网站建设部署与发布,网站前台建设用到哪些工具,网站优化解决方案,wordpress搭建会员简介 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#xff08;完全在国产华为昇腾芯片平台训练#xff09; #xff0c;CodeGeeX2 是基于 ChatGLM2 架构加入代码预训练实现#xff0c;得益于 ChatGLM2 的更优性能#xff0c;Co…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 python3.9 conda activate CodeGeeX2// #或者手动下载并上传至服务器 git clone gitgithub.com:THUDM/CodeGeeX2.git cd CodeGeeX2 conda install -y pytorch2.0.0 torchvision0.15.0 torchaudio2.0.0 pytorch-cuda11.7 -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-toolkit11.7.0 -c nvidiapip 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_ENDPOINThttps://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_codeTrue, cache_dir/mnt/data/CodeGeeX2/model) model AutoModel.from_pretrained(THUDM/codegeex2-6b, trust_remote_codeTrue, devicecuda, 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_tensorspt).to(model.device) outputs model.generate(inputs, max_length256, top_k1) 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_gpusdef get_model():tokenizer AutoTokenizer.from_pretrained(THUDM/codegeex2-6b, trust_remote_codeTrue, cache_dir/mnt/data/CodeGeeX2/model)# gpus文件在demo文件夹中model load_model_on_gpus(THUDM/codegeex2-6b, num_gpus4, cache_dir/mnt/data/CodeGeeX2/model)model model.eval()return tokenizer, modeltokenizer, 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_tensorspt).to(model.device) outputs model.generate(inputs, max_length256, top_k1) 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(titleCodeGeeX2 DEMO)group.add_argument(--model-path,typestr,defaultTHUDM/codegeex2-6b,)group.add_argument(--listen,typestr,default127.0.0.1,)group.add_argument(--port,typeint,default7860,)group.add_argument(--workers,typeint,default1,)group.add_argument( --cpu,actionstore_true,)group.add_argument( --half,actionstore_true,)group.add_argument(--quantize,typeint,defaultNone,)group.add_argument(--chatglm-cpp,actionstore_true,)return parserLANGUAGE_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, tokenizerjson_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 promptinputs tokenizer.encode(prompt, return_tensorspt).to(model.device)response model.generate(inputs,max_lengthmax_length,do_sampletemperature 0,top_ptop_p,top_ktop_k,temperaturetemperature)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 answerdef get_model():tokenizer AutoTokenizer.from_pretrained(THUDM/codegeex2-6b, trust_remote_codeTrue, cache_dir/mnt/data/CodeGeeX2/model)# gpus文件在demo文件夹中model load_model_on_gpus(THUDM/codegeex2-6b, num_gpus3, cache_dir/mnt/data/CodeGeeX2/model)model model.eval()return tokenizer, modelif __name__ __main__: parser argparse.ArgumentParser()parser add_code_generation_args(parser)args, _ parser.parse_known_args()tokenizer, model get_model()uvicorn.run(app, hostargs.listen, portargs.port, workersargs.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/