buffer.py

import os
from typing import Callable, List, Optional, Tuple, Union

import torch
import torch.distributed as dist

from . import deep_ep_cpp
from .deep_ep_cpp import Config, EventHandle

from .utils import EventOverlap, check_nvlink_connections


class Buffer:
    """
    The core expert-parallel (EP) communication buffers for Mixture of Experts (MoE) model, which supports:
        - high-throughput intranode all-to-all (dispatch and combine, using NVLink)
        - high-throughput internode all-to-all (dispatch and combine, using RDMA and NVLink)
        - low-latency all-to-all (dispatch and combine, using RDMA)

    Attributes:
        num_sms: the SMs used in high-throughput kernels.
        rank: the local rank number.
        group_size: the number of ranks in the group.
        group: the communication group.
        num_nvl_bytes: the buffer size for intranode NVLink communication.
        num_rdma_bytes: the buffer size for internode (also for intranode with low-latency mode) RDMA communication.
        runtime: the C++ runtime.
    """

    num_sms: int = 24

    def __init__(
        self,
        group: dist.ProcessGroup,
        num_nvl_bytes: int = 0,
        num_rdma_bytes: int = 0,
        low_latency_mode: bool = False,
        num_qps_per_rank: int = 24,
        allow_nvlink_for_low_latency_mode: bool = True,
        allow_mnnvl: bool = False,
        explicitly_destroy: bool = False,
        enable_shrink: bool = False,
    ) -> None:
        """
        Initialize the communication buffer.

        Arguments:
            group: the communication group.
            num_nvl_bytes: the buffer size for intranode NVLink communication.
            num_rdma_bytes: the buffer size for internode (also for intranode with low-latency mode) RDMA communication.
            low_latency_mode: whether to enable low-latency mode.
            num_qps_per_rank: the number of QPs for RDMA, the low-latency mode requires that this number equals
                to the number of local experts.
            allow_nvlink_for_low_latency_mode: whether allow NVLink traffic for low-latency mode, you should notice
                this is somehow incompatible with the hook-based overlapping.
                Warning: PCIe connections may lead to errors due to memory ordering issues,
                please make sure all connections are via NVLink.
            allow_mnnvl: whether to allow MNNVL
            explicitly_destroy: If this flag is set to True, you need to explicitly call `destroy()` to release resources;
                otherwise, the resources will be released by the destructor.
                Note: Releasing resources in the destructor may cause Python's exception handling process to hang.
            enable_shrink: whether to enable shrink mode. The enable mode allocates a mask buffer to support masking ranks dynamically.
        """
        check_nvlink_connections(group)

        # Initialize the CPP runtime
        self.rank = group.rank()
        self.group_size = group.size()
        self.group = group
        self.num_nvl_bytes = num_nvl_bytes
        self.num_rdma_bytes = num_rdma_bytes
        self.low_latency_mode = low_latency_mode
        self.explicitly_destroy = explicitly_destroy
        self.enable_shrink = enable_shrink
        self.runtime = deep_ep_cpp.Buffer(
            self.rank,
            self.group_size,
            num_nvl_bytes,
            num_rdma_bytes,
            low_latency_mode,
            explicitly_destroy,
            enable_shrink
        )

        # Synchronize device IDs
        device_ids = [
            None,
        ] * self.group_size
        local_device_id = self.runtime.get_local_device_id()
        dist.all_gather_object(device_ids, local_device_id, group)

        # Synchronize IPC handles
        ipc_handles = [
            None,
        ] * self.group_size
        local_ipc_handle = self.runtime.get_local_ipc_handle()
        dist.all_gather_object(ipc_handles, local_ipc_handle, group)

        # Synchronize NVSHMEM unique IDs
        root_unique_id = None
        if self.runtime.get_num_rdma_ranks() > 1 or low_latency_mode:
            # Enable IBGDA
            assert num_qps_per_rank > 0
            os.environ["NVSHMEM_DISABLE_P2P"] = "0" if allow_nvlink_for_low_latency_mode else "1"
            os.environ["NVSHMEM_IB_ENABLE_IBGDA"] = "1"
            os.environ["NVSHMEM_IBGDA_NUM_RC_PER_PE"] = f"{num_qps_per_rank}"
            # Make sure QP depth is always larger than the number of on-flight WRs, so that we can skip WQ slot check
            os.environ["NVSHMEM_QP_DEPTH"] = os.environ.get("NVSHMEM_QP_DEPTH", "1024")

