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🏠 Homepage｜🛠 Extensions VS Code, Jetbrains｜🤗 HF Repo｜📄 Paper

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CodeGeeX2: A More Powerful Multilingual Code Generation Model

CodeGeeX2 is the second-generation model of the multilingual code generation model CodeGeeX (KDD’23), which is implemented based on the ChatGLM2 architecture trained on more code data. Due to the advantage of ChatGLM2, CodeGeeX2 has been comprehensively improved in coding capability (+107% > CodeGeeX; with only 6B parameters, surpassing larger StarCoder-15B for some tasks). It has the following features:

  • More Powerful Coding Capabilities: Based on the ChatGLM2-6B model, CodeGeeX2-6B has been further pre-trained on 600B code tokens, which has been comprehensively improved in coding capability compared to the first-generation. On the HumanEval-X benchmark, all six languages have been significantly improved (Python +57%, C++ +71%, Java +54%, JavaScript +83%, Go +56%, Rust +321%), and in Python it reached 35.9% of Pass@1 one-time pass rate, surpassing the larger StarCoder-15B.
  • More Useful Features: Inheriting the ChatGLM2-6B model features, CodeGeeX2-6B better supports both Chinese and English prompts, maximum 8192 sequence length, and the inference speed is significantly improved compared to the first-generation. After quantization, it only needs 6GB of GPU memory for inference, thus supports lightweight local deployment.
  • Comprehensive AI Coding Assistant: The backend of CodeGeeX plugin (VS Code, Jetbrains) is upgraded, supporting 100+ programming languages, and adding practical functions such as infilling and cross-file completion. Combined with the "Ask CodeGeeX" interactive AI coding assistant, it can be used to solve various programming problems via Chinese or English dialogue, including but not limited to code summarization, code translation, debugging, and comment generation, which helps increasing the efficiency of developpers.
  • Open License: CodeGeeX2-6B weights are fully open to academic research, and please apply for commercial use by filling in the registration form.

AI Coding Assistant

We have developed the CodeGeeX plugin, which supports IDEs such as VS Code, IntelliJ IDEA, PyCharm, GoLand, WebStorm, and Android Studio. The plugin allows you to experience the CodeGeeX2 model's capabilities in code generation and completion, annotation, code translation, and "Ask CodeGeeX" interactive programming, which can help improve your development efficiency. Please download the CodeGeeX plugin in your IDE to get a more comprehensive AI coding experience. You can find more details on our homepage.

Get Started

Use transformers to quickly launch CodeGeeX2-6B：

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True, device='cuda')
model = model.eval()

# remember adding a language tag for better performance
prompt = "# language: Python\n# write a bubble sort function\n"
inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_length=256, top_k=1)
response = tokenizer.decode(outputs[0])

>>> print(response)
# language: Python
# write a bubble sort function


def bubble_sort(list):
    for i in range(len(list) - 1):
        for j in range(len(list) - 1):
            if list[j] > list[j + 1]:
                list[j], list[j + 1] = list[j + 1], list[j]
    return list


print(bubble_sort([5, 2, 1, 8, 4]))

Launch Gradio DEMO:

python ./demo/run_demo.py

❗️Attention:

  • CodeGeeX2 is a base model, which is not instruction-tuned for chatting. It can do tasks like code completion/translation/explaination. To try the instruction-tuned version in CodeGeeX plugins (VS Code, Jetbrains).

  • Programming languages can be controled by adding language tag, e.g., # language: Python. The format should be respected to ensure performance, full list can be found here. Please write comments under the format of the selected programming language to achieve better results.

