Integration of LibKineto CUpti Events and Metrics with PyTorch Profiler
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 30/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- cpp
- Domain
- performance
Research direction
Start by reading the referenced sections of libkineto/src/CuptiRangeProfiler.cpp and libkineto/src/EventProfiler.cpp, then inspect libkineto/test/EventProfilerTest.cpp. Determine and document whether PyTorch Profiler supports these events and metrics, their intended applications, and whether Chrome traces can include them; the test should clarify existing behavior.
Written by the indexing model from the issue text.
Description
Hi team,
It looks like LibKineto can enable GPU hardware counters (Events) and/or GPU Metrics, but the integration with PyTorch profiler is not clear for me. I can see the potential use of the CUDA metrics in kDerivedMetrics, in EventProfilerTest and its initialization in EventProfiler::initMetrics...
So my questions are:
- Is there any documentation on how to use LibKineto CUpti events and metrics within the PyTorch profiler?
- If integration with the PyTorch profiler isn’t the intended use case, what are the main applications for these metrics?
- Can LibKineto produce Chrome traces that include GPU events and/or metrics?
I could not find examples of PyTorch profiler using GPU counter events, which suggests potential integration challenges. Clarifying these points would really help in understanding how to leverage LibKineto’s capabilities. Specially because I am trying to extend LibKineto to support metrics of a different AI hardware accelerator.
- Dominant language
- C++
- Stars
- 992
- Forks
- 270
- Avg merge
- 2d 11h
- Merged PRs (30d)
- 12
Contributor guide
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