def parse_args_executorch()

in optimum/commands/export/executorch.py [0:0]

• 54 lines of code
• 1 McCabe index (conditional complexity)

def parse_args_executorch(parser):
    required_group = parser.add_argument_group("Required arguments")
    required_group.add_argument(
        "-m",
        "--model",
        type=str,
        required=True,
        help="Model ID on huggingface.co or path on disk to load model from.",
    )
    required_group.add_argument(
        "-o",
        "--output_dir",
        type=Path,
        help="Path indicating the directory where to store the generated ExecuTorch model.",
    )
    required_group.add_argument(
        "--task",
        type=str,
        default="text-generation",
        help=(
            "The task to export the model for. Available tasks depend on the model, but are among:"
            f" {str(TasksManager.get_all_tasks())}."
        ),
    )
    required_group.add_argument(
        "--recipe",
        type=str,
        default="xnnpack",
        help='Pre-defined recipes for export to ExecuTorch. Defaults to "xnnpack".',
    )
    required_group.add_argument(
        "--use_custom_sdpa",
        required=False,
        action="store_true",
        help="For decoder-only models to use custom sdpa with static kv cache to boost performance. Defaults to False.",
    )
    required_group.add_argument(
        "--use_custom_kv_cache",
        required=False,
        action="store_true",
        help="For decoder-only models to use custom kv cache for static cache that updates cache using custom op. Defaults to False.",
    )
    required_group.add_argument(
        "--qlinear",
        required=False,
        action="store_true",
        help="Quantization config for linear layers. If set, defaults to '8da4w' w/ groupsize 32.",
    )
    required_group.add_argument(
        "--qembedding",
        required=False,
        action="store_true",
        help="Quantization config for embedding. If set, defaults to int8 channelwise.",
    )