LayerDAG

[Paper]

Table of Contents

• Installation
• Train
• Sample
• Eval
• Frequently Asked Questions
  • Q1: libcusparse.so
• Citation

Installation

conda create -n LayerDAG python=3.10 -y
conda activate LayerDAG
pip install torch==1.12.0+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
conda install -c conda-forge cudatoolkit=11.6
conda clean --all -y
pip install dgl==1.1.0+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html
pip install tqdm einops wandb pydantic pandas
pip install numpy==1.26.3

Train

To train a LayerDAG model,

python train.py --config_file configs/LayerDAG/tpu_tile.yaml

The trained model checkpoint will be saved to a file model_tpu_tile_{time_stamp}.pth.

Sample

For sampling and evaluation,

python sample.py --model_path X

where X is the file model_tpu_tile_{time_stamp}.pth saved above.

Frequently Asked Questions

Q1: libcusparse.so

An error occurs that the program cannot find libcusparse.so, e.g., OSError: libcusparse.so.11: cannot open shared object file: No such file or directory.

To search for the location of it on linux,

find /path/to/directory -name libcusparse.so.11 -exec realpath {} \;

where /path/to/directory is the directory you want to search. Assume that the search returns /home/miniconda3/envs/LayerDAG/lib/libcusparse.so.11. Then you need to manually specify the environment variable as follows.

export LD_LIBRARY_PATH=/home/miniconda3/envs/LayerDAG/lib:$LD_LIBRARY_PATH

Citation

@article{li2024layerdag,
    title={Layer{DAG}: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation},
    author={Mufei Li and Viraj Shitole and Eli Chien and Changhai Man and Zhaodong Wang and Srinivas Sridharan and Ying Zhang and Tushar Krishna and Pan Li},
    journal={arXiv preprint arXiv:2411.02322},
    year={2024}
}