Cool stuff. From my understanding, this is less valuable at inference time and more useful when running offline pre-training data prep.

When tokenizing terabytes of text for your training corpus, the speedup here is probably doing real work in saving you time (and money?). You get a faster iteration cycle when figuring out and adjusting your datasets.

Interesting :

Q: Did you just way over-optimize for a specific CPU and tokenizer? How is it so fast? No, I way over-optimized for every combination of these! The results are very consistent across CPUs (modern x86 and ARM), and across specific tokenizers.

The major improvements are in optimizing heavily an implementation that usually is outsourced to a Regex engine (pretokenization) using SIMD, minimizing branching and other tricks, as well as heavily optimizing caching of pretoken mappings (if a word has been seen before, look it up its encoded tokens efficiently). Caching is a very hard problem in this domain since the cache grows very quickly, and pretoken distributions are very long-tailed.

Finally, interactions with Python are minimized, and threads have minimal interactions with each other.

This is awesome, but tokenization is typically <0.1% of total inference time.

Presumably there's a host of applications that just need to tokenize, though, and this would be great for those!

engineering effort to make something 1000x faster that accounts for 0.1% of total runtime is the most software developer thing imaginable

I had to stare at that chart for a minute just to let the numbers sink in. It's genuinely mind-bending, incredible ship OP

So the question becomes, how many other parts of the inference pipeline have left 1000x optimization opportunities lying on the table?

Practically I would need to wait for hugging face models to adopt this? My harness tokenizer is just an estimate since the model tokenizes on my api calls?

What sort of setups do people have that are bounded by the speed of the tokenizer?

This is really cool, great work!

Quality software here.

Very interesting project! Are there benchmarks for the "compatibility mode" or are all the numbers for the Gigatoken API?

wow, best release all week.

quite excellent software

We should just rewrite everything in Rust, especially bloated Python code, and the world would be a better place. ;) Disclosure: I'm a Rust advocate!