Merge pull request #3050 from alibaba/feature/sync

MNN:Sync: Sync Internal 2.9.6
alibaba · Oct 14, 2024 · a74551b · a74551b
2 parents f830294 + 860fceb
commit a74551b
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diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -25,6 +25,14 @@ set(CMAKE_MODULE_PATH
   ${CMAKE_MODULE_PATH}
   "${CMAKE_CURRENT_LIST_DIR}/cmake"
 )
+
+if(WIN32)
+  if(NOT MSVC)
+    set(CMAKE_MSVC_RUNTIME_LIBRARY "")
+    set(MSVC_RUNTIME_LIBRARY "")
+  endif()
+endif()
+
 # build options
 option(MNN_USE_SYSTEM_LIB "For opencl and vulkan, use system lib or use dlopen" OFF)
 option(MNN_BUILD_HARD "Build -mfloat-abi=hard or not" OFF)
@@ -198,7 +206,7 @@ option(MNN_METAL "Enable Metal" OFF)
 option(MNN_OPENCL "Enable OpenCL" OFF)
 option(MNN_OPENGL "Enable OpenGL" OFF)
 option(MNN_VULKAN "Enable Vulkan" OFF)
-option(MNN_ARM82 "Enable ARM82" OFF)
+option(MNN_ARM82 "Enable ARMv8.2's FP16 Compute" ON)
 option(MNN_ONEDNN "Enable oneDNN" OFF)
 option(MNN_AVX512 "Enable AVX512" OFF)
 option(MNN_CUDA "Enable CUDA" OFF)
@@ -452,6 +460,12 @@ set(MNN_EXTRA_DEPENDS "")
 # Add Thread dependency
 find_package(Threads)
 list(APPEND MNN_EXTRA_DEPENDS ${CMAKE_THREAD_LIBS_INIT})
+if(WIN32)
+  if(NOT MSVC)
+    set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -fuse-ld=lld-link -lmsvcrt")
+    set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fuse-ld=lld-link -lmsvcrt")
+  endif()
+endif()
 
 if (NOT APPLE)
   if(MNN_OPENMP)

diff --git a/MNN.sln b/MNN.sln
@@ -1,36 +0,0 @@
-
-Microsoft Visual Studio Solution File, Format Version 12.00
-# Visual Studio Version 17
-VisualStudioVersion = 17.5.002.0
-MinimumVisualStudioVersion = 10.0.40219.1
-Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "3rd_party", "3rd_party", "{5CD18987-C4CA-49D5-942F-14B15F46B1ED}"
-EndProject
-Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "flatbuffers", "flatbuffers", "{89B04BB7-86CB-4D4F-B65C-C3D0995DBD36}"
-EndProject
-Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "tests", "tests", "{797AC14A-1653-469D-A240-76EF0F36E60A}"
-EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "FlatBuffers.Test", "3rd_party\flatbuffers\tests\FlatBuffers.Test\FlatBuffers.Test.csproj", "{E5A80CC7-62B1-4887-B637-455F34CCC9B3}"
-EndProject
-Global
-	GlobalSection(SolutionConfigurationPlatforms) = preSolution
-		Debug|Any CPU = Debug|Any CPU
-		Release|Any CPU = Release|Any CPU
-	EndGlobalSection
-	GlobalSection(ProjectConfigurationPlatforms) = postSolution
-		{E5A80CC7-62B1-4887-B637-455F34CCC9B3}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{E5A80CC7-62B1-4887-B637-455F34CCC9B3}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{E5A80CC7-62B1-4887-B637-455F34CCC9B3}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{E5A80CC7-62B1-4887-B637-455F34CCC9B3}.Release|Any CPU.Build.0 = Release|Any CPU
-	EndGlobalSection
-	GlobalSection(SolutionProperties) = preSolution
-		HideSolutionNode = FALSE
-	EndGlobalSection
-	GlobalSection(NestedProjects) = preSolution
-		{89B04BB7-86CB-4D4F-B65C-C3D0995DBD36} = {5CD18987-C4CA-49D5-942F-14B15F46B1ED}
-		{797AC14A-1653-469D-A240-76EF0F36E60A} = {89B04BB7-86CB-4D4F-B65C-C3D0995DBD36}
-		{E5A80CC7-62B1-4887-B637-455F34CCC9B3} = {797AC14A-1653-469D-A240-76EF0F36E60A}
-	EndGlobalSection
-	GlobalSection(ExtensibilityGlobals) = postSolution
-		SolutionGuid = {11D826E1-518B-4BC2-8E45-03F5F48170D6}
-	EndGlobalSection
-EndGlobal

