WSL2+RTX3060Ti环境下PyTorch简单运算内存耗尽问题求助

我在WSL2/Ubuntu系统搭配RTX 3060 Ti GPU的环境中运行PyTorch MNIST教程，第一个训练批次就出现Linux内存耗尽、Ubuntu终止进程的问题。简化代码后，以下极简案例仍会触发相同故障：

import torch
x0 = torch.tensor([[1.], [4.]], device='cuda')
w0 = torch.tensor([[2.]], device='cuda')
y0 = torch.nn.functional.linear(x0, w0)    # 此处崩溃，预期返回tensor([[2.], [8.]])

（注：Jupyter内核会因内存耗尽崩溃）

已尝试的排查手段 #

• 确认Shell和PyTorch均可识别GPU，torch.cuda.is_available()返回True
• 在CPU上创建张量运行，代码正常执行
• 改用Python命令行而非Jupyter运行，问题依旧
• 更换CUDA 11.4至12.0对应的多款NVIDIA Windows驱动，无改善
• 重置并重建WSL Ubuntu实例，问题仍存在

环境信息 #

Conda中的PyTorch版本 #

$ conda list | grep torch
pytorch                   1.13.1          py3.10_cuda11.7_cudnn8.5.0_0
pytorch-cuda              11.7                 h67b0de4_1

NVIDIA-SMI输出 #

$ nvidia-smi
Wed Feb 15 15:27:25 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.75       Driver Version: 517.40       CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| 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 GeForce ...  On   | 00000000:01:00.0  On |                  N/A |
|  0%   39C    P8    12W / 200W |    515MiB /  8192MiB |      2%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

/usr/lib/wsl/lib目录文件列表 #

ls -al /usr/lib/wsl/lib
total 74192
drwxr-xr-x 1 root root       40 Feb 15 15:23 .
drwxr-xr-x 4 root root     4096 Feb 15 06:13 ..
-r-xr-xr-x 1 root root   141464 Sep 12 16:54 libcuda.so
-r-xr-xr-x 1 root root   141464 Sep 12 16:54 libcuda.so.1
-r-xr-xr-x 1 root root   141464 Sep 12 16:54 libcuda.so.1.1
-r-xr-xr-x 1 root root   800568 Oct  7 18:46 libd3d12.so
-r-xr-xr-x 1 root root  6224608 Oct  7 18:46 libd3d12core.so
-r-xr-xr-x 1 root root   829248 Oct  7 18:46 libdxcore.so
-r-xr-xr-x 1 root root  5950624 Sep 12 16:54 libnvcuvid.so
-r-xr-xr-x 1 root root  5950624 Sep 12 16:54 libnvcuvid.so.1
-r-xr-xr-x 1 root root  7547400 Sep 12 16:54 libnvdxdlkernels.so
-r-xr-xr-x 1 root root   424400 Sep 12 16:54 libnvidia-encode.so
-r-xr-xr-x 1 root root   424400 Sep 12 16:54 libnvidia-encode.so.1
-r-xr-xr-x 1 root root   212624 Sep 12 16:54 libnvidia-ml.so.1
-r-xr-xr-x 1 root root   354768 Sep 12 16:54 libnvidia-opticalflow.so
-r-xr-xr-x 1 root root   354768 Sep 12 16:54 libnvidia-opticalflow.so.1
-r-xr-xr-x 1 root root 45845584 Sep 12 16:54 libnvwgf2umx.so
-r-xr-xr-x 1 root root   600472 Sep 12 16:54 nvidia-smi

内容的提问来源于stack exchange，提问作者Jim Kelly