Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/google/ml-compiler-opt
Infrastructure for Machine Learning Guided Optimization (MLGO) in LLVM.
https://github.com/google/ml-compiler-opt
Last synced: 2 months ago
JSON representation
Infrastructure for Machine Learning Guided Optimization (MLGO) in LLVM.
- Host: GitHub
- URL: https://github.com/google/ml-compiler-opt
- Owner: google
- License: apache-2.0
- Created: 2020-05-28T23:32:39.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-06T18:56:38.000Z (4 months ago)
- Last Synced: 2024-09-06T21:44:46.444Z (4 months ago)
- Language: Python
- Homepage:
- Size: 2.25 MB
- Stars: 610
- Watchers: 22
- Forks: 91
- Open Issues: 42
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
Awesome Lists containing this project
- awesome-rainmana - google/ml-compiler-opt - Infrastructure for Machine Learning Guided Optimization (MLGO) in LLVM. (Python)
README
# 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. The MLGO framework currently supports two optimizations:
1. inlining-for-size([LLVM RFC](https://lists.llvm.org/pipermail/llvm-dev/2020-April/140763.html));
2. register-allocation-for-performance([LLVM RFC](https://lists.llvm.org/pipermail/llvm-dev/2021-November/153639.html))
The compiler components are both available in the main LLVM repository. This
repository contains the training infrastructure and related tools for MLGO.
We currently use two different ML algorithms: Policy Gradient and Evolution
Strategies to train policies. Currently, this repository only support Policy
Gradient training. The release of Evolution Strategies training is on our
roadmap.
Check out this [demo](docs/inlining-demo/demo.md) for an end-to-end demonstration of how
to train your own inlining-for-size policy from the scratch with Policy
Gradient, or check out this [demo](docs/regalloc-demo/demo.md) for a demonstration of how
to train your own regalloc-for-performance policy.
For more details about MLGO, please refer to our paper
[MLGO: a Machine Learning Guided Compiler Optimizations Framework](https://arxiv.org/abs/2101.04808).
For more details about how to contribute to the project, please refer to
[contributions](docs/contributing.md).
## 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:
```sh
# 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 assumptions for the system are:
* Recent Ubuntu distro, e.g. 20.04
* python 3.8.x/3.9.x/3.10.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](https://llvm.org/docs/CMake.html)
* the build
[bot script](https://github.com/google/ml-compiler-opt/blob/main/buildbot/buildbot_init.sh)
The model training - specific prerequisites are:
Pipenv:
```shell
pip3 install pipenv
```
The actual dependencies:
```shell
pipenv sync --system --categories "packages dev-packages ci"
```
Note that the above command will only work from the root of the repository
since it needs to have `Pipfile.lock` in the working directory at the time
of execution.
The above command will also install all the packages, including development
packages (the `dev-packages` category), and packages only needed in CI (the
`ci` category). If you do not need those, you can omit them from the categories
option.
Optionally, to run tests (run_tests.sh), you also need:
```shell
sudo apt-get install virtualenv
```
Note that the same tensorflow package is also needed for building the 'release'
mode for LLVM.
## Docs
An end-to-end [demo](docs/inlining-demo/demo.md) using Fuchsia as a codebase from which
we extract a corpus and train a model.
[How to add a feature](docs/adding_features.md) guide.
[Extensibility model](docs/extensibility.md).