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Relational Attention: Generalizing Transformers for Graph-Structured Tasks

This is the official code for the Relational Transformer (RT) developed by Cameron Diao and Ricky Loynd. Our code is forked from the CLRS benchmark repository (version date: Sept. 6, 2022).

RT employs a mathematically elegant extension of transformer attention, which incorporates edge vectors as first-class model components. The RT class is defined in _src/processors.py, while relational attention is implemented in _src/base_modules/Basic_Transformer_jax.py.

Installation

For proper package installation, please refer to the setup instructions in the original CLRS README

Alternatively, we provide an environment file (environment.yml) containing the packages used for our experiments. Assuming you have Anaconda installed (including conda), you can create a virtual environment using the environment file as follows:

cd relational-transformer
conda env create -f environment.yml
conda activate rt_clrs

Running Experiments

The run file run.py is located in the examples directory. run.py contains the code we used to obtain the main results of our paper, located in Table 19.

You can reproduce our results evaluating RT on the "Jarvis' March" task by running:

python3 -m clrs.examples.run

Or, for a short test run, you can specify:

python3 -m clrs.examples.run --train_items 320

Run hyperparameters are set in run.py and can be overridden on the command line. The hyperparameters we provide can also be found in Table 2. Before changing any hyperparameters, you should note:

• For any model using attention heads, the hidden size will be overwritten with the value $\mathrm{nb}\textunderscore\mathrm{heads} \cdot \mathrm{head}\textunderscore\mathrm{size}$.

• In the original CLRS benchmark, certain algorithmic tasks have more validation and test samples than others. We follow these task specifications, which are described in the CLRS README

• The current code does not support chunking during training.

Citation

To cite the CLRS Algorithmic Reasoning Benchmark:

@misc{https://doi.org/10.48550/arxiv.2210.05062,
  doi = {10.48550/ARXIV.2210.05062},
  url = {https://arxiv.org/abs/2210.05062},
  author = {Diao, Cameron and Loynd, Ricky},
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Relational Attention: Generalizing Transformers for Graph-Structured Tasks},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

Contributing

This project welcomes contributions and suggestions.