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248 lines
13 KiB
Markdown
248 lines
13 KiB
Markdown
# CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
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This is the official PyTorch implementation for the following EMNLP 2021 paper from Salesforce Research:
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**Title**: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf)
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**Authors**: [Yue Wang](https://yuewang-cuhk.github.io/), [Weishi Wang](https://www.linkedin.com/in/weishi-wang/)
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, [Shafiq Joty](https://raihanjoty.github.io/), and [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home)
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## Updates
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**July 06, 2022**
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We release two large-sized CodeT5 checkpoints at Hugging Face: [Salesforce/codet5-large](https://huggingface.co/Salesforce/codet5-large) and [Salesforce/codet5-large-ntp-py](https://huggingface.co/Salesforce/codet5-large-ntp-py), which are introduced by the paper: [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://arxiv.org/pdf/2207.01780.pdf) by Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C.H. Hoi.
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* CodeT5-large was pretrained using Masked Span Prediction (MSP) objective on CodeSearchNet and achieve new SOTA results on several CodeXGLUE benchmarks. The finetuned checkpoints are released at [here](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/finetuned_models). See Appendix A.1 of the [paper](https://arxiv.org/pdf/2207.01780.pdf) for more details.
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* CodeT5-large-ntp-py was first pretrained using Masked Span Prediction (MSP) objective on CodeSearchNet and GCPY (the Python split of [Github Code](https://huggingface.co/datasets/codeparrot/github-code) data), followed by another 10 epochs on GCPY using Next Token Prediction (NTP) objective.
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CodeT5-large-ntp-py is especially optimized for Python code generation tasks and employed as the foundation model for our [CodeRL](https://github.com/salesforce/CodeRL), yielding new SOTA results on the APPS Python competition-level program synthesis benchmark. See the [paper](https://arxiv.org/pdf/2207.01780.pdf) for more details.
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**Oct 29, 2021**
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We release [fine-tuned checkpoints](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/finetuned_models)
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for all the downstream tasks covered in the paper.
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**Oct 25, 2021**
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We release a CodeT5-base fine-tuned
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checkpoint ([Salesforce/codet5-base-multi-sum](https://huggingface.co/Salesforce/codet5-base-multi-sum)) for
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multilingual code summarzation. Below is how to use this model:
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```python
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from transformers import RobertaTokenizer, T5ForConditionalGeneration
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if __name__ == '__main__':
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tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base')
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model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base-multi-sum')
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text = """def svg_to_image(string, size=None):
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if isinstance(string, unicode):
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string = string.encode('utf-8')
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renderer = QtSvg.QSvgRenderer(QtCore.QByteArray(string))
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if not renderer.isValid():
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raise ValueError('Invalid SVG data.')
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if size is None:
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size = renderer.defaultSize()
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image = QtGui.QImage(size, QtGui.QImage.Format_ARGB32)
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painter = QtGui.QPainter(image)
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renderer.render(painter)
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return image"""
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=20)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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# this prints: "Convert a SVG string to a QImage."
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```
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**Oct 18, 2021**
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We add a [model card](https://github.com/salesforce/CodeT5/blob/main/CodeT5_model_card.pdf) for CodeT5! Please reach out
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if you have any questions about it.
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**Sep 24, 2021**
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CodeT5 is now in [hugginface](https://huggingface.co/)!
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You can simply load the model ([CodeT5-small](https://huggingface.co/Salesforce/codet5-small)
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and [CodeT5-base](https://huggingface.co/Salesforce/codet5-base)) and do the inference:
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```python
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from transformers import RobertaTokenizer, T5ForConditionalGeneration
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tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base')
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model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base')
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text = "def greet(user): print(f'hello <extra_id_0>!')"
