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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 from_config(cls, config: dict[str, Any]) -> "ModelOptFp8Config":
        quant_config = cls.get_from_keys(config, ["quantization"])
        quant_method = quant_config["quant_algo"]
        kv_cache_quant_method = cls.get_from_keys(
            config, ["quantization"]).get("kv_cache_quant_algo")
        exclude_modules = cls.get_from_keys(
            config, ["quantization"]).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)
        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":
    quant_config = cls.get_from_keys(config, ["quantization"])
    quant_method = quant_config["quant_algo"]
    kv_cache_quant_method = cls.get_from_keys(
        config, ["quantization"]).get("kv_cache_quant_algo")
    exclude_modules = cls.get_from_keys(
        config, ["quantization"]).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)
    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

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

    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)

    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.")

        # 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,
        )
        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()

quant_config instance-attribute 

quant_config = quant_config

__init__ 

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

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.")

    # 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,
    )
    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)

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)

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: 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 from_config(cls, config: dict[str, Any]) -> "ModelOptNvFp4Config":
        quant_config = cls.get_from_keys(config, ["quantization"])
        quant_method = quant_config["quant_algo"]
        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)
        if ("group_size" and "kv_cache_quant_algo"
                and "exclude_modules") not in quant_config:
            raise ValueError("NVFP4 quantization requires group size and "
                             "kv_cache_quant_algo specified in "
                             "hf_quant_config.json")
        kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
        group_size = quant_config["group_size"]
        exclude_modules = quant_config["exclude_modules"]
        return cls(is_checkpoint_nvfp4_serialized, kv_cache_quant_algo,
                   exclude_modules, group_size)

    def is_layer_excluded(self, prefix: str, exclude_modules: list):
        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)
        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: 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: 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":
    quant_config = cls.get_from_keys(config, ["quantization"])
    quant_method = quant_config["quant_algo"]
    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)
    if ("group_size" and "kv_cache_quant_algo"
            and "exclude_modules") not in quant_config:
        raise ValueError("NVFP4 quantization requires group size and "
                         "kv_cache_quant_algo specified in "
                         "hf_quant_config.json")
    kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
    group_size = quant_config["group_size"]
    exclude_modules = quant_config["exclude_modules"]
    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)
    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)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def is_layer_excluded(self, prefix: str, exclude_modules: list):
    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

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):
        self.quant_config = quant_config
        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
        self.use_marlin = False
        self.allow_flashinfer_cutlass = False

        if envs.VLLM_USE_FLASHINFER_MOE:
            if self.cutlass_nvfp4_supported and current_platform.is_cuda() \
               and current_platform.is_device_capability(100):
                logger.info_once(
                    "Using FlashInfer kernels for ModelOptNvFp4FusedMoE.")
                self.allow_flashinfer_cutlass = True
            else:
                logger.warning_once(
                    "Flashinfer CUTLASS Fused MoE not supported "
                    "or found on the current platform.")

        if not self.cutlass_nvfp4_supported:
            if is_fp4_marlin_supported():
                self.use_marlin = True
            else:
                raise ValueError("Current platform does not support NVFP4"
                                 " quantization. Please use Blackwell and"
                                 " above.")

        self.fused_experts = None  # type: ignore

    def maybe_swap_experts_impl(
        self,
        moe_parallel_config: FusedMoEParallelConfig,
    ):
        if not self.allow_flashinfer_cutlass:
            return

        logger.debug_once("FlashInferExperts")
        # default to TP/EP case only

        experts_kwargs: dict[str, Any] = {
            "use_nvfp4_w4a4": True,
            "use_dp": moe_parallel_config.dp_size > 1,
            "ep_rank": moe_parallel_config.ep_rank,
            "ep_size": moe_parallel_config.ep_size,
            "tp_rank": moe_parallel_config.tp_rank,
            "tp_size": moe_parallel_config.tp_size,
        }

        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
            FlashInferExperts)
        experts = FlashInferExperts(**experts_kwargs)
        self.fused_experts = mk.FusedMoEModularKernel(
            FlashInferCutlassMoEPrepareAndFinalize(
                quant_dtype=torch.uint8,
                #meaning 2x e2m1 packed in one, kernel requirement
            ),
            experts,
        )

