from contextlib import contextmanager
from typing import Any, List, Optional, Union

import torch
import torch.distributed as dist
from torch.distributed import ProcessGroup

import vllm.envs as envs
from vllm.distributed.parallel_state import (
    get_local_rank, get_tensor_model_parallel_cpu_group)
from vllm.logger import init_logger

try:
    import pynvml

    from vllm._C import custom_ar

    @contextmanager
    def _nvml():
        try:
            pynvml.nvmlInit()
            yield
        finally:
            pynvml.nvmlShutdown()

except ImportError:
    # For AMD GPUs
    custom_ar = None
    pynvml = None

    @contextmanager
    def _nvml():
        try:
            yield
        finally:
            pass


logger = init_logger(__name__)


@_nvml()
def _is_full_nvlink(device_ids: List[int]) -> bool:
    """
    query if the set of gpus are fully connected by nvlink (1 hop)
    Note that `pynvml` is not affected by `CUDA_VISIBLE_DEVICES`,
    so it works on real physical device ids.
    """
    handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in device_ids]
    for i, handle in enumerate(handles):
        for j, peer_handle in enumerate(handles):
            if i < j:
                try:
                    p2p_status = pynvml.nvmlDeviceGetP2PStatus(
                        handle, peer_handle, pynvml.NVML_P2P_CAPS_INDEX_NVLINK)
                    if p2p_status != pynvml.NVML_P2P_STATUS_OK:
                        return False
                except pynvml.NVMLError as error:
                    logger.error(
                        "NVLink detection failed. This is normal if your"
                        " machine has no NVLink equipped.",
                        exc_info=error)
                    return False
    return True


def _can_p2p(rank: int, world_size: int) -> bool:
    from vllm.distributed.utils import gpu_p2p_access_check
    for i in range(world_size):
        if i == rank:
            continue
        if not gpu_p2p_access_check(rank, i):
            return False
    return True


class CustomAllreduce:

    _SUPPORTED_WORLD_SIZES = [2, 4, 6, 8]

    # max_size: max supported allreduce size
    def __init__(self,
                 group: Optional[ProcessGroup] = None,
                 device: Optional[Union[int, str, torch.device]] = None,
                 max_size=8192 * 1024) -> None:
        """
        Args:
            group: the process group to work on. If None, it will use the
                default process group.
            device: the device to bind the CustomAllreduce to. If None,
                it will be bind to f"cuda:{local_rank}".
        It is the caller's responsibility to make sure each communicator
        is bind to a unique device, and all communicators in this group
        are in the same node.
        """
        self._IS_CAPTURING = False
        self.disabled = True

        if custom_ar is None:
            # disable because of missing custom allreduce library
            # e.g. in a non-cuda environment
            return

        group = group or get_tensor_model_parallel_cpu_group()
        self.group = group

        assert dist.get_backend(group) != dist.Backend.NCCL, (
            "CustomAllreduce should be attached to a non-NCCL group.")

        rank = dist.get_rank(group=self.group)
        world_size = dist.get_world_size(group=self.group)
        if world_size == 1:
            # No need to initialize custom allreduce for single GPU case.
            return

        if world_size not in CustomAllreduce._SUPPORTED_WORLD_SIZES:
            logger.warning(
                "Custom allreduce is disabled due to an unsupported world"
                " size: %d. Supported world sizes: %s. To silence this "
                "warning, specify disable_custom_all_reduce=True explicitly.",
                world_size, str(CustomAllreduce._SUPPORTED_WORLD_SIZES))
            return

        if device is None:
            local_rank = get_local_rank()
            device = torch.device(f"cuda:{local_rank}")
        elif isinstance(device, int):
            device = torch.device(f"cuda:{device}")
        elif isinstance(device, str):
            device = torch.device(device)
        # now `device` is a `torch.device` object
        assert isinstance(device, torch.device)
        self.device = device

        cuda_visible_devices = envs.CUDA_VISIBLE_DEVICES
        if cuda_visible_devices:
            device_ids = list(map(int, cuda_visible_devices.split(",")))
        else:
            device_ids = list(range(torch.cuda.device_count()))

        physical_device_id = device_ids[device.index]
        tensor = torch.tensor([physical_device_id],
                              dtype=torch.int,
                              device="cpu")
        gather_list = [
            torch.tensor([0], dtype=torch.int, device="cpu")
            for _ in range(world_size)
        ]
        dist.all_gather(gather_list, tensor, group=self.group)
        physical_device_ids = [t.item() for t in gather_list]

        # test nvlink first, this will filter out most of the cases
        # where custom allreduce is not supported
        # this checks hardware and driver support for NVLink
        full_nvlink = _is_full_nvlink(physical_device_ids)
        if world_size > 2 and not full_nvlink:
            logger.warning(
                "Custom allreduce is disabled because it's not supported on"
                " more than two PCIe-only GPUs. To silence this warning, "
                "specify disable_custom_all_reduce=True explicitly.")
            return
        # test P2P capability, this checks software/cudaruntime support
        # this is expensive to compute at the first time
        # then we cache the result
        if not _can_p2p(rank, world_size):
            logger.warning(
                "Custom allreduce is disabled because your platform lacks "
                "GPU P2P capability or P2P test failed. To silence this "
                "warning, specify disable_custom_all_reduce=True explicitly.")
            return

