from __future__ import annotations

import logging
from contextlib import nullcontext
from dataclasses import dataclass
from typing import TYPE_CHECKING, List, NamedTuple, Optional, Tuple, Union

from sglang.srt.distributed.parallel_state import get_tp_group
from sglang.srt.environ import envs
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.layers import deep_gemm_wrapper
from sglang.srt.layers.dp_attention import get_is_extend_in_batch
from sglang.srt.layers.moe.token_dispatcher.base import (
    BaseDispatcher,
    BaseDispatcherConfig,
    CombineInput,
    CombineInputFormat,
    DispatcherBaseHooks,
    DispatchOutput,
    DispatchOutputFormat,
)
from sglang.srt.layers.moe.topk import TopKOutput
from sglang.srt.layers.moe.utils import (
    DeepEPMode,
    DispatcherOutputDtype,
    get_deepep_config,
    get_deepep_output_dtype,
    is_tbo_enabled,
)
from sglang.srt.utils import (
    get_bool_env_var,
    get_cuda_version,
    is_blackwell,
    is_flashinfer_available,
    is_hip,
    is_npu,
    load_json_config,
)

_is_npu = is_npu()

if TYPE_CHECKING:
    from sglang.srt.batch_overlap.single_batch_overlap import CombineOverlapArgs

try:
    if _is_npu and envs.SGLANG_ZBAL_LOCAL_MEM_SIZE.get() > 0:
        from zbal.zbal.deepep_adaptor import Config
        from zbal.zbal_buffer import Buffer
    else:
        from deep_ep import Buffer, Config

    if not _is_npu:
        from sglang.kernels.ops.quantization.fp8_kernel import (
            sglang_per_token_group_quant_fp8,
        )

    use_deepep = True
except ImportError:
    use_deepep = False

from enum import Enum, IntEnum, auto

import torch
import torch.distributed as dist

_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and is_hip()

logger = logging.getLogger(__name__)


def _is_mnnvl_fabric_supported() -> bool:
    if not is_flashinfer_available():
        return False

    from flashinfer.comm.mnnvl import is_mnnvl_fabric_supported

    return is_mnnvl_fabric_supported(torch.cuda.current_device())


def _deepep_precompile_tp_barrier() -> None:
    # DeepEP's all-to-all operation has a much shorter timeout compared to torch.distributed,
    # so if different ranks compile at different speeds, it may quickly trigger a timeout.
    # To avoid this, we use torch.distributed's barrier during the compile stage.
    # We apply this barrier only in the compile stage to prevent extra all-reduce overhead at runtime.
    if envs.SGLANG_IN_DEEPGEMM_PRECOMPILE_STAGE.get():
        get_tp_group().barrier()


class DeepEPPDispatchHooks(DispatcherBaseHooks):
    def __call__(self, dispatcher: BaseDispatcher):
        for hook_fun in self.hook_dict.values():
            hook_fun(dispatcher)


class DeepEPNormalDispatchOutput(NamedTuple):
    """DeepEP normal dispatch output."""

    hidden_states: torch.Tensor
    hidden_states_scale: Optional[torch.Tensor]
    topk_ids: torch.Tensor
    topk_weights: torch.Tensor
    num_recv_tokens_per_expert: List[int]

    @property
    def format(self) -> DispatchOutputFormat:
        return DispatchOutputFormat.DEEPEP_NORMAL


class DeepEPLLDispatchOutput(NamedTuple):
    """DeepEP low latency dispatch output."""

    hidden_states: torch.Tensor
    hidden_states_scale: Optional[torch.Tensor]
    topk_ids: torch.Tensor
    topk_weights: torch.Tensor
    masked_m: torch.Tensor
    expected_m: int

    @property
    def format(self) -> DispatchOutputFormat:
        return DispatchOutputFormat.DEEPEP_LL


assert isinstance(DeepEPNormalDispatchOutput, DispatchOutput)
assert isinstance(DeepEPLLDispatchOutput, DispatchOutput)


class DeepEPNormalCombineInput(NamedTuple):
    """DeepEP normal combine input."""

