--- language: - 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 automatically complete the code of lines and blocks with GPT2. ## Usage Open source repo:[code-autocomplete](https://github.com/shibing624/code-autocomplete),support GPT2 model, usage: ```python from autocomplete.gpt2_coder import GPT2Coder m = GPT2Coder("shibing624/code-autocomplete-gpt2-base") print(m.generate('import torch.nn as')[0]) ``` 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 nn import torch.nn.functional as F ``` 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_coder.py](https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.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}, } ```