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---
language:
- zh
- en
tags:
- code
- autocomplete
- pytorch
- en
license: "apache-2.0"
---
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
**code-autocomplete** can Automatic completion of code line granularity and block granularity.
## Usage
Open source repo：[code-autocomplete](https://github.com/shibing624/code-autocomplete)，support GPT2 model, usage：
```python
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)
```
Also, use huggingface/transformers：
*Please use 'GPT2' related functions to load this model!*
```python
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:
```shell
from torch import nn
class LSTM(Module):
def __init__(self, *,
n_tokens: int,
embedding_size: int,
hidden_size: int,
n_layers: int):
self.embedding_size = embedding_size
====================
import numpy as np
import torch
import torch.nn as np
from onmt import nnumpy as np
class PredicterDNN(nn.Module):
@classmethod
@parameterized.expand([0.5, 2.5] + (10, 10))
@classmethod
@static
def add(self, sample_rate, max_iters=self.max_iters, mask_fre
====================
import java.util.ArrayList[Tuple[Int]],
====================
def factorial(n): number of elements per dimension,
assert len(n) > 1
n.append(self.n_iters)
n = n_iter(self.n_norm)
def _score(
====================
Process finished with exit code 0
```
Model files：
```
code-autocomplete-gpt2-base
├── config.json
├── merges.txt
├── pytorch_model.bin
├── special_tokens_map.json
├── tokenizer_config.json
└── vocab.json
```
### Train data
#### pytorch_awesome projects source code
download [code-autocomplete](https://github.com/shibing624/code-autocomplete),
```shell
cd autocomplete
python create_dataset.py
```
If you want train code-autocomplete GPT2 model，refer [https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2.py](https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2.py)
### About GPT2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Citation
```latex
@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},
}
```
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