fast-bpe-rs 0.5.2

Fast Byte Pair Encoding (BPE) tokenizer with Python bindings powered by PyO3.

fast-bpe-rs: A Fast Rust BPE Library

A blazing-fast Rust Byte Pair Encoding (BPE) tokenizer with Python bindings for training, encoding, and decoding BPE models.

Naive Vs fast-bpe-rs

Let N be the total number of token positions after splitting, M the number of merges to learn, and k the number of occurrences touched by the current merge.

Aspect	Naive BPE trainer	fast-bpe-rs
Corpus representation	Plain token lists such as Vec<Vec<u32>>	Deduplicated weighted merge sequences
Per-merge work	Recount pairs across the full corpus	Update only neighborhoods touched by the merge
Sequence updates	Rebuild or rewrite token lists repeatedly	In-place splicing in a sparse linked structure backed by Vec<Option<MergeNode>>
Pair statistics	Recomputed from scratch each round	Maintained incrementally as pair -> {count, locations} and count -> set of pairs
Best-pair lookup	Usually depends on the latest full recount	Pulled from the highest non-empty count bucket
Repeated chunks	Counted again and again	Stored once with a frequency weight
Parallelism	Often minimal in simple implementations	Parallel chunk counting and initial pair aggregation with rayon
Training time complexity	Typically O(MN) because each merge triggers another global count pass	O(N) setup, then per merge roughly O(k) local updates instead of O(N) rescans
Space complexity	Usually O(N) plus temporary pair counts	Higher than naive: O(N) corpus state plus pair-location indexes and count buckets

Setup

Install from PyPI

pip install fast-bpe-rs

Use

Small example

from fast_bpe_rs import BPE

bpe = BPE(r"(?s).+")
bpe.train(258, ["low low low low", "lower lower", "newest newest newest"])

ids = bpe.encode("low lower newest")
text = bpe.decode_to_string(ids)

GPT-style split pattern

from fast_bpe_rs import BPE

bpe = BPE(
    r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}"
    r"| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
)

bpe.train(32768, corpus_lines)

Special tokens

from fast_bpe_rs import BPE

bpe = BPE(
    r"(?s).+",
    {
        "<pad>": 600,
        "<eos>": 601,
    },
)

bpe.train(605, ["a<pad>a"])
ids = bpe.encode("a<pad><eos>a")

API

• BPE(split_pattern, special_tokens=None)
• train(vocab_size, docs)
• encode(text) -> list[int]
• decode(token_ids) -> bytes
• decode_to_string(token_ids) -> str

License

Apache 2.0