    hidden_states: torch.Tensor
    topk_ids: torch.Tensor
    topk_weights: torch.Tensor

    @property
    def format(self) -> CombineInputFormat:
        return CombineInputFormat.DEEPEP_NORMAL


class DeepEPLLCombineInput(NamedTuple):
    """DeepEP low latency combine input."""

    hidden_states: torch.Tensor
    topk_ids: torch.Tensor
    topk_weights: torch.Tensor

    @property
    def format(self) -> CombineInputFormat:
        return CombineInputFormat.DEEPEP_LL


assert isinstance(DeepEPNormalCombineInput, CombineInput)
assert isinstance(DeepEPLLCombineInput, CombineInput)


class DeepEPDispatchMode(IntEnum):
    NORMAL = auto()
    LOW_LATENCY = auto()


class DeepEPBuffer:
    """Managing facade for the process-wide DeepEP comm buffer; the state
    itself lives on ``ctx.resources`` (one entry per process)."""

    @classmethod
    def _state(cls):
        from types import SimpleNamespace

        from sglang.srt.runtime_context import get_resources

        buffers = get_resources().buffers
        state = buffers.get("deepep_ep_state")
        if state is None:
            state = SimpleNamespace(
                buffer=None,
                dispatch_mode=None,
                hidden_size=None,
                num_max_dispatch_tokens_per_rank=None,
                num_experts=None,
            )
            buffers["deepep_ep_state"] = state
        return state

    @classmethod
    def get_deepep_buffer(
        cls,
        group: dist.ProcessGroup,
        hidden_size: int,
        param_bytes: int,
        deepep_mode: DeepEPMode,
        num_max_dispatch_tokens_per_rank: int = -1,
        num_experts: int = -1,
    ):
        state = cls._state()
        if state.buffer is not None:
            return state.buffer

        state.hidden_size = hidden_size
        state.num_max_dispatch_tokens_per_rank = num_max_dispatch_tokens_per_rank
        state.num_experts = num_experts

        num_nvl_bytes, num_rdma_bytes = 0, 0
        if deepep_mode.enable_normal():
            hidden_bytes = hidden_size * param_bytes
            for config in (
                DeepEPConfig.get_instance().normal_dispatch_config
                or Buffer.get_dispatch_config(group.size()),
                DeepEPConfig.get_instance().normal_combine_config
                or Buffer.get_combine_config(group.size()),
            ):
                num_nvl_bytes = max(
                    config.get_nvl_buffer_size_hint(hidden_bytes, group.size()),
                    num_nvl_bytes,
                )
                num_rdma_bytes = max(
                    config.get_rdma_buffer_size_hint(hidden_bytes, group.size()),
                    num_rdma_bytes,
                )
        if deepep_mode.enable_low_latency():
            assert num_max_dispatch_tokens_per_rank != -1
            assert num_experts != -1 and num_experts % group.size() == 0
            num_rdma_bytes = max(
                Buffer.get_low_latency_rdma_size_hint(
                    num_max_dispatch_tokens_per_rank,
                    hidden_size,
                    group.size(),
                    num_experts,
                ),
                num_rdma_bytes,
            )

        # We should calculate num_qps_per_rank consistently with DeepEP's test script logic:
        if deepep_mode == DeepEPMode.NORMAL:
            # refer: https://github.com/deepseek-ai/DeepEP/blob/main/tests/test_internode.py#L235
            num_qps_per_rank = DeepEPConfig.get_instance().num_sms
        elif deepep_mode == DeepEPMode.LOW_LATENCY:
            # refer: https://github.com/deepseek-ai/DeepEP/blob/main/tests/test_low_latency.py#L176
            num_qps_per_rank = num_experts // group.size()
        elif deepep_mode == DeepEPMode.AUTO:
            # low-latency and normal mode all need run
            # refer: https://github.com/deepseek-ai/DeepEP/blob/main/tests/test_internode.py#L235
            num_qps_per_rank = max(
                DeepEPConfig.get_instance().num_sms, num_experts // group.size()
            )
        else:
            raise NotImplementedError

