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https://github.com/DefTruth/CUDA-Learn-Notes
🎉 Modern CUDA Learn Notes with PyTorch: fp32/tf32, fp16/bf16, fp8/int8, flash_attn, rope, sgemm, sgemv, warp/block reduce, dot, elementwise, softmax, layernorm, rmsnorm.
https://github.com/DefTruth/CUDA-Learn-Notes
block-reduce cuda cuda-programming elementwise flash-attention flash-attention-2 flash-attention-3 gemm gemv layernorm pytorch rmsnorm softmax triton warp-reduce
Last synced: 16 days ago
JSON representation
🎉 Modern CUDA Learn Notes with PyTorch: fp32/tf32, fp16/bf16, fp8/int8, flash_attn, rope, sgemm, sgemv, warp/block reduce, dot, elementwise, softmax, layernorm, rmsnorm.
- Host: GitHub
- URL: https://github.com/DefTruth/CUDA-Learn-Notes
- Owner: DefTruth
- License: gpl-3.0
- Created: 2022-12-17T08:19:52.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-24T10:55:54.000Z (20 days ago)
- Last Synced: 2024-10-25T02:33:16.654Z (19 days ago)
- Topics: block-reduce, cuda, cuda-programming, elementwise, flash-attention, flash-attention-2, flash-attention-3, gemm, gemv, layernorm, pytorch, rmsnorm, softmax, triton, warp-reduce
- Language: Cuda
- Homepage: https://github.com/DefTruth/cuda-learn-notes
- Size: 217 MB
- Stars: 1,319
- Watchers: 12
- Forks: 147
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-cuda-and-hpc - DefTruth//CUDA-Learn-Notes - Learn-Notes?style=social"/> : 🎉CUDA/C++ 笔记 / 大模型手撕CUDA / 技术博客，更新随缘: flash_attn、sgemm、sgemv、warp reduce、block reduce、dot product、elementwise、softmax、layernorm、rmsnorm、hist etc. (Learning Resources)
- awesome-cuda-and-hpc - DefTruth//CUDA-Learn-Notes - Learn-Notes?style=social"/> : 🎉CUDA/C++ 笔记 / 大模型手撕CUDA / 技术博客，更新随缘: flash_attn、sgemm、sgemv、warp reduce、block reduce、dot product、elementwise、softmax、layernorm、rmsnorm、hist etc. (Learning Resources)
README
📒**CUDA-Learn-Notes**: 🎉CUDA/C++ 笔记 / 大模型手撕CUDA / 技术博客，更新随缘: flash_attn、sgemm、sgemv、warp reduce、block reduce、dot、elementwise、softmax、layernorm、rmsnorm、histogram、relu、sigmoid etc. 更多资料，请关注本人知乎技术博客: [DefTruth on ZhiHu](https://www.zhihu.com/people/qyjdef/posts)
> 想要我的财宝吗？想要的话可以全部给你，去找吧！我把所有财宝都放在那里！—— **哥尔·D·罗杰**
## News 👇👇
Most of my time now is focused on **LLM/VLM** Inference. Please check 📖[Awesome-LLM-Inference](https://github.com/DefTruth/Awesome-LLM-Inference) , 📖[Awesome-SD-Inference](https://github.com/DefTruth/Awesome-SD-Inference)  and 📖[CUDA-Learn-Notes](https://github.com/DefTruth/CUDA-Learn-Notes)  for more details.
## 0x00 📖 博客目录
### 📒 大模型/多模态/SD 推理优化
- [[InternLM/VL系列][万字]📒InternLM2/InternLM2.5/InternViT/InternVL1.5/InternVL2笔记: 核心点解析](https://zhuanlan.zhihu.com/p/702481058)
- [[TensorRT-LLM][5w字]🔥TensorRT-LLM部署调优-指北](https://zhuanlan.zhihu.com/p/699333691)
- [[KV Cache优化]🔥MQA/GQA/YOCO/CLA/MLKV笔记: 层内和层间KV Cache共享](https://zhuanlan.zhihu.com/p/697311739)
- [[Prefill优化]🔥图解vLLM Prefix Prefill Triton Kernel](https://zhuanlan.zhihu.com/p/695799736)
- [[Prefill优化][万字]🔥原理&图解vLLM Automatic Prefix Cache(RadixAttention): 首Token时延优化](https://zhuanlan.zhihu.com/p/693556044)
- [[Attention优化][2w字]🔥原理&图解: 从Online-Softmax到FlashAttention V1/V2/V3](https://zhuanlan.zhihu.com/p/668888063)
- [[Decoding优化]🔥原理&图解FlashDecoding/FlashDecoding++](https://zhuanlan.zhihu.com/p/696075602)
- [[LLaVA系列]📒CLIP/LLaVA/LLaVA1.5/VILA笔记: 核心点解析](https://zhuanlan.zhihu.com/p/683137074)
- [[Attention优化][万字]🔥TensorRT 9.2 MHA/Myelin Optimize vs FlashAttention-2 profile](https://zhuanlan.zhihu.com/p/678873216)
- [[CUDA 12 PTX汇编]📒PRMT指令详解-通用模式](https://zhuanlan.zhihu.com/p/660630414)
- [[CUDA 12 PTX汇编]📒LOP3指令详解](https://zhuanlan.zhihu.com/p/659741469)
- [[LLM推理优化][3w字]🔥高频面试题汇总-大模型手撕CUDA](https://zhuanlan.zhihu.com/p/678903537)
- [[LLM推理优化]🔥WINT8/4-(00): 通俗易懂讲解-快速反量化算法](https://zhuanlan.zhihu.com/p/657072856)
- [[LLM推理优化]🔥WINT8/4-(01): PRMT指令详解及FasterTransformer源码解析](https://zhuanlan.zhihu.com/p/657070837)
- [[LLM推理优化]🔥WINT8/4-(02): 快速反量化之INT8转BF16](https://zhuanlan.zhihu.com/p/657073159)
- [[LLM推理优化]🔥WINT8/4-(03): LOP3指令详解及INT4转FP16/BF16分析](https://zhuanlan.zhihu.com/p/657073857)
- [[LLM推理优化]🔥100+篇: 大模型推理各方向新发展整理](https://zhuanlan.zhihu.com/p/693680304)