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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# simply generate one code span
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generated_ids = model.generate(input_ids, max_length=8)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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# this prints "{user.username}"
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```
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## Introduction
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This repo provides the code for reproducing the experiments
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in [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf)
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. CodeT5 is a new pre-trained encoder-decoder model for programming languages, which is pre-trained on **8.35M**
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functions in 8 programming languages (Python, Java, JavaScript, PHP, Ruby, Go, C, and C#). In total, it achieves
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state-of-the-art results on **14 sub-tasks** in a code intelligence benchmark - [CodeXGLUE](https://github.com/microsoft/CodeXGLUE).
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Paper link: https://arxiv.org/abs/2109.00859
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Blog link: https://blog.salesforceairesearch.com/codet5/
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The code currently includes two pre-trained checkpoints ([CodeT5-small](https://huggingface.co/Salesforce/codet5-small)
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and [CodeT5-base](https://huggingface.co/Salesforce/codet5-base)) and scripts to fine-tune them on 4 generation tasks (
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code summarization, code generation, translation, and refinement) plus 2 understanding tasks (code defect detection and
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clone detection) in CodeXGLUE. We also provide their fine-tuned checkpoints to facilitate the easy replication
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of our paper.
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In practice, CodeT5 can be deployed as an AI-powered coding assistant to boost the productivity of software developers.
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At Salesforce, we build an [AI coding assistant demo](https://github.com/salesforce/CodeT5/raw/main/codet5.gif) using
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CodeT5 as a VS Code plugin to provide three capabilities for Apex developers:
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- **Text-to-code generation**: generate code based on the natural language description.
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- **Code autocompletion**: complete the whole function of code given the target function name.
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- **Code summarization**: generate the summary of a function in natural language description.
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## Table of Contents
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1. [Citation](#citation)
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2. [License](#license)
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3. [Dependency](#dependency)
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4. [Download](#download)
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5. [Fine-tuning](#fine-tuning)
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6. [Get Involved](#get-involved)
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## Citation
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If you find this code to be useful for your research, please consider citing:
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```
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@inproceedings{
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wang2021codet5,
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title={CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation},
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author={Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi},
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booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021},
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year={2021},
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}
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@article{coderl2022,
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title={CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning},
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author={Le, Hung and Wang, Yue and Gotmare, Akhilesh Deepak and Savarese, Silvio and Hoi, Steven C. H.},
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journal={arXiv preprint arXiv:2207.01780},
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year={2022}
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}
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```
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## License
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The code is released under the BSD-3 License (see `LICENSE.txt` for details), but we also ask that users respect the
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following:
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This software should not be used to promote or profit from:
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violence, hate, and division,
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environmental destruction,
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abuse of human rights, or
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the destruction of people's physical and mental health.
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We encourage users of this software to tell us about the applications in which they are putting it to use by emailing
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codeT5@salesforce.com, and to
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use [appropriate](https://arxiv.org/abs/1810.03993) [documentation](https://www.partnershiponai.org/about-ml/) when
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developing high-stakes applications of this model.
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## Dependency
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- Pytorch 1.7.1
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- tensorboard 2.4.1
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- transformers 4.6.1
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- tree-sitter 0.2.2
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## Download
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* [Pre-trained checkpoints](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/pretrained_models)
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* [Fine-tuning data](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/data)
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* [Fine-tuned checkpoints](https://console.cloud.google.com/storage/browser/sfr-codet5-data-research/finetuned_models)
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Instructions to download:
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```
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# pip install gsutil
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cd your-cloned-codet5-path
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gsutil -m cp -r "gs://sfr-codet5-data-research/pretrained_models" .
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gsutil -m cp -r "gs://sfr-codet5-data-research/data" .
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gsutil -m cp -r "gs://sfr-codet5-data-research/finetuned_models" .
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```
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## Fine-tuning
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Go to `sh` folder, set the `WORKDIR` in `exp_with_args.sh` to be your cloned CodeT5 repository path.