            # Reduce gpu memory usage
            # 6 default teams + 1 extra team
            os.environ["NVSHMEM_MAX_TEAMS"] = "7"
            # Disable NVLink SHArP
            os.environ["NVSHMEM_DISABLE_NVLS"] = "1"
            # NOTES: NVSHMEM initialization requires at least 256 MiB
            os.environ["NVSHMEM_CUMEM_GRANULARITY"] = f"{2 ** 29}"

            if not allow_mnnvl:
                # Disable multi-node NVLink detection
                os.environ["NVSHMEM_DISABLE_MNNVL"] = "1"

            # Synchronize using the root ID
            nvshmem_unique_ids = [
                None,
            ] * self.group_size
            if (low_latency_mode and self.rank == 0) or (
                not low_latency_mode and self.runtime.get_rdma_rank() == 0
            ):
                root_unique_id = self.runtime.get_local_nvshmem_unique_id()
            dist.all_gather_object(nvshmem_unique_ids, root_unique_id, group)
            root_unique_id = nvshmem_unique_ids[
                0 if low_latency_mode else self.runtime.get_root_rdma_rank(True)
            ]

        # Make CPP runtime available
        self.runtime.sync(device_ids, ipc_handles, root_unique_id)
        assert self.runtime.is_available()

    def destroy(self):
        """
        Destroy the cpp runtime and release resources.

        """

        assert self.explicitly_destroy, "`explicitly_destroy` flag must be set"

        self.runtime.destroy()
        self.runtime = None

    # @staticmethod
    # def is_sm90_compiled():
    #     return deep_ep_cpp.is_sm90_compiled()

    @staticmethod
    def set_num_sms(new_num_sms: int) -> None:
        """
        Set the number of SMs to use in high-throughput kernels.

        Arguments:
            new_num_sms: the new number to be set.
        """

        assert new_num_sms % 2 == 0, "The SM count must be even"
        Buffer.num_sms = new_num_sms

    @staticmethod
    def capture() -> EventOverlap:
        """
        Capture a CUDA event on the current stream, i.e. `torch.cuda.current_stream()`.

        Returns:
            event: the captured event.
        """
        return EventOverlap(EventHandle())

    @staticmethod
    def get_low_latency_rdma_size_hint(
        num_max_dispatch_tokens_per_rank: int, hidden: int, num_ranks: int, num_experts: int
    ) -> int:
        """
        Get a minimum size requirement for the RDMA buffer. The size calculation will be done with BF16.

        Arguments:
            num_max_dispatch_tokens_per_rank: the maximum number of tokens to dispatch, all the ranks must hold the same value.
            hidden: the hidden dimension of each token.
            num_ranks: the number of EP group ranks.
            num_experts: the number of all experts.

        Returns:
            size: the RDMA buffer size recommended.
        """
        return deep_ep_cpp.get_low_latency_rdma_size_hint(
            num_max_dispatch_tokens_per_rank, hidden, num_ranks, num_experts
        )

    def get_comm_stream(self) -> torch.Stream:
        """
        Get the communication stream.

        Returns:
            stream: the communication stream.
        """
        ts: torch.Stream = self.runtime.get_comm_stream()
        return torch.cuda.Stream(
            stream_id=ts.stream_id, device_index=ts.device_index, device_type=ts.device_type
        )

    def get_local_buffer_tensor(
        self,
        dtype: torch.dtype,
        size: Optional[torch.Size] = None,
        offset: int = 0,
        use_rdma_buffer: bool = False,
    ) -> torch.Tensor:
        """
        Get the raw buffer (slice supported) as a PyTorch tensor.

        Argument:
            dtype: the data type (PyTorch `dtype`) for the tensor.
            size: the slice size (by elements) to get from the buffer.
            offset: the offset of the beginning element.
            use_rdma_buffer: whether to return the RDMA buffer.
        """
        tensor = self.runtime.get_local_buffer_tensor(dtype, offset, use_rdma_buffer)
        if size is None:
            return tensor

        assert tensor.numel() >= size.numel()
        return tensor[: size.numel()].view(size)

    @staticmethod
    def _unpack_bias(bias: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]):
        bias_0, bias_1 = None, None
        if isinstance(bias, torch.Tensor):
            bias_0 = bias
        elif isinstance(bias, tuple):
            assert len(bias) == 2
            bias_0, bias_1 = bias
        return bias_0, bias_1

    @staticmethod
    def get_dispatch_config(num_ranks: int) -> Config:
        """
        Get a recommended dispatch config.

