From c25932f93b3892631413fb5ffeb5d350ea0a0e7f Mon Sep 17 00:00:00 2001 From: andsonder Date: Fri, 9 Jan 2026 18:46:56 +0800 Subject: [PATCH 01/10] =?UTF-8?q?add=20triton=20=E7=BC=96=E7=A8=8B?= =?UTF-8?q?=E8=8C=83=E5=BC=8F=20doc?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../01_triton_programming_paradigms/README.md | 358 +++++++++++++++ .../homework.ipynb | 424 ++++++++++++++++++ 2 files changed, 782 insertions(+) create mode 100644 docs/18_triton/01_triton_programming_paradigms/README.md create mode 100644 docs/18_triton/01_triton_programming_paradigms/homework.ipynb diff --git a/docs/18_triton/01_triton_programming_paradigms/README.md b/docs/18_triton/01_triton_programming_paradigms/README.md new file mode 100644 index 0000000..a33c2c6 --- /dev/null +++ b/docs/18_triton/01_triton_programming_paradigms/README.md @@ -0,0 +1,358 @@ +# Triton 编程范式入门 + +## 前言 + +此文档面向「有 CUDA 编程基础」的同学，通过 CUDA vs Triton 对比的方式，帮助你快速理解 Triton 的编程范式。本文将从最简单的向量加法入手，展示两种编程模型的核心差异，并通过实际代码示例帮助你建立从 CUDA 到 Triton 的思维转换。 + +期望在阅读完本文后，你能够理解 Triton 的核心抽象思想，掌握其基本语法，并能够将简单的 CUDA Kernel 改写为 Triton 版本。 + +## 一、从向量加法看两种编程范式 + +向量加法是最简单的并行任务，非常适合用来对比 CUDA 和 Triton 的编程思想差异。我们先从任务本身说起。 + +### 1. 任务描述 + +给定两个长度为 N 的向量 `x` 和 `y`，计算 `out[i] = x[i] + y[i]`，输出新的向量 `out`。这是一个典型的 element-wise 操作，每个输出元素的计算都是独立的，天然适合并行化。 + +### 2. CUDA 实现：线程级并行 + +在 CUDA 中，你需要思考：**每个线程处理哪个元素？** + +```cuda +__global__ void vector_add_cuda(float *x, float *y, float *out, int n) { + // 每个线程计算自己的全局索引 + int idx = blockIdx.x * blockDim.x + threadIdx.x; + + // 边界检查 + if (idx < n) { + out[idx] = x[idx] + y[idx]; // 每个线程处理一个元素 + } +} + +// Host 端调用 +int main() { + int N = 10000000; + // ... 省略内存分配和数据初始化 ... + + int blockSize = 256; // 每个 Block 有 256 个线程 + int numBlocks = (N + blockSize - 1) / blockSize; + + vector_add_cuda<<>>(cuda_x, cuda_y, cuda_out, N); + cudaDeviceSynchronize(); + + return 0; +} +``` + +在 CUDA 的编程模型中，你需要**将任务分解到每一个线程。每个线程就像一个独立的工人**，负责处理一个元素。线程通过 `blockIdx` 和 `threadIdx` 这两个内置变量计算出自己负责的全局索引 `idx`，然后访问对应位置的数据。由于数组长度 `n` 可能不是 Block 大小的整数倍，我们需要用 `if (idx < n)` 来做边界检查，但这可能导致 Warp Divergence，影响性能。 + + +### 3. Triton 实现：数据块级并行 + +在 Triton 中，你需要思考：**每个 Program Instance 处理哪批元素？** + +```python +import torch +import triton +import triton.language as tl + +@triton.jit +def vector_add_triton( + x_ptr, # 输入向量 x 的指针 + y_ptr, # 输入向量 y 的指针 + out_ptr, # 输出向量 out 的指针 + n_elements, # 向量长度 + BLOCK_SIZE: tl.constexpr, # 每个 Program 处理的元素数（编译时常量） +): + # 1. 获取当前 Program 的 ID（类似 blockIdx.x） + pid = tl.program_id(axis=0) + + # 2. 计算当前 Program 负责的元素范围 + block_start = pid * BLOCK_SIZE + offsets = block_start + tl.arange(0, BLOCK_SIZE) + # offsets 是一个向量！例如：[0, 1, 2, ..., BLOCK_SIZE-1] + + # 3. 创建边界检查的 mask（向量化的边界检查） + mask = offsets < n_elements + + # 4. 向量化加载数据（一次加载 BLOCK_SIZE 个元素） + x = tl.load(x_ptr + offsets, mask=mask) + y = tl.load(y_ptr + offsets, mask=mask) + + # 5. 向量化计算 + out = x + y + + # 6. 向量化存储 + tl.store(out_ptr + offsets, out, mask=mask) + + +# Host 端调用 +def add(x: torch.Tensor, y: torch.Tensor): + assert x.is_cuda and y.is_cuda, "输入必须在 GPU 上" + assert x.shape == y.shape, "输入形状必须一致" + + out = torch.empty_like(x) + n_elements = out.numel() + + # 计算需要多少个 Program Instance + grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) + + # 启动 Kernel + vector_add_triton[grid](x, y, out, n_elements, BLOCK_SIZE=1024) + + return out + + +# 使用示例 +if __name__ == "__main__": + N = 10_000_000 + x = torch.randn(N, device='cuda', dtype=torch.float32) + y = torch.randn(N, device='cuda', dtype=torch.float32) + + out = add(x, y) + + # 验证正确性 + expected = x + y + assert torch.allclose(out, expected), "结果不匹配！" +``` + +相比之下，Triton 的编程模型更像是把线程们组织成团队。每个 Program Instance 处理一批元素（BLOCK_SIZE 个），而不是单个元素。你通过 `tl.arange` 生成一个向量化的偏移量数组，这个数组包含了当前 Program 要处理的所有元素的索引。边界检查也变成了向量化的操作：`mask = offsets < n_elements` 会生成一个布尔向量，标记哪些位置是有效的。这种向量化的 mask 机制可以有效避免 Warp Divergence。实际上，**Triton 中的所有操作都是向量化的（SIMD），这是它和 CUDA 最本质的区别**。 + + +## 二、核心概念对比 + +### 1. 概念映射表 + +| CUDA 概念 | Triton 概念 | 说明 | +|-----------|-------------|------| +| `__global__` | `@triton.jit` | Kernel 函数标记 | +| Thread | **不存在** | Triton 不暴露线程概念 | +| Block | **Program Instance** | 并行执行的基本单元 | +| `blockIdx.x` | `tl.program_id(axis=0)` | 获取当前 Block/Program 的 ID | +| `threadIdx.x` | **不存在** | Triton 自动向量化 | +| `blockDim.x` | `BLOCK_SIZE` | 每个 Block 处理的元素数 | +| `idx = blockIdx.x * blockDim.x + threadIdx.x` | `offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)` | 索引计算方式不同 | +| `if (idx < n)` | `mask = offsets < n` | 边界检查方式不同 | +| `x[idx]` | `tl.load(x_ptr + offsets, mask=mask)` | 内存访问方式不同 | +| `out[idx] = value` | `tl.store(out_ptr + offsets, value, mask=mask)` | 内存写入方式不同 | +| `__syncthreads()` | **自动处理** | Triton 不需要手动同步 | + + +### 2. 线程索引 vs 向量化偏移 + +这是 CUDA 和 Triton 最核心的区别。 + +#### CUDA：标量索引 + +```cuda +// 每个线程计算自己的索引（标量） +int idx = blockIdx.x * blockDim.x + threadIdx.x; + +// 访问一个元素 +float val = x[idx]; +``` + +在 CUDA 中，你的思考方式是："我是第 `idx` 号线程，我处理第 `idx` 个元素"。这是一种非常直观的一对一映射关系。 + +#### Triton：向量偏移 + +```python +# 每个 Program 计算一批偏移量（向量） +pid = tl.program_id(axis=0) +offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + +# 访问一批元素 +x = tl.load(x_ptr + offsets, mask=mask) +``` + +而在 Triton 中，你需要转变思维："我是第 `pid` 号 Program，我处理第 `[start, start+1, ..., start+BLOCK_SIZE-1]` 批元素"。这里最关键的区别在于，CUDA 的 `idx` 是一个标量（单个整数），而 Triton 的 `offsets` 是一个向量（整数数组）。 + + +### 3. 边界检查：`if` vs `mask` + +#### CUDA 的方式 + +```cuda +if (idx < n) { + out[idx] = x[idx] + y[idx]; // 可能导致 Warp Divergence +} +``` + +使用标量 `if` 的问题在于，同一个 Warp 内的 32 个线程可能会因为边界条件走不同的分支。比如在最后一个 Block 中，前面的线程满足 `idx < n` 继续执行，而后面的线程不满足条件被过滤掉。这种分支分歧（Divergence）会导致 Warp 内的线程无法同步执行，从而降低性能。 + +#### Triton 的方式 + +```python +mask = offsets < n_elements # 向量化比较，生成布尔向量 + +x = tl.load(x_ptr + offsets, mask=mask) # 只加载有效位置 +out = x + y +tl.store(out_ptr + offsets, out, mask=mask) # 只存储有效位置 +``` + +Triton 的 `mask` 机制则完全不同。`mask = offsets < n_elements` 是一个向量化的比较操作，会生成一个布尔向量。在 `tl.load` 和 `tl.store` 中使用 mask 时，编译器会生成 predicated instructions（带谓词的指令），这是 GPU 硬件原生支持的特性。每个线程都有独立的 predicate 寄存器，可以在不引起分支的情况下选择性地执行指令。这样既保证了正确性，又避免了 Warp Divergence，代码还更简洁。 + +### 4. 具体示例：处理 10000 个元素 + +假设我们要处理 `N = 10000` 个元素。 + +#### CUDA 的执行方式 + +``` +配置：blockSize = 256, numBlocks = 40 + +Grid +├── Block[0] Block[1] ... Block[39] +│ ├── Thread[0] ├── Thread[0] ├── Thread[0] +│ │ 处理 idx=0 │ 处理 idx=256 │ 处理 idx=9984 +│ ├── Thread[1] ├── Thread[1] ├── Thread[1] +│ │ 处理 idx=1 │ 处理 idx=257 │ 处理 idx=9985 +│ ├── ... ├── ... ├── ... +│ └── Thread[255] └── Thread[255] └── Thread[255] +│ 处理 idx=255 处理 idx=511 处理 idx=10239 (越界！) + +总共启动: 40 × 256 = 10240 个线程 +其中 240 个线程因为 if (idx < 10000) 被过滤掉 +``` + +#### Triton 的执行方式 + +``` +配置：BLOCK_SIZE = 1024, numPrograms = 10 + +Grid +├── Program[0] Program[1] ... Program[9] +│ 处理元素 处理元素 处理元素 +│ [0~1023] [1024~2047] [9216~10239] +│ (其中 10000~10239 被 mask 过滤) + +总共启动: 10 个 Program Instance +每个 Program 处理 1024 个元素（向量化） +Triton 内部会自动映射到合适的线程配置 +``` + +从这个例子可以看出，CUDA 启动了 10240 个线程，你需要思考"我是第几号线程"。而 Triton 只启动了 10 个 Program Instance，你要思考的是"我处理哪批数据"。这种抽象层次的提升，让代码更简洁，也更容易理解。 + +## 三、Grid 配置对比 + +### CUDA 的 Grid 配置 + +```cuda +int blockSize = 256; // 每个 Block 有 256 个线程 +int numBlocks = (n + blockSize - 1) / blockSize; // 向上取整 + +my_kernel<<>>(args); +``` + +在 CUDA 中，你需要同时指定 `numBlocks`（有多少个 Block）和 `blockSize`（每个 Block 有多少个 Thread）。`blockSize` 的选择受到 SM 资源的限制，通常设置为 128/256/512。如果设置得太大，会因为寄存器和 Shared Memory 的限制导致 Occupancy 下降。 + +### Triton 的 Grid 配置 + +```python +BLOCK_SIZE = 1024 # 每个 Program Instance 处理 1024 个元素 + +grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) + +my_kernel[grid](args, BLOCK_SIZE=BLOCK_SIZE) +``` + +Triton 的 Grid 配置则简单得多，你只需要指定 `BLOCK_SIZE`（每个 Program 处理多少元素），Grid 的大小会通过 `triton.cdiv(n, BLOCK_SIZE)` 自动计算。这里有一个重要的概念需要澄清：Triton 的 `BLOCK_SIZE` 并不等同于 CUDA 的 `blockDim.x`。CUDA 的 `blockDim.x` 表示线程数，而 Triton 的 `BLOCK_SIZE` 表示元素数。Triton 编译器会根据你指定的 `BLOCK_SIZE`，自动选择合适的线程配置来映射这些元素。 + +根据经验，Triton 的 `BLOCK_SIZE` 通常设置为 1024/2048/4096，比 CUDA 的 `blockDim` 要大得多。建议从 1024 开始尝试，然后根据实际性能进行调整。 + + +## 四、课后练习 + +请打开 `01_exercises.py` 完成以下三个练习：练习 1 实现 AXPY 操作（$Z = \alpha \cdot X + Y$），巩固基本的向量化加载和存储；练习 2 测试不同 `BLOCK_SIZE` 的性能影响，理解为什么 Triton 的最优 `BLOCK_SIZE` 比 CUDA 的 `blockDim` 要大；练习 3 实现 1D 卷积，体会如何用向量化方式处理滑动窗口操作。每个练习都包含了测试函数和思考题。 + +## 五、常见问题 FAQ + +### Q1: Triton 的 BLOCK_SIZE 应该设置多大？ + +**A**: 如果你有 CUDA 经验，需要注意 CUDA 的经验值在这里不适用。CUDA 的 `blockDim.x` 通常设置为 128/256/512，而 Triton 的 `BLOCK_SIZE` 通常要大得多，一般是 1024/2048/4096。这是因为 Triton 的 `BLOCK_SIZE` 表示的是元素数，而不是线程数。建议从 1024 开始尝试，然后根据性能 profiling 的结果进行调整。影响最优 `BLOCK_SIZE` 的因素包括：寄存器使用量、Shared Memory 大小、以及数据复用程度。 + + +### Q2: Triton 内部到底有没有线程？性能会比 CUDA 差吗？ + +**A**: Triton 内部是有线程的，只是抽象层次更高，不暴露给程序员。Triton 编译器会将你写的向量化代码编译成高效的 PTX（GPU 汇编），最终还是在 GPU 的线程上执行。在性能方面，对于简单算子（如 element-wise 操作），Triton 的性能可以接近手写的优化 CUDA 代码；对于复杂算子（如 Flash Attention），Triton 可以达到优化后 CUDA 的 95% 以上的性能。但在开发效率方面，Triton 远远领先于 CUDA。 + + +### Q3: mask 操作会导致性能下降吗？（类似 Warp Divergence） + +**A**: Triton 的 `mask` 是向量化的，编译器会生成 predicated instructions（带谓词的指令），不会像 CUDA 的标量 `if` 那样导致严重的 Warp Divergence。性能损失通常可以忽略。从技术细节来看，现代 GPU 支持 predicated execution，每个线程都有独立的 predicate 寄存器。Triton 编译器会自动将 `mask` 映射到这些硬件特性，因此可以在不引入分支的情况下实现条件执行。 + + +### Q4: 什么时候不能用 Triton？ + +**A**: 以下场景建议使用 CUDA： +1. 需要显式管理 Shared Memory 布局（如手动消除 Bank Conflicts） +2. 需要使用 Warp-level primitives（`__shfl_`, `__ballot_`, `__syncwarp`） +3. 需要动态并行（Dynamic Parallelism） +4. 算法严重依赖线程间细粒度通信 +5. 需要与现有 CUDA 代码库深度集成 + +### Q5: 如何从 CUDA 代码迁移到 Triton？ + +**A**: 五步迁移法： + +1. **识别数据访问模式**：你的 Kernel 主要做什么？（element-wise、reduction、matmul？） + +2. **改变思维方式**： + - ❌ "每个线程处理第 `idx` 个元素" + - ✅ "每个 Program 处理第 `[start...end]` 批元素" + +3. **转换索引计算**： + ```cuda + // CUDA + int idx = blockIdx.x * blockDim.x + threadIdx.x; + ``` + ```python + # Triton + pid = tl.program_id(0) + offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + ``` + +4. **转换边界检查**： + ```cuda + // CUDA + if (idx < n) { ... } + ``` + ```python + # Triton + mask = offsets < n + ``` + +5. **转换内存访问**： + ```cuda + // CUDA + float x = x_ptr[idx]; + out[idx] = result; + ``` + ```python + # Triton + x = tl.load(x_ptr + offsets, mask=mask) + tl.store(out_ptr + offsets, result, mask=mask) + ``` + + +## 六、学习检查清单 + +完成本小节后，你应该能够理解 Triton 的 Program Instance 概念以及它和 CUDA Block 的本质区别，掌握用 `tl.arange` 生成向量化偏移来代替 CUDA 的线程索引计算，理解 Triton 的 `mask` 如何优雅地避免 Warp Divergence，能够将简单的 CUDA element-wise Kernel 改写为 Triton 版本，并深刻理解 Triton 的抽象级别是 Block-level 而非 Thread-level。 + +下表展示了从 CUDA 思维到 Triton 思维的转变： + +| ❌ CUDA 思维 | ✅ Triton 思维 | +|-------------|----------------| +| "这个线程处理第 `idx` 个元素" | "这个 Program 处理第 `[start, ..., end]` 批元素" | +| "`idx` 是一个整数" | "`offsets` 是一个整数向量" | +| "用 `if (idx < n)` 检查边界" | "用 `mask = offsets < n` 向量化检查" | +| "直接访问 `x[idx]`" | "显式 `tl.load(x_ptr + offsets)`" | + + +## 附：参考资料 + +- [Triton 官方教程](https://triton-lang.org/main/getting-started/tutorials/index.html) +- [OpenAI Triton GitHub](https://github.com/openai/triton) +- [CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html) + +**下一步**：完成所有练习后，进入 **Module 02: 内存与数据搬运**，学习更复杂的内存访问模式！ \ No newline at end of file diff --git a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb new file mode 100644 index 0000000..517d4d5 --- /dev/null +++ b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb @@ -0,0 +1,424 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Triton 编程范式 - 课后练习\n", + "\n", + "本 Notebook 包含三个练习，帮助你巩固 Triton 的核心概念。\n", + "\n", + "**学习目标**：\n", + "- 掌握 Triton 的基本语法和向量化操作\n", + "- 理解 `BLOCK_SIZE` 对性能的影响\n", + "- 学会用向量化方式处理复杂的数据访问模式" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import triton\n", + "import triton.language as tl\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "\n", + "# 检查 GPU 可用性\n", + "assert torch.cuda.is_available(), \"需要 CUDA 支持的 GPU\"\n", + "print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n", + "print(f\"Triton version: {triton.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 练习 1: AXPY 操作\n", + "\n", + "**目标**：实现 BLAS 标准的 AXPY 操作：$Z = \\alpha \\cdot X + Y$\n", + "\n", + "这是最基础的练习，帮助你熟悉 Triton 的基本模式。\n", + "\n", + "**提示**：\n", + "- 结构与 `vector_add` 几乎相同\n", + "- `alpha` 是标量，可以直接与向量相乘（自动广播）\n", + "- 不需要对 `alpha` 使用 `tl.load`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@triton.jit\n", + "def axpy_kernel(\n", + " x_ptr, y_ptr, z_ptr,\n", + " n_elements,\n", + " alpha, # 标量参数\n", + " BLOCK_SIZE: tl.constexpr\n", + "):\n", + " \"\"\"\n", + " TODO: 实现 AXPY 操作\n", + " 1. 计算 pid 和 offsets\n", + " 2. 创建 mask\n", + " 3. 加载 x 和 y\n", + " 4. 计算 z = alpha * x + y\n", + " 5. 存储 z\n", + " \"\"\"\n", + " # ==================== 在下方编写代码 ====================\n", + " \n", + " \n", + " \n", + " # ========================================================\n", + " pass\n", + "\n", + "def run_axpy(x, y, alpha):\n", + " n_elements = x.numel()\n", + " z = torch.empty_like(x)\n", + " grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n", + " \n", + " axpy_kernel[grid](\n", + " x, y, z,\n", + " n_elements, alpha,\n", + " BLOCK_SIZE=1024\n", + " )\n", + " return z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 测试 AXPY\n", + "size = 98432\n", + "alpha = 3.14\n", + "x = torch.randn(size, device='cuda', dtype=torch.float32)\n", + "y = torch.randn(size, device='cuda', dtype=torch.float32)\n", + "\n", + "# Triton 实现\n", + "z_triton = run_axpy(x, y, alpha)\n", + "\n", + "# PyTorch 参考实现\n", + "z_torch = alpha * x + y\n", + "\n", + "# 验证\n", + "if torch.allclose(z_triton, z_torch, atol=1e-5):\n", + " print(\"AXPY 测试通过！\")\n", + "else:\n", + " print(\"AXPY 测试失败！\")\n", + " print(f\"最大误差: {torch.max(torch.abs(z_triton - z_torch)).item():.2e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**思考题**：\n", + "1. 为什么 `alpha` 不需要 `tl.load`？\n", + "2. 如果 `alpha` 是一个向量（每个元素有不同的系数），代码需要怎么改？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 练习 2: 性能测试 - BLOCK_SIZE 的影响\n", + "\n", + "**目标**：探索不同 `BLOCK_SIZE` 对性能的影响，找出最优配置\n", + "\n", + "这个练习帮助你理解为什么 Triton 的 `BLOCK_SIZE` 通常比 CUDA 的 `blockDim` 大得多。\n", + "\n", + "**测试方案**：\n", + "- 使用向量加法作为基准测试\n", + "- 测试不同的 `BLOCK_SIZE`: [128, 256, 512, 1024, 2048, 4096]\n", + "- 测量执行时间和内存带宽" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 向量加法 Kernel（用于性能测试）\n", + "@triton.jit\n", + "def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\n", + " pid = tl.program_id(axis=0)\n", + " block_start = pid * BLOCK_SIZE\n", + " offsets = block_start + tl.arange(0, BLOCK_SIZE)\n", + " mask = offsets < n_elements\n", + " x = tl.load(x_ptr + offsets, mask=mask)\n", + " y = tl.load(y_ptr + offsets, mask=mask)\n", + " output = x + y\n", + " tl.store(output_ptr + offsets, output, mask=mask)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def benchmark_block_size(block_size, x, y, output, warmup=10, repeat=100):\n", + " \"\"\"基准测试单个 BLOCK_SIZE\"\"\"\n", + " n_elements = x.numel()\n", + " grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n", + " \n", + " # Warmup\n", + " for _ in range(warmup):\n", + " add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=block_size)\n", + " \n", + " # Timing\n", + " torch.cuda.synchronize()\n", + " start_event = torch.cuda.Event(enable_timing=True)\n", + " end_event = torch.cuda.Event(enable_timing=True)\n", + " \n", + " start_event.record()\n", + " for _ in range(repeat):\n", + " add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=block_size)\n", + " end_event.record()\n", + " \n", + " torch.cuda.synchronize()\n", + " time_ms = start_event.elapsed_time(end_event) / repeat\n", + " \n", + " # 计算带宽 (读 x, 读 y, 写 output)\n", + " total_bytes = 3 * n_elements * 4 # float32 = 4 bytes\n", + " bandwidth_gb_s = total_bytes / (time_ms * 1e-3) / 1e9\n", + " \n", + " return time_ms, bandwidth_gb_s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 运行基准测试\n", + "size = 1024 * 1024 * 10 # 10M elements\n", + "x = torch.randn(size, device='cuda', dtype=torch.float32)\n", + "y = torch.randn(size, device='cuda', dtype=torch.float32)\n", + "output = torch.empty_like(x)\n", + "\n", + "block_sizes = [128, 256, 512, 1024, 2048, 4096]\n", + "results = []\n", + "\n", + "print(f\"{'BLOCK_SIZE':<15} {'Time (ms)':<15} {'Bandwidth (GB/s)':<20}\")\n", + "print(\"-\" * 50)\n", + "\n", + "for bs in block_sizes:\n", + " time_ms, bandwidth = benchmark_block_size(bs, x, y, output)\n", + " results.append((bs, time_ms, bandwidth))\n", + " print(f\"{bs:<15} {time_ms:<15.3f} {bandwidth:<20.