modelopt.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from fnmatch import fnmatch
from typing import TYPE_CHECKING, Any

import torch
from torch.nn.parameter import Parameter

import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm.logger import init_logger
from vllm.model_executor.kernels.linear import init_fp8_linear_kernel
from vllm.model_executor.layers.attention import Attention, MLAAttention
from vllm.model_executor.layers.fused_moe.activation import MoEActivation
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEConfig,
    FusedMoEQuantConfig,
    RoutingMethodType,
)
from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
    FusedMoEMethodBase,
)
from vllm.model_executor.layers.fused_moe.layer import (
    FusedMoE,
    FusedMoeWeightScaleSupported,
)
from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
    Fp8MoeBackend,
    convert_to_fp8_moe_kernel_format,
    make_fp8_moe_kernel,
    make_fp8_moe_quant_config,
    select_fp8_moe_backend,
)
from vllm.model_executor.layers.fused_moe.oracle.mxfp8 import (
    select_mxfp8_moe_backend,
)
from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import (
    convert_to_nvfp4_moe_kernel_format,
    is_global_sf_supported_for_nvfp4_backend,
    make_nvfp4_moe_kernel,
    make_nvfp4_moe_quant_config,
    select_nvfp4_moe_backend,
)
from vllm.model_executor.layers.linear import (
    LinearBase,
    LinearMethodBase,
    UnquantizedLinearMethod,
)
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig,
    QuantizeMethodBase,
)
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
    swap_w13_to_w31,
)
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
    W8A8BlockFp8LinearOp,
    process_fp8_input_tensor_strategy_moe,
    process_fp8_weight_tensor_strategy_moe,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
    get_marlin_input_dtype,
)
from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
    MXFP8_BLOCK_SIZE,
    MXFP8_SCALE_DTYPE,
    MXFP8_VALUE_DTYPE,
    Mxfp8LinearBackend,
    Mxfp8LinearOp,
    mxfp8_e4m3_quantize,
    swizzle_mxfp8_scale,
)
from vllm.model_executor.layers.quantization.utils.nvfp4_utils import (
    apply_nvfp4_linear,
    convert_to_nvfp4_linear_kernel_format,
    select_nvfp4_linear_backend,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    GroupShape,
    is_layer_skipped,
    kFp8DynamicTokenSym,
    kFp8StaticTensorSym,
    kFp8StaticTokenSym,
    kNvfp4Dynamic,
    kNvfp4Static,
)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    cutlass_block_fp8_supported,
    requantize_with_max_scale,
)
from vllm.model_executor.parameter import (
    BlockQuantScaleParameter,
    ChannelQuantScaleParameter,
    ModelWeightParameter,
    PerTensorScaleParameter,
)
from vllm.model_executor.utils import replace_parameter, set_weight_attrs
from vllm.utils.flashinfer import flashinfer_trtllm_fp8_block_scale_moe

if TYPE_CHECKING:
    from vllm.model_executor.models.utils import WeightsMapper

logger = init_logger(__name__)

QUANT_ALGOS = [
    # FP8 (per-tensor weight + optional static activation scale).
    "FP8",
    # FP8 per-channel weight scale + per-token activation scale.
    "FP8_PER_CHANNEL_PER_TOKEN",
    # FP8 per-block weight-only (ModelOpt may emit this as lowercase).
    "FP8_PB_WO",
    # FP4
    "NVFP4",
    # MXFP8
    "MXFP8",
    # MIXED_PRECISION,
    "MIXED_PRECISION",
]
KV_CACHE_QUANT_ALGOS = ["FP8"]


class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
    """
    Supports loading kv-cache scaling factors from FP8 checkpoints.
    """

    def __init__(self, quant_config: "ModelOptQuantConfigBase"):
        super().__init__(quant_config)


class ModelOptQuantConfigBase(QuantizationConfig):
    LinearMethodCls: type = LinearMethodBase
    FusedMoEMethodCls: type = FusedMoEMethodBase
    KVCacheMethodCls: type = BaseKVCacheMethod

    def __init__(
        self,
        exclude_modules: list[str],
    ):
        super().__init__()
        self.exclude_modules: list[str] = exclude_modules

    def is_layer_excluded(self, prefix: str) -> bool:
        """
        Check if a layer should be excluded from quantization.

        Handles both exact matching (for fused layers) and ModelOpt wildcard matching.

        The ModelOpt exclude_modules list is a list of wildcards.
        """
        if len(self.exclude_modules) == 0:
            return False

        # First check exact matching with fused layer support
        if is_layer_skipped(prefix, self.exclude_modules, self.packed_modules_mapping):
            return True

        # TODO: This special hard coded logic is not needed for quantized checkpoints
        # generated by ModelOpt >= 0.39.0 where they are handled natually by the
        # exclude_modules config. But need to keep them for loading quantized
        # checkpoints generated by older versions. Then check substring matching
        # for patterns not caught by exact match
        for exclude_module in self.exclude_modules:
            # Skip exact matches already handled above
            if exclude_module != prefix and (
                exclude_module in prefix
                or (
                    prefix.startswith("language_model.")
                    and exclude_module in prefix.removeprefix("language_model.")
                )
            ):
                return True

        # modelopt exclude modules are not simple strings, they are wildcards
        for wildcard_pattern in self.exclude_modules:
            if fnmatch(prefix, wildcard_pattern):
                return True

        return False

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> "QuantizeMethodBase | None":
        # handle kv-cache first so we can focus only on weight quantization thereafter
        if isinstance(layer, (Attention, MLAAttention)):
            return self.KVCacheMethodCls(self)

        # handle exclusion
        if self.is_layer_excluded(prefix):
            if isinstance(layer, LinearBase):
                return UnquantizedLinearMethod()
            return None

        # TODO: This special hard coded logic is not needed for quantized checkpoints
        # generated by ModelOpt >= 0.39.0 where they are handled natually by the
        # exclude_modules config. But need to keep them for loading quantized
        # checkpoints generated by older versions. Then check substring matching
        # for patterns not caught by exact match
        if "vision_tower" in prefix or "vision_model" in prefix:
            return UnquantizedLinearMethod()

        # now, the layer is quantized, handle it here
        if isinstance(layer, LinearBase):
            quant_method = self.LinearMethodCls(self)
            if getattr(quant_method, "backend", "") == "marlin":
                quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
            return quant_method
        elif isinstance(layer, FusedMoE):
            quant_method = self.FusedMoEMethodCls(
                quant_config=self, moe_config=layer.moe_config
            )
            if getattr(quant_method, "backend", "") == "marlin":
                quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
            return quant_method

        return None

    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
        if len(self.exclude_modules) > 0:
            # This is a workaround for the weights remapping issue:
            # https://github.com/vllm-project/vllm/issues/28072
            # Right now, the Nvidia ModelOpt library use just one wildcard pattern:
            #        module_path*
            # It gets applied if the whole tree of modules rooted at module_path
            # is not quantized. Here we replace such pattern by 2 patterns that are
            # collectively equivalent to the original pattern:
            #        module_path
            #        module_path.*
            new_exclude_modules = []
            for exclude in self.exclude_modules:
                if len(exclude) >= 2 and exclude[-1] == "*" and exclude[-2] != ".":
                    new_exclude_modules.append(exclude[:-1])
                    new_exclude_modules.append(exclude[:-1] + ".*")
                else:
                    new_exclude_modules.append(exclude)

            self.exclude_modules = hf_to_vllm_mapper.apply_list(new_exclude_modules)

    @staticmethod
    def _extract_modelopt_quant_algo(
        hf_quant_cfg: dict[str, Any] | None,
    ) -> str | None:
        """Extract upper-cased quant_algo from a modelopt config.

