Segmentation fault (core dumped) on importing torch_geometric after installing torch_sparse

[msolki]

🐛 Describe the bug

On a freshly installed Ubuntu 22.04.5 LTS (x86_64) [VM] (version details available below), I followed these steps to install python 3.11:

sudo apt update && sudo apt upgrade

sudo apt install -y python3.11 python3.11-venv python3.11-dev python3.11-distutils

and create a virtual environment:

python3.11 -m venv venv
source venv/bin/activate

Inside virtual environment, I installed pytorch based on pytorch docs:

pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu124

and then based on PyG docs:

pip install torch_geometric

Up until this point everything is fine. Running:

import torch_geometric
print(f'version: {torch_geometric.__version__}')"

returns: version: 2.6.1. But after installing optional dependencies using:

pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.4.1+cu124.html

import torch_geometric

returns:

Segmentation fault (core dumped)

Turns out this issue also happens while importing torch_sparse but importing pyg_lib, torch_scatter, torch_cluster, and torch_spline_conv works fine.

I believe this issue might not be new as I found related examples in pytorch_sparse issues and pytorch_geometric issues as well, but those issues were most likely caused by incompatible dependency versions.

I appreciate your time and I can provide any additional information you might need.

Versions

Collecting environment information...
PyTorch version: 2.4.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.11.0rc1 (main, Aug 12 2022, 10:02:14) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-130-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A40
Nvidia driver version: 550.120
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        48 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               16
On-line CPU(s) list:                  0-15
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 75F3 32-Core Processor
CPU family:                           25
Model:                                1
Thread(s) per core:                   1
Core(s) per socket:                   8
Socket(s):                            2
Stepping:                             1
BogoMIPS:                             5888.90
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch bpext invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr arat umip pku ospke vaes vpclmulqdq rdpid
Hypervisor vendor:                    Xen
Virtualization type:                  full
L1d cache:                            512 KiB (16 instances)
L1i cache:                            512 KiB (16 instances)
L2 cache:                             8 MiB (16 instances)
L3 cache:                             4 GiB (16 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-15
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Mitigation; safe RET
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] nvidia-cublas-cu12==12.4.2.65
[pip3] nvidia-cuda-cupti-cu12==12.4.99
[pip3] nvidia-cuda-nvrtc-cu12==12.4.99
[pip3] nvidia-cuda-runtime-cu12==12.4.99
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.0.44
[pip3] nvidia-curand-cu12==10.3.5.119
[pip3] nvidia-cusolver-cu12==11.6.0.99
[pip3] nvidia-cusparse-cu12==12.3.0.142
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.4.99
[pip3] nvidia-nvtx-cu12==12.4.99
[pip3] torch==2.4.0+cu124
[pip3] torch_cluster==1.6.3+pt24cu124
[pip3] torch-geometric==2.6.1
[pip3] torch_scatter==2.1.2+pt24cu124
[pip3] torch_sparse==0.6.18+pt24cu124
[pip3] torch_spline_conv==1.2.2+pt24cu124
[pip3] torchaudio==2.4.0+cu124
[pip3] torchvision==0.19.0+cu124
[pip3] triton==3.0.0
[conda] Could not collect