forked from metax-maca/op_optimization
oj教程更新
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# FlashAttention Baseline 入门：从环境验证到 KV-Cache Benchmark
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# Flashattention 迁移 Baseline 实战：从性能基线到 XPU-OJ 评测
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## 一、教程定位
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本教程是参赛训练课程的 FlashAttention Baseline 入门模块，主要帮助用户快速跑通 FlashAttention 的 KV-Cache 推理性能基准测试最小可运行流程。
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本教程是参赛训练课程的 **FlashAttention Baseline 入门与评测提交衔接** 模块，主要帮助用户跑通 FlashAttention paged KV‑cache 推理核函数 `flash_attn_with_kvcache` 的基准测试流程，理解 baseline 的输入输出、性能指标和评测含义，并基于 XPU‑OJ 题包完成一个最小正确版 `run_kernel` 的实现与提交。
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完成本教程后，用户应能够：
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需要特别说明：本教程中的 Baseline 主要用于帮助参赛者理解目标算子的调用方式、输入输出结构和性能基线，Baseline 不是最终提交物。最终评测以 XPU‑OJ 题包为准，参赛者需要根据题包中的接口约定，实现自己的 `run_kernel`，并在输出结果对齐 baseline 参考结果的前提下提升性能。
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* 完成环境验证和依赖检查
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完成本教程后，学员应能够：
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* 完成环境验证与依赖检查
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* 理解并配置基准测试参数
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* 理解并配置 KV‑Cache Benchmark 的核心参数
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* 明确Baseline的解读，理解为什么KV-Cache是性能瓶颈
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* 明确 Baseline 的含义，理解 KV‑Cache 为何成为推理性能瓶颈
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* 运行 KV-Cache Benchmark 测试
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* 运行 `flash_attn_with_kvcache` 的 Benchmark 测试并获取性能数据
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* 输出一份 baseline 性能结果记录表，为后续算子优化提供对比基准
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* 输出一份 Baseline 性能结果记录表，为后续算子优化提供对比基准
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* 理解 XPU‑OJ 评测的 `run_kernel` 接口规范与精度要求
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* 基于 OJ 题包接口实现一个最小正确版 `run_kernel`，通过正确性校验
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> Baseline 解读：为什么本教程基于 KV Cache 做性能基线？
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@ -125,17 +131,20 @@
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完成本模块后，你将能够：
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1. 理解 FlashAttention `flash_attn_with_kvcache` 核函数的基本作用与应用场景；
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1. **理解 FlashAttention Paged KV‑Cache 算子的作用**
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明白 `flash_attn_with_kvcache` 在 LLM 推理 decode 阶段如何高效利用分页 KV Cache，减少显存碎片并提升吞吐。
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2. 利用预装专属镜像，完成沐曦 GPU 硬件环境的快速验证；
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2. **完成环境准备与 Benchmark 运行**
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安装所需依赖，运行 `benchmark_kvcache.py`，生成包含执行时间与显存带宽的 CSV 性能记录。
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3. 深入理解 Baseline 的概念，掌握性能测试与正确性测试的联系与区别；
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3. **读懂 Baseline 并定位性能瓶颈**
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分析不同 `batch_size`、`seq_len_kv` 下的带宽曲线，理解显存带宽对 decode 阶段的影响。
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4. 跑通 KV-Cache Benchmark 基准测试脚本；
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4. **理清 Baseline 与 OJ 题包的关系**
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明确基准脚本用于性能参照，OJ 题包定义最终提交接口、数据范围和精度校验标准。
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5. 完成多种 `batch_size × seq_len_kv` 组合的性能测试；
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6. 输出带宽性能结果 CSV 文件并进行结果分析。
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5. **实现并提交一个最小正确版** `**run_kernel**`
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根据题包中的接口约定编写 CUDA 算子，通过 OJ 正确性校验并记录首次提交耗时。
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---
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---
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## 四、前置准备
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开始实战前，请确认你已经完成以下准备：
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### 环境准备
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* **设置领取与兑换算力券**
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* 前往沐曦开发者社区注册账号并完成邮箱验证，申请并获取 MACA 算力代金券兑换码。
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1. 前往沐曦开发者社区注册账号并完成邮箱验证，申请并获取 MACA 算力代金券兑换码。
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链接：[https://developer.metax-tech.com/activities/6](https://developer.metax-tech.com/activities/6)
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链接：https://developer.metax-tech.com/activities/6
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* 登录 模力方舟平台 (Gitee AI)，在“费用中心 -> 算力券”页面输入兑换码完成充值。
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链接：[https://ai.gitee.com/](https://ai.gitee.com/)
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2. 登录 模力方舟平台 (Gitee AI)，在“费用中心 -> 算力券”页面输入兑换码完成充值。 链接：https://ai.gitee.com/
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* **创建并启动实例**
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* 进入 算力市场，筛选“沐曦”芯片厂商，选择合适的 GPU 规格（推荐 曦云 C500 节点）。
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* **关键配置：** 在预装镜像处，务必选择专属开发镜像（`PyTorch Agent/2.8.0/Python 3.12/maca 3.7.2.1`）。
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* 创建完成后，进入算力容器，点击“工具-lab”即可打开 JupyterLab 终端开始项目创作。
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**说明：**由于本次使用的是预装的专属镜像，环境中已经默认安装并配置好了 PyTorch、FlashAttention、einops 等所有依赖包。因此在启动实例后，无需再进行繁琐的依赖库版本验证即可直接进入测试环节。
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1. 进入 算力市场，筛选“沐曦”芯片厂商，选择合适的 GPU 规格（推荐 曦云 C500 节点）。
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2. 关键配置： 在预装镜像处，务必选择专属开发镜像（PyTorch Agent/2.8.0/Python 3.12/maca 3.7.2.1）。
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3. 创建完成后，进入算力容器，点击“工具-lab”即可打开 JupyterLab 终端开始项目创作。
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\*\*说明：\*\*由于本次使用的是预装的专属镜像，环境中已经默认安装并配置好了 PyTorch、FlashAttention、einops 等所有依赖包。因此在启动实例后，无需再进行繁琐的依赖库版本验证即可直接进入测试环节。
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### 代码准备
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* 已获取基准测试脚本 `benchmark_kvcache.py`
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* 获取目标源码（包含 `benchmark_kvcache.py` 及 OJ 题包）
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* 已进入项目目录 `/data/flashattn_baseline`
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* 准备 Benchmark 脚本与 OJ 测试脚本
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---
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### 关键术语
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* **KV-Cache**：缓存历史 Token 的 Key/Value 向量，避免 Transformer 推理时重复计算。
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* **KV-Cache：**缓存历史 Token 的 Key/Value 向量，避免 Transformer 推理时重复计算。
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* **Paged KV-Cache**：将 KV-Cache 分页管理，减少显存碎片，提高利用率。
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* **Paged KV-Cache：**将 KV-Cache 分页管理，减少显存碎片，提高利用率。
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* **Batch Size**：一次处理的样本数，越大并行度越高，但显存占用越大。
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* **Batch Size：**一次处理的样本数，越大并行度越高，但显存占用越大。
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* **seq\_len\_kv**：KV-Cache 中已缓存的历史 Token 数量。
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* **seq\_len\_kv：**KV-Cache 中已缓存的历史 Token 数量。
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* **headdim**：注意力头维度，常见为 64/128/256。
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* **headdim：**注意力头维度，常见为 64/128/256。
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### 核心知识
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* **正确性测试 ：**验证算子输出结果的数学精度是否与标准实现一致，这是**绝对底线**。
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* **正确性测试 ：**验证算子输出结果的数学精度是否与标准实现一致，这是绝对底线。
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* **性能测试 ：**在正确的前提下，测算速度与吞吐。通过建立 **Baseline（基线），**才能量化后续每次代码修改带来的真实收益（加速比）。
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* **性能测试 ：**在正确的前提下，测算速度与吞吐。通过建立Baseline（基线），才能量化后续每次代码修改带来的真实收益（加速比）。
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* **Benchmark （基准测试）：**在固定条件下反复运行同一任务，获取可重复的性能指标，用于建立基线、量化优化效果和定位瓶颈。
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### 关键指标
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* **XPU‑OJ**：比赛官方在线评测平台，最终评测会调用参赛者提交代码中的 `run_kernel`。
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* **Kernel 执行时间**：GPU 核函数运行耗时（ms），使用 GPU 端同步计时获得。
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### 关键指标
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* **Kernel 执行时间：**GPU 核函数运行耗时（ms），使用 GPU 端同步计时获得。
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* **有效带宽：**`数据传输量 (GB) ÷ Kernel 时间 (s)`，越接近理论峰值说明显存带宽利用越充分。
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* **有效带宽：**数据传输量 (GB) ÷ Kernel 时间 (s)，越接近理论峰值说明显存带宽利用越充分。
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### 其他要点
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* **Warmup**：预热若干次（不记录），使 GPU 进入稳定状态。
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* **Warmup：**预热若干次（不记录），使 GPU 进入稳定状态。
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* **Repeat**：正式运行多次，取平均值或中位数以消除波动。
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* **Repeat：**正式运行多次，取平均值或中位数以消除波动。
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* **同步**：调用 `torch.cuda.synchronize()` 确保精确计时。
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* **同步：**调用 torch.cuda.synchronize() 确保精确计时。
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* **数据类型**：本教程使用 `bfloat16`，在精度和性能取得平衡。
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* **数据类型：**本教程使用 bfloat16，在精度和性能取得平衡。
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* **显存占用估算**：`KV-Cache ≈ batch × seq_len_kv × num_heads_k × headdim × 2(K+V) × 字节数`。
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* **显存占用估算：**KV-Cache ≈ batch × seq\_len\_kv × num\_heads\_k × headdim × 2(K+V) × 字节数。
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* **OOM 应对**：减小 batch/seq\_len\_kv、使用更小 dtype 或释放中间变量。
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* **OOM 应对：**减小 batch/seq\_len\_kv、使用更小 dtype 或释放中间变量。
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* **Tensor Core**：现代 GPU（含沐曦 C500）的矩阵乘法专用单元，要求维度对齐为 8 或 16 的倍数。
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* **Tensor Core：**现代 GPU（含沐曦 C500）的矩阵乘法专用单元，要求维度对齐为 8 或 16 的倍数。
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---
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## 六、项目实践：FlashAttention KV-Cache Benchmark
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## 六、项目实践：FlashAttention KV‑Cache Benchmark & OJ 评测
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### 项目目标
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对 FlashAttention 的 paged KV-cache 推理核函数（`flash_attn_with_kvcache`）进行自动化性能基准测试，覆盖多种 `batch_size × seq_len_kv` 组合，输出执行时间和有效显存带宽。
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基准测试脚本 `benchmark_kvcache.py` 的最小闭环包括：
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1. 准备 paged KV-cache 张量和 block table。
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2. 通过 `torch.profiler` 对 kernel 进行计时。
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3. 计算有效显存带宽（GB/s）。
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4. 将结果写入带时间戳的 CSV 文件。
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* 对 FlashAttention 的 paged KV-cache 推理核函数（`flash_attn_with_kvcache`）进行自动化性能基准测试，覆盖多种 `batch_size × seq_len_kv` 组合，输出执行时间和有效显存带宽。
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---
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* 理解 XPU‑OJ 题包的 `run_kernel` 接口，实现一个最小正确版 CUDA 算子，通过所有 OJ 正确性测试用例。
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### 步骤 0：进入创建的实例环境
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选择工具-lab进入实例环境
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### 步骤 1：检查运行环境
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在JupyterLab Terminal中检查运行环境的配置。
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**操作：** 检查 GPU 状态、Python 版本和依赖版本。
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* `mx-smi` 显示沐曦 GPU 信息
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* Python 版本 = 3.8
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* `torch.cuda.is_available()` 返回 `True`
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* 所有依赖版本符合要求
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**常见问题：**
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### 步骤 2：进入项目目录
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**目标：** 进入本模块所需的项目目录。
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目标：进入本模块所需的源码目录。
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<<<<<<< HEAD
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1. 克隆代码仓库
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```Bash
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git clone https://gitlink.org.cn/metax-maca/op_optimization.git
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```
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2. 准备flashattn\_baseline
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在仓库目录 `op_optimization/基于AI Agent开发范式的国产GPU大模型推理算子库优化` 下，找到 `flashattn_baseline` 文件夹。可以将 `flashattn_baseline` 整个目录复制到工作目录 `data/` 下。
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**下一步操作：** 切换到 Flashattn\_Baseline 项目目录。
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**命令示例：**
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```bash
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cd flashattn_baseline/Flashattn_Baselinels -la
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```
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**预期结果：**
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```Plain
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total xx
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drwxr-xr-x 2 root root 4096 Jun 1 09:00 __MACOSX
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- rw-r--r-- 1 root root 5232 Jun 1 09:00 benchmark_kvcache.py
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- rw-r--r-- 1 root root 1440 Jun 1 09:00 benchmark_kvcache_20260526_150953.csv
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...
