Repository: zai-org/CodeGeeX2
Branch: main
Commit: 754a2082356d
Files: 24
Total size: 158.4 KB
Directory structure:
gitextract_b1m4cbos/
├── LICENSE
├── MODEL_LICENSE
├── README.md
├── README_EN.md
├── README_FR.md
├── README_JA.md
├── benchmark/
│ └── humanevalx/
│ ├── go/
│ │ ├── go.mod
│ │ └── go.sum
│ └── rust/
│ └── Cargo.toml
├── demo/
│ ├── example_inputs.jsonl
│ ├── fastapicpu.py
│ ├── gpus.py
│ └── run_demo.py
├── docs/
│ └── zh/
│ └── inference_zh.md
├── evaluation/
│ ├── __init__.py
│ ├── evaluation.py
│ ├── execution.py
│ ├── generation.py
│ ├── inspect_jsonl.py
│ └── utils.py
├── requirements.txt
├── resources/
│ └── wechat.md
└── scripts/
├── run_humanevalx.sh
└── sanity_check.sh
================================================
FILE CONTENTS
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FILE: README.md
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<p align="center">
🏠 <a href="https://codegeex.cn" target="_blank">主页</a>｜🛠 插件 <a href="https://marketplace.visualstudio.com/items?itemName=aminer.codegeex" target="_blank">VS Code</a>, <a href="https://plugins.jetbrains.com/plugin/20587-codegeex" target="_blank">Jetbrains</a>｜🤗 <a href="https://huggingface.co/THUDM/codegeex2-6b" target="_blank">模型下载</a>｜📄 <a href="https://arxiv.org/abs/2303.17568" target="_blank">论文</a>｜👋 加入<a href="resources/wechat.md"target="_blank">微信开发者交流群</a>
</p>
Read this in [English](README_EN.md)<br>
[日本語](README_JA.md)で読む<br>
Lire en [Français](README_FR.md)
⭐️ 最新一代 [CodeGeeX4](https://github.com/THUDM/CodeGeeX4) 模型已经正式开源。
The newest [CodeGeeX4](https://github.com/THUDM/CodeGeeX4) has been released.
# CodeGeeX2: 更强大的多语言代码生成模型
CodeGeeX2 是多语言代码生成模型 [CodeGeeX](https://github.com/THUDM/CodeGeeX) ([KDD’23](https://arxiv.org/abs/2303.17568)) 的第二代模型。不同于一代 CodeGeeX（完全在国产华为昇腾芯片平台训练） ，CodeGeeX2 是基于 [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) 架构加入代码预训练实现，得益于 ChatGLM2 的更优性能，CodeGeeX2 在多项指标上取得性能提升（+107% > CodeGeeX；仅60亿参数即超过150亿参数的 StarCoder-15B 近10%），更多特性包括：
* **更强大的代码能力**：基于 ChatGLM2-6B 基座语言模型，CodeGeeX2-6B 进一步经过了 600B 代码数据预训练，相比一代模型，在代码能力上全面提升，[HumanEval-X](https://huggingface.co/datasets/THUDM/humaneval-x) 评测集的六种编程语言均大幅提升 (Python +57%, C++ +71%, Java +54%, JavaScript +83%, Go +56%, Rust +321\%)，在Python上达到 35.9\% 的 Pass@1 一次通过率，超越规模更大的 StarCoder-15B。
* **更优秀的模型特性**：继承 ChatGLM2-6B 模型特性，CodeGeeX2-6B 更好支持中英文输入，支持最大 8192 序列长度，推理速度较一代 CodeGeeX-13B 大幅提升，量化后仅需6GB显存即可运行，支持轻量级本地化部署。
* **更全面的AI编程助手**：CodeGeeX插件（[VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)）后端升级，支持超过100种编程语言，新增上下文补全、跨文件补全等实用功能。结合 Ask CodeGeeX 交互式AI编程助手，支持中英文对话解决各种编程问题，包括且不限于代码解释、代码翻译、代码纠错、文档生成等，帮助程序员更高效开发。
* **更开放的协议**：CodeGeeX2-6B 权重对学术研究完全开放，填写[登记表](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B)申请商业使用。
## 使用教程
* [快速开始](#快速开始)
* [推理教程（多卡推理，加速推理，多平台推理等）](docs/zh/inference_zh.md)
## AI编程助手
我们开发了支持 VS Code、 IntelliJ IDEA、PyCharm、GoLand、WebStorm、Android Studio 等IDE的 CodeGeeX 插件。在插件中，可以更直接地体验到 CodeGeeX2 模型在代码生成与补全、添加注释、代码翻译及技术问答方面的能力为开发效率带来的提升。欢迎在IDE中下载 CodeGeeX 插件获得更加全面的AI编程体验，详情见[CodeGeeX主页](https://codegeex.cn/)。
## 快速开始
### 使用`transformers`快速调用[CodeGeeX2-6B](https://huggingface.co/THUDM/codegeex2-6b)：
```python
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]))
```
### 启动 Gradio DEMO：
```
python ./demo/run_demo.py
usage: run_demo.py [-h] [--model-path MODEL_PATH] [--example-path EXAMPLE_PATH] [--quantize QUANTIZE]
[--chatglm-cpp] [--fastllm] [--n-gpus N_GPUS] [--gpu GPU] [--cpu] [--auth] [--username yourname]
[--password yourpassword]
[--port PORT] [--listen ADDRESS]
# 若要启用身份验证，请先启用--auth，然后定义--username与--password，如：
python run_demo.py --auth --username user --password password # 若要监听所有地址请指定 --listen 0.0.0.0
```
支持使用 [ChatGLM.cpp](https://github.com/li-plus/chatglm.cpp) 量化推理加速：
```sh
python ./demo/run_demo.py --quantize 4 --chatglm-cpp
```
### 启动FAST API:
```
python ./demo/fastapicpu.py
usage: fastapicpu.py [-h] [--model-path MODEL_PATH] [--listen ADDRESS] [--port PORT] [--workders NUM] [--cpu] [--half] [--quantize QUANTIZE] [--chatglm-cpp]
# --cpu启用cpu --half启用.half()
```
支持使用 [ChatGLM.cpp](https://github.com/li-plus/chatglm.cpp) 量化推理加速，同样添加 `--quantize 4 --chatglm-cpp` 参数即可。
### API使用示例
```
curl -X POST "http://127.0.0.1:7860" \
-H 'Content-Type: application/json' \
-d '{"lang": "Python", "prompt": "# Write a quick sort function"}'
```
❗️请注意：
* CodeGeeX2-6B 是一个基座代码生成模型，不具备聊天能力。请前往插件中体验更全面的 Ask CodeGeeX 聊天功能。
* 在使用 CodeGeeX2-6B 的补全功能时，输入prompt需要遵循特定的格式以获得最好的效果。比如需要在开头加入编程语言标签（`# language: Python`，请查看[完整语言列表](https://github.com/THUDM/CodeGeeX2/blob/main/evaluation/utils.py#L14)），以注释的形式写prompt等。参考`run_demo.py`中的处理。
* 如果显卡不支持`bfloat16`格式，将会输出错误的内容，需要将模型转换成`float16`格式：
```python
model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True).half().cuda()
```
* 如果需要使用多显卡加载模型,可以将以下代码：
```python
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()
```
替换为
```python
def get_model():
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True)
from gpus import load_model_on_gpus
# gpus文件在demo文件夹中
model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=2)
model = model.eval()
return tokenizer, model
tokenizer, model = get_model()
```
## 代码能力评测
CodeGeeX2 作为一个多语言代码生成基座模型，代码能力较上一代大幅提升，以下是在 HumanEval，HumanEval-X, DS1000 基准上的评测结果（评价指标 Pass@k 定义与[论文](https://arxiv.org/abs/2303.17568)中一致）：
### 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** |
> **Pass@1** 使用 `n=20, t=0.2, top_p=0.95`；**Pass@10,Pass@100** 使用 `n=200, t=0.8, top_p=0.95`。
### 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** |
> **Pass@1** 使用 `n=20, t=0.2, top_p=0.95`。
以上结果可使用脚本`scripts/run_humanevalx.sh`复现。环境配置和说明参见[评测环境](https://github.com/THUDM/CodeGeeX/blob/main/codegeex/benchmark/README_zh.md)。
### 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** |
> **Pass@1** 使用 `n=40, t=0.2, top_p=0.5`。
以上结果可使用[DS1000评测代码](https://github.com/HKUNLP/DS-1000.git)复现。
## 量化推理性能
CodeGeeX2 与上一代相比，对部署更加友好。得益于使用 Multi-Query Attention 和 Flash Attention，推理速度更快，且量化后仅需6GB显存即可运行：
### 量化
| **Model** | FP16/BF16 | INT8 | INT4 |
| :--------------: | :-------: | :-----: | :----: |
| CodeGeeX-13B | 26\.9 GB | 14\.7 GB | - |
| **CodeGeeX2-6B** | 13\.1 GB | 8\.2 GB | 5\.5 GB |
> 基于 PyTorch 2.0 测试，利用`torch.nn.functional.scaled_dot_product_attention`实现高效的 Attention 计算。
### 推理
| **Model** | **推理速度 (字符/秒)** |
| :--------------: | :-------------: |
| CodeGeeX-13B | 32 |
| **CodeGeeX2-6B** | 94 |
> `batch_size=1, max_length=2048`，均使用加速框架，测试硬件为`GeForce RTX-3090`。
## 协议
本仓库的代码依照 [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) 协议开源，模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。CodeGeeX2-6B 权重对学术研究完全开放，填写[登记表](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B)申请商业使用。
## 引用
如果觉得我们的工作有帮助，欢迎引用以下论文：
```
@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}
}
```
================================================
FILE: README_EN.md
================================================
<p align="center">
🏠 <a href="https://codegeex.cn" target="_blank">Homepage</a>｜🛠 Extensions <a href="https://marketplace.visualstudio.com/items?itemName=aminer.codegeex" target="_blank">VS Code</a>, <a href="https://plugins.jetbrains.com/plugin/20587-codegeex" target="_blank">Jetbrains</a>｜🤗 <a href="https://huggingface.co/THUDM/codegeex2-6b" target="_blank">HF Repo</a>｜📄 <a href="https://arxiv.org/abs/2303.17568" target="_blank">Paper</a>
</p>
<p align="center">
👋 Join our <a href="https://discord.gg/8gjHdkmAN6" target="_blank">Discord</a>, <a href="https://join.slack.com/t/codegeexworkspace/shared_invite/zt-1s118ffrp-mpKKhQD0tKBmzNZVCyEZLw" target="_blank">Slack</a>, <a href="https://t.me/+IipIayJ32B1jOTg1" target="_blank">Telegram</a>, <a href="resources/wechat.md"target="_blank">WeChat</a>
</p>
查看[中文版](README.md)<br>
[日本語](README_JA.md)で読む<br>
Lire en [Français](README_FR.md)
# CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation model of the multilingual code generation model [CodeGeeX](https://github.com/THUDM/CodeGeeX) ([KDD’23](https://arxiv.org/abs/2303.17568)), which is implemented based on the [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) 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](https://huggingface.co/datasets/THUDM/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](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)) 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](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B).
## 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]( https://codegeex.cn/).
## Get Started
Use `transformers` to quickly launch [CodeGeeX2-6B](https://huggingface.co/THUDM/codegeex2-6b)：
```python
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](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)).
* 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](https://github.com/THUDM/CodeGeeX2/blob/main/evaluation/utils.py#L14). 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:
```python
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:
```python
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
```python
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](https://arxiv.org/abs/2303.17568)):
### 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](https://github.com/THUDM/CodeGeeX/blob/main/codegeex/benchmark/README_zh.md) 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](https://github.com/HKUNLP/DS-1000.git).
## 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](https://www.apache.org/licenses/LICENSE-2.0) license. The model weights are licensed under the [Model License](MODEL_LICENSE). CodeGeeX2-6B weights are open for academic research, and please apply for commercial use by filling in the [registration form](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B).
## 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}
}
```
================================================
FILE: README_FR.md
================================================
<p align="center">
🏠 <a href="https://codegeex.cn" target="_blank">Homepage</a>｜🛠 Extensions <a href="https://marketplace.visualstudio.com/items?itemName=aminer.codegeex" target="_blank">VS Code</a>, <a href="https://plugins.jetbrains.com/plugin/20587-codegeex" target="_blank">Jetbrains</a>｜🤗 <a href="https://huggingface.co/THUDM/codegeex2-6b" target="_blank">HF Repo</a>｜📄 <a href="https://arxiv.org/abs/2303.17568" target="_blank">Paper</a>
</p>
<p align="center">
👋 Rejoignez nous sur <a href="https://discord.gg/8gjHdkmAN6" target="_blank">Discord</a>, <a href="https://join.slack.com/t/codegeexworkspace/shared_invite/zt-1s118ffrp-mpKKhQD0tKBmzNZVCyEZLw" target="_blank">Slack</a>, <a href="https://t.me/+IipIayJ32B1jOTg1" target="_blank">Telegram</a>, <a href="resources/wechat.md"target="_blank">WeChat</a>
</p>
查看[中文版](README.md)<br>
Read this in [English](README_EN.md)<br>
[日本語](README_JA.md)で読む
# CodeGeeX2: Un Modèle de Génération de Code Plus Puissant
CodeGeeX2 est la deuxième itération du modèle de génération de code multilingue [CodeGeeX](https://github.com/THUDM/CodeGeeX) ([KDD’23](https://arxiv.org/abs/2303.17568)), basé sur [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) et entrainé sur un large corpus de code. Grâce à l'architecture ChatGLM2, CodeGeeX2 excelle sur une multitude de tâches de génération de code (+107% > CodeGeeX; avec seulement 6 milliards de paramètres, dépassant StarCoder-15B pour certaines tâches). CodeGeeX2 possède les fonctionnalités suivantes:
* **Capacités de Génération de Code Accrues**: Basé sur ChatGLM2-6B, CodeGeeX2-6B à été entrainé sur un dataset de 600 milliards de tokens de plus ce qui a propulsé ses capacités de génération de code par rapport à la génération précédente. Sur [HumanEval-X](https://huggingface.co/datasets/THUDM/humaneval-x), le modèle opère bien mieux que son prédécesseur (Python +57%, C++ +71%, Java +54%, JavaScript +83%, Go +56%, Rust +321\%). En Python, CodeGeeX atteint un score Pass@1 de 35.9%, surpassant StarCoder-15B malgré le fait que CodeGeeX ait ~3 fois moins de paramètres.
* **Des Fonctionnalités Plus Utiles**: Héritant des fonctionnalités de ChatGLM2-6B, CodeGeeX2-6B prend mieux en charge les prompts en chinois et en anglais, peut ingérer jusqu'à 8192 tokens, et se dotte d'une vitesse de génération en inference fortement accrue comparé à la dernière génération. Après quantisation, CodeGeeX fonctionne sur un GPU avec >6GB de mémoire, permettant un déploiement local efficace.