        Argument:
            num_ranks: the number of ranks.

        Returns:
            config: the recommended config.
        """

        # TODO: automatically tune
        config_map = {
            2: Config(Buffer.num_sms, 24, 256, 6, 128),
            4: Config(Buffer.num_sms, 6, 256, 6, 128),
            8: Config(Buffer.num_sms, 6, 256, 6, 128),
            # 16: Config(Buffer.num_sms, 36, 288, 20, 128),
            16: Config(Buffer.num_sms, 8, 512, 16, 128),
            24: Config(Buffer.num_sms, 8, 288, 32, 128),
            32: Config(Buffer.num_sms, 32, 288, 32, 128),
            64: Config(Buffer.num_sms, 20, 288, 28, 128),
            128: Config(Buffer.num_sms, 20, 560, 32, 128),
            144: Config(Buffer.num_sms, 32, 720, 12, 128),
            160: Config(Buffer.num_sms, 28, 720, 12, 128),
        }
        assert num_ranks in config_map, f"Unsupported number of EP ranks: {num_ranks}"
        return config_map[num_ranks]

    @staticmethod
    def get_combine_config(num_ranks: int) -> Config:
        """
        Get a recommended combine config.

        Argument:
            num_ranks: the number of ranks.

        Returns:
            config: the recommended config.
        """

        # TODO: automatically tune
        config_map = {
            2: Config(Buffer.num_sms, 10, 256, 6, 128),
            4: Config(Buffer.num_sms, 9, 256, 6, 128),
            8: Config(Buffer.num_sms, 4, 256, 6, 128),
            # 16: Config(Buffer.num_sms, 4, 288, 12, 128),
            16: Config(Buffer.num_sms, 8, 512, 16, 128),
            24: Config(Buffer.num_sms, 1, 288, 8, 128),
            32: Config(Buffer.num_sms, 1, 288, 8, 128),
            64: Config(Buffer.num_sms, 1, 288, 20, 128),
            128: Config(Buffer.num_sms, 1, 560, 12, 128),
            144: Config(Buffer.num_sms, 2, 720, 8, 128),
            160: Config(Buffer.num_sms, 2, 720, 8, 128),
        }
        assert num_ranks in config_map, f"Unsupported number of EP ranks: {num_ranks}"
        return config_map[num_ranks]

    # noinspection PyTypeChecker
    def get_dispatch_layout(
        self,
        topk_idx: torch.Tensor,
        num_experts: int,
        previous_event: Optional[EventOverlap] = None,
        async_finish: bool = False,
        allocate_on_comm_stream: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor, torch.Tensor, EventOverlap]:
        """
        Calculate the layout required for later communication.

        Arguments:
            topk_idx: `[num_tokens, num_topk]`, dtype must be `torch.int64`, the expert indices selected by each token,
                `-1` means no selections.
            num_experts: the number of experts.
            previous_event: the event to wait before actually executing the kernel.
            async_finish: the current stream will not wait for the communication kernels to be finished if set.
            allocate_on_comm_stream: control whether all the allocated tensors' ownership to be on the communication stream.

        Returns:
            num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank.
            num_tokens_per_rdma_rank: `[num_rdma_ranks]` with `torch.int`, the number of tokens to be sent to each RDMA
                rank (with the same GPU index), return `None` for intranode settings.
            num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert.
            is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank.
            event: the event after executing the kernel (valid only if `async_finish` is set).
        """
        num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank, event = (
            self.runtime.get_dispatch_layout(
                topk_idx,
                num_experts,
                getattr(previous_event, "event", None),
                async_finish,
                allocate_on_comm_stream,
            )
        )
        return (
            num_tokens_per_rank,
            num_tokens_per_rdma_rank,
            num_tokens_per_expert,
            is_token_in_rank,
            EventOverlap(event),
        )

    # noinspection PyTypeChecker
    def dispatch(
        self,
        x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        handle: Optional[Tuple] = None,
        num_tokens_per_rank: Optional[torch.Tensor] = None,
        num_tokens_per_rdma_rank: Optional[torch.Tensor] = None,
        is_token_in_rank: Optional[torch.Tensor] = None,
        num_tokens_per_expert: Optional[torch.Tensor] = None,
        topk_idx: Optional[torch.Tensor] = None,
        topk_weights: Optional[torch.Tensor] = None,
        expert_alignment: int = 1,
        num_worst_tokens: int = 0,
        config: Optional[Config] = None,
        previous_event: Optional[EventOverlap] = None,
        async_finish: bool = False,
        allocate_on_comm_stream: bool = False,
        num_recv_tokens_per_expert_as_cuda: bool = False,
    ) -> Tuple[
        Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor],
        Optional[torch.Tensor],
        Optional[torch.Tensor],
        List[int],
        torch.Tensor,
        Tuple,
        EventOverlap,
    ]:
        """
        Dispatch tokens to different ranks, both intranode and internode settings are supported.
        Intranode kernels require all the ranks should be visible via NVLink.
        Internode kernels require the ranks in a node should be visible via NVLink, while the ranks with the same GPU
            index should be visible via RDMA.

