puhsu / state-spaces

State space models

State Space Models

Todos:

Til Someday [1/2]

• [-] 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:
  • https://arxiv.org/abs/2110.13985
  • https://arxiv.org/abs/2111.00396

Experiments (mon, tue, wed)

• [ ] 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

Presentation

• 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)