🐛 Describe the bug

Hi all, I am trying to run the c++ example here. I have done all steps except running the final c++ program, which fails with Aborted (Core Dumped) when running the c++ loading function.

To be more specifically, when I remove the torch-geometric Modules from the model in save_mode.py, the c++ program will run smoothly without errors. So I think this is a problem regarding loading torch-geometric modules in c++.

I have checked the compilation and linking of Pytorch-sparse and Pytorch-scatter. Simple c++ test demos using features (e.g. the demo in #1718 and rusty1s/pytorch_scatter#147) from the two libraries could run successfully. How can I handle this error?

Versions

PyTorch version: 2.3.0
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.22.0-rc1
Libc version: glibc-2.39

Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-190-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A100 80GB PCIe
Nvidia driver version: 550.54.15
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.3.0
[pip3] torch_cluster==1.6.3+pt23cu121
[pip3] torch_geometric==2.5.3
[pip3] torch_scatter==2.1.2+pt23cu121
[pip3] torch_sparse==0.6.18+pt23cu121
[pip3] torch_spline_conv==1.2.2+pt23cu121
[pip3] torchaudio==2.3.0
[pip3] torchinfo==1.8.0
[pip3] torchvision==0.18.0
[pip3] triton==2.3.0
[conda] blas 1.0 mkl
[conda] cuda-cudart 12.1.105 0 nvidia
[conda] cuda-cupti 12.1.105 0 nvidia
[conda] cuda-libraries 12.1.0 0 nvidia
[conda] cuda-nvrtc 12.1.105 0 nvidia
[conda] cuda-nvtx 12.1.105 0 nvidia
[conda] cuda-opencl 12.4.127 0 nvidia
[conda] cuda-runtime 12.1.0 0 nvidia
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] libcublas 12.1.0.26 0 nvidia
[conda] libcufft 11.0.2.4 0 nvidia
[conda] libcurand 10.3.5.147 0 nvidia
[conda] libcusolver 11.4.4.55 0 nvidia
[conda] libcusparse 12.0.2.55 0 nvidia
[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
[conda] libnvjitlink 12.1.105 0 nvidia
[conda] mkl 2023.1.0 h213fc3f_46344
[conda] mkl-service 2.4.0 py310h5eee18b_1
[conda] mkl_fft 1.3.8 py310h5eee18b_0
[conda] mkl_random 1.2.4 py310hdb19cb5_0
[conda] numpy 1.26.4 py310h5f9d8c6_0
[conda] numpy-base 1.26.4 py310hb5e798b_0
[conda] pytorch 2.3.0 py3.10_cuda12.1_cudnn8.9.2_0 pytorch
[conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch-cluster 1.6.3+pt23cu121 pypi_0 pypi
[conda] torch-geometric 2.5.3 pypi_0 pypi
[conda] torch-scatter 2.1.2+pt23cu121 pypi_0 pypi
[conda] torch-sparse 0.6.18+pt23cu121 pypi_0 pypi
[conda] torch-spline-conv 1.2.2+pt23cu121 pypi_0 pypi
[conda] torchaudio 2.3.0 py310_cu121 pytorch
[conda] torchinfo 1.8.0 pypi_0 pypi
[conda] torchtriton 2.3.0 py310 pytorch
[conda] torchvision 0.18.0 py310_cu121 pytorch