pytorch 1.3 support

.travis.yml

@@ -17,7 +17,7 @@ before_install:
   - export CXX="g++-4.9"
 install:
   - pip install numpy
-  - pip install -q torch -f https://download.pytorch.org/whl/nightly/cpu/torch.html
+  - pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
   - pip install pycodestyle
   - pip install flake8
   - pip install codecov

cpu/compat.h

+#ifdef VERSION_GE_1_3
+#define DATA_PTR data_ptr
+#else
+#define DATA_PTR data
+#endif

cpu/spspmm.cpp

 #include <torch/extension.h>
 
+#include "compat.h"
+
 at::Tensor degree(at::Tensor row, int64_t num_nodes) {
   auto zero = at::zeros(num_nodes, row.options());
   auto one = at::ones(row.size(0), row.options());
@@ -18,23 +20,23 @@ at::Tensor spspmm_bw(at::Tensor index, at::Tensor indexA, at::Tensor valueA,
                      at::Tensor indexB, at::Tensor valueB, size_t rowA_max,
                      size_t rowB_max) {
 
-  int64_t *index_data = index.data<int64_t>();
+  int64_t *index_data = index.DATA_PTR<int64_t>();
   auto value = at::zeros(index.size(1), valueA.options());
 
   at::Tensor rowA, colA;
   std::tie(rowA, colA) = to_csr(indexA[0], indexA[1], rowA_max);
-  int64_t *rowA_data = rowA.data<int64_t>();
-  int64_t *colA_data = colA.data<int64_t>();
+  int64_t *rowA_data = rowA.DATA_PTR<int64_t>();
+  int64_t *colA_data = colA.DATA_PTR<int64_t>();
 
   at::Tensor rowB, colB;
   std::tie(rowB, colB) = to_csr(indexB[0], indexB[1], rowB_max);
-  int64_t *rowB_data = rowB.data<int64_t>();
-  int64_t *colB_data = colB.data<int64_t>();
+  int64_t *rowB_data = rowB.DATA_PTR<int64_t>();
+  int64_t *colB_data = colB.DATA_PTR<int64_t>();
 
   AT_DISPATCH_FLOATING_TYPES(valueA.scalar_type(), "spspmm_bw", [&] {
-    scalar_t *value_data = value.data<scalar_t>();
-    scalar_t *valueA_data = valueA.data<scalar_t>();
-    scalar_t *valueB_data = valueB.data<scalar_t>();
+    scalar_t *value_data = value.DATA_PTR<scalar_t>();
+    scalar_t *valueA_data = valueA.DATA_PTR<scalar_t>();
+    scalar_t *valueB_data = valueB.DATA_PTR<scalar_t>();
 
     for (int64_t e = 0; e < value.size(0); e++) {
       int64_t i = index_data[e], j = index_data[value.size(0) + e];

cuda/compat.cuh

+#ifdef VERSION_GE_1_3
+#define DATA_PTR data_ptr
+#else
+#define DATA_PTR data
+#endif

cuda/spspmm_kernel.cu

 #include <ATen/ATen.h>
-
 #include <cusparse.h>
 
+#include "compat.cuh"
+
 #define THREADS 1024
 #define BLOCKS(N) (N + THREADS - 1) / THREADS
 
@@ -51,18 +52,18 @@ spspmm_cuda(at::Tensor indexA, at::Tensor valueA, at::Tensor indexB,
 
   // Convert A to CSR format.
   auto row_ptrA = at::empty(m + 1, indexA.options());
-  cusparseXcoo2csr(cusparse_handle, indexA[0].data<int>(), nnzA, k,
-                   row_ptrA.data<int>(), CUSPARSE_INDEX_BASE_ZERO);
+  cusparseXcoo2csr(cusparse_handle, indexA[0].DATA_PTR<int>(), nnzA, k,
+                   row_ptrA.DATA_PTR<int>(), CUSPARSE_INDEX_BASE_ZERO);
   auto colA = indexA[1];
-  cudaMemcpy(row_ptrA.data<int>() + m, &nnzA, sizeof(int),
+  cudaMemcpy(row_ptrA.DATA_PTR<int>() + m, &nnzA, sizeof(int),
              cudaMemcpyHostToDevice);
 
