PyTorch 2.6 support (#397)

update

Author: rusty1s

.github/workflows/building-conda.yml

@@ -11,20 +11,22 @@ jobs:
  fail-fast: false
  matrix:
  os: [ubuntu-20.04, macos-14, windows-2019]
- python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
- torch-version: [2.5.0] # [2.3.0, 2.4.0, 2.5.0]
- cuda-version: ['cpu', 'cu118', 'cu121', 'cu124']
+ python-version: ['3.9', '3.10', '3.11', '3.12']
+ torch-version: [2.6.0] # [2.5.0]
+ cuda-version: ['cpu', 'cu118', 'cu121', 'cu124', 'cu126']
  exclude:
  - torch-version: 2.5.0
- python-version: '3.8'
- - torch-version: 2.3.0
- cuda-version: 'cu124'
+ cuda-version: 'cu126'
+ - torch-version: 2.6.0
+ cuda-version: 'cu121'
  - os: macos-14
  cuda-version: 'cu118'
  - os: macos-14
  cuda-version: 'cu121'
  - os: macos-14
  cuda-version: 'cu124'
+ - os: macos-14
+ cuda-version: 'cu126'
 
  steps:
  - uses: actions/checkout@v2

.github/workflows/building.yml

@@ -0,0 +1,8 @@
+#!/bin/bash
+
+CUDA_HOME=/usr/local/cuda-12.6
+LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
+PATH=${CUDA_HOME}/bin:${PATH}
+
+export FORCE_CUDA=1
+export TORCH_CUDA_ARCH_LIST="5.0+PTX;6.0;7.0;7.5;8.0;8.6;9.0"

.github/workflows/cuda/cu126-Linux-env.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+OS=ubuntu2004
+
+wget -nv https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-${OS}.pin
+sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600
+wget -nv https://developer.download.nvidia.com/compute/cuda/12.6.0/local_installers/cuda-repo-${OS}-12-6-local_12.6.0-560.28.03-1_amd64.deb
+sudo dpkg -i cuda-repo-${OS}-12-6-local_12.6.0-560.28.03-1_amd64.deb
+sudo cp /var/cuda-repo-${OS}-12-6-local/cuda-*-keyring.gpg /usr/share/keyrings/
+
+sudo apt-get -qq update
+sudo apt install cuda-nvcc-12-6 cuda-libraries-dev-12-6
+sudo apt clean
+
+rm -f https://developer.download.nvidia.com/compute/cuda/12.6.0/local_installers/cuda-repo-${OS}-12-6-local_12.6.0-560.28.03-1_amd64.deb

.github/workflows/cuda/cu126-Linux.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+
+CUDA_HOME=/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v12.6
+PATH=${CUDA_HOME}/bin:$PATH
+PATH=/c/Program\ Files\ \(x86\)/Microsoft\ Visual\ Studio/2017/BuildTools/MSBuild/15.0/Bin:$PATH
+
+export FORCE_CUDA=1
+export TORCH_CUDA_ARCH_LIST="6.0+PTX"

.github/workflows/cuda/cu126-Windows-env.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# Install NVIDIA drivers, see:
+# https://github.com/pytorch/vision/blob/master/packaging/windows/internal/cuda_install.bat#L99-L102
+curl -k -L "https://drive.google.com/u/0/uc?id=1injUyo3lnarMgWyRcXqKg4UGnN0ysmuq&export=download" --output "/tmp/gpu_driver_dlls.zip"
+7z x "/tmp/gpu_driver_dlls.zip" -o"/c/Windows/System32"
+
+export CUDA_SHORT=12.6
+export CUDA_URL=https://developer.download.nvidia.com/compute/cuda/${CUDA_SHORT}.0/local_installers
+export CUDA_FILE=cuda_${CUDA_SHORT}.0_560.76_windows.exe
+
+# Install CUDA:
+curl -k -L "${CUDA_URL}/${CUDA_FILE}" --output "${CUDA_FILE}"
+echo ""
+echo "Installing from ${CUDA_FILE}..."
+PowerShell -Command "Start-Process -FilePath \"${CUDA_FILE}\" -ArgumentList \"-s nvcc_${CUDA_SHORT} cuobjdump_${CUDA_SHORT} nvprune_${CUDA_SHORT} cupti_${CUDA_SHORT} cublas_dev_${CUDA_SHORT} cudart_${CUDA_SHORT} cufft_dev_${CUDA_SHORT} curand_dev_${CUDA_SHORT} cusolver_dev_${CUDA_SHORT} cusparse_dev_${CUDA_SHORT} thrust_${CUDA_SHORT} npp_dev_${CUDA_SHORT} nvrtc_dev_${CUDA_SHORT} nvml_dev_${CUDA_SHORT}\" -Wait -NoNewWindow"
+echo "Done!"
+rm -f "${CUDA_FILE}"

.github/workflows/cuda/cu126-Windows.sh

@@ -16,7 +16,7 @@ jobs:
  matrix:
  os: [ubuntu-latest, windows-2019]
  python-version: [3.9]
- torch-version: [2.4.0, 2.5.0]
+ torch-version: [2.5.0, 2.6.0]
 
  steps:
  - uses: actions/checkout@v2

.github/workflows/testing.yml

@@ -32,39 +32,33 @@ Note that only `value` comes with autograd support, as `index` is discrete and t
 
 ### Anaconda
 
-**Update:** You can now install `pytorch-sparse` via [Anaconda](https://anaconda.org/pyg/pytorch-sparse) for all major OS/PyTorch/CUDA combinations 🤗
-Given that you have [`pytorch >= 1.8.0` installed](https://pytorch.org/get-started/locally/), simply run
-
-```
-conda install pytorch-sparse -c pyg
-```
-
 ### Binaries
 
-We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
+We provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
 
-#### PyTorch 2.5
+#### PyTorch 2.6
 
-To install the binaries for PyTorch 2.5.0, simply run
+To install the binaries for PyTorch 2.6.0, simply run
 
 ```
-pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.5.0+${CUDA}.html
+pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.6.0+${CUDA}.html
 ```
 
-where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu121`, or `cu124` depending on your PyTorch installation.
+where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu124`, or `cu126` depending on your PyTorch installation.
 
-| | `cpu` | `cu118` | `cu121` | `cu124` |
+| | `cpu` | `cu118` | `cu124` | `cu126` |
 |-------------|-------|---------|---------|---------|
 | **Linux** | ✅ | ✅ | ✅ | ✅ |
 | **Windows** | ✅ | ✅ | ✅ | ✅ |
 | **macOS** | ✅ | | | |
 
-#### PyTorch 2.4
 
-To install the binaries for PyTorch 2.4.0, simply run
+#### PyTorch 2.5
+
+To install the binaries for PyTorch 2.5.0/2.5.1, simply run
 
 ```
-pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.4.0+${CUDA}.html
+pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.5.0+${CUDA}.html
 ```
 
 where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu121`, or `cu124` depending on your PyTorch installation.
@@ -75,7 +69,7 @@ where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu121`, or `cu124`
 | **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, and PyTorch 2.3.0/2.3.1 (following the same procedure).
+**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, and PyTorch 2.4.0/2.4.1 (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](https://data.pyg.org/whl).