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PyTorch Extension Library of Optimized Scatter Operations

pip install torch-scatter

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Requires Python

>=3.8

Dependencies

    PyTorch Scatter

    PyPI Version Testing Status Linting Status Docs Status Code Coverage


    Documentation

    This package consists of a small extension library of highly optimized sparse update (scatter and segment) operations for the use in PyTorch, which are missing in the main package. Scatter and segment operations can be roughly described as reduce operations based on a given "group-index" tensor. Segment operations require the "group-index" tensor to be sorted, whereas scatter operations are not subject to these requirements.

    The package consists of the following operations with reduction types "sum"|"mean"|"min"|"max":

    In addition, we provide the following composite functions which make use of scatter_* operations under the hood: scatter_std, scatter_logsumexp, scatter_softmax and scatter_log_softmax.

    All included operations are broadcastable, work on varying data types, are implemented both for CPU and GPU with corresponding backward implementations, and are fully traceable.

    Installation

    Anaconda

    Update: You can now install pytorch-scatter via Anaconda for all major OS/PyTorch/CUDA combinations 🤗 Given that you have pytorch >= 1.8.0 installed, simply run

    conda install pytorch-scatter -c pyg
    

    Binaries

    We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see here.

    PyTorch 2.1

    To install the binaries for PyTorch 2.1.0, simply run

    pip install torch-scatter -f https://data.pyg.org/whl/torch-2.1.0+${CUDA}.html
    

    where ${CUDA} should be replaced by either cpu, cu118, or cu121 depending on your PyTorch installation.

    cpucu118cu121
    Linux
    Windows
    macOS

    PyTorch 2.0

    To install the binaries for PyTorch 2.0.0, simply run

    pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
    

    where ${CUDA} should be replaced by either cpu, cu117, or cu118 depending on your PyTorch installation.

    cpucu117cu118
    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 and PyTorch 1.13.0/1.13.1 (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.

    From source

    Ensure that at least PyTorch 1.4.0 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__)"
    >>> 1.4.0
    
    $ echo $PATH
    >>> /usr/local/cuda/bin:...
    
    $ echo $CPATH
    >>> /usr/local/cuda/include:...
    

    Then run:

    pip install torch-scatter
    

    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"
    

    Example

    import torch
    from torch_scatter import scatter_max
    
    src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
    index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
    
    out, argmax = scatter_max(src, index, dim=-1)
    
    print(out)
    tensor([[0, 0, 4, 3, 2, 0],
            [2, 4, 3, 0, 0, 0]])
    
    print(argmax)
    tensor([[5, 5, 3, 4, 0, 1]
            [1, 4, 3, 5, 5, 5]])
    

    Running tests

    pytest
    

    C++ API

    torch-scatter also offers a C++ API that contains C++ equivalent of python models. For this, we need to add TorchLib to the -DCMAKE_PREFIX_PATH (e.g., it may exists in {CONDA}/lib/python{X.X}/site-packages/torch if installed via conda):

    mkdir build
    cd build
    # Add -DWITH_CUDA=on support for CUDA support
    cmake -DCMAKE_PREFIX_PATH="..." ..
    make
    make install