tril 0.2.1

Transformers Reinforcement and Imitation Learning Library

TRIL

Transformers Reinforcement and Imitation Learning Library

TRIL is a modular library for Reinforcment Learning (RL) and Imitation Learning (IL) algorithm development with transformers. We directly build on top of transformers, accelerate, and peft libraries by 🤗 Hugging Face. That way TRIL is able to support open-sourced pretrained models, distributed computing, as well as parameter efficient training. Note we currently support most decoder and encoder-decoder architectures availble in transformers.

Supported Algorithms:

• Behavior Cloning (i.e. Supervised Fine Tuning)
• Proximal Policy Optimization (PPO) (https://arxiv.org/abs/1707.06347)
• Generative Adversarial Imitation Learning (GAIL) (https://arxiv.org/abs/1606.03476)
• PPO++ (https://arxiv.org/pdf/2306.11816)
• AggreVaTeD (https://arxiv.org/pdf/2306.11816)
• Locally Optimal Learning to Search (LOLS) (https://arxiv.org/pdf/2306.11816)
• Direct and Differentiable Locally Optimal Learning to Search (D2LOLS) (https://arxiv.org/pdf/2306.11816)

Supported Tasks:

• IMDB Positive Sentiment (https://arxiv.org/abs/2210.01241)
• CommonGen: Common Sense Generation (https://arxiv.org/abs/1911.03705)
• TL;DR Summarization (https://arxiv.org/pdf/2203.02155.pdf)

---

Planned Algorithms:

• Direct Preference Optimization (DPO) (https://arxiv.org/pdf/2305.18290.pdf)
• Statistical Rejection Sampling Optimization (RSO) (https://arxiv.org/pdf/2309.06657.pdf)
• Phasic Policy Gradient (PPG) (https://arxiv.org/abs/2009.04416)
• Pairwise Proximal Policy Optimization (P3O) (https://arxiv.org/pdf/2310.00212.pdf)
• Advantage-Induced Policy Alignment (APA) (https://arxiv.org/pdf/2306.02231.pdf)
• Advantage-Leftover Lunch RL (A-LoL) (https://arxiv.org/abs/2305.14718)

Planned Tasks:

• Helpfulness and Harmfullness (https://arxiv.org/pdf/2204.05862.pdf)

Installation

To install tril do:

pip install tril

For the run scripts and the example scripts for usage please see the respository.

To setup a development environment we use conda for version control. To install TRIL, please follow these steps

conda create -n tril python=3.10
conda activate tril
pip install -e .

Optionally, for caption_metrics such as CiDER-D and SPICE, please install these additional dependencies.

# Spacy model install
python -m spacy download en_core_web_sm

# CoreNLP library install
cd src/tril/metrics/caption_metrics/spice && bash get_stanford_models.sh

Example Scripts

In the examples directory, there are example scripts to run TRIL algorithms on IMDB positive sentiment generation using pytorch Fully Sharded Data Parallel (FSDP) and TL;DR summarization using deepspeed. The name of each script is of the format, <task>_<alg>.yaml. Run each experiment like the following:

./examples/<task>/<script>

Within each script the command is

accelerate --config <accelerate config> [accelerate args] main.py task=<task config> alg=<alg config> [hydra CLI config specification]

Please see the accelerate launch tutorial for how to launch jobs with accelerate. We provide examples of different accelerate configs in the accelerate_cfgs directoy. For more details on Hydra CLI and config usage please see this tutorial.