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```
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3. 将Flashattn\_Baseline文件加入JupyterLab。
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=======
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1. 克隆代码仓库
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```Bash
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2. 准备flashattn_baseline
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在仓库目录 `op_optimization/基于AI Agent开发范式的国产GPU大模型推理算子库优化` 下，找到 `flashattn_baseline` 文件夹。可以将 `flashattn_baseline` 整个目录复制到工作目录 `data/` 下。
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>>>>>>> upstream/master
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<<<<<<< HEAD
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=======
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**操作：** 切换到基准测试脚本所在目录。
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**命令示例：**
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...
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```
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>>>>>>> upstream/master
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---
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### 步骤 3：配置基准测试参数
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|
|||
ms = run_with_profiler(run_fn, warmup=warmup, reps=repeat, print_result=True, target_kernels=["flash"])
|
||||
|
||||
```
|
||||
---
|
||||
|
||||
## 七、Agent 使用说明
|
||||
### 步骤 7:从 Baseline 到 XPU-OJ 提交
|
||||
|
||||
在本模块中，Agent 可用于以下场景：
|
||||
Baseline benchmark 用于理解目标算子的调用方式、输入输出 shape 和性能基线;XPU-OJ 题包用于定义最终评测接口、数据范围、参考输出和精度要求。
|
||||
|
||||
### Prompt 模板
|
||||
跑完 baseline 后,选手需要完成以下转换:
|
||||
|
||||
**环境验证：**
|
||||
1. 从 benchmark 脚本中理解目标 API,本任务对应的是 `flash_attn.flash_attn_interface` 中的 `flash_attn_with_kvcache`(paged KV cache 布局);
|
||||
|
||||
2. 在对应 OJ 题包中查看 `run_kernel(...)` 接口;
|
||||
|
||||
3. 对照题包中的输入 shape、数据范围和精度要求;
|
||||
|
||||
4. 编写自己的 `run_kernel(...)`;
|
||||
|
||||
5. 提交 OJ,先通过正确性;
|
||||
|
||||
6. 正确性通过后,再对比 baseline / OJ 耗时继续优化。
|
||||
|
||||
|
||||
```Plain
|
||||
请帮我验证当前环境是否满足 FlashAttention KV-Cache Benchmark 的运行要求，包括：
|
||||
1. 沐曦 GPU 是否可见
|
||||
2. PyTorch 版本和 CUDA 支持
|
||||
3. flash-attn 和 einops 是否已安装
|
||||
### 题目说明(FlashAttention KV Cache Decode)
|
||||
|
||||
> 注意:每个子题的接口参数、数据范围和精度要求可能不同,正式要求以对应 XPU-OJ 题包为准。本节以 **FlashAttention KV Cache Decode** 为例,演示从 baseline benchmark 到 XPU-OJ 提交的完整流程。
|
||||
|
||||
* **对应 baseline 脚本**:`flashattn_baseline/baseline/benchmark_kvcache.py`
|
||||
|
||||
* **对应 FlashAttention API**:`flash_attn_with_kvcache`(paged KV cache 版本)
|
||||
|
||||
* **算子说明**:实现 paged KV cache 下的 decode 注意力,每个 batch 只有 1 个 query token,KV cache 按 page 存储,长度由 `seqlen_k` 决定。
|
||||
|
||||
* **对应 OJ 题包**:XPU-OJ 上 `FlashAttention KV Cache Decode` 题(题号 20005)。
|
||||
|
||||
|
||||
### 步骤 8:理解 XPU-OJ 评测接口与精度要求
|
||||
|
||||
**目标**:明确 Baseline 与最终评测提交之间的关系,理解选手需要实现什么。
|
||||
|
||||
完成 baseline benchmark 后,需要注意:baseline 脚本主要用于建立性能基线,**不是最终提交物**。最终评测以 XPU-OJ 题包为准,评测程序会调用选手提交代码中的 `run_kernel`,并将输出结果与 baseline 参考结果进行比较。
|
||||
|
||||
下面以 **FlashAttention KV Cache Decode** 题为例,题包目录中通常包含以下文件:
|
||||
|
||||
* `zh_CN/00_题目描述.md`:说明需要实现的算子功能;
|
||||
|
||||
* `zh_CN/01_接口约定.cuda.md`:说明必须实现的 `run_kernel` 函数签名;
|
||||
|
||||
* `zh_CN/05_数据范围与提示.md`:说明测试范围和精度要求;
|
||||
|
||||
* `testcase_config.py`:定义测试数据生成、baseline 参考实现和正确性校验方式。
|
||||
|
||||
|
||||
#### 1. 必须实现的接口
|
||||
|
||||
选手需要在提交的 CUDA 源码中提供如下 C 符号,函数名、参数类型、顺序必须完全一致,并使用 `extern "C"` 防止 name mangling:
|
||||
|
||||
```cpp
|
||||
#include <stdint.h>
|
||||
#include <cuda_bf16.h>
|
||||
|
||||
extern "C" void run_kernel(
|
||||
const __nv_bfloat16* q,
|
||||
const __nv_bfloat16* k_cache_paged,
|
||||
const __nv_bfloat16* v_cache_paged,
|
||||
__nv_bfloat16* output,
|
||||
const int32_t* cache_seqlens,
|
||||
const int32_t* block_table,
|
||||
int64_t batch_size,
|
||||
int64_t seqlen_k,
|
||||
int64_t seqlen_q,
|
||||
int64_t num_heads,
|
||||
int64_t num_heads_k,
|
||||
int64_t headdim,
|
||||
int64_t page_block_size,
|
||||
int64_t num_blocks,
|
||||
int64_t causal
|
||||
);
|
||||
|
||||
```
|
||||
|
||||
**参数配置建议：**
|
||||
#### 2. 参数说明
|
||||
|
||||
```Plain
|
||||
我需要测试 headdim=128 和 headdim=256 的性能差异，请帮我推荐合适的 batch_sizes 和 seq_lens_kv 扫描范围。
|
||||
| 参数 | 说明 |
|
||||
| --- | --- |
|
||||
| `q` | decode query tensor,shape `(batch_size, seqlen_q, num_heads, headdim)`,连续 `bf16` |
|
||||
| `k_cache_paged` | paged key cache,shape `(num_blocks, page_block_size, num_heads_k, headdim)`,连续 `bf16` |
|
||||
| `v_cache_paged` | paged value cache,shape `(num_blocks, page_block_size, num_heads_k, headdim)`,连续 `bf16` |
|
||||
| `output` | 输出缓冲区,shape `(batch_size, seqlen_q, num_heads, headdim)`,连续 `bf16` |
|
||||
| `cache_seqlens` | 每个 batch 的 KV 长度,shape `(batch_size)`,连续 `int32` |
|
||||
| `block_table` | 每个 batch 的 page 映射表,shape `(batch_size, num_blocks / batch_size)`,连续 `int32` |
|
||||
| `seqlen_q` | query 长度,评测中固定为 `1` |
|
||||
| `page_block_size` | page size,评测中固定为 `16` |
|
||||
| `causal` | 是否启用 causal mask,评测中固定为 `0` |
|
||||
|
||||
`run_kernel` 内部需要自行计算合适的 launch 配置并启动 CUDA kernel。为保证计时准确,**不建议在** `**run_kernel**` **内部做** `**cudaDeviceSynchronize()**` **或显式同步**。
|
||||
|
||||
#### 3. KV cache 布局
|
||||
|
||||
KV cache layout 固定为 `flash_attn_with_kvcache` 的 paged cache 布局:`(num_blocks, page_block_size, num_heads_k, headdim)`。
|
||||
|
||||
第 `t` 个 KV token 位于 `block_table[batch_idx, t / page_block_size]` 指向的物理 page 中,page 内偏移为 `t % page_block_size`。
|
||||
|
||||
例如 `batch_size = 1`、`seqlen_k = 512`、`page_block_size = 16` 时,每个序列需要访问 `32` 个有效 page。
|
||||
|
||||
#### 4. 评测数据范围
|
||||
|
||||
`testcase_config.py` 中定义了本题的测试配置(摘自题包):
|
||||
|
||||
```python
|
||||
HEAD_DIMS = [128]
|
||||
BATCH_SIZES = [1, 4, 16]
|
||||
SEQ_LENS_KV = [1024, 4096, 8192, 16384]
|
||||
SEQ_LEN_Q = 1
|
||||
NUM_HEADS = 8
|
||||
NUM_HEADS_K = 8
|
||||
PAGE_BLOCK_SIZE = 16
|
||||
CAUSAL = 0
|
||||
|
||||
```
|
||||
|
||||
**结果分析：**
|
||||
`num_blocks = max(1024, ceil(seqlen_k / page_block_size) * batch_size * 3)`。
|
||||
|
||||
```Plain
|
||||
请帮我分析这份 benchmark 结果 CSV 文件，找出峰值带宽配置和 OOM 边界。
|
||||
#### 5. 精度要求
|
||||
|
||||
当前 `FlashAttention KV Cache Decode` 题的校验方式为:
|
||||
|
||||
```python
|
||||
torch.allclose(output_t.float(), output_ref.float(), rtol=1e-2, atol=1e-2)
|
||||
|
||||
```
|
||||
---
|
||||
|
||||
## 八、常见问题
|
||||
也就是说,选手实现的输出需要在上述容差范围内与 baseline 输出一致。不同算子的容差可能不同,**正式精度要求以对应 OJ 题包说明为准**。
|
||||
|
||||
### 步骤 9：登录 XPU-OJ 并进入题目页面
|
||||
|
||||
使用组委会统一发放的账号登录 XPU-OJ，并进入对应赛题页面。
|
||||
|
||||
1.打开 XPU-OJ 平台：https://xpuoj.com/
|
||||
|
||||
2.使用组委会统一发放的账号和初始密码登录【后续发布】；
|
||||
|
||||
3.登录后进入比赛 / 题目列表页面；
|
||||
|
||||
4.找到对应题目，例如 `20005 FlashAttention KV Cache Decode`；
|
||||
|
||||
5.点击进入题目详情页，查看题目描述、接口约定、数据范围和提交入口。
|
||||
|
||||
### 步骤 10：在Agent的帮助下提交 OJ 冒烟代码
|
||||
|
||||
**目标：**完成一次最小提交，确认 OJ 提交链路、语言环境和 `run_kernel(...)` 接口可用。
|
||||
|
||||
**操作：**
|
||||
|
||||
1. 在语言下拉框中选择本题支持的提交语言，例如 `MXMACA C++`、`TileLang` 、 `Triton`；
|
||||
|
||||
2. 将实现了题目要求接口的代码复制到提交框中；
|
||||
|
||||
如果你还没有 run\_kernel，应该从哪里开始？
|
||||
|
||||
OJ 最终评测不会直接运行 baseline 脚本，而是调用你提交代码中的 `run_kernel(...)`。 本赛题鼓励参赛者使用 AI Agent 辅助完成代码阅读、接口理解、初版实现、错误定位和性能优化。 如果你还没有自己的 `run_kernel`，可以先让 Agent 阅读题包，并生成一个最小正确版实现思路。
|
||||
|
||||
3. 借助 Agent 从题包生成 run\_kernel 初版
|
||||
|
||||
|
||||
### 10.1 在镜像终端中安装并启动 OpenCode
|
||||
|
||||
1. 首先返回镜像JupyterLab Terminal中, 打开容器内的 **Terminal**(终端)
|
||||
|
||||
2. 执行下面的命令安装 OpenCode:
|
||||
|
||||
```bash
|
||||
curl -fsSL https://opencode.ai/install | bash
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
3. 安装完成后,`cd` 进入赛题文件夹(根据实际目录调整):
|
||||
|
||||
```bash
|
||||
cd xpuoj_problem/
|
||||
|
||||
```
|
||||
|
||||
4. 在该目录下直接输入 `opencode` 并回车,即可进入 OpenCode 的 Agent 界面:
|
||||
|
||||
```bash
|
||||
opencode
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
参考prompt：
|
||||
|
||||
```python
|
||||
本题是 FlashAttention paged KV cache decode 的 CUDA C++ 前向算子。
|
||||
输入输出规范如下：
|
||||
- q: [batch, 1, num_heads, head_dim] (float)
|
||||
- k_cache_paged: [num_blocks, num_heads, page_size, head_dim] (float)
|
||||
- v_cache_paged: [num_blocks, num_heads, page_size, head_dim] (float)
|
||||
- cache_seqlen: [batch] (int)
|
||||
- block_table: [batch, max_num_blocks] (int)
|
||||
- output: [batch, 1, num_heads, head_dim]
|
||||
需调用 run_kernel(q, k_cache_paged, v_cache_paged, cache_seqlen, block_table, output)
|
||||
你需要根据 cache_seqlen 和 block_table 从 paged cache 中取出对应的 K, V，计算 attention 结果，存入 output。
|
||||
参考：out = flash_attn_with_kvcache(q, k_cache_paged, v_cache_paged, cache_seqlen=..., block_table=...)
