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vllm.model_executor.layers.quantization.modelopt

KV_CACHE_QUANT_ALGOS module-attribute 

KV_CACHE_QUANT_ALGOS = ['FP8']

QUANT_ALGOS module-attribute 

QUANT_ALGOS = ['FP8', 'NVFP4']

logger module-attribute 

logger = init_logger(__name__)

ModelOptFp8Config

Bases: QuantizationConfig

Config class for ModelOpt FP8.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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class ModelOptFp8Config(QuantizationConfig):
    """Config class for ModelOpt FP8."""

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

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "modelopt"

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

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

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

    @classmethod
    def override_quantization_method(
            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
        """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 = quant_config.get("quant_algo", "")
                if "FP8" in quant_algo:
                    return "modelopt"
        else:
            # Check for compressed-tensors style config with specific quant_algo
            quant_algo = hf_quant_cfg.get("quant_algo", "")
            if isinstance(quant_algo, str) and "FP8" in quant_algo:
                return "modelopt"

        return None

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "ModelOptFp8Config":
        # Handle both ModelOpt format and compressed-tensors style format
        if "quantization" in config:
            # 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", "")
            if not quant_method:
                raise ValueError("Missing 'quant_algo' in quantization config")
            kv_cache_quant_method = quant_config.get("kv_cache_quant_algo")
            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")
            exclude_modules = config.get("exclude_modules")

        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.")
        is_checkpoint_fp8_serialized = ("FP8" in quant_method)

        return cls(is_checkpoint_fp8_serialized, kv_cache_quant_method,
                   exclude_modules)

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

        This method handles both regular models and multimodal models that use
        the language_model prefix. For multimodal models, it checks if the
        module name (without the language_model prefix) is in the exclude list.
        """
        if self.exclude_modules is None:
            return False

        # Check if any excluded module matches the prefix
        for module in self.exclude_modules:
            if (module in prefix
                    or (prefix.startswith("language_model.")
                        and module in prefix.removeprefix("language_model."))):
                return True
        return False

    def get_quant_method(self, layer: torch.nn.Module,
                         prefix: str) -> Optional["QuantizeMethodBase"]:
        from vllm.attention.layer import Attention  # Avoid circular import
        if isinstance(layer, LinearBase):
            if self.is_layer_excluded(prefix):
                return UnquantizedLinearMethod()
            return ModelOptFp8LinearMethod(self)
        elif isinstance(layer, Attention):
            return ModelOptFp8KVCacheMethod(self)
        elif isinstance(layer, FusedMoE):
            return ModelOptFp8MoEMethod(self, layer)
        return None

exclude_modules instance-attribute 

exclude_modules = exclude_modules

is_checkpoint_fp8_serialized instance-attribute 

is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized

kv_cache_quant_method instance-attribute 

kv_cache_quant_method = kv_cache_quant_method

__init__ 

__init__(
    is_checkpoint_fp8_serialized: bool = False,
    kv_cache_quant_method: Optional[str] = None,
    exclude_modules: Optional[list[str]] = None,
) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(
    self,
    is_checkpoint_fp8_serialized: bool = False,
    kv_cache_quant_method: Optional[str] = None,
    exclude_modules: Optional[list[str]] = None,
) -> None:
    super().__init__()
    self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
    self.kv_cache_quant_method = kv_cache_quant_method
    self.exclude_modules = exclude_modules
    if is_checkpoint_fp8_serialized:
        logger.warning("Detected ModelOpt fp8 checkpoint. Please note that"
                       " the format is experimental and could change.")

from_config classmethod 

from_config(config: dict[str, Any]) -> ModelOptFp8Config
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def from_config(cls, config: dict[str, Any]) -> "ModelOptFp8Config":
    # Handle both ModelOpt format and compressed-tensors style format
    if "quantization" in config:
        # 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", "")
        if not quant_method:
            raise ValueError("Missing 'quant_algo' in quantization config")
        kv_cache_quant_method = quant_config.get("kv_cache_quant_algo")
        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")
        exclude_modules = config.get("exclude_modules")

    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.")
    is_checkpoint_fp8_serialized = ("FP8" in quant_method)

    return cls(is_checkpoint_fp8_serialized, kv_cache_quant_method,
               exclude_modules)

get_config_filenames classmethod 

get_config_filenames() -> list[str]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_config_filenames(cls) -> list[str]:
    return ["hf_quant_config.json"]

get_min_capability classmethod 

get_min_capability() -> int
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_min_capability(cls) -> int:
    return 89

get_name classmethod 

get_name() -> QuantizationMethods
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_name(cls) -> QuantizationMethods:
    return "modelopt"

get_quant_method 

get_quant_method(
    layer: Module, prefix: str
) -> Optional[QuantizeMethodBase]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def get_quant_method(self, layer: torch.nn.Module,
                     prefix: str) -> Optional["QuantizeMethodBase"]:
    from vllm.attention.layer import Attention  # Avoid circular import
    if isinstance(layer, LinearBase):
        if self.is_layer_excluded(prefix):
            return UnquantizedLinearMethod()
        return ModelOptFp8LinearMethod(self)
    elif isinstance(layer, Attention):
        return ModelOptFp8KVCacheMethod(self)
    elif isinstance(layer, FusedMoE):
        return ModelOptFp8MoEMethod(self, layer)
    return None

get_supported_act_dtypes classmethod 

get_supported_act_dtypes() -> list[dtype]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_supported_act_dtypes(cls) -> list[torch.dtype]:
    return [torch.bfloat16, torch.half]

is_layer_excluded 

is_layer_excluded(prefix: str) -> bool

Check if a layer should be excluded from quantization.

This method handles both regular models and multimodal models that use the language_model prefix. For multimodal models, it checks if the module name (without the language_model prefix) is in the exclude list.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def is_layer_excluded(self, prefix: str) -> bool:
    """
    Check if a layer should be excluded from quantization.

    This method handles both regular models and multimodal models that use
    the language_model prefix. For multimodal models, it checks if the
    module name (without the language_model prefix) is in the exclude list.
    """
    if self.exclude_modules is None:
        return False

    # Check if any excluded module matches the prefix
    for module in self.exclude_modules:
        if (module in prefix
                or (prefix.startswith("language_model.")
                    and module in prefix.removeprefix("language_model."))):
            return True
    return False

override_quantization_method classmethod 

override_quantization_method(
    hf_quant_cfg, user_quant
) -> Optional[QuantizationMethods]

Detect if this ModelOpt config should be used based on quantization config.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def override_quantization_method(
        cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
    """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 = quant_config.get("quant_algo", "")
            if "FP8" in quant_algo:
                return "modelopt"
    else:
        # Check for compressed-tensors style config with specific quant_algo
        quant_algo = hf_quant_cfg.get("quant_algo", "")
        if isinstance(quant_algo, str) and "FP8" in quant_algo:
            return "modelopt"

    return None

ModelOptFp8KVCacheMethod

Bases: BaseKVCacheMethod

Supports loading kv-cache scaling factors from FP8 checkpoints.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
    """
    Supports loading kv-cache scaling factors from FP8 checkpoints.
    """

    def __init__(self, quant_config: Union[ModelOptFp8Config,
                                           ModelOptNvFp4Config]):
        super().__init__(quant_config)

__init__ 

__init__(
    quant_config: Union[
        ModelOptFp8Config, ModelOptNvFp4Config
    ],
)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(self, quant_config: Union[ModelOptFp8Config,
                                       ModelOptNvFp4Config]):
    super().__init__(quant_config)

