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torchTT

Tensor-Train decomposition in pytorch

Tensor-Train decomposition package written in Python on top of pytorch. Supports GPU acceleration and automatic differentiation. It also contains routines for solving linear systems in the TT format and performing adaptive cross approximation (the AMEN solver/cross interpolation is inspired form the MATLAB TT-Toolbox). Some routines are implemented in C++ for an increased execution speed.

Installation

Requirements

Following requirements are needed:

The GPU (if available) version of pytorch is recommended to be installed. Read the official installation guide for further info.

Using pip

You can install the package using the pip command:

pip install torchTT

The latest github version can be installed using:

pip install git+https://github.com/ion-g-ion/torchTT

One can also clone the repository and manually install the package:

git clone https://github.com/ion-g-ion/torchTT
cd torchTT
python setup.py install

Using conda

TODO

Components

The main modules/submodules that can be accessed after importing torchtt are briefly desctibed in the following table. Detailed description can be found here.

Component Description
torchtt Basic TT class and basic linear algebra functions.
torchtt.solvers Implementation of the AMEN solver.
torchtt.grad Wrapper for automatic differentiation.
torchtt.manifold Riemannian gradient and projection onto manifolds of tensors with fixed TT rank.
torchtt.nn Basic TT neural network layer.
torchtt.interpolate Cross approximation routines.

Tests

The directory tests/ from the root folder contains all the unittests. To run them use the command:

pytest tests/

Documentation and examples

The documentation can be found here. Following example scripts (as well as python notebooks) are also provied provided as part of the documentation:

The documentation is generated using shpinx with:

make html

after installing the packages

pip install sphinx sphinx_rtd_theme

Author

Ion Gabriel Ion, e-mail: ion.ion.gabriel@gmail.com