- [[LLM推理优化]🔥30+篇: LLM推理论文集-500页PDF💡](https://zhuanlan.zhihu.com/p/669777159)
- [[LLM推理优化]🔥FlashDecoding++: 比FlashDecoding还要快！](https://zhuanlan.zhihu.com/p/665022589)
- [[LLM推理优化]🔥速递：TensorRT-LLM开源，TensorRT 9.1 也来了🤓](https://zhuanlan.zhihu.com/p/662361469)
- [[LLM推理优化]🔥20+篇: LLM推理论文集-300页PDF💡](https://zhuanlan.zhihu.com/p/658091768)
- [[LLM推理优化]🔥PagedAttention论文新鲜出炉](https://zhuanlan.zhihu.com/p/617015570)
### 📒 CV移动端/服务端 推理部署
- [[推理部署]⚡️🔥覆盖云边端全场景，FastDeploy三行代码搞定150+ CV、NLP、Speech模型部署](https://zhuanlan.zhihu.com/p/581326442)
- [[推理部署]💡如何在lite.ai.toolkit(3.5k+🔥stars)中增加您的模型？](https://zhuanlan.zhihu.com/p/523876625)
- [[推理部署]🤓凑个热闹之 美团 YOLOv6 ORT/MNN/TNN/NCNN C++推理部署](https://zhuanlan.zhihu.com/p/533643238)
- [[推理部署]🌔ONNX推理加速技术文档-杂记](https://zhuanlan.zhihu.com/p/524023964)
- [[推理部署]👉Mac源码编译TensorFlow C++指北](https://zhuanlan.zhihu.com/p/524013615)
- [[推理部署]👿1Mb!头部姿态估计: 来讲讲FSANet，一个小而美的模型(含ONNXRuntime/MNN C++实现)](https://zhuanlan.zhihu.com/p/447364201)
- [[推理部署]🤓opencv+ffmpeg编译打包全解指南](https://zhuanlan.zhihu.com/p/472115312)
- [[推理部署]🔧填坑: RobustVideoMatting(5k+🔥star)视频抠图静态ONNX模型转换](https://zhuanlan.zhihu.com/p/459088407)
- [[推理部署]🔥190Kb!SSRNet年龄检测详细解读（含C++工程）](https://zhuanlan.zhihu.com/p/462762797)
- [[推理部署]🔥MGMatting(CVPR2021)人像抠图C++应用记录](https://zhuanlan.zhihu.com/p/464732042)
- [[推理部署]🍅🍅超准确人脸检测(带关键点)YOLO5Face C++工程详细记录](https://zhuanlan.zhihu.com/p/461878005)
- [[推理部署]👋解决: ONNXRuntime(Python) GPU 部署配置记录](https://zhuanlan.zhihu.com/p/457484536)
- [[推理部署]🍅记录SCRFD(CVPR2021)人脸检测C++工程化(含docker镜像)](https://zhuanlan.zhihu.com/p/455165568)
- [[推理部署]👋野路子：记录一个解决onnx转ncnn时op不支持的trick](https://zhuanlan.zhihu.com/p/451446147)
- [[推理部署]🔥升级版NanoDet-Plus MNN/TNN/NCNN/ONNXRuntime C++工程记录](https://zhuanlan.zhihu.com/p/450586647)
- [[推理部署]📒超有用NCNN参考资料整理](https://zhuanlan.zhihu.com/p/449765328)
- [[推理部署]📒超有用MNN参考资料整理](https://zhuanlan.zhihu.com/p/449761992)
- [[推理部署]📒超有用TNN参考资料整理](https://zhuanlan.zhihu.com/p/449769615)
- [[推理部署]📒超有用ONNX参考资料整理](https://zhuanlan.zhihu.com/p/449773663)
- [[推理部署]📒超有用ONNX模型结构参考资料整理](https://zhuanlan.zhihu.com/p/449775926)
- [[推理部署]📒超有用OpenCV-DNN参考资料整理](https://zhuanlan.zhihu.com/p/449778377)
- [[推理部署]📒超有用Tensorflow C++工程化知识点](https://zhuanlan.zhihu.com/p/449788027)
- [[推理部署]📒深度学习模型转换资料整理](https://zhuanlan.zhihu.com/p/449759361)
- [[推理部署]🔥🔥超轻量级NanoDet MNN/TNN/NCNN/ONNXRuntime C++工程记录](https://zhuanlan.zhihu.com/p/443419387)
- [[推理部署]🔥详细记录MGMatting(CVPR2021)🔥MNN、TNN和ONNXRuntime C++移植（长文警告!）](https://zhuanlan.zhihu.com/p/442949027)
- [[推理部署]🔥YOLOX NCNN/MNN/TNN/ONNXRuntime C++工程简记](https://zhuanlan.zhihu.com/p/447364122)
- [[推理部署]🔥手动修改YoloX的tnnproto记录-TNN C++](https://zhuanlan.zhihu.com/p/425668734)
- [[推理部署]🔥🔥🔥 全网最详细 ONNXRuntime C++/Java/Python 资料！](https://zhuanlan.zhihu.com/p/414317269)
- [[推理部署]🔥RobustVideoMatting🔥2021 ONNXRuntime C++工程化记录-实现篇](https://zhuanlan.zhihu.com/p/413280488)
- [[推理部署]🔥RobustVideoMatting🔥2021最新视频抠图来了! C++ 工程化记录-应用篇](https://zhuanlan.zhihu.com/p/412491918)
- [[推理部署]💡ONNXRuntime C++ CMake 工程分析及编译](https://zhuanlan.zhihu.com/p/411887386)
- [[推理部署]🤓如何使用ONNXRuntime C++ API处理NCHW和NHWC输入？](https://zhuanlan.zhihu.com/p/524230808)
- [[推理部署]💡tnn-convert搭建简记-YOLOP转TNN](https://zhuanlan.zhihu.com/p/431418709)
- [[推理部署]💡YOLOP ONNXRuntime C++工程化记录](https://zhuanlan.zhihu.com/p/411651933)
### 📒 C/C++/算法/技术随笔
- [[C++][CMake]👋超有用CMake参考资料整理](https://zhuanlan.zhihu.com/p/449779892)
- [[C++][3W字]💡静态链接和静态库实践指北-原理篇](https://zhuanlan.zhihu.com/p/595527528)
- [[C++]🤓Mac下C++内存检查指北(Valgrind VS Asan)](https://zhuanlan.zhihu.com/p/508470880)
- [[技术随笔]🔥torchlm: 人脸关键点检测库](https://zhuanlan.zhihu.com/p/467211561)
- [[技术随笔]📒200页PDF笔记: 《统计学习方法-李航: 笔记-从原理到实现-基于R》](https://zhuanlan.zhihu.com/p/684885595)
- [[技术随笔]💡如何优雅地git clone和git submodule？](https://zhuanlan.zhihu.com/p/639136221)
- [[技术随笔]📒人脸重建3D参考资料整理](https://zhuanlan.zhihu.com/p/524034741)
- [[技术随笔]📒BlendShapes参考资料整理](https://zhuanlan.zhihu.com/p/524036145)
- [[技术随笔]🛠🛠从源码安装Pytorch3D详细记录及学习资料](https://zhuanlan.zhihu.com/p/512347464)
- [[技术随笔]🍅🍅200页:《统计学习方法：李航》笔记 -从原理到实现](https://zhuanlan.zhihu.com/p/461520847)
## 0x01 📖 Kernel目录
- [x] 📖 [sgemm_naive_f32_kernel](#sgemm)
