psg-mit/programming-with-neural-surrogates-of-programs

Programming with Neural Surrogates of Programs

Citation

@inproceedings{renda2021programming, 
  title={Programming with Neural Surrogates of Programs}, 
  author={Renda, Alex and Ding, Yi and Carbin, Michael}, 
  booktitle={ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!)}, 
  doi = {10.1145/3486607.3486748}, 
  year={2021}, 
}

Dependencies

The codebase has been validated on Debian 10 (Buster) with the following dependencies/versions:

• python=3.7
  • datasets=1.16.1
  • matplotlib=3.5.0
  • numpy=1.21.2
  • onnxruntime=1.10.0
  • pandas=1.3.4
  • pytorch=1.10.0
  • tokenizers=0.10.3
  • tqdm=4.62.3
  • transformers=4.13.0
• numactl
• cmake
• git
• ninja-build
• build-essential

You should also have cuda installed and pytorch configured to use cuda to train efficiently (though it is not required).

How to use

Hyperparameter Search / Surrogate Compilation

To run the initial hyperparameter search, using [JOBS] parallel jobs: python train.py --jobs [JOBS] hyperparameter-search

To print the results of the hyperparameter search (corresponding to Table 4 in the paper), run:

python results.py table-4

To print the results of surrogate compilation (corresponding to Section 3in the paper), first build llvm-mca:

./build_llvm.sh

Them print the results:

python results.py section-3

To generate the telemetry figures in Appendix B, run:

python results.py surrogate-compilation-telemetry

which will create plots in the figures directory.

Surrogate Adaptation

To run the surrpgate adaptation experiments, using [JOBS] parallel jobs: python train.py --jobs [JOBS] adaptation

To generate Figure 2 in the paper:

python results.py figure-2

To generatee the telemetry figures in Appendix B, run:

python results.py surrogate-adaptation-telemetry