        Returns the quant_algo string (upper-cased), or None if the config
        is not a modelopt config.
        """
        if hf_quant_cfg is None:
            return None
        if hf_quant_cfg.get("quant_method", "").lower() != "modelopt":
            return None
        if "quantization" in hf_quant_cfg:
            quant_config = hf_quant_cfg["quantization"]
            if isinstance(quant_config, dict):
                return str(quant_config.get("quant_algo", "")).upper()
            return None
        return str(hf_quant_cfg.get("quant_algo", "")).upper()

    @staticmethod
    def get_config_filenames() -> list[str]:
        return ["hf_quant_config.json"]

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        group_size: int | None,
    ) -> "ModelOptQuantConfigBase":
        raise NotImplementedError("Please implement this function in sub classes")

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "ModelOptQuantConfigBase":
        # Handle both ModelOpt format and compressed-tensors style format
        if "quantization" in config:
            # Traditional ModelOpt format:
            # {"quantization": {"quant_algo": "..."}}
            quant_config = cls.get_from_keys(config, ["quantization"])
            if not isinstance(quant_config, dict):
                raise ValueError("Expected 'quantization' to be a dictionary in config")

            quant_method = quant_config.get("quant_algo")

            # Handle kv_cache_quant_algo with proper type validation
            kv_cache_quant_method = quant_config.get("kv_cache_quant_algo")

            # Handle group_size with proper type validation
            group_size_raw = quant_config.get("group_size")

            # "exclude_modules" is the key in the legacy hf_quant_config.json
            exclude_modules = quant_config.get("exclude_modules", [])
        else:
            # Compressed-tensors style format (config.json quantization_config):
            # {"quant_algo": "...", "quant_method": "modelopt"}
            quant_method = config.get("quant_algo")

            # "kv_cache_scheme" (a dict) instead of "kv_cache_quant_algo" (a string).
            kv_cache_scheme = config.get("kv_cache_scheme")
            if isinstance(kv_cache_scheme, dict) and (
                kv_cache_scheme.get("type") == "float"
                and kv_cache_scheme.get("num_bits") == 8
            ):
                kv_cache_quant_method = "FP8"
            else:
                kv_cache_quant_method = None

            # "ignore" is the key in config.json
            exclude_modules = config.get("ignore", [])
            group_size_raw = config.get("group_size")

        if not quant_method:
            raise ValueError("Missing 'quant_algo' in quantization config")

        # Normalize quant_algo for robust matching (ModelOpt may emit lowercase).
        quant_method = str(quant_method).upper()

        if kv_cache_quant_method is None:
            # No KV cache quantization, keep this branch just to have this comment
            pass
        elif not isinstance(kv_cache_quant_method, str):
            raise ValueError(
                f"kv_cache_quant_algo must be a string, got "
                f"{type(kv_cache_quant_method)}"
            )
        else:
            kv_cache_quant_method = kv_cache_quant_method.upper()

        if not isinstance(exclude_modules, list):
            raise ValueError(
                f"exclude_modules must be a list, got {type(exclude_modules)}"
            )

        if group_size_raw is None:
            group_size = None
        elif isinstance(group_size_raw, int):
            group_size = group_size_raw
        else:
            try:
                group_size = int(group_size_raw)
            except (ValueError, TypeError):
                raise ValueError(
                    f"group_size must be an integer, got {type(group_size_raw)}"
                ) from None

        if quant_method not in QUANT_ALGOS:
            raise ValueError(
                f"ModelOpt currently only supports: {QUANT_ALGOS} "
                "quantizations in vLLM. Please check the "
                "`hf_quant_config.json` file for your model's "
                "quant configuration."
            )
        return cls._from_config(
            quant_method=quant_method,
            kv_cache_quant_method=kv_cache_quant_method,
            exclude_modules=exclude_modules,
            group_size=group_size,
            original_config=config,
        )


class ModelOptFp8Config(ModelOptQuantConfigBase):
    """Config class for ModelOpt FP8."""

    def __init__(
        self,
        quant_method: str,
        is_checkpoint_fp8_serialized: bool,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
    ) -> None:
        super().__init__(exclude_modules)
        self.quant_method = quant_method
        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
        self.kv_cache_quant_method = kv_cache_quant_method
        if is_checkpoint_fp8_serialized:
            logger.warning(
                "Detected ModelOpt fp8 checkpoint (quant_algo=%s). Please note "
                "that the format is experimental and could change.",
                quant_method,
            )

        # Select LinearMethod implementation based on quant_algo.
        if self.quant_method == "FP8":
            self.LinearMethodCls = ModelOptFp8LinearMethod
        elif self.quant_method == "FP8_PER_CHANNEL_PER_TOKEN":
            self.LinearMethodCls = ModelOptFp8PcPtLinearMethod
        elif self.quant_method == "FP8_PB_WO":
            self.LinearMethodCls = ModelOptFp8PbWoLinearMethod
        else:
            raise ValueError(
                "Unsupported ModelOpt FP8 quant_algo for vLLM: "
                f"{self.quant_method}. Supported: FP8 / "
                "FP8_PER_CHANNEL_PER_TOKEN / FP8_PB_WO."
            )

    def get_name(self) -> QuantizationMethods:
        return "modelopt"

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

    @classmethod
    def get_min_capability(cls) -> int:
        return 89

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
        if algo is not None and algo == "FP8":
            return "modelopt"
        return None

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        **kwargs: Any,
    ) -> "ModelOptFp8Config":
        is_checkpoint_fp8_serialized = "FP8" in quant_method

        return cls(
            quant_method,
            is_checkpoint_fp8_serialized,
            kv_cache_quant_method,
            exclude_modules,
        )


class ModelOptFp8LinearMethod(LinearMethodBase):
    """Linear method for Model Optimizer static quantization.
    Supports loading FP8 checkpoints with static weight scale and
    activation scale. Future support might be added for dynamic
    scales.

    Limitations:
    1. Only support per-tensor quantization due to torch._scaled_mm support.
    2. Only support float8_e4m3fn datatype
        Args: quant_config: The ModelOpt quantization config.
    """

    def __init__(self, quant_config: ModelOptFp8Config) -> None:
        self.quant_config = quant_config
        self.fp8_linear = init_fp8_linear_kernel(
            activation_quant_key=kFp8StaticTensorSym,
            weight_quant_key=kFp8StaticTensorSym,
            out_dtype=torch.get_default_dtype(),
            module_name=self.__class__.__name__,
        )

    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 input_size, output_size
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition
        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_fp8_serialized
            else params_dtype
        )
        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition, input_size_per_partition, dtype=weight_dtype
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        if self.quant_config.is_checkpoint_fp8_serialized:
            # WEIGHT SCALE
            weight_scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                weight_loader=weight_loader,
            )
            weight_scale[:] = torch.finfo(torch.float32).min
            layer.register_parameter("weight_scale", weight_scale)
            # INPUT SCALE
            scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                weight_loader=weight_loader,
            )

            scale[:] = torch.finfo(torch.float32).min
            layer.register_parameter("input_scale", scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        weight = layer.weight
        max_w_scale = layer.weight_scale.max()
        if not (layer.weight_scale == layer.weight_scale[0]).all():
            max_w_scale, weight = requantize_with_max_scale(
                layer.weight, layer.weight_scale, layer.logical_widths
            )
        layer.weight = Parameter(weight.t(), requires_grad=False)
        layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
        layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.fp8_linear.apply_weights(layer, x, bias)


class ModelOptFp8PcPtLinearMethod(LinearMethodBase):
    """Linear method for ModelOpt FP8_PER_CHANNEL_PER_TOKEN checkpoints.

