README.md

Code Execution & Sequential ListOps

This directory contains code we used for the two algorithmic tasks: code execution and sequential ListOps.

See Appendix B in our paper for the task descriptions and examples.

Data Generation

Dumped datasets can be found here.

• Code execution:

cd utils
# Set `max_num_vars` in data_generator.py to either 3 or 5
python data_generator.py  --dump_dir my_data_dir --code_length 100  # `code_length` is the number of statements

The dataset can be further customized by changing max_num_vars or --code_length i.e. the number of statements per sequence. It should also not be difficult to extend it to support other statement types.

• ListOps:

cd utils
python nyu_listops.py --dump_dir my_data_dir --only_depth 11  # data for depth 10
python nyu_listops.py --dump_dir my_data_dir --only_depth 16  # data for depth 15

For further options to specify the properties of the ListOps dataset (such as the maximum number of arguments MAX_ARGS, maximum or minimum length MAX_LENGTH/MIN_LENGTH or choice of list operations OPERATORS etc) see nyu_listops.py.

Requirements

• PyTorch (PyT >= 1.6.0 recommended)
• Ninja to compile custom CUDA kernels (pip install ninja)
• Optionally: wandb for monitoring jobs (or disable it by removing the --use_wandb flag; see below)

Training

A generic script to train Transformer model variants on the code execution task is as follows. For ListOps, replace main.py by listops_main.py and provide the data file prefix to the level argument.

Separate paths for TORCH_EXTENSIONS_DIR should be used for code execution and ListOps.

model_type specifies the model type. The models used in the paper are as follows:

• 0: LSTM
• 1: Regular Transformer
• 2: Delta Net
• 3: Delta RNN
• 7: Recurrent Delta Net
• 8: Linear Transformer

export TORCH_EXTENSIONS_DIR="my_dir/torch_extensions2"
DATA_DIR='my_data_dir'

python main.py \
  --data_dir ${DATA_DIR} \
  --level 3 \
  --model_type 2 \
  --num_layer 4 \
  --hidden_size 256 \
  --n_head 16 \
  --ff_factor 4 \
  --dropout 0.1 \
  --batch_size 64 \
  --learning_rate 3e-4 \
  --seed 11 \
  --grad_cummulate 1 \
  --num_epoch 200 \
  --project_name "my_project" \
  --use_wandb \
  --remove_pos_enc

Evaluation

Evalution is automatically run at the end of training using the best performing checkpoint based on the validation accuracy.

References

• https://github.com/wojciechz/learning_to_execute
• Code execution task is designed based on the description by Fan et al. (no public code available)