ModelOptFp8LinearMethod

Bases: 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.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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: Optional[torch.Tensor] = 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)

fp8_linear instance-attribute 

fp8_linear = Fp8LinearOp(
    act_quant_static=True, act_quant_group_shape=PER_TENSOR
)

quant_config instance-attribute 

quant_config = quant_config

__init__ 

__init__(quant_config: ModelOptFp8Config) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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)

apply 

apply(
    layer: Module, x: Tensor, bias: Optional[Tensor] = None
) -> Tensor
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def apply(
    self,
    layer: torch.nn.Module,
    x: torch.Tensor,
    bias: Optional[torch.Tensor] = 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)

create_weights 

create_weights(
    layer: Module,
    input_size_per_partition: int,
    output_partition_sizes: list[int],
    input_size: int,
    output_size: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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)

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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)

ModelOptFp8MoEMethod

Bases: FusedMoEMethodBase

MoE method for ModelOpt FP8. Supports loading FP8 checkpoints with static weight scale and activation scale. Args: quant_config: The ModelOpt quantization config.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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: torch.nn.Module,
    ) -> 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: Optional[FlashinferMoeBackend] = None
        self.fused_experts: Optional[
            mk.FusedMoEModularKernel] = None  # type: ignore
        if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe():
            self.flashinfer_moe_backend = get_flashinfer_moe_backend()
            logger.info_once(
                f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
            )

    def maybe_make_prepare_finalize(
        self,
        moe: FusedMoEConfig,
    ) -> Optional[mk.FusedMoEPrepareAndFinalize]:
        if self.fused_experts is not None or \
            self.flashinfer_moe_backend != FlashinferMoeBackend.CUTLASS:
            return super().maybe_make_prepare_finalize(moe)

        prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
            moe,
            layer=self.layer,
        )
        logger.debug_once("%s", prepare_finalize.__class__.__name__)
        return prepare_finalize

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalize,
        moe: FusedMoEConfig,
    ) -> mk.FusedMoEPermuteExpertsUnpermute:
        experts = select_cutlass_fp8_gemm_impl(
            moe,
            self.layer,
        )
        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")

        w13_weight = ModelWeightParameter(
            data=torch.empty(num_experts,
                             2 * 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)

        if self.quant_config.is_checkpoint_fp8_serialized:
            # WEIGHT SCALES - Per-tensor scaling for ModelOpts
            # Allocate 2 scales for w1 and w3 respectively.
            # They will be combined to a single scale after weight loading.
            w13_weight_scale = PerTensorScaleParameter(
                data=torch.full(
                    (num_experts, 2),
                    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.
        """

        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:

                # 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:
            layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
            register_moe_scaling_factors(layer)
            if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
                rotate_flashinfer_fp8_moe_weights(layer.w13_weight,
                                                  layer.w2_weight)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if enable_eplb:
            raise NotImplementedError(
                "EPLB not supported for `ModelOptFp8MoEMethod` yet.")

        if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
            assert activation == 'silu', (
                f"Expected 'silu' activation but got {activation}")
            assert not renormalize
            return apply_flashinfer_per_tensor_scale_fp8(
                layer=layer,
                hidden_states=x,
                router_logits=router_logits,
                routing_bias=e_score_correction_bias,
                global_num_experts=global_num_experts,
                top_k=top_k,
                num_expert_group=num_expert_group,
                topk_group=topk_group,
                apply_router_weight_on_input=apply_router_weight_on_input)

        # Expert selection
        topk_weights, topk_ids = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias,
            indices_type=self.topk_indices_dtype,
        )

        if self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
            assert not renormalize
            assert activation == 'silu', (
                f"Expected 'silu' activation but got {activation}")
            if self.fused_experts is not None:
                return self.fused_experts(
                    x,
                    layer.w13_weight,
                    layer.w2_weight,
                    topk_weights,
                    topk_ids,
                    inplace=False,
                    activation=activation,
                    global_num_experts=global_num_experts,
                    expert_map=expert_map,
                    apply_router_weight_on_input=apply_router_weight_on_input,
                )
            else:
                return flashinfer_cutlass_moe_fp8(
                    x,
                    layer,
                    topk_weights,
                    topk_ids,
                    inplace=False,
                    activation=activation,
                    global_num_experts=global_num_experts,
                    expert_map=expert_map,
                    apply_router_weight_on_input=apply_router_weight_on_input,
                )
        from vllm.model_executor.layers.fused_moe.fused_moe import (
            fused_experts)
        return fused_experts(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            inplace=True,
            activation=activation,
            use_fp8_w8a8=True,
            per_channel_quant=False,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            w1_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            a1_scale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
            apply_router_weight_on_input=apply_router_weight_on_input,
        )

cutlass_fp8_supported instance-attribute 

cutlass_fp8_supported = cutlass_fp8_supported()

flashinfer_moe_backend instance-attribute 

flashinfer_moe_backend: Optional[FlashinferMoeBackend] = (
    None
)

fused_experts instance-attribute 

fused_experts: Optional[FusedMoEModularKernel] = None

layer instance-attribute 

layer = layer

quant_config instance-attribute 

quant_config = quant_config

__init__ 

__init__(
    quant_config: ModelOptFp8Config, layer: Module
) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(
    self,
    quant_config: ModelOptFp8Config,
    layer: torch.nn.Module,
) -> 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: Optional[FlashinferMoeBackend] = None
    self.fused_experts: Optional[
        mk.FusedMoEModularKernel] = None  # type: ignore
    if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe():
        self.flashinfer_moe_backend = get_flashinfer_moe_backend()
        logger.info_once(
            f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
        )

apply 

apply(
    layer: Module,
    x: Tensor,
    router_logits: Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    e_score_correction_bias: Optional[Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    enable_eplb: bool = False,
    expert_load_view: Optional[Tensor] = None,
    logical_to_physical_map: Optional[Tensor] = None,
    logical_replica_count: Optional[Tensor] = None,
) -> Tensor
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def apply(
    self,
    layer: torch.nn.Module,
    x: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    e_score_correction_bias: Optional[torch.Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    enable_eplb: bool = False,
    expert_load_view: Optional[torch.Tensor] = None,
    logical_to_physical_map: Optional[torch.Tensor] = None,
    logical_replica_count: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    if enable_eplb:
        raise NotImplementedError(
            "EPLB not supported for `ModelOptFp8MoEMethod` yet.")

    if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
        assert activation == 'silu', (
            f"Expected 'silu' activation but got {activation}")
        assert not renormalize
        return apply_flashinfer_per_tensor_scale_fp8(
            layer=layer,
            hidden_states=x,
            router_logits=router_logits,
            routing_bias=e_score_correction_bias,
            global_num_experts=global_num_experts,
            top_k=top_k,
            num_expert_group=num_expert_group,
            topk_group=topk_group,
            apply_router_weight_on_input=apply_router_weight_on_input)