Usage Example

Here is a minimal example of running PPO with TRIL:

import hydra
from accelerate import Accelerator
from tril import tril_run
from tril.logging import Tracker
from tril.algorithms import PPO

@hydra.main(version_base=None, config_path="cfgs", config_name="config") # Hydra Decorator for Config
@tril_run # TRIL decorator for hydra config processing
def run_ppo(cfg):
    # Initialize accelerator for distributed computing
    accelerator = Accelerator()

    # Grab experiment save directory from Hydra
    save_path = hydra.core.hydra_config.HydraConfig.get().runtime.output_dir

    # Instantiate TRIL logger for WandB and CLI logging/saving
    tracker = Tracker(
        save_path,
        OmegaConf.to_container(cfg, resolve=True),
        cfg.project_name,
        cfg.experiment_name,
        cfg.entity_name,
        cfg.log_to_wandb,
        log_level=logging.INFO,
        is_main_process=accelerator.is_main_process,
    )

    # Instantiate Algorithm
    ppo = PPO(cfg, accelerator, tracker)

    # Start learn to train LLM
    ppo.learn()

if __name__ == '__main__':
    run_ppo()

TRIL also provides an AlgorithmRegistry to instantiate algorithms. Please see our main.py to see how our scripts instantiate the algorithms. The list of available algorithms can be seen by the configs in cfgs/task.

Current Task/Algorithm Support Matrix

Algorithm	IMDB	CommonGen	TL;DR
PPO	✅	✅	✅
PPO++	✅	✅	✅
AggreVaTeD	✅	✅	✅
LOLS	✅	✅	✅
D2LOLS	✅	✅	✅
BC	✅	✅	✅
GAIL	✅

Code Structure

The directory structure of the configs, run script, and TRIL components looks like this.

├── cfgs                    <- Hydra configs
│   ├── alg                 <- Algorithm configs (e.g. PPO)
│   ├── task                <- Task configs (e.g. TL;DR summarization)
│   ├── logging             <- Logging configs (e.g. WandB)
│   │
│   └── config.yaml         <- Main config for training
│
├── accelerate_cfgs         <- Accelerate configs
│
├── main.py                 <- TRIL main function
│
├── tril                    <- TRIL src
│   ├── algorithms          <- Algorithm implementations
│   ├── buffers             <- Data Buffer (e.g. OnlineBuffer, PromptBuffer)
│   ├── metrics             <- Evaluation Metrics
│   ├── policies            <- Language Model Policies (e.g. Actor, ActorCritic)
│   ├── rewards             <- Reward Functions
│   ├── tasks               <- Supported Tasks
│   ├── utils               <- Helper functions for TRIL
│   │
│   ├── agent.py            <- Agent contains all torch.nn Modules (i.e. Policy and Reward)
│   ├── base_algorithm.py   <- Algorithm abstract class
│   ├── base_metric.py      <- Metric abstract class
│   ├── base_reward.py      <- Reward abstract class
│   ├── base_task.py        <- Task abstract class
│   └── logging.py          <- TRIL Logger

In each directory's __init__.py, there is a registry to register all supported algorithms, metrics, rewards, and tasks. When extending TRIL, please add the respective addition to one of these registries.

Logging

TRIL support Weights and Biases logging. Please enter your wandb details such as entity_name and project_name into cfgs/logging/wandb.yaml. If you would not like to log to wandb, please set log_to_wandb=False.

By default, we save training and evaluation information in outputs/<experiment_name>/<datetime> You can define experiment_name in cfgs/config.yaml or through Hydra CLI, main.py experiment_name=<name>.

Example WandB Reports

Here is an example WandB Report of how the logging would look like when running multiple different algorithms

• CommonGen Report.
• TL;DR PPO Report.

Citing TRIL

If you use TRIL in your publication, please cite it by using the following BibTeX entry.

@misc{TRIL,
      title={TRIL: Transformers Reinforcement and Imitation Learning Library},
      author={Jonathan D Chang and Kiante Brantley and Rajkumar Ramamurthy and Dipendra Misra and Wen Sun},
      howpublished={\url{https://github.com/Cornell-RL/tril}},
      year={2023}
}

Here is the citation of the accompanying paper for many of the supported algorithms.

@misc{chang2023learning,
      title={Learning to Generate Better Than Your LLM}, 
      author={Jonathan D. Chang and Kiante Brantley and Rajkumar Ramamurthy and Dipendra Misra and Wen Sun},
      year={2023},
      eprint={2306.11816},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Acknowledgements

We would like to acknowledge RL4LMs, TRL, and TRLx for being inspirations for this library.