LookSAM

<h1 align="center"><b>LookSAM Optimizer</b></h1> <h3 align="center"><b>Towards Efficient and Scalable Sharpness-Aware Minimization</b></h3> <p align="center"> <i>~ in Pytorch ~</i> </p>

LookSAM is an accelerated SAM algorithm. Instead of computing the inner gradient ascent every step, LookSAM computer it periodically and reuses the direction that promotes to flat regions.

This is unofficial repository for Towards Efficient and Scalable Sharpness-Aware Minimization. Currently it is only proposed an algorithm without layer-wise adaptive rates (but it will be soon...).

In rewritten step method you are able to fed several arguments:

1. t is a train_index to define index of current batch;
2. samples are input data;
3. targets are input ground-truth data;
4. zero_sam_grad is a boolean value to zero gradients under SAM condition (first step) (see discussion here ;
5. zero_grad is a boolean value for zero gradient after second step;

Unofficial SAM repo is my inspiration :)

Usage

from looksam import LookSAM


model = YourModel()
criterion = YourCriterion()
base_optimizer = YourBaseOptimizer
loader = YourLoader()

optimizer = LookSAM(
    k=10,
    alpha=0.7,
    model=model,
    base_optimizer=base_optimizer,
    rho=0.1,
    **kwargs
)

...

model.train()

for train_index, (samples, targets) in enumerate(loader):
    ...

    loss = criterion(model(samples), targets)
    loss.backward()
    optimizer.step(
        t=train_index, 
        samples=samples, 
        targets=targets, 
        zero_sam_grad=True, 
        zero_grad=True
    )
    ...