MLGO is a framework for integrating ML techniques systematically in LLVM. It replaces human-crafted optimization heuristics in LLVM with machine learned models. Our pioneering project is on the inlining-for-size optimization in LLVM.

We currently use two different ML algorithms: Policy Gradient and Evolution Strategies, to train the inlining-for-size model, and achieve up to 7% size reduction, when compared to state of the art LLVM -Oz. The compiler components are available in the main LLVM repository. This repository contains the training infrastructure and related tools for MLGO.

Currently we only support training inlining-for-size policy with Policy Gradient. We are working on:

1. releasing Evolution Strategies training;
2. more optimization problems other than inlining-for-size.

Check out this demo for an end-to-end demonstration of how to train your own inlining-for-size policy from the scratch with Policy Gradient.

For more details about MLGO, please refer to our paper MLGO: a Machine Learning Guided Compiler Optimizations Framework.

Currently, the assumption for the is:

• Recent Ubuntu distro, e.g. 20.04
• python 3.8.x
• for local training, which is currently the only supported mode, we recommend a high-performance workstation (e.g. 96 hardware threads).

Training assumes a clang build with ML 'development-mode'. Please refer to:

• LLVM documentation
• the build bot script

The model training - specific prerequisites are:

pip3 install --user -r requirements.txt

Where requirements.txt is provided in the root of the repository.

Optionally, to run tests (run_tests.sh), you also need:

sudo apt-get install virtualenv

Note that the same tensorflow package is also needed for building the 'release' mode for LLVM.