converter scrypt fixes
This commit is contained in:
Michael Podvitskiy
parent
e0504d536c
commit
0c69016171
1 changed files with 22 additions and 60 deletions
74
convert.py
74
convert.py
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@ -332,6 +332,9 @@ class Params:
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#
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class BpeVocab:
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tokenizer_model = "gpt2"
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name = "bpe"
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def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
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self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
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if isinstance(self.bpe_tokenizer.get('model'), dict):
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@ -385,17 +388,14 @@ class BpeVocab:
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yield from self.bpe_tokens()
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yield from self.added_tokens()
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def get_tokenizer_model(self) -> str:
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return "gpt2"
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def get_name(self) -> str:
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return "bpe"
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def __repr__(self) -> str:
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return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
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class SentencePieceVocab:
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tokenizer_model = "llama"
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name = "spm"
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def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
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self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
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added_tokens: dict[str, int]
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@ -454,17 +454,14 @@ class SentencePieceVocab:
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yield from self.sentencepiece_tokens()
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yield from self.added_tokens()
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def get_tokenizer_model(self) -> str:
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return "llama"
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def get_name(self) -> str:
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return "spm"
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def __repr__(self) -> str:
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return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
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class HfVocab:
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tokenizer_model = "llama"
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name = "hfft"
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def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
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try:
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from transformers import AutoTokenizer
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@ -561,22 +558,13 @@ class HfVocab:
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yield from self.hf_tokens()
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yield from self.added_tokens()
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def get_tokenizer_model(self) -> str:
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return "llama"
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def get_name(self) -> str:
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return "hfft"
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def __repr__(self) -> str:
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return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
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class NoVocab:
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def get_tokenizer_model(self) -> str:
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return "no_vocab"
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def get_name(self) -> str:
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return "no_vocab"
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tokenizer_model = "no_vocab"
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name = "no_vocab"
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def __repr__(self) -> str:
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return "<NoVocab for a model without integrated vocabulary>"
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@ -969,8 +957,9 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:
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def prepare_vocab(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
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assert not isinstance(vocab, NoVocab)
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check_vocab_size(params, vocab)
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if vocab.name == "no_vocab":
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return
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# Check for a vocab size mismatch
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if params.n_vocab == vocab.vocab_size:
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@ -1065,7 +1054,7 @@ class OutputFile:
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def add_meta_vocab(self, vocab: Vocab) -> None:
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# Ensure that tokenizer_model is added to the GGUF model
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self.gguf.add_tokenizer_model(vocab.get_tokenizer_model())
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self.gguf.add_tokenizer_model(vocab.tokenizer_model)
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# Extract model vocabulary for model conversion
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tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)
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@ -1158,6 +1147,9 @@ class OutputFile:
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# meta data
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of.add_meta_arch(params)
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if vocab.name == "no_vocab":
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of.gguf.add_tokenizer_model(vocab.tokenizer_model)
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else:
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of.add_meta_vocab(vocab)
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of.add_meta_special_vocab(svocab)
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@ -1173,32 +1165,6 @@ class OutputFile:
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of.close()
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@staticmethod
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def write_without_vocab(
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fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab,
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concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
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) -> None:
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assert isinstance(vocab, NoVocab)
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check_vocab_size(params, vocab)
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of = OutputFile(fname_out, endianess=endianess)
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# meta data
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of.add_meta_arch(params)
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of.gguf.add_tokenizer_model(vocab.get_tokenizer_model())
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# tensor info
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for name, lazy_tensor in model.items():
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of.add_tensor_info(name, lazy_tensor)
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of.write_meta()
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of.write_tensor_info()
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# tensor data
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of.write_tensor_data(ftype, model, concurrency)
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of.close()
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def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
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wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type
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@ -1357,7 +1323,7 @@ class VocabFactory:
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raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
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def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab:
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load_merges = vocab.get_name() == "bpe"
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load_merges = vocab.name == "bpe"
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n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
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return gguf.SpecialVocab(
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model_parent_path,
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@ -1513,12 +1479,8 @@ def main(args_in: list[str] | None = None) -> None:
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params.ftype = ftype
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print(f"Writing {outfile}, format {ftype}")
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if not args.no_vocab:
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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.pad_vocab)
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else:
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OutputFile.write_without_vocab(outfile, ftype, params, model, vocab,
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concurrency=args.concurrency, endianess=endianess)
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print(f"Wrote {outfile}")
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