diff --git a/CMakeLists.txt b/CMakeLists.txt index 783580c09..dd7e9b1c5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -58,9 +58,18 @@ option(MNN_ENABLE_COVERAGE "Build with coverage enable" OFF) option(MNN_BUILD_PROTOBUFFER "Build with protobuffer in MNN" ON) option(MNN_BUILD_OPENCV "Build OpenCV api in MNN." OFF) option(MNN_BUILD_LLM "Build llm library based MNN." OFF) +option(MNN_BUILD_DIFFUSION "Build diffusion demo based MNN." OFF) option(MNN_INTERNAL "Build with MNN internal features, such as model authentication, metrics logging" OFF) option(MNN_JNI "Build MNN Jni for java to use" OFF) +IF (OHOS) + include($ENV{NODE_PATH}/@ali/tcpkg/tcpkg.cmake) + export_headers(DIR ${CMAKE_SOURCE_DIR}/include/MNN) + IF (MNN_BUILD_OPENCV) + export_headers(DIR ${CMAKE_SOURCE_DIR}/tools/cv/include/cv) + ENDIF() +ENDIF() + IF (NOT DEFINED MNN_USE_SPARSE_COMPUTE) set(MNN_USE_SPARSE_COMPUTE ON) ENDIF() @@ -263,12 +272,12 @@ endif() option(MNN_USE_CPP11 "Enable MNN use c++11" ON) if (NOT MSVC) if(MNN_CUDA AND MNN_SUPPORT_TRANSFORMER_FUSE) - set(CMAKE_CXX_STANDARD 17) + set(CMAKE_CXX_STANDARD 17) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=gnu99") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++17") elseif(MNN_USE_CPP11) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=gnu99") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") else() set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=gnu99") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x") @@ -493,7 +502,7 @@ endif() IF(MNN_COREML) add_definitions(-DMNN_COREML_ENABLED=1) add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/source/backend/coreml/) - + IF(MNN_SEP_BUILD) list(APPEND MNN_DEPS MNNCoreML) list(APPEND MNN_EXTRA_DEPENDS MNNCoreML) @@ -631,7 +640,7 @@ ENDIF() IF(MNN_BUILD_LLM) # add_definitions(-DMNN_BUILD_LLM) - include(${CMAKE_CURRENT_LIST_DIR}/llm/CMakeLists.txt) + include(${CMAKE_CURRENT_LIST_DIR}/transformers/llm/engine/CMakeLists.txt) ENDIF() # NPU @@ -755,6 +764,9 @@ list(REMOVE_ITEM MNN_TARGETS MNN) IF(MNN_BUILD_DEMO) include(${CMAKE_CURRENT_LIST_DIR}/demo/exec/CMakeLists.txt) ENDIF() +IF(MNN_BUILD_DIFFUSION AND MNN_BUILD_OPENCV AND MNN_IMGCODECS) +include(${CMAKE_CURRENT_LIST_DIR}/transformers/diffusion/CMakeLists.txt) +ENDIF() IF(MNN_BUILD_TOOLS) include(${CMAKE_CURRENT_LIST_DIR}/tools/cpp/CMakeLists.txt) ENDIF() diff --git a/benchmark/benchmark.cpp b/benchmark/benchmark.cpp index 30e4eea2e..f3ee0c55a 100644 --- a/benchmark/benchmark.cpp +++ b/benchmark/benchmark.cpp @@ -127,7 +127,7 @@ std::vector doBench(Model& model, int loop, int warmup = 10, int forward auto modelBuffer = revertor->getBuffer(); const auto bufferSize = revertor->getBufferSize(); - auto net = std::shared_ptr(MNN::Interpreter::createFromBuffer(modelBuffer, bufferSize)); + auto net = std::shared_ptr(MNN::Interpreter::createFromBuffer(modelBuffer, bufferSize), MNN::Interpreter::destroy); revertor.reset(); net->setSessionMode(MNN::Interpreter::Session_Release); MNN::ScheduleConfig config; diff --git a/docs/compile/cmake.md b/docs/compile/cmake.md index 46d04ad9a..092c9d1ec 100644 --- a/docs/compile/cmake.md +++ b/docs/compile/cmake.md @@ -80,6 +80,7 @@ MNN使用CMake构建项目，CMake中的宏定义列表如下： | 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` | -| MNN_SUPPORT_RENDER | 是否支持图形渲染相关算子实现，默认为 `OFF` | +| MNN_SUPPORT_RENDER | 是否支持图形渲染相关算子实现，默认为 `OFF` | | MNN_SUPPORT_TRANSFORMER_FUSE | 是否支持Fuse Transformer相关OP实现，默认为 `OFF` | | MNN_BUILD_LLM | 是否构建基于MNN的llm库和demo，默认为`OFF` | +| MNN_BUILD_DIFFUSION | 是否构建基于MNN的diffusion demo，需要打开MNN_BUILD_OPENCV和MNN_IMGCODECS宏使用 默认为`OFF` | diff --git a/docs/compile/tools.md b/docs/compile/tools.md index d246d8e75..f119c397d 100644 --- a/docs/compile/tools.md +++ b/docs/compile/tools.md @@ -60,6 +60,7 @@ - `winogradExample.out` winograd示例 - `fuseTest` 测试 GPU 自定义算子的功能，目前仅支持 Vulkan Buffer 模式 - `GpuInterTest.out` 测试 GPU 内存输入的功能，目前仅支持 OpenCL Buffer 模式与 OpenGL texture 模式，编译时许打开 MNN_OPENCL 与 MNN_OPENGL + - `LoRA` 将LorA权重添加到模型权重中 ## Benchmark工具 - 相关编译选项 - `MNN_BUILD_BENCHMARK` 是否编译Benchmark工具 diff --git a/express/Executor.cpp b/express/Executor.cpp index c54520ccc..201562e96 100644 --- a/express/Executor.cpp +++ b/express/Executor.cpp @@ -27,6 +27,7 @@ namespace Express { void Executor::setGlobalExecutorConfig(MNNForwardType type, const BackendConfig& config, int numberThread) { std::lock_guard _l(mMutex); + if(type == MNN_FORWARD_AUTO) { ScheduleConfig sConfig; sConfig.type = type; @@ -41,10 +42,12 @@ void Executor::setGlobalExecutorConfig(MNNForwardType type, const BackendConfig& info.numThread = 4; } mAttr->firstType = std::make_pair(type, info.numThread); - - info.user = (BackendConfig*)&config; - std::shared_ptr bn(creator->onCreate(info)); - mRuntimes[mAttr->firstType] = bn; + auto firstIter = mRuntimes.find(mAttr->firstType); + if (firstIter == mRuntimes.end()) { + info.user = (BackendConfig*)&config; + std::shared_ptr bn(creator->onCreate(info)); + mRuntimes[mAttr->firstType] = bn; + } } else { auto creator = MNNGetExtraRuntimeCreator(type); if (nullptr == creator) { @@ -56,11 +59,14 @@ void Executor::setGlobalExecutorConfig(MNNForwardType type, const BackendConfig& Backend::Info info; info.type = type; mAttr->firstType = std::make_pair(type, numberThread); - info.mode = Backend::Info::DIRECT; - info.numThread = numberThread; - info.user = (BackendConfig*)&config; - std::shared_ptr bn(creator->onCreate(info)); - mRuntimes[mAttr->firstType] = bn; + auto firstIter = mRuntimes.find(mAttr->firstType); + if (firstIter == mRuntimes.end()) { + info.mode = Backend::Info::DIRECT; + info.numThread = numberThread; + info.user = (BackendConfig*)&config; + std::shared_ptr bn(creator->onCreate(info)); + mRuntimes[mAttr->firstType] = bn; + } } _refreshRuntime(); } @@ -155,6 +161,10 @@ std::shared_ptr Executor::newExecutor(MNNForwardType type, const BackendConfig& config, int numberThread) { auto creator = MNNGetExtraRuntimeCreator(type); + if(nullptr == creator) { + MNN_ERROR("Don't support %d\n", type); + return nullptr; + } Backend::Info info; info.type = type; info.numThread = numberThread; diff --git a/express/module/StaticModule.cpp b/express/module/StaticModule.cpp index 9bdc86e77..dd63c1718 100644 --- a/express/module/StaticModule.cpp +++ b/express/module/StaticModule.cpp @@ -98,6 +98,21 @@ static std::vector> preRearrangeWeights( // NOLIN } break; } + case MNN::OpType_Attention: { + exe.reset(backend->onCreate({}, {}, op)); + if (exe.get() == nullptr) { + exe.reset(backupBackend->onCreate({}, {}, op)); + } + if (nullptr == exe) { + break; + } + // The exe can't clone + if (!exe->onClone(nullptr, op, nullptr)) { + exe = nullptr; + break; + } + break; + } default: { break; } diff --git a/include/MNN/MNNDefine.h b/include/MNN/MNNDefine.h index ec0956421..45dc30bfb 100644 --- a/include/MNN/MNNDefine.h +++ b/include/MNN/MNNDefine.h @@ -68,7 +68,7 @@ MNN_ERROR("Check failed: %s ==> %s\n", #success, #log); \ #define STR_IMP(x) #x #define STR(x) STR_IMP(x) #define MNN_VERSION_MAJOR 2 -#define MNN_VERSION_MINOR 8 -#define MNN_VERSION_PATCH 4 +#define MNN_VERSION_MINOR 9 +#define MNN_VERSION_PATCH 0 #define MNN_VERSION STR(MNN_VERSION_MAJOR) "." STR(MNN_VERSION_MINOR) "." STR(MNN_VERSION_PATCH) #endif /* MNNDefine_h */ diff --git a/project/harmony/build_64.sh b/project/harmony/build_64.sh new file mode 100755 index 000000000..fefd02c60 --- /dev/null +++ b/project/harmony/build_64.sh @@ -0,0 +1,16 @@ +#!/bin/bash +cmake ../../../ \ +-DCMAKE_TOOLCHAIN_FILE=$HARMONY_HOME/native/build/cmake/ohos.toolchain.cmake \ +-DCMAKE_BUILD_TYPE=Release \ +-DOHOS_ARCH="arm64-v8a" \ +-DOHOS_STL=c++_static \ +-DMNN_USE_LOGCAT=false \ +-DMNN_BUILD_BENCHMARK=ON \ +-DMNN_USE_SSE=OFF \ +-DMNN_SUPPORT_BF16=OFF \ +-DMNN_BUILD_TEST=ON \ +-DOHOS_PLATFORM_LEVEL=9 \ +-DMNN_BUILD_FOR_ANDROID_COMMAND=true \ +-DNATIVE_LIBRARY_OUTPUT=. -DNATIVE_INCLUDE_OUTPUT=. $1 $2 $3 + +make -j4 diff --git a/project/harmony/updateTest.sh b/project/harmony/updateTest.sh new file mode 100755 index 000000000..5a0be5c6f --- /dev/null +++ b/project/harmony/updateTest.sh @@ -0,0 +1,16 @@ +#!/bin/bash +DIR=yanxing + +make -j16 +hdc file send ./libMNN.so /data/local/tmp/$DIR/libMNN.so +hdc file send ./libMNN_Express.so /data/local/tmp/$DIR/libMNN_Express.so +hdc file send ./MNNV2Basic.out /data/local/tmp/$DIR/MNNV2Basic.out +hdc file send ./ModuleBasic.out /data/local/tmp/$DIR/ModuleBasic.out +# hdc shell "cd /data/local/tmp/$DIR && rm -r output" +# hdc shell "cd /data/local/tmp/$DIR && mkdir output" +hdc file send ./unitTest.out /data/local/tmp/$DIR/unitTest.out +hdc file send ./testModel.out /data/local/tmp/$DIR/testModel.out +hdc file send ./testModelWithDescribe.out /data/local/tmp/$DIR/testModelWithDescribe.out +hdc file send ./backendTest.out /data/local/tmp/$DIR/backendTest.out +hdc file send ./timeProfile.out /data/local/tmp/$DIR/timeProfile.out +hdc file send ./run_test.out /data/local/tmp/$DIR/run_test.out diff --git a/project/ios/MNN.xcodeproj/project.pbxproj b/project/ios/MNN.xcodeproj/project.pbxproj index 21fc774f7..f31638c61 100644 --- a/project/ios/MNN.xcodeproj/project.pbxproj +++ b/project/ios/MNN.xcodeproj/project.pbxproj @@ -174,7 +174,6 @@ 489D7A7B2550FDC800AD896A /* MetalUnary.hpp in Headers */ = {isa = PBXBuildFile; fileRef = 489D7A2A2550FDC800AD896A /* MetalUnary.hpp */; }; 489D7A7D2550FDC900AD896A /* MetalConvolution.mm in Sources */ = {isa = PBXBuildFile; fileRef = 489D7A2C2550FDC800AD896A /* MetalConvolution.mm */; }; 489D7A7E2550FDC900AD896A /* MNNMetalContext.mm in Sources */ = {isa = PBXBuildFile; fileRef = 489D7A2D2550FDC800AD896A /* MNNMetalContext.mm */; }; - 489D7A7F2550FDC900AD896A /* MetalReLU.hpp in Headers */ = {isa = PBXBuildFile; fileRef = 489D7A2E2550FDC800AD896A /* MetalReLU.hpp */; }; 489D7A802550FDC900AD896A /* MetalEltwise.hpp in Headers */ = {isa = PBXBuildFile; fileRef = 489D7A2F2550FDC800AD896A /* MetalEltwise.hpp */; }; 489D7A812550FDC900AD896A /* MetalPooling.hpp in Headers */ = {isa = PBXBuildFile; fileRef = 489D7A302550FDC800AD896A /* MetalPooling.hpp */; }; 489D7A822550FDC900AD896A /* MetalPReLU.hpp in Headers */ = {isa = PBXBuildFile; fileRef = 489D7A312550FDC800AD896A /* MetalPReLU.hpp */; }; @@ -184,7 +183,6 @@ 489D7A8A2550FDC900AD896A /* MetalConvolutionDepthwise.mm in Sources */ = {isa = PBXBuildFile; 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}; @@ -1005,7 +1004,6 @@ 489D7A2A2550FDC800AD896A /* MetalUnary.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = MetalUnary.hpp; sourceTree = ""; }; 489D7A2C2550FDC800AD896A /* MetalConvolution.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = MetalConvolution.mm; sourceTree = ""; }; 489D7A2D2550FDC800AD896A /* MNNMetalContext.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = MNNMetalContext.mm; sourceTree = ""; }; - 489D7A2E2550FDC800AD896A /* MetalReLU.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = MetalReLU.hpp; sourceTree = ""; }; 489D7A2F2550FDC800AD896A /* MetalEltwise.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = MetalEltwise.hpp; sourceTree = ""; }; 489D7A302550FDC800AD896A /* MetalPooling.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = MetalPooling.hpp; sourceTree = ""; }; 489D7A312550FDC800AD896A /* MetalPReLU.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = MetalPReLU.hpp; sourceTree = ""; }; @@ -1015,7 +1013,6 @@ 489D7A392550FDC800AD896A /* MetalConvolutionDepthwise.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = MetalConvolutionDepthwise.mm; sourceTree = ""; }; 489D7A3A2550FDC800AD896A /* MetalConvolutionWinograd.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = MetalConvolutionWinograd.hpp; sourceTree = ""; }; 489D7A3B2550FDC800AD896A /* MetalDeconvolution.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = MetalDeconvolution.mm; sourceTree = ""; }; - 489D7A3C2550FDC800AD896A /* MetalReLU.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; 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lastKnownFileType = sourcecode.asm; name = MNNGeluFP16.S; path = ../../../arm82/asm/arm64/MNNGeluFP16.S; sourceTree = ""; }; + 9560EAD52BDE426A00C8D0B6 /* GeometryLayernorm.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = GeometryLayernorm.cpp; sourceTree = ""; }; 956F52E02AB2D692004B13D9 /* ImageProcessUtils.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = ImageProcessUtils.cpp; sourceTree = ""; }; 956F52E22AB2D6A1004B13D9 /* ImageProcessUtils.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = ImageProcessUtils.hpp; sourceTree = ""; }; 958375342A496E5C007C0A3E /* MNNLineDepthWiseInt8AddBiasScale_ARMV82_Unit3X3.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; name = MNNLineDepthWiseInt8AddBiasScale_ARMV82_Unit3X3.S; path = arm/arm64/MNNLineDepthWiseInt8AddBiasScale_ARMV82_Unit3X3.S; sourceTree = ""; }; @@ -1842,6 +1840,7 @@ 4819FB2624C139690050BD09 /* GeometryLSTM.cpp */, 4819FB2424C139680050BD09 /* GeometryPoolGrad.cpp */, 4819FB2A24C139690050BD09 /* GeometryReduce.cpp */, + 9560EAD52BDE426A00C8D0B6 /* GeometryLayernorm.cpp */, 489404DD24A2FC2B001E456C /* GeometryReverseSequence.cpp */, 48FD0349246AA40300456AF5 /* GeometryConvert.cpp */, 48FD12BD2466A88D009E9102 /* GeometryConv2DBackPropFilter.cpp */, @@ -2150,7 +2149,6 @@ 489D7A2A2550FDC800AD896A /* MetalUnary.hpp */, 489D7A2C2550FDC800AD896A /* MetalConvolution.mm */, 489D7A2D2550FDC800AD896A /* MNNMetalContext.mm */, - 489D7A2E2550FDC800AD896A /* MetalReLU.hpp */, 489D7A2F2550FDC800AD896A /* MetalEltwise.hpp */, 489D7A302550FDC800AD896A /* MetalPooling.hpp */, 489D7A312550FDC800AD896A /* MetalPReLU.hpp */, @@ -2160,7 +2158,6 @@ 489D7A392550FDC800AD896A /* MetalConvolutionDepthwise.mm */, 489D7A3A2550FDC800AD896A /* MetalConvolutionWinograd.hpp */, 489D7A3B2550FDC800AD896A /* MetalDeconvolution.mm */, - 489D7A3C2550FDC800AD896A /* MetalReLU.mm */, 489D7A3D2550FDC800AD896A /* MetalPooling.mm */, 489D7A3F2550FDC800AD896A /* MetalConvolution.hpp */, 489D7A402550FDC800AD896A /* MetalScale.mm */, @@ -3070,7 +3067,6 @@ EBECA39924643D320062C7A3 /* Arm82Relu.hpp in Headers */, 4838EA7C2611BFE20027232C /* CPUGridSample.hpp in Headers */, 92FF03A523AA0B5A00AC97F6 /* DeconvolutionWithStride.hpp in Headers */, - 489D7A7F2550FDC900AD896A /* MetalReLU.hpp in Headers */, 92FF03D123AA0B5A00AC97F6 /* CPUTopKV2.hpp in Headers */, 92FF033F23AA0B5A00AC97F6 /* CPUArgMax.hpp in Headers */, 92FF034C23AA0B5A00AC97F6 /* CPUSetDiff1D.hpp in Headers */, @@ -3602,7 +3598,6 @@ 486E1A9924F5078D00C16006 /* CPURandomUniform.cpp in Sources */, 92FF02C823AA0B5A00AC97F6 /* MNNNV21ToBGRUnit.S in Sources */, 92FF045C23AA0B7100AC97F6 /* ShapeBroadcastTo.cpp in Sources */, - 489D7A8D2550FDC900AD896A /* MetalReLU.mm in Sources */, 48747D49245D9D24000B9709 /* RuntimeFactory.cpp in Sources */, 92FF02AE23AA0B5A00AC97F6 /* CPUProposal.cpp in Sources */, 92FF042723AA0B7100AC97F6 /* ShapeMatMul.cpp in Sources */, @@ -3627,6 +3622,7 @@ 92FF025923AA0B5A00AC97F6 /* CPUPoolInt8.cpp in Sources */, 92FF045B23AA0B7100AC97F6 /* ShapeShape.cpp in Sources */, CECF8C87299CAD9400D3875B /* sds.c in Sources */, + 9560EAD62BDE426A00C8D0B6 /* GeometryLayernorm.cpp in Sources */, 4D6D7FD72656896D00F80814 /* SparseConvolutionTiledExecutor.cpp in Sources */, CECF8C82299CAD9400D3875B /* log_api.cpp in Sources */, 92FF03A823AA0B5A00AC97F6 /* WinogradOptFunction.cpp in Sources */, @@ -4131,7 +4127,7 @@ CODE_SIGN_STYLE = Automatic; DEAD_CODE_STRIPPING = YES; DEFINES_MODULE = YES; - DEVELOPMENT_TEAM = 6G7464HHUS; + DEVELOPMENT_TEAM = Q48UX93J22; DYLIB_COMPATIBILITY_VERSION = 1; DYLIB_CURRENT_VERSION = 1; DYLIB_INSTALL_NAME_BASE = "@rpath"; @@ -4218,7 +4214,7 @@ ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon; ASSETCATALOG_COMPILER_LAUNCHIMAGE_NAME = LaunchImage; CODE_SIGN_STYLE = Automatic; - DEVELOPMENT_TEAM = 6G7464HHUS; + DEVELOPMENT_TEAM = Q48UX93J22; GCC_ENABLE_CPP_EXCEPTIONS = NO; GCC_ENABLE_CPP_RTTI = NO; HEADER_SEARCH_PATHS = ( diff --git a/pymnn/CMakeLists.txt b/pymnn/CMakeLists.txt index 6dc42231d..0813adb64 100644 --- a/pymnn/CMakeLists.txt +++ b/pymnn/CMakeLists.txt @@ -17,6 +17,10 @@ option(PYMNN_INTERNAL_SERVING "Internal use only." OFF) option(PYMNN_OPENCV_API "MNN OpenCV API be exposed" ON) option(PYMNN_IMGCODECS "MNN IMGCODECS API be exposed" OFF) +if (OHOS) + include($ENV{NODE_PATH}/@ali/tcpkg/tcpkg.cmake) +endif() + if(PYMNN_INTERNAL_SERVING) file(GLOB_RECURSE SRC ${CMAKE_CURRENT_LIST_DIR}/src/MNN.cc ${CMAKE_CURRENT_LIST_DIR}/src/internal/monitor_service.cc @@ -185,12 +189,20 @@ if(WIN32 OR APPLE OR CMAKE_SYSTEM_NAME MATCHES "^Linux") else() target_include_directories(mnnpybridge PRIVATE ${MNN_DIR}/pymnn/src ${MNN_DIR}/pymnn/android/src/main/c/include) set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${MNN_DIR}/pymnn/android/src/main/jniLibs/${ANDROID_ABI}) - target_link_libraries(mnnpybridge PRIVATE log MNN MNN_Express MNNOpenCV) - if(PYMNN_USE_ALINNPYTHON) - target_link_libraries(mnnpybridge PRIVATE AliNNPython) - endif() - if(PYMNN_NUMPY_USABLE) - target_link_libraries(mnnpybridge PRIVATE numpy_python) + if (OHOS) + target_link_libraries(mnnpybridge PRIVATE tcpkg::mnn) + if(PYMNN_USE_ALINNPYTHON) + target_link_libraries(mnnpybridge PRIVATE tcpkg::alinnpython) + endif() + export_headers(DIR ${CMAKE_SOURCE_DIR}/pip_package/MNN) + else() + target_link_libraries(mnnpybridge PRIVATE log MNN MNN_Express MNNOpenCV) + if(PYMNN_USE_ALINNPYTHON) + target_link_libraries(mnnpybridge PRIVATE AliNNPython) + endif() + if(PYMNN_NUMPY_USABLE) + target_link_libraries(mnnpybridge PRIVATE numpy_python) + endif() endif() endif() diff --git a/schema/current/MNN_generated.h b/schema/current/MNN_generated.h index 45b397ee8..14ae9e4e9 100644 --- a/schema/current/MNN_generated.h +++ b/schema/current/MNN_generated.h @@ -24,6 +24,9 @@ struct ExtraT; struct StringVec; struct StringVecT; +struct AttentionParam; +struct AttentionParamT; + struct FmhaV2Param; struct FmhaV2ParamT; @@ -69,6 +72,8 @@ inline const flatbuffers::TypeTable *ExtraTypeTable(); inline const flatbuffers::TypeTable *StringVecTypeTable(); +inline const flatbuffers::TypeTable *AttentionParamTypeTable(); + inline const flatbuffers::TypeTable *FmhaV2ParamTypeTable(); inline const flatbuffers::TypeTable *FmhcaParamTypeTable(); @@ -261,6 +266,7 @@ enum OpType { OpType_BatchNorm = 267, OpType_ConvTranspose3D = 268, OpType_ZeroGrad = 269, + OpType_Attention = 299, OpType_FmhaV2 = 300, OpType_Fmhca = 301, OpType_SeqLen2Spatial = 302, @@ -281,7 +287,7 @@ enum OpType { OpType_MAX = OpType_GridSample }; -inline const OpType (&EnumValuesOpType())[181] { +inline const OpType (&EnumValuesOpType())[182] { static const OpType values[] = { OpType_AbsVal, OpType_QuantizedAdd, @@ -448,6 +454,7 @@ inline const OpType (&EnumValuesOpType())[181] { OpType_BatchNorm, OpType_ConvTranspose3D, OpType_ZeroGrad, + OpType_Attention, OpType_FmhaV2, OpType_Fmhca, OpType_SeqLen2Spatial, @@ -769,7 +776,7 @@ inline const char * const *EnumNamesOpType() { "", "", "", - "", + "Attention", "FmhaV2", "Fmhca", "SeqLen2Spatial", @@ -1185,11 +1192,12 @@ enum OpParameter { OpParameter_GroupNorm = 95, OpParameter_FmhaV2Param = 96, OpParameter_FmhcaParam = 97, + OpParameter_AttentionParam = 98, OpParameter_MIN = OpParameter_NONE, - OpParameter_MAX = OpParameter_FmhcaParam + OpParameter_MAX = OpParameter_AttentionParam }; -inline const OpParameter (&EnumValuesOpParameter())[98] { +inline const OpParameter (&EnumValuesOpParameter())[99] { static const OpParameter values[] = { OpParameter_NONE, OpParameter_QuantizedAdd, @@ -1288,7 +1296,8 @@ inline const OpParameter (&EnumValuesOpParameter())[98] { OpParameter_CumSum, OpParameter_GroupNorm, OpParameter_FmhaV2Param, - OpParameter_FmhcaParam + OpParameter_FmhcaParam, + OpParameter_AttentionParam }; return values; } @@ -1393,13 +1402,14 @@ inline const char * const *EnumNamesOpParameter() { "GroupNorm", "FmhaV2Param", "FmhcaParam", + "AttentionParam", nullptr }; return names; } inline const char *EnumNameOpParameter(OpParameter e) { - if (e < OpParameter_NONE || e > OpParameter_FmhcaParam) return ""; + if (e < OpParameter_NONE || e > OpParameter_AttentionParam) return ""; const size_t index = static_cast(e); return EnumNamesOpParameter()[index]; } @@ -1796,6 +1806,10 @@ template<> struct OpParameterTraits { static const OpParameter enum_value = OpParameter_FmhcaParam; }; +template<> struct OpParameterTraits { + static const OpParameter enum_value = OpParameter_AttentionParam; +}; + struct OpParameterUnion { OpParameter type; void *value; @@ -2603,6 +2617,14 @@ struct OpParameterUnion { return type == OpParameter_FmhcaParam ? reinterpret_cast(value) : nullptr; } + AttentionParamT *AsAttentionParam() { + return type == OpParameter_AttentionParam ? + reinterpret_cast(value) : nullptr; + } + const AttentionParamT *AsAttentionParam() const { + return type == OpParameter_AttentionParam ? + reinterpret_cast(value) : nullptr; + } }; bool VerifyOpParameter(flatbuffers::Verifier &verifier, const void *obj, OpParameter type); @@ -2900,6 +2922,60 @@ inline flatbuffers::Offset CreateStringVec( flatbuffers::Offset CreateStringVec(flatbuffers::FlatBufferBuilder &_fbb, const StringVecT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct AttentionParamT : public flatbuffers::NativeTable { + typedef AttentionParam TableType; + bool kv_cache; + AttentionParamT() + : kv_cache(true) { + } +}; + +struct AttentionParam FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AttentionParamT NativeTableType; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return AttentionParamTypeTable(); + } + bool kv_cache() const { + return GetField(4, 1) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, 4) && + verifier.EndTable(); + } + AttentionParamT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AttentionParamT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AttentionParamT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AttentionParamBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_kv_cache(bool kv_cache) { + fbb_.AddElement(4, static_cast(kv_cache), 1); + } + explicit AttentionParamBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + AttentionParamBuilder &operator=(const AttentionParamBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAttentionParam( + flatbuffers::FlatBufferBuilder &_fbb, + bool kv_cache = true) { + AttentionParamBuilder builder_(_fbb); + builder_.add_kv_cache(kv_cache); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAttentionParam(flatbuffers::FlatBufferBuilder &_fbb, const AttentionParamT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct FmhaV2ParamT : public flatbuffers::NativeTable { typedef FmhaV2Param TableType; int32_t heads; @@ -3784,6 +3860,9 @@ struct Op FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const FmhcaParam *main_as_FmhcaParam() const { return main_type() == OpParameter_FmhcaParam ? static_cast(main()) : nullptr; } + const AttentionParam *main_as_AttentionParam() const { + return main_type() == OpParameter_AttentionParam ? static_cast(main()) : nullptr; + } const flatbuffers::String *name() const { return GetPointer(10); } @@ -4209,6 +4288,10 @@ template<> inline const FmhcaParam *Op::main_as() const { return main_as_FmhcaParam(); } +template<> inline const AttentionParam *Op::main_as() const { + return main_as_AttentionParam(); +} + struct OpBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -5006,6 +5089,32 @@ inline flatbuffers::Offset CreateStringVec(flatbuffers::FlatBufferBui _data); } +inline AttentionParamT *AttentionParam::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new AttentionParamT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void AttentionParam::UnPackTo(AttentionParamT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = kv_cache(); _o->kv_cache = _e; }; +} + +inline flatbuffers::Offset AttentionParam::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AttentionParamT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAttentionParam(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAttentionParam(flatbuffers::FlatBufferBuilder &_fbb, const AttentionParamT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AttentionParamT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _kv_cache = _o->kv_cache; + return MNN::CreateAttentionParam( + _fbb, + _kv_cache); +} + inline FmhaV2ParamT *FmhaV2Param::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new FmhaV2ParamT(); UnPackTo(_o, _resolver); @@ -5902,6 +6011,10 @@ inline bool VerifyOpParameter(flatbuffers::Verifier &verifier, const void *obj, auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } + case OpParameter_AttentionParam: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } default: return false; } } @@ -6308,6 +6421,10 @@ inline void *OpParameterUnion::UnPack(const void *obj, OpParameter type, const f auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } + case OpParameter_AttentionParam: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } default: return nullptr; } } @@ -6702,6 +6819,10 @@ inline flatbuffers::Offset OpParameterUnion::Pack(flatbuffers::FlatBufferB auto ptr = reinterpret_cast(value); return CreateFmhcaParam(_fbb, ptr, _rehasher).Union(); } + case OpParameter_AttentionParam: { + auto ptr = reinterpret_cast(value); + return CreateAttentionParam(_fbb, ptr, _rehasher).Union(); + } default: return 0; } } @@ -7096,6 +7217,10 @@ inline OpParameterUnion::OpParameterUnion(const OpParameterUnion &u) FLATBUFFERS value = new FmhcaParamT(*reinterpret_cast(u.value)); break; } + case OpParameter_AttentionParam: { + value = new AttentionParamT(*reinterpret_cast(u.value)); + break; + } default: break; } @@ -7588,6 +7713,11 @@ inline void OpParameterUnion::Reset() { delete ptr; break; } + case OpParameter_AttentionParam: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } default: break; } value = nullptr; @@ -7776,12 +7906,13 @@ inline const flatbuffers::TypeTable *OpTypeTypeTable() { { flatbuffers::ET_INT, 0, 0 }, { flatbuffers::ET_INT, 0, 0 }, { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, { flatbuffers::ET_INT, 0, 0 } }; static const flatbuffers::TypeFunction type_refs[] = { OpTypeTypeTable }; - static const int64_t values[] = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 300, 301, 302, 303, 304, 512, 513, 514, 515, 516, 517, 518, 600, 601, 603, 604 }; + static const int64_t values[] = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 299, 300, 301, 302, 303, 304, 512, 513, 514, 515, 516, 517, 518, 600, 601, 603, 604 }; static const char * const names[] = { "AbsVal", "QuantizedAdd", @@ -7948,6 +8079,7 @@ inline const flatbuffers::TypeTable *OpTypeTypeTable() { "BatchNorm", "ConvTranspose3D", "ZeroGrad", + "Attention", "FmhaV2", "Fmhca", "SeqLen2Spatial", @@ -7966,7 +8098,7 @@ inline const flatbuffers::TypeTable *OpTypeTypeTable() { "GridSample" }; static const flatbuffers::TypeTable tt = { - flatbuffers::ST_ENUM, 181, type_codes, type_refs, values, names + flatbuffers::ST_ENUM, 182, type_codes, type_refs, values, names }; return &tt; } @@ -8070,7 +8202,8 @@ inline const flatbuffers::TypeTable *OpParameterTypeTable() { { flatbuffers::ET_SEQUENCE, 0, 93 }, { flatbuffers::ET_SEQUENCE, 0, 94 }, { flatbuffers::ET_SEQUENCE, 0, 95 }, - { flatbuffers::ET_SEQUENCE, 0, 96 } + { flatbuffers::ET_SEQUENCE, 0, 96 }, + { flatbuffers::ET_SEQUENCE, 0, 97 } }; static const flatbuffers::TypeFunction type_refs[] = { QuantizedAddTypeTable, @@ -8169,7 +8302,8 @@ inline const flatbuffers::TypeTable *OpParameterTypeTable() { CumSumTypeTable, GroupNormTypeTable, FmhaV2ParamTypeTable, - FmhcaParamTypeTable + FmhcaParamTypeTable, + AttentionParamTypeTable }; static const char * const names[] = { "NONE", @@ -8269,10 +8403,11 @@ inline const flatbuffers::TypeTable *OpParameterTypeTable() { "CumSum", "GroupNorm", "FmhaV2Param", - "FmhcaParam" + "FmhcaParam", + "AttentionParam" }; static const flatbuffers::TypeTable tt = { - flatbuffers::ST_UNION, 98, type_codes, type_refs, nullptr, names + flatbuffers::ST_UNION, 99, type_codes, type_refs, nullptr, names }; return &tt; } @@ -8376,6 +8511,19 @@ inline const flatbuffers::TypeTable *StringVecTypeTable() { return &tt; } +inline const flatbuffers::TypeTable *AttentionParamTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_BOOL, 0, -1 } + }; + static const char * const names[] = { + "kv_cache" + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, nullptr, nullptr, names + }; + return &tt; +} + inline const flatbuffers::TypeTable *FmhaV2ParamTypeTable() { static const flatbuffers::TypeCode type_codes[] = { { flatbuffers::ET_INT, 0, -1 } diff --git a/schema/default/MNN.fbs b/schema/default/MNN.fbs index 54626df04..b5d8b5756 100644 --- a/schema/default/MNN.fbs +++ b/schema/default/MNN.fbs @@ -187,6 +187,7 @@ enum OpType : int { ZeroGrad, // User define op + Attention = 299, FmhaV2 = 300, Fmhca = 301, SeqLen2Spatial = 302, @@ -218,7 +219,7 @@ table Extra { engine: string; info: [byte]; attr:[Attribute]; - // The Extra Op can be vectorized for execution + // The Extra Op can be vectorized for execution vector: bool; } @@ -226,10 +227,14 @@ table StringVec { data: [string]; } +table AttentionParam { + kv_cache: bool = true; +} + table FmhaV2Param { heads: int; } - + table FmhcaParam { heads: int; } @@ -408,7 +413,8 @@ union OpParameter { CumSum, GroupNorm, FmhaV2Param, - FmhcaParam + FmhcaParam, + AttentionParam } table Op { diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_sdot.S b/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_sdot.S index a46d4bf12..9620c93eb 100644 --- a/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_sdot.S +++ b/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_sdot.S @@ -53,7 +53,7 @@ asm_function MNNGemmHybridInt4FP16_sdot // int32_t useInt8; //}; -//void MNNGemmHybridInt4_sdot(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); +//void MNNGemmHybridInt4_sdot(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); // Auto: x0: C*, x1: A*, x2:B*, x3: src_depth_quad, x4: dst_step, x5: dst_depth_quad, x6: realSize, x7: param @@ -79,8 +79,6 @@ lsl x13, x3, #5 // x13 = src_depth_quad * UNIT * UNIT_SRC / 2(int4) = src_depth_ ld1 {v6.16b, v7.16b}, [x14] // mask movi v14.16b, #15 -// offset -movi v15.16b, #8 TILE_4: cmp x6, #4 blt TILE_1 @@ -120,22 +118,14 @@ LoopSz_TILE_4: // src : 2 x [2 x 8] : v4-5 // weight : 4 x [2 x 8] : v0-3 // dst : 2 x 4 x [4] : v16-23 - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight ld1 {v4.16b, v5.16b}, [x24], x15 // src // int4 to int8: v0, v1, v2, v3 - ushr v9.16b, v0.16b, #4 - ushr v12.16b, v1.16b, #4 - and v8.16b, v0.16b, v14.16b - and v13.16b, v1.16b, v14.16b - sub v9.16b, v9.16b, v15.16b - sub v8.16b, v8.16b, v15.16b - sub v12.16b, v12.16b, v15.16b - sub v13.16b, v13.16b, v15.16b - zip1 v0.16b, v9.16b, v8.16b - zip2 v1.16b, v9.16b, v8.16b - zip1 v2.16b, v12.16b, v13.16b - zip2 v3.16b, v12.16b, v13.16b - + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v14.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v14.16b + mov v10.d[0], v4.d[1] mov v10.d[1], v4.d[0] mov v11.d[1], v5.d[0] @@ -244,21 +234,13 @@ LoopSz_TILE_1: // src : 1 x [1 x 8] : v4 // weight : 4 x [2 x 8] : v0-3 // dst : 1 x 4 x [2] : v16-v19 - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight ld1 {v4.8b}, [x24], x15 // src // int4 to int8: v0, v1, v2, v3 - ushr v16.16b, v0.16b, #4 - ushr v17.16b, v1.16b, #4 - and v18.16b, v0.16b, v14.16b - and v19.16b, v1.16b, v14.16b - sub v16.16b, v16.16b, v15.16b - sub v18.16b, v18.16b, v15.16b - sub v17.16b, v17.16b, v15.16b - sub v19.16b, v19.16b, v15.16b - zip1 v0.16b, v16.16b, v18.16b - zip2 v1.16b, v16.16b, v18.16b - zip1 v2.16b, v17.16b, v19.16b - zip2 v3.16b, v17.16b, v19.16b + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v14.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v14.16b mov v29.d[0], v4.d[1] mov v29.d[1], v4.d[0] diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_smmla.S b/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_smmla.S index 07c65b1fd..62a3053ee 100644 --- a/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_smmla.S +++ b/source/backend/arm82/asm/arm64/low_memory/MNNGemmHybridInt4FP16_smmla.S @@ -64,7 +64,7 @@ asm_function MNNGemmHybridInt4FP16_smmla // int32_t useInt8; //}; -//void MNNGemmHybridInt4_smmla(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); +//void MNNGemmHybridInt4_smmla(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); // Auto: x0: C*, x1: A*, x2:B*, x3: src_depth_quad, x4: dst_step, x5: dst_depth_quad, x6: realSize, x7: param @@ -135,22 +135,14 @@ LoopDz_TILE_8: LoopSz_TILE_8: // src : 2 x [2 x 8] : v4-5 // weight : 4 x [2 x 8] : v0-3 - // dst : 2 x 4 x [4] : v16-23 - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + // dst : 2 x 4 x [4] : v16-31 + ld1 {v8.16b, v9.16b}, [x25], #32 // weight ld1 {v4.16b, v5.16b, v6.16b, v7.16b}, [x24], x15 // src // int4 to int8: v0, v1, v2, v3 - ushr v8.16b, v0.16b, #4 - and v9.16b, v0.16b, v10.16b - sub v8.16b, v8.16b, v11.16b - sub v9.16b, v9.16b, v11.16b - ushr v12.16b, v1.16b, #4 - and v13.16b, v1.16b, v10.16b - sub v12.16b, v12.16b, v11.16b - sub v13.16b, v13.16b, v11.16b - zip1 v0.16b, v8.16b, v9.16b - zip2 v1.16b, v8.16b, v9.16b - zip1 v2.16b, v12.16b, v13.16b - zip2 v3.16b, v12.16b, v13.16b + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v10.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v10.16b .inst 0x4e80a490 // smmla v16.4s, v4.16b, v0.16b // batch=0,1, oc=0,1 .inst 0x4e81a491 // smmla v17.4s, v4.16b, v1.16b // batch=0,1, oc=2,3 @@ -270,21 +262,13 @@ LoopSz_TILE_4: // src : 2 x [2 x 8] : v4-5 // weight : 4 x [2 x 8] : v0-3 // dst : 2 x 4 x [4] : v16-23 - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight ld1 {v4.16b, v5.16b}, [x24], x15 // src // int4 to int8: v0, v1, v2, v3 - ushr v8.16b, v0.16b, #4 - and v9.16b, v0.16b, v10.16b - sub v8.16b, v8.16b, v11.16b - sub v9.16b, v9.16b, v11.16b - ushr v12.16b, v1.16b, #4 - and v13.16b, v1.16b, v10.16b - sub v12.16b, v12.16b, v11.16b - sub v13.16b, v13.16b, v11.16b - zip1 v0.16b, v8.16b, v9.16b - zip2 v1.16b, v8.16b, v9.16b - zip1 v2.16b, v12.16b, v13.16b - zip2 v3.16b, v12.16b, v13.16b + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v10.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v10.16b .inst 0x4e80a490 // smmla v16.4s, v4.16b, v0.16b .inst 0x4e81a491 // smmla v17.4s, v4.16b, v1.16b @@ -313,7 +297,7 @@ LoopSzEnd_TILE_4: trn2 v5.2d, v8.2d, v8.2d // alpha: oc 2,3,2,3 trn1 v6.2d, v9.2d, v9.2d // alpha: oc 4,5,4,5 trn2 v7.2d, v9.2d, v9.2d // alpha: oc 6,7,6,7 - + MulScale_New v16, v17, v18, v19, v0, v4, v5, v6, v7 MulScale_New v20, v21, v22, v23, v1, v4, v5, v6, v7 Float32ToHalf v16, v17, v18, v19, v0, v1 // (batch,oc) v12:(0,0)(0,1)(1,0)(1,1)(0,2)(0,3)(1,3)(1,2) @@ -372,21 +356,13 @@ LoopSz_TILE_2: // src : 1 x [2 x 8] : v4 // weight : 4 x [2 x 8] : v0-3 // dst : 1 x 4 x [4] : v16-19 - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight ld1 {v4.16b}, [x24], x15 // src - // int4 to int8: v0, v1, v2, v3 - ushr v8.16b, v0.16b, #4 - and v9.16b, v0.16b, v10.16b - sub v8.16b, v8.16b, v11.16b - sub v9.16b, v9.16b, v11.16b - ushr v12.16b, v1.16b, #4 - and v13.16b, v1.16b, v10.16b - sub v12.16b, v12.16b, v11.16b - sub v13.16b, v13.16b, v11.16b - zip1 v0.16b, v8.16b, v9.16b - zip2 v1.16b, v8.16b, v9.16b - zip1 v2.16b, v12.16b, v13.16b - zip2 v3.16b, v12.16b, v13.16b + // int4 to int8: v0, v1, v2, v3 + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v10.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v10.16b .inst 0x4e80a490 // smmla v16.4s, v4.16b, v0.16b .inst 0x4e81a491 // smmla v17.4s, v4.16b, v1.16b @@ -409,10 +385,10 @@ LoopSzEnd_TILE_2: trn2 v7.2d, v9.2d, v9.2d // alpha: oc 6,7,6,7 MulScale_New v16, v17, v18, v19, v0, v4, v5, v6, v7 Float32ToHalf v16, v17, v18, v19, v0, v1 // (batch,oc) v12:(0,0)(0,1)(1,0)(1,1)(0,2)(0,3)(1,3)(1,2) - + uzp1 v4.4s, v0.4s, v1.4s uzp2 v5.4s, v0.4s, v1.4s - + Tile2Dequant: ld1 {v16.8h}, [x20], #16 // zero ld1 {v17.8h}, [x21], #16 // bias @@ -463,22 +439,14 @@ LoopSz_TILE_1: // dst : 1 x 4 x [2] : v16-v19 prfm pldl1keep, [x25, #64] // 预取下一次权重数据 prfm pldl1keep, [x24, x15] // 预取下一次源数据 - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight ld1 {v4.8b}, [x24], x15 // src // int4 to int8: v0, v1, v2, v3 - ushr v8.16b, v0.16b, #4 - and v9.16b, v0.16b, v10.16b - sub v8.16b, v8.16b, v11.16b - sub v9.16b, v9.16b, v11.16b - ushr v12.16b, v1.16b, #4 - and v13.16b, v1.16b, v10.16b - sub v12.16b, v12.16b, v11.16b - sub v13.16b, v13.16b, v11.16b - zip1 v0.16b, v8.16b, v9.16b - zip2 v1.16b, v8.16b, v9.16b - zip1 v2.16b, v12.16b, v13.16b - zip2 v3.16b, v12.16b, v13.16b - + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v10.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v10.16b + .inst 0x4e84a410 // smmla v16.4s, v0.16b, v4.16b .inst 0x4e84a431 // smmla v17.4s, v1.16b, v4.16b .inst 0x4e84a452 // smmla v18.4s, v2.16b, v4.16b @@ -505,7 +473,7 @@ LoopSzEnd_TILE_1: fcvtn v17.4h, v20.4s fcvtn2 v17.8h, v21.4s Tile1Dequant: - + ld1 {v1.8h}, [x20], #16 // zero ld1 {v2.8h}, [x21], #16 // bias ld1 {v3.h}[0], [x22] // sums @@ -535,4 +503,4 @@ ldp d12, d13, [sp, #(16 * 1)] ldp d14, d15, [sp], #(16 * 9) ret -#endif \ No newline at end of file +#endif diff --git a/source/backend/cpu/CPUAttention.cpp b/source/backend/cpu/CPUAttention.cpp new file mode 100644 index 000000000..43b4206d9 --- /dev/null +++ b/source/backend/cpu/CPUAttention.cpp @@ -0,0 +1,462 @@ +// +// CPUAttention.cpp +// MNN +// +// Created by MNN on 2024/03/19. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE + +#include +#include "CPUAttention.hpp" +#include "CPUBackend.hpp" +#include "compute/CommonOptFunction.h" +#include "core/Macro.h" +#include "core/Concurrency.h" +#include "core/BufferAllocator.hpp" +#include "core/TensorUtils.hpp" +#include "core/OpCommonUtils.hpp" + +#if defined (__aarch64__) +#define FLOAT16_T __fp16 +#else +#define FLOAT16_T float +#endif + + +namespace MNN { + +template +static void prefill_pack(Tensor* query, Tensor* key, Tensor* value, char* query_ptr, char* key_ptr, char* value_ptr, + int mMaxLength, int mNumHead, int mHeadDim, int mValueH, + int eP, int hP, int query_e, int key_h, int seq_len, int h) { + auto query_src = query->host(); + auto key_src = key->host(); + auto value_src = value->host(); + auto query_dst = reinterpret_cast(query_ptr); + auto key_dst = reinterpret_cast(key_ptr); + auto value_dst = reinterpret_cast(value_ptr); + // transpose query: [seq_len, num_head, head_dim] -> numhead, [seq_len/eP, head_dim, eP] + for (int i = 0; i < query_e; i++) { + for (int j = 0; j < mHeadDim; j++) { + for (int k = 0; k < eP; k++) { + int s = i * eP + k; + if (s < seq_len) { + query_dst[i * mHeadDim * eP + j * eP + k] = query_src[s * mNumHead * mHeadDim + h * mHeadDim + j]; + } + } + } + } + // transpose key: [seq_len, num_head, head_dim] -> numhead, [seq_len/hP, head_dim, hP] + for (int i = 0; i < key_h; i++) { + for (int j = 0; j < mHeadDim; j++) { + for (int k = 0; k < hP; k++) { + int s = i * hP + k; + if (s < seq_len) { + key_dst[i * mHeadDim * hP + j * hP + k] = key_src[s * mNumHead * mHeadDim + h * mHeadDim + j]; + } + } + } + } + // transpose value: [seq_len, num_head, head_dim] -> numhead, [head_dim/hP, seq_len, hP] + for (int i = 0; i < mValueH; i++) { + for (int j = 0; j < seq_len; j++) { + for (int k = 0; k < hP; k++) { + int hd = i * hP + k; + if (hd < mHeadDim) { + value_dst[i * mMaxLength * hP + j * hP + k] = value_src[j * mNumHead * mHeadDim + h * mHeadDim + hd]; + } + } + } + } +} + +template +static void decode_pack(Tensor* query, Tensor* key, Tensor* value, char* query_ptr, char* key_ptr, char* value_ptr, + int mMaxLength, int mPastLength, int mHeadDim, int mValueH, int eP, int hP, int h) { + auto query_src = query->host(); + auto key_src = key->host(); + auto value_src = value->host(); + auto query_dst = reinterpret_cast(query_ptr); + auto key_dst = reinterpret_cast(key_ptr); + auto value_dst = reinterpret_cast(value_ptr); + for (int i = 0; i < mHeadDim; i++) { + query_dst[i * eP] = query_src[h * mHeadDim + i]; + } + // transpose key: [1, num_head, head_dim] -> numhead, [kv_seq_len/hP, head_dim, hP] + int outside_offset = UP_DIV(mPastLength, hP); + int inside_offset = mPastLength % hP; + for (int i = 0; i < mHeadDim; i++) { + key_dst[(outside_offset - (inside_offset != 0)) * mHeadDim * hP + i * hP + inside_offset] = key_src[h * mHeadDim + i]; + } + // transpose value: [1, num_head, head_dim] -> numhead, [head_dim/hP, kv_seq_len, hP] + for (int i = 0; i < mValueH; i++) { + for (int j = 0; j < hP; j++) { + value_dst[i * mMaxLength * hP + mPastLength * hP + j] = value_src[h * mHeadDim + i * hP + j]; + } + } +} + +template +static void prefill_unpack(char* pack_qkv, char* unpack_qkv, int mNumHead, int mHeadDim, int unit, int seq_len) { + auto src_ptr = reinterpret_cast(pack_qkv); + auto dst_ptr = reinterpret_cast(unpack_qkv); + for (int i = 0; i < seq_len; i++) { + for (int j = 0; j < mHeadDim; j++) { + int a = j / unit; + int b = j % unit; + dst_ptr[i * mNumHead * mHeadDim + j] = src_ptr[a * seq_len * unit + i * unit + b]; + } + } +} + +template +static void prefill_softmax(int* mask_ptr, float* mask_qk, float* softmax_qk, char* unpack_qk, char* pack_qk, + float mScale, int eP, int query_e, int seq_len, float min_val) { + T* qk_src = reinterpret_cast(unpack_qk); + T* qk_dst = reinterpret_cast(pack_qk); + for (int i = 0; i < seq_len * seq_len; i++) { + if (mask_ptr[i]) { + mask_qk[i] = qk_src[i] * mScale; + } else { + mask_qk[i] = min_val; + } + } + for (int i = 0; i < seq_len; i++) { + MNNSoftmax(softmax_qk + i * seq_len, mask_qk + i * seq_len, seq_len); + } + for (int i = 0; i < query_e; i++) { + for (int j = 0; j < seq_len; j++) { + for (int k = 0; k < eP; k++) { + int s = i * eP + k; + if (s < seq_len) { + qk_dst[i * seq_len * eP + j * eP + k] = softmax_qk[s * seq_len + j]; + } + } + } + } +} + +template +static void decode_softmax(float* mask_qk, float* softmax_qk, char* unpack_qk, char* pack_qk, + float mScale, int eP, int kv_seq_len) { + T* qk_src = reinterpret_cast(unpack_qk); + T* qk_dst = reinterpret_cast(pack_qk); + for (int i = 0; i < kv_seq_len; i++) { + mask_qk[i] = qk_src[i] * mScale; + } + // softmax + MNNSoftmax(softmax_qk, mask_qk, kv_seq_len); + // pack qk + for (int i = 0; i < kv_seq_len; i++) { + qk_dst[i * eP] = softmax_qk[i]; + } +} + +void CPUAttentionImpl::allocKVCache() { + if (!mKVCache || mPastLength < mMaxLength) { + return; + } + mMaxLength = mPastLength + mExpandChunk; + // past_key: [1, numhead, headdim, maxlen] -> numhead, [headdim, maxlen] -> pack_b -> numhead, [maxlen/hP, head_dim, hP] + mPastKey.reset(Tensor::createDevice({mNumHead, UP_DIV(mMaxLength, hP), mHeadDim, hP})); + // past_value: [1, numhead, maxlen, headdim] -> numhead, [maxlen, headdim] -> pack_b -> numhead, [head_dim/hP, max_len, hP] + mPastValue.reset(Tensor::createDevice({mNumHead, mValueH, mMaxLength, hP})); + backend()->onAcquireBuffer(mPastKey.get(), Backend::STATIC); + backend()->onAcquireBuffer(mPastValue.get(), Backend::STATIC); +} + +void CPUAttentionImpl::reallocKVCache() { + if (!mKVCache || mPastLength < mMaxLength) { + return; + } + mMaxLength = mPastLength + mExpandChunk; + // past_key: [1, numhead, headdim, maxlen] -> numhead, [headdim, maxlen] -> pack_b -> numhead, [maxlen/hP, head_dim, hP] + auto new_key = Tensor::createDevice({mNumHead, UP_DIV(mMaxLength, hP), mHeadDim, hP}); + // past_value: [1, numhead, maxlen, headdim] -> numhead, [maxlen, headdim] -> pack_b -> numhead, [head_dim/hP, max_len, hP] + auto new_value = Tensor::createDevice({mNumHead, mValueH, mMaxLength, hP}); + backend()->onAcquireBuffer(new_key, Backend::STATIC); + backend()->onAcquireBuffer(new_value, Backend::STATIC); + // copy + for (int h = 0; h < mNumHead; h++) { + ::memset(new_key->host() + h * UP_DIV(mMaxLength, hP) * mHeadDim * hP * bytes, 0, UP_DIV(mMaxLength, hP) * mHeadDim * hP * bytes); + ::memset(new_value->host() + h * mValueH * mMaxLength * hP * bytes, 0, mValueH * mMaxLength * hP * bytes); + ::memcpy(new_key->host() + h * UP_DIV(mMaxLength, hP) * mHeadDim * hP * bytes, + mPastKey->host() + h * UP_DIV(mPastLength, hP) * mHeadDim * hP * bytes, + UP_DIV(mPastLength, hP) * mHeadDim * hP * bytes); + for (int i = 0; i < mValueH; i++) { + ::memcpy(new_value->host() + (h * mValueH + i) * mMaxLength * hP * bytes, + mPastValue->host() + (h * mValueH + i) * mPastLength * hP * bytes, + mPastLength * hP * bytes); + } + } + mPastKey.reset(new_key); + mPastValue.reset(new_value); + mTempQK.reset(Tensor::createDevice({mThreadNum, eP + 2, mMaxLength})); + backend()->onAcquireBuffer(mTempQK.get(), Backend::STATIC); +} + +ErrorCode CPUAttentionImpl::onResize(Backend* _backend, const std::vector& inputs, const std::vector& outputs) { + mBackend = _backend; + auto core = static_cast(backend())->functions(); + int unit = core->pack; + bytes = core->bytes; + core->MNNGetMatMulPackMode(&eP, &lP, &hP); + + auto query = inputs[0]; + auto key = inputs[1]; + auto value = inputs[2]; + auto mask = inputs[3]; + auto shape = query->shape(); + int seq_len = shape[1]; + mThreadNum = ((CPUBackend *)backend())->threadNumber(); + mIsDecode = seq_len == 1; + if (mPastLength == 0 || seq_len > 1) { + mPastLength = seq_len; + } + mNumHead = shape[2]; + mHeadDim = shape[3]; + mScale = 1.0 / sqrt(mHeadDim); + mValueH = UP_DIV(mHeadDim, hP); + int query_e = UP_DIV(seq_len, eP); + int key_h = UP_DIV(seq_len, hP); + // mPastLength = 10; + // alloc kv cache + allocKVCache(); + + int tileCount = UP_DIV(mNumHead, mThreadNum); + + // temp_query + mPackQ.reset(Tensor::createDevice({mThreadNum, query_e, mHeadDim, eP})); + mPackQKV.reset(Tensor::createDevice({mThreadNum, UP_DIV(mHeadDim, unit), seq_len, unit})); + if (mIsDecode) { + mTempQK.reset(Tensor::createDevice({mThreadNum, eP + 2, mMaxLength})); + backend()->onAcquireBuffer(mTempQK.get(), Backend::DYNAMIC); + } else { + mTempQK.reset(Tensor::createDevice({mThreadNum, 4, seq_len, seq_len})); + backend()->onAcquireBuffer(mTempQK.get(), Backend::DYNAMIC); + } + backend()->onAcquireBuffer(mPackQ.get(), Backend::DYNAMIC); + backend()->onAcquireBuffer(mPackQKV.get(), Backend::DYNAMIC); + backend()->onReleaseBuffer(mPackQ.get(), Backend::DYNAMIC); + backend()->onReleaseBuffer(mTempQK.get(), Backend::DYNAMIC); + backend()->onReleaseBuffer(mPackQKV.get(), Backend::DYNAMIC); + return NO_ERROR; +} + +ErrorCode CPUAttentionImpl::onExecute(Backend* _backend, const std::vector& inputs, const std::vector& outputs) { + auto core = static_cast(backend())->functions(); + int unit = core->pack; + bytes = core->bytes; + core->MNNGetMatMulPackMode(&eP, &lP, &hP); + mBackend = _backend; + auto matmulUnit = core->MNNPackedMatMul; + auto matmulRemain = core->MNNPackedMatMulRemain; + + auto query = inputs[0]; + auto key = inputs[1]; + auto value = inputs[2]; + auto mask = inputs[3]; + auto shape = query->shape(); + int seq_len = shape[1]; + mThreadNum = ((CPUBackend *)backend())->threadNumber(); + mIsDecode = seq_len == 1; + if (mPastLength == 0 || seq_len > 1) { + mPastLength = seq_len; + } + mNumHead = shape[2]; + mHeadDim = shape[3]; + mScale = 1.0 / sqrt(mHeadDim); + mValueH = UP_DIV(mHeadDim, hP); + int query_e = UP_DIV(seq_len, eP); + int key_h = UP_DIV(seq_len, hP); + // mPastLength = 10; + + int tileCount = UP_DIV(mNumHead, mThreadNum); + + // try calloc kv cache + mPrefill = [=](int tId){ + auto pack_q = mPackQ->host() + tId * query_e * mHeadDim * eP * bytes; + auto pack_qk = mTempQK->host() + tId * 4 * seq_len * seq_len * bytes; + auto unpack_qk = pack_qk + seq_len * seq_len * 2 * bytes; + auto mask_qk = reinterpret_cast(pack_qk); + auto softmax_qk = reinterpret_cast(unpack_qk); + auto pack_qkv = mPackQKV->host() + tId * UP_DIV(mHeadDim, unit) * seq_len * unit * bytes; + + int head_index = tId * tileCount; + for (int h = head_index; h < head_index + tileCount && h < mNumHead; h++) { + // pack for matmul + auto key_dst = mPastKey->host() + h * UP_DIV(mMaxLength, hP) * mHeadDim * hP * bytes; + auto value_dst = mPastValue->host() + h * mValueH * mMaxLength * hP * bytes; + if (bytes == 2) { + prefill_pack(query, key, value, pack_q, key_dst, value_dst, mMaxLength, mNumHead, mHeadDim, mValueH, eP, hP, query_e, key_h, seq_len, h); + } else { + prefill_pack(query, key, value, pack_q, key_dst, value_dst, mMaxLength, mNumHead, mHeadDim, mValueH, eP, hP, query_e, key_h, seq_len, h); + } + // query @ key + int loop_e = seq_len / eP; + int remain = seq_len % eP; + for (int i = 0 ; i < loop_e; i++) { + size_t shapeParameters[6]; + size_t* parameters = shapeParameters; + parameters[0] = eP * bytes; + parameters[1] = mHeadDim; + parameters[2] = seq_len; + parameters[3] = seq_len * unit * bytes; + parameters[4] = 0; + parameters[5] = 0; + matmulUnit((float*)(pack_qk + (i * eP * unit) * bytes), (float*)(pack_q + (i * mHeadDim * eP) * bytes), (float*)key_dst, parameters, nullptr, nullptr, nullptr, nullptr); + } + { + size_t shapeParameters[6]; + size_t* parameters = shapeParameters; + parameters[0] = eP * bytes; + parameters[1] = mHeadDim; + parameters[2] = seq_len; + parameters[3] = seq_len * unit * bytes; + parameters[4] = 0; + parameters[5] = 0; + matmulRemain((float*)(pack_qk + (loop_e * eP * unit) * bytes), (float*)(pack_q + (loop_e * mHeadDim * eP) * bytes), (float*)key_dst, remain, parameters, nullptr, nullptr, nullptr, nullptr); + } + int area_offset[1] {seq_len}; + core->MNNUnpackCUnitTranspose((float*)unpack_qk, (float*)pack_qk, seq_len, seq_len, area_offset); + // div scale and mask + auto mask_ptr = mask->host(); + if (bytes == 2) { + prefill_softmax(mask_ptr, mask_qk, softmax_qk, unpack_qk, pack_qk, mScale, eP, query_e, seq_len, -65504.0); + } else { + prefill_softmax(mask_ptr, mask_qk, softmax_qk, unpack_qk, pack_qk, mScale, eP, query_e, seq_len, std::numeric_limits::lowest()); + } + // qk @ v + for (int i = 0 ; i < loop_e; i++) { + size_t shapeParameters[6]; + size_t* parameters = shapeParameters; + parameters[0] = eP * bytes; + parameters[1] = seq_len; + parameters[2] = mHeadDim; + parameters[3] = seq_len * unit * bytes; + parameters[4] = 0; + parameters[5] = (mMaxLength - seq_len) * hP * bytes; + matmulUnit((float*)(pack_qkv + (i * eP * unit) * bytes), (float*)(pack_qk + (i * seq_len * eP) * bytes), (float*)value_dst, parameters, nullptr, nullptr, nullptr, nullptr); + } + { + size_t shapeParameters[6]; + size_t* parameters = shapeParameters; + parameters[0] = eP * bytes; + parameters[1] = seq_len; + parameters[2] = mHeadDim; + parameters[3] = seq_len * unit * bytes; + parameters[4] = 0; + parameters[5] = (mMaxLength - seq_len) * hP * bytes; + matmulRemain((float*)(pack_qkv + (loop_e * eP * unit) * bytes), (float*)(pack_qk + (loop_e * seq_len * eP) * bytes), (float*)value_dst, remain, parameters, nullptr, nullptr, nullptr, nullptr); + } + // transpose: [head_dim/unit, seq_len, unit] -> [seq_len, num_head, head_dim] + auto dst_ptr = outputs[0]->host() + h * mHeadDim * bytes; + if (bytes == 2) { + prefill_unpack(pack_qkv, dst_ptr, mNumHead, mHeadDim, unit, seq_len); + } else { + prefill_unpack(pack_qkv, dst_ptr, mNumHead, mHeadDim, unit, seq_len); + } + } + }; + + mDecode = [=](int tId) { + int kv_seq_len = mPastLength + 1; + auto pack_q = mPackQ->host() + tId * mHeadDim * eP * bytes; + auto pack_qk = mTempQK->host() + tId * (eP + 2) * kv_seq_len * bytes; + auto unpack_qk = pack_qk + kv_seq_len * eP * bytes; + auto mask_qk = reinterpret_cast(pack_qk); + auto softmax_qk = reinterpret_cast(unpack_qk); + auto pack_qkv = mPackQKV->host() + tId * UP_DIV(mHeadDim, unit) * unit * bytes; + + int head_index = tId * tileCount; + for (int h = head_index; h < head_index + tileCount && h < mNumHead; h++) { + auto key_dst = mPastKey->host() + h * UP_DIV(mMaxLength, hP) * mHeadDim * hP * bytes; + auto value_dst = mPastValue->host() + h * mValueH * mMaxLength * hP * bytes; + // pack for matmul + if (bytes == 2) { + decode_pack(query, key, value, pack_q, key_dst, value_dst, mMaxLength, mPastLength, mHeadDim, mValueH, eP, hP, h); + } else { + decode_pack(query, key, value, pack_q, key_dst, value_dst, mMaxLength, mPastLength, mHeadDim, mValueH, eP, hP, h); + } + // query @ key: [1, head_dim] @ [head_dim, kv_seq_len] -> [1, kv_seq_len] + size_t shapeParameters[6]; + size_t* parameters = shapeParameters; + parameters[0] = eP * bytes; + parameters[1] = mHeadDim; + parameters[2] = kv_seq_len; + parameters[3] = seq_len * unit * bytes; + parameters[4] = 0; + parameters[5] = 0; + matmulRemain((float*)pack_qk, (float*)pack_q, (float*)key_dst, seq_len, parameters, nullptr, nullptr, nullptr, nullptr); + int area_offset[1] {seq_len}; + core->MNNUnpackCUnitTranspose((float*)unpack_qk, (float*)pack_qk, seq_len, kv_seq_len, area_offset); + if (bytes == 2) { + decode_softmax(mask_qk, softmax_qk, unpack_qk, pack_qk, mScale, eP, kv_seq_len); + } else { + decode_softmax(mask_qk, softmax_qk, unpack_qk, pack_qk, mScale, eP, kv_seq_len); + } + // qk @ v: [1, kv_seq_len] @ [kv_seq_len, head_dim] -> [1, head_dim] + { + size_t shapeParameters[6]; + size_t* parameters = shapeParameters; + parameters[0] = eP * bytes; + parameters[1] = kv_seq_len; + parameters[2] = mHeadDim; + parameters[3] = 1 * unit * bytes; + parameters[5] = (mMaxLength - kv_seq_len) * hP * bytes; + matmulRemain((float*)pack_qkv, (float*)pack_qk, (float*)value_dst, 1, parameters, nullptr, nullptr, nullptr, nullptr); + } + // transpose: [head_dim/unit, 1, unit] -> [1, num_head, head_dim] + auto dst_ptr = outputs[0]->host() + h * mHeadDim * bytes; + core->MNNUnpackCUnitTranspose((float*)dst_ptr, (float*)pack_qkv, 1, mHeadDim, area_offset); + } + }; + mFunction = mIsDecode ? mDecode : mPrefill; + reallocKVCache(); + // compute + MNN_CONCURRENCY_BEGIN(tId, mThreadNum) { + mFunction((int)tId); + } + MNN_CONCURRENCY_END(); + mPastLength += mIsDecode; + return NO_ERROR; +} + +CPUAttention::CPUAttention(Backend* backend, bool kv_cahce) : Execution(backend) { + mImpl.reset(new CPUAttentionImpl(backend, kv_cahce)); +} + +CPUAttention::CPUAttention(std::shared_ptr impl, Backend *backend) : Execution(backend), mImpl(impl) {} + +ErrorCode CPUAttention::onResize(const std::vector& inputs, const std::vector& outputs) { + return mImpl->onResize(backend(), inputs, outputs); +} + +ErrorCode CPUAttention::onExecute(const std::vector& inputs, const std::vector& outputs) { + return mImpl->onExecute(backend(), inputs, outputs); +} + +bool CPUAttention::onClone(Backend* bn, const Op* op, Execution** dst) { + if (nullptr == dst) { + return true; + } + *dst = new CPUAttention(mImpl, bn); + return true; +} + +class CPUAttentionCreator : public CPUBackend::Creator { +public: + virtual Execution* onCreate(const std::vector& inputs, const std::vector& outputs, + const MNN::Op* op, Backend* backend) const override { + auto param = op->main_as_AttentionParam(); + return new CPUAttention(backend, param->kv_cache()); + } +}; + +REGISTER_CPU_OP_CREATOR_TRANSFORMER(CPUAttentionCreator, OpType_Attention); + +} // namespace MNN + +#endif diff --git a/source/backend/cpu/CPUAttention.hpp b/source/backend/cpu/CPUAttention.hpp new file mode 100644 index 000000000..1cbc26aba --- /dev/null +++ b/source/backend/cpu/CPUAttention.hpp @@ -0,0 +1,58 @@ +// +// CPUAttention.hpp +// MNN +// +// Created by MNN on 2024/03/19. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE + +#ifndef CPUATTENTION_HPP +#define CPUATTENTION_HPP + +#include +#include "core/Execution.hpp" + +namespace MNN { + +class CPUAttentionImpl { +public: + CPUAttentionImpl(Backend *backend, bool kv_cache) : mBackend(backend), mKVCache(kv_cache) {} + ~CPUAttentionImpl() = default; + ErrorCode onResize(Backend *backend, const std::vector &inputs, const std::vector &outputs); + ErrorCode onExecute(Backend *backend, const std::vector &inputs, const std::vector &outputs); +private: + void allocKVCache(); + void reallocKVCache(); + Backend* backend() { return mBackend; } +private: + Backend* mBackend; + bool mKVCache; + float mScale; + const int mExpandChunk = 64; + int mThreadNum = 1; + bool mIsDecode = false; + int mPastLength = 0, mMaxLength = 0; + std::shared_ptr mPastKey, mPastValue, mTempQK; + std::shared_ptr mPackQ, mPackQKV; + int mNumHead = 0, mHeadDim = 0, mValueH = 0; + int eP, lP, hP, bytes; + std::function mFunction, mPrefill, mDecode; +}; + +class CPUAttention : public Execution { +public: + CPUAttention(Backend *backend, bool kv_cache); + CPUAttention(std::shared_ptr impl, Backend *backend); + virtual ~CPUAttention() = default; + virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; + virtual ErrorCode onExecute(const std::vector &inputs, const std::vector &outputs) override; + virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override; +private: + std::shared_ptr mImpl; +}; +} // namespace MNN + +#endif // CPUATTENTION_HPP +#endif diff --git a/source/backend/cpu/CPUBackend.hpp b/source/backend/cpu/CPUBackend.hpp index ef481b354..7793b696c 100644 --- a/source/backend/cpu/CPUBackend.hpp +++ b/source/backend/cpu/CPUBackend.hpp @@ -93,7 +93,7 @@ class CPUBackend : public Backend { virtual void* onMapTensor(Tensor::MapType mtype, Tensor::DimensionType dtype, const Tensor* srcTensor) override; virtual bool onUnmapTensor(Tensor::MapType mtype, Tensor::DimensionType dtype, const Tensor* dstTensor, void* mapPtr) override; - + virtual void onResizeBegin() override; virtual ErrorCode onResizeEnd() override; @@ -194,6 +194,12 @@ class CastWrapExecution : public Execution { CPUBackend::addCreator(opType, &_temp); \ } +#define REGISTER_CPU_OP_CREATOR_TRANSFORMER(name, opType) \ + void ___##name##__##opType##__() { \ + static name _temp;\ + CPUBackend::addCreator(opType, &_temp); \ + } + } // namespace MNN #endif /* CPUBackend_hpp */ diff --git a/source/backend/cpu/CPULayerNorm.cpp b/source/backend/cpu/CPULayerNorm.cpp index 7446bea24..48225d960 100644 --- a/source/backend/cpu/CPULayerNorm.cpp +++ b/source/backend/cpu/CPULayerNorm.cpp @@ -105,6 +105,10 @@ ErrorCode CPULayerNorm::onResize(const std::vector &inputs, mInnerSize *= inputs.at(0)->length(i); } mInnerSize /= mResource->mGroup; + if (mResource->mIniGammaBeta) { + MNN_ASSERT(mResource->mGamma->size() == mInnerSize * sizeof(float)); + } + return NO_ERROR; } for (int i = 0; i < rank - mResource->mAxis; ++i) { @@ -113,6 +117,9 @@ ErrorCode CPULayerNorm::onResize(const std::vector &inputs, for (int i = rank - mResource->mAxis; i < rank; ++i) { mInnerSize *= inputs.at(0)->length(i); } + if (mResource->mIniGammaBeta) { + MNN_ASSERT(mResource->mGamma->size() == mInnerSize * sizeof(float)); + } if (CPUBackend::getDataType(inputs[0]) == DataType_DT_INT8 || inputs[0]->getType().bytes() == 1) { mInpZero.resize(1); mOutZero.resize(1); diff --git a/source/backend/cpu/CPUOPRegister.cpp b/source/backend/cpu/CPUOPRegister.cpp index b24bcb469..37f868732 100644 --- a/source/backend/cpu/CPUOPRegister.cpp +++ b/source/backend/cpu/CPUOPRegister.cpp @@ -75,6 +75,9 @@ extern void ___CPURasterAndInterpolateCreator__OpType_RasterAndInterpolate__(); extern void ___CPURasterDiffCreator__OpType_RasterDiff__(); extern void ___CPUTextureCreator__OpType_Texture__(); #endif +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE +extern void ___CPUAttentionCreator__OpType_Attention__(); +#endif void registerCPUOps() { ___CPUCropAndResizeCreator__OpType_CropAndResize__(); ___CPUArgMaxCreator__OpType_ArgMax__(); @@ -150,5 +153,8 @@ ___CPURasterAndInterpolateCreator__OpType_RasterAndInterpolate__(); ___CPURasterDiffCreator__OpType_RasterDiff__(); ___CPUTextureCreator__OpType_Texture__(); #endif +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE +___CPUAttentionCreator__OpType_Attention__(); +#endif } } diff --git a/source/backend/cpu/arm/CommonOptFunctionNeon.cpp b/source/backend/cpu/arm/CommonOptFunctionNeon.cpp index 6153281df..06180fcef 100644 --- a/source/backend/cpu/arm/CommonOptFunctionNeon.cpp +++ b/source/backend/cpu/arm/CommonOptFunctionNeon.cpp @@ -3,7 +3,7 @@ #ifdef MNN_USE_NEON #include #include "./FunctionSummary.hpp" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" extern "C" { void MNNTranspose32Bit4x4(int32_t* dstO, const int32_t* srcO, int32_t* dim); diff --git a/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32.S b/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32.S index 2a3b61d48..83548cfd9 100644 --- a/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32.S +++ b/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32.S @@ -43,7 +43,7 @@ asm_function MNNGemmHybridInt4FP32 // int32_t useInt8; //}; -//void MNNGemmHybridInt4FP32(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); +//void MNNGemmHybridInt4FP32(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); // Auto: x0: C*, x1: A*, x2:B*, x3: src_depth_quad, x4: dst_step, x5: dst_depth_quad, x6: realSize, x7: param @@ -120,8 +120,6 @@ LoopSz_TILE_4: // int4->int8 ushr v8.16b, v0.16b, #4 and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b zip1 v0.16b, v8.16b, v9.16b Unit_TILE_4: @@ -244,8 +242,6 @@ LoopSz_TILE_1: // int4->int8 ushr v8.16b, v0.16b, #4 and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b zip1 v0.16b, v8.16b, v9.16b Unit_TILE_1: @@ -261,7 +257,7 @@ LoopSz_TILE_1: smlal v11.4s, v5.4h, v29.4h smlal v12.4s, v5.4h, v30.4h smlal v13.4s, v5.4h, v31.4h - + //.inst 0x4f84e010 // sdot v16.4s, v0.16b, v4.4b[0] subs x26, x26, #1 diff --git a/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_sdot.S b/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_sdot.S index 05839505a..11bf247a5 100644 --- a/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_sdot.S +++ b/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_sdot.S @@ -43,7 +43,7 @@ asm_function MNNGemmHybridInt4FP32_sdot // int32_t useInt8; //}; -//void MNNGemmHybridInt4FP32_sdot(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); +//void MNNGemmHybridInt4FP32_sdot(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); // Auto: x0: C*, x1: A*, x2:B*, x3: src_depth_quad, x4: dst_step, x5: dst_depth_quad, x6: realSize, x7: param @@ -99,11 +99,9 @@ LoopDz_TILE_12: movi v25.4s, #0 movi v26.4s, #0 movi v27.4s, #0 - + // mask movi v14.16b, #15 - // offset - movi v15.16b, #8 LoopSz_TILE_12: // src : 4(batch) x [1 x 4] : v4 // weight : 4(oc) x [1 x 4] : v0 @@ -113,8 +111,6 @@ LoopSz_TILE_12: // int4->int8 ushr v8.16b, v0.16b, #4 and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b zip1 v0.16b, v8.16b, v9.16b .inst 0x4f84e010 // sdot v16.4s, v0.16b, v4.4b[0] // batch0 .inst 0x4fa4e011 // sdot v17.4s, v0.16b, v4.4b[1] // batch1 @@ -203,11 +199,9 @@ LoopDz_TILE_8: movi v21.4s, #0 movi v22.4s, #0 movi v23.4s, #0 - + // mask movi v14.16b, #15 - // offset - movi v15.16b, #8 LoopSz_TILE_8: // src : 4(batch) x [1 x 4] : v4 // weight : 4(oc) x [1 x 4] : v0 @@ -217,8 +211,6 @@ LoopSz_TILE_8: // int4->int8 ushr v8.16b, v0.16b, #4 and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b zip1 v0.16b, v8.16b, v9.16b .inst 0x4f84e010 // sdot v16.4s, v0.16b, v4.4b[0] // batch0 .inst 0x4fa4e011 // sdot v17.4s, v0.16b, v4.4b[1] // batch1 @@ -294,8 +286,6 @@ LoopDz_TILE_4: dup v19.4s, wzr // mask movi v14.16b, #15 - // offset - movi v15.16b, #8 LoopSz_TILE_4: // src : 4(batch) x [1 x 4] : v4 // weight : 4(oc) x [1 x 4] : v0 @@ -305,8 +295,6 @@ LoopSz_TILE_4: // int4->int8 ushr v8.16b, v0.16b, #4 and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b zip1 v0.16b, v8.16b, v9.16b .inst 0x4f84e010 // sdot v16.4s, v0.16b, v4.4b[0] // batch0 .inst 0x4fa4e011 // sdot v17.4s, v0.16b, v4.4b[1] // batch1 @@ -368,8 +356,6 @@ LoopDz_TILE_1: dup v16.4s, wzr // mask movi v14.16b, #15 - // offset - movi v15.16b, #8 LoopSz_TILE_1: // src : 1(batch) x [1 x 4] : v4 // weight : 4(oc) x [1 x 4] : v0 @@ -379,10 +365,8 @@ LoopSz_TILE_1: // int4->int8 ushr v8.16b, v0.16b, #4 and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b zip1 v0.16b, v8.16b, v9.16b - + .inst 0x4f84e010 // sdot v16.4s, v0.16b, v4.4b[0] subs x26, x26, #1 diff --git a/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_smmla.S b/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_smmla.S index 5d5831dcb..aa0b5a383 100644 --- a/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_smmla.S +++ b/source/backend/cpu/arm/arm64/low_memory/MNNGemmHybridInt4FP32_smmla.S @@ -43,7 +43,7 @@ asm_function MNNGemmHybridInt4FP32_smmla // int32_t useInt8; //}; -//void MNNGemmHybridInt4FP32_smmla(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); +//void MNNGemmHybridInt4FP32_smmla(float* C, const int8_t* A, const int8_t* B, size_t src_depth_quad, size_t dst_step, size_t dst_depth_quad, size_t realSize, float** param); // Auto: x0: C*, x1: A*, x2:B*, x3: src_depth_quad, x4: dst_step, x5: dst_depth_quad, x6: realSize, x7: param @@ -106,28 +106,18 @@ LoopDz_TILE_8: // mask movi v10.16b, #15 - // offset - movi v11.16b, #8 LoopSz_TILE_8: // src : 2 x [2 x 8] : v4-5 // weight : 4 x [2 x 8] : v0-3 // dst : 2 x 4 x [4] : v16-23 //ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x25], #64 // weight - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight ld1 {v12.16b, v13.16b, v14.16b, v15.16b}, [x24], x15 // src // int4 to int8: v0, v1, v2, v3 - ushr v4.16b, v0.16b, #4 - and v5.16b, v0.16b, v10.16b - sub v4.16b, v4.16b, v11.16b - sub v5.16b, v5.16b, v11.16b - ushr v8.16b, v1.16b, #4 - and v9.16b, v1.16b, v10.16b - sub v8.16b, v8.16b, v11.16b - sub v9.16b, v9.16b, v11.16b - zip1 v0.16b, v4.16b, v5.16b - zip2 v1.16b, v4.16b, v5.16b - zip1 v2.16b, v8.16b, v9.16b - zip2 v3.16b, v8.16b, v9.16b + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v10.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v10.16b .inst 0x4e80a590 // smmla v16.4s, v12.16b, v0.16b .inst 0x4e81a591 // smmla v17.4s, v12.16b, v1.16b @@ -247,27 +237,17 @@ LoopDz_TILE_4: dup v23.4s, wzr // mask movi v10.16b, #15 - // offset - movi v11.16b, #8 LoopSz_TILE_4: // src : 2 x [2 x 8] : v4-5 // weight : 4 x [2 x 8] : v0-3 // dst : 2 x 4 x [4] : v16-23 //ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x25], #64 // weight - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight // int4 to int8: v0, v1, v2, v3 - ushr v4.16b, v0.16b, #4 - and v5.16b, v0.16b, v10.16b - sub v4.16b, v4.16b, v11.16b - sub v5.16b, v5.16b, v11.16b - ushr v6.16b, v1.16b, #4 - and v7.16b, v1.16b, v10.16b - sub v6.16b, v6.16b, v11.16b - sub v7.16b, v7.16b, v11.16b - zip1 v0.16b, v4.16b, v5.16b - zip2 v1.16b, v4.16b, v5.16b - zip1 v2.16b, v6.16b, v7.16b - zip2 v3.16b, v6.16b, v7.16b + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v10.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v10.16b ld1 {v4.16b, v5.16b}, [x24], x15 // src .inst 0x4e80a490 // smmla v16.4s, v4.16b, v0.16b .inst 0x4e81a491 // smmla v17.4s, v4.16b, v1.16b @@ -349,27 +329,17 @@ LoopDz_TILE_2: dup v19.4s, wzr // mask movi v14.16b, #15 - // offset - movi v15.16b, #8 LoopSz_TILE_2: // src : 1 x [2 x 8] : v4 // weight : 4 x [2 x 8] : v0-3 // dst : 1 x 4 x [4] : v16-19 //ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x25], #64 // weight - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight // int4 to int8: v0, v1, v2, v3 - ushr v8.16b, v0.16b, #4 - and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b - ushr v10.16b, v1.16b, #4 - and v11.16b, v1.16b, v14.16b - sub v10.16b, v10.16b, v15.16b - sub v11.16b, v11.16b, v15.16b - zip1 v0.16b, v8.16b, v9.16b - zip2 v1.16b, v8.16b, v9.16b - zip1 v2.16b, v10.16b, v11.16b - zip2 v3.16b, v10.16b, v11.16b + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v14.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v14.16b ld1 {v4.16b}, [x24], x15 // src .inst 0x4e80a490 // smmla v16.4s, v4.16b, v0.16b .inst 0x4e81a491 // smmla v17.4s, v4.16b, v1.16b @@ -438,28 +408,18 @@ LoopDz_TILE_1: dup v19.4s, wzr // mask movi v14.16b, #15 - // offset - movi v15.16b, #8 LoopSz_TILE_1: // src : 1 x [1 x 8] : v4 // weight : 4 x [2 x 8] : v0-3 // dst : 1 x 4 x [2] : v16-v19 //ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x25], #64 // weight - ld1 {v0.16b, v1.16b}, [x25], #32 // weight + ld1 {v8.16b, v9.16b}, [x25], #32 // weight // int4 to int8: v0, v1, v2, v3 - ushr v8.16b, v0.16b, #4 - and v9.16b, v0.16b, v14.16b - sub v8.16b, v8.16b, v15.16b - sub v9.16b, v9.16b, v15.16b - ushr v10.16b, v1.16b, #4 - and v11.16b, v1.16b, v14.16b - sub v10.16b, v10.16b, v15.16b - sub v11.16b, v11.16b, v15.16b - zip1 v0.16b, v8.16b, v9.16b - zip2 v1.16b, v8.16b, v9.16b - zip1 v2.16b, v10.16b, v11.16b - zip2 v3.16b, v10.16b, v11.16b + ushr v0.16b, v8.16b, #4 + and v1.16b, v8.16b, v14.16b + ushr v2.16b, v9.16b, #4 + and v3.16b, v9.16b, v14.16b ld1 {v4.8b}, [x24], x15 // src .inst 0x4e84a410 // smmla v16.4s, v0.16b, v4.16b .inst 0x4e84a431 // smmla v17.4s, v1.16b, v4.16b diff --git a/source/backend/cpu/compute/CommonOptFunction.cpp b/source/backend/cpu/compute/CommonOptFunction.cpp index ef5b63130..acc2c3c5f 100644 --- a/source/backend/cpu/compute/CommonOptFunction.cpp +++ b/source/backend/cpu/compute/CommonOptFunction.cpp @@ -18,7 +18,7 @@ #include "math/Vec.hpp" #include #include "../CPURuntime.hpp" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" // TODO: Find better way to optimize it #include "../CPUBinary.hpp" #include "../CPUUnary.hpp" @@ -376,7 +376,7 @@ void MNN1BitCopyFast (uint8_t* dstO, const uint8_t* srcO, int size, int stride, #ifdef MNN_USE_SSE std::vector arr(16, val); auto val16 = _mm_loadu_ps((float*)arr.data()); - + for (; cnt >= 16; cnt-=16) { _mm_storeu_ps((float*)dstO, val16); dstO += 16; @@ -427,7 +427,7 @@ void MNNAccumulateSequenceNumber (float* dst, const float* src, int size) { if (size >= 8) { auto sum4_1 = _mm_set_ps1(0.f); auto sum4_2 = _mm_set_ps1(0.f); - + for (; i < size8; i += 8) { auto v4 = _mm_loadu_ps(src); auto u4 = _mm_loadu_ps(src + 4); @@ -435,7 +435,7 @@ void MNNAccumulateSequenceNumber (float* dst, const float* src, int size) { sum4_2 = _mm_add_ps(sum4_2, u4); src += 8; } - + sum4_1 = _mm_add_ps(sum4_1, sum4_2); _mm_storeu_ps(tmp, sum4_1); sum += (tmp[0] + tmp[1] + tmp[2] + tmp[3]); @@ -823,7 +823,7 @@ void MNNGemmHybridInt8FP32(float* C, const int8_t* A, const int8_t* B, size_t sr } } } - + // int32->float for (int cn = 0; cn < pack; ++cn) { float val = (float)tmp[cn] * scale[0]; @@ -873,7 +873,7 @@ void MNNGemmHybridInt4FP32(float* C, const int8_t* A, const int8_t* B, size_t sr } } } - + // int32->float for (int cn = 0; cn < pack; ++cn) { float val = (float)tmp[cn] * scale[0]; @@ -3333,7 +3333,7 @@ void MNNCoreFunctionInit() { gCoreFunction->MNNPoolingAvg = (decltype(gCoreFunction->MNNPoolingAvg))(poolingAvg); // Set min value as 1 << 24 gCoreFunction->MNNPoolingMax = (decltype(gCoreFunction->MNNPoolingMax))(poolingMax); - + gCoreFunction->MNNPoolingMaxWithRedice = (decltype(gCoreFunction->MNNPoolingMaxWithRedice))(poolingMaxWithRedice); // ImageProcess Functions gCoreFunction->MNNRGBAToBGRA = MNNRGBAToBGRA; @@ -3353,7 +3353,7 @@ void MNNCoreFunctionInit() { gCoreFunction->MNN4BitcopyFast = MNN4BitcopyFast; gCoreFunction->MNN2BitcopyFast = MNN2BitcopyFast; gCoreFunction->MNN1BitcopyFast = MNN1BitCopyFast; - + gCoreFunction->MNNAccumulateSequenceNumber = MNNAccumulateSequenceNumber; cpuinfo_arm_isa gCPUInfo; diff --git a/source/backend/cpu/compute/ConvolutionHybrid.cpp b/source/backend/cpu/compute/ConvolutionHybrid.cpp index 0c63ab728..b21ed220c 100644 --- a/source/backend/cpu/compute/ConvolutionHybrid.cpp +++ b/source/backend/cpu/compute/ConvolutionHybrid.cpp @@ -33,19 +33,7 @@ bool ConvolutionHybrid::initQuantizeResource(std::shared_ptrlU = lU; resource->hP = hP; resource->lP = lP; - // Reorder weight - auto dstWInt8 = resource->mWeight->host(); - auto srcWInt8 = int8Info->weight.get(); - // oc, ic -> oc/hP, ic/lP, hP, lP - for (int i = 0; i < hU; i++) { - for (int j = 0; j < lU; j++) { - for (int k = 0; k < hP; k++) { - for (int l = 0; l < lP; l++) { - dstWInt8[i * srcChannel * hP + j * hP * lP + k * lP + l] = srcWInt8[(i * hP + k) * srcChannel + (j * lP + l)]; - } - } - } - } + // Save scale bias resource->mDequantize.mScaleBias.reset(MNN::Tensor::createDevice({hU * hP * 2})); res = resource->backend->onAcquireBuffer(resource->mDequantize.mScaleBias.get(), Backend::STATIC); @@ -56,6 +44,12 @@ bool ConvolutionHybrid::initQuantizeResource(std::shared_ptr(reinterpret_cast(alphaPtr) + hU * hP * bytes); ::memset(alphaPtr, 0, 2 * hU * hP * bytes); int h = int8Info->alpha.size(); + if (int8Info->canUseInt4) { + // int4 to uint4, -8 offset merge to bias + for (int i = 0; i < h/2; ++i) { + int8Info->alpha.get()[2 * i] -= 8 * int8Info->alpha.get()[2 * i + 1]; + } + } if (bytes == 2) { auto core = static_cast(resource->backend)->functions(); if (int8Info->asymmetric) { @@ -86,21 +80,53 @@ bool ConvolutionHybrid::initQuantizeResource(std::shared_ptrmDequantize.bits = 4; - std::shared_ptr weightLow(Tensor::createDevice( - {weightLength})); - auto res = resource->backend->onAcquireBuffer(weightLow.get(), Backend::STATIC); - if (!res) { - return false; + + auto srcPtr = int8Info->weight.get(); + auto dstPtr = resource->mWeight->host(); + // oc, ic -> oc/hP, ic/lP, hP, lP + if (hP == 8 && lP == 8) { + for (int i = 0; i < hU; i++) { + for (int j = 0; j < lU; j++) { + for (int k = 0; k < 2; k++) { + for (int n = 0; n < 16; n++) { + int hp_idx = n / 8; + int lp_idx = n % 8; + int s0 = srcPtr[(i * hP + k * 4 + hp_idx) * srcChannel + (j * lP + lp_idx)]; + int s1 = srcPtr[(i * hP + k * 4 + hp_idx + 2) * srcChannel + (j * lP + lp_idx)]; + int d = (s0 + 8) * 16 + (s1 + 8); + dstPtr[(i * srcChannel * hP + j * hP * lP + k * 32) / 2 + n] = (uint8_t)d; + } + } + } + } + } else { + for (int i = 0; i < hU; i++) { + for (int j = 0; j < lU; j++) { + for (int k = 0; k < hP; k++) { + for (int l = 0; l < lP; l+=2) { + int s0 = srcPtr[(i * hP + k) * srcChannel + (j * lP + l)]; + int s1 = srcPtr[(i * hP + k) * srcChannel + (j * lP + l + 1)]; + int d = (s0 + 8) * 16 + (s1 + 8); + dstPtr[(i * srcChannel * hP + j * hP * lP + k * lP + l) / 2] = d; + } + } + } + } } - auto srcPtr = resource->mWeight->host(); - auto dstPtr = weightLow->host(); - for (int i=0; i < weightLength; ++i) { - int s0 = srcPtr[2 * i + 0]; - int s1 = srcPtr[2 * i + 1]; - int d = (s0 + 8) * 16 + (s1 + 8); - dstPtr[i] = d; + } else { + // Reorder weight for int8 + auto dstWInt8 = resource->mWeight->host(); + auto srcWInt8 = int8Info->weight.get(); + // oc, ic -> oc/hP, ic/lP, hP, lP + for (int i = 0; i < hU; i++) { + for (int j = 0; j < lU; j++) { + for (int k = 0; k < hP; k++) { + for (int l = 0; l < lP; l++) { + dstWInt8[i * srcChannel * hP + j * hP * lP + k * lP + l] = srcWInt8[(i * hP + k) * srcChannel + (j * lP + l)]; + } + } + } } - resource->mWeight = weightLow; } return true; } @@ -129,10 +155,10 @@ ConvolutionHybrid::ConvolutionHybrid(const Convolution2DCommon *common, Backend hPack = unit; lPack = unit; // [oc, ic] => [oc/unit, ic/src_unit, unit, src_unit] - if (unit == 4 && core->supportI8mm) { // Low Memory: use fp32 and smmla. - hPack = 8; - lPack = 8; - } + if (unit == 4 && core->supportI8mm) { // Low Memory: use fp32 and smmla. + hPack = 8; + lPack = 8; + } auto hU = UP_DIV(outputCount, hPack); auto lU = UP_DIV(inputCount, lPack); ConvolutionHybrid::initQuantizeResource(quantInfo, mResource, hU, hPack, lU, lPack, outputCount, (int)originWeightSize / (int)biasSize, common->kernelX() * common->kernelY(), core->bytes); @@ -237,8 +263,8 @@ ErrorCode ConvolutionHybrid::onResize(const std::vector &inputs, const int iTileCount = UP_DIV(iC4, threadNumber); if (unit == 4 && core->supportI8mm) { // Low Memory: use fp32 and smmla. ANeedToPack8 = true; - } - int8_t order[32] = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31, 8, 9, 10, 11, 4, 5, 6, 7, 24, 25, 26, 27, 20, 21, 22, 23}; + } + int8_t order[32] = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31, 8, 9, 10, 11, 4, 5, 6, 7, 24, 25, 26, 27, 20, 21, 22, 23}; allocDynamicQuantInfo(threadNumber, batch, ic, oc, bytes); mDynamicQuant = [=]() { auto maxPtr = mQuantInfo.quant_info.host(); diff --git a/source/backend/cpu/compute/ConvolutionPackFreeWinograd.cpp b/source/backend/cpu/compute/ConvolutionPackFreeWinograd.cpp index bb97db324..a4ccae1db 100644 --- a/source/backend/cpu/compute/ConvolutionPackFreeWinograd.cpp +++ b/source/backend/cpu/compute/ConvolutionPackFreeWinograd.cpp @@ -15,7 +15,7 @@ #include "core/TensorUtils.hpp" #include "math/WingoradGenerater.hpp" #include -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" #ifdef MNN_USE_NEON #include #endif diff --git a/source/backend/cpu/compute/ConvolutionPackWinograd.cpp b/source/backend/cpu/compute/ConvolutionPackWinograd.cpp index ecf1c0510..83b17c77d 100644 --- a/source/backend/cpu/compute/ConvolutionPackWinograd.cpp +++ b/source/backend/cpu/compute/ConvolutionPackWinograd.cpp @@ -15,7 +15,7 @@ #include "core/TensorUtils.hpp" #include "math/WingoradGenerater.hpp" #include -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" constexpr int FULSE_THRESHHOLD_NUMERATOR = 10; constexpr int FULSE_THRESHHOLD_DENOMINATOR = 10; diff --git a/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp b/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp index c4b158306..ff3a0afa1 100644 --- a/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp +++ b/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp @@ -16,7 +16,7 @@ #include "core/TensorUtils.hpp" #include "math/Vec.hpp" #include "core/BufferAllocator.hpp" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" using Vec4 = MNN::Math::Vec; namespace MNN { diff --git a/source/backend/cpu/compute/ConvolutionWinogradImpl.cpp b/source/backend/cpu/compute/ConvolutionWinogradImpl.cpp index 250710e8e..0d597b07b 100644 --- a/source/backend/cpu/compute/ConvolutionWinogradImpl.cpp +++ b/source/backend/cpu/compute/ConvolutionWinogradImpl.cpp @@ -15,7 +15,7 @@ #include "core/TensorUtils.hpp" #include "math/WingoradGenerater.hpp" #include -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" //#define MNN_WINOGRAD_PRINT_REDUCE_RATE diff --git a/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp b/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp index 40da39e3d..10df5d863 100644 --- a/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp +++ b/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp @@ -16,7 +16,7 @@ #include "core/TensorUtils.hpp" #include "math/Vec.hpp" #include "core/BufferAllocator.hpp" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" #define PARAMETERSIZE 6 using Vec4 = MNN::Math::Vec; diff --git a/source/backend/cpu/compute/Int8FunctionsOpt.cpp b/source/backend/cpu/compute/Int8FunctionsOpt.cpp index b8502ff40..e1f29a14e 100644 --- a/source/backend/cpu/compute/Int8FunctionsOpt.cpp +++ b/source/backend/cpu/compute/Int8FunctionsOpt.cpp @@ -10,7 +10,7 @@ #include // for memset #include "Int8FunctionsOpt.h" #include "core/Macro.h" -#include "common/CommonCompute.hpp" +#include "core/CommonCompute.hpp" #include "CommonOptFunction.h" #include "math/Vec.hpp" @@ -1537,7 +1537,7 @@ static void MNNLineDepthWiseInt8AddBiasScaleUnit(int8_t* dst, const int8_t* src, } for (int i = 0; i < pack; ++i) { - + float val = (dstInt32[i] + bias_z[i]) * scale_z[i]; int valOut = roundf(val) + offset; if (valOut > parameters->maxValue + offset) { @@ -1615,7 +1615,7 @@ void MNNMaxPoolInt8(int8_t* dst, int8_t* src, size_t outputWidth, size_t inputWi for (int y = 0; y < kernely; ++y) { for (int x = 0; x < kernelx; ++x) { const int8_t* inputPtr = srcPtr + pack* (x + inputWidth* y); - for (int idx = 0; idx < pack; ++idx) { + for (int idx = 0; idx < pack; ++idx) { results[idx] = std::max(results[idx], *(inputPtr + idx)); } } @@ -2125,7 +2125,7 @@ void MNNCoreInt8FunctionInit() { // pooling gCoreFunc->MNNAvgPoolInt8 = MNNAvgPoolInt8; gCoreFunc->MNNMaxPoolInt8 = MNNMaxPoolInt8; - + // Norm gCoreFunc->MNNNormInt8 = MNNNormInt8; @@ -2143,7 +2143,7 @@ void MNNCoreInt8FunctionInit() { gCoreFunc->MNNPackC4Int8ForMatMul_A = _ArmBasicMNNPackC4ForMatMul_A_L4<12, 4>; // ConvDepthwise gCoreFunc->ConvDepthwise3x3LineInt8_ARM82 = MNNLineDepthWiseInt8AddBiasScale_ARMV82_Unit3X3; - + } if (core->supportI8mm) { // MatMul diff --git a/source/backend/cpu/compute/SparseConvInt8TiledExecutor.cpp b/source/backend/cpu/compute/SparseConvInt8TiledExecutor.cpp index 48b006102..395fa3745 100644 --- a/source/backend/cpu/compute/SparseConvInt8TiledExecutor.cpp +++ b/source/backend/cpu/compute/SparseConvInt8TiledExecutor.cpp @@ -15,7 +15,7 @@ #include "CommonOptFunction.h" #include "core/Concurrency.h" #include "core/TensorUtils.hpp" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" #include "MNN/AutoTime.hpp" #include #ifdef MNN_USE_SSE diff --git a/source/backend/cpu/compute/SparseConvolutionTiledExecutor.cpp b/source/backend/cpu/compute/SparseConvolutionTiledExecutor.cpp index c74ac33a5..ab39bfd9b 100644 --- a/source/backend/cpu/compute/SparseConvolutionTiledExecutor.cpp +++ b/source/backend/cpu/compute/SparseConvolutionTiledExecutor.cpp @@ -16,8 +16,8 @@ #include "core/TensorUtils.hpp" #include "math/Vec.hpp" #include "core/BufferAllocator.hpp" -#include "common/MemoryFormater.h" -#include "common/CommonCompute.hpp" +#include "core/MemoryFormater.h" +#include "core/CommonCompute.hpp" using Vec4 = MNN::Math::Vec; namespace MNN { diff --git a/source/backend/cpu/compute/WinogradOptFunction.cpp b/source/backend/cpu/compute/WinogradOptFunction.cpp index 1af07afa2..23b83eab2 100644 --- a/source/backend/cpu/compute/WinogradOptFunction.cpp +++ b/source/backend/cpu/compute/WinogradOptFunction.cpp @@ -12,7 +12,7 @@ #include #include "core/Macro.h" #include "math/Vec.hpp" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" using Vec4 = MNN::Math::Vec; #define DEFAULT_UNIT 8 diff --git a/source/backend/cpu/x86_x64/avx/GemmAVX2.cpp b/source/backend/cpu/x86_x64/avx/GemmAVX2.cpp