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‎.github/workflows/building.yml‎

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fail-fast: false
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matrix:
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os: [ubuntu-22.04, macos-14, windows-2022, ubuntu-22.04-arm]
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python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
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torch-version: [2.8.0]
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cuda-version: ['cpu', 'cu118', 'cu124', 'cu126', 'cu128', 'cu129']
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python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
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torch-version: [2.9.0, 2.10.0]
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cuda-version: ['cpu', 'cu126', 'cu128', 'cu130']
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exclude:
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- torch-version: 2.6.0
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cuda-version: 'cu128'
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- torch-version: 2.6.0
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cuda-version: 'cu129'
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- torch-version: 2.7.0
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cuda-version: 'cu124'
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- torch-version: 2.7.0
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cuda-version: 'cu129'
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- torch-version: 2.8.0
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cuda-version: 'cu118'
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- torch-version: 2.8.0
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cuda-version: 'cu124'
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- os: macos-14
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cuda-version: 'cu118'
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- os: macos-14
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cuda-version: 'cu124'
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- os: windows-2022
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cuda-version: 'cu130'
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- os: macos-14
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cuda-version: 'cu126'
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- os: macos-14
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cuda-version: 'cu128'
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- os: macos-14
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cuda-version: 'cu129'
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- os: ubuntu-22.04-arm
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cuda-version: 'cu118'
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- os: ubuntu-22.04-arm
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cuda-version: 'cu124'
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cuda-version: 'cu130'
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- os: ubuntu-22.04-arm
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cuda-version: 'cu126'
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- os: ubuntu-22.04-arm
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cuda-version: 'cu128'
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- os: ubuntu-22.04-arm
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cuda-version: 'cu129'
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cuda-version: 'cu130'
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steps:
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- uses: actions/checkout@v4

‎.github/workflows/cuda/cu130-Linux-env.sh‎

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#!/bin/bash
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CUDA_HOME=/usr/local/cuda-13.0
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LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
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PATH=${CUDA_HOME}/bin:${PATH}
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export FORCE_CUDA=1
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export TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;9.0;10.0;12.0+PTX"

‎.github/workflows/cuda/cu130-Linux.sh‎

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#!/bin/bash
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OS=ubuntu2204
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wget -nv https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-${OS}.pin
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sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600
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wget -nv https://developer.download.nvidia.com/compute/cuda/13.0.2/local_installers/cuda-repo-${OS}-13-0-local_13.0.2-580.95.05-1_amd64.deb
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sudo dpkg -i cuda-repo-${OS}-13-0-local_13.0.2-580.95.05-1_amd64.deb
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sudo cp /var/cuda-repo-${OS}-13-0-local/cuda-*-keyring.gpg /usr/share/keyrings/
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sudo apt-get -qq update
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sudo apt install cuda-nvcc-13-0 cuda-libraries-dev-13-0
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sudo apt clean
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rm -f *.deb

‎.github/workflows/testing.yml‎

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fail-fast: false
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matrix:
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os: [ubuntu-latest, windows-latest]
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python-version: [3.9]
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torch-version: [2.7.0, 2.8.0]
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python-version: ['3.10']
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torch-version: [2.9.0, 2.10.0]
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steps:
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- uses: actions/checkout@v4

‎README.md‎

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We provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
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#### PyTorch 2.8
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#### PyTorch 2.10
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To install the binaries for PyTorch 2.8.0, simply run
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To install the binaries for PyTorch 2.10, simply run
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```
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pip install torch-cluster -f https://data.pyg.org/whl/torch-2.8.0+${CUDA}.html
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pip install torch-cluster -f https://data.pyg.org/whl/torch-2.10.0+${CUDA}.html
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```
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where `${CUDA}` should be replaced by either `cpu`, `cu126`, `cu128`, or `cu129` depending on your PyTorch installation.
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where `${CUDA}` should be replaced by either `cpu`, `cu126`, `cu128`, or `cu130` depending on your PyTorch installation.
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| | `cpu` | `cu126` | `cu128` | `cu129` |
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| | `cpu` | `cu126` | `cu128` | `cu130` |
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|-------------|-------|---------|---------|---------|
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| **Linux** | ✅ | ✅ | ✅ | ✅ |
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| **Windows** | ✅ | ✅ | ✅ | ✅ |
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| **macOS** | ✅ | | | |
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#### PyTorch 2.7
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#### PyTorch 2.9
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To install the binaries for PyTorch 2.7.0, simply run
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To install the binaries for PyTorch 2.9, simply run
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```
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pip install torch-cluster -f https://data.pyg.org/whl/torch-2.7.0+${CUDA}.html
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pip install torch-cluster -f https://data.pyg.org/whl/torch-2.9.0+${CUDA}.html
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```
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where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu126`, or `cu128` depending on your PyTorch installation.
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where `${CUDA}` should be replaced by either `cpu`, `cu126`, `cu128`, or `cu130` depending on your PyTorch installation.
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| | `cpu` | `cu118` | `cu126` | `cu128` |
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| | `cpu` | `cu126` | `cu128` | `cu130` |
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|-------------|-------|---------|---------|---------|
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| **Linux** | ✅ | ✅ | ✅ | ✅ |
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| **Windows** | ✅ | ✅ | ✅ | ✅ |
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| **macOS** | ✅ | | | |
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#### PyTorch 2.6
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#### PyTorch 2.8
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To install the binaries for PyTorch 2.6.0, simply run
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To install the binaries for PyTorch 2.8, simply run
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```
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pip install torch-cluster -f https://data.pyg.org/whl/torch-2.6.0+${CUDA}.html
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pip install torch-cluster -f https://data.pyg.org/whl/torch-2.8.0+${CUDA}.html
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```
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where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu124`, or `cu126` depending on your PyTorch installation.
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where `${CUDA}` should be replaced by either `cpu`, `cu126`, `cu128`, or `cu129` depending on your PyTorch installation.
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| | `cpu` | `cu118` | `cu124` | `cu126` |
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| | `cpu` | `cu126` | `cu128` | `cu129` |
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|-------------|-------|---------|---------|---------|
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| **Linux** | ✅ | ✅ | ✅ | ✅ |
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| **Windows** | ✅ | ✅ | ✅ | ✅ |
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| **macOS** | ✅ | | | |
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**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).
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**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, PyTorch 2.5.0/2.5.1, PyTorch 2.6.0, and PyTorch 2.7.0/2.7.1 (following the same procedure).
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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.
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You can look up the latest supported version number [here](https://data.pyg.org/whl).
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‎setup.py‎

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nvcc_flags = os.getenv('NVCC_FLAGS', '')
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nvcc_flags = [] if nvcc_flags == '' else nvcc_flags.split(' ')
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nvcc_flags += ['-O2']
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nvcc_flags += ['-DTORCH_INDUCTOR_CPP_WRAPPER']
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extra_compile_args['nvcc'] = nvcc_flags
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if torch.version.hip:

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