pytorch 1.1.0 update

.travis.yml

@@ -16,8 +16,8 @@ before_install:
   - export CC="gcc-4.9"
   - export CXX="g++-4.9"
 install:
-  - if [[ $TRAVIS_PYTHON_VERSION == 3.5 ]]; then pip install https://download.pytorch.org/whl/cpu/torch-1.0.0-cp35-cp35m-linux_x86_64.whl; fi
-  - if [[ $TRAVIS_PYTHON_VERSION == 3.6 ]]; then pip install https://download.pytorch.org/whl/cpu/torch-1.0.0-cp36-cp36m-linux_x86_64.whl; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == 3.5 ]]; then pip install https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-linux_x86_64.whl; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == 3.6 ]]; then pip install https://download.pytorch.org/whl/cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl; fi
   - pip install pycodestyle
   - pip install flake8
   - pip install codecov

README.md

@@ -26,11 +26,11 @@ All included operations work on varying data types and are implemented both for
 
 ## Installation
 
-Ensure that at least PyTorch 1.0.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:
+Ensure that at least PyTorch 1.1.0 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__)"
->>> 1.0.0
+>>> 1.1.0
 
 $ echo $PATH
 >>> /usr/local/cuda/bin:...

cpu/graclus.cpp

@@ -49,7 +49,7 @@ at::Tensor weighted_graclus(at::Tensor row, at::Tensor col, at::Tensor weight,
   auto cluster = at::full(num_nodes, -1, row.options());
   auto cluster_data = cluster.data<int64_t>();
 
-  AT_DISPATCH_ALL_TYPES(weight.type(), "weighted_graclus", [&] {
+  AT_DISPATCH_ALL_TYPES(weight.scalar_type(), "weighted_graclus", [&] {
     auto weight_data = weight.data<scalar_t>();
 
     for (int64_t i = 0; i < num_nodes; i++) {

cuda/fps_kernel.cu

@@ -189,7 +189,7 @@ at::Tensor fps_cuda(at::Tensor x, at::Tensor batch, float ratio, bool random) {
              cudaMemcpyDeviceToHost);
   auto out = at::empty(k_sum[0], k.options());
 
-  AT_DISPATCH_FLOATING_TYPES(x.type(), "fps_kernel", [&] {
+  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "fps_kernel", [&] {
     FPS_KERNEL(x.size(1), x.data<scalar_t>(), cum_deg.data<int64_t>(),
                cum_k.data<int64_t>(), start.data<int64_t>(),
                dist.data<scalar_t>(), tmp_dist.data<scalar_t>(),

cuda/grid_kernel.cu

@@ -29,7 +29,7 @@ at::Tensor grid_cuda(at::Tensor pos, at::Tensor size, at::Tensor start,
   cudaSetDevice(pos.get_device());
   auto cluster = at::empty(pos.size(0), pos.options().dtype(at::kLong));
 
-  AT_DISPATCH_ALL_TYPES(pos.type(), "grid_kernel", [&] {
+  AT_DISPATCH_ALL_TYPES(pos.scalar_type(), "grid_kernel", [&] {
     grid_kernel<scalar_t><<<BLOCKS(cluster.numel()), THREADS>>>(
         cluster.data<int64_t>(),
         at::cuda::detail::getTensorInfo<scalar_t, int64_t>(pos),

cuda/knn_kernel.cu

@@ -67,7 +67,7 @@ at::Tensor knn_cuda(at::Tensor x, at::Tensor y, size_t k, at::Tensor batch_x,
   auto row = at::empty(y.size(0) * k, batch_y.options());
   auto col = at::full(y.size(0) * k, -1, batch_y.options());
 
-  AT_DISPATCH_FLOATING_TYPES(x.type(), "knn_kernel", [&] {
+  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "knn_kernel", [&] {
     knn_kernel<scalar_t><<<batch_size, THREADS>>>(
         x.data<scalar_t>(), y.data<scalar_t>(), batch_x.data<int64_t>(),
         batch_y.data<int64_t>(), dist.data<scalar_t>(), row.data<int64_t>(),

cuda/nearest_kernel.cu

@@ -71,7 +71,7 @@ at::Tensor nearest_cuda(at::Tensor x, at::Tensor y, at::Tensor batch_x,
 
   auto out = at::empty_like(batch_x);
 
