Merge pull request #2450 from wangzhaode/feature/sync_low_memory

[MNN:Sync] Sync Internal code, support low_memory for conv.
alibaba · Jun 27, 2023 · 24a2e4e · 24a2e4e
2 parents 551974a + 67eceb8
commit 24a2e4e
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Showing 57 changed files with 8,566 additions and 226 deletions.
diff --git a/benchmark/benchmark.cpp b/benchmark/benchmark.cpp
@@ -119,10 +119,12 @@ std::vector<float> doBench(Model& model, int loop, int warmup = 10, int forward
                            int numberThread = 4, int precision = 2, float sparsity = 0.0f, int sparseBlockOC = 1, bool testQuantModel=false) {
     auto revertor = std::unique_ptr<Revert>(new Revert(model.model_file.c_str()));
     if (testQuantModel) {
-        float scale = 0.003, offset = 0.f;
-        revertor->writeExtraDescribeTensor(&scale, &offset);
+        printf("Auto set sparsity=0 when test quantized model in benchmark...\n");
+        revertor->initialize(0, sparseBlockOC, false, true);
+    } else {
+        revertor->initialize(sparsity, sparseBlockOC);
     }
-    revertor->initialize(sparsity, sparseBlockOC);
+
     auto modelBuffer      = revertor->getBuffer();
     const auto bufferSize = revertor->getBufferSize();
     auto net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromBuffer(modelBuffer, bufferSize));

diff --git a/docs/compile/cmake.md b/docs/compile/cmake.md
@@ -80,3 +80,4 @@ MNN使用CMake构建项目，CMake中的宏定义列表如下：
 | MNN_OPENCV_TEST      | 构建MNN的OpenCV功能是否开启单元测试，默认为`OFF` |
 | MNN_OPENCV_BENCH     | 构建MNN的OpenCV功能是否开启性能benchmark，默认为`OFF` |
 | MNN_VULKAN_IMAGE     | 构建MNN的Vulkan后端时采用Image内存模式，以便支持FP16和部分移动端上GPU的加速，默认为`ON` |
+| MNN_LOW_MEMORY       | 是否支持低内存模式，支持低内存模式使用权值量化模型并设置`low_memory`则会使用计算时反量化，默认为`OFF` |
diff --git a/source/backend/arm82/Arm82Backend.cpp b/source/backend/arm82/Arm82Backend.cpp
@@ -40,7 +40,7 @@ bool Arm82Backend::addArm82Creator(OpType t, Arm82Creator* ct) {
     return true;
 }
 
-Arm82Backend::Arm82Backend(const CPURuntime* runtime) : CPUBackend(runtime, BackendConfig::Precision_Low, BackendConfig::Memory_Normal, MNN_FORWARD_CPU_EXTENSION) {
+Arm82Backend::Arm82Backend(const CPURuntime* runtime, BackendConfig::MemoryMode memory) : CPUBackend(runtime, BackendConfig::Precision_Low, memory, MNN_FORWARD_CPU_EXTENSION) {
     mCoreFunctions = Arm82Functions::get();
 }
 

diff --git a/source/backend/arm82/Arm82Backend.hpp b/source/backend/arm82/Arm82Backend.hpp
@@ -29,7 +29,7 @@ namespace MNN {
 class Arm82Backend : public CPUBackend {
 public:
     virtual ~Arm82Backend();
-    Arm82Backend(const CPURuntime* runtime);
+    Arm82Backend(const CPURuntime* runtime, BackendConfig::MemoryMode memory);
     virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
                                 const MNN::Op* op) override;
     virtual Backend::MemObj* onAcquire(const Tensor* nativeTensor, StorageType storageType) override;

diff --git a/source/backend/arm82/Arm82Functions.cpp b/source/backend/arm82/Arm82Functions.cpp
@@ -26,7 +26,14 @@ void MNNPackedMatMulFP16(float* C, const float* A, const float* B, const size_t*
 
 // C(UP_DIV(h,8), e, h8) = B(UP_DIV(h,hP), l, hP) * A(l, e), hP = 24, e >= 1
 // parameter: [aStride, l, h, cStride, bExtraStride]
-void MNNPackedMatMulRemainFP16(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias);
+void MNNPackedMatMulRemainFP16(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b);
+
+#ifdef MNN_LOW_MEMORY
+void MNNPackedMatMulFP16_int4(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b);
+void MNNPackedMatMulRemainFP16_int4(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b);
+void MNNPackedMatMulFP16_int8(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b);
+void MNNPackedMatMulRemainFP16_int8(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b);
+#endif
 
 void MNNConvDwF23MulTransUnitFP16(FLOAT16 **cacheLine, const FLOAT16 *weight, FLOAT16 *dest, size_t ow);
 
@@ -700,6 +707,12 @@ bool Arm82Functions::init() {
     // MatMul
     FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMul, MNNPackedMatMulFP16);
     FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMulRemain, MNNPackedMatMulRemainFP16);
+#ifdef MNN_LOW_MEMORY
+    FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMul_int4, MNNPackedMatMulFP16_int4);
+    FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMulRemain_int4, MNNPackedMatMulRemainFP16_int4);
+    FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMul_int8, MNNPackedMatMulFP16_int8);
+    FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMulRemain_int8, MNNPackedMatMulRemainFP16_int8);
+#endif
     FUNC_PTR_ASSIGN(gInstance->MNNPackC4ForMatMul_A, Arm82MNNPackForMatMul_A);
     FUNC_PTR_ASSIGN(gInstance->MNNGetMatMulPackMode, Arm82MNNGetMatMulPackMode);
     FUNC_PTR_ASSIGN(gInstance->MNNPackForMatMul_B, Arm82MNNPackForMatMul_B);

diff --git a/source/backend/arm82/CMakeLists.txt b/source/backend/arm82/CMakeLists.txt
@@ -7,7 +7,13 @@ if(CMAKE_SYSTEM_PROCESSOR MATCHES "^armv7" OR ARCHS MATCHES "^armv7(;armv7s)?")
     target_compile_options(MNN_Arm82 PRIVATE -march=armv8.2-a+fp16 -mfpu=neon-fp-armv8 -mfloat-abi=softfp -DENABLE_ARMV82)
 elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^aarch64" OR ARCHS STREQUAL "arm64")
     file(GLOB MNN_ARM82_SRCS_ASM "${CMAKE_CURRENT_LIST_DIR}/asm/arm64/*")
+    if (MNN_LOW_MEMORY)
+        file(GLOB MNN_ARM82_SRCS_ASM ${MNN_ARM82_SRCS_ASM} ${CMAKE_CURRENT_LIST_DIR}/asm/arm64/low_memory/*)
+    endif()
     add_library(MNN_Arm82 OBJECT ${MNN_ARM82_SRCS} ${MNN_ARM82_SRCS_ASM})
+    if (MNN_LOW_MEMORY)
+        target_compile_options(MNN_Arm82 PRIVATE -DMNN_LOW_MEMORY)
+    endif()
     target_compile_options(MNN_Arm82 PRIVATE -march=armv8.2-a+fp16 -DENABLE_ARMV82)
 else()
 # Building fat binary requires multiple separate builds and lipo-by-hand under CMake's design