# Adapted from https://github.com/vllm-project/vllm/blob/a6221a144af772fd1a68fe7e627935dc53e81738/vllm/model_executor/layers/fused_moe/layer.py

import importlib.util
import logging
from enum import Enum
from functools import lru_cache
from typing import List, Optional, Tuple

import torch
from packaging import version as pkg_version

from sglang.srt.distributed import (
    get_moe_expert_parallel_rank,
    get_moe_expert_parallel_world_size,
    get_moe_tensor_parallel_rank,
    get_moe_tensor_parallel_world_size,
    get_tp_group,
    tensor_model_parallel_all_reduce,
)
from sglang.srt.distributed.device_communicators.pynccl_allocator import (
    use_symmetric_memory,
)
from sglang.srt.eplb.expert_location import get_global_expert_location_metadata
from sglang.srt.layers.moe.topk import StandardTopKOutput
from sglang.srt.layers.quantization.base_config import (
    QuantizationConfig,
    QuantizeMethodBase,
)
from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_loader.weight_utils import narrow_padded_param_and_loaded_weight
from sglang.srt.utils import cpu_has_amx_support, get_bool_env_var, is_cpu, is_hip

_is_hip = is_hip()
_is_cpu_amx_available = cpu_has_amx_support()
_is_cpu = is_cpu()

logger = logging.getLogger(__name__)


@lru_cache(maxsize=1)
def should_use_flashinfer_trtllm_moe():
    return global_server_args_dict["enable_flashinfer_trtllm_moe"] and (
        not importlib.util.find_spec("flashinfer")
        or pkg_version.parse(__import__("flashinfer").__version__)
        >= pkg_version.parse("0.2.9rc1")
    )


class FusedMoeWeightScaleSupported(Enum):
    TENSOR = "tensor"
    CHANNEL = "channel"
    GROUP = "group"
    BLOCK = "block"


class FusedMoE(torch.nn.Module):
    """FusedMoE layer for MoE models.

    This layer contains both MergedColumnParallel weights (gate_up_proj /
    w13) and RowParallelLinear weights (down_proj/ w2).

    Note: Mixtral uses w1, w2, and w3 for gate, up, and down_proj. We
    copy that naming convention here and handle any remapping in the
    load_weights function in each model implementation.

    Args:
        num_experts: Number of experts in the model
        top_k: Number of experts selected for each token
        hidden_size: Input hidden state size of the transformer
        intermediate_size: Intermediate size of the experts
        params_dtype: Data type for the parameters.
        reduce_results: Whether to all all_reduce on the output of the layer
        renomalize: Whether to renormalize the logits in the fused_moe kernel
        quant_config: Quantization configure.
        inplace: suggestion to compute inplace (modify input activation).
    """

    def __init__(
        self,
        num_experts: int,
        hidden_size: int,
        intermediate_size: int,
        layer_id: int,
        top_k: Optional[int] = None,
        num_fused_shared_experts: int = 0,
        params_dtype: Optional[torch.dtype] = None,
        reduce_results: bool = False,
        quant_config: Optional[QuantizationConfig] = None,
        tp_size: Optional[int] = None,
        prefix: str = "",
        activation: str = "silu",
        apply_router_weight_on_input: bool = False,
        use_presharded_weights: bool = False,
        inplace: bool = True,
        no_combine: bool = False,
        routed_scaling_factor: Optional[float] = None,
        enable_flashinfer_cutlass_moe: Optional[bool] = False,
    ):
        super().__init__()

        if params_dtype is None:
            params_dtype = torch.get_default_dtype()

        self.layer_id = layer_id
        self.top_k = top_k
        self.hidden_size = hidden_size
        self.num_experts = num_experts
        self.num_fused_shared_experts = num_fused_shared_experts
        self.expert_map_cpu = None
        self.expert_map_gpu = None

        if enable_flashinfer_cutlass_moe and quant_config is None:
            logger.warning("Disable flashinfer MoE when quantization config is None.")
            enable_flashinfer_cutlass_moe = False

