setuptools-rust VS tokenizers

For Python specifically, In addition to using rust-cpython or PyO3, maturin makes it really comfortable to build, package, and publish Rust code into Python packages and, if your niche doesn't quite fit, there's setuptools-python which might do it.

Aside from the PyO3 and rust-cpython crates already mentioned, I'd suggest maturin as a way to integrate your build processes or possibly setuptools-rust.

Compiled modules for Python stuff (I'd recommend PyO3 but the last one I started was before that worked on stable Rust, so I used its progenitor, rust-cpython. See also maturin or setuptools-rust).

...but you will need to rename the generated library to match import conventions. setuptools-rust or Maturin can help with that.

Between pyodide, pyo3, rust-cpython, and rustpython, I think Pyo3 is the best way to drop in rust in a python project for a speed up, if that is your goal. Some of the demos show using python from rust, but to me the biggest feature is without a doubt compiling rust code to native python modules. I'm using it to speed up image manipulation backed by numpy arrays.

There’s a setuptools rust [0] extension package that can be used to hook the compilation of the rust into the wheel building or install from source. Maturin [1] seems to be regarded as the new and improved solution for this, but I found that it’s angled toward the using python from rust.

There’s also the rust numpy [2] package by the same org which is fantastic in that it lets you pass a numpy matrix to a native method written in rust and convert it to the rust equivalent data structure, perform whatever transformation you want (in parallel using rayon [3]), and return the array. When building for release, I was seeing speed ups of 100x over numpy on the most matrix mathable function imaginable, and numpy is no joke.

I think there is a lot of potential for these two ecosystems together. If there’s not a python package for something, there’s probably a rust crate.

If anyone is interested the python package that I'm building with some rust backend, its called pyrogis [4] for making custom image manipulations through numpy arrays.

https://github.com/PyO3/setuptools-rust

Hugging Face seems to like Rust. They also wrote Tokenizers in Rust.

Your intuition is right. There are two ways (in increasing order of result performance) : 1. You can simply extend vocab file of the tokenizer and test the predictions 2. You can extend the vocab file and re-train your model on custom data which has these new tokens. Check the following issue on GitHub : https://github.com/huggingface/tokenizers/issues/247

SentencePiece -> implementation of some algorithms (there are several others, https://github.com/microsoft/BlingFire https://github.com/glample/fastBPE https://github.com/huggingface/tokenizers )

In sum I would like the idea to go with Rust as I more or less got to rewrite the whole thing anyway, but I am a bit skeptical if I will be able to interface with everything that might come up at some point. Or probably end up in a wrapper hell if I got to use more C++ libraries. On the other hand there are definitely a few Rust projects out there that might come in handy (for example https://github.com/huggingface/tokenizers). And the build process is pretty awful right now (CMake it is but with lots of hacks).

A full machine learning pipeline usually comprises far more than just the model, and this is the area where Rust may shine (the recent work by HuggingFace and their https://github.com/huggingface/tokenizers library is a good example)

As for other tokenizers, you can take a look at - Huggingface tokenizers library: https://github.com/huggingface/tokenizers - NLTK tokenize: https://www.nltk.org/api/nltk.tokenize.html - Polygot: https://pypi.org/project/polyglot/

Huggingface Tokenizers (https://github.com/huggingface/tokenizers), which are now used by default in their Transformers Python library, use pyO3 and became popular due to the pitch that it encoded text an order of magnitude faster with zero config changes.

It lives up to that claim. (I had issues with return object typing when going between Python/Rust at first but those are more consistent now)

huggingface/tokenizers (Rust): 💥Fast State-of-the-Art Tokenizers optimized for Research and Production