PPOCoder

Official Implementation of Execution-based Code Generation using Deep Reinforcement Learning

Overview

The utilization of programming language (PL) models, pretrained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting specific sequence-level features of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that combines pretrained PL models with Proximal Policy Optimization (PPO) deep reinforcement learning and employs execution feedback as the external source of knowledge into the model optimization. PPOCoder is transferable across different code generation tasks and PLs.

Overview of the PPOCoder with actor and critic models: The action is sampled from the policy based on the given source data $x$ (NL or PL). Then, a reward is obtained for each action to guide and control policy updates. The reward function is composed of four elements: (a) compiler feedback; (b) syntactic matching score based on ASTs; (c) semantic matching score based on DFGs; and (d) KL-divergence penalty between active policy and the reference pretrained model. The critic model estimates value based on the obtained reward and PPOCoder will be optimized with PPO, which takes into account both value and policy optimization.

Environment Installation

To run the code, install the dependencies in requirements.txt.

pip install -r requirements.txt

Datasets

We finetune/evaluate models on the following major dataset benchmarks for different code generation tasks:

• CodeSearchNet (CSN) is available here
• XLCoST is available here
• APPS is available here
• MBPP is available here

We preprocess the data and construct input/output sequences in the same manner as outlined in the original benchmark papers. Unzip and place all benchmarks in the data folder.

Run

We have created run.sh script to execute PPO-based PL model fine-tuning based on the compiler signal. To run the script for different code generation tasks, configure the following parameters:

Parameters	Description	Example Values
l1	Source Language	java
l2	Target Language	cpp
asp	Action Space Size	5
ns	Number of Synthetic Samples	10
data_path	Path to the original data samples	data/xlcost/java-cpp/
output_path	Path to save generations and outputs	saved_results/java-cpp/
baseline_output_dir	Path to the base finetuned CodeT5 (before RL) outputs	baselines/saved_models/java-cpp/
load_model_path	Path to the base finetuned CodeT5 model (before RL) for each downstream task	baselines/saved_models/java-cpp/pytorch_model.bin
max_source_length	Maxmim Source Length	400
max_target_length	Maxmim Target Length	400
train_batch_size	Training Batch Size	32
test_batch_size	Testing Batch Size	48
lr	Learning Rate	1e-6
kl_coef	Initial coefficient of the KL divergence penalty in the reward	0.1
kl_target	Target of the KL which adaptively controls the KL coefficient	1
vf_coef	Coefficient of the vf error in the ppo loss	1e-3
run	Index of the run	1

Running run.sh saves generated programs in a .txt file and the model weights at the end of each epoch.

Citation

If you find the paper or the repo useful, please cite it with

@article{shojaee2023ppocoder,
  title={Execution-based code generation using deep reinforcement learning},
  author={Shojaee, Parshin and Jain, Aneesh and Tipirneni, Sindhu and Reddy, Chandan K},
  journal={arXiv preprint arXiv:2301.13816},
  year={2023}
}