        if not _is_npu:
            total_num_sms = torch.cuda.get_device_properties(
                device="cuda"
            ).multi_processor_count
            if (
                (deepep_mode != DeepEPMode.LOW_LATENCY)
                and not is_tbo_enabled()
                and (DeepEPConfig.get_instance().num_sms < total_num_sms // 2)
            ):
                logger.warning(
                    f"Only use {DeepEPConfig.get_instance().num_sms} SMs for DeepEP communication. "
                    f"This may result in highly suboptimal performance. "
                    f"Consider using --deepep-config to change the behavior."
                )

        use_mnnvl_fabric = _is_mnnvl_fabric_supported()
        buffer_kwargs = dict(
            low_latency_mode=deepep_mode.enable_low_latency(),
            num_qps_per_rank=num_qps_per_rank,
            allow_mnnvl=use_mnnvl_fabric,
        )
        # Use CU_MEM_HANDLE_TYPE_FABRIC on hardware that advertises MNNVL fabric
        # support, so cross-pod GB200/GB300 EP groups use
        # cuMemImportFromShareableHandle instead of the intra-node-only
        # cudaIpcOpenMemHandle. The DeepEP build we ship is keyed on the CUDA major
        # version:
        #   cu13x -> hybrid-ep, which gates fabric behind a use_fabric kwarg, so we
        #            pass it when the device advertises fabric support.
        #   cu12x -> fzyzcjy/DeepEP, which has no use_fabric kwarg but already
        #            auto-enables fabric in C++ when supported, so we skip it:
        #            https://github.com/fzyzcjy/DeepEP/blob/814e508537c6ffc775d59f6f1b9ba43f3a65968c/csrc/deep_ep.cpp#L52
        is_cu12 = get_cuda_version()[0] == 12
        if not is_cu12 and use_mnnvl_fabric:
            buffer_kwargs["use_fabric"] = True

        state.buffer = Buffer(group, num_nvl_bytes, num_rdma_bytes, **buffer_kwargs)
        return state.buffer

    @classmethod
    def clean_buffer(cls):
        state = cls._state()
        if not state.buffer.low_latency_mode:
            return
        state.buffer.clean_low_latency_buffer(
            state.num_max_dispatch_tokens_per_rank,
            state.hidden_size,
            state.num_experts,
        )

    @classmethod
    def set_dispatch_mode_as_normal(cls):
        cls._state().dispatch_mode = DeepEPDispatchMode.NORMAL

    @classmethod
    def set_dispatch_mode_as_low_latency(cls):
        state = cls._state()
        if state.dispatch_mode == DeepEPDispatchMode.NORMAL:
            cls.clean_buffer()
        state.dispatch_mode = DeepEPDispatchMode.LOW_LATENCY

    @classmethod
    def set_dispatch_mode(cls, mode: DeepEPMode):
        if mode.is_low_latency():
            cls.set_dispatch_mode_as_low_latency()
        elif mode.is_normal():
            cls.set_dispatch_mode_as_normal()
        else:
            raise Exception("unsupported mode")


class DeepEPConfig(BaseDispatcherConfig):
    _instance = None

    def __init__(self):
        config_str = get_deepep_config()
        if config_str:
            config_parsed = load_json_config(config_str)
            if torch.distributed.get_rank() == 0:
                logger.info(f"Use DeepEP Config: {config_parsed}")
            config_dispatch = config_parsed["normal_dispatch"]
            config_combine = config_parsed["normal_combine"]

            self.normal_dispatch_config = Config(**config_dispatch)
            self.normal_combine_config = Config(**config_combine)

            assert config_dispatch["num_sms"] == config_combine["num_sms"]
            self.num_sms = config_dispatch["num_sms"]
        else:
            self.normal_dispatch_config = None
            self.normal_combine_config = None
            self.num_sms = Buffer.num_sms

