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, Optional

import torch
from torch.nn import Module
from torch.nn.parameter import Parameter

import vllm.envs as envs
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
from vllm.attention.layer import Attention
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEQuantConfig,
    fp8_w8a8_moe_quant_config,
    nvfp4_moe_quant_config,
)
from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
from vllm.model_executor.layers.fused_moe.layer import (
    FusedMoE,
    FusedMoEMethodBase,
    FusedMoeWeightScaleSupported,
)
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_fp4_moe import (
    build_flashinfer_fp4_cutlass_moe_prepare_finalize,
    flashinfer_trtllm_fp4_moe,
    flashinfer_trtllm_fp4_routed_moe,
    prepare_static_weights_for_trtllm_fp4_moe,
    reorder_w1w3_to_w3w1,
    select_nvfp4_gemm_impl,
)
from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
    FlashinferMoeBackend,
    apply_flashinfer_per_tensor_scale_fp8,
    build_flashinfer_fp8_cutlass_moe_prepare_finalize,
    flashinfer_cutlass_moe_fp8,
    get_flashinfer_moe_backend,
    is_flashinfer_supporting_global_sf,
    register_moe_scaling_factors,
    rotate_flashinfer_fp8_moe_weights,
    select_cutlass_fp8_gemm_impl,
    swap_w13_to_w31,
)
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
    W8A8BlockFp8LinearOp,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
    get_marlin_input_dtype,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
    apply_fp4_marlin_linear,
    is_fp4_marlin_supported,
    prepare_fp4_layer_for_marlin,
    prepare_moe_fp4_layer_for_marlin,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    GroupShape,
    cutlass_fp4_supported,
    is_layer_skipped,
    swizzle_blockscale,
)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    Fp8LinearOp,
    cutlass_block_fp8_supported,
    requantize_with_max_scale,
)
from vllm.model_executor.parameter import (
    BlockQuantScaleParameter,
    ChannelQuantScaleParameter,
    ModelWeightParameter,
    PerTensorScaleParameter,
)
from vllm.scalar_type import scalar_types
from vllm.utils.flashinfer import (
    flashinfer_scaled_fp4_mm,
    has_flashinfer,
    has_flashinfer_moe,
)
from vllm.utils.math_utils import round_up

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",
]
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
    ) -> Optional["QuantizeMethodBase"]:
        # handle kv-cache first so we can focus only on weight quantization thereafter
        if isinstance(layer, Attention):
            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, layer=layer)
            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 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:
            # {"quant_algo": "...", "quant_method": "modelopt"}
            quant_method = config.get("quant_algo")
            kv_cache_quant_method = config.get("kv_cache_quant_algo")
            # "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:
        """Detect if this ModelOpt config should be used based on
        quantization config."""

        if hf_quant_cfg is None:
            return None

        # Use the community standard 'quant_method'
        quant_method = hf_quant_cfg.get("quant_method", "").lower()

        # Only proceed if the method is explicitly "modelopt"
        if quant_method != "modelopt":
            return None

        # Look for ModelOpt-specific config structure
        if "quantization" in hf_quant_cfg:
            quant_config = hf_quant_cfg["quantization"]
            if isinstance(quant_config, dict):
                quant_algo = str(quant_config.get("quant_algo", ""))
                if "FP8" in quant_algo.upper():
                    return "modelopt"
        else:
            # Check for compressed-tensors style config with specific quant_algo
            quant_algo = str(hf_quant_cfg.get("quant_algo", ""))
            if "FP8" in quant_algo.upper():
                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 = Fp8LinearOp(
            act_quant_static=True, act_quant_group_shape=GroupShape.PER_TENSOR
        )

    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: 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(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=layer.input_scale,
            bias=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 = Fp8LinearOp(
            act_quant_static=False, act_quant_group_shape=GroupShape.PER_TOKEN
        )

