from __future__ import annotations

import logging
from dataclasses import dataclass
from fractions import Fraction
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union

import torch

from sglang.srt.layers.parameter import (
    BasevLLMParameter,
    ChannelQuantScaleParameter,
    GroupQuantScaleParameter,
    PackedColumnParameter,
    PackedvLLMParameter,
    RowvLLMParameter,
    permute_param_layout_,
)
from sglang.srt.layers.quantization.base_config import (
    FusedMoEMethodBase,
    LinearMethodBase,
    QuantizationConfig,
    QuantizeMethodBase,
)
from sglang.srt.layers.quantization.marlin_utils import (
    apply_gptq_marlin_linear,
    check_marlin_supported,
    check_marlin_supports_shape,
    marlin_is_k_full,
    marlin_make_empty_g_idx,
    marlin_make_workspace,
    marlin_moe_permute_scales,
    marlin_permute_scales,
    marlin_repeat_scales_on_all_ranks,
    marlin_sort_g_idx,
    marlin_zero_points,
    verify_marlin_supported,
)
from sglang.srt.layers.quantization.utils import (
    get_linear_quant_method,
    get_scalar_types,
    replace_parameter,
    unpack_cols,
)

if TYPE_CHECKING:
    from sglang.srt.layers.moe.topk import TopKOutput

from sglang.srt.utils import is_cuda

_is_cuda = is_cuda()

if _is_cuda:
    from sgl_kernel import fused_marlin_moe, gptq_gemm, gptq_marlin_repack, gptq_shuffle


logger = logging.getLogger(__name__)
ScalarType, scalar_types = get_scalar_types()


def check_marlin_format(hf_quant_cfg: Dict[str, Any]) -> bool:
    # compat: gptqmodel and autogptq (eol) main use checkpoint_format: str
    # compat: autogptq <=0.7.1 is_marlin_format: bool
    return hf_quant_cfg.get("checkpoint_format") == "marlin" or hf_quant_cfg.get(
        "is_marlin_format", False
    )


def gptq_marlin_moe_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
) -> torch.Tensor:
    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
    output = torch.empty(
        (num_experts, size_k // 16, size_n * (num_bits // 2)),
        device=b_q_weight.device,
        dtype=b_q_weight.dtype,
    )
    for e in range(num_experts):
        output[e] = gptq_marlin_repack(b_q_weight[e], perm[e], size_k, size_n, num_bits)
    return output


@dataclass
class MarlinLinearLayerConfig:
    full_weight_shape: tuple[int, int]  # [in, out]
    partition_weight_shape: tuple[int, int]
    weight_type: ScalarType
    act_type: torch.dtype
    group_size: int
    zero_points: bool
    has_g_idx: bool


class GPTQConfig(QuantizationConfig):
    """Config class for GPTQ.

    Reference: https://arxiv.org/abs/2210.17323
    """

    def __init__(
        self,
        weight_bits: int,
        group_size: int,
        desc_act: bool,
        lm_head_quantized: bool,
        dynamic: Dict[str, Dict[str, Union[int, bool]]],
    ) -> None:
        # GPTQModel use `dynamic` config property to allow per module
        # quantization config so each module can be individually optimized.
        # Format is Dict[str, Dict] where key is a regex string that can
        # perform both positive ("+:" prefixed) or negative ("-:" prefixed)
        # matching of a module.
        # Default to positive match, override base quant config mode, if no
        # prefix is used. Value is in dict format of field key and override
        # value.
        # Negative matching will skip quantization init for this module
        # entirely:
        # non-quantized inference. More details and quantization examples can be
        # found at: https://github.com/ModelCloud/GPTQModel
        # Example:
        #  # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
        #  # last 1/4 of the layers 16-21 has 8bit and group_size 64
        # dynamic = {
        #  #`.*\.` matches the layers_node prefix
        #  # positive match layer 10-15
        #  r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
        #  # positive match layer 16-21
        #  r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
        #  r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
        # }
        super().__init__()
        self.dynamic = dynamic

