python-somax 0.0.1

SOMAX: Second-Order Methods for Machine Learning in JAX

Somax

Somax is a library of Second-Order Methods for stochastic optimization written in JAX. Somax is based on the JAXopt StochasticSolver API, and can be used as a drop-in replacement for JAXopt as well as Optax solvers.

Currently supported methods:

• Diagonal Scaling:
  • AdaHessian;
  • Sophia;
• Hessian-free Optimization:
  • Newton-CG;
• Quasi-Newton:
  • Stochastic Quasi-Newton Framework (SQN);
• Gauss-Newton:
  • Exact Gauss-Newton (EGN);
  • Stochastic Gauss-Newton (SGN);
• Natural Gradient:
  • Natural Gradient with Sherman-Morrison-Woodbury formula (SWM-NG).

Future releases:

• Add support for separate "gradient batches" and "curvature batches" for all solvers;
• Add support for Optax rate schedules.

⚠️ Since JAXopt is currently being merged into Optax, Somax at some point will switch to the Optax API as well.

*The catfish in the logo is a nod to "сом", the Belarusian word for "catfish", also pronounced as "som".

Installation

pip install python-somax

Requires JAXopt 0.8.2+.

Quick example

from somax import EGN

# initialize the solver
solver = EGN(
    predict_fun=model.apply,
    loss_type='mse',
    learning_rate=0.1,
    regularizer=1.0,
)

# initialize the solver state
opt_state = solver.init_state(params)

# run the optimization loop
for i in range(10):
    params, opt_state = solver.update(params, opt_state, batch_x, batch_y)

See more in the examples folder.

Citation

@misc{korbit2024somax,
  author = {Nick Korbit},
  title = {{SOMAX}: a library of second-order methods for stochastic optimization written in {JAX}},
  year = {2024},
  url = {https://github.com/cor3bit/somax},
}

See also

Optimization with JAX
Optax: first-order gradient (SGD, Adam, ...) optimisers.
JAXopt: deterministic second-order methods (e.g., Gauss-Newton, Levenberg Marquardt), stochastic first-order methods PolyakSGD, ArmijoSGD.

Awesome Projects
Awesome JAX: a longer list of various JAX projects.
Awesome SOMs: a list of resources for second-order optimization methods in machine learning.

Acknowledgements

Some of the implementation ideas are based on the following repositories:

• Line Search in JAXopt: https://github.com/google/jaxopt/blob/main/jaxopt/_src/armijo_sgd.py#L48

• L-BFGS Inverse Hessian-Gradient product in JAXopt: https://github.com/google/jaxopt/blob/main/jaxopt/_src/lbfgs.py#L44

• AdaHessian (official implementation): https://github.com/amirgholami/adahessian

• AdaHessian (Nestor Demeure's implementation): https://github.com/nestordemeure/AdaHessianJax

• Sophia (official implementation): https://github.com/Liuhong99/Sophia

• Sophia (levanter implementation): https://github.com/stanford-crfm/levanter/blob/main/src/levanter/optim/sophia.py