        Arguments:
            x: `torch.Tensor` or tuple of `torch.Tensor`, for the first type, the shape must be `[num_tokens, hidden]`,
                and type must be `torch.bfloat16`; for the second type, the first element of the tuple must be shaped as
                `[num_tokens, hidden]` with type `torch.float8_e4m3fn`, the second must be `[num_tokens, hidden // 128]`
                 (requiring divisible) with type `torch.float`.
            handle: an optional communication handle, if set, the CPU will reuse the layout information to save some time.
            num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank.
            num_tokens_per_rdma_rank: `[num_rdma_ranks]` with `torch.int`, the number of tokens to be sent to each RDMA
                rank (with the same GPU index), return `None` for intranode settings.
            is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank.
            num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert.
            topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices selected by each token,
                `-1` means no selections.
            topk_weights: `[num_tokens, num_topk]` with `torch.float`, the expert weights of each token to dispatch.
            expert_alignment: align the number of tokens received by each local expert to this variable.
            num_worst_tokens: the worst number of tokens to receive, if specified, there will be no CPU sync, and it
                will be CUDA-graph compatible. Please also notice that this flag is for intranode only.
            config: the performance tuning config.
            previous_event: the event to wait before actually executing the kernel.
            async_finish: the current stream will not wait for the communication kernels to be finished if set.
            allocate_on_comm_stream: control whether all the allocated tensors' ownership to be on the communication stream.
            num_recv_tokens_per_expert_as_cuda: control return num_recv_tokens_per_expert as cuda tensor or python list.
        Returns:
            recv_x: received tokens, the same type and tuple as the input `x`, but the number of tokens equals to the
                received token count.
            recv_topk_idx: received expert indices.
            recv_topk_weights: received expert weights.
            num_recv_tokens_per_expert: Python list or cuda tensor shaped `[num_local_experts]`, the received token count by
                each local expert, aligned to the input `expert_alignment`. If `num_worst_tokens` is specified, the list
                will be empty.
            handle: the returned communication handle.
            event: the event after executing the kernel (valid only if `async_finish` is set).
        """
        # Default config
        config = self.get_dispatch_config(self.group_size) if config is None else config

        # Internode
        if self.runtime.get_num_rdma_ranks() > 1:
            assert num_worst_tokens == 0, "Internode dispatch does not support `num_worst_tokens > 0`"
            return self.internode_dispatch(
                x,
                handle,
                num_tokens_per_rank,
                num_tokens_per_rdma_rank,
                is_token_in_rank,
                num_tokens_per_expert,
                topk_idx,
                topk_weights,
                expert_alignment,
                config,
                previous_event,
                async_finish,
                allocate_on_comm_stream,
            )

