# CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation This is the official PyTorch implementation for the following EMNLP 2021 paper from Salesforce Research: **Title**: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) **Authors**: [Yue Wang](https://yuewang-cuhk.github.io/), [Weishi Wang](https://www.linkedin.com/in/weishi-wang/) , [Shafiq Joty](https://raihanjoty.github.io/), and [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) ![CodeT5 demo](codet5.gif) ## Updates **Oct 29, 2021** We release [fine-tuned checkpoints](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/finetuned_models) for all the downstream tasks covered in the paper. **Oct 25, 2021** We release a CodeT5-base fine-tuned checkpoint ([Salesforce/codet5-base-multi-sum](https://huggingface.co/Salesforce/codet5-base-multi-sum)) for multilingual code summarzation. Below is how to use this model: ```python from transformers import RobertaTokenizer, T5ForConditionalGeneration if __name__ == '__main__': tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base') model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base-multi-sum') text = """def svg_to_image(string, size=None): if isinstance(string, unicode): string = string.encode('utf-8') renderer = QtSvg.QSvgRenderer(QtCore.QByteArray(string)) if not renderer.isValid(): raise ValueError('Invalid SVG data.') if size is None: size = renderer.defaultSize() image = QtGui.QImage(size, QtGui.QImage.Format_ARGB32) painter = QtGui.QPainter(image) renderer.render(painter) return image""" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=20) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) # this prints: "Convert a SVG string to a QImage." ``` **Oct 18, 2021** We add a [model card](https://github.com/salesforce/CodeT5/blob/main/CodeT5_model_card.pdf) for CodeT5! Please reach out if you have any questions about it. **Sep 24, 2021** CodeT5 is now in [hugginface](https://huggingface.co/)! You can simply load the model ([CodeT5-small](https://huggingface.co/Salesforce/codet5-small) and [CodeT5-base](https://huggingface.co/Salesforce/codet5-base)) and do the inference: ```python from transformers import RobertaTokenizer, T5ForConditionalGeneration tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base') model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base') text = "def greet(user): print(f'hello !')" input_ids = tokenizer(text, return_tensors="pt").input_ids # simply generate one code span generated_ids = model.generate(input_ids, max_length=8) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) # this prints "{user.username}" ``` ## Introduction This repo provides the code for reproducing the experiments in [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) . CodeT5 is a new pre-trained encoder-decoder model for programming languages, which is pre-trained on **8.35M** functions in 8 programming languages (Python, Java, JavaScript, PHP, Ruby, Go, C, and C#). In total, it achieves state-of-the-art results on **14 sub-tasks** in a code intelligence benchmark - [CodeXGLUE](https://github.com/microsoft/CodeXGLUE). Paper link: https://arxiv.org/abs/2109.00859 Blog link: https://blog.einstein.ai/codet5/ The code currently includes two pre-trained checkpoints ([CodeT5-small](https://huggingface.co/Salesforce/codet5-small) and [CodeT5-base](https://huggingface.co/Salesforce/codet5-base)) and scripts to fine-tine them on 4 generation tasks ( code summarization, code generation, translation, and refinement) plus 2 understanding tasks (code defect detection and clone detection) in CodeXGLUE. We also provide their fine-tuned checkpoints to facilitate the easy replication of our paper. In practice, CodeT5 can be deployed as an AI-powered coding assistant to boost the productivity of software developers. At Salesforce, we build an [AI coding assistant demo](https://github.com/salesforce/CodeT5/raw/main/codet5.gif) using CodeT5 as a VS Code plugin to provide three capabilities for Apex developers: - **Text-to-code generation**: generate code based on the natural language description. - **Code autocompletion**: complete the whole function of code given the target function name. - **Code summarization**: generate the summary of a function in natural language description. ## Table of Contents 1. [Citation](#citation) 2. [License](#license) 