    # Expert selection
    topk_weights, topk_ids = FusedMoE.select_experts(
        hidden_states=x,
        router_logits=router_logits,
        use_grouped_topk=use_grouped_topk,
        top_k=top_k,
        renormalize=renormalize,
        topk_group=topk_group,
        num_expert_group=num_expert_group,
        custom_routing_function=custom_routing_function,
        scoring_func=scoring_func,
        e_score_correction_bias=e_score_correction_bias,
        indices_type=self.topk_indices_dtype,
    )

    if self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
        assert not renormalize
        assert activation == 'silu', (
            f"Expected 'silu' activation but got {activation}")
        if self.fused_experts is not None:
            return self.fused_experts(
                x,
                layer.w13_weight,
                layer.w2_weight,
                topk_weights,
                topk_ids,
                inplace=False,
                activation=activation,
                global_num_experts=global_num_experts,
                expert_map=expert_map,
                apply_router_weight_on_input=apply_router_weight_on_input,
            )
        else:
            return flashinfer_cutlass_moe_fp8(
                x,
                layer,
                topk_weights,
                topk_ids,
                inplace=False,
                activation=activation,
                global_num_experts=global_num_experts,
                expert_map=expert_map,
                apply_router_weight_on_input=apply_router_weight_on_input,
            )
    from vllm.model_executor.layers.fused_moe.fused_moe import (
        fused_experts)
    return fused_experts(
        x,
        layer.w13_weight,
        layer.w2_weight,
        topk_weights=topk_weights,
        topk_ids=topk_ids,
        inplace=True,
        activation=activation,
        use_fp8_w8a8=True,
        per_channel_quant=False,
        global_num_experts=global_num_experts,
        expert_map=expert_map,
        w1_scale=layer.w13_weight_scale,
        w2_scale=layer.w2_weight_scale,
        a1_scale=layer.w13_input_scale,
        a2_scale=layer.w2_input_scale,
        apply_router_weight_on_input=apply_router_weight_on_input,
    )

create_weights 

create_weights(
    layer: Module,
    num_experts: int,
    hidden_size: int,
    intermediate_size_per_partition: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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")

    w13_weight = ModelWeightParameter(
        data=torch.empty(num_experts,
                         2 * 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)

    if self.quant_config.is_checkpoint_fp8_serialized:
        # WEIGHT SCALES - Per-tensor scaling for ModelOpts
        # Allocate 2 scales for w1 and w3 respectively.
        # They will be combined to a single scale after weight loading.
        w13_weight_scale = PerTensorScaleParameter(
            data=torch.full(
                (num_experts, 2),
                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)

maybe_make_prepare_finalize 

maybe_make_prepare_finalize(
    moe: FusedMoEConfig,
) -> Optional[FusedMoEPrepareAndFinalize]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def maybe_make_prepare_finalize(
    self,
    moe: FusedMoEConfig,
) -> Optional[mk.FusedMoEPrepareAndFinalize]:
    if self.fused_experts is not None or \
        self.flashinfer_moe_backend != FlashinferMoeBackend.CUTLASS:
        return super().maybe_make_prepare_finalize(moe)

    prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
        moe,
        layer=self.layer,
    )
    logger.debug_once("%s", prepare_finalize.__class__.__name__)
    return prepare_finalize

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None

Process FP8 MoE weights after loading from serialized checkpoint. Only supports pre-quantized checkpoints with FP8 weights and scales.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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.
    """

    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:

            # 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:
        layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
        register_moe_scaling_factors(layer)
        if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
            rotate_flashinfer_fp8_moe_weights(layer.w13_weight,
                                              layer.w2_weight)

select_gemm_impl 

select_gemm_impl(
    prepare_finalize: FusedMoEPrepareAndFinalize,
    moe: FusedMoEConfig,
) -> FusedMoEPermuteExpertsUnpermute
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def select_gemm_impl(
    self,
    prepare_finalize: mk.FusedMoEPrepareAndFinalize,
    moe: FusedMoEConfig,
) -> mk.FusedMoEPermuteExpertsUnpermute:
    experts = select_cutlass_fp8_gemm_impl(
        moe,
        self.layer,
    )
    logger.debug_once("Using %s", experts.__class__.__name__)
    return experts

ModelOptNvFp4Config

Bases: QuantizationConfig

Config class for ModelOpt FP4.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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class ModelOptNvFp4Config(QuantizationConfig):
    """Config class for ModelOpt FP4."""

    def __init__(
        self,
        is_checkpoint_nvfp4_serialized: bool,
        kv_cache_quant_algo: Optional[str],
        exclude_modules: list[str],
        group_size: int = 16,
    ) -> None:
        super().__init__()
        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
            self.exclude_modules = exclude_modules

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "modelopt_fp4"

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

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

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

    @classmethod
    def override_quantization_method(
            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
        """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, config: dict[str, Any]) -> "ModelOptNvFp4Config":
        # Handle both traditional 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", "")
            if not quant_method:
                raise ValueError("Missing 'quant_algo' in quantization config")

            # Handle kv_cache_quant_algo with proper type validation
            kv_cache_quant_algo_raw = quant_config.get("kv_cache_quant_algo")
            if kv_cache_quant_algo_raw is None:
                # No KV cache quantization by default
                kv_cache_quant_algo = None
            elif isinstance(kv_cache_quant_algo_raw, str):
                kv_cache_quant_algo = kv_cache_quant_algo_raw
            else:
                raise ValueError(f"kv_cache_quant_algo must be a string, got "
                                 f"{type(kv_cache_quant_algo_raw)}")

            # Handle group_size with proper type validation
            group_size_raw = quant_config.get("group_size")
            if group_size_raw is None:
                group_size = 16  # Default value
            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 "
                                     f"{type(group_size_raw)}") from None

            exclude_modules = quant_config.get("exclude_modules", [])
            if not isinstance(exclude_modules, list):
                raise ValueError(f"exclude_modules must be a list, got "
                                 f"{type(exclude_modules)}")
        else:
            # Compressed-tensors style format:
            # {"quant_algo": "...", "quant_method": "modelopt"}
            quant_method = config.get("quant_algo", "")

            # Handle kv_cache_quant_algo with proper type validation
            kv_cache_quant_algo_raw = config.get("kv_cache_quant_algo")
            if kv_cache_quant_algo_raw is None:
                # No KV cache quantization by default
                kv_cache_quant_algo = None
            elif isinstance(kv_cache_quant_algo_raw, str):
                kv_cache_quant_algo = kv_cache_quant_algo_raw
            else:
                raise ValueError(f"kv_cache_quant_algo must be a string, got "
                                 f"{type(kv_cache_quant_algo_raw)}")

            # Handle group_size with proper type validation
            group_size_raw = config.get("group_size")
            if group_size_raw is None:
                group_size = 16  # Default value
            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 "
                                     f"{type(group_size_raw)}") from None

            exclude_modules = config.get("exclude_modules", [])
            if not isinstance(exclude_modules, list):
                raise ValueError(f"exclude_modules must be a list, got "
                                 f"{type(exclude_modules)}")

        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.")
        is_checkpoint_nvfp4_serialized = ("NVFP4" in quant_method)

        # For FP4, these fields are required
        if is_checkpoint_nvfp4_serialized and "quantization" in config:
            # Check if required fields are present in the quantization config
            quant_config = 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_algo,
                   exclude_modules, group_size)

    def is_layer_excluded(self, prefix: str,
                          exclude_modules: list[str]) -> bool:
        import regex as re
        for pattern in exclude_modules:
            regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
            if re.fullmatch(regex_str, prefix):
                return True
        return False

    def get_quant_method(self, layer: torch.nn.Module,
                         prefix: str) -> Optional["QuantizeMethodBase"]:
        from vllm.attention.layer import Attention  # Avoid circular import
        if isinstance(layer, LinearBase):
            if (is_layer_skipped(prefix, self.exclude_modules)
                    or self.is_layer_excluded(prefix, self.exclude_modules)):
                return UnquantizedLinearMethod()
            return ModelOptNvFp4LinearMethod(self)
        elif isinstance(layer, Attention):
            return ModelOptFp8KVCacheMethod(self)
        elif isinstance(layer, FusedMoE):
            return ModelOptNvFp4FusedMoE(self, layer.moe_config, layer)
        return None

exclude_modules instance-attribute 

exclude_modules = exclude_modules

group_size instance-attribute 

group_size = group_size

is_checkpoint_nvfp4_serialized instance-attribute 

is_checkpoint_nvfp4_serialized = (
    is_checkpoint_nvfp4_serialized
)

kv_cache_quant_algo instance-attribute 

kv_cache_quant_algo = kv_cache_quant_algo

__init__ 

__init__(
    is_checkpoint_nvfp4_serialized: bool,
    kv_cache_quant_algo: Optional[str],
    exclude_modules: list[str],
    group_size: int = 16,
) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(
    self,
    is_checkpoint_nvfp4_serialized: bool,
    kv_cache_quant_algo: Optional[str],
    exclude_modules: list[str],
    group_size: int = 16,
) -> None:
    super().__init__()
    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
        self.exclude_modules = exclude_modules

from_config classmethod 

from_config(config: dict[str, Any]) -> ModelOptNvFp4Config
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def from_config(cls, config: dict[str, Any]) -> "ModelOptNvFp4Config":
    # Handle both traditional 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", "")
        if not quant_method:
            raise ValueError("Missing 'quant_algo' in quantization config")

