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https://github.com/heheda12345/MagPy
https://github.com/heheda12345/MagPy
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/heheda12345/MagPy
- Owner: heheda12345
- License: apache-2.0
- Created: 2023-06-06T15:41:25.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-06-05T08:15:33.000Z (5 months ago)
- Last Synced: 2024-06-15T00:25:58.057Z (5 months ago)
- Language: Python
- Size: 1.33 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MagPy
MagPy is a JIT compiler for PyTorch programs. It can extract the operator graph from PyTorch programs and optimize the graph with a wide range of deep learning graph compilers.
# Installation
MagPy now supports Python 3.9. The support of other Python versions is working in progress.
1. Install CUDA. CUDA 11.8 is recommended.
2. Install dependencies:
```bash
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
```
3. Install MagPy:
```bash
pip install -e .
```
4. Compile a shared library to disable Python integer cache by LD_PRELOAD. This script will generates a ``ldlong.v3.9.12.so'' file in build/ directory. You need to set the LD_PRELOAD environment variable to this file when running the PyTorch program.
```bash
cd scripts
./compile_longobj.sh
```
# Example Usage
The following script compiles and runs a simple PyTorch program with MagPy.
```python
LD_PRELOAD=build/ldlong.v3.9.12.so python test/example.py
```
# Citation
If you find MagPy useful in your research, please consider citing the following paper:
> MagPy: Effective Operator Graph Instantiation for Deep Learning by Execution State Monitoring; Chen Zhang, Rongchao Dong, Haojie Wang, Runxin Zhong, Jike Chen, and Jidong Zhai, Tsinghua University; will be appeared in USENIX ATC'24.