vllm.model_executor.layers.quantization.utils.allspark_utils ¶

def check_allspark_supported_dtype_shape(
    input_size_per_partition: int,
    output_size_per_partition: int,
    group_size: int,
    weight_dtype: ScalarType,
    act_dtype: torch.dtype,
):
    capability_tuple = current_platform.get_device_capability()
    device_capability = -1 if capability_tuple is None else capability_tuple.to_int()

    # For Ampere GPU
    if device_capability >= 80 and device_capability < 90:
        if group_size != -1:
            return (
                False,
                "For Ampere GPU, AllSpark does not support group_size "
                f"= {group_size}. Only group_size = -1 are supported.",
            )

        if weight_dtype not in ALLSPARK_SUPPORTED_QUANT_TYPES:
            return (
                False,
                "For Ampere GPU, AllSpark does not support "
                f"quant type ({weight_dtype}). Only quant type "
                f"({ALLSPARK_SUPPORTED_QUANT_TYPES}) are supported.",
            )

        if (
            input_size_per_partition % ALLSPARK_AMPERE_K_ALIGN != 0
            or output_size_per_partition % ALLSPARK_AMPERE_N_ALIGN != 0
        ):
            return (
                False,
                "AllSpark needs input_size_per_partition % "
                f"{ALLSPARK_AMPERE_K_ALIGN} = 0 and "
                f"output_size_per_partition % {ALLSPARK_AMPERE_N_ALIGN} = 0 "
                "for Ampere GPU optimized kernels.",
            )

        if act_dtype != torch.float16 and act_dtype != torch.bfloat16:
            return (
                False,
                "AllSpark only supports act_dtype = float16 or bfloat16,"
                f"for Ampere GPU, but got act_dtype = {act_dtype}.",
            )
    else:
        return (
            False,
            "AllSpark currently does not support "
            f"device_capability = {device_capability}.",
        )

    return True, None