        # Launch the kernel with cached or non-cached mode
        x, x_scales = x if isinstance(x, tuple) else (x, None)
        if handle is not None:
            assert topk_idx is None and topk_weights is None
            (
                rank_prefix_matrix,
                channel_prefix_matrix,
                recv_channel_prefix_matrix,
                recv_src_idx,
                is_token_in_rank,
                send_head,
            ) = handle
            num_recv_tokens = recv_src_idx.size(0)
            recv_x, recv_x_scales, _, _, _, _, _, _, _, _, _, event = self.runtime.intranode_dispatch(
                x,
                x_scales,
                None,
                None,
                None,
                is_token_in_rank,
                None,
                num_recv_tokens,
                rank_prefix_matrix,
                channel_prefix_matrix,
                expert_alignment,
                num_worst_tokens,
                config,
                getattr(previous_event, "event", None),
                async_finish,
                allocate_on_comm_stream,
            )
            return (
                (recv_x, recv_x_scales) if x_scales is not None else recv_x,
                None,
                None,
                None,
                None,
                EventOverlap(event),
            )
        else:
            assert (
                num_tokens_per_rank is not None
                and is_token_in_rank is not None
                and num_tokens_per_expert is not None
            )
            (
                recv_x,
                recv_x_scales,
                recv_topk_idx,
                recv_topk_weights,
                num_recv_tokens_per_expert_list,
                num_recv_tokens_per_expert_cuda,
                rank_prefix_matrix,
                channel_prefix_matrix,
                recv_channel_prefix_matrix,
                recv_src_idx,
                send_head,
                event,
            ) = self.runtime.intranode_dispatch(
                x,
                x_scales,
                topk_idx,
                topk_weights,
                num_tokens_per_rank,
                is_token_in_rank,
                num_tokens_per_expert,
                0,
                None,
                None,
                expert_alignment,
                num_worst_tokens,
                config,
                getattr(previous_event, "event", None),
                async_finish,
                allocate_on_comm_stream,
            )
            handle = (
                rank_prefix_matrix,
                channel_prefix_matrix,
                recv_channel_prefix_matrix,
                recv_src_idx,
                is_token_in_rank,
                send_head,
            )
            return (
                (recv_x, recv_x_scales) if x_scales is not None else recv_x,
                recv_topk_idx,
                recv_topk_weights,
                (
                    num_recv_tokens_per_expert_cuda
                    if num_recv_tokens_per_expert_as_cuda
                    else num_recv_tokens_per_expert_list
                ),
                handle,
                EventOverlap(event),
            )

    # noinspection PyTypeChecker
    def combine(
        self,
        x: torch.Tensor,
        handle: Tuple,
        topk_weights: Optional[torch.Tensor] = None,
        bias: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] = None,
        config: Optional[Config] = None,
        previous_event: Optional[EventOverlap] = None,
        async_finish: bool = False,
        allocate_on_comm_stream: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], EventOverlap]:
        """
        Combine (reduce) tokens (addition **without** weights) from different ranks, both intranode and internode
            settings are supported.
        Intranode kernels require all the ranks should be visible via NVLink.
        Internode kernels require the ranks in a node should be visible via NVLink, while the ranks with the same GPU
            index should be visible via RDMA.

        Arguments:
            x: `[num_tokens, hidden]` with `torch.bfloat16`, the tokens to send for reducing to its original ranks.
            handle: a must-set communication handle, you can obtain this from the dispatch function.
            topk_weights: `[num_tokens, num_topk]` with `torch.float`, the tokens' top-k weights for reducing to its original ranks.
            config: the performance tuning config.
            previous_event: the event to wait before actually executing the kernel.
            async_finish: the current stream will not wait for the communication kernels to be finished if set.
            allocate_on_comm_stream: control whether all the allocated tensors' ownership to be on the communication stream.

        Returns:
            recv_x: the reduced token from its dispatched ranks.
            recv_topk_weights: the reduced top-k weights from its dispatch ranks.
            event: the event after executing the kernel (valid only if `async_finish` is set).
        """
        # Default config
        config = self.get_combine_config(self.group_size) if config is None else config

        # Internode
        if self.runtime.get_num_rdma_ranks() > 1:
            return self.internode_combine(
                x, handle, topk_weights, bias, config, previous_event, async_finish, allocate_on_comm_stream
            )

        # NOTES: the second `_` is for the sending side, so we should use the third one
        rank_prefix_matrix, _, channel_prefix_matrix, src_idx, is_recv_token_in_rank, send_head = handle
        bias_0, bias_1 = Buffer._unpack_bias(bias)

        # Launch the kernel
        recv_x, recv_topk_weights, event = self.runtime.intranode_combine(
            x,
            topk_weights,
            bias_0,
            bias_1,
            src_idx,
            rank_prefix_matrix,
            channel_prefix_matrix,
            send_head,
            config,
            getattr(previous_event, "event", None),
            async_finish,
            allocate_on_comm_stream,
        )
        return recv_x, recv_topk_weights, EventOverlap(event)

    # noinspection PyTypeChecker
    def internode_dispatch(
        self,
        x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        handle: Optional[Tuple] = None,
        num_tokens_per_rank: Optional[torch.Tensor] = None,
        num_tokens_per_rdma_rank: Optional[torch.Tensor] = None,
        is_token_in_rank: Optional[torch.Tensor] = None,
        num_tokens_per_expert: Optional[torch.Tensor] = None,
        topk_idx: Optional[torch.Tensor] = None,
        topk_weights: Optional[torch.Tensor] = None,
        expert_alignment: int = 1,
        config: Optional[Config] = None,
        previous_event: Optional[EventOverlap] = None,
        async_finish: bool = False,
        allocate_on_comm_stream: bool = False,
    ) -> Tuple[
        Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor],
        Optional[torch.Tensor],
        Optional[torch.Tensor],
        List[int],
        Tuple,
        EventOverlap,
    ]:
        """
        Internode dispatch implementation, for more details, please refer to the `dispatch` docs.
        Normally, you should not directly call this function.
        """
        assert config is not None

