# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import logging
import warnings
from typing import Any, Callable, Dict, List, Optional

import torch

from sglang.srt.layers.linear import LinearBase, set_weight_attrs
from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
from sglang.srt.layers.quantization.base_config import (
    FusedMoEMethodBase,
    LinearMethodBase,
    QuantizationConfig,
    QuantizeMethodBase,
)
from sglang.srt.layers.quantization.marlin_utils import (
    apply_awq_marlin_linear,
    awq_to_marlin_zero_points,
    check_marlin_supported,
    check_marlin_supports_layer,
    check_moe_marlin_supports_layer,
    marlin_make_empty_g_idx,
    marlin_make_workspace,
    marlin_moe_permute_scales,
    marlin_permute_scales,
    moe_awq_to_marlin_zero_points,
    verify_marlin_supported,
    verify_marlin_supports_shape,
)
from sglang.srt.layers.quantization.scalar_type import scalar_types
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import replace_parameter

try:
    from vllm import _custom_ops as ops

    warnings.warn(
        f"Using kernels directly from vllm. This might lead to performance degradation or "
        f"missing functionalities as certain kernels may not be optimized. "
    )
except ImportError:
    ops = None

from sglang.srt.utils import is_cuda, is_hip

_is_cuda = is_cuda()
_is_hip = is_hip()
if _is_cuda:
    from sgl_kernel import awq_dequantize, fused_marlin_moe
elif _is_hip:
    from sglang.srt.layers.quantization.awq_triton import (
        awq_dequantize_triton as awq_dequantize,
    )

    warnings.warn(f"HIP does not support fused_marlin_moe currently.")
else:
    warnings.warn(f"Only CUDA and HIP support AWQ currently.")

logger = logging.getLogger(__name__)


def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
    return any(module_name in prefix for module_name in modules_to_not_convert)


class AWQConfig(QuantizationConfig):
    """Config class for AWQ.

    Reference: https://arxiv.org/abs/2306.00978
    """

    def __init__(
        self,
        weight_bits: int,
        group_size: int,
        zero_point: bool,
        modules_to_not_convert: Optional[List[str]] = None,
    ) -> None:
        super().__init__()
        self.weight_bits = weight_bits
        self.group_size = group_size
        self.zero_point = zero_point
        self.modules_to_not_convert = modules_to_not_convert or []

        if self.weight_bits != 4:
            raise ValueError(
                "Currently, only 4-bit weight quantization is supported for "
                f"AWQ, but got {self.weight_bits} bits."
            )
        self.pack_factor = 32 // self.weight_bits

    def __repr__(self) -> str:
        return (
            f"AWQConfig(weight_bits={self.weight_bits}, "
            f"group_size={self.group_size}, "
            f"zero_point={self.zero_point}, "
            f"modules_to_not_convert={self.modules_to_not_convert})"
        )

    def get_scaled_act_names(self) -> List[str]:
        return []

    def get_name(self) -> str:
        return "awq"

    def get_supported_act_dtypes(self) -> List[torch.dtype]:
        return [torch.half]

    @classmethod
    def get_min_capability(cls) -> int:
        # The AWQ kernel only supports Turing or newer GPUs.
        return 75

    @staticmethod
    def get_config_filenames() -> List[str]:
        return [
            "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
            # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
            "quantize_config.json",
        ]

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> AWQConfig:
        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
        zero_point = cls.get_from_keys(config, ["zero_point"])
        modules_to_not_convert = cls.get_from_keys_or(
            config, ["modules_to_not_convert"], None
        )
        return cls(weight_bits, group_size, zero_point, modules_to_not_convert)

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional[LinearMethodBase]:
        from sglang.srt.layers.linear import LinearBase

        if isinstance(layer, LinearBase):
            if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
                return UnquantizedLinearMethod()
            return AWQLinearMethod(self)
        return None


class AWQMarlinConfig(QuantizationConfig):
    """Config class for AWQ Marlin"""

