From e54f02e16aba3902124cb9da658c0f56ed657351 Mon Sep 17 00:00:00 2001 From: Ding Yue Date: Sat, 13 Apr 2024 16:57:17 +0800 Subject: [PATCH] Qwen1.5-moe-a2.7b --- Qwen1.5/06-Qwen1.5-MoE-A2.7B.md | 87 ++++++++++++++++++++++++++++++++ Qwen1.5/images/image-1.png | Bin 0 -> 86025 bytes Qwen1.5/images/image-2.png | Bin 0 -> 70458 bytes 3 files changed, 87 insertions(+) create mode 100644 Qwen1.5/06-Qwen1.5-MoE-A2.7B.md create mode 100644 Qwen1.5/images/image-1.png create mode 100644 Qwen1.5/images/image-2.png diff --git a/Qwen1.5/06-Qwen1.5-MoE-A2.7B.md b/Qwen1.5/06-Qwen1.5-MoE-A2.7B.md new file mode 100644 index 00000000..4cc05337 --- /dev/null +++ b/Qwen1.5/06-Qwen1.5-MoE-A2.7B.md @@ -0,0 +1,87 @@ +# Qwen1.5-MoE-chat Transformers 部署调用 +## Qwen1.5-MoE-chat介绍 +Qwen1.5-MoE-chat仅使用2.7B激活参数,达到和目前最先进的7B模型如Mistral 7B和Qwen1.5-7B模型相当的能力。相比Qwen1.5-7B,Qwen1.5-MoE-A2.7B的训练成本降低了75%,推理速度则提升至1.74倍。 + +相较于Mixtral-MoE模型,Qwen1.5-MoE在模型结构上有以下改进:使用了finegrained experts,利用已有的Qwen1.8B初始化模型,使用shared experts和routing experts等新的routing机制。 + +完整介绍参照官方blog:[Qwen1.5-MoE:1/3的激活参数量达到7B模型的性能](https://qwenlm.github.io/zh/blog/qwen-moe/) + +## 讲讲显存计算 +显存计算的考虑会随着模型类型不同,任务不同而变化 + +这里的Transformers部署调用是推理任务,因而只需要考虑模型参数、KV Cache、中间结果和输入数据。这里的模型为MoE模型,考虑完整模型参数(14.3B);使用了bf16加载,再考虑中间结果、输入数据和KV Cache等,大概是`2x1.2x14.3`的显存需求,所以我们后面会选择双卡共48G显存 + +更完整的显存计算参照这个blog:[【Transformer 基础系列】手推显存占用](https://zhuanlan.zhihu.com/p/648924115) +## 环境准备 +在autodl平台中租一个**双卡3090等24G(共计48G)显存**的机器,如下图所示镜像选择PyTorch-->2.1.0-->3.10(ubuntu22.04)-->12.1 +接下来打开刚刚租用服务器的JupyterLab, 图像 并且打开其中的终端开始环境配置、模型下载和运行演示。 +![Alt text](images/image-1.png) +pip换源和安装依赖包 +```shell +# 因为涉及到访问github因此最好打开autodl的学术镜像加速 +source /etc/network_turbo +# 升级pip +python -m pip install --upgrade pip +# 更换 pypi 源加速库的安装 +pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple +# 从transformers的github仓库中安装包含qwen1.5-moe的新版本 +pip install git+https://github.com/huggingface/transformers +# 安装需要的python包 +pip install modelscope sentencepiece accelerate fastapi uvicorn requests streamlit transformers_stream_generator +# 安装flash-attention +pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.4.2/flash_attn-2.4.2+cu122torch2.1cxx11abiFALSE-cp310-cp310-linux_x86_64.whl +``` +## 模型下载 +使用ModelScope下载模型 +```python +import torch +from modelscope import snapshot_download, AutoModel, AutoTokenizer +import os +model_dir = snapshot_download('qwen/Qwen1.5-MoE-A2.7B-Chat', cache_dir='/root/autodl-tmp', revision='master') +``` +## 代码准备 +在/root/autodl-tmp路径下新建trains.py文件并在其中输入以下内容 +```python +import torch # 导入torch库,用于深度学习相关操作 +from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig # 三个类分别用于加载分词器、加载因果语言模型和加载生成配置 + +# 将模型路径设置为刚刚下载的模型路径 +model_name = "/root/autodl-tmp/qwen/Qwen1.5-MoE-A2.7B-Chat" + +# 加载语言模型,设置数据类型为bfloat16即混合精度格式以优化性能并减少显存使用,将推理设备设置为`auto`自动选择最佳的设备进行推理,如果没有可用的GPU,它可能会回退到CPU +model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") + +# 加载分词器 +tokenizer = AutoTokenizer.from_pretrained(model_name) + +# 定义input字符串 +prompt = "Give me a short introduction to large language model." +messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prompt} +] +# 使用分词器的apply_chat_template方法来处理messages,转换格式 +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True # 在消息前添加生成提示 +) +# 将text变量中的文本转换为模型输入的格式,指定返回的张量为PyTorch张量("pt") +model_inputs = tokenizer([text], return_tensors="pt").to(device) +# 使用模型的generate方法来生成文本 +generated_ids = model.generate( + model_inputs.input_ids, + max_new_tokens=512 +) +# 从生成的ID中提取出除了原始输入之外的新生成的token +generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) +] +# 使用分词器的batch_decode方法将生成的token ID转换回文本 +response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] +# 显示生成的回答 +print(response) +``` +运行结果如下图所示,模型生成了与代码中prompt对应的结果: + +![alt text](images/image-2.png) \ No newline at end of file diff --git a/Qwen1.5/images/image-1.png b/Qwen1.5/images/image-1.png new file mode 100644 index 0000000000000000000000000000000000000000..3a0f356430d8fb7156c9be1fe2ab6ca7d03b4ccb GIT binary patch literal 86025 zcmaI7cOcte`#(%=il9pE*s7>fn;0=#wW_U_T18Qt*s+7CT}o|5(W<>;kD_*r+G2|> zMr@vZ?$7VOf1msNeV*r^B=3`Ru5-?Hopa6CJ3?1mosyi191jnV@`(ocIUXKS3m)FB zAyQ&oi!xr*Puv&2+jDhgyplnd4cr5vwUU++9$tAY#icnB9^MV&30Uccm&tYpQ6TI1 zS#wK6QnW*2o${@=NLfOD-RxiAzQxEo1WoUmk_*urK8?IfTBZ9_L*VgKXUgYtgcL5G zGd<@wa_jSzva-H=yT${(7p<1NgXz-iQGSORp3TyHdU|@8bug!B*U$F*f<^rgy0sp^ zVCw6q{ryJh$B&T*ca%wiFu=b*QZ+c^HLiXHS((HRx*zu5?<YbL5GULy^1Q=PPa-*EO|IolugNJGGLAuFo8+X$aE?F5`u0Cm) z?4BG(92^|1&&EcKzw=~Tlu7t9cPG9%V*UR#=*(&pat1?!id|auDC=DP`+)tKY1KUh=r| z3Y(VqmO4}TwSPQ#Dkvye`H}pjA*Y|G;Q3cs+P}G!28vDC@%YzdskM9F>Grjw|6*XH z-@yM}##Dd8oK3i6wOB#lch^HOPs@YutNuR0vdhc2eFFo>J|6eA8x4=N*M9G%=bi3+ 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