3c7044728b
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 |
||
|---|---|---|
| .. | ||
| appveyor | ||
| fbcode-dev-setup | ||
| jit | ||
| model_zoo | ||
| onnx | ||
| release | ||
| release_notes | ||
| add_apache_header.sh | ||
| apache_header.txt | ||
| apache_python.txt | ||
| buck_setup.sh | ||
| build_android.sh | ||
| build_host_protoc.sh | ||
| build_ios.sh | ||
| build_local.sh | ||
| build_mobile.sh | ||
| build_pytorch_android.sh | ||
| build_raspbian.sh | ||
| build_tegra_x1.sh | ||
| build_tizen.sh | ||
| build_windows.bat | ||
| diagnose_protobuf.py | ||
| get_python_cmake_flags.py | ||
| proto.ps1 | ||
| read_conda_versions.sh | ||
| README.md | ||
| remove_apache_header.sh | ||
| temp.sh | ||
| xcode_build.rb | ||
This directory contains the useful tools.
build_android.sh
This script is to build PyTorch/Caffe2 library for Android. Take the following steps to start the build:
- set ANDROID_NDK to the location of ndk
export ANDROID_NDK=YOUR_NDK_PATH
- run build_android.sh
#in your PyTorch root directory
bash scripts/build_android.sh
If succeeded, the libraries and headers would be generated to build_android/install directory. You can then copy these files from build_android/install to your Android project for further usage.
You can also override the cmake flags via command line, e.g., following command will also compile the executable binary files:
bash scripts/build_android.sh -DBUILD_BINARY=ON
build_ios.sh
This script is to build PyTorch/Caffe2 library for iOS, and can only be performed on macOS. Take the following steps to start the build:
- Install Xcode from App Store, and configure "Command Line Tools" properly on Xcode.
- Install the dependencies:
brew install cmake automake libtool
- run build_ios.sh
#in your PyTorch root directory
bash scripts/build_ios.sh
If succeeded, the libraries and headers would be generated to build_ios/install directory. You can then copy these files to your Xcode project for further usage.