- [x] 📖 [sgemm_block_tile_k_tile_vec4_f32_kernel](#sgemm)
- [x] 📖 [sgemv_k32_f32_kernel](#sgemv)
- [x] 📖 [sgemv_k128_f32_kernel](#sgemv)
- [x] 📖 [sgemv_k16_f32_kernel](#sgemv)
- [x] 📖 [warp_reduce_sum/max_f32_kernel](#warpreduce)
- [x] 📖 [block_reduce_sum/max_f32_kernel](#warpreduce)
- [x] 📖 [block_all_reduce_f32_kernel](#blockallreduce)
- [x] 📖 [block_all_reduce_vec4_f32_kernel](#blockallreduce)
- [x] 📖 [dot_product_f32_kernel](#dot)
- [x] 📖 [dot_product_vec4_f32_kernel](#dot)
- [x] 📖 [elementwise_f32_kernel](#elementwise)
- [x] 📖 [elementwise_vec4_f32_kernel](#elementwise)
- [x] 📖 [histogram_i32_kernel](#histogram)
- [x] 📖 [histogram_vec4_i32_kernel](#histogram)
- [x] 📖 [softmax_f32_kernel (grid level memory fence)](#softmax)
- [x] 📖 [softmax_vec4_f32_kernel (grid level memory fence)](#softmax)
- [ ] 📖 [safe_softmax_f32_kernel (per token)](#softmax)
- [x] 📖 [sigmoid_f32_kernel](#sigmoid)
- [x] 📖 [sigmoid_vec4_f32_kernel](#sigmoid)
- [ ] 📖 [safe_sigmoid_f32_kernel](#sigmoid)
- [x] 📖 [relu_f32_kernel](#relu)
- [x] 📖 [relu_vec4_f32_kernel](#relu)
- [x] 📖 [layer_norm_f32_kernel (per token)](#layernorm)
- [x] 📖 [layer_norm_vec4_f32_kernel (per token)](#layernorm)
- [ ] 📖 [layer_norm_vec4_f16_kernel (per token)](#layernorm)
- [x] 📖 [rms_norm_f32_kernel (per token)](#rmsnorm)
- [x] 📖 [rms_norm_vec4_f32_kernel (per token)](#rmsnorm)
- [ ] 📖 [rms_norm_vec4_f16_kernel (per token)](#rmsnorm)
- [x] 📖 [flash_attn_1_fwd_f32_kernel](./flash_attn_1_fwd_f32.cu)
- [ ] 📖 flash_attn_2_fwd_f32_kernel
- [ ] 📖 flash_attn_2_fwd_f16_kernel
- [ ] 📖 flash_attn_2_fwd_b16_kernel
- [ ] 📖 flash_attn_2_fwd_f8_kernel
- [ ] 📖 flash_attn_2_split_kv_f16_kernel
- [ ] 📖 flash_attn_2_split_kv_b16_kernel
- [ ] 📖 flash_attn_2_split_kv_f8_kernel
- [ ] 📖 online_softmax_f32_kernel
- [ ] 📖 online_softmax_f16_kernel
- [ ] 📖 online_softmax_b16_kernel
- [ ] 📖 hgemm_f16_kernel
- [ ] 📖 sgemm_dbuf_f32_kernel
## 0x02 sgemm naive, sgemm + block-tile + k-tile + vec4 ([©️back👆🏻](#kernellist))
```c++
#include
#include
#include
#include
#include
#include
#define WARP_SIZE 32
#define INT4(value) (reinterpret_cast(&(value))[0])
#define FLOAT4(value) (reinterpret_cast(&(value))[0])
// SGEMM: Block Tile + K Tile, with smem
// Block Tile (BM, BN) + K Tile (BK=32)
// grid((N + BN - 1) / BN, (M + BM - 1) / BM), block(BN, BM)
// a: MxK, b: KxN, c: MxN, compute: c = a * b, all row major
__global__ void sgemm(float* a, float* b, float* c, int M, int N, int K) {
// [1] Block Tile: 32x32的block处理c上一块32x32的元素计算
// [2] K Tile: 使用共享内存，并将K分块为BK大小的块
constexpr int BM = 32;
constexpr int BN = 32;
constexpr int BK = 32;
__shared__ float s_a[BM][BK], s_b[BK][BN];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int tid = threadIdx.y * blockDim.x + tx; // tid within the block
// load values to shared memory, 32x32 threads working together
// to fetch data along the row direction of a and b both for s_a
// and s_b 32x32x4x2=8KB, we use 32x32 threads within block to
// load 32x32 elements from global memory to shared memory, namely,
// each thread will load 1 element.
int load_smem_a_m = tid / 32; // 0~31, tid / 32, tid / BM, threadIdx.y
int load_smem_a_k = tid % 32; // 0~31, tid % 32, tid % BK, threadIdx.x
int load_smem_b_k = tid / 32; // 0~31, tid / 32, tid / BK, threadIdx.y
int load_smem_b_n = tid % 32; // 0~31, tid % 32, tid % BN, threadIdx.x
int load_gmem_a_m = by * BM + load_smem_a_m; // global row of a and c
int load_gmem_b_n = bx * BN + load_smem_b_n; // global col of b and c
// if (load_gmem_a_m >= M || load_gmem_b_n >= N) return;
float sum = 0.f;
for (int bk = 0; bk < (K + BK - 1) / BK; ++bk) {
int load_gmem_a_k = bk * BK + load_smem_a_k;
int load_gmem_a_addr = load_gmem_a_m * K + load_gmem_a_k;
s_a[load_smem_a_m][load_smem_a_k] = a[load_gmem_a_addr];
int load_gmem_b_k = bk * BK + load_smem_b_k;
int load_gmem_b_addr = load_gmem_b_k * N + load_gmem_b_n;
s_b[load_smem_b_k][load_smem_b_n] = b[load_gmem_b_addr];
__syncthreads();
#pragma unroll
for (int k = 0; k < BK; ++k) {
int comp_smem_a_m = load_smem_a_m;
int comp_smem_b_n = load_smem_b_n;
sum += s_a[comp_smem_a_m][k] * s_b[k][comp_smem_b_n];
}
__syncthreads();
}