    # This method update self.fused_experts
    # only prepare_finalize is not None call select_gemm_impl
    # so when native cutlass fp4, fused_expert is in fuse_moe.py fused_expert
    # when it's not called(TP case), we still have 2 kernels to use.
    def select_gemm_impl(self, prepare_finalize,
                         moe) -> mk.FusedMoEPermuteExpertsUnpermute:

        assert moe is not None
        assert prepare_finalize is not None
        experts = None
        all2all_manager = get_ep_group().device_communicator.all2all_manager
        assert all2all_manager is not None
        if self.allow_flashinfer_cutlass:
            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
                FlashInferExperts)
            logger.debug_once("Using FlashInferExperts")
            experts = FlashInferExperts(
                use_nvfp4_w4a4=True,
                use_dp=moe.moe_parallel_config.dp_size > 1,
                ep_rank=moe.moe_parallel_config.ep_rank,
                ep_size=moe.moe_parallel_config.ep_size,
                tp_rank=moe.moe_parallel_config.tp_rank,
                tp_size=moe.moe_parallel_config.tp_size,
            )
        else:
            assert moe.dp_size > 1
            logger.debug_once("Using CutlassExpertsFp4")
            # Currently CutlassExpertsFp4 doesn't support DP
            raise ValueError(
                "CutlassExpertsFp4 doesn't support DP. "
                "Use flashinfer CUTLASS FusedMoE(VLLM_USE_FLASHINFER_MOE)"
                " backend instead.")

        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 swizzle_blockscale(self, scale: torch.tensor):
        assert (scale.dtype == torch.float8_e4m3fn)
        # Pad and blockwise interleave weight_scale
        scale_ndim = scale.ndim
        if scale.ndim == 2:
            scale = scale.unsqueeze(0)
        assert scale.ndim == 3
        B, M, K = scale.shape
        round_up_multiple = lambda x, m: (x + m - 1) // m * m
        M_padded = round_up_multiple(M, 128)
        K_padded = round_up_multiple(K, 4)
        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
        padded_scale[:B, :M, :K] = scale
        batches, rows, cols = padded_scale.shape
        assert rows % 128 == 0
        assert cols % 4 == 0
        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
                                            cols // 4, 4)
        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
        swizzled_scale = swizzled_scale.contiguous().cuda()
        return (swizzled_scale.reshape(M, K)
                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # GEMM 1
        # The FlashInfer Cutlass fused MoE kernel expects the combined weights
        # to be ordered as [w3, w1], unlike the standard [w1, w3] layout.
        gemm1_weight = layer.w13_weight.data
        gemm1_weight_scale = layer.w13_weight_scale.data

        if self.allow_flashinfer_cutlass:
            dim = -2
            size = gemm1_weight.size(dim)
            assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
            half = size // 2

            # Reorder weight
            w1, w3 = gemm1_weight.split(half, dim=dim)
            gemm1_weight = torch.cat([w3, w1], dim=dim).contiguous()

            # Reorder scale
            s1, s3 = gemm1_weight_scale.split(half, dim=dim)
            gemm1_weight_scale = torch.cat([s3, s1], dim=dim).contiguous()

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

        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)

        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)

        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 = self.swizzle_blockscale(
            layer.w13_weight_scale)

        layer.w13_blockscale_swizzled = Parameter(w13_blockscale_swizzled,
                                                  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
        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)

        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 = self.swizzle_blockscale(layer.w2_weight_scale)

        layer.w2_blockscale_swizzled = 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
            del layer.w13_blockscale_swizzled
            del layer.w2_blockscale_swizzled

    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."

        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)

        if self.use_marlin:
            return torch.ops.vllm.fused_marlin_moe(
                x,
                layer.w13_weight,
                layer.w2_weight,
                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 None:
            # 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_blockscale_swizzled,
                w2_blockscale=layer.w2_blockscale_swizzled,
                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],
                device=x.device,
                expert_map=expert_map,
                apply_router_weight_on_input=apply_router_weight_on_input)
        else:
            # TP or DP case
            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
                is_valid_flashinfer_cutlass_fused_moe)
            assert is_valid_flashinfer_cutlass_fused_moe(
                x, layer.w13_weight, layer.w2_weight), (
                    "Flashinfer CUTLASS Fused MoE not applicable!")

            a1_gscale = torch.min(layer.w13_input_scale_quant)
            a2_gscale = torch.min(layer.w2_input_scale_quant)
            extra_expert_args = {
                'g1_alphas': layer.g1_alphas,
                'g2_alphas': layer.g2_alphas,
                'out_dtype': x.dtype,
                # Avoid confusion with a1_scale and a2_scale
                # where are batch size related.
                'a1_gscale': a1_gscale,
                'a2_gscale': a2_gscale,
            }
            extra_prepare_args = {
                'use_dp': layer.dp_size > 1,
                'local_tokens': x.shape[0],
                'a1_gscale': a1_gscale,
            }
            extra_finalize_args = {
                'use_dp': layer.dp_size > 1,
                'local_tokens': x.shape[0],
            }

