vllm.model_executor.layers.quantization.utils.machete_utils ¶

def check_machete_supports_shape(
    in_features: int, out_featrues: int
) -> tuple[bool, str | None]:
    if in_features % MACHETE_PREPACKED_BLOCK_SHAPE[0] != 0:
        return (
            False,
            "Input features size must be divisible by "
            f"{MACHETE_PREPACKED_BLOCK_SHAPE[0]}",
        )
    if out_featrues % MACHETE_PREPACKED_BLOCK_SHAPE[1] != 0:
        return (
            False,
            "Output features size must be divisible by "
            f"{MACHETE_PREPACKED_BLOCK_SHAPE[1]}",
        )
    return True, None

def query_machete_supported_act_types(zero_points: bool) -> list[ScalarType]:
    return [torch.float16, torch.bfloat16]

def query_machete_supported_group_sizes(act_type: torch.dtype) -> list[int]:
    """
    Queries the supported group sizes for Machete based on the activation type.

    Args:
        act_type: The activation data type (torch.float16, torch.bfloat16).

    Returns:
        A list of supported group sizes. The group size must
        be divisible by `TileShapeK = 128 * 8 // num_bits(act_type)`.
        -1 indicates per-channel quantization.
    """
    if act_type in [torch.float16, torch.bfloat16]:
        return [-1, 64, 128]
    else:
        return [-1, 128]

def query_machete_supported_quant_types(zero_points: bool) -> list[ScalarType]:
    if zero_points:
        return [scalar_types.uint4, scalar_types.uint8]
    else:
        return [scalar_types.uint4b8, scalar_types.uint8b128]