From 3d256d78c6ce0057bf75238cef3b74a8a6c36b97 Mon Sep 17 00:00:00 2001 From: z00919396 Date: Wed, 19 Mar 2025 15:17:40 +0800 Subject: [PATCH] add mindspore-deepseek install script --- README.md | 189 +++++-- doc/README.md | 3 + doc/deepseek/asserts/software_stack.png | Bin 0 -> 223796 bytes script/mindspore-deepseek/README.md | 27 + script/mindspore-deepseek/config.yaml | 40 ++ script/mindspore-deepseek/main.yaml | 9 + .../mindspore-deepseek/workspace/install.yml | 24 + .../roles/prepare/files/lib/ascend_prepare.sh | 43 ++ .../roles/prepare/files/lib/example_config | 19 + .../roles/prepare/files/lib/net_check.sh | 69 +++ .../files/lib/npu_net_config_simple.sh | 25 + .../roles/prepare/files/lib/ray_start.sh | 32 ++ .../roles/prepare/files/lib/set_env.sh | 57 +++ .../roles/prepare/files/lib/start_docker.sh | 67 +++ .../roles/prepare/files/lib/start_ds.sh | 53 ++ .../workspace/roles/prepare/files/prepare.sh | 65 +++ .../workspace/roles/prepare/tasks/main.yml | 34 ++ .../roles/prepare/templates/config.cfg.j2 | 19 + .../roles/start/deepseek/tasks/main.yml | 2 + .../roles/start/ray-master/tasks/main.yml | 2 + .../roles/start/ray-worker/tasks/main.yml | 2 + tool/benchmark/README.md | 91 ++++ tool/benchmark/benchmark_parallel.py | 443 +++++++++++++++++ tool/benchmark/benchmark_utils.py | 466 ++++++++++++++++++ 24 files changed, 1744 insertions(+), 37 deletions(-) create mode 100644 doc/README.md create mode 100755 doc/deepseek/asserts/software_stack.png create mode 100644 script/mindspore-deepseek/README.md create mode 100644 script/mindspore-deepseek/config.yaml create mode 100644 script/mindspore-deepseek/main.yaml create mode 100644 script/mindspore-deepseek/workspace/install.yml create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/ascend_prepare.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/example_config create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/net_check.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/npu_net_config_simple.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/ray_start.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/set_env.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_docker.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_ds.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/files/prepare.sh create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/tasks/main.yml create mode 100644 script/mindspore-deepseek/workspace/roles/prepare/templates/config.cfg.j2 create mode 100644 script/mindspore-deepseek/workspace/roles/start/deepseek/tasks/main.yml create mode 100644 script/mindspore-deepseek/workspace/roles/start/ray-master/tasks/main.yml create mode 100644 script/mindspore-deepseek/workspace/roles/start/ray-worker/tasks/main.yml create mode 100644 tool/benchmark/README.md create mode 100644 tool/benchmark/benchmark_parallel.py create mode 100644 tool/benchmark/benchmark_utils.py diff --git a/README.md b/README.md index b980d04..a086c93 100644 --- a/README.md +++ b/README.md @@ -1,37 +1,152 @@ -# llm_solution - -#### 介绍 -A solution for large model inference, such as DeepSeek, built with full-stack open-source components. - -#### 软件架构 -软件架构说明 - - -#### 安装教程 - -1. xxxx -2. xxxx -3. xxxx - -#### 使用说明 - -1. xxxx -2. xxxx -3. xxxx - -#### 参与贡献 - -1. Fork 本仓库 -2. 新建 Feat_xxx 分支 -3. 提交代码 -4. 新建 Pull Request - - -#### 特技 - -1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md -2. Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com) -3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目 -4. [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目 -5. Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help) -6. Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) +# llm_solution + +2025年，以DeepSeek-R1为代表的AI大模型正以惊人的速度重塑产业格局。短短7天用户破亿、多模态交互与低算力需求突破硬件限制，这些成就印证了AI技术走向规模落地的临界点已至。然而，将AI融入到具体产业，还面临着一些问题： + +**从产业上看：** + +1、**算力与模型的割裂**：厂商需为不同硬件重复适配模型，开发成本陡增； + +2、**生态孤岛化**：各厂商自建技术栈，导致跨平台协作效率低下； + +3、**长尾需求难满足**：中小开发者受限于算力与框架兼容性，难以复用头部模型能力。 + +**从技术上看：** + +1、**混合专家（MoE）架构的适配性挑战**：专家模型与硬件内存存在匹配困境，同时专家负载不均与通信开销过高； + +2、**多模型协同与训推一体的系统挑战**：多模型动态交互、训推状态切换、资源动态分配引发协同困难，训推一体化软件栈的易用性不足； + +3、**长序列推理与稀疏计算的性能挑战**：长序列KVCache存在容量瓶颈；稀疏计算引发的向量化效率下降。 + +DeepSeek引发的挑战本质上是AI规模化落地的必经之痛。解决这些难题需硬件厂商、框架开发者与行业用户深度协同，通过**全栈开放生态共建**与**分层协同性能提升**，实现从单点突破到系统级效能跃迁。 + +为了解决以上问题，**openEuler开源社区与MindSpore社区**，推出面向大模型的全栈开源方案，以**操作系统+AI框架+模型生态**的三层开放架构，替换**操作系统**和**DL框架**，秉承**代码开源+标准开放+生态共建**的理念，打造智能时代的全国产化的数字基座。 + +## 软件架构 +### 全栈开放生态共建 + +![](/doc/deepseek/asserts/software_stack.png) + + +openEuler+MindSpore社区协同提出开源推理方案，以全栈开放架构直击痛点： + +**1. 对上：兼容多元大模型生态，普惠AI** + +a、支持DeepSeek、LLaMA系列等主流模型接入，通过归一化的开源推理软件栈，保证不同模型对资源的动态调配，在生态上做到统一演进，且开箱即优，避免"重复造轮子"； + +b、集成模型微调与蒸馏能力，优化增强RAG流程搭建，结合DeepSeek群体策略优化经验，降低长尾场景定制门槛。 + +**2. 对下：异构算力无缝接入，AI基座** + +a、通过硬件抽象层兼容GPU、NPU及国产芯片，释放DeepSeek低能耗技术红利； + +b、在极致资源约束下，资源动态调度诉求强烈，通过全栈协同优化降低模型资源消耗获得竞争优势。 + + + +### 分层协同性能提升 + +通过openEuler、MindSpore与vLLM/RAY间的分层协同，为DeepSeek-R1大模型带来了吞吐性能与易用性的显著提升。核心技术点如下： + +#### openEuler： + +##### 异构融合调度：负载感知MoE冷热专家，任务细粒度调度提升推理性能 + +1. 负载感知的冷热MoE专家动态识别和并行调度，稀疏MoE计算分层细粒度拆分到不同进程部署在多样算力； + +2. 共享资源细粒度按需控制，支持MoE专家均衡调度，计算/通信细粒度并发； + +3. 针对高并发场景下推理服务、分布式计算组件Host侧资源争用的痛点，利用NUMA感知的细粒度算力与内存资源隔离，提升推理整体性能。 + +##### 异构融合内存：高效管理异构内存，减小系统内存碎片，提升系统推理性能 + +1. 针对推理服务高并发场景，通过线程特性感知的细粒度内存分配、高性能代码段大页机制，在控制内存开销的同时，提升Host侧性能与整体推理吞吐； + +2. 针对MoE架构的稀疏访存特征，通过Host/Device协同内存管理实现多粒度动态混合页与按需内存分配，减少页表访存开销同时提升显存利用效率； + +3. 针对大模型推理服务面临的显存容量挑战，基于MoE架构的稀疏计算特征，利用运行时-OS协同设计实现高效专家超分部署，提升显存利用率与整体推理吞吐。 + +##### 异构融合编译：毕昇编译优化，减少算子下发耗时，提升算子性能 + +1. **架构亲和编译优化**：通过架构亲和的原子指令优化和Malloc、Memcpy高性能库优化，降低各类锁的代价，提高内存利用效率，降低访存开销，进而降低时延，提高吞吐率；算子编译阶段使能智能感知流水优化，基于数据依赖关系深度分析和自适应同步决策机制，自动插入最优同步指令实现高效的多级流水并行；通过昇腾算子抽象层与芯片ISA的智能映射，实现指令级并行优化，极大发挥芯片理论算力； + +2. **多维融合编译优化**：针对算子下发阶段前端性能瓶颈较高的特点，通过CFGO优化技术，借助运行时信息，编译器进行精准的代码布局优化，有效提高程序IPC，降低算子下发时延；多维融合加速能够自动实现向量类算子融合、矩阵-向量类算子融合，减少数据搬运开销，并通过细粒度并行进一步提升算子性能，快速满足用户验证模型算法和提升模型开箱性能。 + +#### MindSpore： + +##### 图编译：将模型编译为计算图，通过模式匹配自动将小算子融为大算子 + +1\.**图生成**：MindSpore通过JIT编译自动将模型的python类或者函数编译成一张完整的计算图，JIT编译提供了多种方式(ast/bytecode/trace）以满足不同场景的用途，覆盖了绝大部分Python语法。 + +2\.**自动融合**：基于计算图通过自动模式匹配实现算子融合，将小算子融合成大颗粒的算子。大算子既减少Host下发的开销，同时也大大缩短了Device的计算时延。在DeepSeek V3/R1模型中实现了QKV/FFN+Split融合、Transpose+BatchMatMul+Transpose融合、Swiglu融合以及Norm类融合，大幅度减少了算子数量。 + +3\.**动态shape支持**：计算图的执行需要支持动态shape以满足推理场景输入输出序列长度以及batch +size的动态变化，相比于静态shape的整图下沉，动态shape的计算图执行需要每个iteration在Host侧重新执执行shape推导以及申请显存等操作，为了避免Host成为瓶颈，MindSpore通过Shape推导和显存申请、算子Tiling数据计算以及算子下发三级流水优化，实现Host计算和Device计算的掩盖。 + +##### 模型压缩：金箍棒工具，快速实现模型量化算法及量化推理全流程 + +金箍棒是华为昇思 MindSpore团队与华为诺亚方舟实验室联合研发的模型压缩工具，依靠 MindSpore Rewrite +模块，为算法开发者屏蔽网络差异和硬件细节，提升算法接入与调优效率，同时提供了可视化、量化损失分析以及Summary等工具。 + +我们使用金箍棒通过不同量化方式，来尝试平衡DeepSeek-R1的精度和性能： + +**8bit权重量化**：对 DeepSeek-R1 进行8bit权重量化，使权重显存占用降为1/2，小batch_size场景推理性能提升明显，但大batch_size场景推理性能变差，分析发现是权重量化矩阵乘算子随着batch_size增大性能会下降。 + +**SmoothQuant 8bit量化**：为提升大batch_size场景的性能，用SmoothQuant 8bit +全量化，测试发现随batch_size增加，吞吐量线性度良好，但网络量化精度损失仍较大。 + +**混合量化**：为降量化精度损失，对精度较敏感的FeedForward层用激活动态量化，损失部分性能提升来提升量化精度，MLA层用Outlier-Suppression+异常值抑制算法替代SmoothQuant进一步提升精度。 + + + + +## 全栈解决方案部署教程 + +### DeepSeek V3&R1部署 + +参考[部署指南](https://gitee.com/openeuler/llm_solution/blob/master/doc/deepseek/DeepSeek-V3&R1%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97.md)，使用一键式部署脚本，20min完成推理服务拉起 + +### EulerCopilot部署 + +参考[EulerCopilot用户指南](https://gitee.com/openeuler/euler-copilot-framework/blob/master/docs/user-guide/README.md)，搭建本地知识库并协同DeepSeek大模型完成智能调优、智能运维等应用； + + + +## 性能 + +### 精度 + +本方案使用8bit权重量化、SmoothQuant +8bit量化和混合量化等技术，最终以CEval精度损失2分的代价，实现了DeepSeek-R1w8a8的大模型部署。 + +| 模型 | CEval精度 | +| ---------------------- | --------- | +| Claude-3.5-Sonnet-1022 | 76.7 | +| GPT-4o 0513 | 76 | +| DeepSeek V3 | 86.5 | +| GPT-4o 0513 | 76 | +| OpenAI o1-mini | 68.9 | +| DeepSeek R1 | 91.8 | +| Deepseek R1 w8a8 | 89.52 | + + + +### 吞吐 + +测试环境： + +1. 