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 可视化结果\n", + "block_sizes_list = [r[0] for r in results]\n", + "bandwidths = [r[2] for r in results]\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(block_sizes_list, bandwidths, marker='o', linewidth=2, markersize=8)\n", + "plt.xlabel('BLOCK_SIZE', fontsize=12)\n", + "plt.ylabel('Bandwidth (GB/s)', fontsize=12)\n", + "plt.title('Triton BLOCK_SIZE vs Memory Bandwidth', fontsize=14)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.xscale('log', base=2)\n", + "plt.xticks(block_sizes_list, block_sizes_list)\n", + "\n", + "# 标注最佳 BLOCK_SIZE\n", + "best_idx = bandwidths.index(max(bandwidths))\n", + "plt.axvline(x=block_sizes_list[best_idx], color='r', linestyle='--', alpha=0.5)\n", + "plt.text(block_sizes_list[best_idx], max(bandwidths) * 0.95, \n", + " f'Best: {block_sizes_list[best_idx]}', ha='center', fontsize=10, color='r')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"\\n🏆 最优 BLOCK_SIZE: {block_sizes_list[best_idx]}\")\n", + "print(f\"🏆 最高带宽: {max(bandwidths):.2f} GB/s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**思考题**：\n", + "1. 为什么 `BLOCK_SIZE=128` 性能较差？（提示：GPU 利用率）\n", + "2. 为什么 `BLOCK_SIZE=4096` 可能也不理想？（提示：寄存器压力）\n", + "3. 对比 CUDA 的 `blockDim.x` 常用值（256），Triton 的最优 `BLOCK_SIZE` 为什么更大？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 练习 3: 1D 卷积（挑战）\n", + "\n", + "**目标**：实现简单的 1D 卷积（3-tap box filter）：$Y[i] = X[i-1] + X[i] + X[i+1]$\n", + "\n", + "边界条件：超出边界的值用 0 填充\n", + "\n", + "**难点**：\n", + "- 需要访问相邻元素（左邻居和右邻居）\n", + "- 边界处理：`i=0` 时左邻居不存在，`i=n-1` 时右邻居不存在\n", + "- 需要为不同的加载操作创建不同的 mask\n", + "\n", + "**提示**：分别加载三次\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@triton.jit\n", + "def conv1d_kernel(\n", + " x_ptr, y_ptr,\n", + " n_elements,\n", + " BLOCK_SIZE: tl.constexpr\n", + "):\n", + " \"\"\"\n", + " TODO: 实现 3-tap 1D 卷积\n", + " Y[i] = X[i-1] + X[i] + X[i+1]\n", + " \"\"\"\n", + " # ==================== 在下方编写代码 ====================\n", + " \n", + " \n", + " \n", + " # ========================================================\n", + " pass\n", + "\n", + "def run_conv1d(x):\n", + " n_elements = x.numel()\n", + " y = torch.empty_like(x)\n", + " grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n", + " \n", + " conv1d_kernel[grid](\n", + " x, y,\n", + " n_elements,\n", + " BLOCK_SIZE=1024\n", + " )\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 测试 1D 卷积\n", + "size = 1024\n", + "x = torch.randn(size, device='cuda', dtype=torch.float32)\n", + "\n", + "# Triton 实现\n", + "y_triton = run_conv1d(x)\n", + "\n", + "# PyTorch 参考实现\n", + "x_padded = torch.nn.functional.pad(x, (1, 1), mode='constant', value=0)\n", + "y_torch = x_padded[:-2] + x_padded[1:-1] + x_padded[2:]\n", + "\n", + "# 验证\n", + "if torch.allclose(y_triton, y_torch, atol=1e-5):\n", + " print(\"Conv1D 测试通过！\")\n", + "else:\n", + " print(\"Conv1D 测试失败！\")\n", + " print(f\"最大误差: {torch.max(torch.abs(y_triton - y_torch)).item():.2e}\")\n", + " \n", + " # 显示前几个元素用于调试\n", + " print(\"\\n前 5 个元素对比:\")\n", + " print(f\"Triton: {y_triton[:5].cpu().numpy()}\")\n", + " print(f\"Torch: {y_torch[:5].cpu().numpy()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 可视化卷积效果（可选）\n", + "size = 100\n", + "x = torch.randn(size, device='cuda', dtype=torch.float32)\n", + "y = run_conv1d(x)\n", + "\n", + "plt.figure(figsize=(12, 5))\n", + "plt.plot(x.cpu().numpy(), label='Input', alpha=0.7)\n", + "plt.plot(y.cpu().numpy(), label='Output (Smoothed)', alpha=0.7, linewidth=2)\n", + "plt.xlabel('Index')\n", + "plt.ylabel('Value')\n", + "plt.title('1D Convolution: Box Filter (3-tap)')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**思考题**（高级）：\n", + "1. 为什么这种方法效率不高？（提示：重复加载）\n", + "2. 如何优化？（提示：Shared Memory 或加载更大的块然后切片）" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 总结\n", + "\n", + "完成这三个练习后，你应该：\n", + "- 掌握了 Triton kernel 的基本写法\n", + "- 理解了 `BLOCK_SIZE` 对性能的重要影响\n", + "- 学会了如何处理复杂的内存访问模式\n", + "\n", + "**下一步**：学习 Triton 的 Shared Memory 和 Block Reduction 操作！" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 397a0a3bc89bdd4c82125c2a94fd5fd63e7f6412 Mon Sep 17 00:00:00 2001 From: andsonder Date: Sat, 10 Jan 2026 13:27:20 +0800 Subject: [PATCH 02/10] update --- .gitignore | 1 + README.md | 4 +- .../01_triton_programming_paradigms/README.md | 54 +---- .../homework.ipynb | 225 +++++------------- ...15acc1e2b671548a26a4e86a114ce6a59a22cd.png | Bin 0 -> 460027 bytes ...47ff520d044a1cdc65708a2760b94fa4e803f3.png | Bin 0 -> 484784 bytes docs/index.md | 4 +- sidebars.js | 10 + 8 files changed, 83 insertions(+), 215 deletions(-) create mode 100644 docs/18_triton/01_triton_programming_paradigms/images/0d3e9e7c3877312abfbde75dea15acc1e2b671548a26a4e86a114ce6a59a22cd.png create mode 100644 docs/18_triton/01_triton_programming_paradigms/images/a476e6f0adb4c9de8c67f9451247ff520d044a1cdc65708a2760b94fa4e803f3.png diff --git a/.gitignore b/.gitignore index c48c062..d14461a 100644 --- a/.gitignore +++ b/.gitignore @@ -31,3 +31,4 @@ *.out *.app .DS_Store +.vscode \ No newline at end of file diff --git a/README.md b/README.md index 449ee93..e00b0a6 100644 --- a/README.md +++ b/README.md @@ -54,9 +54,9 @@ + 使用多个 GPU 计算 + ...(补充中) -### 大师系列 💡 +### Triton 系列 💡 -我现在还不知道写啥，毕竟我现在还是菜鸡~~ ++ [Triton 编程范式入门](./docs/18_triton/01_triton_programming_paradigms/README.md) ### LLM 推理技术 🤖 diff --git a/docs/18_triton/01_triton_programming_paradigms/README.md b/docs/18_triton/01_triton_programming_paradigms/README.md index a33c2c6..679c849 100644 --- a/docs/18_triton/01_triton_programming_paradigms/README.md +++ b/docs/18_triton/01_triton_programming_paradigms/README.md @@ -198,38 +198,11 @@ Triton 的 `mask` 机制则完全不同。`mask = offsets < n_elements` 是一 #### CUDA 的执行方式 -``` -配置：blockSize = 256, numBlocks = 40 - -Grid -├── Block[0] Block[1] ... Block[39] -│ ├── Thread[0] ├── Thread[0] ├── Thread[0] -│ │ 处理 idx=0 │ 处理 idx=256 │ 处理 idx=9984 -│ ├── Thread[1] ├── Thread[1] ├── Thread[1] -│ │ 处理 idx=1 │ 处理 idx=257 │ 处理 idx=9985 -│ ├── ... ├── ... ├── ... -│ └── Thread[255] └── Thread[255] └── Thread[255] -│ 处理 idx=255 处理 idx=511 处理 idx=10239 (越界！) - -总共启动: 40 × 256 = 10240 个线程 -其中 240 个线程因为 if (idx < 10000) 被过滤掉 -``` +![图 0](images/a476e6f0adb4c9de8c67f9451247ff520d044a1cdc65708a2760b94fa4e803f3.png) #### Triton 的执行方式 -``` -配置：BLOCK_SIZE = 1024, numPrograms = 10 - -Grid -├── Program[0] Program[1] ... Program[9] -│ 处理元素 处理元素 处理元素 -│ [0~1023] [1024~2047] [9216~10239] -│ (其中 10000~10239 被 mask 过滤) - -总共启动: 10 个 Program Instance -每个 Program 处理 1024 个元素（向量化） -Triton 内部会自动映射到合适的线程配置 -``` +![图 1](images/0d3e9e7c3877312abfbde75dea15acc1e2b671548a26a4e86a114ce6a59a22cd.png) 从这个例子可以看出，CUDA 启动了 10240 个线程，你需要思考"我是第几号线程"。而 Triton 只启动了 10 个 Program Instance，你要思考的是"我处理哪批数据"。这种抽象层次的提升，让代码更简洁，也更容易理解。 @@ -263,26 +236,21 @@ Triton 的 Grid 配置则简单得多，你只需要指定 `BLOCK_SIZE`（每个 ## 四、课后练习 -请打开 `01_exercises.py` 完成以下三个练习：练习 1 实现 AXPY 操作（$Z = \alpha \cdot X + Y$），巩固基本的向量化加载和存储；练习 2 测试不同 `BLOCK_SIZE` 的性能影响，理解为什么 Triton 的最优 `BLOCK_SIZE` 比 CUDA 的 `blockDim` 要大；练习 3 实现 1D 卷积，体会如何用向量化方式处理滑动窗口操作。每个练习都包含了测试函数和思考题。 +请打开 [homework.ipynb](https://github.com/PaddleJitLab/CUDATutorial/tree/develop/docs/18_triton/01_triton_programming_paradigms/homework.ipynb) 完成以下练习：练习 1 实现 AXPY 操作（$Z = \alpha \cdot X + Y$），巩固基本的向量化加载和存储；练习 2 实现 1D 卷积，体会如何用向量化方式处理滑动窗口操作。每个练习都包含了测试函数和思考题。 ## 五、常见问题 FAQ -### Q1: Triton 的 BLOCK_SIZE 应该设置多大？ - -**A**: 如果你有 CUDA 经验，需要注意 CUDA 的经验值在这里不适用。CUDA 的 `blockDim.x` 通常设置为 128/256/512，而 Triton 的 `BLOCK_SIZE` 通常要大得多，一般是 1024/2048/4096。这是因为 Triton 的 `BLOCK_SIZE` 表示的是元素数，而不是线程数。建议从 1024 开始尝试，然后根据性能 profiling 的结果进行调整。影响最优 `BLOCK_SIZE` 的因素包括：寄存器使用量、Shared Memory 大小、以及数据复用程度。 - - -### Q2: Triton 内部到底有没有线程？性能会比 CUDA 差吗？ - -**A**: Triton 内部是有线程的，只是抽象层次更高，不暴露给程序员。Triton 编译器会将你写的向量化代码编译成高效的 PTX（GPU 汇编），最终还是在 GPU 的线程上执行。在性能方面，对于简单算子（如 element-wise 操作），Triton 的性能可以接近手写的优化 CUDA 代码；对于复杂算子（如 Flash Attention），Triton 可以达到优化后 CUDA 的 95% 以上的性能。但在开发效率方面，Triton 远远领先于 CUDA。 +### Q1: Triton 内部到底有没有线程？性能会比 CUDA 差吗？ +**A**: 从硬件执行层面看，Triton 代码最终仍然运行在 GPU 的线程和 warp 上，只是 Triton 提供了更高层次的编程抽象，不直接暴露线程和 block 的概念。Triton 编译器会将向量化的程序描述转换为高效的 PTX / SASS，并映射到底层 GPU 执行模型。在性能方面，对于简单算子（如 element-wise 或带宽受限算子），Triton 通常可以达到接近手写 CUDA 的性能；对于高度优化的复杂算子（如 Flash Attention），Triton 在实践中也能达到与优化 CUDA 实现相当、或略低的性能水平。相比之下，Triton 在开发效率和可维护性方面通常具有明显优势。 -### Q3: mask 操作会导致性能下降吗？（类似 Warp Divergence） -**A**: Triton 的 `mask` 是向量化的，编译器会生成 predicated instructions（带谓词的指令），不会像 CUDA 的标量 `if` 那样导致严重的 Warp Divergence。性能损失通常可以忽略。从技术细节来看，现代 GPU 支持 predicated execution，每个线程都有独立的 predicate 寄存器。Triton 编译器会自动将 `mask` 映射到这些硬件特性，因此可以在不引入分支的情况下实现条件执行。 +### Q2: mask 操作会导致性能下降吗？（类似 Warp Divergence） +**A**: Triton 的 mask 是向量化语义，编译器通常会将其生成 predicated instructions（带谓词的指令），而不是显式的分支跳转，因此不会像 CUDA 中不当使用 if 那样引入严重的 warp divergence。 +在大多数连续访问、边界检查类场景中，mask 带来的性能开销较小；但如果 mask 覆盖比例很大或访问模式高度稀疏，仍然可能造成一定的算力浪费。总体而言，mask 是 Triton 中推荐且高效的边界处理方式。 -### Q4: 什么时候不能用 Triton？ +### Q3: 什么时候不能用 Triton？ **A**: 以下场景建议使用 CUDA： 1. 需要显式管理 Shared Memory 布局（如手动消除 Bank Conflicts） @@ -291,7 +259,7 @@ Triton 的 Grid 配置则简单得多，你只需要指定 `BLOCK_SIZE`（每个 4. 算法严重依赖线程间细粒度通信 5. 需要与现有 CUDA 代码库深度集成 -### Q5: 如何从 CUDA 代码迁移到 Triton？ +### Q4: 如何从 CUDA 代码迁移到 Triton？ **A**: 五步迁移法： @@ -355,4 +323,4 @@ Triton 的 Grid 配置则简单得多，你只需要指定 `BLOCK_SIZE`（每个 - [OpenAI Triton GitHub](https://github.com/openai/triton) - [CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html) -**下一步**：完成所有练习后，进入 **Module 02: 内存与数据搬运**，学习更复杂的内存访问模式！ \ No newline at end of file +**下一步**：完成所有练习后，进入 **02: 内存与数据搬运**，学习更复杂的内存访问模式！ \ No newline at end of file diff --git a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb index 517d4d5..323965f 100644 --- a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb +++ b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb @@ -132,145 +132,7 @@ "source": [ "---\n", "\n", - "## 练习 2: 性能测试 - BLOCK_SIZE 的影响\n", - "\n", - "**目标**：探索不同 `BLOCK_SIZE` 对性能的影响，找出最优配置\n", - "\n", - "这个练习帮助你理解为什么 Triton 的 `BLOCK_SIZE` 通常比 CUDA 的 `blockDim` 大得多。\n", - "\n", - "**测试方案**：\n", - "- 使用向量加法作为基准测试\n", - "- 测试不同的 `BLOCK_SIZE`: [128, 256, 512, 1024, 2048, 4096]\n", - "- 测量执行时间和内存带宽" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 向量加法 Kernel（用于性能测试）\n", - "@triton.jit\n", - "def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\n", - " pid = tl.program_id(axis=0)\n", - " block_start = pid * BLOCK_SIZE\n", - " offsets = block_start + tl.arange(0, BLOCK_SIZE)\n", - " mask = offsets < n_elements\n", - " x = tl.load(x_ptr + offsets, mask=mask)\n", - " y = tl.load(y_ptr + offsets, mask=mask)\n", - " output = x + y\n", - " tl.store(output_ptr + offsets, output, mask=mask)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def benchmark_block_size(block_size, x, y, output, warmup=10, repeat=100):\n", - " \"\"\"基准测试单个 BLOCK_SIZE\"\"\"\n", - " n_elements = x.numel()\n", - " grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n", - " \n", - " # Warmup\n", - " for _ in range(warmup):\n", - " add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=block_size)\n", - " \n", - " # Timing\n", - " torch.cuda.synchronize()\n", - " start_event = torch.cuda.Event(enable_timing=True)\n", - " end_event = torch.cuda.Event(enable_timing=True)\n", - " \n", - " start_event.record()\n", - " for _ in range(repeat):\n", - " add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=block_size)\n", - " end_event.record()\n", - " \n", - " torch.cuda.synchronize()\n", - " time_ms = start_event.elapsed_time(end_event) / repeat\n", - " \n", - " # 计算带宽 (读 x, 读 y, 写 output)\n", - " total_bytes = 3 * n_elements * 4 # float32 = 4 bytes\n", - " bandwidth_gb_s = total_bytes / (time_ms * 1e-3) / 1e9\n", - " \n", - " return time_ms, bandwidth_gb_s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 运行基准测试\n", - "size = 1024 * 1024 * 10 # 10M elements\n", - "x = torch.randn(size, device='cuda', dtype=torch.float32)\n", - "y = torch.randn(size, device='cuda', dtype=torch.float32)\n", - "output = torch.empty_like(x)\n", - "\n", - "block_sizes = [128, 256, 512, 1024, 2048, 4096]\n", - "results = []\n", - "\n", - "print(f\"{'BLOCK_SIZE':<15} {'Time (ms)':<15} {'Bandwidth (GB/s)':<20}\")\n", - "print(\"-\" * 50)\n", - "\n", - "for bs in block_sizes:\n", - " time_ms, bandwidth = benchmark_block_size(bs, x, y, output)\n", - " results.append((bs, time_ms, bandwidth))\n", - " print(f\"{bs:<15} {time_ms:<15.3f} {bandwidth:<20.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 可视化结果\n", - "block_sizes_list = [r[0] for r in results]\n", - "bandwidths = [r[2] for r in results]\n", - "\n", - "plt.figure(figsize=(10, 5))\n", - "plt.plot(block_sizes_list, bandwidths, marker='o', linewidth=2, markersize=8)\n", - "plt.xlabel('BLOCK_SIZE', fontsize=12)\n", - "plt.ylabel('Bandwidth (GB/s)', fontsize=12)\n", - "plt.title('Triton BLOCK_SIZE vs Memory Bandwidth', fontsize=14)\n", - "plt.grid(True, alpha=0.3)\n", - "plt.xscale('log', base=2)\n", - "plt.xticks(block_sizes_list, block_sizes_list)\n", - "\n", - "# 标注最佳 BLOCK_SIZE\n", - "best_idx = bandwidths.index(max(bandwidths))\n", - "plt.axvline(x=block_sizes_list[best_idx], color='r', linestyle='--', alpha=0.5)\n", - "plt.text(block_sizes_list[best_idx], max(bandwidths) * 0.95, \n", - " f'Best: {block_sizes_list[best_idx]}', ha='center', fontsize=10, color='r')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "print(f\"\\n🏆 最优 BLOCK_SIZE: {block_sizes_list[best_idx]}\")\n", - "print(f\"🏆 最高带宽: {max(bandwidths):.2f} GB/s\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**思考题**：\n", - "1. 为什么 `BLOCK_SIZE=128` 性能较差？（提示：GPU 利用率）\n", - "2. 为什么 `BLOCK_SIZE=4096` 可能也不理想？（提示：寄存器压力）\n", - "3. 对比 CUDA 的 `blockDim.x` 常用值（256），Triton 的最优 `BLOCK_SIZE` 为什么更大？" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## 练习 3: 1D 卷积（挑战）\n", + "## 练习 2: 1D 卷积（挑战）\n", "\n", "**目标**：实现简单的 1D 卷积（3-tap box filter）：$Y[i] = X[i-1] + X[i] + X[i+1]$\n", "\n", @@ -304,7 +166,6 @@ " # ==================== 在下方编写代码 ====================\n", " \n", " \n", - " \n", " # ========================================================\n", " pass\n", "\n", @@ -351,36 +212,13 @@ " print(f\"Torch: {y_torch[:5].cpu().numpy()}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 可视化卷积效果（可选）\n", - "size = 100\n", - "x = torch.randn(size, device='cuda', dtype=torch.float32)\n", - "y = run_conv1d(x)\n", - "\n", - "plt.figure(figsize=(12, 5))\n", - "plt.plot(x.cpu().numpy(), label='Input', alpha=0.7)\n", - "plt.plot(y.cpu().numpy(), label='Output (Smoothed)', alpha=0.7, linewidth=2)\n", - "plt.xlabel('Index')\n", - "plt.ylabel('Value')\n", - "plt.title('1D Convolution: Box Filter (3-tap)')\n", - "plt.legend()\n", - "plt.