SINGD: Structured Inverse-Free Natural Gradient Descent

This package contains the official PyTorch implementation of our memory-efficient and numerically stable KFAC variant, termed SINGD (paper).

The main feature is a torch.optim.Optimizer which works like most PyTorch optimizers and is compatible with:

• Per-parameter options (param_groups)

• Using a learning rate scheduler

• Checkpointing

• Gradient scaling & mixed-precision training

• Gradient accumulation (multiple forward-backwards, then take a step)

• Distributed data-parallel (DDP) training¹

The pre-conditioner matrices support different structures that allow to reduce cost (overview).

Installation

• Stable (recommended):

  pip install singd
  

• Latest version from GitHub main branch:

  pip install git+https://github.com/f-dangel/singd.git@main
  

Usage

• Basic example
• Examples for supported features
• Advanced example
• Supported structures

Limitations

• SINGD does not support graph neural networks (GNN).

• SINGD currently does not support gradient clipping.

• The code has stabilized only recently. Expect things to break and help us improve by filing issues.

Citation

If you find this code useful for your research, consider citing the paper:

@inproceedings{lin2024structured,
  title =        {Structured Inverse-Free Natural Gradient Descent:
                  Memory-Efficient \& Numerically-Stable {KFAC}},
  author =       {Wu Lin and Felix Dangel and Runa Eschenhagen and Kirill
                  Neklyudov and Agustinus Kristiadi and Richard E. Turner and
                  Alireza Makhzani},
  booktitle =    {International Conference on Machine Learning (ICML)},
  year =         2024,
}

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1. We do support standard DDP with one crucial difference: The model should not be wrapped with the DDP wrapper, but the rest, e.g. using the torchrun command stays the same. ↩