    Expected checkpoint structure (per Linear):
    - weight: fp8-e4m3fn, shape [out, in]
    - weight_scale: fp32, shape [out] (per-output-channel)
    - no input_scale (activations are dynamically quantized per-token)
    """

    def __init__(self, quant_config: ModelOptFp8Config) -> None:
        self.quant_config = quant_config
        self.fp8_linear = init_fp8_linear_kernel(
            activation_quant_key=kFp8DynamicTokenSym,
            weight_quant_key=kFp8StaticTokenSym,
            out_dtype=torch.get_default_dtype(),
            module_name=self.__class__.__name__,
        )

    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 input_size, output_size

        if not self.quant_config.is_checkpoint_fp8_serialized:
            raise ValueError(
                "FP8_PER_CHANNEL_PER_TOKEN currently only supports "
                "FP8-serialized checkpoints."
            )

        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition,
                dtype=torch.float8_e4m3fn,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        weight_scale = ChannelQuantScaleParameter(
            data=torch.empty(output_size_per_partition, dtype=torch.float32),
            output_dim=0,
            weight_loader=weight_loader,
        )
        weight_scale[:] = torch.finfo(torch.float32).min
        layer.register_parameter("weight_scale", weight_scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        layer.weight = Parameter(layer.weight.t(), requires_grad=False)
        layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.fp8_linear.apply_weights(layer, x, bias)


class ModelOptFp8PbWoLinearMethod(LinearMethodBase):
    """Linear method for ModelOpt FP8_PB_WO checkpoints.

    ModelOpt exports `weight_scale` as a 4D tensor:
      [out_blk, 1, in_blk, 1]
    where block size is typically 128 for both dims.

    vLLM executes it as FP8 GEMM with *dynamic per-token* activation quant.
    """

    _WEIGHT_BLOCK_SIZE: tuple[int, int] = (128, 128)

    def __init__(self, quant_config: ModelOptFp8Config) -> None:
        self.quant_config = quant_config
        block_n, block_k = self._WEIGHT_BLOCK_SIZE
        self.weight_block_size = list(self._WEIGHT_BLOCK_SIZE)
        self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
            weight_group_shape=GroupShape(block_n, block_k),
            act_quant_group_shape=GroupShape(1, block_k),
            cutlass_block_fp8_supported=cutlass_block_fp8_supported(),
            use_aiter_and_is_supported=False,
        )

    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 input_size, output_size

        if not self.quant_config.is_checkpoint_fp8_serialized:
            raise ValueError(
                "FP8_PB_WO currently only supports FP8-serialized checkpoints."
            )

        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        # Expose block size so the v2 weight loaders can translate offsets from
        # element-space -> block-space for BlockQuantScaleParameter.
        layer.weight_block_size = self.weight_block_size

        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition,
                dtype=torch.float8_e4m3fn,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        block_n, block_k = self._WEIGHT_BLOCK_SIZE
        if output_size_per_partition % block_n != 0:
            raise ValueError(
                "ModelOpt FP8_PB_WO requires out_features divisible by "
                f"{block_n}, got {output_size_per_partition}."
            )
        if input_size_per_partition % block_k != 0:
            raise ValueError(
                "ModelOpt FP8_PB_WO requires in_features divisible by "
                f"{block_k}, got {input_size_per_partition}."
            )

        out_blks = output_size_per_partition // block_n
        in_blks = input_size_per_partition // block_k

        # Match ModelOpt's exported shape so weight loading works without a
        # custom loader: [out_blk, 1, in_blk, 1]
        weight_scale = BlockQuantScaleParameter(
            data=torch.empty((out_blks, 1, in_blks, 1), dtype=torch.float32),
            input_dim=2,
            output_dim=0,
            weight_loader=weight_loader,
        )
        weight_scale[:] = torch.finfo(torch.float32).min
        layer.register_parameter("weight_scale", weight_scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # Keep weight in [out, in] layout for W8A8BlockFp8LinearOp.
        layer.weight = Parameter(layer.weight.data, requires_grad=False)

        scale = layer.weight_scale
        if scale.dim() == 4:
            # [out_blk, 1, in_blk, 1] -> [out_blk, in_blk]
            scale = scale.squeeze(1).squeeze(-1)
        elif scale.dim() != 2:
            raise ValueError(
                "Unexpected ModelOpt FP8_PB_WO weight_scale shape: "
                f"{tuple(scale.shape)}."
            )

        layer.weight_scale = Parameter(scale.contiguous(), requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.w8a8_block_fp8_linear.apply(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=None,
            bias=bias,
        )


class ModelOptFp8MoEMethod(FusedMoEMethodBase):
    """MoE method for ModelOpt FP8.
    Supports loading FP8 checkpoints with static weight scale and
    activation scale.
    Args:
        quant_config: The ModelOpt quantization config.
    """

    def __init__(
        self,
        quant_config: ModelOptFp8Config,
        moe_config: FusedMoEConfig,
    ) -> None:
        super().__init__(moe_config)
        self.quant_config = quant_config
        assert self.quant_config.is_checkpoint_fp8_serialized

        # Select Fp8 MoE backend
        self.fp8_backend, self.experts_cls = select_fp8_moe_backend(
            config=self.moe,
            weight_key=kFp8StaticTensorSym,
            activation_key=kFp8StaticTensorSym,
        )

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalizeModular | None:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalizeModular,
        layer: torch.nn.Module,
    ) -> mk.FusedMoEExpertsModular:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    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,
    ):
        layer.orig_dtype = params_dtype
        layer.num_experts = num_experts

        # Use FP8 dtype if checkpoint is serialized
        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_fp8_serialized
            else params_dtype
        )
        weight_loader = extra_weight_attrs.get("weight_loader")

        w13_num_shards = 2 if self.moe.is_act_and_mul else 1

        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                hidden_size,
                dtype=weight_dtype,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight", w13_weight)

        w2_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                intermediate_size_per_partition,
                dtype=weight_dtype,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_weight", w2_weight)

        # WEIGHT SCALES - Per-tensor scaling for ModelOpts
        # For gated MoE, allocate 2 scales for w1 and w3 respectively.
        # They will be combined to a single scale after weight loading.
        # For non-gated MoE, allocate 1 scale for w13.
        w13_weight_scale = PerTensorScaleParameter(
            data=torch.full(
                (num_experts, w13_num_shards),
                1.0,
                dtype=torch.float32,
            ),
            weight_loader=weight_loader,
        )
        w2_weight_scale = PerTensorScaleParameter(
            data=torch.full((num_experts,), 1.0, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)
        layer.register_parameter("w2_weight_scale", w2_weight_scale)

        # INPUT SCALES - Per-tensor scaling for ModelOpt
        w13_input_scale = PerTensorScaleParameter(
            data=torch.full((num_experts,), 1.0, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        w2_input_scale = PerTensorScaleParameter(
            data=torch.full((num_experts,), 1.0, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_input_scale", w13_input_scale)
        layer.register_parameter("w2_input_scale", w2_input_scale)

    def _setup_kernel(
        self,
        layer: FusedMoE,
        w13: torch.Tensor,
        w2: torch.Tensor,
        w13_scale: torch.Tensor,
        w2_scale: torch.Tensor,
        w13_input_scale: torch.Tensor,
        w2_input_scale: torch.Tensor,
    ):
        w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
            fp8_backend=self.fp8_backend,
            layer=layer,
            w13=w13,
            w2=w2,
            w13_scale=w13_scale,
            w2_scale=w2_scale,
            w13_input_scale=w13_input_scale,
            w2_input_scale=w2_input_scale,
        )