        self.enable_flashinfer_cutlass_moe = enable_flashinfer_cutlass_moe
        self.moe_ep_size = get_moe_expert_parallel_world_size()
        self.moe_ep_rank = get_moe_expert_parallel_rank()
        self.moe_tp_size = get_moe_tensor_parallel_world_size()
        self.moe_tp_rank = get_moe_tensor_parallel_rank()
        assert num_experts % self.moe_ep_size == 0
        self.num_local_experts = num_experts // self.moe_ep_size
        if self.moe_ep_size > 1:
            # TODO(ch-wan): support shared experts fusion
            # Create a tensor of size num_experts filled with -1
            self.expert_map_cpu = torch.full(
                (self.num_experts,), -1, dtype=torch.int32, device="cpu"
            )
            self.expert_map_cpu = torch.full(
                (self.num_experts,), -1, dtype=torch.int32, device="cpu"
            )
            # Create a expert map for the local experts
            self.expert_map_cpu[
                self.moe_ep_rank
                * self.num_local_experts : (self.moe_ep_rank + 1)
                * self.num_local_experts
            ] = torch.arange(0, self.num_local_experts, dtype=torch.int32, device="cpu")

        self.routed_scaling_factor = routed_scaling_factor
        assert intermediate_size % self.moe_tp_size == 0
        self.intermediate_size_per_partition = intermediate_size // self.moe_tp_size
        self.reduce_results = reduce_results
        self.activation = activation
        self.apply_router_weight_on_input = apply_router_weight_on_input
        self.use_presharded_weights = use_presharded_weights
        self.inplace = inplace
        self.no_combine = no_combine

        self.use_triton_kernels = (
            not _is_cpu and global_server_args_dict["enable_triton_kernel_moe"]
        )

        if quant_config is None:
            self.quant_method: Optional[QuantizeMethodBase] = UnquantizedFusedMoEMethod(
                self.use_triton_kernels
            )
        else:
            self.quant_method = quant_config.get_quant_method(self, prefix)
            if self.quant_method.__class__.__name__ == "ModelOptNvFp4FusedMoEMethod":
                self.quant_method.enable_flashinfer_cutlass_moe = (
                    self.enable_flashinfer_cutlass_moe
                )
        assert self.quant_method is not None

        self.quant_config = quant_config
        self.quant_method.create_weights(
            layer=self,
            num_experts=self.num_local_experts,
            hidden_size=hidden_size,
            # FIXME: figure out which intermediate_size to use
            intermediate_size=self.intermediate_size_per_partition,
            intermediate_size_per_partition=self.intermediate_size_per_partition,
            params_dtype=params_dtype,
            weight_loader=self.weight_loader,
        )

    def _load_per_tensor_weight_scale(
        self,
        shard_id: str,
        param: torch.nn.Parameter,
        loaded_weight: torch.Tensor,
        expert_id: int,
    ):
        param_data = param.data
        # for per tensor weight quantization
        if shard_id in ("w1", "w3"):
            # We have to keep the weight scales of w1 and w3 because
            # we need to re-quantize w1/w3 weights after weight loading.
            idx = 0 if shard_id == "w1" else 1
            param_data[expert_id][idx] = loaded_weight
        # If we are in the row parallel case (down_proj)
        elif shard_id == "w2":
            param_data[expert_id] = loaded_weight

    def _load_model_weight_or_group_weight_scale(
        self,
        shard_dim: int,
        expert_data: torch.Tensor,
        shard_id: str,
        loaded_weight: torch.Tensor,
        tp_rank: int,
    ):
        # Load grouped weight scales for group quantization
        # or model weights
        if shard_id == "w2":
            self._load_w2(
                shard_id=shard_id,
                shard_dim=shard_dim,
                loaded_weight=loaded_weight,
                expert_data=expert_data,
                tp_rank=tp_rank,
            )
        elif shard_id in ("w1", "w3"):
            self._load_w13(
                shard_id=shard_id,
                shard_dim=shard_dim,
                loaded_weight=loaded_weight,
                expert_data=expert_data,
                tp_rank=tp_rank,
            )

    def _load_per_channel_weight_scale(
        self,
        expert_data: torch.Tensor,
        shard_dim: int,
        shard_id: str,
        loaded_weight: torch.Tensor,
        tp_rank: int,
    ):
        # for per channel weight quantization
        if shard_id == "w2":
            expert_data.copy_(loaded_weight)
        elif shard_id in ("w1", "w3"):
            self._load_w13(
                shard_id=shard_id,
                shard_dim=shard_dim,
                loaded_weight=loaded_weight,
                expert_data=expert_data,
                tp_rank=tp_rank,
            )

    def _load_w13(
        self,
        expert_data: torch.Tensor,
        shard_dim: int,
        shard_id: str,
        loaded_weight: torch.Tensor,
        tp_rank: int,
    ):

