This repository has been superseded by the library GeoTorch, which offers a much more flexible approach and has many more manifolds implemented

https://github.com/Lezcano/geotorch

This repository will stay here to host the code to reproduce the two papers. If you are looking to use the functionality, please look at GeoTorch's repository.

Dynamic Trivializations: Optimization on manifolds made simple (Orthogonal, Positive Definite, Positive determinant...)

Code and Experiments of the papers:

Trivializations for Gradient-Based Optimization on Manifolds - NeurIPS 2019

Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group - ICML 2019

Start putting orthogonal constraints in your code

Orthogonal Dynamic Trivialization RNN (`dtriv` / `expRNN`)

Just copy the main files into your code and use the class OrthogonalRNN included in the file orthogonal.py. This class implements the dtriv framework for optimization with orthogonal contraints. It has expRNN as a particular case, as described in the remark in Section 7. In the models for the experiments it can be selected through the --mode parameter.

Orthogonal constraints

We implement a class Orthogonal in the file orthogonal.py that can be used both as a static trivialization via the exponential map implementing expRNN, or as a dynamic trivialization, implementing dtriv. It can also be used as a static or a dynamic trivialization with other parametrizations of the orthogonal group, like the Cayley transform. We include the Cayley transform as an example in the experiments as well.

This layer could also be applied to other kinds of layers like CNNs, and as a helper for different kinds of decompositions in linear layers (QR, SVD, Polar, Schur...). To do this, just use the Orthogonal class included in the orthogonal.py file.

This class can serve as an example for how to use the Parametrization class to implement your own class for other manifolds. More on this below.

Optimization step and general recommendations

To optimize with orthogonal constraints we recommend having two optimizers, one for the orthogonal parameters and one for the non orthogonal. We provide a convenience function called get_parameters that, given a model, it returns the constrained parameters to be optimized (skew-symmetric in this case) and the unconstrained parameters (cf. line 138 in 1_copying.py). In the conext of RNNs, we noticed empirically that having the lerning rate of the non-orthogonal parameters to be 10 times that of the orthogonal parameters yields the best performance.

General manifold constraints

The framework presented in the paper "Trivializations for Gradient-Based Optimization on Manifolds" allows to put orthogonal constraints in any given manifold through the use of dynamic parametrizations. In order to create your own, just follow the instructions detailed at the beginning of the class Parametrization in the file parametrization.py.

All one has to do is to implement a class that inherits from it and implements the method retraction. In the Section 6.3 and Section E we describe many different types of trivializations on different manifolds, which can be a good place to look for ideas.

We implemented a class that optimizes over the Stiefel manifold in orthogonal.py as an example. This is the class that we also use for the experiments.

A note on the papers

For the researcher who is mostly interested in the idea and how to implement it in their experiments, a reading order of the papers could be: Sections 1, 3.1, 3.2, 4 of the Cheap paper, and then Sections 1, 5, 6, E of the Trivializations paper.
The NeurIPS paper "Trivializations for Gradient-Based Optimization on Manifolds" is a far reaching generalization of the paper "Cheap Orthogonal Constraints in Neural Networks". As such, some parts of the paper are more abstract, as they are more general. We recommend the interested reader to start reading the ICML paper, and just then, go for the NeurIPS paper.
In both papers, there are certain sections in the appendix that are more technical. These sections are not necessary for the implementation of the algorithms.
Section E of the Trivializations paper can be particularly userful , as it contains many possible applications of the framework in different contexts.

A note on the TIMIT experiment

The TIMIT dataset is not open, but most universities and many other institutions have access to it.

To preprocess the data of the TIMIT dataset, we used the tools provided by Wisdom on the repository:

https://github.com/stwisdom/urnn

As mentioned in the repository, first downsample the TIMIT dataset using the downsample_audio.m present in the matlab folder.

Downsample the TIMIT dataset to 8ksamples/sec using Matlab by running downsample_audio.m from the matlab directory. Make sure you modify the paths in downsample_audio.m for your system.

Create a timit_data folder to store all the files.

After that, modify the file timit_prediction.py and add the following lines after line 529.

np.save("timit_data/lens_train.npy", lens_train)
np.save("timit_data/lens_test.npy", lens_test)
np.save("timit_data/lens_eval.npy", lens_eval)
np.save("timit_data/train_x.npy", np.transpose(train_xdata, [1, 0, 2]))
np.save("timit_data/train_z.npy", np.transpose(train_z, [1, 0, 2]))
np.save("timit_data/test_x.npy",  np.transpose(test_xdata, [1, 0, 2]))
np.save("timit_data/test_z.npy",  np.transpose(test_z, [1, 0, 2]))
np.save("timit_data/eval_x.npy",  np.transpose(eval_xdata, [1, 0, 2]))
np.save("timit_data/eval_z.npy",  np.transpose(eval_z, [1, 0, 2]))

Run this script to save the dataset in a format that can be loaded by the TIMIT dataset loader

import numpy as np
import torch

train_x = torch.tensor(np.load('timit_data/train_x.npy'))
train_y = torch.tensor(np.load('timit_data/train_z.npy'))
lens_train = torch.tensor(np.load("timit_data/lens_train.npy"), dtype=torch.long)

test_x = torch.tensor(np.load('timit_data/test_x.npy'))
test_y = torch.tensor(np.load('timit_data/test_z.npy'))
lens_test = torch.tensor(np.load("timit_data/lens_test.npy"), dtype=torch.long)

val_x = torch.tensor(np.load('timit_data/eval_x.npy'))
val_y = torch.tensor(np.load('timit_data/eval_z.npy'))
lens_val = torch.tensor(np.load("timit_data/lens_eval.npy"), dtype=torch.long)

training_set = (train_x, train_y, lens_train)
test_set = (test_x, test_y, lens_test)
val_set = (val_x, val_y, lens_val)
with open("timit_data/training.pt", 'wb') as f:
    torch.save(training_set, f)
with open("timit_data/test.pt", 'wb') as f:
    torch.save(test_set, f)
with open("timit_data/val.pt", 'wb') as f:
    torch.save(val_set, f)

Cite this work

@inproceedings{lezcano2019trivializations,
    title = {Trivializations for Gradient-Based Optimization on Manifolds},
    author = {Lezcano-Casado, Mario},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {9154--9164},
    year = {2019},
}

@inproceedings{lezcano2019cheap,
  title={Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group},
  author={Lezcano-Casado, Mario and Mart{\'i}nez-Rubio, David},
  booktitle={International Conference on Machine Learning (ICML)},
  pages={3794--3803},
  year={2019}
}

lezcano/expRNN