diff --git a/backupcode/cpubackend/arm/arm32/bf16/MNNConvRunForUnitDepthWise_BF16.S b/backupcode/cpubackend/arm/arm32/bf16/MNNConvRunForUnitDepthWise_BF16.S
diff --git a/backupcode/cpubackend/arm/arm64/bf16/MNNConvRunForUnitDepthWise_BF16.S b/backupcode/cpubackend/arm/arm64/bf16/MNNConvRunForUnitDepthWise_BF16.S
diff --git a/backupcode/cpubackend/bf16/BF16Functions.cpp b/backupcode/cpubackend/bf16/BF16Functions.cpp
@@ -76,23 +76,6 @@ static void _MNNLowpToFp32(const int16_t* src, float* dst, size_t size) {
         ::memcpy(dst, dstTemp, sizeRemain * sizeof(float));
     }
 }
-static void MNNConvRunForUnitDepthWiseBF16(float* dst, const float* src, const float* weight, size_t fw, size_t fh,
-                                size_t weight_y_step, size_t dilateX_step, size_t dilateY_step) {
-    int fx, fy;
-    BFVec4 dstValue(0.0f);
-    const int16_t* src_z    = (const int16_t*)src;
-    const int16_t* weight_z = (const int16_t*)weight;
-    for (fy = 0; fy < fh; ++fy) {
-        const auto src_y    = src_z + fy * dilateY_step;
-        const auto weight_y = weight_z + fy * weight_y_step;
-        for (fx = 0; fx < fw; ++fx) {
-            const auto weight_x = weight_y + 4 * fx;
-            const auto src_x    = src_y + fx * dilateX_step;
-            dstValue = dstValue + BFVec4::load(src_x) * BFVec4::load(weight_x);
-        }
-    }
-    BFVec4::save((int16_t*)dst, dstValue);
-}
 
 static void MNNConvRunForLineDepthwiseBF16(float* dstO, const float* srcO, const float* weightO, size_t width, size_t src_w_setup,
                                 size_t fw, size_t fh, size_t dilateX_step, size_t dilateY_step, size_t height,
@@ -823,7 +806,6 @@ static CoreFunctions* gInstance = nullptr;
 bool BF16Functions::init() {
     gInstance = new CoreFunctions;
     gInstance->MNNConvRunForLineDepthwise = MNNConvRunForLineDepthwiseBF16;
-    gInstance->MNNConvRunForUnitDepthWise = MNNConvRunForUnitDepthWiseBF16;
     gInstance->MNNAxByClampBroadcastUnit = MNNAxByClampBroadcastUnitBF16;
     gInstance->MNNFp32ToLowp = _MNNFp32ToLowp;
     gInstance->MNNLowpToFp32 = _MNNLowpToFp32;
@@ -890,7 +872,6 @@ bool BF16Functions::init() {
     gInstance->MNNPackedMatMul = NEON_MNNPackedMatMul_BF16;
     gInstance->MNNPackedMatMulRemain = NEON_MNNPackedMatMulRemain_BF16;
     gInstance->MNNConvRunForLineDepthwise = NEON_MNNConvRunForLineDepthwise_BF16;
-    gInstance->MNNConvRunForUnitDepthWise = NEON_MNNConvRunForUnitDepthWise_BF16;
     gInstance->MNNAxByClampBroadcastUnit = NEON_MNNAxByClampBroadcastC4_BF16;
 #ifdef __aarch64__
     cpuinfo_arm_isa gCPUInfo;

