mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 05:48:47 +01:00
refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity. - Refactored argument parser setup into 'get_argument_parser' function. - Introduced descriptive comments for each argument in the parser. - Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing. - Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'. - Enhanced error handling to prevent overwriting input data in 'default_output_file'. - Made 'argv' in 'main' an optional parameter for flexibility. - Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency. These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
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parent
8aa5818a20
commit
226cea270e
119
convert.py
119
convert.py
@ -1432,17 +1432,18 @@ class VocabFactory:
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return vocab, special_vocab
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return vocab, special_vocab
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def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
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def default_output_file(model_paths: list[Path], file_type: GGMLFileType) -> Path:
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namestr = {
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namestr = {
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GGMLFileType.AllF32: "f32",
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GGMLFileType.AllF32: "f32",
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GGMLFileType.MostlyF16: "f16",
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GGMLFileType.MostlyF16: "f16",
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GGMLFileType.MostlyQ8_0:"q8_0",
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GGMLFileType.MostlyQ8_0: "q8_0",
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}[file_type]
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}[file_type]
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ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
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ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
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if ret in model_paths:
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if ret in model_paths:
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sys.stderr.write(
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sys.stderr.write(
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f"Error: Default output path ({ret}) would overwrite the input. "
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f"Error: Default output path ({ret}) would overwrite the input. "
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"Please explicitly specify a path using --outfile.\n")
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"Please explicitly specify a path using --out-file.\n"
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)
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sys.exit(1)
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sys.exit(1)
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return ret
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return ret
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@ -1452,29 +1453,107 @@ def do_dump_model(model_plus: ModelPlus) -> None:
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print(f"model_plus.format = {model_plus.format!r}")
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print(f"model_plus.format = {model_plus.format!r}")
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print(f"model_plus.vocab = {model_plus.vocab!r}")
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print(f"model_plus.vocab = {model_plus.vocab!r}")
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for name, lazy_tensor in model_plus.model.items():
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for name, lazy_tensor in model_plus.model.items():
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print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}")
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print(
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f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}"
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)
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def main(args_in: list[str] | None = None) -> None:
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def get_argument_parser() -> ArgumentParser:
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output_choices = ["f32", "f16"]
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output_choices = ["f32", "f16"]
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if np.uint32(1) == np.uint32(1).newbyteorder("<"):
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if np.uint32(1) == np.uint32(1).newbyteorder("<"):
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# We currently only support Q8_0 output on little endian systems.
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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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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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parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
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parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
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parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
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parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
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parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
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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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parser = argparse.ArgumentParser(
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description="Convert a LLaMa model to a GGML compatible file"
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)
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parser.add_argument(
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"model",
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type=Path,
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help="Directory containing the model file or the model file itself (*.pth, *.pt, *.bin)",
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)
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parser.add_argument(
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"--awq-path",
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type=Path,
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help="Path to the Activation-aware Weight Quantization cache file",
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default=None,
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)
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parser.add_argument(
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"--dump",
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action="store_true",
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help="Display the model content without converting it",
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)
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parser.add_argument(
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"--dump-single",
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action="store_true",
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help="Display the content of a single model file without conversion",
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)
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parser.add_argument(
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"--vocab-only",
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action="store_true",
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help="Extract and output only the vocabulary",
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)
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parser.add_argument(
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"--out-type",
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choices=output_choices,
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help="Output format - note: q8_0 may be very slow (default: f16 or f32 based on input)",
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)
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parser.add_argument(
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"--vocab-dir",
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type=Path,
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help="Directory containing the tokenizer.model, if separate from the model file",
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)
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parser.add_argument(
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"--vocab-type",
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choices=["spm", "bpe", "hfft"], # hfft: Hugging Face Fast Tokenizer
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default="spm",
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help="The vocabulary format used to define the tokenizer model (default: spm)",
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)
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parser.add_argument(
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"--pad-vocab",
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action="store_true",
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help="Add padding tokens when the model's vocabulary size exceeds the tokenizer metadata",
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)
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parser.add_argument(
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"--out-file",
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type=Path,
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help="Specify the path for the output file (default is based on input)",
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)
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parser.add_argument(
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"--ctx", type=int, help="Model training context (default is based on input)"
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)
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parser.add_argument(
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"--concurrency",
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type=int,
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help=f"Concurrency used for conversion (default: {DEFAULT_CONCURRENCY})",
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default=DEFAULT_CONCURRENCY,
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)
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parser.add_argument(
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"--big-endian",
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action="store_true",
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help="Indicate that the model is executed on a big-endian machine",
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)
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return parser
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def main(argv: Optional[list[str]] = None) -> None:
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parser = get_argument_parser()
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args = parser.parse_args(argv)
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if args.awq_path:
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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__).parent / 'awq-py'))
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from awq.apply_awq import add_scale_weights
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from awq.apply_awq import add_scale_weights
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