        self.weight_bits = weight_bits
        self.group_size = group_size
        self.desc_act = desc_act
        self.lm_head_quantized = lm_head_quantized
        self.pack_factor = Fraction(32, self.weight_bits)
        if self.weight_bits not in [2, 3, 4, 8]:
            raise ValueError(
                "Currently, only 2/3/4/8-bit weight quantization is "
                f"supported for GPTQ, but got {self.weight_bits} bits."
            )

    def __repr__(self) -> str:
        return (
            f"GPTQConfig(weight_bits={self.weight_bits}, "
            f"group_size={self.group_size}, "
            f"desc_act={self.desc_act}),"
            f"lm_head_quantized={self.lm_head_quantized}), "
            f"dynamic={self.dynamic}"
        )

    def get_scaled_act_names(self) -> List[str]:
        """Returns the activation function names that should be post-scaled.

        For now, this is only used by AWQ.
        """
        raise NotImplementedError

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

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

    @classmethod
    # Need to figure it out
    def get_min_capability(cls) -> int:
        return 60

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

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> GPTQConfig:
        dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
        dynamic = {} if dynamic is None else dynamic

        weight_bits = cls.get_from_keys(config, ["bits"])
        group_size = cls.get_from_keys(config, ["group_size"])
        desc_act = cls.get_from_keys(config, ["desc_act"])
        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
        return cls(weight_bits, group_size, desc_act, lm_head_quantized, dynamic)

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional[LinearMethodBase]:
        # Delay the import to avoid circular dependency
        from sglang.srt.layers.linear import LinearBase
        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE

        if isinstance(layer, FusedMoE):
            raise TypeError("GPTQ Method does not support MoE, please use gptq_marlin")
        else:
            return get_linear_quant_method(
                self, layer, prefix=prefix, linear_method_cls=GPTQLinearMethod
            )


class GPTQMarlinConfig(QuantizationConfig):
    """Config class for GPTQ Marlin"""

    # (num_bits, is_sym) -> quant_type
    TYPE_MAP = {
        (4, True): scalar_types.uint4b8,
        (8, True): scalar_types.uint8b128,
    }

    def __init__(
        self,
        weight_bits: int,
        group_size: int,
        desc_act: bool,
        is_sym: bool,
        lm_head_quantized: bool,
        dynamic: Dict[str, Dict[str, Union[int, bool]]],
        full_config: Dict[str, Any],
    ) -> None:
        super().__init__()
        if desc_act and group_size == -1:
            # In this case, act_order == True is the same as act_order == False
            # (since we have only one group per output channel)
            desc_act = False

        # GPTQModel use `dynamic` config property to allow per module
        # quantization config so each module can be individually optimized.
        # Format is Dict[str, Dict] where key is a regex string that can
        # perform both positive ("+:" prefixed) or negative ("-:" prefixed)
        # matching of a module.
        # Default to positive match, override base quant config mode, if no
        # prefix is used. Value is in dict format of field key and override
        # value.
        # Negative matching will skip quantization init for this module
        # entirely:
        # non-quantized inference. More details and quantization examples can be
        # found at: https://github.com/ModelCloud/GPTQModel
        # Example:
        #  # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
        #  # last 1/4 of the layers 16-21 has 8bit and group_size 64
        # dynamic = {
        #  #`.*\.` matches the layers_node prefix
        #  # positive match layer 10-15
        #  r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
        #  # positive match layer 16-21
        #  r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
        #  r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
        # }
        self.dynamic = dynamic

        self.weight_bits = weight_bits
        self.is_sym = is_sym

        self.pack_factor = 32 // weight_bits  # packed into int32
        self.group_size = group_size
        self.desc_act = desc_act
        self.lm_head_quantized = lm_head_quantized
        self.full_config = full_config

        if (weight_bits, is_sym) not in self.TYPE_MAP:
            raise ValueError(
                "Unsupported quantization config: " f"bits={weight_bits}, sym={is_sym}"
            )

        # (num_bits, is_sym) -> quant_type
        self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]

    def __repr__(self) -> str:
        return (
            f"GPTQMarlinConfig(quant_type={self.quant_type}, "
            f"group_size={self.group_size}, "
            f"desc_act={self.desc_act}, "
            f"lm_head_quantized={self.lm_head_quantized}), "
            f"dynamic={self.dynamic}"
        )

    def get_scaled_act_names(self) -> List[str]:
        """Returns the activation function names that should be post-scaled.