|
||||
要求你的实现与这个 API 的计算结果一致（误差允许1e-5）。
|
||||
请生成一个 run_kernel 函数，优先保证正确性。
|
||||
```
|
||||
|
||||
|
||||
|
||||
输出：
|
||||
|
||||
|
||||
|
||||
为方便参赛者先跑通完整流程,这里直接提供一份完整的冒烟代码,可直接复制粘贴到右侧编辑器,用于验证提交链路是否正常:
|
||||
|
||||
```python
|
||||
#include <stdint.h>
|
||||
#include <cuda_bf16.h>
|
||||
#include <cuda_runtime.h>
|
||||
|
||||
#define PAGE_SIZE 16
|
||||
#define HEAD_DIM 128
|
||||
|
||||
__global__ void paged_attention_kernel(
|
||||
const __nv_bfloat16* __restrict__ q,
|
||||
const __nv_bfloat16* __restrict__ k_cache_paged,
|
||||
const __nv_bfloat16* __restrict__ v_cache_paged,
|
||||
__nv_bfloat16* __restrict__ output,
|
||||
const int32_t* __restrict__ cache_seqlens,
|
||||
const int32_t* __restrict__ block_table,
|
||||
int64_t batch_size,
|
||||
int64_t seqlen_q,
|
||||
int64_t num_heads,
|
||||
int64_t num_heads_k,
|
||||
int64_t headdim,
|
||||
int64_t page_block_size,
|
||||
int64_t blocks_per_batch)
|
||||
{
|
||||
int batch_idx = blockIdx.x / num_heads;
|
||||
int head_idx = blockIdx.x % num_heads;
|
||||
if (batch_idx >= batch_size || head_idx >= num_heads) return;
|
||||
|
||||
int seqlen = cache_seqlens[batch_idx];
|
||||
if (seqlen <= 0) {
|
||||
// 无有效 KV，输出 0
|
||||
int64_t out_base = ((batch_idx * seqlen_q + 0) * num_heads + head_idx) * headdim;
|
||||
for (int i = threadIdx.x; i < headdim; i += blockDim.x)
|
||||
output[out_base + i] = __float2bfloat16(0.0f);
|
||||
return;
|
||||
}
|
||||
|
||||
int tid = threadIdx.x;
|
||||
|
||||
// 加载对应 head 的 query 元素（每个线程负责一个维度）
|
||||
int64_t q_offset = ((batch_idx * seqlen_q + 0) * num_heads + head_idx) * headdim;
|
||||
float q_val = __bfloat162float(q[q_offset + tid]);
|
||||
|
||||
// 全局 softmax 状态（每个线程维护自己维度的累加器）
|
||||
float max_val = -1e38f;
|
||||
float sum_exp = 0.0f;
|
||||
float out_acc = 0.0f;
|
||||
float scale = rsqrtf(static_cast<float>(headdim));
|
||||
|
||||
// 共享内存布局：
|
||||
// K_tile[PAGE_SIZE][HEAD_DIM] (bf16)
|
||||
// V_tile[PAGE_SIZE][HEAD_DIM] (bf16)
|
||||
// partial_scores[PAGE_SIZE][HEAD_DIM] (float, 用于归约点积)
|
||||
__shared__ __nv_bfloat16 K_tile[PAGE_SIZE][HEAD_DIM];
|
||||
__shared__ __nv_bfloat16 V_tile[PAGE_SIZE][HEAD_DIM];
|
||||
__shared__ float partial_scores[PAGE_SIZE][HEAD_DIM];
|
||||
|
||||
int total_pages = (seqlen + PAGE_SIZE - 1) / PAGE_SIZE;
|
||||
|
||||
for (int page = 0; page < total_pages; ++page) {
|
||||
int physical_block = block_table[batch_idx * blocks_per_batch + page];
|
||||
int tokens_this_page = min(seqlen - page * PAGE_SIZE, PAGE_SIZE);
|
||||
|
||||
// 1. 将当前 page 的 K 和 V 从全局显存加载到共享内存
|
||||
// 每个线程负责加载所有 token 的同一个 head 维度
|
||||
const int64_t kv_stride = num_heads_k * headdim; // 每个 (block, offset) 的 stride
|
||||
for (int j = 0; j < tokens_this_page; ++j) {
|
||||
int64_t offset = (physical_block * PAGE_SIZE + j) * kv_stride + head_idx * headdim + tid;
|
||||
K_tile[j][tid] = k_cache_paged[offset];
|
||||
V_tile[j][tid] = v_cache_paged[offset];
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// 2. 计算该 page 内每个 token 与 Q 的部分点积，存入 partial_scores
|
||||
for (int j = 0; j < tokens_this_page; ++j) {
|
||||
float k_val = __bfloat162float(K_tile[j][tid]);
|
||||
partial_scores[j][tid] = q_val * k_val;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// 3. 对 partial_scores 做 tree reduction，得到每个 token 的完整点积
|
||||
#pragma unroll
|
||||
for (int stride = HEAD_DIM / 2; stride > 0; stride >>= 1) {
|
||||
if (tid < stride) {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < tokens_this_page; ++j) {
|
||||
partial_scores[j][tid] += partial_scores[j][tid + stride];
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
// 4. 在线 safe softmax 更新 + V 累加
|
||||
// 4.1 找出本 page 内点积的最大值，结合全局 max 得到 new_max
|
||||
float local_max = -1e38f;
|
||||
#pragma unroll
|
||||
for (int j = 0; j < tokens_this_page; ++j) {
|
||||
local_max = fmaxf(local_max, partial_scores[j][0]);
|
||||
}
|
||||
float new_max = fmaxf(max_val, local_max);
|
||||
|
||||
// 4.2 用旧的 max 对全局状态进行重缩放
|
||||
float rescale = expf(max_val - new_max);
|
||||
sum_exp *= rescale;
|
||||
out_acc *= rescale;
|
||||
max_val = new_max;
|
||||
|
||||
// 4.3 累加 V，并更新 sum_exp
|
||||
#pragma unroll
|
||||
for (int j = 0; j < tokens_this_page; ++j) {
|
||||
float score = partial_scores[j][0] * scale;
|
||||
float weight = expf(score - new_max);
|
||||
sum_exp += weight;
|
||||
|
||||
float v_val = __bfloat162float(V_tile[j][tid]);
|
||||
out_acc += weight * v_val;
|
||||
}
|
||||
|
||||
__syncthreads(); // 准备下一个 page 的共享内存加载
|
||||
}
|
||||
|
||||
// 5. 最终归一化并写回
|
||||
if (sum_exp > 0.0f) {
|
||||
out_acc /= sum_exp;
|
||||
} else {
|
||||
out_acc = 0.0f;
|
||||
}
|
||||
|
||||
int64_t out_offset = ((batch_idx * seqlen_q + 0) * num_heads + head_idx) * headdim + tid;
|
||||
output[out_offset] = __float2bfloat16(out_acc);
|
||||
}
|
||||
|
||||
extern "C" void run_kernel(
|
||||
const __nv_bfloat16* q,
|
||||
const __nv_bfloat16* k_cache_paged,
|
||||
const __nv_bfloat16* v_cache_paged,
|
||||
__nv_bfloat16* output,
|
||||
const int32_t* cache_seqlens,
|
||||
const int32_t* block_table,
|
||||
int64_t batch_size,
|
||||
int64_t seqlen_k,
|
||||
int64_t seqlen_q,
|
||||
int64_t num_heads,
|
||||
int64_t num_heads_k,
|
||||
int64_t headdim,
|
||||
int64_t page_block_size,
|
||||
int64_t num_blocks,
|
||||
int64_t causal)
|
||||
{
|
||||
int64_t blocks_per_batch = num_blocks / batch_size;
|
||||
dim3 grid(batch_size * num_heads);
|
||||
dim3 block(HEAD_DIM);
|
||||
|
||||
paged_attention_kernel<<<grid, block>>>(
|
||||
q, k_cache_paged, v_cache_paged, output,
|
||||
cache_seqlens, block_table,
|
||||
batch_size, seqlen_q, num_heads, num_heads_k, headdim,
|
||||
page_block_size, blocks_per_batch
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
以上代码仅用于说明接口结构，不代表最优实现，也不作为评分参考
|
||||
|
||||
5. 点击提交，等待评测结果返回；
|
||||
|
||||
评测时间与题目测试点数量、队列状态和平台负载有关，通常需要等待数十秒到数分钟。以平台实际返回为准。
|
||||
|
||||
|
||||
|
||||
OJ 对每次提交大致会走这个流程：
|
||||
|
||||
```plaintext
|
||||
XPU-OJ 的一次评测大致流程如下：
|
||||
1. 选手提交代码；
|
||||
2. 平台按所选语言编译或加载提交代码；
|
||||
3. 评测程序构造测试输入；
|
||||
4. 调用选手代码中的 `run_kernel(...)`；
|
||||
5. 调用 `testcase_config.py` 中的 `baseline()` / 参考实现生成 `output_ref`；
|
||||
6. 将 `run_kernel(...)` 的输出与 `output_ref` 做正确性校验；
|
||||
7. 正确性通过后，统计运行耗时或性能指标；
|
||||
8. 根据题目评分规则换算该题得分；
|
||||
9. 更新该题历史最好成绩；
|
||||
10. 汇总各题最好成绩，得到排行榜总分。
|
||||
```
|
||||
|
||||
6. 查看结果
|
||||
|
||||
提交详情会显示状态、得分、时间、内存、编译信息以及各测试点结果。
|
||||
|
||||
|
||||
|
||||
## 从 Baseline 到参赛作品的路径回顾：
|
||||
|
||||
1. 跑通 baseline benchmark，记录原始性能；
|
||||
|
||||
2. 提交冒烟代码，确认 OJ 链路正常；
|
||||
|
||||
3. 使用 Agent 阅读题包，理解输入输出、数据范围和精度要求；
|
||||
|
||||
4. 在 `run_kernel(...)` 中实现最小正确版算子；
|
||||
|
||||
5. 提交 OJ，先通过正确性；
|
||||
|
||||
6. 正确性通过后，再让 Agent 辅助分析性能瓶颈；
|
||||
|
||||
7. 围绕访存、softmax、线程划分、head\_dim 特化、K/V 复用等方向迭代优化；
|
||||
|
||||
8. 保存每轮 Agent Prompt、代码改动、OJ 结果和性能变化，形成可复现的 Agent/Skill 优化流程。
|
||||
|
||||
|
||||
## 七、常见问题
|
||||
|
||||
### Q1: 运行时提示 `ModuleNotFoundError: No module named 'flash_attn '`
|
||||
|
||||
|
|
@ -601,9 +1043,23 @@ ms = run_with_profiler(run_fn, warmup=warmup, reps=repeat, print_result=True, ta
|
|||
|
||||
**原因：** 可能是 warmup 不足或 GPU 未达到稳态。 **解决：** 增加 `warmup` 次数，如 `warmup = 20`。
|
||||
|
||||
---
|
||||
### Q5: 评测状态显示 `**Compile Error**`或提示 `**Undefined reference to run_kernel**`
|
||||
|
||||
## 九、下一步学习建议
|
||||
