tokenizers v1: encode, decode and scaling, measured

As models become faster and workloads scale, that balance begins to shift. Training on massive datasets, serving many concurrent requests, or repeatedly processing long inputs can put enough pressure on the tokenizer that it starves the model of data. This is why we have chosen to heavily focus on performance for the upcoming version 1 of tokenizers.

Tokenization should be light and should scale with your workflow. Your GPUs should never sit idle waiting for the CPU to complete its tokenization. In this article, we look at what makes v1 faster than v0.23, often by tens of times. This work was entirely possible thanks to the rest of the ecosystem. Tokenization is a very active area of open source work, and libraries such as gigatoken, tiktoken, kitoken, tokie, fastokens, wordchipper and ai-tokenizer, as well as many others, have each pushed on what a fast tokenizer can be.

We read that work, and several of the ideas below reached us because another project showed they were worth trying. Before this refactor, tokenizers was nowhere near the performance it could have had,…