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llama.cpp
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convert : automatically fall back to HfVocab if tokenizer.model doesn't exist (#5821)

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Jared Van Bortel 2024-03-02 12:27:26 -05:00 committed by GitHub
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4 changed files with 41 additions and 45 deletions
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4
README.md
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@ -786,7 +786,7 @@ And after 4.45 hours, you will have the final perplexity.
### Interactive mode
If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
Here is an example of a few-shot interaction, invoked with the command
@ -850,7 +850,7 @@ Sample run:
```
== Running in interactive mode. ==
- Press Ctrl+C to interject at any time.
- Press Return to return control to LLaMa.
- Press Return to return control to LLaMA.
- If you want to submit another line, end your input in '\'.
Below is an instruction that describes a task. Write a response that appropriately completes the request.

6
convert-llama-ggml-to-gguf.py
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@ -373,7 +373,7 @@ def handle_metadata(cfg, hp):
raise ValueError('Unable to load metadata')
vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
vocab_factory = convert.VocabFactory(vocab_path)
vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir)
vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir)
convert.check_vocab_size(params, vocab)
return params, vocab, special_vocab
@ -398,8 +398,8 @@ def handle_args():
help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
parser.add_argument("--vocab-dir", type=Path,
help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
parser.add_argument("--vocabtype", default="spm,hfft",
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
return parser.parse_args()

72
convert.py
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@ -1282,35 +1282,32 @@ def load_some_model(path: Path) -> ModelPlus:
class VocabFactory:
_FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"}
def __init__(self, path: Path):
self.path = path
self.files: dict[str, Path | None] = {
"tokenizer.model": None,
"vocab.json": None,
"tokenizer.json": None,
}
self._detect_files()
self.file_paths = self._detect_files()
print(f"Found vocab files: {self.file_paths}")
def _detect_files(self):
for file in self.files.keys():
file_path = self.path / file
parent_file_path = self.path.parent / file
if file_path.exists():
self.files[file] = file_path
elif parent_file_path.exists():
self.files[file] = parent_file_path
print(f"Found vocab files: {self.files}")
def _detect_files(self) -> dict[str, Path | None]:
def locate(file: str) -> Path | None:
if (path := self.path / file).exists():
return path
if (path := self.path.parent / file).exists():
return path
return None
def _select_file(self, vocabtype: str | None) -> Path:
if vocabtype in ["spm", "bpe"]:
for file_key in self.files.keys():
if (file := self.files[file_key]) is not None:
return file
raise FileNotFoundError(f"{vocabtype} vocab not found.")
if vocabtype == "hfft":
# For Hugging Face Fast Tokenizer, return the directory path instead of a specific file
return self.path
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
return {vt: locate(f) for vt, f in self._FILES.items()}
def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]:
for vtype in vocab_types:
try:
path = self.file_paths[vtype]
except KeyError:
raise ValueError(f"Unsupported vocabulary type {vtype}") from None
if path is not None:
return vtype, path
raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab:
load_merges = vocabtype == "bpe"
@ -1322,30 +1319,30 @@ class VocabFactory:
n_vocab=n_vocab,
)
def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
path = self._select_file(vocabtype)
print(f"Loading vocab file '{path}', type '{vocabtype}'")
def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
vocab_type, path = self._select_file(vocab_types)
print(f"Loading vocab file {path!r}, type {vocab_type!r}")
added_tokens_path = path.parent / "added_tokens.json"
vocab: Vocab
if vocabtype == "bpe":
if vocab_type == "bpe":
vocab = BpeVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocabtype == "spm":
elif vocab_type == "spm":
vocab = SentencePieceVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocabtype == "hfft":
elif vocab_type == "hfft":
vocab = HfVocab(
path, added_tokens_path if added_tokens_path.exists() else None
path.parent, added_tokens_path if added_tokens_path.exists() else None
)
else:
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
raise ValueError(vocab_type)
# FIXME: Respect --vocab-dir?
special_vocab = self._create_special_vocab(
vocab,
vocabtype,
vocab_type,
model_parent_path,
)
return vocab, special_vocab
@ -1379,15 +1376,14 @@ def main(args_in: list[str] | None = None) -> None:
if np.uint32(1) == np.uint32(1).newbyteorder("<"):
# We currently only support Q8_0 output on little endian systems.
output_choices.append("q8_0")
vocab_types = ["spm", "bpe", "hfft"]
parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")
parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None)
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm")
parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
@ -1448,7 +1444,7 @@ def main(args_in: list[str] | None = None) -> None:
model_parent_path = model_plus.paths[0].parent
vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
vocab_factory = VocabFactory(vocab_path)
vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path)
if args.vocab_only:
if not args.outfile:

4
examples/infill/infill.cpp
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@ -378,10 +378,10 @@ int main(int argc, char ** argv) {
if (params.interactive) {
const char *control_message;
if (params.multiline_input) {
control_message = " - To return control to LLaMa, end your input with '\\'.\n"
control_message = " - To return control to LLaMA, end your input with '\\'.\n"
" - To return control without starting a new line, end your input with '/'.\n";
} else {
control_message = " - Press Return to return control to LLaMa.\n"
control_message = " - Press Return to return control to LLaMA.\n"
" - To return control without starting a new line, end your input with '/'.\n"
" - If you want to submit another line, end your input with '\\'.\n";
}