index 8a8407a47..56eaf70fb 100644 --- a/source/backend/cpu/x86_x64/avx/GemmAVX2.cpp +++ b/source/backend/cpu/x86_x64/avx/GemmAVX2.cpp @@ -75,7 +75,6 @@ void _AVX_MNNGemmHybridInt4(float* C, const int8_t* A, const int8_t* B, size_t s const float* scale_ptr = param[4]; auto one_int16 = _mm256_set1_epi16(1); auto offset_int8 = _mm256_set1_epi8(128); - auto _int4_signed_8 = _mm256_set1_ps(8); for (int ci = 0; ci < dst_depth_quad; ++ci) { float* dstZ = C + ci * pack * realSize; const int8_t* weight = B + ci * weight_step; @@ -83,14 +82,13 @@ void _AVX_MNNGemmHybridInt4(float* C, const int8_t* A, const int8_t* B, size_t s auto zero = zero_ptr + ci * pack; auto bias = bias_ptr + ci * pack; __m256 alphaValue = _mm256_loadu_ps(alpha); - auto extra_sum = _mm256_mul_ps(_int4_signed_8, alphaValue); for (int j = 0; j < realSize; ++j) { const float* sums = sums_ptr + j; const float* scale = scale_ptr + j; float* dstX = dstZ + j * pack; __m256 scaleValue = _mm256_set1_ps(scale[0]); auto sum_val = _mm256_set1_ps(sums[0]); - __m256 biasValue = _mm256_add_ps(_mm256_loadu_ps(bias), _mm256_mul_ps(_mm256_sub_ps(_mm256_loadu_ps(zero), extra_sum), sum_val)); + __m256 biasValue = _mm256_add_ps(_mm256_loadu_ps(bias), _mm256_mul_ps(_mm256_loadu_ps(zero), sum_val)); const int8_t* srcBatch = A + j * pack; auto oc0123_int16 = _mm256_set1_epi16(0); auto oc4567_int16 = _mm256_set1_epi16(0); @@ -103,10 +101,8 @@ void _AVX_MNNGemmHybridInt4(float* C, const int8_t* A, const int8_t* B, size_t s const uint8_t* weightZ = (uint8_t*)weight + k * weight_stride; auto s0 = _mm256_castpd_si256(_mm256_broadcast_sd((double*)srcZ)); auto wi4 = _mm256_castps_si256(_mm256_loadu_ps((const float*)weightZ)); - auto w_high = _mm256_and_si256(mask, _mm256_srli_epi16(wi4, 4)); - auto w_low = _mm256_and_si256(mask, wi4); - auto w0_ = _mm256_unpacklo_epi8(w_high, w_low); - auto w1_ = _mm256_unpackhi_epi8(w_high, w_low); + auto w0_ = _mm256_and_si256(mask, _mm256_srli_epi16(wi4, 4)); + auto w1_ = _mm256_and_si256(mask, wi4); auto w0 = _mm256_permute2x128_si256(w0_, w1_, 0x20); auto w1 = _mm256_permute2x128_si256(w0_, w1_, 0x31); oc0123_int16 = _mm256_maddubs_epi16(w0, s0); // int16_t sum @@ -194,9 +190,11 @@ void _AVX_MNNGemmHybridInt8(float* C, const int8_t* A, const int8_t* B, size_t s auto oc_04261537 = _mm256_add_epi32(oc_04261537_lo, oc_04261537_hi); auto oc_0426 = _mm256_extractf128_si256(oc_04261537, 0); auto oc_1537 = _mm256_extractf128_si256(oc_04261537, 1); - auto oc_0123 = _mm_unpacklo_epi32(oc_0426, oc_1537); - auto oc_4567 = _mm_unpackhi_epi32(oc_0426, oc_1537); - auto sum8 = _mm256_set_m128i(oc_0123, oc_4567); + auto oc_0145 = _mm_unpacklo_epi32(oc_0426, oc_1537); + auto oc_2367 = _mm_unpackhi_epi32(oc_0426, oc_1537); + auto oc_0123 = _mm_unpacklo_epi64(oc_0145, oc_2367); + auto oc_4567 = _mm_unpackhi_epi64(oc_0145, oc_2367); + auto sum8 = _mm256_set_m128i(oc_4567, oc_0123); __m256 f0 = _mm256_cvtepi32_ps(sum8); __m256 fs = _mm256_mul_ps(_mm256_mul_ps(f0, scaleValue), alphaValue); fs = _mm256_add_ps(biasValue, fs); diff --git a/source/backend/cpu/x86_x64/avx/WinogradFunctions.cpp b/source/backend/cpu/x86_x64/avx/WinogradFunctions.cpp index 8f715a7d8..175962ea1 100644 --- a/source/backend/cpu/x86_x64/avx/WinogradFunctions.cpp +++ b/source/backend/cpu/x86_x64/avx/WinogradFunctions.cpp @@ -9,7 +9,7 @@ #include #include "Vec8.hpp" #include "FunctionSummary.hpp" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" #define PACK_UNIT 8 namespace MNN { diff --git a/source/backend/cpu/x86_x64/sse/GemmFunction.hpp b/source/backend/cpu/x86_x64/sse/GemmFunction.hpp index 5c94843b0..57c2558b0 100644 --- a/source/backend/cpu/x86_x64/sse/GemmFunction.hpp +++ b/source/backend/cpu/x86_x64/sse/GemmFunction.hpp @@ -656,8 +656,8 @@ static inline __m128i _load_int4_to_int8(const uint8_t* src) { int32_t data[4]; int8_t temp[16]; for (int i = 0; i < 8; ++i) { - temp[2 * i] = (src[i] >> 4) - 8; - temp[2 * i +1] = (src[i] & c) - 8; + temp[2 * i] = (src[i] >> 4); + temp[2 * i +1] = (src[i] & c); } auto int8_tx16 = _mm_loadu_si128((const __m128i*)temp); return int8_tx16; diff --git a/source/backend/metal/AllShader.cpp b/source/backend/metal/AllShader.cpp index 2ed682837..620e6ce99 100644 --- a/source/backend/metal/AllShader.cpp +++ b/source/backend/metal/AllShader.cpp @@ -1,32 +1,27 @@ #include "AllShader.hpp" const char* shader_MetalReLU6_metal = -"kernel void relu6_x1(const device M *in [[buffer(0)]],\n" -" device M *out [[buffer(1)]],\n" -" constant float4 &minMax [[buffer(2)]],\n" -" uint gid [[thread_position_in_grid]]) {\n" -" out[int(gid)]=clamp(in[int(gid)],(M)(minMax.x),(M)(minMax.y));\n" -"}\n" -"kernel void relu6_x4(const device M4 *in [[buffer(0)]],\n" +"struct Param {\n" +" float minV;\n" +" float maxV;\n" +" int size;\n" +" int remain;\n" +"};\n" +"kernel void relu6(const device M4 *in [[buffer(0)]],\n" " device M4 *out [[buffer(1)]],\n" -" constant float4 &minMax [[buffer(2)]],\n" -" uint gid [[thread_position_in_grid]]) {\n" -" out[int(gid)]=clamp(in[int(gid)],(M4)minMax.x,(M4)minMax.y);\n" -"}\n" -; -const char* shader_MetalReLU_metal = -"kernel void relu_x1(const device M *in [[buffer(0)]],\n" -" device M *out [[buffer(1)]],\n" -" constant float &slope [[buffer(2)]],\n" -" uint gid [[thread_position_in_grid]]) {\n" -" auto V=in[int(gid)];\n" -" out[int(gid)]=fmax(V,(M)0)+fmin(V,(M)0)*M(slope);\n" +" constant Param &p [[buffer(2)]],\n" +" uint3 gid [[thread_position_in_grid]]) {\n" +" if (gid.x= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.slice*cst.batch) return;\n" " \n" -" int oz=gid.z % cst.slice;\n" +" int oz=gid.z/cst.batch;\n" " int offset_x=(int)gid.x*cst.stride_x-cst.pad_x;\n" " int offset_y=(int)gid.y*cst.stride_y-cst.pad_y;\n" " int sx=max(0,(UP_DIV(-offset_x,cst.dilation_x)));\n" @@ -198,16 +193,16 @@ const char* shader_MetalConvolution_metal = " offset_x += sx*cst.dilation_x;\n" " offset_y += sy*cst.dilation_y;\n" " \n" -" auto z_in=in+idx_b*cst.input_slice*cst.input_size+offset_y*cst.input_width+offset_x;\n" +" auto z_in=in+idx_b*cst.input_size+offset_y*cst.input_width+offset_x;\n" " auto z_wt=wt+idx_c*cst.input_slice*cst.kernel_size+sy*cst.kernel_x+sx;\n" -" auto z_out=out+idx_b*cst.output_slice*cst.output_size+(int)idx_c*cst.output_size+(int)gid.y*cst.output_width+(int)gid.x;\n" +" auto z_out=out+idx_b*cst.output_size+(int)idx_c*cst.batch*cst.output_size+(int)gid.y*cst.output_width+(int)gid.x;\n" " int dilation_h=cst.input_width*cst.dilation_y;\n" " FLOAT4 result=FLOAT4(biasTerms[idx_c]);\n" " for (auto z=0; z=cst.input_width || offset_y<0) ? (M4)0.f : *(z_in+0*cst.input_width+1);\n" " auto in02=(offset_x+2>=cst.input_width || offset_y<0) ? (M4)0.f : *(z_in+0*cst.input_width+2);\n" @@ -312,18 +307,18 @@ const char* shader_MetalConvolution_metal = " if (idx_b >= cst.batch || idx_c >= cst.output_slice) return;\n" " bool valid=(idx_w+1\n" "static inline void template_NHWC_to_NC4HW4(const device IType *in,device OType *out,constant tensor_shape &s,uint2 gid) {\n" -" int b=gid.y/s.slice;\n" -" int z=gid.y % s.slice;\n" +" int b=gid.y % s.batch;\n" +" int z=gid.y/s.batch;\n" " int c=z*4;\n" " \n" " auto off_in=in+b*s.size*s.channel+int(gid.x)*s.channel+c;\n" @@ -653,8 +623,8 @@ const char* shader_MetalBackend_metal = "}\n" "template \n" "static inline void template_NC4HW4_to_NHWC(const device IType *in,device OType *out,constant tensor_shape &s,uint2 gid) {\n" -" int b=gid.y/s.slice;\n" -" int z=gid.y % s.slice;\n" +" int b=gid.y % s.batch;\n" +" int z=gid.y/s.batch;\n" " int c=z*4;\n" " auto off_in=in+int(gid.y)*s.size+int(gid.x);\n" " auto off_out=out+b*s.size*s.channel+int(gid.x)*s.channel+c;\n" @@ -691,8 +661,8 @@ const char* shader_MetalBackend_metal = "}\n" "template \n" "static inline void template_NCHW_to_NC4HW4(const device IType *in,device OType *out,constant tensor_shape &s,uint2 gid) {\n" -" int b=gid.y/s.slice;\n" -" int z=gid.y % s.slice;\n" +" int b=gid.y % s.batch;\n" +" int z=gid.y/s.batch;\n" " int c=z*4;\n" " \n" " auto off_in=in+(b*s.channel+c)*s.size+int(gid.x);\n" @@ -728,8 +698,8 @@ const char* shader_MetalBackend_metal = "}\n" "template \n" "static inline void template_NC4HW4_to_NCHW(const device IType *in,device OType *out,constant tensor_shape &s,uint2 gid) {\n" -" int b=gid.y/s.slice;\n" -" int z=gid.y % s.slice;\n" +" int b=gid.y % s.batch;\n" +" int z=gid.y/s.batch;\n" " int c=z*4;\n" " \n" " auto off_in=in+int(gid.y)*s.size+int(gid.x);\n" @@ -767,8 +737,8 @@ const char* shader_MetalBackend_metal = "template\n" "static inline void template_NHWC_to_NCHW(const device IType* in,\n" " device OType* out,constant tensor_shape &s,uint2 gid) {\n" -" int b=gid.y/s.slice;\n" -" int c4=gid.y % s.slice;\n" +" int b=gid.y % s.batch;\n" +" int c4=gid.y/s.batch;\n" " \n" " auto in_off=(b*s.size+gid.x)*s.channel+c4*4;\n" " auto out_off=(b*s.channel+c4*4)*s.size+gid.x;\n" @@ -793,8 +763,8 @@ const char* shader_MetalBackend_metal = "template\n" "static inline void template_NCHW_to_NHWC(const device IType* in,\n" " device OType* out,constant tensor_shape &s,uint2 gid) {\n" -" int b=gid.y/s.slice;\n" -" int c4=gid.y % s.slice;\n" +" int b=gid.y % s.batch;\n" +" int c4=gid.y/s.batch;\n" " \n" " auto in_off=(b*s.channel+c4*4)*s.size+gid.x;\n" " auto out_off=(b*s.size+gid.x)*s.channel+c4*4;\n" @@ -1452,7 +1422,7 @@ const char* shader_MetalScale_metal = " const device float4 *biasTerms [[buffer(4)]],\n" " uint2 gid [[thread_position_in_grid]]) {\n" " if ((int)gid.x >= s.size || (int)gid.y >= s.steps*s.batch) return;\n" -" int z=gid.y % s.steps;\n" +" int z=gid.y/s.batch;\n" " out[int(gid.y)*s.size+int(gid.x)] =\n" " in [int(gid.y)*s.size+int(gid.x)]*M4(scales[z])+M4(biasTerms[z]);\n" "}\n" @@ -1494,8 +1464,8 @@ const char* shader_MetalDeconvolution_metal = " uint3 gid [[thread_position_in_grid]]) {\n" " if ((int)gid.x >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.batch*cst.output_slice) return;\n" " \n" -" int b=gid.z/cst.output_slice;\n" -" int o=gid.z % cst.output_slice;\n" +" int b=gid.z % cst.batch;\n" +" int o=gid.z/cst.batch;\n" " FLOAT4 result=FLOAT4(biasTerms[o]);\n" " int oy=(int)gid.y+cst.pad_y;\n" " int ox=(int)gid.x+cst.pad_x;\n" @@ -1513,12 +1483,12 @@ const char* shader_MetalDeconvolution_metal = " int min_ix=(ox-max_kx*cst.dilation_x)/cst.stride_x;\n" " \n" " auto o_wt=wt+o*cst.input_slice*cst.kernel_size;\n" -" auto b_in=in+b*cst.input_slice*cst.input_size;\n" +" auto b_in=in+b*cst.input_size;\n" " for (auto z=0; z= min_ky; ky -= cst.delta_ky,iy += cst.delta_iy) {\n" " for (auto kx=max_kx,ix=min_ix; kx >= min_kx; kx -= cst.delta_kx,ix += cst.delta_ix) {\n" " auto wt4=o_wt[z*cst.kernel_size+ky*cst.kernel_x+kx];\n" -" auto in4=b_in[z*cst.input_size+iy*cst.input_width+ix];\n" +" auto in4=b_in[z*cst.input_size*cst.batch+iy*cst.input_width+ix];\n" " result += FLOAT4(in4*wt4);\n" " }\n" " }\n" @@ -1534,7 +1504,7 @@ const char* shader_MetalDeconvolution_metal = " uint3 gid [[thread_position_in_grid]]) {\n" " if ((int)gid.x >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.batch*cst.output_slice) return;\n" " \n" -" FLOAT4 result=FLOAT4(biasTerms[(int)(gid.z % cst.input_slice)]);\n" +" FLOAT4 result=FLOAT4(biasTerms[(int)(gid.z/cst.batch)]);\n" " \n" " int oy=(int)gid.y+cst.pad_y;\n" " int ox=(int)gid.x+cst.pad_x;\n" @@ -1631,10 +1601,11 @@ const char* shader_MetalROIPooling_metal = " int input_width;\n" " int input_height;\n" " int input_size;\n" +" int input_batch;\n" " int output_width;\n" " int output_height;\n" " int output_size;\n" -" int slices;\n" +" int batch;\n" " float spatial_scale;\n" "};\n" "kernel void ROI_pooling(const device M4 *in [[buffer(0)]],\n" @@ -1644,10 +1615,10 @@ const char* shader_MetalROIPooling_metal = " uint3 gid [[thread_position_in_grid]]) {\n" " if ((int)gid.x >= s.output_width || (int)gid.y >= s.output_height) return;\n" " \n" -" int ob=gid.z/s.slices;\n" -" int iz=gid.z % s.slices;\n" +" int ob=gid.z % s.batch;\n" +" int iz=gid.z/s.batch;\n" " \n" -" auto b_roi=roi+ob*8; // roundup(5,4)=8\n" +" auto b_roi=roi+ob*5;\n" " int ib=int(b_roi[0]);\n" " int x1=round(float(b_roi[1])*s.spatial_scale);\n" " int y1=round(float(b_roi[2])*s.spatial_scale);\n" @@ -1656,8 +1627,8 @@ const char* shader_MetalROIPooling_metal = " \n" " int roi_w=max(x2-x1+1,1);\n" " int roi_h=max(y2-y1+1,1);\n" -" auto bin_size_w=(M)roi_w/s.output_width;\n" -" auto bin_size_h=(M)roi_h/s.output_height;\n" +" float bin_size_w=(float)roi_w/(float)s.output_width;\n" +" float bin_size_h=(float)roi_h/(float)s.output_height;\n" " \n" " int w_start=clamp(x1+(int)floor(gid.x*bin_size_w) ,0,s.input_width);\n" " int w_end=clamp(x1+(int)ceil((gid.x+1)*bin_size_w),0,s.input_width);\n" @@ -1665,7 +1636,7 @@ const char* shader_MetalROIPooling_metal = " int h_end=clamp(y1+(int)ceil((gid.y+1)*bin_size_h),0,s.input_height);\n" " \n" " int is_empty=(h_end <= h_start) || (w_end <= w_start);\n" -" auto z_in=in+(ib*s.slices+iz)*s.input_size;\n" +" auto z_in=in+(ib+iz*s.input_batch)*s.input_size;\n" " auto max4=is_empty ? 0 : z_in[h_start*s.input_width+w_start];\n" " for (int y=h_start; y= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.batch*cst.output_slice) return;\n" -" int idx_w=gid.x;\n" -" int idx_h=gid.y;\n" -" int idx_c=gid.z % cst.output_slice;\n" -" int idx_b=gid.z/cst.output_slice;\n" -" auto xy_wt=wt+idx_c*cst.input_slice;\n" -" auto xy_in0=in+(int)idx_b*cst.input_slice*cst.input_size+idx_h*cst.output_width+idx_w;\n" -" auto xy_out=out+(int)idx_b*cst.output_slice*cst.output_size+idx_c*cst.output_size+idx_h*cst.output_width+idx_w;\n" -" auto biasValue=FLOAT4(biasTerms[idx_c]);\n" -" FLOAT4 result0=biasValue;\n" -" for (auto z=0; z= cst.output_size || (int)gid.y >= cst.output_slice || (int)gid.z >= cst.batch) return;\n" " int rx=gid.x*CONV_UNROLL;\n" " int uz=gid.y;\n" " auto xy_wt=wt+uz*cst.input_slice;\n" -" auto xy_in0=in+(int)gid.z*cst.input_slice*cst.input_size+rx+0;\n" -" auto xy_out=out+(int)gid.z*cst.output_slice*cst.output_size+uz*cst.output_size+rx;\n" +" auto xy_in0=in+(int)gid.z*cst.input_size+rx+0;\n" +" auto xy_out=out+(int)gid.z*cst.output_size+uz*cst.output_size*cst.batch+rx;\n" " auto biasValue=FLOAT4(biasTerms[uz]);\n" " FLOAT4 result0=biasValue,result1=biasValue,result2=biasValue,result3=biasValue;\n" " int computeSize=min(cst.output_size-rx,CONV_UNROLL);\n" " auto scale=FLOAT4(dequantScale[uz]);\n" -" auto dequant_bias=FLOAT4(dequantBias[uz]);\n" +" auto dequant_bias=FLOAT4(dequantScale[uz+cst.output_slice]);\n" " for (auto z=0; z= cst.output_size || (int)gid.y >= cst.output_slice || (int)gid.z >= cst.batch) return;\n" " int rx=gid.x*CONV_UNROLL;\n" " int uz=gid.y;\n" " auto xy_wt=wt+uz*cst.input_slice;\n" -" auto xy_in0=in+(int)gid.z*cst.input_slice*cst.input_size+rx+0;\n" -" auto xy_out=out+(int)gid.z*cst.output_slice*cst.output_size+uz*cst.output_size+rx;\n" +" auto xy_in0=in+(int)gid.z*cst.input_size+rx+0;\n" +" auto xy_out=out+(int)gid.z*cst.output_size+uz*cst.output_size*cst.batch+rx;\n" " auto biasValue=FLOAT4(biasTerms[uz]);\n" " FLOAT4 result0=biasValue,result1=biasValue,result2=biasValue,result3=biasValue;\n" " int computeSize=min(cst.output_size-rx,CONV_UNROLL);\n" " auto scale=FLOAT4(dequantScale[uz]);\n" -" auto dequant_bias=FLOAT4(dequantBias[uz]);\n" +" auto dequant_bias=FLOAT4(dequantScale[uz+cst.output_slice]);\n" " for (auto z=0; z> 4)-8,(float)(w_int4[i][0] & 15)-8,(float)(w_int4[i][1] >> 4)-8,(float)(w_int4[i][1] & 15)-8);\n" -" FLOAT4 res;\n" -" for (int j=0; j<4; ++j) {\n" -" float wf=w4[j]*scale[i]+dequant_bias[i];\n" -" res[j]=wf;\n" -" }\n" +" FLOAT4 res=w4*scale[i]+dequant_bias[i];\n" " w_dequant[i]=res;\n" " }\n" " \n" @@ -1845,7 +1780,7 @@ const char* shader_MetalConvolution1x1_metal = " result1 += FLOAT4(in41*w_dequant);\n" " result2 += FLOAT4(in42*w_dequant);\n" " result3 += FLOAT4(in43*w_dequant);\n" -" xy_in0 += cst.input_size;\n" +" xy_in0 += cst.input_size*cst.batch;\n" " }\n" " \n" " /* true */ \n" @@ -1874,8 +1809,8 @@ const char* shader_MetalConvolution1x1_metal = " int rx=gid.x*CONV_UNROLL_L;\n" " int uz=gid.y;\n" " auto xy_wt=wt+uz*cst.input_slice;\n" -" auto xy_in0=in+(int)gid.z*cst.input_slice*cst.input_size+rx+0;\n" -" auto xy_out=out+(int)gid.z*cst.output_slice*cst.output_size+uz*cst.output_size+rx;\n" +" auto xy_in0=in+(int)gid.z*cst.input_size+rx+0;\n" +" auto xy_out=out+(int)gid.z*cst.output_size+uz*cst.batch*cst.output_size+rx;\n" " auto biasValue=FLOAT4(biasTerms[uz]);\n" " FLOAT4 result0=biasValue,result1=biasValue,result2=biasValue,result3=biasValue;\n" " FLOAT4 result4=biasValue,result5=biasValue,result6=biasValue,result7=biasValue;\n" @@ -1898,7 +1833,7 @@ const char* shader_MetalConvolution1x1_metal = " result5 += FLOAT4(in45*w);\n" " result6 += FLOAT4(in46*w);\n" " result7 += FLOAT4(in47*w);\n" -" xy_in0 += cst.input_size;\n" +" xy_in0 += cst.input_size*cst.batch;\n" " }\n" " /* true */ *xy_out=activate(M4(result0),cst.activation);\n" " if (computeSize>1) {xy_out[1]=activate(M4(result1),cst.activation); }\n" @@ -1909,71 +1844,20 @@ const char* shader_MetalConvolution1x1_metal = " if (computeSize>6) {xy_out[6]=activate(M4(result6),cst.activation); }\n" " if (computeSize>7) {xy_out[7]=activate(M4(result7),cst.activation); }\n" "}\n" -"kernel void conv1x1_w4h2(const device M4 *in [[buffer(0)]],\n" -" device M4 *out [[buffer(1)]],\n" -" constant conv1x1_constants& cst [[buffer(2)]],\n" -" const device M4x4 *wt [[buffer(3)]],\n" -" const device M4 *biasTerms [[buffer(4)]],\n" -" uint3 gid [[thread_position_in_grid]]) {\n" -" if ((int)gid.x*4 >= cst.output_width || (int)gid.y*2 >= cst.output_height || (int)gid.z >= cst.batch*cst.output_slice) return;\n" -" int idx_w=gid.x << 2;\n" -" int idx_h=gid.y << 1;\n" -" int idx_c=gid.z % cst.output_slice;\n" -" int idx_b=gid.z/cst.output_slice;\n" -" auto xy_wt=wt+idx_c*cst.input_slice;\n" -" auto xy_in0=in+(int)idx_b*cst.input_slice*cst.input_size+idx_h*cst.output_width+idx_w;\n" -" auto xy_out=out+(int)idx_b*cst.output_slice*cst.output_size+idx_c*cst.output_size+idx_h*cst.output_width+idx_w;\n" -" auto biasValue=FLOAT4(biasTerms[idx_c]);\n" -" FLOAT4 result0=biasValue,result1=biasValue,result2=biasValue,result3=biasValue;\n" -" FLOAT4 result4=biasValue,result5=biasValue,result6=biasValue,result7=biasValue;\n" -" for (auto z=0; z1) {xy_out[1]=activate(M4(result1),cst.activation); }\n" -" if (widthSize>2) {xy_out[2]=activate(M4(result2),cst.activation); }\n" -" if (widthSize>3) {xy_out[3]=activate(M4(result3),cst.activation); }\n" -" \n" -" int heightSize=min(cst.output_height-idx_h,2);\n" -" if(heightSize>1) {\n" -" /* true */ {xy_out[cst.output_width+0]=activate(M4(result4),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_width+1]=activate(M4(result5),cst.activation); }\n" -" if (widthSize>2) {xy_out[cst.output_width+2]=activate(M4(result6),cst.activation); }\n" -" if (widthSize>3) {xy_out[cst.output_width+3]=activate(M4(result7),cst.activation); }\n" -" }\n" -"}\n" "kernel void conv1x1_w4h4(const device M4 *in [[buffer(0)]],\n" " device M4 *out [[buffer(1)]],\n" " constant conv1x1_constants& cst [[buffer(2)]],\n" " const device M4x4 *wt [[buffer(3)]],\n" " const device M4 *biasTerms [[buffer(4)]],\n" " uint3 gid [[thread_position_in_grid]]) {\n" -" if ((int)gid.x*4 >= cst.output_width || (int)gid.y*4 >= cst.output_height || (int)gid.z >= cst.batch*cst.output_slice) return;\n" -" int idx_w=gid.x << 2;\n" -" int idx_h=gid.y << 2;\n" -" int idx_c=gid.z % cst.output_slice;\n" -" int idx_b=gid.z/cst.output_slice;\n" +" if ((int)gid.x*16 >= cst.output_width || (int)gid.y >= cst.batch*cst.output_slice) return;\n" +" int idx_w=gid.x << 4;\n" +" int idx_h=0;\n" +" int idx_c=gid.y/cst.batch;\n" +" int idx_b=gid.y % cst.batch;\n" " auto xy_wt=wt+idx_c*cst.input_slice;\n" -" auto xy_in0=in+(int)idx_b*cst.input_slice*cst.input_size+idx_h*cst.output_width+idx_w;\n" -" auto xy_out=out+(int)idx_b*cst.output_slice*cst.output_size+idx_c*cst.output_size+idx_h*cst.output_width+idx_w;\n" +" auto xy_in0=in+(int)idx_b*cst.input_size+idx_h*cst.output_width+idx_w;\n" +" auto xy_out=out+(int)idx_b*cst.output_size+idx_c*cst.output_size*cst.batch+idx_h*cst.output_width+idx_w;\n" " auto biasValue=FLOAT4(biasTerms[idx_c]);\n" " FLOAT4 result00=biasValue,result01=biasValue,result02=biasValue,result03=biasValue;\n" " FLOAT4 result10=biasValue,result11=biasValue,result12=biasValue,result13=biasValue;\n" @@ -1984,19 +1868,19 @@ const char* shader_MetalConvolution1x1_metal = " auto in01=xy_in0[1];\n" " auto in02=xy_in0[2];\n" " auto in03=xy_in0[3];\n" -" auto in10=xy_in0[cst.output_width+0];\n" -" auto in11=xy_in0[cst.output_width+1];\n" -" auto in12=xy_in0[cst.output_width+2];\n" -" auto in13=xy_in0[cst.output_width+3];\n" -" \n" -" auto in20=xy_in0[cst.output_width+cst.output_width+0];\n" -" auto in21=xy_in0[cst.output_width+cst.output_width+1];\n" -" auto in22=xy_in0[cst.output_width+cst.output_width+2];\n" -" auto in23=xy_in0[cst.output_width+cst.output_width+3];\n" -" auto in30=xy_in0[cst.output_width+cst.output_width+cst.output_width+0];\n" -" auto in31=xy_in0[cst.output_width+cst.output_width+cst.output_width+1];\n" -" auto in32=xy_in0[cst.output_width+cst.output_width+cst.output_width+2];\n" -" auto in33=xy_in0[cst.output_width+cst.output_width+cst.output_width+3];\n" +" auto in10=xy_in0[4];\n" +" auto in11=xy_in0[5];\n" +" auto in12=xy_in0[6];\n" +" auto in13=xy_in0[7];\n" +" \n" +" auto in20=xy_in0[8];\n" +" auto in21=xy_in0[9];\n" +" auto in22=xy_in0[10];\n" +" auto in23=xy_in0[11];\n" +" auto in30=xy_in0[12];\n" +" auto in31=xy_in0[13];\n" +" auto in32=xy_in0[14];\n" +" auto in33=xy_in0[15];\n" " auto w=xy_wt[z];\n" " result00 += FLOAT4(in00*w);\n" " result01 += FLOAT4(in01*w);\n" @@ -2016,33 +1900,25 @@ const char* shader_MetalConvolution1x1_metal = " result32 += FLOAT4(in32*w);\n" " result33 += FLOAT4(in33*w);\n" " \n" -" xy_in0 += cst.input_size;\n" +" xy_in0 += cst.input_size*cst.batch;\n" " }\n" -" int widthSize=min(cst.output_width-idx_w,4);\n" +" int widthSize=min(cst.output_width-idx_w,16);\n" " /* true */ *xy_out=activate(M4(result00),cst.activation);\n" " if (widthSize>1) {xy_out[1]=activate(M4(result01),cst.activation); }\n" " if (widthSize>2) {xy_out[2]=activate(M4(result02),cst.activation); }\n" " if (widthSize>3) {xy_out[3]=activate(M4(result03),cst.activation); }\n" -" \n" -" int heightSize=min(cst.output_height-idx_h,4);\n" -" if(heightSize>1) {\n" -" /* true */ {xy_out[cst.output_width+0]=activate(M4(result10),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_width+1]=activate(M4(result11),cst.activation); }\n" -" if (widthSize>2) {xy_out[cst.output_width+2]=activate(M4(result12),cst.activation); }\n" -" if (widthSize>3) {xy_out[cst.output_width+3]=activate(M4(result13),cst.activation); }\n" -" }\n" -" if(heightSize>2) {\n" -" /* true */ {xy_out[cst.output_width+cst.output_width+0]=activate(M4(result20),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_width+cst.output_width+1]=activate(M4(result21),cst.activation); }\n" -" if (widthSize>2) {xy_out[cst.output_width+cst.output_width+2]=activate(M4(result22),cst.activation); }\n" -" if (widthSize>3) {xy_out[cst.output_width+cst.output_width+3]=activate(M4(result23),cst.activation); }\n" -" }\n" -" if(heightSize>3) {\n" -" /* true */ {xy_out[cst.output_width+cst.output_width+cst.output_width+0]=activate(M4(result30),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_width+cst.output_width+cst.output_width+1]=activate(M4(result31),cst.activation); }\n" -" if (widthSize>2) {xy_out[cst.output_width+cst.output_width+cst.output_width+2]=activate(M4(result32),cst.activation); }\n" -" if (widthSize>3) {xy_out[cst.output_width+cst.output_width+cst.output_width+3]=activate(M4(result33),cst.activation); }\n" -" }\n" +" if (widthSize>4) {xy_out[4]=activate(M4(result10),cst.activation); }\n" +" if (widthSize>5) {xy_out[5]=activate(M4(result11),cst.activation); }\n" +" if (widthSize>6) {xy_out[6]=activate(M4(result12),cst.activation); }\n" +" if (widthSize>7) {xy_out[7]=activate(M4(result13),cst.activation); }\n" +" if (widthSize>8) {xy_out[8]=activate(M4(result20),cst.activation); }\n" +" if (widthSize>9) {xy_out[9]=activate(M4(result21),cst.activation); }\n" +" if (widthSize>10) {xy_out[10]=activate(M4(result22),cst.activation); }\n" +" if (widthSize>11) {xy_out[11]=activate(M4(result23),cst.activation); }\n" +" if (widthSize>12) {xy_out[12]=activate(M4(result30),cst.activation); }\n" +" if (widthSize>13) {xy_out[13]=activate(M4(result31),cst.activation); }\n" +" if (widthSize>14) {xy_out[14]=activate(M4(result32),cst.activation); }\n" +" if (widthSize>15) {xy_out[15]=activate(M4(result33),cst.activation); }\n" "}\n" "kernel void conv1x1_w2c2(const device M4 *in [[buffer(0)]],\n" " device M4 *out [[buffer(1)]],\n" @@ -2050,17 +1926,17 @@ const char* shader_MetalConvolution1x1_metal = " const device M4x4 *wt [[buffer(3)]],\n" " const device M4 *biasTerms [[buffer(4)]],\n" " uint3 gid [[thread_position_in_grid]]) {\n" -" if ((int)gid.x*2 >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z*2 >= cst.batch*cst.output_slice) return;\n" +" if ((int)gid.x*2 >= cst.output_width || (int)gid.y*2 >= cst.batch*cst.output_slice) return;\n" " int channel_pack=(cst.output_channel+7) >> 3;\n" " int idx_w=gid.x << 1;\n" -" int idx_h=gid.y;\n" -" int idx_c=(gid.z % channel_pack) << 1;\n" -" int idx_b=gid.z/channel_pack;\n" +" int idx_h=0;\n" +" int idx_c=(gid.y % channel_pack) << 1;\n" +" int idx_b=gid.y/channel_pack;\n" " \n" " if(idx_b >= cst.batch || idx_c >= cst.output_slice) return;\n" " auto xy_wt=wt+idx_c*cst.input_slice;\n" -" auto xy_in0=in+(int)idx_b*cst.input_slice*cst.input_size+idx_h*cst.output_width+idx_w;\n" -" auto xy_out=out+(int)idx_b*cst.output_slice*cst.output_size+idx_c*cst.output_size+idx_h*cst.output_width+idx_w;\n" +" auto xy_in0=in+(int)idx_b*cst.input_size+idx_h*cst.output_width+idx_w;\n" +" auto xy_out=out+(int)idx_b*cst.output_size+idx_c*cst.output_size*cst.batch+idx_h*cst.output_width+idx_w;\n" " auto biasValue0=FLOAT4(biasTerms[idx_c]);\n" " auto biasValue1=FLOAT4(biasTerms[idx_c+1]);\n" " FLOAT4 result0=biasValue0,result1=biasValue0;\n" @@ -2074,7 +1950,7 @@ const char* shader_MetalConvolution1x1_metal = " result1 += FLOAT4(in41*w0);\n" " result4 += FLOAT4(in40*w1);\n" " result5 += FLOAT4(in41*w1);\n" -" xy_in0 += cst.input_size;\n" +" xy_in0 += cst.input_size*cst.batch;\n" " }\n" " int widthSize=min(cst.output_width-idx_w,2);\n" " /* true */ *xy_out=activate(M4(result0),cst.activation);\n" @@ -2082,65 +1958,26 @@ const char* shader_MetalConvolution1x1_metal = " \n" " int channelSize=min(cst.output_slice-idx_c,2);\n" " if(channelSize>1) {\n" -" /* true */ {xy_out[cst.output_size+0]=activate(M4(result4),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_size+1]=activate(M4(result5),cst.activation); }\n" +" /* true */ {xy_out[cst.output_size*cst.batch +0]=activate(M4(result4),cst.activation); }\n" +" if (widthSize>1) {xy_out[cst.output_size*cst.batch +1]=activate(M4(result5),cst.activation); }\n" " }\n" "}\n" -"kernel void conv1x1_w2h2(const device M4 *in [[buffer(0)]],\n" +"kernel void conv1x1_w4c2(const device M4 *in [[buffer(0)]],\n" " device M4 *out [[buffer(1)]],\n" " constant conv1x1_constants& cst [[buffer(2)]],\n" " const device M4x4 *wt [[buffer(3)]],\n" " const device M4 *biasTerms [[buffer(4)]],\n" " uint3 gid [[thread_position_in_grid]]) {\n" -" if ((int)gid.x*2 >= cst.output_width || (int)gid.y*2 >= cst.output_height || (int)gid.z >= cst.batch*cst.output_slice) return;\n" -" int idx_w=gid.x << 1;\n" -" int idx_h=gid.y << 1;\n" -" int idx_c=gid.z % cst.output_slice;\n" -" int idx_b=gid.z/cst.output_slice;\n" -" auto xy_wt=wt+idx_c*cst.input_slice;\n" -" auto xy_in0=in+(int)idx_b*cst.input_slice*cst.input_size+idx_h*cst.output_width+idx_w;\n" -" auto xy_out=out+(int)idx_b*cst.output_slice*cst.output_size+idx_c*cst.output_size+idx_h*cst.output_width+idx_w;\n" -" auto biasValue=FLOAT4(biasTerms[idx_c]);\n" -" FLOAT4 result0=biasValue,result1=biasValue;\n" -" FLOAT4 result4=biasValue,result5=biasValue;\n" -" for (auto z=0; z1) {xy_out[1]=activate(M4(result1),cst.activation); }\n" -" \n" -" int heightSize=min(cst.output_height-idx_h,2);\n" -" if(heightSize>1) {\n" -" /* true */ {xy_out[cst.output_width+0]=activate(M4(result4),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_width+1]=activate(M4(result5),cst.activation); }\n" -" }\n" -"}\n" -"kernel void conv1x1_w2h2c2(const device M4 *in [[buffer(0)]],\n" -" device M4 *out [[buffer(1)]],\n" -" constant conv1x1_constants& cst [[buffer(2)]],\n" -" const device M4x4 *wt [[buffer(3)]],\n" -" const device M4 *biasTerms [[buffer(4)]],\n" -" uint3 gid [[thread_position_in_grid]]) {\n" -" if ((int)gid.x*2 >= cst.output_width || (int)gid.y*2 >= cst.output_height || (int)gid.z*2 >= cst.batch*cst.output_slice) return;\n" +" if ((int)gid.x*4 >= cst.output_width || (int)gid.y*2 >= cst.batch*cst.output_slice) return;\n" " int channel_pack=(cst.output_channel+7) >> 3;\n" -" int idx_w=gid.x << 1;\n" -" int idx_h=gid.y << 1;\n" -" int idx_c=(gid.z % channel_pack) << 1;\n" -" int idx_b=gid.z/channel_pack;\n" +" int idx_w=gid.x << 2;\n" +" int idx_h=0;\n" +" int idx_c=(gid.y % channel_pack) << 1;\n" +" int idx_b=gid.y/channel_pack;\n" " if(idx_b >= cst.batch || idx_c >= cst.output_slice) return;\n" " auto xy_wt=wt+idx_c*cst.input_slice;\n" -" auto xy_in0=in+(int)idx_b*cst.input_slice*cst.input_size+idx_h*cst.output_width+idx_w;\n" -" auto xy_out=out+(int)idx_b*cst.output_slice*cst.output_size+idx_c*cst.output_size+idx_h*cst.output_width+idx_w;\n" +" auto xy_in0=in+(int)idx_b*cst.input_size+idx_h*cst.output_width+idx_w;\n" +" auto xy_out=out+(int)idx_b*cst.output_size+idx_c*cst.output_size*cst.batch+idx_h*cst.output_width+idx_w;\n" " auto biasValue0=FLOAT4(biasTerms[idx_c]);\n" " auto biasValue1=FLOAT4(biasTerms[idx_c+1]);\n" " FLOAT4 result0=biasValue0,result1=biasValue0;\n" @@ -2150,8 +1987,8 @@ const char* shader_MetalConvolution1x1_metal = " for (auto z=0; z1) {xy_out[1]=activate(M4(result1),cst.activation); }\n" -" \n" -" int heightSize=min(cst.output_height-idx_h,2);\n" -" if(heightSize>1) {\n" -" /* true */ {xy_out[cst.output_width+0]=activate(M4(result4),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_width+1]=activate(M4(result5),cst.activation); }\n" -" }\n" +" if (widthSize>2) {xy_out[2]=activate(M4(result4),cst.activation); }\n" +" if (widthSize>3) {xy_out[3]=activate(M4(result5),cst.activation); }\n" " \n" " int channelSize=min(cst.output_slice-idx_c,2);\n" " if(channelSize>1) {\n" -" /* true */ xy_out[cst.output_size]=activate(M4(result2),cst.activation);\n" -" if (widthSize>1) {xy_out[cst.output_size+1]=activate(M4(result3),cst.activation); }\n" -" \n" -" if(heightSize>1) {\n" -" /* true */ {xy_out[cst.output_size+cst.output_width+0]=activate(M4(result6),cst.activation); }\n" -" if (widthSize>1) {xy_out[cst.output_size+cst.output_width+1]=activate(M4(result7),cst.activation); }\n" -" }\n" +" /* true */ xy_out[cst.output_size*cst.batch]=activate(M4(result2),cst.activation);\n" +" if (widthSize>1) {xy_out[cst.output_size*cst.batch +1]=activate(M4(result3),cst.activation); }\n" +" if (widthSize>2) {xy_out[cst.output_size*cst.batch +2]=activate(M4(result6),cst.activation); }\n" +" if (widthSize>3) {xy_out[cst.output_size*cst.batch +3]=activate(M4(result7),cst.activation); }\n" " }\n" "}\n" ; @@ -2341,7 +2171,7 @@ const char* shader_MetalPReLU_metal = " uint3 gid [[thread_position_in_grid]]) { // size,slice,batch\n" " if ((int)gid.x >= s.size || (int)gid.y >= s.slice) return;\n" " \n" -" int z=gid.z*s.slice+gid.y;\n" +" int z=gid.z+gid.y*s.batch;\n" " auto v4=in[z*s.size+int(gid.x)];\n" " out[z*s.size+int(gid.x)]=select(v4,M4(slope[int(gid.y)])*v4,signbit(v4));\n" "}\n" diff --git a/source/backend/metal/AllShader.hpp b/source/backend/metal/AllShader.hpp index b59f8a8e9..d430897b0 100644 --- a/source/backend/metal/AllShader.hpp +++ b/source/backend/metal/AllShader.hpp @@ -1,7 +1,6 @@ #ifndef MNN_METAL_SHADER_AUTO_GENERATE_H #define MNN_METAL_SHADER_AUTO_GENERATE_H extern const char* shader_MetalReLU6_metal; -extern const char* shader_MetalReLU_metal; extern const char* shader_MetalConvolutionDepthwise_metal; extern const char* shader_MetalConvolutionActivation_metal; extern const char* shader_MetalConvolution_metal; diff --git a/source/backend/metal/MNNMetalContext.h b/source/backend/metal/MNNMetalContext.h index 40bf6a0fb..2159ccf1f 100644 --- a/source/backend/metal/MNNMetalContext.h +++ b/source/backend/metal/MNNMetalContext.h @@ -27,14 +27,6 @@ typedef enum { CPUTransparent } MetalAccess; -typedef struct { - /** wrap size */ - NSUInteger threadExecutionWidth; - /** max threads per thread group */ - NSUInteger maxThreadsPerThreadgroup; - /** run concurrently on z axis or not */ - BOOL zAxisProtected; -} MetalBandwidth; } @interface MNNMetalContext : NSObject @@ -62,14 +54,6 @@ typedef struct { - (id)newDeviceBuffer:(NSUInteger)size bytes:(const void *)bytes access:(MNN::MetalAccess)access; -/** - * @brief load encoder with function name. returns maxTotalThreadsPerThreadgroup of pipeline. - * @param name pipline name - * @param encoder command encoder - * @return bandwidth info for function - */ -- (MNN::MetalBandwidth)load:(NSString *)name encoder:(id)encoder fp16:(BOOL)fp16; - /** * @brief load encoder with function name. returns maxTotalThreadsPerThreadgroup of pipeline. * @param name pipline name @@ -86,16 +70,6 @@ typedef struct { - (BOOL) initWithSharedContext:(const MNNMetalSharedContext*)context dev:(id)device; -/** - * @brief dispatch encoder with default settings - * @param encoder command encoder - * @param threads threads size - * @param bandwidth bandwidth - */ -- (void)dispatchEncoder:(id)encoder - threads:(MTLSize)threads - bandwidth:(MNN::MetalBandwidth)bandwidth; - /** * @brief dispatch encoder with designated threads per threadgroup * @param encoder command encoder @@ -103,10 +77,6 @@ typedef struct { * @param threadsPerGroup thread size per group * @param bandwidth bandwidth */ -- (void)dispatchEncoder:(id)encoder - threads:(MTLSize)threads - threadsPerGroup:(MTLSize)threadsPerGroup - bandwidth:(MNN::MetalBandwidth)bandwidth; - (id)pipelineWithName:(NSString *)name fp16:(BOOL)fp16; - (id)pipelineWithSourceOption:(NSString *)source name:(NSString *)name options:(MTLCompileOptions *)options; - (MTLSize)computeBestGroup:(id) pipeline threads:(MTLSize)threads; diff --git a/source/backend/metal/MNNMetalContext.mm b/source/backend/metal/MNNMetalContext.mm index 6db27d64c..e23fda331 100644 --- a/source/backend/metal/MNNMetalContext.mm +++ b/source/backend/metal/MNNMetalContext.mm @@ -181,25 +181,6 @@ - (MTLResourceOptions)optionForAccess:(MNN::MetalAccess)access { return result; } -- (MetalBandwidth)load:(NSString *)name encoder:(id)encoder fp16:(BOOL)fp16 { - id pipeline = [self pipelineWithName:name fp16:fp16]; - MNN_ASSERT(nil != pipeline); - [encoder setComputePipelineState:pipeline]; -#if MNN_METAL_DEBUG || MNN_METAL_BENCHMARK - if (!name) { - } else if (!encoder.label) { - encoder.label = name; - } else { - NSArray *components = [encoder.label componentsSeparatedByString:@","]; - if (![components containsObject:name]) { - components = [components arrayByAddingObject:name]; - } - encoder.label = [components componentsJoinedByString:@","]; - } -#endif - return {pipeline.threadExecutionWidth, pipeline.maxTotalThreadsPerThreadgroup, NO}; -} - - (NSUInteger)timeUsed:(id)buffer { // Get ns precision time auto start = mach_absolute_time(); @@ -393,37 +374,6 @@ static NSUInteger smallest_log2(NSUInteger integer) { } - (MTLSize)computeBestGroup:(id) bw threads:(MTLSize)t { - if (bw.maxTotalThreadsPerThreadgroup > 64) { - auto res = MTLSizeMake(8, 8, 8); - int reduceNumber = 0; - if (t.depth < 4) { - res.depth = 1; - reduceNumber++; - } - if (t.width < 4) { - res.width = 1; - reduceNumber++; - } - if (t.height < 4) { - res.height = 1; - reduceNumber++; - } - if (reduceNumber == 0) { - return MTLSizeMake(4, 4, 4); - } - if (reduceNumber == 2) { - if (res.width > 1) { - res.width = 64; - } - if (res.height > 1) { - res.height = 64; - } - if (res.depth > 1) { - res.depth = 64; - } - } - return res; - } auto pwarp = smallest_log2(bw.threadExecutionWidth); auto px = smallest_log2(t.width), sx = (NSUInteger)ceil(log2(t.width)); auto py = smallest_log2(t.height), sy = (NSUInteger)ceil(log2(t.height)); @@ -463,91 +413,6 @@ - (MTLSize)computeBestGroup:(id) bw threads:(MTLSize)t } -- (MTLSize)threadsPerGroupWithThreads:(MTLSize)t bandwidth:(MetalBandwidth)bw { - auto pwarp = smallest_log2(bw.threadExecutionWidth); - auto px = smallest_log2(t.width), sx = (NSUInteger)ceil(log2(t.width)); - auto py = smallest_log2(t.height), sy = (NSUInteger)ceil(log2(t.height)); - - // accurately match on x - if (px >= pwarp) { - return {bw.threadExecutionWidth, 1, 1}; - } - // accurately match on xy - else if (px + py >= pwarp && sx < pwarp / 2) { - NSUInteger x = pow(2, px); - return {x, bw.threadExecutionWidth / x, 1}; - } - // similarly match on x - else if (sx >= pwarp) { - return {bw.threadExecutionWidth, 1, 1}; - } - // similarly match on xy - else if (sx + sy >= pwarp) { - NSUInteger x = pow(2, sx); - return {x, bw.threadExecutionWidth / x, 1}; - } - - // on xyz (for most shaders do not protect gid.z, z axis must be accurately match) - auto pz = smallest_log2(t.depth); - auto sz = bw.zAxisProtected ? ceil(log2(t.depth)) : pz; - if (px + py + pz >= pwarp) { - NSUInteger x = pow(2, px), y = pow(2, py); - return {x, y, bw.threadExecutionWidth / x / y}; - } else if (sx + sy + sz >= pwarp) { - NSUInteger x = pow(2, sx), z = pow(2, MIN(sz, pwarp - sx)); - return {x, bw.threadExecutionWidth / x / z, z}; - } else { - NSUInteger z = pow(2, sz); - return {t.width, t.height, z}; - } -} - -- (void)dispatchEncoder:(id)encoder - threads:(MTLSize)threads - bandwidth:(MetalBandwidth)bandwidth { - [self dispatchEncoder:encoder - threads:threads - threadsPerGroup:[self threadsPerGroupWithThreads:threads bandwidth:bandwidth] - bandwidth:bandwidth]; -} - -- (void)dispatchEncoder:(id)encoder - threads:(MTLSize)threads - threadsPerGroup:(MTLSize)threadsPerGroup - bandwidth:(MetalBandwidth)bandwidth { -#if MNN_METAL_DEBUG - if (threads.width == 0 || threads.height == 0 || threads.depth == 0 || threadsPerGroup.width == 0 || - threadsPerGroup.height == 0 || threadsPerGroup.depth == 0) { - printf("[METAL] dispatch error %td %td %td / %td %td %td\n", threads.width, threads.height, threads.depth, - threadsPerGroup.width, threadsPerGroup.height, threadsPerGroup.depth); - } -#endif - - // NSLog(@"dispatch {%td %td %td} with {%td %td %td}", - // threads.width, threads.height, threads.depth, - // threadsPerGroup.width, threadsPerGroup.height, threadsPerGroup.depth); - threadsPerGroup.width = MIN(threadsPerGroup.width, bandwidth.maxThreadsPerThreadgroup); - threadsPerGroup.height = MIN(threadsPerGroup.height, bandwidth.maxThreadsPerThreadgroup); - threadsPerGroup.depth = MIN(threadsPerGroup.depth, bandwidth.maxThreadsPerThreadgroup); -#ifdef MNN_BUILD_FOR_IOS - if (@available(iOS 11.0, *)) { - if ([_device supportsFeatureSet:MTLFeatureSet_iOS_GPUFamily4_v1]) { - [encoder dispatchThreads:threads threadsPerThreadgroup:threadsPerGroup]; - return; - } - } -#endif - MTLSize groups = { - UP_DIV(threads.width, threadsPerGroup.width), UP_DIV(threads.height, threadsPerGroup.height), - UP_DIV(threads.depth, threadsPerGroup.depth), - }; - MNN_ASSERT(threadsPerGroup.width >= 1); - MNN_ASSERT(threadsPerGroup.height >= 1); - MNN_ASSERT(threadsPerGroup.depth >= 1); - - [encoder dispatchThreadgroups:groups threadsPerThreadgroup:threadsPerGroup]; -} - #if MNN_METAL_DEBUG #pragma mark debug - (void)printTensor:(const Tensor *)tensor { diff --git a/source/backend/metal/MetalArgMax.mm b/source/backend/metal/MetalArgMax.mm index b867b9d67..ba0f1e76a 100644 --- a/source/backend/metal/MetalArgMax.mm +++ b/source/backend/metal/MetalArgMax.mm @@ -1,3 +1,11 @@ +// +// MetalArgMax.mm +// MNN +// +// Created by MNN on 2023/12/29. +// Copyright © 2018, Alibaba Group Holding Limited +// + #import "core/Macro.h" #import "MetalCast.hpp" #import "MetalBackend.hpp" diff --git a/source/backend/metal/MetalAttention.mm b/source/backend/metal/MetalAttention.mm new file mode 100644 index 000000000..324266913 --- /dev/null +++ b/source/backend/metal/MetalAttention.mm @@ -0,0 +1,425 @@ +// +// MetalAttention.mm +// MNN +// +// Created by MNN on b'2024/04/29'. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#import "core/Macro.h" +#import "MetalCast.hpp" +#import "MetalBackend.hpp" +#import "MNNMetalContext.h" +#include "MNN_generated.h" + +#if MNN_METAL_ENABLED +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE + +static const char* gMatMulDivMask = R"metal( +#include +#include +using namespace metal; +struct Param { + int query_seq_len; + int key_seq_len; + int head_num; + int head_dim; + float scale; +}; + +kernel void main0(const device T* input0 [[buffer(0)]], + const device T* input1 [[buffer(1)]], + device T* output [[buffer(2)]], + device T* past_key [[buffer(3)]], + const device int* mask [[buffer(4)]], + constant Param& param [[buffer(5)]], + uint3 gid[[thread_position_in_grid]]) { + const int x = gid.x; // query_seq_len + const int y = gid.y; // head_num + const int z = gid.z; // key_seq_len + if (x >= param.query_seq_len || y >= param.head_num || z >= param.key_seq_len) { + return; + } + int query_seq_len = param.query_seq_len; + int key_seq_len = param.key_seq_len; + int head_num = param.head_num; + int head_dim = param.head_dim; + + const int offset = head_num * head_dim; + const int offset_head = y * head_dim; + const device T* A_offset = input0 + x * offset + offset_head; + device T* Pastkey_offset = past_key + z * offset + offset_head; + float Vscale = (float)param.scale; +#ifdef FOR_PREFILL + device const T* B_offset = input1 + z * offset + offset_head; + const int output_offset = y * query_seq_len * key_seq_len; + float out0 = 0.0; + + for(int i = 0; i < head_dim; ++i){ + float A = (float)(A_offset[i]); + float B = (float)(B_offset[i]); + out0 += B * A; + Pastkey_offset[i] = (T)B; + } + + out0 *= Vscale; + + out0 = mask[((x + 0) * key_seq_len + (z + 0))] == 0 ? -FLT_MAX : out0; + output[output_offset + x * key_seq_len + z] = (T)out0; +#else + const device T *B_offset = input1 + offset_head; + float out = 0.0; + if (z == key_seq_len - 1) { + for(int i = 0; i < head_dim; ++i){ + float A = (float)(A_offset[i]); + float B = (float)(B_offset[i]); + out += B * A; + Pastkey_offset[i] = (T)B; + } + } else { + for(int i = 0; i < head_dim; ++i){ + float A = A_offset[i]; + float B = (float)Pastkey_offset[i]; + + out += A * B; + } + } + out *= Vscale; + output[y + z * head_num] = (T)out; +#endif +} + +)metal"; + + +static const char* gMatMulQKV = R"metal( +#include +#include +using namespace metal; +struct Param { + int query_seq_len; + int key_seq_len; + int head_num; + int head_dim; + float scale; +}; +kernel void main0(const device T* input0 [[buffer(0)]], + const device T* input1 [[buffer(1)]], + device T* output [[buffer(2)]], + device T* past_value [[buffer(3)]], + constant Param& param [[buffer(4)]], + uint3 gid[[thread_position_in_grid]]) { + const int x = gid.x; // query_seq_len + const int y = gid.y; // head_num + const int z = gid.z; // head_dim + if (x >= param.query_seq_len || y >= param.head_num || z >= param.head_dim) { + return; + } + int qk_seq_len = param.query_seq_len; + int value_seq_len = param.key_seq_len; + int head_num = param.head_num; + int head_dim = param.head_dim; + const int offset = head_num * head_dim; + const int offset_head = y * head_dim + z; +#ifdef FOR_PREFILL + device const T *A_offset = input0 + (y * qk_seq_len + x) * value_seq_len; + device const T *B_offset = input1 + offset_head; + device T *Pastvalue_offset = past_value + offset_head; + float out = 0.0; + + for(int i = 0; i < value_seq_len; ++i){ + float A0 = (float)A_offset[i]; + float B = (float)B_offset[i*offset]; + out += A0 * B; + Pastvalue_offset[i*offset] = B; + } + output[ x * offset + (y * head_dim + z)] = out; +#else + device const T *A_offset = input0 + y; + device const T *B_offset = input1 + offset_head; + device T *Pastvalue_offset = past_value + offset_head; + float out = 0; + + for(int i = 0; i < value_seq_len - 1; ++i){ + float A = (float)A_offset[i * head_num]; + float B = (float)Pastvalue_offset[i * offset]; + + out += A * B; + } + out += (float)A_offset[(value_seq_len - 1)*head_num] * (float)B_offset[0]; + Pastvalue_offset[(value_seq_len - 1)*offset] = B_offset[0]; + output[(y * head_dim + z)] = (T)out; +#endif + +} +)metal"; + +namespace MNN { +class AttentionBufExecution : public MetalExecution { +public: + struct SharedCache { + std::shared_ptr mPastKey; + std::shared_ptr mPastValue; + int mPastLength = 0, mMaxLength = 0, mKv_seq_len = 0; + }; + AttentionBufExecution(Backend *backend, bool kv_cache); + + virtual ~AttentionBufExecution() = default; + virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; + virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { + if (nullptr == dst) { + return true; + } + auto exe = new AttentionBufExecution(bn, mKVCache); + exe->mCache = mCache; + *dst = exe; + return true; + } + +private: + void _init(); + void reallocKVCache(); + bool mKVCache; + std::shared_ptr mCache; + float mScale; + const int mExpandChunk = 64; + bool mIsDecode = false; + std::shared_ptr mTempQK, mTempSoftMax; + int mNumHead = 0, mHeadDim = 0, mValueH = 0; + id mKernel_softmax; + + id mKernel_qk; + id mKernel_qkv; + id mKernelPrefill_qk; + id mKernelPrefill_qkv; + id mParamQKV; + id mParamSoftmax; +}; + +struct Param { + int query_seq_len; + int key_seq_len; + int head_num; + int head_dim; + float scale; +}; +AttentionBufExecution::AttentionBufExecution(Backend *backend, bool kv_cahce) + : MetalExecution(backend) , mKVCache(kv_cahce){ + _init(); +} +void AttentionBufExecution::_init() { + mCache.reset(new SharedCache); + auto mtbn = static_cast(backend()); + auto context = (__bridge MNNMetalContext *)mtbn->context(); + mKernel_softmax = [context pipelineWithName:@"softmax_plane" fp16:mtbn->useFp16InsteadFp32()]; + mParamQKV = [context newDeviceBuffer:sizeof(Param) access:CPUWriteOnly]; + mParamSoftmax = [context newDeviceBuffer:4 * sizeof(int) access:CPUWriteOnly]; + auto rt = mtbn->runtime(); + std::string T = "float"; + if (mtbn->useFp16InsteadFp32()) { + T = "half"; + } + std::vector keys = { + "matmul_qk_div_mask", + T + }; + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + // Rebuild Pipeline + MTLCompileOptions *decodeOption = [[MTLCompileOptions alloc] init]; + decodeOption.preprocessorMacros = @{ + @"T" : @(T.c_str()), + }; + + MTLCompileOptions* encodeOption = [[MTLCompileOptions alloc] init]; + encodeOption.preprocessorMacros = @{ + @"T" : @(T.c_str()), + @"FOR_PREFILL": @"1" + }; + mKernel_qk = mtbn->makeComputePipelineWithSourceOption(gMatMulDivMask, "main0", decodeOption); + mKernelPrefill_qk = mtbn->makeComputePipelineWithSourceOption(gMatMulDivMask, "main0", encodeOption); + mKernel_qkv = mtbn->makeComputePipelineWithSourceOption(gMatMulQKV, "main0", decodeOption); + mKernelPrefill_qkv = mtbn->makeComputePipelineWithSourceOption(gMatMulQKV, "main0", encodeOption); + + rt->insertPipeline({"matmul_qk_div_mask", T}, mKernel_qk); + rt->insertPipeline({"matmul_qk_div_mask", T, "PREFILL"}, mKernelPrefill_qk); + rt->insertPipeline({"matmul_qkv", T}, mKernel_qkv); + rt->insertPipeline({"matmul_qkv", T, "PREFILL"}, mKernelPrefill_qkv); + } else { + mKernel_qk = rt->findPipeline({"matmul_qk_div_mask", T}); + mKernelPrefill_qk = rt->findPipeline({"matmul_qk_div_mask", T, "PREFILL"}); + mKernel_qkv = rt->findPipeline({"matmul_qkv", T}); + mKernelPrefill_qkv = rt->findPipeline({"matmul_qkv", T, "PREFILL"}); + } + MNN_ASSERT(nil != mKernel_qk); + MNN_ASSERT(nil != mKernel_qkv); + MNN_ASSERT(nil != mKernelPrefill_qk); + MNN_ASSERT(nil != mKernelPrefill_qkv); +} + +void AttentionBufExecution::reallocKVCache() { + if (mCache->mPastLength < mCache->mMaxLength || nullptr == mTempQK || (!mIsDecode)) { + if (mIsDecode) { + mTempQK.reset(Tensor::createDevice({mNumHead, mCache->mMaxLength})); + mTempSoftMax.reset(Tensor::createDevice({mNumHead, mCache->mMaxLength})); + } else { + mTempQK.reset(Tensor::createDevice({mNumHead, mCache->mPastLength, mCache->mPastLength})); + mTempSoftMax.reset(Tensor::createDevice({mNumHead, mCache->mPastLength, mCache->mPastLength})); + } + backend()->onAcquireBuffer(mTempQK.get(), Backend::STATIC); + backend()->onAcquireBuffer(mTempSoftMax.get(), Backend::STATIC); + } + if (!mKVCache || mCache->mPastLength < mCache->mMaxLength) { + return; + } + auto mtbn = static_cast(backend()); + int byte = 4; + if(mtbn->useFp16InsteadFp32()) { + byte = 2; + } + bool needCopy = mCache->mMaxLength > 0; + + size_t old_size = mNumHead * mCache->mMaxLength * mHeadDim * byte; + mCache->mMaxLength = mCache->mPastLength + mExpandChunk; + // past_key: [1, numhead, headdim, maxlen] + auto new_key = Tensor::createDevice({mCache->mMaxLength, mNumHead, mHeadDim}); + // past_value: [1, numhead, maxlen, headdim] + auto new_value = Tensor::createDevice({mCache->mMaxLength, mNumHead, mHeadDim}); + size_t size = mNumHead * mCache->mMaxLength * mHeadDim * byte; + backend()->onAcquireBuffer(new_key, Backend::STATIC); + backend()->onAcquireBuffer(new_value, Backend::STATIC); + if (needCopy) { + auto newKeyBuf = MetalBackend::getBuffer(new_key); + auto new_key_ptr = (uint8_t*)[newKeyBuf.first contents] + newKeyBuf.second; + auto keyBuf = MetalBackend::getBuffer(mCache->mPastKey.get()); + auto key_ptr = (uint8_t*)[keyBuf.first contents] + keyBuf.second;; + ::memcpy(new_key_ptr, key_ptr, old_size); + + auto newValueBuf = MetalBackend::getBuffer(new_value); + auto new_value_ptr = (uint8_t*)[newValueBuf.first contents] + newValueBuf.second; + auto valueBuf = MetalBackend::getBuffer(mCache->mPastValue.get()); + auto value_ptr = (uint8_t*)[valueBuf.first contents] + valueBuf.second; + ::memcpy(new_value_ptr, value_ptr, old_size); + } + mCache->mPastKey.reset(new_key); + mCache->mPastValue.reset(new_value); +} + + +void AttentionBufExecution::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { + auto query = inputs[0]; + auto key = inputs[1]; + auto value = inputs[2]; + auto mask = inputs[3]; + auto mtbn = static_cast(backend()); + auto context = (__bridge MNNMetalContext *)mtbn->context(); + auto shape = query->shape(); + + int seq_len = shape[1]; + mNumHead = shape[2]; + mHeadDim = shape[3]; + mScale = 1.0 / sqrt(mHeadDim); + mIsDecode = seq_len == 1; + if (mCache->mPastLength == 0 || seq_len > 1) { + mCache->mPastLength = seq_len; + } + mCache->mKv_seq_len = mCache->mPastLength; + if(mIsDecode){ + mCache->mKv_seq_len = mCache->mPastLength + 1; + } + reallocKVCache(); + + // Update Parameters + { + auto param = (Param*)mParamQKV.contents; + param->scale = mScale; + param->head_dim = mHeadDim; + param->key_seq_len = mCache->mKv_seq_len; + param->head_num = mNumHead; + param->query_seq_len = seq_len; + } + // For softmax parameter + int inside, outside; + if (mIsDecode) { + inside = mNumHead; + outside = 1; + } else { + inside = 1; + outside = mCache->mKv_seq_len * mNumHead; + } + int axis = mCache->mKv_seq_len; + { + auto softmax = (int*)mParamSoftmax.contents; + // Inside, axis, outside, plane(invalid) + softmax[0] = inside; + softmax[1] = axis; + softmax[2] = outside; + softmax[3] = 0; + } + // Run QK Kernel + { + id pipeline; + if (mIsDecode) { + pipeline = mKernel_qk; + } else { + pipeline = mKernelPrefill_qk; + } + [encoder setComputePipelineState:pipeline]; + MetalBackend::setTensor(query, encoder, 0); + MetalBackend::setTensor(key, encoder, 1); + MetalBackend::setTensor(mTempQK.get(), encoder, 2); + MetalBackend::setTensor(mCache->mPastKey.get(), encoder, 3); + MetalBackend::setTensor(mask, encoder, 4); + [encoder setBuffer:mParamQKV offset:0 atIndex:5]; + auto gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(seq_len, mNumHead, mCache->mKv_seq_len)]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; + } + // Run Softmax Kernel + { + [encoder setComputePipelineState:mKernel_softmax]; + MetalBackend::setTensor(mTempQK.get(), encoder, 0); + MetalBackend::setTensor(mTempSoftMax.get(), encoder, 1); + [encoder setBuffer:mParamSoftmax offset:0 atIndex:2]; + auto gl = [context computeBestGroupAndLocal: mKernel_softmax threads:MTLSizeMake(inside, outside, 1)]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; + } + // Run QKV Kernel + { + id pipeline; + if (mIsDecode) { + pipeline = mKernel_qkv; + } else { + pipeline = mKernelPrefill_qkv; + } + [encoder setComputePipelineState:pipeline]; + MetalBackend::setTensor(mTempSoftMax.get(), encoder, 0); + MetalBackend::setTensor(value, encoder, 1); + MetalBackend::setTensor(outputs[0], encoder, 2); + MetalBackend::setTensor(mCache->mPastValue.get(), encoder, 3); + [encoder setBuffer:mParamQKV offset:0 atIndex:4]; + auto gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(seq_len, mNumHead, mHeadDim)]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; + } + // Update status + if(mIsDecode){ + mCache->mPastLength += 1; + mCache->mKv_seq_len = mCache->mPastLength + 1; + } + + return; +} + +class AttentionBufCreator : public MetalBackend::Creator { +public: + virtual Execution *onCreate(const std::vector &inputs, const MNN::Op *op, Backend *backend, const std::vector &outputs) const override { + auto param = op->main_as_AttentionParam(); + return new AttentionBufExecution(backend, param->kv_cache()); + } +}; +REGISTER_METAL_OP_TRANSFORMER_CREATOR(AttentionBufCreator, OpType_Attention); + +} // namespace MNN +#endif/* MNN_SUPPORT_TRANSFORMER_FUSE */ +#endif + diff --git a/source/backend/metal/MetalBackend.hpp b/source/backend/metal/MetalBackend.hpp index fe7107911..786a89351 100644 --- a/source/backend/metal/MetalBackend.hpp +++ b/source/backend/metal/MetalBackend.hpp @@ -137,7 +137,7 @@ class MetalBackend : public Backend { * @param creator registering creator. */ static void addCreator(OpType type, Creator *creator); - static void setTensor(MNN::Tensor* tensor, id encoder, int index); + static void setTensor(const MNN::Tensor* tensor, id encoder, int index); static std::pair, int> getBuffer(MNN::Tensor* tensor); size_t getTensorSizeInBytes(const Tensor* tensor) const; virtual bool onSelectDynamicAllocator(int index, int maxIndex) override; @@ -264,5 +264,10 @@ class MetalCreatorRegister { MetalBackend::addCreator(opType, new name); \ } +#define REGISTER_METAL_OP_TRANSFORMER_CREATOR(name, opType) \ + void ___##name##__##opType##__() { \ + MetalBackend::addCreator(opType, new name); \ + } + #endif /* MNN_METAL_ENABLED */ #endif /* MetalBackend_hpp */ diff --git a/source/backend/metal/MetalBackend.mm b/source/backend/metal/MetalBackend.mm index 35636c6c3..64187b36c 100644 --- a/source/backend/metal/MetalBackend.mm +++ b/source/backend/metal/MetalBackend.mm @@ -5,12 +5,12 @@ // Created by MNN on 2019/01/30. // Copyright © 2018, Alibaba Group Holding Limited // + #import "backend/metal/MetalBackend.hpp" #define MNN_METAL #import #define METAL_CONST_BUFFER_LIMIT 128 #if MNN_METAL_ENABLED -#import #import "backend/metal/MNNMetalContext.h" #import "core/Macro.h" #import "core/TensorUtils.hpp" @@ -126,24 +126,30 @@ MemChunk chunk() override { auto format = TensorUtils::getDescribe(tensor)->dimensionFormat; size_t size; if (MNN_DATA_FORMAT_NC4HW4 == format && tensor->dimensions() >= 2) { - size_t width = 1; - size_t height = 1; - auto batch = tensor->length(0); - auto channel = tensor->length(1); + int width = 1; + int height = 1; + int batch = tensor->length(0); + int channel = tensor->length(1); if (tensor->dimensions() >= 3) { height = tensor->length(2); } for (int i=3; idimensions(); ++i) { width *= tensor->length(i); } - auto alignC = ROUND_UP(channel, 4); - auto hR = ROUND_UP(height, 4) - height; + int alignC = ROUND_UP(channel, 4); + int hR = ROUND_UP(height, 4) - height; // width parallel 4, may exceed 3 elements - auto wR = ROUND_UP(width + 3, 4) - width; + int wR = ROUND_UP(width + 3, 4) - width; + int bhw = batch * width * height; + int bhwR = UP_DIV(bhw, 16) * 16 - bhw; + int extraPadding = ALIMAX(bhwR, (hR * width + wR)); size = batch * alignC * width * height; - size = size + hR * width * 4 + wR * 4; + size = size + extraPadding * 4; } else { - size = tensor->elementSize(); + size = 1; + for (int i=0; idimensions(); ++i) { + size *= tensor->length(i); + } size = ROUND_UP(size, 4); } if (0 == size) { @@ -356,7 +362,7 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { // shape ((int *)shape.contents)[0] = s; ((int *)shape.contents)[1] = c; - ((int *)shape.contents)[2] = z; + ((int *)shape.contents)[2] = b; ((int *)shape.contents)[3] = b * z; // threads @@ -459,13 +465,6 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { auto dfmt = TensorUtils::getDescribe(dst)->dimensionFormat; auto device = (id)((MetalRuntimeAllocator::MetalBufferAlloc *) (dst->deviceId()))->getBuffer(); auto floats = src->getType().code == halide_type_float; - std::unique_ptr tempSrc; - if (sfmt == dfmt && sfmt == MNN_DATA_FORMAT_NC4HW4) { - tempSrc.reset(new Tensor(src, Tensor::CAFFE)); - MNNCPUCopyBuffer(src, tempSrc.get()); - src = tempSrc.get(); - sfmt = TensorUtils::getDescribe(src)->dimensionFormat; - } // For command queue from user, need user to make sure last frame's gpu work is ready bool needWait = !mRuntime->userSync(); // cast @@ -486,7 +485,8 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { }; ::memcpy(mShapeH2D.contents, limits, sizeof(limits)); auto encoder = [getCommandBufferForBufferCopy() computeCommandEncoder]; - auto bandwidth = [ctx load: @"downcast_float4" encoder:encoder fp16:mUseFloatAsFp16]; + auto pipeline = [ctx pipelineWithName:@"downcast_float4" fp16:mUseFloatAsFp16]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:host offset:0 atIndex:0]; [encoder setBuffer:device offset:TensorUtils::getDescribe(dst)->extra.offset atIndex:1]; @@ -494,7 +494,7 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { //[ctx dispatchEncoder:encoder threads:{sizeC4, 1, 1} bandwidth:bandwidth]; std::pair threads; threads.first = {sizeC4, 1, 1}; - threads.second = {bandwidth.maxThreadsPerThreadgroup, 1, 1}; + threads.second = {[pipeline maxTotalThreadsPerThreadgroup], 1, 1}; threads.second.width = threads.second.width <= threads.first.width ? threads.second.width : threads.first.width; threads.first.width = UP_DIV(threads.first.width, threads.second.width); [encoder dispatchThreadgroups:threads.first threadsPerThreadgroup:threads.second]; @@ -520,13 +520,14 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { auto encoder = [getCommandBufferForBufferCopy() computeCommandEncoder]; auto kernel = kernelForConvert(src->getType(), sfmt, dfmt, Down); MNN_ASSERT(kernel != nil); // unsupported sfmt to dfmt - - auto bandwidth = [ctx load:kernel encoder:encoder fp16:mUseFloatAsFp16]; + auto pipeline = [ctx pipelineWithName:kernel fp16:mUseFloatAsFp16]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:buffer offset:0 atIndex:0]; [encoder setBuffer:device offset:TensorUtils::getDescribe(dst)->extra.offset atIndex:1]; [encoder setBuffer:mShapeH2D offset:0 atIndex:2]; - [ctx dispatchEncoder:encoder threads:size bandwidth:bandwidth]; + auto gl = [ctx computeBestGroupAndLocal:pipeline threads:size]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; [encoder endEncoding]; commit(); //[ctx wait]; @@ -539,16 +540,6 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { auto dfmt = TensorUtils::getDescribe(dst)->dimensionFormat; auto device = (id)((MetalRuntimeAllocator::MetalBufferAlloc *)src->deviceId())->getBuffer(); auto floats = src->getType().code == halide_type_float; - std::shared_ptr tempDst; - if (sfmt == dfmt && sfmt == MNN_DATA_FORMAT_NC4HW4) { - tempDst.reset(new Tensor(dst, Tensor::CAFFE), [dst](void* t) { - auto tensor = (Tensor*)t; - MNNCPUCopyBuffer(tensor, dst); - delete tensor; - }); - dst = tempDst.get(); - dfmt = TensorUtils::getDescribe(dst)->dimensionFormat; - } // cast if (sfmt == dfmt || src->dimensions() <= 1) { if (floats && mUseFloatAsFp16) { @@ -558,7 +549,8 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { NSUInteger size = src->elementSize(); auto encoder = [getCommandBufferForBufferCopy() computeCommandEncoder]; - auto bandwidth = [ctx load: @"upcast_float4" encoder:encoder fp16:mUseFloatAsFp16]; + auto pipeline = [ctx pipelineWithName:@"upcast_float4" fp16:mUseFloatAsFp16]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:device offset:TensorUtils::getDescribe(src)->extra.offset atIndex:0]; [encoder setBuffer:buffer offset:0 atIndex:1]; auto sizeC4 = UP_DIV(size, 4); @@ -573,7 +565,7 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { //[ctx dispatchEncoder:encoder threads:{sizeC4, 1, 1} bandwidth:bandwidth]; std::pair threads; threads.first = {sizeC4, 1, 1}; - threads.second = {bandwidth.maxThreadsPerThreadgroup, 1, 1}; + threads.second = {[pipeline maxTotalThreadsPerThreadgroup], 1, 1}; threads.second.width = threads.second.width <= threads.first.width ? threads.second.width : threads.first.width; threads.first.width = UP_DIV(threads.first.width, threads.second.width); [encoder dispatchThreadgroups:threads.first threadsPerThreadgroup:threads.second]; @@ -597,18 +589,28 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { auto kernel = kernelForConvert(src->getType(), sfmt, dfmt, Up); MNN_ASSERT(kernel != nil); // unsupported sfmt to dfmt - auto bandwidth = [ctx load:kernel encoder:encoder fp16:mUseFloatAsFp16]; + auto pipeline = [ctx pipelineWithName:kernel fp16:mUseFloatAsFp16]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:device offset:TensorUtils::getDescribe(src)->extra.offset atIndex:0]; [encoder setBuffer:buffer offset:0 atIndex:1]; [encoder setBuffer:mShapeD2H offset:0 atIndex:2]; - [ctx dispatchEncoder:encoder threads:size bandwidth:bandwidth]; + auto gl = [ctx computeBestGroupAndLocal:pipeline threads:size]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; [encoder endEncoding]; commit(); wait(); memcpy(dst->host(), buffer.contents, dst->size()); } } - +static const char* gCopy = R"metal( +#include +#include +using namespace metal; +kernel void main0(const device int4 *in [[buffer(0)]], device int4 *out [[buffer(1)]], constant uint4& limit [[buffer(2)]], uint gid [[thread_position_in_grid]]) { + if (gid < limit.x) { + out[int(gid)] = in[int(gid)]; + } +})metal"; void MetalBackend::onCopyDeviceToDevice(const Tensor *src, const Tensor *dst, id encoder, id shape) const { auto ctx = (__bridge MNNMetalContext *)context(); @@ -619,12 +621,28 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { // copy if (sfmt == dfmt || src->dimensions() <= 1) { - auto flt = dst->getType().code == halide_type_float; - auto size = flt ? dst->elementSize() : dst->size(); - auto bandwidth = [ctx load:flt ? @"copy_float" : @"copy_byte" encoder:encoder fp16:mUseFloatAsFp16]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)src->deviceId())->getBuffer() offset:TensorUtils::getDescribe(src)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)dst->deviceId())->getBuffer() offset:TensorUtils::getDescribe(dst)->extra.offset atIndex:1]; - [ctx dispatchEncoder:encoder threads:{(NSUInteger)size, 1, 1} bandwidth:bandwidth]; + auto size = dst->usize(); + if (mUseFloatAsFp16 && dst->getType().code == halide_type_float) { + size = size / 2; + } + size = UP_DIV(size, (4 * sizeof(float))); + std::vector keys = { + "copyC4" + }; + id pipeline = mRuntime->findPipeline(keys); + if (nil == pipeline) { + pipeline = makeComputePipelineWithSourceOption(gCopy, "main0", nil); + mRuntime->insertPipeline(keys, pipeline); + } + [encoder setComputePipelineState:pipeline]; + if (shape == nil) { + shape = getConstBuffer(4 * sizeof(int)); + } + ((uint32_t*)[shape contents])[0] = size; + setTensor(src, encoder, 0); + setTensor(dst, encoder, 1); + [encoder setBuffer:shape offset:0 atIndex:2]; + [encoder dispatchThreadgroups:MTLSizeMake(UP_DIV(size, 256), 1, 1) threadsPerThreadgroup:MTLSizeMake(256, 1, 1)]; } // convert else { @@ -635,11 +653,13 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { } auto size = getTensorShape(shape, src); - auto bandwidth = [ctx load:kernel encoder:encoder fp16:mUseFloatAsFp16]; + auto pipeline = [ctx pipelineWithName:kernel fp16:mUseFloatAsFp16]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:( id)((MetalRuntimeAllocator::MetalBufferAlloc *)(src->buffer().device))->getBuffer() offset:TensorUtils::getDescribe(src)->extra.offset atIndex:0]; [encoder setBuffer:( id)((MetalRuntimeAllocator::MetalBufferAlloc *)(dst->buffer().device))->getBuffer() offset:TensorUtils::getDescribe(dst)->extra.offset atIndex:1]; [encoder setBuffer:shape offset:0 atIndex:2]; - [ctx dispatchEncoder:encoder threads:size bandwidth:bandwidth]; + auto gl = [ctx computeBestGroupAndLocal:pipeline threads:size]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; } if (standalone) { @@ -712,7 +732,7 @@ MTLSize getTensorShape(id shape, const Tensor *tensor) { return _commandBuffer_net; } -void MetalBackend::setTensor(MNN::Tensor* tensor, id encoder, int index) { +void MetalBackend::setTensor(const MNN::Tensor* tensor, id encoder, int index) { [encoder setBuffer:((MetalRuntimeAllocator::MetalBufferAlloc *)tensor->deviceId())->getBuffer() offset:TensorUtils::getDescribe(tensor)->extra.offset atIndex:index]; } std::pair, int> MetalBackend::getBuffer(MNN::Tensor* tensor) { diff --git a/source/backend/metal/MetalCodeGen.py b/source/backend/metal/MetalCodeGen.py index 59af20ef5..9d63fd988 100644 --- a/source/backend/metal/MetalCodeGen.py +++ b/source/backend/metal/MetalCodeGen.py @@ -18,6 +18,7 @@ def genRegister(): f.write(" namespace MNN {\n") f.write("#if MNN_METAL_ENABLED\n") funcs=[] + transformerFuncs = [] for shapath in shaders: with open(shapath,"r") as sha: lines=sha.readlines() @@ -27,10 +28,22 @@ def genRegister(): funcname="___"+x[0]+"__"+x[1]+"__();" funcs.append(funcname) f.write(" extern void "+funcname+"\n") + elif l.startswith('REGISTER_METAL_OP_TRANSFORMER_CREATOR('): + x=l.replace("REGISTER_METAL_OP_TRANSFORMER_CREATOR(","").replace(")","").replace(" ","").replace(";","").replace("\n","").split(",") + funcname="___"+x[0]+"__"+x[1]+"__();" + transformerFuncs.append(funcname) + f.write("#ifdef MNN_SUPPORT_TRANSFORMER_FUSE\n") + f.write(" extern void "+funcname+"\n") + f.write('#endif\n') + pass f.write("void registerMetalOps() {\n") for func in funcs: f.write(" "+func+"\n") + f.write('#ifdef MNN_SUPPORT_TRANSFORMER_FUSE\n') + for func in transformerFuncs: + f.write(" "+func+"\n") + f.write('#endif\n') f.write("}\n#endif\n}") if os.path.isdir(renderPath): shaders=[] diff --git a/source/backend/metal/MetalConvolution.hpp b/source/backend/metal/MetalConvolution.hpp index 150b61eae..139f6a427 100644 --- a/source/backend/metal/MetalConvolution.hpp +++ b/source/backend/metal/MetalConvolution.hpp @@ -22,7 +22,7 @@ class MetalConvolution : public MetalConvolutionCommon { virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override; protected: - virtual void onFloat(const Tensor *input, const Tensor *output, id encoder) override; + virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; private: MetalConvolution(Backend *backend, const MNN::Op *op, std::shared_ptr weight, std::shared_ptr bias); diff --git a/source/backend/metal/MetalConvolution.mm b/source/backend/metal/MetalConvolution.mm index e614b4df0..c1b6ee37c 100755 --- a/source/backend/metal/MetalConvolution.mm +++ b/source/backend/metal/MetalConvolution.mm @@ -175,14 +175,13 @@ return NO_ERROR; } -void MetalConvolution::onFloat(const Tensor *input, const Tensor *output, id encoder) { - auto oc_4 = UP_DIV(output->channel(), 4); +void MetalConvolution::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { + auto input = inputs[0]; + auto output = outputs[0]; - auto bandwidth = (MetalBandwidth){mPipeline.threadExecutionWidth, mPipeline.maxTotalThreadsPerThreadgroup, NO}; - [encoder setComputePipelineState:mPipeline]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; + MetalBackend::setTensor(input, encoder, 0); + MetalBackend::setTensor(output, encoder, 1); [encoder setBuffer:mConstBuffer offset:0 atIndex:2]; MetalBackend::setTensor(mWeight.get(), encoder, 3); MetalBackend::setTensor(mBias.get(), encoder, 4); diff --git a/source/backend/metal/MetalConvolution1x1.hpp b/source/backend/metal/MetalConvolution1x1.hpp index 7e81d41ba..672d433b6 100644 --- a/source/backend/metal/MetalConvolution1x1.hpp +++ b/source/backend/metal/MetalConvolution1x1.hpp @@ -21,10 +21,9 @@ class MetalConvolution1x1 : public MetalConvolutionCommon { virtual ~MetalConvolution1x1() = default; virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override; -protected: - virtual void onFloat(const Tensor *input, const Tensor *output, id encoder) override; + virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; private: - MetalConvolution1x1(Backend *backend, const MNN::Op *op, std::shared_ptr weight, std::shared_ptr bias, std::shared_ptr dequantScale, std::shared_ptr dequantBias, int dequantBits); + MetalConvolution1x1(Backend *backend, const MNN::Op *op, std::shared_ptr weight, std::shared_ptr bias, std::shared_ptr dequantScale, int dequantBits); id mPipeline; std::pair mThreads; }; diff --git a/source/backend/metal/MetalConvolution1x1.mm b/source/backend/metal/MetalConvolution1x1.mm index b768cd4d4..9ca59c59c 100644 --- a/source/backend/metal/MetalConvolution1x1.mm +++ b/source/backend/metal/MetalConvolution1x1.mm @@ -31,11 +31,10 @@ loadWeight(op->main_as_Convolution2D(), ldInt8Weight); } -MetalConvolution1x1::MetalConvolution1x1(Backend *backend, const MNN::Op *op, std::shared_ptr weight, std::shared_ptr bias, std::shared_ptr dequantScale, std::shared_ptr dequantBias, int dequantBits) : MetalConvolutionCommon(backend, op, bias) { +MetalConvolution1x1::MetalConvolution1x1(Backend *backend, const MNN::Op *op, std::shared_ptr weight, std::shared_ptr bias, std::shared_ptr dequantScale, int dequantBits) : MetalConvolutionCommon(backend, op, bias) { mWeight = weight; mBias = bias; - mDequantScale = dequantScale; - mDequantZero = dequantBias; + mDequantScaleBias = dequantScale; mDequantBits = dequantBits; } @@ -47,7 +46,7 @@ if (nullptr == dst) { return true; } - *dst = new MetalConvolution1x1(bn, op, mWeight, mBias, mDequantScale, mDequantZero, mDequantBits); + *dst = new MetalConvolution1x1(bn, op, mWeight, mBias, mDequantScaleBias, mDequantBits); return true; } @@ -55,15 +54,18 @@ MetalConvolutionCommon::onResize(inputs, outputs); // prepare + // For C4NHW4 format, NHW can be fuse to W auto input = inputs[0]; auto output = outputs[0]; - - auto is = input->width() * input->height(); - auto ic_4 = UP_DIV(input->channel(), 4); - auto ow = output->width(); - auto oh = output->height(); - auto os = ow * oh; - auto ob = output->batch(); + int is = input->batch(); + for (int i=2; idimensions(); ++i) { + is *= input->length(i); + } + int ic_4 = UP_DIV(input->channel(), 4); + int ow = is; + int oh = 1; + int os = ow; + int ob = 1; auto oc = output->channel(); auto oc_4 = UP_DIV(output->channel(), 4); auto backend = static_cast(this->backend()); @@ -75,7 +77,7 @@ ::memcpy(mConstBuffer.contents, constants, sizeof(constants)); MetalRuntime* rt = (MetalRuntime *)backend->runtime(); - if (ow == input->width() && oh == input->height() && mDequantScale.get()) { + if (mDequantScaleBias.get()) { NSUInteger gid_x = UP_DIV(ow * oh, 4); NSUInteger gid_y = oc_4; NSUInteger gid_z = ob; @@ -89,8 +91,8 @@ (id)(((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId()))->getBuffer(), mConstBuffer, (((MetalRuntimeAllocator::MetalBufferAlloc *)mWeight->deviceId()))->getBuffer(), ((MetalRuntimeAllocator::MetalBufferAlloc *)mBias->deviceId())->getBuffer(), - (((MetalRuntimeAllocator::MetalBufferAlloc *)mDequantScale->deviceId()))->getBuffer(), - (((MetalRuntimeAllocator::MetalBufferAlloc *)mDequantZero->deviceId()))->getBuffer(), nil]; + (((MetalRuntimeAllocator::MetalBufferAlloc *)mDequantScaleBias->deviceId()))->getBuffer(), + nil]; const Tensor* weight = mWeight.get(); const Tensor* bias = mBias.get(); int buffer_offset[] = { @@ -99,8 +101,7 @@ 0, TensorUtils::getDescribe(weight)->extra.offset, TensorUtils::getDescribe(bias)->extra.offset, - TensorUtils::getDescribe(mDequantScale.get())->extra.offset, - TensorUtils::getDescribe(mDequantZero.get())->extra.offset, + TensorUtils::getDescribe(mDequantScaleBias.get())->extra.offset, 0}; MetalRuntime *rt = (MetalRuntime *)backend->runtime(); @@ -147,9 +148,8 @@ //printf("conv1x1_z4, %d %d %d %d\n", ow, oh, oc_4, ic_4); } } else { - NSString* shaderName[] = {@"conv1x1_w4h2", @"conv1x1_w2h2", @"conv1x1_w4h4", @"conv1x1_w2c2", @"conv1x1_w2h2c2"}; - int itemW[] = {4, 2, 4, 2, 2}; - int itemH[] = {2, 2, 4, 1, 2}; + NSString* shaderName[] = {@"conv1x1_g1z8", @"conv1x1_g1z4", @"conv1x1_w4h4", @"conv1x1_w2c2", @"conv1x1_w4c2"}; + int itemW[] = {8, 4, 16, 2, 4}; int itemC[] = {4, 4, 4, 8, 8}; int actual_kernel = 5; if (oc_4 % 2 != 0) { @@ -168,8 +168,8 @@ for(int knl_idx = 0; knl_idx < actual_kernel; knl_idx++) { id pipeline = [context pipelineWithName:shaderName[knl_idx] fp16:backend->useFp16InsteadFp32()]; NSUInteger gid_x = UP_DIV(ow, itemW[knl_idx]); - NSUInteger gid_y = UP_DIV(oh, itemH[knl_idx]); - NSUInteger gid_z = ob * UP_DIV(oc, itemC[knl_idx]); + NSUInteger gid_y = UP_DIV(oc, itemC[knl_idx]); + NSUInteger gid_z = 1; std::string name = [shaderName[knl_idx] UTF8String]; auto ret = [context getGridAndThreadgroup:pipeline gid:MTLSizeMake(gid_x, gid_y, gid_z) loop:10 buffer:arr runtime:rt shaderName:name offsets:buffer_offset queue:backend->queue()]; @@ -188,16 +188,17 @@ return NO_ERROR; } -void MetalConvolution1x1::onFloat(const Tensor *input, const Tensor *output, id encoder) { +void MetalConvolution1x1::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { + auto input = inputs[0]; + auto output = outputs[0]; [encoder setComputePipelineState:mPipeline]; [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; [encoder setBuffer:mConstBuffer offset:0 atIndex:2]; MetalBackend::setTensor(mWeight.get(), encoder, 3); MetalBackend::setTensor(mBias.get(), encoder, 4); - if (mDequantScale && mDequantZero) { - MetalBackend::setTensor(mDequantScale.get(), encoder, 5); - MetalBackend::setTensor(mDequantZero.get(), encoder, 6); + if (mDequantScaleBias) { + MetalBackend::setTensor(mDequantScaleBias.get(), encoder, 5); } [encoder dispatchThreadgroups:mThreads.first threadsPerThreadgroup:mThreads.second]; } diff --git a/source/backend/metal/MetalConvolutionCommon.hpp b/source/backend/metal/MetalConvolutionCommon.hpp index de1b00333..299551922 100644 --- a/source/backend/metal/MetalConvolutionCommon.hpp +++ b/source/backend/metal/MetalConvolutionCommon.hpp @@ -20,12 +20,10 @@ class MetalConvolutionCommon : public MetalExecution { public: MetalConvolutionCommon(Backend *backend, const MNN::Op *op, std::shared_ptr bias); virtual ~MetalConvolutionCommon() = default; - virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; protected: void loadWeight(const MNN::Convolution2D *conv, bool loadWeightInt8 = false); - virtual void onFloat(const Tensor *input, const Tensor *output, id encoder) = 0; virtual std::shared_ptr weightTransform(int group, int oc, int ic, int kh, int kw, const float *src, bool int8Weight = false, bool int4Weight = false); private: @@ -42,8 +40,7 @@ class MetalConvolutionCommon : public MetalExecution { std::shared_ptr mWeight; std::shared_ptr mBias; - std::shared_ptr mDequantScale; - std::shared_ptr mDequantZero; + std::shared_ptr mDequantScaleBias; int mDequantBits; id mConstBuffer = nil; }; diff --git a/source/backend/metal/MetalConvolutionCommon.mm b/source/backend/metal/MetalConvolutionCommon.mm index 297c17b00..5bdeea2b0 100644 --- a/source/backend/metal/MetalConvolutionCommon.mm +++ b/source/backend/metal/MetalConvolutionCommon.mm @@ -68,10 +68,6 @@ } } -void MetalConvolutionCommon::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { - return onFloat(inputs[0], outputs[0], encoder); -} - template void weightInBlock(int group, int oc, int ic, int kh, int kw, const FType *src, uint8_t* dstOrigion) { auto goc = oc / group; @@ -101,25 +97,24 @@ void weightInBlock(int group, int oc, int ic, int kh, int kw, const FType *src, } } -static std::vector> getDequantScale(float* scale, int size, MetalBackend *backend, bool asymmetric) { +static std::shared_ptr getDequantScale(float* scale, int size, MetalBackend *backend, bool asymmetric) { int outputCount = 0; if (asymmetric) { outputCount = size / 2; } else { outputCount = size; } - std::vector> scaleBias(2); - std::shared_ptr dequantScale(MNN::Tensor::createDevice({outputCount * 4})); - std::shared_ptr dequantBias(MNN::Tensor::createDevice({outputCount * 4})); - bool res = backend->onAcquireBuffer(dequantScale.get(), Backend::STATIC) && backend->onAcquireBuffer(dequantBias.get(), Backend::STATIC); + int alignOutputCount = ALIGN_UP4(outputCount); + std::shared_ptr dequantScale(MNN::Tensor::createDevice({(int)(alignOutputCount * sizeof(float) * 2)})); + bool res = backend->onAcquireBuffer(dequantScale.get(), Backend::STATIC); if (!res) { MNN_ERROR("Buffer allocated error!\n"); - return scaleBias; + return nullptr; } auto buffer0 = MetalBackend::getBuffer(dequantScale.get()); auto dst_scale = (uint8_t*)[buffer0.first contents] + buffer0.second; - auto buffer1 = MetalBackend::getBuffer(dequantBias.get()); - auto dst_bias = (uint8_t*)[buffer1.first contents] + buffer1.second; + ::memset(dst_scale, 0, alignOutputCount * 2 * sizeof(float)); + auto dst_bias = dst_scale + alignOutputCount * sizeof(float); for (int o = 0; o < outputCount; ++o) { float min = 0.0f; float alpha = 0.0f; @@ -131,10 +126,8 @@ void weightInBlock(int group, int oc, int ic, int kh, int kw, const FType *src, } ((float*)dst_scale)[o] = alpha; ((float*)dst_bias)[o] = min; - } - scaleBias[0] = dequantScale; - scaleBias[1] = dequantBias; - return scaleBias; + } + return dequantScale; } void MetalConvolutionCommon::loadWeight(const MNN::Convolution2D *conv, bool loadWeightInt8) { std::shared_ptr qnt = NULL; @@ -157,8 +150,7 @@ void weightInBlock(int group, int oc, int ic, int kh, int kw, const FType *src, auto backend = static_cast(this->backend()); mWeight = weightTransform(group, oc, ic, kh, kw, (float*)qnt->weight.get(), !qnt->canUseInt4, qnt->canUseInt4); auto dequantParams = getDequantScale(qnt->alpha.get(), qnt->alpha.size(), backend, qnt->asymmetric); - mDequantScale = dequantParams[0]; - mDequantZero = dequantParams[1]; + mDequantScaleBias = dequantParams; mDequantBits = qnt->canUseInt4 ? 