FAuST Toolbox -- Flexible Approximate Multi-Layer Sparse Transform

General purpose

The FAuST toolbox contains a C++ code implementing a general framework designed to factorize matrices of interest into multiple sparse factors. It contains a template CPU/GPU C++ code and a Matlab wrapper. A Python wrapper is also available. The algorithms implemented here are described in details in [1]- Le Magoarou

For more information on the FAuST Project, please visit the website of the project: FAµST website

Dependencies

• cuda (preferably cuda 12 latest version but 9 and 11 are also supported). There is a known bug on cuda 11.4 (issue #305). CUDA is optional, only used if cmake option USE_GPU_MOD is ON. The gpu_mod submodule must be checked out in order to enable this function.
• Eigen 3.4.x.
• matio version >= 1.5.7 (current latest version 1.5.23 is supported and advised). matio own dependencies, as hdf5 and zlib. matio dependency can be disabled through cmake option NO_MATIO.
• Python3 (with numpy, scipy and cython) to build the python wrappers.
• Matlab (>= R2017b) to build the matlab wrappers (there is a constraint on the gcc compiler version depending on the used Matlab version, the CMake script indicate if the match is not appropriate).
• libxml2 (Optional, needed with CMake BUILD_READ_XML_FILE).
• CMake >= 3.21.0
• TODO: other?

Cloning the project

git clone git@gitlab.inria.fr:faustgrp/faust.git faust

NOTE: The project git binary objects are heavy (it can take up to ~ 1.5 Gio of data). If your connection is unstable don't download the whole history (using the --depth option -- issue #294).

git clone git@gitlab.inria.fr:faustgrp/faust.git --depth=1 --single-branch faust

NOTE: you can also use the https://gitlab.inria.fr/faustgrp/faust.git URL.

Then if you really need the full git history to work on the project, fetch it (preferably when your Internet connection is good) :

git fetch --unshallow

Build on UNIX

Unpack the directory.
mkdir ./build
cd ./build
cmake .. OR ccmake .. (with Graphical User Interface)
make
make install

Warning 1: The Matlab interface of FAuST requires compiling mex files. The mex compiler compatible with specific versions of gcc depending on the platform used. For more information, please refer to the Mathworks website.

Warning 2: Many CMake build options are available (cf. CMakeLists.txt). It might be quite complicated to deal with them at start (refer to gitlab ci building scripts in ./misc/continuous_integration/jobs/ or the .gitlab-ci.yml root script to get some insight).

Quick Build of the python wrappers (pyfaust) on UNIX (without MATLAB and MATIO)

(With Eigen, Python3 with Cython, numpy and scipy installed)

git clone git@gitlab.inria.fr:faustgrp/faust.git --depth=1  faust_no_matio
cd faust_no_matio/
mkdir build
cd build
cmake -DBUILD_WRAPPER_PYTHON=ON -DNO_MATIO=ON -DNOCPPTESTS=ON ..
# on Linux if clang compiler is not installed add the cmake option -DLINUX_DEFAULT_COMPILER_FOR_PYTHON=ON to defaulty use gcc
make faust_python

Quickest Install on Linux, Windows and macOS (pre-built pakages)

Please refer to the document Installation guides to install the FAUST toolbox. The FAUST toolbox has been tested on the following environments:

• LINUX (fedora 35 - 37 / centos 7, 8 / Ubuntu)
• MACOS X
• WINDOWS (windows 10)

Pre-compiled packages from Gitlab Continuous Integration are also available. Except of course PIP packages, all packages include matlab and python wrappers, below are the latest release links.

• macOS (.pkg) installer
• Windows (.exe) NSI installer
• Linux (.rpm, .deb) packages
• Linux (.rpm, .deb) packages with embedded static matio library
• Python PIP (pre-compiled) packages: for Linux, macOS and Windows 10. Or preferably, refer to pypi pyfaust or anaconda pyfaust.

License

Cf. license.txt

Contacts

Rémi Gribonval: remi.gribonval@inria.fr
Hakim: hakim.hadj-djilani@inria.fr

Credits

Researchers: Luc Le Magoarou Remi Gribonval TODO: add others

Software engineers: Adrien Leman (2016), Nicolas Bellot(2015-2016), Thomas Gautrais (2015), Hakim Hadj-Djilani (2018-), Pascal Carrivain (2023-).

References

[1] Le Magoarou L. and Gribonval R., "Flexible multi-layer sparse approximations of matrices and applications", Journal of Selected Topics in Signal Processing, 2016.