        # Launch the kernel with cached or non-cached mode
        x, x_scales = x if isinstance(x, tuple) else (x, None)
        if handle is not None:
            assert topk_idx is None and topk_weights is None
            (
                is_token_in_rank,
                rdma_channel_prefix_matrix,
                gbl_channel_prefix_matrix,
                recv_rdma_channel_prefix_matrix,
                recv_rdma_rank_prefix_sum,
                recv_gbl_channel_prefix_matrix,
                recv_gbl_rank_prefix_sum,
                recv_src_meta,
                send_rdma_head,
                send_nvl_head,
            ) = handle
            num_recv_tokens = recv_src_meta.size(0)
            num_rdma_recv_tokens = send_nvl_head.size(0)
            recv_x, recv_x_scales, _, _, _, _, _, _, _, _, _, _, _, _, event = (
                self.runtime.internode_dispatch(
                    x,
                    x_scales,
                    topk_idx,
                    topk_weights,
                    None,
                    None,
                    is_token_in_rank,
                    None,
                    num_recv_tokens,
                    num_rdma_recv_tokens,
                    rdma_channel_prefix_matrix,
                    recv_rdma_rank_prefix_sum,
                    gbl_channel_prefix_matrix,
                    recv_gbl_rank_prefix_sum,
                    expert_alignment,
                    config,
                    getattr(previous_event, "event", None),
                    async_finish,
                    allocate_on_comm_stream,
                )
            )
            return (
                (recv_x, recv_x_scales) if x_scales is not None else recv_x,
                None,
                None,
                None,
                None,
                EventOverlap(event),
            )
        else:
            assert (
                num_tokens_per_rank is not None
                and is_token_in_rank is not None
                and num_tokens_per_expert is not None
            )
            (
                recv_x,
                recv_x_scales,
                recv_topk_idx,
                recv_topk_weights,
                num_recv_tokens_per_expert_list,
                rdma_channel_prefix_matrix,
                gbl_channel_prefix_matrix,
                recv_rdma_channel_prefix_matrix,
                recv_rdma_rank_prefix_sum,
                recv_gbl_channel_prefix_matrix,
                recv_gbl_rank_prefix_sum,
                recv_src_meta,
                send_rdma_head,
                send_nvl_head,
                event,
            ) = self.runtime.internode_dispatch(
                x,
                x_scales,
                topk_idx,
                topk_weights,
                num_tokens_per_rank,
                num_tokens_per_rdma_rank,
                is_token_in_rank,
                num_tokens_per_expert,
                0,
                0,
                None,
                None,
                None,
                None,
                expert_alignment,
                config,
                getattr(previous_event, "event", None),
                async_finish,
                allocate_on_comm_stream,
            )
            handle = (
                is_token_in_rank,
                rdma_channel_prefix_matrix,
                gbl_channel_prefix_matrix,
                recv_rdma_channel_prefix_matrix,
                recv_rdma_rank_prefix_sum,
                recv_gbl_channel_prefix_matrix,
                recv_gbl_rank_prefix_sum,
                recv_src_meta,
                send_rdma_head,
                send_nvl_head,
            )
            return (
                (recv_x, recv_x_scales) if x_scales is not None else recv_x,
                recv_topk_idx,
                recv_topk_weights,
                num_recv_tokens_per_expert_list,
                handle,
                EventOverlap(event),
            )

    # noinspection PyTypeChecker
    def internode_combine(
        self,
        x: torch.Tensor,
        handle: Union[tuple, list],
        topk_weights: Optional[torch.Tensor] = None,
        bias: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] = None,
        config: Optional[Config] = None,
        previous_event: Optional[EventOverlap] = None,
        async_finish: bool = False,
        allocate_on_comm_stream: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], EventOverlap]:
        """
        Internode combine implementation, for more details, please refer to the `combine` docs.
        Normally, you should not directly call this function.
        """
        assert config is not None