        # Replace parameters with updated versions. Note that this helper
        # function ensures the replacement is compatible with RL weight reloads.
        replace_parameter(layer, "w13_weight", w13)
        replace_parameter(layer, "w2_weight", w2)
        replace_parameter(layer, "w13_weight_scale", w13_scale)
        replace_parameter(layer, "w2_weight_scale", w2_scale)

        # Setup modular kernel.
        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
        assert self.experts_cls is not None
        self.moe_kernel = make_fp8_moe_kernel(
            moe_quant_config=self.moe_quant_config,
            moe_config=self.moe,
            fp8_backend=self.fp8_backend,
            experts_cls=self.experts_cls,
            routing_tables=layer._maybe_init_expert_routing_tables(),
            shared_experts=layer.shared_experts,
        )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        w13 = layer.w13_weight
        w2 = layer.w2_weight
        w13_scale = layer.w13_weight_scale
        w2_scale = layer.w2_weight_scale
        w13_input_scale = layer.w13_input_scale
        w2_input_scale = layer.w2_input_scale

        # Per tensor kernels require single activation scale. Use the max.
        w13_input_scale, w2_input_scale = process_fp8_input_tensor_strategy_moe(
            w13_input_scale, w2_input_scale
        )
        replace_parameter(layer, "w13_input_scale", w13_input_scale)
        replace_parameter(layer, "w2_input_scale", w2_input_scale)

        # Per tensor kernels require single weight scale for w13 per expert, but
        # on disk there is a scale for w1 and w3. Use the max to requantize.
        shard_size = layer.intermediate_size_per_partition
        w13, w13_scale = process_fp8_weight_tensor_strategy_moe(
            w13,
            w13_scale,
            shard_size,
            num_experts=layer.w13_weight.shape[0],
            is_act_and_mul=self.moe.is_act_and_mul,
        )

        # Shuffle weights to runtime format and setup kernel.
        self._setup_kernel(
            layer, w13, w2, w13_scale, w2_scale, w13_input_scale, w2_input_scale
        )

    def get_fused_moe_quant_config(self, layer: torch.nn.Module) -> FusedMoEQuantConfig:
        w1_scale = layer.w13_weight_scale
        w2_scale = layer.w2_weight_scale
        a1_scale = layer.w13_input_scale
        a2_scale = layer.w2_input_scale

        return make_fp8_moe_quant_config(
            fp8_backend=self.fp8_backend,
            w1_scale=w1_scale,
            w2_scale=w2_scale,
            a1_scale=a1_scale,
            a2_scale=a2_scale,
        )

    def apply_monolithic(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert self.is_monolithic
        assert self.moe_kernel is not None
        return self.moe_kernel.apply_monolithic(
            x,
            layer.w13_weight,
            layer.w2_weight,
            router_logits,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            num_expert_group=layer.num_expert_group,
            topk_group=layer.topk_group,
            e_score_correction_bias=layer.e_score_correction_bias,
            routed_scaling_factor=layer.routed_scaling_factor,
        )

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        shared_experts_input: torch.Tensor | None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert not self.is_monolithic
        assert self.moe_kernel is not None
        return self.moe_kernel.apply(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights,
            topk_ids,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            shared_experts_input=shared_experts_input,
        )


ModelOptFp8Config.LinearMethodCls = ModelOptFp8LinearMethod
ModelOptFp8Config.FusedMoEMethodCls = ModelOptFp8MoEMethod
ModelOptFp8Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod


class ModelOptNvFp4Config(ModelOptQuantConfigBase):
    """Config class for ModelOpt FP4."""

    def __init__(
        self,
        is_checkpoint_nvfp4_serialized: bool,
        kv_cache_quant_algo: str | None,
        exclude_modules: list[str],
        group_size: int = 16,
    ) -> None:
        super().__init__(exclude_modules)
        self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
        if is_checkpoint_nvfp4_serialized:
            logger.warning(
                "Detected ModelOpt NVFP4 checkpoint. Please note that"
                " the format is experimental and could change in future."
            )

            self.group_size = group_size
            self.kv_cache_quant_algo = kv_cache_quant_algo

    def get_name(self) -> QuantizationMethods:
        return "modelopt_fp4"

    def get_supported_act_dtypes(self) -> list[torch.dtype]:
        return [torch.bfloat16, torch.half, torch.float8_e4m3fn]

    @classmethod
    def get_min_capability(cls) -> int:
        return 75

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
        if algo is not None and ("NVFP4" in algo or "FP4" in algo):
            return "modelopt_fp4"
        return None

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        group_size: int | None,
        **kwargs: Any,
    ) -> "ModelOptNvFp4Config":
        is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method

        if group_size is None:
            group_size = 16  # Default value

        # For FP4, these fields are required
        if is_checkpoint_nvfp4_serialized and "quantization" in original_config:
            # Check if required fields are present in the quantization config
            quant_config = original_config["quantization"]
            required_fields = ["group_size", "kv_cache_quant_algo", "exclude_modules"]
            missing_fields = [
                field for field in required_fields if field not in quant_config
            ]
            if missing_fields:
                raise ValueError(
                    f"NVFP4 quantization requires the following fields in "
                    f"hf_quant_config.json: {missing_fields}"
                )

        return cls(
            is_checkpoint_nvfp4_serialized,
            kv_cache_quant_method,
            exclude_modules,
            group_size,
        )


class ModelOptNvFp4LinearMethod(LinearMethodBase):
    """Linear method for Model Optimizer NVFP4.
    Supports loading NVFP4 checkpoints with the following structure:

    input_scale: torch.float32, scalar ,
    weight: NVFP4(represented as byte) Shape: [1, X, y/2]
    weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
    weight_scale_2: torch.float32, scalar,
    Args: quant_config: The ModelOpt quantization config.
    """

    def __init__(self, quant_config: ModelOptNvFp4Config) -> None:
        self.quant_config = quant_config
        self.marlin_input_dtype = None
        self.backend = select_nvfp4_linear_backend()

    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 input_size, output_size
        if not self.quant_config.is_checkpoint_nvfp4_serialized:
            raise ValueError(
                "NVFP4 quantization was selected, "
                " dynamic quantization is not supported."
            )
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        if input_size_per_partition % 16 != 0:
            raise ValueError(
                "Unsupported model when in features size is not multiple of 16"
            )
        # The nvfp4 weight is still represented as
        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_nvfp4_serialized
            else params_dtype
        )
        # Weight
        weight = ModelWeightParameter(
            data=torch.empty(
                # 2 fp4 items are packed in the input dimension
                layer.output_size_per_partition,
                layer.input_size_per_partition // 2,
                dtype=torch.uint8,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        # Input Global Scale
        input_global_scale = PerTensorScaleParameter(
            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("input_scale", input_global_scale)

        # Weight Global Scale
        weight_global_scale = PerTensorScaleParameter(
            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight_scale_2", weight_global_scale)

        # Per Block Weight Scale
        weight_scale = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition // self.quant_config.group_size,
                dtype=weight_dtype,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )

        layer.register_parameter("weight_scale", weight_scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # Rename ModelOpt checkpoint names to standardized names
        input_global_scale = layer.input_scale.max().to(torch.float32)
        layer.input_global_scale = Parameter(input_global_scale, requires_grad=False)
        del layer.input_scale
        weight_global_scale = layer.weight_scale_2.max().to(torch.float32)
        layer.weight_global_scale = Parameter(weight_global_scale, requires_grad=False)
        del layer.weight_scale_2