4:8; } else if (qnt && qnt->weightFloat.size() > 0) { mWeight = weightTransform(group, oc, ic, kh, kw, qnt->weightFloat.get(), false, false); diff --git a/source/backend/metal/MetalConvolutionDepthwise.hpp b/source/backend/metal/MetalConvolutionDepthwise.hpp index ffced0a64..9078c3058 100644 --- a/source/backend/metal/MetalConvolutionDepthwise.hpp +++ b/source/backend/metal/MetalConvolutionDepthwise.hpp @@ -21,7 +21,7 @@ class MetalConvolutionDepthwise : public MetalConvolutionCommon { virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; protected: - virtual void onFloat(const Tensor *input, const Tensor *output, id encoder) override; + virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; virtual std::shared_ptr weightTransform(int group, int oc, int ic, int kh, int kw, const float *src, bool int8Weight=false, bool int4Weight=false) override; private: id mPipeline; diff --git a/source/backend/metal/MetalConvolutionDepthwise.mm b/source/backend/metal/MetalConvolutionDepthwise.mm index 8cabaca7f..546896bb9 100755 --- a/source/backend/metal/MetalConvolutionDepthwise.mm +++ b/source/backend/metal/MetalConvolutionDepthwise.mm @@ -82,12 +82,10 @@ return NO_ERROR; } -void MetalConvolutionDepthwise::onFloat(const Tensor *input, const Tensor *output, id encoder) { +void MetalConvolutionDepthwise::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { [encoder setComputePipelineState:mPipeline]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset: TensorUtils::getDescribe(input)->extra.offset -atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset: TensorUtils::getDescribe(output)->extra.offset -atIndex:1]; + MetalBackend::setTensor(inputs[0], encoder, 0); + MetalBackend::setTensor(outputs[0], encoder, 1); [encoder setBuffer:mConstBuffer offset:0 atIndex:2]; MetalBackend::setTensor(mWeight.get(), encoder, 3); MetalBackend::setTensor(mBias.get(), encoder, 4); diff --git a/source/backend/metal/MetalConvolutionWinograd.hpp b/source/backend/metal/MetalConvolutionWinograd.hpp index d734147d7..bd589ad25 100644 --- a/source/backend/metal/MetalConvolutionWinograd.hpp +++ b/source/backend/metal/MetalConvolutionWinograd.hpp @@ -23,7 +23,7 @@ class MetalConvolutionWinograd : public MetalConvolutionCommon { virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override; protected: - virtual void onFloat(const Tensor *input, const Tensor *output, id encoder) override; + virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; virtual std::shared_ptr weightTransform(int group, int oc, int ic, int kh, int kw, const float *src, bool int8Weight=false, bool int4Weight=false) override; private: diff --git a/source/backend/metal/MetalConvolutionWinograd.mm b/source/backend/metal/MetalConvolutionWinograd.mm index 2848f9779..dcc321782 100644 --- a/source/backend/metal/MetalConvolutionWinograd.mm +++ b/source/backend/metal/MetalConvolutionWinograd.mm @@ -141,32 +141,40 @@ return NO_ERROR; } -void MetalConvolutionWinograd::onFloat(const Tensor *input, const Tensor *output, id encoder) { +void MetalConvolutionWinograd::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { + auto input = inputs[0]; + auto output = outputs[0]; auto backend = static_cast(this->backend()); auto context = (__bridge MNNMetalContext *)backend->context(); { // transform - auto bandwidth = [context load:mKernelX == 3 ? @"winograd_transform_source2_3_1" : @"winograd_transform_source2_5_1" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)mTempSrc->deviceId())->getBuffer() offset:TensorUtils::getDescribe(mTempSrc.get())->extra.offset atIndex:1]; + auto pipeline = [context pipelineWithName:mKernelX == 3 ? @"winograd_transform_source2_3_1" : @"winograd_transform_source2_5_1" fp16:backend->useFp16InsteadFp32()]; + [encoder setComputePipelineState:pipeline]; + MetalBackend::setTensor(input, encoder, 0); + MetalBackend::setTensor(mTempSrc.get(), encoder, 1); [encoder setBuffer:mConstBuffer offset:0 atIndex:2]; - [context dispatchEncoder:encoder threads:mInputTransformThreads bandwidth:bandwidth]; + auto gl = [context computeBestGroupAndLocal:pipeline threads:mInputTransformThreads]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; } { // gemm - auto bandwidth = [context load:@"matmul4x4" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)mTempSrc->deviceId())->getBuffer() offset:TensorUtils::getDescribe(mTempSrc.get())->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)mTempDst->deviceId())->getBuffer() offset:TensorUtils::getDescribe(mTempDst.get())->extra.offset atIndex:1]; + auto pipeline = [context pipelineWithName:@"matmul4x4" fp16:backend->useFp16InsteadFp32()]; + [encoder setComputePipelineState:pipeline]; + MetalBackend::setTensor(mTempSrc.get(), encoder, 0); + MetalBackend::setTensor(mTempDst.get(), encoder, 1); MetalBackend::setTensor(mWeight.get(), encoder, 2); [encoder setBuffer:mShapeBuffer offset:0 atIndex:3]; - [context dispatchEncoder:encoder threads:mMatMulThreads bandwidth:bandwidth]; + auto gl = [context computeBestGroupAndLocal:pipeline threads:mMatMulThreads]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; } { // transform - auto bandwidth = [context load:mKernelX == 3 ? @"winograd_transform_dest2_3_1" : @"winograd_transform_dest2_5_1" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)mTempDst->deviceId())->getBuffer() offset:TensorUtils::getDescribe(mTempDst.get())->extra.offset atIndex:0]; + auto pipeline = [context pipelineWithName:mKernelX == 3 ? @"winograd_transform_dest2_3_1" : @"winograd_transform_dest2_5_1" fp16:backend->useFp16InsteadFp32()]; + [encoder setComputePipelineState:pipeline]; + MetalBackend::setTensor(mTempDst.get(), encoder, 0); MetalBackend::setTensor(mBias.get(), encoder, 1); - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:2]; + MetalBackend::setTensor(output, encoder, 2); [encoder setBuffer:mConstBuffer offset:0 atIndex:3]; - [context dispatchEncoder:encoder threads:mOutputTransformThreads bandwidth:bandwidth]; + auto gl = [context computeBestGroupAndLocal:pipeline threads:mOutputTransformThreads]; + [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; } } std::shared_ptr MetalConvolutionWinograd::weightTransform(int group, int oc, int ic, int kh, int kw, const float *src, bool int8Weight, bool int4Weight) { diff --git a/source/backend/metal/MetalExecution.hpp b/source/backend/metal/MetalExecution.hpp index 5aab8b7a4..6b90a5241 100644 --- a/source/backend/metal/MetalExecution.hpp +++ b/source/backend/metal/MetalExecution.hpp @@ -1,3 +1,11 @@ +// +// MetalExecution.hpp +// MNN +// +// Created by MNN on 2023/11/09. +// Copyright © 2018, Alibaba Group Holding Limited +// + #ifndef MetalExecution_hpp #define MetalExecution_hpp diff --git a/source/backend/metal/MetalExecution.mm b/source/backend/metal/MetalExecution.mm index 1d28023aa..35de88d24 100644 --- a/source/backend/metal/MetalExecution.mm +++ b/source/backend/metal/MetalExecution.mm @@ -1,3 +1,11 @@ +// +// MetalExecution.mm +// MNN +// +// Created by MNN on 2023/11/09. +// Copyright © 2018, Alibaba Group Holding Limited +// + #include "MetalExecution.hpp" #import "backend/metal/MetalBackend.hpp" diff --git a/source/backend/metal/MetalLoop.mm b/source/backend/metal/MetalLoop.mm index 419d8503f..be64819ff 100644 --- a/source/backend/metal/MetalLoop.mm +++ b/source/backend/metal/MetalLoop.mm @@ -1,3 +1,11 @@ +// +// MetalLoop.mm +// MNN +// +// Created by MNN on 2023/12/28. +// Copyright © 2018, Alibaba Group Holding Limited +// + #import "core/Macro.h" #import "MetalCast.hpp" #import "MetalBinary.hpp" diff --git a/source/backend/metal/MetalMatMul.hpp b/source/backend/metal/MetalMatMul.hpp index 258fd5a38..8e70629d2 100644 --- a/source/backend/metal/MetalMatMul.hpp +++ b/source/backend/metal/MetalMatMul.hpp @@ -18,7 +18,7 @@ namespace MNN { class MetalMatMul : public MetalExecution { public: - MetalMatMul(Backend *backend, const MatMul *matmul); + MetalMatMul(Backend *backend, const MatMul *matmul, bool withBias); virtual ~MetalMatMul(); virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; @@ -27,6 +27,8 @@ class MetalMatMul : public MetalExecution { id mConstBuffer = nil; bool mTransposeA = false; bool mTransposeB = false; + id mPipeline; + std::pair mThreads; }; } // namespace MNN diff --git a/source/backend/metal/MetalMatMul.mm b/source/backend/metal/MetalMatMul.mm index db741398e..a6454df06 100755 --- a/source/backend/metal/MetalMatMul.mm +++ b/source/backend/metal/MetalMatMul.mm @@ -18,11 +18,17 @@ int size[4]; int stride[4]; }; -MetalMatMul::MetalMatMul(Backend *backend, const MatMul *matmul) : MetalExecution(backend) { +MetalMatMul::MetalMatMul(Backend *backend, const MatMul *matmul, bool withBias) : MetalExecution(backend) { mTransposeA = matmul->transposeA(); mTransposeB = matmul->transposeB(); auto mkbn = static_cast(backend); mConstBuffer = mkbn->getConstBuffer(sizeof(matP)); + auto context = (__bridge MNNMetalContext *)mkbn->context(); + if (withBias) { + mPipeline = [context pipelineWithName:@"matmul_bias" fp16:mkbn->useFp16InsteadFp32()]; + } else { + mPipeline = [context pipelineWithName:@"matmul" fp16:mkbn->useFp16InsteadFp32()]; + } } MetalMatMul::~MetalMatMul() { auto mkbn = static_cast(backend()); @@ -59,6 +65,9 @@ } ::memcpy(mConstBuffer.contents, &buffer, sizeof(matP)); + auto backend = static_cast(this->backend()); + auto context = (__bridge MNNMetalContext *)static_cast(backend)->context(); + mThreads = [context computeBestGroupAndLocal:mPipeline threads: MTLSizeMake(h, e, 1)]; return NO_ERROR; } @@ -71,25 +80,20 @@ auto h = C->length(1); if (inputs.size() > 2) { - auto bandwidth = [context load:@"matmul_bias" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input0->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input0)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input1->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input1)->extra.offset atIndex:1]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)inputs[2]->deviceId())->getBuffer() offset:TensorUtils::getDescribe(inputs[2])->extra.offset atIndex:2]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:3]; + [encoder setComputePipelineState:mPipeline]; + MetalBackend::setTensor(input0, encoder, 0); + MetalBackend::setTensor(input1, encoder, 1); + MetalBackend::setTensor(inputs[2], encoder, 2); + MetalBackend::setTensor(output, encoder, 3); [encoder setBuffer:mConstBuffer offset:0 atIndex:4]; - [context dispatchEncoder:encoder - threads:{ (NSUInteger)h, (NSUInteger)e, (NSUInteger)1 } - bandwidth:bandwidth]; } else { - auto bandwidth = [context load:@"matmul" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input0->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input0)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input1->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input1)->extra.offset atIndex:1]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:2]; + [encoder setComputePipelineState:mPipeline]; + MetalBackend::setTensor(input0, encoder, 0); + MetalBackend::setTensor(input1, encoder, 1); + MetalBackend::setTensor(output, encoder, 2); [encoder setBuffer:mConstBuffer offset:0 atIndex:3]; - [context dispatchEncoder:encoder - threads:{ (NSUInteger)h, (NSUInteger)e, (NSUInteger)1 } - bandwidth:bandwidth]; } + [encoder dispatchThreadgroups:mThreads.first threadsPerThreadgroup:mThreads.second]; } class MetalMatMulCreator : public MetalBackend::Creator { @@ -100,7 +104,7 @@ MNN_PRINT("metal not support matmul inpt size less than 2\n"); return nullptr; } - return new MetalMatMul(backend, op->main_as_MatMul()); + return new MetalMatMul(backend, op->main_as_MatMul(), inputs.size() > 2); } }; REGISTER_METAL_OP_CREATOR(MetalMatMulCreator, OpType_MatMul); diff --git a/source/backend/metal/MetalOPRegister.mm b/source/backend/metal/MetalOPRegister.mm index 96606e7f3..ef99461a1 100644 --- a/source/backend/metal/MetalOPRegister.mm +++ b/source/backend/metal/MetalOPRegister.mm @@ -12,13 +12,15 @@ extern void ___MetalEltwiseCreator__OpType_Eltwise__(); extern void ___MetalConvolutionCreator__OpType_Convolution__(); extern void ___MetalLayerNormCreator__OpType_LayerNorm__(); +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE + extern void ___AttentionBufCreator__OpType_Attention__(); +#endif extern void ___MetalMatMulCreator__OpType_MatMul__(); extern void ___MetalBinaryCreator__OpType_BinaryOp__(); extern void ___MetalConvolutionDepthwiseCreator__OpType_ConvolutionDepthwise__(); extern void ___MetalDeconvolutionCreator__OpType_Deconvolution__(); extern void ___MetalDeconvolutionCreator__OpType_DeconvolutionDepthwise__(); extern void ___MetalLoopCreator__OpType_While__(); - extern void ___MetalReLUCreator__OpType_ReLU__(); extern void ___MetalPoolingCreator__OpType_Pooling__(); extern void ___MetalScaleCreator__OpType_Scale__(); extern void ___MetalInterpCreator__OpType_Interp__(); @@ -31,6 +33,7 @@ extern void ___MetalFuseCreator__OpType_Extra__(); extern void ___MetalPReLUCreator__OpType_PReLU__(); extern void ___MetalReLU6Creator__OpType_ReLU6__(); + extern void ___MetalReLU6Creator__OpType_ReLU__(); void registerMetalOps() { ___MetalArgMaxCreator__OpType_ArgMax__(); ___MetalArgMaxCreator__OpType_ArgMin__(); @@ -48,7 +51,6 @@ void registerMetalOps() { ___MetalDeconvolutionCreator__OpType_Deconvolution__(); ___MetalDeconvolutionCreator__OpType_DeconvolutionDepthwise__(); ___MetalLoopCreator__OpType_While__(); - ___MetalReLUCreator__OpType_ReLU__(); ___MetalPoolingCreator__OpType_Pooling__(); ___MetalScaleCreator__OpType_Scale__(); ___MetalInterpCreator__OpType_Interp__(); @@ -61,6 +63,10 @@ void registerMetalOps() { ___MetalFuseCreator__OpType_Extra__(); ___MetalPReLUCreator__OpType_PReLU__(); ___MetalReLU6Creator__OpType_ReLU6__(); + ___MetalReLU6Creator__OpType_ReLU__(); +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE + ___AttentionBufCreator__OpType_Attention__(); +#endif } #endif } \ No newline at end of file diff --git a/source/backend/metal/MetalROIPooling.hpp b/source/backend/metal/MetalROIPooling.hpp index 4b707864e..09e70d238 100644 --- a/source/backend/metal/MetalROIPooling.hpp +++ b/source/backend/metal/MetalROIPooling.hpp @@ -24,6 +24,7 @@ class MetalROIPooling : public MetalExecution { private: float mSpatialScale; id mShape; + id mPipeline; }; } // namespace MNN diff --git a/source/backend/metal/MetalROIPooling.mm b/source/backend/metal/MetalROIPooling.mm index 786b3f49d..9407c0c9a 100755 --- a/source/backend/metal/MetalROIPooling.mm +++ b/source/backend/metal/MetalROIPooling.mm @@ -16,7 +16,10 @@ MetalROIPooling::MetalROIPooling(Backend *backend, float spatialScale) : MetalExecution(backend), mSpatialScale(spatialScale) { - // nothing to do + auto mtbn = static_cast(backend); + auto context = (__bridge MNNMetalContext *)mtbn->context(); + mShape = [context newDeviceBuffer:8 * sizeof(int) + sizeof(float) access:CPUWriteOnly]; + mPipeline = [context pipelineWithName:@"ROI_pooling" fp16:mtbn->useFp16InsteadFp32()]; } ErrorCode MetalROIPooling::onResize(const std::vector &inputs, const std::vector &outputs) { auto backend = static_cast(this->backend()); @@ -25,16 +28,15 @@ int iw = input->width(), ih = input->height(); int ow = output->width(), oh = output->height(), oz = UP_DIV(output->channel(), 4), ob = output->batch(); - auto shape = [context newDeviceBuffer:7 * sizeof(int) + sizeof(float) access:CPUWriteOnly]; - ((int *)shape.contents)[0] = iw; - ((int *)shape.contents)[1] = ih; - ((int *)shape.contents)[2] = iw * ih; - ((int *)shape.contents)[3] = ow; - ((int *)shape.contents)[4] = oh; - ((int *)shape.contents)[5] = ow * oh; - ((int *)shape.contents)[6] = oz; - ((float *)shape.contents)[7] = mSpatialScale; - mShape = shape; + ((int *)mShape.contents)[0] = iw; + ((int *)mShape.contents)[1] = ih; + ((int *)mShape.contents)[2] = iw * ih; + ((int *)mShape.contents)[3] = input->batch(); + ((int *)mShape.contents)[4] = ow; + ((int *)mShape.contents)[5] = oh; + ((int *)mShape.contents)[6] = ow * oh; + ((int *)mShape.contents)[7] = ob; + ((float *)mShape.contents)[8] = mSpatialScale; return NO_ERROR; } @@ -44,18 +46,22 @@ auto input = inputs[0], roi = inputs[1], output = outputs[0]; int iw = input->width(), ih = input->height(); int ow = output->width(), oh = output->height(), oz = UP_DIV(output->channel(), 4), ob = output->batch(); - - auto bandwidth = [context load:@"ROI_pooling" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)roi->deviceId())->getBuffer() offset:TensorUtils::getDescribe(roi)->extra.offset atIndex:1]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:2]; + [encoder setComputePipelineState:mPipeline]; + MetalBackend::setTensor(input, encoder, 0); + MetalBackend::setTensor(roi, encoder, 1); + MetalBackend::setTensor(output, encoder, 2); [encoder setBuffer:mShape offset:0 atIndex:3]; + [encoder dispatchThreadgroups:MTLSizeMake(UP_DIV(ow, 16), UP_DIV(oh, 16), ob * oz) threadsPerThreadgroup:MTLSizeMake(16, 16, 1)]; } class MetalROIPoolingCreator : public MetalBackend::Creator { public: virtual Execution *onCreate(const std::vector &inputs, const MNN::Op *op, Backend *backend, const std::vector& outputs) const { + auto roi = inputs[1]; + if (TensorUtils::getDescribe(roi)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4) { + // Don't support old roipooling + return nullptr; + } return new MetalROIPooling(backend, op->main_as_RoiParameters()->spatialScale()); } }; diff --git a/source/backend/metal/MetalRaster.mm b/source/backend/metal/MetalRaster.mm index 28934ed7f..8383b10e6 100644 --- a/source/backend/metal/MetalRaster.mm +++ b/source/backend/metal/MetalRaster.mm @@ -210,7 +210,7 @@ kernel void main0(device int4 *out [[buffer(0)]], fast = false; break; } - if (!OpCommonUtils::canBlitFast(slice, output)) { + if (!OpCommonUtils::canBlitFast(slice, output, 4, true)) { fast = false; break; } @@ -239,7 +239,7 @@ kernel void main0(device int4 *out [[buffer(0)]], for (int v=0; vregions[iter.second[v]]; Tensor::InsideDescribe::Region slice; - OpCommonUtils::turnToPackRegion(oldr, slice, output, 4); + OpCommonUtils::turnToPackRegion(oldr, slice, output, 4, true); slice.dst.offset /= 4; slice.src.offset /= 4; writeSamplerInfo(infoP[v], slice); diff --git a/source/backend/metal/MetalReLU.hpp b/source/backend/metal/MetalReLU.hpp deleted file mode 100644 index 1b968e05e..000000000 --- a/source/backend/metal/MetalReLU.hpp +++ /dev/null @@ -1,28 +0,0 @@ -// -// MetalReLU.hpp -// MNN -// -// Created by MNN on 2019/01/30. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#ifndef MetalReLU_hpp -#define MetalReLU_hpp - -#import "MetalExecution.hpp" -#if MNN_METAL_ENABLED -namespace MNN { - -class MetalReLU : public MetalExecution { -public: - MetalReLU(Backend *backend, float slope); - virtual ~MetalReLU() = default; - virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; - -private: - id mSlope; -}; - -} // namespace MNN -#endif /* MNN_METAL_ENABLED */ -#endif /* MetalReLU_hpp */ diff --git a/source/backend/metal/MetalReLU.mm b/source/backend/metal/MetalReLU.mm deleted file mode 100755 index 9f60ab335..000000000 --- a/source/backend/metal/MetalReLU.mm +++ /dev/null @@ -1,51 +0,0 @@ -// -// MetalReLU.mm -// MNN -// -// Created by MNN on 2019/01/30. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#import "backend/metal/MetalReLU.hpp" -#import "backend/metal/MNNMetalContext.h" -#import "core/Macro.h" -#import "core/Macro.h" -#import "backend/metal/MetalBackend.hpp" - -#if MNN_METAL_ENABLED -namespace MNN { - -MetalReLU::MetalReLU(Backend *backend, float slope) : MetalExecution(backend) { - auto context = (__bridge MNNMetalContext *)static_cast(backend)->context(); - mSlope = [context newDeviceBuffer:sizeof(float) bytes:&slope access:CPUWriteOnly]; -} - -void MetalReLU::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { - auto backend = static_cast(this->backend()); - auto context = (__bridge MNNMetalContext *)backend->context(); - - auto input = inputs[0], output = outputs[0]; - NSUInteger size = output->elementSize(); - auto simd = size % 4 == 0; - if (simd) { - size /= 4; - } - - MNN_ASSERT(mSlope.length == sizeof(float)); - auto bandwidth = [context load:simd ? @"relu_x4" : @"relu_x1" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; - [encoder setBuffer:mSlope offset:0 atIndex:2]; - [context dispatchEncoder:encoder threads:{ size, 1, 1 } bandwidth:bandwidth]; - MNN_PRINT_ENCODER(context, encoder); -} - -class MetalReLUCreator : public MetalBackend::Creator { -public: - virtual Execution *onCreate(const std::vector &inputs, const MNN::Op *op, Backend *backend, const std::vector& outputs) const { - return new MetalReLU(backend, op->main_as_Relu()->slope()); - } -}; -REGISTER_METAL_OP_CREATOR(MetalReLUCreator, OpType_ReLU); -} // namespace MNN -#endif /* MNN_METAL_ENABLED */ diff --git a/source/backend/metal/MetalReLU6.hpp b/source/backend/metal/MetalReLU6.hpp index 96b1929be..69840923f 100644 --- a/source/backend/metal/MetalReLU6.hpp +++ b/source/backend/metal/MetalReLU6.hpp @@ -16,11 +16,12 @@ namespace MNN { class MetalReLU6 : public MetalExecution { public: - MetalReLU6(Backend *backend, float minValue, float maxValue); + MetalReLU6(Backend *backend, float minValue, float maxValue, bool isRelu); virtual ~MetalReLU6() = default; virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; private: id mConst; + id mPipeline; }; } // namespace MNN diff --git a/source/backend/metal/MetalReLU6.mm b/source/backend/metal/MetalReLU6.mm index cd95e1975..6c677bf44 100755 --- a/source/backend/metal/MetalReLU6.mm +++ b/source/backend/metal/MetalReLU6.mm @@ -15,33 +15,39 @@ #if MNN_METAL_ENABLED namespace MNN { -MetalReLU6::MetalReLU6(Backend *backend, float minV, float maxV) : MetalExecution(backend) { +MetalReLU6::MetalReLU6(Backend *backend, float minV, float maxV, bool isRelu) : MetalExecution(backend) { + // For Relu use minV and slope auto metal = static_cast(backend); auto context = (__bridge MNNMetalContext *)metal->context(); - mConst = [context newDeviceBuffer:4 * sizeof(float) access:CPUWriteOnly]; + mConst = [context newDeviceBuffer:4 * sizeof(float) access:CPUWriteOnly]; ((float*)mConst.contents)[0] = minV; ((float*)mConst.contents)[1] = maxV; + if (isRelu) { + mPipeline = [context pipelineWithName:@"relu" fp16:metal->useFp16InsteadFp32()]; + } else { + mPipeline = [context pipelineWithName:@"relu6" fp16:metal->useFp16InsteadFp32()]; + } } void MetalReLU6::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { - auto backend = static_cast(this->backend()); - auto context = (__bridge MNNMetalContext *)backend->context(); auto input = inputs[0], output = outputs[0]; - NSUInteger size = output->elementSize(); - auto simd = size % 4 == 0; - if (simd) { - size /= 4; - } + int size = output->elementSize(); + size = UP_DIV(size, 4); + ((int*)mConst.contents)[2] = size; + ((int*)mConst.contents)[3] = size; - auto bandwidth = [context load:simd ? @"relu6_x4" : @"relu6_x1" encoder:encoder fp16:backend->useFp16InsteadFp32()]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; + [encoder setComputePipelineState:mPipeline]; + MetalBackend::setTensor(input, encoder, 0); + MetalBackend::setTensor(output, encoder, 1); [encoder setBuffer:mConst offset:0 atIndex:2]; - [context dispatchEncoder:encoder threads:{ size, 1, 1 } bandwidth:bandwidth]; + [encoder dispatchThreadgroups:MTLSizeMake(UP_DIV(size, 256), 1, 1) threadsPerThreadgroup:MTLSizeMake(256, 1, 1)]; } class MetalReLU6Creator : public MetalBackend::Creator { public: virtual Execution *onCreate(const std::vector &inputs, const MNN::Op *op, Backend *backend, const std::vector& outputs) const { + if (op->type() == OpType_ReLU) { + return new MetalReLU6(backend, op->main_as_Relu()->slope(), 0.0f, true); + } float minV = 0.0f; float maxV = 6.0f; if (nullptr != op->main()) { @@ -49,9 +55,10 @@ minV = p->minValue(); maxV = p->maxValue(); } - return new MetalReLU6(backend, minV, maxV); + return new MetalReLU6(backend, minV, maxV, false); } }; REGISTER_METAL_OP_CREATOR(MetalReLU6Creator, OpType_ReLU6); +REGISTER_METAL_OP_CREATOR(MetalReLU6Creator, OpType_ReLU); } // namespace MNN #endif /* MNN_METAL_ENABLED */ diff --git a/source/backend/metal/ShaderMap.cpp b/source/backend/metal/ShaderMap.cpp index 622d1f9da..0b6b74780 100644 --- a/source/backend/metal/ShaderMap.cpp +++ b/source/backend/metal/ShaderMap.cpp @@ -3,7 +3,6 @@ namespace MNN { void ShaderMap::init() { mMaps.insert(std::make_pair("shader_MetalReLU6_metal", shader_MetalReLU6_metal)); -mMaps.insert(std::make_pair("shader_MetalReLU_metal", shader_MetalReLU_metal)); mMaps.insert(std::make_pair("shader_MetalConvolutionDepthwise_metal", shader_MetalConvolutionDepthwise_metal)); mMaps.insert(std::make_pair("shader_MetalConvolutionActivation_metal", shader_MetalConvolutionActivation_metal)); mMaps.insert(std::make_pair("shader_MetalConvolution_metal", shader_MetalConvolution_metal)); diff --git a/source/backend/metal/shader/MetalBackend.metal b/source/backend/metal/shader/MetalBackend.metal index f59716c13..973f50fc4 100644 --- a/source/backend/metal/shader/MetalBackend.metal +++ b/source/backend/metal/shader/MetalBackend.metal @@ -1,38 +1,10 @@ struct tensor_shape { int size; int channel; - int slice; + int batch; int batch_slices; }; -kernel void version_func_002(const device uchar *in [[buffer(0)]], - device uchar *out [[buffer(1)]], - uint gid [[thread_position_in_grid]]) { - // do nothing, just for verifying match between mnn and metallib -} - -kernel void copy_byte(const device uchar *in [[buffer(0)]], - device uchar *out [[buffer(1)]], - uint gid [[thread_position_in_grid]]) { - out[int(gid)] = in[int(gid)]; -} - -kernel void copy_float(const device ftype *in [[buffer(0)]], - device ftype *out [[buffer(1)]], - uint gid [[thread_position_in_grid]]) { - out[int(gid)] = in[int(gid)]; -} - -kernel void upcast_float(const device ftype *in [[buffer(0)]], - device float *out [[buffer(1)]], - uint gid [[thread_position_in_grid]]) { - out[int(gid)] = in[int(gid)]; -} -kernel void downcast_float(const device float *in [[buffer(0)]], - device ftype *out [[buffer(1)]], - uint gid [[thread_position_in_grid]]) { - out[int(gid)] = in[int(gid)]; -} struct Limit { uint4 size; }; @@ -55,8 +27,8 @@ kernel void downcast_float4(const device float4 *in [[buffer(0)]], template static inline void template_NHWC_to_NC4HW4(const device IType *in, device OType *out, constant tensor_shape &s, uint2 gid) { - int b = gid.y / s.slice; - int z = gid.y % s.slice; + int b = gid.y % s.batch; + int z = gid.y / s.batch; int c = z * 4; auto off_in = in + b * s.size * s.channel + int(gid.x) * s.channel + c; @@ -93,8 +65,8 @@ kernel void cvt_f_NHWC_to_NC4HW4(const device ftype *in [[buffer(0)]], template static inline void template_NC4HW4_to_NHWC(const device IType *in, device OType *out, constant tensor_shape &s, uint2 gid) { - int b = gid.y / s.slice; - int z = gid.y % s.slice; + int b = gid.y % s.batch; + int z = gid.y / s.batch; int c = z * 4; auto off_in = in + int(gid.y) * s.size + int(gid.x); auto off_out = out + b * s.size * s.channel + int(gid.x) * s.channel + c; @@ -132,8 +104,8 @@ kernel void cvt_f_NC4HW4_to_NHWC(const device ftype4 *in [[buffer(0)]], template static inline void template_NCHW_to_NC4HW4(const device IType *in, device OType *out, constant tensor_shape &s, uint2 gid) { - int b = gid.y / s.slice; - int z = gid.y % s.slice; + int b = gid.y % s.batch; + int z = gid.y / s.batch; int c = z * 4; auto off_in = in + (b * s.channel + c) * s.size + int(gid.x); @@ -170,8 +142,8 @@ kernel void cvt_f_NCHW_to_NC4HW4(const device ftype *in [[buffer(0)]], template static inline void template_NC4HW4_to_NCHW(const device IType *in, device OType *out, constant tensor_shape &s, uint2 gid) { - int b = gid.y / s.slice; - int z = gid.y % s.slice; + int b = gid.y % s.batch; + int z = gid.y / s.batch; int c = z * 4; auto off_in = in + int(gid.y) * s.size + int(gid.x); @@ -210,8 +182,8 @@ kernel void cvt_f_NC4HW4_to_NCHW(const device ftype4 *in [[buffer(0)]], template static inline void template_NHWC_to_NCHW(const device IType* in, device OType* out, constant tensor_shape &s, uint2 gid) { - int b = gid.y / s.slice; - int c4 = gid.y % s.slice; + int b = gid.y % s.batch; + int c4 = gid.y / s.batch; auto in_off = (b * s.size + gid.x) * s.channel + c4 * 4; auto out_off = (b * s.channel + c4 * 4) * s.size + gid.x; @@ -238,8 +210,8 @@ kernel void upcast_f_NHWC_to_NCHW(const device ftype *in [[buffer(0)]], template static inline void template_NCHW_to_NHWC(const device IType* in, device OType* out, constant tensor_shape &s, uint2 gid) { - int b = gid.y / s.slice; - int c4 = gid.y % s.slice; + int b = gid.y % s.batch; + int c4 = gid.y / s.batch; auto in_off = (b * s.channel + c4 * 4) * s.size + gid.x; auto out_off = (b * s.size + gid.x) * s.channel + c4 * 4; diff --git a/source/backend/metal/shader/MetalConvolution.metal b/source/backend/metal/shader/MetalConvolution.metal index 94da2bef7..89a41567c 100644 --- a/source/backend/metal/shader/MetalConvolution.metal +++ b/source/backend/metal/shader/MetalConvolution.metal @@ -102,9 +102,9 @@ kernel void conv(const device ftype4 *in [[buffer(0)]], offset_x += sx * cst.dilation_x; offset_y += sy * cst.dilation_y; - auto z_in = in + idx_b * cst.input_slice * cst.input_size + offset_y * cst.input_width + offset_x; + auto z_in = in + idx_b * cst.input_size + offset_y * cst.input_width + offset_x; auto z_wt = wt + idx_c * cst.input_slice * cst.kernel_size + sy * cst.kernel_x + sx; - auto z_out = out + idx_b * cst.output_slice * cst.output_size + (int)idx_c * cst.output_size + (int)gid.y * cst.output_width + (int)gid.x; + auto z_out = out + idx_b * cst.output_size + (int)idx_c * cst.batch * cst.output_size + (int)gid.y * cst.output_width + (int)gid.x; int dilation_h = cst.input_width * cst.dilation_y; FLOAT4 result = FLOAT4(biasTerms[idx_c]); @@ -112,7 +112,7 @@ kernel void conv(const device ftype4 *in [[buffer(0)]], for (auto y = 0; y < kh; y++) { for (auto x = 0; x < kw; x++) { auto wt4 = z_wt[z * cst.kernel_size + y * cst.kernel_x + x]; - auto in4 = z_in[z * cst.input_size + y * dilation_h + x * cst.dilation_x]; + auto in4 = z_in[z * cst.input_size * cst.batch + y * dilation_h + x * cst.dilation_x]; result += FLOAT4(in4 * wt4); } } @@ -141,15 +141,15 @@ kernel void convk3s1d1p1_w2z4(const device ftype4 *in [[buffer(0)]], int offset_x = (int)gid.x * 2 - cst.pad_x; int offset_y = (int)gid.y - cst.pad_y; - auto z_in = in + idx_b * cst.input_slice * cst.input_size + offset_y * cst.input_width + offset_x; + auto z_in = in + idx_b * cst.input_size + offset_y * cst.input_width + offset_x; auto z_flt = wt + uz[0] * cst.input_slice * cst.kernel_size; - auto z_out = out + idx_b * cst.output_slice * cst.output_size + uz[0] * cst.output_size + idx_h * cst.output_width + idx_w; + auto z_out = out + idx_b * cst.output_size + uz[0] * cst.batch * cst.output_size + idx_h * cst.output_width + idx_w; int ws = cst.input_slice * cst.kernel_size; FLOAT4 result0 = 0, result1 = 0, result2 = 0, result3 = 0; FLOAT4 result4 = 0, result5 = 0, result6 = 0, result7 = 0; - for (auto z = 0; z < cst.input_slice; z++, z_flt += cst.kernel_size, z_in += cst.input_size) { + for (auto z = 0; z < cst.input_slice; z++, z_flt += cst.kernel_size, z_in += (cst.input_size * cst.batch)) { auto in00 = (offset_x<0 || offset_y<0) ? (ftype4)0.f : *(z_in+0*cst.input_width+0); auto in01 = (offset_x+1>=cst.input_width || offset_y<0) ? (ftype4)0.f : *(z_in+0*cst.input_width+1); auto in02 = (offset_x+2>=cst.input_width || offset_y<0) ? (ftype4)0.f : *(z_in+0*cst.input_width+2); @@ -228,19 +228,19 @@ kernel void conv_s1d1p0_w2(const device ftype4 *in [[buffer(0)]], bool valid = (idx_w + 1 < cst.output_width); - auto z_in = in + idx_b * cst.input_slice * cst.input_size + idx_h * cst.input_width + idx_w; + auto z_in = in + idx_b * cst.input_size + idx_h * cst.input_width + idx_w; auto z_wt = wt + idx_c * cst.input_slice * cst.kernel_size; - auto z_out = out + idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; + auto z_out = out + idx_b * cst.output_size + idx_c * cst.batch * cst.output_size + idx_h * cst.output_width + idx_w; FLOAT4 result0 = FLOAT4(biasTerms[idx_c]); FLOAT4 result1 = result0; for (auto z = 0; z < cst.input_slice; z++) { for (auto y = 0; y < cst.kernel_y; y++) { auto wt4 = z_wt[z * cst.kernel_size + y * cst.kernel_x]; - auto in4_0 = z_in[z * cst.input_size + y * cst.input_width]; + auto in4_0 = z_in[z * cst.batch * cst.input_size + y * cst.input_width]; result0 += FLOAT4(in4_0 * wt4); for (auto x = 1; x < cst.kernel_x; x++) { - in4_0 = z_in[z * cst.input_size + y * cst.input_width + x]; + in4_0 = z_in[z * cst.batch * cst.input_size + y * cst.input_width + x]; result1 += FLOAT4(in4_0 * wt4); wt4 = z_wt[z * cst.kernel_size + y * cst.kernel_x + x]; result0 += FLOAT4(in4_0 * wt4); @@ -271,9 +271,9 @@ kernel void conv_s1d1p0_w4(const device ftype4 *in [[buffer(0)]], int3 uz = idx_w + int3(1, 2, 3); bool3 valids = uz.xyz < cst.output_width; - auto z_in = in + idx_b * cst.input_slice * cst.input_size + idx_h * cst.input_width + idx_w; + auto z_in = in + idx_b * cst.input_size + idx_h * cst.input_width + idx_w; auto z_wt = wt + idx_c * cst.input_slice * cst.kernel_size; - auto z_out = out + idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; + auto z_out = out + idx_b * cst.output_size + idx_c * cst.batch * cst.output_size + idx_h * cst.output_width + idx_w; FLOAT4 result0 = FLOAT4(biasTerms[idx_c]); FLOAT4 result1 = result0; @@ -286,7 +286,7 @@ kernel void conv_s1d1p0_w4(const device ftype4 *in [[buffer(0)]], auto wt4_1 = wt_base[1]; auto wt4_2 = wt_base[2]; - auto z_in_base = z_in + z * cst.input_size + y * cst.input_width; + auto z_in_base = z_in + z * cst.batch * cst.input_size + y * cst.input_width; auto in4_0 = z_in_base[0]; result0 += FLOAT4(in4_0 * wt4_0); @@ -346,14 +346,14 @@ kernel void conv_z4(const device ftype4 *in [[buffer(0)]], offset_x += sx * cst.dilation_x; offset_y += sy * cst.dilation_y; - auto z_in = in + idx_b * cst.input_slice * cst.input_size + offset_y * cst.input_width + offset_x; + auto z_in = in + idx_b * cst.input_size + offset_y * cst.input_width + offset_x; auto z_wt = wt + uz[0] * cst.input_slice * cst.kernel_size + sy * cst.kernel_x + sx; - auto z_out = out + idx_b * cst.output_slice * cst.output_size + uz[0] * cst.output_size + idx_h * cst.output_width + idx_w; + auto z_out = out + idx_b * cst.output_size + uz[0] * cst.batch * cst.output_size + idx_h * cst.output_width + idx_w; int ws = cst.input_slice * cst.kernel_size; int dilation_h = cst.input_width * cst.dilation_y; FLOAT4 result0 = 0, result1 = 0, result2 = 0, result3 = 0; - for (auto z = 0; z < cst.input_slice; z++, z_wt += cst.kernel_size, z_in += cst.input_size) { + for (auto z = 0; z < cst.input_slice; z++, z_wt += cst.kernel_size, z_in += cst.input_size * cst.batch) { for (auto y = 0; y < kh; y++) { for (auto x = 0; x < kw; x++) { auto x_wt = z_wt + y * cst.kernel_x + x; @@ -400,14 +400,14 @@ kernel void conv_z2(const device ftype4 *in [[buffer(0)]], offset_x += sx * cst.dilation_x; offset_y += sy * cst.dilation_y; - auto z_in = in + idx_b * cst.input_slice * cst.input_size + offset_y * cst.input_width + offset_x; + auto z_in = in + idx_b * cst.input_size + offset_y * cst.input_width + offset_x; auto z_wt = wt + uz[0] * cst.input_slice * cst.kernel_size + sy * cst.kernel_x + sx; - auto z_out = out + idx_b * cst.output_slice * cst.output_size + uz[0] * cst.output_size + idx_h * cst.output_width + idx_w; + auto z_out = out + idx_b * cst.output_size + uz[0] * cst.batch * cst.output_size + idx_h * cst.output_width + idx_w; int ws = cst.input_slice * cst.kernel_size; int dilation_h = cst.input_width * cst.dilation_y; FLOAT4 result0 = 0, result1 = 0; - for (auto z = 0; z < cst.input_slice; z++, z_wt += cst.kernel_size, z_in += cst.input_size) { + for (auto z = 0; z < cst.input_slice; z++, z_wt += cst.kernel_size, z_in += cst.input_size * cst.batch) { for (auto y = 0; y < kh; y++) { for (auto x = 0; x < kw; x++) { auto x_wt = z_wt + y * cst.kernel_x + x; @@ -418,5 +418,5 @@ kernel void conv_z2(const device ftype4 *in [[buffer(0)]], } } /* true */ *z_out = activate(ftype4(result0 + FLOAT4(biasTerms[uz[0]])), cst.activation); - if (valids) { z_out += cst.output_size; *z_out = activate(ftype4(result1 + FLOAT4(biasTerms[uz[1]])), cst.activation); } + if (valids) { z_out += cst.output_size * cst.batch; *z_out = activate(ftype4(result1 + FLOAT4(biasTerms[uz[1]])), cst.activation); } } diff --git a/source/backend/metal/shader/MetalConvolution1x1.metal b/source/backend/metal/shader/MetalConvolution1x1.metal index 1bddbb596..07b177dd9 100644 --- a/source/backend/metal/shader/MetalConvolution1x1.metal +++ b/source/backend/metal/shader/MetalConvolution1x1.metal @@ -13,36 +13,6 @@ struct conv1x1_constants { conv_activation_type activation; }; -kernel void conv1x1_w1h1(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device ftype4x4 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - uint3 gid [[thread_position_in_grid]]) { - if ((int)gid.x >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.batch * cst.output_slice) return; - int idx_w = gid.x; - int idx_h = gid.y; - int idx_c = gid.z % cst.output_slice; - int idx_b = gid.z / cst.output_slice; - - auto xy_wt = wt + idx_c * cst.input_slice; - auto xy_in0 = in + (int)idx_b * cst.input_slice * cst.input_size + idx_h * cst.output_width + idx_w; - - auto xy_out = out + (int)idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; - auto biasValue = FLOAT4(biasTerms[idx_c]); - FLOAT4 result0 = biasValue; - - for (auto z = 0; z < cst.input_slice; z++) { - auto in40 = xy_in0[0]; - auto w = xy_wt[z]; - - result0 += FLOAT4(in40 * w); - xy_in0 += cst.input_size; - } - - /* true */ *xy_out = activate(ftype4(result0), cst.activation); -} - kernel void conv1x1_g1z4(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant conv1x1_constants& cst [[buffer(2)]], @@ -54,8 +24,8 @@ kernel void conv1x1_g1z4(const device ftype4 *in [[buffer(0)]], int rx = gid.x * CONV_UNROLL; int uz = gid.y; auto xy_wt = wt + uz * cst.input_slice; - auto xy_in0 = in + (int)gid.z * cst.input_slice * cst.input_size + rx + 0; - auto xy_out = out + (int)gid.z * cst.output_slice * cst.output_size + uz * cst.output_size + rx; + auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; + auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.output_size * cst.batch + rx; auto biasValue = FLOAT4(biasTerms[uz]); FLOAT4 result0 = biasValue, result1 = biasValue, result2 = biasValue, result3 = biasValue; int computeSize = min(cst.output_size - rx, CONV_UNROLL); @@ -71,7 +41,7 @@ kernel void conv1x1_g1z4(const device ftype4 *in [[buffer(0)]], result1 += FLOAT4(in41 * w); result2 += FLOAT4(in42 * w); result3 += FLOAT4(in43 * w); - xy_in0 += cst.input_size; + xy_in0 += cst.input_size * cst.batch; } /* true */ *xy_out = activate(ftype4(result0), cst.activation); @@ -86,20 +56,19 @@ kernel void conv1x1_g1z4_w8(const device ftype4 *in [[buffer(0)]], const device MNN::char4x4 *wt [[buffer(3)]], const device ftype4 *biasTerms [[buffer(4)]], const device float4 *dequantScale [[buffer(5)]], - const device float4 *dequantBias [[buffer(6)]], uint3 gid [[thread_position_in_grid]]) { if ((int)gid.x * CONV_UNROLL >= cst.output_size || (int)gid.y >= cst.output_slice || (int)gid.z >= cst.batch) return; int rx = gid.x * CONV_UNROLL; int uz = gid.y; auto xy_wt = wt + uz * cst.input_slice; - auto xy_in0 = in + (int)gid.z * cst.input_slice * cst.input_size + rx + 0; - auto xy_out = out + (int)gid.z * cst.output_slice * cst.output_size + uz * cst.output_size + rx; + auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; + auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.output_size * cst.batch + rx; auto biasValue = FLOAT4(biasTerms[uz]); FLOAT4 result0 = biasValue, result1 = biasValue, result2 = biasValue, result3 = biasValue; int computeSize = min(cst.output_size - rx, CONV_UNROLL); auto scale = FLOAT4(dequantScale[uz]); - auto dequant_bias = FLOAT4(dequantBias[uz]); + auto dequant_bias = FLOAT4(dequantScale[uz + cst.output_slice]); for (auto z = 0; z < cst.input_slice; z++) { auto in40 = (FLOAT4)*xy_in0; @@ -112,20 +81,14 @@ kernel void conv1x1_g1z4_w8(const device ftype4 *in [[buffer(0)]], FLOAT4x4 w_fp32 = FLOAT4x4(FLOAT4(w[0]), FLOAT4(w[1]), FLOAT4(w[2]), FLOAT4(w[3])); FLOAT4x4 w_dequant; for (int i = 0; i < 4; ++i) { - FLOAT4 w4 = w_fp32[i]; - FLOAT4 res; - for (int j = 0; j < 4; ++j) { - float wf = w4[j] * scale[i] + dequant_bias[i]; - res[j] = wf; - } - w_dequant[i] = res; + w_dequant[i] = w_fp32[i] * scale[i] + dequant_bias[i]; } result0 += FLOAT4(in40 * w_dequant); result1 += FLOAT4(in41 * w_dequant); result2 += FLOAT4(in42 * w_dequant); result3 += FLOAT4(in43 * w_dequant); - xy_in0 += cst.input_size; + xy_in0 += cst.input_size * cst.batch; } /* true */ @@ -141,20 +104,19 @@ kernel void conv1x1_g1z4_w4(const device ftype4 *in [[buffer(0)]], const device MNN::uchar4x2 *wt [[buffer(3)]], const device ftype4 *biasTerms [[buffer(4)]], const device float4 *dequantScale [[buffer(5)]], - const device float4 *dequantBias [[buffer(6)]], uint3 gid [[thread_position_in_grid]]) { if ((int)gid.x * CONV_UNROLL >= cst.output_size || (int)gid.y >= cst.output_slice || (int)gid.z >= cst.batch) return; int rx = gid.x * CONV_UNROLL; int uz = gid.y; auto xy_wt = wt + uz * cst.input_slice; - auto xy_in0 = in + (int)gid.z * cst.input_slice * cst.input_size + rx + 0; - auto xy_out = out + (int)gid.z * cst.output_slice * cst.output_size + uz * cst.output_size + rx; + auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; + auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.output_size * cst.batch + rx; auto biasValue = FLOAT4(biasTerms[uz]); FLOAT4 result0 = biasValue, result1 = biasValue, result2 = biasValue, result3 = biasValue; int computeSize = min(cst.output_size - rx, CONV_UNROLL); auto scale = FLOAT4(dequantScale[uz]); - auto dequant_bias = FLOAT4(dequantBias[uz]); + auto dequant_bias = FLOAT4(dequantScale[uz + cst.output_slice]); for (auto z = 0; z < cst.input_slice; z++) { auto in40 = (FLOAT4)*xy_in0; @@ -169,11 +131,7 @@ kernel void conv1x1_g1z4_w4(const device ftype4 *in [[buffer(0)]], for (int i = 0; i < 4; ++i) { // ftype4 w4 = ftype4(w_fp32[i]); FLOAT4 w4 = FLOAT4((float)(w_int4[i][0] >> 4) - 8, (float)(w_int4[i][0] & 15) - 8, (float)(w_int4[i][1] >> 4) - 8, (float)(w_int4[i][1] & 15) - 8); - FLOAT4 res; - for (int j = 0; j < 4; ++j) { - float wf = w4[j] * scale[i] + dequant_bias[i]; - res[j] = wf; - } + FLOAT4 res = w4 * scale[i] + dequant_bias[i]; w_dequant[i] = res; } @@ -181,7 +139,7 @@ kernel void conv1x1_g1z4_w4(const device ftype4 *in [[buffer(0)]], result1 += FLOAT4(in41 * w_dequant); result2 += FLOAT4(in42 * w_dequant); result3 += FLOAT4(in43 * w_dequant); - xy_in0 += cst.input_size; + xy_in0 += cst.input_size * cst.batch; } /* true */ @@ -213,9 +171,9 @@ kernel void conv1x1_g1z8(const device ftype4 *in [[buffer(0)]], int rx = gid.x * CONV_UNROLL_L; int uz = gid.y; auto xy_wt = wt + uz * cst.input_slice; - auto xy_in0 = in + (int)gid.z * cst.input_slice * cst.input_size + rx + 0; + auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; - auto xy_out = out + (int)gid.z * cst.output_slice * cst.output_size + uz * cst.output_size + rx; + auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.batch * cst.output_size + rx; auto biasValue = FLOAT4(biasTerms[uz]); FLOAT4 result0 = biasValue, result1 = biasValue, result2 = biasValue, result3 = biasValue; FLOAT4 result4 = biasValue, result5 = biasValue, result6 = biasValue, result7 = biasValue; @@ -241,7 +199,7 @@ kernel void conv1x1_g1z8(const device ftype4 *in [[buffer(0)]], result5 += FLOAT4(in45 * w); result6 += FLOAT4(in46 * w); result7 += FLOAT4(in47 * w); - xy_in0 += cst.input_size; + xy_in0 += cst.input_size * cst.batch; } /* true */ *xy_out = activate(ftype4(result0), cst.activation); @@ -254,84 +212,23 @@ kernel void conv1x1_g1z8(const device ftype4 *in [[buffer(0)]], if (computeSize > 7) {xy_out[7] = activate(ftype4(result7), cst.activation); } } - -kernel void conv1x1_w4h2(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device ftype4x4 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - uint3 gid [[thread_position_in_grid]]) { - if ((int)gid.x * 4 >= cst.output_width || (int)gid.y * 2 >= cst.output_height || (int)gid.z >= cst.batch * cst.output_slice) return; - - int idx_w = gid.x << 2; - int idx_h = gid.y << 1; - int idx_c = gid.z % cst.output_slice; - int idx_b = gid.z / cst.output_slice; - - auto xy_wt = wt + idx_c * cst.input_slice; - auto xy_in0 = in + (int)idx_b * cst.input_slice * cst.input_size + idx_h * cst.output_width + idx_w; - - auto xy_out = out + (int)idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; - auto biasValue = FLOAT4(biasTerms[idx_c]); - FLOAT4 result0 = biasValue, result1 = biasValue, result2 = biasValue, result3 = biasValue; - FLOAT4 result4 = biasValue, result5 = biasValue, result6 = biasValue, result7 = biasValue; - - for (auto z = 0; z < cst.input_slice; z++) { - auto in40 = xy_in0[0]; - auto in41 = xy_in0[1]; - auto in42 = xy_in0[2]; - auto in43 = xy_in0[3]; - auto in44 = xy_in0[cst.output_width+0]; - auto in45 = xy_in0[cst.output_width+1]; - auto in46 = xy_in0[cst.output_width+2]; - auto in47 = xy_in0[cst.output_width+3]; - - auto w = xy_wt[z]; - - result0 += FLOAT4(in40 * w); - result1 += FLOAT4(in41 * w); - result2 += FLOAT4(in42 * w); - result3 += FLOAT4(in43 * w); - result4 += FLOAT4(in44 * w); - result5 += FLOAT4(in45 * w); - result6 += FLOAT4(in46 * w); - result7 += FLOAT4(in47 * w); - xy_in0 += cst.input_size; - } - - int widthSize = min(cst.output_width - idx_w, 4); - /* true */ *xy_out = activate(ftype4(result0), cst.activation); - if (widthSize > 1) {xy_out[1] = activate(ftype4(result1), cst.activation); } - if (widthSize > 2) {xy_out[2] = activate(ftype4(result2), cst.activation); } - if (widthSize > 3) {xy_out[3] = activate(ftype4(result3), cst.activation); } - - int heightSize = min(cst.output_height - idx_h, 2); - if(heightSize > 1) { - /* true */ {xy_out[cst.output_width+0] = activate(ftype4(result4), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_width+1] = activate(ftype4(result5), cst.activation); } - if (widthSize > 2) {xy_out[cst.output_width+2] = activate(ftype4(result6), cst.activation); } - if (widthSize > 3) {xy_out[cst.output_width+3] = activate(ftype4(result7), cst.activation); } - } -} - - kernel void conv1x1_w4h4(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant conv1x1_constants& cst [[buffer(2)]], const device ftype4x4 *wt [[buffer(3)]], const device ftype4 *biasTerms [[buffer(4)]], uint3 gid [[thread_position_in_grid]]) { - if ((int)gid.x * 4 >= cst.output_width || (int)gid.y * 4 >= cst.output_height || (int)gid.z >= cst.batch * cst.output_slice) return; + if ((int)gid.x * 16 >= cst.output_width || (int)gid.y >= cst.batch * cst.output_slice) return; - int idx_w = gid.x << 2; - int idx_h = gid.y << 2; - int idx_c = gid.z % cst.output_slice; - int idx_b = gid.z / cst.output_slice; + int idx_w = gid.x << 4; + int idx_h = 0; + int idx_c = gid.y / cst.batch; + int idx_b = gid.y % cst.batch; auto xy_wt = wt + idx_c * cst.input_slice; - auto xy_in0 = in + (int)idx_b * cst.input_slice * cst.input_size + idx_h * cst.output_width + idx_w; + auto xy_in0 = in + (int)idx_b * cst.input_size + idx_h * cst.output_width + idx_w; - auto xy_out = out + (int)idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; + auto xy_out = out + (int)idx_b * cst.output_size + idx_c * cst.output_size * cst.batch + idx_h * cst.output_width + idx_w; auto biasValue = FLOAT4(biasTerms[idx_c]); FLOAT4 result00 = biasValue, result01 = biasValue, result02 = biasValue, result03 = biasValue; FLOAT4 result10 = biasValue, result11 = biasValue, result12 = biasValue, result13 = biasValue; @@ -343,19 +240,19 @@ kernel void conv1x1_w4h4(const device ftype4 *in [[buffer(0)]], auto in01 = xy_in0[1]; auto in02 = xy_in0[2]; auto in03 = xy_in0[3]; - auto in10 = xy_in0[cst.output_width+0]; - auto in11 = xy_in0[cst.output_width+1]; - auto in12 = xy_in0[cst.output_width+2]; - auto in13 = xy_in0[cst.output_width+3]; + auto in10 = xy_in0[4]; + auto in11 = xy_in0[5]; + auto in12 = xy_in0[6]; + auto in13 = xy_in0[7]; - auto in20 = xy_in0[cst.output_width+cst.output_width+0]; - auto in21 = xy_in0[cst.output_width+cst.output_width+1]; - auto in22 = xy_in0[cst.output_width+cst.output_width+2]; - auto in23 = xy_in0[cst.output_width+cst.output_width+3]; - auto in30 = xy_in0[cst.output_width+cst.output_width+cst.output_width+0]; - auto in31 = xy_in0[cst.output_width+cst.output_width+cst.output_width+1]; - auto in32 = xy_in0[cst.output_width+cst.output_width+cst.output_width+2]; - auto in33 = xy_in0[cst.output_width+cst.output_width+cst.output_width+3]; + auto in20 = xy_in0[8]; + auto in21 = xy_in0[9]; + auto in22 = xy_in0[10]; + auto in23 = xy_in0[11]; + auto in30 = xy_in0[12]; + auto in31 = xy_in0[13]; + auto in32 = xy_in0[14]; + auto in33 = xy_in0[15]; auto w = xy_wt[z]; @@ -378,34 +275,26 @@ kernel void conv1x1_w4h4(const device ftype4 *in [[buffer(0)]], result32 += FLOAT4(in32 * w); result33 += FLOAT4(in33 * w); - xy_in0 += cst.input_size; + xy_in0 += cst.input_size * cst.batch; } - int widthSize = min(cst.output_width - idx_w, 4); + int widthSize = min(cst.output_width - idx_w, 16); /* true */ *xy_out = activate(ftype4(result00), cst.activation); if (widthSize > 1) {xy_out[1] = activate(ftype4(result01), cst.activation); } if (widthSize > 2) {xy_out[2] = activate(ftype4(result02), cst.activation); } if (widthSize > 3) {xy_out[3] = activate(ftype4(result03), cst.activation); } - - int heightSize = min(cst.output_height - idx_h, 4); - if(heightSize > 1) { - /* true */ {xy_out[cst.output_width+0] = activate(ftype4(result10), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_width+1] = activate(ftype4(result11), cst.activation); } - if (widthSize > 2) {xy_out[cst.output_width+2] = activate(ftype4(result12), cst.activation); } - if (widthSize > 3) {xy_out[cst.output_width+3] = activate(ftype4(result13), cst.activation); } - } - if(heightSize > 2) { - /* true */ {xy_out[cst.output_width+cst.output_width+0] = activate(ftype4(result20), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_width+cst.output_width+1] = activate(ftype4(result21), cst.activation); } - if (widthSize > 2) {xy_out[cst.output_width+cst.output_width+2] = activate(ftype4(result22), cst.activation); } - if (widthSize > 3) {xy_out[cst.output_width+cst.output_width+3] = activate(ftype4(result23), cst.activation); } - } - if(heightSize > 3) { - /* true */ {xy_out[cst.output_width+cst.output_width+cst.output_width+0] = activate(ftype4(result30), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_width+cst.output_width+cst.output_width+1] = activate(ftype4(result31), cst.activation); } - if (widthSize > 2) {xy_out[cst.output_width+cst.output_width+cst.output_width+2] = activate(ftype4(result32), cst.activation); } - if (widthSize > 3) {xy_out[cst.output_width+cst.output_width+cst.output_width+3] = activate(ftype4(result33), cst.activation); } - } + if (widthSize > 4) {xy_out[4] = activate(ftype4(result10), cst.activation); } + if (widthSize > 5) {xy_out[5] = activate(ftype4(result11), cst.activation); } + if (widthSize > 6) {xy_out[6] = activate(ftype4(result12), cst.activation); } + if (widthSize > 7) {xy_out[7] = activate(ftype4(result13), cst.activation); } + if (widthSize > 8) {xy_out[8] = activate(ftype4(result20), cst.activation); } + if (widthSize > 9) {xy_out[9] = activate(ftype4(result21), cst.activation); } + if (widthSize > 10) {xy_out[10] = activate(ftype4(result22), cst.activation); } + if (widthSize > 11) {xy_out[11] = activate(ftype4(result23), cst.activation); } + if (widthSize > 12) {xy_out[12] = activate(ftype4(result30), cst.activation); } + if (widthSize > 13) {xy_out[13] = activate(ftype4(result31), cst.activation); } + if (widthSize > 14) {xy_out[14] = activate(ftype4(result32), cst.activation); } + if (widthSize > 15) {xy_out[15] = activate(ftype4(result33), cst.activation); } } @@ -415,19 +304,19 @@ kernel void conv1x1_w2c2(const device ftype4 *in [[buffer(0)]], const device ftype4x4 *wt [[buffer(3)]], const device ftype4 *biasTerms [[buffer(4)]], uint3 gid [[thread_position_in_grid]]) { - if ((int)gid.x * 2 >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z * 2 >= cst.batch * cst.output_slice) return; + if ((int)gid.x * 2 >= cst.output_width || (int)gid.y * 2 >= cst.batch * cst.output_slice) return; int channel_pack = (cst.output_channel + 7) >> 3; int idx_w = gid.x << 1; - int idx_h = gid.y; - int idx_c = (gid.z % channel_pack) << 1; - int idx_b = gid.z / channel_pack; + int idx_h = 0; + int idx_c = (gid.y % channel_pack) << 1; + int idx_b = gid.y / channel_pack; if(idx_b >= cst.batch || idx_c >= cst.output_slice) return; auto xy_wt = wt + idx_c * cst.input_slice; - auto xy_in0 = in + (int)idx_b * cst.input_slice * cst.input_size + idx_h * cst.output_width + idx_w; + auto xy_in0 = in + (int)idx_b * cst.input_size + idx_h * cst.output_width + idx_w; - auto xy_out = out + (int)idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; + auto xy_out = out + (int)idx_b * cst.output_size + idx_c * cst.output_size * cst.batch + idx_h * cst.output_width + idx_w; auto biasValue0 = FLOAT4(biasTerms[idx_c]); auto biasValue1 = FLOAT4(biasTerms[idx_c+1]); @@ -445,7 +334,7 @@ kernel void conv1x1_w2c2(const device ftype4 *in [[buffer(0)]], result1 += FLOAT4(in41 * w0); result4 += FLOAT4(in40 * w1); result5 += FLOAT4(in41 * w1); - xy_in0 += cst.input_size; + xy_in0 += cst.input_size * cst.batch; } int widthSize = min(cst.output_width - idx_w, 2); @@ -454,79 +343,30 @@ kernel void conv1x1_w2c2(const device ftype4 *in [[buffer(0)]], int channelSize = min(cst.output_slice - idx_c, 2); if(channelSize > 1) { - /* true */ {xy_out[cst.output_size+0] = activate(ftype4(result4), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_size+1] = activate(ftype4(result5), cst.activation); } + /* true */ {xy_out[cst.output_size * cst.batch +0] = activate(ftype4(result4), cst.activation); } + if (widthSize > 1) {xy_out[cst.output_size * cst.batch +1] = activate(ftype4(result5), cst.activation); } } } - -kernel void conv1x1_w2h2(const device ftype4 *in [[buffer(0)]], +kernel void conv1x1_w4c2(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant conv1x1_constants& cst [[buffer(2)]], const device ftype4x4 *wt [[buffer(3)]], const device ftype4 *biasTerms [[buffer(4)]], uint3 gid [[thread_position_in_grid]]) { - if ((int)gid.x * 2 >= cst.output_width || (int)gid.y * 2 >= cst.output_height || (int)gid.z >= cst.batch * cst.output_slice) return; - - int idx_w = gid.x << 1; - int idx_h = gid.y << 1; - int idx_c = gid.z % cst.output_slice; - int idx_b = gid.z / cst.output_slice; - - auto xy_wt = wt + idx_c * cst.input_slice; - auto xy_in0 = in + (int)idx_b * cst.input_slice * cst.input_size + idx_h * cst.output_width + idx_w; - - auto xy_out = out + (int)idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; - auto biasValue = FLOAT4(biasTerms[idx_c]); - FLOAT4 result0 = biasValue, result1 = biasValue; - FLOAT4 result4 = biasValue, result5 = biasValue; - - for (auto z = 0; z < cst.input_slice; z++) { - auto in40 = xy_in0[0]; - auto in41 = xy_in0[1]; - auto in44 = xy_in0[cst.output_width+0]; - auto in45 = xy_in0[cst.output_width+1]; - - auto w = xy_wt[z]; - - result0 += FLOAT4(in40 * w); - result1 += FLOAT4(in41 * w); - result4 += FLOAT4(in44 * w); - result5 += FLOAT4(in45 * w); - xy_in0 += cst.input_size; - } - - int widthSize = min(cst.output_width - idx_w, 2); - /* true */ *xy_out = activate(ftype4(result0), cst.activation); - if (widthSize > 1) {xy_out[1] = activate(ftype4(result1), cst.activation); } - - int heightSize = min(cst.output_height - idx_h, 2); - if(heightSize > 1) { - /* true */ {xy_out[cst.output_width+0] = activate(ftype4(result4), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_width+1] = activate(ftype4(result5), cst.activation); } - } -} - - -kernel void conv1x1_w2h2c2(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device ftype4x4 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - uint3 gid [[thread_position_in_grid]]) { - if ((int)gid.x * 2 >= cst.output_width || (int)gid.y * 2 >= cst.output_height || (int)gid.z * 2 >= cst.batch * cst.output_slice) return; + if ((int)gid.x * 4 >= cst.output_width || (int)gid.y * 2 >= cst.batch * cst.output_slice) return; int channel_pack = (cst.output_channel + 7) >> 3; - int idx_w = gid.x << 1; - int idx_h = gid.y << 1; - int idx_c = (gid.z % channel_pack) << 1; - int idx_b = gid.z / channel_pack; + int idx_w = gid.x << 2; + int idx_h = 0; + int idx_c = (gid.y % channel_pack) << 1; + int idx_b = gid.y / channel_pack; if(idx_b >= cst.batch || idx_c >= cst.output_slice) return; auto xy_wt = wt + idx_c * cst.input_slice; - auto xy_in0 = in + (int)idx_b * cst.input_slice * cst.input_size + idx_h * cst.output_width + idx_w; + auto xy_in0 = in + (int)idx_b * cst.input_size + idx_h * cst.output_width + idx_w; - auto xy_out = out + (int)idx_b * cst.output_slice * cst.output_size + idx_c * cst.output_size + idx_h * cst.output_width + idx_w; + auto xy_out = out + (int)idx_b * cst.output_size + idx_c * cst.output_size * cst.batch + idx_h * cst.output_width + idx_w; auto biasValue0 = FLOAT4(biasTerms[idx_c]); auto biasValue1 = FLOAT4(biasTerms[idx_c+1]); @@ -537,8 +377,8 @@ kernel void conv1x1_w2h2c2(const device ftype4 *in [[buffer(0)]], for (auto z = 0; z < cst.input_slice; z++) { auto in40 = xy_in0[0]; auto in41 = xy_in0[1]; - auto in44 = xy_in0[cst.output_width+0]; - auto in45 = xy_in0[cst.output_width+1]; + auto in44 = xy_in0[2]; + auto in45 = xy_in0[3]; auto w0 = xy_wt[z]; auto w1 = xy_wt[cst.input_slice+z]; @@ -551,27 +391,20 @@ kernel void conv1x1_w2h2c2(const device ftype4 *in [[buffer(0)]], result3 += FLOAT4(in41 * w1); result6 += FLOAT4(in44 * w1); result7 += FLOAT4(in45 * w1); - xy_in0 += cst.input_size; + xy_in0 += cst.input_size * cst.batch; } - int widthSize = min(cst.output_width - idx_w, 2); + int widthSize = min(cst.output_width - idx_w, 4); /* true */ *xy_out = activate(ftype4(result0), cst.activation); if (widthSize > 1) {xy_out[1] = activate(ftype4(result1), cst.activation); } - - int heightSize = min(cst.output_height - idx_h, 2); - if(heightSize > 1) { - /* true */ {xy_out[cst.output_width+0] = activate(ftype4(result4), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_width+1] = activate(ftype4(result5), cst.activation); } - } - + if (widthSize > 2) {xy_out[2] = activate(ftype4(result4), cst.activation); } + if (widthSize > 3) {xy_out[3] = activate(ftype4(result5), cst.activation); } + int channelSize = min(cst.output_slice - idx_c, 2); if(channelSize > 1) { - /* true */ xy_out[cst.output_size] = activate(ftype4(result2), cst.activation); - if (widthSize > 1) {xy_out[cst.output_size+1] = activate(ftype4(result3), cst.activation); } - - if(heightSize > 1) { - /* true */ {xy_out[cst.output_size+cst.output_width+0] = activate(ftype4(result6), cst.activation); } - if (widthSize > 1) {xy_out[cst.output_size+cst.output_width+1] = activate(ftype4(result7), cst.activation); } - } + /* true */ xy_out[cst.output_size * cst.batch] = activate(ftype4(result2), cst.activation); + if (widthSize > 1) {xy_out[cst.output_size * cst.batch +1] = activate(ftype4(result3), cst.activation); } + if (widthSize > 2) {xy_out[cst.output_size * cst.batch +2] = activate(ftype4(result6), cst.activation); } + if (widthSize > 3) {xy_out[cst.output_size * cst.batch +3] = activate(ftype4(result7), cst.activation); } } } diff --git a/source/backend/metal/shader/MetalConvolutionDepthwise.metal b/source/backend/metal/shader/MetalConvolutionDepthwise.metal index 268ebeb02..bff754d7f 100644 --- a/source/backend/metal/shader/MetalConvolutionDepthwise.metal +++ b/source/backend/metal/shader/MetalConvolutionDepthwise.metal @@ -28,7 +28,7 @@ kernel void conv_depthwise(const device ftype4 *in [[buffer(0)]], uint3 gid [[thread_position_in_grid]]) { if ((int)gid.x >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.slice * cst.batch) return; - int oz = gid.z % cst.slice; + int oz = gid.z / cst.batch; int offset_x = (int)gid.x * cst.stride_x - cst.pad_x; int offset_y = (int)gid.y * cst.stride_y - cst.pad_y; int sx = max(0, (UP_DIV(-offset_x, cst.dilation_x))); diff --git a/source/backend/metal/shader/MetalDeconvolution.metal b/source/backend/metal/shader/MetalDeconvolution.metal index 0f5a86fcf..133ca74fa 100644 --- a/source/backend/metal/shader/MetalDeconvolution.metal +++ b/source/backend/metal/shader/MetalDeconvolution.metal @@ -36,8 +36,8 @@ kernel void deconv(const device ftype4 *in [[buffer(0)]], uint3 gid [[thread_position_in_grid]]) { if ((int)gid.x >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.batch * cst.output_slice) return; - int b = gid.z / cst.output_slice; - int o = gid.z % cst.output_slice; + int b = gid.z % cst.batch; + int o = gid.z / cst.batch; FLOAT4 result = FLOAT4(biasTerms[o]); int oy = (int)gid.y + cst.pad_y; @@ -56,12 +56,12 @@ kernel void deconv(const device ftype4 *in [[buffer(0)]], int min_ix = (ox - max_kx * cst.dilation_x) / cst.stride_x; auto o_wt = wt + o * cst.input_slice * cst.kernel_size; - auto b_in = in + b * cst.input_slice * cst.input_size; + auto b_in = in + b * cst.input_size; for (auto z = 0; z < cst.input_slice; z++) { for (auto ky = max_ky, iy = min_iy; ky >= min_ky; ky -= cst.delta_ky, iy += cst.delta_iy) { for (auto kx = max_kx, ix = min_ix; kx >= min_kx; kx -= cst.delta_kx, ix += cst.delta_ix) { auto wt4 = o_wt[z * cst.kernel_size + ky * cst.kernel_x + kx]; - auto in4 = b_in[z * cst.input_size + iy * cst.input_width + ix]; + auto in4 = b_in[z * cst.input_size * cst.batch + iy * cst.input_width + ix]; result += FLOAT4(in4 * wt4); } } @@ -78,7 +78,7 @@ kernel void deconv_depthwise(const device ftype4 *in [[buffer(0)]], uint3 gid [[thread_position_in_grid]]) { if ((int)gid.x >= cst.output_width || (int)gid.y >= cst.output_height || (int)gid.z >= cst.batch * cst.output_slice) return; - FLOAT4 result = FLOAT4(biasTerms[(int)(gid.z % cst.input_slice)]); + FLOAT4 result = FLOAT4(biasTerms[(int)(gid.z / cst.batch)]); int oy = (int)gid.y + cst.pad_y; int ox = (int)gid.x + cst.pad_x; diff --git a/source/backend/metal/shader/MetalPReLU.metal b/source/backend/metal/shader/MetalPReLU.metal index 7082fdca5..24ab1bab7 100644 --- a/source/backend/metal/shader/MetalPReLU.metal +++ b/source/backend/metal/shader/MetalPReLU.metal @@ -19,7 +19,7 @@ kernel void prelu_slopes(const device ftype4 *in [[buffer(0)]], uint3 gid [[thread_position_in_grid]]) { // size, slice, batch if ((int)gid.x >= s.size || (int)gid.y >= s.slice) return; - int z = gid.z * s.slice + gid.y; + int z = gid.z + gid.y * s.batch; auto v4 = in[z * s.size + int(gid.x)]; out[z * s.size + int(gid.x)] = select(v4, ftype4(slope[int(gid.y)]) * v4, signbit(v4)); } diff --git a/source/backend/metal/shader/MetalROIPooling.metal b/source/backend/metal/shader/MetalROIPooling.metal index d5423adc5..a9b8d554e 100644 --- a/source/backend/metal/shader/MetalROIPooling.metal +++ b/source/backend/metal/shader/MetalROIPooling.metal @@ -2,10 +2,11 @@ struct ROI_shape { int input_width; int input_height; int input_size; + int input_batch; int output_width; int output_height; int output_size; - int slices; + int batch; float spatial_scale; }; kernel void ROI_pooling(const device ftype4 *in [[buffer(0)]], @@ -15,10 +16,10 @@ kernel void ROI_pooling(const device ftype4 *in [[buffer(0)]], uint3 gid [[thread_position_in_grid]]) { if ((int)gid.x >= s.output_width || (int)gid.y >= s.output_height) return; - int ob = gid.z / s.slices; - int iz = gid.z % s.slices; + int ob = gid.z % s.batch; + int iz = gid.z / s.batch; - auto b_roi = roi + ob * 8; // roundup(5, 4) = 8 + auto b_roi = roi + ob * 5; int ib = int(b_roi[0]); int x1 = round(float(b_roi[1]) * s.spatial_scale); int y1 = round(float(b_roi[2]) * s.spatial_scale); @@ -27,8 +28,8 @@ kernel void ROI_pooling(const device ftype4 *in [[buffer(0)]], int roi_w = max(x2 - x1 + 1, 1); int roi_h = max(y2 - y1 + 1, 1); - auto bin_size_w = (ftype)roi_w / s.output_width; - auto bin_size_h = (ftype)roi_h / s.output_height; + float bin_size_w = (float)roi_w / (float)s.output_width; + float bin_size_h = (float)roi_h / (float)s.output_height; int w_start = clamp(x1 + (int)floor(gid.x * bin_size_w) , 0, s.input_width); int w_end = clamp(x1 + (int)ceil((gid.x + 1) * bin_size_w), 0, s.input_width); @@ -36,7 +37,7 @@ kernel void ROI_pooling(const device ftype4 *in [[buffer(0)]], int h_end = clamp(y1 + (int)ceil((gid.y + 1) * bin_size_h), 0, s.input_height); int is_empty = (h_end <= h_start) || (w_end <= w_start); - auto z_in = in + (ib * s.slices + iz) * s.input_size; + auto z_in = in + (ib + iz * s.input_batch) * s.input_size; auto max4 = is_empty ? 0 : z_in[h_start * s.input_width + w_start]; for (int y = h_start; y < h_end; y++) { auto y_in = z_in + y * s.input_width; diff --git a/source/backend/metal/shader/MetalReLU.metal b/source/backend/metal/shader/MetalReLU.metal deleted file mode 100644 index ef4ef4f13..000000000 --- a/source/backend/metal/shader/MetalReLU.metal +++ /dev/null @@ -1,15 +0,0 @@ -kernel void relu_x1(const device ftype *in [[buffer(0)]], - device ftype *out [[buffer(1)]], - constant float &slope [[buffer(2)]], - uint gid [[thread_position_in_grid]]) { - auto value = in[int(gid)]; - out[int(gid)] = fmax(value, (ftype)0) + fmin(value, (ftype)0) * ftype(slope); -} - -kernel void relu_x4(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant float &slope [[buffer(2)]], - uint gid [[thread_position_in_grid]]) { - auto value = in[int(gid)]; - out[int(gid)] = fmax(value, (ftype4)0) + fmin(value, (ftype4)0) * ftype4(slope); -} diff --git a/source/backend/metal/shader/MetalReLU6.metal b/source/backend/metal/shader/MetalReLU6.metal index 829c0d90c..7e83b07d6 100644 --- a/source/backend/metal/shader/MetalReLU6.metal +++ b/source/backend/metal/shader/MetalReLU6.metal @@ -1,13 +1,24 @@ -kernel void relu6_x1(const device ftype *in [[buffer(0)]], - device ftype *out [[buffer(1)]], - constant float4 &minMax [[buffer(2)]], - uint gid [[thread_position_in_grid]]) { - out[int(gid)] = clamp(in[int(gid)], (ftype)(minMax.x), (ftype)(minMax.y)); +struct Param { + float minV; + float maxV; + int size; + int remain; +}; +kernel void relu6(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant Param &p [[buffer(2)]], + uint3 gid [[thread_position_in_grid]]) { + if (gid.x < p.size) { + out[int(gid.x)] = clamp(in[int(gid.x)], (ftype4)p.minV, (ftype4)p.maxV); + } } -kernel void relu6_x4(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant float4 &minMax [[buffer(2)]], - uint gid [[thread_position_in_grid]]) { - out[int(gid)] = clamp(in[int(gid)], (ftype4)minMax.x, (ftype4)minMax.y); -} +kernel void relu(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant Param &p [[buffer(2)]], + uint3 gid [[thread_position_in_grid]]) { + if (gid.x < p.size) { + auto value = in[int(gid.x)]; + out[int(gid.x)] = fmax(value, (ftype4)0) + fmin(value, (ftype4)0) * ftype4(p.minV); + } +} \ No newline at end of file diff --git a/source/backend/metal/shader/MetalScale.metal b/source/backend/metal/shader/MetalScale.metal index 78790d5fe..ead56d11e 100644 --- a/source/backend/metal/shader/MetalScale.metal +++ b/source/backend/metal/shader/MetalScale.metal @@ -12,7 +12,7 @@ kernel void scale_ca(const device ftype4 *in [[buffer(0)]], uint2 gid [[thread_position_in_grid]]) { if ((int)gid.x >= s.size || (int)gid.y >= s.steps * s.batch) return; - int z = gid.y % s.steps; + int z = gid.y / s.batch; out[int(gid.y) * s.size + int(gid.x)] = in [int(gid.y) * s.size + int(gid.x)] * ftype4(scales[z]) + ftype4(biasTerms[z]); } diff --git a/source/backend/opencl/core/OpenCLBackend.cpp b/source/backend/opencl/core/OpenCLBackend.cpp index b730cf5dc..74f765912 100644 --- a/source/backend/opencl/core/OpenCLBackend.cpp +++ b/source/backend/opencl/core/OpenCLBackend.cpp @@ -294,6 +294,23 @@ class CLMemReleaseImage : public Backend::MemObj { ImagePool* mBufferPool; }; +float OpenCLBackend::getBytes(const Tensor* tensor) { + float bytes = (float)tensor->getType().bytes(); + if (getOpenCLRuntime()->isSupportedFP16()) {// Fp16 + if (halide_type_float == tensor->getType().code) { + bytes = 2.0; + } + } + auto quant = TensorUtils::getDescribe(tensor)->quantAttr.get(); + if (nullptr != quant && TensorUtils::getDescribe(tensor)->type == DataType_DT_INT8) { + bytes = 1.0; + } + if(tensor->getType().bits == 4) { + bytes = 0.5; + } + return bytes; +} + Backend::MemObj* OpenCLBackend::onAcquire(const Tensor* nativeTensor, StorageType storageType) { #ifdef LOG_VERBOSE MNN_PRINT("Start OpenCLBackend::onAcquireBuffer !\n"); @@ -312,7 +329,7 @@ Backend::MemObj* OpenCLBackend::onAcquire(const Tensor* nativeTensor, StorageTyp #ifndef MNN_OPENCL_BUFFER_CLOSED if(mOpenCLRuntime->getGpuMemType() == BUFFER) { size_t size; - size_t typeSize = 4; + float typeSize = getBytes(nativeTensor); if (nativeTensor->dimensions() >= 2) { auto alignC = ROUND_UP(C, 8); // increment of height and width @@ -331,16 +348,9 @@ Backend::MemObj* OpenCLBackend::onAcquire(const Tensor* nativeTensor, StorageTyp size_t imageHeight = (size_t)N * H; size = imageWidth*imageHeight*cPack; } - cl_channel_type dataType = CL_FLOAT; - //when support and want fp16, use half datatype - if ((nativeTensor->getType().code == halide_type_int || nativeTensor->getType().code == halide_type_uint)){ - if(nativeTensor->getType().bits == 8){ - typeSize = 1; - } - } else if (getOpenCLRuntime()->isSupportedFP16()) { - typeSize = 2; - } - + // Align when int4 memory + size = ROUND_UP(size, 2); + if (storageType == DYNAMIC_SEPERATE) { auto buffer = mBufferPool->alloc(size*typeSize, true); ((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer; @@ -352,23 +362,8 @@ Backend::MemObj* OpenCLBackend::onAcquire(const Tensor* nativeTensor, StorageTyp return new CLMemReleaseBuffer(buffer, mBufferPool); } MNN_ASSERT(storageType == STATIC); -#ifdef MNN_LOW_MEMORY - // for weight quant model's weight - if ((nativeTensor->getType().code == halide_type_int) && - (nativeTensor->getType().bits == 8 || nativeTensor->getType().bits == 4)) { - // int8 quant - size_t alloc_size = size; - if (nativeTensor->getType().bits == 4) { - // int4 quant - alloc_size = size / 2; - } - auto buffer = mStaticBufferPool->alloc(alloc_size); - ((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer; - return new CLMemReleaseBuffer(buffer, mStaticBufferPool.get()); - } -#endif - auto buffer = mStaticBufferPool->alloc(size* - (dataType == CL_HALF_FLOAT ? sizeof(half_float::half) : sizeof(float))); + + auto buffer = mStaticBufferPool->alloc(size*typeSize); ((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer; // fix return new CLMemReleaseBuffer(buffer, mStaticBufferPool.get()); } @@ -569,7 +564,7 @@ ErrorCode OpenCLBackend::onResizeEnd() { mOpenCLRuntime->setCommandQueueProfileDisable(); #endif if(!mRecordings.empty()){ - endRecord(mRecordings.back(), true); + endRecord(mRecordings.back().record, true); } return NO_ERROR; } @@ -1188,8 +1183,25 @@ void OpenCLBackend::clearRecord() const{ #if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER) if(mUseRecordQueue && mDevideOpRecord){ for(int i = 0; i < mRecordings.size(); ++i){ - cl_int res = mOpenCLRuntime->commandQueue().EnqueueRecordingQCOM(mRecordings[i], 0, nullptr, 0, nullptr, - 0, nullptr, 0, nullptr, 0, nullptr, nullptr); + std::vector update_kernel_args; + std::vector update_global_size; + std::vector update_local_size; + for (int j = 0; j < mRecordings[i].updateInfo.size(); ++j){ + for(int k = 0; k < mRecordings[i].updateInfo[j]->update_kernel_args.size(); ++k){ + update_kernel_args.emplace_back(mRecordings[i].updateInfo[j]->update_kernel_args[k]); + update_kernel_args.back().dispatch_index = j; + } + for(int k = 0; k < mRecordings[i].updateInfo[j]->update_global_size.size(); ++k){ + update_global_size.emplace_back(mRecordings[i].updateInfo[j]->update_global_size[k]); + update_global_size.back().dispatch_index = j; + } + for(int k = 0; k < mRecordings[i].updateInfo[j]->update_local_size.size(); ++k){ + update_local_size.emplace_back(mRecordings[i].updateInfo[j]->update_local_size[k]); + update_local_size.back().dispatch_index = j; + } + } + cl_int res = mOpenCLRuntime->commandQueue().EnqueueRecordingQCOM(mRecordings[i].record, update_kernel_args.size(), update_kernel_args.data(), 0, nullptr, + update_global_size.size(), update_global_size.data(), update_local_size.size(), update_local_size.data(), 0, nullptr, nullptr); MNN_CHECK_CL_SUCCESS(res, "EnqueueRecordingQCOM"); } mOpenCLRuntime->commandQueue().finish(); @@ -1202,8 +1214,22 @@ void OpenCLBackend::enqeueRecord() const{ #if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER) if(mUseRecordQueue && !mDevideOpRecord){ for(int i = 0; i < mRecordings.size(); ++i){ - cl_int res = mOpenCLRuntime->commandQueue().EnqueueRecordingQCOM(mRecordings[i], 0, nullptr, 0, nullptr, - 0, nullptr, 0, nullptr, 0, nullptr, nullptr); + std::vector update_kernel_args; + std::vector update_global_size; + std::vector update_local_size; + for (int j = 0; j < mRecordings[i].updateInfo.size(); ++j){ + for(int k = 0; k < mRecordings[i].updateInfo[j]->update_kernel_args.size(); ++k){ + update_kernel_args.emplace_back(mRecordings[i].updateInfo[j]->update_kernel_args[k]); + } + for(int k = 0; k < mRecordings[i].updateInfo[j]->update_global_size.size(); ++k){ + update_global_size.emplace_back(mRecordings[i].updateInfo[j]->update_global_size[k]); + } + for(int k = 0; k < mRecordings[i].updateInfo[j]->update_local_size.size(); ++k){ + update_local_size.emplace_back(mRecordings[i].updateInfo[j]->update_local_size[k]); + } + } + cl_int res = mOpenCLRuntime->commandQueue().EnqueueRecordingQCOM(mRecordings[i].record, update_kernel_args.size(), update_kernel_args.data(), 0, nullptr, + update_global_size.size(), update_global_size.data(), update_local_size.size(), update_local_size.data(), 0, nullptr, nullptr); MNN_CHECK_CL_SUCCESS(res, "EnqueueRecordingQCOM"); } mOpenCLRuntime->commandQueue().finish(); @@ -1215,7 +1241,7 @@ void OpenCLBackend::releaseRecord(){ #if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER) if(mUseRecordQueue && !mDevideOpRecord){ for(int i = 0; i < mRecordings.size(); ++i){ - cl_int res = clReleaseRecordingQCOM(mRecordings[i]); + cl_int res = clReleaseRecordingQCOM(mRecordings[i].record); MNN_CHECK_CL_SUCCESS(res, "clReleaseRecordingQCOM"); } mRecordings.clear(); @@ -1258,8 +1284,9 @@ void OpenCLBackend::endRecord(cl_recording_qcom &recording, bool flag){ res = clEndRecordingQCOM(recording); MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM"); } else if(flag) { + // endRecord for last kernel be recorded when record mode is MNN_GPU_RECORD_BATCH if(!mRecordings.empty()){ - cl_int res = clEndRecordingQCOM(mRecordings.back()); + cl_int res = clEndRecordingQCOM(mRecordings.back().record); mRecordNums = 0; MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM"); } @@ -1270,7 +1297,18 @@ void OpenCLBackend::endRecord(cl_recording_qcom &recording, bool flag){ #endif //ENABLE_OPENCL_TIME_PROFILER } -void OpenCLBackend::recordKernel2d(const std::shared_ptr &kernelW, const std::vector &gws, const std::vector &lws) { +void OpenCLBackend::addRecord(cl_recording_qcom &record, std::vectorupdateInfo){ + if(mDevideOpRecord){ + RecordInfo info; + info.record = record; + for(int i = 0; i < updateInfo.size(); ++i) { + info.updateInfo.emplace_back(updateInfo[i]); + } + mRecordings.emplace_back(info); + } +} + +void OpenCLBackend::recordKernel2d(const std::shared_ptr &kernelW, const std::vector &gws, const std::vector &lws, RecordUpdateInfo *updateInfo) { #if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER) if(!mUseRecordQueue){ return; @@ -1281,17 +1319,35 @@ void OpenCLBackend::recordKernel2d(const std::shared_ptr &kernelW, c #endif cl_int res = CL_SUCCESS; if(!mDevideOpRecord){ + RecordInfo info; + if(updateInfo != nullptr){ + for(int i = 0; i < updateInfo->update_kernel_args.size(); ++i){ + updateInfo->update_kernel_args[i].dispatch_index = mRecordNums; + } + for(int i = 0; i < updateInfo->update_global_size.size(); ++i){ + updateInfo->update_global_size[i].dispatch_index = mRecordNums; + } + for(int i = 0; i < updateInfo->update_local_size.size(); ++i){ + updateInfo->update_local_size[i].dispatch_index = mRecordNums; + } + info.updateInfo.emplace_back(updateInfo); + } if(mRecordNums == 0){ cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res); MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM"); - mRecordings.emplace_back(recording); + info.record = recording; + mRecordings.emplace_back(info); }else if(mRecordNums == mUseRecordableQueueSize){ - res = clEndRecordingQCOM(mRecordings.back()); + res = clEndRecordingQCOM(mRecordings.back().record); MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM"); cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res); MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM"); - mRecordings.emplace_back(recording); + info.record = recording; + mRecordings.emplace_back(info); mRecordNums = 0; + } else if(updateInfo != nullptr){ + auto &lastInfo = mRecordings.back(); + lastInfo.updateInfo.emplace_back(updateInfo); } mRecordNums++; } @@ -1317,7 +1373,7 @@ void OpenCLBackend::recordKernel2d(const std::shared_ptr &kernelW, c #endif //ENABLE_OPENCL_TIME_PROFILER } -void OpenCLBackend::recordKernel3d(const std::shared_ptr &kernelW, const std::vector &gws, const std::vector &lws) { +void OpenCLBackend::recordKernel3d(const std::shared_ptr &kernelW, const std::vector &gws, const std::vector &lws, RecordUpdateInfo *updateInfo) { #if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER) if(!mUseRecordQueue){ return; @@ -1332,17 +1388,35 @@ void OpenCLBackend::recordKernel3d(const std::shared_ptr &kernelW, c internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws[i])); } if(!mDevideOpRecord){ + RecordInfo info; + if(updateInfo != nullptr){ + for(int i = 0; i < updateInfo->update_kernel_args.size(); ++i){ + updateInfo->update_kernel_args[i].dispatch_index = mRecordNums; + } + for(int i = 0; i < updateInfo->update_global_size.size(); ++i){ + updateInfo->update_global_size[i].dispatch_index = mRecordNums; + } + for(int i = 0; i < updateInfo->update_local_size.size(); ++i){ + updateInfo->update_local_size[i].dispatch_index = mRecordNums; + } + info.updateInfo.emplace_back(updateInfo); + } if(mRecordNums == 0){ cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res); MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM"); - mRecordings.emplace_back(recording); + info.record = recording; + mRecordings.emplace_back(info); }else if(mRecordNums == mUseRecordableQueueSize){ - res = clEndRecordingQCOM(mRecordings.back()); + res = clEndRecordingQCOM(mRecordings.back().record); MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM"); cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res); MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM"); - mRecordings.emplace_back(recording); + info.record = recording; + mRecordings.emplace_back(info); mRecordNums = 0; + } else if(updateInfo != nullptr){ + auto &lastInfo = mRecordings.back(); + lastInfo.updateInfo.emplace_back(updateInfo); } mRecordNums++; } diff --git a/source/backend/opencl/core/OpenCLBackend.hpp b/source/backend/opencl/core/OpenCLBackend.hpp index 3e57bc941..ca998b7f4 100644 --- a/source/backend/opencl/core/OpenCLBackend.hpp +++ b/source/backend/opencl/core/OpenCLBackend.hpp @@ -34,6 +34,15 @@ namespace MNN { namespace OpenCL { struct TuneInfo; +struct RecordUpdateInfo{ + std::vector update_kernel_args; + std::vector update_global_size; + std::vector update_local_size; +}; +struct RecordInfo{ + cl_recording_qcom record; + std::vector updateInfo; +}; class CLRuntime : public Runtime { public: CLRuntime(const Backend::Info& info, int platformSize, int platformId, int deviceId = 0, void *contextPtr = nullptr, void *glshared = nullptr); @@ -111,6 +120,7 @@ class OpenCLBackend : public Backend { return mMemory; } + float getBytes(const Tensor* tensor); DataType getDataType(const Tensor* tensor); cl_channel_type fpType(); @@ -125,11 +135,9 @@ class OpenCLBackend : public Backend { bool isDevideOpRecord(){ return mDevideOpRecord; } - void addRecord(cl_recording_qcom &record){ - mRecordings.emplace_back(record); - } - void recordKernel2d(const std::shared_ptr &kernel, const std::vector &gws, const std::vector &lws); - void recordKernel3d(const std::shared_ptr &kernel, const std::vector &gws, const std::vector &lws); + void addRecord(cl_recording_qcom &record, std::vectorupdateInfo); + void recordKernel2d(const std::shared_ptr &kernel, const std::vector &gws, const std::vector &lws, RecordUpdateInfo *updateInfo = nullptr); + void recordKernel3d(const std::shared_ptr &kernel, const std::vector &gws, const std::vector &lws, RecordUpdateInfo *updateInfo = nullptr); void startRecord(cl_recording_qcom &recording); void endRecord(cl_recording_qcom &recording, bool flag = false); @@ -167,7 +175,7 @@ class OpenCLBackend : public Backend { BackendConfig::PrecisionMode mPrecision; BackendConfig::MemoryMode mMemory; bool mIsCreateError{false}; - mutable std::vector mRecordings; + mutable std::vector mRecordings; bool mUseRecordQueue = false; bool mDevideOpRecord = false; uint32_t mRecordNums = 0; @@ -202,6 +210,17 @@ class OpenCLCreatorRegister { } #endif +#ifdef MNN_OPENCL_SEP_BUILD +#define REGISTER_OPENCL_OP_CREATOR_TRANSFORMER(name, opType, memObj) \ + OpenCLCreatorRegister ___OpenCL##name##__##opType##__##memObj##__(opType, memObj) +#else +#define REGISTER_OPENCL_OP_CREATOR_TRANSFORMER(name, opType, memObj) \ + void ___OpenCL##name##__##opType##__##memObj##__() { \ + static name _temp; \ + OpenCLBackend::addCreator(std::make_pair(opType, memObj), &_temp); \ + } +#endif + template class TypedCreator : public OpenCLBackend::Creator { diff --git a/source/backend/opencl/core/OpenCLOPRegister.cpp b/source/backend/opencl/core/OpenCLOPRegister.cpp index 9af375e4c..538a6963d 100644 --- a/source/backend/opencl/core/OpenCLOPRegister.cpp +++ b/source/backend/opencl/core/OpenCLOPRegister.cpp @@ -1,122 +1,127 @@ // This file is generated by Shell for ops register #ifndef MNN_OPENCL_SEP_BUILD - namespace MNN { namespace OpenCL { +extern void ___OpenCLInterp3DBufCreator__OpType_Interp3D__BUFFER__(); +extern void ___OpenCLReductionBufCreator__OpType_Reduction__BUFFER__(); extern void ___OpenCLArgMaxBufCreator__OpType_ArgMax__BUFFER__(); extern void ___OpenCLArgMaxBufCreator__OpType_ArgMin__BUFFER__(); -extern void ___OpenCLScaleCreator__OpType_Scale__IMAGE__(); -extern void ___OpenCLScaleBufCreator__OpType_Scale__BUFFER__(); +extern void ___OpenCLMatMulBufCreator__OpType_MatMul__BUFFER__(); +extern void ___OpenCLRasterBufCreator__OpType_Raster__BUFFER__(); +extern void ___OpenCLLayerNormBufCreator__OpType_LayerNorm__BUFFER__(); +extern void ___OpenCLDepthwiseConvolutionBufCreator__OpType_ConvolutionDepthwise__BUFFER__(); +extern void ___OpenCLInterpBufCreator__OpType_Interp__BUFFER__(); +extern void ___OpenCLBinaryBufCreator__OpType_Eltwise__BUFFER__(); +extern void ___OpenCLBinaryBufCreator__OpType_BinaryOp__BUFFER__(); +extern void ___OpenCLConvolutionBufCreator__OpType_Convolution__BUFFER__(); extern void ___OpenCLSelectBufCreator__OpType_Select__BUFFER__(); -extern void ___OpenCLSoftmaxCreator__OpType_Softmax__IMAGE__(); -extern void ___OpenCLSoftmaxBufCreator__OpType_Softmax__BUFFER__(); +extern void ___OpenCLPoolBufCreator__OpType_Pooling__BUFFER__(); +extern void ___OpenCLDeconvolutionBufCreator__OpType_Deconvolution__BUFFER__(); extern void ___OpenCLCastBufCreator__OpType_Cast__BUFFER__(); -extern void ___OpenCLInterpCreator__OpType_Interp__IMAGE__(); -extern void ___OpenCLInterpBufCreator__OpType_Interp__BUFFER__(); -extern void ___OpenCLGridSampleCreator__OpType_GridSample__IMAGE__(); +extern void ___OpenCLReluBufCreator__OpType_ReLU__BUFFER__(); +extern void ___OpenCLReluBufCreator__OpType_PReLU__BUFFER__(); +extern void ___OpenCLReluBufCreator__OpType_ReLU6__BUFFER__(); +extern void ___OpenCLSoftmaxBufCreator__OpType_Softmax__BUFFER__(); +extern void ___OpenCLLoopBufCreator__OpType_While__BUFFER__(); +extern void ___OpenCLRangeBufCreator__OpType_Range__BUFFER__(); +extern void ___OpenCLUnaryBufCreator__OpType_UnaryOp__BUFFER__(); +extern void ___OpenCLUnaryBufCreator__OpType_Sigmoid__BUFFER__(); +extern void ___OpenCLUnaryBufCreator__OpType_TanH__BUFFER__(); extern void ___OpenCLGridSampleBufCreator__OpType_GridSample__BUFFER__(); +extern void ___OpenCLScaleBufCreator__OpType_Scale__BUFFER__(); +extern void ___OpenCLDepthwiseConvolutionCreator__OpType_ConvolutionDepthwise__IMAGE__(); extern void ___OpenCLMatMulCreator__OpType_MatMul__IMAGE__(); -extern void ___OpenCLMatMulBufCreator__OpType_MatMul__BUFFER__(); -extern void ___OpenCLDeconvolutionCreator__OpType_Deconvolution__IMAGE__(); -extern void ___OpenCLDeconvolutionBufCreator__OpType_Deconvolution__BUFFER__(); -extern void ___OpenCLEltwiseCreator__OpType_BinaryOp__IMAGE__(); -extern void ___OpenCLBinaryBufCreator__OpType_BinaryOp__BUFFER__(); -extern void ___OpenCLEltwiseCreator__OpType_Eltwise__IMAGE__(); -extern void ___OpenCLBinaryBufCreator__OpType_Eltwise__BUFFER__(); -extern void ___OpenCLPoolCreator__OpType_Pooling__IMAGE__(); -extern void ___OpenCLPoolBufCreator__OpType_Pooling__BUFFER__(); -extern void ___OpenCLRoiPoolingCreator__OpType_ROIPooling__IMAGE__(); extern void ___OpenCLUnaryCreator__OpType_UnaryOp__IMAGE__(); extern void ___OpenCLUnaryCreator__OpType_Sigmoid__IMAGE__(); extern void ___OpenCLUnaryCreator__OpType_TanH__IMAGE__(); -extern void ___OpenCLUnaryBufCreator__OpType_UnaryOp__BUFFER__(); -extern void ___OpenCLUnaryBufCreator__OpType_Sigmoid__BUFFER__(); -extern void ___OpenCLUnaryBufCreator__OpType_TanH__BUFFER__(); -extern void ___OpenCLReductionCreator__OpType_Reduction__IMAGE__(); -extern void ___OpenCLReductionBufCreator__OpType_Reduction__BUFFER__(); +extern void ___OpenCLScaleCreator__OpType_Scale__IMAGE__(); +extern void ___OpenCLSoftmaxCreator__OpType_Softmax__IMAGE__(); +extern void ___OpenCLEltwiseCreator__OpType_Eltwise__IMAGE__(); +extern void ___OpenCLEltwiseCreator__OpType_BinaryOp__IMAGE__(); +extern void ___OpenCLRangeCreator__OpType_Range__IMAGE__(); +extern void ___OpenCLRasterCreator__OpType_Raster__IMAGE__(); +extern void ___OpenCLFuseCreator__OpType_Extra__IMAGE__(); +extern void ___OpenCLLoopCreator__OpType_While__IMAGE__(); +extern void ___OpenCLTrainableParamCreator__OpType_TrainableParam__IMAGE__(); extern void ___OpenCLReluCreator__OpType_ReLU__IMAGE__(); extern void ___OpenCLReluCreator__OpType_PReLU__IMAGE__(); extern void ___OpenCLReluCreator__OpType_ReLU6__IMAGE__(); -extern void ___OpenCLReluBufCreator__OpType_ReLU__BUFFER__(); -extern void ___OpenCLReluBufCreator__OpType_PReLU__BUFFER__(); -extern void ___OpenCLReluBufCreator__OpType_ReLU6__BUFFER__(); -extern void ___OpenCLDepthwiseDeconvolutionCreator__OpType_DeconvolutionDepthwise__IMAGE__(); -extern void ___OpenCLRasterCreator__OpType_Raster__IMAGE__(); -extern void ___OpenCLRasterBufCreator__OpType_Raster__BUFFER__(); -extern void ___OpenCLDepthwiseConvolutionCreator__OpType_ConvolutionDepthwise__IMAGE__(); -extern void ___OpenCLDepthwiseConvolutionBufCreator__OpType_ConvolutionDepthwise__BUFFER__(); -extern void ___OpenCLRangeBufCreator__OpType_Range__BUFFER__(); -extern void ___OpenCLInterp3DCreator__OpType_Interp3D__IMAGE__(); -extern void ___OpenCLInterp3DBufCreator__OpType_Interp3D__BUFFER__(); extern void ___OpenCLConvolutionCreator__OpType_Convolution__IMAGE__(); -extern void ___OpenCLConvolutionBufCreator__OpType_Convolution__BUFFER__(); extern void ___OpenCLLayerNormCreator__OpType_LayerNorm__IMAGE__(); -extern void ___OpenCLLayerNormBufCreator__OpType_LayerNorm__BUFFER__(); -extern void ___OpenCLLoopCreator__OpType_While__IMAGE__(); -extern void ___OpenCLLoopBufCreator__OpType_While__BUFFER__(); -extern void ___OpenCLTrainableParamCreator__OpType_TrainableParam__IMAGE__(); -extern void ___OpenCLFuseCreator__OpType_Extra__IMAGE__(); -extern void ___OpenCLRangeCreator__OpType_Range__IMAGE__(); -extern void ___OpenCLCastCreator__OpType_Cast__IMAGE__(); +extern void ___OpenCLReductionCreator__OpType_Reduction__IMAGE__(); +extern void ___OpenCLRoiPoolingCreator__OpType_ROIPooling__IMAGE__(); +extern void ___OpenCLPoolCreator__OpType_Pooling__IMAGE__(); extern void ___OpenCLSelectCreator__OpType_Select__IMAGE__(); +extern void ___OpenCLDeconvolutionCreator__OpType_Deconvolution__IMAGE__(); +extern void ___OpenCLDepthwiseDeconvolutionCreator__OpType_DeconvolutionDepthwise__IMAGE__(); +extern void ___OpenCLInterp3DCreator__OpType_Interp3D__IMAGE__(); +extern void ___OpenCLCastCreator__OpType_Cast__IMAGE__(); +extern void ___OpenCLInterpCreator__OpType_Interp__IMAGE__(); +extern void ___OpenCLGridSampleCreator__OpType_GridSample__IMAGE__(); +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE +extern void ___OpenCLAttentionBufCreator__OpType_Attention__BUFFER__(); +#endif void registerOpenCLOps() { - ___OpenCLArgMaxBufCreator__OpType_ArgMax__BUFFER__(); - ___OpenCLArgMaxBufCreator__OpType_ArgMin__BUFFER__(); - ___OpenCLScaleCreator__OpType_Scale__IMAGE__(); - ___OpenCLScaleBufCreator__OpType_Scale__BUFFER__(); - ___OpenCLSelectBufCreator__OpType_Select__BUFFER__(); - ___OpenCLSoftmaxCreator__OpType_Softmax__IMAGE__(); - ___OpenCLSoftmaxBufCreator__OpType_Softmax__BUFFER__(); - ___OpenCLCastBufCreator__OpType_Cast__BUFFER__(); - ___OpenCLInterpCreator__OpType_Interp__IMAGE__(); - ___OpenCLInterpBufCreator__OpType_Interp__BUFFER__(); - ___OpenCLGridSampleCreator__OpType_GridSample__IMAGE__(); - ___OpenCLGridSampleBufCreator__OpType_GridSample__BUFFER__(); - ___OpenCLMatMulCreator__OpType_MatMul__IMAGE__(); - ___OpenCLMatMulBufCreator__OpType_MatMul__BUFFER__(); - ___OpenCLDeconvolutionCreator__OpType_Deconvolution__IMAGE__(); - ___OpenCLDeconvolutionBufCreator__OpType_Deconvolution__BUFFER__(); - ___OpenCLEltwiseCreator__OpType_BinaryOp__IMAGE__(); - ___OpenCLBinaryBufCreator__OpType_BinaryOp__BUFFER__(); - ___OpenCLEltwiseCreator__OpType_Eltwise__IMAGE__(); - ___OpenCLBinaryBufCreator__OpType_Eltwise__BUFFER__(); - ___OpenCLPoolCreator__OpType_Pooling__IMAGE__(); - ___OpenCLPoolBufCreator__OpType_Pooling__BUFFER__(); - ___OpenCLRoiPoolingCreator__OpType_ROIPooling__IMAGE__(); - ___OpenCLUnaryCreator__OpType_UnaryOp__IMAGE__(); - ___OpenCLUnaryCreator__OpType_Sigmoid__IMAGE__(); - ___OpenCLUnaryCreator__OpType_TanH__IMAGE__(); - ___OpenCLUnaryBufCreator__OpType_UnaryOp__BUFFER__(); - ___OpenCLUnaryBufCreator__OpType_Sigmoid__BUFFER__(); - ___OpenCLUnaryBufCreator__OpType_TanH__BUFFER__(); - ___OpenCLReductionCreator__OpType_Reduction__IMAGE__(); - ___OpenCLReductionBufCreator__OpType_Reduction__BUFFER__(); - ___OpenCLReluCreator__OpType_ReLU__IMAGE__(); - ___OpenCLReluCreator__OpType_PReLU__IMAGE__(); - ___OpenCLReluCreator__OpType_ReLU6__IMAGE__(); - ___OpenCLReluBufCreator__OpType_ReLU__BUFFER__(); - ___OpenCLReluBufCreator__OpType_PReLU__BUFFER__(); - ___OpenCLReluBufCreator__OpType_ReLU6__BUFFER__(); - ___OpenCLDepthwiseDeconvolutionCreator__OpType_DeconvolutionDepthwise__IMAGE__(); - ___OpenCLRasterCreator__OpType_Raster__IMAGE__(); - ___OpenCLRasterBufCreator__OpType_Raster__BUFFER__(); - ___OpenCLDepthwiseConvolutionCreator__OpType_ConvolutionDepthwise__IMAGE__(); - ___OpenCLDepthwiseConvolutionBufCreator__OpType_ConvolutionDepthwise__BUFFER__(); - ___OpenCLRangeBufCreator__OpType_Range__BUFFER__(); - ___OpenCLInterp3DCreator__OpType_Interp3D__IMAGE__(); - ___OpenCLInterp3DBufCreator__OpType_Interp3D__BUFFER__(); - ___OpenCLConvolutionCreator__OpType_Convolution__IMAGE__(); - ___OpenCLConvolutionBufCreator__OpType_Convolution__BUFFER__(); - ___OpenCLLayerNormCreator__OpType_LayerNorm__IMAGE__(); - ___OpenCLLayerNormBufCreator__OpType_LayerNorm__BUFFER__(); - ___OpenCLLoopCreator__OpType_While__IMAGE__(); - ___OpenCLLoopBufCreator__OpType_While__BUFFER__(); - ___OpenCLTrainableParamCreator__OpType_TrainableParam__IMAGE__(); - ___OpenCLFuseCreator__OpType_Extra__IMAGE__(); - ___OpenCLRangeCreator__OpType_Range__IMAGE__(); - ___OpenCLCastCreator__OpType_Cast__IMAGE__(); - ___OpenCLSelectCreator__OpType_Select__IMAGE__(); +___OpenCLInterp3DBufCreator__OpType_Interp3D__BUFFER__(); +___OpenCLReductionBufCreator__OpType_Reduction__BUFFER__(); +___OpenCLArgMaxBufCreator__OpType_ArgMax__BUFFER__(); +___OpenCLArgMaxBufCreator__OpType_ArgMin__BUFFER__(); +___OpenCLMatMulBufCreator__OpType_MatMul__BUFFER__(); +___OpenCLRasterBufCreator__OpType_Raster__BUFFER__(); +___OpenCLLayerNormBufCreator__OpType_LayerNorm__BUFFER__(); +___OpenCLDepthwiseConvolutionBufCreator__OpType_ConvolutionDepthwise__BUFFER__(); +___OpenCLInterpBufCreator__OpType_Interp__BUFFER__(); +___OpenCLBinaryBufCreator__OpType_Eltwise__BUFFER__(); +___OpenCLBinaryBufCreator__OpType_BinaryOp__BUFFER__(); +___OpenCLConvolutionBufCreator__OpType_Convolution__BUFFER__(); +___OpenCLSelectBufCreator__OpType_Select__BUFFER__(); +___OpenCLPoolBufCreator__OpType_Pooling__BUFFER__(); +___OpenCLDeconvolutionBufCreator__OpType_Deconvolution__BUFFER__(); +___OpenCLCastBufCreator__OpType_Cast__BUFFER__(); +___OpenCLReluBufCreator__OpType_ReLU__BUFFER__(); +___OpenCLReluBufCreator__OpType_PReLU__BUFFER__(); +___OpenCLReluBufCreator__OpType_ReLU6__BUFFER__(); +___OpenCLSoftmaxBufCreator__OpType_Softmax__BUFFER__(); +___OpenCLLoopBufCreator__OpType_While__BUFFER__(); +___OpenCLRangeBufCreator__OpType_Range__BUFFER__(); +___OpenCLUnaryBufCreator__OpType_UnaryOp__BUFFER__(); +___OpenCLUnaryBufCreator__OpType_Sigmoid__BUFFER__(); +___OpenCLUnaryBufCreator__OpType_TanH__BUFFER__(); +___OpenCLGridSampleBufCreator__OpType_GridSample__BUFFER__(); +___OpenCLScaleBufCreator__OpType_Scale__BUFFER__(); +___OpenCLDepthwiseConvolutionCreator__OpType_ConvolutionDepthwise__IMAGE__(); +___OpenCLMatMulCreator__OpType_MatMul__IMAGE__(); +___OpenCLUnaryCreator__OpType_UnaryOp__IMAGE__(); +___OpenCLUnaryCreator__OpType_Sigmoid__IMAGE__(); +___OpenCLUnaryCreator__OpType_TanH__IMAGE__(); +___OpenCLScaleCreator__OpType_Scale__IMAGE__(); +___OpenCLSoftmaxCreator__OpType_Softmax__IMAGE__(); +___OpenCLEltwiseCreator__OpType_Eltwise__IMAGE__(); +___OpenCLEltwiseCreator__OpType_BinaryOp__IMAGE__(); +___OpenCLRangeCreator__OpType_Range__IMAGE__(); +___OpenCLRasterCreator__OpType_Raster__IMAGE__(); +___OpenCLFuseCreator__OpType_Extra__IMAGE__(); +___OpenCLLoopCreator__OpType_While__IMAGE__(); +___OpenCLTrainableParamCreator__OpType_TrainableParam__IMAGE__(); +___OpenCLReluCreator__OpType_ReLU__IMAGE__(); +___OpenCLReluCreator__OpType_PReLU__IMAGE__(); +___OpenCLReluCreator__OpType_ReLU6__IMAGE__(); +___OpenCLConvolutionCreator__OpType_Convolution__IMAGE__(); +___OpenCLLayerNormCreator__OpType_LayerNorm__IMAGE__(); +___OpenCLReductionCreator__OpType_Reduction__IMAGE__(); +___OpenCLRoiPoolingCreator__OpType_ROIPooling__IMAGE__(); +___OpenCLPoolCreator__OpType_Pooling__IMAGE__(); +___OpenCLSelectCreator__OpType_Select__IMAGE__(); +___OpenCLDeconvolutionCreator__OpType_Deconvolution__IMAGE__(); +___OpenCLDepthwiseDeconvolutionCreator__OpType_DeconvolutionDepthwise__IMAGE__(); +___OpenCLInterp3DCreator__OpType_Interp3D__IMAGE__(); +___OpenCLCastCreator__OpType_Cast__IMAGE__(); +___OpenCLInterpCreator__OpType_Interp__IMAGE__(); +___OpenCLGridSampleCreator__OpType_GridSample__IMAGE__(); +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE +___OpenCLAttentionBufCreator__OpType_Attention__BUFFER__(); +#endif } } } diff --git a/source/backend/opencl/core/OpenCLRunningUtils.cpp b/source/backend/opencl/core/OpenCLRunningUtils.cpp index 28c87e3c1..bddba3fe4 100644 --- a/source/backend/opencl/core/OpenCLRunningUtils.cpp +++ b/source/backend/opencl/core/OpenCLRunningUtils.cpp @@ -548,9 +548,7 @@ void runKernel2D(const ::std::shared_ptr &kernelw, const std::vector void copyBufferToImage(OpenCLRuntime *runtime, const cl::Buffer &buffer, const cl::Image &image, int w, int h) { std::set buildOptions; - if(runtime->isWeightCpuTransHalf() == false) { - buildOptions.emplace("-DBUFFER_INP_FP32"); - } + buildOptions.emplace("-DBUFFER_INP_FP32"); auto kernelW = runtime->buildKernelWithCache("copy_buffer_to_image2d", "copy_buffer_to_image2d", buildOptions); auto kernel = kernelW->get(); auto status = kernel.setArg(0, buffer); diff --git a/source/backend/opencl/core/runtime/OpenCLRuntime.cpp b/source/backend/opencl/core/runtime/OpenCLRuntime.cpp index 99407c9bb..040993b23 100644 --- a/source/backend/opencl/core/runtime/OpenCLRuntime.cpp +++ b/source/backend/opencl/core/runtime/OpenCLRuntime.cpp @@ -230,9 +230,11 @@ OpenCLRuntime::OpenCLRuntime(const BackendConfig::PrecisionMode precision, const mFirstGPUDevicePtr->getInfo(CL_DEVICE_MAX_COMPUTE_UNITS, &mGPUComputeUnits); mFirstGPUDevicePtr->getInfo(CL_DEVICE_MAX_CLOCK_FREQUENCY, &mMaxFreq); mFirstGPUDevicePtr->getInfo(CL_DEVICE_MAX_MEM_ALLOC_SIZE, &mMaxMemAllocSize); - cl_device_fp_config fpConfig; + cl_device_fp_config fpConfig; auto success = mFirstGPUDevicePtr->getInfo(CL_DEVICE_HALF_FP_CONFIG, &fpConfig); mIsDeviceSupportedFP16 = CL_SUCCESS == success && fpConfig > 0; + bool checkFp16Exetension = getDeviceSupportsExtension(*(mFirstGPUDevicePtr.get()), "cl_khr_fp16"); + mIsDeviceSupportedFP16 = (mIsDeviceSupportedFP16 && checkFp16Exetension); //set gpu mode, tuning level and memory object setGpuMode(cl_mode); @@ -245,11 +247,17 @@ OpenCLRuntime::OpenCLRuntime(const BackendConfig::PrecisionMode precision, const } } - auto permitFloat16 = false; - if (precision == BackendConfig::Precision_Low || (mMemType == BUFFER && precision == BackendConfig::Precision_Normal)) {//buffer mode not support Normal Precision yet - permitFloat16 = true; + mPrecisionLevel = 1; + if (mIsDeviceSupportedFP16) { + if (precision == BackendConfig::Precision_Low) { + mPrecisionLevel = 2; + } else if (precision == BackendConfig::Precision_Normal && mMemType == BUFFER) { + mPrecisionLevel = 0; + } } - mIsSupportedFP16 = mIsDeviceSupportedFP16 && permitFloat16; + + // Is supported fp16 IO storage + mIsSupportedFP16 = (mPrecisionLevel == 2 || mPrecisionLevel == 0); if(getDeviceSupportsExtension(*(mFirstGPUDevicePtr.get()), "cl_arm_integer_dot_product_int8")){ mSupportDotInt8 = true; @@ -257,7 +265,7 @@ OpenCLRuntime::OpenCLRuntime(const BackendConfig::PrecisionMode precision, const if(getDeviceSupportsExtension(*(mFirstGPUDevicePtr.get()), "cl_arm_integer_dot_product_accumulate_int8")){ mSupportDotAccInt8 = true; } - + #if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER) { if((false == OpenCLSymbolsOperator::getOpenclSymbolsPtr()->isQcomError()) @@ -267,7 +275,7 @@ OpenCLRuntime::OpenCLRuntime(const BackendConfig::PrecisionMode precision, const cl_int err; if(MaxRecordableQueueSize > 0){ // TODO: Use setSessionHint to set the number of mUseRecordableQueueSize - mUseRecordableQueueSize = 10; + mUseRecordableQueueSize = MaxRecordableQueueSize; mUseRecordableQueueSize = MaxRecordableQueueSize < mUseRecordableQueueSize ? MaxRecordableQueueSize : mUseRecordableQueueSize; mUseRecordQueue = true; mRecordableQueuePtr = std::make_shared(*mContext, *mFirstGPUDevicePtr, CL_QUEUE_RECORDABLE_QCOM, &err); @@ -435,13 +443,6 @@ std::vector OpenCLRuntime::getMaxImage2DSize() { bool OpenCLRuntime::isSupportedFP16() const { return mIsSupportedFP16; } -bool OpenCLRuntime::isWeightCpuTransHalf() const { -#ifdef USE_HALF_WEIGHT_MEMORY - return mIsSupportedFP16; -#else - return false;//most of time -#endif -} bool OpenCLRuntime::isDeviceSupportedFP16() const { return mIsDeviceSupportedFP16; @@ -536,10 +537,12 @@ std::shared_ptr OpenCLRuntime::buildKernel(const std::string &progra std::shared_ptr OpenCLRuntime::buildKernelWithCache(const std::string &programName, const std::string &kernelName, const std::set &buildOptions, const Tensor *input, const Tensor *output, bool useCache) { std::string buildOptionsStr; - if (mIsSupportedFP16) { - buildOptionsStr = "-DFLOAT=half -DFLOAT2=half2 -DFLOAT3=half3 -DFLOAT4=half4 -DFLOAT8=half8 -DFLOAT16=half16 -DRI_F=read_imageh -DWI_F=write_imageh -DCONVERT_FLOAT4=convert_half4 -DCONVERT_FLOAT8=convert_half8 -DCONVERT_FLOAT16=convert_half16 -DMNN_SUPPORT_FP16"; - } else { - buildOptionsStr = "-DFLOAT=float -DFLOAT2=float2 -DFLOAT3=float3 -DFLOAT4=float4 -DFLOAT8=float8 -DRI_F=read_imagef -DFLOAT16=float16 -DWI_F=write_imagef -DCONVERT_FLOAT4=convert_float4 -DCONVERT_FLOAT8=convert_float8 -DCONVERT_FLOAT16=convert_float16"; + if (mPrecisionLevel == 2) {// Fp16 Memory and fp16 compute + buildOptionsStr = "-DFLOAT=half -DFLOAT2=half2 -DFLOAT3=half3 -DFLOAT4=half4 -DFLOAT8=half8 -DFLOAT16=half16 -DCOMPUTE_FLOAT=half -DCOMPUTE_FLOAT2=half2 -DCOMPUTE_FLOAT3=half3 -DCOMPUTE_FLOAT4=half4 -DCOMPUTE_FLOAT8=half8 -DCOMPUTE_FLOAT16=half16 -DCONVERT_COMPUTE_FLOAT2=convert_half2 -DCONVERT_COMPUTE_FLOAT4=convert_half4 -DCONVERT_COMPUTE_FLOAT8=convert_half8 -DCONVERT_COMPUTE_FLOAT16=convert_half16 -DRI_F=read_imageh -DWI_F=write_imageh -DCONVERT_FLOAT2=convert_half2 -DCONVERT_FLOAT4=convert_half4 -DCONVERT_FLOAT8=convert_half8 -DCONVERT_FLOAT16=convert_half16 -DMNN_SUPPORT_FP16"; + } else if (mPrecisionLevel == 0) {// Fp16 Memory and fp32 compute + buildOptionsStr = "-DFLOAT=half -DFLOAT2=half2 -DFLOAT3=half3 -DFLOAT4=half4 -DFLOAT8=half8 -DFLOAT16=half16 -DCOMPUTE_FLOAT=float -DCOMPUTE_FLOAT2=float2 -DCOMPUTE_FLOAT3=float3 -DCOMPUTE_FLOAT4=float4 -DCOMPUTE_FLOAT8=float8 -DCOMPUTE_FLOAT16=float16 -DCONVERT_COMPUTE_FLOAT2=convert_float2 -DCONVERT_COMPUTE_FLOAT4=convert_float4 -DCONVERT_COMPUTE_FLOAT8=convert_float8 -DCONVERT_COMPUTE_FLOAT16=convert_float16 -DCONVERT_FLOAT2=convert_half2 -DCONVERT_FLOAT4=convert_half4 -DCONVERT_FLOAT8=convert_half8 -DCONVERT_FLOAT16=convert_half16 -DRI_F=read_imageh -DWI_F=write_imageh -DMNN_SUPPORT_FP16"; + } else {// Fp32 Memory and fp32 compute + buildOptionsStr = "-DFLOAT=float -DFLOAT2=float2 -DFLOAT3=float3 -DFLOAT4=float4 -DFLOAT8=float8 -DFLOAT16=float16 -DCOMPUTE_FLOAT=float -DCOMPUTE_FLOAT2=float2 -DCOMPUTE_FLOAT3=float3 -DCOMPUTE_FLOAT4=float4 -DCOMPUTE_FLOAT8=float8 -DCOMPUTE_FLOAT16=float16 -DCONVERT_COMPUTE_FLOAT2=convert_float2 -DCONVERT_COMPUTE_FLOAT4=convert_float4 -DCONVERT_COMPUTE_FLOAT8=convert_float8 -DCONVERT_COMPUTE_FLOAT16=convert_float16 -DRI_F=read_imagef -DFLOAT16=float16 -DWI_F=write_imagef -DCONVERT_FLOAT2=convert_float2 -DCONVERT_FLOAT4=convert_float4 -DCONVERT_FLOAT8=convert_float8 -DCONVERT_FLOAT16=convert_float16"; } if(nullptr != input){ @@ -907,6 +910,7 @@ void OpenCLRuntime::printEventTime(){ return; } int raster_num = 0, raster_time = 0; + unsigned int conv_time = 0, while_time = 0; for(int i = 0; i < mEvents.size(); ++i){ auto event = &mEvents[i].second; cl_int res = event->wait(); @@ -915,10 +919,16 @@ void OpenCLRuntime::printEventTime(){ auto StopNanos = event->getProfilingInfo(); auto kernel_time = (unsigned int)((StopNanos - StartNanos) / 1000.0); mKernelTime += kernel_time; + if(mEvents[i].first == "ConvBuf2D" || (mEvents[i].first.length() >= 11 && mEvents[i].first.substr(0, 11) == "Convolution")) { + conv_time += kernel_time; + } + if((mEvents[i].first.length() >= 5 && mEvents[i].first.substr(0, 5) == "While")) { + while_time += kernel_time; + } MNN_PRINT("kernel time = %d us %s\n", kernel_time, mEvents[i].first.c_str()); } mEvents.clear(); - MNN_PRINT("total kernel time = %d us\n", mKernelTime); + MNN_PRINT("total kernel time = %d us, conv time = %d us, while time = %d us\n", mKernelTime, conv_time, while_time); #endif } } // namespace MNN diff --git a/source/backend/opencl/core/runtime/OpenCLRuntime.hpp b/source/backend/opencl/core/runtime/OpenCLRuntime.hpp index ea7085db9..3e208e806 100644 --- a/source/backend/opencl/core/runtime/OpenCLRuntime.hpp +++ b/source/backend/opencl/core/runtime/OpenCLRuntime.hpp @@ -74,7 +74,6 @@ class OpenCLRuntime { OpenCLRuntime &operator=(const OpenCLRuntime &) = delete; bool isSupportedFP16() const; - bool isWeightCpuTransHalf() const; bool isDeviceSupportedFP16() const; bool isDeviceSupportedLowPower() const; bool isSupportedDotInt8() const; @@ -197,7 +196,9 @@ class OpenCLRuntime { uint32_t mUseRecordableQueueSize; bool mUseRecordQueue = false; bool mDevideOpRecord = true; - bool mIsSupportedFP16 = false; + int mPrecisionLevel; + + bool mIsSupportedFP16 = false; bool mIsDeviceSupportedFP16 = false; bool mIsDeviceSupportedLowPower = false; bool mSupportDotInt8 = false; diff --git a/source/backend/opencl/core/runtime/OpenCLWrapper.cpp b/source/backend/opencl/core/runtime/OpenCLWrapper.cpp index 62cda7e4e..deaf9b627 100644 --- a/source/backend/opencl/core/runtime/OpenCLWrapper.cpp +++ b/source/backend/opencl/core/runtime/OpenCLWrapper.cpp @@ -33,6 +33,14 @@ bool OpenCLSymbols::LoadOpenCLLibrary() { "libGLES_mali.so", "libmali.so", "libOpenCL-pixel.so", + /* + #elif defined(__OHOS__) + "/vendor/lib64/chipsetsdk/libGLES_mali.so", + "/system/lib64/libGLES_mali.so", + "libGLES_mali.so", + "/vendor/lib64/chipsetsdk/libhvgr_v200.so", + "/vendor/lib64/chipsetsdk/libEGI_imp1.so", + */ #if defined(__aarch64__) // Qualcomm Adreno "/system/vendor/lib64/libOpenCL.so", @@ -110,7 +118,7 @@ bool OpenCLSymbols::isSvmError() { bool OpenCLSymbols::isPropError() { return mPropError; } - + bool OpenCLSymbols::isQcomError() { return mQcomError; } @@ -118,11 +126,11 @@ bool OpenCLSymbols::isQcomError() { bool OpenCLSymbols::isGlError() { return mGlError; } - + bool OpenCLSymbols::isCL1_2Error() { return mCL_12Error; } - + bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { #if defined(WIN32) handle_ = LoadLibraryA(library_path.c_str()); @@ -133,38 +141,38 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { if(func_name == nullptr){ \ mIsError = true; \ } - + #define MNN_LOAD_SVM_PTR(func_name) func_name = reinterpret_cast(GetProcAddress(handle_, #func_name)); \ if(func_name == nullptr){ \ mSvmError = true; \ } - + #define MNN_LOAD_PROP_PTR(func_name) func_name = reinterpret_cast(GetProcAddress(handle_, #func_name)); \ if(func_name == nullptr){ \ mPropError = true; \ } - + #define MNN_LOAD_QCOM_PTR(func_name) func_name = reinterpret_cast(GetProcAddress(handle_, #func_name)); \ if(func_name == nullptr){ \ mQcomError = true; \ } - + #define MNN_LOAD_CL_12_PTR(func_name) func_name = reinterpret_cast(GetProcAddress(handle_, #func_name)); \ if(func_name == nullptr){ \ mCL_12Error = true; \ } - + #define MNN_LOAD_GL_PTR(func_name) func_name = reinterpret_cast(GetProcAddress(handle_, #func_name)); \ if(func_name == nullptr){ \ mGlError = true; \ } - + #else handle_ = dlopen(library_path.c_str(), RTLD_NOW | RTLD_LOCAL); if (handle_ == nullptr) { return false; } - + typedef void* (*loadOpenCLPointerFunc)(const char* name); typedef void (*enableOpenCLFunc)(); loadOpenCLPointerFunc loadOpenCLPointer = nullptr; @@ -180,7 +188,7 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { if(func_name == nullptr){ \ mIsError = true; \ } - + #define MNN_LOAD_SVM_PTR(func_name) func_name = reinterpret_cast(dlsym(handle_, #func_name)); \ if(func_name == nullptr && loadOpenCLPointer != nullptr){ \ func_name = reinterpret_cast(loadOpenCLPointer(#func_name)); \ @@ -188,7 +196,7 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { if(func_name == nullptr){ \ mSvmError = true; \ } - + #define MNN_LOAD_PROP_PTR(func_name) func_name = reinterpret_cast(dlsym(handle_, #func_name)); \ if(func_name == nullptr && loadOpenCLPointer != nullptr){ \ func_name = reinterpret_cast(loadOpenCLPointer(#func_name)); \ @@ -196,7 +204,7 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { if(func_name == nullptr){ \ mPropError = true; \ } - + #define MNN_LOAD_QCOM_PTR(func_name) func_name = reinterpret_cast(dlsym(handle_, #func_name)); \ if(func_name == nullptr && loadOpenCLPointer != nullptr){ \ func_name = reinterpret_cast(loadOpenCLPointer(#func_name)); \ @@ -204,7 +212,7 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { if(func_name == nullptr){ \ mQcomError = true; \ } - + #define MNN_LOAD_CL_12_PTR(func_name) func_name = reinterpret_cast(dlsym(handle_, #func_name)); \ if(func_name == nullptr && loadOpenCLPointer != nullptr){ \ func_name = reinterpret_cast(loadOpenCLPointer(#func_name)); \ @@ -212,7 +220,7 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { if(func_name == nullptr){ \ mCL_12Error = true; \ } - + #define MNN_LOAD_GL_PTR(func_name) func_name = reinterpret_cast(dlsym(handle_, #func_name)); \ if(func_name == nullptr && loadOpenCLPointer != nullptr){ \ func_name = reinterpret_cast(loadOpenCLPointer(#func_name)); \ @@ -220,7 +228,7 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { if(func_name == nullptr){ \ mGlError = true; \ } - + #endif MNN_LOAD_FUNCTION_PTR(clGetPlatformIDs); @@ -277,7 +285,7 @@ bool OpenCLSymbols::LoadLibraryFromPath(const std::string &library_path) { MNN_LOAD_CL_12_PTR(clCreateImage); MNN_LOAD_CL_12_PTR(clRetainDevice); MNN_LOAD_CL_12_PTR(clReleaseDevice); - + MNN_LOAD_PROP_PTR(clCreateCommandQueueWithProperties); MNN_LOAD_SVM_PTR(clSVMAlloc); MNN_LOAD_SVM_PTR(clSVMFree); @@ -707,7 +715,7 @@ cl_mem CL_API_CALL clCreateFromGLTexture(cl_context context, auto func = MNN::OpenCLSymbolsOperator::getOpenclSymbolsPtr()->clCreateFromGLTexture; MNN_CHECK_NOTNULL(func); return func(context, flags, target, miplevel, texture, errcode_ret); - + } cl_int CL_API_CALL clEnqueueAcquireGLObjects(cl_command_queue command_queue, diff --git a/source/backend/opencl/execution/buffer/AttentionBufExecution.cpp b/source/backend/opencl/execution/buffer/AttentionBufExecution.cpp new file mode 100644 index 000000000..6006882fd --- /dev/null +++ b/source/backend/opencl/execution/buffer/AttentionBufExecution.cpp @@ -0,0 +1,427 @@ +// +// SoftmaxBufExecution.cpp +// MNN +// +// Created by MNN on 2024/04/11. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef MNN_OPENCL_BUFFER_CLOSED + +#include "backend/opencl/execution/buffer/AttentionBufExecution.hpp" + +namespace MNN { +namespace OpenCL { + +AttentionBufImpl::AttentionBufImpl(const MNN::Op *op, Backend *backend, bool kv_cahce) + : mKVCache(kv_cahce){ + mOpenCLBackend = static_cast(backend); + auto kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("softmax_buf", "softmax_channel", {"-DSOFTMAX_LOCAL_SIZE=512"}); + mMaxWorkGroupSize = static_cast(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel)); +} + +void AttentionBufImpl::allocKVCache() { + if (!mKVCache || mPastLength < mMaxLength) { + return; + } + mMaxLength = mPastLength + mExpandChunk; + int byte = 4; + if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()){ + byte = 2; + } + size_t buffer_size = UP_DIV(mMaxLength, 4) * mNumHead * mHeadDim * 4 * byte; + // past_key: [1, numhead, headdim, maxlen] + mPastKey.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); + // past_value: [1, numhead, maxlen, headdim] + mPastValue.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); +} + +void AttentionBufImpl::reallocKVCache() { + if (!mKVCache || mPastLength < mMaxLength) { + return; + } + int byte = 4; + if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()){ + byte = 2; + } + size_t old_size = mNumHead * UP_DIV(mMaxLength, 4) * mHeadDim * 4 * byte; + mMaxLength = mPastLength + mExpandChunk; + size_t buffer_size = UP_DIV(mMaxLength, 4) * mNumHead * mHeadDim * 4 * byte; + // past_key: [1, numhead, headdim, maxlen] + auto new_key = new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); + // past_value: [1, numhead, maxlen, headdim] + auto new_value = new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); + // copy + cl_int res; + auto new_key_ptr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*new_key, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &res); + auto key_ptr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*mPastKey.get(), true, CL_MAP_READ, 0, old_size, nullptr, nullptr, &res); + if(new_key_ptr != nullptr && key_ptr != nullptr && res == CL_SUCCESS){ + ::memcpy(new_key_ptr, key_ptr, old_size); + }else{ + MNN_ERROR("Map error key_ptr == nullptr \n"); + MNN_ASSERT(false); + } + mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*new_key, new_key_ptr); + mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*mPastKey.get(), key_ptr); + + auto new_value_ptr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*new_value, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &res); + auto value_ptr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*mPastValue.get(), true, CL_MAP_READ, 0, old_size, nullptr, nullptr, &res); + if(new_value_ptr != nullptr && value_ptr != nullptr && res == CL_SUCCESS){ + ::memcpy(new_value_ptr, value_ptr, old_size); + }else{ + MNN_ERROR("Map error value_ptr == nullptr \n"); + MNN_ASSERT(false); + } + mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*new_value, new_value_ptr); + mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*mPastValue.get(), value_ptr); + + mPastKey.reset(new_key); + mPastValue.reset(new_value); + size_t temp_size = UP_DIV(mMaxLength, 4) * mNumHead * 4 * byte; + mTempQK.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, temp_size)); + mTempSoftMax.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, temp_size)); + + // reset memory for args + if(mOpenCLBackend->isUseRecordQueue()){ + mQkUpdateInfo.update_kernel_args[1].arg_value = &(*(mTempQK.get()))(); + mQkUpdateInfo.update_kernel_args[2].arg_value = &(*(mPastKey.get()))(); + mSoftMaxUpdateInfo.update_kernel_args[0].arg_value = &(*(mTempQK.get()))(); + mSoftMaxUpdateInfo.update_kernel_args[1].arg_value = &(*(mTempSoftMax.get()))(); + mQkvUpdateInfo.update_kernel_args[0].arg_value = &(*(mTempSoftMax.get()))(); + mQkvUpdateInfo.update_kernel_args[1].arg_value = &(*(mPastValue.get()))(); + }else{ + cl_int ret = CL_SUCCESS; + ret |= mKernel_qk->get().setArg(5, *mTempQK.get()); + ret |= mKernel_qk->get().setArg(6, *mPastKey.get()); + ret |= mKernel_softmax->get().setArg(3, *mTempQK.get()); + ret |= mKernel_softmax->get().setArg(4, *mTempSoftMax.get()); + ret |= mKernel_qkv->get().setArg(3, *mTempSoftMax.get()); + ret |= mKernel_qkv->get().setArg(6, *mPastValue.get()); + MNN_CHECK_CL_SUCCESS(ret, "reset memory arg for AttentionBufExecution"); + } +} + +int AttentionBufImpl::getLocalSize(int size, int maxGroupSize){ + int local_size = 1; + while(local_size * 2 <= maxGroupSize && local_size * 2 <= size){ + local_size *= 2; + } + return local_size; +} + +ErrorCode AttentionBufImpl::onResize(Backend *backend, const std::vector &inputs, const std::vector &outputs) { + mOpenCLBackend = static_cast(backend); + mOpenCLBackend->startRecord(mRecording); + //clear update arg vector, if prefill and decode use the same one + mOpRecordUpdateInfo.clear(); + mQkUpdateInfo.update_kernel_args.clear(); + mQkUpdateInfo.update_global_size.clear(); + mQkUpdateInfo.update_local_size.clear(); + mSoftMaxUpdateInfo.update_kernel_args.clear(); + mSoftMaxUpdateInfo.update_global_size.clear(); + mSoftMaxUpdateInfo.update_local_size.clear(); + mQkvUpdateInfo.update_kernel_args.clear(); + mQkvUpdateInfo.update_global_size.clear(); + mQkvUpdateInfo.update_local_size.clear(); + + auto query = inputs[0]; + auto key = inputs[1]; + auto value = inputs[2]; + auto mask = inputs[3]; + auto runtime = mOpenCLBackend->getOpenCLRuntime(); + auto shape = query->shape(); + + int seq_len = shape[1]; + mNumHead = shape[2]; + mHeadDim = shape[3]; + mScale = 1.0 / sqrt(mHeadDim); + mIsDecode = seq_len == 1; + mIsFirstDecode = true; + if (mPastLength == 0 || seq_len > 1) { + mPastLength = seq_len; + } + mKv_seq_len = mPastLength; + if(mIsDecode){ + mKv_seq_len = mPastLength + 1; + } + + allocKVCache(); + int byte = 4; + if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()){ + byte = 2; + } + if (mIsDecode) { + size_t buffer_size = UP_DIV(mMaxLength, 4) * mNumHead * 4 * byte; + mTempQK.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); + mTempSoftMax.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); + } else { + size_t buffer_size = UP_DIV(mPastLength, 4) * mPastLength * mNumHead * 4 * byte; + mTempQK.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); + mTempSoftMax.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); + } + + // query * key -> div -> select + { + std::set buildOption; + if(!mIsDecode){ + buildOption.emplace("-DOPENCL_PREFILL_ATTENTION"); + } + if((mHeadDim % 4) != 0){ + buildOption.emplace("-DHEADDIM_LEAVE"); + } + mKernel_qk = runtime->buildKernel("attention_buf", "matmul_qk_div_mask", buildOption, inputs[0], outputs[0]); + mGlobalWorkSizeQk = {static_cast(UP_DIV(seq_len, 4)), static_cast(mNumHead), static_cast(UP_DIV(mKv_seq_len, 4))}; + auto maxWorkGroupSize = static_cast(runtime->getMaxWorkGroupSize(mKernel_qk)); + mGlobalWorkSizeQk2 = UP_DIV(mKv_seq_len, 4); + + uint32_t index = 0; + cl_int ret = CL_SUCCESS; + ret |= mKernel_qk->get().setArg(index++, mGlobalWorkSizeQk[0]); + ret |= mKernel_qk->get().setArg(index++, mGlobalWorkSizeQk[1]); + ret |= mKernel_qk->get().setArg(index++, mGlobalWorkSizeQk2); + ret |= mKernel_qk->get().setArg(index++, openCLBuffer(query)); + ret |= mKernel_qk->get().setArg(index++, openCLBuffer(key)); + ret |= mKernel_qk->get().setArg(index++, *mTempQK.get()); + ret |= mKernel_qk->get().setArg(index++, *mPastKey.get()); + ret |= mKernel_qk->get().setArg(index++, openCLBuffer(mask)); + ret |= mKernel_qk->get().setArg(index++, mScale); + ret |= mKernel_qk->get().setArg(index++, seq_len); + ret |= mKernel_qk->get().setArg(index++, mKv_seq_len); + ret |= mKernel_qk->get().setArg(index++, mNumHead); + ret |= mKernel_qk->get().setArg(index++, mHeadDim); + MNN_CHECK_CL_SUCCESS(ret, "setArg matmul_qk_div_mask"); + + mLocalWorkSizeQk = localWS3DDefault(mGlobalWorkSizeQk, maxWorkGroupSize, runtime, "matmul_qk_div_mask", mKernel_qk).first; + mGlobalWorkSizeQk[0] = ROUND_UP(mGlobalWorkSizeQk[0], std::max((uint32_t)1, mLocalWorkSizeQk[0])); + mGlobalWorkSizeQk[1] = ROUND_UP(mGlobalWorkSizeQk[1], std::max((uint32_t)1, mLocalWorkSizeQk[1])); + mGlobalWorkSizeQk[2] = ROUND_UP(mGlobalWorkSizeQk[2], std::max((uint32_t)1, mLocalWorkSizeQk[2])); + mQkUpdateInfo.update_kernel_args.push_back({0, 2, sizeof(mGlobalWorkSizeQk2), &mGlobalWorkSizeQk2}); + mQkUpdateInfo.update_kernel_args.push_back({0, 5, sizeof(cl_mem), &(*(mTempQK.get()))()}); + mQkUpdateInfo.update_kernel_args.push_back({0, 6, sizeof(cl_mem), &(*(mPastKey.get()))()}); + mQkUpdateInfo.update_kernel_args.push_back({0, 10, sizeof(mKv_seq_len), &mKv_seq_len}); + mQkGlobal_size[0] = mGlobalWorkSizeQk[0]; + mQkGlobal_size[1] = mGlobalWorkSizeQk[1]; + mQkGlobal_size[2] = mGlobalWorkSizeQk[2]; + mQkUpdateInfo.update_global_size.push_back({0, mQkGlobal_size}); + mOpRecordUpdateInfo.emplace_back(&mQkUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_qk, mGlobalWorkSizeQk, mLocalWorkSizeQk, &mQkUpdateInfo); + } + + // softmax + { + auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize); + int localSize = getLocalSize(mKv_seq_len, MaxLocalSize); + if(localSize < 4){ + localSize = 1; + } + int past_len4 = UP_DIV(mKv_seq_len, 4); + mSoftMaxRemainChannels = past_len4 * 4 - mKv_seq_len; + mSoftmaxShape[0] = mNumHead; + mSoftmaxShape[1] = past_len4; + mSoftmaxShape[2] = 1; + mSoftmaxShape[3] = mPastLength; + std::set buildOption; + buildOption.emplace("-DSOFTMAX_LOCAL_SIZE=" + std::to_string(localSize)); + if(!mIsDecode){ + mKernel_softmax = runtime->buildKernel("softmax_buf", "softmax_width", buildOption, inputs[0], outputs[0]); + mGlobalWorkSizeSoftMax = {static_cast(localSize), static_cast(past_len4), static_cast(mNumHead)}; + } else{ + mKernel_softmax = runtime->buildKernel("softmax_buf", "softmax_channel", buildOption, inputs[0], outputs[0]); + mSoftmaxShape[3] = 1; + mGlobalWorkSizeSoftMax = {static_cast(localSize), static_cast(1), static_cast(mNumHead)}; + } + auto maxWorkGroupSize = static_cast(runtime->getMaxWorkGroupSize(mKernel_softmax)); + + uint32_t index = 0; + cl_int ret = CL_SUCCESS; + ret |= mKernel_softmax->get().setArg(index++, mGlobalWorkSizeSoftMax[0]); + ret |= mKernel_softmax->get().setArg(index++, mGlobalWorkSizeSoftMax[1]); + ret |= mKernel_softmax->get().setArg(index++, mGlobalWorkSizeSoftMax[2]); + ret |= mKernel_softmax->get().setArg(index++, *mTempQK.get()); + ret |= mKernel_softmax->get().setArg(index++, *mTempSoftMax.get()); + ret |= mKernel_softmax->get().setArg(index++, mSoftMaxRemainChannels); + ret |= mKernel_softmax->get().setArg(index++, mSoftmaxShape); + MNN_CHECK_CL_SUCCESS(ret, "setArg softmax"); + + mLocalWorkSizeSoftMax = {static_cast(localSize), 1, 1}; + if(localSize == 1){ + mLocalWorkSizeSoftMax = localWS3DDefault(mGlobalWorkSizeSoftMax, maxWorkGroupSize, runtime, "softmax", mKernel_softmax).first; + } + mGlobalWorkSizeSoftMax[0] = ROUND_UP(mGlobalWorkSizeSoftMax[0], std::max((uint32_t)1, mLocalWorkSizeSoftMax[0])); + mGlobalWorkSizeSoftMax[1] = ROUND_UP(mGlobalWorkSizeSoftMax[1], std::max((uint32_t)1, mLocalWorkSizeSoftMax[1])); + mGlobalWorkSizeSoftMax[2] = ROUND_UP(mGlobalWorkSizeSoftMax[2], std::max((uint32_t)1, mLocalWorkSizeSoftMax[2])); + mSoftMaxUpdateInfo.update_kernel_args.push_back({0, 3, sizeof(cl_mem), &(*(mTempQK.get()))()}); + mSoftMaxUpdateInfo.update_kernel_args.push_back({0, 4, sizeof(cl_mem), &(*(mTempSoftMax.get()))()}); + mSoftMaxUpdateInfo.update_kernel_args.push_back({0, 5, sizeof(mSoftMaxRemainChannels), &mSoftMaxRemainChannels}); + mSoftMaxUpdateInfo.update_kernel_args.push_back({0, 6, sizeof(mSoftmaxShape), &mSoftmaxShape}); + mOpRecordUpdateInfo.emplace_back(&mSoftMaxUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_softmax, mGlobalWorkSizeSoftMax, mLocalWorkSizeSoftMax, &mSoftMaxUpdateInfo); + } + + // qk * value + { + std::set buildOption; + if(!mIsDecode){ + buildOption.emplace("-DOPENCL_PREFILL_ATTENTION"); + } + if((mHeadDim % 4) != 0){ + buildOption.emplace("-DHEADDIM_LEAVE"); + } + mKernel_qkv = runtime->buildKernel("attention_buf", "matmul_qkv", buildOption, inputs[0], outputs[0]); + auto maxWorkGroupSize = static_cast(runtime->getMaxWorkGroupSize(mKernel_qkv)); + mGlobalWorkSizeQkv = {static_cast(UP_DIV(seq_len, 4)), static_cast(mNumHead), static_cast(mHeadDim)}; + if(mIsDecode){ + mGlobalWorkSizeQkv = {static_cast(1), static_cast(mNumHead), static_cast(UP_DIV(mHeadDim, 4))}; + } + + uint32_t index = 0; + cl_int ret = CL_SUCCESS; + ret |= mKernel_qkv->get().setArg(index++, mGlobalWorkSizeQkv[0]); + ret |= mKernel_qkv->get().setArg(index++, mGlobalWorkSizeQkv[1]); + ret |= mKernel_qkv->get().setArg(index++, mGlobalWorkSizeQkv[2]); + ret |= mKernel_qkv->get().setArg(index++, *mTempSoftMax.get()); + ret |= mKernel_qkv->get().setArg(index++, openCLBuffer(value)); + ret |= mKernel_qkv->get().setArg(index++, openCLBuffer(outputs[0])); + ret |= mKernel_qkv->get().setArg(index++, *mPastValue.get()); + ret |= mKernel_qkv->get().setArg(index++, seq_len); + ret |= mKernel_qkv->get().setArg(index++, mKv_seq_len); + ret |= mKernel_qkv->get().setArg(index++, mNumHead); + ret |= mKernel_qkv->get().setArg(index++, mHeadDim); + MNN_CHECK_CL_SUCCESS(ret, "setArg matmul_qkv"); + + mLocalWorkSizeQkv = localWS3DDefault(mGlobalWorkSizeQkv, maxWorkGroupSize, runtime, "matmul_qkv", mKernel_qkv).first; + mGlobalWorkSizeQkv[0] = ROUND_UP(mGlobalWorkSizeQkv[0], std::max((uint32_t)1, mLocalWorkSizeQkv[0])); + mGlobalWorkSizeQkv[1] = ROUND_UP(mGlobalWorkSizeQkv[1], std::max((uint32_t)1, mLocalWorkSizeQkv[1])); + mGlobalWorkSizeQkv[2] = ROUND_UP(mGlobalWorkSizeQkv[2], std::max((uint32_t)1, mLocalWorkSizeQkv[2])); + + mQkvUpdateInfo.update_kernel_args.push_back({0, 3, sizeof(cl_mem), &(*(mTempSoftMax.get()))()}); + mQkvUpdateInfo.update_kernel_args.push_back({0, 6, sizeof(cl_mem), &(*(mPastValue.get()))()}); + mQkvUpdateInfo.update_kernel_args.push_back({0, 8, sizeof(mKv_seq_len), &mKv_seq_len}); + mOpRecordUpdateInfo.emplace_back(&mQkvUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_qkv, mGlobalWorkSizeQkv, mLocalWorkSizeQkv, &mQkvUpdateInfo); + } + + mOpenCLBackend->endRecord(mRecording); + return NO_ERROR; +} + +ErrorCode AttentionBufImpl::onExecute(Backend *backend, const std::vector &inputs, const std::vector &outputs) { +#ifdef LOG_VERBOSE + MNN_PRINT("start AttentionBufExecution onExecute !\n"); +#endif + mOpenCLBackend = static_cast(backend); + reallocKVCache(); +#ifdef ENABLE_OPENCL_TIME_PROFILER + { + cl::Event event; + run3DKernelDefault(mKernel_qk, mGlobalWorkSizeQk, mLocalWorkSizeQk, + mOpenCLBackend->getOpenCLRuntime(), &event); + + mOpenCLBackend->getOpenCLRuntime()->pushEvent({"matmul_qk_div_mask", event}); + } + { + cl::Event event; + run3DKernelDefault(mKernel_softmax, mGlobalWorkSizeSoftMax, mLocalWorkSizeSoftMax, + mOpenCLBackend->getOpenCLRuntime(), &event); + + mOpenCLBackend->getOpenCLRuntime()->pushEvent({"softmax", event}); + } + { + cl::Event event; + run3DKernelDefault(mKernel_qkv, mGlobalWorkSizeQkv, mLocalWorkSizeQkv, + mOpenCLBackend->getOpenCLRuntime(), &event); + + mOpenCLBackend->getOpenCLRuntime()->pushEvent({"matmul_qkv", event}); + } +#else + if(mOpenCLBackend->isUseRecordQueue()){ + mOpenCLBackend->addRecord(mRecording, mOpRecordUpdateInfo); + if(mIsDecode){ + if(mIsFirstDecode){ + mIsFirstDecode = false; + }else{ + mPastLength += 1; + mKv_seq_len = mPastLength + 1; + int past_len4 = UP_DIV(mKv_seq_len, 4); + mSoftMaxRemainChannels = past_len4 * 4 - mKv_seq_len; + mSoftmaxShape[1] = past_len4; + mGlobalWorkSizeQk2 = past_len4; + mQkGlobal_size[2] = ROUND_UP(mGlobalWorkSizeQk2, std::max((uint32_t)1, mLocalWorkSizeQk[2])); + } + } +#ifdef LOG_VERBOSE + MNN_PRINT("End AttentionBufExecution onExecute... \n"); +#endif + return NO_ERROR; + } + run3DKernelDefault(mKernel_qk, mGlobalWorkSizeQk, mLocalWorkSizeQk, mOpenCLBackend->getOpenCLRuntime()); + run3DKernelDefault(mKernel_softmax, mGlobalWorkSizeSoftMax, mLocalWorkSizeSoftMax, mOpenCLBackend->getOpenCLRuntime()); + run3DKernelDefault(mKernel_qkv, mGlobalWorkSizeQkv, mLocalWorkSizeQkv, mOpenCLBackend->getOpenCLRuntime()); +#endif + + // decode + if(mIsDecode){ + mPastLength += 1; + mKv_seq_len = mPastLength + 1; + int past_len4 = UP_DIV(mKv_seq_len, 4); + mSoftMaxRemainChannels = past_len4 * 4 - mKv_seq_len; + mSoftmaxShape[1] = past_len4; + cl_int ret = CL_SUCCESS; + mGlobalWorkSizeQk2 = past_len4; + mGlobalWorkSizeQk[2] = ROUND_UP(mGlobalWorkSizeQk2, std::max((uint32_t)1, mLocalWorkSizeQk[2])); + ret |= mKernel_qk->get().setArg(2, mGlobalWorkSizeQk2); + ret |= mKernel_qk->get().setArg(10, mKv_seq_len); + ret |= mKernel_softmax->get().setArg(5, mSoftMaxRemainChannels); + ret |= mKernel_softmax->get().setArg(6, mSoftmaxShape); + ret |= mKernel_qkv->get().setArg(8, mKv_seq_len); + MNN_CHECK_CL_SUCCESS(ret, "reset arg for AttentionBufExecution"); + } +#ifdef LOG_VERBOSE + MNN_PRINT("end AttentionBufExecution onExecute !\n"); +#endif + + return NO_ERROR; +} + +AttentionBufExecution::AttentionBufExecution(const MNN::Op *op, Backend* backend, bool kv_cahce) : CommonExecution(backend, op) { + mImpl.reset(new AttentionBufImpl(op, backend, kv_cahce)); +} + +AttentionBufExecution::AttentionBufExecution(std::shared_ptr impl, const MNN::Op *op, Backend *backend) : CommonExecution(backend, op), mImpl(impl) {} + +ErrorCode AttentionBufExecution::onResize(const std::vector& inputs, const std::vector& outputs) { + return mImpl->onResize(backend(), inputs, outputs); +} + +ErrorCode AttentionBufExecution::onExecute(const std::vector& inputs, const std::vector& outputs) { + return mImpl->onExecute(backend(), inputs, outputs); +} + +bool AttentionBufExecution::onClone(Backend* bn, const Op* op, Execution** dst) { + if (nullptr == dst) { + return true; + } + *dst = new AttentionBufExecution(mImpl, op, bn); + return true; +} + +class AttentionBufCreator : public OpenCLBackend::Creator { +public: + virtual Execution *onCreate(const std::vector &inputs, const std::vector &outputs, + const MNN::Op *op, Backend *backend) const override { + for (int i = 0; i < inputs.size(); ++i) { + TensorUtils::setTensorSupportPack(inputs[i], false); + } + for (int i = 0; i < outputs.size(); ++i) { + TensorUtils::setTensorSupportPack(outputs[i], false); + } + auto param = op->main_as_AttentionParam(); + return new AttentionBufExecution(op, backend, param->kv_cache()); + } +}; +REGISTER_OPENCL_OP_CREATOR(AttentionBufCreator, OpType_Attention, BUFFER); + +} // namespace OpenCL +} // namespace MNN +#endif/* MNN_OPENCL_BUFFER_CLOSED */ + diff --git a/source/backend/opencl/execution/buffer/AttentionBufExecution.hpp b/source/backend/opencl/execution/buffer/AttentionBufExecution.hpp new file mode 100644 index 000000000..1d6ac005b --- /dev/null +++ b/source/backend/opencl/execution/buffer/AttentionBufExecution.hpp @@ -0,0 +1,82 @@ +// +// AttentionBufExecution.hpp +// MNN +// +// Created by MNN on 2024/04/11. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef MNN_OPENCL_BUFFER_CLOSED + +#ifndef AttentionBufExecution_hpp +#define AttentionBufExecution_hpp + +#include "backend/opencl/execution/image/CommonExecution.hpp" + +namespace MNN { +namespace OpenCL { + +class AttentionBufImpl { +public: + AttentionBufImpl(const MNN::Op *op, Backend *backend, bool kv_cache); + + ~AttentionBufImpl() { + if(mRecording != NULL){ +#ifdef MNN_USE_LIB_WRAPPER + clReleaseRecordingQCOM(mRecording); +#endif + } + } + ErrorCode onResize(Backend *backend, const std::vector &inputs, const std::vector &outputs); + ErrorCode onExecute(Backend *backend, const std::vector &inputs, const std::vector &outputs); + +private: + int getLocalSize(int size, int maxGroupSize); + void allocKVCache(); + void reallocKVCache(); + bool mKVCache; + float mScale; + const int mExpandChunk = 64; + bool mIsDecode = false; + bool mIsFirstDecode = true; + int mPastLength = 0, mMaxLength = 0, mKv_seq_len = 0, mSoftMaxRemainChannels = 0; + std::shared_ptr mPastKey, mPastValue, mTempQK, mTempSoftMax; + int mNumHead = 0, mHeadDim = 0, mValueH = 0; + std::shared_ptr mKernel_qk; + std::shared_ptr mKernel_softmax; + std::shared_ptr mKernel_qkv; + std::vector mGlobalWorkSizeQk{1, 1, 1}; + std::vector mLocalWorkSizeQk{1, 1, 1, 1}; + std::vector mGlobalWorkSizeSoftMax{1, 1, 1}; + std::vector mLocalWorkSizeSoftMax{1, 1, 1, 1}; + std::vector mGlobalWorkSizeQkv{1, 1, 1}; + std::vector mLocalWorkSizeQkv{1, 1, 1, 1}; + uint32_t mMaxWorkGroupSize; + OpenCLBackend *mOpenCLBackend; + RecordUpdateInfo mQkUpdateInfo; + RecordUpdateInfo mSoftMaxUpdateInfo; + RecordUpdateInfo mQkvUpdateInfo; + int mGlobalWorkSizeQk2 = 0; + size_t mQkGlobal_size[3]; + int mSoftmaxShape[4]; + cl_recording_qcom mRecording{NULL}; + std::vector mOpRecordUpdateInfo; +}; + +class AttentionBufExecution : public CommonExecution { +public: + AttentionBufExecution(const MNN::Op *op, Backend *backend, bool kv_cache); + AttentionBufExecution(std::shared_ptr impl, const MNN::Op *op, Backend *backend); + + virtual ~AttentionBufExecution() = default; + virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; + virtual ErrorCode onExecute(const std::vector &inputs, const std::vector &outputs) override; + virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override; + +private: + std::shared_ptr mImpl; +}; +} // namespace OpenCL +} // namespace MNN +#endif /* AttentionBufExecution_hpp */ +#endif /* MNN_OPENCL_BUFFER_CLOSED */ diff --git a/source/backend/opencl/execution/buffer/BinaryBufExecution.cpp b/source/backend/opencl/execution/buffer/BinaryBufExecution.cpp index 75d604db0..2455d7943 100644 --- a/source/backend/opencl/execution/buffer/BinaryBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/BinaryBufExecution.cpp @@ -379,7 +379,7 @@ class BinaryBufCreator : public OpenCLBackend::Creator { case BinaryOpOperation_NOTEQUAL: return new BinaryBufExecution(inputs, "convert_float4(-isnotequal(in0,in1))", op, backend); case BinaryOpOperation_MOD: - return new BinaryBufExecution(inputs, "in0-floor(sign(in1)*in0/(fabs(in1)>(float4)((float)0.0000001)?fabs(in1):(float4)((float)0.0000001)))*in1", op, backend); + return new BinaryBufExecution(inputs, "fmod(in0,in1)", op, backend); default: break; } diff --git a/source/backend/opencl/execution/buffer/ConvBufExecution.cpp b/source/backend/opencl/execution/buffer/ConvBufExecution.cpp index 3d5e1c000..e0d6697b9 100644 --- a/source/backend/opencl/execution/buffer/ConvBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/ConvBufExecution.cpp @@ -354,12 +354,7 @@ ConvBufExecution::ConvBufExecution(const std::vector &inputs, const st std::shared_ptr filterBuffer( Tensor::createDevice({mResource->mOutputChannel, ROUND_UP(mResource->mInputChannel, 4), mResource->mKernelWidth, mResource->mKernelHeight})); - int buffer_size = filterBuffer->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + int buffer_size = filterBuffer->elementSize() * sizeof(float); cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); filterBuffer->buffer().device = (uint64_t)(&filterBufferCL); @@ -367,25 +362,10 @@ ConvBufExecution::ConvBufExecution(const std::vector &inputs, const st auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &res); if(ptrCL != nullptr && res == CL_SUCCESS) { ::memset(ptrCL, 0, buffer_size); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int oc=0; ocmOutputChannel; oc++) { - for(int ic=0; icmInputChannel; ic++) { - for(int kh=0; khmKernelHeight; kh++) { - for(int kw=0; kwmKernelWidth; kw++) { - int dst_idx = ((oc * ROUND_UP(mResource->mInputChannel, 4) + ic) * mResource->mKernelHeight + kh)* mResource->mKernelWidth + kw; - int src_idx = ((oc * mResource->mInputChannel + ic) * mResource->mKernelHeight + kh)* mResource->mKernelWidth + kw; - - ((half_float::half*)ptrCL)[dst_idx] = (half_float::half)(mFilterDataPtr[src_idx]); - } - } - } - } - }else{ - const int copy_size = mResource->mKernelWidth * mResource->mKernelHeight * sizeof(float); - for(int oc=0; ocmOutputChannel; oc++) { - for(int ic=0; icmInputChannel; ic++) { - ::memcpy((float *)ptrCL + (oc * ROUND_UP(mResource->mInputChannel, 4) + ic) * mResource->mKernelWidth * mResource->mKernelHeight, mFilterDataPtr + (oc * mResource->mInputChannel + ic) * mResource->mKernelWidth * mResource->mKernelHeight, copy_size); - } + const int copy_size = mResource->mKernelWidth * mResource->mKernelHeight * sizeof(float); + for(int oc=0; ocmOutputChannel; oc++) { + for(int ic=0; icmInputChannel; ic++) { + ::memcpy((float *)ptrCL + (oc * ROUND_UP(mResource->mInputChannel, 4) + ic) * mResource->mKernelWidth * mResource->mKernelHeight, mFilterDataPtr + (oc * mResource->mInputChannel + ic) * mResource->mKernelWidth * mResource->mKernelHeight, copy_size); } } }else{ @@ -397,10 +377,7 @@ ConvBufExecution::ConvBufExecution(const std::vector &inputs, const st mOpenCLBackend->onAcquireBuffer(mResource->mFilter.get(), Backend::STATIC); MNN::OpenCL::BufferConvertor bufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - bool needTrans = false; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - needTrans = true; - } + bool needTrans = true; bufferConvertor.convertToNC4HW4Buffer(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mResource->mFilter.get(), needTrans); } } @@ -697,8 +674,7 @@ ErrorCode ConvBufExecution::onExecute(const std::vector &inputs, const mOpenCLBackend->getOpenCLRuntime()->pushEvent({"ConvBuf2D", event}); #else if(mOpenCLBackend->isUseRecordQueue()){ - if(mOpenCLBackend->isDevideOpRecord()) - mOpenCLBackend->addRecord(mRecording); + mOpenCLBackend->addRecord(mRecording, mOpRecordUpdateInfo); #ifdef LOG_VERBOSE MNN_PRINT("End ConvExecution onExecute... \n"); #endif @@ -729,7 +705,7 @@ class ConvolutionBufCreator : public OpenCLBackend::Creator { #ifdef MNN_LOW_MEMORY { auto conv2dParams = op->main_as_Convolution2D(); - if ((static_cast(backend)->getMemory() == BackendConfig::Memory_Low) && (conv2dParams->quanParameter() != nullptr)) { + if (conv2dParams->quanParameter() != nullptr) { if (((conv2dParams->quanParameter()->type() == 4) || (conv2dParams->quanParameter()->type() == 1) || (conv2dParams->quanParameter()->type() == 2))) { diff --git a/source/backend/opencl/execution/buffer/ConvBufExecution.hpp b/source/backend/opencl/execution/buffer/ConvBufExecution.hpp index 10e3deda2..06fd52127 100644 --- a/source/backend/opencl/execution/buffer/ConvBufExecution.hpp +++ b/source/backend/opencl/execution/buffer/ConvBufExecution.hpp @@ -19,6 +19,7 @@ struct ConvBufResource { const Convolution2DCommon *mConv2dCommonParams; const Convolution2D *mConv2dParams; std::shared_ptr mKernelBuffer; + std::shared_ptr mKernelImage; std::shared_ptr dequantScale; std::shared_ptr dequantOffset; std::shared_ptr mFilter; @@ -33,6 +34,7 @@ struct ConvBufResource { bool mConv1x1Opt = false; bool mConv1x1C8Opt = false; std::shared_ptr mRasterExe; + bool mUseImage = false; }; class ConvBufCommonExecution { diff --git a/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp b/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp index 20e860f77..af8012c8a 100644 --- a/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp +++ b/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp @@ -13,7 +13,7 @@ namespace OpenCL { // set mDequantScale mDequantOffset mNumQuantBit mFilterDataPtr from mConv2dParams void ConvBufLowMemoryExecution::getInfoFromOpLowMemory(std::shared_ptr & quanCommon) { quanCommon = ConvolutionCommon::load(mResource->mConv2dParams, this->backend(), false, true); - if ((mOpenCLBackend->getMemory() == BackendConfig::Memory_Low) && (mResource->mConv2dParams->quanParameter() != nullptr)) { + if (mResource->mConv2dParams->quanParameter() != nullptr) { mLowMemoryFlag = true; } else { MNN_ERROR("Conv buf low memory init error.\n"); @@ -30,44 +30,34 @@ void ConvBufLowMemoryExecution::getInfoFromOpLowMemory(std::shared_ptrmOutputChannel; // set mDequantScale mDequantOffset int numAlphaPack = ROUND_UP(numAlpha, 16); - mResource->dequantScale.reset(Tensor::createDevice({numAlphaPack})); - mResource->dequantOffset.reset(Tensor::createDevice({numAlphaPack})); + mResource->dequantScale.reset(Tensor::createDevice({numAlphaPack})); + mResource->dequantOffset.reset(Tensor::createDevice({numAlphaPack})); + mOpenCLBackend->onAcquireBuffer(mResource->dequantScale.get(), Backend::STATIC); mOpenCLBackend->onAcquireBuffer(mResource->dequantOffset.get(), Backend::STATIC); cl::Buffer &dequantScaleBuffer = openCLBuffer(mResource->dequantScale.get()); cl::Buffer &dequantOffsetBuffer = openCLBuffer(mResource->dequantOffset.get()); // transfer data from src in cpu to dst in gpu - int bytes = mOpenCLBackend->fpBytes(); + int fpBytes = mOpenCLBackend->fpBytes(); cl_int resBias, resScale, resOffset; - void * dequantScaleBufferMap = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(dequantScaleBuffer, true, CL_MAP_WRITE, 0, numAlphaPack * bytes, nullptr, nullptr, &resScale); - void * dequantOffsetBufferMap = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(dequantOffsetBuffer, true, CL_MAP_WRITE, 0, numAlphaPack * bytes, nullptr, nullptr, &resOffset); - ::memset(dequantScaleBufferMap, -1, numAlphaPack * bytes); - ::memset(dequantOffsetBufferMap, 0, numAlphaPack * bytes); + + void * dequantScaleBufferMap = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(dequantScaleBuffer, true, CL_MAP_WRITE, 0, numAlphaPack * sizeof(int32_t), nullptr, nullptr, &resScale); + void * dequantOffsetBufferMap = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(dequantOffsetBuffer, true, CL_MAP_WRITE, 0, numAlphaPack * sizeof(int32_t), nullptr, nullptr, &resOffset); + + ::memset(dequantScaleBufferMap, -1, numAlphaPack * sizeof(int32_t)); + ::memset(dequantOffsetBufferMap, 0, numAlphaPack * sizeof(int32_t)); + if (dequantScaleBufferMap != nullptr && dequantOffsetBufferMap != nullptr && resScale == CL_SUCCESS && resOffset == CL_SUCCESS) { - if (bytes == 2) { - if (quanCommon->asymmetric) { - for (int i = 0; i < numAlpha; ++i) { - ((half_float::half *)dequantOffsetBufferMap)[i] = (half_float::half)dequantAlpha[2 * i]; - ((half_float::half *)dequantScaleBufferMap)[i] = (half_float::half)dequantAlpha[2 * i + 1]; - } - } else { - for (int i = 0; i < numAlpha; ++i) { - ((half_float::half *)dequantScaleBufferMap)[i] = (half_float::half)dequantAlpha[i]; - ((half_float::half *)dequantOffsetBufferMap)[i] = 0.0f; - } + if (quanCommon->asymmetric) { + for (int i = 0; i < numAlpha; ++i) { + ((float *)dequantOffsetBufferMap)[i] = dequantAlpha[2 * i]; + ((float *)dequantScaleBufferMap)[i] = dequantAlpha[2 * i + 1]; } } else { - if (quanCommon->asymmetric) { - for (int i = 0; i < numAlpha; ++i) { - ((float *)dequantOffsetBufferMap)[i] = dequantAlpha[2 * i]; - ((float *)dequantScaleBufferMap)[i] = dequantAlpha[2 * i + 1]; - } - } else { - for (int i = 0; i < numAlpha; ++i) { - ((float *)dequantScaleBufferMap)[i] = dequantAlpha[i]; - ((float *)dequantOffsetBufferMap)[i] = 0.0f; - } + for (int i = 0; i < numAlpha; ++i) { + ((float *)dequantScaleBufferMap)[i] = dequantAlpha[i]; + ((float *)dequantOffsetBufferMap)[i] = 0.0f; } } } else { @@ -82,7 +72,7 @@ void ConvBufLowMemoryExecution::getInfoFromOpLowMemory(std::shared_ptr & quanCommon) { cl_int res; - std::shared_ptr filterBuffer(Tensor::createDevice({ROUND_UP(mResource->mOutputChannel, 8)/*Cout pack set to max 8*/, ROUND_UP(mResource->mInputChannel, packCin), mResource->mKernelWidth, mResource->mKernelHeight})); + std::shared_ptr filterBuffer(Tensor::createDevice({ROUND_UP(mResource->mOutputChannel, packCout), ROUND_UP(mResource->mInputChannel, packCin), mResource->mKernelWidth, mResource->mKernelHeight})); size_t buffer_size = filterBuffer->usize() / sizeof(float); float *dequantAlpha = quanCommon->alpha.get(); // shared part for all cases @@ -93,43 +83,66 @@ void ConvBufLowMemoryExecution::set1x1WeightLowMemory(int packCout, int packCin, // int4 case buffer_size /= 2; } else {/* More types to be supported. */} - mResource->mKernelBuffer.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); - auto kernelBufferPtr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*(mResource->mKernelBuffer.get()), true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &res); - if(kernelBufferPtr != nullptr && res == CL_SUCCESS){ - ::memset(kernelBufferPtr, 0, buffer_size); - + + // Use Image load weights + void *mapPtr = nullptr; + size_t row_pitch; + size_t slice_pitch; + if(UP_DIV(mResource->mInputChannel, packCin) <= 16384 && ROUND_UP(mResource->mOutputChannel, packCout) <= 16384){ + mResource->mUseImage = true; + } + if(mResource->mUseImage) { + if(mNumQuantBit == 4){ + packCin *= 2; + } + size_t w = ROUND_UP(mResource->mOutputChannel, packCout); + size_t h = UP_DIV(mResource->mInputChannel, packCin); + mResource->mKernelImage.reset(new cl::Image2D(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE, cl::ImageFormat(CL_RGBA, CL_FLOAT), w, h, 0, nullptr, &res)); + if (nullptr == mResource->mKernelImage.get() || res != CL_SUCCESS) { + MNN_ERROR("Alloc Image %d x %d error, code:%d \n", w, h, res); + } + mapPtr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapImage(*(mResource->mKernelImage.get()), true, CL_MAP_WRITE, {0, 0, 0}, {w, h, 1}, &row_pitch, &slice_pitch, nullptr, nullptr, &res); + if(mNumQuantBit == 4){ + row_pitch *= 2; + } + } else{ + mResource->mKernelBuffer.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); + mapPtr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*(mResource->mKernelBuffer.get()), true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &res); + row_pitch = ROUND_UP(mResource->mOutputChannel, packCout) * packCin; + } + if(mapPtr != nullptr && res == CL_SUCCESS){ for(int o = 0; o < mResource->mOutputChannel; o++){ float zero = 0; if(quanCommon->asymmetric){ zero = (-dequantAlpha[2 * o + 1])/dequantAlpha[2 * o]; } int i = 0; - for(; i < mResource->mInputChannel; i++){ - int bufferIdx = (o/packCout) * packCin*packCout + (i/packCin)*packCin*ROUND_UP(mResource->mOutputChannel, packCout) + (o%packCout)*packCin + (i%packCin);//(Ci/packCin， Co/packCout，packCout, packCin) + for(; i < mResource->mInputChannel ; i++){ + int bufferIdx = (i/packCin) * row_pitch + o*packCin + (i%packCin);//(Ci/packCin， Co/packCout * packCout * packCin) int filterIdx = o*mResource->mInputChannel + i; if (mNumQuantBit == 8) { // int8 case - ((int8_t *)kernelBufferPtr)[bufferIdx] = (int8_t)(((int8_t *)filterDataPtr)[filterIdx]); + ((int8_t *)mapPtr)[bufferIdx] = (int8_t)(((int8_t *)filterDataPtr)[filterIdx]); } else if (mNumQuantBit == 4){ // int4 case if (bufferIdx % 2 == 0) { - ((uint8_t *)kernelBufferPtr)[bufferIdx / 2] += (uint8_t)((((int8_t *)filterDataPtr)[filterIdx] + 8) * 16); + ((uint8_t *)mapPtr)[bufferIdx / 2] += (uint8_t)((((int8_t *)filterDataPtr)[filterIdx] + 8) * 16); } else { - ((uint8_t *)kernelBufferPtr)[bufferIdx / 2] += (uint8_t)(((int8_t *)filterDataPtr)[filterIdx] + 8); + ((uint8_t *)mapPtr)[bufferIdx / 2] += (uint8_t)(((int8_t *)filterDataPtr)[filterIdx] + 8); } } else {/* More types to be supported. */} } - for(; i < ROUND_UP(mResource->mInputChannel, 4); i++){ - int bufferIdx = (o/packCout) * packCin*packCout + (i/packCin)*packCin*ROUND_UP(mResource->mOutputChannel, packCout) + (i%packCin)*packCout + (o%packCout);//(Ci/packCin， Co/packCout, packCin， packCout) + for(; i < ROUND_UP(mResource->mInputChannel, packCin); i++){ + int bufferIdx = (i/packCin) * row_pitch + o*packCin + (i%packCin);//(Ci/packCin， Co/packCout * packCout * packCin) if (mNumQuantBit == 8) { // int8 case - ((int8_t *)kernelBufferPtr)[bufferIdx] = (int8_t)(zero); + ((int8_t *)mapPtr)[bufferIdx] = (int8_t)(zero); } else if (mNumQuantBit == 4){ // int4 case if (bufferIdx % 2 == 0) { - ((uint8_t *)kernelBufferPtr)[bufferIdx / 2] += (uint8_t)((zero + 8) * 16); + ((uint8_t *)mapPtr)[bufferIdx / 2] += (uint8_t)((zero + 8) * 16); } else { - ((uint8_t *)kernelBufferPtr)[bufferIdx / 2] += (uint8_t)(zero + 8); + ((uint8_t *)mapPtr)[bufferIdx / 2] += (uint8_t)(zero + 8); } } } @@ -138,7 +151,11 @@ void ConvBufLowMemoryExecution::set1x1WeightLowMemory(int packCout, int packCin, MNN_ERROR("set1x1WeightLowMemory: Map error ptrCL == nullptr \n"); MNN_ASSERT(false); } - mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*(mResource->mKernelBuffer.get()), kernelBufferPtr); + if(mResource->mUseImage){ + mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*(mResource->mKernelImage.get()), mapPtr); + } else{ + mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*(mResource->mKernelBuffer.get()), mapPtr); + } } // set mFilter for the general kernels void ConvBufLowMemoryExecution::setGeneralWeightLowMemory(void* filterDataPtr, std::shared_ptr & quanCommon) { @@ -321,13 +338,11 @@ void ConvBufLowMemoryExecution::tuneGemmLowMemory(Tensor * input, Tensor * outpu const int width = outputShape.at(2); const int inputChannelBlocks = UP_DIV(inputChannels, 4); const int outputChannelBlocks = UP_DIV(outChannel, 4); - std::string kernelname = "gemm_conv_buf"; - int global_x = outputChannelBlocks; + int global_y = batch * height; - - const int total_kernel = 3; - std::string kernelName[total_kernel] = {"gemm_conv_c1_buf", "gemm_conv_c2_buf", "gemm_conv_c4_buf",}; - int itemC[total_kernel] = {1, 2, 4}; + const int total_kernel = 5; + std::string kernelName[total_kernel] = {"gemm_conv_c1_buf", "gemm_conv_c2_buf", "gemm_conv_c4_buf", "gemm_conv_c1_image", "gemm_conv_c2_image"}; + int itemC[total_kernel] = {1, 2, 4, 1, 2}; int actual_kernel = total_kernel; std::shared_ptr kernel[total_kernel]; std::vector globalWorkSize[total_kernel]; @@ -337,12 +352,27 @@ void ConvBufLowMemoryExecution::tuneGemmLowMemory(Tensor * input, Tensor * outpu if(width == 1 && height == 1){ buildOption.emplace("-DWIDTH_HEIGHT_1"); } + if(inputChannels % 16 != 0){ buildOption.emplace("-DINPUT_CHANNEL_LEAVE"); + } else if (mResource->mUseImage && mNumQuantBit == 4 && inputChannels % 32 != 0) { + // Image weight-int4 use load32 + buildOption.emplace("-DINPUT_CHANNEL_LEAVE"); } std::string info = std::to_string(inputChannels) + "_" + std::to_string(outChannel); - for (int knl_idx = 0; knl_idx < actual_kernel; knl_idx++) { - kernel[knl_idx] = mOpenCLBackend->getOpenCLRuntime()->buildKernel("gemm_buf", kernelName[knl_idx], buildOption); + if(batch > 1){ + global_y = UP_DIV(batch, 2) * height; + buildOption.emplace("-DBACTH_BLOCK2"); + info += "_BATCH_BLOCK2"; + } + int knl_idx = 0; + actual_kernel = 3; + if(mResource->mUseImage){ + knl_idx = 3; + actual_kernel = total_kernel; + } + for (; knl_idx < actual_kernel; knl_idx++) { + kernel[knl_idx] = mOpenCLBackend->getOpenCLRuntime()->buildKernel("gemv_conv1x1_buf", kernelName[knl_idx], buildOption); uint32_t maxWorkGroupSize = static_cast(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel[knl_idx])); globalWorkSize[knl_idx] = {static_cast(UP_DIV(outChannel, itemC[knl_idx]) * width), static_cast(global_y)}; @@ -351,7 +381,11 @@ void ConvBufLowMemoryExecution::tuneGemmLowMemory(Tensor * input, Tensor * outpu ret |= kernel[knl_idx]->get().setArg(idx++, globalWorkSize[knl_idx][0]); ret |= kernel[knl_idx]->get().setArg(idx++, globalWorkSize[knl_idx][1]); ret |= kernel[knl_idx]->get().setArg(idx++, openCLBuffer(input)); - ret |= kernel[knl_idx]->get().setArg(idx++, *mResource->mKernelBuffer.get()); + if(mResource->mUseImage){ + ret |= kernel[knl_idx]->get().setArg(idx++, *mResource->mKernelImage.get()); + }else{ + ret |= kernel[knl_idx]->get().setArg(idx++, *mResource->mKernelBuffer.get()); + } ret |= kernel[knl_idx]->get().setArg(idx++, openCLBuffer(mResource->dequantScale.get())); ret |= kernel[knl_idx]->get().setArg(idx++, openCLBuffer(mResource->dequantOffset.get())); ret |= kernel[knl_idx]->get().setArg(idx++, openCLBuffer(mResource->mBias.get())); @@ -361,7 +395,7 @@ void ConvBufLowMemoryExecution::tuneGemmLowMemory(Tensor * input, Tensor * outpu ret |= kernel[knl_idx]->get().setArg(idx++, static_cast(batch)); ret |= kernel[knl_idx]->get().setArg(idx++, static_cast(height)); ret |= kernel[knl_idx]->get().setArg(idx++, static_cast(width)); - MNN_CHECK_CL_SUCCESS(ret, "setArg gemm_conv_buf Kernel Select"); + MNN_CHECK_CL_SUCCESS(ret, "setArg gemv_conv1x1_buf Kernel Select"); std::pair, int> retTune; retTune = gws2dLwsTune(kernel[knl_idx], globalWorkSize[knl_idx], kernelName[knl_idx] + info, maxWorkGroupSize); if(min_cost.first > retTune.second) { @@ -374,14 +408,18 @@ void ConvBufLowMemoryExecution::tuneGemmLowMemory(Tensor * input, Tensor * outpu mGlobalWorkSize = {globalWorkSize[min_index][0], globalWorkSize[min_index][1]}; - unit.kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("gemm_buf", kernelName[min_index], buildOption); + unit.kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("gemv_conv1x1_buf", kernelName[min_index], buildOption); //MNN_PRINT("Kernel is %d.\n", min_index); uint32_t idx = 0; cl_int ret = CL_SUCCESS; ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]); ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]); ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input)); - ret |= unit.kernel->get().setArg(idx++, *mResource->mKernelBuffer.get()); + if(mResource->mUseImage){ + ret |= unit.kernel->get().setArg(idx++, *mResource->mKernelImage.get()); + }else{ + ret |= unit.kernel->get().setArg(idx++, *mResource->mKernelBuffer.get()); + } ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mResource->dequantScale.get())); ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mResource->dequantOffset.get())); ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mResource->mBias.get())); @@ -391,7 +429,7 @@ void ConvBufLowMemoryExecution::tuneGemmLowMemory(Tensor * input, Tensor * outpu ret |= unit.kernel->get().setArg(idx++, static_cast(batch)); ret |= unit.kernel->get().setArg(idx++, static_cast(height)); ret |= unit.kernel->get().setArg(idx++, static_cast(width)); - MNN_CHECK_CL_SUCCESS(ret, "setArg gemm_conv_buf"); + MNN_CHECK_CL_SUCCESS(ret, "setArg gemv_conv1x1_buf"); mOpenCLBackend->recordKernel2d(unit.kernel, mGlobalWorkSize, mLocalWorkSize); unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]}; unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1]}; @@ -422,7 +460,7 @@ ConvBufLowMemoryExecution::ConvBufLowMemoryExecution(const std::vector // prepare mDequantScale mDequantOffset mFilterDataPtr getInfoFromOpLowMemory(quanCommon); //select opt conv method - if (mResource->mKernelHeight == mResource->mKernelWidth && mResource->mKernelHeight == 1 && mResource->mStrides[0] == 1 && mResource->mStrides[1] == 1) { + if (mResource->mKernelHeight == mResource->mKernelWidth && mResource->mKernelHeight == 1 && mResource->mStrides[0] == 1 && mResource->mStrides[1] == 1 && conv2dCommonParams->padX() == 0 && conv2dCommonParams->padY() == 0 && conv2dCommonParams->dilateX() == 1 && conv2dCommonParams->dilateY() == 1) { set1x1WeightLowMemory(4, 16, mFilterDataPtr, quanCommon); mResource->mConv1x1Opt = true; }else { diff --git a/source/backend/opencl/execution/buffer/DeconvBufExecution.cpp b/source/backend/opencl/execution/buffer/DeconvBufExecution.cpp index 21d447038..4f1b990b7 100644 --- a/source/backend/opencl/execution/buffer/DeconvBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/DeconvBufExecution.cpp @@ -48,24 +48,13 @@ DeconvBufExecution::DeconvBufExecution(const std::vector &inputs, cons std::shared_ptr filterBuffer( Tensor::createDevice({outputChannel, inputChannel, kernelHeight, kernelWidth})); - int buffer_size = filterBuffer->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = filterBuffer->elementSize() * sizeof(float); cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR, buffer_size); filterBuffer->buffer().device = (uint64_t)(&filterBufferCL); cl_int error; auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(ptrCL != nullptr && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int i=0; ielementSize(); i++) { - ((half_float::half*)ptrCL)[i] = (half_float::half)(filterDataPtrTransformed[i]); - } - }else{ - ::memcpy(ptrCL, filterDataPtrTransformed.data(), filterBuffer->size()); - } + ::memcpy(ptrCL, filterDataPtrTransformed.data(), filterBuffer->size()); }else{ MNN_ERROR("Map error ptrCL == nullptr \n"); } @@ -75,10 +64,7 @@ DeconvBufExecution::DeconvBufExecution(const std::vector &inputs, cons mOpenCLBackend->onAcquireBuffer(mResource->mFilter.get(), Backend::STATIC); MNN::OpenCL::BufferConvertor bufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - bool needTrans = false; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - needTrans = true; - } + bool needTrans = true; bufferConvertor.convertToNC4HW4Buffer(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mResource->mFilter.get(), needTrans); mResource->mBuildOptions.emplace("-DBIAS"); if (conv2dCommonParams->relu() == true) { diff --git a/source/backend/opencl/execution/buffer/DepthwiseConvBufExecution.cpp b/source/backend/opencl/execution/buffer/DepthwiseConvBufExecution.cpp index 2937f4eaa..606e0bda4 100644 --- a/source/backend/opencl/execution/buffer/DepthwiseConvBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/DepthwiseConvBufExecution.cpp @@ -39,24 +39,13 @@ DepthwiseConvBufExecution::DepthwiseConvBufExecution(const std::vector mResource->mFilter.reset(Tensor::createDevice({1, ROUND_UP(filterImageShape[1], 2)/*for kernel C8 read*/, 1, 4 * filterImageShape[0]})); std::shared_ptr filterBuffer(Tensor::createDevice(filterShape)); - int buffer_size = filterBuffer->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = filterBuffer->elementSize() * sizeof(float); cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); filterBuffer->buffer().device = (uint64_t)(&filterBufferCL); cl_int error; auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(ptrCL != nullptr && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for (int i = 0; i < filterBuffer->elementSize(); i++) { - ((half_float::half *)ptrCL)[i] = (half_float::half)(filterDataPtr[i]); - } - } else { - ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); - } + ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); }else{ MNN_ERROR("Map error ptrCL == nullptr \n"); } @@ -65,10 +54,7 @@ DepthwiseConvBufExecution::DepthwiseConvBufExecution(const std::vector mOpenCLBackend->onAcquireBuffer(mResource->mFilter.get(), Backend::STATIC); MNN::OpenCL::BufferConvertor bufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - bool needTrans = false; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - needTrans = true; - } + bool needTrans = true; bufferConvertor.convertToNC4HW4Buffer(filterBuffer.get(), MNN::OpenCL::DW_CONV2D_FILTER, mResource->mFilter.get(), needTrans); if (mResource->mConv2dCommonParams->relu() == true) { diff --git a/source/backend/opencl/execution/buffer/LayerNormBufExecution.cpp b/source/backend/opencl/execution/buffer/LayerNormBufExecution.cpp index e01dc538b..673713141 100644 --- a/source/backend/opencl/execution/buffer/LayerNormBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/LayerNormBufExecution.cpp @@ -94,7 +94,7 @@ ErrorCode LayerNormBufExecution::onEncode(const std::vector &inputs, c Tensor *input = inputs[0]; Tensor *output = outputs[0]; auto runtime = ((OpenCLBackend *)backend())->getOpenCLRuntime(); - auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize); + auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize) / 4; std::vector inputShape = tensorShapeFormat(input); std::vector outputShape = tensorShapeFormat(output); @@ -114,6 +114,14 @@ ErrorCode LayerNormBufExecution::onEncode(const std::vector &inputs, c inner_size *= inputs.at(0)->length(i); } + if (group_ > 1) { + outter_size = inputs[0]->length(0) * group_; + inner_size = 1; + for (int i = 1; i < rank; i++) { + inner_size *= inputs[0]->length(i); + } + inner_size /= group_; + } std::set buildOptions; if(RMSNorm){ @@ -150,6 +158,111 @@ ErrorCode LayerNormBufExecution::onEncode(const std::vector &inputs, c mGWS = {static_cast(local_size), static_cast(1), static_cast(inputBatch)}; + } else if(inner_size == inputWidth * inputHeight * inputChannels / group_ && outter_size == inputBatch * group_){ + mUnits.clear(); + mUnits.resize(3); + std::vector inputShape = tensorShapeFormat(inputs[0]); + int inputWH[] = {inputShape[2], inputShape[1]}; + int region[] = {inputShape[0], UP_DIV(inputShape[3], 4), inputShape[1], inputShape[2]}; + + mInputPlain = std::make_shared(Tensor::createDevice(std::vector{inputShape[0], inputShape[3], ROUND_UP(inputShape[1] * inputShape[2], 4), 1}, Tensor::CAFFE)); + mOpenCLBackend->onAcquireBuffer(mInputPlain.get(), Backend::DYNAMIC); + mOutputPlain = std::make_shared(Tensor::createDevice(std::vector{inputShape[0], inputShape[3], ROUND_UP(inputShape[1] * inputShape[2], 4), 1}, Tensor::CAFFE)); + mOpenCLBackend->onAcquireBuffer(mOutputPlain.get(), Backend::DYNAMIC); + + // convert nc4hw4 to nchw + { + auto &unit = mUnits[0]; + unit.kernel = runtime->buildKernel("buffer_convert_buf", "nc4hw4_buffer_to_nchw_buffer", {}, inputs[0], outputs[0]); + + mGWS = {(uint32_t)(UP_DIV(region[3] * region[1], 16) * 16), + (uint32_t)(UP_DIV(region[2] * region[0], 16) * 16)}; + mLWS = {16, 16}; + unit.globalWorkSize = {mGWS[0], mGWS[1]}; + unit.localWorkSize = {mLWS[0], mLWS[1]}; + + int global_dim0 = region[3] * region[1]; + int global_dim1 = region[2] * region[0]; + + //MNN_CHECK_CL_SUCCESS + uint32_t idx = 0; + cl_int ret = CL_SUCCESS; + ret |= unit.kernel->get().setArg(idx++, global_dim0); + ret |= unit.kernel->get().setArg(idx++, global_dim1); + ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mInputPlain.get())); + ret |= unit.kernel->get().setArg(idx++, inputWH[1]); + ret |= unit.kernel->get().setArg(idx++, inputWH[0]); + ret |= unit.kernel->get().setArg(idx++, inputShape[3]); + ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input)); + MNN_CHECK_CL_SUCCESS(ret, "setArg LayerNormBufExecution with group, convert nc4hw4 to nchw"); + + mOpenCLBackend->recordKernel2d(unit.kernel, mGWS, mLWS); + } + // do group layernorm + { + auto &unit = mUnits[1]; + kernelName = "layernorm_plain_buf"; + local_size = getLocalSize(UP_DIV(inner_size, 4), MaxLocalSize); + buildOptions.emplace("-DLOCAL_SIZE=" + std::to_string(local_size)); + unit.kernel = runtime->buildKernel("layernorm_buf", kernelName, buildOptions); + + mGWS = {static_cast(local_size), + static_cast(1), + static_cast(outter_size)}; + + mLWS = {static_cast(local_size), 1, 1}; + + unit.globalWorkSize = {mGWS[0], mGWS[1], mGWS[2]}; + unit.localWorkSize = {mLWS[0], mLWS[1], mLWS[2]}; + + uint32_t idx = 0; + cl_int ret = CL_SUCCESS; + ret |= unit.kernel->get().setArg(idx++, mGWS[0]); + ret |= unit.kernel->get().setArg(idx++, mGWS[1]); + ret |= unit.kernel->get().setArg(idx++, mGWS[2]); + ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mInputPlain.get())); + ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mOutputPlain.get())); + ret |= unit.kernel->get().setArg(idx++, static_cast(inner_size)); + ret |= unit.kernel->get().setArg(idx++, static_cast(outter_size)); + if(has_gamma_beta_){ + ret |= unit.kernel->get().setArg(idx++, *mGammaBuffer.get()); + ret |= unit.kernel->get().setArg(idx++, *mBetaBuffer.get()); + } + ret |= unit.kernel->get().setArg(idx++, epsilon_); + MNN_CHECK_CL_SUCCESS(ret, "setArg LayerNormBufExecution with group, do group layernorm"); + mOpenCLBackend->recordKernel3d(unit.kernel, mGWS, mLWS); + } + // convert nchw to nc4hw4 + { + auto &unit = mUnits[2]; + + unit.kernel = runtime->buildKernel("buffer_convert_buf", "nchw_buffer_to_nc4hw4_buffer", {}, inputs[0], outputs[0]); + mLWS = {16, 16}; + mGWS = {(uint32_t)UP_DIV(region[3] * region[1], 16) * 16, + (uint32_t)UP_DIV(region[2] * region[0], 16) * 16}; + + unit.globalWorkSize = {mGWS[0], mGWS[1]}; + unit.localWorkSize = {mLWS[0], mLWS[1]}; + + int global_dim0 = region[3] * region[1]; + int global_dim1 = region[2] * region[0]; + + uint32_t idx = 0; + cl_int ret = CL_SUCCESS; + ret |= unit.kernel->get().setArg(idx++, global_dim0); + ret |= unit.kernel->get().setArg(idx++, global_dim1); + ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mOutputPlain.get())); + ret |= unit.kernel->get().setArg(idx++, inputWH[1]); + ret |= unit.kernel->get().setArg(idx++, inputWH[0]); + ret |= unit.kernel->get().setArg(idx++, inputShape[3]); + ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output)); + MNN_CHECK_CL_SUCCESS(ret, "setArg LayerNormBufExecution with group, convert nchw to nc4hw4"); + mOpenCLBackend->recordKernel2d(unit.kernel, mGWS, mLWS); + } + + mOpenCLBackend->onReleaseBuffer(mInputPlain.get(), Backend::DYNAMIC); + mOpenCLBackend->onReleaseBuffer(mOutputPlain.get(), Backend::DYNAMIC); + return NO_ERROR; } mLWS = {static_cast(local_size), 1, 1}; @@ -189,10 +302,6 @@ class LayerNormBufCreator : public OpenCLBackend::Creator { TensorUtils::setTensorSupportPack(outputs[i], false); } const auto* layer_norm_param = op->main_as_LayerNorm(); - int group = layer_norm_param->group(); - if(group > 1){ - return nullptr; - } return new LayerNormBufExecution(inputs, op, backend); } }; diff --git a/source/backend/opencl/execution/buffer/LayerNormBufExecution.hpp b/source/backend/opencl/execution/buffer/LayerNormBufExecution.hpp index 0b9164732..dc925b9d5 100644 --- a/source/backend/opencl/execution/buffer/LayerNormBufExecution.hpp +++ b/source/backend/opencl/execution/buffer/LayerNormBufExecution.hpp @@ -35,6 +35,7 @@ class LayerNormBufExecution : public CommonExecution { std::shared_ptr mGammaBuffer; std::shared_ptr mBetaBuffer; + std::shared_ptr mInputPlain, mOutputPlain; bool has_gamma_beta_ = false; uint32_t mMaxWorkGroupSize; }; diff --git a/source/backend/opencl/execution/buffer/LoopBufExecution.cpp b/source/backend/opencl/execution/buffer/LoopBufExecution.cpp index 175d55e29..60d795ea2 100644 --- a/source/backend/opencl/execution/buffer/LoopBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/LoopBufExecution.cpp @@ -208,7 +208,8 @@ ErrorCode LoopBatchMatMulBufExecution::onEncode(const std::vector &inp const int Width = Shape.at(2); const int Height = Shape.at(1); const int Batch = Shape.at(0); - mTmpTensors[i] = std::make_shared(Tensor::createDevice(std::vector{Batch, Channel, ROUND_UP(Height, 4), ROUND_UP(Width, 4)}, Tensor::CAFFE)); + mTmpTensors[i] = std::make_shared(Tensor::createDevice(std::vector{Batch, Channel, Height, Width}, Tensor::CAFFE)); + mOpenCLBackend->onAcquireBuffer(mTmpTensors[i].get(), Backend::DYNAMIC); Unit unit; diff --git a/source/backend/opencl/execution/buffer/SoftmaxBufExecution.cpp b/source/backend/opencl/execution/buffer/SoftmaxBufExecution.cpp index 09dca38d5..7b8807382 100644 --- a/source/backend/opencl/execution/buffer/SoftmaxBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/SoftmaxBufExecution.cpp @@ -54,7 +54,9 @@ ErrorCode SoftmaxBufExecution::onEncode(const std::vector &inputs, con const auto dims = input->buffer().dimensions; auto runtime = mOpenCLBackend->getOpenCLRuntime(); - auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize); + + auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize) / 4; + int inside = 1; int outside = 1; int channel = 1; diff --git a/source/backend/opencl/execution/buffer/UnaryBufExecution.cpp b/source/backend/opencl/execution/buffer/UnaryBufExecution.cpp index e176e5c1c..75897d2fe 100644 --- a/source/backend/opencl/execution/buffer/UnaryBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/UnaryBufExecution.cpp @@ -253,6 +253,9 @@ class UnaryBufCreator : public OpenCLBackend::Creator { return new UnaryBufExecution("convert_float4(in)>(float4)(-3.0f)?(convert_float4(in)<(float4)(3.0f)?