   // Convert B to CSR format.
   auto row_ptrB = at::empty(k + 1, indexB.options());
-  cusparseXcoo2csr(cusparse_handle, indexB[0].data<int>(), nnzB, k,
-                   row_ptrB.data<int>(), CUSPARSE_INDEX_BASE_ZERO);
+  cusparseXcoo2csr(cusparse_handle, indexB[0].DATA_PTR<int>(), nnzB, k,
+                   row_ptrB.DATA_PTR<int>(), CUSPARSE_INDEX_BASE_ZERO);
   auto colB = indexB[1];
-  cudaMemcpy(row_ptrB.data<int>() + k, &nnzB, sizeof(int),
+  cudaMemcpy(row_ptrB.DATA_PTR<int>() + k, &nnzB, sizeof(int),
              cudaMemcpyHostToDevice);
 
   cusparseMatDescr_t descr = 0;
@@ -74,22 +75,23 @@ spspmm_cuda(at::Tensor indexA, at::Tensor valueA, at::Tensor indexB,
   auto row_ptrC = at::empty(m + 1, indexB.options());
   cusparseXcsrgemmNnz(cusparse_handle, CUSPARSE_OPERATION_NON_TRANSPOSE,
                       CUSPARSE_OPERATION_NON_TRANSPOSE, m, n, k, descr, nnzA,
-                      row_ptrA.data<int>(), colA.data<int>(), descr, nnzB,
-                      row_ptrB.data<int>(), colB.data<int>(), descr,
-                      row_ptrC.data<int>(), &nnzC);
+                      row_ptrA.DATA_PTR<int>(), colA.DATA_PTR<int>(), descr,
+                      nnzB, row_ptrB.DATA_PTR<int>(), colB.DATA_PTR<int>(),
+                      descr, row_ptrC.DATA_PTR<int>(), &nnzC);
   auto colC = at::empty(nnzC, indexA.options());
   auto valueC = at::empty(nnzC, valueA.options());
 
   CSRGEMM(valueC.scalar_type(), cusparse_handle,
           CUSPARSE_OPERATION_NON_TRANSPOSE, CUSPARSE_OPERATION_NON_TRANSPOSE, m,
-          n, k, descr, nnzA, valueA.data<scalar_t>(), row_ptrA.data<int>(),
-          colA.data<int>(), descr, nnzB, valueB.data<scalar_t>(),
-          row_ptrB.data<int>(), colB.data<int>(), descr,
-          valueC.data<scalar_t>(), row_ptrC.data<int>(), colC.data<int>());
+          n, k, descr, nnzA, valueA.DATA_PTR<scalar_t>(),
+          row_ptrA.DATA_PTR<int>(), colA.DATA_PTR<int>(), descr, nnzB,
+          valueB.DATA_PTR<scalar_t>(), row_ptrB.DATA_PTR<int>(),
+          colB.DATA_PTR<int>(), descr, valueC.DATA_PTR<scalar_t>(),
+          row_ptrC.DATA_PTR<int>(), colC.DATA_PTR<int>());
 
   auto rowC = at::empty(nnzC, indexA.options());
-  cusparseXcsr2coo(cusparse_handle, row_ptrC.data<int>(), nnzC, m,
-                   rowC.data<int>(), CUSPARSE_INDEX_BASE_ZERO);
+  cusparseXcsr2coo(cusparse_handle, row_ptrC.DATA_PTR<int>(), nnzC, m,
+                   rowC.DATA_PTR<int>(), CUSPARSE_INDEX_BASE_ZERO);
 
   auto indexC = at::stack({rowC, colC}, 0).toType(at::kLong);
 
@@ -154,9 +156,10 @@ at::Tensor spspmm_bw_cuda(at::Tensor index, at::Tensor indexA,
 
