wehub-resource-sync
113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
"""Command line entrypoint of weight conversion."""
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import argparse
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from pathlib import Path
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from typing import Union
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from mlc_llm.interface.convert_weight import convert_weight
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from mlc_llm.interface.help import HELP
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from mlc_llm.model import MODELS
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from mlc_llm.quantization import QUANTIZATION
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from mlc_llm.support.argparse import ArgumentParser
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from mlc_llm.support.auto_config import detect_config, detect_model_type
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from mlc_llm.support.auto_device import detect_device
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from mlc_llm.support.auto_weight import detect_weight
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def main(argv):
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"""Parse command line argumennts and apply quantization."""
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def _parse_source(path: Union[str, Path], config_path: Path) -> Path:
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if path == "auto":
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return config_path.parent
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path = Path(path)
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if not path.exists():
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raise argparse.ArgumentTypeError(f"Model source does not exist: {path}")
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return path
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def _parse_output(path: Union[str, Path]) -> Path:
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path = Path(path)
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if not path.is_dir():
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path.mkdir(parents=True, exist_ok=True)
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return path
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def _parse_lora_adapter(path: Union[str, Path]) -> Path:
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path = Path(path)
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if not path.exists() or not path.is_dir():
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raise argparse.ArgumentTypeError(f"LoRA adapter directory does not exist: {path}")
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return path
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parser = ArgumentParser("MLC AutoLLM Quantization Framework")
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parser.add_argument(
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"config",
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type=detect_config,
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help=HELP["config"] + " (required)",
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)
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parser.add_argument(
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"--quantization",
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type=str,
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required=True,
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choices=list(QUANTIZATION.keys()),
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help=HELP["quantization"] + " (required, choices: %(choices)s)",
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)
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parser.add_argument(
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"--model-type",
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type=str,
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default="auto",
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choices=["auto", *list(MODELS.keys())],
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help=HELP["model_type"] + ' (default: "%(default)s")',
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)
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parser.add_argument(
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"--device",
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default="auto",
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type=detect_device,
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help=HELP["device_quantize"] + ' (default: "%(default)s")',
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)
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parser.add_argument(
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"--source",
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type=str,
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default="auto",
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help=HELP["source"] + ' (default: "%(default)s")',
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)
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parser.add_argument(
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"--source-format",
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type=str,
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choices=["auto", "huggingface-torch", "huggingface-safetensor", "awq"],
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default="auto",
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help=HELP["source_format"] + ' (default: "%(default)s", choices: %(choices)s")',
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)
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parser.add_argument(
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"--output",
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"-o",
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type=_parse_output,
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required=True,
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help=HELP["output_quantize"] + " (required)",
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)
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parser.add_argument(
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"--lora-adapter",
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type=_parse_lora_adapter,
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default=None,
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help=(
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"Path to a LoRA adapter directory in PEFT format. "
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"When provided, adapter weights are merged into the base model before quantization."
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),
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)
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parsed = parser.parse_args(argv)
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parsed.source, parsed.source_format = detect_weight(
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_parse_source(parsed.source, parsed.config),
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parsed.config,
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parsed.source_format,
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)
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model = detect_model_type(parsed.model_type, parsed.config)
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convert_weight(
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config=parsed.config,
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quantization=QUANTIZATION[parsed.quantization],
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model=model,
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device=parsed.device,
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source=parsed.source,
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source_format=parsed.source_format,
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output=parsed.output,
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lora_adapter=parsed.lora_adapter,
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)
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