convert : remove AWQ remnants (#5768)
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13
convert.py
13
convert.py
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@ -1377,7 +1377,6 @@ def main(args_in: list[str] | None = None) -> None:
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# We currently only support Q8_0 output on little endian systems.
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output_choices.append("q8_0")
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parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")
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parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None)
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parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
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parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
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parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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@ -1393,18 +1392,6 @@ def main(args_in: list[str] | None = None) -> None:
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parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
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args = parser.parse_args(args_in)
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if args.awq_path:
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sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
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from awq.apply_awq import add_scale_weights # type: ignore[import-not-found]
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tmp_model_path = args.model / "weighted_model"
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if tmp_model_path.is_dir():
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print(f"{tmp_model_path} exists as a weighted model.")
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else:
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tmp_model_path.mkdir(parents=True, exist_ok=True)
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print("Saving new weighted model ...")
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add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
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print(f"Saved weighted model at {tmp_model_path}.")
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args.model = tmp_model_path
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if args.dump_single:
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model_plus = lazy_load_file(args.model)
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