        # Index the loaded weight for tp sharding.
        # gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
        shard_size = expert_data.shape[shard_dim] // 2

        # Narrow parameter and load.
        # w1, gate_proj: Load into first logical weight of w13.
        # w3, up_proj: Load into second logical weight of w13.
        # trtllm cutlass kernel assumes differently
        assert shard_id in ("w1", "w3")
        switch_w13 = getattr(self.quant_method, "load_up_proj_weight_first", False)
        if (switch_w13 and shard_id == "w1") or (not switch_w13 and shard_id == "w3"):
            start = shard_size
        else:
            start = 0

        if _is_cpu:
            expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
                expert_data,
                loaded_weight,
                start,
                shard_size * tp_rank,
                shard_dim,
                shard_size,
                not self.use_presharded_weights,
            )
        else:
            if not self.use_presharded_weights:
                if self.use_triton_kernels:
                    loaded_weight = loaded_weight.transpose(-2, -1)
                loaded_weight = loaded_weight.narrow(
                    shard_dim, shard_size * tp_rank, shard_size
                )

            expert_data = expert_data.narrow(shard_dim, start, shard_size)
        expert_data.copy_(loaded_weight)

    def _load_w2(
        self,
        expert_data: torch.Tensor,
        shard_dim: int,
        shard_id: str,
        loaded_weight: torch.Tensor,
        tp_rank: int,
    ):
        """Load w2 weights for down projection.

        Args:
            expert_data: The expert data tensor to load into
            shard_dim: The dimension to shard along
            shard_id: The shard ID (must be "w2")
            loaded_weight: The weight tensor to load from
            tp_rank: The tensor parallel rank
        """
        if not isinstance(expert_data, torch.Tensor) or not isinstance(
            loaded_weight, torch.Tensor
        ):
            raise ValueError("expert_data and loaded_weight must be torch.Tensor")

        if (
            self.quant_config is not None
            and "modelopt" in self.quant_config.get_name()
            and (expert_data.dim() != 2 or loaded_weight.dim() != 2)
        ):
            raise ValueError(
                f"Expected 2D tensors, got expert_data shape {expert_data.shape} and loaded_weight shape {loaded_weight.shape}"
            )

        if shard_id != "w2":
            raise ValueError(f"shard_id must be 'w2', got {shard_id}")

        # Index the loaded weight for tp sharding.
        # down_proj: "RowParallel" so tp sharding on input_dim
        # Narrow parameter and load.
        shard_size = expert_data.shape[shard_dim]

        if _is_cpu:
            expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
                expert_data,
                loaded_weight,
                0,  # param_data_start
                shard_size * tp_rank,
                shard_dim,
                shard_size,
                not self.use_presharded_weights,
            )
        else:
            if not self.use_presharded_weights:
                if self.use_triton_kernels:
                    loaded_weight = loaded_weight.transpose(-2, -1)
                if shard_size * tp_rank + shard_size > loaded_weight.shape[shard_dim]:
                    raise ValueError(
                        f"Shard size {shard_size} at rank {tp_rank} exceeds loaded_weight dimension {loaded_weight.shape[shard_dim]}"
                    )
                loaded_weight = loaded_weight.narrow(
                    shard_dim, shard_size * tp_rank, shard_size
                )

        # w2, down_proj: Load into only logical weight of w2.
        expert_data.copy_(loaded_weight)

    def _load_single_value(
        self, param: torch.nn.Parameter, loaded_weight: torch.Tensor, expert_id: int
    ):
        param_data = param.data

