mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
bc9277dca3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63367 This diff changes the way operator profiling is done in lite predictor benchmarking binary. Instead of using custom callbacks it uses KinetoEdgeCPUProfiler to profile events and then generate operator level metric from it. Since KinetoEvents do not contain cpu clock time, now we report only wallclock time. This unifies various profiling effort that we have for benchmarking purpose. In production we will still use observer based mechanism, but the advantage of using kineto profiler is that we get few other things for free, such as: - chrome trace generation. - operator level memory profiling (to be added) - flop counts (to be added) Furthermore possible we can use python post processing script to parse chrome trace and generate output similar to torch.profiler. (To be done) Test Plan: aibench run Model without debug info: https://www.internalfb.com/intern/aibench/details/219598441154763 Model with debug info and `--print_module_info true` (see Operator summary has now module hierarchy information). https://www.internalfb.com/intern/aibench/details/617154236292985 Reviewed By: raziel Differential Revision: D30327514 fbshipit-source-id: 3bb2f2daaaedfb04bd6f5d9c91292783f9c4344f |
||
|---|---|---|
| .. | ||
| api | ||
| c10d | ||
| common | ||
| dist_autograd | ||
| jit | ||
| lite_interpreter_runtime | ||
| rpc | ||
| tensorexpr | ||
| __init__.py | ||