# taco

**Repository Path**: magicor/taco

## Basic Information

- **Project Name**: taco
- **Description**: https://github.com/tensor-compiler/taco
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-25
- **Last Updated**: 2025-09-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 <taco-directory>
    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=<taco-directory>/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 <taco-directory>/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 <taco-directory>/build
    make test

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

To run the C++ test suite individually:

    cd <taco-directory>
    ./build/bin/taco-test

To run the Python test suite individually:

    cd <taco-directory>
    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<double> A({2,3},   csr);
Tensor<double> B({2,3,4}, csf);
Tensor<double> 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 <taco-directory>
    ./build/bin/taco
    Usage: taco [options] <index expression>

    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).