This is the official implementation of the paper Tree Cross Attention.

Tree Cross Attention (TCA) is a module based on Cross Attention that only retrieves information from a logarithmic O(log(N)) number of tokens for performing inference. TCA organizes the data in a tree structure and performs a tree search at inference time to retrieve the relevant tokens for prediction. Leveraging TCA, we introduce ReTreever, a flexible architecture for token-efficient inference.

Create and activate a conda environment. Install the dependencies as listed in requirements.txt:

conda create --name tca python=3.9
conda activate tca
pip install -r requirements.txt

The default hyperparameters are saved in configs/. Model weights and logs are saved in results/{setting}/{expid}. Note that when running experiments, the {expid} must match between training and evaluation since the model will load weights from results/{setting}/{expid} when evaluating. If training for the first time, evaluation data will be generated and saved in evalsets/{setting}}.

To evaluate Transformer + Cross Attention, add --decoder_type ca to the script.

Training:

python copy_task.py --mode train --expid tca --sequence_length 256

Evaluation:

python copy_task.py --mode eval --expid tca --sequence_length 256

Training:

python gp.py --mode train --expid tca

Evaluation:

python gp.py --mode eval --expid tca --eval_kernel rbf 
python gp.py --mode eval --expid tca --eval_kernel matern 

Download the files from the official CelebA google drive. Specifically, download list_eval_partitions.txt, identity_CelebA.txt, and img_align_celeba.zip and unzip, placing the downloaded files in datasets/celeba folder.

Alternatively, follow the instructions from the official website to download the aforementioned necessary files.

After downloading the data, preprocess it by running python data/celeba.py.

Training:

python celeba.py --mode train --expid tca

Evaluation:

python celeba.py --mode eval --expid tca

Training:

If training for the first time, EMNIST training data will automatically download and save in datasets/emnist.

python emnist.py --mode train --expid tca

Evaluation:

python emnist.py --mode eval --expid tca --class_range 0 10

python emnist.py --mode eval --expid tca --class_range 10 47

For technical details, please check the conference version of our paper.

@inproceedings{
    feng2024tree,
    title={Tree Cross Attention},
    author={Leo Feng and Frederick Tung and Hossein Hajimirsadeghi and Yoshua Bengio and Mohamed Osama Ahmed},
    booktitle={International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=Vw24wtSddM}
}

This code uses parts from the codebases of Transformer Neural Processes, Perceiver, and Pytorch.