问题描述
Python3执行某一个程序时,报Segmentation fault (core dumped)错,且没有其他任何提示,无法查问题。
Segmentation fault (core dumped)多为内存不当操作造成。空指针、野指针的读写操作,数组越界访问,破坏常量等。对每个指针声明后进行初始化为NULL是避免这个问题的好办法。排除此问题的最好办法则是调试。
排查过程
错误排查过程如下:
1. 定位错误,
第一种方式是利用python3的faulthandler
,可定位到出错的代码行,具体操作有两种方式如下:
(1) 在代码中写入faulthandler
import faulthandler
# 在import之后直接添加以下启用代码即可
faulthandler.enable()
# 后边正常写你的代码
(2)直接通过命令行来启用,运行时添加-X faulthandler参数即可:
python -X faulthandler your_script.py
这里我们为了保持代码的纯洁,选用第二种方式
再次执行主程序,发现输出的信息很多了:
Fatal Python error: Segmentation fault
Current thread 0x00007f9f89fa8740 (most recent call first):
File "/home/xinzhepang/anaconda3/envs/train/lib/python3.9/ctypes/__init__.py", line 374 in __init__
File "/home/xinzhepang/anaconda3/envs/train/lib/python3.9/site-packages/torch/_ops.py", line 255 in load_library
File "/home/xinzhepang/anaconda3/envs/train/lib/python3.9/site-packages/torch_sparse/__init__.py", line 19 in <module>
File "<frozen importlib._bootstrap>", line 228 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 850 in exec_module
File "<frozen importlib._bootstrap>", line 680 in _load_unlocked
File "<frozen importlib._bootstrap>", line 986 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1007 in _find_and_load
File "/home/xinzhepang/anaconda3/envs/train/lib/python3.9/site-packages/torch_geometric/data/data.py", line 20 in <module>
File "<frozen importlib._bootstrap>", line 228 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 850 in exec_module
File "<frozen importlib._bootstrap>", line 680 in _load_unlocked
File "<frozen importlib._bootstrap>", line 986 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1007 in _find_and_load
File "/home/xinzhepang/anaconda3/envs/train/lib/python3.9/site-packages/torch_geometric/data/__init__.py", line 1 in <module>
File "<frozen importlib._bootstrap>", line 228 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 850 in exec_module
File "<frozen importlib._bootstrap>", line 680 in _load_unlocked
File "<frozen importlib._bootstrap>", line 986 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1007 in _find_and_load
File "/home/xinzhepang/anaconda3/envs/train/lib/python3.9/site-packages/torch_geometric/__init__.py", line 4 in <module>
File "<frozen importlib._bootstrap>", line 228 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 850 in exec_module
File "<frozen importlib._bootstrap>", line 680 in _load_unlocked
File "<frozen importlib._bootstrap>", line 986 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1007 in _find_and_load
File "<frozen importlib._bootstrap>", line 228 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 972 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1007 in _find_and_load
File "/home/xinzhepang/workspace/iGNC/scripts/bioinfo_training.py", line 6 in <module>
File "<frozen importlib._bootstrap>", line 228 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 850 in exec_module
File "<frozen importlib._bootstrap>", line 680 in _load_unlocked
File "<frozen importlib._bootstrap>", line 986 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1007 in _find_and_load
File "/home/xinzhepang/workspace/iGNC/main.py", line 11 in <module>
./run_main.sh: line 2: 38634 Segmentation fault (core dumped) python -X faulthandler main.py --use_cuda --batch_size 16 --num_workers 2
第二种方式是利用gdb,操作方式如下:
gdb python
(gdb) run /path/to/your_script.py
## wait for segfault ##
(gdb) backtrace
## stack trace of the py code
追踪产生segmenttation fault的位置及代码函数调用情况:
gdb>bt
这样,一般就可以看到出错的代码是哪一句了,还可以打印出相应变量的数值,进行进一步分析。另外需要注意的是,如果机器上跑很多的应用,生成的core又不知道是哪个应用产生的,可以通过下列命令进行查看:file core
可以看到出错的地方是:torch/lib/libtorch_cpu.so
我们执行如下代码:
>>> import torch
>>> print(torch.cuda.current_device())
0
>>> print(torch.cuda.is_available())
True
然后,执行nvidia-smi
命令可知,我们有4块显卡:
Thu Apr 20 11:14:37 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.182.03 Driver Version: 470.182.03 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA TITAN X ... Off | 00000000:02:00.0 Off | N/A |
| 25% 45C P8 13W / 250W | 0MiB / 12196MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA TITAN X ... Off | 00000000:03:00.0 Off | N/A |
| 28% 50C P8 12W / 250W | 0MiB / 12196MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 2 NVIDIA TITAN X ... Off | 00000000:82:00.0 Off | N/A |
| 31% 55C P8 13W / 250W | 0MiB / 12196MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 3 NVIDIA TITAN X ... Off | 00000000:83:00.0 Off | N/A |
| 30% 53C P8 13W / 250W | 0MiB / 12196MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
查看nvcc的版本:
(train) xxxxx@air:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Mar_21_19:15:46_PDT_2021
Cuda compilation tools, release 11.3, V11.3.58
Build cuda_11.3.r11.3/compiler.29745058_0
因此,我们怀疑是CUDA、显卡驱动、pytorch、pytorchvision、torch_scatter、torch_sparse、torch_geometric版本不对应。
2. 解决办法
我们选择重新安装Pytorch 1.10
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
什么是Core:
在使用半导体作为内存的材料前,人类是利用线圈当作内存的材料(发明者为王安),线圈就叫作 core ,用线圈做的内存就叫作 core memory。如今 ,半导体工业澎勃发展,已经没有人用 core memory 了,不过,在许多情况下,人们还是把记忆体叫作 core 。文章来源:https://www.toymoban.com/news/detail-420061.html
什么是Core Dump:
我们在开发(或使用)一个程序时,最怕的就是程序莫明其妙地当掉。虽然系统没事,但我们下次仍可能遇到相同的问题。于是这时操作系统就会把程序当掉 时的内存内容 dump 出来(现在通常是写在一个叫 core 的 file 里面),让 我们或是 debugger 做为参考。这个动作就叫作 core dump。文章来源地址https://www.toymoban.com/news/detail-420061.html
参考资料
- 记录一次Pytorch使用中遇到Segmentation fault (core dumped)的经历
- python遇到Segmentation fault (core dumped)调试方法
- Segmentation fault (core dumped)错误常见原因总结
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