensor.shape[0] == sum(sizes)
            chunk_size = sizes[self.rank_in_group]
        else:
            assert input_tensor.shape[0] % world_size == 0
            chunk_size = input_tensor.shape[0] // world_size
        output_shape = (chunk_size,) + input_tensor.shape[1:]

        output = torch.empty(
            output_shape, dtype=input_tensor.dtype, device=input_tensor.device
        )

        if sizes is not None:
            pynccl_comm.reduce_scatterv(output, input_tensor, sizes=sizes)
        else:
            pynccl_comm.reduce_scatter(output, input_tensor)

        # Reshape before returning
        return output.movedim(0, dim).contiguous()

    def send(self, tensor: torch.Tensor, dst: int | None = None) -> None:
        """Sends a tensor to the destination rank in a blocking way"""
        """NOTE: `dst` is the local rank of the destination rank."""
        if dst is None:
            dst = (self.rank_in_group + 1) % self.world_size

        pynccl_comm = self.pynccl_comm
        if pynccl_comm is not None and not pynccl_comm.disabled:
            pynccl_comm.send(tensor, dst)
        else:
            torch.distributed.send(tensor, self.ranks[dst], self.device_group)

    def recv(
        self, size: torch.Size, dtype: torch.dtype, src: int | None = None
    ) -> torch.Tensor:
        """Receives a tensor from the source rank."""
        """NOTE: `src` is the local rank of the source rank."""
        if src is None:
            src = (self.rank_in_group - 1) % self.world_size

        tensor = torch.empty(size, dtype=dtype, device=self.device)
        pynccl_comm = self.pynccl_comm
        if pynccl_comm is not None and not pynccl_comm.disabled:
            pynccl_comm.recv(tensor, src)
        else:
            torch.distributed.recv(tensor, self.ranks[src], self.device_group)
        return tensor

    def destroy(self):
        if self.pynccl_comm is not None:
            self.pynccl_comm = None
        if self.ca_comm is not None:
            self.ca_comm = None
        if self.all2all_manager is not None:
            self.all2all_manager.destroy()
            self.all2all_manager = None

    def all_gatherv(
        self,
        input_: torch.Tensor | list[torch.Tensor],
        dim: int = 0,
        sizes: list[int] | None = None,
    ):
        if dim != 0:
            raise NotImplementedError("only dim 0 all-gatherv is supported")
        world_size = self.world_size
        pynccl_comm = self.pynccl_comm
        assert pynccl_comm is not None and not pynccl_comm.disabled

        # 'sizes' is not needed if all inputs in the same group have the same
        # shape
        if sizes is not None and all(s == sizes[0] for s in sizes):
            sizes = None

        def _all_gather_single(input_: torch.Tensor, sizes: list[int] | None = None):
            input_size = input_.size()
            if sizes is not None:
                assert len(sizes) == world_size
                assert input_.shape[dim] == sizes[self.rank_in_group], (
                    f"{input_.shape[dim]} != {sizes[self.rank_in_group]}"
                )
                output_size = (sum(sizes),) + input_size[1:]
            else:
                output_size = (input_size[0] * world_size,) + input_size[1:]
            # Allocate output tensor.
            output_tensor = torch.empty(
                output_size, dtype=input_.dtype, device=input_.device
            )
            if sizes is not None:
                pynccl_comm.all_gatherv(output_tensor, input_, sizes=sizes)
            else:
                pynccl_comm.all_gather(output_tensor, input_)
            return output_tensor

        if isinstance(input_, torch.Tensor):
            return _all_gather_single(input_, sizes)

        output_list = []
        pynccl_comm.group_start()
        for inp in input_:
            output_list.append(_all_gather_single(inp, sizes=sizes))
        pynccl_comm.group_end()

        return output_list

    def dispatch(
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
        is_sequence_parallel: bool = False,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        assert self.all2all_manager is not None
        hidden_states, router_logits = self.all2all_manager.dispatch(
            hidden_states, router_logits, is_sequence_parallel
        )
        return hidden_states, router_logits

    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