        # Handle kv_cache_quant_algo with proper type validation
        kv_cache_quant_algo_raw = quant_config.get("kv_cache_quant_algo")
        if kv_cache_quant_algo_raw is None:
            # No KV cache quantization by default
            kv_cache_quant_algo = None
        elif isinstance(kv_cache_quant_algo_raw, str):
            kv_cache_quant_algo = kv_cache_quant_algo_raw
        else:
            raise ValueError(f"kv_cache_quant_algo must be a string, got "
                             f"{type(kv_cache_quant_algo_raw)}")

        # Handle group_size with proper type validation
        group_size_raw = quant_config.get("group_size")
        if group_size_raw is None:
            group_size = 16  # Default value
        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 "
                                 f"{type(group_size_raw)}") from None

        exclude_modules = quant_config.get("exclude_modules", [])
        if not isinstance(exclude_modules, list):
            raise ValueError(f"exclude_modules must be a list, got "
                             f"{type(exclude_modules)}")
    else:
        # Compressed-tensors style format:
        # {"quant_algo": "...", "quant_method": "modelopt"}
        quant_method = config.get("quant_algo", "")

        # Handle kv_cache_quant_algo with proper type validation
        kv_cache_quant_algo_raw = config.get("kv_cache_quant_algo")
        if kv_cache_quant_algo_raw is None:
            # No KV cache quantization by default
            kv_cache_quant_algo = None
        elif isinstance(kv_cache_quant_algo_raw, str):
            kv_cache_quant_algo = kv_cache_quant_algo_raw
        else:
            raise ValueError(f"kv_cache_quant_algo must be a string, got "
                             f"{type(kv_cache_quant_algo_raw)}")

        # Handle group_size with proper type validation
        group_size_raw = config.get("group_size")
        if group_size_raw is None:
            group_size = 16  # Default value
        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 "
                                 f"{type(group_size_raw)}") from None

        exclude_modules = config.get("exclude_modules", [])
        if not isinstance(exclude_modules, list):
            raise ValueError(f"exclude_modules must be a list, got "
                             f"{type(exclude_modules)}")

    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.")
    is_checkpoint_nvfp4_serialized = ("NVFP4" in quant_method)

    # For FP4, these fields are required
    if is_checkpoint_nvfp4_serialized and "quantization" in config:
        # Check if required fields are present in the quantization config
        quant_config = 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_algo,
               exclude_modules, group_size)

get_config_filenames classmethod 

get_config_filenames() -> list[str]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_config_filenames(cls) -> list[str]:
    return ["hf_quant_config.json"]

get_min_capability classmethod 

get_min_capability() -> int
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_min_capability(cls) -> int:
    return 80

get_name classmethod 

get_name() -> QuantizationMethods
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_name(cls) -> QuantizationMethods:
    return "modelopt_fp4"

get_quant_method 

get_quant_method(
    layer: Module, prefix: str
) -> Optional[QuantizeMethodBase]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def get_quant_method(self, layer: torch.nn.Module,
                     prefix: str) -> Optional["QuantizeMethodBase"]:
    from vllm.attention.layer import Attention  # Avoid circular import
    if isinstance(layer, LinearBase):
        if (is_layer_skipped(prefix, self.exclude_modules)
                or self.is_layer_excluded(prefix, self.exclude_modules)):
            return UnquantizedLinearMethod()
        return ModelOptNvFp4LinearMethod(self)
    elif isinstance(layer, Attention):
        return ModelOptFp8KVCacheMethod(self)
    elif isinstance(layer, FusedMoE):
        return ModelOptNvFp4FusedMoE(self, layer.moe_config, layer)
    return None

get_supported_act_dtypes classmethod 

get_supported_act_dtypes() -> list[dtype]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def get_supported_act_dtypes(cls) -> list[torch.dtype]:
    return [torch.bfloat16, torch.half, torch.float8_e4m3fn]

is_layer_excluded 

is_layer_excluded(
    prefix: str, exclude_modules: list[str]
) -> bool
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def is_layer_excluded(self, prefix: str,
                      exclude_modules: list[str]) -> bool:
    import regex as re
    for pattern in exclude_modules:
        regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
        if re.fullmatch(regex_str, prefix):
            return True
    return False

override_quantization_method classmethod 

override_quantization_method(
    hf_quant_cfg, user_quant
) -> Optional[QuantizationMethods]

Detect if this ModelOpt FP4 config should be used based on quantization config.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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@classmethod
def override_quantization_method(
        cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
    """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

ModelOptNvFp4FusedMoE

Bases: FusedMoEMethodBase

MoE Method for FP4 Quantization. Args: quant_config: NVFP4 Quant Config

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
    """
    MoE Method for FP4 Quantization.
    Args:
        quant_config: NVFP4 Quant Config
    """

    def __init__(
        self,
        quant_config: ModelOptNvFp4Config,
        moe: FusedMoEConfig,
        layer: torch.nn.Module,
    ) -> None:
        from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (  # noqa: E501
            detect_nvfp4_moe_support)
        super().__init__(moe)
        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.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.")

    def maybe_make_prepare_finalize(
        self,
        moe: FusedMoEConfig,
    ) -> Optional[mk.FusedMoEPrepareAndFinalize]:
        if (self.allow_flashinfer and self.flashinfer_moe_backend
                == FlashinferMoeBackend.CUTLASS):
            prepare_finalize = (
                build_flashinfer_fp4_cutlass_moe_prepare_finalize(
                    moe,
                    a1_gscale=self.layer.w13_input_scale_quant,
                ))
            logger.debug_once("%s", prepare_finalize.__class__.__name__)
            return prepare_finalize

        return super().maybe_make_prepare_finalize(moe)

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalize,
        moe: FusedMoEConfig,
    ) -> mk.FusedMoEPermuteExpertsUnpermute:
        experts = select_nvfp4_gemm_impl(
            moe,
            g1_alphas=self.layer.g1_alphas,
            g2_alphas=self.layer.g2_alphas,
            a1_gscale=self.layer.w13_input_scale_quant,
            a2_gscale=self.layer.w2_input_scale_quant,
            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")
        # GEMM 1
        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                2 * 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 * 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, 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})

        w13_input_scale = PerTensorScaleParameter(data=torch.empty(
            num_experts, 2, dtype=torch.float32),
                                                  weight_loader=weight_loader)
        layer.register_parameter("w13_input_scale", w13_input_scale)

