Author here! In my case it's mostly pretraining experiments, where you might want to change your data mixture/filtering/processing of training data, and splits are usually done at a token-level instead of a text level. In this case we usually run for days on a huge number of CPUs to finish tokenizing something like DCLM.

From what I can tell it's also useful for inference when considering time-to-first-token (TTFT) as reported by fastokens.[0]

I'm not sure about the proprietary inference engines, but in the open source ones tokenization is done before looking up if a text sequence is present in the KV-cache. If you have a long prefix that's been seen before (say a system prompt), the time for tokenizing that will be a large part of your TTFT. The tokenizer cache should be warmed up in this case, so the throughput for Gigatoken would be significantly higher than reported in the repo.

[0] https://github.com/crusoecloud/fastokens

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janwas 12 hours ago | parent | next [-]

If you are running on large-scale data, have you validated at that scale (comparing results)? From a quick look at the code, it looks like there is a 42-bit hash (computed via single-mul hash function) which can have collisions and thus return the wrong tokens, right?

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wren6991 a day ago | parent | prev | next [-]

> I'm not sure about the proprietary inference engines, but in the open source ones tokenization is done before looking up if a text sequence is present in the KV-cache

Is this necessary? Tokenisation is deterministic, so for a hit/miss check you can lookup on (a hash of) the source text instead of the tokens. You only need the tokens once you're seeking for the exact token index having determined there is a hit. That means tokenisation can proceed in parallel with your cache query, and since these caches are distributed in production systems I imagine the query itself could be slow.

I'm not trying to undermine the utility, and this is obviously excellent work. Being able to tokenise faster on the client also seems useful (precise token counts for context pruning heuristics, instead of `chars / 4`), and on a phone your work translates directly to energy savings. I'm just curious about the cache lookup point.

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marcelroed a day ago | parent [-]

It's usually not as binary as "hit" or "miss" with a prefix cache, and you need to know the token boundaries to know where the cache hit ends.

The current structures used for KV-caching in vLLM and SGLang work by chunking the KV-cache tokens into prefix trees, and you need to hash chunks of tokens in order to look up in these, meaning you need to be able to slice up your tokens by token count.

Again I have no idea what proprietary engines are doing, but this is why open source stuff needs to tokenize before cache lookup at least.

	▲	wren6991 a day ago \| parent [-]
		> and you need to hash chunks of tokens in order to look up in these, meaning you need to be able to slice up your tokens by token count. The hash just has to uniquely identify the contents. I still don't see what stops you from walking the chunk tree by chunks of characters instead of chunks of tokens, then lazily finding the token boundary once you've found the longest common chunk prefix and also (in parallel) tokenized the input.

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fwip 2 days ago | parent | prev | next [-]

Very cool, thanks.

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lostmsu 2 days ago | parent | prev [-]

Can't you tokenize in preloading on demand?

	▲	marcelroed a day ago \| parent [-]
		You can, but this usually results in sequences with padding/truncation, since you won't know how many tokens your inputs map to before you actually tokenize them. This also makes shuffling difficult. In practice every training project I've worked on does tokenization in a separate data processing phase.