Repository: pytorch
Issue: Segmentation fault (SIGSEGV) in c10::detail::getNonDeterministicRandom(bool)
Labels: needs reproduction,module: crash,module: cuda,triaged,module: random

### 🐛 Describe the bug

When running bigger torch models, e.g. `whisperx`, I always receive this error: `Segmentation fault (core dumped)`.
It happens when calling `model.to('cuda')`.

Once I received a different error: `Random_device could not be read`, but I could not reproduce consistently.

Minimum examples like
```
python
Python 3.11.7 | packaged by conda-forge | (main, Dec 23 2023, 14:43:09) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.rand(1).to('cuda')
tensor([0.9164], device='cuda:0')
>>> 
```
do work fine.

Here is the `gdb` backtrace for `whisperx`:
```
Thread 1 "python" received signal SIGSEGV, Segmentation fault.
0x00007fff94b3ddc0 in ?? ()
(gdb) bt
#0  0x00007fff94b3ddc0 in  ()
#1  0x00007fff9813fd73 in c10::detail::getNonDeterministicRandom(bool) ()
    at  /.../.../venvs/whisperx/lib/python3.11/site-packages/torch/lib/libc10.so
#2  0x00007fff1e5a3088 in at::CUDAGeneratorImpl::seed() ()
    at  /.../.../venvs/whisperx/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so
#3  0x00007fff1e5a4196 in at::cuda::detail::getDefaultCUDAGenerator(signed char) ()
    at  /.../.../venvs/whisperx/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so
#4  0x00007fff75454c50 in THCPModule_initExtension(_object*, _object*) ()
    at  /.../.../venvs/whisperx/lib/python3.11/site-packages/torch/lib/libtorch_python.so
#5  0x0000555555738b9e in cfunction_vectorcall_NOARGS (func=0x7fff98537100, args=<optimised out>, nargsf=<optimised out>, kwnames=<optimised out>)
    at /usr/local/src/conda/python-3.11.7/Include/cpython/methodobject.h:52
#6  0x000055555574e5ac in _PyObject_VectorcallTstate (kwnames=<optimised out>, nargsf=<optimised out>, args=<optimised out>, callable=0x7fff98537100, tstate=0x555555ad0218 <_PyRuntime+166328>)
    at /usr/local/src/conda/python-3.11.7/Include/internal/pycore_call.h:92
#7  PyObject_Vectorcall (callable=0x7fff98537100, args=<optimised out>, nargsf=<optimised out>, kwnames=<optimised out>) at /usr/local/src/conda/python-3.11.7/Objects/call.c:299
#8  0x0000555555741a36 in _PyEval_EvalFrameDefault (tstate=<optimised out>, frame=<optimised out>, throwflag=<optimised out>) at /usr/local/src/conda/python-3.11.7/Python/ceval.c:4769
#9  0x0000555555765031 in _PyEval_EvalFrame (throwflag=0, frame=0x7ffff7fb0c18, tstate=0x555555ad0218 <_PyRuntime+166328>)
```

It is the same error for `torch` version 2.0, 2.1, or 2.3 (nightly)

### Versions

```
Collecting environment information...
PyTorch version: 2.3.0.dev20240122+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.11.7 | packaged by conda-forge | (main, Dec 23 2023, 14:43:09) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.5.0-14-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 545.23.08
cuDNN version: Could not collect
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
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             32
On-line CPU(s) list:                0-31
Vendor ID:                          GenuineIntel
Model name:                         13th Gen Intel(R) Core(TM) i9-13900K
CPU family:                         6
Model:                              183
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          1
Stepping:                           1
CPU max MHz:                        5800,0000
CPU min MHz:                        800,0000
BogoMIPS:                           5990.40
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 cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualisation:                     VT-x
L1d cache:                          896 KiB (24 instances)
L1i cache:                          1,3 MiB (24 instances)
L2 cache:                           32 MiB (12 instances)
L3 cache:                           36 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] onnxruntime==1.16.3
[pip3] pytorch-lightning==2.1.3
[pip3] pytorch-metric-learning==2.4.1
[pip3] pytorch-triton==2.2.0+e28a256d71
[pip3] torch==2.3.0.dev20240122+cu121
[pip3] torch-audiomentations==0.11.0
[pip3] torch-pitch-shift==1.2.4
[pip3] torchaudio==2.2.0.dev20240122+cu121
[pip3] torchmetrics==1.3.0.post0
[pip3] torchvision==0.18.0.dev20240122+cu121
[pip3] triton==2.1.0
[conda] numpy                     1.26.3                   pypi_0    pypi
```

cc @ptrblck @pbelevich