两台Atlas 800I A2（8\*64G）。 +2. Ascend HDK Driver 24.1.0版本，Firmware 7.5.0.3.22版本。 +3. openEuler 22.03 LTS版本（内核 5.10）。 + +| 并发数 | 吞吐(Token/s) | +| ------ | ------------- | +| 1 | 16.7 | +| 192 | 1188 | + + + +## 参与贡献 + +欢迎通过issue方式提出您宝贵的建议，共建开箱即优、性能领先的全栈开源国产化推理解决方案 +# llm_solution diff --git a/doc/README.md b/doc/README.md new file mode 100644 index 0000000..5240ded --- /dev/null +++ b/doc/README.md @@ -0,0 +1,3 @@ +### 介绍 + +本目录用于放置安装部署、代码调优、特性介绍等文档 \ No newline at end of file diff --git a/doc/deepseek/asserts/software_stack.png b/doc/deepseek/asserts/software_stack.png new file mode 100755 index 0000000000000000000000000000000000000000..2fa5e20b52b8911e4ffc045b5f954f0454adacfa GIT binary patch literal 223796 zcmeFYbz2J^59ytKxvR|o>HUcD|vh6lDF zv2_o>dPVh0L0VGF8}2X*$s2!Wv7diXr4|LY(D|-TR1_2(4|$CYPeg#Pjvs6%ss8J0 zWL}Qp7JObkwg!JlZ1y+~+?mE@EYZMKO=1v;>J`H0SJ{r`IVKR=VlWI1c{Q%W&?Nvc=&&h`Co$sql&!#k4?yz0`OE? zKFe%hP~qVI<50nf+5fo-!G{bEn_2WYSt8^=$9)BbAN!x@@_#-T{r(C$iNZ@M7yO^& zeujLp{@0BB>uX;qEk0}vD`wCpxzk*wW{XggTpI@WW 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+参考该项目下的DeepSeekV3&R1部署指南-第四章节配置config.yaml后执行以下命令 + +```bash +oedp run install +``` + +### 3. FAQ + +1.下载的权重是CKPT格式的，脚本中默认是safetensor格式，如何修改？ + +```shell +# 修改config.yaml 模型权重类型 +model_type: ckpt +``` + diff --git a/script/mindspore-deepseek/config.yaml b/script/mindspore-deepseek/config.yaml new file mode 100644 index 0000000..35db0fb --- /dev/null +++ b/script/mindspore-deepseek/config.yaml @@ -0,0 +1,40 @@ +all: + children: + masters: + hosts: + master1: + ansible_host: 1.2.3.4 + ansible_port: 22 + ansible_user: root + ansible_password: "" + + workers: + hosts: + worker1: + ansible_host: 2.3.4.5 + ansible_port: 22 + ansible_user: root + ansible_password: "" + + vars: + # 容器镜像 + image_name: hub.oepkgs.net/oedeploy/openeuler/aarch64/deepseek_hyperinfer + image_tag: openeuler22.03-py3.11 + # 推理服务所在容器的名称 + container_name: openeuler_ds + # 模型路径 + model_path: /path/to/model + # ray 开放的端口 + ray_port: 6379 + # 节点个数 + node_num: 2 + # 拉起服务前是否停止其他容器 + is_stop_other_container: 0 + # 推理服务端口 + llm_port: 8000 + # ray集群使用的网卡 + ray_device: enp67s0f0np0 + # 模型权重类型 + model_type: safetensors + # 跳过 ssh 校验（如需禁用此功能，请注释以下配置项） + ansible_ssh_common_args: '-o StrictHostKeyChecking=no' diff --git a/script/mindspore-deepseek/main.yaml b/script/mindspore-deepseek/main.yaml new file mode 100644 index 0000000..dac8b1d --- /dev/null +++ b/script/mindspore-deepseek/main.yaml @@ -0,0 +1,9 @@ +name: mindspore-deepseek +description: auto deploy DeepSeek with mindspore +action: + install: + description: auto deployment + tasks: + - name: install + playbook: install.yml + scope: all diff --git a/script/mindspore-deepseek/workspace/install.yml b/script/mindspore-deepseek/workspace/install.yml new file mode 100644 index 0000000..2d1fec9 --- /dev/null +++ b/script/mindspore-deepseek/workspace/install.yml @@ -0,0 +1,24 @@ +- hosts: + - masters + - workers + roles: + - prepare + any_errors_fatal: true + +- hosts: + - masters + roles: + - start/ray-master + any_errors_fatal: true + +- hosts: + - workers + roles: + - start/ray-worker + any_errors_fatal: true + +- hosts: + - masters + roles: + - start/deepseek + any_errors_fatal: true diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/ascend_prepare.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/ascend_prepare.sh new file mode 100644 index 0000000..235ccbb --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/ascend_prepare.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +# 各个节点均执行 + +# 下载依赖 +PKG_LIST="unzip which initscripts coreutils findutils gawk e2fsprogs util-linux net-tools pciutils gcc make automake autoconf libtool git patch kernel-devel-$(uname -r) kernel-headers-$(uname -r) dkms" + +for pkg in $PKG_LIST; do + if ! rpm -q "$pkg" > /dev/null 2>&1; then + echo "安装 $pkg..." + if ! yum -y install "$pkg"; then + echo "安装 $pkg 失败" + fi + else + echo "$pkg 已安装，跳过" + fi +done + +# 下载驱动 +rm -rf npu-driver +mkdir -p npu-driver/hdk-install +wget -O npu-driver/driver.zip https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Ascend%20HDK/Ascend%20HDK%2024.1.RC3/Ascend-hdk-910b-npu_24.1.rc3_linux-aarch64.zip + +if [ $? -ne 0 ]; then + echo "下载失败" + exit 1 +fi + +# 解压驱动 +cd npu-driver +unzip driver.zip +nested_zip=$(find . -name "*.zip" -not -path "./driver.zip" | head -n 1) +unzip -o "$nested_zip" -d "./hdk-install" + +# 安装驱动 +cd hdk-install +bash install.sh install all +if [ $? -ne 0 ]; then + echo "hdk驱动&固件安装失败" + exit 1 +fi + +echo "hdk驱动&固件安装成功!请重启机器生效" diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/example_config b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/example_config new file mode 100644 index 0000000..d96c038 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/example_config @@ -0,0 +1,19 @@ +#[容器镜像] +IMAGE_NAME=hub.oepkgs.net/oedeploy/openeuler/aarch64/mindspore +IMAGE_TAG=latest +#[推理服务所在容器的名称] +CONTAINER_NAME=openeuler_ds +#[模型路径] +MODEL_PATH=/workspace/deepseekv3 +#[ray开放的端口] +RAY_PORT=6399 +#[节点个数] +NODE_NUM=2 +#[拉起服务前是否停止其他容器] +IS_STOP_OTHER_CONTAINER=0 +#[推理服务端口] +LLM_PORT=8000 +#[ray集群使用的网卡] +RAY_DEVICE=enp67s0f0np0 +#[模型权重类型] +MODEL_TYPE=safetensors \ No newline at end of file diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/net_check.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/net_check.sh new file mode 100644 index 0000000..cca835c --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/net_check.sh @@ -0,0 +1,69 @@ +#!/bin/bash + +# 设置TLS +for i in {0..7}; do + hccn_tool -i $i -tls -s enable 0 +done + +# 检查TLS状态 +for i in {0..7}; do + hccn_tool -i $i -tls -g | grep switch +done + +# 检查链路状态 +for i in {0..7}; do + hccn_tool -i $i -link -g | grep -i 'link status: UP' + if [ $? -ne 0 ]; then + echo "节点npu设备 $i 检测link status不为UP" + exit 1 + fi +done + +# 检查网络健康状态 +for i in {0..7}; do + hccn_tool -i $i -net_health -g | grep -i 'Success' + if [ $? -ne 0 ]; then + echo "节点npu设备 $i 检测net_health不为Success" + fi +done + +# 检查IP信息 +for i in {0..7}; do + hccn_tool -i $i -ip -g +done + +# 添加机器卡间互联检查 +check_inter_device_connection() { + echo -e "${BLUE}请输入目标NPU卡的IP地址 (输入q退出检查):${NC}" + while true; do + read -p "IP地址: " target_ip + if [ "$target_ip" = "q" ]; then + break + fi + + if [[ ! $target_ip =~ ^[0-9]+\.[0-9]+\.[0-9]+\.[0-9]+$ ]]; then + echo -e "${RED}无效的IP地址格式，请重新输入${NC}" + continue + fi + + echo -e "\n${BLUE}正在检查与 $target_ip 的连接...${NC}" + for i in {0..7}; do + echo -e "\n本机设备 $i ping $target_ip:" + hccn_tool -i $i -ping -g address $target_ip + done + + echo -e "\n${BLUE}是否继续检查其他IP？(输入q退出，输入其他继续)${NC}" + read -p "选择: " choice + if [ "$choice" = "q" ]; then + break + fi + done +} + +main() { + if [ "$1" = "--check-connection" ]; then + check_inter_device_connection + fi +} + +main "$@" diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/npu_net_config_simple.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/npu_net_config_simple.sh new file mode 100644 index 0000000..d8acac3 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/npu_net_config_simple.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# 简易辅助脚本，仅配置npu的ip +# sh npu_set_config_simple.sh 1 + +net_config() { + if [ "$1" ]; then + for i in {0..7}; do + hccn_tool -i $i -ip -s address 10.10.0.$1$i netmask 255.255.0.0 + done + else + echo "请输入节点id,从1开始,会使用节点id设置对应的ip地址" + echo "npu会设置ip为10.10.0.{节点}{npu卡id},例如:" + echo "sh npu_set_config_simple.sh 1" + echo "该样例中节点id为1, 卡id为0-7" + echo "设置ip为10.10.0.10 ~ 10.10.0.17" + exit 1 + fi + # 检查IP信息 + for i in {0..7}; do + hccn_tool -i $i -ip -g + done + echo "npu卡ip配置完成" +} + +net_config "$@" diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/ray_start.