Ship the thread pool as its own library so a process has one pool

[shoumikhin]

The thread pool spreads CPU work across cores, and there is meant to
be exactly one per process. Today it lives in a static library, so every component that links it
gets its own private copy.

The wheel ships two copies right now:

_portable_lib.so        has its own thread pool
training extension      has its own thread pool
  -> one process, two pools, each sized for the whole machine

Two pools each sizing themselves to the core count means the process can run roughly twice as
many worker threads as it has cores. They then compete for the same cores, which is slower than
one pool would be, and a setting applied to one pool has no effect on the other.

This change ships the pool as its own shared library that everything links:

executorch/lib/libexecutorch_threadpool.so.1     the one pool
  _portable_lib.so       -> links it
  training extension     -> links it
  your C++ app           -> links it

How you use it

If you just want the default behaviour, nothing changes. If you want to control the pool from
C++:

find_package(executorch REQUIRED)
target_link_libraries(my_app PRIVATE executorch::runtime executorch::threadpool)

Tested

Built the wheel on Linux x86_64 and aarch64 and installed it into a clean environment, then
confirmed by symbol inspection that exactly one shipped library defines the pool accessor,
where before two did. Also linked a C++ application against the component and ran it, and ran
the existing Python tests to confirm behaviour is unchanged for current users.

[shoumikhin]

Stack from ghstack (oldest at bottom):

• Check the shipped device code covers the GPUs a wheel claims #21567
• Publish CUDA wheels so a GPU user does not build from source #21569
• Ship the CUDA delegate as its own library #21531
• Ship the XNNPACK delegate as its own library #21526
• Ship the merged CPU kernels as their own library #21524
• -> Ship the thread pool as its own library so a process has one pool #21522
• Ship a linkable shared runtime and make the Python bindings use it #21514

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21522

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