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https://github.com/tensor-compiler/taco

The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs
https://github.com/tensor-compiler/taco

code-generator library linear-algebra sparse tensor tensor-algebra tensor-algebra-compiler

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The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

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README 

        
The Tensor Algebra Compiler (taco) is a C++ library that computes

tensor algebra expressions on sparse and dense tensors.  It uses novel

compiler techniques to get performance competitive with hand-optimized

kernels in widely used libraries for both sparse tensor algebra and

sparse linear algebra.



You can use taco as a C++ library that lets you load tensors, read

tensors from files, and compute tensor expressions.  You can also use

taco as a code generator that generates C functions that compute

tensor expressions.



Learn more about taco at

[tensor-compiler.org](http://tensor-compiler.org), in the paper

[The Tensor Algebra Compiler](http://tensor-compiler.org/files/kjolstad-oopsla17-tensor-compiler.pdf),

or in [this talk](https://youtu.be/Kffbzf9etLE).  To learn more about

where taco is going in the near-term, see the technical reports on

[optimization](https://arxiv.org/abs/1802.10574) and

[formats](https://arxiv.org/abs/1804.10112).



You can also subscribe to the

[taco-announcements](https://lists.csail.mit.edu/mailman/listinfo/taco-announcements)

email list where we post announcements, RFCs, and notifications of API

changes, or the [taco-discuss](https://lists.csail.mit.edu/mailman/listinfo/taco-discuss)

email list for open discussions and questions.



TL;DR build taco using CMake. Run `make test`.



# Build and test

![Build and Test](https://github.com/RSenApps/taco/workflows/Build%20and%20Test/badge.svg?branch=master)



Build taco using CMake 3.4.0 or greater:



    cd 

    mkdir build

    cd build

    cmake -DCMAKE_BUILD_TYPE=Release ..

    make -j8



Building taco requires `gcc` 5.0 or newer, or `clang` 3.9 or newer.  You can

use a specific compiler or version by setting the `CC` and `CXX` environment

variables before running `cmake`.



## Building Python API

To build taco with the Python API (pytaco), add `-DPYTHON=ON` to the cmake line above. For example:



    cmake -DCMAKE_BUILD_TYPE=Release -DPYTHON=ON ..



You will then need to add the pytaco module to PYTHONPATH:



    export PYTHONPATH=/build/lib:$PYTHONPATH



This requires Python 3.x and some development libraries.  It also requires

NumPy and SciPy to be installed.  For Debian/Ubuntu, the following packages

are needed: `python3 libpython3-dev python3-distutils python3-numpy python3-scipy`.



## Building for OpenMP

To build taco with support for parallel execution (using OpenMP), add `-DOPENMP=ON` to the cmake line above. For example:



    cmake -DCMAKE_BUILD_TYPE=Release -DOPENMP=ON ..



If you are building with the `clang` compiler, you may need to ensure that

the `libomp` development headers are installed.  For Debian/Ubuntu, this is

provided by `libomp-dev`, One of the more specific versions like

`libomp-13-dev` may also work.



## Building for CUDA

To build taco for NVIDIA CUDA, add `-DCUDA=ON` to the cmake line above. For example:



    cmake -DCMAKE_BUILD_TYPE=Release -DCUDA=ON ..



Please also make sure that you have CUDA installed properly and that the following environment variables are set correctly:



    export PATH=/usr/local/cuda/bin:$PATH

    export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

    export LIBRARY_PATH=/usr/local/cuda/lib64:$LIBRARY_PATH



If you do not have CUDA installed, you can still use the taco cli to generate CUDA code with the -cuda flag.



The generated CUDA code will require compute capability 6.1 or higher to run.



## Generating documentation

To generate documentation for the Python API:



    cd /python_bindings

    make html



Before generating the documentation, you must have built the Python API (by

following the [instructions above](#building-python-api)) as well as installed

the following dependencies:



    pip install sphinx

    pip install numpydoc

    pip install sphinx-rtd-theme



## Running tests

To run all tests:



    cd /build

    make test



Tests can be run in parallel by setting `CTEST_PARALLEL_LEVEL=` in the environment (which runs `` tests in parallel).



To run the C++ test suite individually:



    cd 

    ./build/bin/taco-test



To run the Python test suite individually:



    cd 

    python3 build/python_bindings/unit_tests.py



## Code coverage analysis



To enable code coverage analysis, configure with `-DCOVERAGE=ON`.  This requires

the `gcovr` tool to be installed in your PATH.



For best results, the build type should be set to `Debug`.  For example:



    cmake -DCMAKE_BUILD_TYPE=Debug -DCOVERAGE=ON ..



Then to run code coverage analysis:



    make gcovr



This will run the test suite and produce some coverage analysis.  This process

requires that the tests pass, so any failures must be fixed first.

If all goes well, coverage results will be output to the `coverage/` folder.

See `coverage/index.html` for a high level report, and click individual files

to see the line-by-line results.



# Library example



The following sparse tensor-times-vector multiplication example in C++

shows how to use the taco library.



```C++

// Create formats

Format csr({Dense,Sparse});

Format csf({Sparse,Sparse,Sparse});

Format  sv({Sparse});



// Create tensors

Tensor A({2,3},   csr);

Tensor B({2,3,4}, csf);

Tensor c({4},     sv);



// Insert data into B and c

B.insert({0,0,0}, 1.0);

B.insert({1,2,0}, 2.0);

B.insert({1,2,1}, 3.0);

c.insert({0}, 4.0);

c.insert({1}, 5.0);



// Pack inserted data as described by the formats

B.pack();

c.pack();



// Form a tensor-vector multiplication expression

IndexVar i, j, k;

A(i,j) = B(i,j,k) * c(k);



// Compile the expression

A.compile();



// Assemble A's indices and numerically compute the result

A.assemble();

A.compute();



std::cout << A << std::endl;

```



# Code generation tools



If you just need to compute a single tensor kernel you can use the

[taco online tool](http://tensor-compiler.org/codegen.html) to generate

a custom C library.  You can also use the taco command-line tool to

the same effect:



    cd 

    ./build/bin/taco

    Usage: taco [options] 



    Examples:

      taco "a(i) = b(i) + c(i)"                            # Dense vector add

      taco "a(i) = b(i) + c(i)" -f=b:s -f=c:s -f=a:s       # Sparse vector add

      taco "a(i) = B(i,j) * c(j)" -f=B:ds                  # SpMV

      taco "A(i,l) = B(i,j,k) * C(j,l) * D(k,l)" -f=B:sss  # MTTKRP



    Options:

      ...



For more information, see our paper on the taco tools

[taco: A Tool to Generate Tensor Algebra Kernels](http://tensor-compiler.org/files/kjolstad-ase17-taco-tools.pdf).