        For now, this is only used by AWQ.
        """
        raise NotImplementedError

    @classmethod
    def get_name(cls) -> str:
        return "gptq_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]) -> GPTQMarlinConfig:
        dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
        dynamic = {} if dynamic is None else dynamic

        weight_bits = cls.get_from_keys(config, ["bits"])
        group_size = cls.get_from_keys(config, ["group_size"])
        desc_act = cls.get_from_keys(config, ["desc_act"])
        is_sym = cls.get_from_keys(config, ["sym"])
        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
        return cls(
            weight_bits,
            group_size,
            desc_act,
            is_sym,
            lm_head_quantized,
            dynamic,
            config,
        )

    @classmethod
    def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
        is_marlin_format = check_marlin_format(hf_quant_cfg)

        can_convert = cls.is_gptq_marlin_compatible(hf_quant_cfg)

        is_valid_user_quant = (
            user_quant is None or user_quant == "marlin" or user_quant == "gptq_marlin"
        )

        if not is_marlin_format and 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 not is_marlin_format and can_convert and user_quant == "gptq":
            logger.info(
                "Detected that the model can run with gptq_marlin"
                ", however you specified quantization=gptq explicitly,"
                " so forcing gptq. Use quantization=gptq_marlin for"
                " faster inference"
            )
        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional[QuantizeMethodBase]:
        # Delay the import to avoid circular dependency
        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE

        if isinstance(layer, FusedMoE):
            return GPTQMarlinMoEMethod(self)
        return get_linear_quant_method(self, layer, prefix, GPTQMarlinLinearMethod)

    @classmethod
    def is_gptq_marlin_compatible(cls, quant_config: Dict[str, Any]):
        quant_method = quant_config.get("quant_method", "").lower()
        num_bits = quant_config.get("bits")
        group_size = quant_config.get("group_size")
        sym = quant_config.get("sym")
        desc_act = quant_config.get("desc_act")

        if not _is_cuda:
            return False

        if quant_method != "gptq":
            return False

        # Marlin conversion is only valid if required properties are found
        if num_bits is None or group_size is None or sym is None or desc_act is None:
            return False

        if (num_bits, sym) not in cls.TYPE_MAP:
            return False

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


class GPTQLinearMethod(LinearMethodBase):
    """Linear method for GPTQ.

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

    def __init__(self, quant_config: GPTQConfig):
        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,
    ):
        del output_size  # Unused.
        weight_loader = extra_weight_attrs.get("weight_loader")
        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.numerator != 0:
            raise ValueError(
                "The output size is not aligned with the quantized "
                "weight shape. This can be caused by too large "
                "tensor parallel size."
            )

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

        self.use_shuffle = True
        scale_and_zero_size = input_size // group_size
        scale_and_zero_input_dim = None
        if (
            input_size != input_size_per_partition
            and self.quant_config.group_size != -1
        ):
            if self.quant_config.desc_act:
                self.use_shuffle = False
            else:
                # we need to partition qzeros and scales for exllama kernel
                scale_and_zero_size = input_size_per_partition // group_size
                scale_and_zero_input_dim = 0