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

    def prepare_static_weight_layouts_for_trtllm_moe(
        self,
        gemm1_weights: torch.Tensor,
        gemm2_weights: torch.Tensor,
        gemm1_scales_linear_fp4_bytes: torch.Tensor,
        gemm2_scales_linear_fp4_bytes: torch.Tensor,
        hidden_size: int,
        intermediate_size: int,
        num_experts: int,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """Prepare quantized weights for kernel (done offline with weights)."""
        from flashinfer import (reorder_rows_for_gated_act_gemm,
                                shuffle_matrix_a, shuffle_matrix_sf_a)
        epilogue_tile_m = 128  # FIXME: this depends on the kernel internals

        # Convert quantized weights to proper formats
        gemm1_weights_fp4 = gemm1_weights.view(torch.float8_e4m3fn).reshape(
            num_experts, 2 * intermediate_size, hidden_size // 2)  # packed fp4
        gemm1_scales_linear_fp4 = gemm1_scales_linear_fp4_bytes.view(
            torch.float8_e4m3fn).reshape(num_experts, 2 * intermediate_size,
                                         hidden_size //
                                         16)  # fp8 scaling factors

        gemm2_weights_fp4 = gemm2_weights.view(torch.float8_e4m3fn).reshape(
            num_experts, hidden_size, intermediate_size // 2)  # packed fp4
        gemm2_scales_linear_fp4 = gemm2_scales_linear_fp4_bytes.view(
            torch.float8_e4m3fn).reshape(num_experts, hidden_size,
                                         intermediate_size //
                                         16)  # fp8 scaling factors

        # Reorder rows of W1 and scales for fused gated activation
        gemm1_weights_fp4_interleaved = []
        gemm1_scales_fp4_interleaved = []
        for i in range(num_experts):
            gemm1_weights_fp4_interleaved.append(
                reorder_rows_for_gated_act_gemm(gemm1_weights_fp4[i].clone()))
            gemm1_scales_fp4_interleaved.append(
                reorder_rows_for_gated_act_gemm(
                    gemm1_scales_linear_fp4[i].clone()))

        # Stack weights and scales for all experts
        gemm1_weights_fp4_interleaved = torch.stack(
            gemm1_weights_fp4_interleaved).reshape(num_experts,
                                                   2 * intermediate_size,
                                                   hidden_size // 2)
        gemm1_scales_fp4_interleaved = torch.stack(
            gemm1_scales_fp4_interleaved).reshape(num_experts,
                                                  2 * intermediate_size,
                                                  hidden_size // 16)

        # Shuffle weights and scaling factors for transposed mma output
        gemm1_weights_fp4_shuffled = []
        gemm1_scales_fp4_shuffled = []
        gemm2_weights_fp4_shuffled = []
        gemm2_scales_fp4_shuffled = []
        for i in range(num_experts):
            gemm1_weights_fp4_shuffled.append(
                shuffle_matrix_a(
                    gemm1_weights_fp4_interleaved[i].view(torch.uint8),
                    epilogue_tile_m))
            gemm1_scales_fp4_shuffled.append(
                shuffle_matrix_sf_a(
                    gemm1_scales_fp4_interleaved[i].view(torch.uint8),
                    epilogue_tile_m))

            gemm2_weights_fp4_shuffled.append(
                shuffle_matrix_a(gemm2_weights_fp4[i].view(torch.uint8),
                                 epilogue_tile_m))
            gemm2_scales_fp4_shuffled.append(
                shuffle_matrix_sf_a(
                    gemm2_scales_linear_fp4[i].view(torch.uint8),
                    epilogue_tile_m))

        # Stack weights for all experts
        gemm1_weights_fp4_shuffled = torch.stack(gemm1_weights_fp4_shuffled)
        gemm1_scales_fp4_shuffled = (
            torch.stack(gemm1_scales_fp4_shuffled).view(
                torch.float8_e4m3fn).reshape(num_experts,
                                             2 * intermediate_size,
                                             hidden_size // 16))

        gemm2_weights_fp4_shuffled = torch.stack(gemm2_weights_fp4_shuffled)
        gemm2_scales_fp4_shuffled = (
            torch.stack(gemm2_scales_fp4_shuffled).view(
                torch.float8_e4m3fn).reshape(num_experts, hidden_size,
                                             intermediate_size // 16))
        return (gemm1_weights_fp4_shuffled, gemm1_scales_fp4_shuffled,
                gemm2_weights_fp4_shuffled, gemm2_scales_fp4_shuffled)

    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:
            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 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]
        layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2,
                                             requires_grad=False)

        # Common processing for input scales and alphas
        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
        layer.g2_alphas = Parameter(
            (layer.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 / layer.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
            (gemm1_weights_fp4_shuffled, gemm1_scales_fp4_shuffled,
             gemm2_weights_fp4_shuffled, gemm2_scales_fp4_shuffled
             ) = self.prepare_static_weight_layouts_for_trtllm_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
             )

            layer.gemm1_weights_fp4_shuffled = Parameter(
                gemm1_weights_fp4_shuffled, requires_grad=False)
            layer.gemm2_weights_fp4_shuffled = Parameter(
                gemm2_weights_fp4_shuffled, requires_grad=False)
            layer.gemm1_scales_fp4_shuffled = Parameter(
                gemm1_scales_fp4_shuffled, requires_grad=False)
            layer.gemm2_scales_fp4_shuffled = 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,
            )

            # Clean up weights that won't be used by TRT-LLM
            del layer.w2_weight
            del layer.w2_weight_scale
            del layer.w13_weight
            del layer.w13_weight_scale
        else:
            # Non-TRT-LLM processing (Cutlass or non-flashinfer)
            assert (layer.w13_weight_scale.shape[2] % 16 == 0), (
                "Expected weight_scale.dim(1) to be divisible by 16")
            assert (layer.w13_weight_scale.dtype == torch.float8_e4m3fn), (
                "Weight Blockscale must be represented as FP8-E4M3")
            w13_blockscale_swizzled = swizzle_blockscale(
                layer.w13_weight_scale)
            layer.w13_weight_scale = Parameter(w13_blockscale_swizzled,
                                               requires_grad=False)

            assert (layer.w2_weight_scale.shape[2] % 16 == 0), (
                "Expected weight_scale.dim(1) to be divisible by 16")
            assert (layer.w2_weight_scale.dtype == torch.float8_e4m3fn), (
                "Weight Blockscale must be represented as FP8-E4M3")
            w2_blockscale_swizzled = swizzle_blockscale(layer.w2_weight_scale)
            layer.w2_weight_scale = Parameter(w2_blockscale_swizzled,
                                              requires_grad=False)
            layer.w2_weight = Parameter(layer.w2_weight.data,
                                        requires_grad=False)

        if self.use_marlin:
            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

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None,
    ):
        if enable_eplb:
            raise NotImplementedError(
                "EPLB not supported for `ModelOptNvFp4FusedMoE` yet.")
        assert activation == "silu", "Only SiLU activation is supported."