    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: 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(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=None,
            bias=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: 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,
        layer: FusedMoE,
    ) -> None:
        super().__init__(layer.moe_config)
        self.layer = layer
        self.quant_config = quant_config
        from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
            cutlass_fp8_supported,
        )

        self.cutlass_fp8_supported = cutlass_fp8_supported()
        self.flashinfer_moe_backend: FlashinferMoeBackend | None = None
        if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe():
            self.flashinfer_moe_backend = get_flashinfer_moe_backend()
            if (
                self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
                and not self.moe.is_act_and_mul
            ):
                logger.info_once(
                    "Non-gated MoE is not supported for min-latency mode,"
                    "falling back to high-throughput mode"
                )
                self.flashinfer_moe_backend = FlashinferMoeBackend.CUTLASS

            logger.info_once(
                f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
            )

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalize | None:
        # TRT LLM not supported with all2all yet.
        if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
            return None
        elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
            # TP case: avoid convert to ModularKernelMethod - to be refactored.
            if self.moe.dp_size == 1:
                return None

            prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
                self.moe,
                use_deepseek_fp8_block_scale=False,
            )
            logger.debug_once("%s", prepare_finalize.__class__.__name__)
            return prepare_finalize
        return super().maybe_make_prepare_finalize(routing_tables)

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalize,
        layer: torch.nn.Module,
    ) -> mk.FusedMoEPermuteExpertsUnpermute:
        assert self.moe_quant_config is not None
        experts = select_cutlass_fp8_gemm_impl(
            self.moe,
            self.moe_quant_config,
        )
        logger.debug_once("Using %s", experts.__class__.__name__)
        return experts

    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,
    ):
        # 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")

        if self.moe.is_act_and_mul:
            w13_up_dim = 2 * intermediate_size_per_partition
        else:
            w13_up_dim = intermediate_size_per_partition

        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                w13_up_dim,
                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)

        if self.quant_config.is_checkpoint_fp8_serialized:
            # 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.
            if self.moe.is_act_and_mul:
                w13_weight_scale_shape = (num_experts, 2)
            else:
                w13_weight_scale_shape = (num_experts, 1)
            w13_weight_scale = PerTensorScaleParameter(
                data=torch.full(
                    w13_weight_scale_shape,
                    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)

            # Set weight loader attributes for scales
            extra_weight_attrs.update(
                {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
            )

            # 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 process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        """Process FP8 MoE weights after loading from serialized checkpoint.
        Only supports pre-quantized checkpoints with FP8 weights and scales.
        """

        if self.flashinfer_moe_backend is not None:
            self._maybe_pad_intermediate_for_flashinfer(layer)

        layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
        layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)

        from vllm._custom_ops import scaled_fp8_quant
        from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
            per_tensor_dequantize,
        )

        # Handle scale parameters
        if hasattr(layer, "w13_weight_scale") and layer.w13_weight_scale is not None:
            # Fp8 moe kernel needs single weight scale for w13 per expert.
            # We take the max of the w1 and w3 scales
            # then dequant and requant each expert.
            if (
                layer.w13_weight_scale.dim() == 2
                and layer.w13_weight_scale.shape[1] == 2
            ):
                assert self.moe.is_act_and_mul, (
                    "w13_weight_scale should have 2 elements per expert "
                    "only for gated MoE"
                )
                # Get the maximum scale across w1 and w3 for each expert
                max_w13_scales = layer.w13_weight_scale.max(dim=1).values

                # Requantize each expert's weights using the combined scale
                # w13_weight (num_experts, 2 * intermediate_size, hidden_size)
                # where the first intermediate_size rows are w1, the next are w3
                intermediate_size = layer.w13_weight.shape[1] // 2
                for expert_id in range(layer.w13_weight.shape[0]):
                    start = 0
                    for shard_id in range(2):  # w1 and w3
                        # Dequantize using the original scale for this shard
                        dq_weight = per_tensor_dequantize(
                            layer.w13_weight[expert_id][
                                start : start + intermediate_size, :
                            ],
                            layer.w13_weight_scale[expert_id][shard_id],
                        )
                        # Requantize using the combined max scale