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

        g_idx = RowvLLMParameter(
            data=torch.tensor(
                [
                    i // self.quant_config.group_size
                    for i in range(input_size_per_partition)
                ],
                dtype=torch.int32,
            ),
            input_dim=0,
            weight_loader=weight_loader,
        )
        qzeros_args = {
            "data": torch.empty(
                scale_and_zero_size,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            "weight_loader": weight_loader,
        }
        weight_scale_args = {
            "data": torch.empty(
                scale_and_zero_size,
                output_size_per_partition,
                dtype=params_dtype,
            ),
            "weight_loader": weight_loader,
        }
        if scale_and_zero_input_dim is None:
            scales = ChannelQuantScaleParameter(output_dim=1, **weight_scale_args)
            qzeros = PackedColumnParameter(
                output_dim=1,
                packed_dim=1,
                packed_factor=self.quant_config.pack_factor,
                **qzeros_args,
            )

        else:
            scales = GroupQuantScaleParameter(
                output_dim=1, input_dim=0, **weight_scale_args
            )
            qzeros = PackedvLLMParameter(
                input_dim=0,
                output_dim=1,
                packed_dim=1,
                packed_factor=self.quant_config.pack_factor,
                **qzeros_args,
            )

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

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

        # exllama needs to shuffle the weight after the weight is loaded
        # here we do the shuffle on first forward pass
        if self.use_shuffle:
            if self.quant_config.desc_act:
                layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
            else:
                layer.g_idx.data = torch.empty(
                    (0,), dtype=torch.int, device=layer.g_idx.device
                )
            gptq_shuffle(layer.qweight, layer.g_idx, self.quant_config.weight_bits)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        out_shape = x.shape[:-1] + (layer.qweight.shape[-1],)
        reshaped_x = x.reshape(-1, x.shape[-1])

        output = gptq_gemm(
            reshaped_x,
            layer.qweight,
            layer.qzeros,
            layer.scales,
            layer.g_idx,
            self.use_shuffle,
            self.quant_config.weight_bits,
        )
        if bias is not None:
            output.add_(bias)
        return output.reshape(out_shape)


class GPTQMarlinLinearMethod(LinearMethodBase):
    """Linear method for GPTQ Marlin.

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

    _kernel_backends_being_used: set[str] = set()

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

        # Verify supported on platform.
        verify_marlin_supported(
            quant_type=self.quant_config.quant_type,
            group_size=self.quant_config.group_size,
        )

    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:
        output_size_per_partition = sum(output_partition_sizes)
        is_row_parallel = input_size != input_size_per_partition
        weight_loader = extra_weight_attrs.get("weight_loader")

        self.kernel_config = MarlinLinearLayerConfig(
            full_weight_shape=(input_size, output_size),
            partition_weight_shape=(
                input_size_per_partition,
                output_size_per_partition,
            ),
            weight_type=self.quant_config.quant_type,
            act_type=params_dtype,
            group_size=self.quant_config.group_size,
            zero_points=False,
            has_g_idx=self.quant_config.desc_act,
        )
        # Normalize group_size
        if self.quant_config.group_size != -1:
            group_size = self.quant_config.group_size
        else:
            group_size = input_size

        # Determine sharding
        if marlin_repeat_scales_on_all_ranks(
            self.quant_config.desc_act, self.quant_config.group_size, is_row_parallel
        ):
            # By setting scale_dim == None, weight_loader will
            # repeat the scales on each GPU in TP>1 case.
            scales_and_zp_input_dim = None
            scales_and_zp_size = input_size // group_size
        else:
            # By setting scale_dim == 0, weight_loader will
            # shard the scales in TP>1 case.
            scales_and_zp_input_dim = 0
            scales_and_zp_size = input_size_per_partition // group_size

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

        # Activation order
        g_idx = RowvLLMParameter(
            data=torch.empty(
                input_size_per_partition,
                dtype=torch.int32,
            ),
            input_dim=0,
            weight_loader=weight_loader,
        )

        qzeros_args = {
            "data": torch.empty(
                scales_and_zp_size,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            "weight_loader": weight_loader,
        }
        weight_scale_args = {
            "data": torch.empty(
                scales_and_zp_size,
                output_size_per_partition,
                dtype=params_dtype,
            ),
            "weight_loader": weight_loader,
        }

        if scales_and_zp_input_dim is None:
            scales = ChannelQuantScaleParameter(output_dim=1, **weight_scale_args)
            qzeros = PackedColumnParameter(
                output_dim=1,
                packed_dim=1,
                packed_factor=self.quant_config.pack_factor,
                **qzeros_args,
            )

        else:
            scales = GroupQuantScaleParameter(
                output_dim=1, input_dim=0, **weight_scale_args
            )
            qzeros = PackedvLLMParameter(
                input_dim=0,
                output_dim=1,
                packed_dim=1,
                packed_factor=self.quant_config.pack_factor,
                **qzeros_args,
            )