  • If the GPU doesn't support bfloat16 format, it will cause incorrect output. Please convert the model to float16 format:

    model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True).half().cuda()

  • If you need to use Multiple GPUs to load the model, you can use the following code:

    tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True, device='cuda')
model = model.eval()

    Replace with

    def get_model():
    tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True)
    from gpus import load_model_on_gpus
    # The "gpus" file is located in the demo folder
    model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=2)
    model = model.eval()
    return tokenizer, model

tokenizer, model = get_model()

Evaluation

CodeGeeX2 is a base model for multilingual code generation, which has been significantly improved in its coding ability compared to the previous generation. The following are the evaluation results on the HumanEval, HumanEval-X, and DS1000 benchmarks (the evaluation metric Pass@k is the same as in the paper):

HumanEval (Pass@1,10,100)

Model Pass@1 Pass@10 Pass@100
CodeGen-16B-multi 19.2 34.6 55.2
CodeGeeX-13B 22.9 39.6 60.9
Codex-12B 28.8 46.8 72.3
CodeT5Plus-16B-mono 30.9 51.6 76.7
Code-Cushman-001 33.5 54.3 77.4
LLaMA-65B 23.7 - 79.3
LLaMA2-70B 29.9 - -
CodeGen2.5-7B-mono 33.4 58.4 82.7
StarCoder-15B 33.2 61.0 84.7
CodeGeeX2-6B 35.9 62.6 88.3

n=20, t=0.2, top_p=0.95 for Pass@1; n=200, t=0.8, top_p=0.95 for Pass@10 and Pass@100.

HumanEval-X (Pass@1)

Model Python C++ Java JavaScript Go Rust Overall
CodeGen-16B-multi 19.2 18.1 15.0 18.4 13.0 1.8 14.2
CodeGeeX-13B 22.9 17.1 20.0 17.6 14.4 4.3 16.0
Replit-code-v1-3B 22.0 20.1 20.1 20.1 12.2 8.6 17.2
CodeGen2.5-7B-multi 30.6 24.3 29.0 27.5 18.9 20.1 25.1
StarCoder-15B 35.5 28.2 31.5 33.2 21.3 17.8 27.9
CodeGeeX2-6B 35.9 29.3 30.8 32.2 22.5 18.1 28.1

n=20, t=0.2, top_p=0.95 for Pass@1.

The above results can be reproduced by running scripts/run_humanevalx.sh. Refer to HumanEval-X environment for the experiment setups.

DS1000 (Pass@1)

Model Matplotlib Numpy Pandas Pytorch SciPy Scikit-learn TensorFlow Overall
# Samples 155 220 291 68 106 115 45 1000
CodeGen-16B-Mono 31.7 10.9 3.4 7.0 9.0 10.8 15.2 11.7
code-cushman-001 40.7 21.8 7.9 12.4 11.3 18.0 12.2 18.1
Codex-001 41.8 26.6 9.4 9.7 15.0 18.5 17.2 20.2
CodeGeeX2-6B 40.5 25.5 14.5 17.3 19.3 24.0 23.0 23.1
StarCoder-15B 51.7 29.7 11.4 21.4 20.2 29.5 24.5 26.0
Codex-002 57.0 43.1 26.5 41.8 31.8 44.8 39.3 39.2

n=40, t=0.2, top_p=0.5 for Pass@1。

The above results can be reproduced by the code in DS1000 repo.

Inference

CodeGeeX2 is more friendly to deployment than the previous generation. Thanks to the use of Multi-Query Attention and Flash Attention, the inference speed is faster, and only 6GB of GPU memory is required after INT4 quantization.

Quantization

Model FP16/BF16 INT8 INT4
CodeGeeX-13B 26.9 GB 14.7 GB -
CodeGeeX2-6B 13.1 GB 8.2 GB 5.5 GB

Based on PyTorch 2.0, using torch.nn.functional.scaled_dot_product_attention for effecient attention mechanism。

Acceleration

Model Inference speed (token/s)
CodeGeeX-13B 32
CodeGeeX2-6B 94

batch_size=1, max_length=2048, both using acceleration framework, in GeForce RTX-3090。

License

The code in this repository is open source under the Apache-2.0 license. The model weights are licensed under the Model License. CodeGeeX2-6B weights are open for academic research, and please apply for commercial use by filling in the registration form.

Citation

If you find our work helpful, please feel free to cite the following paper:

@inproceedings{zheng2023codegeex,
  title={CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X},
  author={Qinkai Zheng and Xiao Xia and Xu Zou and Yuxiao Dong and Shan Wang and Yufei Xue and Zihan Wang and Lei Shen and Andi Wang and Yang Li and Teng Su and Zhilin Yang and Jie Tang},
  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={5673--5684},
  year={2023}
}