((convert_float4(in)*(convert_float4(in)+(float4)3.0f))/(float4)6.0f):convert_float4(in)):(float4)(0.0f)", op, backend); case UnaryOpOperation_GELU: return new UnaryBufExecution("gelu(convert_float4(in))", op, backend); + case UnaryOpOperation_GELU_STANDARD: + return new UnaryBufExecution("(erf(convert_float4(in)*(float4)0.7071067932881648)+(float4)1.0)*convert_float4(in)*(float4)0.5", op, backend); + default: break; } diff --git a/source/backend/opencl/execution/cl/attention_buf.cl b/source/backend/opencl/execution/cl/attention_buf.cl new file mode 100644 index 000000000..1d7ca6db3 --- /dev/null +++ b/source/backend/opencl/execution/cl/attention_buf.cl @@ -0,0 +1,316 @@ +#ifdef MNN_SUPPORT_FP16 +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#endif + +#define GLOBAL_SIZE_3_DIMS \ + __private const int global_size_dim0, __private const int global_size_dim1, __private const int global_size_dim2, + +#define DEAL_NON_UNIFORM_DIM3(input1, input2, input3) \ + if (input1 >= global_size_dim0 || input2 >= global_size_dim1 || input3 >= global_size_dim2) { \ + return; \ + } + + +__kernel void matmul_qk_div_mask(GLOBAL_SIZE_3_DIMS + __global const FLOAT *input0, // query [1 query_seq_len/4 head_num head_dim 4] + __global const FLOAT *input1, // key [1 key_seq_len/4 head_num head_dim 4] + __global FLOAT *output, // prefill [1 head_num query_seq_len/4 key_seq_len 4] decode[1 head_num key_seq_len/4 4] + __global FLOAT *past_key, // [1 key_seq_len/4 head_num head_dim 4] + __global const int* mask, // [1 1 query_seq_len key_seq_len 4] + __private const float scale, + __private const int query_seq_len, + __private const int key_seq_len, + __private const int head_num, + __private const int head_dim) { + + const int x = get_global_id(0); // query_seq_len / 4 for prefill 1 for decode + const int y = get_global_id(1); // head_num + const int z = get_global_id(2); // key_seq_len / 4 + DEAL_NON_UNIFORM_DIM3(x, y, z); + + const int offset = head_num * head_dim * 4; + const int offset_head = y * head_dim * 4; + __global const FLOAT *A_offset = input0 + x * offset + offset_head; + __global FLOAT *Pastkey_offset = past_key + z * offset + offset_head; + const int z4 = z << 2; + COMPUTE_FLOAT4 Vscale = (COMPUTE_FLOAT4)scale; +#ifdef OPENCL_PREFILL_ATTENTION + __global const FLOAT *B_offset = input1 + z * offset + offset_head; + const int x4 = x << 2; + const int query_seq_len4 = (query_seq_len + 3) / 4; + const int output_offset = y * query_seq_len4 * key_seq_len * 4; + COMPUTE_FLOAT4 out0 = 0; + COMPUTE_FLOAT4 out1 = 0; + COMPUTE_FLOAT4 out2 = 0; + COMPUTE_FLOAT4 out3 = 0; + + const int head_dim4 = (head_dim + 3) / 4; +#ifdef HEADDIM_LEAVE + for(int i = 0; i < head_dim4 - 1; ++i){ + COMPUTE_FLOAT16 A = CONVERT_COMPUTE_FLOAT16(vload16(i, A_offset)); + COMPUTE_FLOAT16 B = CONVERT_COMPUTE_FLOAT16(vload16(i, B_offset)); + + out0 = mad(A.s0123, (COMPUTE_FLOAT4)B.s0, out0); + out1 = mad(A.s0123, (COMPUTE_FLOAT4)B.s1, out1); + out2 = mad(A.s0123, (COMPUTE_FLOAT4)B.s2, out2); + out3 = mad(A.s0123, (COMPUTE_FLOAT4)B.s3, out3); + + out0 = mad(A.s4567, (COMPUTE_FLOAT4)B.s4, out0); + out1 = mad(A.s4567, (COMPUTE_FLOAT4)B.s5, out1); + out2 = mad(A.s4567, (COMPUTE_FLOAT4)B.s6, out2); + out3 = mad(A.s4567, (COMPUTE_FLOAT4)B.s7, out3); + + out0 = mad(A.s89ab, (COMPUTE_FLOAT4)B.s8, out0); + out1 = mad(A.s89ab, (COMPUTE_FLOAT4)B.s9, out1); + out2 = mad(A.s89ab, (COMPUTE_FLOAT4)B.sa, out2); + out3 = mad(A.s89ab, (COMPUTE_FLOAT4)B.sb, out3); + + out0 = mad(A.scdef, (COMPUTE_FLOAT4)B.sc, out0); + out1 = mad(A.scdef, (COMPUTE_FLOAT4)B.sd, out1); + out2 = mad(A.scdef, (COMPUTE_FLOAT4)B.se, out2); + out3 = mad(A.scdef, (COMPUTE_FLOAT4)B.sf, out3); + + vstore16(CONVERT_FLOAT16(B), i, Pastkey_offset); + } + for(int i = (head_dim4 - 1) * 4; i < head_dim; ++i){ + COMPUTE_FLOAT4 A = CONVERT_COMPUTE_FLOAT4(vload4(i, A_offset)); + COMPUTE_FLOAT4 B = CONVERT_COMPUTE_FLOAT4(vload4(i, B_offset)); + + out0 = mad(A, (COMPUTE_FLOAT4)B.s0, out0); + out1 = mad(A, (COMPUTE_FLOAT4)B.s1, out1); + out2 = mad(A, (COMPUTE_FLOAT4)B.s2, out2); + out3 = mad(A, (COMPUTE_FLOAT4)B.s3, out3); + + vstore4(CONVERT_FLOAT4(B), i, Pastkey_offset); + } +#else + for(int i = 0; i < head_dim4; ++i){ + COMPUTE_FLOAT16 A = CONVERT_COMPUTE_FLOAT16(vload16(i, A_offset)); + COMPUTE_FLOAT16 B = CONVERT_COMPUTE_FLOAT16(vload16(i, B_offset)); + + out0 = mad(A.s0123, (COMPUTE_FLOAT4)B.s0, out0); + out1 = mad(A.s0123, (COMPUTE_FLOAT4)B.s1, out1); + out2 = mad(A.s0123, (COMPUTE_FLOAT4)B.s2, out2); + out3 = mad(A.s0123, (COMPUTE_FLOAT4)B.s3, out3); + + out0 = mad(A.s4567, (COMPUTE_FLOAT4)B.s4, out0); + out1 = mad(A.s4567, (COMPUTE_FLOAT4)B.s5, out1); + out2 = mad(A.s4567, (COMPUTE_FLOAT4)B.s6, out2); + out3 = mad(A.s4567, (COMPUTE_FLOAT4)B.s7, out3); + + out0 = mad(A.s89ab, (COMPUTE_FLOAT4)B.s8, out0); + out1 = mad(A.s89ab, (COMPUTE_FLOAT4)B.s9, out1); + out2 = mad(A.s89ab, (COMPUTE_FLOAT4)B.sa, out2); + out3 = mad(A.s89ab, (COMPUTE_FLOAT4)B.sb, out3); + + out0 = mad(A.scdef, (COMPUTE_FLOAT4)B.sc, out0); + out1 = mad(A.scdef, (COMPUTE_FLOAT4)B.sd, out1); + out2 = mad(A.scdef, (COMPUTE_FLOAT4)B.se, out2); + out3 = mad(A.scdef, (COMPUTE_FLOAT4)B.sf, out3); + + vstore16(CONVERT_FLOAT16(B), i, Pastkey_offset); + } +#endif + + out0 *= Vscale; + out1 *= Vscale; + out2 *= Vscale; + out3 *= Vscale; + + out0.s0 = mask[((x4 + 0) * key_seq_len + (z4 + 0)) * 4] == 0 ? -FLT_MAX : out0.s0; + out1.s0 = mask[((x4 + 0) * key_seq_len + (z4 + 1)) * 4] == 0 ? -FLT_MAX : out1.s0; + out2.s0 = mask[((x4 + 0) * key_seq_len + (z4 + 2)) * 4] == 0 ? -FLT_MAX : out2.s0; + out3.s0 = mask[((x4 + 0) * key_seq_len + (z4 + 3)) * 4] == 0 ? -FLT_MAX : out3.s0; + + out0.s1 = mask[((x4 + 1) * key_seq_len + (z4 + 0)) * 4] == 0 ? -FLT_MAX : out0.s1; + out1.s1 = mask[((x4 + 1) * key_seq_len + (z4 + 1)) * 4] == 0 ? -FLT_MAX : out1.s1; + out2.s1 = mask[((x4 + 1) * key_seq_len + (z4 + 2)) * 4] == 0 ? -FLT_MAX : out2.s1; + out3.s1 = mask[((x4 + 1) * key_seq_len + (z4 + 3)) * 4] == 0 ? -FLT_MAX : out3.s1; + + out0.s2 = mask[((x4 + 2) * key_seq_len + (z4 + 0)) * 4] == 0 ? -FLT_MAX : out0.s2; + out1.s2 = mask[((x4 + 2) * key_seq_len + (z4 + 1)) * 4] == 0 ? -FLT_MAX : out1.s2; + out2.s2 = mask[((x4 + 2) * key_seq_len + (z4 + 2)) * 4] == 0 ? -FLT_MAX : out2.s2; + out3.s2 = mask[((x4 + 2) * key_seq_len + (z4 + 3)) * 4] == 0 ? -FLT_MAX : out3.s2; + + out0.s3 = mask[((x4 + 3) * key_seq_len + (z4 + 0)) * 4] == 0 ? -FLT_MAX : out0.s3; + out1.s3 = mask[((x4 + 3) * key_seq_len + (z4 + 1)) * 4] == 0 ? -FLT_MAX : out1.s3; + out2.s3 = mask[((x4 + 3) * key_seq_len + (z4 + 2)) * 4] == 0 ? -FLT_MAX : out2.s3; + out3.s3 = mask[((x4 + 3) * key_seq_len + (z4 + 3)) * 4] == 0 ? -FLT_MAX : out3.s3; + + vstore4(CONVERT_FLOAT4(out0), 0, output + output_offset + x * key_seq_len * 4 + z4 * 4); + if(z4 + 1 >= key_seq_len) return; + vstore4(CONVERT_FLOAT4(out1), 0, output + output_offset + x * key_seq_len * 4 + (z4 + 1) * 4); + if(z4 + 2 >= key_seq_len) return; + vstore4(CONVERT_FLOAT4(out2), 0, output + output_offset + x * key_seq_len * 4 + (z4 + 2) * 4); + if(z4 + 3 >= key_seq_len) return; + vstore4(CONVERT_FLOAT4(out3), 0, output + output_offset + x * key_seq_len * 4 + (z4 + 3) * 4); +#else + __global const FLOAT *B_offset = input1 + offset_head; + const int key_seq_len4 = (key_seq_len + 3) / 4; + COMPUTE_FLOAT4 out = 0; + const int head_dim4 = (head_dim + 3) / 4; + +#ifdef HEADDIM_LEAVE + for(int i = 0; i < head_dim4 - 1; ++i){ + COMPUTE_FLOAT16 A = CONVERT_COMPUTE_FLOAT16(vload16(i, A_offset)); + COMPUTE_FLOAT16 B = CONVERT_COMPUTE_FLOAT16(vload16(i, Pastkey_offset)); + + out = mad((COMPUTE_FLOAT4)A.s0, B.s0123, out); + out = mad((COMPUTE_FLOAT4)A.s4, B.s4567, out); + out = mad((COMPUTE_FLOAT4)A.s8, B.s89ab, out); + out = mad((COMPUTE_FLOAT4)A.sc, B.scdef, out); + } + for(int i = (head_dim4 - 1) * 4; i < head_dim; ++i){ + COMPUTE_FLOAT4 A = CONVERT_COMPUTE_FLOAT4(vload4(i, A_offset)); + COMPUTE_FLOAT4 B = CONVERT_COMPUTE_FLOAT4(vload4(i, Pastkey_offset)); + + out = mad((COMPUTE_FLOAT4)A.s0, B, out); + } +#else + for(int i = 0; i < head_dim4; ++i){ + COMPUTE_FLOAT16 A = CONVERT_COMPUTE_FLOAT16(vload16(i, A_offset)); + COMPUTE_FLOAT16 B = CONVERT_COMPUTE_FLOAT16(vload16(i, Pastkey_offset)); + + out = mad((COMPUTE_FLOAT4)A.s0, B.s0123, out); + out = mad((COMPUTE_FLOAT4)A.s4, B.s4567, out); + out = mad((COMPUTE_FLOAT4)A.s8, B.s89ab, out); + out = mad((COMPUTE_FLOAT4)A.sc, B.scdef, out); + } +#endif + if(z == key_seq_len4 - 1){ + int remain = key_seq_len - z * 4 - 1; + Pastkey_offset += remain; + COMPUTE_FLOAT tmp = 0; + for(int i = 0; i < head_dim; ++i){ + COMPUTE_FLOAT A = A_offset[i*4]; + COMPUTE_FLOAT B = B_offset[i*4]; + Pastkey_offset[i * 4] = B; + tmp += A * B; + } + COMPUTE_FLOAT *out_ptr = (COMPUTE_FLOAT*)&out; + out_ptr[remain] = tmp; + } + out *= Vscale; + vstore4(CONVERT_FLOAT4(out), 0, output + y * key_seq_len4 * 4 + z4); +#endif +} + +__kernel void matmul_qkv(GLOBAL_SIZE_3_DIMS + __global const FLOAT *input0, // qk prefill [1 head_num qk_seq_len/4 value_seq_len 4] decode[1 head_num value_seq_len/4 4] + __global const FLOAT *input1, // [1 value_seq_len/4 head_num head_dim 4] + __global FLOAT *output, // [1 qk_seq_len head_num*head_dim 1 4] + __global FLOAT *past_value, // [1 value_seq_len/4 head_num head_dim 4] + __private const int qk_seq_len, + __private const int value_seq_len, + __private const int head_num, + __private const int head_dim) { + + const int x = get_global_id(0); // prefill qk_seq_len / 4 decode 1 + const int y = get_global_id(1); // head_num + const int z = get_global_id(2); // head_dim + DEAL_NON_UNIFORM_DIM3(x, y, z); + +#ifdef OPENCL_PREFILL_ATTENTION + const int offset = head_num * head_dim * 4; + const int offset_head = y * head_dim * 4 + z * 4; + const int value_seq_len4 = (value_seq_len + 3) / 4; + const int qk_seq_len4 = (qk_seq_len + 3) / 4; + __global const FLOAT *A_offset = input0 + (y * qk_seq_len4 + x) * value_seq_len * 4; + __global const FLOAT *B_offset = input1 + offset_head; + __global FLOAT *Pastvalue_offset = past_value + offset_head; + COMPUTE_FLOAT4 out = 0; + + for(int i = 0; i < value_seq_len4 - 1; ++i){ + int index = i << 2; + COMPUTE_FLOAT4 A0 = CONVERT_COMPUTE_FLOAT4(vload4(index, A_offset)); + COMPUTE_FLOAT4 A1 = CONVERT_COMPUTE_FLOAT4(vload4(index + 1, A_offset)); + COMPUTE_FLOAT4 A2 = CONVERT_COMPUTE_FLOAT4(vload4(index + 2, A_offset)); + COMPUTE_FLOAT4 A3 = CONVERT_COMPUTE_FLOAT4(vload4(index + 3, A_offset)); + COMPUTE_FLOAT4 B = CONVERT_COMPUTE_FLOAT4(vload4(0, B_offset + i * offset)); + + out = mad(A0, (COMPUTE_FLOAT4)B.s0, out); + out = mad(A1, (COMPUTE_FLOAT4)B.s1, out); + out = mad(A2, (COMPUTE_FLOAT4)B.s2, out); + out = mad(A3, (COMPUTE_FLOAT4)B.s3, out); + + vstore4(CONVERT_FLOAT4(B), 0, Pastvalue_offset + i * offset); + } + + COMPUTE_FLOAT4 B = CONVERT_COMPUTE_FLOAT4(vload4(0, B_offset + (value_seq_len4 - 1) * offset)); + vstore4(CONVERT_FLOAT4(B), 0, Pastvalue_offset + (value_seq_len4 - 1) * offset); + COMPUTE_FLOAT *B_ptr = (COMPUTE_FLOAT*)&B; + for(int i = (value_seq_len4 - 1) * 4, j = 0; i < value_seq_len; ++i, ++j){ + COMPUTE_FLOAT4 A0 = CONVERT_COMPUTE_FLOAT4(vload4(i, A_offset)); + out = mad(A0, (COMPUTE_FLOAT4)B_ptr[j], out); + } + vstore4(CONVERT_FLOAT4(out), 0, output + x * offset + (y * head_dim + z) * 4); +#else + const int z4 = z << 2; + const int value_seq_len4 = (value_seq_len + 3) / 4; + const int offset = head_num * head_dim * 4; + const int offset_head = y * head_dim * 4 + z4 * 4; + const int loop = (value_seq_len + 2) / 4; + __global const FLOAT *A_offset = input0 + y * value_seq_len4 * 4; + __global const FLOAT *B_offset = input1 + offset_head; + __global FLOAT *Pastvalue_offset = past_value + offset_head; + COMPUTE_FLOAT4 out = 0; + + for(int i = 0; i < loop - 1; i++){ + COMPUTE_FLOAT4 A = CONVERT_COMPUTE_FLOAT4(vload4(i, A_offset)); + COMPUTE_FLOAT16 B = CONVERT_COMPUTE_FLOAT16(vload16(0, Pastvalue_offset + i * offset)); + + out.s0 += dot(A, B.s0123); + out.s1 += dot(A, B.s4567); + out.s2 += dot(A, B.s89ab); + out.s3 += dot(A, B.scdef); + } + int start = (loop - 1) < 0 ? 0 : (loop - 1); + COMPUTE_FLOAT16 B_Vec = CONVERT_COMPUTE_FLOAT16(vload16(0, Pastvalue_offset + start * offset)); + COMPUTE_FLOAT *B_ptr = (COMPUTE_FLOAT *)&B_Vec; + for(int i = start * 4; i < value_seq_len - 1; ++i){ + COMPUTE_FLOAT A = A_offset[i]; + + int index = i % 4; + out.s0 += A * B_ptr[index]; + out.s1 += A * B_ptr[index+4]; + out.s2 += A * B_ptr[index+8]; + out.s3 += A * B_ptr[index+12]; + } + COMPUTE_FLOAT A = A_offset[value_seq_len - 1]; + COMPUTE_FLOAT B0 = B_offset[0]; + COMPUTE_FLOAT B1 = B_offset[4]; + COMPUTE_FLOAT B2 = B_offset[8]; + COMPUTE_FLOAT B3 = B_offset[12]; + out.s0 += A * B0; + out.s1 += A * B1; + out.s2 += A * B2; + out.s3 += A * B3; + int index = ((value_seq_len - 1) >> 2) * offset + ((value_seq_len - 1) % 4); + +#ifdef HEADDIM_LEAVE + Pastvalue_offset[index] = B0; + output[(y * head_dim + z4) * 4] = out.s0; + if(z4 + 1 >= head_dim) return; + Pastvalue_offset[index + 4] = B1; + output[(y * head_dim + z4 + 1) * 4] = out.s1; + if(z4 + 2 >= head_dim) return; + Pastvalue_offset[index + 8] = B2; + output[(y * head_dim + z4 + 2) * 4] = out.s2; + if(z4 + 3 >= head_dim) return; + Pastvalue_offset[index + 12] = B3; + output[(y * head_dim + z4 + 3) * 4] = out.s3; +#else + Pastvalue_offset[index] = B0; + Pastvalue_offset[index + 4] = B1; + Pastvalue_offset[index + 8] = B2; + Pastvalue_offset[index + 12] = B3; + + output[(y * head_dim + z4) * 4] = out.s0; + output[(y * head_dim + z4 + 1) * 4] = out.s1; + output[(y * head_dim + z4 + 2) * 4] = out.s2; + output[(y * head_dim + z4 + 3) * 4] = out.s3; +#endif + +#endif +} + diff --git a/source/backend/opencl/execution/cl/conv_2d_buf.cl b/source/backend/opencl/execution/cl/conv_2d_buf.cl index c9936deca..ce041937c 100644 --- a/source/backend/opencl/execution/cl/conv_2d_buf.cl +++ b/source/backend/opencl/execution/cl/conv_2d_buf.cl @@ -1,19 +1,6 @@ #ifdef MNN_SUPPORT_FP16 #pragma OPENCL EXTENSION cl_khr_fp16 : enable #endif -#define READ_INPUT_IMAGE(i, base) \ - int in_width_value##i = in_width##i + base; \ - in_width_value##i = \ - select(in_idx + in_width_value##i, -1, (in_width_value##i < 0 || in_width_value##i >= input_shape.y)); \ - in_w_idx = in_width_value##i % input_shape.y; \ - inp_offset = (((in_b_idx*in_channel_block_length + in_channel_block_idx)*input_shape.x + in_h_idx)* input_shape.y + in_w_idx)*4; \ - in##i = (in_width_value##i)==-1 ? (FLOAT4)0 : vload4(0, input+inp_offset); - -#define CALCULATE_OUTPUT(i) \ - out##i = mad(in##i.x, weights0, out##i); \ - out##i = mad(in##i.y, weights1, out##i); \ - out##i = mad(in##i.z, weights2, out##i); \ - out##i = mad(in##i.w, weights3, out##i); #define GLOBAL_SIZE_2_DIMS __private const int global_size_dim0, __private const int global_size_dim1, @@ -46,10 +33,10 @@ void conv_2d_1x1_c4h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int out_h_idx = out_b_h_idx % out_h; // equal to in_h_idx const int out_w4_idx = mul24(out_w_idx, 4); - FLOAT4 out0 = vload4(out_c_idx, (__global FLOAT *)bias_ptr); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias_ptr)); + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; const int intput_width_idx0 = out_w4_idx; @@ -59,15 +46,15 @@ void conv_2d_1x1_c4h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int inp_add = out_h*out_w*4; for (ushort in_channel_block_idx = 0; in_channel_block_idx < in_c_block; ++in_channel_block_idx) { - FLOAT4 in0 = vload4(0, input+inp_offset); - FLOAT4 in1 = vload4(1, input+inp_offset); - FLOAT4 in2 = vload4(2, input+inp_offset); - FLOAT4 in3 = vload4(3, input+inp_offset); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4(vload4(1, input+inp_offset)); + COMPUTE_FLOAT4 in2 = CONVERT_COMPUTE_FLOAT4(vload4(2, input+inp_offset)); + COMPUTE_FLOAT4 in3 = CONVERT_COMPUTE_FLOAT4(vload4(3, input+inp_offset)); - FLOAT4 weights0 = vload4(offset, (__global FLOAT *)kernel_ptr); - FLOAT4 weights1 = vload4(offset + 1, (__global FLOAT *)kernel_ptr); - FLOAT4 weights2 = vload4(offset + 2, (__global FLOAT *)kernel_ptr); - FLOAT4 weights3 = vload4(offset + 3, (__global FLOAT *)kernel_ptr); + COMPUTE_FLOAT4 weights0 = CONVERT_COMPUTE_FLOAT4(vload4(offset, kernel_ptr)); + COMPUTE_FLOAT4 weights1 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 1, kernel_ptr)); + COMPUTE_FLOAT4 weights2 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 2, kernel_ptr)); + COMPUTE_FLOAT4 weights3 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 3, kernel_ptr)); out0.x += dot(weights0, in0); out0.y += dot(weights1, in0); @@ -94,34 +81,34 @@ void conv_2d_1x1_c4h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_block + out_c_idx)*out_h + out_h_idx)* out_w + out_w4_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_w - out_w4_idx; if (remain >= 4) { - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); } else if (remain == 3) { - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); - vstore4(out2, 2, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2, output+out_offset); } else if (remain == 2) { - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); } else if (remain == 1) { - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #else - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); #endif } @@ -148,15 +135,15 @@ void conv_2d_1x1_c8h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int out_h_idx = out_b_h_idx % out_h;//equal to in_h_idx const int out_w4_idx = mul24(out_w_idx, 4); - FLOAT4 out0 = vload4(out_c_idx<<1, (__global FLOAT *)bias_ptr); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx<<1, bias_ptr)); + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; - FLOAT4 out4 = vload4((out_c_idx<<1)+1, (__global FLOAT *)bias_ptr); - FLOAT4 out5 = out4; - FLOAT4 out6 = out4; - FLOAT4 out7 = out4; + COMPUTE_FLOAT4 out4 = CONVERT_COMPUTE_FLOAT4(vload4((out_c_idx<<1)+1, bias_ptr)); + COMPUTE_FLOAT4 out5 = out4; + COMPUTE_FLOAT4 out6 = out4; + COMPUTE_FLOAT4 out7 = out4; const int intput_width_idx0 = out_w4_idx; @@ -167,19 +154,19 @@ void conv_2d_1x1_c8h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int inp_offset = (((out_b_idx*in_c_block + in_channel_block_idx)*out_h + out_h_idx)* out_w + intput_width_idx0)*4; - FLOAT4 in0 = vload4(0, input+inp_offset); - FLOAT4 in1 = vload4(1, input+inp_offset);; - FLOAT4 in2 = vload4(2, input+inp_offset);; - FLOAT4 in3 = vload4(3, input+inp_offset);; - - FLOAT4 weights0 = vload4(offset, (__global FLOAT *)kernel_ptr); - FLOAT4 weights1 = vload4(offset + 1, (__global FLOAT *)kernel_ptr); - FLOAT4 weights2 = vload4(offset + 2, (__global FLOAT *)kernel_ptr); - FLOAT4 weights3 = vload4(offset + 3, (__global FLOAT *)kernel_ptr); - FLOAT4 weights4 = vload4(offset + 4, (__global FLOAT *)kernel_ptr); - FLOAT4 weights5 = vload4(offset + 5, (__global FLOAT *)kernel_ptr); - FLOAT4 weights6 = vload4(offset + 6, (__global FLOAT *)kernel_ptr); - FLOAT4 weights7 = vload4(offset + 7, (__global FLOAT *)kernel_ptr); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4(vload4(1, input+inp_offset)); + COMPUTE_FLOAT4 in2 = CONVERT_COMPUTE_FLOAT4(vload4(2, input+inp_offset)); + COMPUTE_FLOAT4 in3 = CONVERT_COMPUTE_FLOAT4(vload4(3, input+inp_offset)); + + COMPUTE_FLOAT4 weights0 = CONVERT_COMPUTE_FLOAT4(vload4(offset, kernel_ptr)); + COMPUTE_FLOAT4 weights1 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 1, kernel_ptr)); + COMPUTE_FLOAT4 weights2 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 2, kernel_ptr)); + COMPUTE_FLOAT4 weights3 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 3, kernel_ptr)); + COMPUTE_FLOAT4 weights4 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 4, kernel_ptr)); + COMPUTE_FLOAT4 weights5 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 5, kernel_ptr)); + COMPUTE_FLOAT4 weights6 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 6, kernel_ptr)); + COMPUTE_FLOAT4 weights7 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 7, kernel_ptr)); out0.x += dot(weights0, in0); out0.y += dot(weights1, in0); @@ -223,45 +210,45 @@ void conv_2d_1x1_c8h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); - out4 = fmax(out4, (FLOAT4)0); - out5 = fmax(out5, (FLOAT4)0); - out6 = fmax(out6, (FLOAT4)0); - out7 = fmax(out7, (FLOAT4)0); + out4 = fmax(out4, (COMPUTE_FLOAT4)0); + out5 = fmax(out5, (COMPUTE_FLOAT4)0); + out6 = fmax(out6, (COMPUTE_FLOAT4)0); + out7 = fmax(out7, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); - out4 = clamp(out4, (FLOAT4)0, (FLOAT4)6); - out5 = clamp(out5, (FLOAT4)0, (FLOAT4)6); - out6 = clamp(out6, (FLOAT4)0, (FLOAT4)6); - out7 = clamp(out7, (FLOAT4)0, (FLOAT4)6); + out4 = clamp(out4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out5 = clamp(out5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out6 = clamp(out6, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out7 = clamp(out7, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_block + out_c_idx*2)*out_h + out_h_idx)* out_w + out_w4_idx)*4; - __global FLOAT* _tempoutput = output + out_offset; - __global FLOAT* _tempoutput1 = _tempoutput + 4*out_h*out_w; + __global FLOAT * _tempoutput = output + out_offset; + __global FLOAT * _tempoutput1 = _tempoutput + 4*out_h*out_w; #ifdef BLOCK_LEAVE const int remain = out_w - out_w4_idx; if (remain >= 4) { - vstore16((FLOAT16)(out0, out1, out2, out3), 0, _tempoutput); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, _tempoutput); } else if (remain == 3) { - vstore8((FLOAT8)(out0, out1), 0, _tempoutput); - vstore4(out2, 2, _tempoutput); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, _tempoutput); + vstore4(CONVERT_FLOAT4(out2), 2, _tempoutput); } else if (remain == 2) { - vstore8((FLOAT8)(out0, out1), 0, _tempoutput); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, _tempoutput); } else if (remain == 1) { - vstore4(out0, 0, _tempoutput); + vstore4(CONVERT_FLOAT4(out0), 0, _tempoutput); } #ifdef CHANNEL_LEAVE if(out_c_idx*2+1 >= out_c_block) { @@ -269,23 +256,23 @@ void conv_2d_1x1_c8h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, } #endif if (remain >= 4) { - vstore16((FLOAT16)(out4, out5, out6, out7), 0, _tempoutput1); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out4, out5, out6, out7)), 0, _tempoutput1); } else if (remain == 3) { - vstore8((FLOAT8)(out4, out5), 0, _tempoutput1); - vstore4(out6, 2, _tempoutput1); + vstore8(CONVERT_FLOAT8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5))), 0, _tempoutput1); + vstore4(CONVERT_FLOAT4(out6), 2, _tempoutput1); } else if (remain == 2) { - vstore8((FLOAT8)(out4, out5), 0, _tempoutput1); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5)), 0, _tempoutput1); } else if (remain == 1) { - vstore4(out4, 0, _tempoutput1); + vstore4(CONVERT_FLOAT4(out4), 0, _tempoutput1); } #else - vstore16((FLOAT16)(out0, out1, out2, out3), 0, _tempoutput); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, _tempoutput); #ifdef CHANNEL_LEAVE if(out_c_idx*2+1 >= out_c_block) { return; } #endif - vstore16((FLOAT16)(out4, out5, out6, out7), 0, _tempoutput1); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out4, out5, out6, out7)), 0, _tempoutput1); #endif } @@ -312,11 +299,11 @@ void conv_2d_1x1_c8h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int out_h_idx = out_b_h_idx % out_h;//equal to in_h_idx const int out_w2_idx = mul24(out_w_idx, 2); - FLOAT4 out0 = vload4(out_c_idx<<1, (__global FLOAT *)bias_ptr); - FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx<<1, bias_ptr)); + COMPUTE_FLOAT4 out1 = out0; - FLOAT4 out4 = vload4((out_c_idx<<1)+1, (__global FLOAT *)bias_ptr); - FLOAT4 out5 = out4; + COMPUTE_FLOAT4 out4 = CONVERT_COMPUTE_FLOAT4(vload4((out_c_idx<<1)+1, bias_ptr)); + COMPUTE_FLOAT4 out5 = out4; const int intput_width_idx0 = out_w2_idx; @@ -327,17 +314,17 @@ void conv_2d_1x1_c8h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int inp_offset = (((out_b_idx*in_c_block + in_channel_block_idx)*out_h + out_h_idx)* out_w + intput_width_idx0)*4; - FLOAT4 in0 = vload4(0, input+inp_offset); - FLOAT4 in1 = vload4(1, input+inp_offset);; - - FLOAT4 weights0 = vload4(offset, (__global FLOAT *)kernel_ptr); - FLOAT4 weights1 = vload4(offset + 1, (__global FLOAT *)kernel_ptr); - FLOAT4 weights2 = vload4(offset + 2, (__global FLOAT *)kernel_ptr); - FLOAT4 weights3 = vload4(offset + 3, (__global FLOAT *)kernel_ptr); - FLOAT4 weights4 = vload4(offset + 4, (__global FLOAT *)kernel_ptr); - FLOAT4 weights5 = vload4(offset + 5, (__global FLOAT *)kernel_ptr); - FLOAT4 weights6 = vload4(offset + 6, (__global FLOAT *)kernel_ptr); - FLOAT4 weights7 = vload4(offset + 7, (__global FLOAT *)kernel_ptr); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4(vload4(1, input+inp_offset)); + + COMPUTE_FLOAT4 weights0 = CONVERT_COMPUTE_FLOAT4(vload4(offset, kernel_ptr)); + COMPUTE_FLOAT4 weights1 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 1, kernel_ptr)); + COMPUTE_FLOAT4 weights2 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 2, kernel_ptr)); + COMPUTE_FLOAT4 weights3 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 3, kernel_ptr)); + COMPUTE_FLOAT4 weights4 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 4, kernel_ptr)); + COMPUTE_FLOAT4 weights5 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 5, kernel_ptr)); + COMPUTE_FLOAT4 weights6 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 6, kernel_ptr)); + COMPUTE_FLOAT4 weights7 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 7, kernel_ptr)); out0.x += dot(weights0, in0); out0.y += dot(weights1, in0); @@ -361,33 +348,33 @@ void conv_2d_1x1_c8h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); - out4 = fmax(out4, (FLOAT4)0); - out5 = fmax(out5, (FLOAT4)0); + out4 = fmax(out4, (COMPUTE_FLOAT4)0); + out5 = fmax(out5, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); - out4 = clamp(out4, (FLOAT4)0, (FLOAT4)6); - out5 = clamp(out5, (FLOAT4)0, (FLOAT4)6); + out4 = clamp(out4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out5 = clamp(out5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_block + out_c_idx*2)*out_h + out_h_idx)* out_w + out_w2_idx)*4; - __global FLOAT* _tempoutput = output + out_offset; - __global FLOAT* _tempoutput1 = _tempoutput + 4*out_h*out_w; + __global FLOAT * _tempoutput = output + out_offset; + __global FLOAT * _tempoutput1 = _tempoutput + 4*out_h*out_w; #ifdef BLOCK_LEAVE const int remain = out_w - out_w2_idx; if (remain >= 2) { - vstore8((FLOAT8)(out0, out1), 0, _tempoutput); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, _tempoutput); } else if (remain == 1) { - vstore4(out0, 0, _tempoutput); + vstore4(CONVERT_FLOAT4(out0), 0, _tempoutput); } #ifdef CHANNEL_LEAVE if(out_c_idx*2+1 >= out_c_block) { @@ -395,18 +382,18 @@ void conv_2d_1x1_c8h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, } #endif if (remain >= 2) { - vstore8((FLOAT8)(out4, out5), 0, _tempoutput1); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5)), 0, _tempoutput1); } else if (remain == 1) { - vstore4(out4, 0, _tempoutput1); + vstore4(CONVERT_FLOAT4(out4), 0, _tempoutput1); } #else - vstore8((FLOAT8)(out0, out1), 0, _tempoutput); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, _tempoutput); #ifdef CHANNEL_LEAVE if(out_c_idx*2+1 >= out_c_block) { return; } #endif - vstore8((FLOAT8)(out4, out5), 0, _tempoutput1); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5)), 0, _tempoutput1); #endif } @@ -431,7 +418,7 @@ void conv_2d_1x1_c4h1w1(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int out_b_idx = out_b_h_idx / out_h;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_h;//equal to in_h_idx - FLOAT4 out0 = vload4(out_c_idx, (__global FLOAT *)bias_ptr); + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias_ptr)); const int intput_width_idx0 = out_w_idx; for (int in_channel_block_idx = 0; in_channel_block_idx < in_c_block; ++in_channel_block_idx) { @@ -441,12 +428,12 @@ void conv_2d_1x1_c4h1w1(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int inp_offset = (((out_b_idx*in_c_block + in_channel_block_idx)*out_h + out_h_idx)* out_w + intput_width_idx0)*4; - FLOAT4 in0 = vload4(0, input+inp_offset); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); - FLOAT4 weights0 = vload4(offset, (__global FLOAT *)kernel_ptr); - FLOAT4 weights1 = vload4(offset + 1, (__global FLOAT *)kernel_ptr); - FLOAT4 weights2 = vload4(offset + 2, (__global FLOAT *)kernel_ptr); - FLOAT4 weights3 = vload4(offset + 3, (__global FLOAT *)kernel_ptr); + COMPUTE_FLOAT4 weights0 = CONVERT_COMPUTE_FLOAT4(vload4(offset, kernel_ptr)); + COMPUTE_FLOAT4 weights1 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 1, kernel_ptr)); + COMPUTE_FLOAT4 weights2 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 2, kernel_ptr)); + COMPUTE_FLOAT4 weights3 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 3, kernel_ptr)); out0.x += dot(weights0, in0); out0.y += dot(weights1, in0); @@ -455,16 +442,16 @@ void conv_2d_1x1_c4h1w1(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_block + out_c_idx)*out_h + out_h_idx)* out_w + out_w_idx)*4; - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } @@ -491,8 +478,8 @@ void conv_2d_1x1_c4h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int out_w2_idx = mul24(out_w_idx, 2); - FLOAT4 out0 = vload4(out_c_idx, (__global FLOAT *)bias_ptr); - FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias_ptr)); + COMPUTE_FLOAT4 out1 = out0; const int intput_width_idx0 = out_w2_idx; @@ -503,13 +490,13 @@ void conv_2d_1x1_c4h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int inp_offset = (((out_b_idx*in_c_block + in_channel_block_idx)*out_h + out_h_idx)* out_w + intput_width_idx0)*4; - FLOAT4 in0 = vload4(0, input+inp_offset); - FLOAT4 in1 = vload4(1, input+inp_offset); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4(vload4(1, input+inp_offset)); - FLOAT4 weights0 = vload4(offset, (__global FLOAT *)kernel_ptr); - FLOAT4 weights1 = vload4(offset + 1, (__global FLOAT *)kernel_ptr); - FLOAT4 weights2 = vload4(offset + 2, (__global FLOAT *)kernel_ptr); - FLOAT4 weights3 = vload4(offset + 3, (__global FLOAT *)kernel_ptr); + COMPUTE_FLOAT4 weights0 = CONVERT_COMPUTE_FLOAT4(vload4(offset, kernel_ptr)); + COMPUTE_FLOAT4 weights1 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 1, kernel_ptr)); + COMPUTE_FLOAT4 weights2 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 2, kernel_ptr)); + COMPUTE_FLOAT4 weights3 = CONVERT_COMPUTE_FLOAT4(vload4(offset + 3, kernel_ptr)); out0.x += dot(weights0, in0); out0.y += dot(weights1, in0); @@ -523,13 +510,13 @@ void conv_2d_1x1_c4h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_block + out_c_idx)*out_h + out_h_idx)* out_w + out_w2_idx)*4; @@ -538,12 +525,12 @@ void conv_2d_1x1_c4h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, const int remain = out_w - out_w2_idx; if (remain >= 2) { - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); } else if (remain == 1) { - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #else - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); #endif } @@ -574,7 +561,7 @@ void conv_2d_c4h1w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 out0 = vload4(out_c_idx, bias); + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_h_idx_base = mad24(out_h_idx, stride_hw.x, -pad_hw.x); @@ -596,14 +583,14 @@ void conv_2d_c4h1w1(GLOBAL_SIZE_2_DIMS for(int iy = in_h_idx_start; iy < in_h_idx_end; iy += dilate_hw.x) { for(int ix = in_w_idx_start; ix < in_w_idx_end; ix += dilate_hw.y) { int inp_offset = (((out_b_idx * in_c_blocks + in_c_idx) * in_hw.x + iy) * in_hw.y + ix) * 4; - FLOAT4 in0 = vload4(0, input+inp_offset); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); const int filter_w_inc = (ix-in_w_idx_start)/dilate_hw.y; - FLOAT4 weight0 = vload4(filter_w_inc, weight+weight_offset); - FLOAT4 weight1 = vload4(filter_w_inc, weight+weight_offset+weight_oc_offset); - FLOAT4 weight2 = vload4(filter_w_inc, weight+weight_offset+weight_oc_offset*2); - FLOAT4 weight3 = vload4(filter_w_inc, weight+weight_offset+weight_oc_offset*3); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(vload4(filter_w_inc, weight+weight_offset)); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(vload4(filter_w_inc, weight+weight_offset+weight_oc_offset)); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(vload4(filter_w_inc, weight+weight_offset+weight_oc_offset*2)); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(vload4(filter_w_inc, weight+weight_offset+weight_oc_offset*3)); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); @@ -615,15 +602,15 @@ void conv_2d_c4h1w1(GLOBAL_SIZE_2_DIMS } } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } @@ -654,8 +641,8 @@ void conv_2d_c4h1w2(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out1 = out0; const int in_w0_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_w1_idx_base = in_w0_idx_base + stride_hw.y; @@ -679,13 +666,13 @@ void conv_2d_c4h1w2(GLOBAL_SIZE_2_DIMS const int in_w0_idx = fw * dilate_hw.y + in_w0_idx_base; const int in_w1_idx = fw * dilate_hw.y + in_w1_idx_base; - FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base); - FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w0_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w1_idx, input+inp_offset_base)); - FLOAT4 weight0 = vload4(0, weight+weight_offset); - FLOAT4 weight1 = vload4(0, weight+weight_offset+weight_oc_offset); - FLOAT4 weight2 = vload4(0, weight+weight_offset+weight_oc_offset*2); - FLOAT4 weight3 = vload4(0, weight+weight_offset+weight_oc_offset*3); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset)); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset)); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset*2)); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset*3)); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); @@ -702,22 +689,22 @@ void conv_2d_c4h1w2(GLOBAL_SIZE_2_DIMS } } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); if(out_w_idx + 1 >= out_hw.y) return; - vstore4(out1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), 1, output+out_offset); #else - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); #endif } @@ -748,10 +735,10 @@ void conv_2d_c4h1w4(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; const int in_w0_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_w1_idx_base = in_w0_idx_base + stride_hw.y; @@ -779,15 +766,15 @@ void conv_2d_c4h1w4(GLOBAL_SIZE_2_DIMS const int in_w2_idx = fw * dilate_hw.y + in_w2_idx_base; const int in_w3_idx = fw * dilate_hw.y + in_w3_idx_base; - FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base); - FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base); - FLOAT4 in2 = (in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w2_idx, input+inp_offset_base); - FLOAT4 in3 = (in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w3_idx, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w0_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w1_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = (in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w2_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = (in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w3_idx, input+inp_offset_base)); - FLOAT4 weight0 = vload4(0, weight+weight_offset); - FLOAT4 weight1 = vload4(0, weight+weight_offset+weight_oc_offset); - FLOAT4 weight2 = vload4(0, weight+weight_offset+weight_oc_offset*2); - FLOAT4 weight3 = vload4(0, weight+weight_offset+weight_oc_offset*3); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset)); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset)); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset*2)); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset*3)); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); @@ -814,17 +801,17 @@ void conv_2d_c4h1w4(GLOBAL_SIZE_2_DIMS } } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; @@ -832,17 +819,17 @@ void conv_2d_c4h1w4(GLOBAL_SIZE_2_DIMS const int remain = out_hw.y - out_w_idx; if (remain >= 4) { - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); }else if(remain == 3){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); - vstore4(out2, 2, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2, output+out_offset); }else if(remain == 2){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #else - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); #endif } @@ -873,10 +860,10 @@ void conv_2d_c4h4w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_h_blocks;//equal to in_b_idx const int out_h_idx = (out_b_h_idx % out_h_blocks) << 2; - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); @@ -904,15 +891,15 @@ void conv_2d_c4h4w1(GLOBAL_SIZE_2_DIMS const int in_h3_idx = (iy * dilate_hw.x + in_h3_idx_base) * in_hw.y; for(int fw = in_w_idx_start; fw < in_w_idx_end; fw += dilate_hw.y) { - FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base); - FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base); - FLOAT4 in2 = (in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h2_idx + fw, input+inp_offset_base); - FLOAT4 in3 = (in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h3_idx + fw, input+inp_offset_base); - - FLOAT4 weight0 = vload4(0, weight+weight_offset); - FLOAT4 weight1 = vload4(0, weight+weight_offset+weight_oc_offset); - FLOAT4 weight2 = vload4(0, weight+weight_offset+weight_oc_offset*2); - FLOAT4 weight3 = vload4(0, weight+weight_offset+weight_oc_offset*3); + COMPUTE_FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h0_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h1_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = (in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h2_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = (in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h3_idx + fw, input+inp_offset_base)); + + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset)); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset)); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset*2)); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset*3)); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); @@ -939,42 +926,42 @@ void conv_2d_c4h4w1(GLOBAL_SIZE_2_DIMS } } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.x - out_h_idx; if(remain >= 4){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); }else if(remain == 3){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); }else if(remain == 2){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #else - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); #endif } @@ -1005,14 +992,14 @@ void conv_2d_c8h4w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_h_blocks;//equal to in_b_idx const int out_h_idx = (out_b_h_idx % out_h_blocks) << 2; - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; - FLOAT4 out4 = vload4(out_c_idx + 1, bias); - FLOAT4 out5 = out4; - FLOAT4 out6 = out4; - FLOAT4 out7 = out4; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; + COMPUTE_FLOAT4 out4 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx + 1, bias)); + COMPUTE_FLOAT4 out5 = out4; + COMPUTE_FLOAT4 out6 = out4; + COMPUTE_FLOAT4 out7 = out4; const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); @@ -1041,15 +1028,15 @@ void conv_2d_c8h4w1(GLOBAL_SIZE_2_DIMS const int in_h3_idx = (iy * dilate_hw.x + in_h3_idx_base) * in_hw.y; for(int fw = in_w_idx_start; fw < in_w_idx_end; fw += dilate_hw.y) { - FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base); - FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base); - FLOAT4 in2 = (in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h2_idx + fw, input+inp_offset_base); - FLOAT4 in3 = (in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h3_idx + fw, input+inp_offset_base); - - FLOAT4 weight0 = vload4(0, weight+weight_offset); - FLOAT4 weight1 = vload4(0, weight+weight_offset+weight_ic_offset); - FLOAT4 weight2 = vload4(0, weight+weight_offset+weight_ic_offset*2); - FLOAT4 weight3 = vload4(0, weight+weight_offset+weight_ic_offset*3); + COMPUTE_FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h0_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h1_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = (in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h2_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = (in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h3_idx + fw, input+inp_offset_base)); + + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset)); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset)); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset*2)); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset*3)); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); @@ -1071,10 +1058,10 @@ void conv_2d_c8h4w1(GLOBAL_SIZE_2_DIMS out3 = mad(in3.z, weight2, out3); out3 = mad(in3.w, weight3, out3); - weight0 = vload4(0, weight+weight_offset+weight_oc_offset); - weight1 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset); - weight2 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2); - weight3 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3); + weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset)); + weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset)); + weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2)); + weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3)); out4 = mad(in0.x, weight0, out4); out4 = mad(in0.y, weight1, out4); @@ -1101,44 +1088,44 @@ void conv_2d_c8h4w1(GLOBAL_SIZE_2_DIMS } } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); - out4 = fmax(out4, (FLOAT4)0); - out5 = fmax(out5, (FLOAT4)0); - out6 = fmax(out6, (FLOAT4)0); - out7 = fmax(out7, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); + out4 = fmax(out4, (COMPUTE_FLOAT4)0); + out5 = fmax(out5, (COMPUTE_FLOAT4)0); + out6 = fmax(out6, (COMPUTE_FLOAT4)0); + out7 = fmax(out7, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); - out4 = clamp(out4, (FLOAT4)0, (FLOAT4)6); - out5 = clamp(out5, (FLOAT4)0, (FLOAT4)6); - out6 = clamp(out6, (FLOAT4)0, (FLOAT4)6); - out7 = clamp(out7, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out4 = clamp(out4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out5 = clamp(out5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out6 = clamp(out6, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out7 = clamp(out7, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.x - out_h_idx; if(remain >= 4){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); }else if(remain == 3){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); }else if(remain == 2){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ @@ -1147,35 +1134,35 @@ void conv_2d_c8h4w1(GLOBAL_SIZE_2_DIMS #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; if(remain >= 4){ - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); - vstore4(out6, 2 * out_hw.y, output+out_offset); - vstore4(out7, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out7), 3 * out_hw.y, output+out_offset); }else if(remain == 3){ - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); - vstore4(out6, 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2 * out_hw.y, output+out_offset); }else if(remain == 2){ - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out4, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); } #else - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ return; } #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); - vstore4(out6, 2 * out_hw.y, output+out_offset); - vstore4(out7, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out7), 3 * out_hw.y, output+out_offset); #endif } @@ -1206,10 +1193,10 @@ void conv_2d_c8h2w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_h_blocks;//equal to in_b_idx const int out_h_idx = (out_b_h_idx % out_h_blocks) << 1; - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = vload4(out_c_idx + 1, bias); - FLOAT4 out3 = out2; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx + 1, bias)); + COMPUTE_FLOAT4 out3 = out2; const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); @@ -1235,12 +1222,12 @@ void conv_2d_c8h2w1(GLOBAL_SIZE_2_DIMS const int in_h1_idx = (iy * dilate_hw.x + in_h1_idx_base) * in_hw.y; for(int fw = in_w_idx_start; fw < in_w_idx_end; fw += dilate_hw.y) { - FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base); - FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base); - FLOAT4 weight0 = vload4(0, weight+weight_offset); - FLOAT4 weight1 = vload4(0, weight+weight_offset+weight_ic_offset); - FLOAT4 weight2 = vload4(0, weight+weight_offset+weight_ic_offset*2); - FLOAT4 weight3 = vload4(0, weight+weight_offset+weight_ic_offset*3); + COMPUTE_FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h0_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_h1_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset)); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset)); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset*2)); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset*3)); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); @@ -1252,10 +1239,10 @@ void conv_2d_c8h2w1(GLOBAL_SIZE_2_DIMS out1 = mad(in1.z, weight2, out1); out1 = mad(in1.w, weight3, out1); - weight0 = vload4(0, weight+weight_offset+weight_oc_offset); - weight1 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset); - weight2 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2); - weight3 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3); + weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset)); + weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset)); + weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2)); + weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3)); out2 = mad(in0.x, weight0, out2); out2 = mad(in0.y, weight1, out2); @@ -1272,27 +1259,27 @@ void conv_2d_c8h2w1(GLOBAL_SIZE_2_DIMS } } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.x - out_h_idx; if(remain >= 2){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ @@ -1301,22 +1288,22 @@ void conv_2d_c8h2w1(GLOBAL_SIZE_2_DIMS #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; if(remain >= 2){ - vstore4(out2, 0, output+out_offset); - vstore4(out3, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out2, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 0, output+out_offset); } #else - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ return; } #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore4(out2, 0, output+out_offset); - vstore4(out3, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), out_hw.y, output+out_offset); #endif } @@ -1347,15 +1334,15 @@ void conv_2d_c8h1w4(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + COMPUTE_FLOAT4 out0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; - FLOAT4 out4 = vload4(out_c_idx + 1, bias); - FLOAT4 out5 = out4; - FLOAT4 out6 = out4; - FLOAT4 out7 = out4; + COMPUTE_FLOAT4 out4 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx + 1, bias)); + COMPUTE_FLOAT4 out5 = out4; + COMPUTE_FLOAT4 out6 = out4; + COMPUTE_FLOAT4 out7 = out4; const int in_w0_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_w1_idx_base = in_w0_idx_base + stride_hw.y; @@ -1384,15 +1371,15 @@ void conv_2d_c8h1w4(GLOBAL_SIZE_2_DIMS const int in_w2_idx = fw * dilate_hw.y + in_w2_idx_base; const int in_w3_idx = fw * dilate_hw.y + in_w3_idx_base; - FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base); - FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base); - FLOAT4 in2 = (in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w2_idx, input+inp_offset_base); - FLOAT4 in3 = (in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w3_idx, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w0_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w1_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = (in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w2_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = (in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_w3_idx, input+inp_offset_base)); - FLOAT4 weight0 = vload4(0, weight+weight_offset); - FLOAT4 weight1 = vload4(0, weight+weight_offset+weight_ic_offset); - FLOAT4 weight2 = vload4(0, weight+weight_offset+weight_ic_offset*2); - FLOAT4 weight3 = vload4(0, weight+weight_offset+weight_ic_offset*3); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset)); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset)); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset*2)); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_ic_offset*3)); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); @@ -1414,10 +1401,10 @@ void conv_2d_c8h1w4(GLOBAL_SIZE_2_DIMS out3 = mad(in3.z, weight2, out3); out3 = mad(in3.w, weight3, out3); - weight0 = vload4(0, weight+weight_offset+weight_oc_offset); - weight1 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset); - weight2 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2); - weight3 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3); + weight0 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset)); + weight1 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset)); + weight2 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2)); + weight3 = CONVERT_COMPUTE_FLOAT4(vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3)); out4 = mad(in0.x, weight0, out4); out4 = mad(in0.y, weight1, out4); @@ -1444,60 +1431,60 @@ void conv_2d_c8h1w4(GLOBAL_SIZE_2_DIMS } } #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); - out4 = fmax(out4, (FLOAT4)0); - out5 = fmax(out5, (FLOAT4)0); - out6 = fmax(out6, (FLOAT4)0); - out7 = fmax(out7, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); + out4 = fmax(out4, (COMPUTE_FLOAT4)0); + out5 = fmax(out5, (COMPUTE_FLOAT4)0); + out6 = fmax(out6, (COMPUTE_FLOAT4)0); + out7 = fmax(out7, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); - out4 = clamp(out4, (FLOAT4)0, (FLOAT4)6); - out5 = clamp(out5, (FLOAT4)0, (FLOAT4)6); - out6 = clamp(out6, (FLOAT4)0, (FLOAT4)6); - out7 = clamp(out7, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out4 = clamp(out4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out5 = clamp(out5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out6 = clamp(out6, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out7 = clamp(out7, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.y - out_w_idx; if(remain >= 4){ - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); }else if(remain == 3){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); - vstore4(out2, 2, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2, output+out_offset); }else if(remain == 2){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks)return; #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; if(remain >= 4){ - vstore16((FLOAT16)(out4, out5, out6, out7), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out4, out5, out6, out7)), 0, output+out_offset); }else if(remain == 3){ - vstore8((FLOAT8)(out4, out5), 0, output+out_offset); - vstore4(out6, 2, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5)), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2, output+out_offset); }else if(remain == 2){ - vstore8((FLOAT8)(out4, out5), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5)), 0, output+out_offset); }else if(remain == 1){ - vstore4(out4, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); } #else - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks)return; #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore16((FLOAT16)(out4, out5, out6, out7), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out4, out5, out6, out7)), 0, output+out_offset); #endif } diff --git a/source/backend/opencl/execution/cl/conv_2d_c16_subgroup_buf.cl b/source/backend/opencl/execution/cl/conv_2d_c16_subgroup_buf.cl index 3bac8c873..a64f6d9ab 100644 --- a/source/backend/opencl/execution/cl/conv_2d_c16_subgroup_buf.cl +++ b/source/backend/opencl/execution/cl/conv_2d_c16_subgroup_buf.cl @@ -98,19 +98,19 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b2( const uint filter_offset = feature_block * filter_os_pitch; #if SLM_DIV_FACTOR == 1 - FLOAT2 dst = (FLOAT2)(GROUP_READ(biases, feature_block * 16)); + COMPUTE_FLOAT2 dst = (COMPUTE_FLOAT2)((GROUP_READ(biases, feature_block * 16))); #else - FLOAT2 dst; + COMPUTE_FLOAT2 dst; if (feature_sub_block == 0) { - dst = (FLOAT2)(GROUP_READ(biases, feature_block * 16)); + dst = (COMPUTE_FLOAT2)((GROUP_READ(biases, feature_block * 16))); } else { - dst = (FLOAT2)0; + dst = (COMPUTE_FLOAT2)0; } #endif #if SLM_DIV_FACTOR > 1 - __local FLOAT2 sum[WORK_GROUP_SIZE]; + __local COMPUTE_FLOAT2 sum[WORK_GROUP_SIZE]; #endif @@ -129,10 +129,10 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b2( { int xb = 0; for (; xb + 8 <= INPUT_LINE_SIZE; xb += 8) { - FLOAT8 tmp = GROUP_READ8(input, input_offset + + COMPUTE_FLOAT8 tmp = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -144,10 +144,10 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b2( line_cache[xb + 7] = tmp[7]; } for (; xb + 4 <= INPUT_LINE_SIZE; xb += 4) { - FLOAT4 tmp = GROUP_READ4(input, input_offset + + COMPUTE_FLOAT4 tmp = CONVERT_COMPUTE_FLOAT4(GROUP_READ4(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -173,31 +173,31 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b2( src[i] = line_cache[kw * DILATION_WIDTH + STRIDE_WIDTH * i]; #endif } - FLOAT8 weight0 = GROUP_READ8(weights, filter_offset + + COMPUTE_FLOAT8 weight0 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + - kw * filter_x_pitch); - FLOAT8 weight1 = GROUP_READ8(weights, filter_offset + + kw * filter_x_pitch)); + COMPUTE_FLOAT8 weight1 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + kw * filter_x_pitch + - 8 * filter_isv_pitch); - const FLOAT2 src0 = GROUP_SHUFFLE2(src, 0); - const FLOAT2 src1 = GROUP_SHUFFLE2(src, 1); - const FLOAT2 src2 = GROUP_SHUFFLE2(src, 2); - const FLOAT2 src3 = GROUP_SHUFFLE2(src, 3); - const FLOAT2 src4 = GROUP_SHUFFLE2(src, 4); - const FLOAT2 src5 = GROUP_SHUFFLE2(src, 5); - const FLOAT2 src6 = GROUP_SHUFFLE2(src, 6); - const FLOAT2 src7 = GROUP_SHUFFLE2(src, 7); - const FLOAT2 src8 = GROUP_SHUFFLE2(src, 8); - const FLOAT2 src9 = GROUP_SHUFFLE2(src, 9); - const FLOAT2 src10 = GROUP_SHUFFLE2(src, 10); - const FLOAT2 src11 = GROUP_SHUFFLE2(src, 11); - const FLOAT2 src12 = GROUP_SHUFFLE2(src, 12); - const FLOAT2 src13 = GROUP_SHUFFLE2(src, 13); - const FLOAT2 src14 = GROUP_SHUFFLE2(src, 14); - const FLOAT2 src15 = GROUP_SHUFFLE2(src, 15); + 8 * filter_isv_pitch)); + const COMPUTE_FLOAT2 src0 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 0)); + const COMPUTE_FLOAT2 src1 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 1)); + const COMPUTE_FLOAT2 src2 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 2)); + const COMPUTE_FLOAT2 src3 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 3)); + const COMPUTE_FLOAT2 src4 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 4)); + const COMPUTE_FLOAT2 src5 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 5)); + const COMPUTE_FLOAT2 src6 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 6)); + const COMPUTE_FLOAT2 src7 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 7)); + const COMPUTE_FLOAT2 src8 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 8)); + const COMPUTE_FLOAT2 src9 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 9)); + const COMPUTE_FLOAT2 src10 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 10)); + const COMPUTE_FLOAT2 src11 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 11)); + const COMPUTE_FLOAT2 src12 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 12)); + const COMPUTE_FLOAT2 src13 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 13)); + const COMPUTE_FLOAT2 src14 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 14)); + const COMPUTE_FLOAT2 src15 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 15)); dst = mad(weight0.s0, src0, dst); dst = mad(weight0.s1, src1, dst); @@ -229,11 +229,11 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b2( #endif #ifdef RELU - dst = fmax(dst, (FLOAT2)0); + dst = fmax(dst, (COMPUTE_FLOAT2)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT2)0, (FLOAT2)6); + dst = clamp(dst, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); #endif const uint lid_x = sglid % 4; @@ -242,13 +242,13 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b2( if ((feature_block+1)*16 >= output_channel) { for (int i = 0; i < 2 && (x + i) < output_width; i++) { if ((feature_block*16 + lid_y * 4 + lid_x < output_channel)) - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } else { for (int i = 0; i < 2 && (x + i) < output_width; i++) { - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } #if SLM_DIV_FACTOR > 1 @@ -319,19 +319,19 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b4( const uint filter_offset = feature_block * filter_os_pitch; #if SLM_DIV_FACTOR == 1 - FLOAT4 dst = (FLOAT4)(GROUP_READ(biases, feature_block * 16)); + COMPUTE_FLOAT4 dst = (COMPUTE_FLOAT4)((GROUP_READ(biases, feature_block * 16))); #else - FLOAT4 dst; + COMPUTE_FLOAT4 dst; if (feature_sub_block == 0) { - dst = (FLOAT4)(GROUP_READ(biases, feature_block * 16)); + dst = (COMPUTE_FLOAT4)((GROUP_READ(biases, feature_block * 16))); } else { - dst = (FLOAT4)0; + dst = (COMPUTE_FLOAT4)0; } #endif #if SLM_DIV_FACTOR > 1 - __local FLOAT4 sum[WORK_GROUP_SIZE]; + __local COMPUTE_FLOAT4 sum[WORK_GROUP_SIZE]; #endif @@ -350,10 +350,10 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b4( { int xb = 0; for (; xb + 8 <= INPUT_LINE_SIZE; xb += 8) { - FLOAT8 tmp = GROUP_READ8(input, input_offset + + COMPUTE_FLOAT8 tmp = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -365,10 +365,10 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b4( line_cache[xb + 7] = tmp[7]; } for (; xb + 4 <= INPUT_LINE_SIZE; xb += 4) { - FLOAT4 tmp = GROUP_READ4(input, input_offset + + COMPUTE_FLOAT4 tmp = CONVERT_COMPUTE_FLOAT4(GROUP_READ4(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -376,7 +376,7 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b4( line_cache[xb + 3] = tmp[3]; } for (; xb < INPUT_LINE_SIZE; xb++) { - line_cache[xb] = GROUP_READ(input, input_offset + + line_cache[xb] = (COMPUTE_FLOAT)GROUP_READ(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + xb * input_x_pitch); @@ -394,31 +394,31 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b4( src[i] = line_cache[kw * DILATION_WIDTH + STRIDE_WIDTH * i]; #endif } - FLOAT8 weight0 = GROUP_READ8(weights, filter_offset + + COMPUTE_FLOAT8 weight0 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + - kw * filter_x_pitch); - FLOAT8 weight1 = GROUP_READ8(weights, filter_offset + + kw * filter_x_pitch)); + COMPUTE_FLOAT8 weight1 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + kw * filter_x_pitch + - 8 * filter_isv_pitch); - const FLOAT4 src0 = GROUP_SHUFFLE4(src, 0); - const FLOAT4 src1 = GROUP_SHUFFLE4(src, 1); - const FLOAT4 src2 = GROUP_SHUFFLE4(src, 2); - const FLOAT4 src3 = GROUP_SHUFFLE4(src, 3); - const FLOAT4 src4 = GROUP_SHUFFLE4(src, 4); - const FLOAT4 src5 = GROUP_SHUFFLE4(src, 5); - const FLOAT4 src6 = GROUP_SHUFFLE4(src, 6); - const FLOAT4 src7 = GROUP_SHUFFLE4(src, 7); - const FLOAT4 src8 = GROUP_SHUFFLE4(src, 8); - const FLOAT4 src9 = GROUP_SHUFFLE4(src, 9); - const FLOAT4 src10 = GROUP_SHUFFLE4(src, 10); - const FLOAT4 src11 = GROUP_SHUFFLE4(src, 11); - const FLOAT4 src12 = GROUP_SHUFFLE4(src, 12); - const FLOAT4 src13 = GROUP_SHUFFLE4(src, 13); - const FLOAT4 src14 = GROUP_SHUFFLE4(src, 14); - const FLOAT4 src15 = GROUP_SHUFFLE4(src, 15); + 8 * filter_isv_pitch)); + const COMPUTE_FLOAT4 src0 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 0)); + const COMPUTE_FLOAT4 src1 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 1)); + const COMPUTE_FLOAT4 src2 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 2)); + const COMPUTE_FLOAT4 src3 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 3)); + const COMPUTE_FLOAT4 src4 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 4)); + const COMPUTE_FLOAT4 src5 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 5)); + const COMPUTE_FLOAT4 src6 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 6)); + const COMPUTE_FLOAT4 src7 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 7)); + const COMPUTE_FLOAT4 src8 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 8)); + const COMPUTE_FLOAT4 src9 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 9)); + const COMPUTE_FLOAT4 src10 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 10)); + const COMPUTE_FLOAT4 src11 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 11)); + const COMPUTE_FLOAT4 src12 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 12)); + const COMPUTE_FLOAT4 src13 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 13)); + const COMPUTE_FLOAT4 src14 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 14)); + const COMPUTE_FLOAT4 src15 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 15)); dst = mad(weight0.s0, src0, dst); dst = mad(weight0.s1, src1, dst); @@ -450,11 +450,11 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b4( #endif #ifdef RELU - dst = fmax(dst, (FLOAT4)0); + dst = fmax(dst, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT4)0, (FLOAT4)6); + dst = clamp(dst, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const uint lid_x = sglid % 4; @@ -463,13 +463,13 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b4( if ((feature_block+1)*16 >= output_channel) { for (int i = 0; i < 4 && (x + i) < output_width; i++) { if ((feature_block*16 + lid_y * 4 + lid_x < output_channel)) - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } else { for (int i = 0; i < 4 && (x + i) < output_width; i++) { - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } #if SLM_DIV_FACTOR > 1 @@ -540,19 +540,19 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b8( const uint filter_offset = feature_block * filter_os_pitch; #if SLM_DIV_FACTOR == 1 - FLOAT8 dst = (FLOAT8)(GROUP_READ(biases, feature_block * 16)); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(GROUP_READ(biases, feature_block * 16)); #else - FLOAT8 dst; + COMPUTE_FLOAT8 dst; if (feature_sub_block == 0) { - dst = (FLOAT8)(GROUP_READ(biases, feature_block * 16)); + dst = (COMPUTE_FLOAT8)(GROUP_READ(biases, feature_block * 16)); } else { - dst = (FLOAT8)0; + dst = (COMPUTE_FLOAT8)0; } #endif #if SLM_DIV_FACTOR > 1 - __local FLOAT8 sum[WORK_GROUP_SIZE]; + __local COMPUTE_FLOAT8 sum[WORK_GROUP_SIZE]; #endif @@ -571,10 +571,10 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b8( { int xb = 0; for (; xb + 8 <= INPUT_LINE_SIZE; xb += 8) { - FLOAT8 tmp = GROUP_READ8(input, input_offset + + COMPUTE_FLOAT8 tmp = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -586,10 +586,10 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b8( line_cache[xb + 7] = tmp[7]; } for (; xb + 4 <= INPUT_LINE_SIZE; xb += 4) { - FLOAT4 tmp = GROUP_READ4(input, input_offset + + COMPUTE_FLOAT4 tmp = CONVERT_COMPUTE_FLOAT4(GROUP_READ4(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -597,7 +597,7 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b8( line_cache[xb + 3] = tmp[3]; } for (; xb < INPUT_LINE_SIZE; xb++) { - line_cache[xb] = GROUP_READ(input, input_offset + + line_cache[xb] = (COMPUTE_FLOAT)GROUP_READ(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + xb * input_x_pitch); @@ -615,31 +615,31 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b8( src[i] = line_cache[kw * DILATION_WIDTH + STRIDE_WIDTH * i]; #endif } - FLOAT8 weight0 = GROUP_READ8(weights, filter_offset + + COMPUTE_FLOAT8 weight0 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + - kw * filter_x_pitch); - FLOAT8 weight1 = GROUP_READ8(weights, filter_offset + + kw * filter_x_pitch)); + COMPUTE_FLOAT8 weight1 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + kw * filter_x_pitch + - 8 * filter_isv_pitch); - const FLOAT8 src0 = GROUP_SHUFFLE8(src, 0); - const FLOAT8 src1 = GROUP_SHUFFLE8(src, 1); - const FLOAT8 src2 = GROUP_SHUFFLE8(src, 2); - const FLOAT8 src3 = GROUP_SHUFFLE8(src, 3); - const FLOAT8 src4 = GROUP_SHUFFLE8(src, 4); - const FLOAT8 src5 = GROUP_SHUFFLE8(src, 5); - const FLOAT8 src6 = GROUP_SHUFFLE8(src, 6); - const FLOAT8 src7 = GROUP_SHUFFLE8(src, 7); - const FLOAT8 src8 = GROUP_SHUFFLE8(src, 8); - const FLOAT8 src9 = GROUP_SHUFFLE8(src, 9); - const FLOAT8 src10 = GROUP_SHUFFLE8(src, 10); - const FLOAT8 src11 = GROUP_SHUFFLE8(src, 11); - const FLOAT8 src12 = GROUP_SHUFFLE8(src, 12); - const FLOAT8 src13 = GROUP_SHUFFLE8(src, 13); - const FLOAT8 src14 = GROUP_SHUFFLE8(src, 14); - const FLOAT8 src15 = GROUP_SHUFFLE8(src, 15); + 8 * filter_isv_pitch)); + const COMPUTE_FLOAT8 src0 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 0)); + const COMPUTE_FLOAT8 src1 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 1)); + const COMPUTE_FLOAT8 src2 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 2)); + const COMPUTE_FLOAT8 src3 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 3)); + const COMPUTE_FLOAT8 src4 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 4)); + const COMPUTE_FLOAT8 src5 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 5)); + const COMPUTE_FLOAT8 src6 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 6)); + const COMPUTE_FLOAT8 src7 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 7)); + const COMPUTE_FLOAT8 src8 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 8)); + const COMPUTE_FLOAT8 src9 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 9)); + const COMPUTE_FLOAT8 src10 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 10)); + const COMPUTE_FLOAT8 src11 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 11)); + const COMPUTE_FLOAT8 src12 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 12)); + const COMPUTE_FLOAT8 src13 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 13)); + const COMPUTE_FLOAT8 src14 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 14)); + const COMPUTE_FLOAT8 src15 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 15)); dst = mad(weight0.s0, src0, dst); dst = mad(weight0.s1, src1, dst); @@ -671,11 +671,11 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b8( #endif #ifdef RELU - dst = fmax(dst, (FLOAT8)0); + dst = fmax(dst, (COMPUTE_FLOAT8)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT8)0, (FLOAT8)6); + dst = clamp(dst, (COMPUTE_FLOAT8)0, (COMPUTE_FLOAT8)6); #endif const uint lid_x = sglid % 4; @@ -684,13 +684,13 @@ __kernel void conv_2d_buf_subgroup_c16_c4_b8( if ((feature_block+1)*16 >= output_channel) { for (int i = 0; i < 8 && (x + i) < output_width; i++) { if ((feature_block*16 + lid_y * 4 + lid_x < output_channel)) - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } else { for (int i = 0; i < 8 && (x + i) < output_width; i++) { - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } #if SLM_DIV_FACTOR > 1 @@ -761,19 +761,19 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b2( const uint filter_offset = feature_block * filter_os_pitch; #if SLM_DIV_FACTOR == 1 - FLOAT2 dst = (FLOAT2)(GROUP_READ(biases, feature_block * 16)); + COMPUTE_FLOAT2 dst = (COMPUTE_FLOAT2)(GROUP_READ(biases, feature_block * 16)); #else - FLOAT2 dst; + COMPUTE_FLOAT2 dst; if (feature_sub_block == 0) { - dst = (FLOAT2)(GROUP_READ(biases, feature_block * 16)); + dst = (COMPUTE_FLOAT2)(GROUP_READ(biases, feature_block * 16)); } else { - dst = (FLOAT2)0; + dst = (COMPUTE_FLOAT2)0; } #endif #if SLM_DIV_FACTOR > 1 - __local FLOAT2 sum[WORK_GROUP_SIZE]; + __local COMPUTE_FLOAT2 sum[WORK_GROUP_SIZE]; #endif @@ -792,10 +792,10 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b2( { int xb = 0; for (; xb + 8 <= INPUT_LINE_SIZE; xb += 8) { - FLOAT8 tmp = GROUP_READ8(input, input_offset + + COMPUTE_FLOAT8 tmp = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -807,10 +807,10 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b2( line_cache[xb + 7] = tmp[7]; } for (; xb + 4 <= INPUT_LINE_SIZE; xb += 4) { - FLOAT4 tmp = GROUP_READ4(input, input_offset + + COMPUTE_FLOAT4 tmp = CONVERT_COMPUTE_FLOAT4(GROUP_READ4(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -818,7 +818,7 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b2( line_cache[xb + 3] = tmp[3]; } for (; xb < INPUT_LINE_SIZE; xb++) { - line_cache[xb] = GROUP_READ(input, input_offset + + line_cache[xb] = (COMPUTE_FLOAT)GROUP_READ(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + xb * input_x_pitch); @@ -836,31 +836,31 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b2( src[i] = line_cache[kw * DILATION_WIDTH + STRIDE_WIDTH * i]; #endif } - FLOAT8 weight0 = GROUP_READ8(weights, filter_offset + + COMPUTE_FLOAT8 weight0 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + - kw * filter_x_pitch); - FLOAT8 weight1 = GROUP_READ8(weights, filter_offset + + kw * filter_x_pitch)); + COMPUTE_FLOAT8 weight1 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + kw * filter_x_pitch + - 8 * filter_isv_pitch); - const FLOAT2 src0 = GROUP_SHUFFLE2(src, 0); - const FLOAT2 src1 = GROUP_SHUFFLE2(src, 1); - const FLOAT2 src2 = GROUP_SHUFFLE2(src, 2); - const FLOAT2 src3 = GROUP_SHUFFLE2(src, 3); - const FLOAT2 src4 = GROUP_SHUFFLE2(src, 4); - const FLOAT2 src5 = GROUP_SHUFFLE2(src, 5); - const FLOAT2 src6 = GROUP_SHUFFLE2(src, 6); - const FLOAT2 src7 = GROUP_SHUFFLE2(src, 7); - const FLOAT2 src8 = GROUP_SHUFFLE2(src, 8); - const FLOAT2 src9 = GROUP_SHUFFLE2(src, 9); - const FLOAT2 src10 = GROUP_SHUFFLE2(src, 10); - const FLOAT2 src11 = GROUP_SHUFFLE2(src, 11); - const FLOAT2 src12 = GROUP_SHUFFLE2(src, 12); - const FLOAT2 src13 = GROUP_SHUFFLE2(src, 13); - const FLOAT2 src14 = GROUP_SHUFFLE2(src, 14); - const FLOAT2 src15 = GROUP_SHUFFLE2(src, 15); + 8 * filter_isv_pitch)); + const COMPUTE_FLOAT2 src0 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 0)); + const COMPUTE_FLOAT2 src1 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 1)); + const COMPUTE_FLOAT2 src2 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 2)); + const COMPUTE_FLOAT2 src3 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 3)); + const COMPUTE_FLOAT2 src4 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 4)); + const COMPUTE_FLOAT2 src5 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 5)); + const COMPUTE_FLOAT2 src6 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 6)); + const COMPUTE_FLOAT2 src7 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 7)); + const COMPUTE_FLOAT2 src8 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 8)); + const COMPUTE_FLOAT2 src9 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 9)); + const COMPUTE_FLOAT2 src10 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 10)); + const COMPUTE_FLOAT2 src11 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 11)); + const COMPUTE_FLOAT2 src12 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 12)); + const COMPUTE_FLOAT2 src13 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 13)); + const COMPUTE_FLOAT2 src14 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 14)); + const COMPUTE_FLOAT2 src15 = CONVERT_COMPUTE_FLOAT2(GROUP_SHUFFLE2(src, 15)); dst = mad(weight0.s0, src0, dst); dst = mad(weight0.s1, src1, dst); @@ -903,26 +903,26 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b2( #endif #ifdef RELU - dst = fmax(dst, (FLOAT2)0); + dst = fmax(dst, (COMPUTE_FLOAT2)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT2)0, (FLOAT2)6); + dst = clamp(dst, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); #endif if ((feature_block+1)*16 >= output_channel) { for (int i = 0; i < 2; i++) { if ((feature_block*16 + sglid < output_channel) && (x + i) < output_width) - output[output_offset + i * output_x_pitch + sglid] = dst[i]; + output[output_offset + i * output_x_pitch + sglid] = (FLOAT)dst[i]; } } else { if (x + 2 <= output_width || output_width % 2 == 0) { - GROUP_WRITE2(output, output_offset, dst); + GROUP_WRITE2(output, output_offset, CONVERT_FLOAT2(dst)); }else{ for (int i = 0; i < output_width % 2; i++) { - output[output_offset + i * output_x_pitch + sglid] = dst[i]; + output[output_offset + i * output_x_pitch + sglid] = (FLOAT)dst[i]; } } } @@ -994,19 +994,19 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b4( const uint filter_offset = feature_block * filter_os_pitch; #if SLM_DIV_FACTOR == 1 - FLOAT4 dst = (FLOAT4)(GROUP_READ(biases, feature_block * 16)); + COMPUTE_FLOAT4 dst = (COMPUTE_FLOAT4)(GROUP_READ(biases, feature_block * 16)); #else - FLOAT4 dst; + COMPUTE_FLOAT4 dst; if (feature_sub_block == 0) { - dst = (FLOAT4)(GROUP_READ(biases, feature_block * 16)); + dst = (COMPUTE_FLOAT4)(GROUP_READ(biases, feature_block * 16)); } else { - dst = (FLOAT4)0; + dst = (COMPUTE_FLOAT4)0; } #endif #if SLM_DIV_FACTOR > 1 - __local FLOAT4 sum[WORK_GROUP_SIZE]; + __local COMPUTE_FLOAT4 sum[WORK_GROUP_SIZE]; #endif @@ -1025,10 +1025,10 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b4( { int xb = 0; for (; xb + 8 <= INPUT_LINE_SIZE; xb += 8) { - FLOAT8 tmp = GROUP_READ8(input, input_offset + + COMPUTE_FLOAT8 tmp = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -1040,10 +1040,10 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b4( line_cache[xb + 7] = tmp[7]; } for (; xb + 4 <= INPUT_LINE_SIZE; xb += 4) { - FLOAT4 tmp = GROUP_READ4(input, input_offset + + COMPUTE_FLOAT4 tmp = CONVERT_COMPUTE_FLOAT4(GROUP_READ4(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -1051,7 +1051,7 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b4( line_cache[xb + 3] = tmp[3]; } for (; xb < INPUT_LINE_SIZE; xb++) { - line_cache[xb] = GROUP_READ(input, input_offset + + line_cache[xb] = (COMPUTE_FLOAT)GROUP_READ(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + xb * input_x_pitch); @@ -1069,31 +1069,31 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b4( src[i] = line_cache[kw * DILATION_WIDTH + STRIDE_WIDTH * i]; #endif } - FLOAT8 weight0 = GROUP_READ8(weights, filter_offset + + COMPUTE_FLOAT8 weight0 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + - kw * filter_x_pitch); - FLOAT8 weight1 = GROUP_READ8(weights, filter_offset + + kw * filter_x_pitch)); + COMPUTE_FLOAT8 weight1 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + kw * filter_x_pitch + - 8 * filter_isv_pitch); - const FLOAT4 src0 = GROUP_SHUFFLE4(src, 0); - const FLOAT4 src1 = GROUP_SHUFFLE4(src, 1); - const FLOAT4 src2 = GROUP_SHUFFLE4(src, 2); - const FLOAT4 src3 = GROUP_SHUFFLE4(src, 3); - const FLOAT4 src4 = GROUP_SHUFFLE4(src, 4); - const FLOAT4 src5 = GROUP_SHUFFLE4(src, 5); - const FLOAT4 src6 = GROUP_SHUFFLE4(src, 6); - const FLOAT4 src7 = GROUP_SHUFFLE4(src, 7); - const FLOAT4 src8 = GROUP_SHUFFLE4(src, 8); - const FLOAT4 src9 = GROUP_SHUFFLE4(src, 9); - const FLOAT4 src10 = GROUP_SHUFFLE4(src, 10); - const FLOAT4 src11 = GROUP_SHUFFLE4(src, 11); - const FLOAT4 src12 = GROUP_SHUFFLE4(src, 12); - const FLOAT4 src13 = GROUP_SHUFFLE4(src, 13); - const FLOAT4 src14 = GROUP_SHUFFLE4(src, 14); - const FLOAT4 src15 = GROUP_SHUFFLE4(src, 15); + 8 * filter_isv_pitch)); + const COMPUTE_FLOAT4 src0 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 0)); + const COMPUTE_FLOAT4 src1 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 1)); + const COMPUTE_FLOAT4 src2 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 2)); + const COMPUTE_FLOAT4 src3 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 3)); + const COMPUTE_FLOAT4 src4 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 4)); + const COMPUTE_FLOAT4 src5 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 5)); + const COMPUTE_FLOAT4 src6 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 6)); + const COMPUTE_FLOAT4 src7 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 7)); + const COMPUTE_FLOAT4 src8 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 8)); + const COMPUTE_FLOAT4 src9 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 9)); + const COMPUTE_FLOAT4 src10 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 10)); + const COMPUTE_FLOAT4 src11 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 11)); + const COMPUTE_FLOAT4 src12 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 12)); + const COMPUTE_FLOAT4 src13 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 13)); + const COMPUTE_FLOAT4 src14 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 14)); + const COMPUTE_FLOAT4 src15 = CONVERT_COMPUTE_FLOAT4(GROUP_SHUFFLE4(src, 15)); dst = mad(weight0.s0, src0, dst); dst = mad(weight0.s1, src1, dst); @@ -1136,26 +1136,26 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b4( #endif #ifdef RELU - dst = fmax(dst, (FLOAT4)0); + dst = fmax(dst, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT4)0, (FLOAT4)6); + dst = clamp(dst, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif if ((feature_block+1)*16 >= output_channel) { for (int i = 0; i < 4; i++) { if ((feature_block*16 + sglid < output_channel) && (x + i) < output_width) - output[output_offset + i * output_x_pitch + sglid] = dst[i]; + output[output_offset + i * output_x_pitch + sglid] = (FLOAT)dst[i]; } } else { if (x + 4 <= output_width || output_width % 4 == 0) { - GROUP_WRITE4(output, output_offset, dst); + GROUP_WRITE4(output, output_offset, CONVERT_FLOAT4(dst)); }else{ for (int i = 0; i < output_width % 4; i++) { - output[output_offset + i * output_x_pitch + sglid] = dst[i]; + output[output_offset + i * output_x_pitch + sglid] = (FLOAT)dst[i]; } } } @@ -1227,19 +1227,19 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b8( const uint filter_offset = feature_block * filter_os_pitch; #if SLM_DIV_FACTOR == 1 - FLOAT8 dst = (FLOAT8)(GROUP_READ(biases, feature_block * 16)); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(GROUP_READ(biases, feature_block * 16)); #else - FLOAT8 dst; + COMPUTE_FLOAT8 dst; if (feature_sub_block == 0) { - dst = (FLOAT8)(GROUP_READ(biases, feature_block * 16)); + dst = (COMPUTE_FLOAT8)(GROUP_READ(biases, feature_block * 16)); } else { - dst = (FLOAT8)0; + dst = (COMPUTE_FLOAT8)0; } #endif #if SLM_DIV_FACTOR > 1 - __local FLOAT8 sum[WORK_GROUP_SIZE]; + __local COMPUTE_FLOAT8 sum[WORK_GROUP_SIZE]; #endif @@ -1258,10 +1258,10 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b8( { int xb = 0; for (; xb + 8 <= INPUT_LINE_SIZE; xb += 8) { - FLOAT8 tmp = GROUP_READ8(input, input_offset + + COMPUTE_FLOAT8 tmp = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -1273,10 +1273,10 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b8( line_cache[xb + 7] = tmp[7]; } for (; xb + 4 <= INPUT_LINE_SIZE; xb += 4) { - FLOAT4 tmp = GROUP_READ4(input, input_offset + + COMPUTE_FLOAT4 tmp = CONVERT_COMPUTE_FLOAT4(GROUP_READ4(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + - xb * input_x_pitch); + xb * input_x_pitch)); line_cache[xb + 0] = tmp[0]; line_cache[xb + 1] = tmp[1]; @@ -1284,7 +1284,7 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b8( line_cache[xb + 3] = tmp[3]; } for (; xb < INPUT_LINE_SIZE; xb++) { - line_cache[xb] = GROUP_READ(input, input_offset + + line_cache[xb] = (COMPUTE_FLOAT)GROUP_READ(input, input_offset + icb * input_fs_pitch + kh * DILATION_HEIGHT * input_y_pitch + xb * input_x_pitch); @@ -1302,31 +1302,31 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b8( src[i] = line_cache[kw * DILATION_WIDTH + STRIDE_WIDTH * i]; #endif } - FLOAT8 weight0 = GROUP_READ8(weights, filter_offset + + COMPUTE_FLOAT8 weight0 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + - kw * filter_x_pitch); - FLOAT8 weight1 = GROUP_READ8(weights, filter_offset + + kw * filter_x_pitch)); + COMPUTE_FLOAT8 weight1 = CONVERT_COMPUTE_FLOAT8(GROUP_READ8(weights, filter_offset + icb * filter_is_pitch + kh * filter_y_pitch + kw * filter_x_pitch + - 8 * filter_isv_pitch); - const FLOAT8 src0 = GROUP_SHUFFLE8(src, 0); - const FLOAT8 src1 = GROUP_SHUFFLE8(src, 1); - const FLOAT8 src2 = GROUP_SHUFFLE8(src, 2); - const FLOAT8 src3 = GROUP_SHUFFLE8(src, 3); - const FLOAT8 src4 = GROUP_SHUFFLE8(src, 4); - const FLOAT8 src5 = GROUP_SHUFFLE8(src, 5); - const FLOAT8 src6 = GROUP_SHUFFLE8(src, 6); - const FLOAT8 src7 = GROUP_SHUFFLE8(src, 7); - const FLOAT8 src8 = GROUP_SHUFFLE8(src, 8); - const FLOAT8 src9 = GROUP_SHUFFLE8(src, 9); - const FLOAT8 src10 = GROUP_SHUFFLE8(src, 10); - const FLOAT8 src11 = GROUP_SHUFFLE8(src, 11); - const FLOAT8 src12 = GROUP_SHUFFLE8(src, 12); - const FLOAT8 src13 = GROUP_SHUFFLE8(src, 13); - const FLOAT8 src14 = GROUP_SHUFFLE8(src, 14); - const FLOAT8 src15 = GROUP_SHUFFLE8(src, 15); + 8 * filter_isv_pitch)); + const COMPUTE_FLOAT8 src0 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 0)); + const COMPUTE_FLOAT8 src1 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 1)); + const COMPUTE_FLOAT8 src2 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 2)); + const COMPUTE_FLOAT8 src3 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 3)); + const COMPUTE_FLOAT8 src4 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 4)); + const COMPUTE_FLOAT8 src5 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 5)); + const COMPUTE_FLOAT8 src6 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 6)); + const COMPUTE_FLOAT8 src7 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 7)); + const COMPUTE_FLOAT8 src8 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 8)); + const COMPUTE_FLOAT8 src9 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 9)); + const COMPUTE_FLOAT8 src10 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 10)); + const COMPUTE_FLOAT8 src11 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 11)); + const COMPUTE_FLOAT8 src12 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 12)); + const COMPUTE_FLOAT8 src13 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 13)); + const COMPUTE_FLOAT8 src14 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 14)); + const COMPUTE_FLOAT8 src15 = CONVERT_COMPUTE_FLOAT8(GROUP_SHUFFLE8(src, 15)); dst = mad(weight0.s0, src0, dst); dst = mad(weight0.s1, src1, dst); @@ -1369,11 +1369,11 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b8( #endif #ifdef RELU - dst = fmax(dst, (FLOAT8)0); + dst = fmax(dst, (COMPUTE_FLOAT8)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT8)0, (FLOAT8)6); + dst = clamp(dst, (COMPUTE_FLOAT8)0, (COMPUTE_FLOAT8)6); #endif @@ -1381,16 +1381,16 @@ __kernel void conv_2d_buf_subgroup_c16_c16_b8( if ((feature_block+1)*16 >= output_channel) { for (int i = 0; i < 8; i++) { if ((feature_block*16 + sglid < output_channel) && (x + i) < output_width) - output[output_offset + i * output_x_pitch + sglid] = dst[i]; + output[output_offset + i * output_x_pitch + sglid] = (FLOAT)dst[i]; } } else { if (x + 8 <= output_width || output_width % 8 == 0) { - GROUP_WRITE8(output, output_offset, dst); + GROUP_WRITE8(output, output_offset, CONVERT_FLOAT8(dst)); }else{ for (int i = 0; i < output_width % 8; i++) { - output[output_offset + i * output_x_pitch + sglid] = dst[i]; + output[output_offset + i * output_x_pitch + sglid] = (FLOAT)dst[i]; } } } diff --git a/source/backend/opencl/execution/cl/conv_2d_c1_subgroup_buf.cl b/source/backend/opencl/execution/cl/conv_2d_c1_subgroup_buf.cl index c32f04b46..6e4f81324 100644 --- a/source/backend/opencl/execution/cl/conv_2d_c1_subgroup_buf.cl +++ b/source/backend/opencl/execution/cl/conv_2d_c1_subgroup_buf.cl @@ -97,7 +97,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b2( const uint filter_offset = f_block * filter_os_pitch; uint bias_offset = f_block * 16; - FLOAT2 dst = (FLOAT2)(GROUP_READ(biases, bias_offset)); + COMPUTE_FLOAT2 dst = (COMPUTE_FLOAT2)(GROUP_READ(biases, bias_offset)); FLOAT line_cache[INPUT_CHANNEL * INPUT_BLOCK_SIZE]; for (int ic = 0; ic < INPUT_CHANNEL; ic++) @@ -127,7 +127,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b2( { uint offset = filter_offset + kh * filter_y_pitch + kw * filter_x_pitch; - FLOAT wei[INPUT_CHANNEL]; + COMPUTE_FLOAT wei[INPUT_CHANNEL]; __attribute__((opencl_unroll_hint(INPUT_CHANNEL))) for (int ic = 0; ic < INPUT_CHANNEL; ic++) wei[ic] = GROUP_READ(weights, offset + ic * filter_isv_pitch); @@ -139,7 +139,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b2( const uint buf_group = (kw*DILATION_WIDTH + STRIDE_WIDTH * i + (kh*DILATION_HEIGHT) * INPUT_LINE_SIZE) % 16; for (int ic = 0; ic < INPUT_CHANNEL; ic++) { - FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); + COMPUTE_FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); dst[i] = mad(wei[ic], src, dst[i]); } } @@ -147,11 +147,11 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b2( } #ifdef RELU - dst = fmax(dst, (FLOAT2)0); + dst = fmax(dst, (COMPUTE_FLOAT2)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT2)0, (FLOAT2)6); + dst = clamp(dst, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); #endif const uint lid_x = lid % 4; @@ -160,13 +160,13 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b2( if ((f_block+1)*16 >= output_channel) { for (int i = 0; i < 2 && (x + i) < output_width; i++) { if ((f_block*16 + lid_y * 4 < output_pack * 4)) - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } else { for (int i = 0; i < 2 && (x + i) < output_width; i++) { - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } } @@ -234,7 +234,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b4( const uint filter_offset = f_block * filter_os_pitch; uint bias_offset = f_block * 16; - FLOAT4 dst = (FLOAT4)(GROUP_READ(biases, bias_offset)); + COMPUTE_FLOAT4 dst = (COMPUTE_FLOAT4)(GROUP_READ(biases, bias_offset)); FLOAT line_cache[INPUT_CHANNEL * INPUT_BLOCK_SIZE]; for (int ic = 0; ic < INPUT_CHANNEL; ic++) @@ -264,7 +264,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b4( { uint offset = filter_offset + kh * filter_y_pitch + kw * filter_x_pitch; - FLOAT wei[INPUT_CHANNEL]; + COMPUTE_FLOAT wei[INPUT_CHANNEL]; __attribute__((opencl_unroll_hint(INPUT_CHANNEL))) for (int ic = 0; ic < INPUT_CHANNEL; ic++) wei[ic] = GROUP_READ(weights, offset + ic * filter_isv_pitch); @@ -276,7 +276,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b4( const uint buf_group = (kw*DILATION_WIDTH + STRIDE_WIDTH * i + (kh*DILATION_HEIGHT) * INPUT_LINE_SIZE) % 16; for (int ic = 0; ic < INPUT_CHANNEL; ic++) { - FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); + COMPUTE_FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); dst[i] = mad(wei[ic], src, dst[i]); } } @@ -284,11 +284,11 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b4( } #ifdef RELU - dst = fmax(dst, (FLOAT4)0); + dst = fmax(dst, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT4)0, (FLOAT4)6); + dst = clamp(dst, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const uint lid_x = lid % 4; @@ -297,13 +297,13 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b4( if ((f_block+1)*16 >= output_channel) { for (int i = 0; i < 4 && (x + i) < output_width; i++) { if ((f_block*16 + lid_y * 4 < output_pack * 4)) - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } else { for (int i = 0; i < 4 && (x + i) < output_width; i++) { - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } } @@ -371,7 +371,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b8( const uint filter_offset = f_block * filter_os_pitch; uint bias_offset = f_block * 16; - FLOAT8 dst = (FLOAT8)(GROUP_READ(biases, bias_offset)); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(GROUP_READ(biases, bias_offset)); FLOAT line_cache[INPUT_CHANNEL * INPUT_BLOCK_SIZE]; for (int ic = 0; ic < INPUT_CHANNEL; ic++) @@ -401,7 +401,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b8( { uint offset = filter_offset + kh * filter_y_pitch + kw * filter_x_pitch; - FLOAT wei[INPUT_CHANNEL]; + COMPUTE_FLOAT wei[INPUT_CHANNEL]; __attribute__((opencl_unroll_hint(INPUT_CHANNEL))) for (int ic = 0; ic < INPUT_CHANNEL; ic++) wei[ic] = GROUP_READ(weights, offset + ic * filter_isv_pitch); @@ -413,7 +413,7 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b8( const uint buf_group = (kw*DILATION_WIDTH + STRIDE_WIDTH * i + (kh*DILATION_HEIGHT) * INPUT_LINE_SIZE) % 16; for (int ic = 0; ic < INPUT_CHANNEL; ic++) { - FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); + COMPUTE_FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); dst[i] = mad(wei[ic], src, dst[i]); } } @@ -421,11 +421,11 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b8( } #ifdef RELU - dst = fmax(dst, (FLOAT8)0); + dst = fmax(dst, (COMPUTE_FLOAT8)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT8)0, (FLOAT8)6); + dst = clamp(dst, (COMPUTE_FLOAT8)0, (COMPUTE_FLOAT8)6); #endif const uint lid_x = lid % 4; @@ -434,13 +434,13 @@ __kernel void conv_2d_buf_subgroup_c1_c4_b8( if ((f_block+1)*16 >= output_channel) { for (int i = 0; i < 8 && (x + i) < output_width; i++) { if ((f_block*16 + lid_y * 4 < output_pack * 4)) - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } else { for (int i = 0; i < 8 && (x + i) < output_width; i++) { - output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = dst[i]; + output[output_offset + lid_y * output_fs_pitch + i * output_x_pitch + lid_x] = (FLOAT)dst[i]; } } } @@ -507,7 +507,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b2( const uint filter_offset = f_block * filter_os_pitch; uint bias_offset = f_block * 16; - FLOAT2 dst = (FLOAT2)(GROUP_READ(biases, bias_offset)); + COMPUTE_FLOAT2 dst = (COMPUTE_FLOAT2)(GROUP_READ(biases, bias_offset)); FLOAT line_cache[INPUT_CHANNEL * INPUT_BLOCK_SIZE]; for (int ic = 0; ic < INPUT_CHANNEL; ic++) @@ -537,7 +537,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b2( { uint offset = filter_offset + kh * filter_y_pitch + kw * filter_x_pitch; - FLOAT wei[INPUT_CHANNEL]; + COMPUTE_FLOAT wei[INPUT_CHANNEL]; __attribute__((opencl_unroll_hint(INPUT_CHANNEL))) for (int ic = 0; ic < INPUT_CHANNEL; ic++) wei[ic] = GROUP_READ(weights, offset + ic * filter_isv_pitch); @@ -549,7 +549,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b2( const uint buf_group = (kw*DILATION_WIDTH + STRIDE_WIDTH * i + (kh*DILATION_HEIGHT) * INPUT_LINE_SIZE) % 16; for (int ic = 0; ic < INPUT_CHANNEL; ic++) { - FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); + COMPUTE_FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); dst[i] = mad(wei[ic], src, dst[i]); } } @@ -557,11 +557,11 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b2( } #ifdef RELU - dst = fmax(dst, (FLOAT2)0); + dst = fmax(dst, (COMPUTE_FLOAT2)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT2)0, (FLOAT2)6); + dst = clamp(dst, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); #endif if(x == 0){ @@ -579,16 +579,16 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b2( if ((f_block+1)*16 >= output_channel) { for (int i = 0; i < 2; i++) { if ((f_block*16 + lid < output_channel) && (x + i) < output_width) - output[output_offset + i * output_x_pitch + lid] = dst[i]; + output[output_offset + i * output_x_pitch + lid] = (FLOAT)dst[i]; } } else { if (x + 2 <= output_width || output_width % 2 == 0) { - GROUP_WRITE2(output, output_offset, dst); + GROUP_WRITE2(output, output_offset, CONVERT_FLOAT2(dst)); }else{ for (int i = 0; i < output_width % 2; i++) { - output[output_offset + i * output_x_pitch + lid] = dst[i]; + output[output_offset + i * output_x_pitch + lid] = (FLOAT)dst[i]; } } } @@ -656,7 +656,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b4( const uint filter_offset = f_block * filter_os_pitch; uint bias_offset = f_block * 16; - FLOAT4 dst = (FLOAT4)(GROUP_READ(biases, bias_offset)); + COMPUTE_FLOAT4 dst = (COMPUTE_FLOAT4)(GROUP_READ(biases, bias_offset)); FLOAT line_cache[INPUT_CHANNEL * INPUT_BLOCK_SIZE]; for (int ic = 0; ic < INPUT_CHANNEL; ic++) @@ -686,7 +686,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b4( { uint offset = filter_offset + kh * filter_y_pitch + kw * filter_x_pitch; - FLOAT wei[INPUT_CHANNEL]; + COMPUTE_FLOAT wei[INPUT_CHANNEL]; __attribute__((opencl_unroll_hint(INPUT_CHANNEL))) for (int ic = 0; ic < INPUT_CHANNEL; ic++) wei[ic] = GROUP_READ(weights, offset + ic * filter_isv_pitch); @@ -698,7 +698,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b4( const uint buf_group = (kw*DILATION_WIDTH + STRIDE_WIDTH * i + (kh*DILATION_HEIGHT) * INPUT_LINE_SIZE) % 16; for (int ic = 0; ic < INPUT_CHANNEL; ic++) { - FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); + COMPUTE_FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); dst[i] = mad(wei[ic], src, dst[i]); } } @@ -706,11 +706,11 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b4( } #ifdef RELU - dst = fmax(dst, (FLOAT4)0); + dst = fmax(dst, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT4)0, (FLOAT4)6); + dst = clamp(dst, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif if(x == 0){ @@ -728,16 +728,16 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b4( if ((f_block+1)*16 >= output_channel) { for (int i = 0; i < 4; i++) { if ((f_block*16 + lid < output_channel) && (x + i) < output_width) - output[output_offset + i * output_x_pitch + lid] = dst[i]; + output[output_offset + i * output_x_pitch + lid] = (FLOAT)dst[i]; } } else { if (x + 4 <= output_width || output_width % 4 == 0) { - GROUP_WRITE4(output, output_offset, dst); + GROUP_WRITE4(output, output_offset, CONVERT_FLOAT4(dst)); }else{ for (int i = 0; i < output_width % 4; i++) { - output[output_offset + i * output_x_pitch + lid] = dst[i]; + output[output_offset + i * output_x_pitch + lid] = (FLOAT)dst[i]; } } } @@ -805,7 +805,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b8( const uint filter_offset = f_block * filter_os_pitch; uint bias_offset = f_block * 16; - FLOAT8 dst = (FLOAT8)(GROUP_READ(biases, bias_offset)); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(GROUP_READ(biases, bias_offset)); FLOAT line_cache[INPUT_CHANNEL * INPUT_BLOCK_SIZE]; for (int ic = 0; ic < INPUT_CHANNEL; ic++) @@ -835,7 +835,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b8( { uint offset = filter_offset + kh * filter_y_pitch + kw * filter_x_pitch; - FLOAT wei[INPUT_CHANNEL]; + COMPUTE_FLOAT wei[INPUT_CHANNEL]; __attribute__((opencl_unroll_hint(INPUT_CHANNEL))) for (int ic = 0; ic < INPUT_CHANNEL; ic++) wei[ic] = GROUP_READ(weights, offset + ic * filter_isv_pitch); @@ -847,7 +847,7 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b8( const uint buf_group = (kw*DILATION_WIDTH + STRIDE_WIDTH * i + (kh*DILATION_HEIGHT) * INPUT_LINE_SIZE) % 16; for (int ic = 0; ic < INPUT_CHANNEL; ic++) { - FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); + COMPUTE_FLOAT src = GROUP_SHUFFLE(line_cache[ic * INPUT_BLOCK_SIZE + buf_offset], buf_group); dst[i] = mad(wei[ic], src, dst[i]); } } @@ -855,11 +855,11 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b8( } #ifdef RELU - dst = fmax(dst, (FLOAT8)0); + dst = fmax(dst, (COMPUTE_FLOAT8)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT8)0, (FLOAT8)6); + dst = clamp(dst, (COMPUTE_FLOAT8)0, (COMPUTE_FLOAT8)6); #endif if(x == 0){ @@ -877,16 +877,16 @@ __kernel void conv_2d_buf_subgroup_c1_c16_b8( if ((f_block+1)*16 >= output_channel) { for (int i = 0; i < 8; i++) { if ((f_block*16 + lid < output_channel) && (x + i) < output_width) - output[output_offset + i * output_x_pitch + lid] = dst[i]; + output[output_offset + i * output_x_pitch + lid] = (FLOAT)dst[i]; } } else { if (x + 8 <= output_width || output_width % 8 == 0) { - GROUP_WRITE8(output, output_offset, dst); + GROUP_WRITE8(output, output_offset, CONVERT_FLOAT8(dst)); }else{ for (int i = 0; i < output_width % 8; i++) { - output[output_offset + i * output_x_pitch + lid] = dst[i]; + output[output_offset + i * output_x_pitch + lid] = (FLOAT)dst[i]; } } } diff --git a/source/backend/opencl/execution/cl/conv_2d_int_buf.cl b/source/backend/opencl/execution/cl/conv_2d_int_buf.cl index 1f5e7d0a6..c2342b6d4 100644 --- a/source/backend/opencl/execution/cl/conv_2d_int_buf.cl +++ b/source/backend/opencl/execution/cl/conv_2d_int_buf.cl @@ -1,19 +1,6 @@ #ifdef MNN_SUPPORT_FP16 #pragma OPENCL EXTENSION cl_khr_fp16 : enable #endif -#define READ_INPUT_IMAGE(i, base) \ - int in_width_value##i = in_width##i + base; \ - in_width_value##i = \ - select(in_idx + in_width_value##i, -1, (in_width_value##i < 0 || in_width_value##i >= input_shape.y)); \ - in_w_idx = in_width_value##i % input_shape.y; \ - inp_offset = (((in_b_idx*in_channel_block_length + in_channel_block_idx)*input_shape.x + in_h_idx)* input_shape.y + in_w_idx)*4; \ - in##i = (in_width_value##i)==-1 ? (FLOAT4)0 : vload4(0, input+inp_offset); - -#define CALCULATE_OUTPUT(i) \ - out##i = mad(in##i.x, weights0, out##i); \ - out##i = mad(in##i.y, weights1, out##i); \ - out##i = mad(in##i.z, weights2, out##i); \ - out##i = mad(in##i.w, weights3, out##i); #define GLOBAL_SIZE_2_DIMS __private const int global_size_dim0, __private const int global_size_dim1, @@ -33,8 +20,8 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, + __global const float *dequantScale, + __global const float *dequantOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -58,11 +45,12 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - const FLOAT4 dequantScaleC4 = vload4(out_c_idx, dequantScale); - const FLOAT4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); - - FLOAT4 out0 = vload4(out_c_idx, bias); + const float4 dequantScaleC4 = vload4(out_c_idx, dequantScale); + const float4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); + COMPUTE_FLOAT4 vbias = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out0 = (COMPUTE_FLOAT)0; + COMPUTE_FLOAT sum = (COMPUTE_FLOAT)0; const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_h_idx_base = mad24(out_h_idx, stride_hw.x, -pad_hw.x); @@ -83,7 +71,7 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS for(int iy = in_h_idx_start; iy < in_h_idx_end; iy += dilate_hw.x) { for(int ix = in_w_idx_start; ix < in_w_idx_end; ix += dilate_hw.y) { int inp_offset = (((out_b_idx * in_c_blocks + in_c_idx) * in_hw.x + iy) * in_hw.y + ix) * 4; - FLOAT4 in0 = vload4(0, input+inp_offset); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); const int filter_w_inc = (ix-in_w_idx_start)/dilate_hw.y; @@ -92,10 +80,10 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS char4 charWeight1 = vload4(filter_w_inc, weight+weight_offset+weight_oc_offset); char4 charWeight2 = vload4(filter_w_inc, weight+weight_offset+weight_oc_offset*2); char4 charWeight3 = vload4(filter_w_inc, weight+weight_offset+weight_oc_offset*3); - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else uchar2 charWeightInt40 = vload2(filter_w_inc, weight+weight_offset/2); uchar2 charWeightInt41 = vload2(filter_w_inc, weight+weight_offset/2+weight_oc_offset/2); @@ -121,12 +109,12 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif - + sum += (in0.x + in0.y + in0.z + in0.w); out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); out0 = mad(in0.z, weight2, out0); @@ -136,16 +124,19 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS weight_offset += 4*filter_hw.y; } } + + out0 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out0) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum) * dequantOffsetC4); + #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } @@ -157,8 +148,8 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, + __global const float *dequantScale, + __global const float *dequantOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -182,11 +173,14 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - const FLOAT4 dequantScaleC4 = vload4(out_c_idx, dequantScale); - const FLOAT4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); + const float4 dequantScaleC4 = vload4(out_c_idx, dequantScale); + const float4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; + COMPUTE_FLOAT4 vbias = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out0 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT sum0 = 0; + COMPUTE_FLOAT sum1 = 0; const int in_w0_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_w1_idx_base = in_w0_idx_base + stride_hw.y; @@ -210,18 +204,18 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS const int in_w0_idx = fw * dilate_hw.y + in_w0_idx_base; const int in_w1_idx = fw * dilate_hw.y + in_w1_idx_base; - FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base); - FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4((in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4((in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base)); #if (defined USE_LOW_BIT_WEIGHT_INT8) char4 charWeight0 = vload4(0, weight+weight_offset); char4 charWeight1 = vload4(0, weight+weight_offset+weight_oc_offset); char4 charWeight2 = vload4(0, weight+weight_offset+weight_oc_offset*2); char4 charWeight3 = vload4(0, weight+weight_offset+weight_oc_offset*3); - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else uchar2 charWeightInt40 = vload2(0, weight+weight_offset/2); uchar2 charWeightInt41 = vload2(0, weight+weight_offset/2+weight_oc_offset/2); @@ -247,12 +241,15 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif + sum0 += (in0.x + in0.y + in0.z + in0.w); + sum1 += (in1.x + in1.y + in1.z + in1.w); + out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); out0 = mad(in0.z, weight2, out0); @@ -267,23 +264,27 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS } } } + + out0 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out0) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC4); + out1 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out1) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC4); + #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); if(out_w_idx + 1 >= out_hw.y) return; - vstore4(out1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), 1, output+out_offset); #else - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); #endif } @@ -295,8 +296,8 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, + __global const float *dequantScale, + __global const float *dequantOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -320,13 +321,18 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - const FLOAT4 dequantScaleC4 = vload4(out_c_idx, dequantScale); - const FLOAT4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); - - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + const float4 dequantScaleC4 = vload4(out_c_idx, dequantScale); + const float4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); + + COMPUTE_FLOAT4 vbias = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out0 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; + COMPUTE_FLOAT sum0 = 0; + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT sum2 = 0; + COMPUTE_FLOAT sum3 = 0; const int in_w0_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_w1_idx_base = in_w0_idx_base + stride_hw.y; @@ -354,20 +360,20 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS const int in_w2_idx = fw * dilate_hw.y + in_w2_idx_base; const int in_w3_idx = fw * dilate_hw.y + in_w3_idx_base; - FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base); - FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base); - FLOAT4 in2 = (in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w2_idx, input+inp_offset_base); - FLOAT4 in3 = (in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w3_idx, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4((in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4((in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = CONVERT_COMPUTE_FLOAT4((in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w2_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = CONVERT_COMPUTE_FLOAT4((in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w3_idx, input+inp_offset_base)); #if (defined USE_LOW_BIT_WEIGHT_INT8) char4 charWeight0 = vload4(0, weight+weight_offset); char4 charWeight1 = vload4(0, weight+weight_offset+weight_oc_offset); char4 charWeight2 = vload4(0, weight+weight_offset+weight_oc_offset*2); char4 charWeight3 = vload4(0, weight+weight_offset+weight_oc_offset*3); - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else uchar2 charWeightInt40 = vload2(0, weight+weight_offset/2); uchar2 charWeightInt41 = vload2(0, weight+weight_offset/2+weight_oc_offset/2); @@ -393,12 +399,17 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif + sum0 += (in0.x + in0.y + in0.z + in0.w); + sum1 += (in1.x + in1.y + in1.z + in1.w); + sum2 += (in2.x + in2.y + in2.z + in2.w); + sum3 += (in3.x + in3.y + in3.z + in3.w); + out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); out0 = mad(in0.z, weight2, out0); @@ -423,18 +434,22 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS } } } + out0 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out0) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC4); + out1 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out1) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC4); + out2 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out2) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum2) * dequantOffsetC4); + out3 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out3) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum3) * dequantOffsetC4); #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; @@ -442,17 +457,17 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS const int remain = out_hw.y - out_w_idx; if (remain >= 4) { - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); }else if(remain == 3){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); - vstore4(out2, 2, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2, output+out_offset); }else if(remain == 2){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #else - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); #endif } @@ -464,8 +479,8 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, + __global const float *dequantScale, + __global const float *dequantOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -489,13 +504,18 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_h_blocks;//equal to in_b_idx const int out_h_idx = (out_b_h_idx % out_h_blocks) << 2; - const FLOAT4 dequantScaleC4 = vload4(out_c_idx, dequantScale); - const FLOAT4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); + const float4 dequantScaleC4 = vload4(out_c_idx, dequantScale); + const float4 dequantOffsetC4 = vload4(out_c_idx, dequantOffset); - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + COMPUTE_FLOAT4 vbias = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out0 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; + COMPUTE_FLOAT sum0 = 0; + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT sum2 = 0; + COMPUTE_FLOAT sum3 = 0; const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); @@ -523,20 +543,20 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS const int in_h3_idx = (iy * dilate_hw.x + in_h3_idx_base) * in_hw.y; for(int fw = in_w_idx_start; fw < in_w_idx_end; fw += dilate_hw.y) { - FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base); - FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base); - FLOAT4 in2 = (in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h2_idx + fw, input+inp_offset_base); - FLOAT4 in3 = (in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h3_idx + fw, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4((in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4((in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = CONVERT_COMPUTE_FLOAT4((in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h2_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = CONVERT_COMPUTE_FLOAT4((in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h3_idx + fw, input+inp_offset_base)); #if (defined USE_LOW_BIT_WEIGHT_INT8) char4 charWeight0 = vload4(0, weight+weight_offset); char4 charWeight1 = vload4(0, weight+weight_offset+weight_oc_offset); char4 charWeight2 = vload4(0, weight+weight_offset+weight_oc_offset*2); char4 charWeight3 = vload4(0, weight+weight_offset+weight_oc_offset*3); - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else uchar2 charWeightInt40 = vload2(0, weight+weight_offset/2); uchar2 charWeightInt41 = vload2(0, weight+weight_offset/2+weight_oc_offset/2); @@ -562,11 +582,17 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC4, dequantOffsetC4); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC4, dequantOffsetC4); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif + + sum0 += (in0.x + in0.y + in0.z + in0.w); + sum1 += (in1.x + in1.y + in1.z + in1.w); + sum2 += (in2.x + in2.y + in2.z + in2.w); + sum3 += (in3.x + in3.y + in3.z + in3.w); + out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); out0 = mad(in0.z, weight2, out0); @@ -591,43 +617,49 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS } } } + + out0 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out0) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC4); + out1 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out1) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC4); + out2 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out2) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum2) * dequantOffsetC4); + out3 = vbias + CONVERT_COMPUTE_FLOAT4(convert_float4(out3) * dequantScaleC4 + convert_float4((COMPUTE_FLOAT4)sum3) * dequantOffsetC4); + #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.x - out_h_idx; if(remain >= 4){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); }else if(remain == 3){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); }else if(remain == 2){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #else - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); #endif } @@ -639,8 +671,8 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, + __global const float *dequantScale, + __global const float *dequantOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -664,19 +696,25 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_h_blocks;//equal to in_b_idx const int out_h_idx = (out_b_h_idx % out_h_blocks) << 2; - const FLOAT4 dequantScaleC03 = vload4(out_c_idx, dequantScale); - const FLOAT4 dequantOffsetC03 = vload4(out_c_idx, dequantOffset); - const FLOAT4 dequantScaleC47 = vload4(out_c_idx + 1, dequantScale); - const FLOAT4 dequantOffsetC47 = vload4(out_c_idx + 1, dequantOffset); + const float4 dequantScaleC03 = vload4(out_c_idx, dequantScale); + const float4 dequantOffsetC03 = vload4(out_c_idx, dequantOffset); + const float4 dequantScaleC47 = vload4(out_c_idx + 1, dequantScale); + const float4 dequantOffsetC47 = vload4(out_c_idx + 1, dequantOffset); - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; - FLOAT4 out4 = vload4(out_c_idx + 1, bias); - FLOAT4 out5 = out4; - FLOAT4 out6 = out4; - FLOAT4 out7 = out4; + COMPUTE_FLOAT4 vbias0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out0 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; + COMPUTE_FLOAT4 vbias1 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx + 1, bias)); + COMPUTE_FLOAT4 out4 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out5 = out0; + COMPUTE_FLOAT4 out6 = out0; + COMPUTE_FLOAT4 out7 = out0; + COMPUTE_FLOAT sum0 = 0; + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT sum2 = 0; + COMPUTE_FLOAT sum3 = 0; const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); @@ -705,20 +743,20 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS const int in_h3_idx = (iy * dilate_hw.x + in_h3_idx_base) * in_hw.y; for(int fw = in_w_idx_start; fw < in_w_idx_end; fw += dilate_hw.y) { - FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base); - FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base); - FLOAT4 in2 = (in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h2_idx + fw, input+inp_offset_base); - FLOAT4 in3 = (in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h3_idx + fw, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4((in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4((in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = CONVERT_COMPUTE_FLOAT4((in_h2_idx < 0 || in_h2_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h2_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = CONVERT_COMPUTE_FLOAT4((in_h3_idx < 0 || in_h3_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h3_idx + fw, input+inp_offset_base)); #if (defined USE_LOW_BIT_WEIGHT_INT8) char4 charWeight0 = vload4(0, weight+weight_offset); char4 charWeight1 = vload4(0, weight+weight_offset+weight_ic_offset); char4 charWeight2 = vload4(0, weight+weight_offset+weight_ic_offset*2); char4 charWeight3 = vload4(0, weight+weight_offset+weight_ic_offset*3); - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC03, dequantOffsetC03); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else uchar2 charWeightInt40 = vload2(0, weight+weight_offset/2); uchar2 charWeightInt41 = vload2(0, weight+weight_offset/2+weight_ic_offset/2); @@ -744,12 +782,17 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC03, dequantOffsetC03); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif + sum0 += (in0.x + in0.y + in0.z + in0.w); + sum1 += (in1.x + in1.y + in1.z + in1.w); + sum2 += (in2.x + in2.y + in2.z + in2.w); + sum3 += (in3.x + in3.y + in3.z + in3.w); + out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); out0 = mad(in0.z, weight2, out0); @@ -775,10 +818,10 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS charWeight1 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset); charWeight2 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2); charWeight3 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3); - weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC47, dequantOffsetC47); - weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC47, dequantOffsetC47); - weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC47, dequantOffsetC47); - weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC47, dequantOffsetC47); + weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else charWeightInt40 = vload2(0, weight+weight_offset/2+weight_oc_offset/2); charWeightInt41 = vload2(0, weight+weight_offset/2+weight_oc_offset/2+weight_ic_offset/2); @@ -804,10 +847,10 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC47, dequantOffsetC47); - weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC47, dequantOffsetC47); - weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC47, dequantOffsetC47); - weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC47, dequantOffsetC47); + weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif out4 = mad(in0.x, weight0, out4); @@ -834,45 +877,55 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS } } } + + out0 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out0) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC03); + out1 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out1) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC03); + out2 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out2) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum2) * dequantOffsetC03); + out3 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out3) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum3) * dequantOffsetC03); + out4 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out4) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC47); + out5 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out5) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC47); + out6 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out6) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum2) * dequantOffsetC47); + out7 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out7) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum3) * dequantOffsetC47); + #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); - out4 = fmax(out4, (FLOAT4)0); - out5 = fmax(out5, (FLOAT4)0); - out6 = fmax(out6, (FLOAT4)0); - out7 = fmax(out7, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); + out4 = fmax(out4, (COMPUTE_FLOAT4)0); + out5 = fmax(out5, (COMPUTE_FLOAT4)0); + out6 = fmax(out6, (COMPUTE_FLOAT4)0); + out7 = fmax(out7, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); - out4 = clamp(out4, (FLOAT4)0, (FLOAT4)6); - out5 = clamp(out5, (FLOAT4)0, (FLOAT4)6); - out6 = clamp(out6, (FLOAT4)0, (FLOAT4)6); - out7 = clamp(out7, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out4 = clamp(out4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out5 = clamp(out5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out6 = clamp(out6, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out7 = clamp(out7, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.x - out_h_idx; if(remain >= 4){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); }else if(remain == 3){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); }else if(remain == 2){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ @@ -881,35 +934,35 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; if(remain >= 4){ - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); - vstore4(out6, 2 * out_hw.y, output+out_offset); - vstore4(out7, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out7), 3 * out_hw.y, output+out_offset); }else if(remain == 3){ - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); - vstore4(out6, 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2 * out_hw.y, output+out_offset); }else if(remain == 2){ - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out4, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); } #else - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); - vstore4(out2, 2 * out_hw.y, output+out_offset); - vstore4(out3, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), 3 * out_hw.y, output+out_offset); #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ return; } #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore4(out4, 0, output+out_offset); - vstore4(out5, out_hw.y, output+out_offset); - vstore4(out6, 2 * out_hw.y, output+out_offset); - vstore4(out7, 3 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out5), out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2 * out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out7), 3 * out_hw.y, output+out_offset); #endif } @@ -921,8 +974,8 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, + __global const float *dequantScale, + __global const float *dequantOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -946,15 +999,19 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_h_blocks;//equal to in_b_idx const int out_h_idx = (out_b_h_idx % out_h_blocks) << 1; - const FLOAT4 dequantScaleC03 = vload4(out_c_idx, dequantScale); - const FLOAT4 dequantOffsetC03 = vload4(out_c_idx, dequantOffset); - const FLOAT4 dequantScaleC47 = vload4(out_c_idx + 1, dequantScale); - const FLOAT4 dequantOffsetC47 = vload4(out_c_idx + 1, dequantOffset); - - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = vload4(out_c_idx + 1, bias); - FLOAT4 out3 = out2; + const float4 dequantScaleC03 = vload4(out_c_idx, dequantScale); + const float4 dequantOffsetC03 = vload4(out_c_idx, dequantOffset); + const float4 dequantScaleC47 = vload4(out_c_idx + 1, dequantScale); + const float4 dequantOffsetC47 = vload4(out_c_idx + 1, dequantOffset); + + COMPUTE_FLOAT4 vbias0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out0 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 vbias1 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx + 1, bias)); + COMPUTE_FLOAT4 out2 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out3 = out0; + COMPUTE_FLOAT sum0 = 0; + COMPUTE_FLOAT sum1 = 0; const int in_w_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); @@ -980,17 +1037,17 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS const int in_h1_idx = (iy * dilate_hw.x + in_h1_idx_base) * in_hw.y; for(int fw = in_w_idx_start; fw < in_w_idx_end; fw += dilate_hw.y) { - FLOAT4 in0 = (in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base); - FLOAT4 in1 = (in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4((in_h0_idx < 0 || in_h0_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h0_idx + fw, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4((in_h1_idx < 0 || in_h1_idx >= in_hw_size) ? (FLOAT4)0 : vload4(in_h1_idx + fw, input+inp_offset_base)); #if (defined USE_LOW_BIT_WEIGHT_INT8) char4 charWeight0 = vload4(0, weight+weight_offset); char4 charWeight1 = vload4(0, weight+weight_offset+weight_ic_offset); char4 charWeight2 = vload4(0, weight+weight_offset+weight_ic_offset*2); char4 charWeight3 = vload4(0, weight+weight_offset+weight_ic_offset*3); - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC03, dequantOffsetC03); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else uchar2 charWeightInt40 = vload2(0, weight+weight_offset/2); uchar2 charWeightInt41 = vload2(0, weight+weight_offset/2+weight_ic_offset/2); @@ -1016,11 +1073,15 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC03, dequantOffsetC03); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif + + sum0 += (in0.x + in0.y + in0.z + in0.w); + sum1 += (in1.x + in1.y + in1.z + in1.w); + out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); out0 = mad(in0.z, weight2, out0); @@ -1036,10 +1097,10 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS charWeight1 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset); charWeight2 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2); charWeight3 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3); - weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC47, dequantOffsetC47); - weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC47, dequantOffsetC47); - weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC47, dequantOffsetC47); - weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC47, dequantOffsetC47); + weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else charWeightInt40 = vload2(0, weight+weight_offset/2+weight_oc_offset/2); charWeightInt41 = vload2(0, weight+weight_offset/2+weight_oc_offset/2+weight_ic_offset/2); @@ -1065,10 +1126,10 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0& MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1& MOD_NUM) - 8; - weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC47, dequantOffsetC47); - weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC47, dequantOffsetC47); - weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC47, dequantOffsetC47); - weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC47, dequantOffsetC47); + weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif out2 = mad(in0.x, weight0, out2); out2 = mad(in0.y, weight1, out2); @@ -1084,28 +1145,34 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS } } } + + out0 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out0) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC03); + out1 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out1) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC03); + out2 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out2) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC47); + out3 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out3) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC47); + #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.x - out_h_idx; if(remain >= 2){ - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ @@ -1114,22 +1181,22 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; if(remain >= 2){ - vstore4(out2, 0, output+out_offset); - vstore4(out3, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), out_hw.y, output+out_offset); }else if(remain == 1){ - vstore4(out2, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 0, output+out_offset); } #else - vstore4(out0, 0, output+out_offset); - vstore4(out1, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out1), out_hw.y, output+out_offset); #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks){ return; } #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore4(out2, 0, output+out_offset); - vstore4(out3, out_hw.y, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out3), out_hw.y, output+out_offset); #endif } @@ -1141,8 +1208,8 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, + __global const float *dequantScale, + __global const float *dequantOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -1166,20 +1233,25 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS const int out_b_idx = out_b_h_idx / out_hw.x;//equal to in_b_idx const int out_h_idx = out_b_h_idx % out_hw.x; - const FLOAT4 dequantScaleC03 = vload4(out_c_idx, dequantScale); - const FLOAT4 dequantOffsetC03 = vload4(out_c_idx, dequantOffset); - const FLOAT4 dequantScaleC47 = vload4(out_c_idx + 1, dequantScale); - const FLOAT4 dequantOffsetC47 = vload4(out_c_idx + 1, dequantOffset); - - FLOAT4 out0 = vload4(out_c_idx, bias); - FLOAT4 out1 = out0; - FLOAT4 out2 = out0; - FLOAT4 out3 = out0; + const float4 dequantScaleC03 = vload4(out_c_idx, dequantScale); + const float4 dequantOffsetC03 = vload4(out_c_idx, dequantOffset); + const float4 dequantScaleC47 = vload4(out_c_idx + 1, dequantScale); + const float4 dequantOffsetC47 = vload4(out_c_idx + 1, dequantOffset); - FLOAT4 out4 = vload4(out_c_idx + 1, bias); - FLOAT4 out5 = out4; - FLOAT4 out6 = out4; - FLOAT4 out7 = out4; + COMPUTE_FLOAT4 vbias0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT4 out0 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out1 = out0; + COMPUTE_FLOAT4 out2 = out0; + COMPUTE_FLOAT4 out3 = out0; + COMPUTE_FLOAT4 vbias1 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx + 1, bias)); + COMPUTE_FLOAT4 out4 = (COMPUTE_FLOAT4)0; + COMPUTE_FLOAT4 out5 = out0; + COMPUTE_FLOAT4 out6 = out0; + COMPUTE_FLOAT4 out7 = out0; + COMPUTE_FLOAT sum0 = 0; + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT sum2 = 0; + COMPUTE_FLOAT sum3 = 0; const int in_w0_idx_base = mad24(out_w_idx, stride_hw.y, -pad_hw.y); const int in_w1_idx_base = in_w0_idx_base + stride_hw.y; @@ -1208,20 +1280,20 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS const int in_w2_idx = fw * dilate_hw.y + in_w2_idx_base; const int in_w3_idx = fw * dilate_hw.y + in_w3_idx_base; - FLOAT4 in0 = (in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base); - FLOAT4 in1 = (in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base); - FLOAT4 in2 = (in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w2_idx, input+inp_offset_base); - FLOAT4 in3 = (in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w3_idx, input+inp_offset_base); + COMPUTE_FLOAT4 in0 = CONVERT_COMPUTE_FLOAT4((in_w0_idx < 0 || in_w0_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w0_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in1 = CONVERT_COMPUTE_FLOAT4((in_w1_idx < 0 || in_w1_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w1_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in2 = CONVERT_COMPUTE_FLOAT4((in_w2_idx < 0 || in_w2_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w2_idx, input+inp_offset_base)); + COMPUTE_FLOAT4 in3 = CONVERT_COMPUTE_FLOAT4((in_w3_idx < 0 || in_w3_idx >= in_hw.y) ? (FLOAT4)0 : vload4(in_w3_idx, input+inp_offset_base)); #if (defined USE_LOW_BIT_WEIGHT_INT8) char4 charWeight0 = vload4(0, weight+weight_offset); char4 charWeight1 = vload4(0, weight+weight_offset+weight_ic_offset); char4 charWeight2 = vload4(0, weight+weight_offset+weight_ic_offset*2); char4 charWeight3 = vload4(0, weight+weight_offset+weight_ic_offset*3); - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC03, dequantOffsetC03); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else uchar2 charWeightInt40 = vload2(0, weight+weight_offset/2); uchar2 charWeightInt41 = vload2(0, weight+weight_offset/2+weight_ic_offset/2); @@ -1247,12 +1319,17 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - FLOAT4 weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC03, dequantOffsetC03); - FLOAT4 weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC03, dequantOffsetC03); + COMPUTE_FLOAT4 weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + COMPUTE_FLOAT4 weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + COMPUTE_FLOAT4 weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + COMPUTE_FLOAT4 weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif + sum0 += (in0.x + in0.y + in0.z + in0.w); + sum1 += (in1.x + in1.y + in1.z + in1.w); + sum2 += (in2.x + in2.y + in2.z + in2.w); + sum3 += (in3.x + in3.y + in3.z + in3.w); + out0 = mad(in0.x, weight0, out0); out0 = mad(in0.y, weight1, out0); out0 = mad(in0.z, weight2, out0); @@ -1278,10 +1355,10 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS charWeight1 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset); charWeight2 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*2); charWeight3 = vload4(0, weight+weight_offset+weight_oc_offset+weight_ic_offset*3); - weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC47, dequantOffsetC47); - weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC47, dequantOffsetC47); - weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC47, dequantOffsetC47); - weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC47, dequantOffsetC47); + weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #else charWeightInt40 = vload2(0, weight+weight_offset/2+weight_oc_offset/2); charWeightInt41 = vload2(0, weight+weight_offset/2+weight_oc_offset/2+weight_ic_offset/2); @@ -1307,10 +1384,10 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS charWeight3.y = (charWeightInt43.s0 & MOD_NUM) - 8; charWeight3.z = (charWeightInt43.s1 >> 4) - 8; charWeight3.w = (charWeightInt43.s1 & MOD_NUM) - 8; - weight0 = mad(CONVERT_FLOAT4(charWeight0), dequantScaleC47, dequantOffsetC47); - weight1 = mad(CONVERT_FLOAT4(charWeight1), dequantScaleC47, dequantOffsetC47); - weight2 = mad(CONVERT_FLOAT4(charWeight2), dequantScaleC47, dequantOffsetC47); - weight3 = mad(CONVERT_FLOAT4(charWeight3), dequantScaleC47, dequantOffsetC47); + weight0 = CONVERT_COMPUTE_FLOAT4(charWeight0); + weight1 = CONVERT_COMPUTE_FLOAT4(charWeight1); + weight2 = CONVERT_COMPUTE_FLOAT4(charWeight2); + weight3 = CONVERT_COMPUTE_FLOAT4(charWeight3); #endif out4 = mad(in0.x, weight0, out4); @@ -1337,61 +1414,71 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS } } } + + out0 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out0) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC03); + out1 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out1) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC03); + out2 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out2) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum2) * dequantOffsetC03); + out3 = vbias0 + CONVERT_COMPUTE_FLOAT4(convert_float4(out3) * dequantScaleC03 + convert_float4((COMPUTE_FLOAT4)sum3) * dequantOffsetC03); + out4 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out4) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum0) * dequantOffsetC47); + out5 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out5) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum1) * dequantOffsetC47); + out6 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out6) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum2) * dequantOffsetC47); + out7 = vbias1 + CONVERT_COMPUTE_FLOAT4(convert_float4(out7) * dequantScaleC47 + convert_float4((COMPUTE_FLOAT4)sum3) * dequantOffsetC47); + #ifdef RELU - out0 = fmax(out0, (FLOAT4)0); - out1 = fmax(out1, (FLOAT4)0); - out2 = fmax(out2, (FLOAT4)0); - out3 = fmax(out3, (FLOAT4)0); - out4 = fmax(out4, (FLOAT4)0); - out5 = fmax(out5, (FLOAT4)0); - out6 = fmax(out6, (FLOAT4)0); - out7 = fmax(out7, (FLOAT4)0); + out0 = fmax(out0, (COMPUTE_FLOAT4)0); + out1 = fmax(out1, (COMPUTE_FLOAT4)0); + out2 = fmax(out2, (COMPUTE_FLOAT4)0); + out3 = fmax(out3, (COMPUTE_FLOAT4)0); + out4 = fmax(out4, (COMPUTE_FLOAT4)0); + out5 = fmax(out5, (COMPUTE_FLOAT4)0); + out6 = fmax(out6, (COMPUTE_FLOAT4)0); + out7 = fmax(out7, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - out0 = clamp(out0, (FLOAT4)0, (FLOAT4)6); - out1 = clamp(out1, (FLOAT4)0, (FLOAT4)6); - out2 = clamp(out2, (FLOAT4)0, (FLOAT4)6); - out3 = clamp(out3, (FLOAT4)0, (FLOAT4)6); - out4 = clamp(out4, (FLOAT4)0, (FLOAT4)6); - out5 = clamp(out5, (FLOAT4)0, (FLOAT4)6); - out6 = clamp(out6, (FLOAT4)0, (FLOAT4)6); - out7 = clamp(out7, (FLOAT4)0, (FLOAT4)6); + out0 = clamp(out0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out2 = clamp(out2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out3 = clamp(out3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out4 = clamp(out4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out5 = clamp(out5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out6 = clamp(out6, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + out7 = clamp(out7, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((out_b_idx*out_c_blocks + out_c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; #ifdef BLOCK_LEAVE const int remain = out_hw.y - out_w_idx; if(remain >= 4){ - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); }else if(remain == 3){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); - vstore4(out2, 2, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out2), 2, output+out_offset); }else if(remain == 2){ - vstore8((FLOAT8)(out0, out1), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out0, out1)), 0, output+out_offset); }else if(remain == 1){ - vstore4(out0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out0), 0, output+out_offset); } #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks)return; #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; if(remain >= 4){ - vstore16((FLOAT16)(out4, out5, out6, out7), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out4, out5, out6, out7)), 0, output+out_offset); }else if(remain == 3){ - vstore8((FLOAT8)(out4, out5), 0, output+out_offset); - vstore4(out6, 2, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5)), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out6), 2, output+out_offset); }else if(remain == 2){ - vstore8((FLOAT8)(out4, out5), 0, output+out_offset); + vstore8(CONVERT_FLOAT8((COMPUTE_FLOAT8)(out4, out5)), 0, output+out_offset); }else if(remain == 1){ - vstore4(out4, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(out4), 0, output+out_offset); } #else - vstore16((FLOAT16)(out0, out1, out2, out3), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out0, out1, out2, out3)), 0, output+out_offset); #ifdef CHANNEL_LEAVE if(out_c_idx + 1 >= out_c_blocks)return; #endif out_offset = (((out_b_idx*out_c_blocks + out_c_idx + 1)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore16((FLOAT16)(out4, out5, out6, out7), 0, output+out_offset); + vstore16(CONVERT_FLOAT16((COMPUTE_FLOAT16)(out4, out5, out6, out7)), 0, output+out_offset); #endif } diff --git a/source/backend/opencl/execution/cl/depthwise_conv2d_buf.cl b/source/backend/opencl/execution/cl/depthwise_conv2d_buf.cl index 73426c441..586315962 100644 --- a/source/backend/opencl/execution/cl/depthwise_conv2d_buf.cl +++ b/source/backend/opencl/execution/cl/depthwise_conv2d_buf.cl @@ -1,21 +1,6 @@ #ifdef MNN_SUPPORT_FP16 #pragma OPENCL EXTENSION cl_khr_fp16 : enable #endif -#define READ_INPUT_IMAGE(i, base) \ - int inOffset##i = inWidthOffset##i + base; \ - inOffset##i = \ - select(inCurIdx + inOffset##i, -1, (inOffset##i < 0 || inOffset##i >= inputShape.y)); \ - in_c_block_idx = inOffset##i / inputShape.y; \ - in_w_idx = inOffset##i % inputShape.y; \ - inpOffset = (((in_b_idx*channelBlocks + in_c_block_idx)*inputShape.x + in_h_idx)* inputShape.y + in_w_idx)*4; \ - inValue##i = ((inOffset##i)==-1 || inHeightIdx == -1) ? (FLOAT4)0 : vload4(0, input+inpOffset); - - -#define CALCULATE_OUTPUT(i) \ - outValue##i = mad(inValue##i.x, weights0, outValue##i); \ - outValue##i = mad(inValue##i.y, weights1, outValue##i); \ - outValue##i = mad(inValue##i.z, weights2, outValue##i); \ - outValue##i = mad(inValue##i.w, weights3, outValue##i); #define GLOBAL_SIZE_2_DIMS __private const int global_size_dim0, __private const int global_size_dim1, @@ -56,10 +41,10 @@ void depthwise_conv2d_c4h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, const int b_idx = out_b_h_idx / out_hw.x; const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 outValue0 = vload4(c_idx, bias); - FLOAT4 outValue1 = outValue0; - FLOAT4 outValue2 = outValue0; - FLOAT4 outValue3 = outValue0; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue2 = outValue0; + COMPUTE_FLOAT4 outValue3 = outValue0; const int out_w4_idx = out_w_idx << 2; const int in_w_start_0 = out_w4_idx * stride_hw.y - pad_hw.y; @@ -77,14 +62,14 @@ void depthwise_conv2d_c4h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, for (int kw = 0; kw < filter_hw.y; kw++) { const int filter_idx = mad24(kh, filter_hw.y, kw); const int kw_dilate = kw * dilate_hw.y; - FLOAT4 inValue0 = (in_w_start_0+kw_dilate < 0 || in_w_start_0+kw_dilate >= in_hw.y) ? (FLOAT4)0 : vload4(kw_dilate+0, input+inp_offset); - FLOAT4 inValue1 = (in_w_start_1+kw_dilate < 0 || in_w_start_1+kw_dilate >= in_hw.y) ? (FLOAT4)0 : vload4(kw_dilate+1*stride_hw.y, input+inp_offset); - FLOAT4 inValue2 = (in_w_start_2+kw_dilate < 0 || in_w_start_2+kw_dilate >= in_hw.y) ? (FLOAT4)0 : vload4(kw_dilate+2*stride_hw.y, input+inp_offset); - FLOAT4 inValue3 = (in_w_start_3+kw_dilate < 0 || in_w_start_3+kw_dilate >= in_hw.y) ? (FLOAT4)0 : vload4(kw_dilate+3*stride_hw.y, input+inp_offset); + COMPUTE_FLOAT4 inValue0 = (in_w_start_0+kw_dilate < 0 || in_w_start_0+kw_dilate >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw_dilate+0, input+inp_offset)); + COMPUTE_FLOAT4 inValue1 = (in_w_start_1+kw_dilate < 0 || in_w_start_1+kw_dilate >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw_dilate+1*stride_hw.y, input+inp_offset)); + COMPUTE_FLOAT4 inValue2 = (in_w_start_2+kw_dilate < 0 || in_w_start_2+kw_dilate >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw_dilate+2*stride_hw.y, input+inp_offset)); + COMPUTE_FLOAT4 inValue3 = (in_w_start_3+kw_dilate < 0 || in_w_start_3+kw_dilate >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw_dilate+3*stride_hw.y, input+inp_offset)); //NC4HW4 [1, filterShape.x*filterShape.y, 1, channelBlocks] x oc4 //index: [0, filterIdx, 0, inChannelBlockIdx] - FLOAT4 weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights, outValue0); outValue1 = mad(inValue1, weights, outValue1); @@ -94,36 +79,36 @@ void depthwise_conv2d_c4h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, } #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); - outValue1 = fmax(outValue1, (FLOAT4)0); - outValue2 = fmax(outValue2, (FLOAT4)0); - outValue3 = fmax(outValue3, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); + outValue1 = fmax(outValue1, (COMPUTE_FLOAT4)0); + outValue2 = fmax(outValue2, (COMPUTE_FLOAT4)0); + outValue3 = fmax(outValue3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); - outValue1 = clamp(outValue1, (FLOAT4)0, (FLOAT4)6); - outValue2 = clamp(outValue2, (FLOAT4)0, (FLOAT4)6); - outValue3 = clamp(outValue3, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue1 = clamp(outValue1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue2 = clamp(outValue2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue3 = clamp(outValue3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w4_idx)*4; const int remain = out_hw.y - out_w4_idx; if (remain >= 4) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); - vstore4(outValue2, 2, output+out_offset); - vstore4(outValue3, 3, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue3), 3, output+out_offset); } else if (remain == 3) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); - vstore4(outValue2, 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), 2, output+out_offset); } else if (remain == 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } } @@ -152,8 +137,8 @@ void depthwise_conv2d_c4h1w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, const int b_idx = out_b_h_idx / out_hw.x; const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 outValue0 = vload4(c_idx, bias); - FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; const int out_w2_idx = out_w_idx << 1; const int in_w_start_0 = out_w2_idx * stride_hw.y - pad_hw.y; @@ -169,12 +154,12 @@ void depthwise_conv2d_c4h1w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, for (int kw = 0; kw < filter_hw.y; kw++) { const int filter_idx = mad24(kh, filter_hw.y, kw); const int kw_dilate = kw * dilate_hw.y; - FLOAT4 inValue0 = (in_w_start_0+kw_dilate < 0 || in_w_start_0+kw_dilate >= in_hw.y) ? (FLOAT4)0 : vload4(kw_dilate+0, input+inp_offset); - FLOAT4 inValue1 = (in_w_start_1+kw_dilate < 0 || in_w_start_1+kw_dilate >= in_hw.y) ? (FLOAT4)0 : vload4(kw_dilate+1*stride_hw.y, input+inp_offset); + COMPUTE_FLOAT4 inValue0 = (in_w_start_0+kw_dilate < 0 || in_w_start_0+kw_dilate >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw_dilate+0, input+inp_offset)); + COMPUTE_FLOAT4 inValue1 = (in_w_start_1+kw_dilate < 0 || in_w_start_1+kw_dilate >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw_dilate+1*stride_hw.y, input+inp_offset)); //NC4HW4 [1, filterShape.x*filterShape.y, 1, channelBlocks] x oc4 //index: [0, filterIdx, 0, inChannelBlockIdx] - FLOAT4 weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights, outValue0); outValue1 = mad(inValue1, weights, outValue1); @@ -182,23 +167,23 @@ void depthwise_conv2d_c4h1w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, } #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); - outValue1 = fmax(outValue1, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); + outValue1 = fmax(outValue1, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); - outValue1 = clamp(outValue1, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue1 = clamp(outValue1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w2_idx)*4; const int remain = out_hw.y - out_w2_idx; if (remain >= 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } } @@ -227,8 +212,8 @@ void depthwise_conv2d_c4h1w1(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, const int b_idx = out_b_h_idx / out_hw.x; const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 outValue0 = vload4(c_idx, bias); - FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; const int in_w_start_0 = out_w_idx * stride_hw.y - pad_hw.y; const int in_h_start = out_h_idx * stride_hw.x - pad_hw.x; @@ -241,27 +226,27 @@ void depthwise_conv2d_c4h1w1(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, for (int kw = 0; kw < filter_hw.y; kw++) { const int filter_idx = mad24(kh, filter_hw.y, kw); const int kw_dilate = kw * dilate_hw.y; - FLOAT4 inValue0 = (in_w_start_0+kw_dilate < 0 || in_w_start_0+kw_dilate >= in_hw.y) ? (FLOAT4)0 : vload4(kw_dilate+0, input+inp_offset); + COMPUTE_FLOAT4 inValue0 = (in_w_start_0+kw_dilate < 0 || in_w_start_0+kw_dilate >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw_dilate+0, input+inp_offset)); //NC4HW4 [1, filterShape.x*filterShape.y, 1, channelBlocks] x oc4 //index: [0, filterIdx, 0, inChannelBlockIdx] - FLOAT4 weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights, outValue0); } } #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w_idx)*4; - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } __kernel @@ -288,14 +273,14 @@ void depthwise_conv2d_s1_c8h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, const int b_idx = out_b_h_idx / out_hw.x; const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 outValue0 = vload4(c_idx+0, bias); - FLOAT4 outValue1 = outValue0; - FLOAT4 outValue2 = outValue0; - FLOAT4 outValue3 = outValue0; - FLOAT4 outValue4 = vload4(c_idx+1, bias); - FLOAT4 outValue5 = outValue4; - FLOAT4 outValue6 = outValue4; - FLOAT4 outValue7 = outValue4; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx+0, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue2 = outValue0; + COMPUTE_FLOAT4 outValue3 = outValue0; + COMPUTE_FLOAT4 outValue4 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx+1, bias)); + COMPUTE_FLOAT4 outValue5 = outValue4; + COMPUTE_FLOAT4 outValue6 = outValue4; + COMPUTE_FLOAT4 outValue7 = outValue4; const int out_w4_idx = out_w_idx << 2; const int in_w_start_0 = out_w4_idx - pad_hw.y; @@ -313,20 +298,20 @@ void depthwise_conv2d_s1_c8h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, int inp_offset_c1 = (((b_idx*c_blocks + c_idx+1)*in_hw.x + in_h_cur)* in_hw.y + in_w_start_0)*4; for (int kw = 0; kw < filter_hw.y; kw++) { const int filter_idx = mad24(kh, filter_hw.y, kw); - FLOAT4 inValue0 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+0, input+inp_offset_c0); - FLOAT4 inValue1 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+1, input+inp_offset_c0); - FLOAT4 inValue2 = (in_w_start_2+kw < 0 || in_w_start_2+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+2, input+inp_offset_c0); - FLOAT4 inValue3 = (in_w_start_3+kw < 0 || in_w_start_3+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+3, input+inp_offset_c0); - - FLOAT4 inValue4 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+0, input+inp_offset_c1); - FLOAT4 inValue5 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+1, input+inp_offset_c1); - FLOAT4 inValue6 = (in_w_start_2+kw < 0 || in_w_start_2+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+2, input+inp_offset_c1); - FLOAT4 inValue7 = (in_w_start_3+kw < 0 || in_w_start_3+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+3, input+inp_offset_c1); + COMPUTE_FLOAT4 inValue0 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+0, input+inp_offset_c0)); + COMPUTE_FLOAT4 inValue1 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+1, input+inp_offset_c0)); + COMPUTE_FLOAT4 inValue2 = (in_w_start_2+kw < 0 || in_w_start_2+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+2, input+inp_offset_c0)); + COMPUTE_FLOAT4 inValue3 = (in_w_start_3+kw < 0 || in_w_start_3+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+3, input+inp_offset_c0)); + + COMPUTE_FLOAT4 inValue4 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+0, input+inp_offset_c1)); + COMPUTE_FLOAT4 inValue5 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+1, input+inp_offset_c1)); + COMPUTE_FLOAT4 inValue6 = (in_w_start_2+kw < 0 || in_w_start_2+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+2, input+inp_offset_c1)); + COMPUTE_FLOAT4 inValue7 = (in_w_start_3+kw < 0 || in_w_start_3+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+3, input+inp_offset_c1)); //NC4HW4 [1, filterShape.x*filterShape.y, 1, channelBlocks] x oc4 //index: [0, filterIdx, 0, inChannelBlockIdx] - FLOAT4 weights_0 = vload4(0, filter+(filter_idx*c_blocks+c_idx+0)*4); - FLOAT4 weights_1 = vload4(0, filter+(filter_idx*c_blocks+c_idx+1)*4); + COMPUTE_FLOAT4 weights_0 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx+0)*4)); + COMPUTE_FLOAT4 weights_1 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx+1)*4)); outValue0 = mad(inValue0, weights_0, outValue0); outValue1 = mad(inValue1, weights_0, outValue1); @@ -341,65 +326,65 @@ void depthwise_conv2d_s1_c8h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, } #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); - outValue1 = fmax(outValue1, (FLOAT4)0); - outValue2 = fmax(outValue2, (FLOAT4)0); - outValue3 = fmax(outValue3, (FLOAT4)0); - - outValue4 = fmax(outValue4, (FLOAT4)0); - outValue5 = fmax(outValue5, (FLOAT4)0); - outValue6 = fmax(outValue6, (FLOAT4)0); - outValue7 = fmax(outValue7, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); + outValue1 = fmax(outValue1, (COMPUTE_FLOAT4)0); + outValue2 = fmax(outValue2, (COMPUTE_FLOAT4)0); + outValue3 = fmax(outValue3, (COMPUTE_FLOAT4)0); + + outValue4 = fmax(outValue4, (COMPUTE_FLOAT4)0); + outValue5 = fmax(outValue5, (COMPUTE_FLOAT4)0); + outValue6 = fmax(outValue6, (COMPUTE_FLOAT4)0); + outValue7 = fmax(outValue7, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); - outValue1 = clamp(outValue1, (FLOAT4)0, (FLOAT4)6); - outValue2 = clamp(outValue2, (FLOAT4)0, (FLOAT4)6); - outValue3 = clamp(outValue3, (FLOAT4)0, (FLOAT4)6); - - outValue4 = clamp(outValue4, (FLOAT4)0, (FLOAT4)6); - outValue5 = clamp(outValue5, (FLOAT4)0, (FLOAT4)6); - outValue6 = clamp(outValue6, (FLOAT4)0, (FLOAT4)6); - outValue7 = clamp(outValue7, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue1 = clamp(outValue1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue2 = clamp(outValue2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue3 = clamp(outValue3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + + outValue4 = clamp(outValue4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue5 = clamp(outValue5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue6 = clamp(outValue6, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue7 = clamp(outValue7, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w4_idx)*4; const int remain = out_hw.y - out_w4_idx; if (remain >= 4) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); - vstore4(outValue2, 2, output+out_offset); - vstore4(outValue3, 3, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue3), 3, output+out_offset); } else if (remain == 3) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); - vstore4(outValue2, 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), 2, output+out_offset); } else if (remain == 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } if(c_idx + 1 >= c_blocks) return; out_offset += out_hw.x * out_hw.y * 4; if (remain >= 4) { - vstore4(outValue4, 0, output+out_offset); - vstore4(outValue5, 1, output+out_offset); - vstore4(outValue6, 2, output+out_offset); - vstore4(outValue7, 3, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue5), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue6), 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue7), 3, output+out_offset); } else if (remain == 3) { - vstore4(outValue4, 0, output+out_offset); - vstore4(outValue5, 1, output+out_offset); - vstore4(outValue6, 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue5), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue6), 2, output+out_offset); } else if (remain == 2) { - vstore4(outValue4, 0, output+out_offset); - vstore4(outValue5, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue5), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue4, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue4), 0, output+out_offset); } } @@ -428,10 +413,10 @@ void depthwise_conv2d_s1_c8h1w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, const int b_idx = out_b_h_idx / out_hw.x; const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 outValue0 = vload4(c_idx+0, bias); - FLOAT4 outValue1 = outValue0; - FLOAT4 outValue4 = vload4(c_idx+1, bias); - FLOAT4 outValue5 = outValue4; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx+0, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue4 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx+1, bias)); + COMPUTE_FLOAT4 outValue5 = outValue4; const int out_w2_idx = out_w_idx << 1; const int in_w_start_0 = out_w2_idx - pad_hw.y; @@ -447,16 +432,16 @@ void depthwise_conv2d_s1_c8h1w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, int inp_offset_c1 = (((b_idx*c_blocks + c_idx+1)*in_hw.x + in_h_cur)* in_hw.y + in_w_start_0)*4; for (int kw = 0; kw < filter_hw.y; kw++) { const int filter_idx = mad24(kh, filter_hw.y, kw); - FLOAT4 inValue0 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+0, input+inp_offset_c0); - FLOAT4 inValue1 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+1, input+inp_offset_c0); + COMPUTE_FLOAT4 inValue0 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+0, input+inp_offset_c0)); + COMPUTE_FLOAT4 inValue1 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+1, input+inp_offset_c0)); - FLOAT4 inValue4 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+0, input+inp_offset_c1); - FLOAT4 inValue5 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+1, input+inp_offset_c1); + COMPUTE_FLOAT4 inValue4 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+0, input+inp_offset_c1)); + COMPUTE_FLOAT4 inValue5 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+1, input+inp_offset_c1)); //NC4HW4 [1, filterShape.x*filterShape.y, 1, channelBlocks] x oc4 //index: [0, filterIdx, 0, inChannelBlockIdx] - FLOAT4 weights_0 = vload4(0, filter+(filter_idx*c_blocks+c_idx+0)*4); - FLOAT4 weights_1 = vload4(0, filter+(filter_idx*c_blocks+c_idx+1)*4); + COMPUTE_FLOAT4 weights_0 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx+0)*4)); + COMPUTE_FLOAT4 weights_1 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx+1)*4)); outValue0 = mad(inValue0, weights_0, outValue0); outValue1 = mad(inValue1, weights_0, outValue1); @@ -467,39 +452,39 @@ void depthwise_conv2d_s1_c8h1w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, } #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); - outValue1 = fmax(outValue1, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); + outValue1 = fmax(outValue1, (COMPUTE_FLOAT4)0); - outValue4 = fmax(outValue4, (FLOAT4)0); - outValue5 = fmax(outValue5, (FLOAT4)0); + outValue4 = fmax(outValue4, (COMPUTE_FLOAT4)0); + outValue5 = fmax(outValue5, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); - outValue1 = clamp(outValue1, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue1 = clamp(outValue1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); - outValue4 = clamp(outValue4, (FLOAT4)0, (FLOAT4)6); - outValue5 = clamp(outValue5, (FLOAT4)0, (FLOAT4)6); + outValue4 = clamp(outValue4, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue5 = clamp(outValue5, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w2_idx)*4; const int remain = out_hw.y - out_w2_idx; if (remain >= 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } if(c_idx + 1 >= c_blocks) return; out_offset += out_hw.x * out_hw.y * 4; if (remain >= 2) { - vstore4(outValue4, 0, output+out_offset); - vstore4(outValue5, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue4), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue5), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue4, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue4), 0, output+out_offset); } } @@ -527,10 +512,10 @@ void depthwise_conv2d_s1_c4h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, const int b_idx = out_b_h_idx / out_hw.x; const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 outValue0 = vload4(c_idx, bias); - FLOAT4 outValue1 = outValue0; - FLOAT4 outValue2 = outValue0; - FLOAT4 outValue3 = outValue0; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue2 = outValue0; + COMPUTE_FLOAT4 outValue3 = outValue0; const int out_w4_idx = out_w_idx << 2; const int in_w_start_0 = out_w4_idx - pad_hw.y; @@ -540,7 +525,7 @@ void depthwise_conv2d_s1_c4h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, const int in_h_start = out_h_idx - pad_hw.x; - FLOAT4 inValue0, inValue1, inValue2, inValue3; + COMPUTE_FLOAT4 inValue0, inValue1, inValue2, inValue3; for (int kh = 0; kh < filter_hw.x; kh++) { const int in_h_cur = in_h_start + kh; if(in_h_cur < 0 || in_h_cur >= in_hw.x) continue; @@ -548,14 +533,14 @@ void depthwise_conv2d_s1_c4h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, int inp_offset = (((b_idx*c_blocks + c_idx)*in_hw.x + in_h_cur)* in_hw.y + in_w_start_0)*4; for (int kw = 0; kw < filter_hw.y; kw++) { const int filter_idx = mad24(kh, filter_hw.y, kw); - inValue0 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+0, input+inp_offset); - inValue1 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+1, input+inp_offset); - inValue2 = (in_w_start_2+kw < 0 || in_w_start_2+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+2, input+inp_offset); - inValue3 = (in_w_start_3+kw < 0 || in_w_start_3+kw >= in_hw.y) ? (FLOAT4)0 : vload4(kw+3, input+inp_offset); + inValue0 = (in_w_start_0+kw < 0 || in_w_start_0+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+0, input+inp_offset)); + inValue1 = (in_w_start_1+kw < 0 || in_w_start_1+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+1, input+inp_offset)); + inValue2 = (in_w_start_2+kw < 0 || in_w_start_2+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+2, input+inp_offset)); + inValue3 = (in_w_start_3+kw < 0 || in_w_start_3+kw >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(kw+3, input+inp_offset)); //NC4HW4 [1, filterShape.x*filterShape.y, 1, channelBlocks] x oc4 //index: [0, filterIdx, 0, inChannelBlockIdx] - FLOAT4 weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights, outValue0); outValue1 = mad(inValue1, weights, outValue1); @@ -565,36 +550,36 @@ void depthwise_conv2d_s1_c4h1w4(GLOBAL_SIZE_2_DIMS __global const FLOAT *input, } #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); - outValue1 = fmax(outValue1, (FLOAT4)0); - outValue2 = fmax(outValue2, (FLOAT4)0); - outValue3 = fmax(outValue3, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); + outValue1 = fmax(outValue1, (COMPUTE_FLOAT4)0); + outValue2 = fmax(outValue2, (COMPUTE_FLOAT4)0); + outValue3 = fmax(outValue3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); - outValue1 = clamp(outValue1, (FLOAT4)0, (FLOAT4)6); - outValue2 = clamp(outValue2, (FLOAT4)0, (FLOAT4)6); - outValue3 = clamp(outValue3, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue1 = clamp(outValue1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue2 = clamp(outValue2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue3 = clamp(outValue3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w4_idx)*4; const int remain = out_hw.y - out_w4_idx; if (remain >= 4) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); - vstore4(outValue2, 2, output+out_offset); - vstore4(outValue3, 3, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue3), 3, output+out_offset); } else if (remain == 3) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); - vstore4(outValue2, 2, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), 2, output+out_offset); } else if (remain == 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } } @@ -623,96 +608,96 @@ void depthwise_conv2d_k3s1p1_c4h1w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *inp const int b_idx = out_b_h_idx / out_hw.x; const int out_h_idx = out_b_h_idx % out_hw.x; - FLOAT4 outValue0 = vload4(c_idx, bias); - FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; const int out_w2_idx = out_w_idx << 1; const int in_w_start_0 = out_w2_idx - pad_hw.y; const int in_h_start = out_h_idx - pad_hw.x; - FLOAT4 inValue0, inValue1, inValue2, inValue3; + COMPUTE_FLOAT4 inValue0, inValue1, inValue2, inValue3; //first line const int inp_offset = (((b_idx*c_blocks + c_idx)*in_hw.x + in_h_start)* in_hw.y + in_w_start_0)*4; - inValue0 = (in_h_start < 0 || in_w_start_0 < 0 ) ? (FLOAT4)0 : vload4(0, input+inp_offset); - inValue1 = (in_h_start < 0 || in_w_start_0+1 >= in_hw.y) ? (FLOAT4)0 : vload4(1, input+inp_offset); - inValue2 = (in_h_start < 0 || in_w_start_0+2 >= in_hw.y) ? (FLOAT4)0 : vload4(2, input+inp_offset); - inValue3 = (in_h_start < 0 || in_w_start_0+3 >= in_hw.y) ? (FLOAT4)0 : vload4(3, input+inp_offset); + inValue0 = (in_h_start < 0 || in_w_start_0 < 0 ) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); + inValue1 = (in_h_start < 0 || in_w_start_0+1 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(1, input+inp_offset)); + inValue2 = (in_h_start < 0 || in_w_start_0+2 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2, input+inp_offset)); + inValue3 = (in_h_start < 0 || in_w_start_0+3 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(3, input+inp_offset)); int filter_idx = mad24(0, filter_hw.y, 0); - FLOAT4 weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights, outValue0); outValue1 = mad(inValue1, weights, outValue1); filter_idx = mad24(0, filter_hw.y, 1); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue1, weights, outValue0); outValue1 = mad(inValue2, weights, outValue1); filter_idx = mad24(0, filter_hw.y, 2); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue2, weights, outValue0); outValue1 = mad(inValue3, weights, outValue1); //second line - inValue0 = (in_h_start+1 >= in_hw.x || in_w_start_0 < 0 ) ? (FLOAT4)0 : vload4(in_hw.y+0, input+inp_offset); - inValue1 = (in_h_start+1 >= in_hw.x || in_w_start_0+1 >= in_hw.y) ? (FLOAT4)0 : vload4(in_hw.y+1, input+inp_offset); - inValue2 = (in_h_start+1 >= in_hw.x || in_w_start_0+2 >= in_hw.y) ? (FLOAT4)0 : vload4(in_hw.y+2, input+inp_offset); - inValue3 = (in_h_start+1 >= in_hw.x || in_w_start_0+3 >= in_hw.y) ? (FLOAT4)0 : vload4(in_hw.y+3, input+inp_offset); + inValue0 = (in_h_start+1 >= in_hw.x || in_w_start_0 < 0 ) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+0, input+inp_offset)); + inValue1 = (in_h_start+1 >= in_hw.x || in_w_start_0+1 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+1, input+inp_offset)); + inValue2 = (in_h_start+1 >= in_hw.x || in_w_start_0+2 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+2, input+inp_offset)); + inValue3 = (in_h_start+1 >= in_hw.x || in_w_start_0+3 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+3, input+inp_offset)); filter_idx = mad24(1, filter_hw.y, 0); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights, outValue0); outValue1 = mad(inValue1, weights, outValue1); filter_idx = mad24(1, filter_hw.y, 1); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue1, weights, outValue0); outValue1 = mad(inValue2, weights, outValue1); filter_idx = mad24(1, filter_hw.y, 2); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue2, weights, outValue0); outValue1 = mad(inValue3, weights, outValue1); //third line - inValue0 = (in_h_start+2 >= in_hw.x || in_w_start_0 < 0 ) ? (FLOAT4)0 : vload4(2*in_hw.y+0, input+inp_offset); - inValue1 = (in_h_start+2 >= in_hw.x || in_w_start_0+1 >= in_hw.y) ? (FLOAT4)0 : vload4(2*in_hw.y+1, input+inp_offset); - inValue2 = (in_h_start+2 >= in_hw.x || in_w_start_0+2 >= in_hw.y) ? (FLOAT4)0 : vload4(2*in_hw.y+2, input+inp_offset); - inValue3 = (in_h_start+2 >= in_hw.x || in_w_start_0+3 >= in_hw.y) ? (FLOAT4)0 : vload4(2*in_hw.y+3, input+inp_offset); + inValue0 = (in_h_start+2 >= in_hw.x || in_w_start_0 < 0 ) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+0, input+inp_offset)); + inValue1 = (in_h_start+2 >= in_hw.x || in_w_start_0+1 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+1, input+inp_offset)); + inValue2 = (in_h_start+2 >= in_hw.x || in_w_start_0+2 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+2, input+inp_offset)); + inValue3 = (in_h_start+2 >= in_hw.x || in_w_start_0+3 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+3, input+inp_offset)); filter_idx = mad24(2, filter_hw.y, 0); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights, outValue0); outValue1 = mad(inValue1, weights, outValue1); filter_idx = mad24(2, filter_hw.y, 1); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue1, weights, outValue0); outValue1 = mad(inValue2, weights, outValue1); filter_idx = mad24(2, filter_hw.y, 2); - weights = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue2, weights, outValue0); outValue1 = mad(inValue3, weights, outValue1); #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); - outValue1 = fmax(outValue1, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); + outValue1 = fmax(outValue1, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); - outValue1 = clamp(outValue1, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue1 = clamp(outValue1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h_idx)*out_hw.y + out_w2_idx)*4; const int remain = out_hw.y - out_w2_idx; if (remain >= 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); } else if (remain == 1) { - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } } @@ -742,107 +727,107 @@ void depthwise_conv2d_k3s1p1_c4h2w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *inp const int b_idx = out_b_h_idx / out_h_blocks; const int out_h_idx = out_b_h_idx % out_h_blocks; - FLOAT4 outValue0 = vload4(c_idx, bias); - FLOAT4 outValue1 = outValue0; - FLOAT4 outValue2 = outValue0; - FLOAT4 outValue3 = outValue0; + COMPUTE_FLOAT4 outValue0 = CONVERT_COMPUTE_FLOAT4(vload4(c_idx, bias)); + COMPUTE_FLOAT4 outValue1 = outValue0; + COMPUTE_FLOAT4 outValue2 = outValue0; + COMPUTE_FLOAT4 outValue3 = outValue0; const int out_w2_idx = out_w_idx << 1; const int in_w_start = out_w2_idx - pad_hw.y; const int out_h2_idx = out_h_idx << 1; const int in_h_start = out_h2_idx - pad_hw.x; - FLOAT4 inValue0, inValue1, inValue2, inValue3; + COMPUTE_FLOAT4 inValue0, inValue1, inValue2, inValue3; //first line const int inp_offset = (((b_idx*c_blocks + c_idx)*in_hw.x + in_h_start)* in_hw.y + in_w_start)*4; - inValue0 = (in_h_start < 0 || in_w_start < 0 ) ? (FLOAT4)0 : vload4(0, input+inp_offset); - inValue1 = (in_h_start < 0 || in_w_start+1 >= in_hw.y) ? (FLOAT4)0 : vload4(1, input+inp_offset); - inValue2 = (in_h_start < 0 || in_w_start+2 >= in_hw.y) ? (FLOAT4)0 : vload4(2, input+inp_offset); - inValue3 = (in_h_start < 0 || in_w_start+3 >= in_hw.y) ? (FLOAT4)0 : vload4(3, input+inp_offset); + inValue0 = (in_h_start < 0 || in_w_start < 0 ) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset)); + inValue1 = (in_h_start < 0 || in_w_start+1 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(1, input+inp_offset)); + inValue2 = (in_h_start < 0 || in_w_start+2 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2, input+inp_offset)); + inValue3 = (in_h_start < 0 || in_w_start+3 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(3, input+inp_offset)); int filter_idx = mad24(0, filter_hw.y, 0); - FLOAT4 weights0 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights0 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights0, outValue0); outValue1 = mad(inValue1, weights0, outValue1); filter_idx = mad24(0, filter_hw.y, 1); - FLOAT4 weights1 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights1 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue1, weights1, outValue0); outValue1 = mad(inValue2, weights1, outValue1); filter_idx = mad24(0, filter_hw.y, 2); - FLOAT4 weights2 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + COMPUTE_FLOAT4 weights2 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue2, weights2, outValue0); outValue1 = mad(inValue3, weights2, outValue1); //second line - inValue0 = (in_h_start+1 >= in_hw.x || in_w_start < 0 ) ? (FLOAT4)0 : vload4(in_hw.y+0, input+inp_offset); - inValue1 = (in_h_start+1 >= in_hw.x || in_w_start+1 >= in_hw.y) ? (FLOAT4)0 : vload4(in_hw.y+1, input+inp_offset); - inValue2 = (in_h_start+1 >= in_hw.x || in_w_start+2 >= in_hw.y) ? (FLOAT4)0 : vload4(in_hw.y+2, input+inp_offset); - inValue3 = (in_h_start+1 >= in_hw.x || in_w_start+3 >= in_hw.y) ? (FLOAT4)0 : vload4(in_hw.y+3, input+inp_offset); + inValue0 = (in_h_start+1 >= in_hw.x || in_w_start < 0 ) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+0, input+inp_offset)); + inValue1 = (in_h_start+1 >= in_hw.x || in_w_start+1 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+1, input+inp_offset)); + inValue2 = (in_h_start+1 >= in_hw.x || in_w_start+2 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+2, input+inp_offset)); + inValue3 = (in_h_start+1 >= in_hw.x || in_w_start+3 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(in_hw.y+3, input+inp_offset)); DW_CONV_NEXT_LINE_CAL(outValue2, outValue3) filter_idx = mad24(1, filter_hw.y, 0); - weights0 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights0 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights0, outValue0); outValue1 = mad(inValue1, weights0, outValue1); filter_idx = mad24(1, filter_hw.y, 1); - weights1 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights1 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue1, weights1, outValue0); outValue1 = mad(inValue2, weights1, outValue1); filter_idx = mad24(1, filter_hw.y, 2); - weights2 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights2 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue2, weights2, outValue0); outValue1 = mad(inValue3, weights2, outValue1); //third line - inValue0 = (in_h_start+2 >= in_hw.x || in_w_start < 0 ) ? (FLOAT4)0 : vload4(2*in_hw.y+0, input+inp_offset); - inValue1 = (in_h_start+2 >= in_hw.x || in_w_start+1 >= in_hw.y) ? (FLOAT4)0 : vload4(2*in_hw.y+1, input+inp_offset); - inValue2 = (in_h_start+2 >= in_hw.x || in_w_start+2 >= in_hw.y) ? (FLOAT4)0 : vload4(2*in_hw.y+2, input+inp_offset); - inValue3 = (in_h_start+2 >= in_hw.x || in_w_start+3 >= in_hw.y) ? (FLOAT4)0 : vload4(2*in_hw.y+3, input+inp_offset); + inValue0 = (in_h_start+2 >= in_hw.x || in_w_start < 0 ) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+0, input+inp_offset)); + inValue1 = (in_h_start+2 >= in_hw.x || in_w_start+1 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+1, input+inp_offset)); + inValue2 = (in_h_start+2 >= in_hw.x || in_w_start+2 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+2, input+inp_offset)); + inValue3 = (in_h_start+2 >= in_hw.x || in_w_start+3 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(2*in_hw.y+3, input+inp_offset)); DW_CONV_NEXT_LINE_CAL(outValue2, outValue3) filter_idx = mad24(2, filter_hw.y, 0); - weights0 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights0 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue0, weights0, outValue0); outValue1 = mad(inValue1, weights0, outValue1); filter_idx = mad24(2, filter_hw.y, 1); - weights1 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights1 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue1, weights1, outValue0); outValue1 = mad(inValue2, weights1, outValue1); filter_idx = mad24(2, filter_hw.y, 2); - weights2 = vload4(0, filter+(filter_idx*c_blocks+c_idx)*4); + weights2 = CONVERT_COMPUTE_FLOAT4(vload4(0, filter+(filter_idx*c_blocks+c_idx)*4)); outValue0 = mad(inValue2, weights2, outValue0); outValue1 = mad(inValue3, weights2, outValue1); //forth line - inValue0 = (in_h_start+3 >= in_hw.x || in_w_start < 0 ) ? (FLOAT4)0 : vload4(3*in_hw.y+0, input+inp_offset); - inValue1 = (in_h_start+3 >= in_hw.x || in_w_start+1 >= in_hw.y) ? (FLOAT4)0 : vload4(3*in_hw.y+1, input+inp_offset); - inValue2 = (in_h_start+3 >= in_hw.x || in_w_start+2 >= in_hw.y) ? (FLOAT4)0 : vload4(3*in_hw.y+2, input+inp_offset); - inValue3 = (in_h_start+3 >= in_hw.x || in_w_start+3 >= in_hw.y) ? (FLOAT4)0 : vload4(3*in_hw.y+3, input+inp_offset); + inValue0 = (in_h_start+3 >= in_hw.x || in_w_start < 0 ) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(3*in_hw.y+0, input+inp_offset)); + inValue1 = (in_h_start+3 >= in_hw.x || in_w_start+1 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(3*in_hw.y+1, input+inp_offset)); + inValue2 = (in_h_start+3 >= in_hw.x || in_w_start+2 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(3*in_hw.y+2, input+inp_offset)); + inValue3 = (in_h_start+3 >= in_hw.x || in_w_start+3 >= in_hw.y) ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(vload4(3*in_hw.y+3, input+inp_offset)); DW_CONV_NEXT_LINE_CAL(outValue2, outValue3) #ifdef RELU - outValue0 = fmax(outValue0, (FLOAT4)0); - outValue1 = fmax(outValue1, (FLOAT4)0); - outValue2 = fmax(outValue2, (FLOAT4)0); - outValue3 = fmax(outValue3, (FLOAT4)0); + outValue0 = fmax(outValue0, (COMPUTE_FLOAT4)0); + outValue1 = fmax(outValue1, (COMPUTE_FLOAT4)0); + outValue2 = fmax(outValue2, (COMPUTE_FLOAT4)0); + outValue3 = fmax(outValue3, (COMPUTE_FLOAT4)0); #endif #ifdef RELU6 - outValue0 = clamp(outValue0, (FLOAT4)0, (FLOAT4)6); - outValue1 = clamp(outValue1, (FLOAT4)0, (FLOAT4)6); - outValue2 = clamp(outValue2, (FLOAT4)0, (FLOAT4)6); - outValue3 = clamp(outValue3, (FLOAT4)0, (FLOAT4)6); + outValue0 = clamp(outValue0, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue1 = clamp(outValue1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue2 = clamp(outValue2, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); + outValue3 = clamp(outValue3, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); #endif const int out_offset = (((b_idx*c_blocks + c_idx)*out_hw.x + out_h2_idx)*out_hw.y + out_w2_idx)*4; @@ -851,17 +836,17 @@ void depthwise_conv2d_k3s1p1_c4h2w2(GLOBAL_SIZE_2_DIMS __global const FLOAT *inp const int remain_h = out_hw.x - out_h2_idx; if(remain_w >= 2 && remain_h >= 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); - vstore4(outValue2, out_hw.y+0, output+out_offset); - vstore4(outValue3, out_hw.y+1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), out_hw.y+0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue3), out_hw.y+1, output+out_offset); } else if(remain_w == 1 && remain_h >= 2) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue2, out_hw.y+0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue2), out_hw.y+0, output+out_offset); } else if(remain_w >= 2 && remain_h == 1) { - vstore4(outValue0, 0, output+out_offset); - vstore4(outValue1, 1, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue1), 1, output+out_offset); } else { - vstore4(outValue0, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(outValue0), 0, output+out_offset); } } diff --git a/source/backend/opencl/execution/cl/depthwise_conv2d_subgroup_buf.cl b/source/backend/opencl/execution/cl/depthwise_conv2d_subgroup_buf.cl index caca4db1b..7d7698059 100644 --- a/source/backend/opencl/execution/cl/depthwise_conv2d_subgroup_buf.cl +++ b/source/backend/opencl/execution/cl/depthwise_conv2d_subgroup_buf.cl @@ -63,30 +63,30 @@ __kernel void depthwise_conv_2d_buf_c16_c16( const uint filter_offset = c * filter_is_pitch; #ifdef MNN_SUPPORT_FP16 - FLOAT8 dst = (FLOAT8)(as_half(intel_sub_group_block_read_us((__global ushort*)(biases + c * 16)))); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(as_half(intel_sub_group_block_read_us((__global ushort*)(biases + c * 16)))); for(int i = 0; i < FILTER_HEIGHT; ++i){ if ((input_y + i * DILATION_HEIGHT) < 0 || (input_y + i * DILATION_HEIGHT) >= inputHeight) continue; for(int j = 0; j < FILTER_WIDTH; ++j){ - FLOAT wei = as_half(intel_sub_group_block_read_us((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); + COMPUTE_FLOAT wei = as_half(intel_sub_group_block_read_us((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); for(int k = 0; k < 8; ++k){ - FLOAT src = as_half(intel_sub_group_block_read_us((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); + COMPUTE_FLOAT src = as_half(intel_sub_group_block_read_us((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); dst[k] = mad(src, wei, dst[k]); } } } #else - FLOAT8 dst = (FLOAT8)(as_float(intel_sub_group_block_read((__global uint*)(biases + c * 16)))); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(as_float(intel_sub_group_block_read((__global uint*)(biases + c * 16)))); for(int i = 0; i < FILTER_HEIGHT; ++i){ if ((input_y + i * DILATION_HEIGHT) < 0 || (input_y + i * DILATION_HEIGHT) >= inputHeight) continue; for(int j = 0; j < FILTER_WIDTH; ++j){ - FLOAT wei = as_float(intel_sub_group_block_read((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); + COMPUTE_FLOAT wei = as_float(intel_sub_group_block_read((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); for(int k = 0; k < 8; ++k){ - FLOAT src = as_float(intel_sub_group_block_read((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); + COMPUTE_FLOAT src = as_float(intel_sub_group_block_read((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); dst[k] = mad(src, wei, dst[k]); } } @@ -95,18 +95,18 @@ __kernel void depthwise_conv_2d_buf_c16_c16( #ifdef RELU - dst = fmax(dst, (FLOAT8)0); + dst = fmax(dst, (COMPUTE_FLOAT8)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT8)0, (FLOAT8)6); + dst = clamp(dst, (COMPUTE_FLOAT8)0, (COMPUTE_FLOAT8)6); #endif for (int i = 0; i < 8 && (x + i) < outputWidth; i++) { #ifdef MNN_SUPPORT_FP16 - intel_sub_group_block_write_us((__global ushort*)(output + output_offset + i * output_x_pitch), as_ushort(dst[i])); + intel_sub_group_block_write_us((__global ushort*)(output + output_offset + i * output_x_pitch), as_ushort((FLOAT)dst[i])); #else - intel_sub_group_block_write((__global uint*)(output + output_offset + i * output_x_pitch), as_uint(dst[i])); + intel_sub_group_block_write((__global uint*)(output + output_offset + i * output_x_pitch), as_uint((FLOAT)dst[i])); #endif } if(x == 0){ @@ -181,30 +181,30 @@ __kernel void depthwise_conv_2d_buf_c16_c4( const uint filter_offset = c * filter_is_pitch; #ifdef MNN_SUPPORT_FP16 - FLOAT8 dst = (FLOAT8)(as_half(intel_sub_group_block_read_us((__global ushort*)(biases + c * 16)))); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(as_half(intel_sub_group_block_read_us((__global ushort*)(biases + c * 16)))); for(int i = 0; i < FILTER_HEIGHT; ++i){ if ((input_y + i * DILATION_HEIGHT) < 0 || (input_y + i * DILATION_HEIGHT) >= inputHeight) continue; for(int j = 0; j < FILTER_WIDTH; ++j){ - FLOAT wei = as_half(intel_sub_group_block_read_us((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); + COMPUTE_FLOAT wei = as_half(intel_sub_group_block_read_us((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); for(int k = 0; k < 8; ++k){ - FLOAT src = as_half(intel_sub_group_block_read_us((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); + COMPUTE_FLOAT src = as_half(intel_sub_group_block_read_us((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); dst[k] = mad(src, wei, dst[k]); } } } #else - FLOAT8 dst = (FLOAT8)(as_float(intel_sub_group_block_read((__global uint*)(biases + c * 16)))); + COMPUTE_FLOAT8 dst = (COMPUTE_FLOAT8)(as_float(intel_sub_group_block_read((__global uint*)(biases + c * 16)))); for(int i = 0; i < FILTER_HEIGHT; ++i){ if ((input_y + i * DILATION_HEIGHT) < 0 || (input_y + i * DILATION_HEIGHT) >= inputHeight) continue; for(int j = 0; j < FILTER_WIDTH; ++j){ - FLOAT wei = as_float(intel_sub_group_block_read((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); + COMPUTE_FLOAT wei = as_float(intel_sub_group_block_read((__global ushort*)(weights + filter_offset + i * filter_y_pitch + j * filter_x_pitch))); for(int k = 0; k < 8; ++k){ - FLOAT src = as_float(intel_sub_group_block_read((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); + COMPUTE_FLOAT src = as_float(intel_sub_group_block_read((__global ushort*)(input + input_offset + i * DILATION_HEIGHT * input_y_pitch + (j * DILATION_WIDTH + k * STRIDE_WIDTH) * input_x_pitch))); dst[k] = mad(src, wei, dst[k]); } } @@ -213,11 +213,11 @@ __kernel void depthwise_conv_2d_buf_c16_c4( #ifdef RELU - dst = fmax(dst, (FLOAT8)0); + dst = fmax(dst, (COMPUTE_FLOAT8)0); #endif #ifdef RELU6 - dst = clamp(dst, (FLOAT8)0, (FLOAT8)6); + dst = clamp(dst, (COMPUTE_FLOAT8)0, (COMPUTE_FLOAT8)6); #endif const uint lid_x = sglid % 4; diff --git a/source/backend/opencl/execution/cl/gemm_buf.cl b/source/backend/opencl/execution/cl/gemm_buf.cl index e6c307c9f..2022fd9e3 100644 --- a/source/backend/opencl/execution/cl/gemm_buf.cl +++ b/source/backend/opencl/execution/cl/gemm_buf.cl @@ -25,7 +25,7 @@ __kernel void gemm_buf(GLOBAL_SIZE_DIM2 const int pos_y = pos.x / width; const int pos_z = pos.y; - FLOAT16 o = (FLOAT16)0; + COMPUTE_FLOAT16 o = (COMPUTE_FLOAT16)0; int kenerlY = mad24(pos_z, height, pos_y); int srcY = mad24(pos_z, width, pos_x); @@ -37,17 +37,17 @@ __kernel void gemm_buf(GLOBAL_SIZE_DIM2 //index:[0, 0, pos_z*width+pos_y, index+0] //int inp1_offset = (((k * (alpha2*height) + kenerlY) * (srcChannelC4*4) + index)*4 + 0)*4; - FLOAT16 k_v16 = vload16(kenerlY*(srcChannelC4) + k, input1); + COMPUTE_FLOAT16 k_v16 = CONVERT_COMPUTE_FLOAT16(vload16(kenerlY*(srcChannelC4) + k, input1)); //NC4HW4 [alpha*alpha, srcChannelC4, width, 4] x 4 //index: [pos_z, k, pos_x, 0] - FLOAT16 s = vload16(((pos_z*srcChannelC4 + k) * width + pos_x), input0); + COMPUTE_FLOAT16 s = CONVERT_COMPUTE_FLOAT16(vload16(((pos_z*srcChannelC4 + k) * width + pos_x), input0)); - o = mad((FLOAT16)((FLOAT4)s.s0, (FLOAT4)s.s4, (FLOAT4)s.s8, (FLOAT4)s.sc), (FLOAT16)(k_v16.s0123, k_v16.s0123, k_v16.s0123, k_v16.s0123), o); - o = mad((FLOAT16)((FLOAT4)s.s1, (FLOAT4)s.s5, (FLOAT4)s.s9, (FLOAT4)s.sd), (FLOAT16)(k_v16.s4567, k_v16.s4567, k_v16.s4567, k_v16.s4567), o); - o = mad((FLOAT16)((FLOAT4)s.s2, (FLOAT4)s.s6, (FLOAT4)s.sa, (FLOAT4)s.se), (FLOAT16)(k_v16.s89ab, k_v16.s89ab, k_v16.s89ab, k_v16.s89ab), o); - o = mad((FLOAT16)((FLOAT4)s.s3, (FLOAT4)s.s7, (FLOAT4)s.sb, (FLOAT4)s.sf), (FLOAT16)(k_v16.scdef, k_v16.scdef, k_v16.scdef, k_v16.scdef), o); + o = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s0, (COMPUTE_FLOAT4)s.s4, (COMPUTE_FLOAT4)s.s8, (COMPUTE_FLOAT4)s.sc), (COMPUTE_FLOAT16)(k_v16.s0123, k_v16.s0123, k_v16.s0123, k_v16.s0123), o); + o = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s1, (COMPUTE_FLOAT4)s.s5, (COMPUTE_FLOAT4)s.s9, (COMPUTE_FLOAT4)s.sd), (COMPUTE_FLOAT16)(k_v16.s4567, k_v16.s4567, k_v16.s4567, k_v16.s4567), o); + o = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s2, (COMPUTE_FLOAT4)s.s6, (COMPUTE_FLOAT4)s.sa, (COMPUTE_FLOAT4)s.se), (COMPUTE_FLOAT16)(k_v16.s89ab, k_v16.s89ab, k_v16.s89ab, k_v16.s89ab), o); + o = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s3, (COMPUTE_FLOAT4)s.s7, (COMPUTE_FLOAT4)s.sb, (COMPUTE_FLOAT4)s.sf), (COMPUTE_FLOAT16)(k_v16.scdef, k_v16.scdef, k_v16.scdef, k_v16.scdef), o); } __private int out_y_idx = mul24(pos_y, 4); @@ -56,10 +56,10 @@ __kernel void gemm_buf(GLOBAL_SIZE_DIM2 //index: [pos_y, pos_z, 0, pos_x] int out_offset = (((pos_y * alpha2 + pos_z) * 4 + 0) * width + pos_x) * 4; - vstore4(o.s0123, 0, output+out_offset); - vstore4(o.s4567, 0, output+out_offset+4*width); - vstore4(o.s89ab, 0, output+out_offset+8*width); - vstore4(o.scdef, 0, output+out_offset+12*width); + vstore4(CONVERT_FLOAT4(o.s0123), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(o.s4567), 0, output+out_offset+4*width); + vstore4(CONVERT_FLOAT4(o.s89ab), 0, output+out_offset+8*width); + vstore4(CONVERT_FLOAT4(o.scdef), 0, output+out_offset+12*width); } @@ -80,8 +80,8 @@ __kernel void gemm_buf2(GLOBAL_SIZE_DIM2 const int pos_y = pos.x / width_block; const int pos_z = pos.y; - FLOAT16 o0 = (FLOAT16)0; - FLOAT16 o1 = (FLOAT16)0; + COMPUTE_FLOAT16 o0 = (COMPUTE_FLOAT16)0; + COMPUTE_FLOAT16 o1 = (COMPUTE_FLOAT16)0; const int kenerlY = mad24(pos_z, height, pos_y); const int kernel_base = mul24(kenerlY, srcChannelC4); @@ -92,24 +92,24 @@ __kernel void gemm_buf2(GLOBAL_SIZE_DIM2 //index:[0, 0, pos_z*width+pos_y, index+0] //int inp1_offset = (((k * (alpha2*height) + kenerlY) * (srcChannelC4*4) + index)*4 + 0)*4; - FLOAT16 k_v16 = vload16(kernel_base + k, input1); + COMPUTE_FLOAT16 k_v16 = CONVERT_COMPUTE_FLOAT16(vload16(kernel_base + k, input1)); //NC4HW4 [alpha*alpha, srcChannelC4, width, 4] x 4 //index: [pos_z, k, pos_x, 0] const int inp_offset = mad24(k, width, inp_base); - FLOAT16 s = vload16(inp_offset, input0); - - o0 = mad((FLOAT16)((FLOAT4)s.s0, (FLOAT4)s.s4, (FLOAT4)s.s8, (FLOAT4)s.sc), (FLOAT16)(k_v16.s0123, k_v16.s0123, k_v16.s0123, k_v16.s0123), o0); - o0 = mad((FLOAT16)((FLOAT4)s.s1, (FLOAT4)s.s5, (FLOAT4)s.s9, (FLOAT4)s.sd), (FLOAT16)(k_v16.s4567, k_v16.s4567, k_v16.s4567, k_v16.s4567), o0); - o0 = mad((FLOAT16)((FLOAT4)s.s2, (FLOAT4)s.s6, (FLOAT4)s.sa, (FLOAT4)s.se), (FLOAT16)(k_v16.s89ab, k_v16.s89ab, k_v16.s89ab, k_v16.s89ab), o0); - o0 = mad((FLOAT16)((FLOAT4)s.s3, (FLOAT4)s.s7, (FLOAT4)s.sb, (FLOAT4)s.sf), (FLOAT16)(k_v16.scdef, k_v16.scdef, k_v16.scdef, k_v16.scdef), o0); - - s = vload16(inp_offset + 1, input0); - o1 = mad((FLOAT16)((FLOAT4)s.s0, (FLOAT4)s.s4, (FLOAT4)s.s8, (FLOAT4)s.sc), (FLOAT16)(k_v16.s0123, k_v16.s0123, k_v16.s0123, k_v16.s0123), o1); - o1 = mad((FLOAT16)((FLOAT4)s.s1, (FLOAT4)s.s5, (FLOAT4)s.s9, (FLOAT4)s.sd), (FLOAT16)(k_v16.s4567, k_v16.s4567, k_v16.s4567, k_v16.s4567), o1); - o1 = mad((FLOAT16)((FLOAT4)s.s2, (FLOAT4)s.s6, (FLOAT4)s.sa, (FLOAT4)s.se), (FLOAT16)(k_v16.s89ab, k_v16.s89ab, k_v16.s89ab, k_v16.s89ab), o1); - o1 = mad((FLOAT16)((FLOAT4)s.s3, (FLOAT4)s.s7, (FLOAT4)s.sb, (FLOAT4)s.sf), (FLOAT16)(k_v16.scdef, k_v16.scdef, k_v16.scdef, k_v16.scdef), o1); + COMPUTE_FLOAT16 s = CONVERT_COMPUTE_FLOAT16(vload16(inp_offset, input0)); + + o0 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s0, (COMPUTE_FLOAT4)s.s4, (COMPUTE_FLOAT4)s.s8, (COMPUTE_FLOAT4)s.sc), (COMPUTE_FLOAT16)(k_v16.s0123, k_v16.s0123, k_v16.s0123, k_v16.s0123), o0); + o0 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s1, (COMPUTE_FLOAT4)s.s5, (COMPUTE_FLOAT4)s.s9, (COMPUTE_FLOAT4)s.sd), (COMPUTE_FLOAT16)(k_v16.s4567, k_v16.s4567, k_v16.s4567, k_v16.s4567), o0); + o0 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s2, (COMPUTE_FLOAT4)s.s6, (COMPUTE_FLOAT4)s.sa, (COMPUTE_FLOAT4)s.se), (COMPUTE_FLOAT16)(k_v16.s89ab, k_v16.s89ab, k_v16.s89ab, k_v16.s89ab), o0); + o0 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s3, (COMPUTE_FLOAT4)s.s7, (COMPUTE_FLOAT4)s.sb, (COMPUTE_FLOAT4)s.sf), (COMPUTE_FLOAT16)(k_v16.scdef, k_v16.scdef, k_v16.scdef, k_v16.scdef), o0); + + s = CONVERT_COMPUTE_FLOAT16(vload16(inp_offset + 1, input0)); + o1 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s0, (COMPUTE_FLOAT4)s.s4, (COMPUTE_FLOAT4)s.s8, (COMPUTE_FLOAT4)s.sc), (COMPUTE_FLOAT16)(k_v16.s0123, k_v16.s0123, k_v16.s0123, k_v16.s0123), o1); + o1 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s1, (COMPUTE_FLOAT4)s.s5, (COMPUTE_FLOAT4)s.s9, (COMPUTE_FLOAT4)s.sd), (COMPUTE_FLOAT16)(k_v16.s4567, k_v16.s4567, k_v16.s4567, k_v16.s4567), o1); + o1 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s2, (COMPUTE_FLOAT4)s.s6, (COMPUTE_FLOAT4)s.sa, (COMPUTE_FLOAT4)s.se), (COMPUTE_FLOAT16)(k_v16.s89ab, k_v16.s89ab, k_v16.s89ab, k_v16.s89ab), o1); + o1 = mad((COMPUTE_FLOAT16)((COMPUTE_FLOAT4)s.s3, (COMPUTE_FLOAT4)s.s7, (COMPUTE_FLOAT4)s.sb, (COMPUTE_FLOAT4)s.sf), (COMPUTE_FLOAT16)(k_v16.scdef, k_v16.scdef, k_v16.scdef, k_v16.scdef), o1); } __private int out_y_idx = mul24(pos_y, 4); @@ -118,696 +118,14 @@ __kernel void gemm_buf2(GLOBAL_SIZE_DIM2 //index: [pos_y, pos_z, 0, pos_x] int out_offset = (((pos_y * alpha2 + pos_z) * 4 + 0) * width + pos_x) * 4; - vstore4(o0.s0123, 0, output+out_offset); - vstore4(o0.s4567, 0, output+out_offset+4*width); - vstore4(o0.s89ab, 0, output+out_offset+8*width); - vstore4(o0.scdef, 0, output+out_offset+12*width); + vstore4(CONVERT_FLOAT4(o0.s0123), 0, output+out_offset); + vstore4(CONVERT_FLOAT4(o0.s4567), 0, output+out_offset+4*width); + vstore4(CONVERT_FLOAT4(o0.s89ab), 0, output+out_offset+8*width); + vstore4(CONVERT_FLOAT4(o0.scdef), 0, output+out_offset+12*width); if(pos_x + 1 >= width) return; - vstore4(o1.s0123, 1, output+out_offset); - vstore4(o1.s4567, 1, output+out_offset+4*width); - vstore4(o1.s89ab, 1, output+out_offset+8*width); - vstore4(o1.scdef, 1, output+out_offset+12*width); -} - -__kernel void gemm_conv_c4_buf(GLOBAL_SIZE_DIM2 - __global const FLOAT* input, -#if (defined USE_LOW_BIT_WEIGHT_INT8) - __global const char *weight, - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - __global const uchar *weight, - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, -#else - __global const FLOAT *weight, -#endif - __global const FLOAT *bias, - __global FLOAT* output, - __private const int dstChannelC4, - __private const int srcChannelC4, - __private const int batch, - __private const int height, - __private const int width) { - const int out_c_w_idx = get_global_id(0); //c/4 w - const int out_b_h_idx = get_global_id(1); //b h - - UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); - - const int out_c_idx = out_c_w_idx / width; - const int out_w_idx = out_c_w_idx % width; - const int out_b_idx = out_b_h_idx / height; - const int out_h_idx = out_b_h_idx % height; - - FLOAT4 bias0 = vload4(out_c_idx, bias); - FLOAT sum = 0; - FLOAT4 out = 0; - - int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; - int out_offset = ((out_b_idx * dstChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; - int wh = width * height * 4; -#if (defined USE_LOW_BIT_WEIGHT_INT4) - int weight_offset = out_c_idx * 4 * 8; - int weight_oc_offset = dstChannelC4 * 32; -#else - int weight_offset = out_c_idx * 4 * 16; - int weight_oc_offset = dstChannelC4 * 64; -#endif - -#if (defined USE_LOW_BIT_WEIGHT_INT8) || (defined USE_LOW_BIT_WEIGHT_INT4) - const FLOAT4 Scale = vload4(out_c_idx, dequantScale); - const FLOAT4 Offset = vload4(out_c_idx, dequantOffset); -#endif -#ifdef INPUT_CHANNEL_LEAVE - for (int k = 0; k < srcChannelC4/4 - 1; ++k) { -#else - for (int k = 0; k < srcChannelC4/4; ++k) { -#endif -#ifdef WIDTH_HEIGHT_1 - FLOAT16 in = vload16(k, input + input_offset); -#else - int k4 = k << 2; - FLOAT16 in; - in.s0123 = vload4(0, input + input_offset + k4 * wh); - in.s4567 = vload4(0, input + input_offset + (k4 + 1) * wh); - in.s89ab = vload4(0, input + input_offset + (k4 + 2) * wh); - in.scdef = vload4(0, input + input_offset + (k4 + 3) * wh); -#endif -#if (defined USE_LOW_BIT_WEIGHT_INT8) - FLOAT16 weights0 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); - FLOAT16 weights1 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); - FLOAT16 weights2 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 32)); - FLOAT16 weights3 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 48)); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - uchar16 charWeightsInt40 = vload16(0, weight + weight_offset + k * weight_oc_offset); - uchar16 charWeightsInt41 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); - FLOAT16 weights0, weights1, weights2, weights3; - { - char16 charWeights0 = 0; - char16 charWeights1 = 0; - charWeights0.s0 = (charWeightsInt40.s0 >> 4) - 8; - charWeights0.s1 = (charWeightsInt40.s0 & 15) - 8; - charWeights0.s2 = (charWeightsInt40.s1 >> 4) - 8; - charWeights0.s3 = (charWeightsInt40.s1 & 15) - 8; - charWeights0.s4 = (charWeightsInt40.s2 >> 4) - 8; - charWeights0.s5 = (charWeightsInt40.s2 & 15) - 8; - charWeights0.s6 = (charWeightsInt40.s3 >> 4) - 8; - charWeights0.s7 = (charWeightsInt40.s3 & 15) - 8; - charWeights0.s8 = (charWeightsInt40.s4 >> 4) - 8; - charWeights0.s9 = (charWeightsInt40.s4 & 15) - 8; - charWeights0.sa = (charWeightsInt40.s5 >> 4) - 8; - charWeights0.sb = (charWeightsInt40.s5 & 15) - 8; - charWeights0.sc = (charWeightsInt40.s6 >> 4) - 8; - charWeights0.sd = (charWeightsInt40.s6 & 15) - 8; - charWeights0.se = (charWeightsInt40.s7 >> 4) - 8; - charWeights0.sf = (charWeightsInt40.s7 & 15) - 8; - charWeights1.s0 = (charWeightsInt40.s8 >> 4) - 8; - charWeights1.s1 = (charWeightsInt40.s8 & 15) - 8; - charWeights1.s2 = (charWeightsInt40.s9 >> 4) - 8; - charWeights1.s3 = (charWeightsInt40.s9 & 15) - 8; - charWeights1.s4 = (charWeightsInt40.sa >> 4) - 8; - charWeights1.s5 = (charWeightsInt40.sa & 15) - 8; - charWeights1.s6 = (charWeightsInt40.sb >> 4) - 8; - charWeights1.s7 = (charWeightsInt40.sb & 15) - 8; - charWeights1.s8 = (charWeightsInt40.sc >> 4) - 8; - charWeights1.s9 = (charWeightsInt40.sc & 15) - 8; - charWeights1.sa = (charWeightsInt40.sd >> 4) - 8; - charWeights1.sb = (charWeightsInt40.sd & 15) - 8; - charWeights1.sc = (charWeightsInt40.se >> 4) - 8; - charWeights1.sd = (charWeightsInt40.se & 15) - 8; - charWeights1.se = (charWeightsInt40.sf >> 4) - 8; - charWeights1.sf = (charWeightsInt40.sf & 15) - 8; - weights0 = CONVERT_FLOAT16(charWeights0); - weights1 = CONVERT_FLOAT16(charWeights1); - } - - { - char16 charWeights0 = 0; - char16 charWeights1 = 0; - charWeights0.s0 = (charWeightsInt41.s0 >> 4) - 8; - charWeights0.s1 = (charWeightsInt41.s0 & 15) - 8; - charWeights0.s2 = (charWeightsInt41.s1 >> 4) - 8; - charWeights0.s3 = (charWeightsInt41.s1 & 15) - 8; - charWeights0.s4 = (charWeightsInt41.s2 >> 4) - 8; - charWeights0.s5 = (charWeightsInt41.s2 & 15) - 8; - charWeights0.s6 = (charWeightsInt41.s3 >> 4) - 8; - charWeights0.s7 = (charWeightsInt41.s3 & 15) - 8; - charWeights0.s8 = (charWeightsInt41.s4 >> 4) - 8; - charWeights0.s9 = (charWeightsInt41.s4 & 15) - 8; - charWeights0.sa = (charWeightsInt41.s5 >> 4) - 8; - charWeights0.sb = (charWeightsInt41.s5 & 15) - 8; - charWeights0.sc = (charWeightsInt41.s6 >> 4) - 8; - charWeights0.sd = (charWeightsInt41.s6 & 15) - 8; - charWeights0.se = (charWeightsInt41.s7 >> 4) - 8; - charWeights0.sf = (charWeightsInt41.s7 & 15) - 8; - charWeights1.s0 = (charWeightsInt41.s8 >> 4) - 8; - charWeights1.s1 = (charWeightsInt41.s8 & 15) - 8; - charWeights1.s2 = (charWeightsInt41.s9 >> 4) - 8; - charWeights1.s3 = (charWeightsInt41.s9 & 15) - 8; - charWeights1.s4 = (charWeightsInt41.sa >> 4) - 8; - charWeights1.s5 = (charWeightsInt41.sa & 15) - 8; - charWeights1.s6 = (charWeightsInt41.sb >> 4) - 8; - charWeights1.s7 = (charWeightsInt41.sb & 15) - 8; - charWeights1.s8 = (charWeightsInt41.sc >> 4) - 8; - charWeights1.s9 = (charWeightsInt41.sc & 15) - 8; - charWeights1.sa = (charWeightsInt41.sd >> 4) - 8; - charWeights1.sb = (charWeightsInt41.sd & 15) - 8; - charWeights1.sc = (charWeightsInt41.se >> 4) - 8; - charWeights1.sd = (charWeightsInt41.se & 15) - 8; - charWeights1.se = (charWeightsInt41.sf >> 4) - 8; - charWeights1.sf = (charWeightsInt41.sf & 15) - 8; - weights2 = CONVERT_FLOAT16(charWeights0); - weights3 = CONVERT_FLOAT16(charWeights1); - } - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; - -#else - FLOAT16 weights0 = vload16(0, weight + weight_offset + k * weight_oc_offset); - FLOAT16 weights1 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); - FLOAT16 weights2 = vload16(0, weight + weight_offset + k * weight_oc_offset + 32); - FLOAT16 weights3 = vload16(0, weight + weight_offset + k * weight_oc_offset + 48); -#endif - - out.s0 += dot(in.s0123, weights0.s0123); - out.s0 += dot(in.s4567, weights0.s4567); - out.s0 += dot(in.s89ab, weights0.s89ab); - out.s0 += dot(in.scdef, weights0.scdef); - - out.s1 += dot(in.s0123, weights1.s0123); - out.s1 += dot(in.s4567, weights1.s4567); - out.s1 += dot(in.s89ab, weights1.s89ab); - out.s1 += dot(in.scdef, weights1.scdef); - - out.s2 += dot(in.s0123, weights2.s0123); - out.s2 += dot(in.s4567, weights2.s4567); - out.s2 += dot(in.s89ab, weights2.s89ab); - out.s2 += dot(in.scdef, weights2.scdef); - - out.s3 += dot(in.s0123, weights3.s0123); - out.s3 += dot(in.s4567, weights3.s4567); - out.s3 += dot(in.s89ab, weights3.s89ab); - out.s3 += dot(in.scdef, weights3.scdef); - } -#ifdef INPUT_CHANNEL_LEAVE - { - int k = srcChannelC4/4 - 1; - int k4 = k * 4; - FLOAT16 in; - in.s0123 = vload4(0, input + input_offset + k4 * wh); - in.s4567 = k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0; - in.s89ab = k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0; - in.scdef = k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0; -#if (defined USE_LOW_BIT_WEIGHT_INT8) - FLOAT16 weights0 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); - FLOAT16 weights1 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); - FLOAT16 weights2 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 32)); - FLOAT16 weights3 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 48)); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - uchar16 charWeightsInt40 = vload16(0, weight + weight_offset + k * weight_oc_offset); - uchar16 charWeightsInt41 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); - FLOAT16 weights0, weights1, weights2, weights3; - { - char16 charWeights0 = 0; - char16 charWeights1 = 0; - charWeights0.s0 = (charWeightsInt40.s0 >> 4) - 8; - charWeights0.s1 = (charWeightsInt40.s0 & 15) - 8; - charWeights0.s2 = (charWeightsInt40.s1 >> 4) - 8; - charWeights0.s3 = (charWeightsInt40.s1 & 15) - 8; - charWeights0.s4 = (charWeightsInt40.s2 >> 4) - 8; - charWeights0.s5 = (charWeightsInt40.s2 & 15) - 8; - charWeights0.s6 = (charWeightsInt40.s3 >> 4) - 8; - charWeights0.s7 = (charWeightsInt40.s3 & 15) - 8; - charWeights0.s8 = (charWeightsInt40.s4 >> 4) - 8; - charWeights0.s9 = (charWeightsInt40.s4 & 15) - 8; - charWeights0.sa = (charWeightsInt40.s5 >> 4) - 8; - charWeights0.sb = (charWeightsInt40.s5 & 15) - 8; - charWeights0.sc = (charWeightsInt40.s6 >> 4) - 8; - charWeights0.sd = (charWeightsInt40.s6 & 15) - 8; - charWeights0.se = (charWeightsInt40.s7 >> 4) - 8; - charWeights0.sf = (charWeightsInt40.s7 & 15) - 8; - charWeights1.s0 = (charWeightsInt40.s8 >> 4) - 8; - charWeights1.s1 = (charWeightsInt40.s8 & 15) - 8; - charWeights1.s2 = (charWeightsInt40.s9 >> 4) - 8; - charWeights1.s3 = (charWeightsInt40.s9 & 15) - 8; - charWeights1.s4 = (charWeightsInt40.sa >> 4) - 8; - charWeights1.s5 = (charWeightsInt40.sa & 15) - 8; - charWeights1.s6 = (charWeightsInt40.sb >> 4) - 8; - charWeights1.s7 = (charWeightsInt40.sb & 15) - 8; - charWeights1.s8 = (charWeightsInt40.sc >> 4) - 8; - charWeights1.s9 = (charWeightsInt40.sc & 15) - 8; - charWeights1.sa = (charWeightsInt40.sd >> 4) - 8; - charWeights1.sb = (charWeightsInt40.sd & 15) - 8; - charWeights1.sc = (charWeightsInt40.se >> 4) - 8; - charWeights1.sd = (charWeightsInt40.se & 15) - 8; - charWeights1.se = (charWeightsInt40.sf >> 4) - 8; - charWeights1.sf = (charWeightsInt40.sf & 15) - 8; - weights0 = CONVERT_FLOAT16(charWeights0); - weights1 = CONVERT_FLOAT16(charWeights1); - } - - { - char16 charWeights0 = 0; - char16 charWeights1 = 0; - charWeights0.s0 = (charWeightsInt41.s0 >> 4) - 8; - charWeights0.s1 = (charWeightsInt41.s0 & 15) - 8; - charWeights0.s2 = (charWeightsInt41.s1 >> 4) - 8; - charWeights0.s3 = (charWeightsInt41.s1 & 15) - 8; - charWeights0.s4 = (charWeightsInt41.s2 >> 4) - 8; - charWeights0.s5 = (charWeightsInt41.s2 & 15) - 8; - charWeights0.s6 = (charWeightsInt41.s3 >> 4) - 8; - charWeights0.s7 = (charWeightsInt41.s3 & 15) - 8; - charWeights0.s8 = (charWeightsInt41.s4 >> 4) - 8; - charWeights0.s9 = (charWeightsInt41.s4 & 15) - 8; - charWeights0.sa = (charWeightsInt41.s5 >> 4) - 8; - charWeights0.sb = (charWeightsInt41.s5 & 15) - 8; - charWeights0.sc = (charWeightsInt41.s6 >> 4) - 8; - charWeights0.sd = (charWeightsInt41.s6 & 15) - 8; - charWeights0.se = (charWeightsInt41.s7 >> 4) - 8; - charWeights0.sf = (charWeightsInt41.s7 & 15) - 8; - charWeights1.s0 = (charWeightsInt41.s8 >> 4) - 8; - charWeights1.s1 = (charWeightsInt41.s8 & 15) - 8; - charWeights1.s2 = (charWeightsInt41.s9 >> 4) - 8; - charWeights1.s3 = (charWeightsInt41.s9 & 15) - 8; - charWeights1.s4 = (charWeightsInt41.sa >> 4) - 8; - charWeights1.s5 = (charWeightsInt41.sa & 15) - 8; - charWeights1.s6 = (charWeightsInt41.sb >> 4) - 8; - charWeights1.s7 = (charWeightsInt41.sb & 15) - 8; - charWeights1.s8 = (charWeightsInt41.sc >> 4) - 8; - charWeights1.s9 = (charWeightsInt41.sc & 15) - 8; - charWeights1.sa = (charWeightsInt41.sd >> 4) - 8; - charWeights1.sb = (charWeightsInt41.sd & 15) - 8; - charWeights1.sc = (charWeightsInt41.se >> 4) - 8; - charWeights1.sd = (charWeightsInt41.se & 15) - 8; - charWeights1.se = (charWeightsInt41.sf >> 4) - 8; - charWeights1.sf = (charWeightsInt41.sf & 15) - 8; - weights2 = CONVERT_FLOAT16(charWeights0); - weights3 = CONVERT_FLOAT16(charWeights1); - } - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; - -#else - FLOAT16 weights0 = vload16(0, weight + weight_offset + k * weight_oc_offset); - FLOAT16 weights1 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); - FLOAT16 weights2 = vload16(0, weight + weight_offset + k * weight_oc_offset + 32); - FLOAT16 weights3 = vload16(0, weight + weight_offset + k * weight_oc_offset + 48); -#endif - - out.s0 += dot(in.s0123, weights0.s0123); - out.s0 += dot(in.s4567, weights0.s4567); - out.s0 += dot(in.s89ab, weights0.s89ab); - out.s0 += dot(in.scdef, weights0.scdef); - - out.s1 += dot(in.s0123, weights1.s0123); - out.s1 += dot(in.s4567, weights1.s4567); - out.s1 += dot(in.s89ab, weights1.s89ab); - out.s1 += dot(in.scdef, weights1.scdef); - - out.s2 += dot(in.s0123, weights2.s0123); - out.s2 += dot(in.s4567, weights2.s4567); - out.s2 += dot(in.s89ab, weights2.s89ab); - out.s2 += dot(in.scdef, weights2.scdef); - - out.s3 += dot(in.s0123, weights3.s0123); - out.s3 += dot(in.s4567, weights3.s4567); - out.s3 += dot(in.s89ab, weights3.s89ab); - out.s3 += dot(in.scdef, weights3.scdef); - } -#endif - -#if (defined USE_LOW_BIT_WEIGHT_INT8) || (defined USE_LOW_BIT_WEIGHT_INT4) - out = bias0 + mad(out, Scale, sum * Offset); -#endif -#ifdef RELU - out = fmax(out, (FLOAT4)0); -#endif - -#ifdef RELU6 - out = clamp(out, (FLOAT4)0, (FLOAT4)6); -#endif - - vstore4(out, out_c_idx, output+out_offset); -} - -__kernel void gemm_conv_c2_buf(GLOBAL_SIZE_DIM2 - __global const FLOAT* input, -#if (defined USE_LOW_BIT_WEIGHT_INT8) - __global const char *weight, - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - __global const uchar *weight, - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, -#else - __global const FLOAT *weight, -#endif - __global const FLOAT *bias, - __global FLOAT* output, - __private const int dstChannelC4, - __private const int srcChannelC4, - __private const int batch, - __private const int height, - __private const int width) { - const int out_c_w_idx = get_global_id(0); //c/4 w - const int out_b_h_idx = get_global_id(1); //b h - - UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); - - const int out_c_idx = out_c_w_idx / width; - const int out_w_idx = out_c_w_idx % width; - const int out_b_idx = out_b_h_idx / height; - const int out_h_idx = out_b_h_idx % height; - - FLOAT2 bias0 = vload2(out_c_idx, bias); - FLOAT sum = 0; - FLOAT2 out = 0; - - int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; - int out_offset = ((out_b_idx * dstChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; - int wh = width * height * 4; -#if (defined USE_LOW_BIT_WEIGHT_INT4) - int weight_offset = out_c_idx * 2 * 8; - int weight_oc_offset = dstChannelC4 * 32; -#else - int weight_offset = out_c_idx * 2 * 16; - int weight_oc_offset = dstChannelC4 * 64; -#endif - -#if (defined USE_LOW_BIT_WEIGHT_INT8) || (defined USE_LOW_BIT_WEIGHT_INT4) - const FLOAT2 Scale = vload2(out_c_idx, dequantScale); - const FLOAT2 Offset = vload2(out_c_idx, dequantOffset); -#endif - -#ifdef INPUT_CHANNEL_LEAVE - for (int k = 0; k < srcChannelC4/4 - 1; ++k) { -#else - for (int k = 0; k < srcChannelC4/4; ++k) { -#endif -#ifdef WIDTH_HEIGHT_1 - FLOAT16 in = vload16(k, input + input_offset); -#else - FLOAT16 in; - int k4 = k << 2; - in.s0123 = vload4(0, input + input_offset + k4 * wh); - in.s4567 = vload4(0, input + input_offset + (k4 + 1) * wh); - in.s89ab = vload4(0, input + input_offset + (k4 + 2) * wh); - in.scdef = vload4(0, input + input_offset + (k4 + 3) * wh); -#endif -#if (defined USE_LOW_BIT_WEIGHT_INT8) - FLOAT16 weights0 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); - FLOAT16 weights1 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - uchar16 charWeightsInt4 = vload16(0, weight + weight_offset + k * weight_oc_offset); - char16 charWeights0 = 0; - char16 charWeights1 = 0; - charWeights0.s0 = (charWeightsInt4.s0 >> 4) - 8; - charWeights0.s1 = (charWeightsInt4.s0 & 15) - 8; - charWeights0.s2 = (charWeightsInt4.s1 >> 4) - 8; - charWeights0.s3 = (charWeightsInt4.s1 & 15) - 8; - charWeights0.s4 = (charWeightsInt4.s2 >> 4) - 8; - charWeights0.s5 = (charWeightsInt4.s2 & 15) - 8; - charWeights0.s6 = (charWeightsInt4.s3 >> 4) - 8; - charWeights0.s7 = (charWeightsInt4.s3 & 15) - 8; - charWeights0.s8 = (charWeightsInt4.s4 >> 4) - 8; - charWeights0.s9 = (charWeightsInt4.s4 & 15) - 8; - charWeights0.sa = (charWeightsInt4.s5 >> 4) - 8; - charWeights0.sb = (charWeightsInt4.s5 & 15) - 8; - charWeights0.sc = (charWeightsInt4.s6 >> 4) - 8; - charWeights0.sd = (charWeightsInt4.s6 & 15) - 8; - charWeights0.se = (charWeightsInt4.s7 >> 4) - 8; - charWeights0.sf = (charWeightsInt4.s7 & 15) - 8; - - charWeights1.s0 = (charWeightsInt4.s8 >> 4) - 8; - charWeights1.s1 = (charWeightsInt4.s8 & 15) - 8; - charWeights1.s2 = (charWeightsInt4.s9 >> 4) - 8; - charWeights1.s3 = (charWeightsInt4.s9 & 15) - 8; - charWeights1.s4 = (charWeightsInt4.sa >> 4) - 8; - charWeights1.s5 = (charWeightsInt4.sa & 15) - 8; - charWeights1.s6 = (charWeightsInt4.sb >> 4) - 8; - charWeights1.s7 = (charWeightsInt4.sb & 15) - 8; - charWeights1.s8 = (charWeightsInt4.sc >> 4) - 8; - charWeights1.s9 = (charWeightsInt4.sc & 15) - 8; - charWeights1.sa = (charWeightsInt4.sd >> 4) - 8; - charWeights1.sb = (charWeightsInt4.sd & 15) - 8; - charWeights1.sc = (charWeightsInt4.se >> 4) - 8; - charWeights1.sd = (charWeightsInt4.se & 15) - 8; - charWeights1.se = (charWeightsInt4.sf >> 4) - 8; - charWeights1.sf = (charWeightsInt4.sf & 15) - 8; - FLOAT16 weights0 = CONVERT_FLOAT16(charWeights0); - FLOAT16 weights1 = CONVERT_FLOAT16(charWeights1); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; - -#else - FLOAT16 weights0 = vload16(0, weight + weight_offset + k * weight_oc_offset); - FLOAT16 weights1 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); -#endif - out.s0 += dot(in.s0123, weights0.s0123); - out.s0 += dot(in.s4567, weights0.s4567); - out.s0 += dot(in.s89ab, weights0.s89ab); - out.s0 += dot(in.scdef, weights0.scdef); - out.s1 += dot(in.s0123, weights1.s0123); - out.s1 += dot(in.s4567, weights1.s4567); - out.s1 += dot(in.s89ab, weights1.s89ab); - out.s1 += dot(in.scdef, weights1.scdef); - } - -#ifdef INPUT_CHANNEL_LEAVE - { - int k = srcChannelC4/4 - 1; - FLOAT16 in = 0; - int k4 = k * 4; - in.s0123 = vload4(0, input + input_offset + k4 * wh); - in.s4567 = k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0; - in.s89ab = k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0; - in.scdef = k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0; -#if (defined USE_LOW_BIT_WEIGHT_INT8) - FLOAT16 weights0 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); - FLOAT16 weights1 = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - uchar16 charWeightsInt4 = vload16(0, weight + weight_offset + k * weight_oc_offset); - char16 charWeights0 = 0; - char16 charWeights1 = 0; - charWeights0.s0 = (charWeightsInt4.s0 >> 4) - 8; - charWeights0.s1 = (charWeightsInt4.s0 & 15) - 8; - charWeights0.s2 = (charWeightsInt4.s1 >> 4) - 8; - charWeights0.s3 = (charWeightsInt4.s1 & 15) - 8; - charWeights0.s4 = (charWeightsInt4.s2 >> 4) - 8; - charWeights0.s5 = (charWeightsInt4.s2 & 15) - 8; - charWeights0.s6 = (charWeightsInt4.s3 >> 4) - 8; - charWeights0.s7 = (charWeightsInt4.s3 & 15) - 8; - charWeights0.s8 = (charWeightsInt4.s4 >> 4) - 8; - charWeights0.s9 = (charWeightsInt4.s4 & 15) - 8; - charWeights0.sa = (charWeightsInt4.s5 >> 4) - 8; - charWeights0.sb = (charWeightsInt4.s5 & 15) - 8; - charWeights0.sc = (charWeightsInt4.s6 >> 4) - 8; - charWeights0.sd = (charWeightsInt4.s6 & 15) - 8; - charWeights0.se = (charWeightsInt4.s7 >> 4) - 8; - charWeights0.sf = (charWeightsInt4.s7 & 15) - 8; - - charWeights1.s0 = (charWeightsInt4.s8 >> 4) - 8; - charWeights1.s1 = (charWeightsInt4.s8 & 15) - 8; - charWeights1.s2 = (charWeightsInt4.s9 >> 4) - 8; - charWeights1.s3 = (charWeightsInt4.s9 & 15) - 8; - charWeights1.s4 = (charWeightsInt4.sa >> 4) - 8; - charWeights1.s5 = (charWeightsInt4.sa & 15) - 8; - charWeights1.s6 = (charWeightsInt4.sb >> 4) - 8; - charWeights1.s7 = (charWeightsInt4.sb & 15) - 8; - charWeights1.s8 = (charWeightsInt4.sc >> 4) - 8; - charWeights1.s9 = (charWeightsInt4.sc & 15) - 8; - charWeights1.sa = (charWeightsInt4.sd >> 4) - 8; - charWeights1.sb = (charWeightsInt4.sd & 15) - 8; - charWeights1.sc = (charWeightsInt4.se >> 4) - 8; - charWeights1.sd = (charWeightsInt4.se & 15) - 8; - charWeights1.se = (charWeightsInt4.sf >> 4) - 8; - charWeights1.sf = (charWeightsInt4.sf & 15) - 8; - FLOAT16 weights0 = CONVERT_FLOAT16(charWeights0); - FLOAT16 weights1 = CONVERT_FLOAT16(charWeights1); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; - -#else - FLOAT16 weights0 = vload16(0, weight + weight_offset + k * weight_oc_offset); - FLOAT16 weights1 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); -#endif - out.s0 += dot(in.s0123, weights0.s0123); - out.s0 += dot(in.s4567, weights0.s4567); - out.s0 += dot(in.s89ab, weights0.s89ab); - out.s0 += dot(in.scdef, weights0.scdef); - out.s1 += dot(in.s0123, weights1.s0123); - out.s1 += dot(in.s4567, weights1.s4567); - out.s1 += dot(in.s89ab, weights1.s89ab); - out.s1 += dot(in.scdef, weights1.scdef); - } -#endif - -#if (defined USE_LOW_BIT_WEIGHT_INT8) || (defined USE_LOW_BIT_WEIGHT_INT4) - out = bias0 + mad(out, Scale, sum * Offset); -#endif -#ifdef RELU - out = fmax(out, (FLOAT2)0); -#endif - -#ifdef RELU6 - out = clamp(out, (FLOAT2)0, (FLOAT2)6); -#endif - - vstore2(out, out_c_idx, output+out_offset); -} - -__kernel void gemm_conv_c1_buf(GLOBAL_SIZE_DIM2 - __global const FLOAT* input, -#if (defined USE_LOW_BIT_WEIGHT_INT8) - __global const char *weight, - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - __global const uchar *weight, - __global const FLOAT *dequantScale, - __global const FLOAT *dequantOffset, -#else - __global const FLOAT *weight, -#endif - __global const FLOAT *bias, - __global FLOAT* output, - __private const int dstChannelC4, - __private const int srcChannelC4, - __private const int batch, - __private const int height, - __private const int width) { - const int out_c_w_idx = get_global_id(0); //c/4 w - const int out_b_h_idx = get_global_id(1); //b h - - UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); - - const int out_c_idx = out_c_w_idx / width; - const int out_w_idx = out_c_w_idx % width; - const int out_b_idx = out_b_h_idx / height; - const int out_h_idx = out_b_h_idx % height; - - FLOAT bias0 = bias[out_c_idx]; - FLOAT sum = 0; - FLOAT out = 0; - - int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; - int out_offset = ((out_b_idx * dstChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; - int wh = width * height * 4; -#if (defined USE_LOW_BIT_WEIGHT_INT4) - int weight_offset = out_c_idx * 8; - int weight_oc_offset = dstChannelC4 * 32; -#else - int weight_offset = out_c_idx * 16; - int weight_oc_offset = dstChannelC4 * 64; -#endif - -#if (defined USE_LOW_BIT_WEIGHT_INT8) || (defined USE_LOW_BIT_WEIGHT_INT4) - const FLOAT Scale = dequantScale[out_c_idx]; - const FLOAT Offset = dequantOffset[out_c_idx]; -#endif -#ifdef INPUT_CHANNEL_LEAVE - for (int k = 0; k < srcChannelC4/4 - 1; ++k) { -#else - for (int k = 0; k < srcChannelC4/4; ++k) { -#endif -#ifdef WIDTH_HEIGHT_1 - FLOAT16 in = vload16(k, input + input_offset); -#else - FLOAT16 in; - int k4 = k << 2; - in.s0123 = vload4(0, input + input_offset + k4 * wh); - in.s4567 = vload4(0, input + input_offset + (k4 + 1) * wh); - in.s89ab = vload4(0, input + input_offset + (k4 + 2) * wh); - in.scdef = vload4(0, input + input_offset + (k4 + 3) * wh); -#endif -#if (defined USE_LOW_BIT_WEIGHT_INT8) - FLOAT16 weights = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - uchar8 charWeightsInt4 = vload8(0, weight + weight_offset + k * weight_oc_offset); - char16 charWeights = 0; - charWeights.s0 = (charWeightsInt4.s0 >> 4) - 8; - charWeights.s1 = (charWeightsInt4.s0 & 15) - 8; - charWeights.s2 = (charWeightsInt4.s1 >> 4) - 8; - charWeights.s3 = (charWeightsInt4.s1 & 15) - 8; - charWeights.s4 = (charWeightsInt4.s2 >> 4) - 8; - charWeights.s5 = (charWeightsInt4.s2 & 15) - 8; - charWeights.s6 = (charWeightsInt4.s3 >> 4) - 8; - charWeights.s7 = (charWeightsInt4.s3 & 15) - 8; - charWeights.s8 = (charWeightsInt4.s4 >> 4) - 8; - charWeights.s9 = (charWeightsInt4.s4 & 15) - 8; - charWeights.sa = (charWeightsInt4.s5 >> 4) - 8; - charWeights.sb = (charWeightsInt4.s5 & 15) - 8; - charWeights.sc = (charWeightsInt4.s6 >> 4) - 8; - charWeights.sd = (charWeightsInt4.s6 & 15) - 8; - charWeights.se = (charWeightsInt4.s7 >> 4) - 8; - charWeights.sf = (charWeightsInt4.s7 & 15) - 8; - FLOAT16 weights = CONVERT_FLOAT16(charWeights); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#else - FLOAT16 weights = vload16(0, weight + weight_offset + k * weight_oc_offset); -#endif - out += dot(in.s0123, weights.s0123); - out += dot(in.s4567, weights.s4567); - out += dot(in.s89ab, weights.s89ab); - out += dot(in.scdef, weights.scdef); - } -#ifdef INPUT_CHANNEL_LEAVE - { - int k = srcChannelC4/4 - 1; - FLOAT16 in = 0; - int k4 = k * 4; - in.s0123 = vload4(0, input + input_offset + k4 * wh); - in.s4567 = k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0; - in.s89ab = k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0; - in.scdef = k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0; -#if (defined USE_LOW_BIT_WEIGHT_INT8) - FLOAT16 weights = CONVERT_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#elif (defined USE_LOW_BIT_WEIGHT_INT4) - uchar8 charWeightsInt4 = vload8(0, weight + weight_offset + k * weight_oc_offset); - char16 charWeights = 0; - charWeights.s0 = (charWeightsInt4.s0 >> 4) - 8; - charWeights.s1 = (charWeightsInt4.s0 & 15) - 8; - charWeights.s2 = (charWeightsInt4.s1 >> 4) - 8; - charWeights.s3 = (charWeightsInt4.s1 & 15) - 8; - charWeights.s4 = (charWeightsInt4.s2 >> 4) - 8; - charWeights.s5 = (charWeightsInt4.s2 & 15) - 8; - charWeights.s6 = (charWeightsInt4.s3 >> 4) - 8; - charWeights.s7 = (charWeightsInt4.s3 & 15) - 8; - charWeights.s8 = (charWeightsInt4.s4 >> 4) - 8; - charWeights.s9 = (charWeightsInt4.s4 & 15) - 8; - charWeights.sa = (charWeightsInt4.s5 >> 4) - 8; - charWeights.sb = (charWeightsInt4.s5 & 15) - 8; - charWeights.sc = (charWeightsInt4.s6 >> 4) - 8; - charWeights.sd = (charWeightsInt4.s6 & 15) - 8; - charWeights.se = (charWeightsInt4.s7 >> 4) - 8; - charWeights.sf = (charWeightsInt4.s7 & 15) - 8; - FLOAT16 weights = CONVERT_FLOAT16(charWeights); - sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; -#else - FLOAT16 weights = vload16(0, weight + weight_offset + k * weight_oc_offset); -#endif - out += dot(in.s0123, weights.s0123); - out += dot(in.s4567, weights.s4567); - out += dot(in.s89ab, weights.s89ab); - out += dot(in.scdef, weights.scdef); - } -#endif - -#if (defined USE_LOW_BIT_WEIGHT_INT8) || (defined USE_LOW_BIT_WEIGHT_INT4) - out = bias0 + mad(out, Scale, sum * Offset); -#endif -#ifdef RELU - out = fmax(out, )0); -#endif - -#ifdef RELU6 - out = clamp(out, 0, 6); -#endif - - //vstore4(out, pos.x, output+out_offset); - output[out_offset + out_c_idx] = out; + vstore4(CONVERT_FLOAT4(o1.s0123), 1, output+out_offset); + vstore4(CONVERT_FLOAT4(o1.s4567), 1, output+out_offset+4*width); + vstore4(CONVERT_FLOAT4(o1.s89ab), 1, output+out_offset+8*width); + vstore4(CONVERT_FLOAT4(o1.scdef), 1, output+out_offset+12*width); } diff --git a/source/backend/opencl/execution/cl/gemv_conv1x1_buf.cl b/source/backend/opencl/execution/cl/gemv_conv1x1_buf.cl new file mode 100644 index 000000000..ffe697e66 --- /dev/null +++ b/source/backend/opencl/execution/cl/gemv_conv1x1_buf.cl @@ -0,0 +1,1127 @@ +#ifdef MNN_SUPPORT_FP16 +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#endif + +#define GLOBAL_SIZE_DIM2 \ + __private int global_size_dim0, __private int global_size_dim1, + +#define UNIFORM_BOUNDRY_CHECK(index0, index1) \ + if(index0 >= global_size_dim0 || index1 >= global_size_dim1) { \ + return; \ + } + +#define UCHAR16_TO_2CHAR16(a, b, c) \ + a.s0 = (c.s0 >> 4) - 8; a.s1 = (c.s0 & 15) - 8; a.s2 = (c.s1 >> 4) - 8; a.s3 = (c.s1 & 15) - 8; a.s4 = (c.s2 >> 4) - 8; a.s5 = (c.s2 & 15) - 8; a.s6 = (c.s3 >> 4) - 8; a.s7 = (c.s3 & 15) - 8; \ + a.s8 = (c.s4 >> 4) - 8; a.s9 = (c.s4 & 15) - 8; a.sa = (c.s5 >> 4) - 8; a.sb = (c.s5 & 15) - 8; a.sc = (c.s6 >> 4) - 8; a.sd = (c.s6 & 15) - 8; a.se = (c.s7 >> 4) - 8; a.sf = (c.s7 & 15) - 8; \ + b.s0 = (c.s8 >> 4) - 8; b.s1 = (c.s8 & 15) - 8; b.s2 = (c.s9 >> 4) - 8; b.s3 = (c.s9 & 15) - 8; b.s4 = (c.sa >> 4) - 8; b.s5 = (c.sa & 15) - 8; b.s6 = (c.sb >> 4) - 8; b.s7 = (c.sb & 15) - 8; \ + b.s8 = (c.sc >> 4) - 8; b.s9 = (c.sc & 15) - 8; b.sa = (c.sd >> 4) - 8; b.sb = (c.sd & 15) - 8; b.sc = (c.se >> 4) - 8; b.sd = (c.se & 15) - 8; b.se = (c.sf >> 4) - 8; b.sf = (c.sf & 15) - 8; + +#define UCHAR8_TO_CHAR16(a, c) \ + a.s0 = (c.s0 >> 4) - 8; a.s1 = (c.s0 & 15) - 8; a.s2 = (c.s1 >> 4) - 8; a.s3 = (c.s1 & 15) - 8; a.s4 = (c.s2 >> 4) - 8; a.s5 = (c.s2 & 15) - 8; a.s6 = (c.s3 >> 4) - 8; a.s7 = (c.s3 & 15) - 8; \ + a.s8 = (c.s4 >> 4) - 8; a.s9 = (c.s4 & 15) - 8; a.sa = (c.s5 >> 4) - 8; a.sb = (c.s5 & 15) - 8; a.sc = (c.s6 >> 4) - 8; a.sd = (c.s6 & 15) - 8; a.se = (c.s7 >> 4) - 8; a.sf = (c.s7 & 15) - 8; + +#define DOT16X16(a, b, c) \ + c += dot(a.s0123, b.s0123); \ + c += dot(a.s4567, b.s4567); \ + c += dot(a.s89ab, b.s89ab); \ + c += dot(a.scdef, b.scdef); + +__constant sampler_t SAMPLER = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST; + +__kernel void gemm_conv_c4_buf(GLOBAL_SIZE_DIM2 + __global const FLOAT* input, +#if (defined USE_LOW_BIT_WEIGHT_INT8) + __global const char *weight, +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + __global const uchar *weight, +#endif + __global const float *dequantScale, + __global const float *dequantOffset, + __global const FLOAT *bias, + __global FLOAT* output, + __private const int dstChannelC4, + __private const int srcChannelC4, + __private const int batch, + __private const int height, + __private const int width) { + const int out_c_w_idx = get_global_id(0); //c/4 w + const int out_b_h_idx = get_global_id(1); //b h + + UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); + + const int out_c_idx = out_c_w_idx / width; + const int out_w_idx = out_c_w_idx % width; +#ifdef BACTH_BLOCK2 + const int out_b_idx = (out_b_h_idx / height) << 1; +#else + const int out_b_idx = out_b_h_idx / height; +#endif + const int out_h_idx = out_b_h_idx % height; + + COMPUTE_FLOAT4 bias0 = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, bias)); + COMPUTE_FLOAT sum = 0; + COMPUTE_FLOAT4 out = 0; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT4 out1 = 0; + int input_offset1 = (((out_b_idx + 1) * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + bool isValidBatch = out_b_idx + 1 < batch; +#endif + + int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + int out_offset = (((out_b_idx * dstChannelC4 + out_c_idx) * height + out_h_idx) * width + out_w_idx) * 4; +#ifndef WIDTH_HEIGHT_1 + int wh = width * height * 4; +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT4) + int weight_offset = out_c_idx * 4 * 8; + int weight_oc_offset = dstChannelC4 * 32; +#else + int weight_offset = out_c_idx * 4 * 16; + int weight_oc_offset = dstChannelC4 * 64; +#endif + + const COMPUTE_FLOAT4 Scale = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, dequantScale)); + const COMPUTE_FLOAT4 Offset = CONVERT_COMPUTE_FLOAT4(vload4(out_c_idx, dequantOffset)); + +#ifdef INPUT_CHANNEL_LEAVE + const int srcChannelC16 = (srcChannelC4 + 3) >> 2; + for (int k = 0; k < srcChannelC16 - 1; ++k) { +#else + for (int k = 0; k < srcChannelC4/4; ++k) { +#endif +#ifdef WIDTH_HEIGHT_1 + COMPUTE_FLOAT16 in = CONVERT_COMPUTE_FLOAT16(vload16(k, input + input_offset)); +#else + int k4 = k << 2; + COMPUTE_FLOAT16 in; + in.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 1) * wh)); + in.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 2) * wh)); + in.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 3) * wh)); +#endif + sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ +#ifdef WIDTH_HEIGHT_1 + in1 = CONVERT_COMPUTE_FLOAT16(vload16(k, input + input_offset1)); +#else + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 1) * wh)); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 2) * wh)); + in1.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 3) * wh)); +#endif + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT8) + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); + COMPUTE_FLOAT16 weights2 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 32)); + COMPUTE_FLOAT16 weights3 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 48)); +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + uchar16 charWeightsInt40 = vload16(0, weight + weight_offset + k * weight_oc_offset); + uchar16 charWeightsInt41 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); + COMPUTE_FLOAT16 weights0, weights1, weights2, weights3; + { + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt40); + weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + } + + { + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt41); + weights2 = CONVERT_COMPUTE_FLOAT16(charWeights0); + weights3 = CONVERT_COMPUTE_FLOAT16(charWeights1); + } +#endif + DOT16X16(in, weights0, out.s0); + DOT16X16(in, weights1, out.s1); + DOT16X16(in, weights2, out.s2); + DOT16X16(in, weights3, out.s3); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1.s0); + DOT16X16(in1, weights1, out1.s1); + DOT16X16(in1, weights2, out1.s2); + DOT16X16(in1, weights3, out1.s3); +#endif + } +#ifdef INPUT_CHANNEL_LEAVE + { + int k = srcChannelC16 - 1; + int k4 = k * 4; + COMPUTE_FLOAT16 in; + in.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0); + in.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0); + in.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0); + sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; + +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 1) * wh) : (FLOAT4)0); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 2) * wh) : (FLOAT4)0); + in1.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 3) * wh) : (FLOAT4)0); + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT8) + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); + COMPUTE_FLOAT16 weights2 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 32)); + COMPUTE_FLOAT16 weights3 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 48)); +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + uchar16 charWeightsInt40 = vload16(0, weight + weight_offset + k * weight_oc_offset); + uchar16 charWeightsInt41 = vload16(0, weight + weight_offset + k * weight_oc_offset + 16); + COMPUTE_FLOAT16 weights0, weights1, weights2, weights3; + { + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt40); + weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + } + + { + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt41); + weights2 = CONVERT_COMPUTE_FLOAT16(charWeights0); + weights3 = CONVERT_COMPUTE_FLOAT16(charWeights1); + } +#endif + DOT16X16(in, weights0, out.s0); + DOT16X16(in, weights1, out.s1); + DOT16X16(in, weights2, out.s2); + DOT16X16(in, weights3, out.s3); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1.s0); + DOT16X16(in1, weights1, out1.s1); + DOT16X16(in1, weights2, out1.s2); + DOT16X16(in1, weights3, out1.s3); +#endif + } +#endif + + out = bias0 + mad(out, Scale, sum * Offset); +#ifdef RELU + out = fmax(out, (COMPUTE_FLOAT4)0); +#endif + +#ifdef RELU6 + out = clamp(out, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); +#endif + + vstore4(CONVERT_FLOAT4(out), 0, output+out_offset); +#ifdef BACTH_BLOCK2 + if(isValidBatch){ + out_offset += dstChannelC4 * height * width * 4; + out1 = bias0 + mad(out1, Scale, sum1 * Offset); +#ifdef RELU + out1 = fmax(out1, (COMPUTE_FLOAT4)0); +#endif + +#ifdef RELU6 + out1 = clamp(out1, (COMPUTE_FLOAT4)0, (COMPUTE_FLOAT4)6); +#endif + + vstore4(CONVERT_FLOAT4(out1), 0, output+out_offset); + } +#endif +} + +__kernel void gemm_conv_c2_buf(GLOBAL_SIZE_DIM2 + __global const FLOAT* input, +#if (defined USE_LOW_BIT_WEIGHT_INT8) + __global const char *weight, +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + __global const uchar *weight, +#endif + __global const float *dequantScale, + __global const float *dequantOffset, + __global const FLOAT *bias, + __global FLOAT* output, + __private const int dstChannelC4, + __private const int srcChannelC4, + __private const int batch, + __private const int height, + __private const int width) { + const int out_c_w_idx = get_global_id(0); //c/4 w + const int out_b_h_idx = get_global_id(1); //b h + + UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); + + const int out_c_idx = out_c_w_idx / width; + const int out_w_idx = out_c_w_idx % width; +#ifdef BACTH_BLOCK2 + const int out_b_idx = (out_b_h_idx / height) << 1; +#else + const int out_b_idx = out_b_h_idx / height; +#endif + const int out_h_idx = out_b_h_idx % height; + + COMPUTE_FLOAT2 bias0 = CONVERT_COMPUTE_FLOAT2(vload2(out_c_idx, bias)); + COMPUTE_FLOAT sum = 0; + COMPUTE_FLOAT2 out = 0; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT2 out1 = 0; + int input_offset1 = (((out_b_idx + 1) * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + bool isValidBatch = out_b_idx + 1 < batch; +#endif + int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + int out_offset = (((out_b_idx * dstChannelC4 + (out_c_idx * 2) / 4) * height + out_h_idx) * width + out_w_idx) * 4 + ((out_c_idx * 2)%4); +#ifndef WIDTH_HEIGHT_1 + int wh = width * height * 4; +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT4) + int weight_offset = out_c_idx * 2 * 8; + int weight_oc_offset = dstChannelC4 * 32; +#else + int weight_offset = out_c_idx * 2 * 16; + int weight_oc_offset = dstChannelC4 * 64; +#endif + + const COMPUTE_FLOAT2 Scale = CONVERT_COMPUTE_FLOAT2(vload2(out_c_idx, dequantScale)); + const COMPUTE_FLOAT2 Offset = CONVERT_COMPUTE_FLOAT2(vload2(out_c_idx, dequantOffset)); + +#ifdef INPUT_CHANNEL_LEAVE + const int srcChannelC16 = (srcChannelC4 + 3) >> 2; + for (int k = 0; k < srcChannelC16 - 1; ++k) { +#else + for (int k = 0; k < srcChannelC4/4; ++k) { +#endif +#ifdef WIDTH_HEIGHT_1 + COMPUTE_FLOAT16 in = CONVERT_COMPUTE_FLOAT16(vload16(k, input + input_offset)); +#else + COMPUTE_FLOAT16 in; + int k4 = k << 2; + in.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 1) * wh)); + in.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 2) * wh)); + in.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 3) * wh)); +#endif + sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ +#ifdef WIDTH_HEIGHT_1 + in1 = CONVERT_COMPUTE_FLOAT16(vload16(k, input + input_offset1)); +#else + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 1) * wh)); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 2) * wh)); + in1.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 3) * wh)); +#endif + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT8) + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + uchar16 charWeightsInt4 = vload16(0, weight + weight_offset + k * weight_oc_offset); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); +#endif + DOT16X16(in, weights0, out.s0); + DOT16X16(in, weights1, out.s1); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1.s0); + DOT16X16(in1, weights1, out1.s1); +#endif + } + +#ifdef INPUT_CHANNEL_LEAVE + { + int k = srcChannelC16 - 1; + COMPUTE_FLOAT16 in = 0; + int k4 = k * 4; + in.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0); + in.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0); + in.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0); + sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 1) * wh) : (FLOAT4)0); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 2) * wh) : (FLOAT4)0); + in1.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 3) * wh) : (FLOAT4)0); + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT8) + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset + 16)); +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + uchar16 charWeightsInt4 = vload16(0, weight + weight_offset + k * weight_oc_offset); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); +#endif + DOT16X16(in, weights0, out.s0); + DOT16X16(in, weights1, out.s1); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1.s0); + DOT16X16(in1, weights1, out1.s1); +#endif + } +#endif + + out = bias0 + mad(out, Scale, sum * Offset); +#ifdef RELU + out = fmax(out, (COMPUTE_FLOAT2)0); +#endif + +#ifdef RELU6 + out = clamp(out, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); +#endif + + vstore2(CONVERT_FLOAT2(out), 0, output+out_offset); +#ifdef BACTH_BLOCK2 + if(isValidBatch){ + out_offset += dstChannelC4 * height * width * 4; + out1 = bias0 + mad(out1, Scale, sum1 * Offset); +#ifdef RELU + out1 = fmax(out1, (COMPUTE_FLOAT2)0); +#endif + +#ifdef RELU6 + out1 = clamp(out1, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); +#endif + + vstore2(CONVERT_FLOAT2(out1), 0, output+out_offset); + } +#endif +} + +__kernel void gemm_conv_c1_buf(GLOBAL_SIZE_DIM2 + __global const FLOAT* input, +#if (defined USE_LOW_BIT_WEIGHT_INT8) + __global const char *weight, +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + __global const uchar *weight, +#endif + __global const float *dequantScale, + __global const float *dequantOffset, + __global const FLOAT *bias, + __global FLOAT* output, + __private const int dstChannelC4, + __private const int srcChannelC4, + __private const int batch, + __private const int height, + __private const int width) { + const int out_c_w_idx = get_global_id(0); //c/4 w + const int out_b_h_idx = get_global_id(1); //b h + + UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); + + const int out_c_idx = out_c_w_idx / width; + const int out_w_idx = out_c_w_idx % width; +#ifdef BACTH_BLOCK2 + const int out_b_idx = (out_b_h_idx / height) << 1; +#else + const int out_b_idx = out_b_h_idx / height; +#endif + const int out_h_idx = out_b_h_idx % height; + + COMPUTE_FLOAT bias0 = bias[out_c_idx]; + COMPUTE_FLOAT sum = 0; + COMPUTE_FLOAT out = 0; + +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT out1 = 0; + int input_offset1 = (((out_b_idx + 1) * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + bool isValidBatch = out_b_idx + 1 < batch; +#endif + + int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + int out_offset = (((out_b_idx * dstChannelC4 + out_c_idx/4) * height + out_h_idx) * width + out_w_idx) * 4 + (out_c_idx%4); +#ifndef WIDTH_HEIGHT_1 + int wh = width * height * 4; +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT4) + int weight_offset = out_c_idx * 8; + int weight_oc_offset = dstChannelC4 * 32; +#else + int weight_offset = out_c_idx * 16; + int weight_oc_offset = dstChannelC4 * 64; +#endif + + const COMPUTE_FLOAT Scale = dequantScale[out_c_idx]; + const COMPUTE_FLOAT Offset = dequantOffset[out_c_idx]; +#ifdef INPUT_CHANNEL_LEAVE + const int srcChannelC16 = (srcChannelC4 + 3) >> 2; + for (int k = 0; k < srcChannelC16 - 1; ++k) { +#else + for (int k = 0; k < srcChannelC4/4; ++k) { +#endif +#ifdef WIDTH_HEIGHT_1 + COMPUTE_FLOAT16 in = CONVERT_COMPUTE_FLOAT16(vload16(k, input + input_offset)); +#else + COMPUTE_FLOAT16 in; + int k4 = k << 2; + in.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 1) * wh)); + in.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 2) * wh)); + in.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 3) * wh)); +#endif + sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ +#ifdef WIDTH_HEIGHT_1 + in1 = CONVERT_COMPUTE_FLOAT16(vload16(k, input + input_offset1)); +#else + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 1) * wh)); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 2) * wh)); + in1.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 3) * wh)); +#endif + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT8) + COMPUTE_FLOAT16 weights = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + uchar8 charWeightsInt4 = vload8(0, weight + weight_offset + k * weight_oc_offset); + char16 charWeights = 0; + UCHAR8_TO_CHAR16(charWeights, charWeightsInt4); + COMPUTE_FLOAT16 weights = CONVERT_COMPUTE_FLOAT16(charWeights); +#endif + DOT16X16(in, weights, out); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights, out1); +#endif + } +#ifdef INPUT_CHANNEL_LEAVE + { + int k = srcChannelC16 - 1; + COMPUTE_FLOAT16 in = 0; + int k4 = k * 4; + in.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0); + in.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0); + in.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0); + sum += in.s0 + in.s1 + in.s2 + in.s3 + in.s4 + in.s5 + in.s6 + in.s7 + in.s8 + in.s9 + in.sa + in.sb + in.sc + in.sd + in.se + in.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 1) * wh) : (FLOAT4)0); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 2) * wh) : (FLOAT4)0); + in1.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset1 + (k4 + 3) * wh) : (FLOAT4)0); + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif +#if (defined USE_LOW_BIT_WEIGHT_INT8) + COMPUTE_FLOAT16 weights = CONVERT_COMPUTE_FLOAT16(vload16(0, weight + weight_offset + k * weight_oc_offset)); +#elif (defined USE_LOW_BIT_WEIGHT_INT4) + uchar8 charWeightsInt4 = vload8(0, weight + weight_offset + k * weight_oc_offset); + char16 charWeights = 0; + UCHAR8_TO_CHAR16(charWeights, charWeightsInt4); + COMPUTE_FLOAT16 weights = CONVERT_COMPUTE_FLOAT16(charWeights); +#endif + DOT16X16(in, weights, out); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights, out1); +#endif + } +#endif + + out = bias0 + mad(out, Scale, sum * Offset); +#ifdef RELU + out = fmax(out, 0); +#endif + +#ifdef RELU6 + out = clamp(out, 0, 6); +#endif + output[out_offset] = out; +#ifdef BACTH_BLOCK2 + if(isValidBatch){ + out_offset += dstChannelC4 * height * width * 4; + out1 = bias0 + mad(out1, Scale, sum1 * Offset); +#ifdef RELU + out1 = fmax(out1, 0); +#endif + +#ifdef RELU6 + out1 = clamp(out1, 0, 6); +#endif + + output[out_offset] = out1; + } +#endif +} + +__kernel void gemm_conv_c2_image(GLOBAL_SIZE_DIM2 + __global const FLOAT* input, + __read_only image2d_t weight, + __global const float *dequantScale, + __global const float *dequantOffset, + __global const FLOAT *bias, + __global FLOAT* output, + __private const int dstChannelC4, + __private const int srcChannelC4, + __private const int batch, + __private const int height, + __private const int width) { + const int out_c_w_idx = get_global_id(0); //c/4 w + const int out_b_h_idx = get_global_id(1); //b h + UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); + + const int out_c_idx = (out_c_w_idx / width) << 1; + const int out_w_idx = out_c_w_idx % width; +#ifdef BACTH_BLOCK2 + const int out_b_idx = (out_b_h_idx / height) << 1; +#else + const int out_b_idx = out_b_h_idx / height; +#endif + const int out_h_idx = out_b_h_idx % height; + + COMPUTE_FLOAT2 bias0 = CONVERT_COMPUTE_FLOAT2(vload2(0, bias + out_c_idx)); + COMPUTE_FLOAT2 out = 0; + COMPUTE_FLOAT sum = 0; + +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT2 out1 = 0; + int input_offset1 = (((out_b_idx + 1) * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + bool isValidBatch = out_b_idx + 1 < batch; +#endif + + int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + int out_offset = (((out_b_idx * dstChannelC4 + out_c_idx/4) * height + out_h_idx) * width + out_w_idx) * 4 + (out_c_idx % 4); +#ifndef WIDTH_HEIGHT_1 + int wh = width * height * 4; +#endif + + const COMPUTE_FLOAT2 Scale = CONVERT_COMPUTE_FLOAT2(vload2(0, dequantScale + out_c_idx)); + const COMPUTE_FLOAT2 Offset = CONVERT_COMPUTE_FLOAT2(vload2(0, dequantOffset + out_c_idx)); + +#if (defined USE_LOW_BIT_WEIGHT_INT8) +#ifdef INPUT_CHANNEL_LEAVE + const int srcChannelC16 = (srcChannelC4 + 3) >> 2; + for (int k = 0; k < srcChannelC16-1; k++) { +#else + for (int k = 0; k < srcChannelC4/4; k++) { +#endif +#ifdef WIDTH_HEIGHT_1 + COMPUTE_FLOAT16 in0 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset + k * 16)); +#else + COMPUTE_FLOAT16 in0 = 0; + int k4 = k * 4; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 1) * wh)); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 2) * wh)); + in0.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 3) * wh)); + +#endif + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ +#ifdef WIDTH_HEIGHT_1 + in1 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset1 + k * 16)); +#else + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 1) * wh)); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 2) * wh)); + in1.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 3) * wh)); +#endif + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif + + { + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(as_char16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k)))); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(as_char16(read_imagef(weight, SAMPLER, (int2)(out_c_idx + 1, k)))); + DOT16X16(in0, weights0, out.s0); + DOT16X16(in0, weights1, out.s1); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1.s0); + DOT16X16(in1, weights1, out1.s1); +#endif + } + } +#ifdef INPUT_CHANNEL_LEAVE + { + int k = srcChannelC16 - 1; + COMPUTE_FLOAT16 in0 = 0; + int k4 = k * 4; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0); + in0.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0); + + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < vload4(0, input + input_offset1 + (k4 + 1) * wh) : (FLOAT4)0); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < vload4(0, input + input_offset1 + (k4 + 2) * wh) : (FLOAT4)0); + in1.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < vload4(0, input + input_offset1 + (k4 + 3) * wh) : (FLOAT4)0); + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif + + { + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(as_char16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k)))); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(as_char16(read_imagef(weight, SAMPLER, (int2)(out_c_idx + 1, k)))); + DOT16X16(in0, weights0, out.s0); + DOT16X16(in0, weights1, out.s1); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1.s0); + DOT16X16(in1, weights1, out1.s1); +#endif + } + } +#endif +#elif (defined USE_LOW_BIT_WEIGHT_INT4) +#ifdef INPUT_CHANNEL_LEAVE + const int srcChannelC32 = (srcChannelC4 + 7) >> 3; + for (int k = 0; k < srcChannelC32-1; k++) { +#else + for (int k = 0; k < srcChannelC4/8; k++) { +#endif +#ifdef WIDTH_HEIGHT_1 + COMPUTE_FLOAT16 in0 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset + k * 32)); + COMPUTE_FLOAT16 in1 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset + k * 32 + 16)); +#else + COMPUTE_FLOAT16 in0 = 0; + COMPUTE_FLOAT16 in1 = 0; + int k8 = k * 8; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k8 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 1) * wh)); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 2) * wh)); + in0.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 3) * wh)); + + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 4) * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 5) * wh)); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 6) * wh)); + in1.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 7) * wh)); + +#endif + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; + sum += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in2 = 0; + COMPUTE_FLOAT16 in3 = 0; + if(isValidBatch){ +#ifdef WIDTH_HEIGHT_1 + in2 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset1 + k * 32)); + in3 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset1 + k * 32 + 16)); +#else + in2.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k8 * wh)); + in2.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 1) * wh)); + in2.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 2) * wh)); + in2.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 3) * wh)); + + in3.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 4) * wh)); + in3.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 5) * wh)); + in3.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 6) * wh)); + in3.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 7) * wh)); +#endif + sum1 += in2.s0 + in2.s1 + in2.s2 + in2.s3 + in2.s4 + in2.s5 + in2.s6 + in2.s7 + in2.s8 + in2.s9 + in2.sa + in2.sb + in2.sc + in2.sd + in2.se + in2.sf; + sum1 += in3.s0 + in3.s1 + in3.s2 + in3.s3 + in3.s4 + in3.s5 + in3.s6 + in3.s7 + in3.s8 + in3.s9 + in3.sa + in3.sb + in3.sc + in3.sd + in3.se + in3.sf; + } +#endif + + { + uchar16 charWeightsInt4 = as_uchar16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k))); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + DOT16X16(in0, weights0, out.s0); + DOT16X16(in1, weights1, out.s0); +#ifdef BACTH_BLOCK2 + DOT16X16(in2, weights0, out1.s0); + DOT16X16(in3, weights1, out1.s0); +#endif + } + + { + uchar16 charWeightsInt4 = as_uchar16(read_imagef(weight, SAMPLER, (int2)(out_c_idx + 1, k))); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + DOT16X16(in0, weights0, out.s1); + DOT16X16(in1, weights1, out.s1); +#ifdef BACTH_BLOCK2 + DOT16X16(in2, weights0, out1.s1); + DOT16X16(in3, weights1, out1.s1); +#endif + } + } +#ifdef INPUT_CHANNEL_LEAVE + { + int k = srcChannelC32 - 1; + COMPUTE_FLOAT16 in0 = 0; + COMPUTE_FLOAT16 in1 = 0; + int k8 = k * 8; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k8 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 1) * wh) : (FLOAT4)0); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 2) * wh) : (FLOAT4)0); + in0.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 3) * wh) : (FLOAT4)0); + + in1.s0123 = CONVERT_COMPUTE_FLOAT4(k8 + 4 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 4) * wh) : (FLOAT4)0); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 5 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 5) * wh) : (FLOAT4)0); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 6 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 6) * wh) : (FLOAT4)0); + in1.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 7 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 7) * wh) : (FLOAT4)0); + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; + sum += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in2 = 0; + COMPUTE_FLOAT16 in3 = 0; + if(isValidBatch){ + in2.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k8 * wh)); + in2.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 1 < vload4(0, input + input_offset1 + (k8 + 1) * wh) : (FLOAT4)0); + in2.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 2 < vload4(0, input + input_offset1 + (k8 + 2) * wh) : (FLOAT4)0); + in2.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 3 < vload4(0, input + input_offset1 + (k8 + 3) * wh) : (FLOAT4)0); + + in3.s0123 = CONVERT_COMPUTE_FLOAT4(k8 + 4 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 4) * wh) : (FLOAT4)0); + in3.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 5 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 5) * wh) : (FLOAT4)0); + in3.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 6 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 6) * wh) : (FLOAT4)0); + in3.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 7 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 7) * wh) : (FLOAT4)0); + sum1 += in2.s0 + in2.s1 + in2.s2 + in2.s3 + in2.s4 + in2.s5 + in2.s6 + in2.s7 + in2.s8 + in2.s9 + in2.sa + in2.sb + in2.sc + in2.sd + in2.se + in2.sf; + sum1 += in3.s0 + in3.s1 + in3.s2 + in3.s3 + in3.s4 + in3.s5 + in3.s6 + in3.s7 + in3.s8 + in3.s9 + in3.sa + in3.sb + in3.sc + in3.sd + in3.se + in3.sf; + } +#endif + + { + uchar16 charWeightsInt4 = as_uchar16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k))); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + DOT16X16(in0, weights0, out.s0); + DOT16X16(in1, weights1, out.s0); +#ifdef BACTH_BLOCK2 + DOT16X16(in2, weights0, out1.s0); + DOT16X16(in3, weights1, out1.s0); +#endif + } + + { + uchar16 charWeightsInt4 = as_uchar16(read_imagef(weight, SAMPLER, (int2)(out_c_idx + 1, k))); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + DOT16X16(in0, weights0, out.s1); + DOT16X16(in1, weights1, out.s1); +#ifdef BACTH_BLOCK2 + DOT16X16(in2, weights0, out1.s1); + DOT16X16(in3, weights1, out1.s1); +#endif + } + } +#endif +#endif //USE_LOW_BIT_WEIGHT_INT4 + + out = bias0 + mad(out, Scale, sum * Offset); +#ifdef RELU + out = fmax(out, (COMPUTE_FLOAT2)0); +#endif +#ifdef RELU6 + out = clamp(out, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); +#endif + vstore2(CONVERT_FLOAT2(out), 0, output + out_offset); +#ifdef BACTH_BLOCK2 + if(isValidBatch){ + out_offset += dstChannelC4 * height * width * 4; + out1 = bias0 + mad(out1, Scale, sum1 * Offset); +#ifdef RELU + out1 = fmax(out1, (COMPUTE_FLOAT2)0); +#endif + +#ifdef RELU6 + out1 = clamp(out1, (COMPUTE_FLOAT2)0, (COMPUTE_FLOAT2)6); +#endif + + vstore2(CONVERT_FLOAT2(out1), 0, output+out_offset); + } +#endif +} +__kernel void gemm_conv_c1_image(GLOBAL_SIZE_DIM2 + __global const FLOAT* input, + __read_only image2d_t weight, + __global const float *dequantScale, + __global const float *dequantOffset, + __global const FLOAT *bias, + __global FLOAT* output, + __private const int dstChannelC4, + __private const int srcChannelC4, + __private const int batch, + __private const int height, + __private const int width) { + const int out_c_w_idx = get_global_id(0); //c/4 w + const int out_b_h_idx = get_global_id(1); //b h + UNIFORM_BOUNDRY_CHECK(out_c_w_idx, out_b_h_idx); + + const int out_c_idx = out_c_w_idx / width; + const int out_w_idx = out_c_w_idx % width; +#ifdef BACTH_BLOCK2 + const int out_b_idx = (out_b_h_idx / height) << 1; +#else + const int out_b_idx = out_b_h_idx / height; +#endif + const int out_h_idx = out_b_h_idx % height; + + COMPUTE_FLOAT bias0 = bias[out_c_idx]; + COMPUTE_FLOAT out = 0; + COMPUTE_FLOAT sum = 0; + + int input_offset = ((out_b_idx * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + int out_offset = (((out_b_idx * dstChannelC4 + out_c_idx/4)* height + out_h_idx) * width + out_w_idx) * 4 + (out_c_idx%4); +#ifndef WIDTH_HEIGHT_1 + int wh = width * height * 4; +#endif +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT sum1 = 0; + COMPUTE_FLOAT out1 = 0; + int input_offset1 = (((out_b_idx + 1) * srcChannelC4 * height + out_h_idx) * width + out_w_idx) * 4; + bool isValidBatch = out_b_idx + 1 < batch; +#endif + const COMPUTE_FLOAT Scale = dequantScale[out_c_idx]; + const COMPUTE_FLOAT Offset = dequantOffset[out_c_idx]; + +#if (defined USE_LOW_BIT_WEIGHT_INT8) +#ifdef INPUT_CHANNEL_LEAVE + const int srcChannelC16 = (srcChannelC4 + 3) >> 2; + for (int k = 0; k < srcChannelC16-1; k++) { +#else + for (int k = 0; k < srcChannelC4/4; k++) { +#endif +#ifdef WIDTH_HEIGHT_1 + COMPUTE_FLOAT16 in0 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset + k * 16)); +#else + COMPUTE_FLOAT16 in0 = 0; + int k4 = k * 4; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 1) * wh)); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 2) * wh)); + in0.