What sort of setups do people have that are bounded by the speed of the tokenizer?

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

Wait, since when does it matter whether something being hyper-optimized is useful? The computer going brrrr on an interesting problem is in itself the goal!

If you are training an LLM, you need to tokenize the text before it’s trained on. A lot of time this can be done in parallel with the GPU though.

I have spent way too much time waiting 10-15 minutes tokenizing my training dataset only for the run to crash over some minor bug after that. (If I was smarter, I’d test on a smaller batch first.)

It can be useful for checking input token usage before sending it to the model, e.g. preventing calls above a given token bound or grouping requests into batches.

It can also be used by the LLMs to provide the input and output token counts on the different APIs, though I'm not sure if this is how llama.cpp or other OpenAI-like APIs calculate the input/output tokens of a request.

I worked on a system a couple years ago with a BERT-based model (64M parameters) used for classification. The rest of the system could process data at gigabytes per second, and so here tokenization at a measly few megabytes per second really slowed things down. The model inference was more expensive than tokenization, but tokenization was still >10% of total runtime.

I've data where i cannot store metadata that i need to search semantically so i embed it on the fly at every search with static embedding and tokenizing was more than 99% of the cpu time. Granted that was due the naive implementation of the default tokenizer which was o^2 with document length and just switching to a proper scanner solved most of it without going to simd and whatnot, but still.

Pre-training data is pre-tokenized ahead of time before being used to not waste any GPU compute.

A massive speedup like this is a nice efficiency savings on some of these data pipelines for sure.