* **Un Assistant Intelligent dans votre Éditeur**: Les plugins ([VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), et [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)) ont été mis à jour et sont maintenant compatible avec plus de 100 langages de programmation. Le modèle, couplé à l'extension, permet désormais aux utilisateurs de générer du code pour plusieurs fichiers ainsi que de générer et modifier des sections de code. CodeGeeX2 est maintenant capable de résoudre de nombreux problèmes de programmation. Les utilisateurs peuvent profiter de la fonctionnalité "Ask CodeGeeX" pour discuter de manière interactive avec un AI-assistant afin de résumer et d'expliquer du code, traduire du code entre langages, rajouter des commentaires, etc. CodeGeeX permet de maximiser la productivité de ses utilisateurs.
* **License Open-Source**: Les poids du modèle CodeGeeX2-6B sont en accès libre pour toute utilisation dans le cadre de la recherche. Pour toute utilisation commerciale, merci de consulter ce [formulaire](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B).
## Assistant Intelligent
Nous avons développé une extension pour VS Code, IntelliJ IDEA, PyCharm, GoLand, WebStorm, and Android Studio. L'extension permet de profiter des capacités du modèle CodeGeeX2 et de générer, annoter et traduire du code. La fonctionnalité "Ask CodeGeeX" permet de coder de manière interactive et améliore grandement votre productivité. Téléchargez l'extension CodeGeeX dans votre IDE pour une meilleure expérience de développement. Trouvez plus de détail sur notre [site]( https://codegeex.cn/).
## Utilisation
Pour exécuter [CodeGeeX2-6B](https://huggingface.co/THUDM/codegeex2-6b), utilisez la librairie `transformers`：
```python
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()
# TIP: Utilisez un tag pour identifier le langage dans lequel vous souhaitez générer.
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]))
```
Accéder à la démo Gradio:
```
python ./demo/run_demo.py
```
❗️Attention:
* Cette version de CodeGeeX2 est capable de compléter / expliquer / traduire du code mais n'a pas été fine-tuned pour être utilisé comme un chatbot. Pour accéder à la version chatbot de CodeGeeX, utilisez les extensions [VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex) et [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex).
* Pour controller le langage dans lequel CodeGeeX2 opère, utilisez des tags formattés ainsi: `# language: Python`. La liste de tous les langages de programmations que CodeGeeX supporte est accessible [ici](https://github.com/THUDM/CodeGeeX2/blob/main/evaluation/utils.py#L14).
* Si vous avez besoin d'utiliser plusieurs GPU pour charger le modèle, vous pouvez utiliser le code suivant:
```python
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()
```
Remplacer par
```python
def get_model():
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True)
from gpus import load_model_on_gpus
# Le fichier "gpus" se trouve dans le dossier de démonstration
model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=2)
model = model.eval()
return tokenizer, model
tokenizer, model = get_model()
```
## Evaluation
CodeGeeX2 est un modèle de base capable de générer du code en plusieurs langages de programmation et qui est bien plus performant que la version précédente. Voici les capacités de CodeGeeX sur les benchmarks HumanEval, HumanEval-X, et DS1000 (la métrique Pass@k est la même que celle décrite dans ce [papier](https://arxiv.org/abs/2303.17568)):
### 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` pour **Pass@1**; `n=200, t=0.8, top_p=0.95` pour **Pass@10** et **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**.
Les résultats ci-dessus peuvent être reproduits avec le script `scripts/run_humanevalx.sh`. Les environements utilisés sont renseignés [ici](https://github.com/THUDM/CodeGeeX/blob/main/codegeex/benchmark/README_zh.md).
### 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**。
Les résultats ci-dessus peuvent être reproduits avec le code présent sur le repository [HKUNLP/DS-1000](https://github.com/HKUNLP/DS-1000.git).
## Inference
CodeGeeX2 est bien plus simple à déployer que la génération précédente. L'utilisation de "Multi-Query Attention" et "Flash Attention" accélère grandement la vitesse de génération et le modèle n'a besoin que de 6GB de mémoire après avoir été quantisé en INT4.
### Quantisation
| **Model** | FP16/BF16 | INT8 | INT4 |
| :--------------: | :-------: | :-----: | :----: |
| CodeGeeX-13B | 26\.9 GB | 14\.7 GB | - |
| **CodeGeeX2-6B** | 13\.1 GB | 8\.2 GB | 5\.5 GB |
> Résultats obtenus avec PyTorch 2.0, avec `torch.nn.functional.scaled_dot_product_attention` qui est une version plus rapide du calcul de l'attention.
### Accelération
| **Model** | **Inference speed (token/s)** |
| :--------------: | :-------------: |
| CodeGeeX-13B | 32 |
| **CodeGeeX2-6B** | 94 |
> `batch_size=1, max_length=2048` et en utilisant l'accélération des GPUs `GeForce RTX-3090`。
## License
Le code dans ce dépôt est en libre accès selon les droits et devoirs prévu par la license [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). Les poids du modèle sont régis par la [license du modèle](MODEL_LICENSE). Les poids du modèle CodeGeeX2-6B sont en accès libre pour toute utilisation dans le cadre de la recherche. Pour toute utilisation commerciale, merci de consulter ce [formulaire](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B).
## Citation
Si vous trouvez ce projet utile, n'hésitez pas à citer notre papier:
```
@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}
}
```
================================================
FILE: README_JA.md
================================================
<p align="center">
🏠 <a href="https://codegeex.cn" target="_blank">ホームページ</a>｜🛠 拡張 <a href="https://marketplace.visualstudio.com/items?itemName=aminer.codegeex" target="_blank">VS Code</a>, <a href="https://plugins.jetbrains.com/plugin/20587-codegeex" target="_blank">Jetbrains</a>｜🤗 <a href="https://huggingface.co/THUDM/codegeex2-6b" target="_blank">HF Repo</a>｜📄 <a href="https://arxiv.org/abs/2303.17568" target="_blank">論文</a>
</p>
<p align="center">
👋 <a href="https://discord.gg/8gjHdkmAN6" target="_blank">Discord</a> に参加, <a href="https://join.slack.com/t/codegeexworkspace/shared_invite/zt-1s118ffrp-mpKKhQD0tKBmzNZVCyEZLw" target="_blank">Slack</a>, <a href="https://t.me/+IipIayJ32B1jOTg1" target="_blank">Telegram</a>, <a href="resources/wechat.md"target="_blank">WeChat</a>
</p>
查看[中文版](README.md)<br>
Read this in [English](README_EN.md)<br>
Lire en [Français](README_FR.md)
# CodeGeeX2: より強力な多言語コード生成モデル
CodeGeeX2 は、多言語コード生成モデル [CodeGeeX](https://github.com/THUDM/CodeGeeX)([KDD'23](https://arxiv.org/abs/2303.17568)) の第 2 世代モデルであり、より多くのコードデータで学習された [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) アーキテクチャに基づいて実装されています。ChatGLM2 のアドバンテージにより、CodeGeeX2 のコーディング能力は包括的に向上しています(+107% > CodeGeeX; わずか 6B のパラメータで、いくつかのタスクではより大規模な StarCoder-15B を凌駕しています)。以下の特徴があります:
* **より強力なコーディング機能**: CodeGeeX2-6B は、ChatGLM2-6B モデルをベースに、さらに 600B のコードトークンに対して事前学習を行っており、第一世代と比較してコーディング能力が総合的に向上しています。[HumanEval-X](https://huggingface.co/datasets/THUDM/humaneval-x) ベンチマークでは、6 言語すべてで大幅な改善が見られ（Python +57%、C++ +71%、Java +54%、JavaScript +83%、Go +56%、Rust +321%）、Python では Pass@1 一回合格率 35.9% に達し、より大規模な StarCoder-15B を上回りました。
* **その他の便利な機能**: ChatGLM2-6B モデルの特徴を継承し、CodeGeeX2-6B は中国語と英語のプロンプト、最大 8192 シーケンス長をサポートし、推論速度は第一世代と比較して大幅に改善されています。量子化後、推論に必要な GPU メモリは 6GB のみで、軽量なローカル展開をサポートします。
* **包括的な AI コーディングアシスタント**: CodeGeeX プラグイン（[VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex)、[Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)）のバックエンドがアップグレードされ、100 以上のプログラミング言語をサポートし、インフィルやクロスファイル補完などの実用的な機能が追加されました。対話型 AI コーディングアシスタント "Ask CodeGeeX" と組み合わせることで、中国語または英語の対話を通じて、コードの要約、コードの翻訳、デバッグ、コメント生成など、さまざまなプログラミング問題を解決することができ、開発者の作業効率を高めることができます。
* **オープンライセンス**: CodeGeeX2-6B ウェイトは学術研究に全面的に開放しています。商用利用をご希望の方は、[登録フォーム](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B)にご記入の上、お申し込みください。
## AI コーディングアシスタント
VS Code、IntelliJ IDEA、PyCharm、GoLand、WebStorm、Android Studio などの IDE をサポートする CodeGeeX プラグインを開発しました。このプラグインを使用することで、CodeGeeX2 モデルのコード生成と補完、アノテーション、コード変換、"Ask CodeGeeX" 対話型プログラミングなどの機能を体験することができ、開発効率を向上させることができます。より包括的な AI コーディング体験を得るために、IDE に CodeGeeX プラグインをダウンロードしてください。詳しくは[ホームページ](https://codegeex.cn/)をご覧ください。
## 始める
[CodeGeeX2-6B](https://huggingface.co/THUDM/codegeex2-6b) を素早く起動するには、`transformers` を使用します：
```python
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]))
```
Gradio DEMO の起動:
```
python ./demo/run_demo.py
```
❗️注意:
* CodeGeeX2 はベースモデルであり、チャット用の命令チューニングはされていません。コード補完/翻訳/説明のようなタスクは可能です。CodeGeeX のプラグイン([VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex))で命令チューニングされたバージョンを試すことができます。
* プログラミング言語は、`# language: Python` のように `language tag` を追加することで制御できます。パフォーマンスを確保するため、書式を守る必要があります。完全なリストは[こちら](https://github.com/THUDM/CodeGeeX2/blob/main/evaluation/utils.py#L14)にあります。より良い結果を得るためには、選択したプログラミング言語のフォーマットでコメントを書いてください。
* 複数のグラフィックカードを使用してモデルをロードする必要がある場合は、以下のコードを使用できます：
```python
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()
```
をに置き換えてください
```python
def get_model():
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True)
from gpus import load_model_on_gpus
# gpusファイルはdemoフォルダにあります
model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=2)
model = model.eval()
return tokenizer, model
tokenizer, model = get_model()
```
## 評価
CodeGeeX2 は多言語コード生成のベースモデルであり、前世代と比較してコーディング能力が大幅に向上しています。HumanEval、HumanEval-X、DS1000 ベンチマークでの評価結果を以下に示します（評価指標 Pass@k は[論文](https://arxiv.org/abs/2303.17568)と同じです）:
### 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** |
> **Pass@1** 使用 `n=20, t=0.2, top_p=0.95`; **Pass@10** および **Pass@100** を使用 `n=200, t=0.8, top_p=0.95`。
### 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** |
> **Pass@1** 使用 `n=20, t=0.2, top_p=0.95`。
上記の結果は `scripts/run_humanevalx.sh` を実行することで再現できる。実験の設定は [HumanEval-X 環境](https://github.com/THUDM/CodeGeeX/blob/main/codegeex/benchmark/README_zh.md)を参照してください。
### 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** |
> **Pass@1** 使用 `n=40, t=0.2, top_p=0.5`。
上記の結果は [DS1000 repo](https://github.com/HKUNLP/DS-1000.git) のコードで再現できる。
## 推論
CodeGeeX2 は、前世代よりも導入が容易になりました。マルチクエリーアテンションとフラッシュアテンションの使用により、推論速度が速くなり、INT4 量子化後に必要な GPU メモリは 6GB のみです。
### 量子化
| **Model** | FP16/BF16 | INT8 | INT4 |
| :--------------: | :-------: | :-----: | :----: |
| CodeGeeX-13B | 26\.9 GB | 14\.7 GB | - |
| **CodeGeeX2-6B** | 13\.1 GB | 8\.2 GB | 5\.5 GB |
> PyTorch 2.0に基づき、`torch.nn.functional.scaled_dot_product_attention` を使用して、効率的なアテンションメカニズムを実現。
### 加速
| **Model** | **推論速度 (token/秒)** |
| :--------------: | :-------------: |
| CodeGeeX-13B | 32 |
| **CodeGeeX2-6B** | 94 |
> `batch_size=1, max_length=2048`, どちらもアクセラレーションフレームワークを使用、`GeForce RTX-3090` の場合。
## ライセンス
このリポジトリのコードは、[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) ライセンスの下でのオープンソースです。モデルのウェイトは [Model License](MODEL_LICENSE) に基づいてライセンスされています。CodeGeeX2-6B のウェイトは学術研究用に公開されています。商用利用を希望される方は、[登録フォーム](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B)にご記入の上、お申し込みください。
## 引用
私たちの研究がお役に立ちましたら、ぜひ以下の論文を引用してください:
```
@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}
}
```
================================================
FILE: benchmark/humanevalx/go/go.mod
================================================
module humanEval
go 1.18
require (
github.com/go-openapi/inflect v0.19.0
github.com/stretchr/testify v1.8.0
)
require (
github.com/davecgh/go-spew v1.1.1 // indirect
github.com/pmezard/go-difflib v1.0.0 // indirect
gopkg.in/yaml.v3 v3.0.1 // indirect
)
================================================
FILE: benchmark/humanevalx/go/go.sum
================================================
github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/go-openapi/inflect v0.19.0 h1:9jCH9scKIbHeV9m12SmPilScz6krDxKRasNNSNPXu/4=
github.com/go-openapi/inflect v0.19.0/go.mod h1:lHpZVlpIQqLyKwJ4N+YSc9hchQy/i12fJykb83CRBH4=
github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
github.com/stretchr/objx v0.4.0/go.mod h1:YvHI0jy2hoMjB+UWwv71VJQ9isScKT/TqJzVSSt89Yw=
github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
github.com/stretchr/testify v1.8.0 h1:pSgiaMZlXftHpm5L7V1+rVB+AZJydKsMxsQBIJw4PKk=
github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405 h1:yhCVgyC4o1eVCa2tZl7eS0r+SDo693bJlVdllGtEeKM=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
================================================
FILE: benchmark/humanevalx/rust/Cargo.toml
================================================
[package]
name = "rust"
version = "0.1.0"
edition = "2021"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
rand = "0.4"
regex = "1"
md5 = "0.7.0"
================================================
FILE: demo/example_inputs.jsonl
================================================
{"code": "# Write a quick sort function\n", "langauge": "Python"}
{"code": "// 写一个冒泡排序函数\n", "langauge": "C++"}
{"code": "// 写一个二叉树的类\npublic class", "langauge": "Java"}
{"code": "// 矩阵求行列式\n", "langauge": "Matlab"}
{"code": "<!--Write a homepage of CodeGeeX.-->\n", "langauge": "HTML"}
{"code": "// 写一个服务器框架， 接收浏览器发过来的请求，并返回处理后的内容\n", "langauge": "JavaScript"}
{"code": "// Write a binary search function\n", "langauge": "Rust"}
{"code": "-- 查询品类最多的三种食品，但是不包含出现时间在2014年及之后\n", "langauge": "SQL"}
{"code": "// Write a simple file system that allows parallel read/write in Golang\n", "langauge": "Go"}
================================================
FILE: demo/fastapicpu.py
================================================
from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
import uvicorn, json, datetime
import torch
import argparse
try:
import chatglm_cpp
enable_chatglm_cpp = True
except:
print("[WARN] chatglm-cpp not found. Install it by `pip install chatglm-cpp` for better performance. "
"Check out https://github.com/li-plus/chatglm.cpp for more details.")
