This package consists of a small extension library of optimized sparse matrix operations with autograd support. This package currently consists of the following methods:
All included operations work on varying data types and are implemented both for CPU and GPU. To avoid the hazzle of creating torch.sparse_coo_tensor, this package defines operations on sparse tensors by simply passing index and value tensors as arguments (with same shapes as defined in PyTorch). Note that only value comes with autograd support, as index is discrete and therefore not differentiable.
We provide pip wheels for all major OS/PyTorch/CUDA combinations, see here.
To install the binaries for PyTorch 2.8.0, simply run
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.8.0+${CUDA}.html
where ${CUDA} should be replaced by either cpu, cu126, cu128, or cu129 depending on your PyTorch installation.
cpu cu126 cu128 cu129 Linux ✅ ✅ ✅ ✅ Windows ✅ ✅ ✅ ✅ macOS ✅
To install the binaries for PyTorch 2.7.0, simply run
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.7.0+${CUDA}.html
where ${CUDA} should be replaced by either cpu, cu118, cu126, or cu128 depending on your PyTorch installation.
cpu cu118 cu126 cu128 Linux ✅ ✅ ✅ ✅ Windows ✅ ✅ ✅ ✅ macOS ✅
To install the binaries for PyTorch 2.6.0, simply run
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.6.0+${CUDA}.html
where ${CUDA} should be replaced by either cpu, cu118, cu124, or cu126 depending on your PyTorch installation.
cpu cu118 cu124 cu126 Linux ✅ ✅ ✅ ✅ 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, PyTorch 1.12.0/1.12.1, PyTorch 1.13.0/1.13.1, PyTorch 2.0.0/2.0.1, PyTorch 2.1.0/2.1.1/2.1.2, PyTorch 2.2.0/2.2.1/2.2.2, PyTorch 2.3.0/2.3.1, PyTorch 2.4.0/2.4.1, and PyTorch 2.5.0/2.5.1 (following the same procedure). For older versions, you need to explicitly specify the latest supported version number or install via pip install --no-index in order to prevent a manual installation from source. You can look up the latest supported version number here.
Ensure that at least PyTorch 1.7.0 is installed and verify that cuda/bin and cuda/include are in your $PATH and $CPATH respectively, 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 build torch-sparse with METIS support, e.g. for partioning, please download and install the METIS library by following the instructions in the Install.txt file. Note that METIS needs to be installed with 64 bit IDXTYPEWIDTH by changing include/metis.h. Afterwards, set the environment variable WITH_METIS=1.
Then run:
pip install torch-scatter torch-sparse
When 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 via TORCH_CUDA_ARCH_LIST, e.g.:
export TORCH_CUDA_ARCH_LIST="6.0 6.1 7.2+PTX 7.5+PTX"
torch_sparse.coalesce(index, value, m, n, op="add") -> (torch.LongTensor, torch.Tensor)
Row-wise sorts index and removes duplicate entries. Duplicate entries are removed by scattering them together. For scattering, any operation of torch_scatter can be used.
"add")import torch
from torch_sparse import coalesce
index = 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]])
torch_sparse.transpose(index, value, m, n) -> (torch.LongTensor, torch.Tensor)
Transposes dimensions 0 and 1 of a sparse matrix.
False, will not coalesce the output. (default: True)import torch
from torch_sparse import transpose
index = 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]])
torch_sparse.spmm(index, value, m, n, matrix) -> torch.Tensor
Matrix product of a sparse matrix with a dense matrix.
import torch
from torch_sparse import spmm
index = 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]])
torch_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 be coalesced (use the coalesced attribute to force).
True, will coalesce both input sparse matrices. (default: False)import torch from torch_sparse import spspmm indexA = 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])
torch-sparse also offers a C++ API that contains C++ equivalent of python models. For this, we need to add TorchLib to the -DCMAKE_PREFIX_PATH (run import torch; print(torch.utils.cmake_prefix_path) to obtain it).
mkdir build
cd build
# Add -DWITH_CUDA=on support for CUDA support
cmake -DCMAKE_PREFIX_PATH="..." ..
make
make install
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