# NPU 示例 ## NPU 工具链 经常在 AI 芯片上部署算法模型的同学都知道，想要把模型部署到芯片上的 NPU 中运行，都需要使用芯片原厂提供的 NPU 工具链，这里我们使用的是 Pulsar2。 ![](../res/pulsar2.png) - [Pulsar2 在线文档](https://pulsar2-docs.readthedocs.io/zh-cn/latest/index.html) - [安装指导](https://pulsar2-docs.readthedocs.io/zh-cn/latest/user_guides_quick/quick_start_prepare.html) - [快速上手](https://pulsar2-docs.readthedocs.io/zh-cn/latest/user_guides_quick/quick_start_ax650.html) - [NPU 算子支持列表](https://pulsar2-docs.readthedocs.io/zh-cn/latest/appendix/op_support_list_ax650.html) - [大模型转换](https://pulsar2-docs.readthedocs.io/zh-cn/latest/appendix/build_llm.html) ## AXCL-Samples AXCL-Samples 由 爱芯元智 主导开发。该项目实现了常见的深度学习开源算法在基于 爱芯元智 的 SoC 实现的 PCIE算力卡 产品上的运行的示例代码，方便社区开发者进行快速评估和适配。 - [axcl-samples](https://github.com/AXERA-TECH/axcl-samples)； - 该仓库采用最简单的方式展示常用的开源模型，例如 Ultralytics 的 YOLO 系列，DepthAnything，YOLO-Worldv2 等等。 ### 获取示例 - AXCL-Samples 的预编译 ModelZoo 请参考 - [百度网盘](https://pan.baidu.com/s/1MAAKElTI2wgiDehvd2Q1lA?pwd=p1k6) ### YOLO11x 基于 Ultralytics YOLO11 系列模型详细的模型导出、量化、编译的流程请参考[《基于 AX650N 部署 YOLO11》](https://zhuanlan.zhihu.com/p/772269394) ``` (base) axera@raspberrypi:~/temp $ ./axcl_yolo11 -i ssd_horse.jpg -m yolo11x.axmodel -------------------------------------- model file : yolo11x.axmodel image file : ssd_horse.jpg img_h, img_w : 640 640 -------------------------------------- post process cost time:1.44 ms -------------------------------------- Repeat 1 times, avg time 24.69 ms, max_time 24.69 ms, min_time 24.69 ms -------------------------------------- detection num: 6 17: 96%, [ 216, 71, 423, 370], horse 16: 93%, [ 144, 203, 196, 345], dog 0: 89%, [ 273, 14, 349, 231], person 2: 88%, [ 1, 105, 132, 197], car 0: 82%, [ 431, 124, 451, 178], person 19: 46%, [ 171, 137, 202, 169], cow -------------------------------------- ``` ![](../res/yolo11_out.jpg) ### YOLO11x-Seg ``` (base) axera@raspberrypi:~/temp $ ./axcl_yolo11_seg -i ssd_horse.jpg -m yolo11x-seg.axmodel -------------------------------------- model file : yolo11x-seg.axmodel image file : ssd_horse.jpg img_h, img_w : 640 640 -------------------------------------- post process cost time:3.12 ms -------------------------------------- Repeat 1 times, avg time 34.75 ms, max_time 34.75 ms, min_time 34.75 ms -------------------------------------- detection num: 6 17: 96%, [ 216, 71, 423, 370], horse 16: 93%, [ 144, 203, 196, 345], dog 0: 89%, [ 273, 14, 349, 231], person 2: 88%, [ 1, 105, 132, 197], car 0: 82%, [ 431, 124, 451, 178], person 19: 46%, [ 171, 137, 202, 169], cow -------------------------------------- ``` ![](../res/yolo11_seg_out.jpg) ### YOLO11x-Pose ``` axera@raspberrypi:~/temp $ ./axcl_yolo11_pose -i football.jpg -m yolo11x-pose.axmodel -------------------------------------- model file : yolo11x-pose.