from __future__ import annotations

import logging
from typing import TYPE_CHECKING, Optional

import torch

from sglang.srt.distributed.parallel_state import get_moe_expert_parallel_world_size
from sglang.srt.layers.moe.ep_moe.kernels import (
    ep_gather,
    ep_scatter,
    moe_ep_deepgemm_preprocess,
    post_reorder_triton_kernel,
    silu_and_mul_masked_post_quant_fwd,
    tma_align_input_scale,
)
from sglang.srt.layers.moe.fused_moe_triton.layer import FlashInferFusedMoE, FusedMoE
from sglang.srt.layers.moe.topk import TopKOutput
from sglang.srt.layers.moe.utils import DeepEPMode, should_use_flashinfer_trtllm_moe
from sglang.srt.layers.quantization import deep_gemm_wrapper
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.fp8 import (
    Fp8Config,
    Fp8MoEMethod,
    get_tile_tokens_dim,
)
from sglang.srt.layers.quantization.fp8_kernel import (
    is_fp8_fnuz,
    sglang_per_token_group_quant_fp8,
)
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.utils import ceil_div, dispose_tensor, get_bool_env_var, is_hip, is_npu

if TYPE_CHECKING:
    from sglang.srt.layers.moe.token_dispatcher import (
        DeepEPLLOutput,
        DeepEPNormalOutput,
        DispatchOutput,
    )

_is_hip = is_hip()
_is_npu = is_npu()
_is_fp8_fnuz = is_fp8_fnuz()
_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip

if not (_is_npu or _is_hip):
    from sgl_kernel import silu_and_mul

if _use_aiter:
    from aiter import ActivationType, QuantType
    from aiter.fused_moe import fused_moe
    from aiter.ops.shuffle import shuffle_weight

logger = logging.getLogger(__name__)


# TODO(kaixih@nvidia): ideally we should merge this logic into
# `fill_gateup_input_triton_kernel` to directly generate e8m0 scale.
@torch.compile
def _cast_to_e8m0_with_rounding_up(x: torch.Tensor) -> torch.Tensor:
    temp = x.to(torch.float32).view(torch.int32)
    exp = torch.bitwise_right_shift(temp, 23)
    mant = torch.bitwise_and(temp, 0x7FFFFF)
    is_ru = torch.logical_and(
        torch.logical_and((mant > 0), (exp != 0xFE)),
        ~torch.logical_and((exp == 0), (mant <= 0x400000)),
    )
    exp = torch.where(is_ru, exp + 1, exp)
    new_x = exp.to(torch.uint8).view(torch.int)
    return new_x.transpose(1, 2).contiguous().transpose(1, 2)


class EPMoE(FusedMoE):
    """
    MoE Expert Parallel Impl


    """

    def __init__(
        self,
        num_experts: int,
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        layer_id: int,
        num_fused_shared_experts: int = 0,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        tp_size: Optional[int] = None,
        prefix: str = "",
        activation: str = "silu",
        routed_scaling_factor: Optional[float] = None,
        activation_alpha: Optional[float] = None,
        swiglu_limit: Optional[float] = None,
        with_bias: bool = False,
    ):
        super().__init__(
            num_experts=num_experts,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            num_fused_shared_experts=num_fused_shared_experts,
            layer_id=layer_id,
            top_k=top_k,
            params_dtype=params_dtype,
            quant_config=quant_config,
            tp_size=tp_size,
            prefix=prefix,
            activation=activation,
            # apply_router_weight_on_input=apply_router_weight_on_input,
            routed_scaling_factor=routed_scaling_factor,
            activation_alpha=activation_alpha,
            swiglu_limit=swiglu_limit,
            with_bias=with_bias,
        )

        self.start_expert_id = self.moe_ep_rank * self.num_local_experts
        self.end_expert_id = self.start_expert_id + self.num_local_experts - 1

        self.intermediate_size = intermediate_size

        if isinstance(quant_config, Fp8Config):
            self.use_block_quant = getattr(self.quant_method, "block_quant", False)
            self.block_shape = (
                self.quant_method.quant_config.weight_block_size
                if self.use_block_quant
                else None
            )
            self.use_fp8_w8a8 = True
            self.fp8_dtype = torch.float8_e4m3fn
            self.activation_scheme = quant_config.activation_scheme
        else:
            self.use_fp8_w8a8 = False
            self.use_block_quant = False
            self.block_shape = None
            self.activation_scheme = None

    def forward(self, hidden_states: torch.Tensor, topk_output: TopKOutput):
        if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8_w8a8:
            return self.forward_deepgemm(hidden_states, topk_output)
        else:
            return super().forward(hidden_states, topk_output)

