code-autocomplete 0.0.1

Code AutoComplete

Code AutoComplete

code-autocomplete, a code completion plugin for Python.

code-autocomplete实现了Python代码行粒度和块粒度自动补全功能。

Guide

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

Feature

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

Install

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

基于GPT2模型预测补全代码，通过如下命令调用：

from autocomplete.gpt2 import Infer
m = Infer(model_name="gpt2", model_dir="shibing624/code-autocomplete-gpt2-base", use_cuda=False)
i = m.predict('import torch.nn as')
print(i)

output:

import torch.nn as nn

当然，你也可使用官方的huggingface/transformers调用：

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

import os
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = GPT2Tokenizer.from_pretrained("shibing624/code-autocomplete-gpt2-base")
model = GPT2LMHeadModel.from_pretrained("shibing624/code-autocomplete-gpt2-base")
model.to(device)
prompts = [
    """from torch import nn
    class LSTM(Module):
        def __init__(self, *,
                     n_tokens: int,
                     embedding_size: int,
                     hidden_size: int,
                     n_layers: int):""",
    """import numpy as np
    import torch
    import torch.nn as""",
    "import java.util.ArrayList;",
    "def factorial(n):",
]
for prompt in prompts:
    input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt').to(device)
    outputs = model.generate(input_ids=input_ids,
                             max_length=64 + len(prompt),
                             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)
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(decoded)
    print("=" * 20)

output:

from torch import nn
class LSTM(Module):
    def __init__(self, *,
                 n_tokens: int,
                 embedding_size: int,
                 hidden_size: int,
                 n_layers: int):
        self.hidden_size = hidden_size
        self.embedding_size = embedding_size

====================

import numpy as np
import torch
import torch.nn as nn

====================
...

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：

@misc{code-autocomplete,
  author = {Xu Ming},
  title = {code-autocomplete: Code AutoComplete with GPT model},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  url = {https://github.com/shibing624/code-autocomplete},
}

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

• https://github.com/galois-autocompleter/galois-autocompleter