sidnb13/tokengt

[NeurIPS'22] Tokenized Graph Transformer (TokenGT), in PyTorch

Tokenized Graph Transformer (PyTorch)

Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong
NeurIPS 2022

Setting up experiments

Using the provided Docker image (recommended)

docker pull jw9730/tokengt:latest
docker run -it --gpus=all --ipc=host --name=tokengt -v /home:/home jw9730/tokengt:latest bash
# upon completion, you should be at /tokengt inside the container

Using the provided Dockerfile

git clone --recursive https://github.com/jw9730/tokengt.git /tokengt
cd tokengt
docker build --no-cache --tag tokengt:latest .
docker run -it --gpus all --ipc=host --name=tokengt -v /home:/home tokengt:latest bash
# upon completion, you should be at /tokengt inside the container

Using pip

sudo apt-get update
sudo apt-get install python3.9
git clone --recursive https://github.com/jw9730/tokengt.git tokengt
cd tokengt
bash install.sh

Running experiments

Synthetic second-order equivariant basis approximation

cd equivariant-basis-approximation/scripts

# Train and save logs, ckpts, and attention maps (--save_display)
bash [INPUT]-[NODE_IDENTIFIER]-[TYPE_IDENTIFIER].sh

# Test and save attention maps (--save_display)
bash [INPUT]-[NODE_IDENTIFIER]-[TYPE_IDENTIFIER]-test.sh

# For the visualization of saved attention maps, please see viz_multi.ipynb

PCQM4Mv2 large-scale graph regression

cd large-scale-regression/scripts

# TokenGT (ORF)
bash pcqv2-orf.sh

# TokenGT (Lap)
bash pcqv2-lap.sh

# TokenGT (Lap) + Performer
bash pcqv2-lap-performer-finetune.sh

# TokenGT (ablated)
bash pcqv2-ablated.sh

# Attention distance plot for TokenGT (ORF)
bash visualize-pcqv2-orf.sh

# Attention distance plot for TokenGT (Lap)
bash visualize-pcqv2-lap.sh

Pre-Trained Models

We provide checkpoints of TokenGT (ORF) and TokenGT (Lap), both trained with PCQM4Mv2. Please download ckpts.zip from this link. Then, unzip ckpts and place it in the large-scale-regression/scripts directory, so that each trained checkpoint is located at large-scale-regression/scripts/ckpts/pcqv2-tokengt-[NODE_IDENTIFIER]-trained/checkpoint_best.pt. After that, you can resume the training from these checkpoints by adding the option --pretrained-model-name pcqv2-tokengt-[NODE_IDENTIFIER]-trained to the training scripts.

References

Our implementation uses code from the following repositories:

• Performer for FAVOR+ attention kernel
• Graph Transformer, SignNet, and SAN for Laplacian eigenvectors
• Graphormer for PCQM4Mv2 experiment pipeline
• timm for stochastic depth regularization

Citation

If you find our work useful, please consider citing it:

@article{kim2022pure,
  author    = {Jinwoo Kim and Tien Dat Nguyen and Seonwoo Min and Sungjun Cho and Moontae Lee and Honglak Lee and Seunghoon Hong},
  title     = {Pure Transformers are Powerful Graph Learners},
  journal   = {arXiv},
  volume    = {abs/2207.02505},
  year      = {2022},
  url       = {https://arxiv.org/abs/2207.02505}
}

Acknowledgements

The development of this open-sourced code was supported in part by the National Research Foundation of Korea (NRF) (No. 2021R1A4A3032834).