Package: torch-sparse-carate
BadgesThe repo was not touched, however the quality may significantly be improved usingliaBadges Parent PackagePyTorch SparseThis package consists of a small extension library of optimized sparse matrix operations with autograd support.
This package currently consists of the following methods:CoalesceTransposeSparse Dense Matrix MultiplicationSparse Sparse Matrix MultiplicationAll included operations work on varying data types and are implemented both for CPU and GPU.
To avoid the hazzle of creatingtorch.sparse_coo_tensor, this package defines operations on sparse tensors by simply passingindexandvaluetensors as arguments (with same shapes as defined in PyTorch).
Note that onlyvaluecomes with autograd support, asindexis discrete and therefore not differentiable.InstallationAnacondaUpdate:You can now installpytorch-sparseviaAnacondafor all major OS/PyTorch/CUDA combinations 🤗
Given that you havepytorch >= 1.8.0installed, simply runconda install pytorch-sparse -c pygBinariesWe alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, seehere.PyTorch 2.0To install the binaries for PyTorch 2.0.0, simply runpip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.htmlwhere${CUDA}should be replaced by eithercpu,cu117, orcu118depending on your PyTorch installation.cpucu117cu118Linux✅✅✅Windows✅✅✅macOS✅PyTorch 1.13To install the binaries for PyTorch 1.13.0, simply runpip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.htmlwhere${CUDA}should be replaced by eithercpu,cu116, orcu117depending on your PyTorch installation.cpucu116cu117Linux✅✅✅Windows✅✅✅macOS✅Note:Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0, PyTorch 1.6.0, PyTorch 1.7.0/1.7.1, PyTorch 1.8.0/1.8.1, PyTorch 1.9.0, PyTorch 1.10.0/1.10.1/1.10.2, PyTorch 1.11.0 and PyTorch 1.12.0/1.12.1 (following the same procedure).
For older versions, you need to explicitly specify the latest supported version number or install viapip install --no-indexin order to prevent a manual installation from source.
You can look up the latest supported version numberhere.From sourceEnsure that at least PyTorch 1.7.0 is installed and verify thatcuda/binandcuda/includeare in your$PATHand$CPATHrespectively,e.g.:$ python -c "import torch; print(torch.__version__)"
>>> 1.7.0

$ echo $PATH
>>> /usr/local/cuda/bin:...

$ echo $CPATH
>>> /usr/local/cuda/include:...If you want to additionally buildtorch-sparsewith METIS support,e.g.for partioning, please download and install theMETIS libraryby following the instructions in theInstall.txtfile.
Note that METIS needs to be installed with 64 bitIDXTYPEWIDTHby changinginclude/metis.h.
Afterwards, set the environment variableWITH_METIS=1.Then run:pip install torch-scatter torch-sparseWhen running in a docker container without NVIDIA driver, PyTorch needs to evaluate the compute capabilities and may fail.
In this case, ensure that the compute capabilities are set viaTORCH_CUDA_ARCH_LIST,e.g.:export TORCH_CUDA_ARCH_LIST="6.0 6.1 7.2+PTX 7.5+PTX"FunctionsCoalescetorch_sparse.coalesce(index, value, m, n, op="add") -> (torch.LongTensor, torch.Tensor)Row-wise sortsindexand removes duplicate entries.
Duplicate entries are removed by scattering them together.
