CodeT5/CodeT5+

CodeT5+

Official research release for the CodeT5+ models (220M, 770M, 2B, 6B 16B) for a wide range of Code Understanding and Generation tasks.

Title: CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Authors: Yue Wang*, Hung Le*, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi (* indicates equal contribution)

What is this about?

CodeT5+ is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. encoder-only, decoder-only, and encoder-decoder) to support a wide range of code understanding and generation tasks.

To train CodeT5+, we introduce a diverse set of pretraining tasks including span denoising, causal language modeling, contrastive learning, and text-code matching to learn rich representations from both unimodal code data and bimodal code-text data. Additionally, to efficiently scale up the model, we propose a simple yet effective compute-efficient pretraining method to initialize our model with frozen off-the-shelf LLMs such as CodeGen. Furthermore, we explore instruction tuning to align the model with natural language instructions following Code Alpaca. See the below overview of CodeT5+.

Released Models

We implemented a family of CodeT5+ models, with model size ranging from 220M to 16B. Note that CodeT5+ 220M and 770M employ the same architecture of CodeT5-base and large respectively and are pretrained from scratch, while CodeT5+ 2B, 6B, 16B employ a "shallow encoder and deep decoder" architecture with the shallow encoder initialized from CodeGen-mono 350M and the deep decoder initialized from CodeGen-mono 2B, 6B, 16B, respectively. InstructCodeT5+ 16B is our instruction-tuned model from CodeT5+ 16B. Note that as this model utilizes instruction tuning data curated using OpenAI API, the checkpoint of InstructCodeT5+ 16B is licensed for research and non-commercial use only.

We release the following CodeT5+ models at Huggingface:

• CodeT5+ 220M and 770M: codet5p-220m and codet5p-770m.
• CodeT5+ 220M and 770M that are further tuned on Python subset: codet5p-220m-py and codet5p-770m-py.
• CodeT5+ 2B, 6B, 16B: codet5p-2b, codet5p-6b, and codet5p-16b.
• InstructCodeT5+ 16B: instructcodet5p-16b.

How to Use?

All CodeT5+ models and tokenizers can be easily loaded using the AutoModelForSeq2SeqLM and AutoTokenizer functionality. For tokenizers, CodeT5+ 220M and 770M employ the same tokenizer as the original CodeT5 while CodeT5+ 2B, 6B, 16B employ the same tokenizer as CodeGen. To load CodeT5+ 2B, 6B, 16B, please set trust_remote_code=True as the model class is defined in the Huggingface repo.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

checkpoint = "Salesforce/instructcodet5p-16b"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint,
                                              torch_dtype=torch.float16,
                                              low_cpu_mem_usage=True,
                                              trust_remote_code=True).to(device)

inputs = tokenizer.encode("def print_hello():", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_length=12)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Reproduce the Results

HumanEval

TBA

Citation

@article{wang2023codet5plus,
  title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
  author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
  journal={arXiv preprint},
  year={2023}
}