CodeGeeX2模型安装
pytorch与cuda对应版本：https://pytorch.org/get-started/previous-versions/demo.gpus为CodeGeeX2官方仓库的demo文件夹下的gps文件，需要修改num_gpus为机器实际的值，可以通过。cuda tookit版本：https://anaconda.org/nvidia/cuda-toolkit。由于huggingface国
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简介
CodeGeeX2 是多语言代码生成模型 CodeGeeX (KDD’23) 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 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%）
官方仓库地址：https://github.com/THUDM/CodeGeeX2/tree/main
部署步骤
安装依赖
conda create --name CodeGeeX2 python=3.9
conda activate CodeGeeX2
// #或者手动下载并上传至服务器
git clone git@github.com:THUDM/CodeGeeX2.git
cd CodeGeeX2
conda install -y pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
# 一般来说，不需要安装这个，用显卡驱动自带的就行。
# conda install -y cuda-toolkit=11.7.0 -c nvidia
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
下载模型数据
由于huggingface国内无法访问，我们使用镜像站下载
pip install -U huggingface_hub -i https://mirrors.aliyun.com/pypi/simple/
export HF_ENDPOINT=https://hf-mirror.com
# 下载THUDM/codegeex2-6b模型到本地目录：/mnt/data/CodeGeeX2/model
huggingface-cli download --resume-download --local-dir-use-symlinks False THUDM/codegeex2-6b --cache-dir /mnt/data/CodeGeeX2/model
启动
quick start - 单gpu
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True, cache_dir="/mnt/data/CodeGeeX2/model")
model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True, device='cuda', cache_dir="/mnt/data/CodeGeeX2/model")
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)
quick start - 多gpu
demo.gpus为CodeGeeX2官方仓库的demo文件夹下的gps文件，需要修改num_gpus为机器实际的值，可以通过nvidia-smi -L查看GPU个数。
from transformers import AutoTokenizer, AutoModel
from demo.gpus import load_model_on_gpus
def get_model():
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True, cache_dir="/mnt/data/CodeGeeX2/model")
# gpus文件在demo文件夹中
model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=4, cache_dir="/mnt/data/CodeGeeX2/model")
model = model.eval()
return tokenizer, model
tokenizer, model = get_model()
# 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)
quick start - http api
from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
from demo.gpus import load_model_on_gpus
import uvicorn, json, datetime
import argparse
#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()
@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', 1)
if lang != "None":
prompt = LANGUAGE_TAG[lang] + "\n" + prompt
inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
response = model.generate(inputs,
max_length=max_length,
do_sample=temperature > 0,
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
def get_model():
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True, cache_dir="/mnt/data/CodeGeeX2/model")
# gpus文件在demo文件夹中
model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=3, cache_dir="/mnt/data/CodeGeeX2/model")
model = model.eval()
return tokenizer, model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser = add_code_generation_args(parser)
args, _ = parser.parse_known_args()
tokenizer, model = get_model()
uvicorn.run(app, host=args.listen, port=args.port, workers=args.workers)
启动命令
python web_quick_start.py --listen '0.0.0.0' --port 7860 --workders 100
# 另一个终端中调用
curl -X POST "http://127.0.0.1:7860" \
-H 'Content-Type: application/json' \
-d '{"lang": "Python", "prompt": "# Write a quick sort function"}'
参考资料
校验torch安装结果
import torch
print(torch.cuda.is_available())
# 返回gpu数量；
print(torch.cuda.device_count())
# 返回gpu名字，设备索引默认从0开始；
print(torch.cuda.get_device_name(0))
print(torch.cuda.current_device())
# 查看nvidia cuda toolkit版本
nvcc -V
版本查询
cuda tookit版本：https://anaconda.org/nvidia/cuda-toolkit
pytorch与cuda对应版本：https://pytorch.org/get-started/previous-versions/
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