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You can use `run_exp.py` to run a broad set of experiments by simply passing the `model_tag`, `task`, and `sub_task`
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arguments. In total, we support five models (i.e., ['roberta', 'codebert', 'bart_base', 'codet5_small', 'codet5_base'])
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and six tasks (i.e., ['summarize', 'concode', 'translate', 'refine', 'defect', 'clone']). For each task, we use
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the `sub_task` to specify which specific datasets to fine-tne on. Below is the full list:
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| \--task | \--sub\_task | Description |
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| --------- | ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
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| summarize | ruby/javascript/go/python/java/php | code summarization task on [CodeSearchNet](https://arxiv.org/abs/1909.09436) data with six PLs |
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| concode | none | text-to-code generation on [Concode](https://aclanthology.org/D18-1192.pdf) data |
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| translate | java-cs/cs-java | code-to-code translation between [Java and C#](https://arxiv.org/pdf/2102.04664.pdf) |
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| refine | small/medium | code refinement on [code repair data](https://arxiv.org/pdf/1812.08693.pdf) with small/medium functions |
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| defect | none | code defect detection in [C/C++ data](https://proceedings.neurips.cc/paper/2019/file/49265d2447bc3bbfe9e76306ce40a31f-Paper.pdf) |
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| clone | none | code clone detection in [Java data](https://arxiv.org/pdf/2002.08653.pdf) |
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For example, if you want to run CodeT5-base model on the code summarization task for Python, you can simply run:
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```
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python run_exp.py --model_tag codet5_base --task summarize --sub_task python
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```
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For multi-task training, you can type:
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```
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python run_exp.py --model_tag codet5_base --task multi_task --sub_task none
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```
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Besides, you can specify:
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```
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model_dir: where to save fine-tuning checkpoints
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res_dir: where to save the performance results
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summary_dir: where to save the training curves
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data_num: how many data instances to use, the default -1 is for using the full data
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gpu: the index of the GPU to use in the cluster
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```
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You can also revise the suggested
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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.
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Please refer to the argument flags in [configs.py](https://github.com/salesforce/CodeT5/blob/main/configs.py) for the full
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available options. The saved training curves in `summary_dir` can be visualized using [tensorboard](https://pypi.org/project/tensorboard/).
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Note that we employ one A100 GPU for all fine-tuning experiments.
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### How to reproduce the results using the released finetuned checkpoints?
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* Remove the `--do_train --do_eval --do_eval_bleu` and reserve only `--do_test` at [here](https://github.com/salesforce/CodeT5/blob/5b37c34f4bbbfcfd972c24a9dd1f45716568ecb5/sh/exp_with_args.sh#L84).
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* Pass the path of your downloaded finetuned checkpoint to load at [here](https://github.com/salesforce/CodeT5/blob/5b37c34f4bbbfcfd972c24a9dd1f45716568ecb5/run_gen.py#L366), e.g., `file = "CodeT5/finetuned_models/summarize_python_codet5_base.bin"`
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* Run the program: `python run_exp.py --model_tag codet5_base --task summarize --sub_task python`
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### How to fine-tune on your own task and dataset?
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If you want to fine-tune on your dataset, you can add your own task and sub_task in `configs.py` ([here](https://github.com/salesforce/CodeT5/blob/d27512d23ba6130e089e571d8c3e399760db1c31/configs.py#L11)) and add your data path and the function to read in `utils.py` ([here](https://github.com/salesforce/CodeT5/blob/5bb41e21b07fee73f310476a91ded00e385290d7/utils.py#L103) and [here](https://github.com/salesforce/CodeT5/blob/5bb41e21b07fee73f310476a91ded00e385290d7/utils.py#L149)). The read function can be implemented in `_utils.py` similar to [this one](https://github.com/salesforce/CodeT5/blob/aaf9c4a920c4986abfd54a74f5456b056b6409e0/_utils.py#L213). If your task to add is a generation task, you can simply reuse or customize the `run_gen.py`. For understanding tasks, please refer to `run_defect.py` and `run_clone.py`.
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## Get Involved
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Please create a GitHub issue if you have any questions, suggestions, requests or bug-reports. We welcome PRs!
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