kgTransformer

This is the original implementation for KDD 2022 paper Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries.

Prerequisites

• pytorch>=1.7.1,<=1.9
  • Note: Pytorch version greater than 1.9 has OOM bugs. See pytorch/pytorch#67680.
• pytorch-geometric
  • See PyG implementation. For older PyTorch version, check out here
• fastmoe

Example installation using conda:

# Use the cuda version that matches your nvidia driver and pytorch
conda install "pytorch>=1.7.1,<=1.9" cudatoolkit=11.3 pyg -c pyg -c pytorch -y

# To compile fastmoe, CUDA `nvcc` toolchain is required.
# If not exists, it can be installed with conda:
conda install cudatoolkit-dev=11.3 "gxx_linux-64<=10" nccl -c conda-forge -y
# `nvcc` does not support gcc>10 as of 2022/06.

# Download fastmoe submodule if not already downloaded
git submodule update --init
cd fastmoe
pip install -e .

Reproduction

The parameters in the paper is preloaded in configs/. Change root_dir option for the location to save model checkpoints.

Dataset can be downloaded from http://snap.stanford.edu/betae/KG_data.zip. The location for the extracted dataset should be specified in the data_dir in the config files. For exmpale, if the FB15k-237-q2b dataset is in /data/FB15k-237-q2b, this is what the data_dir options should be set.

Alternatively, pretrained models are available at OneDrive.

To reproduce all results for FB15k-237:

kgt="python main.py -c configs/fb15k-237.json"
# run pretrain1 & pretrain2
$kgt pretrain1 pretrain2
# Do multi-task finetuning for all tasks
$kgt reasoning_multi_1e5 kgt reasoning_multi_1e6
# Do single-task finetuning for each task
$kgt reasoning_1p reasoning_2p reasoning_3p
$kgt reasoning_2i reasoning_3i
$kgt reasoning_ip reasoning_pi
$kgt reasoning_2u reasoning_up

For NELL995:

kgt="python main.py -c configs/nell995.json"
# run pretrain
$kgt pretrain
# Do multi-task finetuning
$kgt reasoning_multi
# Do finetuning for tasks
$kgt reasoning_1p reasoning_2p reasoning_3p
$kgt reasoning_2i reasoning_3i
$kgt reasoning_ip reasoning_pi
$kgt reasoning_2u reasoning_up

Documentation

Model

There are two main implementations for kgTransformer, both of which resides in model.py.

• KGTransformer is the original implementation that uses sparse operations to perform attention calculations, causing randomness beyond seed's control in the CUDA multi-thread parallel execution.
• D_KGTransformer is the alternative version that uses matrix attention to avoid reproducibility issues.

Our current implementation is based on D_KGTransformer.

Training

Training-related utilities can be found in train.py. They accept Iterator's that yield batched data, identical to the output of a torch.utils.data.DataLoader. The most useful functions are main_mp() and ft_test().

TrainClient scatters data onto different workers and perform multi-GPU training based on torch.nn.parallel.DistributedDataParallel. Example usage can be found in main_mp().

Config Files

Each config file is a JSON key-value mapping that maps a task name to a task. The tasks can be run directly from the command line:

python main.py <task_name> [<task_name>...]

In a specific task, base option specifies the task it should inherit from. type option specifies the type of operation of this configuration. See main.py for a full list of available options.

Troubleshooting

NCCL Unhandled System Error

We observed that Infiniband is not supported by fastmoe on some machines.

NCCL with Infiniband can be disabled using an environment variable.

export NCCL_IB_DISABLE=1

CUDA Out of Memory

Adjust batch size and retry. If the issue persists, downgrade pytorch to as early as possible (e.g. LTS 1.8.2 as of 2022/07). This is possibly due to memory issues in higher pytorch versions. See pytorch/pytorch#67680 for more information.

Citation

If you are interested in our work and wish to give us a credit, you can use the following BibTeX:

@inproceedings{liu2022kgxfmr,
  title={Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries},
  author={Liu, Xiao and Zhao, Shiyu and Su, Kai and Cen, Yukuo and Qiu, Jiezhong and Zhang, Mengdi and Wu, Wei and Dong, Yuxiao and Tang, Jie},
  booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2022}
}