Introduction

uniMASK is a generalization of BERT models with flexible abstractions for performing inference on subportions of sequences. Masking and prediction can occur both on the token level (as in traditional transformer), or even on subportions of tokens.

You can find the full paper here

Getting Started

To install uniMASK, run:

conda create -n uniMASK python=3.7
conda activate uniMASK
pip install -e .

uniMASK requires D4RL. You may install as detailed in the D4RL repo, e.g., by running:

pip install git+https://github.com/Farama-Foundation/d4rl@master#egg=d4rl

For CUDA support, you may need to reinstall pytorch in CUDA mode, for example:

pip install torch --extra-index-url https://download.pytorch.org/whl/cu116

To verify that the installation was successful, run pytest.

Reproducing results from the paper

Minigrid heatmap (figure 7)

Note: Reproducing all runs can a long time. We recommend parallelizing runs. In each script, the first line (comment) contains an example of how to use GNU Parallel towards this end.

1. Run the commands found in minigrid_repro.sh.
2. Fine-tune the pre-trained models generated in the previous step by running the commands in minigrid_ft_repro.sh.
3. Generate the heatmaps from these runs by running minigrid_heatmap.sh (no parallelization here).
4. You may then find the heatmap at uniMASK/scripts.

Maze2D results

To reproduce the Maze2D table in the paper:

1. Run wandb sweeps: medium_maze_sweep_all.yaml and medium_maze_sweep_DT.yaml
2. Run finetuning runs: maze_ft_final.sh
3. Parse results with Parse wandb Maze Experiments.ipynb

File structure

• scripts/train.py: the main script from running uniMASK -- start here.
• data/: where rollouts (datasets) and trained models (transformer_runs) are stored.
• envs/: data-handling and evaluation for each supported environment. Currently
• scripts/: reproducing results from the paper, and running uniMASK in general.
• batches.py: has all data pipeline processing classes (FactorSeq, TokenSeq, FullTokenSeq, Batch, SubBatch)
• sequences.py:
• trainer.py: the Trainer class handles the training loop for all models.
• transformer.py: contains the transformer model class itself.
• transformer_train.py: interface and config setting for training a transformer, through Trainer class.
• utils.py: misc utilities, namely math functions, gpu handling, profiling, etc.
• transformer_eval.py: interface for getting predictions from transformer (currently empty).