I. Preparation

pip install -r requirements.txt
python setup.py develop

II. Explanation of run_slurm.py

(i) Preliminaries

The authors used run_slurm.py to run experiments.

The file contains six segments of code, with each segment headed by if False. The six segments correspond to the following (this information is also commented in run_slurm.py):

• (1) training vanilla models (RNNs and LSTMs)
• (2) evaluating vanilla model using regular decoding algorithms (for decoding algorithms, please refer to the decoding algorithm section below)
• (3) evaluating vanilla model using consistent sampling algorithms
• (4) training self-terminating RNN models
• (5) training self-terminating LSTM models
• (6) evaluating self-terminating RNN+LSTM models

Note that by default, we train each model using 10 different random seeds. The number of random seeds can be easily adjusted from run_slurm.py.

In the end of the segments we show how to modify parameters to run with BPE-tokenized dataset..

(ii) Things to do before using run_slurm.py

• Set the values in user_folders per the example in run_slurm.py
• Adjust partition choices in run_slurm.py, and adjust corresponding GPU options (to find the spot to do so, one can search args.partition)
• After training and before final evaluation, one should adjust sweep_dirs in the evaluation segments of the code, to refer to absolute locations of checkpoint folders; examples are included in run_slurm.py

(iii) How to use run_slurm.py

To use one segment, users can set the corresponding if False to if True and run python run_slurm.py.

Alternatively, if users are not in a slurm environment, or if users prefer to run our code through command line, one can print out the actual python commands by including the flag --print-commands.

III. Decoding algorithms in evaluate.py

• When the model is a self-terminating RNN/LSTM, evaluate.py only uses greedy decoding algorithm.
• When the model is a regular RNN/LSTM...
  • if --consistent-sampling 0, then evaluate.py uses the following decoding algorithms: greedy decoding, ancestral sampling, beam search with beam size 2 and 4, top-k decoding with k=2 and k=4, and nucleus sampling with mu=0.2 and mu=0.4.
  • if --consistent-sampling 1, then evaluate.py uses the following decoding algorithms: consistent top-k decoding with k=2 and k=4, and consistent nucleus sampling with mu=0.2 and mu=0.4.

IV. GPT-2 experiments

The self-terminating wrapper supports Transformers 3.3.1 (current version on Oct 2020)

The gpt2 folder contains all the necessary wrappers to use self-terminating layer in HuggingFace pretrained model.

• (1) Tokenize wikitext-103 dataset: prepare-wikitext.py
• (2) Fine-tune GPT-2 or self-terminating GPT-2: train_line.py