        # Pre-compute alpha and inverse for runtime quantization
        layer.alpha = Parameter(
            layer.input_global_scale * layer.weight_global_scale, requires_grad=False
        )
        layer.input_global_scale_inv = Parameter(
            (1.0 / layer.input_global_scale).to(torch.float32), requires_grad=False
        )

        # Convert layer to NVFP4 linear kernel format
        convert_to_nvfp4_linear_kernel_format(self.backend, layer)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return apply_nvfp4_linear(
            backend=self.backend,
            layer=layer,
            x=x,
            bias=bias,
        )


class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
    """
    MoE Method for FP4 Quantization.
    Args:
        quant_config: NVFP4 Quant Config
    """

    def __init__(
        self,
        quant_config: ModelOptNvFp4Config,
        moe_config: FusedMoEConfig,
    ) -> None:
        super().__init__(moe_config)
        self.quant_config = quant_config
        # Select experts implementation.
        self.nvfp4_backend, self.experts_cls = select_nvfp4_moe_backend(
            config=self.moe,
            weight_key=kNvfp4Static,
            activation_key=kNvfp4Dynamic,
        )

        self.use_global_sf = is_global_sf_supported_for_nvfp4_backend(
            self.nvfp4_backend
        )

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalizeModular | None:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    def uses_weight_scale_2_pattern(self) -> bool:
        """
        FP4 variants use 'weight_scale_2' pattern for per-tensor weight scales.
        """
        return True

    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,
    ):
        assert self.quant_config.is_checkpoint_nvfp4_serialized

        layer.num_experts = num_experts
        layer.params_dtype = params_dtype
        layer.quant_config = self.quant_config
        weight_dtype = torch.uint8
        weight_scale_dtype = torch.float8_e4m3fn
        weight_loader = extra_weight_attrs.get("weight_loader")
        global_num_experts = extra_weight_attrs.get("global_num_experts")
        w13_num_shards = 2 if self.moe.is_act_and_mul else 1
        # GEMM 1
        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                # 2 fp4 items are packed in the input dimension
                hidden_size // 2,
                dtype=weight_dtype,
            ),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight", w13_weight)

        # GEMM 2
        w2_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                # 2 fp4 items are packed in the input dimension
                intermediate_size_per_partition // 2,
                dtype=weight_dtype,
            ),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_weight", w2_weight)

        w13_weight_scale = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                # 2 fp4 items are packed in the input dimension
                hidden_size // self.quant_config.group_size,
                dtype=weight_scale_dtype,
            ),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)

        w2_weight_scale = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                # 2 fp4 items are packed in the input dimension
                intermediate_size_per_partition // self.quant_config.group_size,
                dtype=weight_scale_dtype,
            ),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_weight_scale", w2_weight_scale)

        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
        )

        w13_weight_scale_2 = PerTensorScaleParameter(
            data=torch.empty(num_experts, w13_num_shards, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)

        w2_weight_scale_2 = PerTensorScaleParameter(
            data=torch.empty(num_experts, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)

        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
        )

        global_sf_num_experts = (
            global_num_experts if self.use_global_sf else num_experts
        )
        w13_input_scale = PerTensorScaleParameter(
            data=torch.empty(
                global_sf_num_experts,
                w13_num_shards,
                dtype=torch.float32,
            ),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_input_scale", w13_input_scale)

        w2_input_scale = PerTensorScaleParameter(
            data=torch.empty(global_sf_num_experts, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_input_scale", w2_input_scale)

    def process_weights_after_loading(self, layer: FusedMoE) -> None:
        """
        Convert NVFP4 MoE weights into kernel format and setup the kernel.
        """

        # Use a single gscale for w13.
        if self.moe.is_act_and_mul and not torch.allclose(
            layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1]
        ):
            logger.warning_once(
                "w1_weight_scale_2 must match w3_weight_scale_2. "
                "Accuracy may be affected."
            )
        w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0].contiguous()

        (
            w13,
            w13_scale,
            w13_scale_2,
            a13_scale,
            w2,
            w2_scale,
            w2_scale_2,
            a2_scale,
        ) = convert_to_nvfp4_moe_kernel_format(
            nvfp4_backend=self.nvfp4_backend,
            layer=layer,
            w13=layer.w13_weight,
            w13_scale=layer.w13_weight_scale,
            w13_scale_2=w13_weight_scale_2,
            a13_scale=layer.w13_input_scale,
            w2=layer.w2_weight,
            w2_scale=layer.w2_weight_scale,
            w2_scale_2=layer.w2_weight_scale_2,
            a2_scale=layer.w2_input_scale,
            is_act_and_mul=self.moe.is_act_and_mul,
        )

        replace_parameter(layer, "w13_weight", w13)
        replace_parameter(layer, "w13_weight_scale", w13_scale)
        replace_parameter(layer, "w13_weight_scale_2", w13_scale_2)
        replace_parameter(layer, "w13_input_scale", a13_scale)
        replace_parameter(layer, "w2_weight", w2)
        replace_parameter(layer, "w2_weight_scale", w2_scale)
        replace_parameter(layer, "w2_weight_scale_2", w2_scale_2)
        replace_parameter(layer, "w2_input_scale", a2_scale)

        # Setup modular kernel.
        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
        assert self.experts_cls is not None
        self.moe_kernel = make_nvfp4_moe_kernel(
            moe_quant_config=self.moe_quant_config,
            moe_config=self.moe,
            experts_cls=self.experts_cls,
            shared_experts=layer.shared_experts,
            routing_tables=layer._maybe_init_expert_routing_tables(),
        )
        self.moe_kernel.fused_experts.process_weights_after_loading(layer)

    def get_fused_moe_quant_config(self, layer: torch.nn.Module) -> FusedMoEQuantConfig:
        return make_nvfp4_moe_quant_config(
            backend=self.nvfp4_backend,
            w13_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            w13_scale_2=layer.w13_weight_scale_2,
            w2_scale_2=layer.w2_weight_scale_2,
            a13_scale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
        )

    @property
    def supports_eplb(self) -> bool:
        return True

    def apply_monolithic(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert self.is_monolithic
        assert self.moe_kernel is not None
        return self.moe_kernel.apply_monolithic(
            x,
            layer.w13_weight,
            layer.w2_weight,
            router_logits,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            num_expert_group=layer.num_expert_group,
            topk_group=layer.topk_group,
            e_score_correction_bias=layer.e_score_correction_bias,
            routed_scaling_factor=layer.routed_scaling_factor,
        )

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        shared_experts_input: torch.Tensor | None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert not self.is_monolithic
        assert self.moe_kernel is not None
        return self.moe_kernel.apply(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights,
            topk_ids,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            shared_experts_input=shared_experts_input,
        )


ModelOptNvFp4Config.LinearMethodCls = ModelOptNvFp4LinearMethod
ModelOptNvFp4Config.FusedMoEMethodCls = ModelOptNvFp4FusedMoE
ModelOptNvFp4Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod


class ModelOptMxFp8Config(ModelOptQuantConfigBase):
    """Config class for ModelOpt MXFP8."""