diff --git a/docs/compile/cmake.md b/docs/compile/cmake.md
@@ -38,7 +38,7 @@ MNN使用CMake构建项目，CMake中的宏定义列表如下：
 | MNN_OPENCL           | 是否构建`OpenCL`后端，默认为`OFF` |
 | MNN_OPENGL           | 是否构建`OpenGL`后端，默认为`OFF` |
 | MNN_VULKAN           | 是否构建`Vulkan`后端，默认为`OFF` |
-| MNN_ARM82            | 是否构建`Armv8.2`后端，默认为`OFF` |
+| MNN_ARM82            | 编译ARM架构时，是否构建`Armv8.2`后端，以支持FP16计算，默认为`ON` |
 | MNN_ONEDNN           | 是否使用`oneDNN`，默认为`OFF` |
 | MNN_AVX512           | 是否构建`avx512`后端，默认为`OFF` |
 | MNN_CUDA             | 是否构建`Cuda`后端，默认为`OFF` |

diff --git a/docs/compile/engine.md b/docs/compile/engine.md
@@ -22,37 +22,45 @@
         ```bash
         mkdir build && cd build && cmake .. && make -j8
         ```
-## Windows
+## Windows(非ARM架构)
 - 环境要求
   - Microsoft Visual Studio >= 2017
   - cmake >= 3.13
-  - powershell
   - Ninja
 - 相关编译选项
   - 同`Linux/MacOS`
 - 具体步骤
-  1. opencl/vulkan
-     - *(可选)*下载GPU Caps Viewer，你可以通过这个工具来查看本机设备的详细信息（opencl、opengl、vulkan等）
-     - sdk和驱动准备
-        - [opencl sdk](https://github.com/GPUOpen-LibrariesAndSDKs/OCL-SDK/releases)，将opencl sdk目录的路径加到AMDAPPSDKROOT环境变量
-        - [vulkan sdk](https://vulkan.lunarg.com/)，将vulkan skd路径加入VULKAN_SDK环境变量，以备cmake查找
-        - [AMD opencl驱动](https://www.amd.com/zh-hans/support)
-        - [NVIDIA opencl驱动](https://developer.nvidia.com/opencl)
-        - [AMD vulkan驱动](https://community.amd.com/community/gaming/blog/2016/02/16/radeon-gpus-are-ready-for-the-vulkan-graphics-api)
-  2. 编译
-     - 64位编译：在设置中找到vcvars64.bat（适用于 VS 2017 的 x64 本机工具命令提示）并单击，打开VS编译x64架构程序的虚拟环境
-     - 32位编译：在设置中找到vcvarsamd64_x86.bat（VS 2017的 x64_x86 交叉工具命令提示符）并单击，打开VS交叉编译x86架构程序的虚拟环境 
-     - 在虚拟环境中执行如下编译命令：
-        ```bash
-        cd /path/to/MNN
-        ./schema/generate.ps1 # 非必须
-        mkdir build && cd build
-        cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release -DMNN_BUILD_SHARED_LIBS=OFF -DMNN_WIN_RUNTIME_MT=OFF
-        ninja
-        ```
-     - 若需要编译模型转换工具，cmake 命令加上 -DMNN_BUILD_CONVERTER=ON -DMNN_BUILD_SHARED_LIBS=OFF -DMNN_WIN_RUNTIME_MT=ON
-     - 若需要编译 MNN CUDA，MNN_WIN_RUNTIME_MT 和 MNN_BUILD_SHARED_LIBS 需要设成 ON ，另外加上 -DMNN_CUDA=ON: cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release -DMNN_BUILD_SHARED_LIBS=ON -DMNN_WIN_RUNTIME_MT=ON -DMNN_CUDA=ON
-     - Windows 上建议使用 Interpreter::destroy , Tensor::destroy , Module::destroy 等方法进行 MNN 相关内存对象的析构，不要直接使用 delete （直接使用 delete 在 -DMNN_WIN_RUNTIME_MT=ON 时会出问题）
+  - 64位编译：在设置中找到vcvars64.bat（适用于 VS 2017 的 x64 本机工具命令提示）并单击，打开VS编译x64架构程序的虚拟环境
+  - 32位编译：在设置中找到vcvarsamd64_x86.bat（VS 2017的 x64_x86 交叉工具命令提示符）并单击，打开VS交叉编译x86架构程序的虚拟环境 
+  - 在虚拟环境中执行如下编译命令：
+     ```bash
+     cd /path/to/MNN
+     ./schema/generate.ps1 # 非必须
+     mkdir build && cd build
+     cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release -DMNN_BUILD_SHARED_LIBS=OFF -DMNN_WIN_RUNTIME_MT=OFF
+     ninja
+     ```
+  - 若需要编译模型转换工具，cmake 命令加上 -DMNN_BUILD_CONVERTER=ON -DMNN_BUILD_SHARED_LIBS=OFF -DMNN_WIN_RUNTIME_MT=ON
+  - 若需要编译 MNN CUDA，MNN_WIN_RUNTIME_MT 和 MNN_BUILD_SHARED_LIBS 需要设成 ON ，另外加上 -DMNN_CUDA=ON: cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release -DMNN_BUILD_SHARED_LIBS=ON -DMNN_WIN_RUNTIME_MT=ON -DMNN_CUDA=ON
+  - Windows 上建议使用 Interpreter::destroy , Tensor::destroy , Module::destroy 等方法进行 MNN 相关内存对象的析构，不要直接使用 delete （直接使用 delete 在 -DMNN_WIN_RUNTIME_MT=ON 时会出问题）
+
+## Windows(ARM架构)
+- 环境要求
+  - Microsoft Visual Studio >= 2017
+  - cmake >= 3.13
+  - Ninja
+  - Clang
+    - Clang 安装参考: https://learn.microsoft.com/en-us/cpp/build/clang-support-msbuild?view=msvc-170#install-1
+- 相关编译选项
+  - 同`Linux/MacOS`
+- 具体步骤
+  - 打开vs的ARM64命令行工具
+  - 进入 MNN 根目录
+  - mkdir build && cd build
+  - cmake .. -G Ninja -DCMAKE_C_COMPILER="C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\Llvm\ARM64\bin\clang.exe" -DCMAKE_CXX_COMPILER="C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\Llvm\ARM64\bin\clang++.exe"  -DCMAKE_LINKER="C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\Llvm\ARM64\bin\lld.exe" -DCMAKE_BUILD_TYPE=Release
+    - Visual Studio 安装路径不一致的，可自行修改脚本
+  - ninja -j16
+
 ## Android
 - 环境要求
   - cmake >= 3.10

