recurrent-memory-transformer VS safari

i find the github link https://github.com/booydar/t5-experiments/tree/scaling-report

Here's a list of tools for scaling up transformer context that have github repos:

* FlashAttention: In my experience, the current best solution for n² attention, but it's very hard to scale it beyond the low tens of thousands of tokens. Code: https://github.com/HazyResearch/flash-attention

* Heinsen Routing: In my experience, the current best solution for n×m attention. I've used it to pull up more than a million tokens as context. It's not a substitute for n² attention. Code: https://github.com/glassroom/heinsen_routing

* RWKV: A sort-of-recurrent model which claims to have performance comparable to n² attention in transformers. In my limited experience, it doesn't. Others agree: https://twitter.com/arankomatsuzaki/status/16390003799784038... . Code: https://github.com/BlinkDL/RWKV-LM

* RMT (this method): I'm skeptical that the recurrent connections will work as well as n² attention in practice, but I'm going to give it a try. Code: https://github.com/booydar/t5-experiments/tree/scaling-repor...

In addition, there's a group at Stanford working on state-space models that looks promising to me. The idea is to approximate n² attention dynamically using only O(n log n) compute. There's no code available, but here's a blog post about it: https://hazyresearch.stanford.edu/blog/2023-03-27-long-learn...

If anyone here has other suggestions for working with long sequences (hundreds of thousands to millions of tokens), I'd love to learn about them.

Checking the actual results: https://github.com/booydar/t5-experiments/blob/a6c478754530cdee2a67974e44a0c1b6dbad92c4/results/babilong.csv, I think it's cute, but not a real breakthrough.

As all...

https://github.com/booydar/t5-experiments/tree/scaling-repor...

> Also, we know that transformers can scale

Do we have strong evidence that other models don't scale or have we just put more time into transformers?

Convolutional resnets look to scale on vision and language: (cv) https://arxiv.org/abs/2301.00808, (cv) https://arxiv.org/abs/2110.00476, (nlp) https://github.com/HazyResearch/safari

MLPs also seem to scale: (cv) https://arxiv.org/abs/2105.01601, (cv) https://arxiv.org/abs/2105.03404

I mean I don't see a strong reason to turn away from attention as well but I also don't think anyone's thrown a billion parameter MLP or Conv model at a problem. We've put a lot of work into attention, transformers, and scaling these. Thousands of papers each year! Definitely don't see that for other architectures. The ResNet Strikes back paper is a great paper for one reason being that it should remind us all to not get lost in the hype and that our advancements are coupled. We learned a lot of training techniques since the original ResNet days and pushing those to ResNets also makes them a lot better and really closes the gaps. At least in vision (where I research). It is easy to railroad in research where we have publish or perish and hype driven reviewing.

After a very quick read, that's my understanding too: It's just KNN search. So I agree on points 1-3. When something works well, I don't care much about point 4.

I've had only mixed success with KNN search. Maybe I haven't done it right? Nothing seems to work quite as well for me as explicit token-token interactions by some form of attention, which as we all know is too costly for long sequences (O(n²)). Lately I've been playing with https://github.com/hazyresearch/safari , which uses a lot less compute and seems promising. Otherwise, for long sequences I've yet to find something better than https://github.com/HazyResearch/flash-attention for n×n interactions and https://github.com/glassroom/heinsen_routing for n×m interactions. If anyone here has other suggestions, I'd love to hear about them.

Code: https://github.com/HazyResearch/safari

the code is here https://github.com/hazyresearch/safari you should try it and let us know your verdict.