        # Unpack handle and bias
        (
            is_combined_token_in_rank,
            _,
            _,
            rdma_channel_prefix_matrix,
            rdma_rank_prefix_sum,
            gbl_channel_prefix_matrix,
            gbl_rank_prefix_sum,
            src_meta,
            send_rdma_head,
            send_nvl_head,
        ) = handle
        bias_0, bias_1 = Buffer._unpack_bias(bias)

        # Launch the kernel
        combined_x, combined_topk_weights, event = self.runtime.internode_combine(
            x,
            topk_weights,
            bias_0,
            bias_1,
            src_meta,
            is_combined_token_in_rank,
            rdma_channel_prefix_matrix,
            rdma_rank_prefix_sum,
            gbl_channel_prefix_matrix,
            send_rdma_head,
            send_nvl_head,
            config,
            getattr(previous_event, "event", None),
            async_finish,
            allocate_on_comm_stream,
        )
        return combined_x, combined_topk_weights, EventOverlap(event)

    def clean_low_latency_buffer(
        self, num_max_dispatch_tokens_per_rank: int, hidden: int, num_experts: int
    ) -> None:
        """
        As low-latency kernels require part of the buffer to be zero-initialized, so it is vital to clean the buffer
            if the buffer is dirty at some time.
        For example, after running the normal dispatch/combine, you must run this function before executing any
            low-latency kernel.

        Arguments:
            num_max_dispatch_tokens_per_rank: the maximum number of tokens to dispatch, all the ranks must hold the same value.
            hidden: the hidden dimension of each token.
            num_experts: the number of all experts.
        """
        self.runtime.clean_low_latency_buffer(num_max_dispatch_tokens_per_rank, hidden, num_experts)

    # noinspection PyTypeChecker
    def low_latency_dispatch(self, x: torch.Tensor, topk_idx: torch.Tensor,
                             num_max_dispatch_tokens_per_rank: int, num_experts: int,
                             use_fp8: bool = True, async_finish: bool = False, return_recv_hook: bool = False) -> \
            Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, Tuple, EventOverlap, Callable]:
        """
        A low-latency implementation for dispatching with IBGDA.
        This kernel requires all the ranks (no matter intranode or internode) should be visible via RDMA
            (specifically, IBGDA must be enabled).
        Even for ranks in the same node, NVLink are fully disabled for simplicity.
        Warning: as there are only two buffers, and the returned tensors reuse the buffer, you can not hold more than 2
            low-latency kernels' result tensor at a single moment.

        Arguments:
            x: `torch.Tensor` with `torch.bfloat16`, shaped as `[num_tokens, hidden]`, only several hidden shapes are
                supported. The number of tokens to be dispatched must be less than `num_max_dispatch_tokens_per_rank`.
            topk_idx: `torch.Tensor` with `torch.int64`, shaped as `[num_tokens, num_topk]`, only several top-k shapes
                are supported. `-1` indices (not selecting any expert) are supported.
            num_max_dispatch_tokens_per_rank: the maximum number of tokens to dispatch, all the ranks must hold the same value.
            num_experts: the number of all experts.
            use_fp8: whether to enable FP8 casting, with this, the received data will be a tuple of FP8 tensor and scaling factors.
            async_finish: the current stream will not wait for the communication kernels to be finished if set.
            return_recv_hook: return a receiving hook if set. If set, the kernel will just do the RDMA request issues,
                but **without actually receiving the data**. You must call the received hook to make sure the data's arrival.
                If you not set this flag, the kernel will ensure the data's arrival.

        Returns:
            recv_x: a tensor or tuple with received tokens for each expert.
                With `use_fp8=True`: the first element is a `torch.Tensor` shaped as
                `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden]` with `torch.float8_e4m3fn`.
                The second tensor is the corresponding scales for the first element with shape
                `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden // 128]` with `torch.float`.
                Notice that, the last-two-dimension of the scaling tensors are in column-major for TMA compatibility.
                With `use_fp8=False`, the result would be a tensor shaped as
                `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden]` with `torch.bfloat16`.
                Moreover, not all tokens are valid, only some of the `num_max_dispatch_tokens_per_rank * num_ranks` are,
                as we do not synchronize CPU received count with GPU (also not incompatible with CUDA graph if synced).
            recv_count: a tensor shaped `[num_local_experts]` with type `torch.int`, indicating how many tokens each
                expert receive. As mentioned before, all not tokens are valid in `recv_x`.
            handle: the communication handle to be used in the `low_latency_combine` function.
            event: the event after executing the kernel (valid only if `async_finish` is set).
            hook: the receiving hook function (valid only if `return_recv_hook` is set).
        """
        packed_recv_x, packed_recv_x_scales, packed_recv_count, packed_recv_src_info, packed_recv_layout_range, event, hook = \
            self.runtime.low_latency_dispatch(x, topk_idx,
                                              num_max_dispatch_tokens_per_rank, num_experts,
                                              use_fp8, async_finish, return_recv_hook)
        handle = (packed_recv_src_info, packed_recv_layout_range, num_max_dispatch_tokens_per_rank, x.size(1), num_experts)
        tensors_to_record = (x, topk_idx,
                             packed_recv_x, packed_recv_x_scales, packed_recv_count,
                             packed_recv_src_info, packed_recv_layout_range)
        return (packed_recv_x, packed_recv_x_scales) if use_fp8 else packed_recv_x, packed_recv_count, handle, \
            EventOverlap(event, tensors_to_record if async_finish else None), hook

