[c10d] Faster coalescing (#98793)
### Description
The PR aims at reducing CPU overhead of context manager style coalescing.
By "context manager style coalescing", we mean:
Sync style:
```
with _coalescing_manager():
for i in range(num_coll):
dist.all_reduce(tensors[i])
```
Async style:
```
with _coalescing_manager(async_ops=True) as cm:
for i in range(num_coll):
dist.all_reduce(tensors[i])
cm.wait()
```
In previous implementation, each collective in the `num_coll` loop actually calls into the C++ backend, accumulating pybind overhead.
In the new implementation, we capture the collectives at Python level, and only fire towards C++ at the exit of the coalescing manager.
### Tests
In current PR, the "fast path" only applies to all-reduce.
- Flattened 512M: 16.38 ms, including CPU time 131.21 us
- Old _coalescing_manager 64 x 8M: 22.19 ms, including CPU time 2865 us
- New _coalescing_manager 64 x 8M: 16.93 ms, including CPU time 635 us
Hence a 4x reduction in CPU overhead (dependent on `num_coll`).
Cc @mrshenli @kumpera @wanchaol @fegin
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98793
Approved by: https://github.com/kumpera
