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Contribution

Contributing to Intel® Extension for PyTorch*

Thank you for your interest in contributing to Intel® Extension for PyTorch*! Before you begin writing code, it is important that you share your intention to contribute with the team, based on the type of contribution:

  1. You want to propose a new feature and implement it.
    • Post about your intended feature in an issue, and we shall discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it.
  2. You want to implement a feature or bug-fix for an outstanding issue.
    • Search for your issue in the issue list.
    • Pick an issue and comment that you'd like to work on the feature or bug-fix.
    • If you need more context on a particular issue, please ask and we shall provide.

Once you implement and test your feature or bug-fix, please submit a Pull Request to https://github.com/intel/intel-extension-for-pytorch.

Developing Intel® Extension for PyTorch*

A full set of instructions on installing Intel® Extension for PyTorch* from source is here: https://github.com/intel/intel-extension-for-pytorch#install-extension-by-compiling-from-source

To develop on your machine, here are some tips:

  1. Uninstall all existing Intel® Extension for PyTorch* installs. You may need to run pip uninstall intel_extension_for_pytorch multiple times. You'll know intel_extension_for_pytorch is fully uninstalled when you see WARNING: Skipping intel_extension_for_pytorch as it is not installed. (You should only have to pip uninstall a few times, but you can always uninstall with timeout or in a loop if you're feeling lazy.)
yes | pip uninstall intel_extension_for_pytorch
  1. Clone a copy of Intel® Extension for PyTorch* from source:
git clone https://github.com/intel/intel-extension-for-pytorch.git
cd intel-extension-for-pytorch

2.1. If you already have Intel® Extension for PyTorch* from source, update it:

git pull --rebase
git submodule sync --recursive
git submodule update --init --recursive --jobs 0

If you want to have no-op incremental rebuilds (which are fast), see the section below titled "Make no-op build fast."

  1. Install Intel® Extension for PyTorch* in develop mode:

The change you have to make is to replace

python setup.py install

with

python setup.py develop

This mode will symlink the Python files from the current local source tree into the Python install. Hence, if you modify a Python file, you do not need to reinstall PyTorch again and again. This is especially useful if you are only changing Python files.

For example:

  • Install local Intel® Extension for PyTorch* in develop mode
  • modify your Python file intel_extension_for_pytorch/__init__.py (for example)
  • test functionality
  • modify your Python file intel_extension_for_pytorch/__init__.py
  • test functionality
  • modify your Python file intel_extension_for_pytorch/__init__.py
  • test functionality

You do not need to repeatedly install after modifying Python files (.py). However, you would need to reinstall if you modify Python interface (.pyi, .pyi.in) or non-Python files (.cpp, .cc, .cu, .h, ...).

In case you want to reinstall, make sure that you uninstall Intel® Extension for PyTorch* first by running pip uninstall intel_extension_for_pytorch until you see WARNING: Skipping intel_extension_for_pytorch as it is not installed; next run python setup.py clean. After that, you can install in develop mode again.

Tips and Debugging

  • A prerequisite to installing Intel® Extension for PyTorch* is CMake. We recommend installing it with Homebrew with brew install cmake if you are developing on MacOS or Linux system.
  • Our setup.py requires Python >= 3.6
  • If you run into errors when running python setup.py develop, here are some debugging steps:
    1. Run printf '#include <stdio.h>\nint main() { printf("Hello World");}'|clang -x c -; ./a.out to make sure your CMake works and can compile this simple Hello World program without errors.
    2. Nuke your build directory. The setup.py script compiles binaries into the build folder and caches many details along the way, which saves time the next time you build. If you're running into issues, you can always rm -rf build from the toplevel pytorch directory and start over.
    3. If you have made edits to the Intel® Extension for PyTorch* repo, commit any change you'd like to keep and clean the repo with the following commands (note that clean really removes all untracked files and changes.):
    git submodule deinit -f .
git clean -xdf
python setup.py clean
git submodule update --init --recursive --jobs 0 # very important to sync the submodules
python setup.py develop                          # then try running the command again
    1. The main step within python setup.py develop is running make from the build directory. If you want to experiment with some environment variables, you can pass them into the command:
    ENV_KEY1=ENV_VAL1[, ENV_KEY2=ENV_VAL2]* python setup.py develop

Unit testing

Python Unit Testing

All PyTorch test suites are located in the test folder and start with test_. Run individual test suites using the command python test/cpu/FILENAME.py, where FILENAME represents the file containing the test suite you wish to run.