**原因：**C++函数名被修饰（Name Mangling）或参数类型/顺序与接口约定不符。 **解决：**在 run\_kernel 前添加 extern "C"，并严格逐字核对参数的类型和修饰符。
|
||||
|
||||
#### Q6: 评测状态显示 `**Wrong Answer**`，提示 torch.allclose校验失败
|
||||
|
||||
**原因：**bfloat16精度截断溢出、线程同步缺失或无效Token（Padding区域）处理错误。 **解决：**累加和 Softmax 强制转为 float32 计算；检查 \_\_syncthreads() 逻辑；增加 seqlen 的边界判空。
|
||||
|
||||
#### Q7: 评测状态显示 `**Runtime Error**` (非法内存访问/段错误)
|
||||
|
||||
**原因：**Paged KV 地址映射索引错误、尾部 Page 越界读取，或线程块维度超限。 **解决：**仔细核对物理块寻址公式；增加当前 Page 有效 Token 数量的越界判断；检查单 Block 线程数配置。
|
||||
|
||||
#### Q8: 评测状态显示`**Time Limit Exceeded**` (评测超时)
|
||||
|
||||
**原因：**发散分支内的 \_\_syncthreads() 导致内核死锁、误加主机端同步指令或并行度划分错误导致串行。 **解决：**确保同步指令在所有线程必经路径上；移除主机端多余的 cudaDeviceSynchronize()；优化 <<<grid, block>>> 参数以提升并行度。
|
||||
|
||||
## 八、下一步学习建议
|
||||
|
||||
完成本模块后，建议继续学习以下内容：
|
||||
|
||||
|
|
@ -615,5 +1071,6 @@ ms = run_with_profiler(run_fn, warmup=warmup, reps=repeat, print_result=True, ta
|
|||
|
||||
4. **性能对比分析** — 将 baseline 结果与优化后结果进行对比
|
||||
|
||||
|
||||
---
|
||||
5. **OJ 题包深度解析** — 学习阅读题包中的 `testcase_config.py`，掌握本地构造边界用例与独立 Debug 的能力
|
||||
|
||||
6. **评测打榜与极限优化** — 在通过 OJ 正确性校验的基础上，挑战排行榜（Leaderboard），不断逼近硬件理论带宽极限
|
||||
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,160,0.0580,45.25
|
||||
2,512,8,160,0.0611,85.84
|
||||
4,512,8,160,0.0656,159.96
|
||||
8,512,8,160,0.0699,300.40
|
||||
16,512,8,160,0.1321,317.92
|
||||
32,512,8,160,0.2002,419.52
|
||||
64,512,8,160,0.3383,496.43
|
||||
128,512,8,160,0.6669,503.61
|
||||
1,1024,8,160,0.1129,46.45
|
||||
2,1024,8,160,0.1190,88.18
|
||||
4,1024,8,160,0.1224,171.49
|
||||
8,1024,8,160,0.1287,326.07
|
||||
16,1024,8,160,0.2479,338.49
|
||||
32,1024,8,160,0.3767,445.54
|
||||
64,1024,8,160,0.6419,523.01
|
||||
128,1024,8,160,1.2804,524.37
|
||||
1,2048,8,160,0.2270,46.20
|
||||
2,2048,8,160,0.2299,91.22
|
||||
4,2048,8,160,0.2349,178.63
|
||||
8,2048,8,160,0.2447,342.96
|
||||
16,2048,8,160,0.4773,351.60
|
||||
32,2048,8,160,0.7279,461.07
|
||||
64,2048,8,160,1.2559,534.49
|
||||
128,2048,8,160,2.5613,524.15
|
||||
1,4096,8,160,0.4460,47.02
|
||||
2,4096,8,160,0.4513,92.94
|
||||
4,4096,8,160,0.4593,182.64
|
||||
8,4096,8,160,0.4813,348.64
|
||||
16,4096,8,160,0.9363,358.43
|
||||
32,4096,8,160,1.4552,461.21
|
||||
64,4096,8,160,2.5615,524.05
|
||||
128,4096,8,160,5.1420,522.11
|
||||
1,8192,8,160,0.8847,47.41
|
||||
2,8192,8,160,0.8944,93.80
|
||||
4,8192,8,160,0.9094,184.51
|
||||
8,8192,8,160,0.9625,348.64
|
||||
16,8192,8,160,1.8550,361.80
|
||||
32,8192,8,160,2.9567,453.97
|
||||
64,8192,8,160,5.1398,522.30
|
||||
128,8192,8,160,10.2972,521.41
|
||||
1,16384,8,160,1.7608,47.64
|
||||
2,16384,8,160,1.7786,94.33
|
||||
4,16384,8,160,1.8143,184.95
|
||||
8,16384,8,160,1.9317,347.42
|
||||
16,16384,8,160,3.7301,359.83
|
||||
32,16384,8,160,5.9216,453.33
|
||||
64,16384,8,160,10.2668,522.94
|
||||
128,16384,8,160,20.6062,521.09
|
||||
|
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,192,0.0458,68.82
|
||||
2,512,8,192,0.0515,122.32
|
||||
4,512,8,192,0.0574,219.28
|
||||
8,512,8,192,0.0607,414.80
|
||||
16,512,8,192,0.1147,439.27
|
||||
32,512,8,192,0.1763,571.40
|
||||
64,512,8,192,0.2978,676.79
|
||||
128,512,8,192,0.5874,686.11
|
||||
1,1024,8,192,0.0946,66.55
|
||||
2,1024,8,192,0.1033,121.85
|
||||
4,1024,8,192,0.1073,234.66
|
||||
8,1024,8,192,0.1131,445.23
|
||||
16,1024,8,192,0.2165,465.24
|
||||
32,1024,8,192,0.3347,601.80
|
||||
64,1024,8,192,0.5701,706.63
|
||||
128,1024,8,192,1.1302,712.88
|
||||
1,2048,8,192,0.1943,64.79
|
||||
2,2048,8,192,0.1992,126.38
|
||||
4,2048,8,192,0.2059,244.52
|
||||
8,2048,8,192,0.2174,463.13
|
||||
16,2048,8,192,0.4202,479.24
|
||||
32,2048,8,192,0.6503,619.36
|
||||
64,2048,8,192,1.1158,721.93
|
||||
128,2048,8,192,2.2250,724.05
|
||||
1,4096,8,192,0.3834,65.65
|
||||
2,4096,8,192,0.3904,128.95
|
||||
4,4096,8,192,0.4043,249.04
|
||||
8,4096,8,192,0.4267,471.92
|
||||
16,4096,8,192,0.8271,486.90
|
||||
32,4096,8,192,1.2840,627.28
|
||||
64,4096,8,192,2.2148,727.29
|
||||
128,4096,8,192,4.3819,735.21
|
||||
1,8192,8,192,0.7566,66.52
|
||||
2,8192,8,192,0.7712,130.54
|
||||
4,8192,8,192,0.7974,252.49
|
||||
8,8192,8,192,0.8433,477.47
|
||||
16,8192,8,192,1.6433,490.09
|
||||
32,8192,8,192,2.5573,629.84
|
||||
64,8192,8,192,4.3785,735.73
|
||||
128,8192,8,192,8.7303,737.99
|
||||
1,16384,8,192,1.5068,66.81
|
||||
2,16384,8,192,1.5350,131.16
|
||||
4,16384,8,192,1.5868,253.76
|
||||
8,16384,8,192,1.6778,479.99
|
||||
16,16384,8,192,3.2750,491.81
|
||||
32,16384,8,192,5.0659,635.88
|
||||
64,16384,8,192,8.7435,736.85
|
||||
128,16384,8,192,17.5040,736.13
|
||||
|
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,224,0.1254,29.29
|
||||
2,512,8,224,0.1412,52.05
|
||||
4,512,8,224,0.1497,98.13
|
||||
8,512,8,224,0.1533,191.70
|
||||
16,512,8,224,0.1913,307.17
|
||||
32,512,8,224,0.3292,357.08
|
||||
64,512,8,224,0.5187,453.28
|
||||
128,512,8,224,0.9522,493.84
|
||||
1,1024,8,224,0.2727,26.93
|
||||
2,1024,8,224,0.2836,51.78
|
||||
4,1024,8,224,0.2890,101.63
|
||||
8,1024,8,224,0.2959,198.55
|
||||
16,1024,8,224,0.3696,317.93
|
||||
32,1024,8,224,0.6408,366.75
|
||||
64,1024,8,224,1.0081,466.21
|
||||
128,1024,8,224,1.8548,506.78
|
||||
1,2048,8,224,0.5515,26.63
|
||||
2,2048,8,224,0.5575,52.67
|
||||
4,2048,8,224,0.5666,103.65
|
||||
8,2048,8,224,0.5803,202.42
|
||||
16,2048,8,224,0.7250,324.05
|
||||
32,2048,8,224,1.2593,373.14
|
||||
64,2048,8,224,1.9890,472.48
|
||||
128,2048,8,224,3.6905,509.28
|
||||
1,4096,8,224,1.0939,26.84
|
||||
2,4096,8,224,1.1044,53.18
|
||||
4,4096,8,224,1.1219,104.69
|
||||
8,4096,8,224,1.1500,204.26
|
||||
16,4096,8,224,1.4390,326.48
|
||||
32,4096,8,224,2.4992,375.97
|
||||
64,4096,8,224,4.0082,468.86
|
||||
128,4096,8,224,7.3372,512.26
|
||||
1,8192,8,224,2.1775,26.97
|
||||
2,8192,8,224,2.1989,53.41
|
||||
4,8192,8,224,2.2338,105.15
|
||||
8,8192,8,224,2.3268,201.90
|
||||
16,8192,8,224,2.8806,326.18
|
||||
32,8192,8,224,5.0187,374.43
|
||||
64,8192,8,224,8.0323,467.90
|
||||
128,8192,8,224,14.6300,513.78
|
||||
1,16384,8,224,4.3360,27.09
|
||||
2,16384,8,224,4.3820,53.60
|
||||
4,16384,8,224,4.5006,104.38
|
||||
8,16384,8,224,4.6987,199.96
|
||||
16,16384,8,224,5.7361,327.59
|
||||
32,16384,8,224,10.1291,371.03
|
||||
64,16384,8,224,16.0745,467.60
|
||||
128,16384,8,224,OOM,OOM
|
||||
|
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,256,0.0877,47.89
|
||||
2,512,8,256,0.0921,91.17
|
||||
4,512,8,256,0.0940,178.74
|
||||
8,512,8,256,0.0964,348.52
|
||||
16,512,8,256,0.1450,463.27
|
||||
32,512,8,256,0.2250,597.21
|
||||
64,512,8,256,0.3609,744.43
|
||||
128,512,8,256,0.6932,775.25
|
||||
1,1024,8,256,0.1747,48.04
|
||||
2,1024,8,256,0.1762,95.27
|
||||
4,1024,8,256,0.1784,188.22
|
||||
8,1024,8,256,0.1817,369.53
|
||||
16,1024,8,256,0.2796,480.25
|
||||
32,1024,8,256,0.4339,619.00
|
||||
64,1024,8,256,0.6960,771.73
|
||||
128,1024,8,256,1.3439,799.36
|
||||
1,2048,8,256,0.3410,49.21
|
||||
2,2048,8,256,0.3439,97.60
|
||||
4,2048,8,256,0.3469,193.52
|
||||
8,2048,8,256,0.3533,379.94
|
||||
16,2048,8,256,0.5461,491.67
|
||||
32,2048,8,256,0.8493,632.28
|
||||
64,2048,8,256,1.3667,785.82
|
||||
128,2048,8,256,2.6465,811.64
|
||||
1,4096,8,256,0.6742,49.77
|
||||
2,4096,8,256,0.6777,99.03
|
||||
4,4096,8,256,0.6836,196.36
|
||||
8,4096,8,256,0.6950,386.31
|
||||
16,4096,8,256,1.0803,497.02
|
||||
32,4096,8,256,1.6794,639.44
|
||||
64,4096,8,256,2.7101,792.50