    def forward_deepgemm(
        self,
        hidden_states: torch.Tensor,
        topk_output: TopKOutput,
    ):

        self.w13_weight_fp8 = (
            self.w13_weight,
            (
                self.w13_weight_scale_inv
                if self.use_block_quant
                else self.w13_weight_scale
            ),
        )
        self.w2_weight_fp8 = (
            self.w2_weight,
            self.w2_weight_scale_inv if self.use_block_quant else self.w2_weight_scale,
        )

        assert self.quant_method is not None
        assert self.activation == "silu"
        hidden_states_shape = hidden_states.shape
        hidden_states_dtype = hidden_states.dtype
        hidden_states_device = hidden_states.device

        topk_weights, topk_ids, _ = topk_output

        if not self.use_block_quant:
            # Convert per-tensor quant to per-block quant by repeating scales for forward_deepgemm
            scale_block_size = 128
            w13_weight_scale_n = 2 * (
                (self.intermediate_size + scale_block_size - 1) // scale_block_size
            )
            w13_weight_scale_k = (
                hidden_states_shape[-1] + scale_block_size - 1
            ) // scale_block_size
            w13_weight_scale = (
                self.w13_weight_scale.unsqueeze(1)
                .repeat_interleave(w13_weight_scale_n, dim=1)
                .unsqueeze(2)
                .repeat_interleave(w13_weight_scale_k, dim=2)
            )
            self.w13_weight_fp8 = (
                self.w13_weight,
                w13_weight_scale,
            )
            w2_weight_scale_n = (
                hidden_states_shape[-1] + scale_block_size - 1
            ) // scale_block_size
            w2_weight_scale_k = (
                self.intermediate_size + scale_block_size - 1
            ) // scale_block_size
            w2_weight_scale = (
                self.w2_weight_scale.unsqueeze(1)
                .repeat_interleave(w2_weight_scale_n, dim=1)
                .unsqueeze(2)
                .repeat_interleave(w2_weight_scale_k, dim=2)
            )
            self.w2_weight_fp8 = (
                self.w2_weight,
                w2_weight_scale,
            )

        # PreReorder
        m_max, masked_m, expected_m, src2dst, gateup_input, gateup_input_scale = (
            moe_ep_deepgemm_preprocess(
                topk_ids,
                self.num_experts,
                hidden_states,
                self.top_k,
                self.start_expert_id,
                self.end_expert_id,
                self.block_shape,
            )
        )

        dispose_tensor(hidden_states)

        if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
            b, s_mn, s_k = gateup_input_scale.shape
            assert (
                s_mn % 4 == 0 and s_k % 4 == 0
            ), f"scales must be aligned to 4, but got ({b}, {s_mn}, {s_k})"

        # GroupGemm-0
        gateup_input_fp8 = (
            gateup_input,
            (
                _cast_to_e8m0_with_rounding_up(gateup_input_scale)
                if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
                else deep_gemm_wrapper.get_col_major_tma_aligned_tensor(
                    gateup_input_scale
                )
            ),
        )
        num_groups, m, k = gateup_input_fp8[0].size()
        n = self.w13_weight.size(1)
        gateup_output = torch.empty(
            (num_groups, m, n), device=hidden_states_device, dtype=torch.bfloat16
        )
        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_masked(
            gateup_input_fp8,
            self.w13_weight_fp8,
            gateup_output,
            masked_m,
            expected_m,
            recipe=(1, 128, 128) if deep_gemm_wrapper.DEEPGEMM_BLACKWELL else None,
        )
        del gateup_input
        del gateup_input_fp8

        # Act
        down_input = torch.empty(
            (
                gateup_output.shape[0],
                gateup_output.shape[1],
                gateup_output.shape[2] // 2,
            ),
            device=hidden_states_device,
            dtype=self.fp8_dtype,
        )
        scale_block_size = 128
        down_input_scale = torch.empty(
            (
                gateup_output.shape[0],
                gateup_output.shape[1],
                gateup_output.shape[2] // 2 // scale_block_size,
            ),
            device=hidden_states_device,
            dtype=torch.float32,
        )
        silu_and_mul_masked_post_quant_fwd(
            gateup_output,
            down_input,
            down_input_scale,
            scale_block_size,
            masked_m,
            scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
        )
        del gateup_output

