from autocomplete.gpt2_coder import GPT2Coder

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

Also, use huggingface/transformers：

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

python

import os
from transformers import GPT2Tokenizer, GPT2LMHeadModel

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

tokenizer = GPT2Tokenizer.from_pretrained("shibing624/code-autocomplete-distilgpt2-python")
model = GPT2LMHeadModel.from_pretrained("shibing624/code-autocomplete-distilgpt2-python")

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')
    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-distilgpt2-python
├── 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,

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

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 and first released at this page.

Disclaimer: The team releasing GPT-2 also wrote a model card 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},
}