pytorch

22fb9294 - [iOS][coreml] Add CoreML memory observer (#76251)

[iOS][coreml] Add CoreML memory observer (#76251) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/76251 Add an observer to `PTMCoreMLExecutor` so we can inspect OOMs in production to help with T115554493. The behaviour of the logger is as such: 1. Each time a model is compiled, there is a chance we publish all logs to QPL. This is determined by the randomly generated `_model_load_id` and `_sample_thresh`. 2. If we are publishing all logs, then every `_sample_every` inferences will be logged via QPL. 3. Every QPL log will collect memory metrics before and after model compilation/inference 4. If memory pressure is not normal (remaining mem < 400 MB) before or after compilation/inference, then that compilation/inference will be logged to QPL no matter what. Test Plan: We can test in pytorch playground and inspect the QPL logs through Flipper: ``` arc focus2 -b pp-ios -a ModelRunner -a //xplat/caffe2/c10:c10Apple -a //xplat/caffe2:torch_mobile_coreApple -a //xplat/caffe2/fb/dynamic_pytorch:dynamic_pytorch_implApple -a //xplat/caffe2:coreml_delegateApple -a ModelRunnerDevOps -a //xplat/caffe2:torch_mobile_all_opsApple -a coreml_memory_observer -a //xplat/perflogger:perfloggerApple -fd --force-with-wrong-xcode ``` To check results in Hive/Scuba, test in instagram: ``` arc focus2 -b igios-no-extensions -a //fbobjc/Apps/Instagram/AppLibraries/Core/QPL/IGPerformanceLogging:IGPerformanceLogging -a //xplat/caffe2/c10:c10Apple -a //xplat/caffe2:torch_mobile_coreApple -a //xplat/caffe2/fb/dynamic_pytorch:dynamic_pytorch_implApple -a //xplat/caffe2:coreml_delegateApple -a //xplat/caffe2:torch_mobile_all_opsApple -a //xplat/perflogger:perfloggerApple -a coreml_memory_observerApple -c pt.enable_qpl=1 --force-with-wrong-xcode ``` Note that we need to change `_sample_thresh` to ensure logs show up. Reviewed By: kimishpatel Differential Revision: D35511873 fbshipit-source-id: 59f2fa2d021178ceab1fcf5ee94b2f15ceca32ee (cherry picked from commit 8b8af55410ea1231693ee980c80d8a749f5ad870)