Latent Execution for Neural Program Synthesis

This repo provides the code to replicate the experiments in the paper

Xinyun Chen, Dawn Song, Yuandong Tian, Latent Execution for Neural Program Synthesis, in NeurIPS 2021.

Paper [arXiv] [NeurIPS]

Prerequisites

PyTorch

Dataset

Sample Usage

1. To run our full latent program synthesizer (LaSynth):

python run.py --latent_execution --operation_predictor --decoder_self_attention

2. To run our program synthesizer without partial program execution (NoPartialExecutor):

python run.py --latent_execution --operation_predictor --decoder_self_attention --no_partial_execution

3. To run the RobustFill model:

python run.py

4. To run the Property Signatures model:

python run.py --use_properties

Run experiments

In the following we list some important arguments for experiments:

• --data_folder: path to the dataset.
• --model_dir: path to the directory that stores the models.
• --load_model: path to the pretrained model (optional).
• --eval: adding this command will enable the evaluation mode; otherwise, the model will be trained by default.
• --num_epochs: number of training epochs. The default value is 10, but usually 1 epoch is enough for a decent performance.
• --log_interval LOG_INTERVAL: saving checkpoints every LOG_INTERVAL steps.
• --latent_execution: Enable the model to learn the latent executor module.
• --no_partial_execution: Enable the model to learn the latent executor module, but this module is not used by the program synthesizer, and only adds to the training loss.
• --operation_predictor: Enable the model to learn the operation predictor module.
• --use_properties: Run the Property Signatures baseline.
• --iterative_retraining_prog_gen: Decode training programs for iterative retraining.

More details can be found in arguments.py.

Citation

If you use the code in this repo, please cite the following paper:

@inproceedings{chen2021latent,
  title={Latent Execution for Neural Program Synthesis},
  author={Chen, Xinyun and Song, Dawn and Tian, Yuandong},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021}
}

License

This repo is CC-BY-NC licensed, as found in the LICENSE file.

References

[1] Devlin et al., RobustFill: Neural Program Learning under Noisy I/O, ICML 2017.

[2] Odena and Sutton, Learning to Represent Programs with Property Signatures, ICLR 2020.

[3] Chen et al., Execution-Guided Neural Program Synthesis, ICLR 2019.