-  AT_DISPATCH_FLOATING_TYPES(x.type(), "nearest_kernel", [&] {
+  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "nearest_kernel", [&] {
     nearest_kernel<scalar_t><<<x.size(0), THREADS>>>(
         x.data<scalar_t>(), y.data<scalar_t>(), batch_x.data<int64_t>(),
         batch_y.data<int64_t>(), out.data<int64_t>(), x.size(1));

cuda/proposal.cuh

@@ -77,7 +77,7 @@ __global__ void propose_kernel(int64_t *__restrict__ cluster, int64_t *proposal,
 
 void propose(at::Tensor cluster, at::Tensor proposal, at::Tensor row,
              at::Tensor col, at::Tensor weight) {
-  AT_DISPATCH_ALL_TYPES(weight.type(), "propose_kernel", [&] {
+  AT_DISPATCH_ALL_TYPES(weight.scalar_type(), "propose_kernel", [&] {
     propose_kernel<scalar_t><<<BLOCKS(cluster.numel()), THREADS>>>(
         cluster.data<int64_t>(), proposal.data<int64_t>(), row.data<int64_t>(),
         col.data<int64_t>(), weight.data<scalar_t>(), cluster.numel());

cuda/radius_kernel.cu

@@ -62,7 +62,7 @@ at::Tensor radius_cuda(at::Tensor x, at::Tensor y, float radius,
   auto row = at::full(y.size(0) * max_num_neighbors, -1, batch_y.options());
   auto col = at::full(y.size(0) * max_num_neighbors, -1, batch_y.options());
 
-  AT_DISPATCH_FLOATING_TYPES(x.type(), "radius_kernel", [&] {
+  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "radius_kernel", [&] {
     radius_kernel<scalar_t><<<batch_size, THREADS>>>(
         x.data<scalar_t>(), y.data<scalar_t>(), batch_x.data<int64_t>(),
         batch_y.data<int64_t>(), row.data<int64_t>(), col.data<int64_t>(),

cuda/response.cuh

@@ -82,7 +82,7 @@ __global__ void respond_kernel(int64_t *__restrict__ cluster, int64_t *proposal,
 
 void respond(at::Tensor cluster, at::Tensor proposal, at::Tensor row,
              at::Tensor col, at::Tensor weight) {
-  AT_DISPATCH_ALL_TYPES(weight.type(), "respond_kernel", [&] {
+  AT_DISPATCH_ALL_TYPES(weight.scalar_type(), "respond_kernel", [&] {
     respond_kernel<scalar_t><<<BLOCKS(cluster.numel()), THREADS>>>(
         cluster.data<int64_t>(), proposal.data<int64_t>(), row.data<int64_t>(),
         col.data<int64_t>(), weight.data<scalar_t>(), cluster.numel());

setup.py

@@ -27,7 +27,7 @@ if CUDA_HOME is not None:
                       ['cuda/rw.cpp', 'cuda/rw_kernel.cu']),
     ]
 
-__version__ = '1.2.4'
+__version__ = '1.3.0'
 url = 'https://github.com/rusty1s/pytorch_cluster'
 
 install_requires = ['scipy']

test/utils.py

@@ -3,6 +3,7 @@ from torch.testing import get_all_dtypes
 
 dtypes = get_all_dtypes()
 dtypes.remove(torch.half)
+dtypes.remove(torch.bool)
 
 grad_dtypes = [torch.float, torch.double]
 

torch_cluster/__init__.py

@@ -6,7 +6,7 @@ from .knn import knn, knn_graph
 from .radius import radius, radius_graph
 from .rw import random_walk
 
-__version__ = '1.2.4'
+__version__ = '1.3.0'
 
 __all__ = [
     'graclus_cluster',