Summary:
Pull Request resolved: pytorch#895

Internal
# Context
With the sched_ext effort we are trying to build custom Linux schedulers that provide a small performance boost to AI training and improve the resource isolation on the trainer hosts. The latter is necessary to avoid cases when noisy neighbor processes, like data loaders, slow down the GPU training.

More details in this note: https://fb.workplace.com/notes/1118655556176038

By naming the forward pass thread we can use its name and assign it a higher priority at the linux scheduler level. The backward pass is named inside the Pytorch implementation but the forward pass needs to be named at the application level.

We did the same thing in PyPer, APS, MVAI which are the largest trainer frameworks for reco models, consuming 70%  of fleet level GPU hours for recommender systems.

# This Diff
Adds core lines
```
if torch.multiprocessing._get_thread_name() != "trainer_main":
    torch.multiprocessing._set_thread_name("trainer_main")
```
to train/eval/predict scripts. We can check the preexisting name to avoid renaming the same thread.

Reviewed By: diego-urgell

Differential Revision: D61924982