vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w4a16_mxfp4 ¶

class CompressedTensorsW4A16Mxfp4(CompressedTensorsScheme):
    """
    Compressed tensors scheme for MXFP4 weight-only quantization.

    Supports models quantized with the compressed-tensors mxfp4-pack-quantized
    format.

    MXFP4 format:
    - 4-bit float weights (E2M1) packed into uint8
    - Per-group E8M0 scales with group_size=32
    - No global scale (unlike NVFP4)
    """

    def __init__(self):
        self.group_size = 32

    @classmethod
    def get_min_capability(cls) -> int:
        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
        layer.params_dtype = params_dtype

        # Packed FP4 weights (2 values per byte)
        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition // 2,
                dtype=torch.uint8,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight_packed", weight)

        # Per-group E8M0 scales
        weight_scale = GroupQuantScaleParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition // self.group_size,
                dtype=torch.uint8,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight_scale", weight_scale)

    def process_weights_after_loading(self, layer) -> None:
        # Rename weight_packed to weight that marlin expects
        layer.weight = Parameter(layer.weight_packed.data, requires_grad=False)
        del layer.weight_packed

        prepare_fp4_layer_for_marlin(layer)

    def apply_weights(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return apply_fp4_marlin_linear(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            weight_scale_2=None,
            workspace=layer.workspace,
            size_n=layer.output_size_per_partition,
            size_k=layer.input_size_per_partition,
            bias=bias,
        )