Add support for using caffe2::ThreadPool in pytorch mobile QNNPACK. (#23658)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23658
**How things work for caffe2:**
Caffe2 Ops -> NNPACK/QNNPACK -> pthreadpool_compute_1/2/3/4d_tiled -> pthreadpool_compute_1d (caffe2 shim) -> caffe2::ThreadPool
**Before this PR:**
Pytorch Ops -> NNPACK/QNNPACK -> pthreadpool_compute_1/2/3/4d_tiled -> pthreadpool_compute_1d (third_party implementation without mobile optimization)
caffe2::ThreadPool is optimized for mobile. This change leverages this logic for pytorch mobile as a temporary solution improve pytorch mobile perf. It is guarded by the C10_MOBILE macro.
For server side we return nullptr.
**Plan for next steps:**
Implement a mobile version of "at::parallel_for" which uses caffe2::ThreadPool internally so all ATen/TH multithreading usage is mobile optimized.
Refactor QNNPACK and/or pthreadpool to explicitly using "at::parallel_for" primitive to replace pthreadpool_compute_1d for Pytorch.
After QNNPACK is refactored, we will delete the mobile_threadpool() API.
ghstack-source-id: 88073396
Reviewed By: dreiss
Differential Revision: D16594020
fbshipit-source-id: 9f94600756d5f86d24a12a2fd7df3eebd0994f1d
