Skip to content

CGCL-codes/naturalcc

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
gui
 
 
ncc
 
 
 
 
 
 
run
 
 
 
 
 
 
 
 
 
 
 
 
 
 

NaturalCC

NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models for many software engineering tasks, e.g., code summarization, code retrieval, code completion, code clone detection and type inference. Our vision is to bridge the gap between programming language and natural language through machine learning techniques.

Version Python pytorch license


🔖 News

  • [May 10] We have merged the source code of "What Do They Capture? - A Structural Analysis of Pre-Trained Language Models for Source Code" into NaturalCC.

Features

  • A collection of code corpus with data preprocessing
  • Performance benchmark
  • Mixed precision training
    • Nvidia APEX
    • Automatic Mixed Precision
  • Multi-GPU training
  • Better logging output
  • Various Implementations:
    • tensorflow gradient clipping
    • optimizers or learning schedulers
    • baseline models
    • binary data formats

🚀 Installation

Requirements

  • PyTorch version >= 1.6.0
  • Python version >= 3.6
  • GCC/G++ > 5.0
  • For training new models, you'll also need an NVIDIA GPU, NCCL and Cuda Toolkit installed.
  • (optional) For faster training, you need to install NVIDIA's apex library.

1. Install prerequisite libraries

git clone https://github.com/CGCL-codes/naturalcc && cd naturalcc
pip install -r requirements.txt

Once you installed prerequisite libraries, you can check them via python -m env_test

2. Build or install NaturalCC

Export your NaturalCC cache directory (data and models will be saved in this directory) to user variables(~/.bashrc or ~/.zshrc in Linux, ~/.zsh_profile or ~/.bash_profile in macOS).

# Linux
echo "export NCC=<path_to_store ncc_data>" >> ~/.bashrc
# macOS
echo "export NCC=<path_to_store ncc_data>" >> ~/.bash_profile

Note: PyCharm cannot get environment variables and, therefore, we recommend you to register your NCC variable at ncc/__init__.py.

Compile Cython files to accelerate programs and register NaturalCC into your pip list

# compile for debug
# python setup.py build_ext --inplace
# install 
pip install --editable ./

3. Half precision computation (optional)

NaturalCC supports half precision training.

  • If your Pytorch.__version__ < 1.6.0 and nvcc -V is runnable, please install apex.
  • Otherwise, use Automatic Mixed Precision (AMP). Available Now (set amp: 1 in yaml file, An example).

4. Install GCC/G++ with conda (if you do not have permission)

Since NCC is build via Cython, your GCC/G++ version should be greater than 4.9. If you have the root permission, update GCC/G++; otherwise, install GCC/G++ with conda.

# install GCC/G++ with conda
conda install -c anaconda gxx_linux-64
conda install -c conda-forge gcc_linux-64
cd ~/anaconda/envs/XXX/bin
ln -s x86_64-conda_cos6-linux-gnu-gcc gcc
ln -s x86_64-conda_cos6-linux-gnu-g++ g++
# check
conda deactivate
conda activate XXX
>> type "gcc/g++ -v" in terminals

📚 Dataset

Currently, we have processed the following datasets:

🤖 Implementations

Code retrieval (search)

Code completion

Heterogeneous mapping

Code summarization

Structural Analysis of Pre-Trained Language Models for Source Code

📋 Experiments

Code Summarization

Dataset: Python (Wan et al.)

BLEU-4 METEOR ROUGE-L Cost Logs
Seq2Seq+Attn 25.57 14.40 39.41 0.09s/b click here
Tree2Seq+Attn 23.35 12.59 36.49 0.48s/b click here
Transformer 30.64 17.65 44.59 0.26s/b click here
Transformer+RPE 31.57 17.74 45.18 0.27s/b click here
PLBART 32.71 18.13 46.05 0.80s/b TBC

Code Retrieval

Dataset: CodeSearchNet (Husain et al.)

