Max_Unhold
October 15, 2022, 12:48pm
1
Hello, I am getting the error
CUDA error: insufficient resources when calling
`cusparseSpGEMM_compute( handle, opA, opB, &alpha,
matA, matB, &beta, matC, computeType,
CUSPARSE_SPGEMM_DEFAULT, spgemmDesc, &bufferSize2, dBuffer2)
when using different sizes of sparse matrix multiplication on the GPU (not the actual number of values but for the dimension that are taken when creating a new matrix.)
How do I debug this error in general?
What are the maximal dimensions that is allowed for this operation on CUDA?
Why do I get this error only when using a GPU, not the CPU?
Could you post a minimal, executable code snippet to reproduce the issue as well as the output of python -m torch.utils.collect_env, please?
i’m getting same error,python -m torch.utils.collect_env
PyTorch version: 2.0.0+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A
OS: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31
Python version: 3.10.9 (main, Jan 11 2023, 15:21:40) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.10.0-21-amd64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: Quadro RTX 8000
GPU 1: Quadro RTX 8000
Nvidia driver version: 515.76
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
Byte Order: Little Endian
Address sizes: 40 bits physical, 48 bits virtual
CPU(s): 25
On-line CPU(s) list: 0-24
Thread(s) per core: 1
Core(s) per socket: 25
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz
Stepping: 7
CPU MHz: 2099.968
BogoMIPS: 4199.93
L1d cache: 800 KiB
L1i cache: 800 KiB
L2 cache: 100 MiB
L3 cache: 16 MiB
NUMA node0 CPU(s): 0-24
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Mitigation; IBRS
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; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single pti ssbd ibrs ibpb fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat umip pku ospke avx512_vnni md_clear
Versions of relevant libraries:
[pip3] numpy==1.24.2
[pip3] torch==2.0.0
[pip3] torch-cluster==1.6.1+pt20cu117
[pip3] torch-geometric==2.3.0
[pip3] torch-scatter==2.1.1+pt20cu117
[pip3] torch-sparse==0.6.17+pt20cu117
[pip3] torch-spline-conv==1.2.2+pt20cu117
[pip3] torchvision==0.15.1
[conda] numpy 1.24.2 pypi_0 pypi
[conda] torch 2.0.0 pypi_0 pypi
[conda] torch-cluster 1.6.1+pt20cu117 pypi_0 pypi
[conda] torch-geometric 2.3.0 pypi_0 pypi
[conda] torch-scatter 2.1.1+pt20cu117 pypi_0 pypi
[conda] torch-sparse 0.6.17+pt20cu117 pypi_0 pypi
[conda] torch-spline-conv 1.2.2+pt20cu117 pypi_0 pypi
[conda] torchvision 0.15.1 pypi_0 pypi