enable_chatglm_cpp = False
#获取选项
def add_code_generation_args(parser):
group = parser.add_argument_group(title="CodeGeeX2 DEMO")
group.add_argument(
"--model-path",
type=str,
default="THUDM/codegeex2-6b",
)
group.add_argument(
"--listen",
type=str,
default="127.0.0.1",
)
group.add_argument(
"--port",
type=int,
default=7860,
)
group.add_argument(
"--workers",
type=int,
default=1,
)
group.add_argument(
"--cpu",
action="store_true",
)
group.add_argument(
"--half",
action="store_true",
)
group.add_argument(
"--quantize",
type=int,
default=None,
)
group.add_argument(
"--chatglm-cpp",
action="store_true",
)
return parser
LANGUAGE_TAG = {
"Abap" : "* language: Abap",
"ActionScript" : "// language: ActionScript",
"Ada" : "-- language: Ada",
"Agda" : "-- language: Agda",
"ANTLR" : "// language: ANTLR",
"AppleScript" : "-- language: AppleScript",
"Assembly" : "; language: Assembly",
"Augeas" : "// language: Augeas",
"AWK" : "// language: AWK",
"Basic" : "' language: Basic",
"C" : "// language: C",
"C#" : "// language: C#",
"C++" : "// language: C++",
"CMake" : "# language: CMake",
"Cobol" : "// language: Cobol",
"CSS" : "/* language: CSS */",
"CUDA" : "// language: Cuda",
"Dart" : "// language: Dart",
"Delphi" : "{language: Delphi}",
"Dockerfile" : "# language: Dockerfile",
"Elixir" : "# language: Elixir",
"Erlang" : f"% language: Erlang",
"Excel" : "' language: Excel",
"F#" : "// language: F#",
"Fortran" : "!language: Fortran",
"GDScript" : "# language: GDScript",
"GLSL" : "// language: GLSL",
"Go" : "// language: Go",
"Groovy" : "// language: Groovy",
"Haskell" : "-- language: Haskell",
"HTML" : "<!--language: HTML-->",
"Isabelle" : "(*language: Isabelle*)",
"Java" : "// language: Java",
"JavaScript" : "// language: JavaScript",
"Julia" : "# language: Julia",
"Kotlin" : "// language: Kotlin",
"Lean" : "-- language: Lean",
"Lisp" : "; language: Lisp",
"Lua" : "// language: Lua",
"Markdown" : "<!--language: Markdown-->",
"Matlab" : f"% language: Matlab",
"Objective-C" : "// language: Objective-C",
"Objective-C++": "// language: Objective-C++",
"Pascal" : "// language: Pascal",
"Perl" : "# language: Perl",
"PHP" : "// language: PHP",
"PowerShell" : "# language: PowerShell",
"Prolog" : f"% language: Prolog",
"Python" : "# language: Python",
"R" : "# language: R",
"Racket" : "; language: Racket",
"RMarkdown" : "# language: RMarkdown",
"Ruby" : "# language: Ruby",
"Rust" : "// language: Rust",
"Scala" : "// language: Scala",
"Scheme" : "; language: Scheme",
"Shell" : "# language: Shell",
"Solidity" : "// language: Solidity",
"SPARQL" : "# language: SPARQL",
"SQL" : "-- language: SQL",
"Swift" : "// language: swift",
"TeX" : f"% language: TeX",
"Thrift" : "/* language: Thrift */",
"TypeScript" : "// language: TypeScript",
"Vue" : "<!--language: Vue-->",
"Verilog" : "// language: Verilog",
"Visual Basic" : "' language: Visual Basic",
}
app = FastAPI()
def device():
if enable_chatglm_cpp and args.chatglm_cpp:
print("Using chatglm-cpp to improve performance")
dtype = "f16" if args.half else "f32"
if args.quantize in [4, 5, 8]:
dtype = f"q{args.quantize}_0"
model = chatglm_cpp.Pipeline(args.model_path, dtype=dtype)
return model
print("chatglm-cpp not enabled, falling back to transformers")
if not args.cpu:
if not args.half:
model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True).cuda()
else:
model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True).cuda().half()
if args.quantize in [4, 8]:
print(f"Model is quantized to INT{args.quantize} format.")
model = model.half().quantize(args.quantize)
else:
model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True)
return model.eval()
@app.post("/")
async def create_item(request: Request):
global model, tokenizer
json_post_raw = await request.json()
json_post = json.dumps(json_post_raw)
json_post_list = json.loads(json_post)
lang = json_post_list.get('lang')
prompt = json_post_list.get('prompt')
max_length = json_post_list.get('max_length', 128)
top_p = json_post_list.get('top_p', 0.95)
temperature = json_post_list.get('temperature', 0.2)
top_k = json_post_list.get('top_k', 0)
if lang != "None":
prompt = LANGUAGE_TAG[lang] + "\n" + prompt
if enable_chatglm_cpp and args.chatglm_cpp:
response = model.generate(prompt,
max_length=max_length,
do_sample=temperature > 0,
top_p=top_p,
top_k=top_k,
temperature=temperature)
else:
response = model.chat(tokenizer,
prompt,
max_length=max_length,
top_p=top_p,
top_k=top_k,
temperature=temperature)
now = datetime.datetime.now()
time = now.strftime("%Y-%m-%d %H:%M:%S")
answer = {
"response": response,
"lang": lang,
"status": 200,
"time": time
}
log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
print(log)
return answer
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser = add_code_generation_args(parser)
args, _ = parser.parse_known_args()
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
model = device()
uvicorn.run(app, host=args.listen, port=args.port, workers=args.workers)
================================================
FILE: demo/gpus.py
================================================
import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
# transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
# windows下 model.device 会被设置成 transformer.word_embeddings.device
# linux下 model.device 会被设置成 lm_head.device
# 在调用chat或者stream_chat时,input_ids会被放到model.device上
# 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
# 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
# 本文件来源于https://github.com/THUDM/ChatGLM-6B/blob/main/utils.py
# 仅此处做少许修改以支持ChatGLM2,CodeGeeX2
device_map = {
'transformer.embedding.word_embeddings': 0,
'transformer.encoder.final_layernorm': 0,
'transformer.output_layer': 0,
'transformer.rotary_pos_emb': 0,
'lm_head': 0
}
used = 2
gpu_target = 0
for i in range(num_trans_layers):
if used >= per_gpu_layers:
gpu_target += 1
used = 0
assert gpu_target < num_gpus
device_map[f'transformer.encoder.layers.{i}'] = gpu_target
used += 1
return device_map
def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map: Optional[Dict[str, int]] = None, **kwargs) -> Module:
if num_gpus < 2 and device_map is None:
model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half().cuda()
else:
from accelerate import dispatch_model
model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half()
if device_map is None:
device_map = auto_configure_device_map(num_gpus)
model = dispatch_model(model, device_map=device_map)
return model
================================================
FILE: demo/run_demo.py
================================================
import os
import json
import numpy
import torch
import random
import argparse
import gradio as gr
from transformers import AutoTokenizer, AutoModel
try:
# Should first install fastllm (https://github.com/ztxz16/fastllm.git)
from fastllm_pytools import llm
enable_fastllm = True
except:
print("fastllm disabled.")
enable_fastllm = False
try:
from gpus import load_model_on_gpus
enable_multiple_gpus = True
except:
print("Multiple GPUs support disabled.")
enable_multiple_gpus = False
try:
import chatglm_cpp
enable_chatglm_cpp = True
except:
print("[WARN] chatglm-cpp not found. Install it by `pip install chatglm-cpp` for better performance. "
"Check out https://github.com/li-plus/chatglm.cpp for more details.")
enable_chatglm_cpp = False
def get_model(args):
if not args.cpu:
if torch.cuda.is_available():
device = f"cuda:{args.gpu}"
elif torch.backends.mps.is_built():
device = "mps"
else:
device = "cpu"
else:
device = "cpu"
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
if args.n_gpus > 1 and enable_multiple_gpus:
# 如需实现多显卡模型加载,传入"n_gpus"为需求的显卡数量 / To enable Multiple GPUs model loading, please adjust "n_gpus" to the desired number of graphics cards.
print(f"Runing on {args.n_gpus} GPUs.")
model = load_model_on_gpus(args.model_path, num_gpus=args.n_gpus)
model = model.eval()
elif enable_chatglm_cpp and args.chatglm_cpp:
print("Using chatglm-cpp to improve performance")
dtype = "f16"
if args.quantize in [4, 5, 8]:
dtype = f"q{args.quantize}_0"
model = chatglm_cpp.Pipeline(args.model_path, dtype=dtype)
else:
model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True)
model = model.eval()
if enable_fastllm and args.fastllm:
print("fastllm enabled.")
model = model.half()
llm.set_device_map(device)
if args.quantize in [4, 8]:
model = llm.from_hf(model, dtype=f"int{args.quantize}")
else:
model = llm.from_hf(model, dtype="float16")
else:
print("chatglm-cpp and fastllm not installed, using transformers.")
if args.quantize in [4, 8]:
print(f"Model is quantized to INT{args.quantize} format.")
model = model.half().quantize(args.quantize)
model = model.to(device)
return tokenizer, model
def add_code_generation_args(parser):
group = parser.add_argument_group(title="CodeGeeX2 DEMO")
group.add_argument(
"--model-path",
type=str,
default="THUDM/codegeex2-6b",
)
group.add_argument(
"--example-path",
type=str,
default=None,
)
group.add_argument(
"--quantize",
type=int,
default=None,
)
group.add_argument(
"--chatglm-cpp",
action="store_true",
)
group.add_argument(
"--fastllm",
action="store_true",
)
group.add_argument(
"--n-gpus",
type=int,
default=1,
)
group.add_argument(
"--gpu",
type=int,
default=0,
)
group.add_argument(
"--cpu",
action="store_true",
)
group.add_argument(
"--listen",
type=str,
default="127.0.0.1",
)
group.add_argument(
"--port",
type=int,
default=7860,
)
group.add_argument(
"--username",
type=str,
default=None,
)
group.add_argument(
"--password",
type=str,
default=None,
)
group.add_argument(
"--auth",
action="store_true",
)
return parser
# 更完编程语言列表请查看 evaluation/utils.py / Full list of supported languages in evaluation/utils.py
LANGUAGE_TAG = {
"Abap" : "* language: Abap",
"ActionScript" : "// language: ActionScript",
"Ada" : "-- language: Ada",
"Agda" : "-- language: Agda",
"ANTLR" : "// language: ANTLR",
"AppleScript" : "-- language: AppleScript",
"Assembly" : "; language: Assembly",
"Augeas" : "// language: Augeas",
"AWK" : "// language: AWK",
"Basic" : "' language: Basic",
"C" : "// language: C",
"C#" : "// language: C#",
"C++" : "// language: C++",
"CMake" : "# language: CMake",
"Cobol" : "// language: Cobol",
"CSS" : "/* language: CSS */",
"CUDA" : "// language: Cuda",
"Dart" : "// language: Dart",
"Delphi" : "{language: Delphi}",
"Dockerfile" : "# language: Dockerfile",
"Elixir" : "# language: Elixir",
"Erlang" : f"% language: Erlang",
"Excel" : "' language: Excel",
"F#" : "// language: F#",
"Fortran" : "!language: Fortran",
"GDScript" : "# language: GDScript",
"GLSL" : "// language: GLSL",
"Go" : "// language: Go",
"Groovy" : "// language: Groovy",
"Haskell" : "-- language: Haskell",
"HTML" : "<!--language: HTML-->",
"Isabelle" : "(*language: Isabelle*)",
"Java" : "// language: Java",
"JavaScript" : "// language: JavaScript",
"Julia" : "# language: Julia",
"Kotlin" : "// language: Kotlin",
"Lean" : "-- language: Lean",
"Lisp" : "; language: Lisp",
"Lua" : "// language: Lua",
"Markdown" : "<!--language: Markdown-->",
"Matlab" : f"% language: Matlab",
"Objective-C" : "// language: Objective-C",
"Objective-C++": "// language: Objective-C++",
"Pascal" : "// language: Pascal",
"Perl" : "# language: Perl",
"PHP" : "// language: PHP",
"PowerShell" : "# language: PowerShell",
"Prolog" : f"% language: Prolog",
"Python" : "# language: Python",
"R" : "# language: R",
"Racket" : "; language: Racket",
"RMarkdown" : "# language: RMarkdown",
"Ruby" : "# language: Ruby",
"Rust" : "// language: Rust",
"Scala" : "// language: Scala",
"Scheme" : "; language: Scheme",
"Shell" : "# language: Shell",
"Solidity" : "// language: Solidity",
"SPARQL" : "# language: SPARQL",
"SQL" : "-- language: SQL",
"Swift" : "// language: swift",
"TeX" : f"% language: TeX",
"Thrift" : "/* language: Thrift */",
"TypeScript" : "// language: TypeScript",
"Vue" : "<!--language: Vue-->",
"Verilog" : "// language: Verilog",
"Visual Basic" : "' language: Visual Basic",
}
def set_random_seed(seed):
"""Set random seed for reproducability."""
