DiGress: Discrete Denoising diffusion models for graph generation

Warning: The code has been updated after experiments were run for the paper. If you don't manage to reproduce the paper results, please write to us so that we can investigate the issue.

For the conditional generation experiments, check the guidance branch.

Environment installation

• Download anaconda/miniconda if needed
• Create a rdkit environment that directly contains rdkit: conda create -c conda-forge -n digress rdkit python=3.9
• Install graph-tool (https://graph-tool.skewed.de/): conda install -c conda-forge graph-tool
• Install the nvcc drivers for your cuda version. For example, conda install -c "nvidia/label/cuda-11.3.1" cuda-nvcc
• Install pytorch 1.10 or 1.11 (https://pytorch.org/)
• Install pytorch-geometric. Your version should match the pytorch version that is installed (https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
• Install other packages using the requirement file: pip install -r requirements.txt
• Install mini-moses: pip install git+https://github.com/igor-krawczuk/mini-moses
• Run pip install -e .
• Navigate to the ./src/analysis/orca directory and compile orca.cpp: g++ -O2 -std=c++11 -o orca orca.cpp

Download the data

• QM9 and Guacamol should download by themselves when you run the code.
• For the community, SBM and planar datasets, data can be found at https://github.com/KarolisMart/SPECTRE/tree/main/data
• Moses dataset can be found at https://github.com/molecularsets/moses/tree/master/data

If you want to run Guacamol on the filtered data, either download it from https://drive.switch.ch/index.php/s/pjlZ8A7PADiBGrr or follow these instructions:

• Set filter_dataset=True in guacamol_dataset.py
• Run main.py with cfg.dataset.filtered=False
• Delete data/guacamol/guacamol_pyg/processed
• Run main.py with cfg.dataset.filtered=True

Run the code

• All code is currently launched through python3 main.py. Check hydra documentation (https://hydra.cc/) for overriding default parameters.
• To run the debugging code: python3 main.py +experiment=debug.yaml. We advise to try to run the debug mode first before launching full experiments.
• To run a code on only a few batches: python3 main.py general.name=test.
• To run the continuous model: python3 main.py model=continuous
• To run the discrete model: python3 main.py
• You can specify the dataset with python3 main.py dataset=guacamol. Look at configs/dataset for the list of datasets that are currently available

Checkpoints

The following checkpoints should work with the latest commit:

• Planar: https://drive.switch.ch/index.php/s/hRWLp8gOGOGFzgR \ Performance of this checkpoint:

  • Test NLL: 1135.6080
  • {'spectre': 0.006211824145982536, 'clustering': 0.0563302653184386, 'orbit': 0.00980205113753696, 'planar_acc': 0.85, 'sampling/frac_unique': 1.0, 'sampling/frac_unique_non_iso': 1.0, 'sampling/frac_unic_non_iso_valid': 0.85, 'sampling/frac_non_iso': 1.0}

• MOSES (the model in the paper was trained a bit longer than this one): https://drive.switch.ch/index.php/s/DBbvfMmezjg6KUm \ Performance of this checkpoint:

  • Test NLL: 203.8171
  • {'valid': 0.86032, 'unique@1000': 1.0, 'unique@10000': 0.9999, 'FCD/Test': 0.6176261401223826, 'SNN/Test': 0.5493580505032953, 'Frag/Test': 0.9986637035374839, 'Scaf/Test': 0.8997144919185305, 'FCD/TestSF': 1.2799741890619032, 'SNN/TestSF': 0.5231424506655995, 'Frag/TestSF': 0.9968362360368359, 'Scaf/TestSF': 0.11830576038721641, 'IntDiv': 0.8550915438149056, 'IntDiv2': 0.8489191659624407, 'Filters': 0.9707550678817184, 'logP': 0.02719348046624242, 'SA': 0.05725088257521343, 'QED': 0.0043940205061221965, 'weight': 0.7913020095007184, 'Novelty': 0.9442790697674419}

• SBM: https://drive.switch.ch/index.php/s/rxWFVQX4Cu4Vq5j \ Performance of this checkpoint:

  • Test NLL: 4757.903
  • {'spectre': 0.0060240439382095445, 'clustering': 0.05020166160905111, 'orbit': 0.04615866844490847, 'sbm_acc': 0.675, 'sampling/frac_unique': 1.0, 'sampling/frac_unique_non_iso': 1.0, 'sampling/frac_unic_non_iso_valid': 0.625, 'sampling/frac_non_iso': 1.0}

The following checkpoints require to revert to commit 682e59019dd33073b1f0f4d3aaba7de6a308602e and run pip install -e .:

• Guacamol: https://drive.switch.ch/index.php/s/jjM3pIHdxWrUGOH

Generated samples

We provide the generated samples for some of the models. If you have retrained a model from scratch for which the samples are not available yet, we would be very happy if you could send them to us!

Troubleshooting

PermissionError: [Errno 13] Permission denied: '/home/vignac/DiGress/src/analysis/orca/orca': You probably did not compile orca.

Use DiGress on a new dataset

To implement a new dataset, you will need to create a new file in the src/datasets folder. Depending on whether you are considering molecules or abstract graphs, you can base this file on moses_dataset.py or spectre_datasets.py, for example. This file should implement a Dataset class to process the data (check PyG documentation), as well as a DatasetInfos class that is used to define the noise model and some metrics.

For molecular datasets, you'll need to specify several things in the DatasetInfos:

• The atom_encoder, which defines the one-hot encoding of the atom types in your dataset
• The atom_decoder, which is simply the inverse mapping of the atom encoder
• The atomic weight for each atom atype
• The most common valency for each atom type

The node counts and the distribution of node types and edge types can be computed automatically using functions from AbstractDataModule.

Once the dataset file is written, the code in main.py can be adapted to handle the new dataset, and a new file can be added in configs/dataset.

Cite the paper

@inproceedings{
vignac2023digress,
title={DiGress: Discrete Denoising diffusion for graph generation},
author={Clement Vignac and Igor Krawczuk and Antoine Siraudin and Bohan Wang and Volkan Cevher and Pascal Frossard},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=UaAD-Nu86WX}
}