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fuchsia / third_party / github.com / google / ml-compiler-opt / refs/heads/upstream/progress
commit92366906444732eabd43870ce63f75ceb5a95431[log] [tgz]
authorJacob Hegna <jacobhegna@gmail.com>Fri Sep 10 13:32:24 2021 -0500
committerJacob Hegna <jacobhegna@gmail.com>Fri Sep 10 13:32:24 2021 -0500
tree4efccfb5f4ba8ff5175493caeaa17287f90af2e6
parentc235bbbd3bdf4a55233cdd82965b49b0de5cd4cd [diff]
Add progress feature to -Oz inlining policy.
1 file changed
tree: 4efccfb5f4ba8ff5175493caeaa17287f90af2e6
  1. buildbot/
  2. compiler_opt/
  3. docs/
  4. .gitignore
  5. LICENSE
  6. README.md
  7. requirements.txt
  8. run_tests.sh
README.md

Infrastructure for MLGO --- a Machine Learning Guided Compiler Optimizations Framework.

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.

Pretrained models

We occasionally release pretrained models that may be used as-is with LLVM. Models are released as github releases, and are named as [task]-[major-version].[minor-version].The versions are semantic: the major version corresponds to breaking changes on the LLVM/compiler side, and the minor version corresponds to model updates that are independent of the compiler.

When building LLVM, there is a flag -DLLVM_INLINER_MODEL_PATH which you may set to the path to your inlining model. If the path is set to download, then cmake will download the most recent (compatible) model from github to use. Other values for the flag could be:

# Model is in /tmp/model, i.e. there is a file /tmp/model/saved_model.pb along
# with the rest of the tensorflow saved_model files produced from training.
-DLLVM_INLINER_MODEL_PATH=/tmp/model

# Download the most recent compatible model
-DLLVM_INLINER_MODEL_PATH=download

Prerequisites

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:

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.