For scattering, any operation oftorch_scattercan be used.Parametersindex(LongTensor)- The index tensor of sparse matrix.value(Tensor)- The value tensor of sparse matrix.m(int)- The first dimension of sparse matrix.n(int)- The second dimension of sparse matrix.op(string, optional)- The scatter operation to use. (default:"add")Returnsindex(LongTensor)- The coalesced index tensor of sparse matrix.value(Tensor)- The coalesced value tensor of sparse matrix.Exampleimporttorchfromtorch_sparseimportcoalesceindex=torch.tensor([[1,0,1,0,2,1],[0,1,1,1,0,0]])value=torch.Tensor([[1,2],[2,3],[3,4],[4,5],[5,6],[6,7]])index,value=coalesce(index,value,m=3,n=2)print(index)
tensor([[0, 1, 1, 2],
        [1, 0, 1, 0]])
print(value)
tensor([[6.0, 8.0],
        [7.0, 9.0],
        [3.0, 4.0],
        [5.0, 6.0]])Transposetorch_sparse.transpose(index, value, m, n) -> (torch.LongTensor, torch.Tensor)Transposes dimensions 0 and 1 of a sparse matrix.Parametersindex(LongTensor)- The index tensor of sparse matrix.value(Tensor)- The value tensor of sparse matrix.m(int)- The first dimension of sparse matrix.n(int)- The second dimension of sparse matrix.coalesced(bool, optional)- If set toFalse, will not coalesce the output. (default:True)Returnsindex(LongTensor)- The transposed index tensor of sparse matrix.value(Tensor)- The transposed value tensor of sparse matrix.Exampleimporttorchfromtorch_sparseimporttransposeindex=torch.tensor([[1,0,1,0,2,1],[0,1,1,1,0,0]])value=torch.Tensor([[1,2],[2,3],[3,4],[4,5],[5,6],[6,7]])index,value=transpose(index,value,3,2)print(index)
tensor([[0, 0, 1, 1],
        [1, 2, 0, 1]])
print(value)
tensor([[7.0, 9.0],
        [5.0, 6.0],
        [6.0, 8.0],
        [3.0, 4.0]])Sparse Dense Matrix Multiplicationtorch_sparse.spmm(index, value, m, n, matrix) -> torch.TensorMatrix product of a sparse matrix with a dense matrix.Parametersindex(LongTensor)- The index tensor of sparse matrix.value(Tensor)- The value tensor of sparse matrix.m(int)- The first dimension of sparse matrix.n(int)- The second dimension of sparse matrix.matrix(Tensor)- The dense matrix.Returnsout(Tensor)- The dense output matrix.Exampleimporttorchfromtorch_sparseimportspmmindex=torch.tensor([[0,0,1,2,2],[0,2,1,0,1]])value=torch.Tensor([1,2,4,1,3])matrix=torch.Tensor([[1,4],[2,5],[3,6]])out=spmm(index,value,3,3,matrix)print(out)
tensor([[7.0, 16.0],
        [8.0, 20.0],
        [7.0, 19.0]])Sparse Sparse Matrix Multiplicationtorch_sparse.spspmm(indexA, valueA, indexB, valueB, m, k, n) -> (torch.LongTensor, torch.Tensor)Matrix product of two sparse tensors.
Both input sparse matrices need to becoalesced(use thecoalescedattribute to force).ParametersindexA(LongTensor)- The index tensor of first sparse matrix.valueA(Tensor)- The value tensor of first sparse matrix.indexB(LongTensor)- The index tensor of second sparse matrix.valueB(Tensor)- The value tensor of second sparse matrix.m(int)- The first dimension of first sparse matrix.k(int)- The second dimension of first sparse matrix and first dimension of second sparse matrix.n(int)- The second dimension of second sparse matrix.coalesced(bool, optional): If set toTrue, will coalesce both input sparse matrices. (default:False)Returnsindex(LongTensor)- The output index tensor of sparse matrix.value(Tensor)- The output value tensor of sparse matrix.Exampleimporttorchfromtorch_sparseimportspspmmindexA=torch.tensor([[0,0,1,2,2],[1,2,0,0,1]])valueA=torch.Tensor([1,2,3,4,5])indexB=torch.tensor([[0,2],[1,0]])valueB=torch.Tensor([2,4])indexC,valueC=spspmm(indexA,valueA,indexB,valueB,3,3,2)print(indexC)
tensor([[0, 1, 2],
        [0, 1, 1]])
print(valueC)
tensor([8.0, 6.0, 8.0])Running testspytestC++ APItorch-sparsealso offers a C++ API that contains C++ equivalent of python models.
For this, we need to addTorchLibto the-DCMAKE_PREFIX_PATH(e.g., it may exists in{CONDA}/lib/python{X.X}/site-packages/torchif installed viaconda):mkdir build
cd build
# Add -DWITH_CUDA=on support for CUDA support
cmake -DCMAKE_PREFIX_PATH="..." ..
make
make install