int store_gmem_c_m = load_gmem_a_m;
int store_gmem_c_n = load_gmem_b_n;
int store_gmem_c_addr = store_gmem_c_m * N + store_gmem_c_n;
c[store_gmem_c_addr] = sum;
}
// SGEMM: Block Tile + Thread Tile + K Tile + Vec4, with smem
// BK:TILE_K=8 BM=BN=128
// TM=TN=8 增加计算密度 BM/TM=16 BN/TN=16
// dim3 blockDim(BN/TN, BM/TM);
// dim3 gridDim((N + BN - 1) / BN, (M + BM - 1) / BM)
__global__ void sgemm_thread_tile_vec4(
float* a, float* b, float* c, int M, int N, int K) {
// [1] Block Tile: 一个16x16的block处理C上大小为128X128的一个目标块
// [2] Thread Tile: 每个thread负责计算TM*TN(8*8)个元素，增加计算密度
// [3] K Tile: 将K分块，每块BK大小，迭代(K+BK-1/BK)次，
// 每次计算TM*TN个元素各自的部分乘累加
// [4] Vectorize: 减少load和store指令，使用float4
constexpr int BM = 128;
constexpr int BN = 128;
constexpr int BK = 8;
constexpr int TM = 8;
constexpr int TN = 8;
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int tid = threadIdx.y * blockDim.x + tx; // tid within the block
__shared__ float s_a[BM][BK], s_b[BK][BN]; // 2*128*8*4=8KB
// 0. 先计算shared memory中的索引
// tid和需要加载的smem s_a[BM][BK] 之间的索引关系 BM=128 BK=8 按行读取 A行主序
// 对于s_a每行8个数据，每个线程读取4个，需要2个线程；总共128行，需要128x2刚好256线程
int load_smem_a_m = tid / 2; // tid/2 (128/8)*(128/8)=256 threads per block, tid/2->[0,128), BM=128 0~127
int load_smem_a_k = (tid % 2 == 0) ? 0 : 4; // (tid%2 == 0) ? 0 : 4, col of s_a 0,4
// tid和需要加载的smem s_b[BK][BN] 之间的索引关系 BK=8 BN=128 按行读取 B行主序
// 对于s_b每行128个数据，每个线程读4个数据，需要32个线程；总共8行，需要32x8=256个线程
int load_smem_b_k = tid / 32; // tid/32, row of s_b 256/32=8 行 0~7
int load_smem_b_n = (tid % 32) * 4; // (tid % 32) * 4, col of s_b 0,4,...,124
// 1. 再计算全局内存中的索引
// 要加载到s_a中的元素对应到A全局内存中的行数 每个block负责出C中大小为BM*BN的块
int load_gmem_a_m = by * BM + load_smem_a_m; // global row of a and c
int load_gmem_b_n = bx * BN + load_smem_b_n; // global col of b and c
float r_c[TM][TN] = {0.0}; // 8x8
// 2. 先对K进行分块，每块BK大小
for (int bk = 0; bk < (K + BK - 1) / BK; ++bk) {
// 加载数据到共享内存smem s_a BM*BK 128*8 vectorize float4
int load_gmem_a_k = bk * BK + load_smem_a_k; // global col of a
int load_gmem_a_addr = load_gmem_a_m * K + load_gmem_a_k;
FLOAT4(s_a[load_smem_a_m][load_smem_a_k]) = FLOAT4(a[load_gmem_a_addr]);
// 加载数据到共享内存smem s_b BK*BN 8*128 vectorize float4
int load_gmem_b_k = bk * BK + load_smem_b_k; // global row of b
int load_gmem_b_addr = load_gmem_b_k * N + load_gmem_b_n;
FLOAT4(s_b[load_smem_b_k][load_smem_b_n]) = FLOAT4(b[load_gmem_b_addr]);
__syncthreads();
#pragma unroll
for (int k = 0; k < BK; k++) {
// 3. 每个线程负责计算BM*BN(12x128)中的TM*TN(8x8)个元素
#pragma unroll
for (int m = 0; m < TM; m++) {
#pragma unroll
for (int n = 0; n < TN; n++) {
// k from 0~7，0 ~ BK, ty and tx range from 0 to 15, 16x8=128
int comp_smem_a_m = ty * TM + m; // 128*8 128/TM(8)=16 M方向 16线程
int comp_smem_b_n = tx * TN + n; // 8*128 128/TN(8)=16 N方向 16线程
r_c[m][n] += s_a[comp_smem_a_m][k] * s_b[k][comp_smem_b_n];
}
}
}
__syncthreads();
}
#pragma unroll
for (int m = 0; m < TM; ++m) {
int store_gmem_c_m = by * BM + ty * TM + m;
#pragma unroll
for (int n = 0; n < TN; n += 4) {
int store_gmem_c_n = bx * BN + tx * TN + n;
int store_gmem_c_addr = store_gmem_c_m * N + store_gmem_c_n;
FLOAT4(c[store_gmem_c_addr]) = FLOAT4(r_c[m][n]);
}
}
}
```
这里gemm的实现比较简单，只使用了CUDA Cores，并且只实现Block Tile + K Tile以及Block Tile + K Tile+Thread Tile+向量化的版本。主要在于如何加载gmem中的数据到smem，也就是把全局内存中的数据索引mapping到共享内存中的。核心思维：把一个block中的线程id按照线性来理解，然后把这个线性的id和全局内存索引以及共享内存索引进行匹配。比如Block Tile + K Tile的实现，block内一共32x32个Threads，需要加载到smem的数据也是32x32，那么，最简单的做法，只需要每个线程加载一个互不重复数据即可。NOTE，本文的gemm kernel修改自：[紫气东来：CUDA（三）：通用矩阵乘法：从入门到熟练](https://zhuanlan.zhihu.com/p/657632577)
## 0x03 warp/block reduce sum/max ([©️back👆🏻](#kernellist))
```C++
// Warp Reduce Sum
template
__device__ __forceinline__ float warp_reduce_sum(float val) {
#pragma unroll
for (int mask = kWarpSize >> 1; mask >= 1; mask >>= 1) {
val += __shfl_xor_sync(0xffffffff, val, mask);
}
return val;
}
// Warp Reduce Max
template
__device__ __forceinline__ float warp_reduce_max(float val) {
#pragma unroll
for (int mask = kWarpSize >> 1; mask >= 1; mask >>= 1) {
val = fmaxf(val, __shfl_xor_sync(0xffffffff, val, mask));
}
return val;
}
// Block reduce sum/max/min device helper for Layer/RMS Norm/Softmax etc.
