Repository: pytorch
Issue: Error in SVD cusolver on Linux
Labels: needs reproduction,triaged,module: linear algebra

## 🐛 Bug

The svd-functions (svd, svdvals) return an error when called on certain vectors on linux, while it works on Windows
`RuntimeError: cusolver error: CUSOLVER_STATUS_EXECUTION_FAILED, when calling cusolverDnSgesvdj( handle, jobz, econ, m, n, A, lda, S, U, ldu, V, ldv, static_cast<float*>(dataPtr.get()), lwork, info, params)`



## To Reproduce
```
cpt = torch.load('tensor.pth')
a = cpt['t']
torch.linalg.svd(a)
```

## Expected behavior

No error should be given, function is executed

## Environment

### Windows Environment:
```
PyTorch version: 1.10.0
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Microsoft Windows 11 Home
GCC version: Could not collect
Clang version: Could not collect
CMake version: version 3.22.0
Libc version: N/A

Python version: 3.8.11 (default, Aug  3 2021, 06:49:12) [MSC v.1916 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22000-SP0
Is CUDA available: True
CUDA runtime version: 11.3.58
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
Nvidia driver version: 466.85
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.20.3
[pip3] pytorch-lightning==1.4.1
[pip3] torch==1.10.0
[pip3] torchmetrics==0.4.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.11.0
[pip3] torchvision==0.11.0
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               11.3.1               h59b6b97_2
[conda] mkl                       2021.3.0           haa95532_524
[conda] mkl-service               2.4.0            py38h2bbff1b_0
[conda] mkl_fft                   1.3.0            py38h277e83a_2
[conda] mkl_random                1.2.2            py38hf11a4ad_0
[conda] numpy                     1.20.3           py38ha4e8547_0
[conda] numpy-base                1.20.3           py38hc2deb75_0
[conda] pytorch                   1.10.0          py3.8_cuda11.3_cudnn8_0    pytorch
[conda] pytorch-lightning         1.4.1              pyhd8ed1ab_0    conda-forge
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchmetrics              0.4.1              pyhd8ed1ab_0    conda-forge
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchtext                 0.11.0                     py38    pytorch
[conda] torchvision               0.11.0               py38_cu113    pytorch
```

### Linux Environment:
```
PyTorch version: 1.10.0
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Red Hat Enterprise Linux Server release 7.9 (Maipo) (x86_64)
GCC version: (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
Clang version: Could not collect
CMake version: version 2.8.12.2
Libc version: glibc-2.17

Python version: 3.8.12 (default, Oct 12 2021, 13:49:34)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.49.1.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.3.109
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-32GB-LS
GPU 1: Tesla V100-SXM2-32GB-LS
GPU 2: Tesla V100-SXM2-32GB-LS
GPU 3: Tesla V100-SXM2-32GB-LS
GPU 4: Tesla V100-SXM2-32GB-LS
GPU 5: Tesla V100-SXM2-32GB-LS
GPU 6: Tesla V100-SXM2-32GB-LS
GPU 7: Tesla V100-SXM2-32GB-LS

Nvidia driver version: 450.156.00
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.20.3
[pip3] pytorch-lightning==1.5.3
[pip3] torch==1.10.0
[pip3] torchmetrics==0.6.0
[pip3] torchvision==0.11.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libblas                   3.9.0            12_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            12_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            12_linux64_mkl    conda-forge
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] numpy                     1.20.3           py38h9894fe3_1    conda-forge
[conda] pytorch                   1.10.0          py3.8_cuda11.3_cudnn8.2.0_0    pytorch
[conda] pytorch-lightning         1.5.3              pyhd8ed1ab_0    conda-forge
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchmetrics              0.6.0              pyhd8ed1ab_0    conda-forge
[conda] torchvision               0.11.1               py38_cu113    pytorch
```

[tensor.zip](https://github.com/pytorch/pytorch/files/7632377/tensor.zip)



cc @jianyuh @nikitaved @pearu @mruberry @walterddr @IvanYashchuk @xwang233 @Lezcano