    def __init__(
        self,
        is_checkpoint_mxfp8_serialized: bool,
        kv_cache_quant_algo: str | None,
        exclude_modules: list[str],
    ) -> None:
        super().__init__(exclude_modules)
        self.is_checkpoint_mxfp8_serialized = is_checkpoint_mxfp8_serialized

        if not is_checkpoint_mxfp8_serialized:
            raise ValueError(
                "MXFP8 quantization requires a serialized checkpoint. "
                "Dynamic quantization is not supported."
            )

        logger.warning(
            "Detected ModelOpt MXFP8 checkpoint. Please note that "
            "the format is experimental and could change in future."
        )

        self.kv_cache_quant_algo = kv_cache_quant_algo

    def get_name(self) -> QuantizationMethods:
        return "modelopt_mxfp8"

    def get_supported_act_dtypes(self) -> list[torch.dtype]:
        return [torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        # MXFP8 hardware acceleration requires Blackwell (SM100) or newer
        return 100

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
        if algo is not None and "MXFP8" in algo:
            return "modelopt_mxfp8"
        return None

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        **kwargs: Any,
    ) -> "ModelOptMxFp8Config":
        is_checkpoint_mxfp8_serialized = "MXFP8" in quant_method.upper()

        # For MXFP8, validate required fields in the config
        if is_checkpoint_mxfp8_serialized and "quantization" in original_config:
            quant_config = original_config["quantization"]
            required_fields = ["kv_cache_quant_algo", "exclude_modules"]
            missing_fields = [
                field for field in required_fields if field not in quant_config
            ]
            if missing_fields:
                raise ValueError(
                    f"MXFP8 quantization requires the following fields in "
                    f"hf_quant_config.json: {missing_fields}"
                )

        return cls(
            is_checkpoint_mxfp8_serialized,
            kv_cache_quant_method,
            exclude_modules,
        )


class ModelOptMxFp8LinearMethod(LinearMethodBase):
    """Linear method for ModelOpt MXFP8 quantization."""

    def __init__(self, quant_config: ModelOptMxFp8Config) -> None:
        self.quant_config = quant_config

        if not self.quant_config.is_checkpoint_mxfp8_serialized:
            raise ValueError(
                "MXFP8 currently only supports serialized checkpoints. "
                "Dynamic quantization is not supported."
            )

        self.backend: Mxfp8LinearBackend = Mxfp8LinearBackend.FLASHINFER_CUTLASS
        self.mxfp8_linear_op = Mxfp8LinearOp(backend=self.backend)
        logger.info_once("Using %s backend for MXFP8 GEMM", self.backend.value)

    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 input_size, output_size

        if not self.quant_config.is_checkpoint_mxfp8_serialized:
            raise ValueError(
                "MXFP8 quantization was selected, but checkpoint is not "
                "MXFP8 serialized. Dynamic quantization is not supported."
            )

        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        if input_size_per_partition % MXFP8_BLOCK_SIZE != 0:
            raise ValueError(
                f"MXFP8 requires input dimension to be divisible by "
                f"{MXFP8_BLOCK_SIZE}, got {input_size_per_partition}"
            )

        # Weight tensor: FP8 E4M3 format
        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition,
                dtype=MXFP8_VALUE_DTYPE,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        # Weight scale tensor (E8M0 encoded as uint8), one scale per block of 32 along K
        weight_scale = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition // MXFP8_BLOCK_SIZE,
                dtype=MXFP8_SCALE_DTYPE,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight_scale", weight_scale)

    def _process_weights_after_loading_scale_2d(self, layer: torch.nn.Module) -> None:
        """Not swizzled - MXFP8 GEMM emulation"""
        weight = layer.weight.data  # [N, K]
        N, K = weight.shape
        scale_k = K // MXFP8_BLOCK_SIZE

        # Slice weight_scale to match weight dimensions (handles padding)
        weight_scale = layer.weight_scale.data[:N, :scale_k].contiguous()

        layer.weight = Parameter(weight.contiguous(), requires_grad=False)
        layer.weight_scale = Parameter(weight_scale, requires_grad=False)

    def _process_weights_after_loading_scale_1d(self, layer: torch.nn.Module) -> None:
        """Swizzled - MXFP8 GEMM Flashinfer CUTLASS"""
        weight = layer.weight.data  # [N, K]
        N, K = weight.shape

        # 2D weight scale
        weight_scale = layer.weight_scale.data

        # Swizzle the weight scales
        scale_k = K // MXFP8_BLOCK_SIZE
        weight_scale_2d = weight_scale[:N, :scale_k].contiguous()
        weight_scale_swizzled = swizzle_mxfp8_scale(weight_scale_2d, M=N, K=K)

        layer.weight = Parameter(weight.contiguous(), requires_grad=False)
        layer.weight_scale = Parameter(
            weight_scale_swizzled.contiguous(), requires_grad=False
        )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # Validate weight tensor
        if layer.weight.ndim != 2:
            raise ValueError(
                f"MXFP8 weight must be 2D tensor [N, K], got {layer.weight.ndim}D "
                f"with shape {tuple(layer.weight.shape)}"
            )

        if layer.weight.dtype != MXFP8_VALUE_DTYPE:
            raise ValueError(
                f"MXFP8 weight must be {MXFP8_VALUE_DTYPE} (FP8 E4M3), "
                f"got {layer.weight.dtype}. The checkpoint may not be properly "
                f"quantized with MXFP8."
            )

        # Validate weight scale tensor (should be 2D, not swizzled)
        assert layer.weight_scale.ndim == 2, (
            f"MXFP8 weight scale must be 2D, got {layer.weight_scale.ndim}D"
        )
        assert layer.weight_scale.dtype == MXFP8_SCALE_DTYPE, (
            f"MXFP8 weight scale must be {MXFP8_SCALE_DTYPE},"
            f" got {layer.weight_scale.dtype}"
        )

        if self.backend == Mxfp8LinearBackend.EMULATION:
            # Swizzled layout is not used
            self._process_weights_after_loading_scale_2d(layer)
            return

        assert self.backend == Mxfp8LinearBackend.FLASHINFER_CUTLASS
        # Swizzled layout is required for Flashinfer CUTLASS
        self._process_weights_after_loading_scale_1d(layer)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        if layer.weight.dtype != MXFP8_VALUE_DTYPE:
            raise ValueError(
                f"Weight dtype {layer.weight.dtype} != expected {MXFP8_VALUE_DTYPE}"
            )
        if layer.weight_scale.dtype != MXFP8_SCALE_DTYPE:
            raise ValueError(
                f"Weight scale dtype {layer.weight_scale.dtype} != "
                f"expected {MXFP8_SCALE_DTYPE}"
            )

        return self.mxfp8_linear_op.apply(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            out_dtype=x.dtype,
            bias=bias,
        )


class ModelOptMxFp8FusedMoE(FusedMoEMethodBase):
    """FlashInfer TRTLLM MXFP8 block-scale MoE for ModelOpt checkpoints."""

    def __init__(
        self,
        quant_config: ModelOptMxFp8Config,
        moe_config: FusedMoEConfig,
    ) -> None:
        super().__init__(moe_config)
        self.quant_config = quant_config
        assert self.quant_config.is_checkpoint_mxfp8_serialized

        self.mxfp8_backend, _ = select_mxfp8_moe_backend(self.moe)

    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,
    ):
        layer.intermediate_size_per_partition = intermediate_size_per_partition
        layer.hidden_size = hidden_size
        layer.orig_dtype = params_dtype

        if hidden_size % MXFP8_BLOCK_SIZE != 0:
            raise ValueError(
                f"MXFP8 MoE requires hidden_size divisible by {MXFP8_BLOCK_SIZE}, "
                f"got {hidden_size}."
            )
        if intermediate_size_per_partition % MXFP8_BLOCK_SIZE != 0:
            raise ValueError(
                "MXFP8 MoE requires intermediate_size_per_partition divisible by "
                f"{MXFP8_BLOCK_SIZE}, got {intermediate_size_per_partition}."
            )

        layer.num_experts = num_experts
        weight_loader = extra_weight_attrs.get("weight_loader")
        w13_num_shards = 2 if self.moe.is_act_and_mul else 1