// grid 1D block 1D, grid(N/128), block(128)
template
__device__ __forceinline__ float block_reduce_sum(float val) {
// always <= 32 warps per block (limited by 1024 threads per block)
constexpr int NUM_WARPS = (NUM_THREADS + WARP_SIZE - 1) / WARP_SIZE;
int warp = threadIdx.x / WARP_SIZE;
int lane = threadIdx.x % WARP_SIZE;
static __shared__ float shared[NUM_WARPS];
val = warp_reduce_sum(val);
if (lane == 0) shared[warp] = val;
__syncthreads();
val = (lane < NUM_WARPS) ? shared[lane] : 0.0f;
val = warp_reduce_sum(val);
return val;
}
template
__device__ __forceinline__ float block_reduce_max(float val) {
// always <= 32 warps per block (limited by 1024 threads per block)
constexpr int NUM_WARPS = (NUM_THREADS + WARP_SIZE - 1) / WARP_SIZE;
int warp = threadIdx.x / WARP_SIZE;
int lane = threadIdx.x % WARP_SIZE;
static __shared__ float shared[NUM_WARPS];
val = warp_reduce_max(val);
if (lane == 0) shared[warp] = val;
__syncthreads();
val = (lane < NUM_WARPS) ? shared[lane] : -FLT_MAX;
val = warp_reduce_max(val);
return val;
}
```
warp reduce几乎已经成为大部分reduce kernel的标准写法了，比如vLLM中，就是这种经典的写法。所以，先搞懂warp reduce（也就是搞懂各种warp functions的用法），再去写其他kernel，思路就会容易很多。需要注意的是，warp函数处理的是寄存器上的数据，也就是说，此时，没必要先加载数据到smem，再进行reduce，直接加载到寄存器即可（以前犯过这个小错误...）。Warp Functions建议参考：[jhang：CUDA编程入门之Warp-Level Primitives](https://zhuanlan.zhihu.com/p/572820783)
## 0x04 block all reduce + vec4 ([©️back👆🏻](#kernellist))
```c++
// Block All Reduce Sum
// grid(N/128), block(128)
// a: Nx1, y=sum(a)
template
__global__ void block_all_reduce_sum(float* a, float* y, int N) {
int tid = threadIdx.x;
int idx = blockIdx.x * NUM_THREADS + tid;
constexpr int NUM_WARPS = (NUM_THREADS + WARP_SIZE - 1) / WARP_SIZE;
__shared__ float reduce_smem[NUM_WARPS];
// keep the data in register is enougth for warp operaion.
float sum = (idx < N) ? a[idx] : 0.0f;
int warp = tid / WARP_SIZE;
int lane = tid % WARP_SIZE;
// perform warp sync reduce.
sum = warp_reduce_sum(sum);
// warp leaders store the data to shared memory.
if (lane == 0) reduce_smem[warp] = sum;
__syncthreads(); // make sure the data is in shared memory.
// the first warp compute the final sum.
sum = (lane < NUM_WARPS) ? reduce_smem[lane] : 0.0f;
if (warp == 0) sum = warp_reduce_sum(sum);
if (tid == 0) atomicAdd(y, sum);
}
// Block All Reduce Sum + float4
// grid(N/128), block(128/4)
// a: Nx1, y=sum(a)
template
__global__ void block_all_reduce_sum_vec4(float* a, float* y, int N) {
int tid = threadIdx.x;
int idx = (blockIdx.x * NUM_THREADS + tid) * 4;
constexpr int NUM_WARPS = (NUM_THREADS + WARP_SIZE - 1) / WARP_SIZE;
__shared__ float reduce_smem[NUM_WARPS];
float4 reg_a = FLOAT4(a[idx]);
// keep the data in register is enougth for warp operaion.
float sum = (idx < N) ? (reg_a.x + reg_a.y + reg_a.z + reg_a.w) : 0.0f;
int warp = tid / WARP_SIZE;
int lane = tid % WARP_SIZE;
// perform warp sync reduce.
sum = warp_reduce_sum(sum);
// warp leaders store the data to shared memory.
if (lane == 0) reduce_smem[warp] = sum;
__syncthreads(); // make sure the data is in shared memory.
// the first warp compute the final sum.
sum = (lane < NUM_WARPS) ? reduce_smem[lane] : 0.0f;
if (warp == 0) sum = warp_reduce_sum(sum);
if (tid == 0) atomicAdd(y, sum);
}
```
block all reduce是在warp reduce的基础上进行的，reduce_smem这部分的共享内存申请无法避免，这是用来同步每个warp之间得到局部结果。注意，最后，还需要atomicAdd做一个block级别的原子操作，以得到全局的和。float4向量化优化访存，可以减缓WarpScheduler发送指令的压力。
## 0x05 sgemv k32/k128/k16 kernel ([©️back👆🏻](#kernellist))
```C++
// SGEMV: Warp SGEMV K32
// 假设K为32的倍数，每个warp负责一行
// grid(M/4), block(32,4) blockDim.x=32=K, blockDim.y=4
// a: MxK, x: Kx1, y: Mx1, compute: y = a * x
__global__ void sgemv_k32(float* a, float* x, float* y, int M, int K) {
int tx = threadIdx.x; // 0~31
int ty = threadIdx.y; // 0~4
int bx = blockIdx.x; // 0~M/4
int lane = tx % WARP_SIZE; // 0~31
int m = bx * blockDim.y + ty; // (0~M/4) * 4 + (0~3)
if (m < M) {
float sum = 0.0f;
int NUM_WARPS = (K + WARP_SIZE - 1) / WARP_SIZE;
#pragma unroll
for (int w = 0; w < NUM_WARPS; ++w) {
// 若NUM_WARPS>=2，先将当前行的数据累加到第一个warp中
int k = w * WARP_SIZE + lane;
sum += a[m * K + k] * x[k];
}
sum = warp_reduce_sum(sum);
if (lane == 0) y[m] = sum;
}
}
// SGEMV: Warp SGEMV K128 + Vec4
// 假设K为128的倍数 float4
// grid(M/4), block(32,4) blockDim.x=32=K, blockDim.y=4
// a: MxK, x: Kx1, y: Mx1, compute: y = a * x
__global__ void sgemv_k128(float* a, float* x, float* y, int M, int K) {
// 每个线程负责4个元素，一个warp覆盖128个元素
int tx = threadIdx.x; // 0~31
int ty = threadIdx.y; // 0~3
int bx = blockIdx.x; // 0~M/4
int lane = tx % WARP_SIZE; // 0~31
int m = blockDim.y * bx + ty; // (0~M/4) * 4 + (0~3)
if (m < M) {
float sum = 0.0f;
// process 4*WARP_SIZE elements per warp.