        if self.allow_flashinfer and \
            self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
            import flashinfer

            from vllm.model_executor.models.llama4 import Llama4MoE

            a1_gscale = layer.w13_input_scale_quant
            (hidden_states_fp4,
             hidden_states_scale_linear_fp4) = flashinfer.fp4_quantize(
                 x,
                 a1_gscale,
                 is_sf_swizzled_layout=False,
             )
            use_llama4_routing = \
                custom_routing_function is Llama4MoE.custom_routing_function
            routing_method_type = flashinfer.RoutingMethodType.DeepSeekV3
            if use_llama4_routing:
                routing_method_type = flashinfer.RoutingMethodType.Llama4
            out = flashinfer.fused_moe.trtllm_fp4_block_scale_moe(
                routing_logits=router_logits
                if use_llama4_routing else router_logits.to(torch.float32),
                routing_bias=e_score_correction_bias,
                hidden_states=hidden_states_fp4,
                hidden_states_scale=hidden_states_scale_linear_fp4.view(
                    torch.float8_e4m3fn).flatten(),
                gemm1_weights=layer.gemm1_weights_fp4_shuffled.data,
                gemm1_weights_scale=layer.gemm1_scales_fp4_shuffled.data.view(
                    torch.float8_e4m3fn),
                gemm1_bias=None,
                gemm1_alpha=None,
                gemm1_beta=None,
                gemm1_clamp_limit=None,
                gemm2_weights=layer.gemm2_weights_fp4_shuffled.data,
                gemm2_weights_scale=layer.gemm2_scales_fp4_shuffled.data.view(
                    torch.float8_e4m3fn),
                gemm2_bias=None,
                output1_scale_scalar=layer.g1_scale_c.data,
                output1_scale_gate_scalar=layer.g1_alphas.data,
                output2_scale_scalar=layer.g2_alphas.data,
                num_experts=global_num_experts,
                top_k=top_k,
                n_group=num_expert_group
                if num_expert_group is not None else 0,
                topk_group=topk_group if topk_group is not None else 0,
                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=None,
                tile_tokens_dim=_get_tile_tokens_dim(x.shape[0], top_k,
                                                     layer.local_num_experts),
                routing_method_type=routing_method_type,
                do_finalize=True,
            )[0]
            return out

        topk_weights, topk_ids = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias,
            indices_type=self.topk_indices_dtype)

        if self.use_marlin:
            return torch.ops.vllm.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=apply_router_weight_on_input,
                global_num_experts=global_num_experts,
                expert_map=expert_map)

        if self.fused_experts is not None:
            assert self.allow_flashinfer and \
               self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS

            assert is_valid_flashinfer_cutlass_fused_moe(
                x, layer.w13_weight, layer.w2_weight), (
                    "Flashinfer CUTLASS Fused MoE not applicable!")

            out = self.fused_experts(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                inplace=False,  # TODO(shuw): fix later, now output is high prec
                activation=activation,
                global_num_experts=global_num_experts,
                expert_map=expert_map,
                w1_scale=layer.w13_weight_scale,
                w2_scale=layer.w2_weight_scale,
                apply_router_weight_on_input=apply_router_weight_on_input,
            )
        elif (self.allow_flashinfer
              and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS):
            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
                flashinfer_cutlass_moe_fp4)

            out = flashinfer_cutlass_moe_fp4(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                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,
                inplace=False,  # TODO(shuw): fix later, now output is high prec
                activation=activation,
                global_num_experts=global_num_experts,
                expert_map=expert_map,
                apply_router_weight_on_input=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)
            out = cutlass_moe_fp4(
                a=x,
                w1_fp4=layer.w13_weight,
                w2_fp4=layer.w2_weight,
                w1_blockscale=layer.w13_weight_scale,
                w2_blockscale=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,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                m=x.shape[0],
                n=layer.w2_weight.shape[2] * 2,
                k=x.shape[1],
                e=layer.w13_weight.shape[0],
                expert_map=expert_map,
                apply_router_weight_on_input=apply_router_weight_on_input)

        return out

allow_flashinfer instance-attribute 

allow_flashinfer = allow_flashinfer

cutlass_nvfp4_supported instance-attribute 

cutlass_nvfp4_supported = cutlass_supported

flashinfer_moe_backend instance-attribute 

flashinfer_moe_backend = None

layer instance-attribute 

layer = layer

quant_config instance-attribute 

quant_config = quant_config

use_marlin instance-attribute 

use_marlin = use_marlin

__init__ 

__init__(
    quant_config: ModelOptNvFp4Config,
    moe: FusedMoEConfig,
    layer: Module,
) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(
    self,
    quant_config: ModelOptNvFp4Config,
    moe: FusedMoEConfig,
    layer: torch.nn.Module,
) -> None:
    from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (  # noqa: E501
        detect_nvfp4_moe_support)
    super().__init__(moe)
    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.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.")

apply 

apply(
    layer: Module,
    x: Tensor,
    router_logits: Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    e_score_correction_bias: Optional[Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    enable_eplb: bool = False,
    expert_load_view: Optional[Tensor] = None,
    logical_to_physical_map: Optional[Tensor] = None,
    logical_replica_count: Optional[Tensor] = None,
)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def apply(
    self,
    layer: torch.nn.Module,
    x: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    e_score_correction_bias: Optional[torch.Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    enable_eplb: bool = False,
    expert_load_view: Optional[torch.Tensor] = None,
    logical_to_physical_map: Optional[torch.Tensor] = None,
    logical_replica_count: Optional[torch.Tensor] = None,
):
    if enable_eplb:
        raise NotImplementedError(
            "EPLB not supported for `ModelOptNvFp4FusedMoE` yet.")
    assert activation == "silu", "Only SiLU activation is supported."

    if self.allow_flashinfer and \
        self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
        import flashinfer

        from vllm.model_executor.models.llama4 import Llama4MoE

        a1_gscale = layer.w13_input_scale_quant
        (hidden_states_fp4,
         hidden_states_scale_linear_fp4) = flashinfer.fp4_quantize(
             x,
             a1_gscale,
             is_sf_swizzled_layout=False,
         )
        use_llama4_routing = \
            custom_routing_function is Llama4MoE.custom_routing_function
        routing_method_type = flashinfer.RoutingMethodType.DeepSeekV3
        if use_llama4_routing:
            routing_method_type = flashinfer.RoutingMethodType.Llama4
        out = flashinfer.fused_moe.trtllm_fp4_block_scale_moe(
            routing_logits=router_logits
            if use_llama4_routing else router_logits.to(torch.float32),
            routing_bias=e_score_correction_bias,
            hidden_states=hidden_states_fp4,
            hidden_states_scale=hidden_states_scale_linear_fp4.view(
                torch.float8_e4m3fn).flatten(),
            gemm1_weights=layer.gemm1_weights_fp4_shuffled.data,
            gemm1_weights_scale=layer.gemm1_scales_fp4_shuffled.data.view(
                torch.float8_e4m3fn),
            gemm1_bias=None,
            gemm1_alpha=None,
            gemm1_beta=None,
            gemm1_clamp_limit=None,
            gemm2_weights=layer.gemm2_weights_fp4_shuffled.data,
            gemm2_weights_scale=layer.gemm2_scales_fp4_shuffled.data.view(
                torch.float8_e4m3fn),
            gemm2_bias=None,
            output1_scale_scalar=layer.g1_scale_c.data,
            output1_scale_gate_scalar=layer.g1_alphas.data,
            output2_scale_scalar=layer.g2_alphas.data,
            num_experts=global_num_experts,
            top_k=top_k,
            n_group=num_expert_group
            if num_expert_group is not None else 0,
            topk_group=topk_group if topk_group is not None else 0,
            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=None,
            tile_tokens_dim=_get_tile_tokens_dim(x.shape[0], top_k,
                                                 layer.local_num_experts),
            routing_method_type=routing_method_type,
            do_finalize=True,
        )[0]
        return out

    topk_weights, topk_ids = FusedMoE.select_experts(
        hidden_states=x,
        router_logits=router_logits,
        use_grouped_topk=use_grouped_topk,
        top_k=top_k,
        renormalize=renormalize,
        topk_group=topk_group,
        num_expert_group=num_expert_group,
        custom_routing_function=custom_routing_function,
        scoring_func=scoring_func,
        e_score_correction_bias=e_score_correction_bias,
        indices_type=self.topk_indices_dtype)

    if self.use_marlin:
        return torch.ops.vllm.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=apply_router_weight_on_input,
            global_num_experts=global_num_experts,
            expert_map=expert_map)

    if self.fused_experts is not None:
        assert self.allow_flashinfer and \
           self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS

        assert is_valid_flashinfer_cutlass_fused_moe(
            x, layer.w13_weight, layer.w2_weight), (
                "Flashinfer CUTLASS Fused MoE not applicable!")

