Support NUMA Binding for Callable Entrypoints, Take 2 (pytorch#161183)

# Context
In pytorch#160163, we added support for NUMA binding for `Callable` entrypoints to `elastic_launch`. This requires special consideration, because they go through a different path to spawn subprocesses compared to `str` entrypoints, a path which does not provide a straightforward way to utilize `numactl` CLI. See pytorch#160006 for a full description of the challenges.

Although pytorch#160163 worked in initial local experiments, we ran into some linker errors in other environments when we tried to call `numactl`. This appeared to be due to interactions with how the `LD_PRELOAD` environment variable was being set.

# This PR
On further thought, the most straightforward, foolproof solution here is to use [the trick that @d4l3k suggested.](pytorch#160006 (comment))

Specifically, for each local rank `i`:
1. The parent process sets its own CPU affinity to what local rank `i`'s should be.
2. Then, the parent spawns the subprocess for local rank `i`.
3. Finally, the parent resets its own CPU affinity to what it was originally.

There were other solutions that would work just for `Callable` entrypoints, but I believe this is the simplest one that can work for *both* `str` and `Callable`, and it's pretty simple.

This required a bit of refactoring:
1. Turn all the `_get_.*_numactl_options` into functions which return a set of logical CPUs to bind to, rather than options like `--cpunodebind=0`.
2. Instead of wrapping commands with `numactl`, use `os.sched_setaffinity` to bind to the CPUs from (1.).
3. Put this all inside a context manager which encapsulates applying and restoring the bindings in the parent process.
4. Use the context manager for both `str` and `Callable` paths

# Test Plan
## Automated
`$ pytest test/test_numa_binding.py`

## Manual
See [doc.](https://docs.google.com/document/d/1vxD-OKYBTT27jbBwtW9iz9g0tNM0u-i0tiTJg_ieQA8/edit?tab=t.0) Meta only, but TLDR tried out every combination of `str`, `Callable`, binding disabled, and binding enabled on the same model and saw 2x SM utilization for binding enabled.

Pull Request resolved: pytorch#161183
Approved by: https://github.com/d4l3k

Author: pdesupinski

docs/source/conf.py

@@ -1221,6 +1221,9 @@
     "reduce_typed_storage_child",
     "storage_from_cache",
     # torch.multiprocessing.spawn
+    # Added docstring for this but I think we need to go through
+    # and add the entire torch.multiprocessing.spawn module to a .rst...
+    "should_use_parallel_start",
     "start_processes",
     # torch.nn.functional
     "adaptive_max_pool1d_with_indices",  # documented as adaptive_max_pool1d