ihaeyong/fishleg

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FishLeg

Repository containing the official PyTorch implementation of the Fisher-Legendre Second Order Optimization method.

Overview

This library contains the official PyTorch implementation of the FishLeg optimizer as introduced in Fisher-Legendre (FishLeg) optimization of deep neural networks .
FishLeg is a learnt second-order optimization method that uses natural gradients and ideas from Legendre-Fenchel duality to learn a direct and efficiently evaluated model for the product of the inverse Fisher with any vector in an online manner. Thanks to its generality, we expect FishLeg to facilitate handling various neural network architectures. The library's primary goal is to provide researchers and developers with an easy-to-use implementation of the FishLeg optimizer and curvature estimator.

Installation

FishLeg is written in Python, and only requires PyTorch > 1.8.
The example scripts have additional requirements defined in examples/requirements.txt
The FishLeg library does not requires dedicated installation.

Usage

FishLeg requires minimal code modifications to introduce it in existing training scripts.

from optim.FishLeg import FishLeg, FISH_LIKELIHOODS

...
likelihood = FISH_LIKELIHOODS["FixedGaussian".lower()](sigma=1.0, device=device)

    def nll(model, data_x, data_y):
        pred_y = model.forward(data_x)
        return likelihood.nll(data_y, pred_y)

    def draw(model, data_x):
        pred_y = model.forward(data_x)
        return likelihood.draw(pred_y)



...

model = nn.Sequential(...).to(device)
optimizer =  opt = FishLeg(
        model,
        draw,
        nll,
        aux_loader,
        lr=eta_fl,
        eps=eps,
        beta=beta,
        weight_decay=1e-5,
        update_aux_every=10,
        aux_lr=aux_eta,
        aux_betas=(0.9, 0.999),
        aux_eps=1e-8,
        damping=damping,
        pre_aux_training=25,
        sgd_lr=eta_sgd,
        device=device,
    )

...

See examples/autoencoder.py for an usage demostration.
See the FishLeg documentation for a detailed list of parameters.

Citation

@article{garcia2022FishLeg,
  title={Fisher-Legendre (FishLeg) optimization of deep neural networks},
  author={Garcia, Jezabel R and Freddi, Federica and Fotiadis, Stathi1 and Li, Maolin and Vakili, Sattar, and Bernacchia, Alberto and Hennequin,Guillaume },
  journal={},
  year={2023}
}

Contributing to FishLeg

We love your input! We want to make contributing to this project as easy and transparent as possible, whether it's:

• Creating new layers
• Creating new approximations of the Qv product
• Reporting a bug
• Discussing the current state of the code
• Submitting a fix
• Proposing new features

We Develop with Github

We use github to host code, to track issues and feature requests, as well as accept pull requests.

We Use Github Flow, So All Code Changes Happen Through Pull Requests

Pull requests are the best way to propose changes to the codebase (we use Github Flow). We actively welcome your pull requests:

1. Fork the repo and create your branch from master.
2. If you've added code that should be tested, add tests.
3. If necessary update the documentation.
4. Ensure the test suite passes.
5. Make sure your code lints.
6. Issue that pull request!

Any contributions you make will be under the MIT Software License

Report bugs using Github's issues

We use GitHub issues to track public bugs. Report a bug by opening a new issue; it's that easy!

Write bug reports with detail, background, and sample code

Great Bug Reports tend to have:

• A quick summary and/or background
• Steps to reproduce
  • Be specific!
  • Give sample code if you can.
• What you expected would happen
• What actually happens
• Notes (possibly including why you think this might be happening, or stuff you tried that didn't work)