24ca7e91e6
[1/N] Use internal linkage in torch/csrc C++ files. ( #150930 )
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Turn more functions and variables into static if they are not used outside the cpp files. Unused functions are removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150930
Approved by: https://github.com/Skylion007
Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com >
2025-04-11 02:19:31 +00:00
8fa81a6066
Enable misc-use-internal-linkage check and apply fixes ( #148948 )
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Enables clang-tidy rule [`misc-use-internal-linkage`](https://clang.llvm.org/extra/clang-tidy/checks/misc/use-internal-linkage.html ). This new check was introduced in Clang-Tidy 18 and is available due to recent update of Clang-Tidy 19.
The check marks functions and variables used only in the translation unit as static. Therefore undesired symbols are not leaked into other units, more link time optimisations are possible and the resulting binaries may be smaller.
The detected violations were mostly fixed by using static. In other cases, the symbols were indeed consumed by others files, then their declaring headers were included. Still some declarations were wrong and have been fixed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148948
Approved by: https://github.com/Skylion007
2025-03-12 14:22:56 +00:00
4a019047ad
Enable nested namespace check in clang-tidy ( #118506 )
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It is time to enable nested namespaces in the code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118506
Approved by: https://github.com/albanD
2024-01-31 00:32:35 +00:00
f20e4eab7b
Fix ITT unit-tests if PyTorch is compiled with USE_ITT=OFF ( #86199 )
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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
3c7044728b
Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch ( #63289 )
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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
1454515253
Revert "Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch ( #63289 )"
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This reverts commit f988aa2b3ff77d5aa010bdaae4e52c6ee345c04d.
Reverted https://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
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
