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vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w4a16_nvfp4

__all__ module-attribute 

__all__ = ['CompressedTensorsW4A16Fp4']

CompressedTensorsW4A16Fp4

Bases: CompressedTensorsScheme

Source code in vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py 
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class CompressedTensorsW4A16Fp4(CompressedTensorsScheme):

    def __init__(self, has_input_global_scale: bool = False):
        self.has_input_global_scale = has_input_global_scale
        self.group_size = 16

    @classmethod
    def get_min_capability(cls) -> int:
        # dont restrict as emulations
        return 80

    def create_weights(self, layer: torch.nn.Module,
                       output_partition_sizes: list[int],
                       input_size_per_partition: int,
                       params_dtype: torch.dtype, weight_loader: Callable,
                       **kwargs):
        output_size_per_partition = sum(output_partition_sizes)
        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
        weight = ModelWeightParameter(data=torch.empty(
            sum(output_partition_sizes),
            input_size_per_partition // 2,
            dtype=torch.uint8),
                                      input_dim=1,
                                      output_dim=0,
                                      weight_loader=weight_loader)
        layer.register_parameter("weight_packed", weight)

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

        # Per Group Weight Scale
        weight_scale = GroupQuantScaleParameter(data=torch.empty(
            sum(output_partition_sizes),
            input_size_per_partition // self.group_size,
            dtype=torch.float8_e4m3fn,
        ),
                                                input_dim=1,
                                                output_dim=0,
                                                weight_loader=weight_loader)

        layer.register_parameter("weight_scale", weight_scale)

        if self.has_input_global_scale:
            input_global_scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes),
                                 dtype=torch.float32),
                weight_loader=weight_loader)
            layer.register_parameter("input_global_scale", input_global_scale)

    def process_weights_after_loading(self, layer) -> None:
        # Process parameters for marlin repacking

        # Rename weight_packed to weight that marlin expects
        layer.weight = Parameter(layer.weight_packed.data, requires_grad=False)
        del layer.weight_packed
        # Rename weight_global_scale to weight_scale_2 that marlin expects
        # Note: ct stores the inverse of what is expected by the marlin kernel
        layer.weight_scale_2 = Parameter(
            1 / layer.weight_global_scale.max().to(torch.float32),
            requires_grad=False)
        del layer.weight_global_scale

        if self.has_input_global_scale:
            layer.input_global_scale = torch.nn.Parameter(
                layer.input_global_scale.data, requires_grad=False)

        prepare_fp4_layer_for_marlin(layer)

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

group_size instance-attribute 

group_size = 16

has_input_global_scale instance-attribute 

has_input_global_scale = has_input_global_scale

__init__ 

__init__(has_input_global_scale: bool = False)
Source code in vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py 
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def __init__(self, has_input_global_scale: bool = False):
    self.has_input_global_scale = has_input_global_scale
    self.group_size = 16

apply_weights 

apply_weights(
    layer: Module, x: Tensor, bias: Optional[Tensor] = None
) -> Tensor
Source code in vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py 
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def apply_weights(self,
                  layer: torch.nn.Module,
                  x: torch.Tensor,
                  bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    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)

create_weights 

create_weights(
    layer: Module,
    output_partition_sizes: list[int],
    input_size_per_partition: int,
    params_dtype: dtype,
    weight_loader: Callable,
    **kwargs,
)
Source code in vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py 
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def create_weights(self, layer: torch.nn.Module,
                   output_partition_sizes: list[int],
                   input_size_per_partition: int,
                   params_dtype: torch.dtype, weight_loader: Callable,
                   **kwargs):
    output_size_per_partition = sum(output_partition_sizes)
    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
    weight = ModelWeightParameter(data=torch.empty(
        sum(output_partition_sizes),
        input_size_per_partition // 2,
        dtype=torch.uint8),
                                  input_dim=1,
                                  output_dim=0,
                                  weight_loader=weight_loader)
    layer.register_parameter("weight_packed", weight)

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

    # Per Group Weight Scale
    weight_scale = GroupQuantScaleParameter(data=torch.empty(
        sum(output_partition_sizes),
        input_size_per_partition // self.group_size,
        dtype=torch.float8_e4m3fn,
    ),
                                            input_dim=1,
                                            output_dim=0,
                                            weight_loader=weight_loader)

    layer.register_parameter("weight_scale", weight_scale)

    if self.has_input_global_scale:
        input_global_scale = PerTensorScaleParameter(
            data=torch.empty(len(output_partition_sizes),
                             dtype=torch.float32),
            weight_loader=weight_loader)
        layer.register_parameter("input_global_scale", input_global_scale)

get_min_capability classmethod 

get_min_capability() -> int
Source code in vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py 
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@classmethod
def get_min_capability(cls) -> int:
    # dont restrict as emulations
    return 80

process_weights_after_loading 

process_weights_after_loading(layer) -> None
Source code in vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py 
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def process_weights_after_loading(self, layer) -> None:
    # Process parameters for marlin repacking

    # Rename weight_packed to weight that marlin expects
    layer.weight = Parameter(layer.weight_packed.data, requires_grad=False)
    del layer.weight_packed
    # Rename weight_global_scale to weight_scale_2 that marlin expects
    # Note: ct stores the inverse of what is expected by the marlin kernel
    layer.weight_scale_2 = Parameter(
        1 / layer.weight_global_scale.max().to(torch.float32),
        requires_grad=False)
    del layer.weight_global_scale

    if self.has_input_global_scale:
        layer.input_global_scale = torch.nn.Parameter(
            layer.input_global_scale.data, requires_grad=False)

    prepare_fp4_layer_for_marlin(layer)