| """Native ByteLevel BPE tokenizer and explicit role-token chat formatting.""" |
| from __future__ import annotations |
| from pathlib import Path |
|
|
| SPECIAL=['<|unk|>','<|pad|>','<|bos|>','<|eos|>','<|im_start|>','<|im_end|>','<|eot_id|>'] |
| TOKENIZER_FILE='tokenizer.json' |
|
|
|
|
| def load_tokenizer(): |
| from tokenizers import Tokenizer |
| from .settings import path |
| p=path('tokenizer.json') |
| if not p.exists():raise FileNotFoundError('Tokenizer missing: run python -m velqora tokenizer') |
| return Tokenizer.from_file(str(p)) |
|
|
|
|
| def train_tokenizer(corpus_file,vocab_size=32000): |
| from tokenizers import Tokenizer,models,pre_tokenizers,decoders,trainers |
| from .settings import path |
| tokenizer=Tokenizer(models.BPE(unk_token='<|unk|>')) |
| tokenizer.pre_tokenizer=pre_tokenizers.ByteLevel(add_prefix_space=False) |
| tokenizer.decoder=decoders.ByteLevel() |
| trainer=trainers.BpeTrainer(vocab_size=vocab_size,min_frequency=2, |
| initial_alphabet=pre_tokenizers.ByteLevel.alphabet(), |
| special_tokens=SPECIAL,show_progress=True) |
| tokenizer.train([str(corpus_file)],trainer) |
| tokenizer.save(str(path(TOKENIZER_FILE))) |
| print(f'Saved tokenizer with {tokenizer.get_vocab_size()} tokens') |
| return tokenizer |
|
|
|
|
| def require_ids(tokenizer): |
| return {key: token_id(tokenizer,key) for key in SPECIAL} |
|
|
|
|
| def token_id(tokenizer,text): |
| x=tokenizer.token_to_id(text) |
| if x is None:raise ValueError(f'Missing special token: {text}') |
| return x |
|
|
|
|
| def text_ids(tokenizer,text): |
| return tokenizer.encode(text,add_special_tokens=False).ids |
|
|
|
|
| def chat_ids(tokenizer,messages,add_assistant_prefix=False,system=None): |
| """Return (ids, loss_mask). Targets are assistant text/eot, not prompts or role markers.""" |
| spec=require_ids(tokenizer) |
| start,end,eot=spec['<|im_start|>'],spec['<|im_end|>'],spec['<|eot_id|>'] |
| result=[];mask=[] |
| if system is not None: |
| messages=[{'role':'system','content':system}]+list(messages) |
| for m in messages: |
| role=m.get('role') |
| content=m.get('content') |
| if role not in ('user','assistant','system') or not isinstance(content,str):continue |
| prefix=[start]+text_ids(tokenizer,role+'\n') |
| content_tokens=text_ids(tokenizer,content) |
| suffix=[eot if role=='assistant' else end] |
| result.extend(prefix);mask.extend([0]*len(prefix)) |
| result.extend(content_tokens);mask.extend([1 if role=='assistant' else 0]*len(content_tokens)) |
| result.extend(suffix);mask.extend([1 if role=='assistant' else 0]) |
| if add_assistant_prefix: |
| tail=[start]+text_ids(tokenizer,'assistant\n') |
| result.extend(tail);mask.extend([0]*len(tail)) |
| return result,mask |
|
|
|
|
| def decode_reply(tokenizer,ids): |
| spec=require_ids(tokenizer) |
| clean=[] |
| for i in ids: |
| if i in (spec['<|eot_id|>'],spec['<|im_end|>'],spec['<|eos|>']):break |
| clean.append(i) |
| return tokenizer.decode(clean,skip_special_tokens=True) |
|
|