                        (
                            layer.w13_weight[expert_id][
                                start : start + intermediate_size, :
                            ],
                            _,
                        ) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])

                        start += intermediate_size

                # Update the scale parameter to be per-expert
                layer.w13_weight_scale = Parameter(max_w13_scales, requires_grad=False)
            else:
                layer.w13_weight_scale = Parameter(
                    layer.w13_weight_scale.data, requires_grad=False
                )

        if hasattr(layer, "w2_weight_scale") and layer.w2_weight_scale is not None:
            layer.w2_weight_scale = Parameter(
                layer.w2_weight_scale.data, requires_grad=False
            )
        # Input scales must be equal for each expert in fp8 MoE layers.
        if hasattr(layer, "w13_input_scale") and layer.w13_input_scale is not None:
            layer.w13_input_scale = Parameter(
                layer.w13_input_scale.max(), requires_grad=False
            )
        if hasattr(layer, "w2_input_scale") and layer.w2_input_scale is not None:
            layer.w2_input_scale = Parameter(
                layer.w2_input_scale.max(), requires_grad=False
            )

        if self.flashinfer_moe_backend is not None:
            if self.moe.is_act_and_mul:
                layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
            if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
                rotate_flashinfer_fp8_moe_weights(layer.w13_weight, layer.w2_weight)
        register_moe_scaling_factors(layer)

    def _maybe_pad_intermediate_for_flashinfer(self, layer: torch.nn.Module) -> None:
        """Pad intermediate size so FlashInfer kernels' alignment constraints hold.

        Some FlashInfer FP8 MoE kernels require the (gated) intermediate size
        used for GEMM to be divisible by a small alignment value. When this is
        not satisfied (e.g. with certain tensor-parallel sizes), we pad the
        gate/up and down projection weights along the intermediate dim.
        """
        if not hasattr(layer, "w13_weight") or not hasattr(layer, "w2_weight"):
            return

        # Current local intermediate size (per partition) is the K dimension of
        # the down projection.
        num_experts, hidden_size, intermediate = layer.w2_weight.shape

        min_alignment = 16
        padded_intermediate = round_up(intermediate, min_alignment)

        if padded_intermediate == intermediate:
            return

        logger.info(
            "Padding intermediate size from %d to %d for up/down projection weights.",
            intermediate,
            padded_intermediate,
        )

        up_mult = 2 if self.moe.is_act_and_mul else 1
        padded_gate_up_dim = up_mult * padded_intermediate

        # Pad w13 and w12 along its intermediate dimension.
        w13 = layer.w13_weight.data
        padded_w13 = w13.new_zeros((num_experts, padded_gate_up_dim, hidden_size))
        padded_w13[:, : w13.shape[1], :] = w13
        layer.w13_weight.data = padded_w13

        w2 = layer.w2_weight.data
        padded_w2 = w2.new_zeros((num_experts, hidden_size, padded_intermediate))
        padded_w2[:, :, :intermediate] = w2
        layer.w2_weight.data = padded_w2

        if hasattr(layer, "intermediate_size_per_partition"):
            layer.intermediate_size_per_partition = padded_intermediate

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> FusedMoEQuantConfig | None:
        if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
            return None

        return fp8_w8a8_moe_quant_config(
            w1_scale=layer.w13_weight_scale,
            g1_alphas=layer.output1_scales_gate_scalar.squeeze(),
            w2_scale=layer.w2_weight_scale,
            g2_alphas=layer.output2_scales_scalar.squeeze(),
            a1_scale=layer.w13_input_scale,
            a1_gscale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
            a2_gscale=layer.w2_input_scale_inv,
            per_act_token_quant=False,
        )

