A100 GPU MIG feature support for trainer

@Borda is already working on this.

Since Nov 7, 2022.

## 🚀 Feature

A100 GPU MIG feature support for trainer

### Motivation

Hi, could you update trainer class for MIG feature GPU?

For our LSF scheduler , -gpu "num=1:mig=1" will give you a single smallest possible slice (1/7th)
 -gpu "num=1:mig=7" gives you a single entire A100 GPU (80G memory)(equivalent to the previous "num=1")

What will be the number of GPU argument for trainer? The real number of GPU , or the slice number?
for example num=1:mig=4, should it be gpu =1 or 4?

I found that torch.cuda.device_count() returns 1 but I am not sure if I pass the num 1 to -gpu of trainer, will it use 10G memory or 40G memory?

How about num=2:mig=4?

Can we pass -1 to use all available gpu? How will pytorchlightning decide the number of gpus? Can we get a print information ?

### Pitch

It would be better to make -gpus obvious,
for example: -gpus and -migs or treat the total slice of MIG from one A100 as one gpu.

### Alternatives