|
||||
128,4096,8,256,5.2543,817.52
|
||||
1,8192,8,256,1.3375,50.18
|
||||
2,8192,8,256,1.3448,99.81
|
||||
4,8192,8,256,1.3564,197.91
|
||||
8,8192,8,256,1.3799,389.08
|
||||
16,8192,8,256,2.1465,500.25
|
||||
32,8192,8,256,3.3342,644.12
|
||||
64,8192,8,256,5.3983,795.67
|
||||
128,8192,8,256,10.4691,820.55
|
||||
1,16384,8,256,2.6697,50.28
|
||||
2,16384,8,256,2.6817,100.10
|
||||
4,16384,8,256,2.7049,198.49
|
||||
8,16384,8,256,2.7533,390.00
|
||||
16,16384,8,256,4.2789,501.89
|
||||
32,16384,8,256,6.6476,646.11
|
||||
64,16384,8,256,10.7723,797.43
|
||||
128,16384,8,256,OOM,OOM
|
||||
|
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,32,0.0257,20.45
|
||||
2,512,8,32,0.0256,41.02
|
||||
4,512,8,32,0.0258,81.28
|
||||
8,512,8,32,0.0265,158.45
|
||||
16,512,8,32,0.0396,212.30
|
||||
32,512,8,32,0.0516,325.43
|
||||
64,512,8,32,0.0721,465.83
|
||||
128,512,8,32,0.1270,529.03
|
||||
1,1024,8,32,0.0461,22.75
|
||||
2,1024,8,32,0.0465,45.15
|
||||
4,1024,8,32,0.0477,88.04
|
||||
8,1024,8,32,0.0548,153.23
|
||||
16,1024,8,32,0.0734,228.71
|
||||
32,1024,8,32,0.0958,350.42
|
||||
64,1024,8,32,0.1334,503.15
|
||||
128,1024,8,32,0.2381,564.04
|
||||
1,2048,8,32,0.0872,24.06
|
||||
2,2048,8,32,0.0904,46.42
|
||||
4,2048,8,32,0.1028,81.59
|
||||
8,2048,8,32,0.1067,157.25
|
||||
16,2048,8,32,0.1428,235.10
|
||||
32,2048,8,32,0.1818,369.13
|
||||
64,2048,8,32,0.2554,525.57
|
||||
128,2048,8,32,0.4622,580.86
|
||||
1,4096,8,32,0.1730,24.25
|
||||
2,4096,8,32,0.1955,42.91
|
||||
4,4096,8,32,0.2020,83.05
|
||||
8,4096,8,32,0.2140,156.83
|
||||
16,4096,8,32,0.2777,241.65
|
||||
32,4096,8,32,0.3542,378.99
|
||||
64,4096,8,32,0.4990,538.05
|
||||
128,4096,8,32,0.9099,590.13
|
||||
1,8192,8,32,0.3820,21.96
|
||||
2,8192,8,32,0.3913,42.88
|
||||
4,8192,8,32,0.4127,81.31
|
||||
8,8192,8,32,0.4224,158.88
|
||||
16,8192,8,32,0.5490,244.51
|
||||
32,8192,8,32,0.6960,385.70
|
||||
64,8192,8,32,0.9870,543.98
|
||||
128,8192,8,32,1.8100,593.25
|
||||
1,16384,8,32,0.7655,21.92
|
||||
2,16384,8,32,0.8067,41.59
|
||||
4,16384,8,32,0.8228,81.56
|
||||
8,16384,8,32,0.8397,159.85
|
||||
16,16384,8,32,1.0910,246.04
|
||||
32,16384,8,32,1.3824,388.37
|
||||
64,16384,8,32,1.9663,546.08
|
||||
128,16384,8,32,3.6107,594.78
|
||||
|
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,512,0.3588,23.40
|
||||
2,512,8,512,0.3651,46.00
|
||||
4,512,8,512,0.3736,89.89
|
||||
8,512,8,512,0.3856,174.22
|
||||
16,512,8,512,0.7472,179.80
|
||||
32,512,8,512,1.1447,234.72
|
||||
64,512,8,512,1.9549,274.89
|
||||
128,512,8,512,3.8962,275.85
|
||||
1,1024,8,512,0.7261,23.12
|
||||
2,1024,8,512,0.7354,45.65
|
||||
4,1024,8,512,0.7496,89.57
|
||||
8,1024,8,512,0.7746,173.35
|
||||
16,1024,8,512,1.5049,178.46
|
||||
32,1024,8,512,2.3111,232.42
|
||||
64,1024,8,512,3.9538,271.70
|
||||
128,1024,8,512,7.8811,272.62
|
||||
1,2048,8,512,1.4636,22.93
|
||||
2,2048,8,512,1.4826,45.27
|
||||
4,2048,8,512,1.5109,88.86
|
||||
8,2048,8,512,1.5549,172.68
|
||||
16,2048,8,512,3.0237,177.60
|
||||
32,2048,8,512,4.6439,231.27
|
||||
64,2048,8,512,7.9560,269.99
|
||||
128,2048,8,512,15.8741,270.63
|
||||
1,4096,8,512,2.9312,22.90
|
||||
2,4096,8,512,2.9675,45.24
|
||||
4,4096,8,512,3.0243,88.77
|
||||
8,4096,8,512,3.1127,172.50
|
||||
16,4096,8,512,6.0753,176.76
|
||||
32,4096,8,512,9.3182,230.49
|
||||
64,4096,8,512,15.9642,269.07
|
||||
128,4096,8,512,31.8313,269.89
|
||||
1,8192,8,512,5.8843,22.81
|
||||
2,8192,8,512,5.9344,45.24
|
||||
4,8192,8,512,6.0465,88.80
|
||||
8,8192,8,512,6.2334,172.27
|
||||
16,8192,8,512,12.1594,176.62
|
||||
32,8192,8,512,18.6826,229.90
|
||||
64,8192,8,512,32.0055,268.41
|
||||
128,8192,8,512,OOM,OOM
|
||||
1,16384,8,512,11.8153,22.72
|
||||
2,16384,8,512,11.9237,45.03
|
||||
4,16384,8,512,12.1671,88.25
|
||||
8,16384,8,512,12.4948,171.88
|
||||
16,16384,8,512,24.3414,176.45
|
||||
32,16384,8,512,37.3907,229.74
|
||||
64,16384,8,512,OOM,OOM
|
||||
128,16384,8,512,OOM,OOM
|
||||
|
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,64,0.0404,25.99
|
||||
2,512,8,64,0.0399,52.60
|
||||
4,512,8,64,0.0413,101.69
|
||||
8,512,8,64,0.0482,174.25
|
||||
16,512,8,64,0.0540,310.86
|
||||
32,512,8,64,0.0629,533.75
|
||||
64,512,8,64,0.0833,806.14
|
||||
128,512,8,64,0.1104,1216.59
|
||||
1,1024,8,64,0.0747,28.08
|
||||
2,1024,8,64,0.0766,54.77
|
||||
4,1024,8,64,0.0891,94.17
|
||||
8,1024,8,64,0.0918,182.94
|
||||
16,1024,8,64,0.1044,321.41
|
||||
32,1024,8,64,0.1179,569.43
|
||||
64,1024,8,64,0.1566,857.28
|
||||
128,1024,8,64,0.2078,1292.17
|
||||
1,2048,8,64,0.1455,28.84
|
||||
2,2048,8,64,0.1684,49.82
|
||||
4,2048,8,64,0.1730,97.01
|
||||
8,2048,8,64,0.1850,181.39
|
||||
16,2048,8,64,0.2009,334.18
|
||||
32,2048,8,64,0.2268,592.01
|
||||
64,2048,8,64,0.3002,894.44
|
||||
128,2048,8,64,0.4027,1333.64
|
||||
1,4096,8,64,0.3265,25.69
|
||||
2,4096,8,64,0.3322,50.51
|
||||
4,4096,8,64,0.3522,95.27
|
||||
8,4096,8,64,0.3632,184.79
|
||||
16,4096,8,64,0.3942,340.56
|
||||
32,4096,8,64,0.4456,602.47
|
||||
64,4096,8,64,0.5927,905.94
|
||||
128,4096,8,64,0.7938,1352.87
|
||||
1,8192,8,64,0.6508,25.78
|
||||
2,8192,8,64,0.6879,48.78
|
||||
4,8192,8,64,0.7008,95.77
|
||||
8,8192,8,64,0.7199,186.44
|
||||
16,8192,8,64,0.7786,344.79
|
||||
32,8192,8,64,0.8798,610.25
|
||||
64,8192,8,64,1.1745,914.30
|
||||
128,8192,8,64,1.5728,1365.50
|
||||
1,16384,8,64,1.3524,24.81
|
||||
2,16384,8,64,1.3698,48.99
|
||||
4,16384,8,64,1.3923,96.40
|
||||
8,16384,8,64,1.4267,188.16
|
||||
16,16384,8,64,1.5451,347.47
|
||||
32,16384,8,64,1.7622,609.32
|
||||
64,16384,8,64,2.3392,918.09
|
||||
128,16384,8,64,3.1332,1370.84
|
||||
|
|
|
@ -0,0 +1,49 @@
|
|||
batch_size,seq_len_kv,heads,headdim,time_ms,bandwidth_GB_s
|
||||
1,512,8,96,0.0407,38.67
|
||||
2,512,8,96,0.0398,79.02
|
||||
4,512,8,96,0.0431,146.08
|
||||
8,512,8,96,0.0495,254.61
|
||||
16,512,8,96,0.0698,360.64
|
||||
32,512,8,96,0.1117,450.87
|
||||
64,512,8,96,0.1780,566.16
|
||||
128,512,8,96,0.3329,605.28
|
||||
1,1024,8,96,0.0732,43.01
|
||||
2,1024,8,96,0.0794,79.29
|
||||
4,1024,8,96,0.0871,144.54
|
||||
8,1024,8,96,0.0934,269.52
|
||||
16,1024,8,96,0.1297,388.14
|
||||
32,1024,8,96,0.2114,476.36
|
||||
64,1024,8,96,0.3379,596.08
|
||||
128,1024,8,96,0.6327,636.68
|
||||
1,2048,8,96,0.1505,41.80
|
||||
2,2048,8,96,0.1619,77.76
|
||||
4,2048,8,96,0.1713,146.94
|
||||
8,2048,8,96,0.1780,282.84
|
||||
16,2048,8,96,0.2492,404.09
|
||||
32,2048,8,96,0.4088,492.55
|
||||
64,2048,8,96,0.6575,612.55
|
||||
128,2048,8,96,1.2457,646.61
|
||||
1,4096,8,96,0.3099,40.61
|
||||
2,4096,8,96,0.3259,77.23
|
||||
4,4096,8,96,0.3346,150.42
|
||||
8,4096,8,96,0.3467,290.41
|
||||
16,4096,8,96,0.4888,411.94
|
||||
32,4096,8,96,0.8055,499.94
|
||||
64,4096,8,96,1.3209,609.72
|
||||
128,4096,8,96,2.4810,649.25
|
||||
1,8192,8,96,0.6343,39.68
|
||||
2,8192,8,96,0.6437,78.20
|
||||
4,8192,8,96,0.6601,152.50
|
||||
8,8192,8,96,0.6826,294.97
|
||||
16,8192,8,96,0.9688,415.64
|
||||
32,8192,8,96,1.6057,501.55
|
||||
64,8192,8,96,2.6527,607.19
|
||||
128,8192,8,96,4.9464,651.27
|
||||
1,16384,8,96,1.2581,40.01
|
||||
2,16384,8,96,1.2812,78.57
|
||||
4,16384,8,96,1.3112,153.55
|
||||
8,16384,8,96,1.3653,294.92
|
||||
16,16384,8,96,1.9351,416.16
|
||||
32,16384,8,96,3.2277,499.01
|
||||
64,16384,8,96,5.3192,605.60
|
||||
128,16384,8,96,9.8747,652.44
|
||||
|