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed)
def main():
parser = argparse.ArgumentParser()
parser = add_code_generation_args(parser)
args, _ = parser.parse_known_args()
tokenizer, model = get_model(args)
examples = []
if args.example_path is None:
example_path = os.path.join(os.path.split(os.path.realpath(__file__))[0], "example_inputs.jsonl")
else:
example_path = args.example_path
# Load examples for gradio DEMO
with open(example_path, "r", encoding="utf-8") as f:
for line in f:
examples.append(list(json.loads(line).values()))
def predict(
prompt,
lang,
seed,
out_seq_length,
temperature,
top_k,
top_p,
):
set_random_seed(seed)
if lang != "None":
prompt = LANGUAGE_TAG[lang] + "\n" + prompt
if enable_fastllm and args.fastllm:
model.direct_query = True
outputs = model.chat(tokenizer,
prompt,
max_length=out_seq_length,
top_p=top_p,
top_k=top_k,
temperature=temperature)
response = prompt + outputs[0]
elif enable_chatglm_cpp and args.chatglm_cpp:
inputs = tokenizer([prompt], return_tensors="pt")
pipeline = model
outputs = pipeline.generate(prompt,
max_length=inputs['input_ids'].shape[-1] + out_seq_length,
do_sample=temperature > 0,
top_p=top_p,
top_k=top_k,
temperature=temperature)
response = prompt + outputs
else:
inputs = tokenizer([prompt], return_tensors="pt")
inputs = inputs.to(model.device)
outputs = model.generate(**inputs,
max_length=inputs['input_ids'].shape[-1] + out_seq_length,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
pad_token_id=2,
eos_token_id=2)
response = tokenizer.decode(outputs[0])
return response
with gr.Blocks(title="CodeGeeX2 DEMO") as demo:
gr.Markdown(
"""
<p align="center">
<img src="https://raw.githubusercontent.com/THUDM/CodeGeeX2/main/resources/codegeex_logo.png">
</p>
""")
gr.Markdown(
"""
<p align="center">
🏠 <a href="https://codegeex.cn" target="_blank">Homepage</a>｜💻 <a href="https://github.com/THUDM/CodeGeeX2" target="_blank">GitHub</a>｜🛠 Tools <a href="https://marketplace.visualstudio.com/items?itemName=aminer.codegeex" target="_blank">VS Code</a>, <a href="https://plugins.jetbrains.com/plugin/20587-codegeex" target="_blank">Jetbrains</a>｜🤗 <a href="https://huggingface.co/THUDM/codegeex2-6b" target="_blank">Download</a>｜📄 <a href="https://arxiv.org/abs/2303.17568" target="_blank">Paper</a>
</p>
""")
gr.Markdown(
"""
这是 CodeGeeX2 的简易DEMO。请注意：
* CodeGeeX2 是一个基座模型，它可以完成代码补全/翻译/解释等任务，没有针对聊天进行指令微调。可以在 CodeGeeX 插件[VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex)、[Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)中体验指令微调后的版本。
* 可以通过添加`language tag`来控制编程语言，例如`# language: Python`，查看[完整支持语言列表](https://github.com/THUDM/CodeGeeX2/blob/main/evaluation/utils.py#L14)。
* 按照所选编程语言的格式写注释可以获得更好的结果，请参照下方给出的示例。
This is the DEMO for CodeGeeX2. Please note that:
* 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](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)).
* 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](https://github.com/THUDM/CodeGeeX2/blob/main/evaluation/utils.py#L14).
* Write comments under the format of the selected programming language to achieve better results, see examples below.
""")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(lines=14, placeholder='Please enter the description or select an example input below.',label='Input')
with gr.Row():
gen = gr.Button("Generate")
clr = gr.Button("Clear")
outputs = gr.Textbox(lines=15, label='Output')
gr.Markdown(
"""
Generation Parameter
""")
with gr.Row():
with gr.Row():
seed = gr.Slider(maximum=10000, value=8888, step=1, label='Seed')
with gr.Row():
out_seq_length = gr.Slider(maximum=8192, value=128, minimum=1, step=1, label='Output Sequence Length')
temperature = gr.Slider(maximum=1, value=0.2, minimum=0, label='Temperature')
with gr.Row():
top_k = gr.Slider(maximum=100, value=0, minimum=0, step=1, label='Top K')
top_p = gr.Slider(maximum=1, value=0.95, minimum=0, label='Top P')
with gr.Row():
lang = gr.Radio(
choices=["None"] + list(LANGUAGE_TAG.keys()), value='None', label='Programming Language')
inputs = [prompt, lang, seed, out_seq_length, temperature, top_k, top_p]
gen.click(fn=predict, inputs=inputs, outputs=outputs)
clr.click(fn=lambda value: gr.update(value=""), inputs=clr, outputs=prompt)
gr_examples = gr.Examples(examples=examples, inputs=[prompt, lang],
label="Example Inputs (Click to insert an examplet it into the input box)",
examples_per_page=20)
if not args.auth:
demo.launch(server_name=args.listen, server_port=args.port)
else:
demo.launch(server_name=args.listen, server_port=args.port, auth=(args.username, args.password))
#如果需要监听0.0.0.0和其他端口 可以改成 demo.launch(server_name="0.0.0.0", server_port=6666)
#如果需要加密码 demo.launch(server_name="0.0.0.0", server_port=6666, auth=("admin", "password"))
if __name__ == '__main__':
with torch.no_grad():
main()
================================================
FILE: docs/zh/inference_zh.md
================================================
# CodeGeeX2推理教程
CodeGeeX2 是多语言代码生成模型 [CodeGeeX](https://github.com/THUDM/CodeGeeX) ([KDD’23](https://arxiv.org/abs/2303.17568)) 的第二代模型，更强，更快，更轻量，是适合本地部署的AI代码生成助手。CodeGeeX2 支持在多种不同平台上进行推理，本教程将会介绍几种不同的推理方式，包括CPU推理，多卡推理，加速推理等。
- [快速开始](#快速开始)
- [多精度/量化推理](#多精度/量化推理)
- [多GPU推理](#多GPU推理)
- [Mac推理](#Mac推理)
- [fastllm加速推理](#fastllm加速推理)
- [ChatGLM.cpp量化推理](#chatglmcpp-量化推理)
## 快速开始
下载本仓库并使用`pip`安装环境依赖：
```shell
git clone https://github.com/THUDM/CodeGeeX2
cd CodeGeeX2
pip install -r requirements.txt
```
使用`transformers`快速调用[CodeGeeX2-6B](https://huggingface.co/THUDM/codegeex2-6b)，将自动下载权重到本地：
```python
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') # 如使用CPU推理，device='cpu'
model = model.eval()
# CodeGeeX2支持100种编程语言，加入语言标签引导生成相应的语言
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) # 示例中使用greedy decoding，检查输出结果是否对齐
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]))
```
亦可以手动下载权重：
```shell
# huggingface下载
git clone https://huggingface.co/THUDM/codegeex2-6b
```
将tokenizer和model路径改为本地路径：
```python
model_path = "/path/to/codegeex2-6b"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
```
## 多精度/量化推理
CodeGeeX2 使用BF16训练，推理时支持BF16/FP16/INT8/INT4，可以根据显卡显存选择合适的精度格式：
| **Model** | FP16/BF16 | INT8 | INT4 |
| :--------------: | :-------: | :------: | :-----: |
| CodeGeeX-13B | 26\.9 GB | 14\.7 GB | - |
| **CodeGeeX2-6B** | 13\.1 GB | 8\.2 GB | 5\.5 GB |
默认使用BF16精度进行推理，如显卡不支持BF16（❗️如使用错误的格式，推理结果将出现乱码），需要转换为FP16格式：
```python
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().to("cuda")
```
量化推理以INT4为例，可以下载转换好的权重（[INT4权重](https://huggingface.co/THUDM/codegeex2-6b-int4)）或手动转换，如果显卡不支持BF16，也需要先转换为FP16格式：
```python
# 下载转换好的权重
model = AutoModel.from_pretrained("THUDM/codegeex2-6b-int4", trust_remote_code=True)
# 手动转换权重
model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True).quantize(4).to("cuda")
# 如果显卡不支持BF16，需要先转换为FP16格式
model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True).half().quantize(4).to("cuda")
```
## 多GPU推理
用[gpus.py](https://github.com/THUDM/CodeGeeX2/blob/main/demo/gpus.py)实现多GPU推理：
```python
from gpus import load_model_on_gpus
model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=2)
```
## Mac推理
对于搭载了 Apple Silicon 或者 AMD GPU 的 Mac，可以使用 MPS 后端运行。参考 Apple 的 [官方说明](https://developer.apple.com/metal/pytorch) 安装 PyTorch-Nightly（正确的版本号应该是2.x.x.dev2023xxxx，如2.1.0.dev20230729）：
```shell
pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
```
在 MacOS 上只支持从本地加载模型（提前下载权重[codegeex2-6b](https://huggingface.co/THUDM/codegeex2-6b)，[codegeex2-6b-int4](https://huggingface.co/THUDM/codegeex2-6b-int4)），支持FP16/INT8/INT4格式，并使用 mps 后端：
```python
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().to('mps')
```
## fastllm加速推理
可以使用[fastllm](https://github.com/ztxz16/fastllm)对 CodeGeeX2 进行加速，fastllm是目前支持GLM架构的最快开源框架。首先安装fastllm_pytools：
```shell
git clone https://github.com/ztxz16/fastllm
cd fastllm
mkdir build
cd build
# 使用GPU编译，需要添加CUDA路径：export CUDA_HOME=/usr/local/cuda/bin:$PATH，export PATH=$PATH:$CUDA_HOME/bin
cmake .. -DUSE_CUDA=ON # 如果不使用GPU编译 cmake .. -DUSE_CUDA=OFF
make -j
cd tools && python setup.py install # 确认安装是否成功，在python中 import fastllm_pytools 不报错
```
如出现架构不支持的报错，需要调整`CMakeLists.txt`，注释掉下面一行：
```shell
# set(CMAKE_CUDA_ARCHITECTURES "native")
```
如果是E5系列的CPU可能会出现下面的编译报错
```
error: inlining failed in call to ‘always_inline’ ‘__m256i _mm256_add_epi32(__m256i, __m256i)’: target specific option mismatch
```
此时将'CmakeLists.txt'的第20行修改如下即可编译成功:
```
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread --std=c++17 -O2")
```
将huggingface转换成fastllm格式：
```python
# 原本的调用代码
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)
# 加入下面这两行，将huggingface模型转换成fastllm模型
from fastllm_pytools import llm
model = llm.from_hf(model, tokenizer, dtype="float16") # dtype支持 "float16", "int8", "int4"
```
fastllm中模型接口和huggingface不完全相同，可以参考[demo/run_demo.py](https://github.com/THUDM/CodeGeeX2/blob/main/demo/run_demo.py)中的相关实现：
```python
model.direct_query = True
outputs = model.chat(tokenizer,
prompt,
max_length=out_seq_length,
top_p=top_p,
top_k=top_k,
temperature=temperature)
response = outputs[0]
```
## ChatGLM.cpp 量化推理
[ChatGLM.cpp](https://github.com/li-plus/chatglm.cpp) 是类似 LLaMA.cpp 的全平台量化加速方案，支持 q4_0/q4_1/q5_0/q5_1/q8_0 多种量化精度，CPU/CUDA/Metal 多种后端，仅用一行代码实现推理加速。
首先安装 chatglm-cpp。如需使用 CUDA 加速，需要添加环境变量 `CMAKE_ARGS="-DGGML_CUBLAS=ON"`；如果仅使用 CPU 加速，将该环境变量去掉即可。
```sh
CMAKE_ARGS="-DGGML_CUBLAS=ON" pip install chatglm-cpp -v
```
仅需一行代码即可量化加速 Hugging Face 模型，`dtype` 可指定 `q4_0`, `q4_1`, `q5_0`, `q5_1`, `q8_0`, `f16`，表示不同的量化类型。
```python
>>> import chatglm_cpp
>>>
>>> pipeline = chatglm_cpp.Pipeline("THUDM/codegeex2-6b", dtype="q4_0") # Load HF model and quantize it into int4
Loading checkpoint shards: 100%|███████████████████████████████████████████████| 7/7 [00:09<00:00, 1.33s/it]
Processing model states: 100%|█████████████████████████████████████████████| 199/199 [00:21<00:00, 9.21it/s]
...
>>> print(pipeline.generate("# language: Python\n# write a bubble sort function\n", do_sample=False))
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, 4, 3, 2, 1]))
```
ChatGLM.cpp 已集成到本仓库，demo 添加选项 `--quantize 4 --chatglm-cpp` 即可开启 int4 (q4_0) 量化加速，例如：
```sh
python ./demo/run_demo.py --quantize 4 --chatglm-cpp
```
Fast API 同样支持 ChatGLM.cpp 加速，添加同样参数启动服务：
```sh
python ./demo/fastapicpu.py --quantize 4 --chatglm-cpp
```
测试服务接口：
```sh
curl -X POST "http://127.0.0.1:7860" \
-H 'Content-Type: application/json' \
-d '{"lang": "Python", "prompt": "# Write a bubble sort function", "max_length": 512}'
```
================================================
FILE: evaluation/__init__.py
================================================
================================================
FILE: evaluation/evaluation.py
================================================
import os
import re
import sys
import fire
import json
import gzip
import glob
import numpy as np
from typing import *
from tqdm.auto import tqdm
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from execution import check_correctness
from utils import Logger, IMPORT_HELPER, read_dataset, stream_jsonl_all, estimate_pass_at_k
LANGUAGE_NAME = {
"CPP" : "cpp",
"Go" : "go",
"Java" : "java",
"JavaScript" : "js",
"Python" : "python",
"Rust" : "rust",
}
def postprocess_generation(sample, generation_mode="completion"):
code = sample["generation"]
if generation_mode == "instruction":
if "```" in code:
pattern = r'```(.*?)\n(.*?)```'
matches = re.findall(pattern, code, re.DOTALL)
for match in matches:
code = match[1]
break
sample["generation"] = code
return sample
def process_test(sample, problems, dataset_type, language_type, generation_mode):
if dataset_type == "humanevalx":
task_id = sample["task_id"]
prompt = problems[task_id]["prompt"]
test = problems[task_id]["test"]
code = sample["generation"]
# Pre-process for different languages
if language_type == "python":
test_setup = "\n".join(IMPORT_HELPER["python"]) + "\n"
test_string = test_setup + prompt + code + "\n" + test + "\n"
elif language_type == "cpp":
test_set_up = ""
for s in IMPORT_HELPER["cpp"]:
if s not in prompt:
test_set_up += s + "\n"
test_string = test_set_up + "\n" + prompt + code + "\n" + test
elif language_type == "java":
test_string = prompt + code + "\n" + test
elif language_type == "js" or language_type == "javascript":
test_string = prompt + code + "\n" + test
elif language_type == "go":
import_string = problems[task_id]["import"]
prompt = prompt.replace(import_string, "")
test = problems[task_id]["test"]
test_setup = problems[task_id]["test_setup"]
other_pkgs = []
for pkg in IMPORT_HELPER["go"]:
if pkg not in test_setup:
p = pkg.split("/")[-1]
if p + "." in code:
other_pkgs.append(f"\"{pkg}\"")
if other_pkgs:
import_other_pkgs = "import (\n" + " ".join([p + "\n" for p in other_pkgs]) + ")"
test_string = test_setup + "\n" + import_other_pkgs + "\n" + prompt + code + "\n" + test
else:
test_string = test_setup + "\n" + prompt + code + "\n" + test
elif language_type == "rust":
main = "\nfn main(){ \n } \n"
test_string = main + prompt + code + test
elif dataset_type == "mbpp":
task_id = sample["task_id"]
prompt = sample["prompt"]
test = "\n".join(problems[task_id]["test_list"]) + "\n" + "\n".join(problems[task_id]["challenge_test_list"])
code = sample["generation"]
test_setup = "\n".join(IMPORT_HELPER["python"]) + "\n"
test_string = test_setup + "\n" + prompt + code + "\n" + problems[task_id]["test_setup_code"] + "\n" + test + "\n"
return test_string
def evaluate_functional_correctness(
input_path: str = None,
output_path: str = None,
log_path: str = None,
tmp_dir: str = "./",
n_workers: int = 32,
timeout: float = 5.0,
k: List[int] = [1, 10, 100],
model_name: str = None,
problem_file: str = None,
language_type: str = None,
dataset_type: str = "humanevalx",
generation_mode: str = "completion",
test_groundtruth: bool = False,
):
if log_path is None:
log_path = os.path.join(output_path, "evaluation.log")
logger = Logger(__name__, log_file=log_path)
if os.path.isdir(input_path):
input_list = glob.glob(input_path + '/*generation*.jsonl')
sample_jsonl = []
for input_file in input_list:
sample_jsonl += stream_jsonl_all(input_file)
else:
input_file = input_path
sample_jsonl = stream_jsonl_all(input_file)
problems = read_dataset(problem_file, dataset_type=dataset_type)
if output_path is not None:
os.makedirs(output_path, exist_ok=True)
with ThreadPoolExecutor(max_workers=n_workers) as executor:
futures = []
completion_id = Counter()
n_samples = 0
results = defaultdict(list)
if test_groundtruth:
logger.info("Testing ground truth...")
