Basic framework for Distributed Autograd context. (#24875)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24875
As per https://github.com/pytorch/pytorch/issues/23110, each autograd pass
would be assigned a unique autograd_context_id. In this change we introduce a
DistAutogradContainer per worker which holds information for each autograd pass
currently running.
DistAutogradContainer has a map from the autograd_context_id to
DistAutogradContext (which holds all the relevant information for the autograd
pass). DistAutogradContext currently only stores the autograd_context_id and
more information would be added to it later as we build out the rest of the
framework.
The autograd_context_id is a 64 bit globally unique integer where the first 16
bits are the worker_id and next 48 bits are auto-incrementing for uniqueness.
Sample python code on how this would be used for distributed autograd:
```
import torch.distributed.autograd as dist_autograd
worker_id = 0
dist_autograd.init(worker_id)
with dist_autograd.context() as context_id:
# forward pass...
# backward pass...
# optimizer step...
```
ghstack-source-id: 89119248
Test Plan: unit tests.
Differential Revision: D16356694
fbshipit-source-id: d1a8678da0c2af611758dbb5d624d554212330ce
