code-autocomplete 0.0.4

Code AutoComplete

Code AutoComplete

code-autocomplete, a code completion plugin for Python.

code-autocomplete can automatically complete the code of lines and blocks with GPT2.

Guide

• Feature
• Install
• Usage
• Contact
• Citation
• Reference

Feature

• GPT2-based code completion
• Code completion for Python, other language is coming soon
• Line and block code completion
• Train(Fine-tune GPT2) and predict model with your own data

Demo

HuggingFace Demo: https://huggingface.co/spaces/shibing624/code-autocomplete

Install

pip3 install torch # conda install pytorch
pip3 install -U code-autocomplete

or

git clone https://github.com/shibing624/code-autocomplete.git
cd code-autocomplete
python3 setup.py install

Usage

Code Completion

Model upload to HF's model hub:

• DistilGPT2-python: shibing624/code-autocomplete-distilgpt2-python (fine-tuned distilgpt2, model size: 319MB)
• GPT2-python: shibing624/code-autocomplete-gpt2-base (fine-tuned gpt2, model size: 487MB)

Use with code-autocomplete

example: base_demo.py

from autocomplete.gpt2_coder import GPT2Coder

m = GPT2Coder("shibing624/code-autocomplete-gpt2-base")
print(m.generate('import torch.nn as')[0])

distilgpt2 fine-tuned code autocomplete model, you can use the following code:

example: distilgpt2_demo.py

import sys

sys.path.append('..')
from autocomplete.gpt2_coder import GPT2Coder

m = GPT2Coder("shibing624/code-autocomplete-distilgpt2-python")
print(m.generate('import torch.nn as')[0])

output:

import torch.nn as nn
import torch.nn.functional as F

Use with huggingface/transformers：

example: use_transformers_gpt2.py

Please use 'GPT2' related functions to load this model!

import os
from transformers import GPT2Tokenizer, GPT2LMHeadModel

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

tokenizer = GPT2Tokenizer.from_pretrained("shibing624/code-autocomplete-gpt2-base")
model = GPT2LMHeadModel.from_pretrained("shibing624/code-autocomplete-gpt2-base")
prompts = [
    "import numpy as np",
    "import torch.nn as",
    'parser.add_argument("--num_train_epochs",',
    "def set_seed(",
    "def factorial",
]
for prompt in prompts:
    input_ids = tokenizer(prompt, return_tensors='pt').input_ids
    outputs = model.generate(input_ids=input_ids,
                             max_length=64 + len(input_ids[0]),
                             temperature=1.0,
                             top_k=50,
                             top_p=0.95,
                             repetition_penalty=1.0,
                             do_sample=True,
                             num_return_sequences=1,
                             length_penalty=2.0,
                             early_stopping=True,
                             pad_token_id=tokenizer.eos_token_id,
                             eos_token_id=tokenizer.eos_token_id,
                             )
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print("Input :", prompt)
    print("Output:", decoded)
    print("=" * 20)

output:

import numpy as np
====================
import torch.nn as nn
import torchvision.transforms as transforms
====================
parser.add_argument("--num_train_epochs", type=int, default=50, help="Number of training epochs.")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size of validation/test data.")
====================
def set_seed(self):
====================
def factorial(n: int) -> int:

Train your own model with Dataset

Build dataset

This allows to customize dataset building. Below is an example of the building process.

Let's use Python codes from Awesome-pytorch-list

1. We want the model to help auto-complete codes at a general level. The codes of The Algorithms suits the need.
2. This code from this project is well written (high-quality codes).

dataset tree:

examples/download/python
├── train.txt
└── valid.txt
└── test.txt

There are three ways to build dataset:

1. Use the huggingface/datasets library load the dataset huggingface datasets https://huggingface.co/datasets/shibing624/source_code

pip3 install datasets

from datasets import load_dataset
dataset = load_dataset("shibing624/source_code", "python") # python or java or cpp
print(dataset)
print(dataset['test'][0:10])

output:

DatasetDict({
    train: Dataset({
        features: ['text'],
        num_rows: 5215412
    })
    validation: Dataset({
        features: ['text'],
        num_rows: 10000
    })
    test: Dataset({
        features: ['text'],
        num_rows: 10000
    })
})
{'text': [
"            {'max_epochs': [1, 2]},\n", 
'            refit=False,\n', '            cv=3,\n', 
"            scoring='roc_auc',\n", '        )\n', 
'        search.fit(*data)\n', 
'', 
'    def test_module_output_not_1d(self, net_cls, data):\n', 
'        from skorch.toy import make_classifier\n', 
'        module = make_classifier(\n'
]}

2. Download dataset from Cloud

Name	Source	Download	Size
Python+Java+CPP source code	Awesome-pytorch-list(5.22 Million lines)	github_source_code.zip	105M

download dataset and unzip it, put to examples/.

3. Get source code from scratch and build dataset

prepare_data.py

cd examples
python prepare_data.py --num_repos 260

Train and predict model

example: train_gpt2.py

cd examples
python train_gpt2.py --do_train --do_predict --num_epochs 15 --model_dir outputs-fine-tuned --model_name gpt2

Server

start FastAPI server:

example: server.py

cd examples
python server.py

open url: http://0.0.0.0:8001/docs

Contact

• Issue(建议) ：
• 邮件我：xuming: [e-mail]
• 微信我： 加我微信号：xuming624, 备注：个人名称-公司-NLP 进NLP交流群。

Citation

如果你在研究中使用了code-autocomplete，请按如下格式引用：

APA:

Xu, M. code-autocomplete: Code AutoComplete with GPT2 model (Version 0.0.4) [Computer software]. https://github.com/shibing624/code-autocomplete

BibTeX:

@software{Xu_code-autocomplete_Code_AutoComplete,
author = {Xu, Ming},
title = {code-autocomplete: Code AutoComplete with GPT2 model},
url = {https://github.com/shibing624/code-autocomplete},
version = {0.0.4}
}

License

授权协议为 The Apache License 2.0，可免费用做商业用途。请在产品说明中附加code-autocomplete的链接和授权协议。

Contribute

项目代码还很粗糙，如果大家对代码有所改进，欢迎提交回本项目，在提交之前，注意以下两点：

• 在tests添加相应的单元测试
• 使用python setup.py test来运行所有单元测试，确保所有单测都是通过的

之后即可提交PR。

Reference

• gpt-2-simple
• galois-autocompleter