Mark Huang: belief

Transcript context

…Yeah. Like, are you able to compare it with like other forms of scaling that people have been talking about? Alibi comes to mind, yarn is being talked about a lot by a news research. And then there's other forms which are like, not exactly directly related, but like ring attention comes up a lot that we had a really good session with StrongCompute in the Latent Space Discord talking about all these approaches. I just wonder if you want to compare and contrast like rope versus the other stuff. Yeah, I think Alibi, we haven't compared with that one specifically, mostly because I've noticed some of the newer architectures don't actually employ it a lot. I think the last architecture that actually really employed it was the Mosaic MPT model class. And then almost all the models these days are all rope scaling. And then effectively, you can use yarn with that as well. We just did the theta scaling specifically because of its empirical elegance, really easy and like it was well understood by us. The other one that I know that in the open source that people are applying, which uses more of a LoRa based approach, which is really interesting too, is the one that Wing has been employing, which is Pose. We sort of help them evaluate some of the models. With respect to like the performance of it, it does start to break down a little bit more on the longer, longer context. So like 500,000 to a million, it appeared that it doesn't hold as well specifically for like needle in the haystack. It's still TBD as evaluations. It's a sparse high dimensional space where you're just like evaluating performance across so many different things and then trying to map it back to like, hey, here's the thing that I actually cared about from the start and I have like a thousand different evaluations and they tell me something but not the entire picture. And as for like ring attention specifically, we employed ring attention in order to do the training. So we combined flash attention and ring attention together with like a really specific network topology on our GPUs to be able to maximize the memory bandwidth. Yeah. As far as I understand, like ring attention, a lot of people credit it for Gemini's million token context, but actually it's just a better utilization of GPUs. Like, yeah, that's really what it is. You mentioned in our show notes, Zhang Peiyuan's easy context repo. I have seen that come up quite a bit. What does that do as, you know, like how important is it as ring attention implementation? I know there's like maybe another one that was done by Lucid Reins or one of the other open source people. But like, what is easy context? Is that the place to go? Like, did you evaluate a bunch of things to implement ring attention?…

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