Package: bpeasy
bpeasyOverviewbpeasyis a Python package that provides a tokenizer trainer, implementing in 400 lines of rust an efficient version of Byte Pair Encoding (BPE). The implementation largely follows the huggingfacetokenizerslibrary, but makes opinionated decisions to simplify the tokenizer training specifically to:Treat text data at the byte-level first --- all text is converted to bytes before training rather than using a character-level approach (like in Huggingface).Always use a regex-based split pre-tokenizer. This is a customisable regex that is applied to the text before training. This regex decides where to split the text and limits what kind of tokens are possible. This is technically possible in Huggingface but is not well documented. We also use thefancy-regexcrate which supports a richer set of regex features than theregexcrate used in Huggingface.Useint64types for counting to allow for training on much larger datasets without the risk of overflow.You can think ofbpeasyas thetiktokentraining code that never was.See thebenchmarkssection for a comparison with the Huggingface library.InstallationSimply install the package using pip:pipinstallbpeasyTrainingThe training function is designed to be bare-bones and returns the trained tokenizer vocab as a dictionary of bytes to integers. This is to allow for maximum flexibility in how you want to use the tokenizer. For example, you can use then port these to tiktoken or Huggingface tokenizers (see below).# should be an iterator over striterator=jsonl_content_iterator(args)# example regex from GPT-4regex_pattern=r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""# returns the vocab (dict[bytes, int])vocab=bpeasy.train_bpe(iterator,regex_pattern,args.max_sentencepiece_length,# max length of tokensargs.vocab_size,# max size of vocab)Alternatively, you can also train using the basic tokenizer class provided:frombpeasy.tokenizerimportBPEasyTokenizertokenizer=BPEasyTokenizer.train(iterator,# iterator over strvocab_size=vocab_size,max_token_length=max_token_length,regex_pattern=regex_pattern,special_tokens=["
","<pad>","
"],fill_to_nearest_multiple_of_eight=True,name="bpeasy",)Encoding/DecodingTo test your tokenizer you can use theBPEasyTokenizerclass, which is a wrapper around thetiktoken.Encodingmodule, simplifying the handling of vocabularies, special tokens, and regex patterns for tokenization.frombpeasy.tokenizerimportBPEasyTokenizeryour_special_tokens=["
","<pad>","
"]tokenizer=BPEasyTokenizer(vocab=vocab,regex_pattern=regex_pattern,special_tokens=your_special_tokens,fill_to_nearest_multiple_of_eight=True,# pad vocab to multiple of 8name="bpeasy"# optional name for the tokenizer)test="hello_world"# encode and decode uses the tiktoken functionsencoded=tokenizer.encode(test)decoded=tokenizer.decode(encoded)>"hello_world"You can also usetiktokendirectly, but you would need to handle the special tokens and regex pattern yourself:importtiktokenvocab=bpeasy.train_bpe(...)special_tokens=["
","<pad>","
"]# Sort the vocab by ranksorted_vocab=sorted(list(vocab.items()),key=lambdax:x[1])# add special tokensspecial_token_ranks={}forspecial_tokeninspecial_tokens:special_token_ranks[special_token]=len(sorted_vocab)sorted_vocab.append((special_token.encode("utf-8"),len(sorted_vocab)))full_vocab=dict(sorted_vocab)encoder=tiktoken.Encoding(name=name,pat_str=regex_pattern,mergeable_ranks=full_vocab,special_tokens=special_token_ranks,)Save/Load tokenizer from fileWe provide basic utility functions to save and load the tokenizer from a json file.tokenizer.save("path_to_file.json")tokenizer=BPEasyTokenizer.from_file("path_to_file.json")Export to HuggingFace formatWe also support exporting the tokenizer to the HuggingFace format, which can then be used directly with the HuggingFacetransformerslibrary.frombpeasy.tokenizerimportBPEasyTokenizerfromtranstokenizer=BPEasyTokenizer(...)tokenizer.export_to_huggingface_format("hf_tokenizer.json")fromtransformersimportPreTrainedTokenizerFasthf_tokenizer=PreTrainedTokenizerFast(tokenizer_file="hf_tokenizer.json")Export vocab totiktokentxt formatfrombpeasyimportvocab=bpeasy.train_bpe(...)# saves the vocab to a tiktoken txt file formatsave_vocab_to_tiktoken(vocab,"vocab.txt",special_tokens=["
","<pad>","
"])If you want to use thetiktokentxt format, you will still need to handle the regex and special tokens yourself, as shown above,ContributingContributions are welcome! Please open an issue if you have any suggestions or improvements.LicenseThis project is licensed under the MIT License.