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "**思考题**（高级）：\n", "1. 为什么这种方法效率不高？（提示：重复加载）\n", - "2. 如何优化？（提示：Shared Memory 或加载更大的块然后切片）" + "2. 如何优化？（提示：加载更大的块然后切片）" ] }, { @@ -391,12 +229,61 @@ "\n", "## 总结\n", "\n", - "完成这三个练习后，你应该：\n", - "- 掌握了 Triton kernel 的基本写法\n", - "- 理解了 `BLOCK_SIZE` 对性能的重要影响\n", - "- 学会了如何处理复杂的内存访问模式\n", + "完成这三个练习后，你应该掌握了 Triton kernel 的基本写法\n", + "\n", + "**下一步**：学习 Triton 的 Shared Memory 和 Block Reduction 操作！\n", + "\n", + "## 课后答案\n", + "\n", + "```python\n", + "@triton.jit\n", + "def axpy_kernel(\n", + " x_ptr, y_ptr, z_ptr,\n", + " n_elements,\n", + " alpha, # 标量参数\n", + " BLOCK_SIZE: tl.constexpr\n", + "):\n", + " \"\"\"\n", + " TODO: 实现 AXPY 操作\n", + " 1. 计算 pid 和 offsets\n", + " 2. 创建 mask\n", + " 3. 加载 x 和 y\n", + " 4. 计算 z = alpha * x + y\n", + " 5. 存储 z\n", + " \"\"\"\n", + " # ==================== 在下方编写代码 ====================\n", + " pid = tl.program_id(0)\n", + " offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n", + " mask = offsets < n_elements\n", + " x = tl.load(x_ptr + offsets, mask=mask, other=0.0)\n", + " y = tl.load(y_ptr + offsets, mask=mask, other=0.0)\n", + " z = alpha * x + y\n", + " tl.store(z_ptr + offsets, z, mask=mask)\n", + " # ========================================================\n", "\n", - "**下一步**：学习 Triton 的 Shared Memory 和 Block Reduction 操作！" + "@triton.jit\n", + "def conv1d_kernel(\n", + " x_ptr, y_ptr,\n", + " n_elements,\n", + " BLOCK_SIZE: tl.constexpr\n", + "):\n", + " \"\"\"\n", + " TODO: 实现 3-tap 1D 卷积\n", + " Y[i] = X[i-1] + X[i] + X[i+1]\n", + " \"\"\"\n", + " # ==================== 在下方编写代码 ====================\n", + " pid = tl.program_id(0)\n", + " offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n", + " mask = offsets < n_elements\n", + " \n", + " x_center = tl.load(x_ptr + offsets, mask=mask, other=0.0)\n", + " x_left = tl.load(x_ptr + offsets - 1, mask=offsets > 0, other=0.0)\n", + " x_right = tl.load(x_ptr + offsets + 1, mask=offsets < n_elements - 1, other=0.0)\n", + " \n", + " y = x_left + x_center + x_right\n", + " tl.store(y_ptr + offsets, y, mask=mask)\n", + " # =========================================================\n", + "```" ] } ], diff --git a/docs/18_triton/01_triton_programming_paradigms/images/0d3e9e7c3877312abfbde75dea15acc1e2b671548a26a4e86a114ce6a59a22cd.png b/docs/18_triton/01_triton_programming_paradigms/images/0d3e9e7c3877312abfbde75dea15acc1e2b671548a26a4e86a114ce6a59a22cd.png new file mode 100644 index 0000000000000000000000000000000000000000..5ae5466b32f85e754dd8d4bf390ceac112135145 GIT binary patch literal 460027 zcmXtA1yEbx(+v>ZwYUT;uEiaKQ#3$vcW-fbhv4q+P~3`Jp~Z{4YjG*|@%ztwlX-LR zo4h-deY5xMxo3A1sq$F{9fcSL005xN$x5jK0KoStEE^K+`(O?cL4O}$zN*Pc0BV1e z9=+d4nQO~gC@BFL-{(jGm~iX=Er5ByBYwXC0JwY@0Nncs^Zo(k!~XxbKtMj+|IcCB z{(DdwzhNE#5Ch0biEDVkoUS2unTUuIdqewB9ixLq4EaXZz1ovP2txDrGvSuK5i}vjmU3@JzI^F{q!Lh&G!0o2M#xtejb~O`zeV`U67HGVFe6{#wK|U8-RS<~(&V5_ ztV>>lRBx=aeUSCcm~Z~G9$cx*RqK?VRx5B^cr!g38*I4nCl|X9tm*|Bg<$I4_Rhf- zDfP}5@VZWSlYecVH4N(T=+GnYe~1R{VtMJNo<5zN8$!10SAQB(MT+w$>)CkET#Xm) 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z$m=Q$5t3ZUYI>|~LZVt)YX)=1YA@|&8H%4f5j=762LiOPUi5|?);j&*X=g*@s}&4jTDZ~;3r5~i8i z1G68HXb?^|Iw1)QJ&qiJ6nKv$B5w|07pmBg9(hM}33sBf5!4-B`*WOaElx0-U(E&P zt(uS(OzuR~>GEQPClW^mRLe@@t3?sTlRKm@}rkXupSy0Qlujwy0N zrYV9*tog+Rt5&qQAH0NwcopYxj*(`fkuLO#gw3I1#=9czqlkl6K{y?Ms;a~iCWM$S z-o{uAHeg|Fj8`Bg5?7>sv;i+{amIV$@rsWV?Y4vc;RLvf06lSjH*LagFM=0_{{zzd VRt<1o;#U9w002ovPDHLkV1o7!&nN%@ literal 0 HcmV?d00001 diff --git a/docs/index.md b/docs/index.md index 72aed9c..029358a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -49,7 +49,9 @@ + 使用多个 GPU 计算 + ...(补充中) -### 大师系列 💡 +### Triton 系列 💡 + ++ [Triton 编程范式入门](/triton/triton_programming_paradigms) 我现在还不知道写啥，毕竟我现在还是菜鸡~~ diff --git a/sidebars.js b/sidebars.js index bbe0e53..2c9ba24 100644 --- a/sidebars.js +++ b/sidebars.js @@ -168,6 +168,16 @@ const sidebars = { } ], }, + { + type: 'category', + label: 'Triton 系列', + items: [ + { + type: 'autogenerated', + dirName: '18_triton/01_triton_programming_paradigms' + }, + ] + }, { type: 'category', label: 'LLM 推理技术', From 141b438bb8dd0e83bbf39d4212fa99ca65652a15 Mon Sep 17 00:00:00 2001 From: andsonder Date: Sat, 10 Jan 2026 13:30:18 +0800 Subject: [PATCH 03/10] update index --- docs/index.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/index.md b/docs/index.md index 029358a..a6438d3 100644 --- a/docs/index.md +++ b/docs/index.md @@ -53,8 +53,6 @@ + [Triton 编程范式入门](/triton/triton_programming_paradigms) -我现在还不知道写啥，毕竟我现在还是菜鸡~~ - ### LLM 推理技术 🤖 + [FlashAttention v1 - 原理篇](/flash_attn/flash_attn_v1_part1) From e6edc475a1558a558fba91a9163115fe593b597f Mon Sep 17 00:00:00 2001 From: andsonder Date: Sat, 10 Jan 2026 13:32:58 +0800 Subject: [PATCH 04/10] format doc --- docs/18_triton/01_triton_programming_paradigms/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/18_triton/01_triton_programming_paradigms/README.md b/docs/18_triton/01_triton_programming_paradigms/README.md index 679c849..206daee 100644 --- a/docs/18_triton/01_triton_programming_paradigms/README.md +++ b/docs/18_triton/01_triton_programming_paradigms/README.md @@ -240,7 +240,7 @@ Triton 的 Grid 配置则简单得多，你只需要指定 `BLOCK_SIZE`（每个 ## 五、常见问题 FAQ -### Q1: Triton 内部到底有没有线程？性能会比 CUDA 差吗？ +### Q1: Triton 内部到底有没有线程？性能会比 CUDA 差吗 **A**: 从硬件执行层面看，Triton 代码最终仍然运行在 GPU 的线程和 warp 上，只是 Triton 提供了更高层次的编程抽象，不直接暴露线程和 block 的概念。Triton 编译器会将向量化的程序描述转换为高效的 PTX / SASS，并映射到底层 GPU 执行模型。在性能方面，对于简单算子（如 element-wise 或带宽受限算子），Triton 通常可以达到接近手写 CUDA 的性能；对于高度优化的复杂算子（如 Flash Attention），Triton 在实践中也能达到与优化 CUDA 实现相当、或略低的性能水平。相比之下，Triton 在开发效率和可维护性方面通常具有明显优势。 @@ -250,7 +250,7 @@ Triton 的 Grid 配置则简单得多，你只需要指定 `BLOCK_SIZE`（每个 **A**: Triton 的 mask 是向量化语义，编译器通常会将其生成 predicated instructions（带谓词的指令），而不是显式的分支跳转，因此不会像 CUDA 中不当使用 if 那样引入严重的 warp divergence。 在大多数连续访问、边界检查类场景中，mask 带来的性能开销较小；但如果 mask 覆盖比例很大或访问模式高度稀疏，仍然可能造成一定的算力浪费。总体而言，mask 是 Triton 中推荐且高效的边界处理方式。 -### Q3: 什么时候不能用 Triton？ +### Q3: 什么时候不能用 Triton **A**: 以下场景建议使用 CUDA： 1. 需要显式管理 Shared Memory 布局（如手动消除 Bank Conflicts） @@ -259,7 +259,7 @@ Triton 的 Grid 配置则简单得多，你只需要指定 `BLOCK_SIZE`（每个 4. 算法严重依赖线程间细粒度通信 5. 需要与现有 CUDA 代码库深度集成 -### Q4: 如何从 CUDA 代码迁移到 Triton？ +### Q4: 如何从 CUDA 代码迁移到 Triton **A**: 五步迁移法： From ca80d13e5b07c565bebda78f1d449fa032ebe56d Mon Sep 17 00:00:00 2001 From: andsonder Date: Mon, 12 Jan 2026 00:23:45 +0800 Subject: [PATCH 05/10] init triton 02 md --- .gitignore | 7 +- .../README.md | 693 ++++++++++++++ .../homework.ipynb | 860 ++++++++++++++++++ ...8f34a65d99c6ebd33e1084f74416d0aa259dde.png | Bin 0 -> 89201 bytes 4 files changed, 1559 insertions(+), 1 deletion(-) create mode 100644 docs/18_triton/02_triton_memory_and_data_movement/README.md create mode 100644 docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb create mode 100644 docs/18_triton/02_triton_memory_and_data_movement/images/3a87ae7803e31e2baaf06184248f34a65d99c6ebd33e1084f74416d0aa259dde.png diff --git a/.gitignore b/.gitignore index d14461a..d46913f 100644 --- a/.gitignore +++ b/.gitignore @@ -30,5 +30,10 @@ *.exe *.out *.app + .DS_Store -.vscode \ No newline at end of file +node_modules +.docusaurus +build +.vscode + diff --git a/docs/18_triton/02_triton_memory_and_data_movement/README.md b/docs/18_triton/02_triton_memory_and_data_movement/README.md new file mode 100644 index 0000000..a9adf67 --- /dev/null +++ b/docs/18_triton/02_triton_memory_and_data_movement/README.md @@ -0,0 +1,693 @@ +# Triton 内存与数据搬运 + +## 前言 + +在上一节中，我们学习了 Triton 的核心编程范式，理解了 Program Instance 的概念，掌握了用 `tl.arange` 进行向量化操作，也学会了用 mask 处理边界。但当时我们只处理了简单的 1D 向量操作。 + +真实世界的应用往往更复杂：我们需要处理**多维数据**（如图像的 2D 矩阵），需要理解**内存布局**对性能的影响，还需要处理**更复杂的边界情况**（如 padding）。 + +这一节，我们将从 1D 走向 2D，探索 Triton 的内存管理和数据搬运机制。 + +期望在阅读完本文后，你能够： +- 理解多维数组的 stride 概念，掌握 2D 地址计算方法 +- 理解内存连续性对性能的影响 +- 掌握 Triton 的高级加载参数（cache_modifier、eviction_policy） +- 学会使用数据复用技巧减少内存访问 + +--- + +## 一、从 1D 到 2D - 指针与多维地址计算 + +还记得 Triton 01 中的向量加法吗？我们用 `offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)` 来计算一维索引。 + +现在，如果我们需要处理一个矩阵，应该怎么办？ + +### 1.1 任务描述 + +给定一个 M×N 的矩阵 `A`，计算其转置 `B = A^T`。 + +这是一个经典的 2D 操作，能很好地展示多维地址计算。在内存中，矩阵是按**行优先**顺序存储的。例如，一个 3×4 的矩阵： + +``` +A = [[1, 2, 3, 4], + [5, 6, 7, 8], + [9, 10, 11, 12]] +``` + +在内存中的实际布局是：`[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]` + +如果要访问 `A[i][j]`（第 i 行，第 j 列），内存地址是： +``` +物理地址 = base_ptr + i * N + j +``` + +这里的关键概念是 **stride（步长）**： +- **行 stride** = N（每行的元素数，跨过一行需要移动 N 个位置） +- **列 stride** = 1（每列的元素间隔） + +### 1.2 CUDA 实现 + +在 CUDA 中，处理 2D 数据需要使用 2D grid 和 2D block： + +```cuda +__global__ void transpose_cuda(float *A, float *B, int M, int N) { + // 每个线程计算自己的 2D 坐标 + int row = blockIdx.y * blockDim.y + threadIdx.y; + int col = blockIdx.x * blockDim.x + threadIdx.x; + + // 边界检查 + if (row < M && col < N) { + B[col * M + row] = A[row * N + col]; // 转置：行列交换 + } +} +``` + +在 CUDA 中，每个线程处理一个元素。你需要： +- 用 `dim3` 定义 2D 的 block 和 grid +- 通过 `threadIdx.x/y` 和 `blockIdx.x/y` 计算每个线程的坐标 +- 用 `if (row < M && col < N)` 做边界检查 +- 手动计算转置后的索引 + +### 1.3 Triton 实现 + +在 Triton 中，咱们不需要一个个线程地思考，而是**以数据块为单位**： + +```python +@triton.jit +def transpose_triton( + a_ptr, b_ptr, + M, N, + stride_am, stride_an, # A 的行 stride 和列 stride + stride_bm, stride_bn, # B 的行 stride 和列 stride + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, +): + # 1. 获取 2D Program ID + pid_m = tl.program_id(axis=0) # 行方向的 Program ID + pid_n = tl.program_id(axis=1) # 列方向的 Program ID + + # 2. 计算当前块负责的行列范围（向量化） + rows = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + cols = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + + # 3. 创建 2D mask（broadcast 机制） + mask = (rows[:, None] < M) & (cols[None, :] < N) + + # 4. 计算指针并加载数据 + a_ptrs = a_ptr + (rows[:, None] * stride_am + cols[None, :] * stride_an) + a = tl.load(a_ptrs, mask=mask) + + # 5. 存储（转置：交换 rows 和 cols 的位置） + b_ptrs = b_ptr + (cols[None, :] * stride_bm + rows[:, None] * stride_bn) + tl.store(b_ptrs, a, mask=mask) +``` + +#### 核心变化 + +**从 1D 到 2D，主要有三个变化**： + +| 1D（向量加法） | 2D（矩阵转置） | +|---------------|---------------| +| `pid = tl.program_id(0)` | `pid_m = tl.program_id(0)`, `pid_n = tl.program_id(1)` | +| `offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)` | `rows = ...`, `cols = ...`（两个独立的偏移数组） | +| `mask = offsets < n_elements` | `mask = (rows[:, None] < M) & (cols[None, :] < N)` | + +#### Broadcast 机制 + +这里有个非常重要的技巧：**Broadcast（广播）** + +```python +rows = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) # 形状: (BLOCK_SIZE_M,) +cols = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) # 形状: (BLOCK_SIZE_N,) + +# 添加新维度后： +rows[:, None] # 形状: (BLOCK_SIZE_M, 1) +cols[None, :] # 形状: (1, BLOCK_SIZE_N) + +# 相加后自动 broadcast 到 (BLOCK_SIZE_M, BLOCK_SIZE_N) +mask = (rows[:, None] < M) & (cols[None, :] < N) +``` + +这种 broadcast 机制让我们能够用简洁的代码生成 2D 的坐标网格，而不需要像 CUDA 那样写两层循环。 + +> **💡 底层原理：Broadcast 是如何编译的？** +> +> 你可能会担心：`rows[:, None] * stride + cols[None, :]` 这种操作会不会在运行时构造大数组？ +> +> 完全不会！Triton 编译器会将这个 broadcast 操作**展开为标量代码**，而不是运行时构造 2D 数组。编译器知道 `rows` 和 `cols` 的编译时值，会直接生成对应的内存访问指令。 +> +> 对于 BLOCK_SIZE=64 的情况，编译后类似： +> ```cuda +> // 伪代码 +> for (int i = 0; i < 64; i++) { +> for (int j = 0; j < 64; j++) { +> ptr[i][j] = base + (pid_m * 64 + i) * stride_am + (pid_n * 64 + j) * stride_an; +> } +> } +> ``` +> 然后编译器会进一步优化这个循环，自动进行内存合并访问（coalescing），让相邻线程访问相邻内存。 + +### 1.4 Host 端调用 + +```python +def transpose(a: torch.Tensor) -> torch.Tensor: + """ + 转置矩阵 a + + Args: + a: (M, N) 的 tensor + + Returns: + (N, M) 的转置 tensor + """ + M, N = a.shape + b = torch.empty(N, M, device=a.device, dtype=a.dtype) + + # Grid 配置：2D grid + grid = lambda meta: ( + triton.cdiv(M, meta['BLOCK_SIZE_M']), # 行方向的 Program 数 + triton.cdiv(N, meta['BLOCK_SIZE_N']), # 列方向的 Program 数 + ) + + # 启动 kernel + transpose_triton[grid]( + a, b, + M, N, + a.stride(0), a.stride(1), # PyTorch 自动获取 stride + b.stride(0), b.stride(1), + BLOCK_SIZE_M=64, + BLOCK_SIZE_N=64, + ) + + return b +``` + + +## 二、内存连续性与访问模式 + +理解了 2D 地址计算后，咱们来看看一个对性能影响巨大的因素：**内存连续性**。 + +### 2.1 什么是内存连续性？ + +在 GPU 编程中，有一个非常重要的性能优化原则：**Memory Coalescing**。 + +简单来说，当相邻的线程访问相邻的内存地址时，GPU 可以将这些访问合并为一个大的内存事务，从而大幅提高带宽利用率。 + +**好例子 vs 坏例子**： + +```python +# 好的访问：连续访问 +x = torch.randn(1024, 1024, device='cuda') +print(x.is_contiguous()) # True +# 在内存中：[x[0,0], x[0,1], ..., x[0,1023], x[1,0], x[1,1], ...] + +# 坏的访问：转置后不连续 +y = x.T # 转置 +print(y.is_contiguous()) # False +# 在内存中：[x[0,0], x[1,0], ..., x[1023,0], x[0,1], x[1,1], ...] +``` + +### 2.2 实验对比 + +让我们用实际代码看看连续性对性能的影响： + +```python +@triton.jit +def vector_add_contiguous(x_ptr, y_ptr, out_ptr, n_elements, BLOCK_SIZE: tl.constexpr): + """连续访问版本""" + pid = tl.program_id(0) + offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + + x = tl.load(x_ptr + offsets, mask=mask) + y = tl.load(y_ptr + offsets, mask=mask) + out = x + y + tl.store(out_ptr + offsets, out, mask=mask) + + +@triton.jit +def vector_add_strided(x_ptr, y_ptr, out_ptr, n_elements, stride, BLOCK_SIZE: tl.constexpr): + """跨步访问版本""" + pid = tl.program_id(0) + offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + + # 跨步访问（非连续） + x = tl.load(x_ptr + offsets * stride, mask=mask) + y = tl.load(y_ptr + offsets * stride, mask=mask) + out = x + y + tl.store(out_ptr + offsets * stride, out, mask=mask) +``` + +**性能测试结果**（典型值）： + +``` +Size | Stride | Time (ms) | Slowdown +--------|--------|-----------|---------- +1048576 | 1 | 5.23 | 1.00x +1048576 | 2 | 6.87 | 1.31x +1048576 | 4 | 9.45 | 1.81x +1048576 | 8 | 12.34 | 2.36x +``` + +可以看到，随着 stride 增大，访问速度明显下降！ + +> **💡 底层原理：为什么 stride 越大越慢？** +> +> GPU 的内存访问是以**事务（transaction）为单位**的。一个 warp（32 个线程）访问连续的 32 个 float（128 字节）时，可以合并为**单个内存事务**。 +> +> 但当 stride=8 时，这 32 个线程访问的是间隔 8 个元素的数据： +> ``` +> 线程 0: addr[0] +> 线程 1: addr[8] +> 线程 2: addr[16] +> ... +> ``` +> +> 这些地址跨越了多个 cache line，GPU 需要发起**多个内存事务**，浪费了大量带宽。 +> +> Triton 编译器会尽量优化访问模式，但如果数据本身的内存布局不连续，编译器也无力回天。所以**保持输入 tensor 的连续性**非常重要。 + +### 2.3 如何检查和修复 + +PyTorch 提供了方便的 API： + +```python +# 检查连续性 +x = torch.randn(1024, 1024, device='cuda') +print(x.is_contiguous()) # True + +y = x.T # 转置 +print(y.is_contiguous()) # False + +# 查看实际内存布局 +print(x.stride()) # (1024, 1) - 行优先，连续 +print(y.stride()) # (1, 1024) - 列优先，不连续 + +# 使其连续 +y_contiguous = y.contiguous() +print(y_contiguous.is_contiguous()) # True +``` + +在 Triton 中，stride 信息是通过参数传递给 kernel 的： + +```python +transpose_triton[grid]( + a, b, + M, N, + a.stride(0), a.stride(1), # 获取实际的 stride 值 + b.stride(0), b.stride(1), + BLOCK_SIZE_M=64, + BLOCK_SIZE_N=64, +) +``` + +### 2.4 性能优化建议 + +1. **尽量使用连续的 tensor**：在 PyTorch 中使用 `.contiguous()` 确保内存连续 +2. **注意转置操作**：转置后的 tensor 通常不连续，考虑是否真的需要转置 +3. **避免频繁的切片**：切片可能产生不连续的 tensor +4. **使用 stride 参数**：Triton 支持任意 stride，但性能会受影响 + + + +## 三、向量化加载与存储进阶 + +在 Triton 01 中，我们只用了 `tl.load(ptr, mask=mask)` 的基础形式。实际上，`tl.load` 还有很多强大的参数可以帮助我们优化性能。 + +### 3.1 tl.load 的高级参数 + +```python +tl.load( + pointer, # 指针（标量或向量） + mask=None, # 边界掩码 + other=None, # mask=False 时的默认值 + cache_modifier="", # 缓存控制 hint + eviction_policy="",# 缓存驱逐策略 +) +``` + +### 3.2 cache_modifier 详解 + +`cache_modifier` 参数告诉 GPU 的缓存系统如何处理这些数据。 + +| 修饰符 | 含义 | 使用场景 | +|--------|------|----------| +| `""` | 默认 | 通用场景，让硬件自动决定 | +| `".ca"` | Cache All | 数据会被多次访问，建议缓存 | +| `".cg"` | Cache Global | 数据会被频繁访问，缓存在 L1/L2 | +| `".cs"` | Cache Stream | 数据只使用一次，不缓存（流式） | + +**原理：GPU 缓存层次结构** + +现代 GPU 有多层缓存： + +![picture 0](images/3a87ae7803e31e2baaf06184248f34a65d99c6ebd33e1084f74416d0aa259dde.png) + + +`cache_modifier` 就是在提示 Triton：这个数据应该放在哪一层缓存最合适 + +让我们看一个完整的矩阵乘法例子： + +```python +@triton.jit +def matmul_kernel( + a_ptr, b_ptr, c_ptr, + M, N, K, + stride_am, stride_ak, + stride_bk, stride_bn, + stride_cm, stride_cn, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, +): + pid = tl.program_id(0) + pid_m = pid // (triton.cdiv(N, BLOCK_SIZE_N)) + pid_n = pid % (triton.cdiv(N, BLOCK_SIZE_N)) + + # 当前块负责的输出范围 + rm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + rn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + + # 分块计算：沿着 K 维度切分 + for k in range(0, K, BLOCK_SIZE_K): + rk = k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K) + + # A 的块：形状 (BLOCK_SIZE_M, BLOCK_SIZE_K) + a_ptrs = a_ptr + (rm[:, None] * stride_am + rk[None, :] * stride_ak) + + # B 的块：形状 (BLOCK_SIZE_K, BLOCK_SIZE_N) + b_ptrs = b_ptr + (rk[:, None] * stride_bk + rn[None, :] * stride_bn) + + mask = (rm[:, None] < M) & (rn[None, :] < N) & (rk[None, :] < K) + + # A 的这个块在当前 k 迭代中只访问一次 + # 下一次循环 (k+1) 会加载完全不同的数据 + a = tl.load(a_ptrs, mask=mask, other=0.0, cache_modifier=".ca") + + # B 的这个块会被多个 output 块复用 + # 不同的 (pid_m, pid_n) 可能访问相同的 B 块 + b = tl.load(b_ptrs, mask=mask, other=0.0, cache_modifier=".cg") + + accumulator += tl.dot(a, b) +``` + + +- **A 矩阵**：对于固定的 `(pid_m, pid_n)`，在循环中每次 `k` 迭代加载的是 A 的不同列块。当前 `k` 加载的数据在 `k+1` 时不会再被用到，所以**每个块只访问一次**。 + +- **B 矩阵**：虽然当前循环中也只访问一次，但 B 的同一块数据可能被**不同的 Program**（不同的 `pid_m`）访问和复用。使用 `.cg` 可以让它留在缓存中，供其他 Program 使用。 + + + +在大多数情况下，使用默认策略即可。cache_modifier 是**hint**（提示），不是强制命令，最终决策权在 Triton 手中。只有在性能瓶颈明显时，才考虑手动调整并测试效果。 + +> **💡 底层原理：cache_modifier 如何影响 PTX？** +> +> 当你指定 `cache_modifier=".cg"` 时，Triton 会生成不同的 PTX 指令： +> +> ```ptx +> // 默认 +> ld.global.f32 {%r1}, [%ptr]; +> +> // .cg (Cache at Global level - L2) +> ld.global.cg.f32 {%r1}, [%ptr]; +> +> // .ca (Cache All - L1 + L2) +> ld.global.ca.f32 {%r1}, [%ptr]; +> ``` +> +> 这些是 PTX 的缓存操作符，直接控制 GPU 的 L1/L2 缓存行为。`.cg` 告诉 GPU 这个数据值得留在 L2 缓存中，`.ca` 则表示连 L1 也值得缓存。 +> +> Triton 编译器会自动将这些 hints 转换为对应的 PTX 指令，无需你手动编写汇编。 + +### 3.3 eviction_policy 的使用 + +`eviction_policy` 控制缓存行的替换策略。 + +| 策略 | 含义 | 使用场景 | +|------|------|----------| +| `"evict_first"` | 优先驱逐 | 临时数据、只读一次 | +| `"evict_last"` | 最后驱逐 | 需要保留的数据 | + +**示例**： + +```python +# 只读数据，用完后立即驱逐 +data = tl.load( + input_ptr + offsets, + mask=mask, + other=0.0, + eviction_policy="evict_first" +) +``` + +cache hints 的效果因 GPU 架构和数据访问模式而异，需要实际测试来确定最佳策略。在大多数情况下，使用默认策略即可，只有在性能瓶颈明显时才考虑手动调整。 + + +## 四、数据复用优化 + +在 Triton 01 作业中的 1D 卷积，朴素实现是这样的： + +```python +# 朴素方法：每个元素单独加载 +x_left = tl.load(x_ptr + offsets - 1, mask=..., other=0.0) +x_center = tl.load(x_ptr + offsets, mask=..., other=0.0) +x_right = tl.load(x_ptr + offsets + 1, mask=..., other=0.0) +result = x_left + x_center + x_right +``` + +问题来了：**相邻的 Program 会重复加载相同的数据！** + +### 4.1 优化方法 + +解决方案很简单：**一次性加载更多的数据，然后在 SRAM 中切片** + +```python +@triton.jit +def conv1d_optimized(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): + """ + 优化版 1D 卷积：Y[i] = X[i-1] + X[i] + X[i+1] + + 优化策略：加载更大的块，然后切片，减少重复加载 + """ + pid = tl.program_id(0) + + # 加载 BLOCK_SIZE + 2 个元素（左边 1 个，右边 1 个） + block_start = pid * BLOCK_SIZE - 1 # 向前偏移 1 + offsets = block_start + tl.arange(0, BLOCK_SIZE + 2) + + # 边界处理 + mask = (offsets >= 0) & (offsets < n_elements) + + # 一次性加载所有需要的元素 + x = tl.load(input_ptr + offsets, mask=mask, other=0.0) + + # 切片获取所需的邻居 + x_left = x[:-2] # [0, 1, 2, ..., BLOCK_SIZE-1] + x_center = x[1:-1] # [1, 2, 3, ..., BLOCK_SIZE] + x_right = x[2:] # [2, 3, 4, ..., BLOCK_SIZE+1] + + # 计算 + result = x_left + x_center + x_right + + # 存储结果 + output_offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + output_mask = output_offsets < n_elements + tl.store(output_ptr + output_offsets, result, mask=output_mask) +``` + +### 4.2 对比分析 + +| 方法 | 加载次数 | 数据复用 | 性能 | +|------|---------|---------|------| +| 朴素方法 | 3 × BLOCK_SIZE | 无 | 慢 | +| 优化方法 | 1 × (BLOCK_SIZE + 2) | 高 | 快 | + +内存访问减少了约 **3 倍**！ + +### 4.3 与 CUDA Shared Memory 的对比 + +| 特性 | CUDA | Triton | +|------|------|--------| +| 显式声明 | `__shared__ float smem[1024];` | 无需显式声明 | +| 手动同步 | `__syncthreads();` | 自动处理 | +| Bank Conflict | 需要手动处理 | 编译器自动优化 | + +Triton 的优势在于**简洁性**：你只需要关注"我需要什么数据"，编译器会自动处理"如何高效地访问这些数据"。 + +> **💡 底层原理：Triton 如何自动优化内存访问？** +> +> 在优化版 1D 卷积中，我们加载了 `BLOCK_SIZE + 2` 个元素，然后用切片 `x[:-2]`, `x[1:-1]`, `x[2:]`。 +> +> Triton 编译器会分析这些切片的使用模式： +> 1. 发现 `x` 被多次访问（三次切片操作） +> 2. 自动将 `x` 分配到寄存器或 Shared Memory（取决于大小） +> 3. 切片操作编译为**指针偏移**，不产生实际数据拷贝 +> +> 相当于 CUDA 中手动写： +> ```cuda +> __shared__ float x[BLOCK_SIZE + 2]; +> // ... 加载到 smem ... +> float x_left = x[0]; // 直接索引，无拷贝 +> float x_center = x[1]; +> float x_right = x[2]; +> ``` +> +> 此外，Triton 编译器还会自动检测 Bank Conflict。如果发现访问模式会导致 conflict，它会尝试重排数据或访问顺序——这是手动 CUDA 编程中需要费力优化的事。 + + +## 五、高级工具：tl.make_block_ptr + +前面我们学习了如何手动计算 2D/多维张量的地址。但实际上 Triton 提供了一个更强大的工具：`tl.make_block_ptr`。 + +### 5.1 什么是 Block Pointer？ + +`tl.make_block_ptr` 是一个专门用于处理多维张量访问的高级工具。它可以： +- 自动处理多维地址计算 +- 支持任意维度的张量 +- 自动优化内存访问模式 + +### 5.2 基本用法 + +```python +@triton.jit +def using_block_ptr(...): + # 创建一个 2D block pointer + a_ptr = tl.make_block_ptr( + base=a_ptr, # 基地址 + shape=(M, N), # 张量形状 + strides=(stride_am, stride_an), # stride + offsets=(pid_m * BLOCK_SIZE_M, pid_n * BLOCK_SIZE_N), # 当前块偏移 + block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_N), # 块大小 + order=(1, 0), # 维度顺序：列连续（Row-Major） + ) + + # 加载数据（注意：必须显式指定 boundary_check！） + a = tl.load(a_ptr, boundary_check=(0, 1), padding_option="zero") + + # 使用数据 + result = a * 2.0 +``` + +注意使用 `tl.make_block_ptr` 时，`tl.load` **不会自动处理边界**！你必须显式指定 `boundary_check` 参数，否则当矩阵尺寸不是 BLOCK_SIZE 的倍数时，代码会越界访问或读取错误数据。 + +### 5.3 在循环中使用 Block Pointer + +Block Pointer 真正的优势在于循环迭代。普通指针需要手动计算 `ptr += stride`，而 Block Pointer 有专属的神器：`tl.advance`。 + +**普通指针的循环**（需要手动计算偏移）： + +```python +@triton.jit +def matmul_manual_pointer(...): + for k in range(0, K, BLOCK_SIZE_K): + # 手动计算下一块的地址 + a_ptrs = a_ptr + (rm[:, None] * stride_am + (k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K))[None, :] * stride_ak) + a = tl.load(a_ptrs, mask=mask, other=0.0) + # ... 计算 ... +``` + +**Block Pointer 的循环**（自动前进）： + +```python +@triton.jit +def matmul_block_ptr(...): + # 初始化 Block Pointer + a_block_ptr = tl.make_block_ptr( + base=a_ptr, + shape=(M, K), + strides=(stride_am, stride_ak), + offsets=(pid_m * BLOCK_SIZE_M, 0), + block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_K), + order=(1, 0), + ) + + for k in range(0, K, BLOCK_SIZE_K): + # 加载当前块 + a = tl.load(a_block_ptr, boundary_check=(0, 1), padding_option="zero") + + # ... 计算 ... + + # 移动到下一个 K 块（沿着第 1 维移动 BLOCK_SIZE_K） + a_block_ptr = tl.advance(a_block_ptr, (0, BLOCK_SIZE_K)) +``` + +`tl.advance` 会更新 Block Pointer 的内部状态，使其指向下一个逻辑块，完全不需要手动计算复杂的 stride 偏移。这在矩阵乘法、卷积等需要反复遍历张量的场景中非常方便。 + +> **💡 底层原理：tl.advance 是如何实现的？** +> +> `tl.advance(a_block_ptr, (0, BLOCK_SIZE_K))` 不会运行时计算复杂地址。 +> +> Block Pointer 是一个**编译期抽象**，它记录了： +> - 基地址指针 +> - 各维度的 stride +> - 当前块偏移 +> +> `tl.advance` 只需要更新偏移量，编译器会直接生成： +> ```ptx +> // 假设沿着第二维移动 BLOCK_SIZE_K +> add.u64 %ptr, %base_ptr, %new_offset; // 单个加法指令！ +> ``` +> +> 相比手动计算 `ptr += stride0 * delta0 + stride1 * delta1`，效率完全相同，但代码更清晰，不易出错。 + +### 5.4 tl.store 的特殊说明 + +使用 Block Pointer 写入时有一个重要的限制：**tl.store 不支持 padding_option**。 + +```python +# 读取时可以指定 padding_option +a = tl.load(a_block_ptr, boundary_check=(0, 1), padding_option="zero") + +# 写入时不能指定 padding_option +# tl.store(c_block_ptr, result, boundary_check=(0, 1), padding_option="zero") # 错误 +tl.store(c_block_ptr, result, boundary_check=(0, 1)) # 正确 +``` + +这是逻辑上的必然：读取时越界可以补 0，但写入时越界是非法的。Triton 会自动屏蔽掉越界的写入操作（通过 boundary_check）。 + +### 5.5 关于 order 参数的深入理解 + +`order` 参数应该按照 **stride 从小到大**（即变化最快到最慢）的顺序排列维度。 + +对于 Row-Major（行优先）的矩阵： + +- 列索引（第 1 维）变化最快，stride = 1 +- 行索引（第 0 维）变化最慢，stride = N + +因此 `order=(1, 0)`，把变化最快的维放在前面。Triton 会利用这个信息来优化数据搬运（如 Swizzling 以避免 Bank Conflict），最大化 L2 Cache 的命中率。 + +> **💡 底层原理：order 如何影响数据搬运？** +> +> `order` 参数不仅影响代码正确性，还直接影响性能。 +> +> 当你指定 `order=(1, 0)` 时，Triton 编译器知道数据是**列连续**的。在从 DRAM 搬运数据到 SRAM 时，它会： +> 1. 按照连续维度优先加载数据（提高内存吞吐） +> 2. 进行 **Swizzling** 优化：重新排列数据在 Shared Memory 中的布局，避免 Bank Conflict +> 3. 生成向量化加载指令（如 `ld.global.nc.f32.v4`，一次加载 4 个 float） +> +> 如果 `order` 设置错误，不仅访问模式会低效，编译器也无法进行这些优化。所以在使用 Block Pointer 时，**正确设置 order 非常关键**。 + +### 5.6 什么时候使用 Block Pointer？ + +| 场景 | 推荐方式 | +|------|---------| +| 简单的 1D/2D 操作 | 手动计算（更直观） | +| 复杂的多维张量（3D+） | Block Pointer（更简洁） | +| 快速原型开发 | Block Pointer | + +Block Pointer 是 Triton 的高级特性，在处理复杂张量操作时非常有用。 + +## 六、总结 + +完成本节学习后，你应该理解了 stride 概念，能处理 2D/多维张量的地址计算。掌握了 Triton 的 broadcast 机制（`[:, None]` 和 `[None, :]`），也理解了内存连续性对性能的影响。在编程技能方面，你能够实现 2D/多维操作的 Triton kernel，可以使用 cache_modifier 和 eviction_policy 优化访存，还能通过数据复用减少内存访问次数。同时，你也了解了 `tl.make_block_ptr` 这个高级工具的使用场景。 + +从思维转换的角度来看，CUDA 的思维方式是"这个线程处理第 (row, col) 个元素"，而 Triton 的思维方式转变为"这个 Program 处理第 [rows...][cols...] 批元素"。在边界检查上，CUDA 用 `if (row < M && col < N)`，而 Triton 用 `mask = (rows[:, None] < M) & (cols[None, :] < N)` 进行向量化检查。在数据管理方面，CUDA 需要手动管理 Shared Memory 和同步，而 Triton 只需加载更大的块、使用切片操作，编译器会自动优化。 + +## 参考资料 + + +1. [Triton Tutorial 04: Matrix Multiplication](https://triton-lang.org/main/getting-started/tutorials/04-matrix-multiplication.html) +2. [Triton Tutorial 05: Softmax](https://triton-lang.org/main/getting-started/tutorials/05-softmax.html) \ No newline at end of file diff --git a/docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb b/docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb new file mode 100644 index 0000000..540baad --- /dev/null +++ b/docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb @@ -0,0 +1,860 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Triton 内存与数据搬运 - 课后练习\n", + "\n", + "本 Notebook 包含三个练习，难度递进，帮助你巩固内存管理知识。\n", + "\n", + "**学习目标**：\n", + "- 掌握 2D 地址计算和 stride 处理\n", + "- 理解内存连续性对性能的影响\n", + "- 学会优化数据复用，减少重复加载\n", + "- 使用 cache hints 优化访存性能\n", + "- 处理复杂的边界情况" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import triton\n", + "import triton.language as tl\n", + "import time\n", + "\n", + "# 检查 GPU 可用性\n", + "assert torch.cuda.is_available(), \"需要 CUDA 支持的 GPU\"\n", + "print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n", + "print(f\"Triton version: {triton.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 练习 1: 优化 2D 卷积\n", + "\n", + "**目标**：实现一个高效的 2D 卷积（3×3 box filter），要求：\n", + "1. 使用数据复用技术，减少重复加载\n", + "2. 正确处理边界和 padding\n", + "3. 支持 stride\n", + "\n", + "**提示**：\n", + "1. 参考教程中的 `conv1d_optimized` 方法\n", + "2. 加载 `(BLOCK_SIZE_H + 2) × (BLOCK_SIZE_W + 2)` 的块\n", + "3. 使用切片操作获取邻居数据\n", + "4. 注意边界处理" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@triton.jit\n", + "def conv2d_box_filter_kernel(\n", + " input_ptr, output_ptr,\n", + " H, W,\n", + " stride_h, stride_w,\n", + " BLOCK_SIZE_H: tl.constexpr,\n", + " BLOCK_SIZE_W: tl.constexpr,\n", + "):\n", + " \"\"\"\n", + " TODO: 实现优化的 2D 卷积\n", + " Y[i][j] = sum(X[i-1:i+2][j-1:j+2])\n", + " \n", + " 步骤：\n", + " 1. 计算 Program ID\n", + " 2. 加载 (BLOCK_SIZE_H + 2) × (BLOCK_SIZE_W + 2) 的块\n", + " 3. 创建边界 mask\n", + " 4. 使用切片获取 3×3 邻居\n", + " 5. 求和\n", + " 6. 存储结果\n", + " \"\"\"\n", + " # ==================== 在下方编写代码 ====================\n", + " \n", + " \n", + " \n", + " # ========================================================\n", + " pass\n", + "\n", + "def conv2d_box_filter(input_tensor):\n", + " \"\"\"\n", + " Host 端包装函数\n", + " \n", + " Args:\n", + " input_tensor: (H, W) 的 tensor\n", + " \n", + " Returns:\n", + " (H, W) 的输出 tensor\n", + " \"\"\"\n", + " H, W = input_tensor.shape\n", + " output = torch.empty_like(input_tensor)\n", + " \n", + " # Grid 配置\n", + " grid = lambda meta: (\n", + " triton.cdiv(H, meta['BLOCK_SIZE_H']),\n", + " triton.cdiv(W, meta['BLOCK_SIZE_W']),\n", + " )\n", + " \n", + " # 启动 kernel\n", + " conv2d_box_filter_kernel[grid](\n", + " input_tensor, output,\n", + " H, W,\n", + " input_tensor.stride(0), input_tensor.stride(1),\n", + " BLOCK_SIZE_H=64,\n", + " BLOCK_SIZE_W=64,\n", + " )\n", + " \n", + " return output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 测试 2D 卷积\n", + "def test_conv2d():\n", + " H, W = 512, 512\n", + " input_tensor = torch.randn(H, W, device='cuda', dtype=torch.float32)\n", + " \n", + " # Triton 实现\n", + " output_triton = conv2d_box_filter(input_tensor)\n", + " \n", + " # PyTorch 参考实现\n", + " output_torch = torch.nn.functional.avg_pool2d(\n", + " input_tensor.unsqueeze(0), \n", + " kernel_size=3, \n", + " stride=1, \n", + " padding=1\n", + " ).squeeze(0) * 9 # avg_pool2d 会除以 9，所以乘回去\n", + " \n", + " # 验证\n", + " if torch.allclose(output_triton, output_torch, atol=1e-4):\n", + " print(\"✓ 2D 卷积测试通过！\")\n", + " else:\n", + " print(\"✗ 2D 卷积测试失败！\")\n", + " print(f\"最大误差: {torch.max(torch.abs(output_triton - output_torch)).item():.2e}\")\n", + " print(f\"\\n前 5x5 元素对比:\")\n", + " print(f\"Triton:\\n{output_triton[:5, :5]}\")\n", + " print(f\"Torch:\\n{output_torch[:5, :5]}\")\n", + "\n", + "test_conv2d()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 性能对比\n", + "def benchmark_conv2d():\n", + " sizes = [(256, 256), (512, 512), (1024, 1024)]\n", + " \n", + " print(f\"{'Size':>15} | {'Triton (ms)':>12} | {'Torch (ms)':>12} | {'Speedup':>10}\")\n", + " print(\"-\" * 60)\n", + " \n", + " for H, W in sizes:\n", + " input_tensor = torch.randn(H, W, device='cuda', dtype=torch.float32)\n", + " \n", + " # Triton 实现\n", + " torch.cuda.synchronize()\n", + " t0 = time.time()\n", + " for _ in range(100):\n", + " output = conv2d_box_filter(input_tensor)\n", + " torch.cuda.synchronize()\n", + " t_triton = (time.time() - t0) * 1000\n", + " \n", + " # PyTorch 实现\n", + " torch.cuda.synchronize()\n", + " t0 = time.time()\n", + " for _ in range(100):\n", + " output_torch = torch.nn.functional.avg_pool2d(\n", + " input_tensor.unsqueeze(0), kernel_size=3, stride=1, padding=1\n", + " ).squeeze(0) * 9\n", + " torch.cuda.synchronize()\n", + " t_torch = (time.time() - t0) * 1000\n", + " \n", + " speedup = t_torch / t_triton\n", + " print(f\"{H}x{W:>10} | {t_triton:>12.2f} | {t_torch:>12.2f} | {speedup:>10.2f}x\")\n", + "\n", + "benchmark_conv2d()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**思考题**：\n", + "1. 如果卷积核大小变为 5×5，代码需要如何修改？\n", + "2. 如何使用 cache_modifier 进一步优化性能？\n", + "3. 这种方法和 CUDA 的 Shared Memory 有什么异同？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**提示**（点击展开）：\n", + "\n", + "
    \n", + "提示 1：如何计算加载块的坐标\n", + "\n", + "加载块需要包含 padding，所以要从 -1 开始：\n", + "```python\n", + "# 加载块的坐标\n", + "h_load = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H + 2) - 1\n", + "w_load = pid_w * BLOCK_SIZE_W + tl.arange(0, BLOCK_SIZE_W + 2) - 1\n", + "```\n", + "
    \n", + "\n", + "
    \n", + "提示 2：如何创建 mask\n", + "\n", + "需要检查加载的坐标是否在有效范围内：\n", + "```python\n", + "mask = (h_load[:, None] >= 0) & (h_load[:, None] < H) & \\\n", + " (w_load[None, :] >= 0) & (w_load[None, :] < W)\n", + "```\n", + "
    \n", + "\n", + "
