So many tokens, so little time: Introducing a faster, more flexible byte-pair tokenizer

Large language models (LLMs) like those used by GitHub Copilot rely on tokenization, converting bytes into tokens, to function efficiently, which poses scaling challenges due to the non-incremental nature of traditional tokenization algorithms like byte-pair encoding (BPE). GitHub addressed these challenges by developing a novel, open-source BPE algorithm that scales linearly and performs better than existing libraries, accommodating GitHub Copilot’s expanding user base and feature set. The solution enhances the efficiency of tokenization, which is crucial for retrieval augmented generation (RAG) methods, by allowing dynamic construction of prompts and maintaining token counts. GitHub's algorithm improves upon the traditional BPE by enabling linear encoding and implementing dynamic programming principles to maintain valid tokenizations efficiently, significantly outperforming other implementations in both typical and worst-case scenarios when benchmarked.

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