Quasi-Recurrent Neural Network (QRNN) for Tensorflow

This repository contains a Tensorflow implementation of Salesforce Research's Quasi-Recurrent Neural Networks paper. It supports batch-major or time-major inputs in single or double precision.

From the authors:

The QRNN provides similar accuracy to the LSTM but can be betwen 2 and 17 times faster than the highly optimized NVIDIA cuDNN LSTM implementation depending on the use case.

To install, simply run:

pip3 install qrnn

If you use this code or their results in your research, you should cite:

@article{bradbury2016quasi,
  title={{Quasi-Recurrent Neural Networks}},
  author={Bradbury, James and Merity, Stephen and Xiong, Caiming and Socher, Richard},
  journal={International Conference on Learning Representations (ICLR 2017)},
  year={2017}
}

The original PyTorch implementation of the QRNN can be found here.

Requirements

• Tensorflow 1.4 (pip install tensorflow or pip install tensorflow-gpu)
• GCC
• CUDA (optional, needed for GPU support)

Testing

python3 test/test_fo_pool.py

TODOs:

• create wheels for Fedora, Ubuntu, etc...