Show HN: TokenDagger – A tokenizer faster than OpenAI's Tiktoken

192•matthewolfe•6h ago

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.

Comments

chrismustcode•5h ago

There’s something beautiful about creating a drop in replacement for something that improves performance substantially.

ScyllaDB comes to mind

matthewolfe•5h ago

Agreed. I figured nobody would use it otherwise.

parhamn•5h ago

Put it in there readme & description. It's a big selling point.

matthewolfe•5h ago

Thanks, I clarified it.

pvg•5h ago

To be fair, many people have token stabbing needs.

npalli•5h ago

Kudos, I think (in the short term at least) there is a large amount of perf. optimization to be found by coding parts of the whole AI/ML infrastructure in C++ like this one, not as a rewrite (god no!) but drop in and fix key bottlenecks. Anytime I see someone (seems Chinese engineers are good at this) put something out in C++, good chance some solid engineering tradeoffs have been made and dramatic improvement will be seen.

matthewolfe•3h ago

Agreed. A former mentor of mine told me a nice way of viewing software development:

1. Make it work. 2. Make it fast. 3. Make it pretty.

Transformers & LLMs have been developed to a point where they work quite well. I feel as though we're at a stage where most substantial progress is being made on the performance side.

diggan•3h ago

Heh, seems people I've been learning from been biased away from beauty, as I know that as "Make It Work, Make It Right, Make It Fast".

abybaddi009•2h ago

What's the difference between make it work and make it right? Aren't they the same thing?

stavros•2h ago

Yeah, if it's not right, it doesn't work.

darknoon•2h ago

In ML, often it does work to a degree even if it's not 100% correct. So getting it working at all is all about hacking b/c most ideas are bad and don't work. Then you'll find wins by incrementally correcting issues with the math / data / floating point precision / etc.

DSingularity•2h ago

Not true. Things can work with hacks. Your standards might consider it unacceptable to have hacks. So you can have a “make it right” stage.

gabrielhidasy•1h ago

Depends on your definition of "right" and "work". It could be a big ball of mud that always returns exactly the required response (so it 'works'), but be hellish hard change and very picky about dependencies and environment (so it's not 'right').

stavros•1h ago

Nope, it's right, but it's not pretty.

robertfw•2h ago

Making it work can be a hacky, tech debt laden implementation. Making it right involves refactoring/rewriting with an eye towards maintainability, testability, etc etc

gopalv•2h ago

> make it work and make it right?

My mentor used say it is the difference between a screw and glue.

You can glue some things together and prove that it works, but eventually you learn that anytime you had to break something to fix it, you should've used a screw.

It is trade off in coupling - the glue binds tightly over the entire surface but a screw concentrates the loads, so needs maintenance to stay tight.

You only really know which is "right" it if you test it to destruction.

All of that advice is probably sounding date now, even in material science the glue might be winning (see the Tesla bumper or Lotus Elise bonding videos - every screw is extra grams).

kevindamm•2h ago

I've usually heard/said it as

  1. Make it
  2. Make it work
  3. Make it work better

(different circumstances have different nuances about what "better" means, it isn't always performance optimization; some do substitute "faster" for "better" here, but I think it loses generality then).

gabrielhidasy•1h ago

I always heard the "Make it Right" as "Make it Beautiful", where Right and Beautiful would mean "non-hacky, easily maintainable, easily extendable, well tested, and well documented"

binarymax•56m ago

The Huggingface transformers lib is currently undergoing a refactor to get rid of cruft and make it more extensible, hopefully with some perf gains.

jotux•43m ago

A similar concept dates back to 30BC: https://en.wikipedia.org/wiki/De_architectura

Firmitas, utilitas, venustas - Strong, useful, and beautiful.

saretup•3h ago

And while we’re at it, let’s move away from Python altogether. In the long run it doesn’t make sense just because it’s the language ML engineers are familiar with.

tbalsam•2h ago

No! This is not good.

Iteration speed trumps all in research, most of what Python does is launch GPU operations, if you're having slowdowns from Pythonland then you're doing something terribly wrong.

