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https://github.com/chriscummins/programl
A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
https://github.com/chriscummins/programl
compiler-irs control-flow data-flow graph-neural-networks graph-representation llvm llvm-ir machine-learning programming-languages
Last synced: 5 days ago
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A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
- Host: GitHub
- URL: https://github.com/chriscummins/programl
- Owner: ChrisCummins
- License: other
- Created: 2019-11-25T02:49:35.000Z (about 5 years ago)
- Default Branch: development
- Last Pushed: 2024-05-22T23:14:58.000Z (7 months ago)
- Last Synced: 2024-12-10T22:14:05.445Z (12 days ago)
- Topics: compiler-irs, control-flow, data-flow, graph-neural-networks, graph-representation, llvm, llvm-ir, machine-learning, programming-languages
- Language: C++
- Homepage:
- Size: 51.1 MB
- Stars: 305
- Watchers: 9
- Forks: 63
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
ProGraML: Program Graphs for Machine Learning
An expressive, language-independent representation of programs.
Check the website
for more information.
## Introduction
ProGraML is a representation for programs as input to a machine learning model.
The key features are:
1. **Simple:** Everything is available through a `pip install`, no compilation
required. Supports several programming languages (*C, C++, LLVM-IR, XLA*) and
several graph formats (*NetworkX, DGL, Graphviz, JSON*) out of the box.
2. **Expressive:** Captures every control, data, and call relation across entire
programs. The representation is independent of the source language. Features
and labels can be added at any granularity to support whole-program,
per-instruction, or per-relation reasoning tasks.
3. **Fast:** The core graph construction is implemented in C++ with a low
overhead interface to Python. Every API method supports simple and efficient
parallelization through an `executor` parameter.
To get stuck in and play around with our graph representation, visit:
Or if papers are more your ☕, have a read of ours:
## Supported Programming Languages
The following programming languages and compiler IRs are supported
out-of-the-box:
Language
API Calls
Supported Versions
C
programl.from_cpp(),
programl.from_clang()
Up to ISO C 2017
C++
programl.from_cpp(),
programl.from_clang()
Up to ISO C++ 2020 DIS
LLVM-IR
programl.from_llvm_ir()
3.8.0, 6.0.0, 10.0.0
XLA
programl.from_xla_hlo_proto()
2.0.0
Is your favorite language not supported here? Submit a [feature
request](https://github.com/ChrisCummins/ProGraML/issues/new/choose)!
## Getting Started
Install the latest release of the Python package using:
```
pip install -U programl
```
The API is very simple, comprising graph *creation* ops, graph *transform* ops,
and graph *serialization* ops. Here is a quick demo of each:
```py
>>> import programl as pg
# Construct a program graph from C++:
>>> G = pg.from_cpp("""
... #include
...
... int main(int argc, char** argv) {
... std::cout << "Hello, world!" << std::endl;
... return 0;
... }
... """)
# A program graph is a protocol buffer:
>>> type(G).__name__
'ProgramGraph'
# Convert the graph to NetworkX:
>>> pg.to_networkx(G)
# Save the graph for later:
>>> pg.save_graphs('file.data', [G])
```
For further details check out the [API
reference](https://chriscummins.cc/ProGraML/api/python.html).
## Contributing
Patches, bug reports, feature requests are welcome! Please use the
[issue tracker](https://github.com/ChrisCummins/ProGraML/issues) to file a
bug report or question. If you would like to help out with the code, please
read [this document](CONTRIBUTING.md).
## Citation
If you use ProGraML in any of your work, please cite [this
paper](https://chriscummins.cc/pub/2021-icml.pdf):
```
@inproceedings{cummins2021a,
title={{ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations}},
author={Cummins, Chris and Fisches, Zacharias and Ben-Nun, Tal and Hoefler, Torsten and O'Boyle, Michael and Leather, Hugh},
booktitle = {Thirty-eighth International Conference on Machine Learning (ICML)},
year={2021}
}
```