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
|
@ -0,0 +1,330 @@
|
|||
# guide
|
||||
|
||||
# FlashAttention KV Cache Decode - 参赛指南
|
||||
|
||||
## 一、登录 XPU-OJ 平台
|
||||
|
||||
打开浏览器，访问：\*\*https://xpuoj.com/\*\*
|
||||
|
||||
1. 等待组委会统一发放 XPU-OJ 账号;
|
||||
|
||||
2. 使用分配的用户名和初始密码登录平台
|
||||
|
||||
|
||||
|
||||
|
||||
点击 **"登录"** 进入平台。
|
||||
|
||||
## 二、进入比赛
|
||||
|
||||
### 2.1 点击导航栏"比赛"
|
||||
|
||||
登录成功后,会跳转到 XPUOJ 平台首页。
|
||||
|
||||
找到页面顶部的导航栏,点击 **"比赛"** 标签,进入比赛列表页。
|
||||
|
||||
|
||||
|
||||
### 2.2 找到目标比赛
|
||||
|
||||
在比赛列表中,根据比赛状态(全部 / 未开始 / 进行中 / 已结束)找到目标比赛。
|
||||
|
||||
|
||||
|
||||
## 三、进入题目
|
||||
|
||||
### 3.1 在题目列表中找到目标题目
|
||||
|
||||
进入比赛后,页面会展示该比赛的**题目列表**。每道题都有编号(1 ~ N)和标题。
|
||||
|
||||
在列表中找到 **FlashAttention KV Cache Decode** 这道题(前缀为 `FlashAttention`),点击标题即可进入题目详情页。
|
||||
|
||||
|
||||
|
||||
### 3.2 查看题目要求
|
||||
|
||||
进入题目详情页后,可以看到以下几个区域:
|
||||
|
||||
* **左侧**:题目描述、接口约定、参数说明
|
||||
|
||||
* **右侧**:代码编辑器,用于编写并提交代码
|
||||
|
||||
|
||||
请仔细阅读左侧的 **题目描述** 和 **接口约定**,重点关注:
|
||||
|
||||
* 入口函数名(本题为 `run_kernel`)
|
||||
|
||||
* 必传的参数列表及其类型、顺序
|
||||
|
||||
* 编译/运行环境(语言选择,目标硬件 C500)
|
||||
|
||||
|
||||
### 3.3 编写并提交代码
|
||||
|
||||
在右侧的代码编辑器中,按照题目要求填入完整代码(可点击右上角"重置"恢复初始模板)。
|
||||
|
||||
#### 最小正确性代码(可先复制跑通)
|
||||
|
||||
为方便参赛者先跑通完整流程,这里提供一份 **最小正确性代码**,可直接复制粘贴到右侧编辑器,用于验证提交链路是否正常:
|
||||
|
||||
```cpp
|
||||
|
||||
#include <stdint.h>
|
||||
|
||||
#include <cuda_bf16.h>
|
||||
|
||||
#include <cuda_runtime.h>
|
||||
|
||||
|
||||
#define HEAD_DIM 128
|
||||
|
||||
|
||||
__global__ void paged_attention_kernel(
|
||||
|
||||
const __nv_bfloat16* q,
|
||||
|
||||
const __nv_bfloat16* k_cache_paged,
|
||||
|
||||
const __nv_bfloat16* v_cache_paged,
|
||||
|
||||
__nv_bfloat16* output,
|
||||
|
||||
const int32_t* cache_seqlens,
|
||||
|
||||
const int32_t* block_table,
|
||||
|
||||
int64_t batch_size,
|
||||
|
||||
int64_t seqlen_q,
|
||||
|
||||
int64_t num_heads,
|
||||
|
||||
int64_t num_heads_k,
|
||||
|
||||
int64_t headdim,
|
||||
|
||||
int64_t page_block_size,
|
||||
|
||||
int64_t blocks_per_batch)
|
||||
|
||||
{
|
||||
|
||||
int batch_idx = blockIdx.x / num_heads;
|
||||
|
||||
int head_idx = blockIdx.x % num_heads;
|
||||
|
||||
if (batch_idx >= batch_size || head_idx >= num_heads) return;
|
||||
|
||||
|
||||
int seqlen = cache_seqlens[batch_idx];
|
||||
|
||||
int tid = threadIdx.x;
|
||||
|
||||
|
||||
// 加载对应 head 的 query 元素
|
||||
|
||||
int64_t q_offset = ((batch_idx * seqlen_q + 0) * num_heads + head_idx) * headdim;
|
||||
|
||||
float q_val = __bfloat162float(q[q_offset + tid]);
|
||||
|
||||
|
||||
// Online safe softmax 状态
|
||||
|
||||
float max_val = -1e38f;
|
||||
|
||||
float sum_exp = 0.0f;
|
||||
|
||||
float out_acc = 0.0f;
|
||||
|
||||
float scale = 1.0f / sqrtf(static_cast<float>(headdim));
|
||||
|
||||
|
||||
// 静态共享内存,避免动态分配可能带来的兼容性问题
|
||||
|
||||
__shared__ float s_score[HEAD_DIM];
|
||||
|
||||
|
||||
for (int token = 0; token < seqlen; ++token) {
|
||||
|
||||
int page_idx = token / page_block_size;
|
||||
|
||||
int page_offset = token % page_block_size;
|
||||
|
||||
int physical_block = block_table[batch_idx * blocks_per_batch + page_idx];
|
||||
|
||||
|
||||
// 读取 key 元素
|
||||
|
||||
const __nv_bfloat16* k_ptr = k_cache_paged
|
||||
|
||||
+ (physical_block * page_block_size + page_offset) * (num_heads_k * headdim)
|
||||
|
||||
+ head_idx * headdim;
|
||||
|
||||
float k_val = __bfloat162float(k_ptr[tid]);
|
||||
|
||||
|
||||
// 点积 -> 共享内存归约
|
||||
|
||||
s_score[tid] = q_val * k_val;
|
||||
|
||||
__syncthreads();
|
||||
|
||||
|
||||
for (int stride = HEAD_DIM >> 1; stride > 0; stride >>= 1) {
|
||||
|
||||
if (tid < stride) {
|
||||
|
||||
s_score[tid] += s_score[tid + stride];
|
||||
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
}
|
||||
|
||||
float score = s_score[0] * scale;
|
||||
|
||||
|
||||
// 更新 softmax 状态
|
||||
|
||||
float new_max = fmaxf(max_val, score);
|
||||
|
||||
float rescale = expf(max_val - new_max);
|
||||
|
||||
sum_exp = sum_exp * rescale + expf(score - new_max);
|
||||
|
||||
out_acc = out_acc * rescale;
|
||||
|
||||
max_val = new_max;
|
||||
|
||||
|
||||
// 读取 value 元素,并累加(用最新 max 的权重)
|
||||
|
||||
const __nv_bfloat16* v_ptr = v_cache_paged
|
||||
|
||||
+ (physical_block * page_block_size + page_offset) * (num_heads_k * headdim)
|
||||
|
||||
+ head_idx * headdim;
|
||||
|
||||
float v_val = __bfloat162float(v_ptr[tid]);
|
||||
|
||||
out_acc += expf(score - max_val) * v_val;
|
||||
|
||||
|
||||
__syncthreads(); // 确保下次迭代共享内存可安全复用
|
||||
|
||||
}
|
||||
|
||||
|
||||
if (seqlen > 0) {
|
||||
|
||||
out_acc /= sum_exp;
|
||||
|
||||
} else {
|
||||
|
||||
out_acc = 0.0f;
|
||||
|
||||
}
|
||||
|
||||
|
||||
int64_t out_offset = ((batch_idx * seqlen_q + 0) * num_heads + head_idx) * headdim + tid;
|
||||
|
||||
output[out_offset] = __float2bfloat16(out_acc);
|
||||
|
||||
}
|
||||
|
||||
|
||||
extern "C" void run_kernel(
|
||||
|
||||
const __nv_bfloat16* q,
|
||||
|
||||
const __nv_bfloat16* k_cache_paged,
|
||||
|
||||
const __nv_bfloat16* v_cache_paged,
|
||||
|
||||
__nv_bfloat16* output,
|
||||
|
||||
const int32_t* cache_seqlens,
|
||||
|
||||
const int32_t* block_table,
|
||||
|
||||
int64_t batch_size,
|
||||
|
||||
int64_t seqlen_k,
|
||||
|
||||
int64_t seqlen_q,
|
||||
|
||||
int64_t num_heads,
|
||||
|
||||
int64_t num_heads_k,
|
||||
|
||||
int64_t headdim,
|
||||
|
||||
int64_t page_block_size,
|
||||
|
||||
int64_t num_blocks,
|
||||
|
||||
int64_t causal)
|
||||
|
||||
{
|
||||
|
||||
int64_t blocks_per_batch = num_blocks / batch_size;
|
||||
|
||||
dim3 grid(batch_size * num_heads);
|
||||
|
||||
dim3 block(HEAD_DIM);
|
||||
|
||||
|
||||
paged_attention_kernel<<<grid, block>>>(
|
||||
|
||||
q, k_cache_paged, v_cache_paged, output,
|
||||
|
||||
cache_seqlens, block_table,
|
||||
|
||||
batch_size, seqlen_q, num_heads, num_heads_k, headdim,
|
||||
|
||||
page_block_size, blocks_per_batch
|
||||
|
||||
);
|
||||
|
||||
}
|
||||
```
|
||||
|
||||
完成后:
|
||||
|
||||
1. 在编辑器下方 **语言** 选项中,根据代码实际情况选择对应语言(可选 `Triton` / `CUDA Maca` / `MXMACA C++` / `TileLang` 等)
|
||||
|
||||
2. 选择 **目标硬件** 为 `C500`
|
||||
|
||||
3. 点击右上角 **"提交"** 按钮
|
||||
|
||||
|
||||
|
||||
|
||||
## 四、查看提交结果
|
||||
|
||||
点击 **"提交"** 后,系统会自动跳转到提交记录页,展示本次提交的详细信息。
|
||||
|
||||
页面顶部会显示一行汇总信息,包括:
|
||||
|
||||
* **状态** —— 评测结果(如 `Accepted` 表示通过)
|
||||
|
||||
* **分数** —— 本次提交获得的分数
|
||||
|
||||
* **题目** —— 对应的题目名称
|
||||
|
||||
* **用时** —— 程序运行耗时
|
||||
|
||||
* **内存** —— 占用内存大小
|
||||
|
||||
* **答案** —— 提交所用的语言/硬件
|
||||
|
||||
* **提交时间** —— 提交的时刻
|
||||
|
||||
|
||||
|
||||
|
||||
页面下方会展开 **编译信息** 和 **各测试点**(样例、测试点 #1 ~ #N)的结果,逐个显示:
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
{
|
||||
"id": 197,
|
||||
"displayId": 20005,
|
||||
"type": "Traditional",
|
||||
"isPublic": false,
|
||||
"locales": [
|
||||
"zh_CN"
|
||||
],
|
||||
"samples": [
|
||||
{
|
||||
"inputData": "1\n",
|
||||
"outputData": ""
|
||||
}
|
||||
],
|
||||
"problemTagIds": []
|
||||
}
|
||||
|
|
@ -0,0 +1,298 @@
|
|||
from __future__ import annotations
|
||||
|
||||
|
||||
HEAD_DIMS = [128]
|
||||
BATCH_SIZES = [1, 4, 16]
|
||||
SEQ_LENS_KV = [1024, 4096, 8192, 16384]
|
||||
SEQ_LEN_Q = 1
|
||||
NUM_HEADS = 8
|
||||
NUM_HEADS_K = 8
|
||||
PAGE_BLOCK_SIZE = 16
|
||||
CAUSAL = 0
|
||||
|
||||
|
||||
def _build_cases():