   AT_DISPATCH_FLOATING_TYPES(valueA.scalar_type(), "spspmm_bw", [&] {
     spspmm_bw_kernel<scalar_t><<<BLOCKS(value.numel()), THREADS>>>(
-        index.data<int64_t>(), value.data<scalar_t>(), rowA.data<int64_t>(),
-        colA.data<int64_t>(), valueA.data<scalar_t>(), rowB.data<int64_t>(),
-        colB.data<int64_t>(), valueB.data<scalar_t>(), value.numel());
+        index.DATA_PTR<int64_t>(), value.DATA_PTR<scalar_t>(),
+        rowA.DATA_PTR<int64_t>(), colA.DATA_PTR<int64_t>(),
+        valueA.DATA_PTR<scalar_t>(), rowB.DATA_PTR<int64_t>(),
+        colB.DATA_PTR<int64_t>(), valueB.DATA_PTR<scalar_t>(), value.numel());
   });
 
   return value;

cuda/unique_kernel.cu

 #include <ATen/ATen.h>
 
+#include "compat.cuh"
+
 #define THREADS 1024
 #define BLOCKS(N) (N + THREADS - 1) / THREADS
 
@@ -23,7 +25,7 @@ std::tuple<at::Tensor, at::Tensor> unique_cuda(at::Tensor src) {
   auto mask = at::zeros(src.numel(), src.options().dtype(at::kByte));
   AT_DISPATCH_ALL_TYPES(src.scalar_type(), "grid_cuda_kernel", [&] {
     unique_cuda_kernel<scalar_t><<<BLOCKS(src.numel()), THREADS>>>(
-        src.data<scalar_t>(), mask.data<uint8_t>(), src.numel());
+        src.DATA_PTR<scalar_t>(), mask.DATA_PTR<uint8_t>(), src.numel());
   });
 
   src = src.masked_select(mask);

setup.py

@@ -3,7 +3,17 @@ from setuptools import setup, find_packages
 import torch
 from torch.utils.cpp_extension import CppExtension, CUDAExtension, CUDA_HOME
 
-ext_modules = [CppExtension('torch_sparse.spspmm_cpu', ['cpu/spspmm.cpp'])]
+TORCH_MAJOR = int(torch.__version__.split('.')[0])
+TORCH_MINOR = int(torch.__version__.split('.')[1])
+
+extra_compile_args = []
+if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR > 2):
+    extra_compile_args += ['-DVERSION_GE_1_3']
+
+ext_modules = [
+    CppExtension('torch_sparse.spspmm_cpu', ['cpu/spspmm.cpp'],
+                 extra_compile_args=extra_compile_args)
+]
 cmdclass = {'build_ext': torch.utils.cpp_extension.BuildExtension}
 
 if CUDA_HOME is not None:
@@ -13,15 +23,16 @@ if CUDA_HOME is not None:
         extra_link_args = ['-lcusparse', '-l', 'cusparse']
 
     ext_modules += [
-        CUDAExtension(
-            'torch_sparse.spspmm_cuda',
-            ['cuda/spspmm.cpp', 'cuda/spspmm_kernel.cu'],
-            extra_link_args=extra_link_args),
+        CUDAExtension('torch_sparse.spspmm_cuda',
+                      ['cuda/spspmm.cpp', 'cuda/spspmm_kernel.cu'],
+                      extra_link_args=extra_link_args,
+                      extra_compile_args=extra_compile_args),
         CUDAExtension('torch_sparse.unique_cuda',
-                      ['cuda/unique.cpp', 'cuda/unique_kernel.cu']),
+                      ['cuda/unique.cpp', 'cuda/unique_kernel.cu'],
+                      extra_compile_args=extra_compile_args),
     ]
 
-__version__ = '0.4.0'
+__version__ = '0.4.1'
 url = 'https://github.com/rusty1s/pytorch_sparse'
 
 install_requires = ['scipy']

torch_sparse/__init__.py

@@ -5,7 +5,7 @@ from .eye import eye
 from .spmm import spmm
 from .spspmm import spspmm
 
-__version__ = '0.4.0'
+__version__ = '0.4.1'
 
 __all__ = [
     '__version__',