        # GroupGemm-1
        n = self.w2_weight.size(1)
        down_input_fp8 = (
            down_input,
            (
                down_input_scale
                if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
                else deep_gemm_wrapper.get_col_major_tma_aligned_tensor(
                    down_input_scale
                )
            ),
        )
        down_output = torch.empty(
            (num_groups, m, n), device=hidden_states_device, dtype=torch.bfloat16
        )
        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_masked(
            down_input_fp8,
            self.w2_weight_fp8,
            down_output,
            masked_m,
            expected_m,
            recipe=(1, 128, 128) if deep_gemm_wrapper.DEEPGEMM_BLACKWELL else None,
        )
        del down_input
        del down_input_fp8

        # PostReorder
        output = torch.empty(
            hidden_states_shape, dtype=hidden_states_dtype, device=hidden_states_device
        )
        post_reorder_triton_kernel[(hidden_states_shape[0],)](
            down_output,
            output,
            src2dst,
            topk_ids,
            topk_weights,
            self.start_expert_id,
            self.end_expert_id,
            self.top_k,
            hidden_states_shape[1],
            m_max * self.start_expert_id,
            BLOCK_SIZE=512,
        )
        if self.routed_scaling_factor is not None:
            output *= self.routed_scaling_factor
        return output


class DeepEPMoE(EPMoE):
    """
    MoE Expert Parallel Impl based on DeepEP (https://github.com/deepseek-ai/DeepEP/tree/main)
    """

    _has_printed = False

    def __init__(
        self,
        num_experts: int,
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        layer_id: int,
        num_fused_shared_experts: int = 0,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        tp_size: Optional[int] = None,
        prefix: str = "",
        activation: str = "silu",
        routed_scaling_factor: Optional[float] = None,
        deepep_mode: DeepEPMode = DeepEPMode.AUTO,
    ):
        super().__init__(
            num_experts=num_experts,
            top_k=top_k,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            layer_id=layer_id,
            num_fused_shared_experts=num_fused_shared_experts,
            params_dtype=params_dtype,
            quant_config=quant_config,
            tp_size=tp_size,
            prefix=prefix,
            activation=activation,
            routed_scaling_factor=routed_scaling_factor,
        )
        self.deepep_mode = deepep_mode

        # TODO: move to the beginning of the file
        from sglang.srt.distributed.parallel_state import get_tp_group
        from sglang.srt.two_batch_overlap import MaybeTboDeepEPDispatcher

        self.deepep_dispatcher = MaybeTboDeepEPDispatcher(
            group=get_tp_group().device_group,
            router_topk=self.top_k,
            permute_fusion=True,
            num_experts=self.num_experts,
            num_local_experts=self.num_local_experts,
            hidden_size=hidden_size,
            params_dtype=params_dtype,
            deepep_mode=deepep_mode,
            async_finish=True,  # TODO
            return_recv_hook=True,
        )

        if self.deepep_mode.enable_low_latency():
            assert (
                deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
            ), f"DeepEP {self.deepep_mode} mode requires deep_gemm"
        if _use_aiter:
            # expert_mask is of size (self.num_local_experts + 1),
            # the extra 1 is for invalid rank_id (in original deepep, the invalid rank_id is -1, but aiter does not allow -1, we use a mask to make those ids invalid)
            # for instance, if we have 4 experts on this rank, we would have a expert_mask like:
            #     self.expert_mask = [1, 1, 1, 1, 0]
            # idx from 0-3 is valid and will be processed, while idx == 4 will be masked out
            self.expert_mask = torch.zeros(
                (self.num_local_experts + 1),
                device=torch.cuda.current_device(),
                dtype=torch.int,
            )
            # the last one is invalid rank_id
            self.expert_mask[:-1] = 1
        else:
            self.w13_weight_fp8 = (
                self.w13_weight,
                (
                    self.w13_weight_scale_inv
                    if self.use_block_quant
                    else self.w13_weight_scale
                ),
            )
            self.w2_weight_fp8 = (
                self.w2_weight,
                (
                    self.w2_weight_scale_inv
                    if self.use_block_quant
                    else self.w2_weight_scale
                ),
            )

    def forward(
        self,
        hidden_states: torch.Tensor,
        topk_idx: torch.Tensor,
        topk_weights: torch.Tensor,
        forward_batch: ForwardBatch,
    ):
        dispatch_output = self.dispatch(
            hidden_states, topk_idx, topk_weights, forward_batch
        )
        hidden_states = self.moe_impl(dispatch_output)
        hidden_states = self.combine(
            hidden_states,
            dispatch_output.topk_idx,
            dispatch_output.topk_weights,
            forward_batch,
        )
        return hidden_states

    def dispatch(
        self,
        hidden_states: torch.Tensor,
        topk_idx: torch.Tensor,
        topk_weights: torch.Tensor,
        forward_batch: ForwardBatch,
    ):
        return self.deepep_dispatcher.dispatch(
            hidden_states=hidden_states,
            topk_idx=topk_idx,
            topk_weights=topk_weights,
            forward_batch=forward_batch,
        )