int NUM_WARPS = (((K + WARP_SIZE - 1) / WARP_SIZE) + 4 - 1) / 4;
#pragma unroll
for (int w = 0; w < NUM_WARPS; ++w) {
int k = (w * WARP_SIZE + lane) * 4;
float4 reg_x = FLOAT4(x[k]);
float4 reg_a = FLOAT4(a[m * K + k]);
sum += (reg_a.x * reg_x.x + reg_a.y * reg_x.y
+ reg_a.z * reg_x.z + reg_a.w * reg_x.w);
}
sum = warp_reduce_sum(sum);
if(lane == 0) y[m] = sum;
}
}
// SGEMV: Warp SGEMV K16
// 假设K为16 < 32,每个warp负责2行，每行有16个元素
// NUM_THREADS=128, NUM_WARPS=NUM_THREADS/WARP_SIZE;
// NUM_ROWS=NUM_WARPS * ROW_PER_WARP, grid(M/NUM_ROWS), block(32,NUM_WARPS)
// a: MxK, x: Kx1, y: Mx1, compute: y = a * x
template
__global__ void sgemv_k16(float* A, float* x, float* y, int M, int K) {
constexpr int K_WARP_SIZE = (WARP_SIZE + ROW_PER_WARP - 1) / ROW_PER_WARP;
int tx = threadIdx.x; // 0~31
int ty = threadIdx.y; // 0~NUM_WARPS
int bx = blockIdx.x; // 0~M/NUM_ROWS (NUM_ROWS=NUM_WARPS * ROW_PER_WARP)
int lane = tx % WARP_SIZE; // 0~31
int k = lane % K_WARP_SIZE; // 0~15
// gloabl row of a: MxK and y:Mx1, blockDim.y=NUM_WARPS
int m = (blockDim.y * bx + ty) * ROW_PER_WARP + lane / K_WARP_SIZE;
if (m < M) {
float sum = A[m * K + k] * x[k];
sum = warp_reduce_sum(sum);
// 注意是k == 0，而不是lane == 0
if(k == 0) y[m] = sum;
}
}
```
估计有些大佬倒立都能写sgemv的各种优化版了，核心思路其实也是基于warp reduce，考虑K的不同情况进行优化。本文的sgemv kernel修改自：[有了琦琦的棍子：深入浅出GPU优化系列：gemv优化](https://zhuanlan.zhihu.com/p/494144694)
## 0x06 dot product, dot product + vec4 ([©️back👆🏻](#kernellist))
```c++
// Dot Product
// grid(N/128), block(128)
// a: Nx1, b: Nx1, y=sum(elementwise_mul(a,b))
template
__global__ void dot(float* a, float* b, float* y, int N) {
int tid = threadIdx.x;
int idx = blockIdx.x * NUM_THREADS + tid;
constexpr int NUM_WARPS = (NUM_THREADS + WARP_SIZE - 1) / WARP_SIZE;
__shared__ float reduce_smem[NUM_WARPS];
// keep the data in register is enougth for warp operaion.
float prod = (idx < N) ? a[idx] * b[idx] : 0.0f;
int warp = tid / WARP_SIZE;
int lane = tid % WARP_SIZE;
// perform warp sync reduce.
prod = warp_reduce_sum(prod);
// warp leaders store the data to shared memory.
if (lane == 0) reduce_smem[warp] = prod;
__syncthreads(); // make sure the data is in shared memory.
// the first warp compute the final sum.
prod = (lane < NUM_WARPS) ? reduce_smem[lane] : 0.0f;
if (warp == 0) prod = warp_reduce_sum(prod);
if (tid == 0) atomicAdd(y, prod);
}
// Dot Product + Vec4
// grid(N/128), block(128/4)
// a: Nx1, b: Nx1, y=sum(elementwise_mul(a,b))
template
__global__ void dot_vec4(float* a, float* b, float* y, int N) {
int tid = threadIdx.x;
int idx = (blockIdx.x * NUM_THREADS + tid) * 4;
constexpr int NUM_WARPS = (NUM_THREADS + WARP_SIZE - 1) / WARP_SIZE;
__shared__ float reduce_smem[NUM_WARPS];
float4 reg_a = FLOAT4(a[idx]);
float4 reg_b = FLOAT4(b[idx]);
float prod = (idx < N) ? (reg_a.x * reg_b.x + reg_a.y * reg_b.y
+ reg_a.z * reg_b.z + reg_a.w * reg_b.w) : 0.0f;
int warp = tid / WARP_SIZE;
int lane = tid % WARP_SIZE;
// perform warp sync reduce.
prod = warp_reduce_sum(prod);
// warp leaders store the data to shared memory.
if (lane == 0) reduce_smem[warp] = prod;
__syncthreads(); // make sure the data is in shared memory.
// the first warp compute the final sum.
prod = (lane < NUM_WARPS) ? reduce_smem[lane] : 0.0f;
if (warp == 0) prod = warp_reduce_sum(prod);
if (tid == 0) atomicAdd(y, prod);
}
```
dot product kernel的核心就是block reduce，不多说了。
## 0x07 elementwise, elementwise + vec4 ([©️back👆🏻](#kernellist))
```c++
// ElementWise Add
// grid(N/128), block(128)
// a: Nx1, b: Nx1, c: Nx1, c = elementwise_add(a, b)
__global__ void elementwise_add(float* a, float* b, float* c, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) c[idx] = a[idx] + b[idx];
}
// ElementWise Add + Vec4
// grid(N/128), block(128/4)
// a: Nx1, b: Nx1, c: Nx1, c = elementwise_add(a, b)
__global__ void elementwise_add_vec4(float* a, float* b, float* c, int N) {
int idx = 4 * (blockIdx.x * blockDim.x + threadIdx.x);
if (idx < N) {
float4 reg_a = FLOAT4(a[idx]);
float4 reg_b = FLOAT4(b[idx]);
float4 reg_c;
reg_c.x = reg_a.x + reg_b.x;
reg_c.y = reg_a.y + reg_b.y;
reg_c.z = reg_a.z + reg_b.z;
reg_c.w = reg_a.w + reg_b.w;
FLOAT4(c[idx]) = reg_c;
}
}
```
elementwise可以考虑加点向量化进行访存优化。
## 0x08 histogram, histogram + vec4
```c++
// Histogram
// grid(N/128), block(128)
// a: Nx1, y: count histogram
__global__ void histogram(int* a, int* y, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) atomicAdd(&(y[a[idx]]), 1);
}
// Histogram + Vec4
// grid(N/128), block(128/4)
// a: Nx1, y: count histogram
__global__ void histogram_vec4(int* a, int* y, int N) {
int idx = 4 * (blockIdx.x * blockDim.x + threadIdx.x);
if (idx < N) {
int4 reg_a = INT4(a[idx]);
atomicAdd(&(y[reg_a.x]), 1);
atomicAdd(&(y[reg_a.y]), 1);
atomicAdd(&(y[reg_a.z]), 1);
atomicAdd(&(y[reg_a.w]), 1);
}
}
```
统计频数直方图，很简单，两行代码搞定。
## 0x09 softmax, softmax + vec4 (grid level memory fence) ([©️back👆🏻](#kernellist))
```c++
// Softmax x: N, y: N
// grid(N/128), block(K=128)
template
__global__ void softmax(float* x, float* y, float* total, int N) {
const int tid = threadIdx.x;
const int idx = blockIdx.x * blockDim.x + tid;
constexpr int NUM_WARPS = (NUM_THREADS + WARP_SIZE - 1) / WARP_SIZE;
__shared__ float reduce_smem[NUM_WARPS];
float sum = (idx < N) ? expf(x[idx]) : 0.0f;
int warp = tid / WARP_SIZE;
int lane = tid % WARP_SIZE;
sum = warp_reduce_sum(sum);
if (lane == 0) reduce_smem[warp] = sum;
__syncthreads();
// compute the final sum in each warp
sum = (lane < NUM_WARPS) ? reduce_smem[lane] : 0.0f;
sum = warp_reduce_sum(sum); // sum(e^x_0,...,e^x_n-1)
// get the total sum of all blocks.
