mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency. - Improved path resolution and import adjustments for robustness. - Thoughtfully handled 'awq-path' and conditional logic for the weighted model. - Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading. - Strengthened error handling and user feedback for a more user-friendly experience. - Structured output file handling with clear conditions and defaults. - Streamlined and organized the 'main' function for better logic flow. - Passed 'sys.argv[1:]' to 'main' for adaptability and testability. These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
This commit is contained in:
teleprint-me
parent
226cea270e
commit
0614c338f8
84
convert.py
84
convert.py
@ -1555,8 +1555,9 @@ def main(argv: Optional[list[str]] = None) -> None:
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args = parser.parse_args(argv)
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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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sys.path.insert(1, str(Path(__file__).resolve().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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@ -1575,74 +1576,83 @@ def main(argv: Optional[list[str]] = None) -> None:
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if not args.vocab_only:
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model_plus = load_some_model(args.model)
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else:
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model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
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model_plus = ModelPlus(
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model={}, paths=[args.model / "dummy"], format="none", vocab=None
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)
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if args.dump:
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do_dump_model(model_plus)
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return
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endianess = gguf.GGUFEndian.LITTLE
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if args.bigendian:
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if args.big_endian:
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endianess = gguf.GGUFEndian.BIG
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params = Params.load(model_plus)
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if params.n_ctx == -1:
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if args.ctx is None:
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raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"
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raise Exception(
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"The model doesn't have a context size, and you didn't specify one with --ctx\n"
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"Please specify one with --ctx:\n"
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" - LLaMA v1: --ctx 2048\n"
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" - LLaMA v2: --ctx 4096\n")
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" - LLaMA v2: --ctx 4096\n"
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)
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params.n_ctx = args.ctx
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if args.outtype:
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if args.out_type:
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params.ftype = {
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"f32": GGMLFileType.AllF32,
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"f16": GGMLFileType.MostlyF16,
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"q8_0": GGMLFileType.MostlyQ8_0,
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}[args.outtype]
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}[args.out_type]
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print(f"params = {params}")
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vocab: Vocab
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model_parent_path = model_plus.paths[0].parent
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vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
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vocab_factory = VocabFactory(vocab_path)
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vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
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if args.vocab_only:
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if not args.outfile:
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raise ValueError("need --outfile if using --vocab-only")
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# FIXME: Try to respect vocab_dir somehow?
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vocab = VocabLoader(params, args.vocab_dir or args.model)
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special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
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load_merges = True,
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n_vocab = vocab.vocab_size)
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outfile = args.outfile
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OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
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endianess = endianess, pad_vocab = args.padvocab)
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print(f"Wrote {outfile}")
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if not args.out_file:
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raise ValueError("need --out-file if using --vocab-only")
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out_file = args.out_file
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OutputFile.write_vocab_only(
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out_file,
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params,
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vocab,
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special_vocab,
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endianess=endianess,
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pad_vocab=args.pad_vocab,
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)
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print(f"Wrote {out_file}")
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return
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if model_plus.vocab is not None and args.vocab_dir is None:
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vocab = model_plus.vocab
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else:
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vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
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vocab = VocabLoader(params, vocab_dir)
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# FIXME: Try to respect vocab_dir somehow?
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print(f"Vocab info: {vocab}")
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special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
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load_merges = True,
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n_vocab = vocab.vocab_size)
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print(f"Special vocab info: {special_vocab}")
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model = model_plus.model
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model = convert_model_names(model, params)
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ftype = pick_output_type(model, args.outtype)
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ftype = pick_output_type(model, args.out_type)
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model = convert_to_output_type(model, ftype)
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outfile = args.outfile or default_outfile(model_plus.paths, ftype)
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out_file = args.out_file or default_output_file(model_plus.paths, ftype)
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params.ftype = ftype
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print(f"Writing {outfile}, format {ftype}")
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print(f"Writing {out_file}, format {ftype}")
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OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
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concurrency = args.concurrency, endianess = endianess, pad_vocab = args.padvocab)
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print(f"Wrote {outfile}")
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OutputFile.write_all(
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out_file,
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ftype,
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params,
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model,
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vocab,
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special_vocab,
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concurrency=args.concurrency,
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endianess=endianess,
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pad_vocab=args.pad_vocab,
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)
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print(f"Wrote {out_file}")
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if __name__ == '__main__':
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main()
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if __name__ == "__main__":
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main(sys.argv[1:]) # Exclude the first element (script name) from sys.argv
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