diff --git a/docs/tools/quant.md b/docs/tools/quant.md
@@ -39,8 +39,43 @@ MNN现已推出基于TensorFlow/Pytorch的模型压缩工具mnncompress，请查
 | ADMM | 使用ADMM方法进行权值量化 |
 
 ## 多输入模型的参数设置的特别说明(MNN现阶段仅支持输入数据类型是非图片的多输入模型)
-| input_type | `str` | 输入数据的类型，"sequence" |
-| path | `str` | 存放校正特征量化系数的输入数据目录 |，例如该目录下包含2个输入数据集input_0和input_1，子目录input_0和input_1中包含模型的输入数据和一个input.json文件。input_0和input_1分别是两个输入输出信息文件夹，可使用 testMNNFromOnnx.py 等脚本生成，参考模型转换的正确性校验部分。
+| 需要特别指定的参数 | 设置值 |
+|--------------------|------|
+| input_type | `str`：输入数据的类型，"sequence" |
+| path | `str`：存放校正特征量化系数的输入数据目录 |，
+例如在quant.json文件中 "path": "/home/data/inputs_dir/"，你所构造的矫正数据集有两个，分别存放在input_0和input_1子目录下，即"/home/data/inputs_dir/input_0"和"/home/data/inputs_dir/input_1".由GetMNNInfo工具可以得到模型的输入输出名称，例如该模型的输入有三个：data0, data1, data2，输出有两个：out1, out2. 那么在input_0和input_1子目录下分别有六个文件：data0.txt, data1.txt, data2.txt, out1.txt, out2.txt, input.json. 其中的五个文件名要和模型的输入输出名对应，最后一个input.json文件则描述的是输入名和对应的shape内容：
+```json
+{
+    "inputs": [
+        {
+            "name": "data0",
+            "shape": [
+                2,
+                4,
+		        64,
+		        64
+            ]
+        },
+	        {
+            "name": "data1",
+            "shape": [
+                1
+            ]
+        },
+        {
+            "name": "data2",
+            "shape": [
+                2,
+                512,
+                768
+            ]
+        }
+    ],
+    "outputs": [
+        "out1", "out2"
+    ]
+}
+```
 
 ## 量化模型的使用
 和浮点模型同样使用方法，输入输出仍然为浮点类型