    @classmethod
    def get_instance(cls):
        if cls._instance is None:
            cls._instance = DeepEPConfig()
        return cls._instance


class _DeepEPDispatcherImplBase:
    def __init__(
        self,
        group: torch.distributed.ProcessGroup,
        router_topk: int,
        permute_fusion: bool,
        num_experts: int,
        num_local_experts: int,
        hidden_size: int,
        params_dtype: torch.dtype,
        deepep_mode: DeepEPMode,
    ):
        if not use_deepep:
            raise ImportError(
                "DeepEP is not installed. Please install DeepEP package from "
                "https://github.com/deepseek-ai/deepep."
            )

        self.group = group
        self.router_topk = router_topk
        self.permute_fusion = permute_fusion
        self.num_experts = num_experts
        self.num_local_experts = num_local_experts
        self.hidden_size = hidden_size
        self.params_dtype = params_dtype
        self.deepep_mode = deepep_mode

        self.params_bytes = 2
        # A large value will lead to large memory occupation, thus users should change it accordingly
        self.num_max_dispatch_tokens_per_rank = (
            envs.SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK.get()
        )
        # DeepEP internode_ll dispatch uses FINISHED_SUM_TAG=1024
        # and the logic requires num-tokens-sent-from-one-rank-to-another-rank less than it
        assert self.num_max_dispatch_tokens_per_rank <= 1024

        self.handle = None

        self.quant_config: Optional[dict] = None

        self.overlap_args: Optional[CombineOverlapArgs] = None
        self.meta_overlap_args: Optional[dict] = None

        self.set_deepep_dispatcher_dtype()

    def dispatch_a(
        self,
        hidden_states: torch.Tensor,
        topk_output: TopKOutput,
    ):
        raise NotImplementedError

    def dispatch_b(self, *args, **kwargs):
        raise NotImplementedError

    def combine_a(
        self,
        hidden_states: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
    ):
        raise NotImplementedError

    def combine_b(self, *args, **kwargs):
        raise NotImplementedError

    def _get_buffer(self):
        raise NotImplementedError

    def set_quant_config(self, quant_config: dict) -> None:
        self.quant_config = quant_config
        self.set_deepep_dispatcher_dtype()

    def set_deepep_dispatcher_dtype(self) -> None:
        self.deepep_output_dtype = get_deepep_output_dtype(self)

        # Configuration mapping for each dtype
        config_map = {
            DispatcherOutputDtype.BF16: {
                "use_fp8": False,
                "use_nvfp4": False,
            },
            DispatcherOutputDtype.FP8: {
                "use_fp8": True,
                "use_nvfp4": False,
            },
            # Needed for Ascend A2/A3 NPU case,
            # despite the use_fp8 flag,
            # quantization will be performed in int8
            DispatcherOutputDtype.INT8: {
                "use_fp8": True,
                "use_nvfp4": False,
            },
            DispatcherOutputDtype.NVFP4: {
                "use_fp8": False,
                "use_nvfp4": True,
            },
        }

        # Validate and apply hardware-specific adjustments
        self._validate_and_adjust_dtype()

        # Apply configuration
        config = config_map[self.deepep_output_dtype]
        self.use_fp8 = config["use_fp8"]
        self.use_nvfp4 = config["use_nvfp4"]

        # Handle environment variables
        if _is_npu:
            self._update_int8_quant_env()

    def _validate_and_adjust_dtype(self) -> None:
        """Validate dtype against hardware and adjust if necessary."""
        if _is_npu:
            if self.deepep_output_dtype == DispatcherOutputDtype.FP8:
                logger.warning_once(
                    "Ascend A2/A3 NPU does not support fp8 "
                    "deepep_dispatcher_output_dtype, switching to int8..."
                )
                self.deepep_output_dtype = DispatcherOutputDtype.INT8
            elif self.deepep_output_dtype == DispatcherOutputDtype.NVFP4:
                raise RuntimeError(
                    "Ascend A2/A3 NPU does not support nvfp4 deepep_dispatcher_output_dtype."
                )
        else:
            if self.deepep_output_dtype == DispatcherOutputDtype.INT8:
                logger.warning_once(
                    "GPU does not support int8 "
                    "deepep_dispatcher_output_dtype, switching to fp8..."
                )
                self.deepep_output_dtype = DispatcherOutputDtype.FP8
            # NVFP4 is supported on GPU, no adjustment needed

    def _update_int8_quant_env(self) -> None:
        """TODO adapt different quantization schemes for base model and draft model on NPU"""
        pass

