RuntimeError: CUDA error: an illegal memory access was encountered

[Riki123666]

🐛 Describe the bug

When I use GPU other than the cuda:0, the following problems occur:
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

I installed it through the following command:
pip install --no-index pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.1 +cu116.html

Versions

Collecting environment information...
PyTorch version: 1.12.1
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 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: 3.8.20 (default, Oct 3 2024, 15:24:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to:
GPU models and configuration:
GPU 0: Tesla P40
GPU 1: Tesla P40
GPU 2: Tesla P40
GPU 3: Tesla P40
GPU 4: Tesla P40
GPU 5: Tesla P40
GPU 6: Tesla P40
GPU 7: Tesla P40
GPU 8: Tesla P40
GPU 9: Tesla P40

Nvidia driver version: 535.183.01
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: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 72
On-line CPU(s) list: 0-71
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10GHz
CPU family: 6
Model: 79
Thread(s) per core: 2
Core(s) per socket: 18
Socket(s): 2
Stepping: 1
CPU max MHz: 2100.0000
CPU min MHz: 1200.0000
BogoMIPS: 4200.46
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm arat pln pts vnmi md_clear flush_l1d
Virtualization: VT-x
L1d cache: 1.1 MiB (36 instances)
L1i cache: 1.1 MiB (36 instances)
L2 cache: 9 MiB (36 instances)
L3 cache: 90 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-17,36-53
NUMA node1 CPU(s): 18-35,54-71
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] pytorch-metric-learning==2.8.1
[pip3] torch==1.12.1
[pip3] torch-cluster==1.6.0+pt112cu113
[pip3] torch_geometric==2.3.1
[pip3] torch-scatter==2.1.0+pt112cu113
[pip3] torch-sparse==0.6.16+pt112cu113
[pip3] torch-spline-conv==1.2.1+pt112cu113
[pip3] torchaudio==0.12.1
[pip3] torchmetrics==1.5.2
[pip3] torchvision==0.13.1
[conda] blas 1.0 mkl
[conda] cudatoolkit 11.3.1 ha36c431_9 nvidia
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] mkl 2023.1.0 h213fc3f_46344
[conda] mkl-service 2.4.0 py38h5eee18b_1
[conda] mkl_fft 1.3.8 py38h5eee18b_0
[conda] mkl_random 1.2.4 py38hdb19cb5_0
[conda] numpy 1.24.3 py38hf6e8229_1
[conda] numpy-base 1.24.3 py38h060ed82_1
[conda] pytorch 1.12.1 py3.8_cuda11.3_cudnn8.3.2_0 pytorch
[conda] pytorch-metric-learning 2.8.1 pypi_0 pypi
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch-cluster 1.6.0+pt112cu113 pypi_0 pypi
[conda] torch-geometric 2.3.1 pypi_0 pypi
[conda] torch-scatter 2.1.0+pt112cu113 pypi_0 pypi
[conda] torch-sparse 0.6.16+pt112cu113 pypi_0 pypi
[conda] torch-spline-conv 1.2.1+pt112cu113 pypi_0 pypi
[conda] torchaudio 0.12.1 py38_cu113 pytorch
[conda] torchmetrics 1.5.2 pyhe5570ce_0 conda-forge
[conda] torchvision 0.13.1 py38_cu113 pytorch