torch-secorder 0.0.1

A PyTorch library for second-order optimization and analysis

torch-secorder

A PyTorch-native library for efficient second-order computations in deep neural networks.

Overview

Torch-Secorder provides efficient implementations of second-order optimization utilities for PyTorch, including:

• Hessian-Vector Products (HVP)
• Jacobian-Vector Products (JVP)
• Vector-Jacobian Products (VJP)
• Gauss-Newton matrix computations
• Hessian trace estimation

These tools are essential for:

• Second-order optimization methods
• Natural gradient descent
• Curvature-based regularization
• Neural network analysis and debugging

Features

• Hessian-Vector Products (HVP)

  • Computation of Hv for any vector v
  • Trace estimation using Hutchinson's method
  • Support for distributed computation
  • Automatic parameter handling

• Hessian Diagonal

  • Computation of Hessian diagonal elements
  • Trace estimation using diagonal elements
  • Support for custom vectors
  • Gradient requirement validation

• Trace Estimation

  • HVP-based: Uses Hutchinson's method with random vectors
  • Diagonal-based: Uses exact diagonal elements
  • Both methods compute the same quantity
  • Choose based on model size and accuracy requirements

• Model Integration

  • PyTorch model integration
  • Parameter management
  • Loss function support
  • Model-specific computations

Installation

pip install torch-secorder

Or install from source:

git clone https://github.com/pybrainn/torch-secorder.git
cd torch-secorder
pip install -e .

Quick Start

See our documentation for detailed examples and tutorials.

Documentation

• API Reference
• Examples

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Citation

If you use this library in your research, please cite:

@software{torch_secorder2025,
  author = PyBrainn,
  title = {Torch-Secorder: Second-Order Optimization for PyTorch},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/pybrainn/torch-secorder}
}