    \n", + "提示 3：如何切片\n", + "\n", + "使用切片获取 3×3 窗口：\n", + "```python\n", + "# 3×3 邻居求和\n", + "result = (\n", + " input_block[:-2, :-2] + input_block[:-2, 1:-1] + input_block[:-2, 2:] +\n", + " input_block[1:-1, :-2] + input_block[1:-1, 1:-1] + input_block[1:-1, 2:] +\n", + " input_block[2:, :-2] + input_block[2:, 1:-1] + input_block[2:, 2:]\n", + ")\n", + "```\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 练习 2: 优化矩阵乘法内存访问\n", + "\n", + "**目标**：实现一个高效的矩阵乘法 kernel，运用 cache hints 优化访存\n", + "\n", + "**提示**：\n", + "1. 参考教程中的 `matmul_with_cache_hints`\n", + "2. 使用 `cache_modifier` 参数优化加载\n", + "3. 注意 stride 的正确传递\n", + "4. 处理非 BLOCK_SIZE 整数倍的情况" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@triton.jit\n", + "def matmul_kernel(\n", + " a_ptr, b_ptr, c_ptr,\n", + " M, N, K,\n", + " stride_am, stride_ak,\n", + " stride_bk, stride_bn,\n", + " stride_cm, stride_cn,\n", + " BLOCK_SIZE_M: tl.constexpr,\n", + " BLOCK_SIZE_N: tl.constexpr,\n", + " BLOCK_SIZE_K: tl.constexpr,\n", + "):\n", + " \"\"\"\n", + " TODO: 实现带 cache hints 的矩阵乘法\n", + " \n", + " 步骤：\n", + " 1. 获取 Program ID\n", + " 2. 初始化累加器\n", + " 3. 分块循环加载 A 和 B（使用 cache_modifier）\n", + " 4. 计算 dot product\n", + " 5. 存储结果\n", + " \"\"\"\n", + " # ==================== 在下方编写代码 ====================\n", + " \n", + " \n", + " \n", + " # ========================================================\n", + " pass\n", + "\n", + "def matmul(a, b):\n", + " \"\"\"\n", + " Host 端包装函数\n", + " \n", + " Args:\n", + " a: (M, K) tensor\n", + " b: (K, N) tensor\n", + " \n", + " Returns:\n", + " (M, N) tensor\n", + " \"\"\"\n", + " M, K = a.shape\n", + " K2, N = b.shape\n", + " assert K == K2, \"矩阵维度不匹配\"\n", + " \n", + " c = torch.empty(M, N, device=a.device, dtype=a.dtype)\n", + " \n", + " # Grid 配置\n", + " grid = lambda meta: (\n", + " triton.cdiv(M, meta['BLOCK_SIZE_M']) * triton.cdiv(N, meta['BLOCK_SIZE_N']),\n", + " )\n", + " \n", + " # 启动 kernel\n", + " matmul_kernel[grid](\n", + " a, b, c,\n", + " M, N, K,\n", + " a.stride(0), a.stride(1),\n", + " b.stride(0), b.stride(1),\n", + " c.stride(0), c.stride(1),\n", + " BLOCK_SIZE_M=64,\n", + " BLOCK_SIZE_N=64,\n", + " BLOCK_SIZE_K=32,\n", + " )\n", + " \n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 测试矩阵乘法\n", + "def test_matmul():\n", + " M, N, K = 512, 512, 512\n", + " \n", + " a = torch.randn(M, K, device='cuda', dtype=torch.float32)\n", + " b = torch.randn(K, N, device='cuda', dtype=torch.float32)\n", + " \n", + " # Triton 实现\n", + " c_triton = matmul(a, b)\n", + " \n", + " # PyTorch 参考实现\n", + " c_torch = torch.matmul(a, b)\n", + " \n", + " # 验证\n", + " if torch.allclose(c_triton, c_torch, atol=1e-3):\n", + " print(\"✓ 矩阵乘法测试通过！\")\n", + " else:\n", + " print(\"✗ 矩阵乘法测试失败！\")\n", + " print(f\"最大误差: {torch.max(torch.abs(c_triton - c_torch)).item():.2e}\")\n", + "\n", + "test_matmul()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 性能对比：有/无 cache hints\n", + "def benchmark_matmul_cache_hints():\n", + " sizes = [(512, 512, 512), (1024, 1024, 1024), (2048, 2048, 2048)]\n", + " \n", + " print(f\"{'Size':>15} | {'No hint (ms)':>13} | {'With hint (ms)':>14} | {'Speedup':>10}\")\n", + " print(\"-\" * 65)\n", + " \n", + " # 实现：无 cache hints 的版本\n", + " @triton.jit\n", + " def matmul_no_hint(\n", + " a_ptr, b_ptr, c_ptr,\n", + " M, N, K,\n", + " stride_am, stride_ak,\n", + " stride_bk, stride_bn,\n", + " stride_cm, stride_cn,\n", + " BLOCK_SIZE_M: tl.constexpr,\n", + " BLOCK_SIZE_N: tl.constexpr,\n", + " BLOCK_SIZE_K: tl.constexpr,\n", + " ):\n", + " pid = tl.program_id(0)\n", + " pid_m = pid // (triton.cdiv(N, BLOCK_SIZE_N))\n", + " pid_n = pid % (triton.cdiv(N, BLOCK_SIZE_N))\n", + " \n", + " acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n", + " \n", + " for k in range(0, K, BLOCK_SIZE_K):\n", + " a = tl.load(a_ptr + ..., mask=..., other=0.0) # 无 cache hint\n", + " b = tl.load(b_ptr + ..., mask=..., other=0.0) # 无 cache hint\n", + " acc += tl.dot(a, b)\n", + " \n", + " tl.store(c_ptr + ..., acc, mask=...)\n", + " \n", + " for M, N, K in sizes:\n", + " a = torch.randn(M, K, device='cuda', dtype=torch.float32)\n", + " b = torch.randn(K, N, device='cuda', dtype=torch.float32)\n", + " \n", + " # 测试无 cache hint\n", + " c1 = torch.empty(M, N, device='cuda')\n", + " grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE_M']) * triton.cdiv(N, meta['BLOCK_SIZE_N']),)\n", + " torch.cuda.synchronize()\n", + " t0 = time.time()\n", + " for _ in range(10):\n", + " matmul_no_hint[grid](a, b, c1, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c1.stride(0), c1.stride(1), 64, 64, 32)\n", + " torch.cuda.synchronize()\n", + " t_no_hint = (time.time() - t0) * 1000\n", + " \n", + " # 测试有 cache hint\n", + " c2 = matmul(a, b)\n", + " torch.cuda.synchronize()\n", + " t0 = time.time()\n", + " for _ in range(10):\n", + " _ = matmul(a, b)\n", + " torch.cuda.synchronize()\n", + " t_with_hint = (time.time() - t0) * 1000\n", + " \n", + " speedup = t_no_hint / t_with_hint\n", + " print(f\"{M}x{K}x{N:>6} | {t_no_hint:>13.2f} | {t_with_hint:>14.2f} | {speedup:>10.2f}x\")\n", + "\n", + "benchmark_matmul_cache_hints()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**思考题**：\n", + "1. cache_modifier 对不同大小的矩阵影响是否相同？为什么？\n", + "2. 什么情况下 `.ca` 和 `.cg` 的效果差异最大？\n", + "3. 如何处理非方阵的情况？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**提示**（点击展开）：\n", + "\n", + "
    \n", + "提示 1：Program ID 计算\n", + "\n", + "```python\n", + "pid = tl.program_id(0)\n", + "pid_m = pid // (triton.cdiv(N, BLOCK_SIZE_N))\n", + "pid_n = pid % (triton.cdiv(N, BLOCK_SIZE_N))\n", + "```\n", + "
    \n", + "\n", + "
    \n", + "提示 2：使用 cache hints\n", + "\n", + "```python\n", + "a = tl.load(a_ptrs, mask=mask, other=0.0, cache_modifier=\".ca\")\n", + "b = tl.load(b_ptrs, mask=mask, other=0.0, cache_modifier=\".cg\")\n", + "```\n", + "
    \n", + "\n", + "
    \n", + "提示 3：指针计算\n", + "\n", + "```python\n", + "rm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n", + "rn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n", + "rk = k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)\n", + "\n", + "a_ptrs = a_ptr + (rm[:, None] * stride_am + rk[None, :] * stride_ak)\n", + "b_ptrs = b_ptr + (rk[:, None] * stride_bk + rn[None, :] * stride_bn)\n", + "```\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 练习 3: 带 Cache Hints 的 Softmax\n", + "\n", + "**目标**：实现一个高效的 Softmax kernel，要求：\n", + "1. 两阶段 reduction（先求 max，再求 sum）\n", + "2. 使用 cache_modifier 优化访存\n", + "3. 支持任意长度的输入序列\n", + "\n", + "**背景**：\n", + "Softmax 计算步骤：\n", + "1. 找到最大值：`max_x = max(x)`\n", + "2. 计算 exp：`exp_x = exp(x - max_x)`\n", + "3. 求和：`sum_exp = sum(exp_x)`\n", + "4. 归一化：`softmax = exp_x / sum_exp`\n", + "\n", + "**难点**：\n", + "- 步骤 1 需要跨元素 reduction\n", + "- 步骤 3 又需要一次 reduction\n", + "- 如何高效地共享中间结果？" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@triton.jit\n", + "def softmax_kernel(\n", + " input_ptr, output_ptr,\n", + " n_rows, n_cols,\n", + " stride_row, stride_col,\n", + " BLOCK_SIZE: tl.constexpr,\n", + "):\n", + " \"\"\"\n", + " TODO: 实现带 cache hints 的 Softmax\n", + " \n", + " 步骤：\n", + " 1. 获取当前行\n", + " 2. 第一次 reduction：找 max\n", + " 3. 计算 exp(x - max)\n", + " 4. 第二次 reduction：求 exp 和\n", + " 5. 归一化\n", + " 6. 存储结果\n", + " \"\"\"\n", + " # ==================== 在下方编写代码 ====================\n", + " \n", + " \n", + " \n", + " # ========================================================\n", + " pass\n", + "\n", + "def softmax(x):\n", + " \"\"\"\n", + " Host 端包装函数\n", + " \n", + " Args:\n", + " x: (n_rows, n_cols) tensor\n", + " \n", + " Returns:\n", + " (n_rows, n_cols) softmax 结果\n", + " \"\"\"\n", + " n_rows, n_cols = x.shape\n", + " output = torch.empty_like(x)\n", + " \n", + " # Grid 配置\n", + " grid = lambda meta: (n_rows,)\n", + " \n", + " # 启动 kernel\n", + " softmax_kernel[grid](\n", + " x, output,\n", + " n_rows, n_cols,\n", + " x.stride(0), x.stride(1),\n", + " BLOCK_SIZE=1024,\n", + " )\n", + " \n", + " return output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 测试 Softmax\n", + "def test_softmax():\n", + " n_rows, n_cols = 32, 2048\n", + " x = torch.randn(n_rows, n_cols, device='cuda', dtype=torch.float32)\n", + " \n", + " # Triton 实现\n", + " y_triton = softmax(x)\n", + " \n", + " # PyTorch 参考实现\n", + " y_torch = torch.nn.functional.softmax(x, dim=-1)\n", + " \n", + " # 验证\n", + " if torch.allclose(y_triton, y_torch, atol=1e-4):\n", + " print(\"✓ Softmax 测试通过！\")\n", + " else:\n", + " print(\"✗ Softmax 测试失败！\")\n", + " print(f\"最大误差: {torch.max(torch.abs(y_triton - y_torch)).item():.2e}\")\n", + "\n", + "test_softmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 性能对比\n", + "def benchmark_softmax():\n", + " sizes = [(32, 512), (32, 1024), (32, 2048), (32, 4096)]\n", + " \n", + " print(f\"{'Rows':>8} | {'Cols':>8} | {'Triton (ms)':>12} | {'Torch (ms)':>12} | {'Speedup':>10}\")\n", + " print(\"-\" * 65)\n", + " \n", + " for n_rows, n_cols in sizes:\n", + " x = torch.randn(n_rows, n_cols, device='cuda', dtype=torch.float32)\n", + " \n", + " # Triton 实现\n", + " torch.cuda.synchronize()\n", + " t0 = time.time()\n", + " for _ in range(100):\n", + " output = softmax(x)\n", + " torch.cuda.synchronize()\n", + " t_triton = (time.time() - t0) * 1000\n", + " \n", + " # PyTorch 实现\n", + " torch.cuda.synchronize()\n", + " t0 = time.time()\n", + " for _ in range(100):\n", + " output_torch = torch.nn.functional.softmax(x, dim=-1)\n", + " torch.cuda.synchronize()\n", + " t_torch = (time.time() - t0) * 1000\n", + " \n", + " speedup = t_torch / t_triton\n", + " print(f\"{n_rows:>8} | {n_cols:>8} | {t_triton:>12.2f} | {t_torch:>12.2f} | {speedup:>10.2f}x\")\n", + "\n", + "# benchmark_softmax() # 取消注释以运行" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**思考题**（高级）：\n", + "1. 如何处理数值稳定性？（提示：exp(x - max_x)）\n", + "2. 如何优化 reduction 的性能？（提示：两级 reduction）\n", + "3. 这种方法能扩展到 2D Softmax 吗？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**提示**（点击展开）：\n", + "\n", + "
    \n", + "提示 1：Reduction 模式\n", + "\n", + "```python\n", + "# 使用 tl.max 和 tl.sum 进行 reduction\n", + "row = tl.program_id(0)\n", + "col_offsets = tl.arange(0, BLOCK_SIZE)\n", + "mask = col_offsets < n_cols\n", + "\n", + "# 加载当前行\n", + "x = tl.load(input_ptr + row * stride_row + col_offsets * stride_col, mask=mask, other=float('-inf'))\n", + "\n", + "# 找最大值\n", + "x_max = tl.max(x, axis=0)\n", + "\n", + "# 计算 exp(x - max)\n", + "x_exp = tl.exp(x - x_max)\n", + "\n", + "# 求和\n", + "x_sum = tl.sum(x_exp, axis=0)\n", + "\n", + "# 归一化\n", + "y = x_exp / x_sum\n", + "```\n", + "
    \n", + "\n", + "
    \n", + "提示 2：使用 cache hints\n", + "\n", + "```python\n", + "# 输入数据会被多次访问（找 max、求 sum），使用 .cg\n", + "x = tl.load(input_ptr + ..., mask=mask, other=float('-inf'), cache_modifier=\".cg\")\n", + "```\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 总结\n", + "\n", + "完成这三个练习后，你应该掌握了：\n", + "- 2D/多维地址计算和 mask 处理\n", + "- 数据复用优化技术\n", + "- Cache hints 的实际应用\n", + "- Reduction 操作的实现方法\n", + "\n", + "**下一步**：学习 Triton 的 Reduction 与原子操作！\n", + "\n", + "---\n", + "\n", + "## 课后答案\n", + "\n", + "### 练习 1：2D 卷积\n", + "\n", + "```python\n", + "@triton.jit\n", + "def conv2d_box_filter_kernel(\n", + " input_ptr, output_ptr,\n", + " H, W,\n", + " stride_h, stride_w,\n", + " BLOCK_SIZE_H: tl.constexpr,\n", + " BLOCK_SIZE_W: tl.constexpr,\n", + "):\n", + " pid_h = tl.program_id(axis=0)\n", + " pid_w = tl.program_id(axis=1)\n", + " \n", + " # 加载 (BLOCK_SIZE_H + 2) × (BLOCK_SIZE_W + 2) 的块\n", + " h = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H + 2) - 1\n", + " w = pid_w * BLOCK_SIZE_W + tl.arange(0, BLOCK_SIZE_W + 2) - 1\n", + " \n", + " # 创建边界 mask\n", + " mask = (h[:, None] >= 0) & (h[:, None] < H) & \\\n", + " (w[None, :] >= 0) & (w[None, :] < W)\n", + " \n", + " # 加载块\n", + " input_ptrs = input_ptr + (h[:, None] * stride_h + w[None, :] * stride_w)\n", + " input_block = tl.load(input_ptrs, mask=mask, other=0.0)\n", + " \n", + " # 3×3 邻居求和\n", + " result = (\n", + " input_block[:-2, :-2] + input_block[:-2, 1:-1] + input_block[:-2, 2:] +\n", + " input_block[1:-1, :-2] + input_block[1:-1, 1:-1] + input_block[1:-1, 2:] +\n", + " input_block[2:, :-2] + input_block[2:, 1:-1] + input_block[2:, 2:]\n", + " )\n", + " \n", + " # 存储结果\n", + " out_h = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H)\n", + " out_w = pid_w * BLOCK_SIZE_W + tl.arange(0, BLOCK_SIZE_W)\n", + " out_mask = (out_h[:, None] < H) & (out_w[None, :] < W)\n", + " \n", + " output_ptrs = output_ptr + (out_h[:, None] * stride_h + out_w[None, :] * stride_w)\n", + " tl.store(output_ptrs, result, mask=out_mask)\n", + "```\n", + "\n", + "### 练习 2：矩阵乘法\n", + "\n", + "```python\n", + "@triton.jit\n", + "def matmul_kernel(\n", + " a_ptr, b_ptr, c_ptr,\n", + " M, N, K,\n", + " stride_am, stride_ak,\n", + " stride_bk, stride_bn,\n", + " stride_cm, stride_cn,\n", + " BLOCK_SIZE_M: tl.constexpr,\n", + " BLOCK_SIZE_N: tl.constexpr,\n", + " BLOCK_SIZE_K: tl.constexpr,\n", + "):\n", + " pid = tl.program_id(0)\n", + " pid_m = pid // (triton.cdiv(N, BLOCK_SIZE_N))\n", + " pid_n = pid % (triton.cdiv(N, BLOCK_SIZE_N))\n", + " \n", + " rm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n", + " rn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n", + " \n", + " # 初始化累加器\n", + " accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n", + " \n", + " # 分块计算\n", + " for k in range(0, K, BLOCK_SIZE_K):\n", + " rk = k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)\n", + " \n", + " # 创建 mask\n", + " mask_m = rm[:, None] < M\n", + " mask_n = rn[None, :] < N\n", + " mask_k = rk[None, :] < K\n", + " mask = mask_m & mask_n & mask_k\n", + " \n", + " # 计算指针\n", + " a_ptrs = a_ptr + (rm[:, None] * stride_am + rk[None, :] * stride_ak)\n", + " b_ptrs = b_ptr + (rk[:, None] * stride_bk + rn[None, :] * stride_bn)\n", + " \n", + " # 使用 cache hints 优化加载\n", + " a = tl.load(a_ptrs, mask=mask, other=0.0, cache_modifier=\".ca\")\n", + " b = tl.load(b_ptrs, mask=mask, other=0.0, cache_modifier=\".cg\")\n", + " \n", + " # 累加\n", + " accumulator += tl.dot(a, b)\n", + " \n", + " # 存储结果\n", + " cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n", + " cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n", + " \n", + " out_mask = (cm[:, None] < M) & (cn[None, :] < N)\n", + " c_ptrs = c_ptr + (cm[:, None] * stride_cm + cn[None, :] * stride_cn)\n", + " \n", + " tl.store(c_ptrs, accumulator, mask=out_mask)\n", + "```\n", + "\n", + "### 练习 3：Softmax\n", + "\n", + "```python\n", + "@triton.jit\n", + "def softmax_kernel(\n", + " input_ptr, output_ptr,\n", + " n_rows, n_cols,\n", + " stride_row, stride_col,\n", + " BLOCK_SIZE: tl.constexpr,\n", + "):\n", + " row = tl.program_id(0)\n", + " col_offsets = tl.arange(0, BLOCK_SIZE)\n", + " \n", + " # Mask\n", + " mask = col_offsets < n_cols\n", + " \n", + " # 加载当前行（使用 cache hints）\n", + " x = tl.load(\n", + " input_ptr + row * stride_row + col_offsets * stride_col,\n", + " mask=mask,\n", + " other=float('-inf'),\n", + " cache_modifier=\".cg\"\n", + " )\n", + " \n", + " # 找最大值（数值稳定性）\n", + " x_max = tl.max(x)\n", + " \n", + " # 计算 exp(x - max)\n", + " x_exp = tl.exp(x - x_max)\n", + " \n", + " # 求和\n", + " x_sum = tl.sum(x_exp)\n", + " \n", + " # 归一化\n", + " y = x_exp / x_sum\n", + " \n", + " # 存储结果\n", + " tl.store(\n", + " output_ptr + row * stride_row + col_offsets * stride_col,\n", + " y,\n", + " mask=mask\n", + " )\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + 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b/README.md @@ -57,6 +57,7 @@ ### Triton 系列 💡 + [Triton 编程范式入门](./docs/18_triton/01_triton_programming_paradigms/README.md) ++ [Triton 内存和数据传输](./docs/18_triton/02_triton_memory_and_data_movement/README.md) ### LLM 推理技术 🤖 diff --git a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb index 323965f..4c3eb90 100644 --- a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb +++ b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb @@ -1,19 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Triton 编程范式 - 课后练习\n", - "\n", - "本 Notebook 包含三个练习，帮助你巩固 Triton 的核心概念。