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/ray_start.sh new file mode 100644 index 0000000..826eb12 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/ray_start.sh @@ -0,0 +1,32 @@ +#!/bin/bash +current_path=$( + cd $(dirname $0/) + pwd +) +ENV_FILE=/root/.bashrc +source $current_path/config.cfg +source $ENV_FILE +ray_start() { + ps -ef | grep "python" | grep -v grep | awk '{print $2}' | xargs kill + ray stop + + if [ "$1" ]; then + # 从节点 + nohup ray start --address=$1:$RAY_PORT & + else + # 主节点 + nohup ray start --head --include-dashboard=False --port=$RAY_PORT & + sleep 5 + for i in {1..10}; do + ray status | grep '8.0 NPU' + if [ $? -eq 0 ]; then + echo "主节点ray已启动" + break + fi + sleep 3 + done + fi + echo "ray 已在后台运行" +} + +ray_start "$@" diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/set_env.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/set_env.sh new file mode 100644 index 0000000..e43b877 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/set_env.sh @@ -0,0 +1,57 @@ +#!/bin/bash + +# 各个节点的容器内均执行 +current_path=$( + cd $(dirname $0/) + pwd +) +source $current_path/config.cfg + +ENV_ARG=' +# openeuler_deepseek_env_config +export ASCEND_CUSTOM_PATH=$ASCEND_HOME_PATH/../ +export MS_ENABLE_LCCL=off +export HCCL_OP_EXPANSION_MODE=AIV +export vLLM_MODEL_BACKEND=MindFormers +export vLLM_MODEL_MEMORY_USE_GB=53 +export MS_DEV_RUNTIME_CONF="parallel_dispatch_kernel:True" +export MS_ALLOC_CONF="enable_vmm:True" +export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +export ASCEND_TOTAL_MEMORY_GB=64 +export HCCL_CONNECT_TIMEOUT=7200 +export MS_COMPILER_CACHE_ENABLE=1 +' + +NET_ENV=" +export GLOO_SOCKET_IFNAME=$RAY_DEVICE +export TP_SOCKET_IFNAME=$RAY_DEVICE +export HCCL_SOCKET_IFNAME=$RAY_DEVICE +" + +if [ $NODE_NUM -eq 2 ]; then + YAML_FILE='/root/Python-3.11/lib/python3.11/site-packages/research/deepseek3/deepseek_r1_671b/predict_deepseek_r1_671b_w8a8.yaml' +elif [ $NODE_NUM -eq 4 ]; then + YAML_FILE='/root/Python-3.11/lib/python3.11/site-packages/research/deepseek3/deepseek_r1_671b/predict_deepseek_r1_671b.yaml' +fi + + +# 修改权重类型 +sed -e 's/^load_ckpt_format.*/load_ckpt_format: "'$MODEL_TYPE'"/' -i $YAML_FILE +if [ "$MODEL_TYPE" = "ckpt" ]; then + sed -e 's/^auto_trans_ckpt.*/auto_trans_ckpt: False/' -i $YAML_FILE +fi + +YAML_ENV="export MINDFORMERS_MODEL_CONFIG=$YAML_FILE" + +ENV_FILE=/root/.bashrc + +if grep -q "openeuler_deepseek_env_config" /root/.bashrc; then + echo "存在已配置的环境变量，详见容器内/root/.bashrc" + exit 0 +fi + +echo "$ENV_ARG" >> $ENV_FILE +echo "$NET_ENV" >> $ENV_FILE +echo "$YAML_ENV" >> $ENV_FILE +source $ENV_FILE + diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_docker.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_docker.sh new file mode 100644 index 0000000..5e97f7f --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_docker.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +# 各个节点均执行 +current_path=$( + cd $(dirname $0/) + pwd +) +source $current_path/config.cfg +# 安装docker +yum install docker -y + +# 检测镜像是否已被拉取 +docker images | grep $IMAGE_NAME | grep $IMAGE_TAG +if [ $? -ne 0 ]; then + docker pull $IMAGE_NAME:$IMAGE_TAG + if [ $? -ne 0 ]; then + echo "docker镜像拉取失败，请手动下载并加载" + exit 1 + fi +fi + +# 停止所有现存容器实例 +if [ $IS_STOP_OTHER_CONTAINER -ne 0 ]; then + docker stop $(docker ps -aq) +fi + +# 如果存在名称相同的容器，则直接使用 +docker ps -a | grep $IMAGE_NAME:$IMAGE_TAG | grep $CONTAINER_NAME +if [ $? -eq 0 ]; then + echo "发现容器 $CONTAINER_NAME 已存在，直接使用" + docker start $CONTAINER_NAME + exit 0 +fi + +# 如果存在名称相同，但镜像不同容器，则报错 +docker ps -a | grep $CONTAINER_NAME +if [ $? -eq 0 ]; then + echo "发现容器名称 $CONTAINER_NAME 已被使用，请排查" + exit 1 +fi + +# 拉起容器实例 +docker run -itd --privileged --hostname $(hostname) --name=$CONTAINER_NAME --net=host \ + --shm-size 500g \ + --device=/dev/davinci0 \ + --device=/dev/davinci1 \ + --device=/dev/davinci2 \ + --device=/dev/davinci3 \ + --device=/dev/davinci4 \ + --device=/dev/davinci5 \ + --device=/dev/davinci6 \ + --device=/dev/davinci7 \ + --device=/dev/davinci_manager \ + --device=/dev/hisi_hdc \ + --device /dev/devmm_svm \ + -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \ + -v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \ + -v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \ + -v /usr/local/sbin:/usr/local/sbin \ + -v /usr/sbin:/usr/sbin \ + -v /etc/hccn.conf:/etc/hccn.conf \ + -v /usr/bin/netstat:/usr/bin/netstat \ + -v /usr/bin/lscpu:/usr/bin/lscpu \ + -v $MODEL_PATH:$MODEL_PATH \ + --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ + $IMAGE_NAME:$IMAGE_TAG \ + bash diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_ds.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_ds.sh new file mode 100644 index 0000000..0a637ae --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/lib/start_ds.sh @@ -0,0 +1,53 @@ +#!/bin/bash +current_path=$( + cd $(dirname $0/) + pwd +) +ENV_FILE=/root/.bashrc +source $current_path/config.cfg +source $ENV_FILE +# 仅主节点运行 + +if [ $NODE_NUM -eq 2 ]; then + NPU_NUM=16.0 + PARALLEL=16 +elif [ $NODE_NUM -eq 4 ]; then + NPU_NUM=32.0 + PARALLEL=32 +fi + +ray_status=0 +for i in {1..10}; do + ray status | grep "$NPU_NUM NPU" + if [ $? -eq 0 ]; then + echo "ray集群已全部拉起" + ray_status=1 + break + fi + sleep 3 +done + +if [ $ray_status -eq 0 ]; then + echo "ray集群超时" + exit 1 +fi + +#拉起服务 +rm -rf ds.log +nohup python3 -m vllm_mindspore.entrypoints vllm.entrypoints.openai.api_server --model "$MODEL_PATH" --port=$LLM_PORT --trust_remote_code --tensor_parallel_size=$PARALLEL --max-num-seqs=192 --max_model_len=32768 --max-num-batched-tokens=16384 --block-size=32 --gpu-memory-utilization=0.93 --num-scheduler-steps=8 --distributed-executor-backend=ray &> ds.log & +#检测推理服务是否拉起 +llm_status=0 +for i in {1..7200}; do + netstat -ntlp | grep $LLM_PORT + if [ $? -eq 0 ]; then + echo "推理服务已拉起，端口$LLM_PORT已打开" + llm_status=1 + break + fi + sleep 1 +done + +if [ $llm_status -eq 0 ]; then + echo "推理服务拉起超时，请手动确认" + exit 1 +fi \ No newline at end of file diff --git a/script/mindspore-deepseek/workspace/roles/prepare/files/prepare.sh b/script/mindspore-deepseek/workspace/roles/prepare/files/prepare.sh new file mode 100644 index 0000000..c000112 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/files/prepare.sh @@ -0,0 +1,65 @@ +#!/bin/bash +current_path=$( + cd $(dirname $0/) + pwd +) +source $current_path/lib/config.cfg + +cp_into_container() { + docker cp $current_path/lib $CONTAINER_NAME:/workspace +} + +main() { + set -e + chmod -R +x $current_path/lib + + systemctl stop firewalld + systemctl stop iptables + + # 检查防火墙是否启动，如果启动则检查端口是否在防火墙白名单中，如果不存在则添加到白名单中 + status=$(systemctl status firewalld | grep -E "Active" | awk -F":" '{print $2}' | awk -F" " '{print $1}') + if [[ "${status}" == "active" ]]; then + # ray 端口防火墙检查 + port_ray=$(firewall-cmd --query-port=$RAY_PORT/tcp) + if [[ "${port_ray}" == "no" ]]; then + port_ray=$(firewall-cmd --zone=public --add-port=$RAY_PORT/tcp --permanent) + firewall-cmd --reload + fi + port_ray=$(firewall-cmd --query-port=$RAY_PORT/tcp) + if [[ "${port_ray}" != "yes" ]]; then + echo -e "防火墙开启 $RAY_PORT端口失败" + exit 1 + fi + port_llm=$(firewall-cmd --query-port=$LLM_PORT/tcp) + if [[ "${port_llm}" == "no" ]]; then + port_llm=$(firewall-cmd --zone=public --add-port=$LLM_PORT/tcp --permanent) + firewall-cmd --reload + fi + port_llm=$(firewall-cmd --query-port=$LLM_PORT/tcp) + if [[ "${port_llm}" != "yes" ]]; then + echo -e "防火墙开启 $LLM_PORT端口失败" + exit 1 + fi + fi + + # 检测需要部署的节点ip数量 + if [ [ $NODE_NUM -ne 2 ] && [ $NODE_NUM -ne 4 ] ]; then + echo "当前仅支持两/四节点部署,当前数量是$NODE_NUM" + exit 1 + fi + + # 1. 启动Docker容器并复制文件 + $current_path/lib/start_docker.sh + cp_into_container + + # 2. 执行组网检查 + $current_path/lib/net_check.sh + + #进入容器执行 + # 3. 