PyTorch 2.0 wheels (#169)

* update

* update

* update

* update

.github/workflows/building-conda.yml

@@ -13,27 +13,18 @@ jobs:
         # We have trouble building for Windows - drop for now.
         os: [ubuntu-18.04, macos-10.15]  # windows-2019
         python-version: ['3.7', '3.8', '3.9', '3.10']
-        torch-version: [1.13.0]  # [1.12.0, 1.13.0]
-        cuda-version: ['cpu', 'cu102', 'cu113', 'cu116', 'cu117']
+        torch-version: [1.13.0, 2.0.0]
+        # We have trouble building for `cu116` due to PyTorch 1.13.0 bugs
+        cuda-version: ['cpu', 'cu117', 'cu118']
         exclude:
-          - torch-version: 1.12.0
-            cuda-version: 'cu117'
-          - torch-version: 1.13.0
-            cuda-version: 'cu102'
+          - torch-version: 2.0.0
+            python-version: '3.7'
           - torch-version: 1.13.0
-            cuda-version: 'cu113'
-          - os: macos-10.15
-            cuda-version: 'cu102'
-          - os: macos-10.15
-            cuda-version: 'cu113'
-          - os: macos-10.15
-            cuda-version: 'cu116'
+            cuda-version: 'cu118'
           - os: macos-10.15
             cuda-version: 'cu117'
-          - os: windows-2019
-            cuda-version: 'cu102'
-          - os: windows-2019  # Complains about CUDA mismatch.
-            python-version: '3.7'
+          - os: macos-10.15
+            cuda-version: 'cu118'
 
     steps:
       - uses: actions/checkout@v2

.github/workflows/building.yml

@@ -11,26 +11,24 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-18.04, macos-10.15, windows-2019]
-        python-version: ['3.7', '3.8', '3.9', '3.10']
-        torch-version: [1.12.0, 1.13.0]
-        cuda-version: ['cpu', 'cu102', 'cu113', 'cu116', 'cu117']
+        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
+        torch-version: [1.13.0, 2.0.0]
+        cuda-version: ['cpu', 'cu116', 'cu117', 'cu118']
         exclude:
-          - torch-version: 1.12.0
-            cuda-version: 'cu117'
           - torch-version: 1.13.0
-            cuda-version: 'cu102'
+            python-version: '3.11'
+          - torch-version: 2.0.0
+            python-version: '3.7'
           - torch-version: 1.13.0
-            cuda-version: 'cu113'
-          - os: macos-10.15
-            cuda-version: 'cu102'
-          - os: macos-10.15
-            cuda-version: 'cu113'
+            cuda-version: 'cu118'
+          - torch-version: 2.0.0
+            cuda-version: 'cu116'
           - os: macos-10.15
             cuda-version: 'cu116'
           - os: macos-10.15
             cuda-version: 'cu117'
-          - os: windows-2019
-            cuda-version: 'cu102'
+          - os: macos-10.15
+            cuda-version: 'cu118'
 
     steps:
       - uses: actions/checkout@v2

.github/workflows/cuda/cu118-Linux-env.sh

+#!/bin/bash
+
+CUDA_HOME=/usr/local/cuda-11.8
+LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
+PATH=${CUDA_HOME}/bin:${PATH}
+
+export FORCE_CUDA=1
+export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"

.github/workflows/cuda/cu118-Linux.sh

+#!/bin/bash
+
+OS=ubuntu1804
+
+wget -nv https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-${OS}.pin
+sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600
+wget -nv https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-${OS}-11-8-local_11.8.0-520.61.05-1_amd64.deb
+sudo dpkg -i cuda-repo-${OS}-11-8-local_11.8.0-520.61.05-1_amd64.deb
+sudo cp /var/cuda-repo-${OS}-11-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
+
+sudo apt-get -qq update
+sudo apt install cuda-nvcc-11-8 cuda-libraries-dev-11-8
+sudo apt clean
+
+rm -f https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-${OS}-11-8-local_11.8.0-520.61.05-1_amd64.deb