        # GEMM 1 weights: [E, (2I or I), H]
        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                hidden_size,
                dtype=MXFP8_VALUE_DTYPE,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight", w13_weight)

        # GEMM 2 weights: [E, H, I]
        w2_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                intermediate_size_per_partition,
                dtype=MXFP8_VALUE_DTYPE,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_weight", w2_weight)

        # Per-block (K=32) E8M0 scales.
        w13_weight_scale = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                hidden_size // MXFP8_BLOCK_SIZE,
                dtype=MXFP8_SCALE_DTYPE,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)

        w2_weight_scale = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                intermediate_size_per_partition // MXFP8_BLOCK_SIZE,
                dtype=MXFP8_SCALE_DTYPE,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_weight_scale", w2_weight_scale)

        # Ensure the generic MoE weight-loader treats these as block scales.
        set_weight_attrs(
            layer.w13_weight_scale,
            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value},
        )
        set_weight_attrs(
            layer.w2_weight_scale,
            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value},
        )

    @staticmethod
    def _check_weight_dtypes(layer: torch.nn.Module) -> None:
        """Validate weight and scale dtypes before processing."""
        expected = {
            "w13_weight": MXFP8_VALUE_DTYPE,
            "w2_weight": MXFP8_VALUE_DTYPE,
            "w13_weight_scale": MXFP8_SCALE_DTYPE,
            "w2_weight_scale": MXFP8_SCALE_DTYPE,
        }
        for name, expected_dtype in expected.items():
            actual = getattr(layer, name).dtype
            if actual != expected_dtype:
                raise ValueError(
                    f"Expected {name} dtype {expected_dtype}, got {actual}."
                )

    def _shuffle_weights_for_trtllm(self, layer: torch.nn.Module) -> None:
        """Shuffle weights and scales into FlashInfer TRTLLM MXFP8 layout."""
        from flashinfer import (
            reorder_rows_for_gated_act_gemm,
            shuffle_matrix_a,
            shuffle_matrix_sf_a,
        )

        epilogue_tile_m = 128
        num_experts = layer.w13_weight.shape[0]
        is_gated = self.moe.is_act_and_mul
        intermediate_size_factor = 2 if is_gated else 1

        w13_weight = layer.w13_weight.data
        w13_scale = layer.w13_weight_scale.data
        if is_gated:
            # FI TRTLLM gated kernels use W31 ordering. Model checkpoints store
            # gated projection as W13, so convert once before shuffling.
            w13_weight = swap_w13_to_w31(w13_weight)
            w13_scale = swap_w13_to_w31(w13_scale)

        w13_weight_shuffled = []
        w2_weight_shuffled = []
        w13_scale_shuffled = []
        w2_scale_shuffled = []
        for i in range(num_experts):
            w13_i = w13_weight[i].reshape(
                intermediate_size_factor * layer.intermediate_size_per_partition, -1
            )
            w13_sf_i = w13_scale[i].reshape(
                intermediate_size_factor * layer.intermediate_size_per_partition, -1
            )
            if is_gated:
                # Reorder rows for gated activation layout expected by TRTLLM.
                w13_i = reorder_rows_for_gated_act_gemm(w13_i.clone())
                w13_sf_i = reorder_rows_for_gated_act_gemm(w13_sf_i.clone())

            w13_shuffled_i = shuffle_matrix_a(w13_i.view(torch.uint8), epilogue_tile_m)
            w2_shuffled_i = shuffle_matrix_a(
                layer.w2_weight.data[i].view(torch.uint8), epilogue_tile_m
            )
            w13_weight_shuffled.append(
                w13_shuffled_i.contiguous().view(MXFP8_VALUE_DTYPE)
            )
            w2_weight_shuffled.append(
                w2_shuffled_i.contiguous().view(MXFP8_VALUE_DTYPE)
            )
            w13_sf_shuffled_i = shuffle_matrix_sf_a(
                w13_sf_i.view(torch.uint8).reshape(
                    intermediate_size_factor * layer.intermediate_size_per_partition,
                    -1,
                ),
                epilogue_tile_m,
            )
            w2_sf_shuffled_i = shuffle_matrix_sf_a(
                layer.w2_weight_scale.data[i]
                .view(torch.uint8)
                .reshape(layer.hidden_size, -1),
                epilogue_tile_m,
            )
            w13_scale_shuffled.append(
                w13_sf_shuffled_i.contiguous().view(MXFP8_SCALE_DTYPE)
            )
            w2_scale_shuffled.append(
                w2_sf_shuffled_i.contiguous().view(MXFP8_SCALE_DTYPE)
            )

        replace_parameter(
            layer, "w13_weight", torch.stack(w13_weight_shuffled).contiguous()
        )
        replace_parameter(
            layer, "w2_weight", torch.stack(w2_weight_shuffled).contiguous()
        )
        replace_parameter(
            layer,
            "w13_weight_scale",
            torch.stack(w13_scale_shuffled).contiguous(),
        )
        replace_parameter(
            layer,
            "w2_weight_scale",
            torch.stack(w2_scale_shuffled).contiguous(),
        )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        if getattr(layer, "_already_called_process_weights_after_loading", False):
            return

        self._check_weight_dtypes(layer)
        self._shuffle_weights_for_trtllm(layer)
        layer._already_called_process_weights_after_loading = True

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalizeModular | None:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalizeModular,
        layer: torch.nn.Module,
    ) -> mk.FusedMoEExpertsModular:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> FusedMoEQuantConfig | None:
        # TRTLLM MXFP8 path is monolithic and does not use modular kernel config.
        return None

    @property
    def is_monolithic(self) -> bool:
        return self.mxfp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM

    def apply_monolithic(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        from flashinfer.fused_moe.core import (
            ActivationType,
            Fp8QuantizationType,
        )

        assert self.mxfp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM

        if layer.enable_eplb:
            raise NotImplementedError(
                "EPLB is not supported for FlashInfer TRTLLM MXFP8 MoE backend."
            )

        supported_activations = [MoEActivation.SILU]
        if layer.activation not in supported_activations:
            raise NotImplementedError(
                "FlashInfer TRTLLM MXFP8 MoE supports only "
                f"{supported_activations}, got {layer.activation}."
            )

        # Map vLLM MoEActivation to FlashInfer ActivationType.
        activation_map = {
            MoEActivation.SILU: ActivationType.Swiglu,
            MoEActivation.RELU2_NO_MUL: ActivationType.Relu2,
        }
        fi_activation_type: ActivationType = activation_map[layer.activation]

        # DeepSeekV3 routing requires float32 logits; others expect bfloat16.
        if layer.routing_method_type == RoutingMethodType.DeepSeekV3:
            assert router_logits.dtype == torch.float32, (
                "DeepSeekV3 routing requires float32 router_logits, "
                f"got {router_logits.dtype}."
            )
        else:
            router_logits = router_logits.to(torch.bfloat16)