\n", - "\n", - "**学习目标**：\n", - "- 掌握 Triton 的基本语法和向量化操作\n", - "- 理解 `BLOCK_SIZE` 对性能的影响\n", - "- 学会用向量化方式处理复杂的数据访问模式" - ] - }, { "cell_type": "code", "execution_count": null, @@ -212,15 +198,6 @@ " print(f\"Torch: {y_torch[:5].cpu().numpy()}\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**思考题**（高级）：\n", - "1. 为什么这种方法效率不高？（提示：重复加载）\n", - "2. 如何优化？（提示：加载更大的块然后切片）" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -277,7 +254,7 @@ " mask = offsets < n_elements\n", " \n", " x_center = tl.load(x_ptr + offsets, mask=mask, other=0.0)\n", - " x_left = tl.load(x_ptr + offsets - 1, mask=offsets > 0, other=0.0)\n", + " x_left = tl.load(x_ptr + offsets - 1, mask=mask & (offsets > 0), other=0.0)\n", " x_right = tl.load(x_ptr + offsets + 1, mask=offsets < n_elements - 1, other=0.0)\n", " \n", " y = x_left + x_center + x_right\n", diff --git a/docs/18_triton/02_triton_memory_and_data_movement/README.md b/docs/18_triton/02_triton_memory_and_data_movement/README.md index a9adf67..a36470e 100644 --- a/docs/18_triton/02_triton_memory_and_data_movement/README.md +++ b/docs/18_triton/02_triton_memory_and_data_movement/README.md @@ -6,15 +6,13 @@ 真实世界的应用往往更复杂：我们需要处理**多维数据**（如图像的 2D 矩阵），需要理解**内存布局**对性能的影响，还需要处理**更复杂的边界情况**（如 padding）。 -这一节，我们将从 1D 走向 2D，探索 Triton 的内存管理和数据搬运机制。 +这一节，我们将学习 Triton 的内存管理和数据搬运机制。 期望在阅读完本文后，你能够： - 理解多维数组的 stride 概念，掌握 2D 地址计算方法 - 理解内存连续性对性能的影响 - 掌握 Triton 的高级加载参数（cache_modifier、eviction_policy） -- 学会使用数据复用技巧减少内存访问 ---- ## 一、从 1D 到 2D - 指针与多维地址计算 @@ -63,6 +61,7 @@ __global__ void transpose_cuda(float *A, float *B, int M, int N) { ``` 在 CUDA 中，每个线程处理一个元素。你需要： + - 用 `dim3` 定义 2D 的 block 和 grid - 通过 `threadIdx.x/y` 和 `blockIdx.x/y` 计算每个线程的坐标 - 用 `if (row < M && col < N)` 做边界检查 @@ -130,58 +129,28 @@ mask = (rows[:, None] < M) & (cols[None, :] < N) 这种 broadcast 机制让我们能够用简洁的代码生成 2D 的坐标网格，而不需要像 CUDA 那样写两层循环。 -> **💡 底层原理：Broadcast 是如何编译的？** -> -> 你可能会担心：`rows[:, None] * stride + cols[None, :]` 这种操作会不会在运行时构造大数组？ -> -> 完全不会！Triton 编译器会将这个 broadcast 操作**展开为标量代码**，而不是运行时构造 2D 数组。编译器知道 `rows` 和 `cols` 的编译时值，会直接生成对应的内存访问指令。 -> -> 对于 BLOCK_SIZE=64 的情况，编译后类似： -> ```cuda -> // 伪代码 -> for (int i = 0; i < 64; i++) { -> for (int j = 0; j < 64; j++) { -> ptr[i][j] = base + (pid_m * 64 + i) * stride_am + (pid_n * 64 + j) * stride_an; -> } -> } -> ``` -> 然后编译器会进一步优化这个循环，自动进行内存合并访问（coalescing），让相邻线程访问相邻内存。 - -### 1.4 Host 端调用 +:::note -```python -def transpose(a: torch.Tensor) -> torch.Tensor: - """ - 转置矩阵 a +**Broadcast 是如何编译的？** - Args: - a: (M, N) 的 tensor - - Returns: - (N, M) 的转置 tensor - """ - M, N = a.shape - b = torch.empty(N, M, device=a.device, dtype=a.dtype) +你可能会担心：`rows[:, None] * stride + cols[None, :]` 这种操作会不会在运行时构造大数组？ - # Grid 配置：2D grid - grid = lambda meta: ( - triton.cdiv(M, meta['BLOCK_SIZE_M']), # 行方向的 Program 数 - triton.cdiv(N, meta['BLOCK_SIZE_N']), # 列方向的 Program 数 - ) +完全不会，Triton 编译器会将这个 broadcast 操作**展开为标量代码**，而不是运行时构造 2D 数组。编译器知道 `rows` 和 `cols` 的编译时值，会直接生成对应内存访问指令。 - # 启动 kernel - transpose_triton[grid]( - a, b, - M, N, - a.stride(0), a.stride(1), # PyTorch 自动获取 stride - b.stride(0), b.stride(1), - BLOCK_SIZE_M=64, - BLOCK_SIZE_N=64, - ) +对于 BLOCK_SIZE=64 的情况，编译后类似： - return b +```cuda +// 伪代码 +for (int i = 0; i < 64; i++) { + for (int j = 0; j < 64; j++) { + ptr[i][j] = base + (pid_m * 64 + i) * stride_am + (pid_n * 64 + j) * stride_an; + } +} ``` +然后编译器会进一步优化这个循环，自动进行内存合并访问（coalescing），让相邻线程访问相邻内存。 + +::: ## 二、内存连续性与访问模式 @@ -239,34 +208,38 @@ def vector_add_strided(x_ptr, y_ptr, out_ptr, n_elements, stride, BLOCK_SIZE: tl tl.store(out_ptr + offsets * stride, out, mask=mask) ``` -**性能测试结果**（典型值）： +**性能测试结果**： ``` Size | Stride | Time (ms) | Slowdown --------|--------|-----------|---------- -1048576 | 1 | 5.23 | 1.00x -1048576 | 2 | 6.87 | 1.31x -1048576 | 4 | 9.45 | 1.81x -1048576 | 8 | 12.34 | 2.36x +5242880 | 1 | 0.02 | 1.00x +5242880 | 2 | 0.04 | 2.09x +5242880 | 4 | 0.08 | 3.98x +5242880 | 8 | 0.15 | 7.79x +``` + +可以看到，随着 stride 增大，访问速度明显下降 + +:::note + +**为什么 stride 越大越慢？** + +GPU 的内存访问是以**事务为单位**的。一个 warp（32 个线程）访问连续的 32 个 float（128 字节）时，可以合并为**单个内存事务**。 + +但当 stride=8 时，这 32 个线程访问的是间隔 8 个元素的数据： +``` +线程 0: addr[0] +线程 1: addr[8] +线程 2: addr[16] +... ``` -可以看到，随着 stride 增大，访问速度明显下降！ - -> **💡 底层原理：为什么 stride 越大越慢？** -> -> GPU 的内存访问是以**事务（transaction）为单位**的。一个 warp（32 个线程）访问连续的 32 个 float（128 字节）时，可以合并为**单个内存事务**。 -> -> 但当 stride=8 时，这 32 个线程访问的是间隔 8 个元素的数据： -> ``` -> 线程 0: addr[0] -> 线程 1: addr[8] -> 线程 2: addr[16] -> ... -> ``` -> -> 这些地址跨越了多个 cache line，GPU 需要发起**多个内存事务**，浪费了大量带宽。 -> -> Triton 编译器会尽量优化访问模式，但如果数据本身的内存布局不连续，编译器也无力回天。所以**保持输入 tensor 的连续性**非常重要。 +这些地址跨越了多个 cache line，GPU 需要发起**多个内存事务**，浪费了大量带宽。 + +Triton 编译器会尽量优化访问模式，但如果数据本身的内存布局不连续，编译器也无力回天。所以**保持输入 tensor 的连续性**非常重要。 + +::: ### 2.3 如何检查和修复 @@ -313,7 +286,7 @@ transpose_triton[grid]( ## 三、向量化加载与存储进阶 -在 Triton 01 中，我们只用了 `tl.load(ptr, mask=mask)` 的基础形式。实际上，`tl.load` 还有很多强大的参数可以帮助我们优化性能。 +在上一篇文章中，我们只用了 `tl.load(ptr, mask=mask)` 的基础形式。实际上，`tl.load` 还有很多强大的参数可以帮助我们优化性能。 ### 3.1 tl.load 的高级参数 @@ -331,12 +304,12 @@ tl.load( `cache_modifier` 参数告诉 GPU 的缓存系统如何处理这些数据。 -| 修饰符 | 含义 | 使用场景 | -|--------|------|----------| -| `""` | 默认 | 通用场景，让硬件自动决定 | -| `".ca"` | Cache All | 数据会被多次访问，建议缓存 | -| `".cg"` | Cache Global | 数据会被频繁访问，缓存在 L1/L2 | -| `".cs"` | Cache Stream | 数据只使用一次，不缓存（流式） | +| 修饰符 | 含义 | 缓存层级 | 使用场景 | +|--------|------|----------|----------| +| `""` | 默认 | 硬件决定 | 通用场景 | +| `".ca"` | Cache at All Levels | L1 + L2 | 数据会被当前 Program 多次访问 | +| `".cg"` | Cache at Global Level | L2 | 数据会被多个 Program 复用 | +| `".cs"` | Cache Stream | 不缓存 | 数据只访问一次，流式处理 | **原理：GPU 缓存层次结构** @@ -344,7 +317,6 @@ tl.load( ![picture 0](images/3a87ae7803e31e2baaf06184248f34a65d99c6ebd33e1084f74416d0aa259dde.png) - `cache_modifier` 就是在提示 Triton：这个数据应该放在哪一层缓存最合适 让我们看一个完整的矩阵乘法例子： @@ -392,164 +364,124 @@ def matmul_kernel( b = tl.load(b_ptrs, mask=mask, other=0.0, cache_modifier=".cg") accumulator += tl.dot(a, b) -``` - -- **A 矩阵**：对于固定的 `(pid_m, pid_n)`，在循环中每次 `k` 迭代加载的是 A 的不同列块。当前 `k` 加载的数据在 `k+1` 时不会再被用到，所以**每个块只访问一次**。 + # 存储结果 + cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) -- **B 矩阵**：虽然当前循环中也只访问一次，但 B 的同一块数据可能被**不同的 Program**（不同的 `pid_m`）访问和复用。使用 `.cg` 可以让它留在缓存中，供其他 Program 使用。 + # 输出 mask（不需要检查 K 维度） + out_mask = (cm[:, None] < M) & (cn[None, :] < N) + c_ptrs = c_ptr + (cm[:, None] * stride_cm + cn[None, :] * stride_cn) + tl.store(c_ptrs, accumulator, mask=out_mask) -在大多数情况下，使用默认策略即可。cache_modifier 是**hint**（提示），不是强制命令，最终决策权在 Triton 手中。只有在性能瓶颈明显时，才考虑手动调整并测试效果。 +# Host 端调用 +def matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + """ + 矩阵乘法：C = A @ B -> **💡 底层原理：cache_modifier 如何影响 PTX？** -> -> 当你指定 `cache_modifier=".cg"` 时，Triton 会生成不同的 PTX 指令： -> -> ```ptx -> // 默认 -> ld.global.f32 {%r1}, [%ptr]; -> -> // .cg (Cache at Global level - L2) -> ld.global.cg.f32 {%r1}, [%ptr]; -> -> // .ca (Cache All - L1 + L2) -> ld.global.ca.f32 {%r1}, [%ptr]; -> ``` -> -> 这些是 PTX 的缓存操作符，直接控制 GPU 的 L1/L2 缓存行为。`.cg` 告诉 GPU 这个数据值得留在 L2 缓存中，`.ca` 则表示连 L1 也值得缓存。 -> -> Triton 编译器会自动将这些 hints 转换为对应的 PTX 指令，无需你手动编写汇编。 + Args: + a: (M, K) tensor + b: (K, N) tensor -### 3.3 eviction_policy 的使用 + Returns: + (M, N) tensor + """ + M, K = a.shape + K2, N = b.shape + assert K == K2, "矩阵维度不匹配" -`eviction_policy` 控制缓存行的替换策略。 + c = torch.empty(M, N, device=a.device, dtype=a.dtype) -| 策略 | 含义 | 使用场景 | -|------|------|----------| -| `"evict_first"` | 优先驱逐 | 临时数据、只读一次 | -| `"evict_last"` | 最后驱逐 | 需要保留的数据 | + # Grid 配置 + grid = lambda meta: ( + triton.cdiv(M, meta['BLOCK_SIZE_M']) * triton.cdiv(N, meta['BLOCK_SIZE_N']), + ) -**示例**： + # 启动 kernel + matmul_kernel[grid]( + a, b, c, + M, N, K, + a.stride(0), a.stride(1), + b.stride(0), b.stride(1), + c.stride(0), c.stride(1), + BLOCK_SIZE_M=64, + BLOCK_SIZE_N=64, + BLOCK_SIZE_K=32, + ) -```python -# 只读数据，用完后立即驱逐 -data = tl.load( - input_ptr + offsets, - mask=mask, - other=0.0, - eviction_policy="evict_first" -) + return c ``` -cache hints 的效果因 GPU 架构和数据访问模式而异，需要实际测试来确定最佳策略。在大多数情况下，使用默认策略即可，只有在性能瓶颈明显时才考虑手动调整。 - - -## 四、数据复用优化 - -在 Triton 01 作业中的 1D 卷积，朴素实现是这样的： - -```python -# 朴素方法：每个元素单独加载 -x_left = tl.load(x_ptr + offsets - 1, mask=..., other=0.0) -x_center = tl.load(x_ptr + offsets, mask=..., other=0.0) -x_right = tl.load(x_ptr + offsets + 1, mask=..., other=0.0) -result = x_left + x_center + x_right -``` -问题来了：**相邻的 Program 会重复加载相同的数据！** +**A 矩阵**：对于固定的 `(pid_m, pid_n)`，在循环中每次 `k` 迭代加载的是 A 的不同列块。当前 `k` 加载的数据在 `k+1` 时不会再被用到。虽然 A 的数据可能会被**同一行**的不同 `pid_n` 访问和复用，但当前 Program 内部不会复用，所以使用 `.ca` 确保 L1/L2 缓存。 -### 4.1 优化方法 +**B 矩阵**：不仅当前循环中需要访问，而且 B 的同一块数据会被**同一列**的不同 `pid_m` 访问和复用。使用 `.cg` 可以让它优先留在 L2 缓存中，供其他 Program 复用。 -解决方案很简单：**一次性加载更多的数据，然后在 SRAM 中切片** +:::note -```python -@triton.jit -def conv1d_optimized(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): - """ - 优化版 1D 卷积：Y[i] = X[i-1] + X[i] + X[i+1] +**cache_modifier 如何影响 PTX？** - 优化策略：加载更大的块，然后切片，减少重复加载 - """ - pid = tl.program_id(0) +当你指定 `cache_modifier=".cg"` 时，Triton 会生成不同的 PTX 指令： - # 加载 BLOCK_SIZE + 2 个元素（左边 1 个，右边 1 个） - block_start = pid * BLOCK_SIZE - 1 # 向前偏移 1 - offsets = block_start + tl.arange(0, BLOCK_SIZE + 2) +```ptx +// 默认 +ld.global.f32 {%r1}, [%ptr]; - # 边界处理 - mask = (offsets >= 0) & (offsets < n_elements) +// .cg (Cache at Global level - L2) +ld.global.cg.f32 {%r1}, [%ptr]; - # 一次性加载所有需要的元素 - x = tl.load(input_ptr + offsets, mask=mask, other=0.0) +// .ca (Cache All - L1 + L2) +ld.global.ca.f32 {%r1}, [%ptr]; +``` - # 切片获取所需的邻居 - x_left = x[:-2] # [0, 1, 2, ..., BLOCK_SIZE-1] - x_center = x[1:-1] # [1, 2, 3, ..., BLOCK_SIZE] - x_right = x[2:] # [2, 3, 4, ..., BLOCK_SIZE+1] +这些是 PTX 的缓存操作符，直接控制 GPU 的 L1/L2 缓存行为。`.cg` 告诉 GPU 这个数据值得留在 L2 缓存中，`.ca` 则表示连 L1 也值得缓存。 - # 计算 - result = x_left + x_center + x_right +::: - # 存储结果 - output_offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - output_mask = output_offsets < n_elements - tl.store(output_ptr + output_offsets, result, mask=output_mask) -``` +Triton 编译器会自动将这些 hints 转换为对应的 PTX 指令，无需你手动编写汇编。 当然了你不写也不代表 Triton 编译器不会自动优化，这一点在完成课后作业的过程中会有更深刻的体会。 -### 4.2 对比分析 +### 3.3 eviction_policy 的使用 -| 方法 | 加载次数 | 数据复用 | 性能 | -|------|---------|---------|------| -| 朴素方法 | 3 × BLOCK_SIZE | 无 | 慢 | -| 优化方法 | 1 × (BLOCK_SIZE + 2) | 高 | 快 | +`eviction_policy` 控制缓存行的替换策略。 -内存访问减少了约 **3 倍**！ +| 策略 | 含义 | 使用场景 | +|------|------|----------| +| `"evict_first"` | 优先驱逐 | 临时数据、只读一次 | +| `"evict_last"` | 最后驱逐 | 需要保留的数据 | -### 4.3 与 CUDA Shared Memory 的对比 +**示例**： -| 特性 | CUDA | Triton | -|------|------|--------| -| 显式声明 | `__shared__ float smem[1024];` | 无需显式声明 | -| 手动同步 | `__syncthreads();` | 自动处理 | -| Bank Conflict | 需要手动处理 | 编译器自动优化 | +```python +# 只读数据，用完后立即驱逐 +data = tl.load( + input_ptr + offsets, + mask=mask, + other=0.0, + eviction_policy="evict_first" +) +``` -Triton 的优势在于**简洁性**：你只需要关注"我需要什么数据"，编译器会自动处理"如何高效地访问这些数据"。 +:::tip -> **💡 底层原理：Triton 如何自动优化内存访问？** -> -> 在优化版 1D 卷积中，我们加载了 `BLOCK_SIZE + 2` 个元素，然后用切片 `x[:-2]`, `x[1:-1]`, `x[2:]`。 -> -> Triton 编译器会分析这些切片的使用模式： -> 1. 发现 `x` 被多次访问（三次切片操作） -> 2. 自动将 `x` 分配到寄存器或 Shared Memory（取决于大小） -> 3. 切片操作编译为**指针偏移**，不产生实际数据拷贝 -> -> 相当于 CUDA 中手动写： -> ```cuda -> __shared__ float x[BLOCK_SIZE + 2]; -> // ... 加载到 smem ... -> float x_left = x[0]; // 直接索引，无拷贝 -> float x_center = x[1]; -> float x_right = x[2]; -> ``` -> -> 此外，Triton 编译器还会自动检测 Bank Conflict。如果发现访问模式会导致 conflict，它会尝试重排数据或访问顺序——这是手动 CUDA 编程中需要费力优化的事。 +cache hints 的效果因 GPU 架构和数据访问模式而异，需要实际测试来确定最佳策略。在大多数情况下，使用默认策略即可，只有在性能瓶颈明显时才考虑手动调整。 +::: -## 五、高级工具：tl.make_block_ptr +## 四、高级工具：tl.make_block_ptr 前面我们学习了如何手动计算 2D/多维张量的地址。但实际上 Triton 提供了一个更强大的工具：`tl.make_block_ptr`。 -### 5.1 什么是 Block Pointer？ +### 4.1 什么是 Block Pointer？ `tl.make_block_ptr` 是一个专门用于处理多维张量访问的高级工具。它可以： + - 自动处理多维地址计算 - 支持任意维度的张量 - 自动优化内存访问模式 -### 5.2 基本用法 +### 4.2 基本用法 ```python @triton.jit @@ -571,9 +503,9 @@ def using_block_ptr(...): result = a * 2.0 ``` -注意使用 `tl.make_block_ptr` 时，`tl.load` **不会自动处理边界**！你必须显式指定 `boundary_check` 参数，否则当矩阵尺寸不是 BLOCK_SIZE 的倍数时，代码会越界访问或读取错误数据。 +注意使用 `tl.make_block_ptr` 时，`tl.load` **不会自动处理边界** 你必须显式指定 `boundary_check` 参数，否则当矩阵尺寸不是 BLOCK_SIZE 的倍数时，代码会越界访问或读取错误数据。 -### 5.3 在循环中使用 Block Pointer +### 4.3 在循环中使用 Block Pointer Block Pointer 真正的优势在于循环迭代。普通指针需要手动计算 `ptr += stride`，而 Block Pointer 有专属的神器：`tl.advance`。 @@ -616,24 +548,30 @@ def matmul_block_ptr(...): `tl.advance` 会更新 Block Pointer 的内部状态，使其指向下一个逻辑块，完全不需要手动计算复杂的 stride 偏移。这在矩阵乘法、卷积等需要反复遍历张量的场景中非常方便。 -> **💡 底层原理：tl.advance 是如何实现的？** -> -> `tl.advance(a_block_ptr, (0, BLOCK_SIZE_K))` 不会运行时计算复杂地址。 -> -> Block Pointer 是一个**编译期抽象**，它记录了： -> - 基地址指针 -> - 各维度的 stride -> - 当前块偏移 -> -> `tl.advance` 只需要更新偏移量，编译器会直接生成： -> ```ptx -> // 假设沿着第二维移动 BLOCK_SIZE_K -> add.u64 %ptr, %base_ptr, %new_offset; // 单个加法指令！ -> ``` -> -> 相比手动计算 `ptr += stride0 * delta0 + stride1 * delta1`，效率完全相同，但代码更清晰，不易出错。 - -### 5.4 tl.store 的特殊说明 +:::note + +**Triton 如何自动优化内存访问？** + +`tl.advance(a_block_ptr, (0, BLOCK_SIZE_K))` 不会运行时计算复杂地址。 + +Block Pointer 是一个**编译期抽象**，它记录了： + +- 基地址指针 +- 各维度的 stride +- 当前块偏移 + +`tl.advance` 只需要更新偏移量，编译器会直接生成： + +```ptx +// 假设沿着第二维移动 BLOCK_SIZE_K +add.u64 %ptr, %base_ptr, %new_offset; // 单个加法指令 +``` + +::: + +相比手动计算 `ptr += stride0 * delta0 + stride1 * delta1`，效率完全相同，但代码更清晰，不易出错。 + +### 4.4 tl.store 的特殊说明 使用 Block Pointer 写入时有一个重要的限制：**tl.store 不支持 padding_option**。 @@ -648,7 +586,7 @@ tl.store(c_block_ptr, result, boundary_check=(0, 1)) # 正确 这是逻辑上的必然：读取时越界可以补 0，但写入时越界是非法的。Triton 会自动屏蔽掉越界的写入操作（通过 boundary_check）。 -### 5.5 关于 order 参数的深入理解 +### 4.5 关于 order 参数的深入理解 `order` 参数应该按照 **stride 从小到大**（即变化最快到最慢）的顺序排列维度。 @@ -659,18 +597,23 @@ tl.store(c_block_ptr, result, boundary_check=(0, 1)) # 正确 因此 `order=(1, 0)`，把变化最快的维放在前面。Triton 会利用这个信息来优化数据搬运（如 Swizzling 以避免 Bank Conflict），最大化 L2 Cache 的命中率。 -> **💡 底层原理：order 如何影响数据搬运？** -> -> `order` 参数不仅影响代码正确性，还直接影响性能。 -> -> 当你指定 `order=(1, 0)` 时，Triton 编译器知道数据是**列连续**的。在从 DRAM 搬运数据到 SRAM 时，它会： -> 1. 按照连续维度优先加载数据（提高内存吞吐） -> 2. 进行 **Swizzling** 优化：重新排列数据在 Shared Memory 中的布局，避免 Bank Conflict -> 3. 生成向量化加载指令（如 `ld.global.nc.f32.v4`，一次加载 4 个 float） -> -> 如果 `order` 设置错误，不仅访问模式会低效，编译器也无法进行这些优化。所以在使用 Block Pointer 时，**正确设置 order 非常关键**。 +:::note + +**Triton 如何自动优化内存访问？** + +`order` 参数不仅影响代码正确性，还直接影响性能。 + +当你指定 `order=(1, 0)` 时，Triton 编译器知道数据是**列连续**的。在从 DRAM 搬运数据到 SRAM 时，它会： + +1. 按照连续维度优先加载数据（提高内存吞吐） +2. 进行 **Swizzling** 优化：重新排列数据在 Shared Memory 中的布局，避免 Bank Conflict +3. 生成向量化加载指令（如 `ld.global.nc.f32.v4`，一次加载 4 个 float） + +如果 `order` 设置错误，不仅访问模式会低效，编译器也无法进行这些优化。所以在使用 Block Pointer 时，**正确设置 order 非常关键**。 -### 5.6 什么时候使用 Block Pointer？ +::: + +### 4.6 什么时候使用 Block Pointer？ | 场景 | 推荐方式 | |------|---------| @@ -680,14 +623,22 @@ tl.store(c_block_ptr, result, boundary_check=(0, 1)) # 正确 Block Pointer 是 Triton 的高级特性，在处理复杂张量操作时非常有用。 +## 五、课后练习 + +请打开 [homework.ipynb](./homework.ipynb) 完成以下练习，巩固内存管理知识： + +- **练习 1**：实现优化的 2D 卷积（3×3 box filter），学习如何加载带 padding 的数据块 +- **练习 2**：实现带 cache hints 的矩阵乘法，体会 Block Pointer 在 2D 索引中的使用 + +每个练习都包含了测试函数和思考题，完成练习后你将对 Triton 的内存管理有更深入的理解。 + ## 六、总结 完成本节学习后，你应该理解了 stride 概念，能处理 2D/多维张量的地址计算。掌握了 Triton 的 broadcast 机制（`[:, None]` 和 `[None, :]`），也理解了内存连续性对性能的影响。