dasguptar / treelstm.pytorch

Tree LSTM implementation in PyTorch
MIT License
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deep-learning deeplearning machine-learning machinelearning pytorch recursive-neural-networks tree-lstm treelstm

Tree-Structured Long Short-Term Memory Networks

This is a PyTorch implementation of Tree-LSTM as described in the paper Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks by Kai Sheng Tai, Richard Socher, and Christopher Manning. On the semantic similarity task using the SICK dataset, this implementation reaches:

Requirements

Usage

Before delving into how to run the code, here is a quick overview of the contents:

Next, these are the different ways to run the code here to train a TreeLSTM model.

Local Python Environment

If you have a working Python3 environment, simply run the following sequence of steps:

- bash fetch_and_preprocess.sh
- pip install -r requirements.txt
- python main.py

Pure Docker Environment

If you want to use a Docker container, simply follow these steps:

- docker build -t treelstm .
- docker run -it treelstm bash
- bash fetch_and_preprocess.sh
- python main.py

Local Filesystem + Docker Environment

If you want to use a Docker container, but want to persist data and checkpoints in your local filesystem, simply follow these steps:

- bash fetch_and_preprocess.sh
- docker build -t treelstm .
- docker run -it --mount type=bind,source="$(pwd)",target="/root/treelstm.pytorch" treelstm bash
- python main.py

NOTE: Setting the environment variable OMP_NUM_THREADS=1 usually gives a speedup on the CPU. Use it like OMP_NUM_THREADS=1 python main.py. To run on a GPU, set the CUDA_VISIBLE_DEVICES instead. Usually, CUDA does not give much speedup here, since we are operating at a batchsize of 1.

Notes

Acknowledgements

Shout-out to Kai Sheng Tai for the original LuaTorch implementation, and to the Pytorch team for the fun library.

Contact

Riddhiman Dasgupta

This is my first PyTorch based implementation, and might contain bugs. Please let me know if you find any!

License

MIT