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- post process cost time:0.59 ms -------------------------------------- Repeat 1 times, avg time 25.02 ms, max_time 25.02 ms, min_time 25.02 ms -------------------------------------- detection num: 6 0: 94%, [1350, 337, 1632, 1036], person 0: 93%, [ 492, 477, 658, 1000], person 0: 92%, [ 756, 219, 1126, 1154], person 0: 91%, [ 0, 354, 314, 1108], person 0: 73%, [ 0, 530, 81, 1017], person 0: 54%, [ 142, 589, 239, 1013], person -------------------------------------- ``` ![](../res/yolo11_pose_out.jpg) ### YOLO World v2 YOLO-Worldv2 该模型的详细模型导出、量化、编译的流程请参考[《再谈 YOLO World 部署》](https://zhuanlan.zhihu.com/p/721856217) - 模型：yoloworldv2_4cls_50_npu3.axmodel - 输入图片：ssd_horse.jpg - 输入文本：dog.bin, 对应的 4 分类 'dog' 'horse' 'sheep' 'cow' ``` axera@raspberrypi:~/temp $ ./axcl_yolo_world_open_vocabulary -m yoloworldv2_4cls_50_npu3.axmodel -t dog.bin -i ssd_horse.jpg -------------------------------------- model file : yoloworldv2_4cls_50_npu3.axmodel image file : ssd_horse.jpg text_feature file : dog.bin img_h, img_w : 640 640 -------------------------------------- post process cost time:0.35 ms -------------------------------------- Repeat 1 times, avg time 4.47 ms, max_time 4.47 ms, min_time 4.47 ms -------------------------------------- detection num: 2 1: 91%, [ 215, 71, 421, 374], class2 0: 67%, [ 144, 204, 197, 346], class1 -------------------------------------- ``` ![](../res/yolo_world_out.jpg) ### YOLOv7-Face ``` axera@raspberrypi:~/temp $ ./axcl_yolov7_face -m yolov7-face.axmodel -i selfie.jpg -------------------------------------- model file : yolov7-face.axmodel image file : selfie.jpg img_h, img_w : 640 640 -------------------------------------- post process cost time:8.28 ms -------------------------------------- Repeat 1 times, avg time 12.17 ms, max_time 12.17 ms, min_time 12.17 ms -------------------------------------- detection num: 277 0: 91%, [1137, 869, 1283, 1065], face 0: 91%, [1424, 753, 1570, 949], face ...... 0: 20%, [1120, 570, 1145, 604], face 0: 20%, [1025, 390, 1041, 413], face ``` ![](../res/yolov7_face_out.jpg) ### DepthAnything DepthAnything 该模型的详细模型导出、量化、编译的流程请参考[《基于 AX650N 的 Depth Anything》](https://zhuanlan.zhihu.com/p/681378259) ``` axera@raspberrypi:~/temp/axcl-samples/build $ ./install/bin/ax_depth_anything -m depth_anything.axmodel -i ssd_horse.jpg -------------------------------------- model file : depth_anything.axmodel image file : ssd_horse.jpg img_h, img_w : 384 640 -------------------------------------- post process cost time:4.43 ms -------------------------------------- Repeat 1 times, avg time 44.02 ms, max_time 44.02 ms, min_time 44.02 ms -------------------------------------- ``` ![](../res/depth_anything_out.png) ## LLM 示例 - 模型转请参考[大模型编译文档](https://pulsar2-docs.readthedocs.io/zh-cn/latest/appendix/build_llm.html) - 预编译 ModelZoo-LLM 请参考[百度网盘](https://pan.baidu.com/s/1grJNjcpUln-fDBisJxuvCA?pwd=mys8) - 上板执行程序 main_pcie 