3. [Dependency](#dependency) 4. [Download](#download) 5. [Fine-tuning](#fine-tuning) 6. [Get Involved](#get-involved) ## Citation If you find this code to be useful for your research, please consider citing. ``` @inproceedings{ wang2021codet5, title={CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation}, author={Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi}, booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021}, year={2021}, } ``` ## License The code is released under the BSD-3 License (see `LICENSE.txt` for details), but we also ask that users respect the following: This software should not be used to promote or profit from: violence, hate, and division, environmental destruction, abuse of human rights, or the destruction of people's physical and mental health. We encourage users of this software to tell us about the applications in which they are putting it to use by emailing codeT5@salesforce.com, and to use [appropriate](https://arxiv.org/abs/1810.03993) [documentation](https://www.partnershiponai.org/about-ml/) when developing high-stakes applications of this model. ## Dependency - Pytorch 1.7.1 - tensorboard 2.4.1 - transformers 4.6.1 - tree-sitter 0.2.2 ## Download * [Pre-trained checkpoints](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/pretrained_models) * [Fine-tuning data](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/data) * [Fine-tuned checkpoints](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/finetuned_models) Instructions to download: ``` # pip install gsutil cd your-cloned-codet5-path gsutil -m cp -r "gs://sfr-codet5-data-research/pretrained_models" . gsutil -m cp -r "gs://sfr-codet5-data-research/data" . gsutil -m cp -r "gs://sfr-codet5-data-research/finetuned_models" . ``` ## Fine-tuning Go to `sh` folder, set the `WORKDIR` in `exp_with_args.sh` to be your cloned CodeT5 repository path. You can use `run_exp.py` to run a broad set of experiments by simply passing the `model_tag`, `task`, and `sub_task` arguments. In total, we support five models (i.e., ['roberta', 'codebert', 'bart_base', 'codet5_small', 'codet5_base']) and six tasks (i.e., ['summarize', 'concode', 'translate', 'refine', 'defect', 'clone']). For each task, we use the `sub_task` to specify which specific datasets to fine-tine on. Below is the full list: | \--task | \--sub\_task | Description | | --------- | ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | | summarize | ruby/javascript/go/python/java/php | code summarization task on [CodeSearchNet](https://arxiv.org/abs/1909.09436) data with six PLs | | concode | none | text-to-code generation on [Concode](https://aclanthology.org/D18-1192.pdf) data | | translate | java-cs/cs-java | code-to-code translation between [Java and C#](https://arxiv.org/pdf/2102.04664.pdf) | | refine | small/medium | code refinement on [code repair data](https://arxiv.org/pdf/1812.08693.pdf) with small/medium functions | | defect | none | code defect detection in [C/C++ data](https://proceedings.neurips.cc/paper/2019/file/49265d2447bc3bbfe9e76306ce40a31f-Paper.pdf) | | clone | none | code clone detection in [Java data](https://arxiv.org/pdf/2002.08653.pdf) | For example, if you want to run CodeT5-base model on the code summarization task for Python, you can simply run: ``` python run_exp.py --model_tag codet5_base --task summarize --sub_task python ``` Besides, you can specify: ``` model_dir: where to save fine-tuning checkpoints res_dir: where to save the performance results summary_dir: where to save the training curves data_num: how many data instances to use, the default -1 is for using the full data gpu: the index of the GPU to use in the cluster ``` You can also revise the suggested arguments [here](https://github.com/salesforce/CodeT5/blob/0bf3c0c43e92fcf54d9df68c793ac22f2b60aad4/sh/run_exp.py#L14) or directly customize the [exp_with_args.sh](https://github.com/salesforce/CodeT5/blob/main/sh/exp_with_args.sh) bash file. Please refer to the argument flags in [configs.py](https://github.com/salesforce/CodeT5/blob/main/configs.py) for the full available options. The saved training curves in `summary_dir` can be visualized using [tensorboard](https://pypi.org/project/tensorboard/). ## Get Involved Please create a GitHub issue if you have any questions, suggestions, requests or bug-reports. We welcome PRs!