        # Input scales can be loaded directly and should be equal.
        param_data[expert_id] = loaded_weight

    def _load_g_idx(
        self,
        shard_id: str,
        expert_data: torch.Tensor,
        shard_dim: int,
        loaded_weight: torch.Tensor,
        tp_rank: int,
    ):

        if shard_id == "w2":
            self._load_w2(
                shard_id=shard_id,
                shard_dim=shard_dim,
                loaded_weight=loaded_weight,
                expert_data=expert_data,
                tp_rank=tp_rank,
            )
        else:
            assert shard_id in ("w1", "w3")
            expert_data.copy_(loaded_weight)

    def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
        if self.expert_map_cpu is None:
            return expert_id
        return self.expert_map_cpu[expert_id].item()

    def weight_loader(
        self,
        param: torch.nn.Parameter,
        loaded_weight: torch.Tensor,
        weight_name: str,
        shard_id: str,
        expert_id: int,
    ) -> None:

        global_expert_location_metadata = get_global_expert_location_metadata()
        if global_expert_location_metadata is None:
            self._weight_loader_impl(
                param=param,
                loaded_weight=loaded_weight,
                weight_name=weight_name,
                shard_id=shard_id,
                expert_id=expert_id,
            )
            return

        if expert_id >= self.num_experts - self.num_fused_shared_experts:
            # This is a shared expert.
            physical_expert_ids = [expert_id]
        else:
            physical_expert_ids = (
                global_expert_location_metadata.logical_to_all_physical(
                    self.layer_id, expert_id
                )
            )

        for physical_expert_id in physical_expert_ids:
            self._weight_loader_physical(
                param=param,
                loaded_weight=loaded_weight,
                weight_name=weight_name,
                shard_id=shard_id,
                expert_id=physical_expert_id,
            )

    def _weight_loader_physical(
        self,
        param: torch.nn.Parameter,
        loaded_weight: torch.Tensor,
        weight_name: str,
        shard_id: str,
        expert_id: int,
    ) -> None:
        expert_id = self._map_global_expert_id_to_local_expert_id(expert_id)
        if expert_id == -1:
            return
        self._weight_loader_impl(
            param=param,
            loaded_weight=loaded_weight,
            weight_name=weight_name,
            shard_id=shard_id,
            expert_id=expert_id,
        )

    def _weight_loader_impl(
        self,
        param: torch.nn.Parameter,
        loaded_weight: torch.Tensor,
        weight_name: str,
        shard_id: str,
        expert_id: int,
    ) -> None:

        tp_rank = self.moe_tp_rank

        # compressed-tensors checkpoints with packed weights are stored flipped
        # TODO (mgoin): check self.quant_method.quant_config.quant_format
        # against known CompressionFormat enum values that have this quality
        loaded_weight = (
            loaded_weight.t().contiguous()
            if (
                self.quant_method.__class__.__name__
                == "CompressedTensorsWNA16MoEMethod"
            )
            else loaded_weight
        )

        if shard_id not in ("w1", "w2", "w3"):
            raise ValueError(
                f"shard_id must be ['w1','w2','w3'] but " f"got {shard_id}."
            )

        # Flashinfer assumes w31 format for w13_weight. Same for the scales.
        if should_use_flashinfer_trtllm_moe():
            shard_id = {"w1": "w3", "w3": "w1", "w2": "w2"}[shard_id]

        WEIGHT_SCALE_SUPPORTED = [e.value for e in FusedMoeWeightScaleSupported]
        # Fetch the dim to shard the parameter/loaded weight
        # based on the shard id. This will be whatever
        # dimension intermediate_size is used.
        SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}

        expert_data = param.data[expert_id]

        # is_transposed: if the dim to shard the weight
        # should be flipped. Required by GPTQ, compressed-tensors
        # should be whatever dimension intermediate_size is
        is_transposed = getattr(param, "is_transposed", False)
        shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
        if self.use_triton_kernels:
            is_transposed = True
        if is_transposed:
            shard_dim = int(not shard_dim)

        # Case input scale: input_scale loading is only supported for fp8
        if "input_scale" in weight_name:
            # INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust input_scale for e4m3fnuz (AMD)
            if _is_hip and get_bool_env_var("SGLANG_INT4_WEIGHT"):
                loaded_weight = loaded_weight * 2.0