设置容器内环境变量 + docker exec -it $CONTAINER_NAME /workspace/lib/set_env.sh + +} + +# 执行主函数 +main "$@" diff --git a/script/mindspore-deepseek/workspace/roles/prepare/tasks/main.yml b/script/mindspore-deepseek/workspace/roles/prepare/tasks/main.yml new file mode 100644 index 0000000..f31adb3 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/tasks/main.yml @@ -0,0 +1,34 @@ +- name: Set hostname + hostname: + name: "{{ inventory_hostname }}" + +- name: Set dir_path as a fact + set_fact: + dir_path: "/home/mindspore-deepseek" + +- name: Create directory + file: + path: "{{ dir_path }}" + state: directory + mode: '0644' + +- name: Copy prepare.sh to hosts + copy: + src: prepare.sh + dest: "{{ dir_path }}" + mode: '0755' + +- name: Copy lib to hosts + copy: + src: lib + dest: "{{ dir_path }}" + mode: '0644' + +- name: Copy config.cfg to hosts + template: + src: config.cfg.j2 + dest: "{{ dir_path }}/lib/config.cfg" + mode: '0644' + +- name: Execute prepare.sh + shell: sh {{ dir_path }}/prepare.sh diff --git a/script/mindspore-deepseek/workspace/roles/prepare/templates/config.cfg.j2 b/script/mindspore-deepseek/workspace/roles/prepare/templates/config.cfg.j2 new file mode 100644 index 0000000..de9cb18 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/prepare/templates/config.cfg.j2 @@ -0,0 +1,19 @@ +#[容器镜像] +IMAGE_NAME={{ image_name }} +IMAGE_TAG={{ image_tag }} +#[推理服务所在容器的名称] +CONTAINER_NAME={{ container_name }} +#[模型路径] +MODEL_PATH={{ model_path }} +#[ray开放的端口] +RAY_PORT={{ ray_port }} +#[节点个数] +NODE_NUM={{ node_num }} +#[拉起服务前是否停止其他容器] +IS_STOP_OTHER_CONTAINER={{ is_stop_other_container }} +#[推理服务端口] +LLM_PORT={{ llm_port }} +#[ray集群使用的网卡] +RAY_DEVICE={{ ray_device }} +#[模型权重类型] +MODEL_TYPE={{ model_type }} \ No newline at end of file diff --git a/script/mindspore-deepseek/workspace/roles/start/deepseek/tasks/main.yml b/script/mindspore-deepseek/workspace/roles/start/deepseek/tasks/main.yml new file mode 100644 index 0000000..c1a5fee --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/start/deepseek/tasks/main.yml @@ -0,0 +1,2 @@ +- name: Start DeepSeek on master + shell: docker exec {{ container_name }} /workspace/lib/start_ds.sh diff --git a/script/mindspore-deepseek/workspace/roles/start/ray-master/tasks/main.yml b/script/mindspore-deepseek/workspace/roles/start/ray-master/tasks/main.yml new file mode 100644 index 0000000..9fbb45f --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/start/ray-master/tasks/main.yml @@ -0,0 +1,2 @@ +- name: Start Ray on master + shell: docker exec {{ container_name }} /workspace/lib/ray_start.sh diff --git a/script/mindspore-deepseek/workspace/roles/start/ray-worker/tasks/main.yml b/script/mindspore-deepseek/workspace/roles/start/ray-worker/tasks/main.yml new file mode 100644 index 0000000..a002379 --- /dev/null +++ b/script/mindspore-deepseek/workspace/roles/start/ray-worker/tasks/main.yml @@ -0,0 +1,2 @@ +- name: Start Ray on worker + shell: docker exec {{ container_name }} /workspace/lib/ray_start.sh {{ hostvars[groups['masters'][0]].ansible_host }} diff --git a/tool/benchmark/README.md b/tool/benchmark/README.md new file mode 100644 index 0000000..d294d2b --- /dev/null +++ b/tool/benchmark/README.md @@ -0,0 +1,91 @@ +# benchmark性能测试脚本 + +本测试脚本旨在对在线推理服务进行性能评估。 + +### 测试参数 + +可以通过以下命令查看benchmark测试参数 + +```shell +# python benchmark_parallel.py -h +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. +usage: benchmark_parallel.py [-h] [--backend {vllm,mindspore,base,tgi,openai,trt,embedding,openai-chat}] [--host HOST] + [--port PORT] [--url URL] [--app-code APP_CODE] --tokenizer TOKENIZER [--best-of BEST_OF] + [--use-beam-search] [--request-rate REQUEST_RATE] [--epochs EPOCHS] + [--parallel-num PARALLEL_NUM [PARALLEL_NUM ...]] + [--output-tokens OUTPUT_TOKENS [OUTPUT_TOKENS ...]] + [--prompt-tokens PROMPT_TOKENS [PROMPT_TOKENS ...]] [--benchmark-csv BENCHMARK_CSV] + [--seed SEED] [--served-model-name SERVED_MODEL_NAME] + [--num-scheduler-steps NUM_SCHEDULER_STEPS] [--enable-prefix-caching ENABLE_PREFIX_CACHING] + [--prefix-caching-num PREFIX_CACHING_NUM] [--use-spec-decode] + [--num-speculative-tokens NUM_SPECULATIVE_TOKENS] [--dataset-type {random,sharegpt,human-eval}] + [--dataset-path DATASET_PATH] [--use-real-dataset-output-tokens] + [--use-pd-separate USE_PD_SEPARATE] + +Benchmark the serving prefill performance. + +options: + -h, --help show this help message and exit + --backend {vllm,mindspore,base,tgi,openai,trt,embedding,openai-chat} + --host HOST + --port PORT + --url URL + --app-code APP_CODE + --tokenizer TOKENIZER + Name or path of the tokenizer. + --best-of BEST_OF Generates `best_of` sequences per prompt and returns the best one. + --use-beam-search + --request-rate REQUEST_RATE + Number of requests per second. If this is inf, then all the requests are sent at time 0. Otherwise, + we use Poisson process to synthesize the request arrival times. + --epochs EPOCHS Number of epochs. + --parallel-num PARALLEL_NUM [PARALLEL_NUM ...] + Number of parallel request number. + --output-tokens OUTPUT_TOKENS [OUTPUT_TOKENS ...] + Max tokens to process. + --prompt-tokens PROMPT_TOKENS [PROMPT_TOKENS ...] + Max tokens to process. + --benchmark-csv BENCHMARK_CSV + Path to the csv. + --seed SEED + --served-model-name SERVED_MODEL_NAME + --num-scheduler-steps NUM_SCHEDULER_STEPS + --enable-prefix-caching ENABLE_PREFIX_CACHING + --prefix-caching-num PREFIX_CACHING_NUM + --use-spec-decode + --num-speculative-tokens NUM_SPECULATIVE_TOKENS + the step if spec decode, default -1 for disable accept rate statistic. + --dataset-type {random,sharegpt,human-eval} + --dataset-path DATASET_PATH + --use-real-dataset-output-tokens + --use-pd-separate USE_PD_SEPARATE + +``` + +### 多batch测试 + +可以参考以下命令进行192-batch多并发测试 + +```bash +python benchmark_parallel.py --backend openai --host [主服务IP] --port [推理接口] --tokenizer [权重路径] --num-scheduler-steps=8 --epochs 1 --parallel-num 192 --prompt-tokens 256 --output-tokens 256 +``` + +最终输出如下： + +```shell +2025-03-19 08:25:32,725 - benchmark_utils - INFO - 所有请求耗时: 13.5293 s +2025-03-19 08:25:32,726 - benchmark_utils - INFO - 请求吞吐: 14.1914 requests/s +2025-03-19 08:25:32,726 - benchmark_utils - INFO - 输出tokens总吞吐: 3633.0039 tokens/s +2025-03-19 08:25:32,728 - benchmark_utils - INFO - 首tokens时延TP90: 7958.1101 ms +2025-03-19 08:25:32,729 - benchmark_utils - INFO - 首tokens时延TP99: 7964.8968 ms +2025-03-19 08:25:32,729 - benchmark_utils - INFO - 最大首tokens时延: 7966.0322 ms +2025-03-19 08:25:32,729 - benchmark_utils - INFO - 平均首tokens时延: 5049.2075 ms +2025-03-19 08:25:32,740 - benchmark_utils - INFO - 增量时延TP90: 159.5743 ms +2025-03-19 08:25:32,744 - benchmark_utils - INFO - 增量时延TP99: 175.7760 ms +2025-03-19 08:25:32,746 - benchmark_utils - INFO - 最大增量时延: 207.5 ms +2025-03-19 08:25:32,749 - benchmark_utils - INFO - 平均增量时延: 20.8 ms +2025-03-19 08:25:42,751 - benchmark_utils - INFO - ['输入长度', '输出长度', '并发数', '输出tokens总吞吐', '首tokens时延TP90（ms）', '首tokens时延TP99（ms）', '最大首tokens时延（ms）', '平均首tokens时延（ms）', '增量时延TP90（ms）', '增量时延TP99（ms）', '最大增量时延（ms）', '平均增量时延（ms）'] +2025-03-19 08:25:42,753 - benchmark_utils - INFO - [256.0, 256.0, 192, 3633.0039, 7958.1101, 7964.8968, 7966.0322, 5049.2075, 159.5743, 175.776, 207.5029, 20.7627] +2025-03-19 08:25:42,753 - __main__ - INFO - Benchmark parallel finished +``` + diff --git a/tool/benchmark/benchmark_parallel.py b/tool/benchmark/benchmark_parallel.py new file mode 100644 index 0000000..7eea809 --- /dev/null +++ b/tool/benchmark/benchmark_parallel.py @@ -0,0 +1,443 @@ +import argparse +import asyncio +import json +import os +import random +import logging +from typing import AsyncGenerator, List, Tuple, Optional + +import numpy as np +from tqdm import tqdm +from transformers import PreTrainedTokenizerBase + +from benchmark_utils import (generate_str, get_tokenizer, get_api_url, + get_request_data, do_request, + save_to_csv, statistics_and_print_performance_data, + generate_hello_str, check_multi_step) + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +CACHE_DATASET = None + + +def sample_random_requests( + tokenizer: PreTrainedTokenizerBase, + prompt_tokens: int, + output_tokens: int, + enable_prefix_caching: bool = False, + prefix_caching_num: int = 0 +) -> List[Tuple[str, int, int]]: + prompt = generate_str(tokenizer, prompt_tokens) + requests_list=[(prompt, prompt_tokens, output_tokens)] + if enable_prefix_caching and prefix_caching_num: + # construct prompt with the same prefix. + caching_prompt = generate_hello_str(tokenizer, prefix_caching_num) + generate_str(tokenizer, + prompt_tokens - prefix_caching_num) + requests_list.append((caching_prompt, prompt_tokens, output_tokens)) + return requests_list + + +# just copy from vllm. +def sample_sharegpt_requests( + dataset_path, + requests_num, + tokenizer: PreTrainedTokenizerBase, + modified_output_len: Optional[int] = None, +) -> List[Tuple[str, int, int]]: + if modified_output_len is not None and modified_output_len < 4: + raise ValueError("output_len too small") + # Load the dataset. + global CACHE_DATASET + if CACHE_DATASET is None: + with open(dataset_path) as f: + dataset = json.load(f) + # Filter out the conversations with less than 2 turns. + # Only keep the first two turns of each conversation. + dataset = [subset for subset in dataset if len(subset["conversations"]) >= 2] + dataset = [(subset["conversations"][0]["value"], + subset["conversations"][1]["value"]) for subset in dataset] + + # Shuffle the dataset. + random.shuffle(dataset) + + CACHE_DATASET = dataset + else: + dataset = CACHE_DATASET + + # Filter out sequences that are too long or too short + filtered_dataset: List[Tuple[str, int, int]] = [] + for i in range(len(dataset)): + if len(filtered_dataset) == requests_num: + break + + # Tokenize the prompts and completions. + prompt = dataset[i][0] + prompt_token_ids = tokenizer(prompt).input_ids + output = dataset[i][1] + output_token_ids = tokenizer(output).input_ids + prompt_len = len(prompt_token_ids) + output_len = len(output_token_ids + ) if modified_output_len is None else modified_output_len + if prompt_len < 4 or output_len < 4: + # Filter sequences that are too short. + continue + if prompt_len > 1024 or prompt_len + output_len > 2048: + # Filter sequences that are too long. + continue + filtered_dataset.append((prompt, prompt_len, output_len)) + + return filtered_dataset + + +def sample_human_eval_requests( + dataset_path: str, + requests_num: int, + tokenizer: PreTrainedTokenizerBase, + modified_output_len: Optional[int] = None, +): + # Load the dataset. + global CACHE_DATASET + if CACHE_DATASET is None: + with open(dataset_path, encoding='utf-8') as f: + dataset = [json.loads(line) for line in f] + # Filter out the conversations with less than 2 turns. + dataset = [(data["prompt"], data["canonical_solution"]) for data in dataset] + + # Shuffle the dataset. + + random.shuffle(dataset) + CACHE_DATASET = dataset + else: + dataset = CACHE_DATASET + + # Filter out sequences that are too long or too short + filtered_dataset: List[Tuple[str, int, int]] = [] + for i in range(len(dataset)): + if len(filtered_dataset) == requests_num: + break + + # Tokenize the prompts and completions. + prompt = dataset[i][0] + prompt_token_ids = tokenizer(prompt).input_ids + output = dataset[i][1] + output_token_ids = tokenizer(output).input_ids + prompt_len = len(prompt_token_ids) + output_len = len(output_token_ids + ) if modified_output_len is None else modified_output_len + if prompt_len == 0 or output_len == 0: + continue + + filtered_dataset.append((prompt, prompt_len, output_len)) + + return filtered_dataset + + +async def get_request( + input_requests, + req_rate +) -> AsyncGenerator[Tuple[str, int, int], None]: + input_requests = iter(input_requests) + for req in input_requests: + yield req + + if req_rate == float("inf"): + # If the request rate is infinite, then we don't need to wait. + continue + # Sample the request interval from the exponential distribution. + interval = np.random.exponential(1.0 / req_rate) + # The next request will be sent after the interval. + await asyncio.sleep(interval) + + +async def send_request( + request_latency_record: List, + backend: str, + api_url: str, + prompt: str, + prompt_len: int, + output_len: int, + best_of: int, + use_beam_search: bool, + app_code: str = None, + model: str = None, + served_model_name: str = None, + num_scheduler_steps: int = 1, + use_spec_decode: bool = False +) -> None: + headers, pload, confirm_error_output = get_request_data(backend, + prompt, + prompt_len, + output_len, + best_of, + use_beam_search, + app_code, + model, + served_model_name, + use_spec_decode) + + time_record, chunk_record = await do_request(api_url, headers, pload, confirm_error_output, + output_len, num_scheduler_steps, use_spec_decode) + + output_tokens = len(time_record) - 1 + + # output_tokens will smaller than output_len when use spec decode, fix it in update_spec_output_tokens function. + if not use_spec_decode and output_tokens < output_len: + logger.error(f"output_tokens: %d < output_len: %d", output_tokens, output_len) + + request_latency_record.append((prompt_len, output_len, time_record, chunk_record)) + + +async def benchmark( + request_latency_record: List, + backend: str, + api_url: str, + input_requests: List[Tuple[str, int, int]], + best_of: int, + use_beam_search: bool, + request_rate: float, + parallel_num: int, + epochs: int, + app_code: str = None, + model: str = None, + served_model_name: str = None, + num_scheduler_steps: int = 1, + use_spec_decode: bool = False +) -> None: + input_index = 0 + for ep in tqdm(range(epochs), desc="epoch"): + input_parallel = [] + for id in range(parallel_num): + input_parallel.append(input_requests[input_index]) + input_index += 1 + if input_index >= len(input_requests): + input_index = 0 + + tasks: List[asyncio.Task] = [] + async for request in get_request(input_parallel, request_rate): + prompt, prompt_len, output_len = request + task = asyncio.create_task(send_request(request_latency_record, + backend, api_url, prompt, + prompt_len, output_len, + best_of, use_beam_search, + app_code, + model, + served_model_name, + num_scheduler_steps, + use_spec_decode)) + tasks.append(task) + await asyncio.gather(*tasks) + + +def group_get_output_tokens_per_step(output_list, tokenizer): + token_list = tokenizer.batch_encode_plus(output_list, add_special_tokens=False, return_attention_mask=False)[ + "input_ids"] + token_len_list = [0] + [len(token) for token in token_list] + # use max(end - start, 1) to ensure at lest 1 output token (when the model output eos token, the client will + # receive empty str). + return [max(end - start, 1) for (end, start) in zip(token_len_list[1:], token_len_list[:-1])] + + +def update_spec_output_tokens(request_latency_record, tokenizer, backend): + def decode_openai_output(chunk_list): + output_list = [] + cur_output = "" + for chunk in chunk_list: + if chunk.startswith(b'data:'): + output = chunk[5:].strip().decode("utf-8") + else: + output = chunk.strip().strip().decode("utf-8").rstrip("\0") + + if output != '[DONE]': + output = json.loads(output) + output = output["choices"][0]["text"] + cur_output += output + output_list.append(cur_output) + + return output_list + + def decode_vllm_output(chunk_list): + output_list = [] + for chunk in chunk_list: + output = chunk.strip().strip().decode("utf-8").rstrip("\0") + output = json.loads(output)["text"][0] + output_list.append(output) + + return output_list + + if backend == "openai": + request_output_list = [decode_openai_output(chunk_list) for _, _, _, chunk_list in request_latency_record] + elif backend == "vllm": + request_output_list = [decode_vllm_output(chunk_list) for _, _, _, chunk_list in request_latency_record] + else: + logger.warning( + f"Backend {backend} is not supported in spec decode benchmark, it might return the incorrect results.") + return + + request_output_lens_list = [group_get_output_tokens_per_step(output_list, tokenizer) for output_list in + request_output_list] + + for req_index in range(len(request_latency_record)): + (prompt_len, output_len, time_record, chunk_record) = request_latency_record[req_index] + output_lens_per_step = request_output_lens_list[req_index] + new_time_record = [time_record[0]] + for o_len, timestamp in zip(output_lens_per_step, time_record[1:]): + new_time_record.extend([timestamp] * o_len) + + output_tokens = len(new_time_record) - 1 + if len(new_time_record) - 1 < output_len: + logger.warning(f"output_tokens: %d < output_len: %d, maybe caused by the difference between the number of " + f"tokens re-encoded by the tokenizer and the number of original tokens.", output_tokens, output_len) + + # replace chunk_record to output_step for acceptance rate of speculative decode. + request_latency_record[req_index] = (prompt_len, output_len, new_time_record, len(output_lens_per_step)) + + +def get_dataset_requests(args, tokenizer, prompt_tokens, output_tokens, parallel_num): + if args.dataset_type == "random": + logger.info( + f"Benchmark running with parallel_num: %d, " + f"prompt_tokens: %d, output_tokens: %d", + parallel_num, prompt_tokens, output_tokens + ) + return sample_random_requests(tokenizer, prompt_tokens, output_tokens, + args.enable_prefix_caching, args.prefix_caching_num) + else: + if not os.path.exists(args.dataset_path): + raise ValueError(f"Dataset path {args.dataset_path} is not existed.") + + logger.info( + f"Benchmark running with parallel_num: %d, " + f"dataset: %s, output_tokens: %s", + parallel_num, + args.dataset_type, "real" if args.use_real_dataset_output_tokens else str(output_tokens) + ) + + if args.dataset_type == "sharegpt": + return sample_sharegpt_requests(args.dataset_path, parallel_num * args.epochs, tokenizer, + None if args.use_real_dataset_output_tokens else output_tokens) + elif args.dataset_type == "human-eval": + return sample_human_eval_requests(args.dataset_path, parallel_num * args.epochs, + tokenizer, + None if args.use_real_dataset_output_tokens else output_tokens) + else: + raise ValueError("Unsupport dataset.") + + +def main(args: argparse.Namespace): + logger.info(args) + random.seed(args.seed) + np.random.seed(args.seed) + + if args.enable_prefix_caching and not args.prefix_caching_num: + logger.error("The prefix_caching_num parameter must be seted for prefix_caching.") + return + + api_url = get_api_url(args.backend, args.host, args.port, args.url) + tokenizer = get_tokenizer(args.tokenizer) + + # if not args.use_spec_decode: + # is_multi_step = check_multi_step(args, api_url, tokenizer, args.prompt_tokens[0], args.output_tokens[0]) + # if not is_multi_step: + # logger.error("The service does not use multi_step or num-scheduler-steps is different from the service.") + # return + # + # logger.info(f"Warmup ...") + # input_requests = sample_random_requests(tokenizer, args.prompt_tokens[0], args.output_tokens[0]) + # asyncio.run( + # benchmark([], args.backend, api_url, input_requests, args.best_of, + # args.use_beam_search, args.request_rate, 4, 1, args.app_code, args.tokenizer, + # args.served_model_name, args.num_scheduler_steps, args.use_spec_decode) + # ) + + if args.dataset_type != "random": + logging.info("When use sharegpt or human-eval dataset, the number of the --input-tokens will be ignored.") + if args.use_real_dataset_output_tokens: + logging.info("When use --use-real-dataset-output-tokens, the number of the --output-tokens will be ignored.") + if args.use_spec_decode and args.num_speculative_tokens >= 0: + logging.info("When enable --use-spec-decode and --num-speculative-tokens >= 0, the acceptance rate of " + "speculative inference will be collected. Ensure that --num-speculative-tokens in the benchmark is " + "equal to --num-speculative-tokens in the vllm service.") + + all_latency_record = [] + for parallel_num in args.parallel_num: + for i, prompt_tokens in enumerate(args.prompt_tokens): + output_tokens = args.output_tokens[i] + + input_requests = get_dataset_requests(args, tokenizer, prompt_tokens, output_tokens, parallel_num) + + request_latency_record: List[Tuple[int, int, List]] = [] + asyncio.run(benchmark(request_latency_record, args.backend, api_url, input_requests, args.best_of, + args.use_beam_search, args.request_rate, parallel_num, + args.epochs, args.app_code, args.tokenizer, + args.served_model_name, args.num_scheduler_steps, args.use_spec_decode)) + if args.use_spec_decode: + update_spec_output_tokens(request_latency_record, tokenizer, args.backend) + + statistics_and_print_performance_data(args, prompt_tokens, output_tokens, parallel_num, + request_latency_record, all_latency_record) + + if args.dataset_type == "random": + all_latency_record.sort() + else: + # the mean input tokens and output tokens of real dataset will be a little diffferent in defferent batch_size, + # so just sort by batch_size + all_latency_record.sort(key=lambda x: x[2]) + + + benchmark_head = ["输入长度", "输出长度", "并发数", + "输出tokens总吞吐", + "首tokens时延TP90（ms）", "首tokens时延TP99（ms）", "最大首tokens时延（ms）", "平均首tokens时延（ms）", + "增量时延TP90（ms）", "增量时延TP99（ms）", "最大增量时延（ms）", "平均增量时延（ms）"] + + if args.use_spec_decode and args.num_speculative_tokens >= 0 and len(all_latency_record[-1]) != len(benchmark_head): + accept_head = ["投机接受率TP90", "投机接受率TP99", "投机最大接受率", "投机最小接受率", "投机平均接受率"] + benchmark_head = benchmark_head + accept_head + + save_to_csv(benchmark_head, all_latency_record, args.benchmark_csv) + + logger.info("Benchmark parallel finished") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Benchmark the serving prefill performance.") + parser.add_argument("--backend", type=str, default="mindspore", + choices=["vllm", "mindspore", "base", "tgi", "openai", "trt"]) + parser.add_argument("--host", type=str, default="localhost") + parser.add_argument("--port", type=int, default=9288) + parser.add_argument("--url", type=str, default="") + parser.add_argument("--app-code", type=str, default=None) + parser.add_argument("--tokenizer", type=str, required=True, + help="Name or path of the tokenizer.") + parser.add_argument("--best-of", type=int, default=1, + help="Generates `best_of` sequences per prompt and " + "returns the best one.") + parser.add_argument("--use-beam-search", action="store_true") + parser.add_argument("--request-rate", type=float, default=float("inf"), + help="Number of requests per second. If this is inf, " + "then all the requests are sent at time 0. " + "Otherwise, we use Poisson process to synthesize " + "the request arrival times.") + parser.add_argument("--epochs", type=int, default=10, + help="Number of epochs.") + parser.add_argument("--parallel-num", nargs='+', type=int, default=[1, 4, 8], + help="Number of parallel request number.") + parser.add_argument("--output-tokens", nargs='+', type=int, default=[256, 256, 256], + help="Max tokens to process.") + parser.add_argument("--prompt-tokens", nargs='+', type=int, default=[512, 1024, 2048], + help="Max tokens to process.") + parser.add_argument("--benchmark-csv", type=str, default="benchmark_parallel.csv", + help="Path to the csv.") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--served-model-name", type=str, default=None) + parser.add_argument("--num-scheduler-steps", type=int, default=1) + parser.add_argument("--enable-prefix-caching", type=bool, default=False) + parser.add_argument("--prefix-caching-num", type=int, default=0) + parser.add_argument("--use-spec-decode", action="store_true") + parser.add_argument("--num-speculative-tokens", type=int, default=-1, help="the step if spec decode, default -1 for disable accept rate statistic.") + parser.add_argument("--dataset-type", default="random", choices=["random", "sharegpt", "human-eval"]) + parser.add_argument("--dataset-path", default="") + parser.add_argument("--use-real-dataset-output-tokens", action="store_true") + args_global = parser.parse_args() + main(args_global) diff --git a/tool/benchmark/benchmark_utils.py b/tool/benchmark/benchmark_utils.py new file mode 100644 index 0000000..982314e --- /dev/null +++ b/tool/benchmark/benchmark_utils.py @@ -0,0 +1,466 @@ +import asyncio +import csv +import json +import logging +import os +import stat +import time +from typing import Union + +import aiohttp +import numpy as np +import requests +from tqdm import tqdm +from transformers import (AutoTokenizer, PreTrainedTokenizer, + PreTrainedTokenizerFast) + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) +IS_DEBUG = int(os.environ.get("BENCKMARK_DEBUG", 0)) +EPOCH_NUM = 10 +# The size of the data block returned in each iteration is not greater than 8192. Therefore, chunk_size is 8192. +CHUNK_SIZE = 8192 +TIMEOUT = 3 * 3600 + +if IS_DEBUG: + import matplotlib.lines as mlines + import matplotlib.pyplot as plt + + +def get_tokenizer( + transformer_tokenizer_path: str, +) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]: + """Gets a tokenizer for the given model name via Huggingface.""" + tokenizer = AutoTokenizer.from_pretrained(transformer_tokenizer_path, trust_remote_code=True) + + return tokenizer + + +def generate_hello_str(tokenizer, length, hello_token="Hello"): + text = hello_token * (length - 1) + completion_token_ids = tokenizer([text]).input_ids + while len(completion_token_ids[0]) < length: + text += hello_token + completion_token_ids = tokenizer([text]).input_ids + + return text + + +def generate_str(tokenizer, length): + vocab_size = tokenizer.vocab_size + ids = np.random.randint(vocab_size / 4, vocab_size / 3, length) + text = tokenizer.decode(ids) + + completion_token_ids = tokenizer([text]).input_ids + if IS_DEBUG: + logger.info("len(completion_token_ids[0]) %d, length %d ", len(completion_token_ids[0]), length) + + epoch = EPOCH_NUM + while len(completion_token_ids[0]) != length and epoch > 0: + while len(completion_token_ids[0]) > length: + diff = len(completion_token_ids[0]) - length + now_length = ids.shape[0] - diff + ids = ids[:now_length] + text = tokenizer.decode(ids) + completion_token_ids = tokenizer([text]).input_ids + if IS_DEBUG: + logger.info("len(completion_token_ids[0]) %d, %d ", len(completion_token_ids[0]), length) + + while len(completion_token_ids[0]) < length: + diff = length - len(completion_token_ids[0]) + diff_ids = np.random.randint(vocab_size / 4, vocab_size / 3, diff) + ids = np.append(ids, diff_ids) + text = tokenizer.decode(ids) + completion_token_ids = tokenizer([text]).input_ids + if IS_DEBUG: + logger.info("len(completion_token_ids[0]) %d, %d ", len(completion_token_ids[0]), length) + + epoch -= 1 + + if len(completion_token_ids[0]) != length: + text = generate_hello_str(tokenizer, length) + + if IS_DEBUG: + logger.info(text) + return text + + +def get_api_url(backend, host, port, url): + if url is not None and len(url) > 0: + return url + + if backend == "mindspore": + api_url = f"http://{host}:{port}/models/llama2/generate" + elif backend == "base": + api_url = f"http://{host}:{port}/v1/generate" + elif backend == "tgi": + api_url = f"https://{host}:{port}/generate_stream" + elif backend == "openai": + api_url = f"http://{host}:{port}/v1/completions" + elif backend == "trt": + api_url = f"http://{host}:{port}/v2/models/ensemble/generate_stream" + else: + api_url = f"http://{host}:{port}/generate" + return api_url + + +def get_request_data( + backend: str, + prompt: str, + prompt_len: int, + output_len: int, + best_of: int, + use_beam_search: bool, + app_code: str = None, + model: str = None, + served_model_name: str = None, + use_spec_decode: bool = False +): + confirm_error_output = False + + if app_code is not None and len(app_code) > 0: + headers = {"User-Agent": "Benchmark Client", + 'Content-Type': 'application/json', + 'X-Apig-AppCode': app_code} + else: + headers = {"User-Agent": "Benchmark Client"} + + if backend == "vllm": + pload = { + "prompt": prompt, + "temperature": 0.0, + "top_p": 0.8, + "top_k": 5, + "max_tokens": output_len, + "ignore_eos": True, + "stream": True, + } + if use_spec_decode: + pload["skip_special_tokens"] = False + confirm_error_output = True + elif backend == "openai": + if served_model_name is None: + served_name = model + else: + served_name = served_model_name + pload = { + "prompt": prompt, + "temperature": 0, + "top_p": 0.8, + "top_k": 5, + "max_tokens": output_len, + "ignore_eos": True, + "model": served_name, + "stream": True, + } + if use_spec_decode: + pload["skip_special_tokens"] = False + confirm_error_output = True + elif backend == "mindspore": + params = { + "max_new_tokens": output_len, + "do_sample": False, + "ignore_eos": True, + "return_full_text": False + } + pload = { + "inputs": prompt, + "parameters": params, + "stream": True + } + elif backend == "base": + pload = { + "prompt": prompt, + "max_tokens": (prompt_len + output_len), + "model_name": "llama2", + "do_sample": False, + "stream": True, + "debug": 2 + } + elif backend == "tgi": + params = { + "best_of": best_of, + "max_new_tokens": output_len, + "do_sample": False, + "ignore_eos_token": True, + "decoder_input_details": False + } + pload = { + "inputs": prompt, + "parameters": params, + } + confirm_error_output = True + elif backend == "trt": + headers = {"Content-Type": "text/event-stream; charset=utf-8"} + params = { + "max_tokens": output_len, + "min_length": output_len, + "bad_words": "", + "stop_words": "", + "ignore_eos": True, + "stream": True + } + pload = { + "text_input": prompt, + "parameters": params, + } + confirm_error_output = True + else: + raise ValueError(f"Unknown backend: {backend}") + + return headers, pload, confirm_error_output + + +async def do_request(api_url, headers, pload, confirm_error_output, output_len, num_scheduler_steps, + use_spec_decode=False): + timeout = aiohttp.ClientTimeout(total=TIMEOUT) + first_token = True + async with aiohttp.ClientSession(timeout=timeout, connector=aiohttp.TCPConnector(ssl=False)) as session: + while True: + last_chunk = None + prefill_start_time = time.perf_counter() + time_record = [prefill_start_time] + chunk_record = [] + async with session.post(api_url, headers=headers, json=pload) as response: + async for chunk, _ in response.content.iter_chunks(): + if len(chunk.strip()) > 0: + last_chunk = chunk + return_token_num = 1 if first_token else min(num_scheduler_steps, output_len) + time_record.extend([time.perf_counter()] * return_token_num) + first_token = False + output_len -= return_token_num + # for maximum block depth + if len(chunk.strip()) > 0 and use_spec_decode: + chunk_record.append(last_chunk) + + if confirm_error_output: + if last_chunk.startswith(b'data:'): + output = last_chunk[5:].strip().decode("utf-8") + else: + output = last_chunk.strip().strip().decode("utf-8").rstrip("\0") + + if IS_DEBUG: + logger.info(output) + if output == '[DONE]': + break + try: + output = json.loads(output) + except Exception: + logger.error("Exception") + break + + # Re-send the request if it failed. + if "error" not in output: + break + else: + logger.error("request failed, %s, retry", output) + await asyncio.sleep(0.1) + else: + break + return time_record, chunk_record + + +def check_multi_step(args, api_url, tokenizer, prompt_len, output_len): + prompt = generate_str(tokenizer, prompt_len) + headers, pload, confirm_error_output = get_request_data(args.backend, + prompt, + prompt_len, + output_len, + args.best_of, + args.use_beam_search, + args.app_code, + args.tokenizer, + args.served_model_name) + return_num = 0 + response = requests.post(api_url, headers=headers, json=pload, stream=True, timeout=TIMEOUT) + response.raise_for_status() + for chunk in response.iter_content(chunk_size=CHUNK_SIZE): + return_num += 1 + + # openai流式最后返回[done],其余接口没有 + end_chunk_num = 1 if args.backend == 'openai' else 0 + # 首token + 是否有返回[done] + chunk_num + theory_return_num = 1 + end_chunk_num + (output_len - 1) // args.num_scheduler_steps + # (output_len - 1) % num_scheduler_steps 非整除情况下最后返回包含剩余token的chunk + theory_return_num += 1 if (output_len - 1) % args.num_scheduler_steps else 0 + if theory_return_num == return_num: + return True + return False + + +def statistics_and_print_performance_data(args, prompt_tokens, output_tokens, parallel_num, + request_latency_record, all_latency_record): + benchmark_start_time = np.min([time_record[0] for _, _, time_record, _ in request_latency_record]) + benchmark_end_time = np.max([time_record[-1] for _, _, time_record, _ in request_latency_record]) + benchmark_time = benchmark_end_time - benchmark_start_time + logger.info("所有请求耗时: %.4f s", benchmark_time) + + benchmark_requests = args.epochs * parallel_num / benchmark_time + logger.info("请求吞吐: %.4f requests/s", benchmark_requests) + + total_output_tokens = np.sum([ + output_len + for _, output_len, _, _ in request_latency_record + ]) + total_output_token_throughput = total_output_tokens / benchmark_time + logger.info("输出tokens总吞吐: %.4f tokens/s", total_output_token_throughput) + + prefill_latency_list = [ + time_record[1] - time_record[0] + for _, _, time_record, _ in request_latency_record + ] + + p90_prefill_latency = np.percentile(prefill_latency_list, 90) * 1000 + logger.info("首tokens时延TP90: %.4f ms", p90_prefill_latency) + + p99_prefill_latency = np.percentile(prefill_latency_list, 99) * 1000 + logger.info("首tokens时延TP99: %.4f ms", p99_prefill_latency) + + max_prefill_latency = np.max(prefill_latency_list) * 1000 + logger.info("最大首tokens时延: %.4f ms", max_prefill_latency) + + avg_prefill_latency = np.mean(prefill_latency_list) * 1000 + logger.info("平均首tokens时延: %.4f ms", avg_prefill_latency) + + prefill_ranges = [ + (time_record[0], time_record[1]) + for _, _, time_record, _ in request_latency_record + ] + + def in_ranges(ranges, start, end): + for time_range in ranges: + if start <= time_range[1] and end >= time_range[0]: + return True + return False + + decode_latency_list = [ + end - start + for _, _, time_record, _ in request_latency_record + for start, end in zip(time_record[1:-1], time_record[2:]) + if not in_ranges(prefill_ranges, start, end) + ] + + # 去掉开始的0（multi-step场景） + start_index = 0 + for latency_index, latency in enumerate(decode_latency_list): + if latency > 0: + start_index = latency_index + break + decode_latency_list = decode_latency_list[start_index:] + + p90_decode_latency = np.percentile(decode_latency_list, 90) * 1000 + logger.info("增量时延TP90: %.4f ms", p90_decode_latency) + + p99_decode_latency = np.percentile(decode_latency_list, 99) * 1000 + logger.info("增量时延TP99: %.4f ms", p99_decode_latency) + + max_decode_latency = np.max(decode_latency_list) * 1000 + logger.info("最大增量时延: %.1f ms", max_decode_latency) + + avg_decode_latency = np.mean(decode_latency_list) * 1000 + logger.info("平均增量时延: %.1f ms", avg_decode_latency) + + if IS_DEBUG: + plot_time_record(benchmark_start_time, benchmark_time, request_latency_record, + f"{parallel_num}_{prompt_tokens}_{output_tokens}.jpg") + + avg_prompt_token = np.mean([prompt_len for prompt_len, _, _, _ in request_latency_record]) + avg_output_token = np.mean([output_len for _, output_len, _, _ in request_latency_record]) + + if getattr(args, "height", None) is None: + latency_record = (avg_prompt_token, avg_output_token, parallel_num, + total_output_token_throughput, + p90_prefill_latency, p99_prefill_latency, max_prefill_latency, avg_prefill_latency, + p90_decode_latency, p99_decode_latency, max_decode_latency, avg_decode_latency) + else: + image_shape = str(args.height) + ',' + str(args.width) + latency_record = (avg_prompt_token, avg_output_token, parallel_num, image_shape, + total_output_token_throughput, + p90_prefill_latency, p99_prefill_latency, max_prefill_latency, avg_prefill_latency, + p90_decode_latency, p99_decode_latency, max_decode_latency, avg_decode_latency) + + # If the benchmark backend supports speculative inference, request_latency_record is replaced with output_step, + # which is an int value. Otherwise, the original chunk_list is retained as a list. + is_spec_support_backend = isinstance(request_latency_record[0][-1], int) + if getattr(args, "use_spec_decode", False) and getattr(args, "num_speculative_tokens", -1) >= 0 \ + and is_spec_support_backend: + accept_rate_list = [(output_len - 1) / ((output_step - 1) * (args.num_speculative_tokens + 1)) for + _, output_len, _, output_step in request_latency_record] + + p90_accept_rate = np.percentile(accept_rate_list, 90) + logger.info("投机接受率TP90: %.4f", p90_accept_rate) + + p99_accept_rate = np.percentile(accept_rate_list, 99) + logger.info("投机接受率TP99: %.4f", p99_accept_rate) + + max_accept_rate = np.max(accept_rate_list) + logger.info("投机最大接受率: %.2f", max_accept_rate) + + min_accept_rate = np.min(accept_rate_list) + logger.info("投机最小接受率: %.2f", min_accept_rate) + + avg_accept_rate = np.mean(accept_rate_list) + logger.info("投机平均接受率: %.2f", avg_accept_rate) + + accept_rate_record = (p90_accept_rate, p99_accept_rate, max_accept_rate, min_accept_rate, avg_accept_rate) + + latency_record = latency_record + accept_rate_record + + time.sleep(10) + + all_latency_record.append(latency_record) + + +def plot_time_record(benchmark_start_time, benchmark_time, request_latency_record, name="parallel.jpg"): + def newline(ax, p1, p2, color='skyblue'): + line = mlines.Line2D([p1[0], p2[0]], [p1[1], p2[1]], color=color, linewidth=20, markersize=100, marker=".", + markerfacecolor=color) + ax.add_line(line) + + fig_size_x = 256 + fig_size_y = 128 + fig, ax = plt.subplots(1, 1, figsize=(fig_size_x, fig_size_y), facecolor='#f7f7f7', dpi=80) + time_records = [time_record for _, _, time_record in request_latency_record] + time_records = (time_records - benchmark_start_time) * 1000 + for idx, time_record in enumerate(tqdm(time_records, desc="plot_time_record")): + idx = idx * 1 + newline(ax, [time_record[0], idx], [time_record[1], idx], color='red') + for start, end in zip(time_record[1:-1], time_record[2:]): + newline(ax, [start, idx], [end, idx]) + + ax.set_facecolor('#f7f7f7') + ax.set(xlim=(0, (benchmark_time * 1000) + 10), ylim=(-1, len(time_records) * 1), ylabel='request') + font_size = round(fig_size_x / 3) + plt.xticks(fontsize=font_size) + plt.yticks(fontsize=font_size) + plt.xlabel('time', fontsize=fig_size_x) + plt.ylabel('request', fontsize=fig_size_x) + logger.info(f"save fig ...") + plt.savefig(name) + + +def save_to_csv(benchmark_head, records, csv_path): + logger.info(benchmark_head) + sprtr_idx = csv_path.rfind('/') + if sprtr_idx > 0: + csv_dir = csv_path[0:sprtr_idx] + if len(csv_dir) > 1: + os.makedirs(csv_dir, exist_ok=True) + + # 设置文件打开的标志 + flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL + # 设置文件权限，仅授予文件所有者读写权限 + mode = stat.S_IWUSR | stat.S_IRUSR + # 使用 os.open 打开文件描述符 + fd = os.open(csv_path, flags, mode) + with os.fdopen(fd, 'w', encoding='utf-8-sig', newline='') as csv_file: + writer = csv.writer(csv_file) + writer.writerow(benchmark_head) + for items in records: + to_csv = [] + for item in items: + if isinstance(item, float): + item = round(item, 4) + to_csv.append(item) + logger.info(to_csv) + writer.writerow(to_csv) -- Gitee