            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_blockscale_swizzled,
                w2_scale=layer.w2_blockscale_swizzled,
                apply_router_weight_on_input=apply_router_weight_on_input,
                extra_expert_args=extra_expert_args,
                extra_prepare_args=extra_prepare_args,
                extra_finalize_args=extra_finalize_args,
            )
        return out

allow_flashinfer_cutlass instance-attribute 

allow_flashinfer_cutlass = False

cutlass_nvfp4_supported instance-attribute 

cutlass_nvfp4_supported = cutlass_fp4_supported()

fused_experts instance-attribute 

fused_experts = None

quant_config instance-attribute 

quant_config = quant_config

use_marlin instance-attribute 

use_marlin = False

__init__ 

__init__(quant_config: ModelOptNvFp4Config)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(self, quant_config: ModelOptNvFp4Config):
    self.quant_config = quant_config
    self.cutlass_nvfp4_supported = cutlass_fp4_supported()
    self.use_marlin = False
    self.allow_flashinfer_cutlass = False

    if envs.VLLM_USE_FLASHINFER_MOE:
        if self.cutlass_nvfp4_supported and current_platform.is_cuda() \
           and current_platform.is_device_capability(100):
            logger.info_once(
                "Using FlashInfer kernels for ModelOptNvFp4FusedMoE.")
            self.allow_flashinfer_cutlass = True
        else:
            logger.warning_once(
                "Flashinfer CUTLASS Fused MoE not supported "
                "or found on the current platform.")

    if not self.cutlass_nvfp4_supported:
        if is_fp4_marlin_supported():
            self.use_marlin = True
        else:
            raise ValueError("Current platform does not support NVFP4"
                             " quantization. Please use Blackwell and"
                             " above.")

    self.fused_experts = None  # type: ignore

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."

    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)

    if self.use_marlin:
        return torch.ops.vllm.fused_marlin_moe(
            x,
            layer.w13_weight,
            layer.w2_weight,
            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 None:
        # 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_blockscale_swizzled,
            w2_blockscale=layer.w2_blockscale_swizzled,
            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],
            device=x.device,
            expert_map=expert_map,
            apply_router_weight_on_input=apply_router_weight_on_input)
    else:
        # TP or DP case
        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
            is_valid_flashinfer_cutlass_fused_moe)
        assert is_valid_flashinfer_cutlass_fused_moe(
            x, layer.w13_weight, layer.w2_weight), (
                "Flashinfer CUTLASS Fused MoE not applicable!")

        a1_gscale = torch.min(layer.w13_input_scale_quant)
        a2_gscale = torch.min(layer.w2_input_scale_quant)
        extra_expert_args = {
            'g1_alphas': layer.g1_alphas,
            'g2_alphas': layer.g2_alphas,
            'out_dtype': x.dtype,
            # Avoid confusion with a1_scale and a2_scale
            # where are batch size related.
            'a1_gscale': a1_gscale,
            'a2_gscale': a2_gscale,
        }
        extra_prepare_args = {
            'use_dp': layer.dp_size > 1,
            'local_tokens': x.shape[0],
            'a1_gscale': a1_gscale,
        }
        extra_finalize_args = {
            'use_dp': layer.dp_size > 1,
            'local_tokens': x.shape[0],
        }

        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_blockscale_swizzled,
            w2_scale=layer.w2_blockscale_swizzled,
            apply_router_weight_on_input=apply_router_weight_on_input,
            extra_expert_args=extra_expert_args,
            extra_prepare_args=extra_prepare_args,
            extra_finalize_args=extra_finalize_args,
        )
    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_swap_experts_impl 

maybe_swap_experts_impl(
    moe_parallel_config: FusedMoEParallelConfig,
)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def maybe_swap_experts_impl(
    self,
    moe_parallel_config: FusedMoEParallelConfig,
):
    if not self.allow_flashinfer_cutlass:
        return

    logger.debug_once("FlashInferExperts")
    # default to TP/EP case only

    experts_kwargs: dict[str, Any] = {
        "use_nvfp4_w4a4": True,
        "use_dp": moe_parallel_config.dp_size > 1,
        "ep_rank": moe_parallel_config.ep_rank,
        "ep_size": moe_parallel_config.ep_size,
        "tp_rank": moe_parallel_config.tp_rank,
        "tp_size": moe_parallel_config.tp_size,
    }

    from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
        FlashInferExperts)
    experts = FlashInferExperts(**experts_kwargs)
    self.fused_experts = mk.FusedMoEModularKernel(
        FlashInferCutlassMoEPrepareAndFinalize(
            quant_dtype=torch.uint8,
            #meaning 2x e2m1 packed in one, kernel requirement
        ),
        experts,
    )