Python is an excellent (and yes, fast!) language for orchestrating and calling ML stuff. If C++ code is needed, call it as a module.

bigyabai•2h ago

It makes plenty of sense. Python handles strings well, has a great package ecosystem, and is easy to write/learn for non-programmers. It can be easily embedded into a notebook (which is huge for academics) and is technically a "write once run anywhere" platform in theory. It's great.

If you think Python is a bad language for AI integrations, try writing one in a compiled language.

janalsncm•1h ago

Most of that is already happening under the hood. A lot of performance-sensitive code is already written in C or cython. For example numpy, scikit learn, pandas. Lots of torch code is either C or CUDA.

ML researchers aren’t using python because they are dumb. They use it because what takes 8 lines in Java can be done with 2 or 3 (including import json) in python for example.

ipsum2•2h ago

Sort of. The key bottlenecks are not in tokenization, but running the actual CUDA kernels. Python actually has very little overhead. (See VLLM, which is primarily in Python). So when people (like deepseek) 'rewrite in C++', they're usually just rewriting CUDA kernels to be more efficient.

konsalexee•5h ago

> simplifying the algorithm to forego regex matching special tokens at all

Does that mean there could be cases with less quality in terms of tokenization?

matthewolfe•5h ago

The output should be identical, assuming no bugs.

The Tiktoken implementation takes a collection of all special tokens upon initialization and compiles them into a regex by joining them with `|` [0]. Then the actual encoding process checks for matches on this expression.

Models like Llama 4 define a list of 1,135 special tokens. Notably, 1,115 of those are "reserved" special tokens! So this yields a huge regexp of special tokens that shouldn't be considered at all.

TokenDagger does not do this. Instead, simple string matching is used. This works because we don't need to consider the entire special vocabulary every time. The caller of `encode` must explicitly define which special tokens should be considered [1]. So it's faster to check against the much smaller list we _know_ is being used.

[0] https://github.com/openai/tiktoken/blob/main/src/lib.rs#L476

[1] https://github.com/openai/tiktoken/blob/main/tiktoken/core.p...

manishsharan•4h ago

Is there a tokenizer someone can recommend for code ? I have tried CodeBert but maybe I am using it wrong as my results with it were pretty bad.

fkyoureadthedoc•4h ago

Would be cool to see WASM bindings for this here https://github.com/dqbd/tiktoken

Or maybe even your speedups from "b" in the pure js implementation

p0•4h ago

How does this compare to the BPE crate [1]? Its main selling point is support for incrementally re-tokenising text, but it's also faster than tiktoken.

[1] https://crates.io/crates/bpe

matthewolfe•3h ago

I'm working on incremental re-tokenizing next. Then I'll run some benchmarks against this crate too.

frabcus•4h ago

Is there any way we can get local tokenizers for other LLMs? e.g. Gemini only offer a remote API for their tokenizer. Is it proprietary? Could we infer the token mapping somehow efficiently by making lots of calls?

weberer•3h ago

I thought Gemini used SentencePiece

https://github.com/google/sentencepiece

Deathmax•3h ago

Gemini uses SentencePiece [1], and the proprietary Gemini models share the same tokenizer vocabulary as Gemma [2, 3, 4].

Out of the large proprietary western AI labs (OpenAI, Anthropic, Google), only Anthropic with Claude 3 and newer lack local tokenizers.

[1] https://github.com/google/sentencepiece

[2] https://github.com/googleapis/python-aiplatform/blob/main/ve...

[3] https://storage.googleapis.com/deepmind-media/gemma/gemma-2-...: "We inherit from the large Gemini vocabulary (256k entries)."

[4] https://storage.googleapis.com/deepmind-media/gemma/Gemma3Re...: "We use the same tokenizer as Gemini 2.0."

matthewolfe•3h ago

A lot of model-specific tokenizers have reference implementations ([0], [1]). Underlying them is a core algorithm like SentencePiece or Byte-pair encoding (BPE). Tiktoken and TokenDagger are BPE implementations. The wrapping "tokenizer" mostly deals with the quirks of the vocabulary and handling special tokens.

For this project, I think there is value in building some of these model-specific quirks into the library. Could see some minor performance gains and generally make it easier to integrate with. It's probably not too much work to keep up with newer models. Tokenizers change much less frequently.

[0] https://github.com/meta-llama/llama-models/blob/01dc8ce46fec...

