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279 points matthewolfe | 1 comments | | HN request time: 0s | source

TokenDagger is a drop-in replacement for OpenAI’s Tiktoken (the tokenizer behind Llama 3, Mistral, GPT-3.*, etc.). It’s written in C++ 17 with thin Python bindings, keeps the exact same BPE vocab/special-token rules, and focuses on raw speed.

I’m teaching myself LLM internals by re-implementing the stack from first principles. Profiling TikToken’s Python/Rust implementation showed a lot of time was spent doing regex matching. Most of my perf gains come from a) using a faster jit-compiled regex engine; and b) simplifying the algorithm to forego regex matching special tokens at all.

Benchmarking code is included. Notable results show: - 4x faster code sample tokenization on a single thread. - 2-3x higher throughput when tested on a 1GB natural language text file.

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pama ◴[] No.44423489[source]
Cool. Would it be possible to eliminate that little vocab format conversion requirement for the vocab I see in the test against tiktoken? It would be nice to have a fully compatible drop in replacement without having to think about details. It also would be nice to have examples that work the other way around: initialize tiktoken as you normally would, including any specialized extension of standard tokenizers, and then use that initialized tokenizer to initialize a new tokendagger and test identity of results.
replies(2): >>44425102 #>>44425489 #
1. matthewolfe ◴[] No.44425102[source]
Ah good catch. Updating this right now.