    def set_overlap_args(
        self, combine_overlap_args: CombineOverlapArgs, meta_overlap_args: dict
    ) -> None:
        self.overlap_args = combine_overlap_args
        self.meta_overlap_args = meta_overlap_args

    def clear_overlap_args(self) -> None:
        self.overlap_args = None
        self.meta_overlap_args = None


class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
    def __init__(self, async_finish: bool, **kwargs):
        super().__init__(**kwargs)

        self.async_finish = async_finish
        self.src2dst = None
        self.quant_config = {}

    def dispatch_a(
        self,
        hidden_states: torch.Tensor,
        topk_output: TopKOutput,
    ):
        topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids
        topk_ids = topk_ids.to(torch.int64)
        if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8:
            # TODO hard code 128 block quant,use fp8 communication
            hidden_states = sglang_per_token_group_quant_fp8(
                hidden_states,
                128,
                column_major_scales=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
                scale_tma_aligned=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
                scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
            )
        previous_event = Buffer.capture() if self.async_finish else None
        return hidden_states, topk_ids, topk_weights, previous_event

    def dispatch_b(self, hidden_states, topk_ids, topk_weights, previous_event):
        (
            hidden_states,
            topk_ids,
            topk_weights,
            num_recv_tokens_per_expert,
            event,
        ) = self._dispatch_core(hidden_states, topk_ids, topk_weights, previous_event)
        event.current_stream_wait() if self.async_finish else ()

        if isinstance(hidden_states, tuple):
            hidden_states, hidden_states_scale = hidden_states
        else:
            hidden_states_scale = None

        return DeepEPNormalDispatchOutput(
            hidden_states,
            hidden_states_scale,
            topk_ids,
            topk_weights,
            num_recv_tokens_per_expert,
        )

    def _dispatch_core(
        self,
        x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
        previous_event,
    ):
        buffer = self._get_buffer()
        (
            num_tokens_per_rank,
            num_tokens_per_rdma_rank,
            num_tokens_per_expert,
            is_token_in_rank,
            previous_event,
        ) = buffer.get_dispatch_layout(
            topk_ids,
            self.num_experts,
            previous_event=previous_event,
            async_finish=self.async_finish,
            allocate_on_comm_stream=previous_event is not None,
        )
        # FIXME: `handle` should be transmitted with tokens from dispatch to combine.
        # However, doing this would incur an unknown synchronization error, but keeping
        # `handle` as a member variable works.

        _deepep_precompile_tp_barrier()
        (
            recv_x,
            recv_topk_ids,
            recv_topk_weights,
            num_recv_tokens_per_expert,
            self.handle,
            event,
        ) = buffer.dispatch(
            x,
            topk_idx=topk_ids,
            topk_weights=topk_weights,
            num_tokens_per_rank=num_tokens_per_rank,
            num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
            is_token_in_rank=is_token_in_rank,
            num_tokens_per_expert=num_tokens_per_expert,
            previous_event=previous_event,
            async_finish=self.async_finish,
            allocate_on_comm_stream=(previous_event is not None) and self.async_finish,
            expert_alignment=128 if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM else 1,
            config=DeepEPConfig.get_instance().normal_dispatch_config,
        )
        get_global_expert_distribution_recorder().on_deepep_dispatch_normal(
            num_recv_tokens_per_expert,
            num_tokens_per_rank=num_tokens_per_rank,
            num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
            num_tokens_per_expert=num_tokens_per_expert,
        )

        return (
            recv_x,
            recv_topk_ids,
            recv_topk_weights,
            num_recv_tokens_per_expert,
            event,
        )

    def combine_a(
        self,
        hidden_states: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
    ):

        if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM or _use_aiter or _is_npu:
            output = hidden_states
        else:
            raise NotImplementedError()  # triton runner was supported but it's temporarily disabled

        previous_event = Buffer.capture() if self.async_finish else None
        return output, previous_event

    def combine_b(self, output, previous_event):
        hidden_states, event = self._combine_core(output, previous_event)
        event.current_stream_wait() if self.async_finish else ()
        self.handle = None
        self.src2dst = None
        return hidden_states