            # this is needed for compressed-tensors only
            loaded_weight = loaded_weight.to(param.data.device)

            if (
                "compressed" in self.quant_method.__class__.__name__.lower()
                and param.data[expert_id] != 1
                and (param.data[expert_id] - loaded_weight).abs() > 1e-5
            ):
                raise ValueError(
                    "input_scales of w1 and w3 of a layer "
                    f"must be equal. But got {param.data[expert_id]} "
                    f"vs. {loaded_weight}"
                )

            self._load_single_value(
                param=param, loaded_weight=loaded_weight, expert_id=expert_id
            )
            return

        # Case g_idx
        if "g_idx" in weight_name:
            self._load_g_idx(
                shard_dim=0,
                shard_id=shard_id,
                loaded_weight=loaded_weight,
                expert_data=expert_data,
                tp_rank=tp_rank,
            )
            return

        if "ModelOpt" in self.quant_method.__class__.__name__:
            # Determine per-tensor weight scale patterns based on variant
            is_fp4_variant = (
                "ModelOptNvFp4FusedMoEMethod" in self.quant_method.__class__.__name__
            )

            # FP4 uses "weight_scale_2" for per-tensor, FP8 uses "weight_scale" for per-tensor
            per_tensor_conditions = (
                "weight_scale_2" in weight_name
                if is_fp4_variant
                else "weight_scale" in weight_name
            ) or "input_scale" in weight_name

            if per_tensor_conditions:
                self._load_per_tensor_weight_scale(
                    shard_id=shard_id,
                    param=param,
                    loaded_weight=loaded_weight,
                    expert_id=expert_id,
                )
            elif "weight" in weight_name:
                self._load_model_weight_or_group_weight_scale(
                    shard_id=shard_id,
                    shard_dim=shard_dim,
                    loaded_weight=loaded_weight,
                    expert_data=expert_data,
                    tp_rank=tp_rank,
                )
            return

        # Case weight scales and zero_points
        if "scale" in weight_name or "zero" in weight_name or "offset" in weight_name:
            # load the weight scales and zp based on the quantization scheme
            # supported weight scales/zp can be found in
            # FusedMoeWeightScaleSupported
            # TODO @dsikka: once hardened, refactor to use vLLM Parameters
            # specific to each case
            quant_method = getattr(param, "quant_method", None)
            if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
                # INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust INT4 column-wise scaling number to e4m3fnuz (AMD)
                if _is_hip and get_bool_env_var("SGLANG_INT4_WEIGHT"):
                    loaded_weight = loaded_weight * 0.5

                self._load_per_channel_weight_scale(
                    shard_id=shard_id,
                    shard_dim=shard_dim,
                    loaded_weight=loaded_weight,
                    expert_data=expert_data,
                    tp_rank=tp_rank,
                )
            elif quant_method in [
                FusedMoeWeightScaleSupported.GROUP.value,
                FusedMoeWeightScaleSupported.BLOCK.value,
            ]:
                self._load_model_weight_or_group_weight_scale(
                    shard_id=shard_id,
                    shard_dim=shard_dim,
                    loaded_weight=loaded_weight,
                    expert_data=expert_data,
                    tp_rank=tp_rank,
                )
            elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
                # INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust FP8 per-tensor scaling number for e4m3fnuz (AMD)
                if _is_hip and get_bool_env_var("SGLANG_INT4_WEIGHT"):
                    loaded_weight = loaded_weight * 2.0

                self._load_per_tensor_weight_scale(
                    shard_id=shard_id,
                    param=param,
                    loaded_weight=loaded_weight,
                    expert_id=expert_id,
                )
            else:
                raise ValueError(
                    f"quant method must be one of {WEIGHT_SCALE_SUPPORTED}"
                )
            return

        # Case weight_shape
        if "weight_shape" in weight_name:
            # only required by compressed-tensors
            self._load_single_value(
                param=param, loaded_weight=loaded_weight, expert_id=expert_id
            )
            return

