# crossbeam

**Repository Path**: mirrors_google-research/crossbeam

## Basic Information

- **Project Name**: crossbeam
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-23
- **Last Updated**: 2026-02-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CrossBeam: Learning to Search in Bottom-Up Program Synthesis

This repository contains the source code associated with the paper published at
ICLR'22 ([OpenReview](https://openreview.net/forum?id=qhC8mr2LEKq)):

> Kensen Shi, Hanjun Dai, Kevin Ellis, and Charles Sutton. **CrossBeam:
> Learning to Search in Bottom-Up Program Synthesis.** International Conference
> on Learning Representations (ICLR), 2022.

In this research project, we train a neural model to learn a hands-on search
policy for bottom-up program synthesis, in an effort to tame the search space
blowup.

To cite this work, you can use the following BibTeX entry:
```
@inproceedings{shi2022crossbeam,
    title={{CrossBeam}: Learning to Search in Bottom-Up Program Synthesis},
    author={Kensen Shi and Hanjun Dai and Kevin Ellis and Charles Sutton},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=qhC8mr2LEKq},
}
```

## Setup

For dependencies, first install
[PyTorch](https://pytorch.org/get-started/locally/) with CUDA and
`torch-scatter`, for example with the following commands:

```
pip3 install torch==1.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip3 install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
```

Then run the following from the root `crossbeam/` directory:

```
pip3 install -e .
```

You can also append the `--user` option if you don't want to install globally.

## Running tests

From the root `crossbeam` directory, run:

```
pytest
```

## Generate training and test datasets

The synthetic training data is only needed for re-training models and is not
included in this repo. The following steps will generate the synthetic training
data:

```
./crossbeam/datasets/run_bustle_gen.sh
./crossbeam/datasets/run_logic_gen.sh
./crossbeam/datasets/run_logic_clean_and_shard.sh
```

Test datasets are in `crossbeam/data/`.

## Train the model

Trained models are included in this repo in `trained_models/`. If you wish to
re-train the models, first generate training data with the commands above, and
then follow the steps below.

#### BUSTLE domain:

Navigate to `crossbeam/experiment/bustle` directory, make any necessary edits
to `bustle_main_dist.sh` including the data folder and number of GPUs to use,
and run the script to train a model:

```
cd crossbeam/experiment/bustle
./bustle_main_dist.sh
```

The default hyperparameters should mirror the settings in the paper.

#### Logic domain:

Similar to the case above, run the following script:

```
cd crossbeam/experiment/logic_synthesis
./dist_run_logic.sh
```

You can set the `usegreattransformer` argument to either `1` or `0`, to use the
Transformer encoder or a simple MLP.

## Running the trained model on test datasets

From the root `crossbeam/` directory, run:

```
./iclr2022/bustle_experiments.sh
./iclr2022/logic_experiments.sh
```

These commands will save results as JSON files in `iclr2022/bustle_results` and
`iclr2022/logic_results`.

## Disclaimer

This is not an official Google product.