        out = self.fused_experts(
            hidden_states=x,
            w1=layer.w13_weight,
            w2=layer.w2_weight,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            inplace=False,  # TODO(shuw): fix later, now output is high prec
            activation=activation,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            w1_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            apply_router_weight_on_input=apply_router_weight_on_input,
        )
    elif (self.allow_flashinfer
          and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS):
        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
            flashinfer_cutlass_moe_fp4)

        out = flashinfer_cutlass_moe_fp4(
            hidden_states=x,
            w1=layer.w13_weight,
            w2=layer.w2_weight,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            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,
            inplace=False,  # TODO(shuw): fix later, now output is high prec
            activation=activation,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            apply_router_weight_on_input=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)
        out = cutlass_moe_fp4(
            a=x,
            w1_fp4=layer.w13_weight,
            w2_fp4=layer.w2_weight,
            w1_blockscale=layer.w13_weight_scale,
            w2_blockscale=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,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            m=x.shape[0],
            n=layer.w2_weight.shape[2] * 2,
            k=x.shape[1],
            e=layer.w13_weight.shape[0],
            expert_map=expert_map,
            apply_router_weight_on_input=apply_router_weight_on_input)

    return out

create_weights 

create_weights(
    layer: Module,
    num_experts: int,
    hidden_size: int,
    intermediate_size_per_partition: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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")
    # GEMM 1
    w13_weight = ModelWeightParameter(
        data=torch.empty(
            num_experts,
            2 * 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 * 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, 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})

    w13_input_scale = PerTensorScaleParameter(data=torch.empty(
        num_experts, 2, dtype=torch.float32),
                                              weight_loader=weight_loader)
    layer.register_parameter("w13_input_scale", w13_input_scale)

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

maybe_make_prepare_finalize 

maybe_make_prepare_finalize(
    moe: FusedMoEConfig,
) -> Optional[FusedMoEPrepareAndFinalize]
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def maybe_make_prepare_finalize(
    self,
    moe: FusedMoEConfig,
) -> Optional[mk.FusedMoEPrepareAndFinalize]:
    if (self.allow_flashinfer and self.flashinfer_moe_backend
            == FlashinferMoeBackend.CUTLASS):
        prepare_finalize = (
            build_flashinfer_fp4_cutlass_moe_prepare_finalize(
                moe,
                a1_gscale=self.layer.w13_input_scale_quant,
            ))
        logger.debug_once("%s", prepare_finalize.__class__.__name__)
        return prepare_finalize

    return super().maybe_make_prepare_finalize(moe)

prepare_static_weight_layouts_for_trtllm_moe 

prepare_static_weight_layouts_for_trtllm_moe(
    gemm1_weights: Tensor,
    gemm2_weights: Tensor,
    gemm1_scales_linear_fp4_bytes: Tensor,
    gemm2_scales_linear_fp4_bytes: Tensor,
    hidden_size: int,
    intermediate_size: int,
    num_experts: int,
) -> tuple[Tensor, Tensor, Tensor, Tensor]

Prepare quantized weights for kernel (done offline with weights).

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def prepare_static_weight_layouts_for_trtllm_moe(
    self,
    gemm1_weights: torch.Tensor,
    gemm2_weights: torch.Tensor,
    gemm1_scales_linear_fp4_bytes: torch.Tensor,
    gemm2_scales_linear_fp4_bytes: torch.Tensor,
    hidden_size: int,
    intermediate_size: int,
    num_experts: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    """Prepare quantized weights for kernel (done offline with weights)."""
    from flashinfer import (reorder_rows_for_gated_act_gemm,
                            shuffle_matrix_a, shuffle_matrix_sf_a)
    epilogue_tile_m = 128  # FIXME: this depends on the kernel internals

    # Convert quantized weights to proper formats
    gemm1_weights_fp4 = gemm1_weights.view(torch.float8_e4m3fn).reshape(
        num_experts, 2 * intermediate_size, hidden_size // 2)  # packed fp4
    gemm1_scales_linear_fp4 = gemm1_scales_linear_fp4_bytes.view(
        torch.float8_e4m3fn).reshape(num_experts, 2 * intermediate_size,
                                     hidden_size //
                                     16)  # fp8 scaling factors

    gemm2_weights_fp4 = gemm2_weights.view(torch.float8_e4m3fn).reshape(
        num_experts, hidden_size, intermediate_size // 2)  # packed fp4
    gemm2_scales_linear_fp4 = gemm2_scales_linear_fp4_bytes.view(
        torch.float8_e4m3fn).reshape(num_experts, hidden_size,
                                     intermediate_size //
                                     16)  # fp8 scaling factors

    # Reorder rows of W1 and scales for fused gated activation
    gemm1_weights_fp4_interleaved = []
    gemm1_scales_fp4_interleaved = []
    for i in range(num_experts):
        gemm1_weights_fp4_interleaved.append(
            reorder_rows_for_gated_act_gemm(gemm1_weights_fp4[i].clone()))
        gemm1_scales_fp4_interleaved.append(
            reorder_rows_for_gated_act_gemm(
                gemm1_scales_linear_fp4[i].clone()))

    # Stack weights and scales for all experts
    gemm1_weights_fp4_interleaved = torch.stack(
        gemm1_weights_fp4_interleaved).reshape(num_experts,
                                               2 * intermediate_size,
                                               hidden_size // 2)
    gemm1_scales_fp4_interleaved = torch.stack(
        gemm1_scales_fp4_interleaved).reshape(num_experts,
                                              2 * intermediate_size,
                                              hidden_size // 16)

    # Shuffle weights and scaling factors for transposed mma output
    gemm1_weights_fp4_shuffled = []
    gemm1_scales_fp4_shuffled = []
    gemm2_weights_fp4_shuffled = []
    gemm2_scales_fp4_shuffled = []
    for i in range(num_experts):
        gemm1_weights_fp4_shuffled.append(
            shuffle_matrix_a(
                gemm1_weights_fp4_interleaved[i].view(torch.uint8),
                epilogue_tile_m))
        gemm1_scales_fp4_shuffled.append(
            shuffle_matrix_sf_a(
                gemm1_scales_fp4_interleaved[i].view(torch.uint8),
                epilogue_tile_m))

        gemm2_weights_fp4_shuffled.append(
            shuffle_matrix_a(gemm2_weights_fp4[i].view(torch.uint8),
                             epilogue_tile_m))
        gemm2_scales_fp4_shuffled.append(
            shuffle_matrix_sf_a(
                gemm2_scales_linear_fp4[i].view(torch.uint8),
                epilogue_tile_m))

    # Stack weights for all experts
    gemm1_weights_fp4_shuffled = torch.stack(gemm1_weights_fp4_shuffled)
    gemm1_scales_fp4_shuffled = (
        torch.stack(gemm1_scales_fp4_shuffled).view(
            torch.float8_e4m3fn).reshape(num_experts,
                                         2 * intermediate_size,
                                         hidden_size // 16))

    gemm2_weights_fp4_shuffled = torch.stack(gemm2_weights_fp4_shuffled)
    gemm2_scales_fp4_shuffled = (
        torch.stack(gemm2_scales_fp4_shuffled).view(
            torch.float8_e4m3fn).reshape(num_experts, hidden_size,
                                         intermediate_size // 16))
    return (gemm1_weights_fp4_shuffled, gemm1_scales_fp4_shuffled,
            gemm2_weights_fp4_shuffled, gemm2_scales_fp4_shuffled)