    # num_bits -> type
    TYPE_MAP = {
        4: scalar_types.uint4,
        8: scalar_types.uint8,
    }

    def __init__(
        self,
        weight_bits: int,
        group_size: int,
        zero_point: bool,
        lm_head_quantized: bool,
        modules_to_not_convert: Optional[list[str]],
        full_config: dict[str, Any],
    ) -> None:
        super().__init__()
        self.pack_factor = 32 // weight_bits  # packed into int32
        self.group_size = group_size
        self.zero_point = zero_point
        self.lm_head_quantized = lm_head_quantized
        self.weight_bits = weight_bits
        self.modules_to_not_convert = modules_to_not_convert or []
        self.full_config = full_config

        if self.weight_bits not in self.TYPE_MAP:
            raise ValueError(
                f"Unsupported num_bits = {self.weight_bits}. "
                f"Supported num_bits = {self.TYPE_MAP.keys()}"
            )

        self.quant_type = self.TYPE_MAP[self.weight_bits]

        verify_marlin_supported(
            self.quant_type, group_size=self.group_size, has_zp=self.zero_point
        )

    def __repr__(self) -> str:
        return (
            f"AWQMarlinConfig(quant_type={self.quant_type}, "
            f"group_size={self.group_size}, "
            f"zero_point={self.zero_point}, "
            f"lm_head_quantized={self.lm_head_quantized}, "
            f"modules_to_not_convert={self.modules_to_not_convert})"
        )

    def get_scaled_act_names(self) -> List[str]:
        return []

    @classmethod
    def get_name(cls) -> str:
        return "awq_marlin"

    @classmethod
    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
        return [torch.half, torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        return 80

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return ["quantize_config.json"]

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> AWQMarlinConfig:
        weight_bits = cls.get_from_keys(config, ["bits"])
        group_size = cls.get_from_keys(config, ["group_size"])
        zero_point = cls.get_from_keys(config, ["zero_point"])
        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
        modules_to_not_convert = cls.get_from_keys_or(
            config, ["modules_to_not_convert"], None
        )
        return cls(
            weight_bits,
            group_size,
            zero_point,
            lm_head_quantized,
            modules_to_not_convert,
            config,
        )

    @classmethod
    def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
        can_convert = cls.is_awq_marlin_compatible(hf_quant_cfg)
        is_valid_user_quant = (
            user_quant is None or user_quant == "marlin" or user_quant == "awq_marlin"
        )

        if can_convert and is_valid_user_quant:
            msg = (
                "The model is convertible to {} during runtime."
                " Using {} kernel.".format(cls.get_name(), cls.get_name())
            )
            logger.info(msg)
            return cls.get_name()

        if can_convert and user_quant == "awq":
            logger.info(
                "Detected that the model can run with awq_marlin"
                ", however you specified quantization=awq explicitly,"
                " so forcing awq. Use quantization=awq_marlin for"
                " faster inference"
            )
        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional[QuantizeMethodBase]:
        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
        from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead

        if isinstance(layer, LinearBase) or (
            isinstance(layer, ParallelLMHead) and self.lm_head_quantized
        ):
            if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
                return UnquantizedLinearMethod()
            # Check if the layer is supported by AWQMarlin.
            if not check_marlin_supports_layer(layer, self.group_size):
                logger.warning_once(
                    "Layer '%s' is not supported by AWQMarlin. Falling back to unoptimized AWQ kernels.",  # noqa: E501
                    prefix,
                )
                return AWQConfig.from_config(self.full_config).get_quant_method(
                    layer, prefix
                )
            return AWQMarlinLinearMethod(self)
        elif isinstance(layer, FusedMoE):
            from sglang.srt.layers.quantization.moe_wna16 import MoeWNA16Config

            if not check_moe_marlin_supports_layer(layer, self.group_size):
                logger.warning_once(
                    f"Layer '{prefix}' is not supported by AWQMoeMarlin. "
                    "Falling back to Moe WNA16 kernels."
                )
                return MoeWNA16Config.from_config(self.full_config).get_quant_method(
                    layer, prefix
                )
            return AWQMoEMethod(self)
        return None

    @classmethod
    def is_awq_marlin_compatible(cls, quant_config: dict[str, Any]):
        # Extract data from quant config.
        quant_method = quant_config.get("quant_method", "").lower()
        num_bits = quant_config.get("bits")
        group_size = quant_config.get("group_size")
        zero_point = quant_config.get("zero_point")

        if not _is_cuda:
            return False

        if quant_method != "awq":
            return False

        # If we cannot find the info needed in the config, cannot convert.
        if num_bits is None or group_size is None or zero_point is None:
            return False

        if num_bits not in cls.TYPE_MAP:
            return False

        return check_marlin_supported(
            quant_type=cls.TYPE_MAP[num_bits], group_size=group_size, has_zp=zero_point
        )


class AWQLinearMethod(LinearMethodBase):
    """Linear method for AWQ.