else:
logger.info("Testing generation...")
for sample in sample_jsonl:
task_id = sample["task_id"]
if language_type is None:
language_type = LANGUAGE_NAME[task_id.split("/")[0]]
if test_groundtruth:
if dataset_type == "humanevalx":
sample["generation"] = sample["canonical_solution"]
sample["prompt"] = problems[task_id]["prompt"]
if dataset_type == "mbpp":
sample["generation"] = sample["code"]
sample["prompt"] = problems[task_id]["prompt"]
sample = postprocess_generation(sample, generation_mode)
sample["test_code"] = process_test(sample, problems, dataset_type, language_type, generation_mode)
if sample["test_code"] is None:
continue
if "completion_id" in sample:
completion_id_ = sample["completion_id"]
else:
completion_id_ = completion_id[task_id]
args = (task_id, sample, language_type, timeout, tmp_dir, completion_id_)
future = executor.submit(check_correctness, *args)
futures.append(future)
completion_id[task_id] += 1
n_samples += 1
if len(completion_id) == len(problems):
evaluate_pass_at_k = True
else:
evaluate_pass_at_k = False
logger.info("Running test suites...")
for future in tqdm(as_completed(futures), total=len(futures)):
result = future.result()
results[result["task_id"]].append((result["completion_id"], result))
# Calculate pass@k.
total, correct = [], []
for result in results.values():
passed = [r[1]["passed"] for r in result]
total.append(len(passed))
correct.append(sum(passed))
total = np.array(total)
correct = np.array(correct)
if evaluate_pass_at_k:
ks = k
pass_at_k = {f"pass@{k}": estimate_pass_at_k(total, correct, k).mean()
for k in ks if (total >= k).all()}
logger.info(pass_at_k)
else:
logger.info("Total: {}".format(np.sum(total)))
logger.info("Correct: {}".format(np.sum(correct)))
if test_groundtruth:
out_file = os.path.join(output_path, "ground_truth.jsonl")
else:
out_file = os.path.join(output_path, "result-" + input_file.split("/")[-2] + "." + input_file.split("/")[-1].split(".")[-1])
logger.info("Writing to: {}".format(out_file))
if out_file.endswith(".gz"):
fp = gzip.GzipFile(fileobj=open(out_file, "wb"), mode="wb")
for res in results.values():
for r in res:
fp.write((json.dumps(r[1], ensure_ascii=False) + "\n").encode("utf-8"))
else:
fp = open(out_file, 'w')
for res in results.values():
for r in res:
fp.write(json.dumps(r[1], ensure_ascii=False) + "\n")
fp.close()
if test_groundtruth:
logger.info("Ground-truth test finished.")
else:
logger.info("Evaluation finished.")
def main():
fire.Fire(evaluate_functional_correctness)
if __name__ == "__main__":
sys.exit(main())
================================================
FILE: evaluation/execution.py
================================================
import io
import os
import signal
import random
import gzip
import json
import tempfile
import platform
import subprocess
import contextlib
import faulthandler
import multiprocessing
from typing import *
def dicts_to_jsonl(data_list: list, filename: str, compress: bool = True) -> None:
"""
Method saves list of dicts into jsonl file.
:param data: (list) list of dicts to be stored,
:param filename: (str) path to the output file. If suffix .jsonl is not given then methods appends
.jsonl suffix into the file.
:param compress: (bool) should file be compressed into a gzip archive?
"""
sjsonl = '.jsonl'
sgz = '.gz'
# Check filename
if not filename.endswith(sjsonl):
filename = filename + sjsonl
# Save data
if compress:
filename = filename + sgz
with gzip.open(filename, 'w') as compressed:
for ddict in data_list:
jout = json.dumps(ddict) + '\n'
jout = jout.encode('utf-8')
compressed.write(jout)
else:
with open(filename, 'w') as out:
for ddict in data_list:
jout = json.dumps(ddict) + '\n'
out.write(jout)
def check_correctness(
task_id: str,
sample: dict,
language_type: str,
timeout: float = 3.0,
tmp_dir: str = None,
completion_id: Optional[int] = None,
) -> Dict:
"""
Evaluates the functional correctness of a completion by running the test
suite provided in the problem.
"""
def unsafe_execute(tmp_dir):
random_id = random.uniform(1, 1000)
if "python" in language_type.lower():
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
rmtree = shutil.rmtree
rmdir = os.rmdir
chdir = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
try:
exec_globals = {}
with swallow_io():
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec(sample["test_code"], exec_globals)
result.append("passed")
except TimeoutException:
result.append("timed out")
except AssertionError as e:
result.append(f"failed: AssertionError")
except BaseException as e:
result.append(f"failed: {e}")
# Needed for cleaning up.
shutil.rmtree = rmtree
os.rmdir = rmdir
os.chdir = chdir
elif "go" in language_type.lower():
assert tmp_dir is not None, "Go should be evaluated in a dir where necessary module files installed."
import os
import shutil
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(f"main_test.go", 'w').write(sample["test_code"])
try:
exec_result = None
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec_result = subprocess.run(["go", "test", f"-timeout={timeout}s", "main_test.go"], timeout=timeout, capture_output=True)
if exec_result.returncode == 0:
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
shutil.rmtree(tmp_dir)
elif "js" in language_type.lower():
import os
import shutil
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(f"test.js", 'w').write(sample["test_code"])
try:
exec_result = None
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec_result = subprocess.run(["node", "test.js"], timeout=timeout, capture_output=True)
if exec_result.stderr.decode():
err = exec_result.stderr.decode()
result.append(f"failed: {err}")
elif exec_result.stdout.decode():
err = exec_result.stdout.decode()
result.append(f"failed: {err}")
else:
result.append("passed")
except TimeoutException:
result.append("timed out")
shutil.rmtree(tmp_dir)
elif "cpp" in language_type.lower():
import os
import shutil
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(f"test.cpp", 'w').write(sample["test_code"])
if "162" in task_id:
compilation_result = subprocess.run(["/usr/bin/g++", "-std=c++11", "test.cpp", "-lcrypto", "-lssl"],
timeout=timeout,
capture_output=True)
else:
compilation_result = subprocess.run(["/usr/bin/g++", "-std=c++11", "test.cpp"], timeout=timeout,
capture_output=True)
if compilation_result.returncode != 0:
if compilation_result.stderr:
err = compilation_result.stderr.decode()
else:
err = compilation_result.stdout.decode()
result.append(f"failed: compilation error: {err}")
else:
try:
exec_result = None
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec_result = subprocess.run(["./a.out"], timeout=timeout, capture_output=True)
if exec_result.returncode == 0:
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
shutil.rmtree(tmp_dir)
elif "rust" in language_type.lower():
import os
WD: str = os.path.dirname(tmp_dir)
RUST_DIR: str = os.path.join(WD, "rust")
RUST_SRC: str = os.path.join(RUST_DIR, "src")
RUST_BIN: str = os.path.join(RUST_SRC, "bin")
RUST_TMP_DIR: str = os.path.join(RUST_DIR, "tmp")
RUST_LOGS: str = os.path.join(RUST_TMP_DIR, "logs")
RUST_EXT: str = ".rs"
# Create mandatory tmp directories
os.makedirs(RUST_TMP_DIR, exist_ok=True)
os.makedirs(RUST_LOGS, exist_ok=True)
os.makedirs(RUST_SRC, exist_ok=True)
os.makedirs(RUST_BIN, exist_ok=True)
with tempfile.NamedTemporaryFile(dir = RUST_BIN, delete=False) as f:
# temporal file name
file_prefix = sample["task_id"].lower().replace("/", "_")
file_name:str = file_prefix +RUST_EXT
os.rename(f.name, os.path.join(RUST_BIN, file_name))
# Sample to pure Rust function
rust_code: str = sample["test_code"]
# dump the rust source code in the target temporal file
f.write(rust_code.encode('utf-8'))
# Proceed towards Rust binaries compilation. Therefore move to Rust module root dir.
os.chdir(RUST_DIR)
# Two possible outcomes
# Pass OR Fail compilation
log_filename: str = file_prefix + ".jsonl"
log_path: str = os.path.join(RUST_LOGS, log_filename)
cargo_check: str = "cargo check --bin " + file_prefix + " --message-format json >> " + log_path
# Compilation build status
returned_val_compilation: int
# Overwrite file content
if os.path.exists(log_path):
if(file_size := os.path.getsize(log_path)) >= 0:
os.remove(log_path)
returned_val_compilation = os.system(cargo_check)
else:
returned_val_compilation = os.system(cargo_check)
# 0 means success
if returned_val_compilation == 0:
#Execution pipeline
cargo_test: str = "cargo test --bin " +file_prefix+ " --message-format json >> " + log_path
returned_val_execution = os.system(cargo_test)
if returned_val_execution == 0:
result.append("passed")
else:
result.append(f"failed: execution error")
else:
result.append(f"failed: compilation error")
elif "java" in language_type.lower():
assert tmp_dir is not None, "Java should be evaluated in a temporary dir."
import os
import shutil
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(os.path.join(tmp_dir, "Main.java"), 'w').write(sample["test_code"])
res = "failed: unknown error"
compile_returncode = -1
for _ in range(5):
try:
compilation_result = subprocess.run(['javac', os.path.join(tmp_dir, "Main.java")], timeout=5,
capture_output=True)
compile_returncode = compilation_result.returncode
break
except subprocess.TimeoutExpired as e:
continue
if compile_returncode != 0:
res = "failed: compilation error"
else:
exec_result = None
try:
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec_result = subprocess.run([f'java', '-cp', tmp_dir, 'Main'], timeout=timeout, capture_output=True)
if exec_result.returncode == 0:
res = "passed"
elif exec_result.returncode == 1:
if "AssertionError" in exec_result.stderr.decode('unicode-escape'):
res = "failed: wrong answer"
else:
res = f"failed: {exec_result.stderr.decode()}"
except subprocess.TimeoutExpired as e:
res = "time out"
except BaseException as e:
res = f"failed: {e}"
result.append(res)
shutil.rmtree(tmp_dir)
manager = multiprocessing.Manager()
result = manager.list()
p = multiprocessing.Process(target=unsafe_execute, args=(tmp_dir,))
p.start()
p.join(timeout=timeout + 1)
if p.is_alive():
p.kill()
if not result:
result.append("timed out")
return {
"task_id" : task_id,
"completion_id": completion_id,
"test_code" : sample["test_code"],
"prompt" : sample["prompt"],
"generation" : sample["generation"],
"result" : result[0],
"passed" : result[0] == "passed",
"finish" : -1 if "finish" not in sample else sample["finish"],
"file" : "" if "file" not in sample else sample["file"],
"output" : [] if "output" not in sample else sample["output"],
}
# Copyright (c) OpenAI (https://openai.com)
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
# ============================================================================
@contextlib.contextmanager
def time_limit(seconds: float):
def signal_handler(signum, frame):
raise TimeoutException("Timed out!")
signal.setitimer(signal.ITIMER_REAL, seconds)
signal.signal(signal.SIGALRM, signal_handler)
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL, 0)
@contextlib.contextmanager
def swallow_io():
stream = WriteOnlyStringIO()
with contextlib.redirect_stdout(stream):
with contextlib.redirect_stderr(stream):
with redirect_stdin(stream):
yield
@contextlib.contextmanager
def create_tempdir():
with tempfile.TemporaryDirectory() as dirname:
with chdir(dirname):
yield dirname
class TimeoutException(Exception):
pass
class WriteOnlyStringIO(io.StringIO):
""" StringIO that throws an exception when it's read from """
def read(self, *args, **kwargs):
raise IOError
def readline(self, *args, **kwargs):
raise IOError
def readlines(self, *args, **kwargs):
raise IOError
def readable(self, *args, **kwargs):
""" Returns True if the IO object can be read. """
return False
class redirect_stdin(contextlib._RedirectStream): # type: ignore
_stream = 'stdin'
@contextlib.contextmanager
def chdir(root):
if root == ".":
yield
return
cwd = os.getcwd()
os.chdir(root)
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(cwd)
def reliability_guard(maximum_memory_bytes: Optional[int] = None):
"""
This disables various destructive functions and prevents the generated code
from interfering with the test (e.g. fork bomb, killing other processes,
removing filesystem files, etc.)
WARNING
This function is NOT a security sandbox. Untrusted code, including, model-
generated code, should not be blindly executed outside of one. See the
Codex paper for more information about OpenAI's code sandbox, and proceed
with caution.
