Integrate apply_optim_in_backward with DDP (#89194)
Allow _apply_optim_in_backward to work with DDP.
Example:
```
dist.init_process_group("nccl", rank=rank, world_size=2)
torch.cuda.set_device(rank)
e = enc().cuda(rank)
_apply_optimizer_in_backward(
optimizer_class=torch.optim.SGD,
params=e.parameters(),
optimizer_kwargs={"lr": 0.03},
)
e = DDP(e, device_ids=[rank])
inp = torch.randn(1, 10, device=rank)
e(inp).sum().backward()
```
Constraints:
1. Custom communication hook not yet supported
2. _apply_optim_in_backward needs to be called _before_ wrapping model in DDP.
3. DDP will remove the gradient hooks _apply_optim_in_backward registers, so these gradient hooks will not be fired and cannot be used.
4. All DDP managed parameters have grads set to None by default once optimizer is applied. There is no support for setting only some parameter grads to None, this must be done manually by user (and DDP_OVERLAPPED_OPTIM_SET_GRADS_TO_NONE=0 needs to be set.)
Differential Revision: [D41329694](https://our.internmc.facebook.com/intern/diff/D41329694/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D41329694/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89194
Approved by: https://github.com/zhaojuanmao