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
            if layer.enable_eplb:
                raise NotImplementedError(
                    "EPLB not supported for `ModelOptFp8MoEMethod` yet."
                )
            assert layer.activation == "silu", (
                f"Expected 'silu' activation but got {layer.activation}"
            )

            assert not layer.renormalize
            return apply_flashinfer_per_tensor_scale_fp8(
                layer=layer,
                hidden_states=x,
                router_logits=router_logits,
                routing_bias=layer.e_score_correction_bias,
                global_num_experts=layer.global_num_experts,
                top_k=layer.top_k,
                num_expert_group=layer.num_expert_group,
                topk_group=layer.topk_group,
                apply_router_weight_on_input=layer.apply_router_weight_on_input,
            )

        # Expert selection
        topk_weights, topk_ids = layer.select_experts(
            hidden_states=x,
            router_logits=router_logits,
        )

        if self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
            assert layer.activation in ("silu", "relu2_no_mul"), (
                "Expected activation to be in ('silu', 'relu2_no_mul'),"
                f"but got {layer.activation}"
            )
            return flashinfer_cutlass_moe_fp8(
                x,
                layer,
                topk_weights,
                topk_ids,
                inplace=False,
                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,
            )
        else:
            from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts

            assert self.moe_quant_config is not None

            return fused_experts(
                x,
                layer.w13_weight,
                layer.w2_weight,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                inplace=True,
                activation=layer.activation,
                quant_config=self.moe_quant_config,
                global_num_experts=layer.global_num_experts,
                expert_map=layer.expert_map,
                apply_router_weight_on_input=layer.apply_router_weight_on_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:
        """Detect if this ModelOpt FP4 config should be used based on
        quantization config."""
        if hf_quant_cfg is None:
            return None

        # Use the community standard 'quant_method'
        quant_method = hf_quant_cfg.get("quant_method", "").lower()

        # Only proceed if the method is explicitly "modelopt"
        if quant_method != "modelopt":
            return None

        # Look for ModelOpt-specific config structure
        if "quantization" in hf_quant_cfg:
            quant_config = hf_quant_cfg["quantization"]
            if isinstance(quant_config, dict):
                quant_algo = quant_config.get("quant_algo", "")
                if "NVFP4" in quant_algo:
                    return "modelopt_fp4"
        else:
            # Check for compressed-tensors style config with specific
            # quant_algo field
            quant_algo = hf_quant_cfg.get("quant_algo", "")
            if isinstance(quant_algo, str) and "FP4" in quant_algo.upper():
                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 = "none"
        if envs.VLLM_NVFP4_GEMM_BACKEND is None:
            if has_flashinfer():
                self.backend = "flashinfer-cutlass"
            elif cutlass_fp4_supported():
                self.backend = "cutlass"
            elif is_fp4_marlin_supported():
                self.backend = "marlin"
        elif envs.VLLM_NVFP4_GEMM_BACKEND.startswith("flashinfer-"):
            self.backend = envs.VLLM_NVFP4_GEMM_BACKEND
            assert has_flashinfer(), f"FlashInfer is required for {self.backend}"
        elif envs.VLLM_NVFP4_GEMM_BACKEND == "cutlass":
            self.backend = "cutlass"
            assert cutlass_fp4_supported(), f"Cutlass is required for {self.backend}"

        if self.backend == "none":
            raise ValueError(
                "No valid NVFP4 GEMM backend found. "
                "Please check your platform capability."
            )

        logger.info_once(f"Using {self.backend} for NVFP4 GEMM")

    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 Weight Scale
        input_scale = PerTensorScaleParameter(
            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("input_scale", input_scale)

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

        # 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: Module) -> None:
        # global scales:
        input_scale_2 = layer.input_scale.max().to(torch.float32)
        layer.input_scale = Parameter(input_scale_2, requires_grad=False)

        weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
        layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)

        layer.alpha = Parameter(
            layer.input_scale * layer.weight_scale_2, requires_grad=False
        )

        # Calculate `1 / input_scale` so that we don't need to do so at runtime
        layer.input_scale_inv = Parameter(
            (1 / layer.input_scale).to(torch.float32), requires_grad=False
        )

