tokenizers v1: encode, decode and scaling, measured

The tokenizer has not historically been the bottleneck within ML workflows. Compute-wise, tokenization is light compared to the heavy modeling happening in the rest of the pipeline. Yet, in some cases, it has rapidly become key to accelerating (or slowing down) your machine learning work.

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, so contributing to it may not have seemed worth it. With this refactor, we hope to make clear that we intend tokenizers to be a library worth contributing to.

We also thank IBM, NVIDIA, and the ExecuTorch team for contributing patches and helping us test across a wide range of hardware to broaden platform support.

Results

We showcase results for the release candidate of tokenizers v1 against other widely used alternatives. We go over single-threaded, multi-threaded, scaling across threads, per-model comparison, per-language comparison, latency, decoding throughput, memory heap, as well as crate size.