"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))
resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))
if not platform.uname().system == 'Darwin':
resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes))
faulthandler.disable()
import builtins
builtins.exit = None
builtins.quit = None
import os
os.environ['OMP_NUM_THREADS'] = '1'
os.kill = None
os.system = None
os.putenv = None
os.remove = None
os.removedirs = None
os.rmdir = None
os.fchdir = None
os.setuid = None
os.fork = None
os.forkpty = None
os.killpg = None
os.rename = None
os.renames = None
os.truncate = None
os.replace = None
os.unlink = None
os.fchmod = None
os.fchown = None
os.chmod = None
os.chown = None
os.chroot = None
os.fchdir = None
os.lchflags = None
os.lchmod = None
os.lchown = None
os.getcwd = None
os.chdir = None
import shutil
shutil.rmtree = None
shutil.move = None
shutil.chown = None
import subprocess
subprocess.Popen = None # type: ignore
__builtins__['help'] = None
import sys
sys.modules['ipdb'] = None
sys.modules['joblib'] = None
sys.modules['resource'] = None
sys.modules['psutil'] = None
sys.modules['tkinter'] = None
================================================
FILE: evaluation/generation.py
================================================
import os
import zmq
import time
import json
import torch
import random
import socket
import argparse
from typing import *
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, StoppingCriteria
from utils import Logger, read_dataset, process_extra_prompt, is_code_generation_finished, cleanup_code
logger = Logger(__name__)
def add_code_generation_specific_args(parser):
group = parser.add_argument_group("Code Generation")
group.add_argument(
"--hostfile",
type=str,
default="./hostfile",
)
group.add_argument(
"--channel-ip",
type=str,
default=None,
help="IP for ZeroMQ channel",
)
group.add_argument(
"--channel-port",
type=int,
default=5555,
help="Port for ZeroMQ channel",
)
group.add_argument(
"--master-port",
type=int,
default=6007,
help="Port for distributed channel",
)
group.add_argument(
"--model-per-device",
type=int,
default=1,
help="Number of models per device",
)
group.add_argument(
"--max-length",
type=int,
default=8192,
help="Max sequence length",
)
group.add_argument(
"--top-p",
type=float,
default=1.0,
help="Top-p Probability for sampling",
)
group.add_argument(
"--top-k",
type=int,
default=0,
help="Top-k for sampling",
)
group.add_argument(
"--temperature",
type=float,
default=1.0,
help="Temperature for sampling",
)
group.add_argument(
"--greedy",
type=int,
default=0,
help="Use greedy decoding instead of sampling",
)
group.add_argument(
"--seed",
type=int,
default=42,
help="Random seed",
)
group.add_argument(
"--micro-batch-size",
type=int,
default=1,
help="Micro batch size for each GPU",
)
group.add_argument(
"--samples-per-problem",
type=int,
default=200,
help="Number of samples to generate for each problem",
)
group.add_argument(
"--gen-node-world-size",
type=int,
default=1,
help="Number of machines to use for generation",
)
group.add_argument(
'--task-name',
default="generation",
help='Name of task',
)
group.add_argument(
'--model-name',
default="codegeex2-6b",
help='Name of model, support ["codegeex2-6b", "starcoder", "replit-code-v1-3b", "codegen25-7b-multi", "codegen25-7b-mono", "codegen-16B-multi"]',
)
group.add_argument(
'--data-path',
required=True,
)
group.add_argument(
'--output-path',
required=True,
)
group.add_argument(
'--log-path',
default=None,
help='Path to log output',
)
group.add_argument(
'--model-path',
required=True,
)
group.add_argument(
'--dataset-type',
default="humanevalx",
help='Identify the evaluation dataset [humanevalx]',
)
group.add_argument(
'--language-type',
default="python",
help='Identify the type of programming language to generate',
)
group.add_argument(
'--generation-mode',
default="instruction",
)
class CodeStoppingCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length` or meet the code generation stopping criteria.
"""
def __init__(
self,
max_length: int,
micro_batch_size: int,
tokenizer,
dataset_type: str,
language_type: str,
prompt: str,
):
self.max_length = max_length
self.tokenizer = tokenizer
self.dataset_type = dataset_type
self.language_type = language_type
self.prompt = prompt
self.stop_index = [-1 for _ in range(micro_batch_size)]
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
for i, input_id in enumerate(input_ids):
if self.stop_index[i] > -1:
continue
code = self.tokenizer.decode(input_id)
code = code[len(self.prompt):]
if is_code_generation_finished(
code,
dataset_type=self.dataset_type,
language_type=self.language_type) or input_id.shape[-1] >= self.max_length:
self.stop_index[i] = len(code) + len(self.prompt)
if all([s != -1 for s in self.stop_index]):
return True
return False
def run_generation_distributed(args, model, tokenizer):
logger.info(f"Connecting to tcp://{args.channel_ip}:{args.channel_port}")
context = zmq.Context()
socket = context.socket(zmq.REQ)
socket.connect(f"tcp://{args.channel_ip}:{args.channel_port}")
os.makedirs(args.output_path, exist_ok=True)
output_path = os.path.join(
args.output_path,
f"{args.task_name}-t{args.temperature}-topp{args.top_p}-ns{args.samples_per_problem}-rank{args.rank}.jsonl",
)
def process(obj):
results = []
prompt = obj["prompt"]
if args.generation_mode == "instruction":
inputs = tokenizer([prompt] * args.micro_batch_size, return_tensors="pt")
inputs = inputs.to(model.device)
outputs = model.generate(**inputs,
max_length=args.max_length,
do_sample=True if not args.greedy else False,
use_cache=True,
top_p=args.top_p,
top_k=args.top_k,
temperature=args.temperature,
pad_token_id=tokenizer.eos_token_id)
for i, output in enumerate(outputs):
response = tokenizer.decode(output)
res = obj.copy()
res["generation"] = response[len(prompt):].strip()
results.append(res)
elif args.generation_mode == "completion":
inputs = tokenizer([prompt for _ in range(args.micro_batch_size)], return_tensors="pt")
inputs = inputs.to(model.device)
stop_criteria = CodeStoppingCriteria(
max_length=args.max_length,
micro_batch_size=args.micro_batch_size,
tokenizer=tokenizer,
dataset_type=args.dataset_type,
language_type=args.language_type,
prompt=prompt)
outputs = model.generate(**inputs,
max_length=args.max_length,
do_sample=True if not args.greedy else False,
use_cache=True,
stopping_criteria=[stop_criteria],
top_p=args.top_p,
top_k=args.top_k,
temperature=args.temperature,
pad_token_id=tokenizer.eos_token_id)
for i, output in enumerate(outputs):
response = tokenizer.decode(output)
res = obj.copy()
res["generation_raw"] = response
res["generation"] = cleanup_code(
response[len(prompt):],
dataset_type=args.dataset_type,
language_type=args.language_type)
results.append(res)
return results
fout = open(output_path, "w", encoding="utf-8")
while True:
socket.send_json({"rank": args.rank, "action": "pull"})
resp = socket.recv_json()
try:
if resp["task_id"] is None:
break
current_spec = resp["task_id"]
results = process(current_spec)
for res in results:
fout.write(json.dumps(res, ensure_ascii=False) + "\n")
fout.flush()
socket.send_json(
{
"rank" : args.rank,
"action" : "success",
"task_id": current_spec['task_id']
}
)
socket.recv()
except Exception as e:
logger.error(f"*** (rank={args.rank}) crashed.")
logger.error(f" error: {repr(e)}")
socket.send_json(
{
"rank" : args.rank,
"action" : "fail",
"task_id": current_spec['task_id']
}
)
socket.recv()
continue
def main(args, node_rank: int, local_rank: int, master_port: int, num_devices: int):
world_size = args.gen_node_world_size * num_devices
args.rank = num_devices * node_rank + local_rank
args.world_size = world_size
logger.info(f"Generating on rank {args.rank} of {args.world_size}")
try:
if args.model_name in ["codegeex2-6b"]:
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
else:
tokenizer = AutoTokenizer.from_pretrained(args.model_path, clean_up_tokenization_spaces=False, trust_remote_code=True)
if args.model_name in ["codegeex2-6b"]:
model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True).to("cuda:{}".format(local_rank % torch.cuda.device_count()))
elif args.model_name in ["starcoder", "replit-code-v1-3b", "codegen25-7b-multi", "codegen25-7b-mono", "codegen-16B-multi"]:
model = AutoModelForCausalLM.from_pretrained(args.model_path, trust_remote_code=True).to("cuda:{}".format(local_rank % torch.cuda.device_count()))
else:
try:
model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True).to("cuda:{}".format(local_rank % torch.cuda.device_count()))
except:
logger.error(f"Model {args.model_name} not supported.")
raise NotImplementedError
except Exception as e:
logger.error(e)
model = model.eval()
# Generate samples.
run_generation_distributed(args, model, tokenizer)
logger.info(f"rank={args.rank} worker finished, waiting ...")
exit(0)
def server(args):
logger.info(f"[ server ] starting ...")
entries = read_dataset(args.data_path, dataset_type=args.dataset_type)
assert args.samples_per_problem % args.micro_batch_size == 0, "samples_per_problem should be divisible by batch_size"
for entry in entries.values():
entry["prompt"] = process_extra_prompt(
entry["prompt"],
language_type=args.language_type,
dataset_type=args.dataset_type,
generation_mode=args.generation_mode,
)
res = []
for entry in entries.values():
res.extend([entry] * (args.samples_per_problem // args.micro_batch_size))
random.shuffle(res)
all_entries = res
# setup zeromq channel
logger.info(f"[ server ] starting up on port {args.channel_port}")
context = zmq.Context()
logger.info(f"[ server ] creating socket")
socket = context.socket(zmq.REP)
logger.info(f"[ server ] binding to port {args.channel_port}")
socket.bind(f"tcp://*:{args.channel_port}")
logger.info(
f"[ server ] loaded {len(entries)} entries, generating {len(entries) * args.samples_per_problem} samples",
)
remaining_entries = all_entries.copy()
running_workers = args.gen_node_world_size * torch.cuda.device_count()
num_finished = 0
logger.info(f"[ server ] listening for requests ...")
start_time = time.perf_counter()
while True:
# Wait for next request from client
msg = socket.recv_json()
rank = msg["rank"]
action = msg["action"]
if action == "pull":
if len(remaining_entries) == 0:
socket.send_json({"task_id": None})
running_workers -= 1
logger.info(f"[ server ] Shutting down worker {rank}, remaining {running_workers} workers")
if running_workers == 0 and num_finished == len(all_entries):
logger.info(f"[ server ] All workers finished")
break
else:
entry = remaining_entries.pop()
time_elapsed = time.perf_counter() - start_time
logger.info(f"[ server ] Sending entry {entry['task_id']} to worker {rank}")
remaining = (
len(remaining_entries)
/ (len(all_entries) - len(remaining_entries))
* time_elapsed
)
time_per_sampple = 0.0 if num_finished == 0 else time_elapsed / num_finished / args.micro_batch_size
logger.info(
f"[ server ] total {len(all_entries)}, assigned {len(all_entries) - len(remaining_entries)}, finished {num_finished}, elapsed {time_elapsed:.4f}, speed {time_per_sampple:.4f}s/sample, remaining {remaining:.4f}",
)
socket.send_json({"task_id": entry})
else:
if action == "success":
logger.info(f"[ server ] {msg['task_id']} is finished")
socket.send_json({"pong": 1})
else:
logger.info(f"[ server ] {msg['task_id']} is not finished")
remaining_entries.append(msg['task_id'])
socket.send_json({"pong": 1})
break
num_finished += 1
if __name__ == "__main__":
torch.multiprocessing.set_start_method("spawn")
parser = argparse.ArgumentParser()
add_code_generation_specific_args(parser)
args = parser.parse_args()
if args.log_path is None:
args.log_path = os.path.join(args.output_path, "generation.log")
logger.info("start method: " + torch.multiprocessing.get_start_method())
processes = []
num_devices = torch.cuda.device_count()
hosts = open(args.hostfile, "r").readlines()
hosts = [host.strip() for host in hosts]
master_port = args.master_port
node_rank = None
for i in range(len(hosts)):
if hosts[i] == socket.gethostbyname(socket.gethostname()):
node_rank = i
break
assert (
node_rank is not None
), f"Could not find hostname ({socket.gethostbyname(socket.gethostname())}) in hostlist"
# launch server
if socket.gethostbyname(socket.gethostname()) == hosts[0]:
server_process = torch.multiprocessing.Process(target=server, args=(args,))
logger.info(f"Launching server ...")
server_process.start()
processes.append(server_process)
for i in range(num_devices):
local_rank = i
logger.info(f"launching local rank {i}")
p = torch.multiprocessing.Process(
target=main,
args=(args, node_rank, local_rank, master_port, num_devices),
)
p.start()
processes.append(p)
for p in processes:
p.join()
================================================
FILE: evaluation/inspect_jsonl.py
================================================
import fire
import json
import numpy as np
from typing import *
from utils import Logger
def main(
data_path: str = "./test.jsonl",
threshold: int = -1,
random: int = 0,
log_path: str = 'inspect_jsonl.txt',
random_rate: float = 0.5,
):
logger = Logger(__name__, log_file=log_path, log_mode="file", disable_formatter=True)
n = 0
with open(data_path, "r") as f:
for i, line in enumerate(f):
if i == 0:
logger.info("Data has the following keys")
obj = json.loads(line)
logger.info(obj.keys())
if threshold > 0 and n > threshold:
break
if random and np.random.randint(10) > 10 * random_rate:
continue
obj = json.loads(line)
n += 1
logger.info(f"========== Sample {i} ==========")
if 'code' in obj:
try:
code_splits = obj['code'].split("\n")
logger.info(f"Length of chars: {len(obj['code'])}, length of lines: {len(code_splits)}.")
except:
pass
for j, k in enumerate(obj.keys()):
logger.info(f"** Key {j}: {k} **")
logger.info(obj[k])
print(f"Log saved in {log_path}")
if __name__ == "__main__":
fire.Fire(main)
================================================
FILE: evaluation/utils.py
================================================
import re
import json
import gzip
import torch
import numpy
import random
import logging
import itertools
import numpy as np
from typing import *
LANGUAGE_TAG = {
"c" : "// language: C",
"c++" : "// language: C++",
"cpp" : "// language: C++",
"c#" : "// language: C#",
"csharp" : "// language: C#",
"c-sharp" : "// language: C#",
"css" : "/* language: CSS */",
"cuda" : "// language: Cuda",
"dart" : "// language: Dart",
"lua" : "// language: Lua",
"objectivec" : "// language: Objective-C",
"objective-c" : "// language: Objective-C",
"objective-c++": "// language: Objective-C++",
"python" : "# language: Python",
"perl" : "# language: Perl",
"prolog" : f"% language: Prolog",
"swift" : "// language: swift",
"lisp" : "; language: Lisp",
"java" : "// language: Java",
"scala" : "// language: Scala",
"tex" : f"% language: TeX",
"vue" : "<!--language: Vue-->",
"markdown" : "<!--language: Markdown-->",
"html" : "<!--language: HTML-->",
"php" : "// language: PHP",
"js" : "// language: JavaScript",
"javascript" : "// language: JavaScript",
"typescript" : "// language: TypeScript",
"go" : "// language: Go",
"shell" : "# language: Shell",
"rust" : "// language: Rust",
"sql" : "-- language: SQL",
"kotlin" : "// language: Kotlin",
"vb" : "' language: Visual Basic",
"ruby" : "# language: Ruby",
"pascal" : "// language: Pascal",
"r" : "# language: R",
"fortran" : "!language: Fortran",
"lean" : "-- language: Lean",
"matlab" : f"% language: Matlab",
"delphi" : "{language: Delphi}",
"scheme" : "; language: Scheme",
"basic" : "' language: Basic",
"assembly" : "; language: Assembly",
"groovy" : "// language: Groovy",
"abap" : "* language: Abap",
"gdscript" : "# language: GDScript",
"haskell" : "-- language: Haskell",
"julia" : "# language: Julia",
"elixir" : "# language: Elixir",
"excel" : "' language: Excel",
"clojure" : "; language: Clojure",
"actionscript" : "// language: ActionScript",
"solidity" : "// language: Solidity",
"powershell" : "# language: PowerShell",
"erlang" : f"% language: Erlang",
"cobol" : "// language: Cobol",
"alloy" : "/* language: Alloy */",
"awk" : "// language: AWK",
"thrift" : "/* language: Thrift */",
"sparql" : "# language: SPARQL",
"augeas" : "// language: Augeas",
"cmake" : "# language: CMake",
"f-sharp" : "// language: F#",
"stan" : "// language: Stan",
"isabelle" : "(*language: Isabelle*)",
"dockerfile" : "# language: Dockerfile",
"rmarkdown" : "# language: RMarkdown",
"literate-agda": "-- language: Literate Agda",
"tcl" : "// language: Augeas",
"glsl" : "// language: GLSL",
"antlr" : "// language: ANTLR",
"verilog" : "// language: Verilog",
"racket" : "; language: Racket",
"standard-ml" : "(*language:Standard ML*)",
"elm" : "-- language: Elm",
"yaml" : "# language: YAML",
"smalltalk" : "'' language: Smalltalk",
"ocaml" : "(*language: OCaml*)",
"idris" : "-- language: Idris",
"visual-basic" : "' language: Visual Basic",
"protocol-buffer": "// language: Protocol Buffer",
"bluespec" : "// language: Bluespec",
"applescript" : "-- language: AppleScript",
"makefile" : "# language: Makefile",
"tcsh" : "# language: TCSH",
"maple" : "# language: Maple",
"systemverilog": "// language: SystemVerilog",
"literate-coffeescript": "# language: Literate CoffeeScript",
"vhdl" : "-- language: VHDL",
"restructuredtext": ".. language: reStructuredText",
"sas" : "* language: SAS",
"literate-haskell": "> language: Literate Haskell",
"java-server-pages": "// language: Java Server Pages",
"coffeescript" : "# language: CoffeeScript",
"emacs-lisp" : "; language: Emacs Lisp",
"mathematica" : "// language: Mathematica",
"xslt" : "<!--language: XSLT-->",
"zig" : "// language: Zig",
"common-lisp" : "; language: Common Lisp",
"stata" : "* language: Stata",
"agda" : "-- language: Agda",
"ada" : "-- language: Ada",
}
LANGUAGE_COMMENT_SIGN = {}
for lang in LANGUAGE_TAG:
LANGUAGE_COMMENT_SIGN[lang] = LANGUAGE_TAG[lang].split("language:")[0].strip()
IMPORT_HELPER = {
"python": [
"import math",
"import re",
"import sys",
"import copy",
"import datetime",
"import itertools",
"import collections",
"import heapq",
"import statistics",
"import functools",
"import hashlib",
"import numpy",
"import numpy as np",
"import string",
"from typing import *",
"from collections import *",
],
"go" : [
"math",
"strings",
"fmt",
"strconv",
"time",
"bytes",
"regexp",
"sort",
"math/rand",
"crypto/md5",
],
"cpp" : [
"#include<stdlib.h>",
"#include<algorithm>",
"#include<math.h>",
"#include<stdio.h>",
"#include<vector>",
"#include<string>",
"#include<climits>",
"#include<cstring>",
"#include<iostream>",
],
}
def set_random_seed(seed):
"""Set random seed for reproducability."""