        # Swizzle the weight blockscale.
        # contracting dimension is input dimension
        # block_size = 16;
        assert layer.weight_scale.dtype == torch.float8_e4m3fn, (
            "Weight Block scale must be represented as FP8-E4M3"
        )

        if self.backend == "marlin":
            prepare_fp4_layer_for_marlin(layer)
            del layer.alpha
            del layer.input_scale
        elif self.backend == "flashinfer-trtllm":
            # FlashInfer TRTLLM FP4 GEMM requires a different weight layout.
            # FlashInfer provides nvfp4_quantize to quantize + shuffle the
            # layout but we use our own quantization so we have to call
            # shuffles ourselves.
            from flashinfer import shuffle_matrix_a, shuffle_matrix_sf_a

            weight = layer.weight.data
            weight_scale = layer.weight_scale.data

            epilogue_tile_m = 128
            weight = shuffle_matrix_a(weight.view(torch.uint8), epilogue_tile_m)
            weight_scale = (
                shuffle_matrix_sf_a(weight_scale.view(torch.uint8), epilogue_tile_m)
                .reshape(weight_scale.shape)
                .view(torch.float8_e4m3fn)
            )

            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
            layer.weight = Parameter(weight, requires_grad=False)
        else:
            swizzled_weight_scale = swizzle_blockscale(layer.weight_scale)
            layer.weight_scale = Parameter(swizzled_weight_scale, requires_grad=False)
            layer.weight = Parameter(layer.weight.data, requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        if self.backend == "marlin":
            return apply_fp4_marlin_linear(
                input=x,
                weight=layer.weight,
                weight_scale=layer.weight_scale,
                weight_scale_2=layer.weight_scale_2,
                workspace=layer.workspace,
                size_n=layer.output_size_per_partition,
                size_k=layer.input_size_per_partition,
                bias=bias,
                input_dtype=self.marlin_input_dtype,
            )

        output_dtype = x.dtype
        output_shape = [x.shape[0], layer.weight.shape[0]]

        # quantize BF16 or FP16 to (FP4 and interleaved block scale)
        x_fp4, x_blockscale = scaled_fp4_quant(x, layer.input_scale_inv)

        # validate dtypes of quantized input, input block scale,
        # weight and weight_blockscale
        assert x_fp4.dtype == torch.uint8
        assert layer.weight.dtype == torch.uint8
        assert x_blockscale.dtype == torch.float8_e4m3fn
        assert layer.weight_scale.dtype == torch.float8_e4m3fn
        assert layer.alpha.dtype == torch.float32

        mm_args = (
            x_fp4,
            layer.weight,
            x_blockscale,
            layer.weight_scale,
            layer.alpha,
            output_dtype,
        )
        if self.backend.startswith("flashinfer-"):
            backend_name = self.backend[len("flashinfer-") :]
            out = flashinfer_scaled_fp4_mm(*mm_args, backend=backend_name)
        else:
            assert self.backend == "cutlass"
            out = cutlass_scaled_fp4_mm(*mm_args)

        if bias is not None:
            out = out + bias
        return out.view(*output_shape)


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

    def __init__(
        self,
        quant_config: ModelOptNvFp4Config,
        layer: FusedMoE,
    ) -> None:
        from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (
            detect_nvfp4_moe_support,  # noqa: E501
        )

        super().__init__(layer.moe_config)
        self.quant_config = quant_config
        self.layer = layer
        _nvfp4 = detect_nvfp4_moe_support(self.__class__.__name__)
        self.cutlass_nvfp4_supported = _nvfp4.cutlass_supported
        self.allow_flashinfer = _nvfp4.allow_flashinfer
        self.use_marlin = _nvfp4.use_marlin
        self.marlin_input_dtype = None
        self.flashinfer_moe_backend = None
        if self.allow_flashinfer:
            self.flashinfer_moe_backend = get_flashinfer_moe_backend()
            logger.info_once(
                f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
                " for ModelOptNvFp4FusedMoE."
            )
        elif self.use_marlin:
            logger.info_once("Using Marlin for ModelOptNvFp4FusedMoE.")
        else:
            logger.info_once("Using Cutlass for ModelOptNvFp4FusedMoE.")

