Taylor Robie
b8f14b7877
[Profiler][Minor] Group and consolidate stub APIs ( #85510 )
...
There is a concept in profiler of a stub that wraps a profiling API. It was introduced for CUDA profiling before Kineto, and ITT has adopted it to call into VTune APIs. However for the most part we don't really interact with them when developing the PyTorch profiler.
Thus it makes sense to unify the fallback registration mechanism and create a subfolder to free up real estate in the top level `torch/csrc/profiler` directory.
Differential Revision: [D39108647](https://our.internmc.facebook.com/intern/diff/D39108647/ )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85510
Approved by: https://github.com/aaronenyeshi
2022-10-14 05:38:46 +00:00
Jing Xu
f20e4eab7b
Fix ITT unit-tests if PyTorch is compiled with USE_ITT=OFF ( #86199 )
...
Fixes https://github.com/pytorch/pytorch/pull/84848#discussion_r986329680
@malfet @slgong-fb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86199
Approved by: https://github.com/malfet
2022-10-04 21:57:05 +00:00
Jing Xu
3c7044728b
Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch ( #63289 )
...
More detailed description of benefits can be found at #41001 . This is Intel's counterpart of NVidia’s NVTX (https://pytorch.org/docs/stable/autograd.html#torch.autograd.profiler.emit_nvtx ).
ITT is a functionality for labeling trace data during application execution across different Intel tools.
For integrating Intel(R) VTune Profiler into Kineto, ITT needs to be integrated into PyTorch first. It works with both standalone VTune Profiler [(https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html )) and Kineto-integrated VTune functionality in the future.
It works for both Intel CPU and Intel XPU devices.
Pitch
Add VTune Profiler's ITT API function calls to annotate PyTorch ops, as well as developer customized code scopes on CPU, like NVTX for NVidia GPU.
This PR rebases the code changes at https://github.com/pytorch/pytorch/pull/61335 to the latest master branch.
Usage example:
```
with torch.autograd.profiler.emit_itt():
for i in range(10):
torch.itt.range_push('step_{}'.format(i))
model(input)
torch.itt.range_pop()
```
cc @ilia-cher @robieta @chaekit @gdankel @bitfort @ngimel @orionr @nbcsm @guotuofeng @guyang3532 @gaoteng-git
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63289
Approved by: https://github.com/malfet
2022-07-13 13:50:15 +00:00
PyTorch MergeBot
1454515253
Revert "Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch ( #63289 )"
...
This reverts commit f988aa2b3fhttps://github.com/pytorch/pytorch/pull/63289 on behalf of https://github.com/malfet due to broke trunk, see f988aa2b3f
2022-06-30 12:49:41 +00:00
Jing Xu
f988aa2b3f
Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch ( #63289 )
...
More detailed description of benefits can be found at #41001 . This is Intel's counterpart of NVidia’s NVTX (https://pytorch.org/docs/stable/autograd.html#torch.autograd.profiler.emit_nvtx ).
ITT is a functionality for labeling trace data during application execution across different Intel tools.
For integrating Intel(R) VTune Profiler into Kineto, ITT needs to be integrated into PyTorch first. It works with both standalone VTune Profiler [(https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html )) and Kineto-integrated VTune functionality in the future.
It works for both Intel CPU and Intel XPU devices.
Pitch
Add VTune Profiler's ITT API function calls to annotate PyTorch ops, as well as developer customized code scopes on CPU, like NVTX for NVidia GPU.
This PR rebases the code changes at https://github.com/pytorch/pytorch/pull/61335 to the latest master branch.
Usage example:
```
with torch.autograd.profiler.emit_itt():
for i in range(10):
torch.itt.range_push('step_{}'.format(i))
model(input)
torch.itt.range_pop()
```
cc @ilia-cher @robieta @chaekit @gdankel @bitfort @ngimel @orionr @nbcsm @guotuofeng @guyang3532 @gaoteng-git
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63289
Approved by: https://github.com/malfet
2022-06-30 05:14:03 +00:00