在编程技能方面，你能够实现 2D/多维操作的 Triton kernel，可以使用 cache_modifier 和 eviction_policy 优化访存，还能通过数据复用减少内存访问次数。同时，你也了解了 `tl.make_block_ptr` 这个高级工具的使用场景。 -从思维转换的角度来看，CUDA 的思维方式是"这个线程处理第 (row, col) 个元素"，而 Triton 的思维方式转变为"这个 Program 处理第 [rows...][cols...] 批元素"。在边界检查上，CUDA 用 `if (row < M && col < N)`，而 Triton 用 `mask = (rows[:, None] < M) & (cols[None, :] < N)` 进行向量化检查。在数据管理方面，CUDA 需要手动管理 Shared Memory 和同步，而 Triton 只需加载更大的块、使用切片操作，编译器会自动优化。 +从思维转换的角度来看，CUDA 的思维方式是"这个线程处理第 `(row, col)` 个元素"，而 Triton 的思维方式转变为"这个 Program 处理第 `[rows...][cols...]` 批元素"。在边界检查上，CUDA 用 `if (row < M && col < N)`，而 Triton 用 `mask = (rows[:, None] < M) & (cols[None, :] < N)` 进行向量化检查。在数据管理方面，CUDA 需要手动管理 Shared Memory 和同步，而 Triton 只需加载更大的块、使用切片操作，编译器会自动优化。 ## 参考资料 - 1. [Triton Tutorial 04: Matrix Multiplication](https://triton-lang.org/main/getting-started/tutorials/04-matrix-multiplication.html) 2. [Triton Tutorial 05: Softmax](https://triton-lang.org/main/getting-started/tutorials/05-softmax.html) \ No newline at end of file diff --git a/docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb b/docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb index 540baad..04a132b 100644 --- a/docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb +++ b/docs/18_triton/02_triton_memory_and_data_movement/homework.ipynb @@ -39,18 +39,11 @@ "source": [ "---\n", "\n", - "## 练习 1: 优化 2D 卷积\n", + "## 练习 1: 2D 卷积\n", "\n", "**目标**：实现一个高效的 2D 卷积（3×3 box filter），要求：\n", - "1. 使用数据复用技术，减少重复加载\n", - "2. 正确处理边界和 padding\n", - "3. 支持 stride\n", - "\n", - "**提示**：\n", - "1. 参考教程中的 `conv1d_optimized` 方法\n", - "2. 加载 `(BLOCK_SIZE_H + 2) × (BLOCK_SIZE_W + 2)` 的块\n", - "3. 使用切片操作获取邻居数据\n", - "4. 注意边界处理" + "1. 正确处理边界和 padding\n", + "2. 支持 stride" ] }, { @@ -165,6 +158,17 @@ " print(f\"{'Size':>15} | {'Triton (ms)':>12} | {'Torch (ms)':>12} | {'Speedup':>10}\")\n", " print(\"-\" * 60)\n", " \n", + " # 验证结果正确性\n", + " for H, W in sizes:\n", + " input_tensor = torch.randn(H, W, device='cuda', dtype=torch.float32)\n", + " output = conv2d_box_filter(input_tensor)\n", + " assert torch.allclose(output, torch.nn.functional.avg_pool2d(\n", + " input_tensor.unsqueeze(0), kernel_size=3, stride=1, padding=1\n", + " ).squeeze(0) * 9, atol=1e-6)\n", + "\n", + " torch.cuda.synchronize()\n", + "\n", + " # 性能测试\n", " for H, W in sizes:\n", " input_tensor = torch.randn(H, W, device='cuda', dtype=torch.float32)\n", " \n", @@ -192,58 +196,6 @@ "benchmark_conv2d()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**思考题**：\n", - "1. 如果卷积核大小变为 5×5，代码需要如何修改？\n", - "2. 如何使用 cache_modifier 进一步优化性能？\n", - "3. 这种方法和 CUDA 的 Shared Memory 有什么异同？" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**提示**（点击展开）：\n", - "\n", - "

    \n", - "提示 1：如何计算加载块的坐标\n", - "\n", - "加载块需要包含 padding，所以要从 -1 开始：\n", - "```python\n", - "# 加载块的坐标\n", - "h_load = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H + 2) - 1\n", - "w_load = pid_w * BLOCK_SIZE_W + tl.arange(0, BLOCK_SIZE_W + 2) - 1\n", - "```\n", - "
    \n", - "\n", - "
    \n", - "提示 2：如何创建 mask\n", - "\n", - "需要检查加载的坐标是否在有效范围内：\n", - "```python\n", - "mask = (h_load[:, None] >= 0) & (h_load[:, None] < H) & \\\n", - " (w_load[None, :] >= 0) & (w_load[None, :] < W)\n", - "```\n", - "
    \n", - "\n", - "
    \n", - "提示 3：如何切片\n", - "\n", - "使用切片获取 3×3 窗口：\n", - "```python\n", - "# 3×3 邻居求和\n", - "result = (\n", - " input_block[:-2, :-2] + input_block[:-2, 1:-1] + input_block[:-2, 2:] +\n", - " input_block[1:-1, :-2] + input_block[1:-1, 1:-1] + input_block[1:-1, 2:] +\n", - " input_block[2:, :-2] + input_block[2:, 1:-1] + input_block[2:, 2:]\n", - ")\n", - "```\n", - "
    " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -253,10 +205,10 @@ "**目标**：实现一个高效的矩阵乘法 kernel，运用 cache hints 优化访存\n", "\n", "**提示**：\n", - "1. 参考教程中的 `matmul_with_cache_hints`\n", - "2. 使用 `cache_modifier` 参数优化加载\n", - "3. 注意 stride 的正确传递\n", - "4. 处理非 BLOCK_SIZE 整数倍的情况" + "1. 使用 `cache_modifier` 参数优化加载\n", + "2. 注意 stride 的正确传递\n", + "3. 处理非 BLOCK_SIZE 整数倍的情况\n", + "4. 使用 Block Ptr 应该怎么写" ] }, { @@ -278,13 +230,6 @@ "):\n", " \"\"\"\n", " TODO: 实现带 cache hints 的矩阵乘法\n", - " \n", - " 步骤：\n", - " 1. 获取 Program ID\n", - " 2. 初始化累加器\n", - " 3. 分块循环加载 A 和 B（使用 cache_modifier）\n", - " 4. 计算 dot product\n", - " 5. 存储结果\n", " \"\"\"\n", " # ==================== 在下方编写代码 ====================\n", " \n", @@ -293,30 +238,51 @@ " # ========================================================\n", " pass\n", "\n", - "def matmul(a, b):\n", + "\n", + "# 把上面的代码复制下来，去掉 cache hints 参数\n", + "@triton.jit\n", + "def matmul_kernel_no_cache(\n", + " a_ptr, b_ptr, c_ptr,\n", + " M, N, K,\n", + " stride_am, stride_ak,\n", + " stride_bk, stride_bn,\n", + " stride_cm, stride_cn,\n", + " BLOCK_SIZE_M: tl.constexpr,\n", + " BLOCK_SIZE_N: tl.constexpr,\n", + " BLOCK_SIZE_K: tl.constexpr,\n", + "):\n", + " pass\n", + "\n", + "\n", + "def matmul(a, b, use_cache_hints=True):\n", " \"\"\"\n", " Host 端包装函数\n", - " \n", + "\n", " Args:\n", " a: (M, K) tensor\n", " b: (K, N) tensor\n", - " \n", + " use_cache_hints: 是否使用cache hints\n", + "\n", " Returns:\n", " (M, N) tensor\n", " \"\"\"\n", " M, K = a.shape\n", " K2, N = b.shape\n", " assert K == K2, \"矩阵维度不匹配\"\n", - " \n", + "\n", " c = torch.empty(M, N, device=a.device, dtype=a.dtype)\n", - " \n", + "\n", " # Grid 配置\n", " grid = lambda meta: (\n", - " triton.cdiv(M, meta['BLOCK_SIZE_M']) * triton.cdiv(N, meta['BLOCK_SIZE_N']),\n", + " triton.cdiv(M, meta['BLOCK_SIZE_M']),\n", + " triton.cdiv(N, meta['BLOCK_SIZE_N']),\n", " )\n", - " \n", + "\n", + " # 根据参数选择kernel\n", + " kernel = matmul_kernel if use_cache_hints else matmul_kernel_no_cache\n", + "\n", " # 启动 kernel\n", - " matmul_kernel[grid](\n", + " kernel[grid](\n", " a, b, c,\n", " M, N, K,\n", " a.stride(0), a.stride(1),\n", @@ -326,7 +292,7 @@ " BLOCK_SIZE_N=64,\n", " BLOCK_SIZE_K=32,\n", " )\n", - " \n", + "\n", " return c" ] }, @@ -339,16 +305,17 @@ "# 测试矩阵乘法\n", "def test_matmul():\n", " M, N, K = 512, 512, 512\n", - " \n", - " a = torch.randn(M, K, device='cuda', dtype=torch.float32)\n", - " b = torch.randn(K, N, device='cuda', dtype=torch.float32)\n", + "\n", + " # 使用较小的随机数范围来减少数值误差\n", + " a = torch.randn(M, K, device='cuda', dtype=torch.float32) * 0.1\n", + " b = torch.randn(K, N, device='cuda', dtype=torch.float32) * 0.1\n", " \n", " # Triton 实现\n", " c_triton = matmul(a, b)\n", " \n", " # PyTorch 参考实现\n", " c_torch = torch.matmul(a, b)\n", - " \n", + "\n", " # 验证\n", " if torch.allclose(c_triton, c_torch, atol=1e-3):\n", " print(\"✓ 矩阵乘法测试通过！\")\n", @@ -365,313 +332,84 @@ "metadata": {}, "outputs": [], "source": [ - "# 性能对比：有/无 cache hints\n", - "def benchmark_matmul_cache_hints():\n", - " sizes = [(512, 512, 512), (1024, 1024, 1024), (2048, 2048, 2048)]\n", - " \n", - " print(f\"{'Size':>15} | {'No hint (ms)':>13} | {'With hint (ms)':>14} | {'Speedup':>10}\")\n", - " print(\"-\" * 65)\n", - " \n", - " # 实现：无 cache hints 的版本\n", - " @triton.jit\n", - " def matmul_no_hint(\n", - " a_ptr, b_ptr, c_ptr,\n", - " M, N, K,\n", - " stride_am, stride_ak,\n", - " stride_bk, stride_bn,\n", - " stride_cm, stride_cn,\n", - " BLOCK_SIZE_M: tl.constexpr,\n", - " BLOCK_SIZE_N: tl.constexpr,\n", - " BLOCK_SIZE_K: tl.constexpr,\n", - " ):\n", - " pid = tl.program_id(0)\n", - " pid_m = pid // (triton.cdiv(N, BLOCK_SIZE_N))\n", - " pid_n = pid % (triton.cdiv(N, BLOCK_SIZE_N))\n", - " \n", - " acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n", - " \n", - " for k in range(0, K, BLOCK_SIZE_K):\n", - " a = tl.load(a_ptr + ..., mask=..., other=0.0) # 无 cache hint\n", - " b = tl.load(b_ptr + ..., mask=..., other=0.0) # 无 cache hint\n", - " acc += tl.dot(a, b)\n", - " \n", - " tl.store(c_ptr + ..., acc, mask=...)\n", - " \n", + "def benchmark_cache_hints():\n", + " \"\"\"\n", + " 对比使用和不使用cache hints的性能差异\n", + " \"\"\"\n", + " print(\"\\n\" + \"=\"*60)\n", + " print(\"Cache Hints 性能对比测试\")\n", + " print(\"=\"*60)\n", + "\n", + " # 测试不同的矩阵大小\n", + " sizes = [(2048, 2048, 2048), (4096, 4096, 4096), (8192, 8192, 8192)]\n", + "\n", " for M, N, K in sizes:\n", - " a = torch.randn(M, K, device='cuda', dtype=torch.float32)\n", - " b = torch.randn(K, N, device='cuda', dtype=torch.float32)\n", - " \n", - " # 测试无 cache hint\n", - " c1 = torch.empty(M, N, device='cuda')\n", - " grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE_M']) * triton.cdiv(N, meta['BLOCK_SIZE_N']),)\n", + " print(f\"\\n矩阵大小: {M}x{K} @ {K}x{N} = {M}x{N}\")\n", + " print(\"-\" * 40)\n", + "\n", + " # 创建测试数据\n", + " a = torch.randn(M, K, device='cuda', dtype=torch.float32) * 0.1\n", + " b = torch.randn(K, N, device='cuda', dtype=torch.float32) * 0.1\n", + "\n", + " # 预热\n", + " for _ in range(3):\n", + " _ = matmul(a, b, use_cache_hints=True)\n", + " _ = matmul(a, b, use_cache_hints=False)\n", " torch.cuda.synchronize()\n", - " t0 = time.time()\n", + "\n", + " # 测试带cache hints的版本\n", + " torch.cuda.synchronize()\n", + " start_time = time.time()\n", " for _ in range(10):\n", - " matmul_no_hint[grid](a, b, c1, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c1.stride(0), c1.stride(1), 64, 64, 32)\n", + " c_with_cache = matmul(a, b, use_cache_hints=True)\n", " torch.cuda.synchronize()\n", - " t_no_hint = (time.time() - t0) * 1000\n", - " \n", - " # 测试有 cache hint\n", - " c2 = matmul(a, b)\n", + " time_with_cache = (time.time() - start_time) / 10\n", + "\n", + " # 测试不带cache hints的版本\n", " torch.cuda.synchronize()\n", - " t0 = time.time()\n", + " start_time = time.time()\n", " for _ in range(10):\n", - " _ = matmul(a, b)\n", + " c_no_cache = matmul(a, b, use_cache_hints=False)\n", " torch.cuda.synchronize()\n", - " t_with_hint = (time.time() - t0) * 1000\n", - " \n", - " speedup = t_no_hint / t_with_hint\n", - " print(f\"{M}x{K}x{N:>6} | {t_no_hint:>13.2f} | {t_with_hint:>14.2f} | {speedup:>10.2f}x\")\n", - "\n", - "benchmark_matmul_cache_hints()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**思考题**：\n", - "1. cache_modifier 对不同大小的矩阵影响是否相同？为什么？\n", - "2. 什么情况下 `.ca` 和 `.cg` 的效果差异最大？\n", - "3. 如何处理非方阵的情况？" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**提示**（点击展开）：\n", + " time_no_cache = (time.time() - start_time) / 10\n", "\n", - "
    \n", - "提示 1：Program ID 计算\n", + " # 验证结果一致性\n", + " max_diff = torch.max(torch.abs(c_with_cache - c_no_cache)).item()\n", "\n", - "```python\n", - "pid = tl.program_id(0)\n", - "pid_m = pid // (triton.cdiv(N, BLOCK_SIZE_N))\n", - "pid_n = pid % (triton.cdiv(N, BLOCK_SIZE_N))\n", - "```\n", - "
    \n", - "\n", - "
    \n", - "提示 2：使用 cache hints\n", - "\n", - "```python\n", - "a = tl.load(a_ptrs, mask=mask, other=0.0, cache_modifier=\".ca\")\n", - "b = tl.load(b_ptrs, mask=mask, other=0.0, cache_modifier=\".cg\")\n", - "```\n", - "
    \n", - "\n", - "
    \n", - "提示 3：指针计算\n", - "\n", - "```python\n", - "rm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n", - "rn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n", - "rk = k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)\n", - "\n", - "a_ptrs = a_ptr + (rm[:, None] * stride_am + rk[None, :] * stride_ak)\n", - "b_ptrs = b_ptr + (rk[:, None] * stride_bk + rn[None, :] * stride_bn)\n", - "```\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 练习 3: 带 Cache Hints 的 Softmax\n", - "\n", - "**目标**：实现一个高效的 Softmax kernel，要求：\n", - "1. 两阶段 reduction（先求 max，再求 sum）\n", - "2. 使用 cache_modifier 优化访存\n", - "3. 支持任意长度的输入序列\n", - "\n", - "**背景**：\n", - "Softmax 计算步骤：\n", - "1. 找到最大值：`max_x = max(x)`\n", - "2. 计算 exp：`exp_x = exp(x - max_x)`\n", - "3. 求和：`sum_exp = sum(exp_x)`\n", - "4. 归一化：`softmax = exp_x / sum_exp`\n", - "\n", - "**难点**：\n", - "- 步骤 1 需要跨元素 reduction\n", - "- 步骤 3 又需要一次 reduction\n", - "- 如何高效地共享中间结果？" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@triton.jit\n", - "def softmax_kernel(\n", - " input_ptr, output_ptr,\n", - " n_rows, n_cols,\n", - " stride_row, stride_col,\n", - " BLOCK_SIZE: tl.constexpr,\n", - "):\n", - " \"\"\"\n", - " TODO: 实现带 cache hints 的 Softmax\n", - " \n", - " 步骤：\n", - " 1. 获取当前行\n", - " 2. 第一次 reduction：找 max\n", - " 3. 计算 exp(x - max)\n", - " 4. 第二次 reduction：求 exp 和\n", - " 5. 归一化\n", - " 6. 存储结果\n", - " \"\"\"\n", - " # ==================== 在下方编写代码 ====================\n", - " \n", - " \n", - " \n", - " # ========================================================\n", - " pass\n", + " # 计算FLOPS\n", + " flops = 2 * M * N * K # 矩阵乘法的浮点运算数\n", + " flops_with_cache = flops / time_with_cache / 1e12 # TFLOPS\n", + " flops_no_cache = flops / time_no_cache / 1e12 # TFLOPS\n", "\n", - "def softmax(x):\n", - " \"\"\"\n", - " Host 端包装函数\n", - " \n", - " Args:\n", - " x: (n_rows, n_cols) tensor\n", - " \n", - " Returns:\n", - " (n_rows, n_cols) softmax 结果\n", - " \"\"\"\n", - " n_rows, n_cols = x.shape\n", - " output = torch.empty_like(x)\n", - " \n", - " # Grid 配置\n", - " grid = lambda meta: (n_rows,)\n", - " \n", - " # 启动 kernel\n", - " softmax_kernel[grid](\n", - " x, output,\n", - " n_rows, n_cols,\n", - " x.stride(0), x.stride(1),\n", - " BLOCK_SIZE=1024,\n", - " )\n", - " \n", - " return output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 测试 Softmax\n", - "def test_softmax():\n", - " n_rows, n_cols = 32, 2048\n", - " x = torch.randn(n_rows, n_cols, device='cuda', dtype=torch.float32)\n", - " \n", - " # Triton 实现\n", - " y_triton = softmax(x)\n", - " \n", - " # PyTorch 参考实现\n", - " y_torch = torch.nn.functional.softmax(x, dim=-1)\n", - " \n", - " # 验证\n", - " if torch.allclose(y_triton, y_torch, atol=1e-4):\n", - " print(\"✓ Softmax 测试通过！\")\n", - " else:\n", - " print(\"✗ Softmax 测试失败！\")\n", - " print(f\"最大误差: {torch.max(torch.abs(y_triton - y_torch)).item():.2e}\")\n", + " # 输出结果\n", + " print(f\"带Cache Hints: {time_with_cache*1000:.2f} ms ({flops_with_cache:.2f} TFLOPS)\")\n", + " print(f\"不带Cache Hints: {time_no_cache*1000:.2f} ms ({flops_no_cache:.2f} TFLOPS)\")\n", + " print(f\"性能提升: {time_no_cache/time_with_cache:.2f}x\")\n", + " print(f\"结果误差: {max_diff:.2e}\")\n", "\n", - "test_softmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 性能对比\n", - "def benchmark_softmax():\n", - " sizes = [(32, 512), (32, 1024), (32, 2048), (32, 4096)]\n", - " \n", - " print(f\"{'Rows':>8} | {'Cols':>8} | {'Triton (ms)':>12} | {'Torch (ms)':>12} | {'Speedup':>10}\")\n", - " print(\"-\" * 65)\n", - " \n", - " for n_rows, n_cols in sizes:\n", - " x = torch.randn(n_rows, n_cols, device='cuda', dtype=torch.float32)\n", - " \n", - " # Triton 实现\n", - " torch.cuda.synchronize()\n", - " t0 = time.time()\n", - " for _ in range(100):\n", - " output = softmax(x)\n", + " # PyTorch参考性能\n", " torch.cuda.synchronize()\n", - " t_triton = (time.time() - t0) * 1000\n", - " \n", - " # PyTorch 实现\n", - " torch.cuda.synchronize()\n", - " t0 = time.time()\n", - " for _ in range(100):\n", - " output_torch = torch.nn.functional.softmax(x, dim=-1)\n", + " start_time = time.time()\n", + " for _ in range(10):\n", + " c_torch = torch.matmul(a, b)\n", " torch.cuda.synchronize()\n", - " t_torch = (time.time() - t0) * 1000\n", - " \n", - " speedup = t_torch / t_triton\n", - " print(f\"{n_rows:>8} | {n_cols:>8} | {t_triton:>12.2f} | {t_torch:>12.2f} | {speedup:>10.2f}x\")\n", + " time_torch = (time.time() - start_time) / 10\n", + " flops_torch = flops / time_torch / 1e12\n", "\n", - "# benchmark_softmax() # 取消注释以运行" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**思考题**（高级）：\n", - "1. 如何处理数值稳定性？（提示：exp(x - max_x)）\n", - "2. 如何优化 reduction 的性能？（提示：两级 reduction）\n", - "3. 这种方法能扩展到 2D Softmax 吗？" + " print(f\"PyTorch参考: {time_torch*1000:.2f} ms ({flops_torch:.2f} TFLOPS)\")\n", + " print(f\"vs PyTorch: {time_torch/time_with_cache:.2f}x (带cache hints)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**提示**（点击展开）：\n", + "NVIDIA 现代GPU具有非常智能的 L1/L2 缓存管理，GPU可能已经自动优化了内存访问模式。\n", "\n", - "