    def _combine_core(self, x: torch.Tensor, previous_event):
        buffer = self._get_buffer()
        _deepep_precompile_tp_barrier()
        combined_x, _, event = buffer.combine(
            x,
            self.handle,
            async_finish=self.async_finish,
            previous_event=previous_event,
            allocate_on_comm_stream=previous_event is not None,
            config=DeepEPConfig.get_instance().normal_combine_config,
        )
        return combined_x, event

    def _get_buffer(self):
        DeepEPBuffer.set_dispatch_mode_as_normal()

        return DeepEPBuffer.get_deepep_buffer(
            self.group,
            self.hidden_size,
            self.params_bytes,
            self.deepep_mode,
            self.num_max_dispatch_tokens_per_rank,
            self.num_experts,
        )


class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
    def __init__(self, return_recv_hook: bool, **kwargs):
        super().__init__(**kwargs)

        """
        num_max_dispatch_tokens_per_rank: the actual batch size in the decoding engine should be less than 256
        https://github.com/deepseek-ai/DeepEP?tab=readme-ov-file#example-use-in-inference-decoding
        """
        self.return_recv_hook = return_recv_hook
        self.device_module = torch.get_device_module()
        self.quant_config = {}

    def dispatch_a(
        self,
        hidden_states: torch.Tensor,
        topk_output: TopKOutput,
    ):
        buffer = self._get_buffer()
        topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids
        topk_ids = topk_ids.to(torch.int64)
        expected_m = (
            hidden_states.shape[0] * buffer.group_size * topk_ids.shape[1]
            + self.num_experts
        ) // self.num_experts
        hidden_states, masked_m, event, hook = self._dispatch_core(
            hidden_states,
            topk_ids,
            topk_weights,
        )
        return (
            hidden_states,
            topk_ids,
            topk_weights,
            masked_m,
            expected_m,
            event,
            hook,
        )

    def dispatch_b(
        self,
        hidden_states,
        topk_ids,
        topk_weights,
        masked_m,
        expected_m,
        event,
        hook,
    ):
        hook() if self.return_recv_hook else event.current_stream_wait()

        get_global_expert_distribution_recorder().on_deepep_dispatch_low_latency(
            masked_m
        )

        if isinstance(hidden_states, tuple):
            hidden_states, hidden_states_scale = hidden_states
        else:
            hidden_states_scale = None

        deepep_output = DeepEPLLDispatchOutput(
            hidden_states,
            hidden_states_scale,
            topk_ids,
            topk_weights,
            masked_m,
            expected_m,
        )
        return deepep_output

    def _dispatch_core(
        self,
        hidden_states: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
    ):
        input_global_scale = self.quant_config.get("input_global_scale", None)

        # round_scale / use_ue8m0 are FP8-DeepGEMM specific; they cause DeepEP
        # to return int32-packed UE8M0 scales that don't feed the flashinfer
        # cutedsl kernel.
        fp8_deepgemm_scale_opts = (
            dict(
                round_scale=deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
                and deep_gemm_wrapper.DEEPGEMM_BLACKWELL,
                use_ue8m0=deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
                and deep_gemm_wrapper.DEEPGEMM_BLACKWELL,
            )
            if self.use_fp8
            else dict()
        )

        buffer = self._get_buffer()
        _deepep_precompile_tp_barrier()
        packed_recv_hidden, self.packed_recv_count, self.handle, event, hook = (
            buffer.low_latency_dispatch(
                hidden_states,
                topk_ids,
                self.num_max_dispatch_tokens_per_rank,
                self.num_experts,
                use_fp8=self.use_fp8,
                **(dict(topk_weights=topk_weights) if _is_npu else dict()),
                **(dict(use_nvfp4=True) if self.use_nvfp4 else dict()),
                **(
                    dict(x_global_scale=input_global_scale)
                    if input_global_scale is not None
                    else dict()
                ),
                async_finish=not self.return_recv_hook,
                return_recv_hook=self.return_recv_hook,
                **fp8_deepgemm_scale_opts,
            )
        )
        return packed_recv_hidden, self.packed_recv_count, event, hook

