PyTorch KFAC

This repository is meant to port most of the original KFAC repository to PyTorch.

Why should I use KFAC?

KFAC is an approximation of the natural gradients, i.e., it is an approximate second-order method allowing for larger step sizes at the cost of compute time. This should enable faster convergence.

Usage

Installation

git clone https://github.com/n-gao/pytorch-kfac.git
cd pytorch-kfac
python setup.py install

Example

An example notebook is provided in examples.

One has to enable the tracking of the forward pass and backward pass. This prevents the optimizer to track other passes, e.g., for validation.

kfac = torch_kfac.KFAC(model, learning_rate=1e-3, damping=1e-3)
...
model.zero_grad()
with kfac.track_forward():
  # forward pass
  loss = ...
with kfac.track_backward():
  # backward pass
  loss.backward()
kfac.step()

# Do some validation

Features

This implementation features the following features:

• Regular and Adam momentum
• Adaptive damping
• Weight decay
• Norm constraint

The following layers are currently supported:

• nn.Linear
• nn.Conv1d, nn.Conv2d, nn.Conv3d

Further, the code is designed to be easily extendible by implementing the torch_kfac.layers.Layer interface with a custom layer. Unsupported layers fall back to classical gradient descent.

TODOs

• Due to the different structure of the KFAC optimizer, implementing the torch.optim.optimizer.Optimizer interface could only work by hacks, but you are welcome to add this. This implies that torch.optim.lr_scheduler are not available for KFAC. However, the learning rate can still be manually be edited by changing the .learning_rate property.

• Currently it the optimizer assumes that the output is reduced by mean, other reductions are not supported yet. So if you use e.g. torch.nn.CrossEntropyLoss make sure to have reduction='mean'.

• Add more examples

• Add support for shared layers (,e.g., RNNs)

• Documentation

• Add support for distributed training (This is partially possible by synchronizing the covariance matrices and gradients manually.)

• optimizer.zero_gard is not available yet, you can use model.zero_grad

References

Orignal Repsotiry

tensorflow/kfac

Literature

This work is based on:

• Martens et Grosse's 'Optimizing Neural Networks with Kronecker-factored Approximate Curvature'
• Grosse et Marten's 'A Kronecker-factored approximate Fisher matrix for convolution layers'