        # Treat 0 as "unset" for compatibility with ungrouped routing configs.
        n_group = layer.num_expert_group or None
        topk_group = layer.topk_group or None

        hidden_states_mxfp8, hidden_states_scale = mxfp8_e4m3_quantize(
            x,
            is_sf_swizzled_layout=False,
        )

        kwargs: dict = dict(
            routing_logits=router_logits,
            routing_bias=layer.e_score_correction_bias,
            hidden_states=hidden_states_mxfp8,
            hidden_states_scale=hidden_states_scale,
            gemm1_weights=layer.w13_weight,
            gemm1_weights_scale=layer.w13_weight_scale,
            gemm2_weights=layer.w2_weight,
            gemm2_weights_scale=layer.w2_weight_scale,
            num_experts=layer.global_num_experts,
            top_k=layer.top_k,
            # Keep Optional semantics: FlashInfer expects None for non-grouped
            # routing (e.g. Qwen3 Renormalize), not 0.
            n_group=n_group,
            topk_group=topk_group,
            intermediate_size=layer.intermediate_size_per_partition,
            local_expert_offset=layer.ep_rank * layer.local_num_experts,
            local_num_experts=layer.local_num_experts,
            routed_scaling_factor=layer.routed_scaling_factor,
            routing_method_type=layer.routing_method_type,
            use_shuffled_weight=True,
            weight_layout=0,
            fp8_quantization_type=Fp8QuantizationType.MxFp8,
        )

        if fi_activation_type != ActivationType.Swiglu:
            raise NotImplementedError(
                "FlashInfer TRTLLM MXFP8 MoE supports only Swiglu activation, "
                f"got {fi_activation_type}."
            )

        return flashinfer_trtllm_fp8_block_scale_moe(**kwargs)

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        shared_experts_input: torch.Tensor | None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert not self.is_monolithic
        raise NotImplementedError(
            "Non-monolithic MXFP8 MoE path is not yet implemented."
        )


# Register the method classes for ModelOptMxFp8Config
ModelOptMxFp8Config.LinearMethodCls = ModelOptMxFp8LinearMethod
ModelOptMxFp8Config.FusedMoEMethodCls = ModelOptMxFp8FusedMoE
ModelOptMxFp8Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod


class ModelOptMixedPrecisionConfig(ModelOptQuantConfigBase):
    """Config class for ModelOpt MIXED_PRECISION.

    Supports checkpoints where different layers use different quantization
    algorithms (e.g., FP8 for dense layers and NVFP4 for MoE experts).
    The per-layer algorithm is specified in the ``quantized_layers`` dict
    inside ``config.json``'s ``quantization_config`` (preferred) or the
    legacy ``hf_quant_config.json``.
    """

    def __init__(
        self,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        quantized_layers: dict[str, dict[str, Any]],
        fp8_config: ModelOptFp8Config,
        nvfp4_config: ModelOptNvFp4Config,
    ) -> None:
        super().__init__(exclude_modules)
        self.kv_cache_quant_method = kv_cache_quant_method
        self.quantized_layers = quantized_layers
        self.fp8_config = fp8_config
        self.nvfp4_config = nvfp4_config

    def get_name(self) -> QuantizationMethods:
        return "modelopt_mixed"

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

    @classmethod
    def get_min_capability(cls) -> int:
        return 89

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
        if algo is not None and algo == "MIXED_PRECISION":
            return "modelopt_mixed"
        return None

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        group_size: int | None,
        **kwargs: Any,
    ) -> "ModelOptMixedPrecisionConfig":
        if "quantization" in original_config:
            quantized_layers = original_config["quantization"].get(
                "quantized_layers", {}
            )
        else:
            quantized_layers = original_config.get("quantized_layers", {})

        if not quantized_layers:
            raise ValueError(
                "MIXED_PRECISION quant_algo requires a non-empty "
                "'quantized_layers' mapping in the quantization config."
            )

        # Determine group_size from the first NVFP4 entry if not provided.
        if group_size is None:
            for layer_info in quantized_layers.values():
                if layer_info.get("quant_algo", "").upper() == "NVFP4":
                    group_size = layer_info.get("group_size", 16)
                    break
        if group_size is None:
            group_size = 16

        fp8_config = ModelOptFp8Config(
            quant_method="FP8",
            is_checkpoint_fp8_serialized=True,
            kv_cache_quant_method=kv_cache_quant_method,
            exclude_modules=[],
        )
        nvfp4_config = ModelOptNvFp4Config(
            is_checkpoint_nvfp4_serialized=True,
            kv_cache_quant_algo=kv_cache_quant_method,
            exclude_modules=[],
            group_size=group_size,
        )

        return cls(
            kv_cache_quant_method=kv_cache_quant_method,
            exclude_modules=exclude_modules,
            quantized_layers=quantized_layers,
            fp8_config=fp8_config,
            nvfp4_config=nvfp4_config,
        )

    def _resolve_quant_algo(self, prefix: str) -> str | None:
        """Look up the quant_algo for a vLLM-side layer prefix.

        Tries three strategies in order:
        1. Direct lookup in ``quantized_layers``.
        2. Packed/fused-layer lookup (unfuse via ``packed_modules_mapping``).
        3. Prefix-based lookup for FusedMoE (any child key starts with
           ``prefix + "."``).

        Returns the upper-cased quant_algo string, or *None* if the prefix
        is not found.
        """
        # 1. Direct lookup
        if prefix in self.quantized_layers:
            return self.quantized_layers[prefix]["quant_algo"].upper()

        # 2. Packed / fused layer lookup
        proj_name = prefix.rsplit(".", 1)[-1]
        if self.packed_modules_mapping and proj_name in self.packed_modules_mapping:
            algos: set[str] = set()
            base = prefix.rsplit(".", 1)[0]
            for shard_name in self.packed_modules_mapping[proj_name]:
                shard_prefix = f"{base}.{shard_name}"
                if shard_prefix in self.quantized_layers:
                    algos.add(self.quantized_layers[shard_prefix]["quant_algo"].upper())
            if len(algos) == 1:
                return algos.pop()
            if len(algos) > 1:
                raise ValueError(
                    f"Mixed quant_algo within fused layer {prefix}: "
                    f"{algos}. All shards must use the same quantization."
                )

        # 3. Prefix-based lookup (for FusedMoE / parent modules)
        prefix_dot = prefix + "."
        for key, info in self.quantized_layers.items():
            if key.startswith(prefix_dot):
                return info["quant_algo"].upper()

        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> "QuantizeMethodBase | None":
        """Return quantize-method based on layer."""
        # KV-cache quantization
        if isinstance(layer, Attention):
            if self.kv_cache_quant_method:
                return ModelOptFp8KVCacheMethod(self)
            return None

        # Excluded layers
        if self.is_layer_excluded(prefix):
            if isinstance(layer, LinearBase):
                return UnquantizedLinearMethod()
            return None

        quant_algo = self._resolve_quant_algo(prefix)

        if isinstance(layer, LinearBase):
            if quant_algo == "FP8":
                return ModelOptFp8LinearMethod(self.fp8_config)
            if quant_algo == "NVFP4":
                return ModelOptNvFp4LinearMethod(self.nvfp4_config)
            # Layer not in quantized_layers — leave unquantized
            return UnquantizedLinearMethod()

        if isinstance(layer, FusedMoE):
            if quant_algo == "FP8":
                return ModelOptFp8MoEMethod(
                    quant_config=self.fp8_config,
                    moe_config=layer.moe_config,
                )
            if quant_algo == "NVFP4":
                return ModelOptNvFp4FusedMoE(
                    quant_config=self.nvfp4_config,
                    moe_config=layer.moe_config,
                )
            return None

        return None

    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
        super().apply_vllm_mapper(hf_to_vllm_mapper)
        if self.quantized_layers:
            self.quantized_layers = hf_to_vllm_mapper.apply_dict(self.quantized_layers)