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
    # The FlashInfer Cutlass fused MoE kernel expects the combined weights
    # to be ordered as [w3, w1], unlike the standard [w1, w3] layout.
    gemm1_weight = layer.w13_weight.data
    gemm1_weight_scale = layer.w13_weight_scale.data

    if self.allow_flashinfer_cutlass:
        dim = -2
        size = gemm1_weight.size(dim)
        assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
        half = size // 2

        # Reorder weight
        w1, w3 = gemm1_weight.split(half, dim=dim)
        gemm1_weight = torch.cat([w3, w1], dim=dim).contiguous()

        # Reorder scale
        s1, s3 = gemm1_weight_scale.split(half, dim=dim)
        gemm1_weight_scale = torch.cat([s3, s1], dim=dim).contiguous()

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

    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)

    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)

    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 = self.swizzle_blockscale(
        layer.w13_weight_scale)

    layer.w13_blockscale_swizzled = Parameter(w13_blockscale_swizzled,
                                              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
    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)

    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 = self.swizzle_blockscale(layer.w2_weight_scale)

    layer.w2_blockscale_swizzled = 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
        del layer.w13_blockscale_swizzled
        del layer.w2_blockscale_swizzled

select_gemm_impl 

select_gemm_impl(
    prepare_finalize, moe
) -> FusedMoEPermuteExpertsUnpermute
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def select_gemm_impl(self, prepare_finalize,
                     moe) -> mk.FusedMoEPermuteExpertsUnpermute:

    assert moe is not None
    assert prepare_finalize is not None
    experts = None
    all2all_manager = get_ep_group().device_communicator.all2all_manager
    assert all2all_manager is not None
    if self.allow_flashinfer_cutlass:
        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
            FlashInferExperts)
        logger.debug_once("Using FlashInferExperts")
        experts = FlashInferExperts(
            use_nvfp4_w4a4=True,
            use_dp=moe.moe_parallel_config.dp_size > 1,
            ep_rank=moe.moe_parallel_config.ep_rank,
            ep_size=moe.moe_parallel_config.ep_size,
            tp_rank=moe.moe_parallel_config.tp_rank,
            tp_size=moe.moe_parallel_config.tp_size,
        )
    else:
        assert moe.dp_size > 1
        logger.debug_once("Using CutlassExpertsFp4")
        # Currently CutlassExpertsFp4 doesn't support DP
        raise ValueError(
            "CutlassExpertsFp4 doesn't support DP. "
            "Use flashinfer CUTLASS FusedMoE(VLLM_USE_FLASHINFER_MOE)"
            " backend instead.")

    return experts

swizzle_blockscale 

swizzle_blockscale(scale: tensor)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def swizzle_blockscale(self, scale: torch.tensor):
    assert (scale.dtype == torch.float8_e4m3fn)
    # Pad and blockwise interleave weight_scale
    scale_ndim = scale.ndim
    if scale.ndim == 2:
        scale = scale.unsqueeze(0)
    assert scale.ndim == 3
    B, M, K = scale.shape
    round_up_multiple = lambda x, m: (x + m - 1) // m * m
    M_padded = round_up_multiple(M, 128)
    K_padded = round_up_multiple(K, 4)
    padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
    padded_scale[:B, :M, :K] = scale
    batches, rows, cols = padded_scale.shape
    assert rows % 128 == 0
    assert cols % 4 == 0
    padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
                                        cols // 4, 4)
    swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
    swizzled_scale = swizzled_scale.contiguous().cuda()
    return (swizzled_scale.reshape(M, K)
            if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))

