torchvision 0.21.0+cu126 RuntimeError: operator torchvision::nms does not exist

[AndrewTsao]

🐛 Describe the bug

micromamba create -n torchvision python=3.12
micromamba activate torchvision
pip install torchvision

import torchvision

throw error:

Python 3.12.9 | packaged by conda-forge | (main, Mar  4 2025, 22:48:41) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchvision
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torchvision/__init__.py", line 10, in <module>
    from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils  # usort:skip
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torchvision/_meta_registrations.py", line 163, in <module>
    @torch.library.register_fake("torchvision::nms")
     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torch/library.py", line 828, in register
    use_lib._register_fake(op_name, func, _stacklevel=stacklevel + 1)
  File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torch/library.py", line 198, in _register_fake
    handle = entry.fake_impl.register(func_to_register, source)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torch/_library/fake_impl.py", line 31, in register
    if torch._C._dispatch_has_kernel_for_dispatch_key(self.qualname, "Meta"):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: operator torchvision::nms does not exist

Versions

Collecting environment information...
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: CentOS Linux release 7.9.2009 (Core) (x86_64)
GCC version: (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
Clang version: Could not collect
CMake version: version 3.31.0
Libc version: glibc-2.17

Python version: 3.12.9 | packaged by conda-forge | (main, Mar 4 2025, 22:48:41) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.31.1.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: Tesla V100S-PCIE-32GB
GPU 1: Tesla V100S-PCIE-32GB

Nvidia driver version: 550.54.15
cuDNN version: Probably one of the following:
/opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.5
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
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 1
Core(s) per socket: 24
Socket(s): 4
NUMA node(s): 4
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Platinum 8268 CPU @ 2.90GHz
Stepping: 5
CPU MHz: 3499.859
CPU max MHz: 3900.0000
CPU min MHz: 1000.0000
BogoMIPS: 5800.00
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 33792K
NUMA node0 CPU(s): 0-23
NUMA node1 CPU(s): 24-47
NUMA node2 CPU(s): 48-71
NUMA node3 CPU(s): 72-95
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 aperfmperf eagerfpu 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 epb cat_l3 cdp_l3 invpcid_single intel_ppin intel_pt ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_pkg_req pku ospke spec_ctrl intel_stibp flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==2.2.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] torch==2.6.0+cu124
[pip3] torchvision==0.21.0+cu126
[pip3] triton==3.2.0
[conda] Could not collect

[NicolasHug]

Hi @AndrewTsao , I'm not sure this is the reason but:

[pip3] torch==2.6.0+cu124
[pip3] torchvision==0.21.0+cu126

try to make the cuda toolkit version match?