.github/workflows/cuda/cu118-Windows-env.sh

+#!/bin/bash
+
+CUDA_HOME=/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.8
+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/cu118-Windows.sh

+#!/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=11.8
+export CUDA_URL=https://developer.download.nvidia.com/compute/cuda/${CUDA_SHORT}.0/local_installers
+export CUDA_FILE=cuda_${CUDA_SHORT}.0_522.06_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/linting.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.9
+          python-version: 3.8
 
       - name: Install dependencies
         run: |
@@ -26,30 +26,3 @@ jobs:
       - name: Run linting
         run: |
           flake8 .
-
-  pyroma:
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        torch-version: [1.13.0]
-
-    steps:
-      - uses: actions/checkout@v2
-
-      - name: Set up Python
-        uses: actions/setup-python@v2
-        with:
-          python-version: 3.9
-
-      - name: Install PyTorch ${{ matrix.torch-version }}
-        run: |
-          pip install torch==${{ matrix.torch-version }} --extra-index-url https://download.pytorch.org/whl/cpu
-
-      - name: Install dependencies
-        run: |
-          pip install pyroma
-
-      - name: Check package metadata
-        run: |
-          pyroma --min=10 .

.github/workflows/testing.yml

@@ -15,8 +15,8 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: [3.7]
-        torch-version: [1.12.0, 1.13.0]
+        python-version: [3.8]
+        torch-version: [1.13.0, 2.0.0]
 
     steps:
       - uses: actions/checkout@v2

CMakeLists.txt

 cmake_minimum_required(VERSION 3.0)
 project(torchcluster)
 set(CMAKE_CXX_STANDARD 14)
-set(TORCHCLUSTER_VERSION 1.6.0)
+set(TORCHCLUSTER_VERSION 1.6.1)
 
 option(WITH_CUDA "Enable CUDA support" OFF)
 option(WITH_PYTHON "Link to Python when building" ON)

README.md

@@ -43,39 +43,39 @@ conda install pytorch-cluster -c pyg
 
 We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
 
-#### PyTorch 1.13
+#### PyTorch 2.0
 
-To install the binaries for PyTorch 1.13.0, simply run
+To install the binaries for PyTorch 2.0.0, simply run
 
 ```
-pip install torch-spline-conv -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html
+pip install torch-spline-conv -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
 ```
 
-where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation.
+where `${CUDA}` should be replaced by either `cpu`, `cu117`, or `cu118` depending on your PyTorch installation.
 
-|             | `cpu` | `cu116` | `cu117` |
+|             | `cpu` | `cu117` | `cu118` |
 |-------------|-------|---------|---------|
 | **Linux**   | ✅    | ✅      | ✅      |
 | **Windows** | ✅    | ✅      | ✅      |
 | **macOS**   | ✅    |         |         |
 
-#### PyTorch 1.12
+#### PyTorch 1.13
 
-To install the binaries for PyTorch 1.12.0, simply run
+To install the binaries for PyTorch 1.13.0, simply run
 
 ```
-pip install torch-spline-conv -f https://data.pyg.org/whl/torch-1.12.0+${CUDA}.html
+pip install torch-spline-conv -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html
 ```
 
-where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu113`, or `cu116` depending on your PyTorch installation.
+where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation.
 