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed)
def stream_jsonl(filename: str) -> Iterable[Dict]:
"""
Parses each jsonl line and yields it as a dictionary
"""
if filename.endswith(".gz"):
with open(filename, "rb") as gzfp:
with gzip.open(gzfp, "rt") as fp:
for line in fp:
if any(not x.isspace() for x in line):
yield json.loads(line)
else:
with open(filename, "r") as fp:
for line in fp:
if any(not x.isspace() for x in line):
yield json.loads(line)
def stream_jsonl_all(filename: str) -> Iterable[Dict]:
results = []
if filename.endswith(".gz"):
fp = gzip.open(open(filename, "rb"), "rt")
else:
fp = open(filename, "r")
for line in fp:
if any(not x.isspace() for x in line):
results.append(json.loads(line))
fp.close()
return results
def read_dataset(
data_file: str = None,
dataset_type: str = "humanevalx",
) -> Dict:
if "humanevalx" in dataset_type.lower():
dataset = {task["task_id"]: task for task in stream_jsonl(data_file)}
elif "mbpp" in dataset_type.lower():
problems = {task["task_id"]: task for task in stream_jsonl(data_file)}
task_ids = sorted(problems.keys())[10:510]
dataset = {}
for task_id in task_ids:
sample = problems[task_id]
description = sample["text"]
test_example = sample["test_list"][0]
prompt = f'"""\n{description}\n{test_example}\n"""\n'
sample["prompt"] = prompt
dataset[task_id] = sample
elif "ds1000" in dataset_type.lower():
# install ds1000 from https://github.com/HKUNLP/DS-1000
from ds1000 import DS1000Dataset
ds1000 = DS1000Dataset(source_dir=data_file, libs="all", mode="Completion")
for lib in ds1000.libs:
for problem_id in range(len(ds1000[lib])):
prefix = ""
suffix = ""
insert_flag = False
first_line_flag = True
# extract prefix and suffix of the prompt
for line in ds1000[lib][problem_id]["prompt"].split("\n"):
if "[insert]" in line:
insert_flag = True
continue
if first_line_flag:
first_line_flag = False
else:
line = "\n" + line
if not insert_flag:
prefix += line
else:
suffix += line
else:
raise f"Dataset: {dataset_type} not supported."
return dataset
def read_translation_dataset(
data_file_src: str = None,
data_file_tgt: str = None,
lang_src: str = None,
lang_tgt: str = None,
dataset_type: str = "humanevalx",
) -> Dict:
if "humanevalx" in dataset_type.lower():
dataset_src = {task["task_id"]: task for task in stream_jsonl(data_file_src)}
dataset_tgt = {task["task_id"].split("/")[-1]: task for task in stream_jsonl(data_file_tgt)}
for k, sample in dataset_src.items():
prompt = "code translation\n"
if lang_src == "cpp":
prompt += "C++:\n"
elif lang_src == "js":
prompt += "JavaScript:\n"
else:
prompt += f"{lang_src}:\n".capitalize()
prompt += dataset_src[k]["declaration"] + "\n" + dataset_src[k]["canonical_solution"].rstrip() + "\n"
if lang_tgt == "cpp":
prompt += "C++:\n"
elif lang_tgt == "js":
prompt += "JavaScript:\n"
else:
prompt += f"{lang_tgt}:\n".capitalize()
prompt += dataset_tgt[k.split("/")[-1]]["declaration"]
dataset_src[k]["prompt"] = prompt
else:
raise f"Dataset: {dataset_type} not supported."
return dataset_src
def process_extra_prompt(
prompt: str,
language_type: str = "python",
dataset_type: str = None,
generation_mode: str = "completion",
) -> str:
"""
Processes the extra prompt.
"""
language = language_type.lower()
if dataset_type == "humanevalx":
extra_prompt = ""
# extra_prompt = LANGUAGE_TAG[language] + "\n"
prompt = prompt.strip()
if generation_mode == "instruction":
return "问：" + extra_prompt + prompt + "\n答："
return extra_prompt + prompt
elif dataset_type == "mbpp":
extra_prompt = ""
prompt = prompt.strip()
return extra_prompt + prompt
else:
return prompt
def is_code_generation_finished(
code: str,
dataset_type: str = None,
language_type: str = None,
):
"""
Checks whether the generated code is finished.
"""
if dataset_type == "mbpp":
end_words = ["\ndef", "\nassert"]
for w in end_words:
if w == "\ndef":
if code.count(w) > 1:
return True
else:
if w in code:
return True
else:
if language_type.lower() == "python":
for line in code.split("\n"):
if len(line.strip()) > 0 and line[0] != ' ' and line[0] != '\t':
return True
end_words = ["\ndef", "\nclass", "\nif", "\n#", "\nprint"]
for w in end_words:
if w in code:
return True
elif language_type.lower() == "java":
if code.count("{") + 1 == code.count("}"):
return True
elif language_type.lower() == "go":
if "\nfunc main(" in code:
return True
if code.count("{") + 1 == code.count("}"):
return True
elif language_type.lower() == "js":
if code.count("{") + 1 == code.count("}"):
return True
elif language_type.lower() == "cpp":
if "\nint main()" in code:
return True
if code.count("{") + 1 == code.count("}"):
return True
elif language_type.lower() == "rust":
if "\nfn main()" in code:
return True
if code.count("{") + 1 == code.count("}"):
return True
return False
# Modified from https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/lm_eval/tasks/mbpp.py
stop_words=["\nclass", "\nassert", '\n"""', "\nprint", "\nif"]
def first_block(string, stop_words):
"""Split off first block of code by scanning for class, def etc. on newlines."""
return re.split("|".join(stop_words), string)[0].rstrip()
def cleanup_code(
code: str,
dataset_type: str = None,
language_type: str = None,
):
"""
Cleans up the generated code.
"""
if dataset_type == "mbpp":
end_words = ["\nassert", "\ndef"]
for w in end_words:
if w == "\ndef":
if code.count(w) > 1:
code = code[:code.rfind(w)]
else:
code = code[:code.rfind(w)]
code = first_block(code, stop_words)
elif dataset_type == "humanevalx":
if language_type.lower() == "python":
code_splits = code.split("\n")
is_empty_line = False
ind_empty_line = None
for i, line in enumerate(code_splits):
if len(line.strip()) > 0 and line[0] != ' ' and line[0] != '\t':
is_empty_line = True
ind_empty_line = i
break
if is_empty_line:
code = "\n".join(code_splits[:ind_empty_line])
else:
end_words = ["\ndef", "\nclass", "\n#", "\nassert", '\n"""', "\nprint", "\nif", "\n\n\n"]
for w in end_words:
if w in code:
code = code[:code.rfind(w)]
elif language_type.lower() == "java":
main_pos = code.find("public static void main")
if main_pos != -1:
code = code[:main_pos] + '}'
if '}' in code:
code = code[:code.rfind('}')] + '}'
if code.count('{') + 1 == code.count('}'):
code += "\n}"
elif language_type.lower() == "go":
if "\nfunc main(" in code:
code = code[:code.rfind("func main(")]
if '}' in code:
code = code[:code.rfind('}')] + '}'
elif language_type.lower() == "cpp":
if "\nint main()" in code:
code = code[:code.rfind("int main()")]
if '}' in code:
code = code[:code.rfind('}')] + '}'
elif language_type.lower() == "js":
if '}' in code:
code = code[:code.rfind('}')] + '}'
elif language_type.lower() == "rust":
if '}' in code:
code = code[:code.rfind('}')] + '}'
return code
def estimate_pass_at_k(
num_samples: Union[int, List[int], np.ndarray],
num_correct: Union[List[int], np.ndarray],
k: int
) -> np.ndarray:
"""
Estimates pass@k of each problem and returns them in an array.
"""
def estimator(n: int, c: int, k: int) -> float:
"""
Calculates 1 - comb(n - c, k) / comb(n, k).
"""
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
if isinstance(num_samples, int):
num_samples_it = itertools.repeat(num_samples, len(num_correct))
else:
assert len(num_samples) == len(num_correct)
num_samples_it = iter(num_samples)
return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])
class Logger:
def __init__(self, name, log_level=logging.INFO, log_file=None, log_mode="both", disable_formatter=False):
self.logger = logging.getLogger(name)
self.logger.setLevel(log_level)
self.formatter = logging.Formatter('%(asctime)s [%(levelname)s] %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
# Log to console
if log_mode == "both" or log_mode == "terminal":
console_handler = logging.StreamHandler()
if not disable_formatter:
console_handler.setFormatter(self.formatter)
self.logger.addHandler(console_handler)
# Log to file
if log_file is not None:
if log_mode == "both" or log_mode == "file":
file_handler = logging.FileHandler(log_file, mode='w')
if not disable_formatter:
file_handler.setFormatter(self.formatter)
self.logger.addHandler(file_handler)
def add_file_handler(self, file_name):
file_handler = logging.FileHandler(file_name, mode='w')
file_handler.setFormatter(self.formatter)
self.logger.addHandler(file_handler)
def debug(self, message):
self.logger.debug(message)
def info(self, message):
self.logger.info(message)
def warning(self, message):
self.logger.warning(message)
def error(self, message):
self.logger.error(message)
def critical(self, message):
self.logger.critical(message)
================================================
FILE: requirements.txt
================================================
protobuf
transformers>=4.30.2
accelerate
cpm_kernels
torch>=2.0
sentencepiece
gradio
================================================
FILE: resources/wechat.md
================================================
<div align="center">
<img src=join_wechat.png width="60%"/>
<p> 扫码关注公众号加入「CodeGeeX交流群」 </p>
<p> Scan the QR code to join the "CodeGeeX WeChat Group" </p>
</div>
================================================
FILE: scripts/run_humanevalx.sh
================================================
#!/bin/bash
# This script is used to generate solutions of HumanEval-X.
# Examples (MODE=(gen, eval, both)):
# MODE=gen bash ./scripts/run_humanevalx.sh
if [ -z "$MODE" ]
then
MODE="both"
fi
SCRIPT_PATH=$(realpath "$0")
SCRIPT_DIR=$(dirname "$SCRIPT_PATH")
MAIN_DIR=$(dirname "$SCRIPT_DIR")
# enviroment settings
HOSTLIST=$SCRIPT_DIR/hostlist
WORLD_SIZE=1
DATASET=humanevalx
GENERATION_MODE=completion
MODEL_NAME=codegeex2-6b
MODEL_PATH=/pathto/codegeex2-6b/
N_CPU_WORKERS=16
TIMEOUT=5
# generation settings
## pass@1 greedy
NUM_SAMPLES=1
MICRO_BSZ=1
TEMP=1.0
TOPK=1
TOPP=1.0
MAX_LENGTH=1024
SEED=42
GREEDY=1
## pass@1 estimated
# NUM_SAMPLES=20
# MICRO_BSZ=1
# TEMP=0.2
# TOPK=0
# TOPP=0.95
# MAX_LENGTH=1024
# SEED=42
# GREEDY=0
## pass@10 & pass@100
# NUM_SAMPLES=200
# MICRO_BSZ=4
# TEMP=0.8
# TOPK=0
# TOPP=0.95
# MAX_LENGTH=1024
# SEED=42
# GREEDY=0
for l in python java js cpp go rust;
do
LANGUAGE=$l
DATA_DIR=$MAIN_DIR/benchmark/$DATASET/
DATA_PATH=$DATA_DIR/$DATASET\_$LANGUAGE.jsonl.gz
OUTPUT_PATH=$MAIN_DIR/output/$DATASET/$LANGUAGE
TODAY=$(date +%y%m%d)
CHANNEL_PORT=$(expr $RANDOM + 5000)
MASTER_PORT=$(expr $RANDOM + 8000)
JOB_ID=$MODEL_NAME-$LANGUAGE-greedy$GREEDY-ns$NUM_SAMPLES-t$TEMP-topp$TOPP-seed$SEED
mkdir -p "$OUTPUT_PATH/$JOB_ID"
# evaluation settings
EVAL_INPUT_PATH=$OUTPUT_PATH/$JOB_ID
EVAL_OUTPUT_PATH=$OUTPUT_PATH/$JOB_ID
# nccl options
OPTIONS_NCCL="export NCCL_DEBUG=warn; export NCCL_IB_DISABLE=0; export NCCL_IB_GID_INDEX=3"
OPTIONS_PATH="export PATH=$PATH; export LD_LIBRARY_PATH=$LD_LIBRARY_PATH"
CWD=$(pwd)
gen_func() {
echo "Generating......"
