#tokenizer #nlp #huggingface #aleph #alpha #rustic


A fast implementation of a wordpiece-inspired tokenizer

4 releases (2 breaking)

0.3.1 Jul 13, 2020
0.3.0 Jun 18, 2020
0.2.0 May 13, 2020
0.1.0 Apr 17, 2020

#1787 in Algorithms


362 lines


Rust docs.rs License: MIT/Apache

We at Aleph Alpha are big fans of huggingface's tokenizers crate. Kudos for this great library. There is only one downside: The interface is optimized for the bindings, not for working with it from within Rust.

So we took it as an inspiration and tried to improve on some things. First we wanted to see how fast we could make it while implementing the same Model trait. We based our implementation on the very good fst crate. Then we added our own interface to play to Rust's strengths (mainly avoiding needless allocation, re-using data, generics).

We are very happy with the improved performance. In our tests, we found our tokenizer performed mostly linearly with whatever data was thrown at it, while the huggingface wordpiece tokenizer performs quadratically worse with longer multi-token words. The following single-core runtimes in µs were measured for a set of benchmarks:

# AlephAlphaTokenizer ~ as Model wordpiece
0 749.950 1274.923 2025.289
1 1010.120 1511.214 1900.441
2 1775.973 2648.909 2995.574
3 2263.436 3598.771 12978.049
4 2262.490 3403.918 4864.752
5 2808.373 4456.960 18623.648
6 2783.996 4015.472 5362.356
7 3160.517 5048.136 9946.745
8 3016.781 4742.037 8066.818
9 3497.266 5626.896 8662.281
10 4446.626 6679.859 10584.524

(This was measured on an Intel(R) Core(TM) i7-7600U CPU @ 2.80GHz running on a Fedora kernel 5.6.15-300.fc32.x86_64 with all mitigations enabled)

As you can see, using our tokenizer as a model is faster than huggingface's wordpiece tokenizer by at least 13%, often more. Using the rustic interface, we can omit a lot of allocation and memory copying, so we are at least 60% faster.

To re-run the benchmark, call cargo bench --all-features. Otherwise only the AlephAlphaTokenizer will be benchmarked.


This package is licensed under MIT or Apache License Version 2, at your discretion.


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