llama : add AWQ for llama, llama2, mpt, and mistral models (#4593)
* update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci --------- Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io> Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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8 changed files with 443 additions and 5 deletions
14
convert.py
14
convert.py
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@ -1187,6 +1187,7 @@ 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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@ -1200,6 +1201,19 @@ def main(args_in: list[str] | None = None) -> None:
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parser.add_argument("--padvocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
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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
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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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do_dump_model(model_plus)
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