        self.disabled = False
        # buffers memory are owned by this Python class and passed to C++
        # meta data composes of two parts: meta data for synchronization
        # (256 bytes) and a temporary buffer for storing intermediate
        # allreduce results.
        self.meta = torch.zeros(custom_ar.meta_size() + max_size,
                                dtype=torch.uint8,
                                device=self.device)
        # This is a pre-registered IPC buffer. In eager mode, input tensors
        # are first copied into this buffer before allreduce is performed
        self.buffer = torch.empty(max_size,
                                  dtype=torch.uint8,
                                  device=self.device)
        # This is a buffer for storing the tuples of pointers pointing to
        # IPC buffers from all ranks. Each registered tuple has size of
        # 8*world_size bytes where world_size is at most 8. Allocating 8MB
        # is enough for 131072 such tuples. The largest model I've seen only
        # needs less than 10000 of registered tuples.
        self.rank_data = torch.empty(8 * 1024 * 1024,
                                     dtype=torch.uint8,
                                     device=self.device)
        self.max_size = max_size
        self.rank = rank
        self.world_size = world_size
        handles, offsets = self._get_ipc_meta(self.meta)
        self.full_nvlink = full_nvlink
        self._ptr = custom_ar.init_custom_ar(self.meta, self.rank_data,
                                             handles, offsets, rank,
                                             self.full_nvlink)
        self.register_buffer(self.buffer)

    @contextmanager
    def capture(self):
        """
        The main responsibility of this context manager is the 
        `register_graph_buffers` call at the end of the context.
        It records all the buffer addresses used in the CUDA graph.
        """
        try:
            self._IS_CAPTURING = True
            yield
        finally:
            self._IS_CAPTURING = False
            if not self.disabled:
                self.register_graph_buffers()

    def _get_ipc_meta(self, inp: torch.Tensor):
        data = inp.untyped_storage()._share_cuda_()
        shard_data = (
            data[1],  # ipc handle to base ptr
            data[3],  # offset of base ptr
        )
        return self._gather_ipc_meta(shard_data)

    def _gather_ipc_meta(self, shard_data):
        # Note: don't use `[[None]] * self.world_size` here
        # because it will create a list of the same reference
        all_data: List[Optional[Any]] = [[None]
                                         for i in range(self.world_size)]
        all_data[self.rank][0] = shard_data

        ranks = dist.get_process_group_ranks(group=self.group)
        ranks.sort()
        for i, rank in enumerate(ranks):
            dist.broadcast_object_list(all_data[i],
                                       src=rank,
                                       group=self.group,
                                       device="cpu")

        # we cannot directly use `dist.all_gather_object` here
        # because it is incompatible with `gloo` backend under inference mode.
        # see https://github.com/pytorch/pytorch/issues/126032 for details.

        handles = []
        offsets = []
        for i in range(len(all_data)):
            handles.append(all_data[i][0][0])  # type: ignore
            offsets.append(all_data[i][0][1])  # type: ignore
        return handles, offsets

    def register_buffer(self, inp: torch.Tensor):
        handles, offsets = self._get_ipc_meta(inp)
        custom_ar.register_buffer(self._ptr, inp, handles, offsets)

    def register_graph_buffers(self):
        handle, offset = custom_ar.get_graph_buffer_ipc_meta(self._ptr)
        handles, offsets = self._gather_ipc_meta((bytes(handle), offset))
        logger.info("Registering %d cuda graph addresses", len(offset))
        custom_ar.register_graph_buffers(self._ptr, handles, offsets)

    def should_custom_ar(self, inp: torch.Tensor):
        return custom_ar.should_custom_ar(inp, self.max_size, self.world_size,
                                          self.full_nvlink)

    # all reduce, assuming inp tensor is IPC registered with register_buffer,
    # or, in the context of cuda graphs, register_graph_buffers
    def all_reduce_reg(self, inp: torch.Tensor, out: torch.Tensor = None):
        if out is None:
            out = torch.empty_like(inp)
        custom_ar.all_reduce_reg(self._ptr, inp, out)
        return out

    # all reduce, assuming inp tensor is NOT IPC registered
    def all_reduce_unreg(self, inp: torch.Tensor, out: torch.Tensor = None):
        if out is None:
            out = torch.empty_like(inp)
        custom_ar.all_reduce_unreg(self._ptr, inp, self.buffer, out)
        return out

    def custom_all_reduce(self, input: torch.Tensor) -> Optional[torch.Tensor]:
        # when custom allreduce is disabled, this will be None
        if self.disabled:
            return None
        if self._IS_CAPTURING:
            if torch.cuda.is_current_stream_capturing():
                if self.should_custom_ar(input):
                    return self.all_reduce_reg(input)
            else:
                if self.should_custom_ar(input):
                    # if warm up, mimic the allocation pattern
                    # since custom allreduce is out-of-place
                    return torch.empty_like(input)
        else:
            # note: outside of cuda graph context,
            # custom allreduce incurs a cost of cudaMemcpy, which should
            # be small(<=1% of overall latency) compared to the performance
            # gains of using custom kernels
            if self.should_custom_ar(input):
                return self.all_reduce_unreg(input)

        return None

    def close(self):
        if not self.disabled and self._ptr:
            custom_ar.dispose(self._ptr)
            self._ptr = 0

    def __del__(self):
        self.close()