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalize | None:
        if self.use_marlin or (
            self.allow_flashinfer
            and self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
        ):
            return None
        elif (
            self.allow_flashinfer
            and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS
        ):
            # TP case: avoid convert to ModularKernelMethod - to be refactored.
            if self.moe.dp_size == 1:
                return None
            # For now, fp4 moe only works with the flashinfer dispatcher.
            prepare_finalize = build_flashinfer_fp4_cutlass_moe_prepare_finalize(
                self.moe
            )
            logger.debug_once("%s", prepare_finalize.__class__.__name__)
            return prepare_finalize
        else:
            return super().maybe_make_prepare_finalize(routing_tables)

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalize,
        layer: torch.nn.Module,
    ) -> mk.FusedMoEPermuteExpertsUnpermute:
        assert self.moe_quant_config is not None
        experts = select_nvfp4_gemm_impl(
            self.moe,
            self.moe_quant_config,
            allow_flashinfer=self.allow_flashinfer,
        )
        logger.debug_once("Using %s", experts.__class__.__name__)
        return experts

    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,
    ):
        if not self.quant_config.is_checkpoint_nvfp4_serialized:
            raise ValueError(
                "NVFP4 quantization was selected, "
                " dynamic quantization is not supported."
            )

        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")
        # GEMM 1
        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                (2 if self.moe.is_act_and_mul else 1) * 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,
                (2 if self.moe.is_act_and_mul else 1) * 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, 2 if self.moe.is_act_and_mul else 1, 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}
        )

        use_global_sf = self.allow_flashinfer and is_flashinfer_supporting_global_sf(
            self.flashinfer_moe_backend
        )
        global_scale_num_experts = global_num_experts if use_global_sf else num_experts

        w13_input_scale = PerTensorScaleParameter(
            data=torch.empty(
                global_scale_num_experts,
                2 if self.moe.is_act_and_mul else 1,
                dtype=torch.float32,
            ),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_input_scale", w13_input_scale)

        w2_input_scale = PerTensorScaleParameter(
            data=torch.empty(global_scale_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: torch.nn.Module) -> None:
        # GEMM 1 processing
        gemm1_weight = layer.w13_weight.data
        gemm1_weight_scale = layer.w13_weight_scale.data

        if (
            self.allow_flashinfer
            and (
                self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS
                or self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
            )
            and self.moe.is_act_and_mul
        ):
            gemm1_weight, gemm1_weight_scale = reorder_w1w3_to_w3w1(
                gemm1_weight, gemm1_weight_scale, dim=-2
            )

        layer.w13_weight = Parameter(gemm1_weight, requires_grad=False)
        layer.w13_weight_scale = Parameter(gemm1_weight_scale, requires_grad=False)

        # Common processing for w13_weight_scale_2
        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()
        layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2, requires_grad=False)

        # Common processing for input scales and alphas
        use_global_sf = self.allow_flashinfer and is_flashinfer_supporting_global_sf(
            self.flashinfer_moe_backend
        )
        if use_global_sf:
            # For backends provide by Flashinfer, the input global scales are
            # shared across all experts.
            w13_input_scale = (
                layer.w13_input_scale.max().to(torch.float32).expand(layer.num_experts)
            )
        else:
            w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(torch.float32)
        layer.g1_alphas = Parameter(
            (w13_input_scale * w13_weight_scale_2).to(torch.float32),
            requires_grad=False,
        )

        # This is for quantization, so we need to invert it.
        layer.w13_input_scale_quant = Parameter(
            (1 / w13_input_scale).to(torch.float32), requires_grad=False
        )

        # GEMM 2 processing
        if use_global_sf:
            # For backends provide by Flashinfer, the input global scales are
            # shared across all experts.
            w2_input_scale = (
                layer.w2_input_scale.max().to(torch.float32).expand(layer.num_experts)
            )
        else:
            w2_input_scale = layer.w2_input_scale
        layer.g2_alphas = Parameter(
            (w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
            requires_grad=False,
        )

