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convert : remove AWQ remnants (#8320)
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@ -2445,7 +2445,7 @@ class Gemma2Model(Model):
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raise ValueError("query_pre_attn_scalar must be equal to n_embd / n_head")
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unusem
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del bid # unused
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# lm_head is not used in llama.cpp, while autoawq will include this tensor in model
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# To prevent errors, skip loading lm_head.weight.
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@ -3225,10 +3225,6 @@ def parse_args() -> argparse.Namespace:
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"--vocab-only", action="store_true",
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help="extract only the vocab",
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)
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parser.add_argument(
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"--awq-path", type=Path, default=None,
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help="Path to scale awq cache file",
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)
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parser.add_argument(
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"--outfile", type=Path,
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help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
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@ -3306,19 +3302,6 @@ def main() -> None:
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dir_model = args.model
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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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dir_model = tmp_model_path
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if tmp_model_path.is_dir():
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logger.info(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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logger.info("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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logger.info(f"Saved weighted model at {tmp_model_path}.")
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if not dir_model.is_dir():
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logger.error(f'Error: {args.model} is not a directory')
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sys.exit(1)
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