[Bug] undefined symbol: __nvJitLinkAddData_12_5, version libnvJitLink.so.12

[xiangcp]

Your current environment information

onediff 1.2.1.dev6 oneflow 0.9.1.dev20240730+cu121

nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2023 NVIDIA Corporation Built on Tue_Feb__7_19:32:13_PST_2023 Cuda compilation tools, release 12.1, V12.1.66 Build cuda_12.1.r12.1/compiler.32415258_0

🐛 Describe the bug

Traceback (most recent call last): File "/home/dbt/miniconda3/envs/sd_webui/lib/python3.10/site-packages/onediff/utils/import_utils.py", line 16, in check_module_availability importlib.import_module(module_name) File "/home/dbt/miniconda3/envs/sd_webui/lib/python3.10/importlib/init.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "", line 1050, in _gcd_import File "", line 1027, in _find_and_load File "", line 1006, in _find_and_load_unlocked File "", line 688, in _load_unlocked File "", line 883, in exec_module File "", line 241, in _call_with_frames_removed File "/home/dbt/miniconda3/envs/sd_webui/lib/python3.10/site-packages/oneflow/init.py", line 26, in import oneflow._oneflow_internal ImportError: /home/dbt/miniconda3/envs/sd_webui/lib/python3.10/site-packages/oneflow/../nvidia/cusparse/lib/libcusparse.so.12: undefined symbol: __nvJitLinkAddData_12_5, version libnvJitLink.so.12

环境内没有tensorflow，我cuda是12.1的版本。

[strint]

Can you try checking the import order of oneflow and pytorch, and then adjust the order: it's best to place import torch before import onediff and oneflow.

可以试下看看 oneflow 和 pytorch 的 import 顺序，然后调下顺序看看：最好把 import torch 放在 import onediff 和 oneflow 的前面

[xiangcp]

Can you try checking the import order of oneflow and pytorch, and then adjust the order: it's best to place import torch before import onediff and oneflow.

可以试下看看 oneflow 和 pytorch 的 import 顺序，然后调下顺序看看：最好把 import torch 放在 import onediff 和 oneflow 的前面

这个是SD webui的插件，torch的import都前面

[strint]

帮忙用这个发下更完整的环境信息，估计是和某个依赖的版本有关：

https://github.com/siliconflow/onediff/wiki/How-to-Run-or-Debug-OneDiff#get-onediff-environment-info

看你用的 GPU 设备型号是什么，可能也有关系

[xiangcp]

帮忙用这个发下更完整的环境信息，估计是和某个依赖的版本有关：

https://github.com/siliconflow/onediff/wiki/How-to-Run-or-Debug-OneDiff#get-onediff-environment-info

看你用的 GPU 设备型号是什么，可能也有关系

完整的结果： Collecting environment information... PyTorch version: 2.1.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OneFlow version: none Nexfort version: none OneDiff version: 1.2.1.dev6 OneDiffX version: none

OS: Rocky Linux 9.4 (Blue Onyx) (x86_64) GCC version: (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3) Clang version: Could not collect CMake version: version 3.29.3 Libc version: glibc-2.34

Python version: 3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.14.0-427.18.1.el9_4.x86_64-x86_64-with-glibc2.34 Is CUDA available: True CUDA runtime version: 12.1.66 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 Ti GPU 1: NVIDIA GeForce RTX 3090 Ti GPU 2: NVIDIA GeForce RTX 3090 Ti GPU 3: NVIDIA GeForce RTX 3090 Ti

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, 57 bits virtual Byte Order: Little Endian CPU(s): 112 On-line CPU(s) list: 0-111 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6330 CPU @ 2.00GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 28 Socket(s): 2 Stepping: 6 CPU(s) scaling MHz: 100% CPU max MHz: 3100.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 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 art 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 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 2.6 MiB (56 instances) L1i cache: 1.8 MiB (56 instances) L2 cache: 70 MiB (56 instances) L3 cache: 84 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-27,56-83 NUMA node1 CPU(s): 28-55,84-111 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected 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; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] diffusers==0.27.2 [pip3] numpy==1.26.2 [pip3] onnx==1.16.0 [pip3] onnxruntime==1.17.3 [pip3] onnxruntime-gpu==1.17.1 [pip3] open-clip-torch==2.20.0 [pip3] pytorch-lightning==1.9.4 [pip3] torch==2.1.0+cu121 [pip3] torchaudio==2.1.0+cu121 [pip3] torchdiffeq==0.2.3 [pip3] torchmetrics==1.3.2 [pip3] torchsde==0.2.6 [pip3] torchvision==0.16.0+cu121 [pip3] transformers==4.30.2 [pip3] triton==2.1.0 [conda] numpy 1.26.2 pypi_0 pypi [conda] open-clip-torch 2.20.0 pypi_0 pypi [conda] pytorch-lightning 1.9.4 pypi_0 pypi [conda] torch 2.1.0+cu121 pypi_0 pypi [conda] torchaudio 2.1.0+cu121 pypi_0 pypi [conda] torchdiffeq 0.2.3 pypi_0 pypi [conda] torchmetrics 1.3.2 pypi_0 pypi [conda] torchsde 0.2.6 pypi_0 pypi [conda] torchvision 0.16.0+cu121 pypi_0 pypi [conda] triton 2.1.0 pypi_0 pypi