-|             | `cpu` | `cu102` | `cu113` | `cu116` |
-|-------------|-------|---------|---------|---------|
-| **Linux**   | ✅    | ✅      | ✅      | ✅      |
-| **Windows** | ✅    |         | ✅      | ✅      |
-| **macOS**   | ✅    |         |         |         |
+|             | `cpu` | `cu116` | `cu117` |
+|-------------|-------|---------|---------|
+| **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 and PyTorch 1.11.0 (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 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 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).
 

conda/pytorch-cluster/README.md

 ```
-./build_conda.sh 3.9 1.13.0 cu116  # python, pytorch and cuda version
+./build_conda.sh 3.9 2.0.0 cu117  # python, pytorch and cuda version
 ```

conda/pytorch-cluster/build_conda.sh

@@ -10,6 +10,9 @@ if [ "${CUDA_VERSION}" = "cpu" ]; then
   export CONDA_CUDATOOLKIT_CONSTRAINT="cpuonly  # [not osx]"
 else
   case $CUDA_VERSION in
+    cu118)
+      export CONDA_CUDATOOLKIT_CONSTRAINT="pytorch-cuda==11.8.*"
+      ;;
     cu117)
       export CONDA_CUDATOOLKIT_CONSTRAINT="pytorch-cuda==11.7.*"
       ;;

conda/pytorch-cluster/meta.yaml

 package:
   name: pytorch-cluster
-  version: 1.6.0
+  version: 1.6.1
 
 source:
   path: ../..

setup.py

@@ -11,7 +11,7 @@ from torch.__config__ import parallel_info
 from torch.utils.cpp_extension import (CUDA_HOME, BuildExtension, CppExtension,
                                        CUDAExtension)
 
-__version__ = '1.6.0'
+__version__ = '1.6.1'
 URL = 'https://github.com/rusty1s/pytorch_cluster'
 
 WITH_CUDA = False

test/test_nearest.py

@@ -38,33 +38,27 @@ def test_nearest(dtype, device):
     batch_x = tensor([0, 0, 0, 0, 1, 1, 1, 1], torch.long, device)
     batch_y = tensor([0, 0, 0, 0], torch.long, device)
     with pytest.raises(ValueError):
-        out = nearest(x, y, batch_x, batch_y)
+        nearest(x, y, batch_x, batch_y)
 
     # Invalid input: instance 1 only in batch_x (implicitly as batch_y=None)
     with pytest.raises(ValueError):
-        out = nearest(x, y, batch_x, batch_y=None)
-
-    # Valid input: instance 1 only in batch_y
-    batch_x = tensor([0, 0, 0, 0, 0, 0, 0, 0], torch.long, device)
-    batch_y = tensor([0, 0, 1, 1], torch.long, device)
-    out = nearest(x, y, batch_x, batch_y)
-    assert out.tolist() == [0, 0, 1, 1, 0, 0, 1, 1]
+        nearest(x, y, batch_x, batch_y=None)
 
     # Invalid input: instance 2 only in batch_x
     # (i.e.instance in the middle missing)
     batch_x = tensor([0, 0, 1, 1, 2, 2, 3, 3], torch.long, device)
     batch_y = tensor([0, 1, 3, 3], torch.long, device)
     with pytest.raises(ValueError):
-        out = nearest(x, y, batch_x, batch_y)
+        nearest(x, y, batch_x, batch_y)
 
     # Invalid input: batch_x unsorted
     batch_x = tensor([0, 0, 1, 0, 0, 0, 0], torch.long, device)
     batch_y = tensor([0, 0, 1, 1], torch.long, device)
     with pytest.raises(ValueError):
-        out = nearest(x, y, batch_x, batch_y)
+        nearest(x, y, batch_x, batch_y)
 
     # Invalid input: batch_y unsorted
     batch_x = tensor([0, 0, 0, 0, 1, 1, 1, 1], torch.long, device)
     batch_y = tensor([0, 0, 1, 0], torch.long, device)
     with pytest.raises(ValueError):
-        out = nearest(x, y, batch_x, batch_y)
+        nearest(x, y, batch_x, batch_y)

torch_cluster/__init__.py

@@ -3,7 +3,7 @@ import os.path as osp
 
 import torch
 
-__version__ = '1.6.0'
+__version__ = '1.6.1'
 
 for library in [
         '_version', '_grid', '_graclus', '_fps', '_rw', '_sampler', '_nearest',

torch_cluster/nearest.py

 from typing import Optional
 
-import torch
 import scipy.cluster
+import torch
 
 
-def nearest(x: torch.Tensor, y: torch.Tensor,
-            batch_x: Optional[torch.Tensor] = None,
-            batch_y: Optional[torch.Tensor] = None) -> torch.Tensor:
+def nearest(
+    x: torch.Tensor,
+    y: torch.Tensor,
+    batch_x: Optional[torch.Tensor] = None,
+    batch_y: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
     r"""Clusters points in :obj:`x` together which are nearest to a given query
     point in :obj:`y`.
 
