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, in the paper The Tensor Algebra Compiler, or in this talk. To learn more about where taco is going in the near-term, see the technical reports on optimization and formats.

You can also subscribe to the taco-announcements email list where we post announcements, RFCs, and notifications of API changes, or the taco-discuss email list for open discussions and questions.

TL;DR build taco using CMake. Run taco-test in the bin directory.

Build and test

Build taco using CMake 2.8.3 or greater:

cd <taco-directory>
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j8

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>/python_bindings/:$PYTHONPATH

pytaco requires NumPy and SciPy to be installed.

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 ..

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.

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 python_bindings/unit_tests.py

Library example

The following sparse tensor-times-vector multiplication example in C++ shows how to use the taco library.

// 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 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.