Implement coalesced reduce_scatter_tensor (#103561)
Map of #101157.
This PR adds support for coalesced `reduce_scatter_tensor` calls in the following syntax:
Sync communication style:
```
with dist._coalescing_manager():
for i in range(num_coll):
dist.reduce_scatter_tensor(output_tensors[i], input_tensors[i])
```
Async communication style:
```
with dist._coalescing_manager(async_ops=True) as cm:
for i in range(num_coll):
dist.reduce_scatter_tensor(output_tensors[i], input_tensors[i])
# do a bunch of other things
cm.wait()
# do things that depend on the reduce-scatters' results
```
Each `reduce_scatter_tensor` call can be independent in terms of their data and buffer locations. But could be executed in parallel by supported backends (like NCCL).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103561
Approved by: https://github.com/fegin