# set master ip for zmq server
if [ -z "$HOSTLIST" ]; then
ZMQ_ADDR=$(hostname -i)
echo "$ZMQ_ADDR" > "./hostfile"
HOSTLIST="./hostfile"
else
ZMQ_ADDR=$(cat $HOSTLIST | head -n 1)
fi
echo "master_ip: $ZMQ_ADDR"
# run generation
RUN_CMD="python \
$MAIN_DIR/evaluation/generation.py \
--hostfile $HOSTLIST \
--channel-ip $ZMQ_ADDR \
--channel-port $CHANNEL_PORT \
--master-port $MASTER_PORT \
--model-path $MODEL_PATH \
--temperature $TEMP \
--top-p $TOPP \
--top-k $TOPK \
--greedy $GREEDY \
--max-length $MAX_LENGTH \
--micro-batch-size $MICRO_BSZ \
--samples-per-problem $NUM_SAMPLES \
--model-name $MODEL_NAME \
--dataset-type $DATASET \
--language-type $LANGUAGE \
--generation-mode $GENERATION_MODE \
--data-path $DATA_PATH \
--output-path $OUTPUT_PATH/$JOB_ID \
--log-path $OUTPUT_PATH/$JOB_ID/$TODAY-generation.log \
--gen-node-world-size $WORLD_SIZE \
--seed $SEED"
RUN_CMD="$OPTIONS_NCCL; $OPTIONS_PATH; $RUN_CMD"
RUN_CMD="cd $CWD; $RUN_CMD"
if (( WORLD_SIZE != 1 )); then
RUN_CMD="pdsh -R ssh -w ^$HOSTLIST \"$RUN_CMD\""
fi
eval "$RUN_CMD"
}
eval_func() {
echo "Evaluating......"
if [ $LANGUAGE = rust ]; then
TIMEOUT=300
echo "Setting timeout to $TIMEOUT for Rust"
fi
RUN_CMD="python \
$MAIN_DIR/evaluation/evaluation.py \
--input_path $EVAL_INPUT_PATH \
--output_path $EVAL_OUTPUT_PATH \
--log-path $OUTPUT_PATH/$JOB_ID/$TODAY-evaluation.log \
--model_name $MODEL_NAME \
--language_type $LANGUAGE \
--dataset_type $DATASET \
--generation_mode $GENERATION_MODE \
--n_workers $N_CPU_WORKERS \
--tmp_dir $MAIN_DIR/benchmark/$DATASET/$LANGUAGE \
--problem_file $DATA_PATH \
--timeout $TIMEOUT"
# inspecting results
INSPECT_CMD="python \
$MAIN_DIR/evaluation/inspect_jsonl.py \
--data_path $EVAL_OUTPUT_PATH/result-$JOB_ID.jsonl \
--log-path $OUTPUT_PATH/$JOB_ID/$TODAY-inspect.txt"
eval "$RUN_CMD && $INSPECT_CMD"
}
case $MODE in
"gen")
gen_func
;;
"eval")
eval_func
;;
"both")
gen_func
eval_func
;;
*)
echo "Unsupported MODE (gen, eval, both): $MODE"
exit 1
;;
esac
done
================================================
FILE: scripts/sanity_check.sh
================================================
#!/bin/bash
# This script is used to check the correctness of code generation benchmarks.
SCRIPT_PATH=$(realpath "$0")
SCRIPT_DIR=$(dirname "$SCRIPT_PATH")
MAIN_DIR=$(dirname "$SCRIPT_DIR")
# enviroment settings
DATASET=humanevalx
GENERATION_MODE=completion
N_CPU_WORKERS=16
TIMEOUT=5
# Check HumanEval-X
for l in python java js cpp go rust;
do
LANGUAGE=$l
echo "Evaluating $l"
DATA_DIR=$MAIN_DIR/benchmark/$DATASET/
DATA_PATH=$DATA_DIR/$DATASET\_$LANGUAGE.jsonl.gz
OUTPUT_PATH=$MAIN_DIR/output/$DATASET/$LANGUAGE
JOB_ID=sanity-check-$LANGUAGE
mkdir -p "$OUTPUT_PATH/$JOB_ID"
# evaluation settings
EVAL_INPUT_PATH=$DATA_PATH
EVAL_OUTPUT_PATH=$OUTPUT_PATH/$JOB_ID
if [ $LANGUAGE = rust ]; then
TIMEOUT=300
echo "Setting timeout to $TIMEOUT for Rust"
fi
RUN_CMD="python \
$MAIN_DIR/evaluation/evaluation.py \
--test_groundtruth=True \
--input_path $EVAL_INPUT_PATH \
--output_path $EVAL_OUTPUT_PATH \
--log-path $OUTPUT_PATH/$JOB_ID/$TODAY-evaluation.log \
--model_name $MODEL_NAME \
--language_type $LANGUAGE \
--dataset_type $DATASET \
--generation_mode $GENERATION_MODE \
--n_workers $N_CPU_WORKERS \
--tmp_dir $MAIN_DIR/benchmark/$DATASET/$LANGUAGE \
--problem_file $DATA_PATH \
--timeout $TIMEOUT"
eval "$RUN_CMD"
done
gitextract_b1m4cbos/
├── LICENSE
├── MODEL_LICENSE
├── README.md
├── README_EN.md
├── README_FR.md
├── README_JA.md
├── benchmark/
│ └── humanevalx/
│ ├── go/
│ │ ├── go.mod
│ │ └── go.sum
│ └── rust/
│ └── Cargo.toml
├── demo/
│ ├── example_inputs.jsonl
│ ├── fastapicpu.py
│ ├── gpus.py
│ └── run_demo.py
├── docs/
│ └── zh/
│ └── inference_zh.md
├── evaluation/
│ ├── __init__.py
│ ├── evaluation.py
│ ├── execution.py
│ ├── generation.py
│ ├── inspect_jsonl.py
│ └── utils.py
├── requirements.txt
├── resources/
│ └── wechat.md
└── scripts/
├── run_humanevalx.sh
└── sanity_check.sh
SYMBOL INDEX (53 symbols across 8 files)
FILE: demo/fastapicpu.py
function add_code_generation_args (line 17) | def add_code_generation_args(parser):
function device (line 129) | def device():
function create_item (line 153) | async def create_item(request: Request):
FILE: demo/gpus.py
function auto_configure_device_map (line 8) | def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
function load_model_on_gpus (line 45) | def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpu...
FILE: demo/run_demo.py
function get_model (line 35) | def get_model(args):
function add_code_generation_args (line 81) | def add_code_generation_args(parser):
function set_random_seed (line 221) | def set_random_seed(seed):
function main (line 228) | def main():
FILE: evaluation/evaluation.py
function postprocess_generation (line 29) | def postprocess_generation(sample, generation_mode="completion"):
function process_test (line 43) | def process_test(sample, problems, dataset_type, language_type, generati...
function evaluate_functional_correctness (line 94) | def evaluate_functional_correctness(
function main (line 214) | def main():
FILE: evaluation/execution.py
function dicts_to_jsonl (line 16) | def dicts_to_jsonl(data_list: list, filename: str, compress: bool = True...
function check_correctness (line 45) | def check_correctness(
function time_limit (line 409) | def time_limit(seconds: float):
function swallow_io (line 422) | def swallow_io():
function create_tempdir (line 431) | def create_tempdir():
class TimeoutException (line 437) | class TimeoutException(Exception):
class WriteOnlyStringIO (line 441) | class WriteOnlyStringIO(io.StringIO):
method read (line 444) | def read(self, *args, **kwargs):
method readline (line 447) | def readline(self, *args, **kwargs):
method readlines (line 450) | def readlines(self, *args, **kwargs):
method readable (line 453) | def readable(self, *args, **kwargs):
class redirect_stdin (line 458) | class redirect_stdin(contextlib._RedirectStream): # type: ignore
function chdir (line 463) | def chdir(root):
function reliability_guard (line 477) | def reliability_guard(maximum_memory_bytes: Optional[int] = None):
FILE: evaluation/generation.py
function add_code_generation_specific_args (line 17) | def add_code_generation_specific_args(parser):
class CodeStoppingCriteria (line 145) | class CodeStoppingCriteria(StoppingCriteria):
method __init__ (line 150) | def __init__(
method __call__ (line 166) | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTen...
function run_generation_distributed (line 183) | def run_generation_distributed(args, model, tokenizer):
function main (line 283) | def main(args, node_rank: int, local_rank: int, master_port: int, num_de...
function server (line 315) | def server(args):
FILE: evaluation/inspect_jsonl.py
function main (line 9) | def main(
FILE: evaluation/utils.py
function set_random_seed (line 172) | def set_random_seed(seed):
function stream_jsonl (line 179) | def stream_jsonl(filename: str) -> Iterable[Dict]:
function stream_jsonl_all (line 196) | def stream_jsonl_all(filename: str) -> Iterable[Dict]:
function read_dataset (line 210) | def read_dataset(
function read_translation_dataset (line 257) | def read_translation_dataset(
function process_extra_prompt (line 290) | def process_extra_prompt(
function is_code_generation_finished (line 315) | def is_code_generation_finished(
function first_block (line 368) | def first_block(string, stop_words):
function cleanup_code (line 373) | def cleanup_code(
function estimate_pass_at_k (line 435) | def estimate_pass_at_k(
class Logger (line 461) | class Logger:
method __init__ (line 462) | def __init__(self, name, log_level=logging.INFO, log_file=None, log_mo...
method add_file_handler (line 483) | def add_file_handler(self, file_name):
method debug (line 488) | def debug(self, message):
method info (line 491) | def info(self, message):
method warning (line 494) | def warning(self, message):
method error (line 497) | def error(self, message):
method critical (line 500) | def critical(self, message):
Condensed preview — 24 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (171K chars).
[
{
"path": "LICENSE",
"chars": 11336,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "MODEL_LICENSE",
"chars": 2329,
"preview": "The CodeGeeX License\n\n1. Definitions\n\n“Licensor” means the CodeGeeX Model Team that distributes its Software.\n\n“Software"
},
{
"path": "README.md",
"chars": 10537,
"preview": "\n\n<p align=\"center\">\n 🏠 <a href=\"https://codegeex.cn\" target=\"_blank\">主页</a>｜🛠 插件 <a "
},
{
"path": "README_EN.md",
"chars": 12411,
"preview": "\n\n<p align=\"center\">\n 🏠 <a href=\"https://codegeex.cn\" target=\"_blank\">Homepage</a>｜🛠 "
},
{
"path": "README_FR.md",
"chars": 12706,
"preview": "\n\n<p align=\"center\">\n 🏠 <a href=\"https://codegeex.cn\" target=\"_blank\">Homepage</a>｜🛠 "
},
{
"path": "README_JA.md",
"chars": 10116,
"preview": "\n\n<p align=\"center\">\n 🏠 <a href=\"https://codegeex.cn\" target=\"_blank\">ホームページ</a>｜🛠 拡張"
},
{
"path": "benchmark/humanevalx/go/go.mod",
"chars": 261,
"preview": "module humanEval\n\ngo 1.18\n\nrequire (\n\tgithub.com/go-openapi/inflect v0.19.0\n\tgithub.com/stretchr/testify v1.8.0\n)\n\nrequi"
},
{
"path": "benchmark/humanevalx/go/go.sum",
"chars": 1520,
"preview": "github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=\ngithub.com/davecgh/go-spew v1.1"
},
{
"path": "benchmark/humanevalx/rust/Cargo.toml",
"chars": 213,
"preview": "[package]\nname = \"rust\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n# See more keys and their definitions at https://doc.rust-la"
},
{
"path": "demo/example_inputs.jsonl",
"chars": 600,
"preview": "{\"code\": \"# Write a quick sort function\\n\", \"langauge\": \"Python\"}\n{\"code\": \"// 写一个冒泡排序函数\\n\", \"langauge\": \"C++\"}\n{\"code\":"
},
{
"path": "demo/fastapicpu.py",
"chars": 7100,
"preview": "from fastapi import FastAPI, Request\nfrom transformers import AutoTokenizer, AutoModel\nimport uvicorn, json, datetime\nim"
},
{
"path": "demo/gpus.py",
"chars": 2152,
"preview": "import os\r\nfrom typing import Dict, Tuple, Union, Optional\r\n\r\nfrom torch.nn import Module\r\nfrom transformers import Auto"
},
{
"path": "demo/run_demo.py",
"chars": 13940,
"preview": "import os\nimport json\nimport numpy\nimport torch\nimport random\nimport argparse\nimport gradio as gr\n\nfrom transformers imp"
},
{
"path": "docs/zh/inference_zh.md",
"chars": 6876,
"preview": "# CodeGeeX2推理教程\n\nCodeGeeX2 是多语言代码生成模型 [CodeGeeX](https://github.com/THUDM/CodeGeeX) ([KDD’23](https://arxiv.org/abs/2303"
},
{
"path": "evaluation/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "evaluation/evaluation.py",
"chars": 7981,
"preview": "import os\nimport re\nimport sys\nimport fire\nimport json\nimport gzip\nimport glob\nimport numpy as np\n\nfrom typing import *\n"
},
{
"path": "evaluation/execution.py",
"chars": 21800,
"preview": "import io\nimport os\nimport signal\nimport random\nimport gzip\nimport json\nimport tempfile\nimport platform\nimport subproces"
},
{
"path": "evaluation/generation.py",
"chars": 15389,
"preview": "import os\nimport zmq\nimport time\nimport json\nimport torch\nimport random\nimport socket\nimport argparse\n\nfrom typing impor"
},
{
"path": "evaluation/inspect_jsonl.py",
"chars": 1377,
"preview": "import fire\nimport json\nimport numpy as np\n\nfrom typing import *\nfrom utils import Logger\n\n\ndef main(\n data_path: str"
},
{
"path": "evaluation/utils.py",
"chars": 17410,
"preview": "import re\nimport json\nimport gzip\nimport torch\nimport numpy\nimport random\nimport logging\nimport itertools\nimport numpy a"
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{
"path": "requirements.txt",
"chars": 84,
"preview": "protobuf\ntransformers>=4.30.2\naccelerate\ncpm_kernels\ntorch>=2.0\nsentencepiece\ngradio"
},
{
"path": "resources/wechat.md",
"chars": 162,
"preview": "<div align=\"center\">\n<img src=join_wechat.png width=\"60%\"/>\n\n<p> 扫码关注公众号加入「CodeGeeX交流群」 </p>\n<p> Scan the QR code to joi"
},
{
"path": "scripts/run_humanevalx.sh",
"chars": 4453,
"preview": "#!/bin/bash\n# This script is used to generate solutions of HumanEval-X.\n\n# Examples (MODE=(gen, eval, both)):\n# MODE=gen"
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{
"path": "scripts/sanity_check.sh",
"chars": 1418,
"preview": "#!/bin/bash\n# This script is used to check the correctness of code generation benchmarks.\n\nSCRIPT_PATH=$(realpath \"$0\")\n"
}
]
About this extraction
This page contains the full source code of the zai-org/CodeGeeX2 GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 24 files (158.4 KB), approximately 44.6k tokens, and a symbol index with 53 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.