diff options
Diffstat (limited to 'convert-gptneox-hf-to-gguf.py')
| -rwxr-xr-x | convert-gptneox-hf-to-gguf.py | 48 |
1 files changed, 7 insertions, 41 deletions
diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index 60679a2f..d4e85f51 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -19,29 +19,6 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) import gguf -# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py - - -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a corresponding list of unicode strings. - The reversible bpe codes work on unicode strings. - This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. - When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a significant percentage of your normal, say, 32K bpe vocab. - To avoid that, we want lookup tables between utf-8 bytes and unicode strings. - And avoids mapping to whitespace/control characters the bpe code barfs on. - """ - bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8+n) - n += 1 - return dict(zip(bs, (chr(n) for n in cs))) - def count_model_parts(dir_model: Path) -> int: num_parts = 0 @@ -130,6 +107,8 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) print("gguf: get tokenizer metadata") tokens: list[bytearray] = [] +scores: list[float] = [] +toktypes: list[int] = [] # gpt2 tokenizer gguf_writer.add_tokenizer_model("gpt2") @@ -145,28 +124,15 @@ vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) assert max(tokenizer.vocab.values()) < vocab_size reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} -byte_encoder = bytes_to_unicode() -byte_decoder = {v: k for k, v in byte_encoder.items()} for i in range(vocab_size): - if i in reverse_vocab: - try: - text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) - except KeyError: - text = bytearray() - for c in reverse_vocab[i]: - if ord(c) < 256: # single byte character - text.append(byte_decoder[ord(c)]) - else: # multibyte special token character - text.extend(c.encode('utf-8')) - else: - print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") - pad_token = f"[PAD{i}]".encode("utf8") - text = bytearray(pad_token) - - tokens.append(text) + tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]") + scores.append(0.0) # dummy + toktypes.append(gguf.TokenType.NORMAL) gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) special_vocab.add_to_gguf(gguf_writer) |