        # This is for quantization, so we need to invert it.
        layer.w2_input_scale_quant = Parameter(
            (1 / w2_input_scale).to(torch.float32), requires_grad=False
        )

        # TensorRT-LLM specific processing
        if (
            self.allow_flashinfer
            and self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
        ):
            # Prepare static weights for TRT-LLM kernel
            # alternate: prepare_static_weight_layouts_for_trtllm_moe
            (
                gemm1_weights_fp4_shuffled,
                gemm1_scales_fp4_shuffled,
                gemm2_weights_fp4_shuffled,
                gemm2_scales_fp4_shuffled,
            ) = prepare_static_weights_for_trtllm_fp4_moe(
                layer.w13_weight,
                layer.w2_weight,
                layer.w13_weight_scale,
                layer.w2_weight_scale,
                layer.w2_weight.size(-2),  # hidden_size
                layer.w13_weight.size(-2) // 2,  # intermediate_size
                layer.w13_weight.size(0),  # num_experts
            )
            logger.debug_once("Finished shuffling weights for TRT-LLM MOE")

            layer.w13_weight = Parameter(
                gemm1_weights_fp4_shuffled, requires_grad=False
            )
            layer.w2_weight = Parameter(gemm2_weights_fp4_shuffled, requires_grad=False)
            layer.w13_weight_scale = Parameter(
                gemm1_scales_fp4_shuffled, requires_grad=False
            )
            layer.w2_weight_scale = Parameter(
                gemm2_scales_fp4_shuffled, requires_grad=False
            )

            # Additional parameter needed for TRT-LLM
            layer.g1_scale_c = Parameter(
                (layer.w2_input_scale_quant * layer.g1_alphas).to(torch.float32),
                requires_grad=False,
            )
        elif self.use_marlin:
            # Marlin processing
            prepare_moe_fp4_layer_for_marlin(layer)
            del layer.g1_alphas
            del layer.g2_alphas
            del layer.w13_input_scale_quant
            del layer.w2_input_scale_quant
        else:
            # Non-TRT-LLM processing (Cutlass or non-flashinfer)
            w13_blockscale_swizzled = swizzle_blockscale(layer.w13_weight_scale)
            layer.w13_weight_scale = Parameter(
                w13_blockscale_swizzled, requires_grad=False
            )

            w13_weight = layer.w13_weight
            intermediate_size_pad = w13_blockscale_swizzled.size(1) - w13_weight.size(1)
            if intermediate_size_pad:
                # padding gated activations will require to split w1 and w3
                # and pad them individually
                assert not self.moe.is_act_and_mul, (
                    "The intermediate size required padding, "
                    "but padding is not implemented for gated activations"
                )

                layer.w13_weight = Parameter(
                    torch.nn.functional.pad(
                        w13_weight, (0, 0, 0, intermediate_size_pad)
                    ),
                    requires_grad=False,
                )
                layer.w2_weight = Parameter(
                    torch.nn.functional.pad(
                        layer.w2_weight, (0, intermediate_size_pad // 2, 0, 0)
                    ),
                    requires_grad=False,
                )
                layer.w2_weight_scale = Parameter(
                    torch.nn.functional.pad(
                        layer.w2_weight_scale, (0, intermediate_size_pad // 16)
                    ),
                    requires_grad=False,
                )

            w2_blockscale_swizzled = swizzle_blockscale(layer.w2_weight_scale)
            layer.w2_weight_scale = Parameter(
                w2_blockscale_swizzled, requires_grad=False
            )

    def prepare_dp_allgather_tensor(
        self,
        layer: FusedMoE,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> tuple[torch.Tensor, list[torch.Tensor]]:
        """Optionally prepare extra tensors to carry through DP allgather/EP."""
        import flashinfer

        a1_gscale = layer.w13_input_scale_quant
        hidden_states_fp4, hidden_states_sf = flashinfer.fp4_quantize(
            hidden_states,
            a1_gscale,
            is_sf_swizzled_layout=False,
        )
        extra_tensors: list[torch.Tensor] = [hidden_states_sf]
        return hidden_states_fp4, extra_tensors