|
||||
cases = []
|
||||
for headdim in HEAD_DIMS:
|
||||
for seqlen_k in SEQ_LENS_KV:
|
||||
for batch_size in BATCH_SIZES:
|
||||
cases.append(
|
||||
(
|
||||
batch_size,
|
||||
seqlen_k,
|
||||
SEQ_LEN_Q,
|
||||
NUM_HEADS,
|
||||
NUM_HEADS_K,
|
||||
headdim,
|
||||
PAGE_BLOCK_SIZE,
|
||||
CAUSAL,
|
||||
)
|
||||
)
|
||||
return cases
|
||||
|
||||
|
||||
TESTCASES = _build_cases()
|
||||
|
||||
|
||||
def getNumOfTestcases() -> int:
|
||||
return len(TESTCASES)
|
||||
|
||||
|
||||
try:
|
||||
from pathlib import Path
|
||||
from typing import List, Tuple, Union
|
||||
import math
|
||||
import sys
|
||||
|
||||
import torch
|
||||
|
||||
KernelArg = Union[torch.Tensor, int, float]
|
||||
CURRENT_CASE = None
|
||||
|
||||
def _ensure_flashattn_importable():
|
||||
try:
|
||||
from flash_attn.flash_attn_interface import flash_attn_with_kvcache # noqa: F401
|
||||
|
||||
return
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
here = Path(__file__).resolve()
|
||||
for parent in here.parents:
|
||||
candidate = parent / "flashattn"
|
||||
if (candidate / "flash_attn").is_dir():
|
||||
sys.path.insert(0, str(candidate))
|
||||
return
|
||||
|
||||
def _get_testcase_index() -> int:
|
||||
try:
|
||||
raw = input().strip()
|
||||
except EOFError:
|
||||
return 0
|
||||
if raw == "":
|
||||
return 0
|
||||
try:
|
||||
testcase_id = int(raw.split()[0])
|
||||
except ValueError:
|
||||
return 0
|
||||
if 1 <= testcase_id <= len(TESTCASES):
|
||||
return testcase_id - 1
|
||||
if 0 <= testcase_id < len(TESTCASES):
|
||||
return testcase_id
|
||||
return 0
|
||||
|
||||
def _compute_reps(batch_size: int, seq_len: int, head_dim: int, base_reps: int = 100) -> int:
|
||||
workload = batch_size * seq_len * head_dim
|
||||
if workload < 1e5:
|
||||
return base_reps
|
||||
if workload < 1e6:
|
||||
return base_reps // 2
|
||||
if workload < 1e7:
|
||||
return base_reps // 4
|
||||
if workload < 1e8:
|
||||
return base_reps // 8
|
||||
if workload < 1e9:
|
||||
return base_reps // 16
|
||||
return base_reps // 32
|
||||
|
||||
def _get_num_blocks(batch_size: int, seqlen_k: int, page_block_size: int) -> int:
|
||||
num_blocks = math.ceil(seqlen_k / page_block_size) * batch_size * 3
|
||||
return max(1024, num_blocks)
|
||||
|
||||
def getTestCaseSize() -> Tuple[List[Tuple[int, ...]], Tuple[int, int]]:
|
||||
testcase_id = _get_testcase_index()
|
||||
global CURRENT_CASE
|
||||
(
|
||||
batch_size,
|
||||
seqlen_k,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
page_block_size,
|
||||
causal,
|
||||
) = TESTCASES[testcase_id]
|
||||
num_blocks = _get_num_blocks(batch_size, seqlen_k, page_block_size)
|
||||
blocks_per_batch = num_blocks // batch_size
|
||||
CURRENT_CASE = (
|
||||
batch_size,
|
||||
seqlen_k,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
page_block_size,
|
||||
num_blocks,
|
||||
causal,
|
||||
20260720 + testcase_id,
|
||||
)
|
||||
warmup = 3
|
||||
iters = max(1, _compute_reps(batch_size, seqlen_k, headdim))
|
||||
return [
|
||||
(batch_size, seqlen_q, num_heads, headdim),
|
||||
(num_blocks, page_block_size, num_heads_k, headdim),
|
||||
(num_blocks, page_block_size, num_heads_k, headdim),
|
||||
(batch_size, seqlen_q, num_heads, headdim),
|
||||
(batch_size,),
|
||||
(batch_size, blocks_per_batch),
|
||||
(), (), (), (), (), (), (), (), (),
|
||||
], (warmup, iters)
|
||||
|
||||
def genTestCase(testcase_sizes, device: str = "cuda") -> List[KernelArg]:
|
||||
del testcase_sizes
|
||||
(
|
||||
batch_size,
|
||||
seqlen_k,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
page_block_size,
|
||||
num_blocks,
|
||||
causal,
|
||||
seed,
|
||||
) = CURRENT_CASE
|
||||
gen = torch.Generator(device=device)
|
||||
gen.manual_seed(seed)
|
||||
dtype = torch.bfloat16
|
||||
blocks_per_batch = num_blocks // batch_size
|
||||
q = torch.randn(
|
||||
batch_size,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
headdim,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
generator=gen,
|
||||
).contiguous()
|
||||
k_cache_paged = torch.randn(
|
||||
num_blocks,
|
||||
page_block_size,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
generator=gen,
|
||||
).contiguous()
|
||||
v_cache_paged = torch.randn(
|
||||
num_blocks,
|
||||
page_block_size,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
generator=gen,
|
||||
).contiguous()
|
||||
output = torch.empty(
|
||||
batch_size,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
headdim,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
)
|
||||
cache_seqlens = torch.full((batch_size,), seqlen_k, dtype=torch.int32, device=device)
|
||||
block_table = torch.randperm(num_blocks, dtype=torch.int32, device=device, generator=gen).reshape(
|
||||
batch_size,
|
||||
blocks_per_batch,
|
||||
)
|
||||
return [
|
||||
q,
|
||||
k_cache_paged,
|
||||
v_cache_paged,
|
||||
output,
|
||||
cache_seqlens,
|
||||
block_table,
|
||||
batch_size,
|
||||
seqlen_k,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
page_block_size,
|
||||
num_blocks,
|
||||
causal,
|
||||
]
|
||||
|
||||
def baseline(
|
||||
q,
|
||||
k_cache_paged,
|
||||
v_cache_paged,
|
||||
output,
|
||||
cache_seqlens,
|
||||
block_table,
|
||||
batch_size,
|
||||
seqlen_k,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
page_block_size,
|
||||
num_blocks,
|
||||
causal,
|
||||
):
|
||||
_ensure_flashattn_importable()
|
||||
from flash_attn.flash_attn_interface import flash_attn_with_kvcache
|
||||
|
||||
out = flash_attn_with_kvcache(
|
||||
q,
|
||||
k_cache_paged,
|
||||
v_cache_paged,
|
||||
None,
|
||||
None,
|
||||
cache_seqlens=cache_seqlens,
|
||||
cache_batch_idx=None,
|
||||
block_table=block_table,
|
||||
causal=bool(causal),
|
||||
window_size=(-1, -1),
|
||||
rotary_interleaved=False,
|
||||
alibi_slopes=None,
|
||||
num_splits=1,
|
||||
)
|
||||
output.copy_(out)
|
||||
return [
|
||||
q,
|
||||
k_cache_paged,
|
||||
v_cache_paged,
|
||||
output,
|
||||
cache_seqlens,
|
||||
block_table,
|
||||
batch_size,
|
||||
seqlen_k,
|
||||
seqlen_q,
|
||||
num_heads,
|
||||
num_heads_k,
|
||||
headdim,
|
||||
page_block_size,
|
||||
num_blocks,
|
||||
causal,
|
||||
]
|
||||
|
||||
def check(
|
||||
testcase_sizes,
|
||||
original_input_tensors,
|
||||
target_kernel_input_tensors,
|
||||
baseline_input_tensors,
|
||||
rtol=1e-2,
|
||||
atol=1e-2,
|
||||
) -> bool:
|
||||
del testcase_sizes, original_input_tensors
|
||||
output_t = target_kernel_input_tensors[3]
|
||||
output_ref = baseline_input_tensors[3]
|
||||
if output_t.shape != output_ref.shape:
|
||||
print(f"[FAIL] shape mismatch: target {output_t.shape}, ref {output_ref.shape}", file=sys.stderr)
|
||||
return False
|
||||
if output_t.dtype != output_ref.dtype:
|
||||
print(f"[FAIL] dtype mismatch: target {output_t.dtype}, ref {output_ref.dtype}", file=sys.stderr)
|
||||
return False
|
||||
if not torch.allclose(output_t.float(), output_ref.float(), rtol=rtol, atol=atol):
|
||||
diff = (output_t.float() - output_ref.float()).abs()
|
||||
print(
|
||||
f"[FAIL] allclose failed: max_abs_diff={float(diff.max().item()):.6f}, "
|
||||
f"mean_abs_diff={float(diff.mean().item()):.6f} (rtol={rtol}, atol={atol})",
|
||||
file=sys.stderr,
|
||||
)
|
||||
return False
|
||||
return True
|
||||
except Exception:
|
||||
pass
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