    Args:
        quant_config: The AWQ quantization config.
    """

    def __init__(self, quant_config: AWQConfig):
        self.quant_config = quant_config

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: List[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        if input_size_per_partition % self.quant_config.group_size != 0:
            raise ValueError(
                "The input size is not aligned with the quantized "
                "weight shape. This can be caused by too large "
                "tensor parallel size."
            )

        output_size_per_partition = sum(output_partition_sizes)
        if output_size_per_partition % self.quant_config.pack_factor != 0:
            raise ValueError(
                "The output size is not aligned with the quantized "
                "weight shape. This can be caused by too large "
                "tensor parallel size."
            )

        weight_loader = extra_weight_attrs.get("weight_loader")
        qweight = PackedvLLMParameter(
            data=torch.empty(
                input_size_per_partition,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=1,
            packed_factor=self.quant_config.pack_factor,
            weight_loader=weight_loader,
        )

        qzeros = PackedvLLMParameter(
            data=torch.empty(
                input_size_per_partition // self.quant_config.group_size,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=1,
            packed_factor=self.quant_config.pack_factor,
            weight_loader=weight_loader,
        )

        scales = GroupQuantScaleParameter(
            data=torch.empty(
                input_size_per_partition // self.quant_config.group_size,
                output_size_per_partition,
                dtype=params_dtype,
            ),
            input_dim=0,
            output_dim=1,
            weight_loader=weight_loader,
        )

        layer.register_parameter("qweight", qweight)
        layer.register_parameter("qzeros", qzeros)
        layer.register_parameter("scales", scales)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
        layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
        layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        qweight = layer.qweight
        scales = layer.scales
        qzeros = layer.qzeros
        pack_factor = self.quant_config.pack_factor
        out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
        reshaped_x = x.reshape(-1, x.shape[-1])
        out = awq_dequantize(qweight, scales, qzeros)
        out = torch.matmul(reshaped_x, out)

        if bias is not None:
            out.add_(bias)
        return out.reshape(out_shape)


class AWQMarlinLinearMethod(LinearMethodBase):
    """Linear method for AWQ Marlin.

    Args:
        quant_config: The AWQ Marlin quantization config.
    """

    def __init__(self, quant_config: AWQMarlinConfig) -> None:
        self.quant_config = quant_config

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: list[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ) -> None:
        del output_size
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")

        # Normalize group_size
        if self.quant_config.group_size != -1:
            group_size = self.quant_config.group_size
        else:
            group_size = input_size

        verify_marlin_supports_shape(
            output_size_per_partition=output_size_per_partition,
            input_size_per_partition=input_size_per_partition,
            input_size=input_size,
            group_size=group_size,
        )

        qweight = PackedvLLMParameter(
            data=torch.empty(
                input_size_per_partition,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=1,
            packed_factor=self.quant_config.pack_factor,
            weight_loader=weight_loader,
        )

        num_groups = input_size_per_partition // group_size

        qzeros = PackedvLLMParameter(
            data=torch.empty(
                num_groups,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=1,
            packed_factor=self.quant_config.pack_factor,
            weight_loader=weight_loader,
        )

        scales = GroupQuantScaleParameter(
            data=torch.empty(
                num_groups,
                output_size_per_partition,
                dtype=params_dtype,
            ),
            input_dim=0,
            output_dim=1,
            weight_loader=weight_loader,
        )

        layer.register_parameter("qweight", qweight)
        layer.register_parameter("qzeros", qzeros)
        layer.register_parameter("scales", scales)

        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition
        layer.num_groups = num_groups

    # TODO: Update this docs
    # Checkpoints are serialized in AutoAWQ format, which is different from the
    # marlin format. This function is called after the weights are loaded.
    # Here, we handle the repacking
    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        device = layer.qweight.device
        layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
        layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
        layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)