    # noinspection PyTypeChecker
    def low_latency_combine(self, x: torch.Tensor, topk_idx: torch.Tensor, topk_weights: torch.Tensor,
                            handle: tuple, zero_copy: bool = False, async_finish: bool = False,
                            return_recv_hook: bool = False, out: Optional[torch.Tensor] = None) -> \
            Tuple[torch.Tensor, EventOverlap, Callable]:
        """
        A low-latency implementation for combining tokens (reduce **with weights**) with IBGDA.
        This kernel requires all the ranks (no matter intranode or internode) should be visible via RDMA
            (specifically, IBGDA must be enabled).
        Even for ranks in the same node, NVLink are fully disabled for simplicity.
        Warning: as there are only two buffers, and the returned tensors reuse the buffer, you can not hold more than 2
            low-latency kernels' result tensor at a single moment.

        Arguments:
            x: `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden]` with `torch.bfloat16`,
                the local calculated tokens to be sent to this original rank and reduced.
            topk_idx: `[num_combined_tokens, num_topk]` with `torch.int64`, the expert indices selected by the dispatched
                tokens. `-1` indices (not selecting any expert) are supported. Note that, `num_combined_tokens` equals
                to the number of dispatched tokens.
            topk_weights: `[num_combined_tokens, num_topk]` with `torch.float`, the expert weights selected by the dispatched
                tokens. The received tokens will be reduced with the weights in this tensor.
            handle: the communication handle given by the `dispatch` function.
            zero_copy: whether the tensor is already copied into the RDMA buffer, should be cooperative
                with `get_next_low_latency_combine_buffer`.
            async_finish: the current stream will not wait for the communication kernels to be finished if set.
            return_recv_hook: return a receiving hook if set. If set, the kernel will just do the RDMA request issues,
                but **without actually receiving the data**. You must call the received hook to make sure the data's arrival.
                If you not set this flag, the kernel will ensure the data's arrival.
            out: the in-place output tensor, if set, the kernel will write the result to this tensor and return it directly.

        Returns:
            combined_x: the reduced token tensor, with shape `[num_combined_tokens, num_topk]` and type `torch.bfloat16`.
            event: the event after executing the kernel (valid only if `async_finish` is set).
            hook: the receiving hook function (valid only if `return_recv_hook` is set).
        """
        src_info, layout_range, num_max_dispatch_tokens_per_rank, hidden, num_experts = handle
        combined_x, event, hook = self.runtime.low_latency_combine(x, topk_idx, topk_weights, src_info, layout_range,
                                                                   num_max_dispatch_tokens_per_rank, num_experts,
                                                                   zero_copy, async_finish, return_recv_hook, out)
        tensors_to_record = (x, topk_idx, topk_weights, src_info, layout_range, combined_x)
        return combined_x, EventOverlap(event, tensors_to_record if async_finish else None), hook

    def get_next_low_latency_combine_buffer(self, handle: object):
        """
        Get the raw registered RDMA buffer tensor for next low-latency combine, so that the next combine kernel can skip the copying.

        Arguments:
            handle: the communication handle given by the `dispatch` function.

        Returns:
            buffer: the raw RDMA low-latency buffer as a BF16 PyTorch tensor with shape
                `[num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank, hidden]`, you should fill this buffer
                by yourself.
        """
        src_info, layout_range, num_max_dispatch_tokens_per_rank, hidden, num_experts = handle
        return self.runtime.get_next_low_latency_combine_buffer(num_max_dispatch_tokens_per_rank, hidden, num_experts)