    def moe_impl(self, dispatch_output: DispatchOutput):
        if _use_aiter:
            # in forward_aiter, we skip token permutation and unpermutation, which have been fused inside aiter kernel
            return self.forward_aiter(dispatch_output)
        if dispatch_output.format.is_deepep_normal():
            assert deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8_w8a8
            return self.forward_deepgemm_contiguous(dispatch_output)
        elif dispatch_output.format.is_deepep_ll():
            assert deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8_w8a8
            return self.forward_deepgemm_masked(dispatch_output)
        else:
            raise ValueError(
                f"Dispatch output format {dispatch_output.format} is not supported"
            )

    def combine(
        self,
        hidden_states: torch.Tensor,
        topk_idx: torch.Tensor,
        topk_weights: torch.Tensor,
        forward_batch: ForwardBatch,
    ):
        return self.deepep_dispatcher.combine(
            hidden_states=hidden_states,
            topk_idx=topk_idx,
            topk_weights=topk_weights,
            forward_batch=forward_batch,
        )

    def forward_aiter(
        self,
        dispatch_output: DeepEPNormalOutput,
    ):
        hidden_states, topk_idx, topk_weights = (
            dispatch_output.hidden_states,
            dispatch_output.topk_idx,
            dispatch_output.topk_weights,
        )
        if hidden_states.shape[0] == 0:
            return hidden_states
        # in original deepep, idx == -1 meaning invalid and will not be processed.
        # aiter does not accept -1, we use a expert mask to make these idx invalid
        # (idx == num_local_experts) meaning not used in aiter fused_moe
        topk_idx_copy = topk_idx.to(torch.int32)
        topk_idx_copy[topk_idx_copy == -1] = self.num_local_experts

        return fused_moe(
            hidden_states,
            self.w13_weight,
            self.w2_weight,
            topk_weights,
            topk_idx_copy,
            w1_scale=self.w13_weight_scale_inv,
            w2_scale=self.w2_weight_scale_inv,
            quant_type=QuantType.per_128x128,
            activation=(
                ActivationType.Silu
                if self.activation == "silu"
                else ActivationType.Gelu
            ),
            expert_mask=self.expert_mask,
        )

    def forward_deepgemm_contiguous(
        self,
        dispatch_output: DeepEPNormalOutput,
    ):
        hidden_states_fp8, topk_idx, topk_weights, num_recv_tokens_per_expert = (
            dispatch_output
        )
        hidden_states_fp8, hidden_states_scale = hidden_states_fp8
        assert self.quant_method is not None
        assert self.activation == "silu"
        if num_recv_tokens_per_expert is None:
            return hidden_states_fp8.bfloat16()
        all_tokens = sum(num_recv_tokens_per_expert)
        if all_tokens <= 0:
            return hidden_states_fp8.bfloat16()
        M, K = hidden_states_fp8.size()
        N = self.w13_weight.size(1)
        scale_block_size = 128

        hidden_states_fp8_shape = hidden_states_fp8.shape
        hidden_states_fp8_device = hidden_states_fp8.device
        hidden_states_fp8_dtype = hidden_states_fp8.dtype

        input_tensor = [
            torch.empty(
                (all_tokens, K),
                device=hidden_states_fp8.device,
                dtype=hidden_states_fp8.dtype,
            ),
            (
                # TODO check whether need `zeros`
                torch.zeros(
                    (ceil_div(K // 128, 4), all_tokens),
                    device=hidden_states_fp8.device,
                    dtype=torch.int,
                ).transpose(0, 1)
                if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
                else torch.empty(
                    (all_tokens, K // 128),
                    device=hidden_states_fp8.device,
                    dtype=torch.float32,
                )
            ),
        ]
        m_indices = torch.empty(
            all_tokens, device=hidden_states_fp8.device, dtype=torch.int32
        )
        output_index = torch.empty_like(topk_idx)

        num_recv_tokens_per_expert_gpu = torch.tensor(
            num_recv_tokens_per_expert,
            dtype=torch.int32,
            pin_memory=True,
            device="cpu",
        ).cuda(non_blocking=True)
        expert_start_loc = torch.empty_like(num_recv_tokens_per_expert_gpu)

        ep_scatter(
            hidden_states_fp8,
            hidden_states_scale,
            topk_idx,
            num_recv_tokens_per_expert_gpu,
            expert_start_loc,
            input_tensor[0],
            input_tensor[1],
            m_indices,
            output_index,
            scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
        )
        dispose_tensor(hidden_states_fp8)