        # Allocate marlin workspace
        layer.workspace = marlin_make_workspace(device)

        # Repack weights from AWQ format to marlin format.
        marlin_qweight = ops.awq_marlin_repack(
            layer.qweight,
            size_k=layer.input_size_per_partition,
            size_n=layer.output_size_per_partition,
            num_bits=self.quant_config.quant_type.size_bits,
        )
        replace_parameter(layer, "qweight", marlin_qweight)

        # Permute scales from AWQ format to marlin format.
        marlin_scales = marlin_permute_scales(
            layer.scales,
            size_k=layer.input_size_per_partition,
            size_n=layer.output_size_per_partition,
            group_size=self.quant_config.group_size,
        )
        replace_parameter(layer, "scales", marlin_scales)

        # Permute zero-points from AWQ format to marlin format.
        marlin_zp = awq_to_marlin_zero_points(
            layer.qzeros,
            size_k=layer.num_groups,
            size_n=layer.output_size_per_partition,
            num_bits=self.quant_config.quant_type.size_bits,
        )
        replace_parameter(layer, "qzeros", marlin_zp)

        # Not-used
        layer.g_idx = marlin_make_empty_g_idx(device)
        layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        return apply_awq_marlin_linear(
            input=x,
            weight=layer.qweight,
            weight_scale=layer.scales,
            weight_zp=layer.qzeros,
            g_idx=layer.g_idx,
            g_idx_sort_indices=layer.g_idx_sort_indices,
            workspace=layer.workspace,
            quant_type=self.quant_config.quant_type,
            output_size_per_partition=layer.output_size_per_partition,
            input_size_per_partition=layer.input_size_per_partition,
            bias=bias,
        )


class AWQMoEMethod(FusedMoEMethodBase):

    def __init__(self, quant_config: AWQMarlinConfig):
        self.quant_config = quant_config
        if self.quant_config.weight_bits != 4:
            raise ValueError("AWQMoEMethod only supports 4bit now.")
        self.quant_type = scalar_types.uint4

    def create_weights(
        self,
        layer: torch.nn.Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size_per_partition: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        # Delay the import to avoid circular dependency
        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported

        extra_weight_attrs.update(
            {
                "is_transposed": True,
                "quant_method": FusedMoeWeightScaleSupported.GROUP.value,
            }
        )

        w13_qweight = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size,
                2 * intermediate_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_qweight", w13_qweight)
        set_weight_attrs(w13_qweight, extra_weight_attrs)

        w2_qweight = torch.nn.Parameter(
            torch.empty(
                num_experts,
                intermediate_size_per_partition,
                hidden_size // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_qweight", w2_qweight)
        set_weight_attrs(w2_qweight, extra_weight_attrs)

        num_groups_w13 = hidden_size // self.quant_config.group_size
        num_groups_w2 = intermediate_size_per_partition // self.quant_config.group_size

        # WEIGHT_SCALES
        # Allocate 2 scales for w1 and w3 respectively.
        w13_scales = torch.nn.Parameter(
            torch.empty(
                num_experts,
                num_groups_w13,
                intermediate_size_per_partition * 2,
                dtype=params_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_scales", w13_scales)
        set_weight_attrs(w13_scales, extra_weight_attrs)

        w2_scales = torch.nn.Parameter(
            torch.empty(num_experts, num_groups_w2, hidden_size, dtype=params_dtype),
            requires_grad=False,
        )
        layer.register_parameter("w2_scales", w2_scales)
        set_weight_attrs(w2_scales, extra_weight_attrs)

