is_cuda_out_of_memory Misses CUDA OOM Errors
🐛 Bug
pytorch_lightning.utilities.memory.is_cuda_out_of_memory does not work reliably, causing things like auto_scale_batch_size tuning to fail.
Please reproduce using the BoringModel
I'm afraid I don't know what BoringModel is.
To Reproduce
This returns false. is_cuda_out_of_memory(RuntimeError('CUDA error: out of memory'))
The function only checks for the string CUDA out of memory.
Expected behavior
The above should return True.
Environment

CUDA:
- GPU:
- NVIDIA GeForce RTX 3090
- NVIDIA GeForce RTX 2060 SUPER
- available:         True
- version:           11.1
Packages:
- numpy:             1.20.1
- pyTorch_debug:     False
- pyTorch_version:   1.8.0+cu111
- pytorch-lightning: 1.2.4
- tqdm:              4.59.0
System:
- OS:                Linux
- architecture:
- 64bit
- ELF
- processor:
- python:            3.7.9
- version:           #1 SMP Tue Jun 23 12:58:10 UTC 2020

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 $$P      d$'     ,    $$P    Shell: bash 5.0.3
 $$:      $$.   -    ,d$$'    Terminal: /dev/pts/4
 $$;      Y$b._   _,d$P'      CPU: AMD Ryzen Threadripper 3970X 32- (64) @ 3.693GHz
 Y$$.    `.`"Y$$$$P"'         Memory: 23149MiB / 257643MiB
 `$$b      "-.__
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