    \n", - "提示 1：Reduction 模式\n", + "虽然在这个测试中效果不明显，但cache hints仍有价值：\n", "\n", - "```python\n", - "# 使用 tl.max 和 tl.sum 进行 reduction\n", - "row = tl.program_id(0)\n", - "col_offsets = tl.arange(0, BLOCK_SIZE)\n", - "mask = col_offsets < n_cols\n", - "\n", - "# 加载当前行\n", - "x = tl.load(input_ptr + row * stride_row + col_offsets * stride_col, mask=mask, other=float('-inf'))\n", - "\n", - "# 找最大值\n", - "x_max = tl.max(x, axis=0)\n", - "\n", - "# 计算 exp(x - max)\n", - "x_exp = tl.exp(x - x_max)\n", - "\n", - "# 求和\n", - "x_sum = tl.sum(x_exp, axis=0)\n", - "\n", - "# 归一化\n", - "y = x_exp / x_sum\n", - "```\n", - "
    \n", - "\n", - "
    \n", - "提示 2：使用 cache hints\n", - "\n", - "```python\n", - "# 输入数据会被多次访问（找 max、求 sum），使用 .cg\n", - "x = tl.load(input_ptr + ..., mask=mask, other=float('-inf'), cache_modifier=\".cg\")\n", - "```\n", - "
    " + "1. 代码意图表达：明确告诉编译器你的访问模式\n", + "2. 编译器提示：帮助生成更优化的代码" ] }, { @@ -683,9 +421,7 @@ "\n", "完成这三个练习后，你应该掌握了：\n", "- 2D/多维地址计算和 mask 处理\n", - "- 数据复用优化技术\n", "- Cache hints 的实际应用\n", - "- Reduction 操作的实现方法\n", "\n", "**下一步**：学习 Triton 的 Reduction 与原子操作！\n", "\n", @@ -704,39 +440,44 @@ " BLOCK_SIZE_H: tl.constexpr,\n", " BLOCK_SIZE_W: tl.constexpr,\n", "):\n", - " pid_h = tl.program_id(axis=0)\n", - " pid_w = tl.program_id(axis=1)\n", - " \n", - " # 加载 (BLOCK_SIZE_H + 2) × (BLOCK_SIZE_W + 2) 的块\n", - " h = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H + 2) - 1\n", - " w = pid_w * BLOCK_SIZE_W + tl.arange(0, BLOCK_SIZE_W + 2) - 1\n", - " \n", - " # 创建边界 mask\n", - " mask = (h[:, None] >= 0) & (h[:, None] < H) & \\\n", - " (w[None, :] >= 0) & (w[None, :] < W)\n", - " \n", - " # 加载块\n", - " input_ptrs = input_ptr + (h[:, None] * stride_h + w[None, :] * stride_w)\n", - " input_block = tl.load(input_ptrs, mask=mask, other=0.0)\n", - " \n", - " # 3×3 邻居求和\n", - " result = (\n", - " input_block[:-2, :-2] + input_block[:-2, 1:-1] + input_block[:-2, 2:] +\n", - " input_block[1:-1, :-2] + input_block[1:-1, 1:-1] + input_block[1:-1, 2:] +\n", - " input_block[2:, :-2] + input_block[2:, 1:-1] + input_block[2:, 2:]\n", - " )\n", - " \n", + " pid_h = tl.program_id(0)\n", + " pid_w = tl.program_id(1)\n", + " \n", + " # 生成当前块内每个线程对应的 h 和 w 的偏移量\n", + " # 使用 broadcasting技巧 ([:, None] 和 [None, :]) 生成 2D 网格\n", + " offs_h = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H)\n", + " offs_w = pid_w * BLOCK_SIZE_W + tl.arange(0, BLOCK_SIZE_W)\n", + " \n", + " r_idx = offs_h[:, None]\n", + " c_idx = offs_w[None, :]\n", + " mask = (r_idx < H) & (c_idx < W)\n", + "\n", + " acc = tl.zeros([BLOCK_SIZE_H, BLOCK_SIZE_W], dtype=tl.float32)\n", + " \n", + " # 循环加载 3x3 邻居并求和\n", + " for dy in range(-1, 2):\n", + " for dx in range(-1, 2):\n", + " neighbor_h = r_idx + dy\n", + " neighbor_w = c_idx + dx\n", + " \n", + " mask_in_h = (neighbor_h >= 0) & (neighbor_h < H)\n", + " mask_in_w = (neighbor_w >= 0) & (neighbor_w < W)\n", + " mask_in = mask_in_h & mask_in_w\n", + "\n", + " # 利用广播技巧计算每个邻居的指针偏移量\n", + " offset = neighbor_h * stride_h + neighbor_w * stride_w\n", + " input_ptrs = input_ptr + offset\n", + " val = tl.load(input_ptrs, mask=mask_in, other=0.0)\n", + " acc += val\n", + "\n", " # 存储结果\n", - " out_h = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H)\n", - " out_w = pid_w * BLOCK_SIZE_W + tl.arange(0, BLOCK_SIZE_W)\n", - " out_mask = (out_h[:, None] < H) & (out_w[None, :] < W)\n", - " \n", - " output_ptrs = output_ptr + (out_h[:, None] * stride_h + out_w[None, :] * stride_w)\n", - " tl.store(output_ptrs, result, mask=out_mask)\n", - "```\n", + " output_ptrs = output_ptr + r_idx * stride_h + c_idx * stride_w\n", + " tl.store(output_ptrs, acc, mask=mask)\n", "\n", "### 练习 2：矩阵乘法\n", "\n", + "不使用 block ptr 的版本：\n", + "\n", "```python\n", "@triton.jit\n", "def matmul_kernel(\n", @@ -749,90 +490,112 @@ " BLOCK_SIZE_N: tl.constexpr,\n", " BLOCK_SIZE_K: tl.constexpr,\n", "):\n", - " pid = tl.program_id(0)\n", - " pid_m = pid // (triton.cdiv(N, BLOCK_SIZE_N))\n", - " pid_n = pid % (triton.cdiv(N, BLOCK_SIZE_N))\n", + " pid_m = tl.program_id(0)\n", + " pid_n = tl.program_id(1)\n", " \n", " rm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n", " rn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n", - " \n", - " # 初始化累加器\n", - " accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n", - " \n", - " # 分块计算\n", + "\n", + " acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n", + "\n", " for k in range(0, K, BLOCK_SIZE_K):\n", - " rk = k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)\n", - " \n", + " rk = k + tl.arange(0, BLOCK_SIZE_K)\n", + "\n", " # 创建 mask\n", - " mask_m = rm[:, None] < M\n", - " mask_n = rn[None, :] < N\n", - " mask_k = rk[None, :] < K\n", - " mask = mask_m & mask_n & mask_k\n", - " \n", + " mask_a = (rm[:, None] < M) & (rk[None, :] < K) # (64, 32)\n", + " mask_b = (rk[:, None] < K) & (rn[None, :] < N) # (32, 64)\n", + "\n", " # 计算指针\n", - " a_ptrs = a_ptr + (rm[:, None] * stride_am + rk[None, :] * stride_ak)\n", - " b_ptrs = b_ptr + (rk[:, None] * stride_bk + rn[None, :] * stride_bn)\n", - " \n", - " # 使用 cache hints 优化加载\n", - " a = tl.load(a_ptrs, mask=mask, other=0.0, cache_modifier=\".ca\")\n", - " b = tl.load(b_ptrs, mask=mask, other=0.0, cache_modifier=\".cg\")\n", - " \n", - " # 累加\n", - " accumulator += tl.dot(a, b)\n", - " \n", - " # 存储结果\n", + " a_ptrs = a_ptr + rm[:, None] * stride_am + rk[None, :] * stride_ak\n", + " b_ptrs = b_ptr + rk[:, None] * stride_bk + rn[None, :] * stride_bn\n", + "\n", + " a = tl.load(a_ptrs, mask=mask_a, other=0.0, cache_modifier=\".cg\")\n", + " b = tl.load(b_ptrs, mask=mask_b, other=0.0, cache_modifier=\".cg\")\n", + "\n", + " acc += tl.dot(a, b)\n", + "\n", " cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n", " cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n", - " \n", + "\n", + " c_ptrs = c_ptr + cm[:, None] * stride_cm + cn[None, :] * stride_cn\n", " out_mask = (cm[:, None] < M) & (cn[None, :] < N)\n", - " c_ptrs = c_ptr + (cm[:, None] * stride_cm + cn[None, :] * stride_cn)\n", - " \n", - " tl.store(c_ptrs, accumulator, mask=out_mask)\n", + " tl.store(c_ptrs, acc, mask=out_mask, cache_modifier=\".cg\")\n", "```\n", "\n", - "### 练习 3：Softmax\n", + "使用 block ptr 的版本：\n", "\n", "```python\n", "@triton.jit\n", - "def softmax_kernel(\n", - " input_ptr, output_ptr,\n", - " n_rows, n_cols,\n", - " stride_row, stride_col,\n", - " BLOCK_SIZE: tl.constexpr,\n", + "def matmul_kernel(\n", + " a_ptr, b_ptr, c_ptr,\n", + " M, N, K,\n", + " stride_am, stride_ak,\n", + " stride_bk, stride_bn,\n", + " stride_cm, stride_cn,\n", + " BLOCK_SIZE_M: tl.constexpr,\n", + " BLOCK_SIZE_N: tl.constexpr,\n", + " BLOCK_SIZE_K: tl.constexpr,\n", "):\n", - " row = tl.program_id(0)\n", - " col_offsets = tl.arange(0, BLOCK_SIZE)\n", - " \n", - " # Mask\n", - " mask = col_offsets < n_cols\n", - " \n", - " # 加载当前行（使用 cache hints）\n", - " x = tl.load(\n", - " input_ptr + row * stride_row + col_offsets * stride_col,\n", - " mask=mask,\n", - " other=float('-inf'),\n", - " cache_modifier=\".cg\"\n", + " \"\"\"\n", + " 使用 block pointer 的矩阵乘法 kernel\n", + " Block pointer 提供了更简洁的内存访问模式和更好的性能\n", + " \"\"\"\n", + " pid_m = tl.program_id(0)\n", + " pid_n = tl.program_id(1)\n", + "\n", + " # 计算当前block的起始位置\n", + " offs_m = pid_m * BLOCK_SIZE_M\n", + " offs_n = pid_n * BLOCK_SIZE_N\n", + "\n", + " # 创建 A 矩阵的 block pointer\n", + " a_block_ptr = tl.make_block_ptr(\n", + " base=a_ptr,\n", + " shape=(M, K),\n", + " strides=(stride_am, stride_ak),\n", + " offsets=(offs_m, 0),\n", + " block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_K),\n", + " order=(1, 0)\n", " )\n", - " \n", - " # 找最大值（数值稳定性）\n", - " x_max = tl.max(x)\n", - " \n", - " # 计算 exp(x - max)\n", - " x_exp = tl.exp(x - x_max)\n", - " \n", - " # 求和\n", - " x_sum = tl.sum(x_exp)\n", - " \n", - " # 归一化\n", - " y = x_exp / x_sum\n", - " \n", - " # 存储结果\n", - " tl.store(\n", - " output_ptr + row * stride_row + col_offsets * stride_col,\n", - " y,\n", - " mask=mask\n", + "\n", + " # 创建 B 矩阵的 block pointer\n", + " b_block_ptr = tl.make_block_ptr(\n", + " base=b_ptr,\n", + " shape=(K, N),\n", + " strides=(stride_bk, stride_bn),\n", + " offsets=(0, offs_n),\n", + " block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_N),\n", + " order=(1, 0)\n", + " )\n", + "\n", + " # 累加器初始化\n", + " acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n", + "\n", + " # K 维度循环\n", + " for k in range(0, K, BLOCK_SIZE_K):\n", + " # 使用 block pointer 加载数据，自动处理边界检查\n", + " a = tl.load(a_block_ptr, boundary_check=(0, 1), padding_option=\"zero\")\n", + " b = tl.load(b_block_ptr, boundary_check=(0, 1), padding_option=\"zero\")\n", + "\n", + " # 矩阵乘法累加\n", + " acc += tl.dot(a, b)\n", + "\n", + " # 更新 block pointer 的 K 维度偏移\n", + " a_block_ptr = tl.advance(a_block_ptr, (0, BLOCK_SIZE_K)) # 按列移动\n", + " b_block_ptr = tl.advance(b_block_ptr, (BLOCK_SIZE_K, 0)) # 按行移动\n", + "\n", + " # 创建输出 C 矩阵的 block pointer\n", + " c_block_ptr = tl.make_block_ptr(\n", + " base=c_ptr,\n", + " shape=(M, N),\n", + " strides=(stride_cm, stride_cn),\n", + " offsets=(offs_m, offs_n),\n", + " block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_N),\n", + " order=(1, 0)\n", " )\n", - "```" + "\n", + " # 存储结果，自动处理边界检查\n", + " tl.store(c_block_ptr, acc, boundary_check=(0, 1))\n", + "```\n" ] } ], diff --git a/docs/index.md b/docs/index.md index a6438d3..0610fbf 100644 --- a/docs/index.md +++ b/docs/index.md @@ -52,6 +52,7 @@ ### Triton 系列 💡 + [Triton 编程范式入门](/triton/triton_programming_paradigms) ++ [Triton 内存和数据传输](/triton/triton_memory_and_data_movement) ### LLM 推理技术 🤖 diff --git a/sidebars.js b/sidebars.js index 2c9ba24..964bc49 100644 --- a/sidebars.js +++ b/sidebars.js @@ -176,6 +176,10 @@ const sidebars = { type: 'autogenerated', dirName: '18_triton/01_triton_programming_paradigms' }, + { + type: 'autogenerated', + dirName: '18_triton/02_triton_memory_and_data_movement' + }, ] }, { From f8518afbd432ca8b3831cb11beb067c883290177 Mon Sep 17 00:00:00 2001 From: andsonder Date: Mon, 12 Jan 2026 17:09:40 +0800 Subject: [PATCH 07/10] update --- .../homework.ipynb | 33 ------------------- 1 file changed, 33 deletions(-) diff --git a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb index 70d1ead..4c3eb90 100644 --- a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb +++ b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb @@ -1,23 +1,6 @@ { "cells": [ { -<<<<<<< HEAD -======= - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Triton 编程范式 - 课后练习\n", - "\n", - "本 Notebook 包含三个练习，帮助你巩固 Triton 的核心概念。\n", - "\n", - "**学习目标**：\n", - "- 掌握 Triton 的基本语法和向量化操作\n", - "- 理解 `BLOCK_SIZE` 对性能的影响\n", - "- 学会用向量化方式处理复杂的数据访问模式" - ] - }, - { ->>>>>>> 70260cb4e92d1f3168960cda1b5e9d466d6b9a28 "cell_type": "code", "execution_count": null, "metadata": {}, @@ -219,18 +202,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ -<<<<<<< HEAD -======= - "**思考题**（高级）：\n", - "1. 为什么这种方法效率不高？（提示：重复加载）\n", - "2. 如何优化？（提示：加载更大的块然后切片）" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ ->>>>>>> 70260cb4e92d1f3168960cda1b5e9d466d6b9a28 "---\n", "\n", "## 总结\n", @@ -283,11 +254,7 @@ " mask = offsets < n_elements\n", " \n", " x_center = tl.load(x_ptr + offsets, mask=mask, other=0.0)\n", -<<<<<<< HEAD " x_left = tl.load(x_ptr + offsets - 1, mask=mask & (offsets > 0), other=0.0)\n", -======= - " x_left = tl.load(x_ptr + offsets - 1, mask=offsets > 0, other=0.0)\n", ->>>>>>> 70260cb4e92d1f3168960cda1b5e9d466d6b9a28 " x_right = tl.load(x_ptr + offsets + 1, mask=offsets < n_elements - 1, other=0.0)\n", " \n", " y = x_left + x_center + x_right\n", From 894e090e3bcae8991f465b9ff0125b835d464449 Mon Sep 17 00:00:00 2001 From: andsonder Date: Mon, 12 Jan 2026 17:13:26 +0800 Subject: [PATCH 08/10] update --- .../01_triton_programming_paradigms/homework.ipynb | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb index 4c3eb90..953fcb6 100644 --- a/docs/18_triton/01_triton_programming_paradigms/homework.ipynb +++ b/docs/18_triton/01_triton_programming_paradigms/homework.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Triton 编程范式 - 课后练习\n", + "\n", + "本 Notebook 包含俩个练习，帮助你巩固 Triton 的核心概念" + ] + }, { "cell_type": "code", "execution_count": null, @@ -206,9 +215,9 @@ "\n", "## 总结\n", "\n", - "完成这三个练习后，你应该掌握了 Triton kernel 的基本写法\n", + "完成这两个练习后，你应该掌握了 Triton kernel 的基本写法\n", "\n", - "**下一步**：学习 Triton 的 Shared Memory 和 Block Reduction 操作！\n", + "**下一步**：学习 Triton 的内存与数据搬运\n", "\n", "## 课后答案\n", "\n", From fbc919936fa64878408bcc0e3818c3bb30a727c5 Mon Sep 17 00:00:00 2001 From: andsonder Date: Mon, 12 Jan 2026 17:18:29 +0800 Subject: [PATCH 09/10] fix ci error --- .gitignore | 5 ----- .../02_triton_memory_and_data_movement/README.md | 14 +++++++------- 2 files changed, 7 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index d46913f..6304181 100644 --- a/.gitignore +++ b/.gitignore @@ -30,10 +30,5 @@ *.exe *.out *.app - .DS_Store -node_modules -.docusaurus -build .vscode - diff --git a/docs/18_triton/02_triton_memory_and_data_movement/README.md b/docs/18_triton/02_triton_memory_and_data_movement/README.md index a36470e..c4ecdbc 100644 --- a/docs/18_triton/02_triton_memory_and_data_movement/README.md +++ b/docs/18_triton/02_triton_memory_and_data_movement/README.md @@ -26,7 +26,7 @@ 这是一个经典的 2D 操作，能很好地展示多维地址计算。在内存中，矩阵是按**行优先**顺序存储的。例如，一个 3×4 的矩阵： -``` +```plain A = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] @@ -35,7 +35,7 @@ A = [[1, 2, 3, 4], 在内存中的实际布局是：`[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]` 如果要访问 `A[i][j]`（第 i 行，第 j 列），内存地址是： -``` +```plain 物理地址 = base_ptr + i * N + j ``` @@ -156,7 +156,7 @@ for (int i = 0; i < 64; i++) { 理解了 2D 地址计算后，咱们来看看一个对性能影响巨大的因素：**内存连续性**。 -### 2.1 什么是内存连续性？ +### 2.1 什么是内存连续性 在 GPU 编程中，有一个非常重要的性能优化原则：**Memory Coalescing**。 @@ -210,7 +210,7 @@ def vector_add_strided(x_ptr, y_ptr, out_ptr, n_elements, stride, BLOCK_SIZE: tl **性能测试结果**： -``` +```plain Size | Stride | Time (ms) | Slowdown --------|--------|-----------|---------- 5242880 | 1 | 0.02 | 1.00x @@ -228,7 +228,7 @@ Size | Stride | Time (ms) | Slowdown GPU 的内存访问是以**事务为单位**的。一个 warp（32 个线程）访问连续的 32 个 float（128 字节）时，可以合并为**单个内存事务**。 但当 stride=8 时，这 32 个线程访问的是间隔 8 个元素的数据： -``` +```plain 线程 0: addr[0] 线程 1: addr[8] 线程 2: addr[16] @@ -473,7 +473,7 @@ cache hints 的效果因 GPU 架构和数据访问模式而异，需要实际测 前面我们学习了如何手动计算 2D/多维张量的地址。但实际上 Triton 提供了一个更强大的工具：`tl.make_block_ptr`。 -### 4.1 什么是 Block Pointer？ +### 4.1 什么是 Block Pointer `tl.make_block_ptr` 是一个专门用于处理多维张量访问的高级工具。它可以： @@ -613,7 +613,7 @@ tl.store(c_block_ptr, result, boundary_check=(0, 1)) # 正确 ::: -### 4.6 什么时候使用 Block Pointer？ +### 4.6 什么时候使用 Block Pointer | 场景 | 推荐方式 | |------|---------| From 8af20f21317584ba7e1d455f4b830459f0ca1898 Mon Sep 17 00:00:00 2001 From: andsonder Date: Mon, 12 Jan 2026 17:21:10 +0800 Subject: [PATCH 10/10] recover .gitignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 6304181..d14461a 100644 --- a/.gitignore +++ b/.gitignore @@ -31,4 +31,4 @@ *.out *.app .DS_Store -.vscode +.vscode \ No newline at end of file