        # Case model weights
        if "weight" in weight_name:
            self._load_model_weight_or_group_weight_scale(
                shard_id=shard_id,
                shard_dim=shard_dim,
                loaded_weight=loaded_weight,
                expert_data=expert_data,
                tp_rank=tp_rank,
            )
            return

    def forward(self, hidden_states: torch.Tensor, topk_output: StandardTopKOutput):
        assert self.quant_method is not None

        if self.moe_ep_size > 1 and not self.enable_flashinfer_cutlass_moe:
            if self.expert_map_cpu is not None and self.expert_map_gpu is None:
                # If we are in EP mode, we need to move the expert map to GPU.
                self.expert_map_gpu = self.expert_map_cpu.to(device="cuda")

        if self.expert_map_gpu is not None:
            topk_output = topk_output._replace(
                topk_ids=self.expert_map_gpu[topk_output.topk_ids]
            )

        # Matrix multiply.
        with use_symmetric_memory(get_tp_group()) as sm:
            final_hidden_states = self.quant_method.apply(
                layer=self,
                x=hidden_states,
                topk_output=topk_output,
                activation=self.activation,
                apply_router_weight_on_input=self.apply_router_weight_on_input,
                routed_scaling_factor=self.routed_scaling_factor,
                **(
                    dict(
                        tp_rank=self.moe_tp_rank,
                        tp_size=self.moe_tp_size,
                        ep_rank=self.moe_ep_rank,
                        ep_size=self.moe_ep_size,
                    )
                    if self.quant_method.__class__.__name__
                    == "ModelOptNvFp4FusedMoEMethod"
                    else {}
                ),
            )
            sm.tag(final_hidden_states)

        if self.reduce_results and (self.moe_tp_size > 1 or self.moe_ep_size > 1):
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)

        return final_hidden_states

    @classmethod
    def make_expert_params_mapping(
        cls,
        ckpt_gate_proj_name: str,
        ckpt_down_proj_name: str,
        ckpt_up_proj_name: str,
        num_experts: int,
    ) -> List[Tuple[str, str, int, str]]:

        return [
            # (param_name, weight_name, expert_id, shard_id)
            (
                (
                    "experts.w13_"
                    if weight_name in [ckpt_gate_proj_name, ckpt_up_proj_name]
                    else "experts.w2_"
                ),
                f"experts.{expert_id}.{weight_name}.",
                expert_id,
                shard_id,
            )
            for expert_id in range(num_experts)
            for shard_id, weight_name in [
                ("w1", ckpt_gate_proj_name),
                ("w2", ckpt_down_proj_name),
                ("w3", ckpt_up_proj_name),
            ]
        ]

    @classmethod
    def make_expert_input_scale_params_mapping(
        cls,
        num_experts: int,
    ) -> List[Tuple[str, str, int, str]]:
        # (param_name, weight_name, expert_id, shard_id)
        return [
            (
                "experts.w13_" if shard_id in ["w1", "w3"] else "experts.w2_",
                f"experts.{expert_id}.{shard_id}.",
                expert_id,
                shard_id,
            )
            for expert_id in range(num_experts)
            for shard_id in ["w1", "w2", "w3"]
        ]


class FlashInferFusedMoE(FusedMoE):
    def __init__(self, *args, **kwargs):
        renormalize = kwargs.pop("renormalize", True)
        num_fused_shared_experts = kwargs.pop("num_fused_shared_experts", 0)
        use_grouped_topk = kwargs.pop("use_grouped_topk", False)
        num_expert_group = kwargs.pop("num_expert_group", None)
        topk_group = kwargs.pop("topk_group", None)
        correction_bias = kwargs.pop("correction_bias", None)
        super().__init__(*args, **kwargs)
        self.renormalize = renormalize
        self.num_fused_shared_experts = num_fused_shared_experts
        self.use_grouped_topk = use_grouped_topk
        if self.use_grouped_topk:
            assert num_expert_group is not None and topk_group is not None
        self.num_expert_group = num_expert_group
        self.topk_group = topk_group
        self.correction_bias = correction_bias

    def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
        assert self.quant_method is not None
        assert (
            self.renormalize
        ), "Renormalize is required for flashinfer blockscale fp8 moe"
        assert (
            self.num_fused_shared_experts == 0
        ), "Fused shared experts are not supported for flashinfer blockscale fp8 moe"
        # Matrix multiply.
        final_hidden_states = self.quant_method.apply_with_router_logits(
            layer=self,
            x=hidden_states,
            router_logits=router_logits,
            activation=self.activation,
            routed_scaling_factor=self.routed_scaling_factor,
        )

        if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)

        return final_hidden_states