    def combine_a(
        self,
        hidden_states: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
    ):
        hidden_states, event, hook = self._combine_core(
            hidden_states,
            topk_ids,
            topk_weights,
        )
        return hidden_states, event, hook

    def combine_b(self, hidden_states, event, hook):
        overlap_args = self.overlap_args
        if overlap_args is not None:
            overlap_args.stream.wait_stream(self.device_module.current_stream())

        hook() if self.return_recv_hook else event.current_stream_wait()

        if overlap_args is not None:
            self.device_module.current_stream().wait_stream(overlap_args.stream)

        return hidden_states

    def _combine_core(
        self,
        hidden_states: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
    ):
        buffer = self._get_buffer()
        overlap_args = self.overlap_args
        meta_overlap_args = self.meta_overlap_args

        ctx = nullcontext()
        if overlap_args is not None:
            overlap_args.stream.wait_event(overlap_args.wait_event)
            ctx = torch.cuda.stream(overlap_args.stream)

            if is_blackwell():
                overlap_args_dict = dict(
                    overlap=overlap_args.overlap,
                    src_signals=overlap_args.signal,
                    src_signal_expect_value=overlap_args.threshold,
                )
            else:
                overlap_args_dict = dict(
                    overlap=overlap_args.overlap,
                    packed_recv_count=self.packed_recv_count,
                    comp_signal=overlap_args.signal,
                    block_m=meta_overlap_args["block_m"],
                    threshold=meta_overlap_args["threshold"],
                    num_sms=overlap_args.num_sms,
                )
        else:
            overlap_args_dict = {}

        with ctx:
            _deepep_precompile_tp_barrier()
            combined_hidden_states, event, hook = buffer.low_latency_combine(
                x=hidden_states,
                topk_idx=topk_ids,
                topk_weights=topk_weights,
                handle=self.handle,
                async_finish=not self.return_recv_hook,
                return_recv_hook=self.return_recv_hook,
                **overlap_args_dict,
            )

        self.packed_recv_count = self.handle = None
        return combined_hidden_states, event, hook

    def _get_buffer(self):
        DeepEPBuffer.set_dispatch_mode_as_low_latency()
        return DeepEPBuffer.get_deepep_buffer(
            self.group,
            self.hidden_size,
            self.params_bytes,
            self.deepep_mode,
            self.num_max_dispatch_tokens_per_rank,
            self.num_experts,
        )


@dataclass
class _Stage(Enum):
    INITIAL = auto()
    AFTER_DISPATCH_A = auto()
    AFTER_DISPATCH_B = auto()
    AFTER_COMBINE_A = auto()


class DeepEPDispatcher(BaseDispatcher):
    def __init__(
        self,
        group: torch.distributed.ProcessGroup,
        router_topk: int,
        permute_fusion: bool = False,
        num_experts: int = None,
        num_local_experts: int = None,
        hidden_size: int = None,
        params_dtype: torch.dtype = None,
        deepep_mode: DeepEPMode = DeepEPMode.AUTO,
        async_finish: bool = False,
        return_recv_hook: bool = False,
    ):
        super().__init__()

        self.deepep_mode = deepep_mode

        common_kwargs = dict(
            group=group,
            router_topk=router_topk,
            permute_fusion=permute_fusion,
            num_experts=num_experts,
            num_local_experts=num_local_experts,
            hidden_size=hidden_size,
            params_dtype=params_dtype,
            deepep_mode=deepep_mode,
        )

        if self.deepep_mode.enable_low_latency():
            self._low_latency_dispatcher = _DeepEPDispatcherImplLowLatency(
                return_recv_hook=return_recv_hook,
                **common_kwargs,
            )
        if self.deepep_mode.enable_normal():
            self._normal_dispatcher = _DeepEPDispatcherImplNormal(
                async_finish=async_finish,
                **common_kwargs,
            )

        self._stage = _Stage.INITIAL
        self._deepep_dispatch_hooks = DeepEPPDispatchHooks()