if (tid == 0) atomicAdd(total, sum);
__threadfence(); // grid level memory fence 注意这里需要网格级别的内存同步
// e^x_i/sum(e^x_0,...,e^x_n-1)
if (idx < N) y[idx] = block_smem[tid] / (*total);
}
// Softmax x: N, y: N
// grid(N/128), block(K=128)
template
__global__ void softmax_v2(float* x, float* y, float* total, int N) {
const int tid = threadIdx.x;
const int idx = blockIdx.x * blockDim.x + tid;
float exp_val = (idx < N) ? expf(x[idx]) : 0.0f;
float sum = block_reduce_sum(exp_val);
// get the total sum of all blocks.
if (tid == 0) atomicAdd(total, sum);
__threadfence(); // grid level memory fence 注意这里需要网格级别的内存同步
// e^x_i/sum(e^x_0,...,e^x_n-1)
if (idx < N) y[idx] = exp_val / (*total);
}
// Softmax Vec4 x: N, y: N
// grid(N/128), block(128/4)
template
__global__ void softmax_v2_vec4(float* x, float* y, float* total, int N) {
const int tid = threadIdx.x;
const int idx = (blockIdx.x * blockDim.x + tid) * 4;
float4 reg_x = FLOAT4(x[idx]);
float4 reg_exp;
reg_exp.x = (idx < N) ? expf(reg_x.x) : 0.0f;
reg_exp.y = (idx < N) ? expf(reg_x.y) : 0.0f;
reg_exp.z = (idx < N) ? expf(reg_x.z) : 0.0f;
reg_exp.w = (idx < N) ? expf(reg_x.w) : 0.0f;
float exp_val = (reg_exp.x + reg_exp.y + reg_exp.z + reg_exp.w);
float sum = block_reduce_sum(exp_val);
// get the total sum of all blocks.
if (tid == 0) atomicAdd(total, sum);
__threadfence(); // grid level memory fence 注意这里需要网格级别的内存同步
// e^x_i/sum(e^x_0,...,e^x_n-1)
if (idx < N) {
float4 reg_y;
reg_y.x = reg_exp.x / (*total);
reg_y.y = reg_exp.y / (*total);
reg_y.z = reg_exp.z / (*total);
reg_y.w = reg_exp.w / (*total);
FLOAT4(y[idx]) = reg_y;
}
}
```
softmax稍微要注意的就是内存同步的问题，这里，你需要做一个网格级别的同步，而不能仅仅是block级别，否则拿不到全局的exp sum作为分母项。因此使用 __threadfence 这个网格及内存同步操作。不过效率我还没测过，实在要高效的话，可能得整成FA2那样的 1-pass + online softmax的实现。不过，如果是面试的话，就不要太为难自己了...，但是FA1/FA2的论文很经典，强烈建议多读几遍。
## 0x0a sigmoid, sigmoid + vec4 ([©️back👆🏻](#kernellist))
```c++
// Sigmoid x: N, y: N y=1/(1+exp(-x))
// grid(N/128), block(K=128)
__global__ void sigmoid(float* x, float* y, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) y[idx] = 1.0f / (1.0f + expf(-x[idx]));
}
// Sigmoid x: N, y: N y=1/(1+exp(-x)) Vec4
// grid(N/128), block(128/4)
__global__ void sigmoid_vec4(float* x, float* y, int N) {
int idx = (blockIdx.x * blockDim.x + threadIdx.x) * 4;
if (idx < N) {
float4 reg_x = FLOAT4(x[idx]);
float4 reg_y;
reg_y.x = 1.0f / (1.0f + expf(-reg_x.x));
reg_y.y = 1.0f / (1.0f + expf(-reg_x.y));
reg_y.z = 1.0f / (1.0f + expf(-reg_x.z));
reg_y.w = 1.0f / (1.0f + expf(-reg_x.w));
FLOAT4(y[idx]) = reg_y;
}
}
```
## 0x0b relu, relu + vec4 ([©️back👆🏻](#kernellist))
```c++
// Relu x: N, y: N y=max(0,x)
// grid(N/128), block(K=128)
__global__ void relu(float* x, float* y, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) y[idx] = fmaxf(0.0f, x[idx]);
}
// Relu x: N, y: N y=max(0,x) Vec4
// grid(N/128/4), block(128/4)
__global__ void relu_vec4(float* x, float* y, int N) {
int idx = (blockIdx.x * blockDim.x + threadIdx.x) * 4;
if (idx < N) {
float4 reg_x = FLOAT4(x[idx]);
float4 reg_y;
reg_y.x = fmaxf(0.0f, reg_x.x);
reg_y.y = fmaxf(0.0f, reg_x.y);
reg_y.z = fmaxf(0.0f, reg_x.z);
reg_y.w = fmaxf(0.0f, reg_x.w);
FLOAT4(y[idx]) = reg_y;
}
}
```
## 0x0c layer_norm, layer_norm + vec4 ([©️back👆🏻](#kernellist))
```c++
// Layer Norm: x: NxK(K=128<1024), y': NxK, y'=x-mean(x)/std(x) each row
// mean(x) = sum(x)/K, 1/std(x) = rsqrtf( sum( (x-mean(x))^2 )/K ) each row
// grid(N*K/K), block(K<1024) N=batch_size*seq_len, K=hidden_size
// y=y'*g + b (g: scale, b: bias)
template
__global__ void layer_norm(float* x, float* y, float g, float b, int N, int K) {
int tid = threadIdx.x; // 0..K-1
int bid = blockIdx.x; // 0..N-1
int idx = bid * blockDim.x + threadIdx.x;
const float epsilon = 1e-5f;
__shared__ float s_mean; // shared within block
__shared__ float s_variance; // shared within block
float value = (idx < N * K) ? x[idx] : 0.0f; // load once only
float sum = block_reduce_sum(value);
if (tid == 0) s_mean = sum / (float) K;
// wait for s_mean in shared memory to be ready for all threads
__syncthreads();
float variance = (value - s_mean) * (value - s_mean);
variance = block_reduce_sum(variance);
if (tid == 0) s_variance = rsqrtf(variance / (float) K + epsilon);
// wait for s_variance in shared memory to be ready for all threads
__syncthreads();
if (idx < N * K) y[idx] = ((value - s_mean) * s_variance) * g + b;
}
// Layer Norm Vec4: x: NxK(K=128<1024), y': NxK, y'=x-mean(x)/std(x) each row
// mean(x) = sum(x)/K, 1/std(x) = rsqrtf( sum( (x-mean(x))^2 )/K ) each row
// grid(N*K/K), block(K/4<1024) N=batch_size*seq_len, K=hidden_size
// y=y'*g + b (g: scale, b: bias)
template
__global__ void layer_norm_vec4(float* x, float* y, float g, float b, int N, int K) {
int tid = threadIdx.x; // 0..K-1
int bid = blockIdx.x; // 0..N-1
int idx = (bid * blockDim.x + threadIdx.x) * 4;
const float epsilon = 1e-5f;
__shared__ float s_mean; // shared within block
__shared__ float s_variance; // shared within block
float4 reg_x = FLOAT4(x[idx])
float value = (idx < N * K) ? (reg_x.x + reg_x.y
+ reg_x.z + reg_x.w) : 0.0f;
float sum = block_reduce_sum(value);
if (tid == 0) s_mean = sum / (float) K;
// wait for s_mean in shared memory to be ready for all threads
__syncthreads();
float4 reg_x_hat;
reg_x_hat.x = reg_x.x - s_mean;
reg_x_hat.y = reg_x.y - s_mean;
reg_x_hat.z = reg_x.z - s_mean;
reg_x_hat.w = reg_x.w - s_mean;
float variance = reg_x_hat.x * reg_x_hat.x + reg_x_hat.y * reg_x_hat.y
+ reg_x_hat.z * reg_x_hat.z + reg_x_hat.w * reg_x_hat.w;
variance = block_reduce_sum(variance);
if (tid == 0) s_variance = rsqrtf(variance / (float) K + epsilon);
// wait for s_variance in shared memory to be ready for all threads
__syncthreads();
float4 reg_y;
reg_y.x = reg_x_hat.x * s_variance * g + b;
reg_y.y = reg_x_hat.y * s_variance * g + b;
reg_y.z = reg_x_hat.z * s_variance * g + b;
reg_y.w = reg_x_hat.w * s_variance * g + b;
if (idx < N * K) FLOAT4(y[idx]) = reg_y;
}
```
layer norm实现的核心同样也是block reduce和warp reduce，然后再整点向量化...
