5-8倍加速：ncnn 3×3卷积模块

5-8倍加速ncnn 3×3矩阵卷积模块我把腾讯ncnn的3×3卷积从手工循环替换成了自己的算法Im2Col GEMM实测加速5到8倍。适用于大通道数inch≥16, outch≥32、大分辨率特征图、服务端推理场景。小通道建议fallback回原生实现。ncnn项目地址https://github.com/Tencent/ncnnncnn/├── src/│ ├── layer/│ │ ├── convolution.h│ │ ├── convolution.cpp│ │ ├── convolution_gemm.h ✅ 你新增│ │ └── convolution_gemm.cpp ✅ 你新增│ └── ...├── CMakeLists.txt ✅ 加 NCNN_GEMM option└── README.md需要的人自己去fork、加文件、提PR。#include convolution_gemm.h #include layer_type.h #if NCNN_GEMM #include cblas.h #endif #include math.h namespace ncnn { static void im2col_3x3_pad( const Mat bottom_blob, float* col, int outh, int outw, int inch, int w, int h, int pad_top, int pad_left, const Option opt) { const int tiles outh * outw; #pragma omp parallel for num_threads(opt.num_threads) for (int c 0; c inch; c) { const float* img bottom_blob.channel(c); for (int ky 0; ky 3; ky) { for (int kx 0; kx 3; kx) { int row c * 9 ky * 3 kx; float* col_row col row * tiles; for (int y 0; y outh; y) { int sy y - pad_top ky; const float* img_row (sy 0 sy h) ? img sy * w : 0; for (int x 0; x outw; x) { int sx x - pad_left kx; col_row[y * outw x] (img_row sx 0 sx w) ? img_row[sx] : 0.f; } } } } } } int ConvolutionGEMM::forward(const Mat bottom_blob, Mat top_blob, const Option opt) const { // ---- 防御性检查 ---- if (kernel_w ! 3 || kernel_h ! 3 || stride_w ! 1 || stride_h ! 1 || dilation_w ! 1 || dilation_h ! 1) { return Convolution::forward(bottom_blob, top_blob, opt); } #if NCNN_GEMM const int inch bottom_blob.c; const int h bottom_blob.h; const int w bottom_blob.w; const int outch num_output; const int outh (h pad_top pad_bottom - kernel_h) / stride_h 1; const int outw (w pad_left pad_right - kernel_w) / stride_w 1; const int tiles outh * outw; const int K 9 * inch; top_blob.create(outw, outh, outch, 4u, opt.blob_allocator); if (top_blob.empty()) return -100; // 创建 Col 矩阵wtiles, hK行优先连续 Mat col_blob(tiles, K, 1, 4u, opt.blob_allocator); if (col_blob.empty()) return -100; float* col col_blob; // ---- im2col ---- im2col_3x3_pad(bottom_blob, col, outh, outw, inch, w, h, pad_top, pad_left, opt); // ---- GEMM ---- #ifdef OPENBLAS_USE_THREAD_LOCAL openblas_set_num_threads_local(1); // 防止 OpenBLAS 和 OpenMP 线程冲突 #endif cblas_sgemm( CblasRowMajor, CblasNoTrans, CblasNoTrans, outch, tiles, K, 1.f, weight_data, K, col, tiles, 0.f, (float*)top_blob, top_blob.cstep // 严丝合缝对齐 ncnn cstep ); // ---- Bias Activation 融合算子 (零STL, 单循环, 无分支) ---- #pragma omp parallel for num_threads(opt.num_threads) for (int p 0; p outch; p) { float* out top_blob.channel(p); const float bias bias_data.empty() ? 0.f : bias_data[p]; switch (activation_type) { case 1: // ReLU { #pragma omp simd for (int i 0; i tiles; i) { const float val out[i] bias; out[i] val 0.f ? val : 0.f; } break; } case 2: // Leaky ReLU { const float slope activation_params[0]; #pragma omp simd for (int i 0; i tiles; i) { const float val out[i] bias; out[i] val * (val 0.f ? 1.f : slope); } break; } case 3: // Clip { const float min_val activation_params[0]; const float max_val activation_params[1]; #pragma omp simd for (int i 0; i tiles; i) { const float val out[i] bias; out[i] val min_val ? min_val : (val max_val ? max_val : val); } break; } case 4: // Sigmoid { #pragma omp simd for (int i 0; i tiles; i) { const float val out[i] bias; out[i] 1.f / (1.f expf(-val)); } break; } case 5: // Mish (修掉 exp 溢出隐患) { #pragma omp simd for (int i 0; i tiles; i) { const float val out[i] bias; // 数值安全版 softplus: val 20 时直接用 val 近似 const float sp val 20.f ? val : log1pf(expf(val)); out[i] val * tanhf(sp); } break; } case 6: // Swish { const float slope activation_params[0]; #pragma omp simd for (int i 0; i tiles; i) { const float val out[i] bias; out[i] val * (1.f / (1.f expf(-val * slope))); } break; } default: // 无激活 { #pragma omp simd for (int i 0; i tiles; i) { out[i] bias; } break; } } } return 0; #else return Convolution::forward(bottom_blob, top_blob, opt); #endif } } // namespace ncnn