        # DeepEP/Mooncake/Nixl mark invalid topk slots with -1; the AITER
        # pre_permute reroutes them to a sink slot at index num_local_experts,
        # which is masked off here.
        self.expert_mask_gpu = None
        if _use_aiter and num_local_experts is not None:
            expert_mask = torch.zeros(
                num_local_experts + 1,
                device=torch.cuda.current_device(),
                dtype=torch.int,
            )
            expert_mask[:-1] = 1
            self.expert_mask_gpu = expert_mask

    def dispatch(
        self,
        hidden_states: torch.Tensor,
        topk_output: TopKOutput,
    ) -> DispatchOutput:
        self.dispatch_a(hidden_states, topk_output)
        if self._deepep_dispatch_hooks is not None:
            self._deepep_dispatch_hooks(self)
        ret = self.dispatch_b()
        return ret

    def dispatch_a(
        self,
        hidden_states: torch.Tensor,
        topk_output: TopKOutput,
    ):
        self._update_stage(_Stage.INITIAL, _Stage.AFTER_DISPATCH_A)
        inner_state = self._get_impl().dispatch_a(
            hidden_states=hidden_states,
            topk_output=topk_output,
        )
        self._dispatch_intermediate_state = inner_state

    def dispatch_b(self):
        self._update_stage(_Stage.AFTER_DISPATCH_A, _Stage.AFTER_DISPATCH_B)
        inner_state = self._dispatch_intermediate_state
        del self._dispatch_intermediate_state
        return self._get_impl().dispatch_b(*inner_state)

    def combine(
        self,
        combine_input: CombineInput,
    ) -> torch.Tensor:
        self.combine_a(combine_input)
        ret = self.combine_b()
        return ret

    def combine_a(
        self,
        combine_input: CombineInput,
    ):
        hidden_states, topk_ids, topk_weights = combine_input
        self._update_stage(_Stage.AFTER_DISPATCH_B, _Stage.AFTER_COMBINE_A)
        inner_state = self._get_impl().combine_a(
            hidden_states=hidden_states,
            topk_ids=topk_ids,
            topk_weights=topk_weights,
        )
        self._combine_intermediate_state = inner_state

    def combine_b(self):
        self._update_stage(_Stage.AFTER_COMBINE_A, _Stage.INITIAL)
        inner_state = self._combine_intermediate_state
        del self._combine_intermediate_state
        return self._get_impl().combine_b(*inner_state)

    def _get_impl(self) -> _DeepEPDispatcherImplBase:
        is_extend_in_batch = get_is_extend_in_batch()
        resolved_deepep_mode = self.deepep_mode.resolve(is_extend_in_batch)
        if resolved_deepep_mode == DeepEPMode.NORMAL:
            return self._normal_dispatcher
        elif resolved_deepep_mode == DeepEPMode.LOW_LATENCY:
            return self._low_latency_dispatcher
        else:
            raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")

    def _update_stage(self, old_stage, new_stage):
        assert self._stage == old_stage
        self._stage = new_stage

    def set_quant_config(self, quant_config: dict):
        super().set_quant_config(quant_config)
        if self.deepep_mode.enable_low_latency():
            self._low_latency_dispatcher.set_quant_config(quant_config)
        if self.deepep_mode.enable_normal():
            self._normal_dispatcher.set_quant_config(quant_config)

    def set_overlap_args(
        self, combine_overlap_args: CombineOverlapArgs, meta_overlap_args: dict
    ):
        super().set_overlap_args(combine_overlap_args, meta_overlap_args)
        if self.deepep_mode.enable_low_latency():
            self._low_latency_dispatcher.set_overlap_args(
                combine_overlap_args, meta_overlap_args
            )
        if self.deepep_mode.enable_normal():
            self._normal_dispatcher.set_overlap_args(
                combine_overlap_args, meta_overlap_args
            )

    def clear_overlap_args(self):
        super().clear_overlap_args()
        if self.deepep_mode.enable_low_latency():
            self._low_latency_dispatcher.clear_overlap_args()
        if self.deepep_mode.enable_normal():
            self._normal_dispatcher.clear_overlap_args()

    def register_deepep_dispatch_hook(self, hook):
        return self._deepep_dispatch_hooks.register_hook(hook)