@@ -43,9 +46,9 @@ def nearest(x: torch.Tensor, y: torch.Tensor,
     assert x.size(1) == y.size(1)
 
     if batch_x is not None and (batch_x[1:] - batch_x[:-1] < 0).any():
-        raise ValueError("batch_x is not sorted")
+        raise ValueError("'batch_x' is not sorted")
     if batch_y is not None and (batch_y[1:] - batch_y[:-1] < 0).any():
-        raise ValueError("batch_y is not sorted")
+        raise ValueError("'batch_y' is not sorted")
 
     if x.is_cuda:
         if batch_x is not None:
@@ -72,30 +75,32 @@ def nearest(x: torch.Tensor, y: torch.Tensor,
         else:
             ptr_y = torch.tensor([0, y.size(0)], device=y.device)
 
-        # if an instance in batch_x is non-empty, it must be
-        # non-empty in batch_y as well
-        instance_nonempty_x = (ptr_x[:-1] != ptr_x[1:])
-        instance_nonempty_y = (ptr_y[:len(ptr_x)-1] != ptr_y[1:len(ptr_x)])
-        if (len(ptr_x) > len(ptr_y) or
-                (instance_nonempty_x & ~instance_nonempty_y).any()):
-            raise ValueError("Some batch index occurs in batch_x "
-                             "that does not occur in batch_y")
+        # If an instance in `batch_x` is non-empty, it must be non-empty in
+        # `batch_y `as well:
+        nonempty_ptr_x = (ptr_x[1:] - ptr_x[:-1]) > 0
+        nonempty_ptr_y = (ptr_y[1:] - ptr_y[:-1]) > 0
+        if not torch.equal(nonempty_ptr_x, nonempty_ptr_y):
+            raise ValueError("Some batch indices occur in 'batch_x' "
+                             "that do not occur in 'batch_y'")
 
         return torch.ops.torch_cluster.nearest(x, y, ptr_x, ptr_y)
+
     else:
-        if (batch_x is None) != (batch_y is None):
-            raise ValueError("Either both or none of batch_x, batch_y "
-                             "may be None")
+
+        if batch_x is None and batch_y is not None:
+            batch_x = x.new_zeros(x.size(0), dtype=torch.long)
+        if batch_y is None and batch_x is not None:
+            batch_y = y.new_zeros(y.size(0), dtype=torch.long)
 
         # Translate and rescale x and y to [0, 1].
         if batch_x is not None and batch_y is not None:
-            # if an instance in batch_x is non-empty, it must be
-            # non-empty in batch_y as well
-            if not torch.isin(torch.unique_consecutive(batch_x),
-                              torch.unique_consecutive(batch_y),
-                              assume_unique=True).all():
-                raise ValueError("Some batch index occurs in batch_x "
-                                 "that does not occur in batch_y")
+            # If an instance in `batch_x` is non-empty, it must be non-empty in
+            # `batch_y `as well:
+            unique_batch_x = batch_x.unique_consecutive()
+            unique_batch_y = batch_y.unique_consecutive()
+            if not torch.equal(unique_batch_x, unique_batch_y):
+                raise ValueError("Some batch indices occur in 'batch_x' "
+                                 "that do not occur in 'batch_y'")
 
             assert x.dim() == 2 and batch_x.dim() == 1
             assert y.dim() == 2 and batch_y.dim() == 1