---
|
||||
sectionTitle: "题目描述"
|
||||
type: "Text"
|
||||
---
|
||||
你需要实现 FlashAttention paged KV cache decode 的 CUDA C++ 前向算子。
|
||||
|
||||
本题输入采用 `flash_attn_with_kvcache` 在 `flashattn/benchmarks/benchmark_kvcache.py` 中使用的 paged KV cache 配置。每个 batch 只有 1 个 query token，KV cache 长度为 `seqlen_k`，K/V cache 按 page 存储。
|
||||
|
||||
评测程序会调用你提交代码中的 `run_kernel` 函数。你需要根据 `cache_seqlens` 和 `block_table` 读取 paged KV cache，并将结果写入 `output`。
|
||||
|
||||
baseline 使用 benchmark 中的 FlashAttention Python API：
|
||||
|
||||
```python
|
||||
out = flash_attn_with_kvcache(
|
||||
q, k_cache_paged, v_cache_paged, None, None,
|
||||
cache_seqlens=cache_seqlens,
|
||||
cache_batch_idx=None,
|
||||
block_table=block_table,
|
||||
causal=False,
|
||||
window_size=(-1, -1),
|
||||
rotary_interleaved=False,
|
||||
alibi_slopes=None,
|
||||
num_splits=1,
|
||||
)
|
||||
output.copy_(out)
|
||||
```
|
||||
|
||||
如何提交代码详见[评测指南](/d/2)。
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
---
|
||||
sectionTitle: "接口约定"
|
||||
type: "codeSample"
|
||||
lang: "cuda"
|
||||
---
|
||||
你必须在提交的 CUDA 源码中提供如下 **C 符号**，函数名、参数类型、顺序必须完全一致，并使用 `extern "C"` 防止 name mangling：
|
||||
|
||||
```cpp
|
||||
#include <stdint.h>
|
||||
#include <cuda_bf16.h>
|
||||
|
||||
extern "C" void run_kernel(
|
||||
const __nv_bfloat16* q,
|
||||
const __nv_bfloat16* k_cache_paged,
|
||||
const __nv_bfloat16* v_cache_paged,
|
||||
__nv_bfloat16* output,
|
||||
const int32_t* cache_seqlens,
|
||||
const int32_t* block_table,
|
||||
int64_t batch_size,
|
||||
int64_t seqlen_k,
|
||||
int64_t seqlen_q,
|
||||
int64_t num_heads,
|
||||
int64_t num_heads_k,
|
||||
int64_t headdim,
|
||||
int64_t page_block_size,
|
||||
int64_t num_blocks,
|
||||
int64_t causal
|
||||
);
|
||||
```
|
||||
|
||||
### 参数说明
|
||||
|
||||
* `q`：decode query tensor，shape `(batch_size, seqlen_q, num_heads, headdim)`，连续 `bf16`
|
||||
* `k_cache_paged`：paged key cache，shape `(num_blocks, page_block_size, num_heads_k, headdim)`，连续 `bf16`
|
||||
* `v_cache_paged`：paged value cache，shape `(num_blocks, page_block_size, num_heads_k, headdim)`，连续 `bf16`
|
||||
* `output`：输出缓冲区，shape `(batch_size, seqlen_q, num_heads, headdim)`，连续 `bf16`
|
||||
* `cache_seqlens`：每个 batch 的 KV 长度，shape `(batch_size)`，连续 `int32`
|
||||
* `block_table`：每个 batch 的 page 映射表，shape `(batch_size, num_blocks / batch_size)`，连续 `int32`
|
||||
* `seqlen_q`：query 长度，评测中固定为 `1`
|
||||
* `page_block_size`：page size，评测中固定为 `16`
|
||||
* `causal`：是否启用 causal mask，评测中固定为 `0`
|
||||
|
||||
`run_kernel` 内部需要自行计算合适的 launch 配置并启动 CUDA kernel。为保证计时准确，不建议在 `run_kernel` 内部做 `cudaDeviceSynchronize()` 或显式同步。
|
||||
|
|
@ -0,0 +1,57 @@
|
|||
---
|
||||
sectionTitle: "接口约定"
|
||||
type: "codeSample"
|
||||
lang: "tilelang"
|
||||
---
|
||||
你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致：
|
||||
|
||||
```python
|
||||
import tilelang
|
||||
import tilelang.language as T
|
||||
from tilelang import jit
|
||||
|
||||
real_kernel = None
|
||||
|
||||
@jit
|
||||
def build_kernel(*args):
|
||||
@T.prim_func
|
||||
def kernel(*args):
|
||||
...
|
||||
return kernel
|
||||
|
||||
def run_kernel(
|
||||
q, # Tensor[bf16], shape (batch_size, seqlen_q, num_heads, headdim)
|
||||
k_cache_paged, # Tensor[bf16], shape (num_blocks, page_block_size, num_heads_k, headdim)
|
||||
v_cache_paged, # Tensor[bf16], shape (num_blocks, page_block_size, num_heads_k, headdim)
|
||||
output, # Tensor[bf16], shape (batch_size, seqlen_q, num_heads, headdim)
|
||||
cache_seqlens, # Tensor[int32], shape (batch_size)
|
||||
block_table, # Tensor[int32], shape (batch_size, num_blocks / batch_size)
|
||||
batch_size, # int64
|
||||
seqlen_k, # int64
|
||||
seqlen_q, # int64
|
||||
num_heads, # int64
|
||||
num_heads_k, # int64
|
||||
headdim, # int64
|
||||
page_block_size, # int64
|
||||
num_blocks, # int64
|
||||
causal, # int64
|
||||
):
|
||||
global real_kernel
|
||||
if real_kernel is None:
|
||||
real_kernel = build_kernel(...)
|
||||
real_kernel(q, k_cache_paged, v_cache_paged, output,
|
||||
cache_seqlens, block_table,
|
||||
batch_size, seqlen_k, seqlen_q, num_heads,
|
||||
num_heads_k, headdim, page_block_size, num_blocks, causal)
|
||||
```
|
||||
|
||||
### 参数说明
|
||||
|
||||
* `q`：decode query tensor，连续 `bfloat16`
|
||||
* `k_cache_paged/v_cache_paged`：paged KV cache，连续 `bfloat16`
|
||||
* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果
|
||||
* `cache_seqlens/block_table`：paged KV metadata，连续 `int32`
|
||||
* `page_block_size`：评测中固定为 `16`
|
||||
* `causal`：评测中固定为 `0`
|
||||
|
||||
`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 TileLang kernel。
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
---
|
||||
sectionTitle: "接口约定"
|
||||
type: "codeSample"
|
||||
lang: "triton"
|
||||
---
|
||||
你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致：
|
||||
|
||||
```python
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
@triton.jit
|
||||
def your_kernel(...):
|
||||
...
|
||||
|
||||
def run_kernel(
|
||||
q, # Tensor[bf16], shape (batch_size, seqlen_q, num_heads, headdim)
|
||||
k_cache_paged, # Tensor[bf16], shape (num_blocks, page_block_size, num_heads_k, headdim)
|
||||
v_cache_paged, # Tensor[bf16], shape (num_blocks, page_block_size, num_heads_k, headdim)
|
||||
output, # Tensor[bf16], shape (batch_size, seqlen_q, num_heads, headdim)
|
||||
cache_seqlens, # Tensor[int32], shape (batch_size)
|
||||
block_table, # Tensor[int32], shape (batch_size, num_blocks / batch_size)
|
||||
batch_size, # int64
|
||||
seqlen_k, # int64
|
||||
seqlen_q, # int64
|
||||
num_heads, # int64
|
||||
num_heads_k, # int64
|
||||
headdim, # int64
|
||||
page_block_size, # int64
|
||||
num_blocks, # int64
|
||||
causal, # int64
|
||||
):
|
||||
...
|
||||
```
|
||||
|
||||
### 参数说明
|
||||
|
||||
* `q`：decode query tensor，连续 `bfloat16`
|
||||
* `k_cache_paged/v_cache_paged`：paged KV cache，连续 `bfloat16`
|
||||
* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果
|
||||
* `cache_seqlens/block_table`：paged KV metadata，连续 `int32`
|
||||
* `page_block_size`：评测中固定为 `16`
|
||||
* `causal`：评测中固定为 `0`
|
||||
|
||||
`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 Triton kernel。
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
---
|
||||
sectionTitle: "输入格式"
|
||||
type: "Text"
|
||||
---
|
||||
本题输入由评测程序在 GPU 上构造，并按接口约定中的顺序传入 `run_kernel`。
|
||||
|
||||
`q/k_cache_paged/v_cache_paged/output` 均为连续 `torch.bfloat16` CUDA tensor，`cache_seqlens/block_table` 均为连续 `torch.int32` CUDA tensor。
|
||||
|
||||
KV cache layout 固定为 `flash_attn_with_kvcache` 的 paged cache 布局：`(num_blocks, page_block_size, num_heads_k, headdim)`。
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
---
|
||||
sectionTitle: "输出格式"
|
||||
type: "Text"
|
||||
---
|
||||
输出写入 `output`，shape 为 `(batch_size, 1, num_heads, headdim)`，类型为 `bfloat16`。
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
---
|
||||
sectionTitle: "样例"
|
||||
type: "Text"
|
||||
---
|
||||
若 `batch_size = 1`、`seqlen_k = 512`、`page_block_size = 16`，则每个序列需要访问 `32` 个有效 page：
|
||||
|
||||
```text
|
||||
cache_seqlens = [512]
|
||||
block_table.shape = (1, num_blocks)
|
||||
```
|
||||
|
||||
第 `t` 个 KV token 位于 `block_table[0, t / 16]` 指向的物理 page 中，page 内偏移为 `t % 16`。
|
||||
|
|
@ -0,0 +1 @@
|
|||
FlashAttention KV Cache Decode
|
||||
Loading…
Reference in New Issue