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

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        device = getattr(layer, "qweight").device
        c = self.kernel_config

        check_marlin_supports_shape(
            c.partition_weight_shape[1],  # out_features
            c.partition_weight_shape[0],  # in_features
            c.full_weight_shape[0],  # in_features
            c.group_size,
        )

        row_parallel = c.partition_weight_shape[0] != c.full_weight_shape[0]
        self.is_k_full = marlin_is_k_full(c.has_g_idx, row_parallel)

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

        # Default names since marlin requires empty parameters for these,
        # TODO: remove this requirement from marlin (allow optional tensors)
        self.w_q_name = "qweight"
        self.w_s_name = "scales"
        self.w_zp_name = "qzeros"
        self.w_gidx_name = "g_idx"

        def _transform_param(
            layer: torch.nn.Module, name: Optional[str], fn: Callable
        ) -> None:
            if name is not None and getattr(layer, name, None) is not None:

                old_param = getattr(layer, name)
                new_param = fn(old_param)
                # replace the parameter with torch.nn.Parameter for TorchDynamo
                # compatibility
                replace_parameter(
                    layer, name, torch.nn.Parameter(new_param.data, requires_grad=False)
                )

        def transform_w_q(x):
            assert isinstance(x, BasevLLMParameter)
            permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
            x.data = gptq_marlin_repack(
                x.data.contiguous(),
                perm=layer.g_idx_sort_indices,
                size_k=c.partition_weight_shape[0],
                size_n=c.partition_weight_shape[1],
                num_bits=c.weight_type.size_bits,
            )
            return x

        def transform_w_s(x):
            assert isinstance(x, BasevLLMParameter)
            permute_param_layout_(x, input_dim=0, output_dim=1)
            x.data = marlin_permute_scales(
                x.data.contiguous(),
                size_k=c.partition_weight_shape[0],
                size_n=c.partition_weight_shape[1],
                group_size=c.group_size,
            )
            return x

        if c.has_g_idx:
            g_idx, g_idx_sort_indices = marlin_sort_g_idx(
                getattr(layer, self.w_gidx_name)
            )
            _transform_param(layer, self.w_gidx_name, lambda _: g_idx)
            layer.g_idx_sort_indices = g_idx_sort_indices
        else:
            setattr(layer, self.w_gidx_name, marlin_make_empty_g_idx(device))
            layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)

        if c.zero_points:
            grouped_k = (
                c.partition_weight_shape[0] // c.group_size if c.group_size != -1 else 1
            )
            _transform_param(
                layer,
                self.w_zp_name,
                lambda x: marlin_zero_points(
                    unpack_cols(
                        x.t(),
                        c.weight_type.size_bits,
                        grouped_k,
                        c.partition_weight_shape[1],
                    ),
                    size_k=grouped_k,
                    size_n=c.partition_weight_shape[1],
                    num_bits=c.weight_type.size_bits,
                ),
            )
        else:
            setattr(layer, self.w_zp_name, marlin_make_empty_g_idx(device))
        _transform_param(layer, self.w_q_name, transform_w_q)
        _transform_param(layer, self.w_s_name, transform_w_s)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        c = self.kernel_config

        def _get_weight_params(
            layer: torch.nn.Module,
        ) -> tuple[
            torch.Tensor,  # w_q
            torch.Tensor,  # w_s
            Optional[torch.Tensor],  # w_zp,
            Optional[torch.Tensor],  # w_gidx
        ]:
            return (
                getattr(layer, self.w_q_name),
                getattr(layer, self.w_s_name),
                getattr(layer, self.w_zp_name or "", None),
                getattr(layer, self.w_gidx_name or "", None),
            )

        w_q, w_s, w_zp, w_gidx = _get_weight_params(layer)