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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:
        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 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]
    layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2,
                                         requires_grad=False)

    # Common processing for input scales and alphas
    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
    layer.g2_alphas = Parameter(
        (layer.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 / layer.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
        (gemm1_weights_fp4_shuffled, gemm1_scales_fp4_shuffled,
         gemm2_weights_fp4_shuffled, gemm2_scales_fp4_shuffled
         ) = self.prepare_static_weight_layouts_for_trtllm_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
         )

        layer.gemm1_weights_fp4_shuffled = Parameter(
            gemm1_weights_fp4_shuffled, requires_grad=False)
        layer.gemm2_weights_fp4_shuffled = Parameter(
            gemm2_weights_fp4_shuffled, requires_grad=False)
        layer.gemm1_scales_fp4_shuffled = Parameter(
            gemm1_scales_fp4_shuffled, requires_grad=False)
        layer.gemm2_scales_fp4_shuffled = 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,
        )

        # Clean up weights that won't be used by TRT-LLM
        del layer.w2_weight
        del layer.w2_weight_scale
        del layer.w13_weight
        del layer.w13_weight_scale
    else:
        # Non-TRT-LLM processing (Cutlass or non-flashinfer)
        assert (layer.w13_weight_scale.shape[2] % 16 == 0), (
            "Expected weight_scale.dim(1) to be divisible by 16")
        assert (layer.w13_weight_scale.dtype == torch.float8_e4m3fn), (
            "Weight Blockscale must be represented as FP8-E4M3")
        w13_blockscale_swizzled = swizzle_blockscale(
            layer.w13_weight_scale)
        layer.w13_weight_scale = Parameter(w13_blockscale_swizzled,
                                           requires_grad=False)

        assert (layer.w2_weight_scale.shape[2] % 16 == 0), (
            "Expected weight_scale.dim(1) to be divisible by 16")
        assert (layer.w2_weight_scale.dtype == torch.float8_e4m3fn), (
            "Weight Blockscale must be represented as FP8-E4M3")
        w2_blockscale_swizzled = swizzle_blockscale(layer.w2_weight_scale)
        layer.w2_weight_scale = Parameter(w2_blockscale_swizzled,
                                          requires_grad=False)
        layer.w2_weight = Parameter(layer.w2_weight.data,
                                    requires_grad=False)

    if self.use_marlin:
        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

select_gemm_impl 

select_gemm_impl(
    prepare_finalize: FusedMoEPrepareAndFinalize,
    moe: FusedMoEConfig,
) -> FusedMoEPermuteExpertsUnpermute
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def select_gemm_impl(
    self,
    prepare_finalize: mk.FusedMoEPrepareAndFinalize,
    moe: FusedMoEConfig,
) -> mk.FusedMoEPermuteExpertsUnpermute:
    experts = select_nvfp4_gemm_impl(
        moe,
        g1_alphas=self.layer.g1_alphas,
        g2_alphas=self.layer.g2_alphas,
        a1_gscale=self.layer.w13_input_scale_quant,
        a2_gscale=self.layer.w2_input_scale_quant,
        allow_flashinfer=self.allow_flashinfer,
    )
    logger.debug_once("Using %s", experts.__class__.__name__)
    return experts

uses_weight_scale_2_pattern 

uses_weight_scale_2_pattern() -> bool

FP4 variants use 'weight_scale_2' pattern for per-tensor weight scales.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def uses_weight_scale_2_pattern(self) -> bool:
    """
    FP4 variants use 'weight_scale_2' pattern for per-tensor weight scales.
    """
    return True

ModelOptNvFp4LinearMethod

Bases: 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.

Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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

        if envs.VLLM_USE_TRTLLM_FP4_GEMM:
            assert has_flashinfer(), "TRTLLM FP4 GEMM requires FlashInfer"
            self.backend = "flashinfer-trtllm"
        elif has_flashinfer():
            self.backend = "flashinfer-cutlass"
        elif cutlass_fp4_supported():
            self.backend = "cutlass"
        elif is_fp4_marlin_supported():
            self.backend = "marlin"
        else:
            raise ValueError("Current platform does not support NVFP4"
                             " quantization. Please use Blackwell and"
                             " above.")

    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)

        # 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 == "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)

            if self.backend == "marlin":
                prepare_fp4_layer_for_marlin(layer)
                del layer.alpha
                del layer.input_scale

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = 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)

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

        # quantize BF16 or FP16 to (FP4 and interleaved block scale)
        s_quant = 1 / layer.input_scale
        x_fp4, x_blockscale = scaled_fp4_quant(x, s_quant)

        # 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 == "flashinfer-trtllm":
            out = flashinfer_scaled_fp4_mm(*mm_args, backend="trtllm")
        elif self.backend == "flashinfer-cutlass":
            out = flashinfer_scaled_fp4_mm(*mm_args, backend="cutlass")
        else:
            out = cutlass_scaled_fp4_mm(*mm_args)

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

backend instance-attribute 

backend = 'flashinfer-trtllm'

quant_config instance-attribute 

quant_config = quant_config

__init__ 

__init__(quant_config: ModelOptNvFp4Config) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(self, quant_config: ModelOptNvFp4Config) -> None:
    self.quant_config = quant_config

    if envs.VLLM_USE_TRTLLM_FP4_GEMM:
        assert has_flashinfer(), "TRTLLM FP4 GEMM requires FlashInfer"
        self.backend = "flashinfer-trtllm"
    elif has_flashinfer():
        self.backend = "flashinfer-cutlass"
    elif cutlass_fp4_supported():
        self.backend = "cutlass"
    elif is_fp4_marlin_supported():
        self.backend = "marlin"
    else:
        raise ValueError("Current platform does not support NVFP4"
                         " quantization. Please use Blackwell and"
                         " above.")

apply 

apply(
    layer: Module, x: Tensor, bias: Optional[Tensor] = None
) -> Tensor
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def apply(
    self,
    layer: torch.nn.Module,
    x: torch.Tensor,
    bias: Optional[torch.Tensor] = 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)

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

    # quantize BF16 or FP16 to (FP4 and interleaved block scale)
    s_quant = 1 / layer.input_scale
    x_fp4, x_blockscale = scaled_fp4_quant(x, s_quant)

    # 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 == "flashinfer-trtllm":
        out = flashinfer_scaled_fp4_mm(*mm_args, backend="trtllm")
    elif self.backend == "flashinfer-cutlass":
        out = flashinfer_scaled_fp4_mm(*mm_args, backend="cutlass")
    else:
        out = cutlass_scaled_fp4_mm(*mm_args)

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

create_weights 

create_weights(
    layer: Module,
    input_size_per_partition: int,
    output_partition_sizes: list[int],
    input_size: int,
    output_size: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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)

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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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)

    # 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 == "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)

        if self.backend == "marlin":
            prepare_fp4_layer_for_marlin(layer)
            del layer.alpha
            del layer.input_scale

_get_tile_tokens_dim 

_get_tile_tokens_dim(
    num_tokens: int, top_k: int, num_experts: int
) -> int
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def _get_tile_tokens_dim(num_tokens: int, top_k: int, num_experts: int) -> int:
    # Guess tokens per expert assuming perfect expert distribution first.
    num_tokens_per_expert = (num_tokens * top_k) // num_experts
    # And pad the number to the next power of 2.
    tile_tokens_dim = next_power_of_2(num_tokens_per_expert)
    # Cap to 8-64 tokens per CTA tile as it's the range supported by the kernel.
    tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
    return tile_tokens_dim