[BUILD] enable dgl sparse build in default (#5175)

* enable dgl sparse build in default

* disable sparse build on cugraph

* update

CMakeLists.txt

@@ -36,7 +36,7 @@ dgl_option(USE_HDFS "Build with HDFS support" OFF) # Set env HADOOP_HDFS_HOME if
 dgl_option(REBUILD_LIBXSMM "Clean LIBXSMM build cache at every build" OFF) # Set env HADOOP_HDFS_HOME if needed
 dgl_option(USE_EPOLL "Build with epoll for socket communicator" ON)
 dgl_option(TP_BUILD_LIBUV "Build libuv together with tensorpipe (only impacts Linux)" ON)
-dgl_option(BUILD_SPARSE "Build DGL sparse library" OFF)
+dgl_option(BUILD_SPARSE "Build DGL sparse library" ON)
 dgl_option(TORCH_PYTHON_INTERPS "Python interpreter used to build tensoradapter and DGL sparse library" python3)
 
 # Set debug compile option for gdb, only happens when -DCMAKE_BUILD_TYPE=DEBUG

benchmarks/scripts/build_dgl_asv.sh

@@ -8,7 +8,8 @@ DEVICE=${DGL_BENCH_DEVICE:-cpu}
 pip install -r /asv/torch_gpu_pip.txt
 
 # build
-CMAKE_VARS="-DUSE_OPENMP=ON -DBUILD_TORCH=ON -DBUILD_SPARSE=ON -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda"
+# 'CUDA_TOOLKIT_ROOT_DIR' is always required for sparse build as torch1.13.1+cu116 is installed.
+CMAKE_VARS="-DUSE_OPENMP=ON -DBUILD_TORCH=ON -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda"
 if [[ $DEVICE == "gpu" ]]; then
     CMAKE_VARS="-DUSE_CUDA=ON -DUSE_NCCL=ON $CMAKE_VARS"
 fi

tests/scripts/build_dgl.sh

@@ -15,7 +15,10 @@ if [ "$1" != "cugraph" ]; then
     # We do not build pytorch for cugraph because currently building
     # pytorch against all the supported cugraph versions is not supported
     # See issue: https://github.com/rapidsai/cudf/issues/8510
-    CMAKE_VARS="$CMAKE_VARS -DBUILD_TORCH=ON -DBUILD_SPARSE=ON -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
+    CMAKE_VARS="$CMAKE_VARS -DBUILD_TORCH=ON -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
+else
+    # Disable sparse build as cugraph docker image lacks cuDNN.
+    CMAKE_VARS="$CMAKE_VARS -DBUILD_SPARSE=OFF"
 fi
 
 #This is implemented to detect underlying architecture and enable arch specific optimization.