请参考[ax-llm pcie 分支](https://github.com/AXERA-TECH/ax-llm/tree/axcl-llm-prefill) ### Tokenizer 解析器 **tokenizer 解析准备** 为了更方便、更准确的进行 LLM DEMO 展示，我们采用 transformers 内置的 tokenizer 解析服务，因此需要安装 python 环境和 transformers 库 安装 miniconda ``` wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh chmod a+x Miniconda3-latest-Linux-aarch64.sh ./Miniconda3-latest-Linux-aarch64.sh ``` 启用 python 环境 ``` conda create --name axcl python=3.9 conda activate axcl ``` 安装 transformers ``` pip install transformers==4.41.1 -i https://mirrors.aliyun.com/pypi/simple ``` ### Qwen2.5 拷贝相关文件到 Host **文件说明** ``` (base) axera@raspberrypi:~/qwen2.5-0.5b-prefill-ax650 $ tree . ├── main_pcie_prefill ├── qwen2.5-0.5B-prefill-ax650 │   ├── model.embed_tokens.weight.bfloat16.bin │   ├── qwen2_p128_l0_together.axmodel │   ├── qwen2_p128_l10_together.axmodel ...... │   ├── qwen2_p128_l8_together.axmodel │   ├── qwen2_p128_l9_together.axmodel │   └── qwen2_post.axmodel ├── qwen2.5_tokenizer │   ├── merges.txt │   ├── tokenizer_config.json │   ├── tokenizer.json │   └── vocab.json ├── qwen2.5_tokenizer.py └── run_qwen2.5_0.5B_prefill_pcie.sh ``` **启动 tokenizer 解析器** 运行 tokenizer 服务，Host ip 默认为 localhost，端口号设置为 12345，正在运行后信息如下 ``` (axcl) axera@raspberrypi:~/qwen2.5-0.5b-prefill-ax650 $ python qwen2.5_tokenizer.py --port 12345 None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used. Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. None None 151645 <|im_end|> <|im_start|>system You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|> <|im_start|>user hello world<|im_end|> <|im_start|>assistant [151644, 8948, 198, 2610, 525, 1207, 16948, 11, 3465, 553, 54364, 14817, 13, 1446, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 14990, 1879, 151645, 198, 151644, 77091, 198] http://localhost:12345 ``` **运行 Qwen 2.5** ``` (base) axera@raspberrypi:~/qtang/llama_axera_cpp $ ./run_qwen2_0.5B_prefill_pcie.sh [I][ Init][ 129]: LLM init start 7% | ███ | 2 / 27 [1.30s<17.54s, 1.54 count/s] embed_selector init okcat: /proc/ax_proc/mem_cmm_info: No such file or directory 11% | ████ | 3 / 27 [1.75s<15.74s, 1.72 count/s] init 0 axmodel ok,remain_cmm(-1 MB)cat: /proc/ax_proc/mem_cmm_info: No such file or directory ...... 96% | ███████████████████████████████ | 26 / 27 [7.34s<7.62s, 3.54 count/s] init 23 axmodel ok,remain_cmm(-1 MB)cat: /proc/ax_proc/mem_cmm_info: No such file or directory 100% | ████████████████████████████████ | 27 / 27 [12.84s<12.84s, 2.10 count/s] init post axmodel ok,remain_cmm(-1 MB) [I][ Init][ 253]: max_token_len : 1023 [I][ Init][ 258]: kv_cache_size : 128, kv_cache_num: 1023 [I][ Init][ 266]: prefill_token_num : 128 [I][ Init][ 348]: LLM init ok Type "q" to exit, Ctrl+c to stop current running >> 你是谁？ [I][ Run][ 511]: ttft: 128.70 ms 我是来自阿里云的大规模语言模型，我叫通义千问。 [N][ Run][ 636]: hit eos,avg 26.44 token/s >> 深圳在哪里？ [I][ Run][ 511]: ttft: 128.96 ms 深圳位于中国广东省，是中国的经济中心之一。 [N][ Run][ 636]: hit eos,avg 25.89 token/s >> q ``` ### InternVL2-1B InternVL2-1B 的详细模型导出、量化、编译的流程请参考[《基于 AX650N/AX630C 部署多模态大模型 InternVL2-1B》](https://zhuanlan.zhihu.com/p/4118849355) 拷贝相关文件到 Host **文件说明** ``` (axcl) axera@raspberrypi:~/internvl2-1b-448-ax650 $ tree . ├── internvl2_tokenizer │   ├── added_tokens.json │   ├── merges.txt │   ├── special_tokens_map.json │   ├── tokenizer_config.json │   └── vocab.json ├── internvl2_tokenizer_448.py ├── internvl_448 │   ├── intervl_vision_part_448.axmodel │   ├── model.embed_tokens.weight.bfloat16.bin │   ├── qwen2_p320_l0_together.axmodel ...... │   ├── qwen2_p320_l9_together.axmodel │   └── qwen2_post.axmodel ├── main_internvl ├── main_internvl_pcie ├── run_internvl2_448_ax650.sh └── run_internvl2_448_pcie.sh ``` **启动 tokenizer 解析器** 运行 tokenizer 服务，Host ip 默认为 localhost，端口号设置为 12345，正在运行后信息如下 ``` (axcl_test) axera@raspberrypi:~/internvl2-1b-448-ax650 $ python internvl2_tokenizer_448.py --port 12345 None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used. Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. None None 151645 <|im_end|> [151644, 8948, 198, 56568, 104625, 100633, 104455, 104800, 101101, 32022, ...... 5501, 7512, 279, 2168, 19620, 13, 151645, 151644, 77091, 198] 310 [151644, 8948, 198, 56568, 104625, 100633, 104455, 104800, 101101, 32022, ......151645, 151644, 77091, 198] http://localhost:12345 ``` **运行 InternVL2-1B** 测试图片 ![](../res/ssd_car.jpg) 输出信息 ``` (base) axera@raspberrypi:~/internvl2-1b-448-ax650 $ ./run_internvl2_448_pcie.sh [I][ Init][ 135]: LLM init start bos_id: -1, eos_id: 151645 7% | ███ | 2 / 27 [0.95s<12.82s, 2.11 count/s] embed_selector init okcat: /proc/ax_proc/mem_cmm_info: No such file or directory 11% | ████ | 3 / 27 [1.40s<12.61s, 2.14 count/s] init 0 axmodel ok,remain_cmm(-1 MB)cat: /proc/ax_proc/mem_cmm_info: No such file or directory ...... 100% | ████████████████████████████████ | 27 / 27 [8.99s<8.99s, 3.00 count/s] init post axmodel ok,remain_cmm(-1 MB) [I][ Init][ 292]: max_token_len : 1023 [I][ Init][ 297]: kv_cache_size : 128, kv_cache_num: 1023 [I][ Init][ 305]: prefill_token_num : 320 [I][ Init][ 307]: vpm_height : 448,vpm_width : 448 [I][ Init][ 389]: LLM init ok Type "q" to exit, Ctrl+c to stop current running prompt >> who are you? image >> [I][ Run][ 626]: ttft: 425.78 ms I am an AI assistant whose name is InternVL, developed jointly by Shanghai AI Lab and SenseTime. [N][ Run][ 751]: hit eos,avg 29.24 token/s prompt >> 图片中有什么? image >> ./ssd_car.jpg [I][ Encode][ 468]: image encode time : 4202.367188 ms, size : 229376 [I][ Run][ 626]: ttft: 425.97 ms 这张图片展示了一辆红色的双层巴士，巴士上有一个广告，广告上写着“THINGS GET MORE EXCITING WHEN YOU SAY YES”（当你说“是”时，事情会变得更加有趣）。巴士停在城市街道的一侧，街道两旁有建筑物和行人。图片中还有一位穿着黑色外套的女士站在巴士前微笑。 [N][ Run][ 751]: hit eos,avg 29.26 token/s prompt >> q (base) axera@raspberrypi:~/internvl2-1b-448-ax650 $ ``` ## 音频大模型 本章节展示常用的 ASR（自动语音识别）、TTS（文本转语音）模型示例。 ### Whisper - 本小节只指导如何在 Raspberry Pi 5 上运行预编译好的基于 Whipser Small 的语音转文字示例； - 模型转换、示例源码编译请参考 [whisper.axcl](https://github.com/ml-inory/whisper.axcl)。 **下载** ``` git clone https://github.com/ml-inory/whisper.axcl.git ``` **预编译模型** 预编译模型下载([百度网盘](https://pan.baidu.com/s/1tOHVMZCin0A68T5HmKRJyg?pwd=axyz)) 下载后放到 models 下 **编译** ``` cd whisper.axcl mkdir -p build && cd build cmake -DCMAKE_INSTALL_PREFIX=../install -DCMAKE_BUILD_TYPE=Release .. make install -j4 ``` **运行 Whisper** ``` cd install ./whisper -w ../demo.wav ``` **运行结果** ``` (base) axera@raspberrypi:~/qtang/whisper.axcl/install $ ./whisper -w ../demo.wav encoder: ../models/small-encoder.axmodel decoder_main: ../models/small-decoder-main.axmodel decoder_loop: ../models/small-decoder-loop.axmodel wav_file: ../demo.wav language: zh Load encoder take 3336.25 ms Load decoder_main take 6091.89 ms Load decoder_loop take 5690.05 ms Read positional_embedding Encoder run take 190.26 ms First token: 17556 take 51.49ms Next Token: 20844 take 30.15 ms Next Token: 7781 take 30.21 ms Next Token: 20204 take 30.20 ms Next Token: 28455 take 30.17 ms Next Token: 31962 take 30.02 ms Next Token: 6336 take 30.09 ms Next Token: 254 take 30.22 ms Next Token: 2930 take 30.14 ms Next Token: 236 take 30.14 ms Next Token: 36135 take 30.12 ms Next Token: 15868 take 30.18 ms Next Token: 252 take 30.01 ms Next Token: 1546 take 30.17 ms Next Token: 46514 take 30.17 ms Next Token: 50257 take 30.15 ms All Token: take 503.68ms, 31.77 token/s All take 735.09ms Result: 甚至出现交易几乎停滞的情况 (base) axera@raspberrypi:~/qtang/whisper.axcl/install $ ``` ### MeloTTS - 本小节只指导如何在 Raspberry Pi 5 上运行预编译好的 MeloTTS 文字转语音示例； - 模型转换、示例源码编译请参考 [melotts.axcl](https://github.com/ml-inory/melotts.axcl)。 **下载** ``` git clone https://github.com/ml-inory/melotts.axcl.git ``` **预编译模型** ``` cd melotts.axcl ./download_models.sh ``` **编译** x86_64 平台 ``` ./build.sh ``` aarch64 平台 ``` ./build_aarch64.sh ``` **运行 MeloTTS** 在 melotts.axcl 项目根目录下运行 ``` ./install/melotts -s 句子 ``` **运行结果** ``` (base) axera@raspberrypi:~/melotts.axcl $ ./install/melotts encoder: ./models/encoder.onnx decoder: ./models/decoder.axmodel lexicon: ./models/lexicon.txt token: ./models/tokens.txt sentence: 爱芯元智半导体股份有限公司，致力于打造世界领先的人工智能感知与边缘计算芯片。服务智慧城市、智能驾驶、机器人的海量普惠的应用 wav: output.wav speed: 0.800000 sample_rate: 44100 Load encoder Load decoder model Encoder run take 191.25ms decoder slice num: 9 Decode slice(1/9) take 39.90ms Decode slice(2/9) take 39.66ms Decode slice(3/9) take 39.98ms Decode slice(4/9) take 39.57ms Decode slice(5/9) take 40.28ms Decode slice(6/9) take 39.68ms Decode slice(7/9) take 39.59ms Decode slice(8/9) take 39.58ms Decode slice(9/9) take 41.11ms Saved audio to output.wav (base) axera@raspberrypi:~/melotts.axcl $ ```