[1] https://github.com/mistralai/mistral-common/tree/main/src/mi...

pama•4h ago

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.

matthewolfe•2h ago

Ah good catch. Updating this right now.

matthewolfe•1h ago

Alright, 0.1.1 should now be a true drop-in replacement. I'll write up some examples soon.

janwilmake•4h ago

You know what's also faster to roughly get the amount of tokens? string.length/5

_flux•1h ago

It is not helpful in actual tokenization, though.

EGreg•4h ago

What about pairing this with BigBird and Mamba?

pamelafox•3h ago

Just curious whether it's possible to push any of your performance improvements to tiktoken itself?

matthewolfe•3h ago

I probably will. Was hesitant initially, because adding PCRE2 as a dependency might cause issues to existing projects. I believe this was discussed briefly in a closed PR with other performance improvements.

b0a04gl•3h ago

if dagger builds a byte level DFA for special tokens and resolves overlaps via longest match, how does it handle inputs with partial matches at chunk boundaries, say a stream ends mid token like <|endo , does it buffer forward or require lookahead

kevmo314•3h ago

Nice work! I tried something similar a while back ago: https://github.com/kevmo314/tokie

The takeaway I also found was that the running cost was really dominated by pretokenization (the regex). It's cool to see that you found a faster way to run the regex, but have you tried comparing the performance of just swapping out the regex engine and leaving the actual BPE to tiktoken? I wonder if that is upstreamable?

matthewolfe•3h ago

Cool!

I've reached out to the guy who maintains Tiktoken to talk about this.

polynomial•3h ago

Just to note that Tiktoken is still the tokenizer behind the GPT-4x series, it just uses a different token model. (Post only says GPT-3, implying they were using something else for subsequent iterations.)

silentsea90•2h ago

"I’m teaching myself LLM internals by re-implementing the stack from first principles." - curious what resources you're using? Any books or courses, or just building it straight up? Great work!

matthewolfe•2h ago

Modal's GPU glossary is a good overview about how GPUs work [0]. Karpathy's LLM overview is a good high level overview on LLMs [1]. 3b1b's video (and subsequent videos) on transformers was excellent at helping me understand the math at a high level [2]. This matrix multiplication optimization worklog helped me understand writing better CUDA (not for beginner intro though) [3].

During this process I also asked ChatGPT a lot of questions.

I'm definitely open to suggestions about "how to learn" with all the new tools we have. I felt this has not been straightforward to figure out.

[0] https://modal.com/gpu-glossary

[1] https://www.youtube.com/watch?v=7xTGNNLPyMI

[2] https://www.youtube.com/watch?v=wjZofJX0v4M

[3] https://siboehm.com/articles/22/CUDA-MMM

matrix2596•2h ago

is is possible for your tokenizer to give different tokenization ever then openai tokenizer? i am asking because there are multiple ways to tokenize the same string?? sry if i am mistaken

matthewolfe•2h ago

Should be the same. Both use Byte-Pair Encoding (BPE) as underlying algo.

Tiberium•1h ago

Can you also compare the performance with https://github.com/huggingface/tokenizers/? Would be helpful, since the benchmark in the tiktoken readme seems to be very outdated.

binarymax•55m ago

Anecdotally I've always found tiktoken to be far slower than huggingface tokenizers. I'm not sure why, as I haven't dug into tiktoken, but I'm a heavy user of HF's rust tokenizers

superlopuh•1h ago

Can someone familiar with performance of LLMs please tell me how important this is to the overall perf? I'm interested in looking into optimizing tokenizers, and have not yet run the measurements. I would have assumed that the cost is generally dominated by matmuls but am encouraged by the reception of this post in the comments.

serjester•50m ago

Tokenizing text is ridiculously small part of the overall computation that goes into serving a request. With that said if you’re doing this on petabytes of data, never hurts to have something faster.

refibrillator•45m ago

Tokenization is typically done on CPU and is rarely (if ever) a bottleneck for training or inference.

GPU kernels typically dominate in terms of wall clock time, the only exception might be very small models.

Thus the latency of tokenization can essentially be “hidden”, by having the CPU prepare the next batch while the GPU finishes the current batch.

luppy47474•31m ago

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Show HN: TokenDagger – A tokenizer faster than OpenAI's Tiktoken

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