## 0x0d rms_norm, rms_norm + vec4 ([©️back👆🏻](#kernellist))
```c++
// RMS Norm: x: NxK(K=128<1024), y': NxK, y'=x/rms(x) each row
// 1/rms(x) = rsqrtf( sum(x^2)/K ) each row
// grid(N*K/K), block(K<1024) N=batch_size*seq_len, K=hidden_size
// y=y'*g (g: scale)
template
__global__ void rms_norm(float* x, float* y, float g, int N, int K) {
int tid = threadIdx.x; // 0..K-1
int bid = blockIdx.x; // 0..N-1
int idx = bid * blockDim.x + threadIdx.x;
const float epsilon = 1e-5f;
__shared__ float s_variance; // shared within block
float value = (idx < N * K) ? x[idx] : 0.0f; // load once only
float variance = value * value;
variance = block_reduce_sum(variance);
if (tid == 0) s_variance = rsqrtf(variance / (float) K + epsilon);
// wait for s_variance in shared memory to be ready for all threads
__syncthreads();
if (idx < N * K) y[idx] = (value * s_variance) * g;
}
// RMS Norm Vec4: x: NxK(K=128<1024), y': NxK, y'=x/rms(x) each row
// 1/rms(x) = rsqrtf( sum(x^2)/K ) each row
// grid(N*K/K), block(K/4<1024) N=batch_size*seq_len, K=hidden_size
// y=y'*g (g: scale)
template
__global__ void rms_norm_vec4(float* x, float* y, float g, int N, int K) {
int tid = threadIdx.x; // 0..K-1
int bid = blockIdx.x; // 0..N-1
int idx = (bid * blockDim.x + threadIdx.x) * 4;
const float epsilon = 1e-5f;
__shared__ float s_variance; // shared within block
float4 reg_x = FLOAT4(x[idx]);
float variance = (idx < N * K) ? (reg_x.x * reg_x.x + reg_x.y * reg_x.y
+ reg_x.z * reg_x.z + reg_x.w * reg_x.w) : 0.0f;
variance = block_reduce_sum(variance);
if (tid == 0) s_variance = rsqrtf(variance / (float) K + epsilon);
// wait for s_variance in shared memory to be ready for all threads
__syncthreads();
float4 reg_y;
reg_y.x = reg_x.x * s_variance * g;
reg_y.y = reg_x.y * s_variance * g;
reg_y.z = reg_x.z * s_variance * g;
reg_y.w = reg_x.w * s_variance * g;
if (idx < N * K) FLOAT4(y[idx]) = reg_y;
}
```
rms norm实现的核心同样也是block reduce和warp reduce...，然后再加点float4向量化什么的。
## 0x0e NMS ([©️back👆🏻](#kernellist))
```c++
struct Box {
float x1, y1, x2, y2, score;
float area() const {return (std::abs(x2 - x1 + 1)) * std::abs(y2 - y1 + 1); }
float iou_of(const Box& other) const{
float inner_x1 = x1 > other.x1 ? x1 : other.x1;
float inner_y1 = y1 > other.y1 ? y1 : other.y1;
float inner_x2 = x2 < other.x2 ? x2 : other.x2;
float inner_y2 = y2 < other.y2 ? y2 : other.y2;
float inner_h = inner_y2 - inner_y1 + 1.0f;
float inner_w = inner_x2 - inner_x1 + 1.0f;
float inner_area = inner_h * inner_w;
return (inner_area / (area() + tbox.area() - inner_area));
}
}
void hard_nms(std::vector &input, std::vector &output, float iou_threshold){
if (input.empty()) return;
std::sort(input.begin(), input.end(),[](Box& a, Box& b) { return a.score > b.score; });
int box_num = input.size();
std::vector merged(box_num, 0);
for (int i = 0; i < box_num; ++i) {
if (merged[i]) continue;
merged[i] = 1;
for (int j = i + 1; j < box_num; ++j) {
if (merged[j]) continue;
float iou = input[i].iou_of(input[j]);
if (iou > iou_threshold) merged[j] = 1;
}
output.push_back(input[i]);
}
}
```
CV相关的经常会要手撕NMS，也记录下。
## 0x0f 总结 ([©️back👆🏻](#kernellist))
可以发现，大部分kernel的基本写法都是依赖warp reduce和block reduce的，基本上只要熟练应用warp functions各种场景的写法，应该问题不大；softmax需要考虑网格级同步的问题，或者online softmax以及FlashAttention；sgemm的优化是个很大的课题，不是案例中写的这么简单，但是入门的话，基本就是tiling的思想以及如何做索引之间的mapping；sgemv的优化则主要考虑K不同的值（因为M为1了），比如K=16,64,128等情况下，如何按照warp来处理；relu、sigmoid等都是elementwise的操作，很好实现，可以再考虑加点向量化优化访存；layer norm和rms norm在数学上其实也是挺清晰简单的，落实到cuda kernel时，只要按照逐个token来处理，headdim没有超过1024的情况下（一个block最多可以放1024个threads），可以放到一个block处理，这样并行化就很好写。当然，核心还是warp reduce和block reduce；NMS是乱入的，没有CUDA版本，别问了...
## ©️License
GNU General Public License v3.0
## References
- [flash-attention-minimal](https://github.com/tspeterkim/flash-attention-minimal): Flash Attention in ~100 lines of CUDA (forward pass only)
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