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):
        self.quant_config = quant_config
        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
        self.use_marlin = False

        if not self.cutlass_nvfp4_supported:
            if is_fp4_marlin_supported():
                self.use_marlin = True
            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 swizzle_blockscale(self, scale: torch.tensor):
        assert (scale.dtype == torch.float8_e4m3fn)
        # Pad and blockwise interleave weight_scale
        scale_ndim = scale.ndim
        if scale.ndim == 2:
            scale = scale.unsqueeze(0)
        assert scale.ndim == 3
        B, M, K = scale.shape
        round_up_multiple = lambda x, m: (x + m - 1) // m * m
        M_padded = round_up_multiple(M, 128)
        K_padded = round_up_multiple(K, 4)
        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
        padded_scale[:B, :M, :K] = scale
        batches, rows, cols = padded_scale.shape
        assert rows % 128 == 0
        assert cols % 4 == 0
        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
                                            cols // 4, 4)
        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
        swizzled_scale = swizzled_scale.contiguous().cuda()
        return (swizzled_scale.reshape(M, K)
                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))

    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.shape[1] % 16 == 0), (
            "Expected weight_scale.dim(1) to be divisible by 16")
        assert (layer.weight_scale.dtype == torch.float8_e4m3fn), (
            "Weight Block scale must be represented as FP8-E4M3")
        swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)

        layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
                                                requires_grad=False)
        layer.weight = Parameter(layer.weight.data, requires_grad=False)

        if self.use_marlin:
            prepare_fp4_layer_for_marlin(layer)
            del layer.alpha
            del layer.input_scale
            del layer.weight_scale_swizzled

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if self.use_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_swizzled.dtype == torch.float8_e4m3fn)
        assert (layer.alpha.dtype == torch.float32)

        out = cutlass_scaled_fp4_mm(x_fp4, layer.weight, x_blockscale,
                                    layer.weight_scale_swizzled, layer.alpha,
                                    output_dtype)
        if bias is not None:
            out = out + bias
        return out.view(*output_shape)

cutlass_nvfp4_supported instance-attribute 

cutlass_nvfp4_supported = cutlass_fp4_supported()

quant_config instance-attribute 

quant_config = quant_config

use_marlin instance-attribute 

use_marlin = False

__init__ 

__init__(quant_config: ModelOptNvFp4Config)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def __init__(self, quant_config: ModelOptNvFp4Config):
    self.quant_config = quant_config
    self.cutlass_nvfp4_supported = cutlass_fp4_supported()
    self.use_marlin = False

    if not self.cutlass_nvfp4_supported:
        if is_fp4_marlin_supported():
            self.use_marlin = True
        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.use_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_swizzled.dtype == torch.float8_e4m3fn)
    assert (layer.alpha.dtype == torch.float32)

    out = cutlass_scaled_fp4_mm(x_fp4, layer.weight, x_blockscale,
                                layer.weight_scale_swizzled, layer.alpha,
                                output_dtype)
    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.shape[1] % 16 == 0), (
        "Expected weight_scale.dim(1) to be divisible by 16")
    assert (layer.weight_scale.dtype == torch.float8_e4m3fn), (
        "Weight Block scale must be represented as FP8-E4M3")
    swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)

    layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
                                            requires_grad=False)
    layer.weight = Parameter(layer.weight.data, requires_grad=False)

    if self.use_marlin:
        prepare_fp4_layer_for_marlin(layer)
        del layer.alpha
        del layer.input_scale
        del layer.weight_scale_swizzled

swizzle_blockscale 

swizzle_blockscale(scale: tensor)
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def swizzle_blockscale(self, scale: torch.tensor):
    assert (scale.dtype == torch.float8_e4m3fn)
    # Pad and blockwise interleave weight_scale
    scale_ndim = scale.ndim
    if scale.ndim == 2:
        scale = scale.unsqueeze(0)
    assert scale.ndim == 3
    B, M, K = scale.shape
    round_up_multiple = lambda x, m: (x + m - 1) // m * m
    M_padded = round_up_multiple(M, 128)
    K_padded = round_up_multiple(K, 4)
    padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
    padded_scale[:B, :M, :K] = scale
    batches, rows, cols = padded_scale.shape
    assert rows % 128 == 0
    assert cols % 4 == 0
    padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
                                        cols // 4, 4)
    swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
    swizzled_scale = swizzled_scale.contiguous().cuda()
    return (swizzled_scale.reshape(M, K)
            if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))

cutlass_fp4_supported 

cutlass_fp4_supported() -> bool
Source code in vllm/model_executor/layers/quantization/modelopt.py 
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def cutlass_fp4_supported() -> bool:
    if not current_platform.is_cuda():
        return False
    capability_tuple = current_platform.get_device_capability()
    capability = -1 if capability_tuple is None else capability_tuple.to_int()
    return cutlass_scaled_mm_supports_fp4(capability)