        gateup_output = torch.empty(
            (all_tokens, N),
            device=hidden_states_fp8_device,
            dtype=torch.bfloat16,
        )
        if not deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
            input_tensor[1] = tma_align_input_scale(input_tensor[1])
        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig(
            input_tensor, self.w13_weight_fp8, gateup_output, m_indices
        )
        del input_tensor
        down_input = torch.empty(
            (
                all_tokens,
                N // 2,
            ),
            device=gateup_output.device,
            dtype=torch.bfloat16,
        )
        silu_and_mul(gateup_output.view(-1, N), down_input)
        del gateup_output
        down_output = torch.empty(
            (all_tokens, K),
            device=hidden_states_fp8_device,
            dtype=torch.bfloat16,
        )
        down_input_fp8, down_input_scale = sglang_per_token_group_quant_fp8(
            down_input,
            scale_block_size,
            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,
        )
        del down_input
        if not deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
            down_input_scale = tma_align_input_scale(down_input_scale)
        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig(
            (down_input_fp8, down_input_scale),
            self.w2_weight_fp8,
            down_output,
            m_indices,
        )
        del down_input_fp8, down_input_scale

        gather_out = torch.empty(
            hidden_states_fp8_shape,
            device=hidden_states_fp8_device,
            dtype=torch.bfloat16,
        )
        ep_gather(down_output, topk_idx, topk_weights, output_index, gather_out)

        return gather_out

    def forward_deepgemm_masked(
        self,
        dispatch_output: DeepEPLLOutput,
    ):
        hidden_states_fp8, _, _, masked_m, expected_m = dispatch_output
        assert self.quant_method is not None
        assert self.activation == "silu"

        # GroupGemm-0
        num_groups, m, k = hidden_states_fp8[0].size()
        n = self.w13_weight.size(1)
        expected_m = min(expected_m, m)
        gateup_output = torch.empty(
            (num_groups, m, n), device=hidden_states_fp8[0].device, dtype=torch.bfloat16
        )
        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_masked(
            hidden_states_fp8,
            self.w13_weight_fp8,
            gateup_output,
            masked_m,
            expected_m,
            recipe=(1, 128, 128) if deep_gemm_wrapper.DEEPGEMM_BLACKWELL else None,
        )
        dispose_tensor(hidden_states_fp8[0])

        # Act
        down_input = torch.empty(
            (
                gateup_output.shape[0],
                gateup_output.shape[1],
                gateup_output.shape[2] // 2,
            ),
            device=gateup_output.device,
            dtype=self.fp8_dtype,
        )
        scale_block_size = 128
        down_input_scale = torch.empty(
            (
                gateup_output.shape[0],
                gateup_output.shape[1],
                gateup_output.shape[2] // 2 // scale_block_size,
            ),
            device=gateup_output.device,
            dtype=torch.float32,
        )
        silu_and_mul_masked_post_quant_fwd(
            gateup_output,
            down_input,
            down_input_scale,
            scale_block_size,
            masked_m,
            scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
        )
        del gateup_output

        # GroupGemm-1
        n = self.w2_weight.size(1)
        down_input_fp8 = (
            down_input,
            (
                down_input_scale
                if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
                else deep_gemm_wrapper.get_col_major_tma_aligned_tensor(
                    down_input_scale
                )
            ),
        )
        down_output = torch.empty(
            (num_groups, m, n), device=down_input.device, dtype=torch.bfloat16
        )
        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_masked(
            down_input_fp8,
            self.w2_weight_fp8,
            down_output,
            masked_m,
            expected_m,
            recipe=(1, 128, 128) if deep_gemm_wrapper.DEEPGEMM_BLACKWELL else None,
        )

        return down_output


def get_moe_impl_class():
    if global_server_args_dict["moe_a2a_backend"].is_deepep():
        return DeepEPMoE

    # NEW: Direct FP4 detection (bypasses EP requirements)
    # Check for FP4 quantization with TRTLLM flag, regardless of EP
    if global_server_args_dict.get("enable_flashinfer_trtllm_moe", False):
        try:
            # Check the quantization argument directly
            quantization = global_server_args_dict.get("quantization")
            if quantization == "modelopt_fp4":
                from sglang.srt.layers.moe.fused_moe_triton.layer import (
                    FlashInferFP4MoE,
                )

                return FlashInferFP4MoE
        except:
            pass

    if should_use_flashinfer_trtllm_moe():
        return FlashInferFusedMoE
    if global_server_args_dict["enable_flashinfer_cutlass_moe"]:
        return FusedMoE
    if get_moe_expert_parallel_world_size() > 1:
        return EPMoE
    return FusedMoE