For example, to run all the TorchScript JIT tests (located at test/cpu/test_jit.py), you would run:

python test/cpu/test_jit.py

You can narrow down what you're testing even further by specifying the name of an individual test with TESTCLASSNAME.TESTNAME. Here, TESTNAME is the name of the test you want to run, and TESTCLASSNAME is the name of the class in which it is defined.

Going off the above example, let's say you want to run test_Sequential, which is defined as part of the TestJit class in test/cpu/test_jit.py. Your command would be:

python test/test_jit.py TestJit.test_Sequential

The expecttest and hypothesis libraries must be installed to run the tests. mypy is an optional dependency, and pytest may help run tests more selectively. All these packages can be installed with conda or pip.

Better local unit tests with pytest

We don't officially support pytest, but it works well with our unittest tests and offers a number of useful features for local developing. Install it via pip install pytest.

If you want to just run tests that contain a specific substring, you can use the -k flag:

pytest test/cpu/test_nn.py -k Loss -v

The above is an example of testing a change to all Loss functions: this command runs tests such as TestNN.test_BCELoss and TestNN.test_MSELoss and can be useful to save keystrokes.

Local linting

You can run the same linting steps that are used in CI locally via make:

# Lint all files
make lint -j 6  # run lint (using 6 parallel jobs)

# Lint only the files you have changed
make quicklint -j 6

These jobs may require extra dependencies that aren't dependencies of Intel® Extension for PyTorch* itself, so you can install them via this command, which you should only have to run once:

make setup_lint

To run a specific linting step, use one of these targets or see the Makefile for a complete list of options.

# Check for tabs, trailing newlines, etc.
make quick_checks

make flake8

make mypy

make cmakelint

make clang-tidy

To run a lint only on changes, add the CHANGED_ONLY option:

make <name of lint> CHANGED_ONLY=--changed-only

C++ Unit Testing

Intel® Extension for PyTorch* offers tests located in the test/cpp folder. These tests are written in C++ and use the Google Test testing framework. After compiling Intel® Extension for PyTorch* from source, the test runner binaries will be written to the build/bin folder. The command to run one of these tests is ./build/bin/FILENAME --gtest_filter=TESTSUITE.TESTNAME, where TESTNAME is the name of the test you'd like to run and TESTSUITE is the suite that test is defined in.

For example, if you wanted to run the test MayContainAlias, which is part of the test suite ContainerAliasingTest in the file test/cpp/jit/test_alias_analysis.cpp, the command would be:

./build/bin/test_jit --gtest_filter=ContainerAliasingTest.MayContainAlias

Writing documentation

So you want to write some documentation and don't know where to start?

Intel® Extension for PyTorch* uses Google style for formatting docstrings. Length of line inside docstrings block must be limited to 80 characters to fit into Jupyter documentation popups.

Building documentation

To build the documentation:

  1. Build and install Intel® Extension for PyTorch*

  2. Install the prerequisites

cd docs
pip install -r requirements.txt
  1. Generate the documentation HTML files. The generated files will be in docs/_build/html.
make clean
make html

Tips

The .rst source files live in docs/tutorials. Some of the .rst files pull in docstrings from Intel® Extension for PyTorch* Python code (for example, via the autofunction or autoclass directives). To vastly shorten doc build times, it is helpful to remove the files you are not working on, only keeping the base index.rst file and the files you are editing. The Sphinx build will produce missing file warnings but will still complete.