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> FusedMoEQuantConfig | None:
        if (
            self.use_marlin
            or self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
        ):
            return None

        return nvfp4_moe_quant_config(
            w1_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            g1_alphas=layer.g1_alphas,
            g2_alphas=layer.g2_alphas,
            a1_gscale=layer.w13_input_scale_quant,
            a2_gscale=layer.w2_input_scale_quant,
        )

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

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        if not self.moe.is_act_and_mul:
            assert (
                self.allow_flashinfer
                and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS
            ), (
                "Non-gated activations are only supported by the"
                " flashinfer CUTLASS backend for modelopt checkpoints"
            )

        if (
            self.allow_flashinfer
            and self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
            and not layer.enable_eplb
        ):
            return flashinfer_trtllm_fp4_moe(
                layer=layer,
                x=x,
                router_logits=router_logits,
                top_k=layer.top_k,
                global_num_experts=layer.global_num_experts,
                num_expert_group=layer.num_expert_group,
                topk_group=layer.topk_group,
                custom_routing_function=layer.custom_routing_function,
                e_score_correction_bias=layer.e_score_correction_bias,
            )

        # Hidden_states in select_experts is only used to extract metadata
        if isinstance(x, tuple):
            x_routing, _ = x
        else:
            x_routing = x
        topk_weights, topk_ids = layer.select_experts(
            hidden_states=x_routing,
            router_logits=router_logits,
        )

        # EPLB path
        if (
            self.allow_flashinfer
            and self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
        ):
            return flashinfer_trtllm_fp4_routed_moe(
                layer=layer,
                x=x,
                topk_ids=topk_ids,
                topk_weights=topk_weights,
                top_k=layer.top_k,
                global_num_experts=layer.global_num_experts,
            )

        if self.use_marlin:
            return fused_marlin_moe(
                x,
                layer.w13_weight,
                layer.w2_weight,
                None,
                None,
                layer.w13_weight_scale,
                layer.w2_weight_scale,
                router_logits,
                topk_weights,
                topk_ids,
                global_scale1=layer.w13_weight_scale_2,
                global_scale2=layer.w2_weight_scale_2,
                quant_type_id=scalar_types.float4_e2m1f.id,
                apply_router_weight_on_input=layer.apply_router_weight_on_input,
                global_num_experts=layer.global_num_experts,
                expert_map=layer.expert_map,
                input_dtype=self.marlin_input_dtype,
            )

        elif self.allow_flashinfer:
            assert self.flashinfer_moe_backend in (
                FlashinferMoeBackend.CUTLASS,
                FlashinferMoeBackend.CUTEDSL,
            )
            if self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
                from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
                    flashinfer_cutlass_moe_fp4,
                )

                flashinfer_fn_moe_fp4 = flashinfer_cutlass_moe_fp4
            else:
                from vllm.model_executor.layers.fused_moe.flashinfer_cutedsl_moe import (  # noqa: E501
                    flashinfer_cutedsl_moe_fp4,
                )

                flashinfer_fn_moe_fp4 = flashinfer_cutedsl_moe_fp4

            assert self.moe_quant_config is not None
            return flashinfer_fn_moe_fp4(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                quant_config=self.moe_quant_config,
                inplace=False,
                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,
            )
        else:
            # If no modular kernel is provided, use cutlass_moe_fp4 for TP case
            # only (no EP).
            from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4

            assert self.moe_quant_config is not None
            return cutlass_moe_fp4(
                a=x,
                w1_fp4=layer.w13_weight,
                w2_fp4=layer.w2_weight,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                quant_config=self.moe_quant_config,
                expert_map=layer.expert_map,
                apply_router_weight_on_input=layer.apply_router_weight_on_input,
                # TODO: derive from arguments
                m=x.shape[0],
                n=layer.w2_weight.shape[2] * 2,
                k=x.shape[1],
                e=layer.w13_weight.shape[0],
            )


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