        # `process_weights_after_loading` will ensure w_zp and w_gidx are not
        #  None for marlin
        return apply_gptq_marlin_linear(
            input=x,
            weight=w_q,
            weight_scale=w_s,
            weight_zp=w_zp,  # type: ignore
            g_idx=w_gidx,  # type: ignore
            g_idx_sort_indices=layer.g_idx_sort_indices,
            workspace=self.workspace,
            wtype=c.weight_type,
            input_size_per_partition=c.partition_weight_shape[0],
            output_size_per_partition=c.partition_weight_shape[1],
            is_k_full=self.is_k_full,
            bias=bias,
        )


class GPTQMarlinMoEMethod(FusedMoEMethodBase):
    """MoE Marlin method with quantization."""

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

    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.linear import set_weight_attrs
        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported

        intermediate_size = extra_weight_attrs.pop("intermediate_size")

        self.is_k_full = (not self.quant_config.desc_act) or (
            intermediate_size_per_partition == intermediate_size
        )

        if self.quant_config.group_size != -1:
            scales_size13 = hidden_size // self.quant_config.group_size
            w2_scales_size = (
                intermediate_size
                if self.quant_config.desc_act
                else intermediate_size_per_partition
            )
            scales_size2 = w2_scales_size // self.quant_config.group_size
            strategy = FusedMoeWeightScaleSupported.GROUP.value
        else:
            scales_size13 = 1
            scales_size2 = 1
            strategy = FusedMoeWeightScaleSupported.CHANNEL.value

        extra_weight_attrs.update({"quant_method": strategy, "is_transposed": True})
        # Fused gate_up_proj (column parallel)
        w13_qweight = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size // self.quant_config.pack_factor,
                2 * intermediate_size_per_partition,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_qweight", w13_qweight)
        set_weight_attrs(w13_qweight, extra_weight_attrs)
        # down_proj (row parallel)
        w2_qweight = torch.nn.Parameter(
            torch.empty(
                num_experts,
                intermediate_size_per_partition // self.quant_config.pack_factor,
                hidden_size,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_qweight", w2_qweight)
        set_weight_attrs(w2_qweight, extra_weight_attrs)
        # up_proj scales
        w13_scales = torch.nn.Parameter(
            torch.empty(
                num_experts,
                scales_size13,
                2 * intermediate_size_per_partition,
                dtype=torch.half,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_scales", w13_scales)
        set_weight_attrs(w13_scales, extra_weight_attrs)
        # down_proj scales
        w2_scales = torch.nn.Parameter(
            torch.empty(num_experts, scales_size2, hidden_size, dtype=torch.half),
            requires_grad=False,
        )
        layer.register_parameter("w2_scales", w2_scales)
        set_weight_attrs(w2_scales, extra_weight_attrs)
        # dont shard the w2 scales when running act order
        set_weight_attrs(w2_scales, {"load_full_w2": self.quant_config.desc_act})
        # up_proj scales
        w13_qzeros = torch.nn.Parameter(
            torch.empty(
                num_experts,
                scales_size13,
                2 * intermediate_size_per_partition // self.quant_config.pack_factor,
                dtype=params_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_qzeros", w13_qzeros)
        set_weight_attrs(w13_qzeros, extra_weight_attrs)
        # down_proj scales
        w2_qzeros = torch.nn.Parameter(
            torch.empty(
                num_experts,
                scales_size2,
                hidden_size // self.quant_config.pack_factor,
                dtype=params_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_qzeros", w2_qzeros)
        set_weight_attrs(w2_qzeros, extra_weight_attrs)
        # dont shard the w2 scales when running act order
        set_weight_attrs(w2_qzeros, {"load_full_w2": self.quant_config.desc_act})
        w13_g_idx = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_g_idx", w13_g_idx)
        set_weight_attrs(w13_g_idx, extra_weight_attrs)
        w2_g_idx = torch.nn.Parameter(
            torch.empty(
                num_experts,
                intermediate_size_per_partition,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_g_idx", w2_g_idx)
        set_weight_attrs(w2_g_idx, extra_weight_attrs)
        w13_g_idx_sort_indices = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_g_idx_sort_indices", w13_g_idx_sort_indices)
        set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
        w2_g_idx_sort_indices = torch.nn.Parameter(
            torch.empty(
                num_experts,
                intermediate_size_per_partition,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_g_idx_sort_indices", w2_g_idx_sort_indices)
        set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:

        # Process act_order
        if self.quant_config.desc_act:
            # Get sorting based on g_idx
            num_experts = layer.w13_g_idx.shape[0]
            w13_g_idx_sort_indices = torch.empty_like(layer.w13_g_idx)
            w2_g_idx_sort_indices = torch.empty_like(layer.w2_g_idx)
            w13_sorted_g_idx = torch.empty_like(layer.w13_g_idx)
            w2_sorted_g_idx = torch.empty_like(layer.w2_g_idx)
            for e in range(num_experts):
                w13_g_idx_sort_indices[e] = torch.argsort(layer.w13_g_idx[e]).to(
                    torch.int32
                )
                w2_g_idx_sort_indices[e] = torch.argsort(layer.w2_g_idx[e]).to(
                    torch.int32
                )
                w13_sorted_g_idx[e] = layer.w13_g_idx[e][w13_g_idx_sort_indices[e]]
                w2_sorted_g_idx[e] = layer.w2_g_idx[e][w2_g_idx_sort_indices[e]]
            replace_parameter(layer, "w13_g_idx", w13_sorted_g_idx)
            replace_parameter(layer, "w2_g_idx", w2_sorted_g_idx)
            replace_parameter(layer, "w13_g_idx_sort_indices", w13_g_idx_sort_indices)
            replace_parameter(layer, "w2_g_idx_sort_indices", w2_g_idx_sort_indices)
        else:
            # Reset g_idx related tensors
            num_experts = layer.w13_g_idx.shape[0]
            device = layer.w13_g_idx.device
            layer.w13_g_idx = torch.nn.Parameter(
                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
                requires_grad=False,
            )
            layer.w2_g_idx = torch.nn.Parameter(
                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
                requires_grad=False,
            )
            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,
            )
        # Repack weights
        marlin_w13_qweight = gptq_marlin_moe_repack(
            layer.w13_qweight,
            layer.w13_g_idx_sort_indices,
            layer.w13_qweight.shape[1] * self.quant_config.pack_factor,
            layer.w13_qweight.shape[2],
            self.quant_config.weight_bits,
        )
        replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
        marlin_w2_qweight = gptq_marlin_moe_repack(
            layer.w2_qweight,
            layer.w2_g_idx_sort_indices,
            layer.w2_qweight.shape[1] * self.quant_config.pack_factor,
            layer.w2_qweight.shape[2],
            self.quant_config.weight_bits,
        )
        replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
        # Repack scales
        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.w2_scales.shape[1]
            * (
                self.quant_config.group_size
                if self.quant_config.group_size != -1
                else self.quant_config.pack_factor
            ),
            size_n=layer.w2_scales.shape[2],
            group_size=self.quant_config.group_size,
        )
        replace_parameter(layer, "w2_scales", marlin_w2_scales)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        topk_output: TopKOutput,
        *,
        activation: str = "silu",
        **kwargs,
    ) -> torch.Tensor:
        # Delay the import to avoid circular dependency

        assert activation == "silu", "Only SiLU activation is supported."

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

        topk_weights, topk_ids, router_logits = topk_output

        return fused_marlin_moe(
            x,
            layer.w13_qweight,
            layer.w2_qweight,
            layer.w13_scales,
            layer.w2_scales,
            router_logits,
            topk_weights,
            topk_ids,
            g_idx1=layer.w13_g_idx,
            g_idx2=layer.w2_g_idx,
            sort_indices1=layer.w13_g_idx_sort_indices,
            sort_indices2=layer.w2_g_idx_sort_indices,
            num_bits=self.quant_config.weight_bits,
            is_k_full=self.is_k_full,
        ).to(orig_dtype)