        # WEIGHT_ZERO_POINT
        # Allocate 2 zero points for w1 and w3 respectively.
        w13_qzeros = torch.nn.Parameter(
            torch.empty(
                num_experts,
                num_groups_w13,
                2 * intermediate_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_qzeros", w13_qzeros)
        set_weight_attrs(w13_qzeros, extra_weight_attrs)

        w2_qzeros = torch.nn.Parameter(
            torch.empty(
                num_experts,
                num_groups_w2,
                hidden_size // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_qzeros", w2_qzeros)
        set_weight_attrs(w2_qzeros, extra_weight_attrs)

        device = layer.w13_qweight.device
        layer.workspace = marlin_make_workspace(device, 4)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        num_experts = layer.w13_qweight.shape[0]
        device = layer.w13_qweight.device

        layer.w13_g_idx_sort_indices = torch.nn.Parameter(
            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
            requires_grad=False,
        )
        layer.w2_g_idx_sort_indices = torch.nn.Parameter(
            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
            requires_grad=False,
        )

        marlin_w13_qweight = ops.awq_marlin_moe_repack(
            layer.w13_qweight,
            layer.w13_g_idx_sort_indices,
            size_k=layer.w13_qweight.shape[1],
            size_n=layer.w13_qweight.shape[2] * self.quant_config.pack_factor,
            num_bits=self.quant_config.weight_bits,
        )
        replace_parameter(layer, "w13_qweight", marlin_w13_qweight)

        marlin_w2_qweight = ops.awq_marlin_moe_repack(
            layer.w2_qweight,
            layer.w2_g_idx_sort_indices,
            size_k=layer.w2_qweight.shape[1],
            size_n=layer.w2_qweight.shape[2] * self.quant_config.pack_factor,
            num_bits=self.quant_config.weight_bits,
        )
        replace_parameter(layer, "w2_qweight", marlin_w2_qweight)

        # hidden_size->intermediate_size
        marlin_w13_scales = marlin_moe_permute_scales(
            s=layer.w13_scales,
            size_k=layer.intermediate_size_per_partition,
            size_n=layer.w13_scales.shape[2],
            group_size=self.quant_config.group_size,
        )

        replace_parameter(layer, "w13_scales", marlin_w13_scales)

        marlin_w2_scales = marlin_moe_permute_scales(
            s=layer.w2_scales,
            size_k=layer.intermediate_size_per_partition,
            size_n=layer.w2_scales.shape[2],
            group_size=self.quant_config.group_size,
        )
        replace_parameter(layer, "w2_scales", marlin_w2_scales)

        marlin_w13_zp = moe_awq_to_marlin_zero_points(
            layer.w13_qzeros,
            size_k=layer.w13_qzeros.shape[1],
            size_n=layer.w13_qzeros.shape[2] * self.quant_config.pack_factor,
            num_bits=self.quant_config.weight_bits,
        )
        replace_parameter(layer, "w13_qzeros", marlin_w13_zp)

        marlin_w2_zp = moe_awq_to_marlin_zero_points(
            layer.w2_qzeros,
            size_k=layer.w2_qzeros.shape[1],
            size_n=layer.w2_qzeros.shape[2] * self.quant_config.pack_factor,
            num_bits=self.quant_config.weight_bits,
        )
        replace_parameter(layer, "w2_qzeros", marlin_w2_zp)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        num_fused_shared_experts: int = 0,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        routed_scaling_factor: Optional[float] = None,
    ) -> torch.Tensor:
        # Delay the import to avoid circular dependency
        from sglang.srt.layers.moe.topk import select_experts

        assert activation == "silu", "Only SiLU activation is supported."
        assert (
            scoring_func == "softmax"
        ), "Only softmax score func is supported for now."

        # The input must currently be float16
        orig_dtype = x.dtype
        x = x.half()

        topk_weights, topk_ids = select_experts(
            hidden_states=x,
            router_logits=router_logits,
            top_k=top_k,
            use_grouped_topk=use_grouped_topk,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            num_fused_shared_experts=num_fused_shared_experts,
            custom_routing_function=custom_routing_function,
            correction_bias=correction_bias,
            routed_scaling_factor=routed_scaling_factor,
        )

        return fused_marlin_moe(
            x,
            layer.w13_qweight,
            layer.w2_qweight,
            layer.w13_scales,
            layer.w2_scales,
            router_logits,
            topk_weights,
            topk_ids,
            sort_indices1=layer.w13_g_idx_sort_indices,
            sort_indices2=layer.w2_g_idx_sort_indices,
            w1_zeros=layer.w13_qzeros,
            w2_zeros=layer.w2_qzeros,
            num_bits=self.quant_config.weight_bits,
        ).to(orig_dtype)