MRR Go Java JS PHP Python Ruby Cost Logs
NBOW 66.59 59.92 47.15 54.75 63.33 42.86 0.16s/b click here
ConV1d 70.87 60.49 38.81 61.92 67.29 36.53 0.30s/b click here
BiRNN 65.80 48.60 23.23 51.36 48.28 19.35 0.74s/b click here
SelfAttn 78.45 66.55 50.38 65.78 79.09 47.96 0.25s/b click here

Code Completion

Dataset: Py150 (official processed) (raw)

MRR Attr Num Name Param Tokens Cost Logs
LSTM 51.67 47.45 46.52 66.06 73.73 0.31s/b click here
GPT-2 70.37 62.20 63.84 73.54 82.17 0.43s/b click here
TravTrans 72.08 68.55 76.33 71.08 83.17 0.43s/b click here

Type Inference

Dataset: CodeSearchNet-Java (Husain et al.)

Acc@1 (All types) Acc@5 (All types) Acc@1 (Any types) Acc@5 (Any types) Cost Logs
DeepTyper 0.52 0.67 0.43 0.67 0.42s/b TBC
Transformer 0.32 0.64 0.37 0.75 0.85s/b TBC

Heterogeneous Mapping

Dataset: OpenCL (Grewe et al.)

Accuracy AMD NVIDIA
Static mapping 58.82 56.91
Decision tree 70.29 74.56
Inst2vec 82.79 81.76
DeepTune 83.24 80.15

🏫 Examples & Tutorials

All the running commands here should be executed in the root of project folder (the path of your naturalcc). For example, in my environment I will stay at /data/wanyao/Dropbox/ghproj-v100/naturalcc.

We also have more detailed READMEs to start your tutorial of NaturalCC.

Step 1: Download and process a dataset from datasets, and follow the instructions from the README.md file.

# ref: dataset/python_wan/README.md
# download dataset
bash dataset/python_wan/download.sh
# clean data
python -m dataset.python_wan.clean
# cast data attributes into different files
python -m dataset.python_wan.attributes_cast

# ref: dataset/python_wan/summarization/README.md
# save code tokens and docstirng tokens into MMAP format
python -m dataset.python_wan.summarization.preprocess

Step 2 (optional): Register your self-defined models

  • If you want to create a new model, please add your model at ncc/models and ncc/modules.

  • If your training policy are more complex than we thought, you should update your criterions and training procedure at ncc/criterions and ncc/trainers, respectively.

    Do not forget to update your self defined module at ncc/XX/__init__.py.

Step 3: Training and inference.

  • Select a task and a model from task list and follow the instructions in its README.md to start your learning.
# ref: run/summarization/transformer/README.md
# train
CUDA_VISIBLE_DEVICES=0,1,2,3 nohup python -m run.summarization.transformer.train -f config/python_wan/python > run/summarization/transformer/config/python_wan/python.log 2>&1 &
# inference
CUDA_VISIBLE_DEVICES=0 python -m run.summarization.transformer.eval -f config/python_wan/python -o run/summarization/transformer/config/python_wan/python.txt

FAQ

Please fell free to contact me if you have any troubles.

😘 License and Acknowledgement

NaturalCC is MIT-licensed. The license applies to the pre-trained models as well. This project is also highly inspired by Fairseq and AllenNLP.

🔗 Related Links

Paper
NaturalCC-demo
About us: XCodeMind

❤️ Citation

Please cite as:

@inproceedings{wan2022naturalcc,
              author    = {Yao Wan and
                           Yang He and
                           Zhangqian Bi and
                           Jianguo Zhang and
                           Yulei Sui and
                           Hongyu Zhang and
                           Kazuma Hashimoto and
                           Hai Jin and
                           Guandong Xu and
                           Caiming Xiong and
                           Philip S. Yu},
              title     = {NaturalCC: An Open-Source Toolkit for Code Intelligence},
              booktitle   = {Proceedings of 44th International Conference on Software Engineering, Companion Volume},
              publisher = ACM,
              year      = {2022}
            }