grokfast-pytorch 0.0.11

Grokfast

Grokfast - Pytorch (wip)

Explorations into "Grokfast, Accelerated Grokking by Amplifying Slow Gradients", out of Seoul National University in Korea. In particular, will compare it with NAdam on modular addition as well as a few other tasks, since I am curious why those experiments are left out of the paper. If it holds up, will polish it up into a nice package for quick use.

The official repository can be found here

Install

$ pip install grokfast-pytorch

Usage

import torch
from torch import nn

# toy model

model = nn.Linear(10, 1)

# import GrokFastAdamW and instantiate with parameters

from grokfast_pytorch import GrokFastAdamW

opt = GrokFastAdamW(
    model.parameters(),
    lr = 1e-4,
    weight_decay = 1e-2
)

# forward and backwards

loss = model(torch.randn(10))
loss.backward()

# optimizer step

opt.step()
opt.zero_grad()

Todo

• run all experiments on small transformer
  • modular addition
  • pathfinder-x
  • run against nadam and some other optimizers
  • see if exp_avg could be repurposed for amplifying slow grads
• add the foreach version only if above experiments turn out well

Citations

@inproceedings{Lee2024GrokfastAG,
    title   = {Grokfast: Accelerated Grokking by Amplifying Slow Gradients},
    author  = {Jaerin Lee and Bong Gyun Kang and Kihoon Kim and Kyoung Mu Lee},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:270123846}
}

@misc{kumar2024maintaining,
    title={Maintaining Plasticity in Continual Learning via Regenerative Regularization},
    author={Saurabh Kumar and Henrik Marklund and Benjamin Van Roy},
    year={2024},
    url={https://openreview.net/forum?id=lyoOWX0e0O}
}

@inproceedings{anonymous2024the,
    title   = {The Complexity Dynamics of Grokking},
    author  = {Anonymous},
    booktitle = {Submitted to The Thirteenth International Conference on Learning Representations},
    year    = {2024},
    url     = {https://openreview.net/forum?id=07N9jCfIE4},
    note    = {under review}
}