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image scene graph generation benchmark
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RuntimeError: CUDA error: invalid device function #13

Closed mohaoran93 closed 3 years ago

mohaoran93 commented 3 years ago

When I try to run 

python tools/test_sg_net.py --config-file sgg_configs/vgattr/vinvl_x152c4.yaml TEST.IMS_PER_BATCH 1 MODEL.WEIGHT models/vinvl/vinvl_vg_x152c4.pth MODEL.ROI_HEADS.NMS_FILTER 1 \
MODEL.ROI_HEADS.SCORE_THRESH 0.2 DATA_DIR /my_path_to_prepard_tsv/dataset/tsv TEST.IGNORE_BOX_REGRESSION True MODEL.ATTRIBUTE_ON True TEST.OUTPUT_FEATURE True

with environment

PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 16.04.7 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
CMake version: version 3.5.1

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-32GB
GPU 1: Tesla V100-SXM2-32GB
GPU 2: Tesla V100-SXM2-32GB
GPU 3: Tesla V100-SXM2-32GB
GPU 4: Tesla V100-SXM2-32GB
GPU 5: Tesla V100-SXM2-32GB
GPU 6: Tesla V100-SXM2-32GB
GPU 7: Tesla V100-SXM2-32GB

Nvidia driver version: 418.67
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5

Versions of relevant libraries:
[pip3] numpy==1.19.2
[pip3] torch==1.4.0
[pip3] torchvision==0.5.0
[conda] blas                      1.0                         mkl  
[conda] mkl                       2020.2                      256  
[conda] mkl-service               2.3.0            py37he8ac12f_0  
[conda] mkl_fft                   1.3.0            py37h54f3939_0  
[conda] mkl_random                1.1.1            py37h0573a6f_0  
[conda] pytorch                   1.4.0           py3.7_cuda10.1.243_cudnn7.6.3_0    pytorch
[conda] torchvision               0.5.0                py37_cu101    pytorch
        Pillow (8.2.0)

I encountered the error as following

RuntimeError: CUDA error: invalid device function (launch_kernel at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/ATen/native/cuda/Loops.cuh:103)
frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x47 (0x7f3f8ee64627 in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: void at::native::gpu_index_kernel<__nv_dl_wrapper_t<__nv_dl_tag<void (*)(at::TensorIterator&, c10::ArrayRef<long>, c10::ArrayRef<long>), &(void at::native::index_kernel_impl<at::native::OpaqueType<8> >(at::TensorIterator&, c10::ArrayRef<long>, c10::ArrayRef<long>)), 1u>> >(at::TensorIterator&, c10::ArrayRef<long>, c10::ArrayRef<long>, __nv_dl_wrapper_t<__nv_dl_tag<void (*)(at::TensorIterator&, c10::ArrayRef<long>, c10::ArrayRef<long>), &(void at::native::index_kernel_impl<at::native::OpaqueType<8> >(at::TensorIterator&, c10::ArrayRef<long>, c10::ArrayRef<long>)), 1u>> const&) + 0x78d (0x7f3f9670368d in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: <unknown function> + 0x571bf32 (0x7f3f966fcf32 in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: <unknown function> + 0x571c298 (0x7f3f966fd298 in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #4: <unknown function> + 0x16957eb (0x7f3f926767eb in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #5: at::native::index(at::Tensor const&, c10::ArrayRef<at::Tensor>) + 0x47e (0x7f3f926725ae in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #6: <unknown function> + 0x1c0155a (0x7f3f92be255a in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #7: <unknown function> + 0x1c06023 (0x7f3f92be7023 in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #8: <unknown function> + 0x3820d1a (0x7f3f94801d1a in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #9: <unknown function> + 0x1c06023 (0x7f3f92be7023 in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #10: at::Tensor::index(c10::ArrayRef<at::Tensor>) const + 0x191 (0x7f3fc1465931 in /miniconda/envs/py37/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #11: nms_cuda(at::Tensor, float) + 0x7e8 (0x7f3f6982407b in ./maskrcnn_benchmark/_C.cpython-37m-x86_64-linux-gnu.so)
frame #12: nms(at::Tensor const&, at::Tensor const&, float) + 0x790 (0x7f3f697eabb0 in ./maskrcnn_benchmark/_C.cpython-37m-x86_64-linux-gnu.so)
frame #13: <unknown function> + 0x53b97 (0x7f3f697fbb97 in ./maskrcnn_benchmark/_C.cpython-37m-x86_64-linux-gnu.so)
frame #14: <unknown function> + 0x5004d (0x7f3f697f804d in ./maskrcnn_benchmark/_C.cpython-37m-x86_64-linux-gnu.so)
<omitting python frames>

I tried both to set up my env by using option 1 and docker image, both environments give me the same error. If anyone also has the same issue, please guide me.

hanxiaotian commented 3 years ago

Hi Haoran, I have tried the same command on my machine, it worked fine. Here's my environment

PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 20.04 LTS
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
CMake version: version 3.16.3

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Probably one of the following:
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.1
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.1
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.1
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.1
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8

Versions of relevant libraries:
[pip3] numpy==1.19.2
[pip3] torch==1.4.0
[pip3] torchvision==0.5.0
[conda] blas                      1.0                         mkl
[conda] mkl                       2020.2                      256
[conda] mkl-service               2.3.0            py37he8ac12f_0
[conda] mkl_fft                   1.2.0            py37h23d657b_0
[conda] mkl_random                1.1.1            py37h0573a6f_0
[conda] pytorch                   1.4.0           py3.7_cuda10.1.243_cudnn7.6.3_0    pytorch
[conda] torchvision               0.5.0                py37_cu101    pytorch
        Pillow (8.0.1)
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.14       Driver Version: 470.14       CUDA Version: 11.3     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ... WDDM  | 00000000:21:00.0  On |                  N/A |
| 30%   35C    P8    26W / 250W |   2338MiB / 11264MiB |      6%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
mohaoran93 commented 3 years ago

Hi Xiaotian, Thank you for you your information. Just for anyone else having the same issue, I have 

Docker version 19.03.12

and host machine has 

NVIDIA-SMI 418.67       Driver Version: 418.67       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
pooravdesai commented 3 years ago

Hi mohaoran93, check the nvcc version being used for the compilation (nvcc --version). I faced the same issue. I tried with nvcc 9.2 and 10.0 but they didn't work probably because they don't match the cudatoolkit version 10.1 here. I used nvcc 11.2 and it worked. run setup.py file with the updated nvcc.