- [-] Experiments (sanity checks)
On sequential cifar (reproduce table 1 from https://arxiv.org/abs/2110.13985)
- [X] Train a Transformer
- [X] Train LSSM (done: dev/test 0.8697/0.862)
- [ ] Train S4 (almost here) @nakhodnov17
- [X] Papers Talk trough:
- [ ] Transformer on LRA (path-X (maybe smaller if it’s a problem), should fail: probably OOM) @vladyur (table 4)
- [ ] CIFAR autoregressive generation (table 7)
- S4 @nakhodnov17
- Transformer @vladyur or @ViktorooTg
- [ ] Transformer with flash-attention on LRA (path-X, should be ok) @vladyur or @ViktorooTg
links:
- https://habr.com/ru/post/669506/
- https://github.com/HazyResearch/flash-attention
- оно есть в xformers, можно посмотреть как используют операцию https://github.com/facebookresearch/xformers/blob/main/xformers/benchmarks/benchmark_mem_eff_attention.py, по сути просот подменяют MHSA на то что написали во flash-attention
- [ ] Потенциально в самом конце (скорее всего не успеем) @anyone: сравниться с https://github.com/ctlllll/SGConv/blob/main/gconv.py
- prepare for the final presentation
- lssl is ok on CIFAR (much better than a transformer?), but
- s4 works (high probability)
- transformer works (and fails where it should)
- Our benchmarks: cifar pixel level, cifar density estimation, path-x binclass (add speed + memory benchmarks for the presentation)