grokfast-pytorch 0.0.1

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.

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)

# forward and backwards

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

# optimizer step

opt.step()
opt.zero_grad()

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}
}