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k4 + 3) * wh)); + +#endif + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ +#ifdef WIDTH_HEIGHT_1 + in1 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset1 + k * 16)); +#else + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 1) * wh)); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 2) * wh)); + in1.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k4 + 3) * wh)); +#endif + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif + + { + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(as_char16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k)))); + DOT16X16(in0, weights0, out); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1); +#endif + } + } +#ifdef INPUT_CHANNEL_LEAVE + { + int k = srcChannelC16 - 1; + COMPUTE_FLOAT16 in0 = 0; + int k4 = k * 4; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k4 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 1) * wh) : (FLOAT4)0); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 2) * wh) : (FLOAT4)0); + in0.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k4 + 3) * wh) : (FLOAT4)0); + + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in1 = 0; + if(isValidBatch){ + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k4 * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(k4 + 1 < vload4(0, input + input_offset1 + (k4 + 1) * wh) : (FLOAT4)0); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(k4 + 2 < vload4(0, input + input_offset1 + (k4 + 2) * wh) : (FLOAT4)0); + in1.scdef = CONVERT_COMPUTE_FLOAT4(k4 + 3 < vload4(0, input + input_offset1 + (k4 + 3) * wh) : (FLOAT4)0); + sum1 += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; + } +#endif + + { + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(as_char16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k)))); + DOT16X16(in0, weights0, out); +#ifdef BACTH_BLOCK2 + DOT16X16(in1, weights0, out1); +#endif + } + } +#endif +#elif (defined USE_LOW_BIT_WEIGHT_INT4) +#ifdef INPUT_CHANNEL_LEAVE + const int srcChannelC32 = (srcChannelC4 + 7) >> 3; + for (int k = 0; k < srcChannelC32-1; k++) { +#else + for (int k = 0; k < srcChannelC4/8; k++) { +#endif +#ifdef WIDTH_HEIGHT_1 + COMPUTE_FLOAT16 in0 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset + k * 32)); + COMPUTE_FLOAT16 in1 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset + k * 32 + 16)); +#else + COMPUTE_FLOAT16 in0 = 0; + COMPUTE_FLOAT16 in1 = 0; + int k8 = k * 8; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k8 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 1) * wh)); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 2) * wh)); + in0.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 3) * wh)); + + in1.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 4) * wh)); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 5) * wh)); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 6) * wh)); + in1.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + (k8 + 7) * wh)); + +#endif + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; + sum += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in2 = 0; + COMPUTE_FLOAT16 in3 = 0; + if(isValidBatch){ +#ifdef WIDTH_HEIGHT_1 + in2 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset1 + k * 32)); + in3 = CONVERT_COMPUTE_FLOAT16(vload16(0, input + input_offset1 + k * 32 + 16)); +#else + in2.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k8 * wh)); + in2.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 1) * wh)); + in2.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 2) * wh)); + in2.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 3) * wh)); + + in3.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 4) * wh)); + in3.s4567 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 5) * wh)); + in3.s89ab = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 6) * wh)); + in3.scdef = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + (k8 + 7) * wh)); +#endif + sum1 += in2.s0 + in2.s1 + in2.s2 + in2.s3 + in2.s4 + in2.s5 + in2.s6 + in2.s7 + in2.s8 + in2.s9 + in2.sa + in2.sb + in2.sc + in2.sd + in2.se + in2.sf; + sum1 += in3.s0 + in3.s1 + in3.s2 + in3.s3 + in3.s4 + in3.s5 + in3.s6 + in3.s7 + in3.s8 + in3.s9 + in3.sa + in3.sb + in3.sc + in3.sd + in3.se + in3.sf; + } +#endif + { + uchar16 charWeightsInt4 = as_uchar16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k))); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + DOT16X16(in0, weights0, out); + DOT16X16(in1, weights1, out); +#ifdef BACTH_BLOCK2 + DOT16X16(in2, weights0, out1); + DOT16X16(in3, weights1, out1); +#endif + } + } + +#ifdef INPUT_CHANNEL_LEAVE + { + int k = srcChannelC32 - 1; + COMPUTE_FLOAT16 in0 = 0; + COMPUTE_FLOAT16 in1 = 0; + int k8 = k * 8; + in0.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset + k8 * wh)); + in0.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 1 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 1) * wh) : (FLOAT4)0); + in0.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 2 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 2) * wh) : (FLOAT4)0); + in0.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 3 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 3) * wh) : (FLOAT4)0); + + in1.s0123 = CONVERT_COMPUTE_FLOAT4(k8 + 4 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 4) * wh) : (FLOAT4)0); + in1.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 5 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 5) * wh) : (FLOAT4)0); + in1.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 6 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 6) * wh) : (FLOAT4)0); + in1.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 7 < srcChannelC4 ? vload4(0, input + input_offset + (k8 + 7) * wh) : (FLOAT4)0); + sum += in0.s0 + in0.s1 + in0.s2 + in0.s3 + in0.s4 + in0.s5 + in0.s6 + in0.s7 + in0.s8 + in0.s9 + in0.sa + in0.sb + in0.sc + in0.sd + in0.se + in0.sf; + sum += in1.s0 + in1.s1 + in1.s2 + in1.s3 + in1.s4 + in1.s5 + in1.s6 + in1.s7 + in1.s8 + in1.s9 + in1.sa + in1.sb + in1.sc + in1.sd + in1.se + in1.sf; +#ifdef BACTH_BLOCK2 + COMPUTE_FLOAT16 in2 = 0; + COMPUTE_FLOAT16 in3 = 0; + if(isValidBatch){ + in2.s0123 = CONVERT_COMPUTE_FLOAT4(vload4(0, input + input_offset1 + k8 * wh)); + in2.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 1 < vload4(0, input + input_offset1 + (k8 + 1) * wh) : (FLOAT4)0); + in2.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 2 < vload4(0, input + input_offset1 + (k8 + 2) * wh) : (FLOAT4)0); + in2.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 3 < vload4(0, input + input_offset1 + (k8 + 3) * wh) : (FLOAT4)0); + + in3.s0123 = CONVERT_COMPUTE_FLOAT4(k8 + 4 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 4) * wh) : (FLOAT4)0); + in3.s4567 = CONVERT_COMPUTE_FLOAT4(k8 + 5 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 5) * wh) : (FLOAT4)0); + in3.s89ab = CONVERT_COMPUTE_FLOAT4(k8 + 6 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 6) * wh) : (FLOAT4)0); + in3.scdef = CONVERT_COMPUTE_FLOAT4(k8 + 7 < srcChannelC4 ? vload4(0, input + input_offset1 + (k8 + 7) * wh) : (FLOAT4)0); + sum1 += in2.s0 + in2.s1 + in2.s2 + in2.s3 + in2.s4 + in2.s5 + in2.s6 + in2.s7 + in2.s8 + in2.s9 + in2.sa + in2.sb + in2.sc + in2.sd + in2.se + in2.sf; + sum1 += in3.s0 + in3.s1 + in3.s2 + in3.s3 + in3.s4 + in3.s5 + in3.s6 + in3.s7 + in3.s8 + in3.s9 + in3.sa + in3.sb + in3.sc + in3.sd + in3.se + in3.sf; + } +#endif + + { + uchar16 charWeightsInt4 = as_uchar16(read_imagef(weight, SAMPLER, (int2)(out_c_idx, k))); + char16 charWeights0 = 0; + char16 charWeights1 = 0; + UCHAR16_TO_2CHAR16(charWeights0, charWeights1, charWeightsInt4); + COMPUTE_FLOAT16 weights0 = CONVERT_COMPUTE_FLOAT16(charWeights0); + COMPUTE_FLOAT16 weights1 = CONVERT_COMPUTE_FLOAT16(charWeights1); + DOT16X16(in0, weights0, out); + DOT16X16(in1, weights1, out); +#ifdef BACTH_BLOCK2 + DOT16X16(in2, weights0, out1); + DOT16X16(in3, weights1, out1); +#endif + } + } +#endif +#endif //USE_LOW_BIT_WEIGHT_INT4 + + out = bias0 + mad(out, Scale, sum * Offset); +#ifdef RELU + out = fmax(out, 0); +#endif +#ifdef RELU6 + out = clamp(out, 0, 6); +#endif + output[out_offset] = out; +#ifdef BACTH_BLOCK2 + if(isValidBatch){ + out_offset += dstChannelC4 * height * width * 4; + out1 = bias0 + mad(out1, Scale, sum1 * Offset); +#ifdef RELU + out1 = fmax(out1, 0); +#endif + +#ifdef RELU6 + out1 = clamp(out1, 0, 6); +#endif + + output[out_offset] = out1; + } +#endif +} + diff --git a/source/backend/opencl/execution/cl/grid_sample_buf.cl b/source/backend/opencl/execution/cl/grid_sample_buf.cl index 9d78bad19..391a88163 100644 --- a/source/backend/opencl/execution/cl/grid_sample_buf.cl +++ b/source/backend/opencl/execution/cl/grid_sample_buf.cl @@ -33,7 +33,7 @@ static int CLAMP(int v, int min, int max) { return v; } -FLOAT4 sample(int h, int w, +COMPUTE_FLOAT4 sample(int h, int w, const int offset_base, __global const FLOAT *buffer, int height, int width, @@ -51,7 +51,7 @@ FLOAT4 sample(int h, int w, w = CLAMP(w, 0, width - 1); } int offset = (offset_base + h) * width + w; - return vload4(offset, buffer); + return CONVERT_COMPUTE_FLOAT4(vload4(offset, buffer)); } __kernel void nearest_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT* input, @@ -92,8 +92,8 @@ __kernel void nearest_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT* input, const int slice_blocks = (output_height + 3) / 4; // output_width_block_idx means gird y offset, 2 means grid width const int grid_offset = ((output_batch_idx * slice_blocks + slice) * output_width + output_width_block_idx) * 2; - FLOAT4 grid_x = vload4(grid_offset, grid); - FLOAT4 grid_y = vload4(grid_offset + 1, grid); + COMPUTE_FLOAT4 grid_x = CONVERT_COMPUTE_FLOAT4(vload4(grid_offset, grid)); + COMPUTE_FLOAT4 grid_y = CONVERT_COMPUTE_FLOAT4(vload4(grid_offset + 1, grid)); const float arr[8] = {grid_x.x, grid_y.x, grid_x.y, grid_y.y, grid_x.z, grid_y.z, grid_x.w, grid_y.w}; @@ -111,10 +111,10 @@ __kernel void nearest_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT* input, int nh = floor(in_grid_y + 0.5f); const int inp_offset_base = (output_batch_idx * channelBlocks + output_channel_block_idx) * input_height; - FLOAT4 value = sample(nh, nw, inp_offset_base, input, input_height, input_width, paddingMode); + COMPUTE_FLOAT4 value = sample(nh, nw, inp_offset_base, input, input_height, input_width, paddingMode); const int output_offset = ((output_batch_idx * channelBlocks + output_channel_block_idx ) * output_height + output_height_idx) * output_width + output_width_block_idx; - vstore4(value, output_offset, output); + vstore4(CONVERT_FLOAT4(value), output_offset, output); } __kernel void bilinear_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT* input, @@ -141,8 +141,8 @@ __kernel void bilinear_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT* input, const int slice_blocks = (output_height + 3) / 4; // output_width_block_idx means gird y offset, 2 means grid width const int grid_offset = ((output_batch_idx * slice_blocks + slice) * output_width + output_width_block_idx) * 2; - FLOAT4 grid_x = vload4(grid_offset, grid); - FLOAT4 grid_y = vload4(grid_offset + 1, grid); + COMPUTE_FLOAT4 grid_x = CONVERT_COMPUTE_FLOAT4(vload4(grid_offset, grid)); + COMPUTE_FLOAT4 grid_y = CONVERT_COMPUTE_FLOAT4(vload4(grid_offset + 1, grid)); const float arr[8] = {grid_x.x, grid_y.x, grid_x.y, grid_y.y, grid_x.z, grid_y.z, grid_x.w, grid_y.w}; @@ -165,14 +165,14 @@ __kernel void bilinear_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT* input, // bilinear interpolation const int inp_offset_base = (output_batch_idx * channelBlocks + output_channel_block_idx) * input_height; - FLOAT4 i00 = sample(in_h0, in_w0, inp_offset_base, input, input_height, input_width, paddingMode); - FLOAT4 i01 = sample(in_h0, in_w1, inp_offset_base, input, input_height, input_width, paddingMode); - FLOAT4 i10 = sample(in_h1, in_w0, inp_offset_base, input, input_height, input_width, paddingMode); - FLOAT4 i11 = sample(in_h1, in_w1, inp_offset_base, input, input_height, input_width, paddingMode); + COMPUTE_FLOAT4 i00 = sample(in_h0, in_w0, inp_offset_base, input, input_height, input_width, paddingMode); + COMPUTE_FLOAT4 i01 = sample(in_h0, in_w1, inp_offset_base, input, input_height, input_width, paddingMode); + COMPUTE_FLOAT4 i10 = sample(in_h1, in_w0, inp_offset_base, input, input_height, input_width, paddingMode); + COMPUTE_FLOAT4 i11 = sample(in_h1, in_w1, inp_offset_base, input, input_height, input_width, paddingMode); - FLOAT4 value = CONVERT_FLOAT4(((FLOAT4)x_weight * CONVERT_FLOAT4(i00) + (FLOAT4)(1.0f - x_weight) * CONVERT_FLOAT4(i01)) * (FLOAT4)y_weight + - ((FLOAT4)x_weight * CONVERT_FLOAT4(i10) + (FLOAT4)(1.0f - x_weight) * CONVERT_FLOAT4(i11)) * (FLOAT4)(1.0f- y_weight)); + COMPUTE_FLOAT4 value = CONVERT_COMPUTE_FLOAT4(((COMPUTE_FLOAT4)x_weight * CONVERT_COMPUTE_FLOAT4(i00) + (COMPUTE_FLOAT4)(1.0f - x_weight) * CONVERT_COMPUTE_FLOAT4(i01)) * (COMPUTE_FLOAT4)y_weight + + ((COMPUTE_FLOAT4)x_weight * CONVERT_COMPUTE_FLOAT4(i10) + (COMPUTE_FLOAT4)(1.0f - x_weight) * CONVERT_COMPUTE_FLOAT4(i11)) * (COMPUTE_FLOAT4)(1.0f- y_weight)); const int output_offset = ((output_batch_idx * channelBlocks + output_channel_block_idx ) * output_height + output_height_idx) * output_width + output_width_block_idx; - vstore4(value, output_offset, output); + vstore4(CONVERT_FLOAT4(value), output_offset, output); } diff --git a/source/backend/opencl/execution/cl/layernorm_buf.cl b/source/backend/opencl/execution/cl/layernorm_buf.cl index 90457d629..3608a7af5 100644 --- a/source/backend/opencl/execution/cl/layernorm_buf.cl +++ b/source/backend/opencl/execution/cl/layernorm_buf.cl @@ -227,3 +227,100 @@ __kernel void layernorm_chw_buf(__private int global_dim0, __private int global_ } } } + + +__kernel void layernorm_plain_buf(__private int global_dim0, __private int global_dim1, __private int global_dim2, + __global const FLOAT * input, + __global FLOAT * output, + __private const int inside, + __private const int outside, +#ifdef GAMMA_BETA + __global const FLOAT *gamma, + __global const FLOAT *beta, +#endif + __private float epsilon){ + int3 pos = (int3)(get_global_id(0), get_global_id(1), get_global_id(2)); + COMPUTE_FLOAT local sum[LOCAL_SIZE]; + if (pos.x < global_dim0 && pos.y < global_dim1 && pos.z < global_dim2) { + const int idx_out = pos.z; + const int lid = get_local_id(0); + const int offset = idx_out * inside; + const int inside_v4 = (inside + 3) >> 2; + const int inside_remain = inside - ((inside_v4-1) << 2); + + COMPUTE_FLOAT4 in_sum = 0; + for(int i = lid; i < inside_v4 - 1; i+=LOCAL_SIZE){ + COMPUTE_FLOAT4 in = CONVERT_COMPUTE_FLOAT4(vload4(i, input + offset)); + in_sum += in; + } + sum[lid] = in_sum.x + in_sum.y + in_sum.z+ in_sum.w; + + COMPUTE_FLOAT4 in_left = 0; + if(lid == inside_v4 - 1) { + in_left = CONVERT_COMPUTE_FLOAT4(vload4(inside_v4 - 1, input + offset)); + sum[lid] = sum[lid] + in_left.x; + if(inside_remain > 1) { + sum[lid] = sum[lid] + in_left.y; + } + if(inside_remain > 2) { + sum[lid] = sum[lid] + in_left.z; + } + if(inside_remain > 3) { + sum[lid] = sum[lid] + in_left.w; + } + } + + barrier(CLK_LOCAL_MEM_FENCE); + for(int i = LOCAL_SIZE/2; i > 0; i /= 2){ + if (lid < i) + sum[lid] = sum[lid] + sum[lid + i]; + barrier(CLK_LOCAL_MEM_FENCE); + } + + COMPUTE_FLOAT4 mean = sum[0] / (COMPUTE_FLOAT4)inside; + + in_sum = 0; + for(int i = lid; i < inside_v4 - 1; i+=LOCAL_SIZE){ + COMPUTE_FLOAT4 in = CONVERT_COMPUTE_FLOAT4(vload4(i, input + offset)); + in_sum += (in - mean) * (in - mean); + } + sum[lid] = in_sum.x + in_sum.y + in_sum.z + in_sum.w; + + if(lid == inside_v4 - 1) { + COMPUTE_FLOAT4 in_left = CONVERT_COMPUTE_FLOAT4(vload4(inside_v4 - 1, input + offset)); + in_sum = (in_left - mean) * (in_left - mean); + sum[lid] = sum[lid] + in_sum.x; + if(inside_remain > 1) { + sum[lid] = sum[lid] + in_sum.y; + } + if(inside_remain > 2) { + sum[lid] = sum[lid] + in_sum.z; + } + if(inside_remain > 3) { + sum[lid] = sum[lid] + in_sum.w; + } + } + barrier(CLK_LOCAL_MEM_FENCE); + for(int i = LOCAL_SIZE/2; i > 0; i /= 2){ + if (lid < i) + sum[lid] = sum[lid] + sum[lid + i]; + barrier(CLK_LOCAL_MEM_FENCE); + } + COMPUTE_FLOAT4 square_sum = sum[0] / (COMPUTE_FLOAT4)inside; + COMPUTE_FLOAT4 value = (COMPUTE_FLOAT4)1.0f / (COMPUTE_FLOAT4)sqrt(square_sum + (COMPUTE_FLOAT4)epsilon); + /* + if(pos.x == 0) { + printf("ln: %d, mean:%f square:%f, value:%f\n", pos.z, (float)mean.x, (float)sum[0], (float)value.x); + } + */ + for(int i = lid; i < inside_v4; i+=LOCAL_SIZE){ + COMPUTE_FLOAT4 in = CONVERT_COMPUTE_FLOAT4(vload4(i, input + offset)); +#ifdef GAMMA_BETA + COMPUTE_FLOAT4 out = (in - mean) * value * CONVERT_COMPUTE_FLOAT4(vload4(i, gamma)) + CONVERT_COMPUTE_FLOAT4(vload4(i, beta)); +#else + COMPUTE_FLOAT4 out = (in - mean) * value; +#endif + vstore4(CONVERT_FLOAT4(out), i, output + offset); + } + } +} diff --git a/source/backend/opencl/execution/cl/matmul_buf.cl b/source/backend/opencl/execution/cl/matmul_buf.cl index a0e9413fd..4d2b65756 100644 --- a/source/backend/opencl/execution/cl/matmul_buf.cl +++ b/source/backend/opencl/execution/cl/matmul_buf.cl @@ -24,40 +24,40 @@ __kernel void matmul_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a, const int height_idx = get_global_id(1);// output H DEAL_NON_UNIFORM_DIM2(width_blocks_idx, height_idx); - FLOAT4 a; - FLOAT4 b0 = 0, b1 = 0, b2 = 0, b3 = 0; - FLOAT4 v_zero = (FLOAT4)((FLOAT)0.0); + COMPUTE_FLOAT4 a; + COMPUTE_FLOAT4 b0 = 0, b1 = 0, b2 = 0, b3 = 0; + COMPUTE_FLOAT4 v_zero = (COMPUTE_FLOAT4)((COMPUTE_FLOAT)0.0); #ifdef BIAS - FLOAT4 temp = vload4(width_blocks_idx, input_c); + COMPUTE_FLOAT4 temp = CONVERT_COMPUTE_FLOAT4(vload4(width_blocks_idx, input_c)); - FLOAT result0 = temp.x; - FLOAT result1 = temp.y; - FLOAT result2 = temp.z; - FLOAT result3 = temp.w; + COMPUTE_FLOAT result0 = temp.x; + COMPUTE_FLOAT result1 = temp.y; + COMPUTE_FLOAT result2 = temp.z; + COMPUTE_FLOAT result3 = temp.w; #else - FLOAT result0 = 0; - FLOAT result1 = 0; - FLOAT result2 = 0; - FLOAT result3 = 0; + COMPUTE_FLOAT result0 = 0; + COMPUTE_FLOAT result1 = 0; + COMPUTE_FLOAT result2 = 0; + COMPUTE_FLOAT result3 = 0; #endif const int remain = channel_blocks*4 - channels; for (short pos = 0; pos < channel_blocks - 1; pos += 1) { const int inpa_offset = height_idx * channel_blocks + pos; - a = vload4(inpa_offset, input_a); + a = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); const int inpb_offset = (pos*4) * width_blocks + width_blocks_idx; - b0 = vload4(inpb_offset, input_b); - b1 = vload4(inpb_offset + width_blocks, input_b); - b2 = vload4(inpb_offset + width_blocks*2, input_b); - b3 = vload4(inpb_offset + width_blocks*3, input_b); + b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + b1 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks, input_b)); + b2 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*2, input_b)); + b3 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*3, input_b)); - FLOAT4 btmp0 = (FLOAT4)(b0.s0, b1.s0, b2.s0, b3.s0); - FLOAT4 btmp1 = (FLOAT4)(b0.s1, b1.s1, b2.s1, b3.s1); - FLOAT4 btmp2 = (FLOAT4)(b0.s2, b1.s2, b2.s2, b3.s2); - FLOAT4 btmp3 = (FLOAT4)(b0.s3, b1.s3, b2.s3, b3.s3); + COMPUTE_FLOAT4 btmp0 = (COMPUTE_FLOAT4)(b0.s0, b1.s0, b2.s0, b3.s0); + COMPUTE_FLOAT4 btmp1 = (COMPUTE_FLOAT4)(b0.s1, b1.s1, b2.s1, b3.s1); + COMPUTE_FLOAT4 btmp2 = (COMPUTE_FLOAT4)(b0.s2, b1.s2, b2.s2, b3.s2); + COMPUTE_FLOAT4 btmp3 = (COMPUTE_FLOAT4)(b0.s3, b1.s3, b2.s3, b3.s3); result0 += dot(a, btmp0); result1 += dot(a, btmp1); @@ -67,14 +67,14 @@ __kernel void matmul_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a, { const int inpa_offset = height_idx * channel_blocks + channel_blocks - 1; - a = vload4(inpa_offset, input_a); + a = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); const int inpb_offset = ((channel_blocks - 1)*4) * width_blocks + width_blocks_idx; - b0 = vload4(inpb_offset, input_b); - b1 = ((remain >= 3) ? v_zero : vload4(inpb_offset + width_blocks, input_b)); - b2 = ((remain >= 2) ? v_zero : vload4(inpb_offset + width_blocks*2, input_b)); - b3 = ((remain >= 1) ? v_zero : vload4(inpb_offset + width_blocks*3, input_b)); + b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + b1 = (remain >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks, input_b)); + b2 = (remain >= 2) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*2, input_b)); + b3 = (remain >= 1) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*3, input_b)); if (remain == 3) { a.y = 0; a.z = 0; @@ -86,10 +86,10 @@ __kernel void matmul_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a, a.w = 0;; } - FLOAT4 btmp0 = (FLOAT4)(b0.s0, b1.s0, b2.s0, b3.s0); - FLOAT4 btmp1 = (FLOAT4)(b0.s1, b1.s1, b2.s1, b3.s1); - FLOAT4 btmp2 = (FLOAT4)(b0.s2, b1.s2, b2.s2, b3.s2); - FLOAT4 btmp3 = (FLOAT4)(b0.s3, b1.s3, b2.s3, b3.s3); + COMPUTE_FLOAT4 btmp0 = (COMPUTE_FLOAT4)(b0.s0, b1.s0, b2.s0, b3.s0); + COMPUTE_FLOAT4 btmp1 = (COMPUTE_FLOAT4)(b0.s1, b1.s1, b2.s1, b3.s1); + COMPUTE_FLOAT4 btmp2 = (COMPUTE_FLOAT4)(b0.s2, b1.s2, b2.s2, b3.s2); + COMPUTE_FLOAT4 btmp3 = (COMPUTE_FLOAT4)(b0.s3, b1.s3, b2.s3, b3.s3); result0 += dot(a, btmp0); result1 += dot(a, btmp1); @@ -98,7 +98,7 @@ __kernel void matmul_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a, } const int out_offset = height_idx * width_blocks + width_blocks_idx; - vstore4((FLOAT4)(result0, result1, result2, result3), out_offset, output_c); + vstore4(CONVERT_FLOAT4((COMPUTE_FLOAT4)(result0, result1, result2, result3)), out_offset, output_c); } __kernel void matmul_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a, @@ -115,35 +115,35 @@ __kernel void matmul_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a const int height_idx = get_global_id(1); DEAL_NON_UNIFORM_DIM2(width_blocks_idx, height_idx); - FLOAT4 a; - FLOAT4 b0 = 0, b1 = 0, b2 = 0, b3 = 0; - FLOAT4 v_zero = (FLOAT4)((FLOAT)0.0); + COMPUTE_FLOAT4 a; + COMPUTE_FLOAT4 b0 = 0, b1 = 0, b2 = 0, b3 = 0; + COMPUTE_FLOAT4 v_zero = (COMPUTE_FLOAT4)((COMPUTE_FLOAT)0.0); #ifdef BIAS - FLOAT4 temp = vload4(width_blocks_idx, input_c); - FLOAT result0 = temp.x; - FLOAT result1 = temp.y; - FLOAT result2 = temp.z; - FLOAT result3 = temp.w; + COMPUTE_FLOAT4 temp = CONVERT_COMPUTE_FLOAT4(vload4(width_blocks_idx, input_c)); + COMPUTE_FLOAT result0 = temp.x; + COMPUTE_FLOAT result1 = temp.y; + COMPUTE_FLOAT result2 = temp.z; + COMPUTE_FLOAT result3 = temp.w; #else - FLOAT result0 = 0; - FLOAT result1 = 0; - FLOAT result2 = 0; - FLOAT result3 = 0; + COMPUTE_FLOAT result0 = 0; + COMPUTE_FLOAT result1 = 0; + COMPUTE_FLOAT result2 = 0; + COMPUTE_FLOAT result3 = 0; #endif const int remaina = channel_blocks*4 - channels; const int remainb = (width_blocks_idx+1)*4 - width; for (short pos = 0; pos < channel_blocks - 1; pos += 1) { const int inpa_offset = height_idx * channel_blocks + pos; - a = vload4(inpa_offset, input_a); + a = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); const int inpb_offset = (width_blocks_idx*4) * channel_blocks + pos; - b0 = vload4(inpb_offset, input_b); - b1 = ((remainb >= 3) ? v_zero : vload4(inpb_offset + channel_blocks, input_b)); - b2 = ((remainb >= 2) ? v_zero : vload4(inpb_offset + channel_blocks*2, input_b)); - b3 = ((remainb >= 1) ? v_zero : vload4(inpb_offset + channel_blocks*3, input_b)); + b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + b1 = (remainb >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks, input_b)); + b2 = (remainb >= 2) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*2, input_b)); + b3 = (remainb >= 1) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*3, input_b)); result0 += dot(a, b0); result1 += dot(a, b1); @@ -153,14 +153,14 @@ __kernel void matmul_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a { const int inpa_offset = height_idx * channel_blocks + channel_blocks - 1; - a = vload4(inpa_offset, input_a); + a = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); const int inpb_offset = (width_blocks_idx*4) * channel_blocks + channel_blocks - 1; - b0 = vload4(inpb_offset, input_b); - b1 = ((remainb >= 3) ? v_zero : vload4(inpb_offset + channel_blocks, input_b)); - b2 = ((remainb >= 2) ? v_zero : vload4(inpb_offset + channel_blocks*2, input_b)); - b3 = ((remainb >= 1) ? v_zero : vload4(inpb_offset + channel_blocks*3, input_b)); + b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + b1 = (remainb >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks, input_b)); + b2 = (remainb >= 2) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*2, input_b)); + b3 = (remainb >= 1) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*3, input_b)); if (remaina == 3) { a.y = 0; @@ -179,7 +179,7 @@ __kernel void matmul_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a result3 += dot(a, b3); } const int out_offset = height_idx * width_blocks + width_blocks_idx; - vstore4((FLOAT4)(result0, result1, result2, result3), out_offset, output_c); + vstore4(CONVERT_FLOAT4((COMPUTE_FLOAT4)(result0, result1, result2, result3)), out_offset, output_c); } @@ -200,43 +200,43 @@ __kernel void matmul_transA_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a DEAL_NON_UNIFORM_DIM2(width_blocks_idx, height_blocks_idx); - FLOAT4 v_zero = (FLOAT4)((FLOAT)0.0); + COMPUTE_FLOAT4 v_zero = (COMPUTE_FLOAT4)((COMPUTE_FLOAT)0.0); #ifdef BIAS - FLOAT4 result0 = vload4(width_blocks_idx, input_c); - FLOAT4 result1 = result0; - FLOAT4 result2 = result0; - FLOAT4 result3 = result0; + COMPUTE_FLOAT4 result0 = CONVERT_COMPUTE_FLOAT4(vload4(width_blocks_idx, input_c)); + COMPUTE_FLOAT4 result1 = result0; + COMPUTE_FLOAT4 result2 = result0; + COMPUTE_FLOAT4 result3 = result0; #else - FLOAT4 result0 = 0; - FLOAT4 result1 = 0; - FLOAT4 result2 = 0; - FLOAT4 result3 = 0; + COMPUTE_FLOAT4 result0 = 0; + COMPUTE_FLOAT4 result1 = 0; + COMPUTE_FLOAT4 result2 = 0; + COMPUTE_FLOAT4 result3 = 0; #endif const int remain = channel_blocks*4 - channels; for (short pos = 0; pos < channel_blocks - 1; pos += 1) { const int inpa_offset = (4*pos) * height_blocks + height_blocks_idx; - FLOAT4 a0 = vload4(inpa_offset, input_a); - FLOAT4 a1 = vload4(inpa_offset + height_blocks, input_a); - FLOAT4 a2 = vload4(inpa_offset + height_blocks*2, input_a); - FLOAT4 a3 = vload4(inpa_offset + height_blocks*3, input_a); + COMPUTE_FLOAT4 a0 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); + COMPUTE_FLOAT4 a1 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks, input_a)); + COMPUTE_FLOAT4 a2 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*2, input_a)); + COMPUTE_FLOAT4 a3 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*3, input_a)); const int inpb_offset = (4*pos) * width_blocks + width_blocks_idx; - FLOAT4 b0 = vload4(inpb_offset, input_b); - FLOAT4 b1 = vload4(inpb_offset + width_blocks, input_b); - FLOAT4 b2 = vload4(inpb_offset + width_blocks*2, input_b); - FLOAT4 b3 = vload4(inpb_offset + width_blocks*3, input_b); - - FLOAT4 a0_trans = (FLOAT4)(a0.x, a1.x, a2.x, a3.x); - FLOAT4 a1_trans = (FLOAT4)(a0.y, a1.y, a2.y, a3.y); - FLOAT4 a2_trans = (FLOAT4)(a0.z, a1.z, a2.z, a3.z); - FLOAT4 a3_trans = (FLOAT4)(a0.w, a1.w, a2.w, a3.w); + COMPUTE_FLOAT4 b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + COMPUTE_FLOAT4 b1 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks, input_b)); + COMPUTE_FLOAT4 b2 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*2, input_b)); + COMPUTE_FLOAT4 b3 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*3, input_b)); + + COMPUTE_FLOAT4 a0_trans = (COMPUTE_FLOAT4)(a0.x, a1.x, a2.x, a3.x); + COMPUTE_FLOAT4 a1_trans = (COMPUTE_FLOAT4)(a0.y, a1.y, a2.y, a3.y); + COMPUTE_FLOAT4 a2_trans = (COMPUTE_FLOAT4)(a0.z, a1.z, a2.z, a3.z); + COMPUTE_FLOAT4 a3_trans = (COMPUTE_FLOAT4)(a0.w, a1.w, a2.w, a3.w); - FLOAT4 b0_trans = (FLOAT4)(b0.x, b1.x, b2.x, b3.x); - FLOAT4 b1_trans = (FLOAT4)(b0.y, b1.y, b2.y, b3.y); - FLOAT4 b2_trans = (FLOAT4)(b0.z, b1.z, b2.z, b3.z); - FLOAT4 b3_trans = (FLOAT4)(b0.w, b1.w, b2.w, b3.w); + COMPUTE_FLOAT4 b0_trans = (COMPUTE_FLOAT4)(b0.x, b1.x, b2.x, b3.x); + COMPUTE_FLOAT4 b1_trans = (COMPUTE_FLOAT4)(b0.y, b1.y, b2.y, b3.y); + COMPUTE_FLOAT4 b2_trans = (COMPUTE_FLOAT4)(b0.z, b1.z, b2.z, b3.z); + COMPUTE_FLOAT4 b3_trans = (COMPUTE_FLOAT4)(b0.w, b1.w, b2.w, b3.w); //matmul result0.x += dot(a0_trans, b0_trans); @@ -262,26 +262,26 @@ __kernel void matmul_transA_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a { const int inpa_offset = (4*(channel_blocks - 1)) * height_blocks + height_blocks_idx; - FLOAT4 a0 = vload4(inpa_offset, input_a); - FLOAT4 a1 = ((remain >= 3) ? v_zero : vload4(inpa_offset + height_blocks, input_a)); - FLOAT4 a2 = ((remain >= 2) ? v_zero : vload4(inpa_offset + height_blocks*2, input_a)); - FLOAT4 a3 = ((remain >= 1) ? v_zero : vload4(inpa_offset + height_blocks*3, input_a)); + COMPUTE_FLOAT4 a0 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); + COMPUTE_FLOAT4 a1 = ((remain >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks, input_a))); + COMPUTE_FLOAT4 a2 = ((remain >= 2) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*2, input_a))); + COMPUTE_FLOAT4 a3 = ((remain >= 1) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*3, input_a))); const int inpb_offset = (4*(channel_blocks - 1)) * width_blocks + width_blocks_idx; - FLOAT4 b0 = vload4(inpb_offset, input_b); - FLOAT4 b1 = ((remain >= 3) ? v_zero : vload4(inpb_offset + width_blocks, input_b)); - FLOAT4 b2 = ((remain >= 3) ? v_zero : vload4(inpb_offset + width_blocks*2, input_b)); - FLOAT4 b3 = ((remain >= 3) ? v_zero : vload4(inpb_offset + width_blocks*3, input_b)); - - FLOAT4 a0_trans = (FLOAT4)(a0.x, a1.x, a2.x, a3.x); - FLOAT4 a1_trans = (FLOAT4)(a0.y, a1.y, a2.y, a3.y); - FLOAT4 a2_trans = (FLOAT4)(a0.z, a1.z, a2.z, a3.z); - FLOAT4 a3_trans = (FLOAT4)(a0.w, a1.w, a2.w, a3.w); + COMPUTE_FLOAT4 b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + COMPUTE_FLOAT4 b1 = ((remain >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks, input_b))); + COMPUTE_FLOAT4 b2 = ((remain >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*2, input_b))); + COMPUTE_FLOAT4 b3 = ((remain >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + width_blocks*3, input_b))); + + COMPUTE_FLOAT4 a0_trans = (COMPUTE_FLOAT4)(a0.x, a1.x, a2.x, a3.x); + COMPUTE_FLOAT4 a1_trans = (COMPUTE_FLOAT4)(a0.y, a1.y, a2.y, a3.y); + COMPUTE_FLOAT4 a2_trans = (COMPUTE_FLOAT4)(a0.z, a1.z, a2.z, a3.z); + COMPUTE_FLOAT4 a3_trans = (COMPUTE_FLOAT4)(a0.w, a1.w, a2.w, a3.w); - FLOAT4 b0_trans = (FLOAT4)(b0.x, b1.x, b2.x, b3.x); - FLOAT4 b1_trans = (FLOAT4)(b0.y, b1.y, b2.y, b3.y); - FLOAT4 b2_trans = (FLOAT4)(b0.z, b1.z, b2.z, b3.z); - FLOAT4 b3_trans = (FLOAT4)(b0.w, b1.w, b2.w, b3.w); + COMPUTE_FLOAT4 b0_trans = (COMPUTE_FLOAT4)(b0.x, b1.x, b2.x, b3.x); + COMPUTE_FLOAT4 b1_trans = (COMPUTE_FLOAT4)(b0.y, b1.y, b2.y, b3.y); + COMPUTE_FLOAT4 b2_trans = (COMPUTE_FLOAT4)(b0.z, b1.z, b2.z, b3.z); + COMPUTE_FLOAT4 b3_trans = (COMPUTE_FLOAT4)(b0.w, b1.w, b2.w, b3.w); //matmul result0.x += dot(a0_trans, b0_trans); @@ -307,13 +307,13 @@ __kernel void matmul_transA_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a const int out_offset = (4*height_blocks_idx) * width_blocks + width_blocks_idx; - vstore4(result0, out_offset, output_c); + vstore4(CONVERT_FLOAT4(result0), out_offset, output_c); if(4*height_blocks_idx+1 >= height) return; - vstore4(result1, out_offset + width_blocks, output_c); + vstore4(CONVERT_FLOAT4(result1), out_offset + width_blocks, output_c); if(4*height_blocks_idx+2 >= height) return; - vstore4(result2, out_offset + width_blocks*2, output_c); + vstore4(CONVERT_FLOAT4(result2), out_offset + width_blocks*2, output_c); if(4*height_blocks_idx+3 >= height) return; - vstore4(result3, out_offset + width_blocks*3, output_c); + vstore4(CONVERT_FLOAT4(result3), out_offset + width_blocks*3, output_c); } __kernel void matmul_transA_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* input_a, @@ -333,39 +333,39 @@ __kernel void matmul_transA_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* DEAL_NON_UNIFORM_DIM2(width_blocks_idx, height_blocks_idx); - FLOAT4 v_zero = (FLOAT4)((FLOAT)0.0); + COMPUTE_FLOAT4 v_zero = (COMPUTE_FLOAT4)((COMPUTE_FLOAT)0.0); #ifdef BIAS - FLOAT4 result0 = vload4(width_blocks_idx, input_c); + COMPUTE_FLOAT4 result0 = CONVERT_COMPUTE_FLOAT4(vload4(width_blocks_idx, input_c)); - FLOAT4 result1 = result0; - FLOAT4 result2 = result0; - FLOAT4 result3 = result0; + COMPUTE_FLOAT4 result1 = result0; + COMPUTE_FLOAT4 result2 = result0; + COMPUTE_FLOAT4 result3 = result0; #else - FLOAT4 result0 = 0; - FLOAT4 result1 = 0; - FLOAT4 result2 = 0; - FLOAT4 result3 = 0; + COMPUTE_FLOAT4 result0 = 0; + COMPUTE_FLOAT4 result1 = 0; + COMPUTE_FLOAT4 result2 = 0; + COMPUTE_FLOAT4 result3 = 0; #endif const int remaina = channel_blocks * 4 - channels; const int remainb = (width_blocks_idx + 1) * 4 - width; for (short pos = 0; pos < channel_blocks - 1; pos += 1) { const int inpa_offset = (4*pos) * height_blocks + height_blocks_idx; - FLOAT4 a0 = vload4(inpa_offset, input_a); - FLOAT4 a1 = vload4(inpa_offset + height_blocks, input_a); - FLOAT4 a2 = vload4(inpa_offset + height_blocks*2, input_a); - FLOAT4 a3 = vload4(inpa_offset + height_blocks*3, input_a); + COMPUTE_FLOAT4 a0 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); + COMPUTE_FLOAT4 a1 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks, input_a)); + COMPUTE_FLOAT4 a2 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*2, input_a)); + COMPUTE_FLOAT4 a3 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*3, input_a)); const int inpb_offset = (4*width_blocks_idx) * channel_blocks + pos; - FLOAT4 b0 = vload4(inpb_offset, input_b); - FLOAT4 b1 = ((remainb >= 3) ? v_zero : vload4(inpb_offset + channel_blocks, input_b)); - FLOAT4 b2 = ((remainb >= 2) ? v_zero : vload4(inpb_offset + channel_blocks*2, input_b)); - FLOAT4 b3 = ((remainb >= 1) ? v_zero : vload4(inpb_offset + channel_blocks*3, input_b)); + COMPUTE_FLOAT4 b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + COMPUTE_FLOAT4 b1 = ((remainb >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks, input_b))); + COMPUTE_FLOAT4 b2 = ((remainb >= 2) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*2, input_b))); + COMPUTE_FLOAT4 b3 = ((remainb >= 1) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*3, input_b))); - FLOAT4 a0_trans = (FLOAT4)(a0.x, a1.x, a2.x, a3.x); - FLOAT4 a1_trans = (FLOAT4)(a0.y, a1.y, a2.y, a3.y); - FLOAT4 a2_trans = (FLOAT4)(a0.z, a1.z, a2.z, a3.z); - FLOAT4 a3_trans = (FLOAT4)(a0.w, a1.w, a2.w, a3.w); + COMPUTE_FLOAT4 a0_trans = (COMPUTE_FLOAT4)(a0.x, a1.x, a2.x, a3.x); + COMPUTE_FLOAT4 a1_trans = (COMPUTE_FLOAT4)(a0.y, a1.y, a2.y, a3.y); + COMPUTE_FLOAT4 a2_trans = (COMPUTE_FLOAT4)(a0.z, a1.z, a2.z, a3.z); + COMPUTE_FLOAT4 a3_trans = (COMPUTE_FLOAT4)(a0.w, a1.w, a2.w, a3.w); //matmul result0.x += dot(a0_trans, b0); @@ -391,21 +391,21 @@ __kernel void matmul_transA_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* { const int inpa_offset = (4*(channel_blocks-1)) * height_blocks + height_blocks_idx; - FLOAT4 a0 = vload4(inpa_offset, input_a); - FLOAT4 a1 = ((remaina >= 3) ? v_zero : vload4(inpa_offset + height_blocks, input_a)); - FLOAT4 a2 = ((remaina >= 2) ? v_zero : vload4(inpa_offset + height_blocks*2, input_a)); - FLOAT4 a3 = ((remaina >= 1) ? v_zero : vload4(inpa_offset + height_blocks*3, input_a)); + COMPUTE_FLOAT4 a0 = CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset, input_a)); + COMPUTE_FLOAT4 a1 = ((remaina >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks, input_a))); + COMPUTE_FLOAT4 a2 = ((remaina >= 2) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*2, input_a))); + COMPUTE_FLOAT4 a3 = ((remaina >= 1) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpa_offset + height_blocks*3, input_a))); const int inpb_offset = (4*width_blocks_idx) * channel_blocks + channel_blocks-1; - FLOAT4 b0 = vload4(inpb_offset, input_b); - FLOAT4 b1 = ((remainb >= 3) ? v_zero : vload4(inpb_offset + channel_blocks, input_b)); - FLOAT4 b2 = ((remainb >= 2) ? v_zero : vload4(inpb_offset + channel_blocks*2, input_b)); - FLOAT4 b3 = ((remainb >= 1) ? v_zero : vload4(inpb_offset + channel_blocks*3, input_b)); + COMPUTE_FLOAT4 b0 = CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset, input_b)); + COMPUTE_FLOAT4 b1 = ((remainb >= 3) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks, input_b))); + COMPUTE_FLOAT4 b2 = ((remainb >= 2) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*2, input_b))); + COMPUTE_FLOAT4 b3 = ((remainb >= 1) ? v_zero : CONVERT_COMPUTE_FLOAT4(vload4(inpb_offset + channel_blocks*3, input_b))); - FLOAT4 a0_trans = (FLOAT4)(a0.x, a1.x, a2.x, a3.x); - FLOAT4 a1_trans = (FLOAT4)(a0.y, a1.y, a2.y, a3.y); - FLOAT4 a2_trans = (FLOAT4)(a0.z, a1.z, a2.z, a3.z); - FLOAT4 a3_trans = (FLOAT4)(a0.w, a1.w, a2.w, a3.w); + COMPUTE_FLOAT4 a0_trans = (COMPUTE_FLOAT4)(a0.x, a1.x, a2.x, a3.x); + COMPUTE_FLOAT4 a1_trans = (COMPUTE_FLOAT4)(a0.y, a1.y, a2.y, a3.y); + COMPUTE_FLOAT4 a2_trans = (COMPUTE_FLOAT4)(a0.z, a1.z, a2.z, a3.z); + COMPUTE_FLOAT4 a3_trans = (COMPUTE_FLOAT4)(a0.w, a1.w, a2.w, a3.w); //matmul result0.x += dot(a0_trans, b0); @@ -431,11 +431,11 @@ __kernel void matmul_transA_transB_buf(GLOBAL_SIZE_2_DIMS __global const FLOAT* const int out_offset = (4*height_blocks_idx) * width_blocks + width_blocks_idx; - vstore4(result0, out_offset, output_c); + vstore4(CONVERT_FLOAT4(result0), out_offset, output_c); if(4*height_blocks_idx+1 >= height) return; - vstore4(result1, out_offset + width_blocks, output_c); + vstore4(CONVERT_FLOAT4(result1), out_offset + width_blocks, output_c); if(4*height_blocks_idx+2 >= height) return; - vstore4(result2, out_offset + width_blocks*2, output_c); + vstore4(CONVERT_FLOAT4(result2), out_offset + width_blocks*2, output_c); if(4*height_blocks_idx+3 >= height) return; - vstore4(result3, out_offset + width_blocks*3, output_c); + vstore4(CONVERT_FLOAT4(result3), out_offset + width_blocks*3, output_c); } diff --git a/source/backend/opencl/execution/cl/opencl_program.cc b/source/backend/opencl/execution/cl/opencl_program.cc index b18253f2c..ae5bd9565 100644 --- a/source/backend/opencl/execution/cl/opencl_program.cc +++ b/source/backend/opencl/execution/cl/opencl_program.cc @@ -21,7 +21,7 @@ extern const std::map> OpenCLProgramMap #ifndef MNN_OPENCL_BUFFER_CLOSED { "grid_sample_buf", - { 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} }, #endif { @@ -46,6 +46,12 @@ extern const std::map> OpenCLProgramMap "winogradTransformSource2_3_1", { 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} }, +#ifndef MNN_OPENCL_BUFFER_CLOSED +{ + "gemv_conv1x1_buf", + { 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} @@ -54,14 +60,14 @@ extern const std::map> OpenCLProgramMap #ifdef MNN_SUPPORT_INTEL_SUBGROUP { "conv_2d_c1_subgroup_buf", - { 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} }, #endif #endif #ifndef MNN_OPENCL_BUFFER_CLOSED { "conv_2d_int_buf", - { 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} }, #endif #ifndef MNN_OPENCL_BUFFER_CLOSED @@ -87,7 +93,7 @@ extern const std::map> OpenCLProgramMap #ifndef MNN_OPENCL_BUFFER_CLOSED { "raster_buf", - { 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} }, #endif #ifndef MNN_OPENCL_BUFFER_CLOSED @@ -102,7 +108,7 @@ extern const std::map> OpenCLProgramMap #ifdef MNN_SUPPORT_INTEL_SUBGROUP { "depthwise_conv2d_subgroup_buf", - { 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} }, #endif #endif @@ -114,14 +120,14 @@ extern const std::map> OpenCLProgramMap #ifdef MNN_SUPPORT_INTEL_SUBGROUP { "pooling_subgroup_buf", - { 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} }, #endif #endif #ifndef MNN_OPENCL_BUFFER_CLOSED { "pooling_buf", - { 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} }, #endif { @@ -137,7 +143,7 @@ extern const std::map> OpenCLProgramMap #ifndef MNN_OPENCL_BUFFER_CLOSED { "depthwise_conv2d_buf", - { 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} }, #endif #ifndef MNN_OPENCL_BUFFER_CLOSED @@ -167,7 +173,7 @@ extern const std::map> OpenCLProgramMap #ifndef MNN_OPENCL_BUFFER_CLOSED { "gemm_buf", - { 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} }, #endif { @@ -193,6 +199,12 @@ extern const std::map> OpenCLProgramMap #endif #endif #ifndef MNN_OPENCL_BUFFER_CLOSED +{ + "attention_buf", + { 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} + }, +#endif +#ifndef MNN_OPENCL_BUFFER_CLOSED #ifdef MNN_SUPPORT_INTEL_SUBGROUP { "unary_subgroup_buf", @@ -215,13 +227,13 @@ extern const std::map> OpenCLProgramMap #ifndef MNN_OPENCL_BUFFER_CLOSED { "scale_buf", - { 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} }, #endif #ifndef MNN_OPENCL_BUFFER_CLOSED { "matmul_buf", - { 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} }, #endif { @@ -231,7 +243,7 @@ extern const std::map> OpenCLProgramMap #ifndef MNN_OPENCL_BUFFER_CLOSED { "conv_2d_buf", - { 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} }, #endif { @@ -245,13 +257,13 @@ extern const std::map> OpenCLProgramMap #ifndef MNN_OPENCL_BUFFER_CLOSED { "layernorm_buf", - { 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} }, #endif #ifndef MNN_OPENCL_BUFFER_CLOSED { "softmax_buf", - { 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} }, #endif #ifndef MNN_OPENCL_BUFFER_CLOSED @@ -264,7 +276,7 @@ extern const std::map> OpenCLProgramMap #ifdef MNN_SUPPORT_INTEL_SUBGROUP { "conv_2d_c16_subgroup_buf", - { 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} }, #endif #endif diff --git a/source/backend/opencl/execution/cl/pooling_buf.cl b/source/backend/opencl/execution/cl/pooling_buf.cl index d8e77a898..1340973d1 100644 --- a/source/backend/opencl/execution/cl/pooling_buf.cl +++ b/source/backend/opencl/execution/cl/pooling_buf.cl @@ -30,7 +30,7 @@ __kernel void pooling(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, const int ih_start = mad24(oh_idx, stride_shape.x, -pad_shape.x); #ifdef POOL_AVG - FLOAT4 result = (FLOAT4)(0); + COMPUTE_FLOAT4 result = (COMPUTE_FLOAT4)(0); const int inp_offset = (((b_idx*channel_block+c_idx)*input_shape.x+ih_start)*input_shape.y+iw_start)*4; #ifdef COUNT_INCLUDE_PADDING int total_count = (min(ih_start + kernel_shape.x, input_shape.x + pad_shape.x) - ih_start) * (min(iw_start + kernel_shape.y, input_shape.y + pad_shape.y) - iw_start); @@ -47,16 +47,16 @@ __kernel void pooling(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, if(iw_cur < 0 || iw_cur >= input_shape.y) { continue; } - FLOAT4 inp_data = vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4); + COMPUTE_FLOAT4 inp_data = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4)); result += inp_data; #ifndef COUNT_INCLUDE_PADDING total_count++; #endif } } - result = result / (FLOAT4)(1.0*total_count); + result = result / (COMPUTE_FLOAT4)(1.0*total_count); #else - FLOAT4 result = (FLOAT4)(-FLT_MAX); + COMPUTE_FLOAT4 result = (COMPUTE_FLOAT4)(-FLT_MAX); #if RETURN_REDICE int4 redice = (int4)0; #endif @@ -71,7 +71,7 @@ __kernel void pooling(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, if(iw_cur < 0 || iw_cur >= input_shape.y) { continue; } - FLOAT4 inp_data = vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4); + COMPUTE_FLOAT4 inp_data = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4)); #if RETURN_REDICE redice = inp_data > result ? (int4)((ih_start + kh) * input_shape.y + iw_start + kw) : redice; #endif @@ -81,7 +81,7 @@ __kernel void pooling(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, #endif const int out_offset = (((b_idx*channel_block + c_idx)*output_shape.x + oh_idx)* output_shape.y + ow_idx)*4; - vstore4(result, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(result), 0, output+out_offset); #if RETURN_REDICE vstore4(CONVERT_FLOAT4(redice), 0, rediceOutput+out_offset); #endif @@ -102,22 +102,22 @@ __kernel void global_pooling_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, const int output_batch_idx = get_global_id(2); #ifdef POOL_AVG - FLOAT4 output_result = 0; + COMPUTE_FLOAT4 output_result = 0; #else - FLOAT4 output_result = (FLOAT4)(-FLT_MAX); + COMPUTE_FLOAT4 output_result = (COMPUTE_FLOAT4)(-FLT_MAX); #if RETURN_REDICE int4 redice = (int4)0; int4 local rediceId[LOCAL_SIZE]; #endif #endif - FLOAT4 local sum[LOCAL_SIZE]; + COMPUTE_FLOAT4 local sum[LOCAL_SIZE]; const int inp_offset = ((output_batch_idx*channel_block+output_channel_idx)*input_shape.x)*input_shape.y*4; const int size = input_shape.x * input_shape.y; for(int i = local_id; i < size; i+=LOCAL_SIZE){ int w = i % input_shape.y;; int h = i / input_shape.y; - FLOAT4 in = vload4(0, input+inp_offset+(h*input_shape.y+w)*4); + COMPUTE_FLOAT4 in = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset+(h*input_shape.y+w)*4)); #ifdef POOL_AVG output_result += in; #else @@ -153,7 +153,7 @@ __kernel void global_pooling_buf(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, #endif const int out_offset = (output_batch_idx*channel_block + output_channel_idx)*4; - vstore4(output_result, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(output_result), 0, output+out_offset); #if RETURN_REDICE redice = rediceId[0]; vstore4(CONVERT_FLOAT4(redice), 0, rediceOutput+out_offset); diff --git a/source/backend/opencl/execution/cl/pooling_subgroup_buf.cl b/source/backend/opencl/execution/cl/pooling_subgroup_buf.cl index 3f4ebd6fe..304c3b903 100644 --- a/source/backend/opencl/execution/cl/pooling_subgroup_buf.cl +++ b/source/backend/opencl/execution/cl/pooling_subgroup_buf.cl @@ -34,7 +34,7 @@ __kernel void pooling_c4_c4(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, const int ih_start = mad24(oh_idx, STRIDE_Y, -pad_shape.x); #ifdef POOL_AVG - FLOAT4 result = (FLOAT4)(0); + COMPUTE_FLOAT4 result = (COMPUTE_FLOAT4)(0); const int inp_offset = (((b_idx*in_channel_block+c_idx)*input_shape.x+ih_start)*input_shape.y+iw_start+input_pad_left)*4; #ifdef COUNT_INCLUDE_PADDING int total_count = (min(ih_start + KERNEL_Y, input_shape.x + pad_shape.x) - ih_start) * (min(iw_start + KERNEL_X, input_shape.y + pad_shape.y) - iw_start); @@ -51,16 +51,16 @@ __kernel void pooling_c4_c4(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, if(iw_cur < 0 || iw_cur >= input_shape.y) { continue; } - FLOAT4 inp_data = vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4); + COMPUTE_FLOAT4 inp_data = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4)); result += inp_data; #ifndef COUNT_INCLUDE_PADDING total_count++; #endif } } - result = result / (FLOAT4)(1.0*total_count); + result = result / (COMPUTE_FLOAT4)(1.0*total_count); #else - FLOAT4 result = (FLOAT4)(-FLT_MAX); + COMPUTE_FLOAT4 result = (COMPUTE_FLOAT4)(-FLT_MAX); #if RETURN_REDICE int4 redice = (int4)0; #endif @@ -75,7 +75,7 @@ __kernel void pooling_c4_c4(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, if(iw_cur < 0 || iw_cur >= input_shape.y) { continue; } - FLOAT4 inp_data = vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4); + COMPUTE_FLOAT4 inp_data = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4)); #if RETURN_REDICE redice = inp_data > result ? (int4)((ih_start + kh) * input_shape.y + iw_start + kw) : redice; #endif @@ -85,7 +85,7 @@ __kernel void pooling_c4_c4(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, #endif const int out_offset = (((b_idx*in_channel_block + c_idx)*output_shape.x + oh_idx)* output_shape.y + ow_idx + output_pad_left)*4; - vstore4(result, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(result), 0, output+out_offset); #if RETURN_REDICE vstore4(CONVERT_FLOAT4(redice), 0, rediceOutput+(((b_idx*in_channel_block + c_idx)*output_shape.x + oh_idx)* output_shape.y + ow_idx)*4); #endif @@ -118,7 +118,7 @@ __kernel void pooling_c4_c16(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, const int dst_width = output_shape.y + output_pad_left + output_pad_right; #ifdef POOL_AVG - FLOAT4 result = (FLOAT4)(0); + COMPUTE_FLOAT4 result = (COMPUTE_FLOAT4)(0); const int inp_offset = (((b_idx*in_channel_block+c_idx)*input_shape.x+ih_start)*input_shape.y+iw_start+input_pad_left)*4; #ifdef COUNT_INCLUDE_PADDING int total_count = (min(ih_start + KERNEL_Y, input_shape.x + pad_shape.x) - ih_start) * (min(iw_start + KERNEL_X, input_shape.y + pad_shape.y) - iw_start); @@ -135,16 +135,16 @@ __kernel void pooling_c4_c16(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, if(iw_cur < 0 || iw_cur >= input_shape.y) { continue; } - FLOAT4 inp_data = vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4); + COMPUTE_FLOAT4 inp_data = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4)); result += inp_data; #ifndef COUNT_INCLUDE_PADDING total_count++; #endif } } - result = result / (FLOAT4)(1.0*total_count); + result = result / (COMPUTE_FLOAT4)(1.0*total_count); #else - FLOAT4 result = (FLOAT4)(-FLT_MAX); + COMPUTE_FLOAT4 result = (COMPUTE_FLOAT4)(-FLT_MAX); #if RETURN_REDICE int4 redice = (int4)0; #endif @@ -159,7 +159,7 @@ __kernel void pooling_c4_c16(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, if(iw_cur < 0 || iw_cur >= input_shape.y) { continue; } - FLOAT4 inp_data = vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4); + COMPUTE_FLOAT4 inp_data = CONVERT_COMPUTE_FLOAT4(vload4(0, input+inp_offset+(kh*input_shape.y+kw)*4)); #if RETURN_REDICE redice = inp_data > result ? (int4)((ih_start + kh) * input_shape.y + iw_start + kw) : redice; #endif @@ -170,7 +170,7 @@ __kernel void pooling_c4_c16(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, const int c_left = (c_idx % 4) * 4; const int out_offset = (((b_idx*out_channel_block + c_idx/4)*output_shape.x + oh_idx)* dst_width + ow_idx + output_pad_left)*16 + c_left; - vstore4(result, 0, output+out_offset); + vstore4(CONVERT_FLOAT4(result), 0, output+out_offset); #if RETURN_REDICE vstore4(CONVERT_FLOAT4(redice), 0, rediceOutput+(((b_idx*out_channel_block + c_idx)*output_shape.x + oh_idx)* output_shape.y + ow_idx)*4); #endif @@ -214,20 +214,20 @@ __kernel void pooling_c16_c16(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, const int dst_width = output_shape.y + output_pad_left + output_pad_right; #ifdef POOL_AVG - FLOAT8 result = (FLOAT8)(0); - FLOAT8 w_start = (FLOAT8)(iw_start, iw_start + STRIDE_X, iw_start + STRIDE_X * 2, iw_start + STRIDE_X * 3, iw_start + STRIDE_X * 4, iw_start + STRIDE_X * 5, iw_start + STRIDE_X * 6, iw_start + STRIDE_X * 7); + COMPUTE_FLOAT8 result = (COMPUTE_FLOAT8)(0); + COMPUTE_FLOAT8 w_start = (COMPUTE_FLOAT8)(iw_start, iw_start + STRIDE_X, iw_start + STRIDE_X * 2, iw_start + STRIDE_X * 3, iw_start + STRIDE_X * 4, iw_start + STRIDE_X * 5, iw_start + STRIDE_X * 6, iw_start + STRIDE_X * 7); #ifdef COUNT_INCLUDE_PADDING - FLOAT8 w_size = fmin(w_start + KERNEL_X, input_shape.y + pad_shape.y) - w_start; - FLOAT8 total_count = (FLOAT8)(min(ih_start + KERNEL_Y, input_shape.x + pad_shape.x) - ih_start) * w_size; + COMPUTE_FLOAT8 w_size = fmin(w_start + KERNEL_X, input_shape.y + pad_shape.y) - w_start; + COMPUTE_FLOAT8 total_count = (COMPUTE_FLOAT8)(min(ih_start + KERNEL_Y, input_shape.x + pad_shape.x) - ih_start) * w_size; #else - w_start = fmax(w_start, (FLOAT8)0); - FLOAT8 w_end = fmin(w_start + KERNEL_X, (FLOAT8)input_shape.y); + w_start = fmax(w_start, (COMPUTE_FLOAT8)0); + COMPUTE_FLOAT8 w_end = fmin(w_start + KERNEL_X, (COMPUTE_FLOAT8)input_shape.y); float h_start = fmax((float)ih_start, 0); float h_end = fmin(h_start + KERNEL_Y, (float)input_shape.x); - FLOAT8 total_count = (w_end - w_start) * (FLOAT8)(h_end - h_start); + COMPUTE_FLOAT8 total_count = (w_end - w_start) * (COMPUTE_FLOAT8)(h_end - h_start); #endif #else - FLOAT8 result = (FLOAT8)(-FLT_MAX); + COMPUTE_FLOAT8 result = (COMPUTE_FLOAT8)(-FLT_MAX); #if RETURN_REDICE int8 redice = (int8)0; #endif @@ -251,14 +251,14 @@ __kernel void pooling_c16_c16(GLOBAL_SIZE_3_DIMS __global const FLOAT *input, #ifdef POOL_AVG line_cache[i] = 0; #else - line_cache[i] = (FLOAT)(-FLT_MAX); + line_cache[i] = (COMPUTE_FLOAT)(-FLT_MAX); #endif } } for(int kw=0; kw= 4 int lid = get_local_id(0); - FLOAT4 local sum[SOFTMAX_LOCAL_SIZE]; + COMPUTE_FLOAT4 local sum[SOFTMAX_LOCAL_SIZE]; - FLOAT4 maxValue = (FLOAT4)-FLT_MAX; + COMPUTE_FLOAT4 maxValue = (COMPUTE_FLOAT4)-FLT_MAX; for (int i = lid; i < shape.y - 1; i+=SOFTMAX_LOCAL_SIZE) { - maxValue = fmax(maxValue, vload4(i*shape.z*shape.w, input+offset)); + maxValue = fmax(maxValue, CONVERT_COMPUTE_FLOAT4(vload4(i*shape.z*shape.w, input+offset))); } sum[lid] = maxValue; @@ -48,7 +48,7 @@ __kernel void softmax_channel(GLOBAL_SIZE_3_DIMS maxValue.x = fmax(maxValue.x, maxValue.z); maxValue.x = fmax(maxValue.x, maxValue.w); - FLOAT4 input_data = vload4((shape.y - 1) *shape.z*shape.w, input+offset); + COMPUTE_FLOAT4 input_data = CONVERT_COMPUTE_FLOAT4(vload4((shape.y - 1) *shape.z*shape.w, input+offset)); if (remain_channels == 0) { maxValue.x = fmax(maxValue.x, input_data.x); maxValue.x = fmax(maxValue.x, input_data.y); @@ -65,9 +65,9 @@ __kernel void softmax_channel(GLOBAL_SIZE_3_DIMS maxValue.x = fmax(maxValue.x, input_data.x); } - FLOAT4 sumValue = (FLOAT4)0; + COMPUTE_FLOAT4 sumValue = (COMPUTE_FLOAT4)0; for (int i = lid; i < shape.y - 1; i+=SOFTMAX_LOCAL_SIZE) { - sumValue += exp(vload4(i*shape.z*shape.w, input+offset) - (FLOAT4)maxValue.x); + sumValue += exp(CONVERT_COMPUTE_FLOAT4(vload4(i*shape.z*shape.w, input+offset)) - (COMPUTE_FLOAT4)maxValue.x); } sum[lid] = sumValue; barrier(CLK_LOCAL_MEM_FENCE); @@ -97,20 +97,20 @@ __kernel void softmax_channel(GLOBAL_SIZE_3_DIMS sumValue.x += exp(input_data.x); } for(int i = lid; i < shape.y; i+=SOFTMAX_LOCAL_SIZE){ - FLOAT4 value = exp(vload4(i*shape.z*shape.w, input+offset) - maxValue.x) / sumValue.x; - vstore4(value, i*shape.z*shape.w, output+offset); + COMPUTE_FLOAT4 value = exp(CONVERT_COMPUTE_FLOAT4(vload4(i*shape.z*shape.w, input+offset)) - maxValue.x) / sumValue.x; + vstore4(CONVERT_FLOAT4(value), i*shape.z*shape.w, output+offset); } #else - FLOAT4 maxValue = (FLOAT4)-FLT_MAX; + COMPUTE_FLOAT4 maxValue = (COMPUTE_FLOAT4)-FLT_MAX; for (int i = 0; i < shape.y - 1; i++) { - maxValue = fmax(maxValue, vload4(i*shape.z*shape.w, input+offset)); + maxValue = fmax(maxValue, CONVERT_COMPUTE_FLOAT4(vload4(i*shape.z*shape.w, input+offset))); } maxValue.x = fmax(maxValue.x, maxValue.y); maxValue.x = fmax(maxValue.x, maxValue.z); maxValue.x = fmax(maxValue.x, maxValue.w); - FLOAT4 input_data = vload4((shape.y - 1) *shape.z*shape.w, input+offset); + COMPUTE_FLOAT4 input_data = CONVERT_COMPUTE_FLOAT4(vload4((shape.y - 1) *shape.z*shape.w, input+offset)); if (remain_channels == 0) { maxValue.x = fmax(maxValue.x, input_data.x); maxValue.x = fmax(maxValue.x, input_data.y); @@ -127,9 +127,9 @@ __kernel void softmax_channel(GLOBAL_SIZE_3_DIMS maxValue.x = fmax(maxValue.x, input_data.x); } - FLOAT4 sumValue = (FLOAT4)0; + COMPUTE_FLOAT4 sumValue = (COMPUTE_FLOAT4)0; for (int i = 0; i < shape.y - 1; i++) { - sumValue += exp(vload4(i*shape.z*shape.w, input+offset) - (FLOAT4)maxValue.x); + sumValue += exp(CONVERT_COMPUTE_FLOAT4(vload4(i*shape.z*shape.w, input+offset)) - (COMPUTE_FLOAT4)maxValue.x); } sumValue.x = sumValue.x + sumValue.y + sumValue.z + sumValue.w; input_data -= maxValue.x; @@ -149,8 +149,8 @@ __kernel void softmax_channel(GLOBAL_SIZE_3_DIMS sumValue.x += exp(input_data.x); } for(int i = 0; i < shape.y; i++){ - FLOAT4 value = exp(vload4(i*shape.z*shape.w, input+offset) - maxValue.x) / sumValue.x; - vstore4(value, i*shape.z*shape.w, output+offset); + COMPUTE_FLOAT4 value = exp(CONVERT_COMPUTE_FLOAT4(vload4(i*shape.z*shape.w, input+offset)) - maxValue.x) / sumValue.x; + vstore4(CONVERT_FLOAT4(value), i*shape.z*shape.w, output+offset); } #endif } @@ -172,12 +172,12 @@ __kernel void softmax_height(GLOBAL_SIZE_3_DIMS const int offset = (((b*shape.y+c)*shape.z+0)*shape.w+w)*4; #if SOFTMAX_LOCAL_SIZE >= 4 int lid = get_local_id(0); - FLOAT4 local sum[SOFTMAX_LOCAL_SIZE]; + COMPUTE_FLOAT4 local sum[SOFTMAX_LOCAL_SIZE]; /*Compute Max */ - FLOAT4 maxValue = (FLOAT4)(-FLT_MAX); + COMPUTE_FLOAT4 maxValue = (COMPUTE_FLOAT4)(-FLT_MAX); for (int i=lid; i= 4 int lid = get_local_id(0); - FLOAT4 local sum[SOFTMAX_LOCAL_SIZE]; + COMPUTE_FLOAT4 local sum[SOFTMAX_LOCAL_SIZE]; /*Compute Max */ - FLOAT4 maxValue = (FLOAT4)(-FLT_MAX); + COMPUTE_FLOAT4 maxValue = (COMPUTE_FLOAT4)(-FLT_MAX); for (int i=lid; i &inputs, const int idx = 0; #else if(openCLBackend->isUseRecordQueue()){ - if(openCLBackend->isDevideOpRecord()) - openCLBackend->addRecord(mRecording); + openCLBackend->addRecord(mRecording, mOpRecordUpdateInfo); return NO_ERROR; } #endif diff --git a/source/backend/opencl/execution/image/CommonExecution.hpp b/source/backend/opencl/execution/image/CommonExecution.hpp index f8ecaea6a..c5a043969 100644 --- a/source/backend/opencl/execution/image/CommonExecution.hpp +++ b/source/backend/opencl/execution/image/CommonExecution.hpp @@ -43,6 +43,7 @@ class CommonExecution : public Execution { const MNN::Op *mOp; OpType mOpType; cl_recording_qcom mRecording{NULL}; + std::vector mOpRecordUpdateInfo; }; } // namespace OpenCL } // namespace MNN diff --git a/source/backend/opencl/execution/image/ConvExecution.cpp b/source/backend/opencl/execution/image/ConvExecution.cpp index 440ba5ac9..74673a469 100644 --- a/source/backend/opencl/execution/image/ConvExecution.cpp +++ b/source/backend/opencl/execution/image/ConvExecution.cpp @@ -25,28 +25,13 @@ ConvCommonExecution::ConvCommonExecution(const Convolution2D *conv2dParams, Back int biasSize = conv2dParams->bias()->size(); const float *biasDataPtr = conv2dParams->bias()->data(); - int buffer_size = ALIGN_UP4(biasSize); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + int buffer_size = ALIGN_UP4(biasSize) * sizeof(float); cl::Buffer biasBuffer(runtime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); cl_int error; - auto biasPtrCL = runtime->commandQueue().enqueueMapBuffer( - biasBuffer, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); + auto biasPtrCL = runtime->commandQueue().enqueueMapBuffer(biasBuffer, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(biasPtrCL != nullptr && error == CL_SUCCESS){ - if(runtime->isWeightCpuTransHalf()){ - for(int i=0; i &inputs, const std::vec std::shared_ptr filterBuffer( Tensor::createDevice({outputChannel, inputChannel, kernelWidth, kernelHeight})); - int buffer_size = filterBuffer->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = filterBuffer->elementSize() * sizeof(float); cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); filterBuffer->buffer().device = (uint64_t)(&filterBufferCL); @@ -219,13 +199,8 @@ ConvExecution::ConvExecution(const std::vector &inputs, const std::vec auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(ptrCL != nullptr && error == CL_SUCCESS) { ::memset(ptrCL, 0, buffer_size); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int i = 0 ; i < filterBuffer->elementSize(); i++){ - ((half_float::half*)ptrCL)[i] = (half_float::half)(filterDataPtr[i]); - } - }else{ - ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); - } + ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); + }else{ MNN_ERROR("Map error ptrCL == nullptr \n"); } @@ -243,10 +218,7 @@ ConvExecution::ConvExecution(const std::vector &inputs, const std::vec mResource->mFilter.get()->buffer().device = (uint64_t)mResource->mKernelBuffer.get(); MNN::OpenCL::BufferConvertor bufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - bool needTrans = false; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - needTrans = true; - } + bool needTrans = true; bufferConvertor.convertToNC4HW4Buffer(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mResource->mFilter.get(), needTrans); } else #endif @@ -255,10 +227,7 @@ ConvExecution::ConvExecution(const std::vector &inputs, const std::vec mOpenCLBackend->onAcquireBuffer(mResource->mFilter.get(), Backend::STATIC); MNN::OpenCL::ImageBufferConvertor imageBufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - std::string buildOption = ""; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - buildOption = "-DBUFFER_INP_FP32"; - } + std::string buildOption = "-DBUFFER_INP_FP32"; imageBufferConvertor.convertBufferToImage(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mResource->mFilter.get(), false, buildOption); } } diff --git a/source/backend/opencl/execution/image/ConvWinograd.cpp b/source/backend/opencl/execution/image/ConvWinograd.cpp index 547c7081b..25f793286 100644 --- a/source/backend/opencl/execution/image/ConvWinograd.cpp +++ b/source/backend/opencl/execution/image/ConvWinograd.cpp @@ -100,29 +100,15 @@ ConvWinograd::ConvWinograd(const MNN::Op *op, Backend* backend) : CommonExecutio mResource->mBias.reset(new cl::Image2D(runTime->context(), CL_MEM_READ_WRITE, cl::ImageFormat(CL_RGBA, imageChannelType), UP_DIV(co, 4), 1, 0, nullptr, nullptr)); - int buffer_size = ALIGN_UP4(co); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = ALIGN_UP4(co) * sizeof(float); std::shared_ptr biasBuffer( new cl::Buffer(runTime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size)); cl_int error; auto biasC = queue.enqueueMapBuffer(*biasBuffer, CL_TRUE, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(biasC != nullptr && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int i=0; ibias()->data()[i]); - } - for(int i=co; ibias()->data(), co * sizeof(float)); - } + ::memset(biasC, 0, buffer_size); + ::memcpy(biasC, conv2D->bias()->data(), co * sizeof(float)); }else{ MNN_ERROR("Map error biasC == nullptr \n"); } @@ -140,24 +126,13 @@ ConvWinograd::ConvWinograd(const MNN::Op *op, Backend* backend) : CommonExecutio generator.transformWeight(weightDest.get(), sourceWeight.get()); auto weightDestSize = weightDest->size(); - buffer_size = weightDest->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + buffer_size = weightDest->elementSize() * sizeof(float); cl::Buffer weightBuffer(runTime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); { cl_int error; auto weightPtr = queue.enqueueMapBuffer(weightBuffer, CL_TRUE, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(weightPtr != nullptr && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int i=0; ielementSize(); i++) { - ((half_float::half*)weightPtr)[i] = (half_float::half)(weightDest->host()[i]); - } - }else{ - ::memcpy(weightPtr, weightDest->host(), buffer_size); - } + ::memcpy(weightPtr, weightDest->host(), buffer_size); } else{ MNN_ERROR("Map error weightPtr == nullptr \n"); } diff --git a/source/backend/opencl/execution/image/DeconvExecution.cpp b/source/backend/opencl/execution/image/DeconvExecution.cpp index c61e93d1c..b16abb3ec 100644 --- a/source/backend/opencl/execution/image/DeconvExecution.cpp +++ b/source/backend/opencl/execution/image/DeconvExecution.cpp @@ -41,24 +41,13 @@ DeconvExecution::DeconvExecution(const std::vector &inputs, const MNN: std::shared_ptr filterBuffer( Tensor::createDevice({outputChannel, inputChannel, kernelHeight, kernelWidth})); - int buffer_size = filterBuffer->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = filterBuffer->elementSize() * sizeof(float); cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR, buffer_size); filterBuffer->buffer().device = (uint64_t)(&filterBufferCL); cl_int error; auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(ptrCL != nullptr && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int i=0; ielementSize(); i++) { - ((half_float::half*)ptrCL)[i] = (half_float::half)(filterDataPtrTransformed[i]); - } - }else{ - ::memcpy(ptrCL, filterDataPtrTransformed.data(), filterBuffer->size()); - } + ::memcpy(ptrCL, filterDataPtrTransformed.data(), filterBuffer->size()); }else{ MNN_ERROR("Map error ptrCL == nullptr \n"); } @@ -68,10 +57,7 @@ DeconvExecution::DeconvExecution(const std::vector &inputs, const MNN: mOpenCLBackend->onAcquireBuffer(mResource->mFilter.get(), Backend::STATIC); MNN::OpenCL::ImageBufferConvertor imageBufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - std::string buildOption = ""; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - buildOption = "-DBUFFER_INP_FP32"; - } + std::string buildOption = "-DBUFFER_INP_FP32"; imageBufferConvertor.convertBufferToImage(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mResource->mFilter.get(), false, buildOption); mResource->mBuildOptions.emplace("-DBIAS"); diff --git a/source/backend/opencl/execution/image/DepthwiseConvExecution.cpp b/source/backend/opencl/execution/image/DepthwiseConvExecution.cpp index ba5bc93bc..dbdbd1bbd 100644 --- a/source/backend/opencl/execution/image/DepthwiseConvExecution.cpp +++ b/source/backend/opencl/execution/image/DepthwiseConvExecution.cpp @@ -42,24 +42,13 @@ DepthwiseConvExecution::DepthwiseConvExecution(const std::vector &inpu mResource->mFilter.reset(Tensor::createDevice({1, filterImageShape[1], 1, 4 * filterImageShape[0]})); std::shared_ptr filterBuffer(Tensor::createDevice(filterShape)); - int buffer_size = filterBuffer->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = filterBuffer->elementSize() * sizeof(float); cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size); filterBuffer->buffer().device = (uint64_t)(&filterBufferCL); cl_int error; auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(ptrCL != nullptr && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for (int i = 0; i < filterBuffer->elementSize(); i++) { - ((half_float::half *)ptrCL)[i] = (half_float::half)(filterDataPtr[i]); - } - } else { - ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); - } + ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); }else{ MNN_ERROR("Map error ptrCL == nullptr \n"); } @@ -68,10 +57,7 @@ DepthwiseConvExecution::DepthwiseConvExecution(const std::vector &inpu mOpenCLBackend->onAcquireBuffer(mResource->mFilter.get(), Backend::STATIC); MNN::OpenCL::ImageBufferConvertor imageBufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - std::string buildOption = ""; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - buildOption = "-DBUFFER_INP_FP32"; - } + std::string buildOption = "-DBUFFER_INP_FP32"; imageBufferConvertor.convertBufferToImage(filterBuffer.get(), MNN::OpenCL::DW_CONV2D_FILTER, mResource->mFilter.get(), false, buildOption); if (mResource->mConv2dCommonParams->relu() == true) { diff --git a/source/backend/opencl/execution/image/DepthwiseDeconvExecution.cpp b/source/backend/opencl/execution/image/DepthwiseDeconvExecution.cpp index d157373ea..da85385a7 100644 --- a/source/backend/opencl/execution/image/DepthwiseDeconvExecution.cpp +++ b/source/backend/opencl/execution/image/DepthwiseDeconvExecution.cpp @@ -38,24 +38,13 @@ DepthwiseDeconvExecution::DepthwiseDeconvExecution(const std::vector & mResource->mFilter.reset(Tensor::createDevice({1, filterImageShape[1], 1, 4 * filterImageShape[0]})); std::shared_ptr filterBuffer(Tensor::createDevice(filterShape)); - int buffer_size = filterBuffer->elementSize(); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = filterBuffer->elementSize() * sizeof(float); cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR, buffer_size); filterBuffer->buffer().device = (uint64_t)(&filterBufferCL); cl_int error; auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(nullptr != ptrCL && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int i=0; ielementSize(); i++) { - ((half_float::half*)ptrCL)[i] = (half_float::half)(filterDataPtr[i]); - } - }else{ - ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); - } + ::memcpy(ptrCL, filterDataPtr, filterBuffer->size()); }else{ MNN_ERROR("Map error ptrCL == nullptr \n"); } @@ -63,10 +52,7 @@ DepthwiseDeconvExecution::DepthwiseDeconvExecution(const std::vector & mOpenCLBackend->onAcquireBuffer(mResource->mFilter.get(), Backend::STATIC); MNN::OpenCL::ImageBufferConvertor imageBufferConvertor{mOpenCLBackend->getOpenCLRuntime()}; - std::string buildOption = ""; - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){ - buildOption = "-DBUFFER_INP_FP32"; - } + std::string buildOption = "-DBUFFER_INP_FP32"; imageBufferConvertor.convertBufferToImage(filterBuffer.get(), MNN::OpenCL::DW_CONV2D_FILTER, mResource->mFilter.get(), false, buildOption); if (mResource->mConv2dCommonParams->relu() == true) { mResource->mBuildOptions.emplace("-DRELU"); diff --git a/source/backend/opencl/execution/image/ReluExecution.cpp b/source/backend/opencl/execution/image/ReluExecution.cpp index 583777f68..3a8c95f8a 100644 --- a/source/backend/opencl/execution/image/ReluExecution.cpp +++ b/source/backend/opencl/execution/image/ReluExecution.cpp @@ -20,28 +20,14 @@ ReluExecution::ReluExecution(const std::vector &inputs, const MNN::Op int preluSize = mPreluParamPtr->slopeCount(); const float *preluDataPtr = mPreluParamPtr->slope()->data(); - int buffer_size = ALIGN_UP4(preluSize); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = ALIGN_UP4(preluSize) * sizeof(float); cl::Buffer preluBuffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR, buffer_size); cl_int error; auto preluDataPtrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer( preluBuffer, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(preluDataPtrCL != nullptr && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for(int i=0; i &inputs, const MNN::O int scaleSize = scaleParams->scaleData()->size(); const float *scaleDataPtr = scaleParams->scaleData()->data(); - int buffer_size = ALIGN_UP4(scaleSize); - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + size_t buffer_size = ALIGN_UP4(scaleSize) * sizeof(float); cl::Buffer scaleBuffer(openclBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR, buffer_size); cl_int error; auto scalePtrCL = openclBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer( scaleBuffer, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(nullptr != scalePtrCL && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for (int i = 0; i < scaleSize; i++) { - ((half_float::half *)scalePtrCL)[i] = (half_float::half)(scaleDataPtr[i]); - } - for(int i=scaleSize; i &inputs, const MNN::O MNN_ASSERT(biasSize == scaleSize); const float *biasDataPtr = scaleParams->biasData()->data(); - int buffer_size = ALIGN_UP4(biasSize); - if(openclBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) { - buffer_size *= sizeof(half_float::half); - } else { - buffer_size *= sizeof(float); - } + int buffer_size = ALIGN_UP4(biasSize) * sizeof(float); cl::Buffer biasBuffer(openclBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR, buffer_size); cl_int error; auto biasPtrCL = openclBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer( biasBuffer, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error); if(nullptr != biasPtrCL && error == CL_SUCCESS){ - if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){ - for (int i = 0; i < biasSize; i++) { - ((half_float::half *)biasPtrCL)[i] = (half_float::half)(biasDataPtr[i]); - } - for(int i=biasSize; i& inputs, const s class VulkanROIPoolingCreator : public VulkanBackend::Creator { public: virtual VulkanBasicExecution* onCreate(const std::vector& inputs, const std::vector& outputs, const MNN::Op* op, Backend* bn) const override { + if (TensorUtils::getDescribe(inputs[1])->dimensionFormat == MNN_DATA_FORMAT_NC4HW4) { + // Don't support old op version + return nullptr; + } return new VulkanROIPooling(bn, op->main_as_RoiParameters()->spatialScale()); } }; diff --git a/source/backend/vulkan/buffer/execution/glsl/roipooling.comp b/source/backend/vulkan/buffer/execution/glsl/roipooling.comp index c30868003..6d21e0016 100644 --- a/source/backend/vulkan/buffer/execution/glsl/roipooling.comp +++ b/source/backend/vulkan/buffer/execution/glsl/roipooling.comp @@ -1,5 +1,5 @@ #version 440 core -layout(std140) buffer; +layout(std430) buffer; layout(set = 0, binding = 0) writeonly buffer outputBuffer{ vec4 data[]; }uOutput; @@ -9,7 +9,7 @@ layout(set = 0, binding = 1) readonly buffer inputBuffer{ }uInput; layout(set = 0, binding = 2) readonly buffer roiBuffer{ - vec4 data[]; + float data[]; }uRoI; layout(set = 0, binding = 3) uniform constBuffer @@ -32,13 +32,16 @@ void main() int roiBatchIndex = pos.z % uRoiPoolingParam.outputImgSize.w; int inputZIndex = pos.z / uRoiPoolingParam.outputImgSize.w; // 0, xmin, ymin, xmax, ymax - vec4 roiData0 = uRoI.data[roiBatchIndex]; - vec4 roiData1 = uRoI.data[roiBatchIndex + uRoiPoolingParam.outputImgSize.w]; - int inputBatch = int(roiData0.x); - int x1 = int(round(float(roiData0.y) * uRoiPoolingParam.spatialScale)); - int y1 = int(round(float(roiData0.z) * uRoiPoolingParam.spatialScale)); - int x2 = int(round(float(roiData0.w) * uRoiPoolingParam.spatialScale)); - int y2 = int(round(float(roiData1.x) * uRoiPoolingParam.spatialScale)); + float roiData0 = uRoI.data[roiBatchIndex * 5 + 0]; + float roiData1 = uRoI.data[roiBatchIndex * 5 + 1]; + float roiData2 = uRoI.data[roiBatchIndex * 5 + 2]; + float roiData3 = uRoI.data[roiBatchIndex * 5 + 3]; + float roiData4 = uRoI.data[roiBatchIndex * 5 + 4]; + int inputBatch = int(roiData0); + int x1 = int(round(float(roiData1) * uRoiPoolingParam.spatialScale)); + int y1 = int(round(float(roiData2) * uRoiPoolingParam.spatialScale)); + int x2 = int(round(float(roiData3) * uRoiPoolingParam.spatialScale)); + int y2 = int(round(float(roiData4) * uRoiPoolingParam.spatialScale)); int roiW = max(x2 - x1 + 1, 1); int roiH = max(y2 - y1 + 1, 1); diff --git a/source/common/CommonCompute.hpp b/source/core/CommonCompute.hpp similarity index 100% rename from source/common/CommonCompute.hpp rename to source/core/CommonCompute.hpp diff --git a/source/core/ConvolutionCommon.cpp b/source/core/ConvolutionCommon.cpp index 9e1f8d524..9070fe9c7 100644 --- a/source/core/ConvolutionCommon.cpp +++ b/source/core/ConvolutionCommon.cpp @@ -11,412 +11,10 @@ #include "backend/cpu/compute/CommonOptFunction.h" #include "half.hpp" #include "core/OpCommonUtils.hpp" +#include "core/IDSTDecoder.hpp" namespace MNN { -static inline void *MNNMemoryAllocAlignZeroAlign(size_t size) { - return MNNMemoryCallocAlign(size, MNN_MEMORY_ALIGN_DEFAULT); -} -static int ReadBlobDim(unsigned char *&myfile, unsigned int* shape, int shapeBufCnt, bool useInt32) { - int uSize = myfile[0]; - myfile++; - if (uSize > 4) { - printf("Read shape error!\n"); - return 0; - } - int copyLength = uSize; - if (copyLength > shapeBufCnt) { - copyLength = shapeBufCnt; - } - if (useInt32) { - ::memcpy(shape, myfile, sizeof(unsigned int) * copyLength); - myfile += copyLength * sizeof(unsigned int); - } else { - std::vector tmpShapeInt16(copyLength); - ::memcpy(tmpShapeInt16.data(), myfile, copyLength * sizeof(int16_t)); - for (int i=0; i> (8 + uShift)) & cMask; - } else { - idx = (tmp[iOffset / 8] >> uShift) & cMask; - } - iOffset += iNeedBits; - if (iOffset % 8 == 0) { - tmp += iOffset / 8; - iOffset = 0; - } - arr[i] = idx; - } -} - -// fixme!!! not efficiency -typedef struct _SIMPLE_SET { - int8_t *UniSet; - uint32_t UniSetSize; - uint32_t CurUniCnt; -} SIMPLE_SET, *PSIMPLE_SET; - -static PSIMPLE_SET CreateSimpleSet(uint32_t maxSize) { - PSIMPLE_SET set = (PSIMPLE_SET)calloc(1, sizeof(SIMPLE_SET)); - if (set == nullptr) - return nullptr; - set->UniSet = (int8_t *)calloc(maxSize, sizeof(int8_t)); - set->UniSetSize = maxSize; - set->CurUniCnt = 0; - return set; -} - -static void SimpleRank(int8_t *data, uint32_t cnt, int up) { - if (up) { - for (uint32_t i = 0; i < cnt; i++) { - for (uint32_t j = i + 1; j < cnt; j++) { - if (data[i] > data[j]) { - int8_t tmp = data[i]; - data[i] = data[j]; - data[j] = tmp; - } - } - } - } else { - for (uint32_t i = 0; i < cnt; i++) { - for (uint32_t j = i + 1; j < cnt; j++) { - if (data[i] < data[j]) { - int8_t tmp = data[i]; - data[i] = data[j]; - data[j] = tmp; - } - } - } - } -} - -static void InsertSimpleSet(PSIMPLE_SET set, int8_t value) { - if (set->CurUniCnt >= set->UniSetSize) - return; - for (uint32_t i = 0; i < set->CurUniCnt; i++) { - if (set->UniSet[i] == value) - return; - } - set->UniSet[set->CurUniCnt++] = value; - // SimpleRank(set->UniSet, set->CurUniCnt, 1); -} - -void DestorySimpleSet(PSIMPLE_SET set) { - if (set->UniSet != nullptr) - free(set->UniSet); - free(set); -} - -typedef struct _SIMPLE_MAP { - int8_t *CharCharMap; - uint32_t CharMapSize; - uint32_t CurMapCnt; -} SIMPLE_MAP, *PSIMPLE_MAP; - -static PSIMPLE_MAP CreateSimpleMap(uint32_t MaxCnt) { - PSIMPLE_MAP map = (PSIMPLE_MAP)calloc(1, sizeof(SIMPLE_MAP)); - if (map == nullptr) - return nullptr; - map->CharMapSize = MaxCnt * sizeof(int8_t); - map->CurMapCnt = 0; - map->CharCharMap = (int8_t *)calloc(1, MaxCnt * 2); - return map; -} - -static void DestroySimpleMap(PSIMPLE_MAP map) { - if (map->CharCharMap) - free(map->CharCharMap); - free(map); -} -static void InsertMap(PSIMPLE_MAP map, int8_t k, int8_t v) { - for (uint32_t i = 0; i < map->CurMapCnt; i++) { - if (map->CharCharMap[i * 2] == k) { - map->CharCharMap[i * 2 + 1] = v; - return; - } - } - if (map->CurMapCnt >= map->CharMapSize) - return; - map->CharCharMap[map->CurMapCnt * 2] = k; - map->CharCharMap[map->CurMapCnt * 2 + 1] = v; - map->CurMapCnt++; -} - -static int8_t FindInMap(PSIMPLE_MAP map, int8_t k, int *found) { - for (uint32_t i = 0; i < map->CurMapCnt; i++) { - if (map->CharCharMap[i * 2] == k) { - if (found != nullptr) - *found = 1; - return map->CharCharMap[i * 2 + 1]; - } - } - if (found != nullptr) - *found = 0; - return 0; -} - -static void StreamSizeRead(void *dst, int unit, size_t count, unsigned char *&file) { - ::memcpy(dst, file, unit * count); - file += (unit * count); -} - -static bool isLinearSample(const std::vector& sample, int bit) { - const int offset = 1 << (bit - 1); - const int size = 1 << bit; - if (sample.size() != size) { - return false; - } - for (int i = 0; i < sample.size(); i++) { - if (static_cast(sample[i]) != i - offset) { - return false; - } - } - return true; -} - -static int8_t *ReadQuanData_c(unsigned char *&s, size_t* len, ConvolutionCommon::Int8Common* result, bool shapeInt32) { - int8_t *blob = nullptr; - uint8_t *idxBuf = nullptr; - uint8_t *idxBytes = nullptr; - uint32_t dataCnt = 1; - - do { - // blob shape - unsigned int shape[32] = {0}; - uint32_t shapeDim = (uint32_t)ReadBlobDim(s, shape, 32, shapeInt32); - if (shapeDim == 0 || shapeDim > 32) - break; - for (uint32_t i = 0; i < shapeDim; i++) - dataCnt *= shape[i]; - - // sample - uint32_t sampleCnt = 0; - StreamSizeRead(&sampleCnt, 1, 1, s); - if (sampleCnt == 0) { - sampleCnt = 256; - } - result->weightMap.resize(sampleCnt); - auto samples = result->weightMap.data(); - if (samples == nullptr) - break; - StreamSizeRead(samples, 1, sampleCnt, s); - SimpleRank(samples, sampleCnt, 1); - uint32_t idxBitsCnt = atLestBitsCnt(sampleCnt); - idxBitsCnt = idxBitsCnt < 1 ? 1 : idxBitsCnt; - // index - size_t idxBufSize = ceil(idxBitsCnt * dataCnt * 0.125); - idxBuf = (uint8_t *)MNNMemoryAllocAlignZeroAlign(idxBufSize); - if (nullptr == idxBuf) { - MNN_ERROR("Not enought memory\n"); - break; - } - StreamSizeRead(idxBuf, 1, idxBufSize, s); - if (idxBitsCnt == 4) { - dataCnt = UP_DIV(dataCnt, 2) * 2; - } - blob = (int8_t *)MNNMemoryAllocAlignZeroAlign((size_t)dataCnt); - if (nullptr == blob) { - break; - } - - if (isLinearSample(result->weightMap, idxBitsCnt) && (idxBitsCnt == 4 || idxBitsCnt == 8)) { - // fast sample for bit = 4 or 8 - if (idxBitsCnt == 4) { - for (int i = 0; i < idxBufSize; i++) { - int val = idxBuf[i]; - int x1 = val / 16; - int x2 = val % 16; - blob[2 * i] = x1 - 8; - blob[2 * i + 1] = x2 - 8; - } - } - if (idxBitsCnt == 8) { - for (int i = 0; i < idxBufSize; i++) { - int val = idxBuf[i]; - blob[i] = val - 128; - } - } - } else { - // split index value into bytes - idxBytes = (uint8_t *)MNNMemoryAllocAlignZeroAlign(dataCnt * sizeof(uint8_t)); - if (idxBitsCnt == 0 || nullptr == idxBytes) { - break; - } - SplitBufToArray(idxBuf, (uint32_t)idxBufSize, idxBytes, (uint32_t)dataCnt, (uint32_t)idxBitsCnt); - int i = 0; - for (; i < dataCnt; i++) { - if (idxBytes[i] >= sampleCnt) { - MNN_PRINT("iNeedBits is %u\nRead quan weights error with idx:%d\n", idxBitsCnt, (int)idxBytes[i]); - break; - } - blob[i] = samples[idxBytes[i]]; - } - - if (i < dataCnt) { - MNNMemoryFreeAlign(blob); - blob = nullptr; - break; - } - MNNMemoryFreeAlign(idxBytes); - idxBytes = nullptr; - } - } while (0); - - if (idxBuf != nullptr) - MNNMemoryFreeAlign(idxBuf); - if (idxBytes != nullptr) - MNNMemoryFreeAlign(idxBytes); - if (len) - *len = blob ? dataCnt : 0; - return blob; -} - -static int8_t *ReadSparseQuanData_c(unsigned char *&myfile, size_t* len, const float* alpha_ptr, size_t alpha_size, ConvolutionCommon::Int8Common* result, bool useInt32) { // MNN_ERROR("sparse:%d\n", 1); - unsigned int shape[32]; - uint32_t ucMapSize = 0; - PSIMPLE_SET setWeight = CreateSimpleSet(256); - if (setWeight == nullptr) { - return nullptr; - } - std::shared_ptr __autoReleaseSetWeight(nullptr, [setWeight](void *) { DestorySimpleSet(setWeight); }); - unsigned int nnz; - unsigned char iIdxNeedBits; - int8_t *blob = nullptr; - // 1. weights blob shape(unsigned int32) - int ShapeDim = ReadBlobDim(myfile, shape, 32, useInt32); - size_t Size = sizeof(int8_t); - for (int i = 0; i < ShapeDim; i++) - Size *= shape[i]; - blob = (int8_t *)MNNMemoryAllocAlignZeroAlign((size_t)Size); - if (blob == nullptr) - return nullptr; - // 2. nnz - StreamSizeRead(&nnz, 4, 1, myfile); - // 3. max_step use # bits () (unsigned char) - StreamSizeRead(&iIdxNeedBits, 1, 1, myfile); - // read idx array - // 4. buf for steps ceil(nnz*step need bits/8) - AutoStorage arrIdxBuffer(nnz); - unsigned char *arrIdx = arrIdxBuffer.get(); - if (nullptr == arrIdx) { - return nullptr; - } - { - size_t bufLen = (size_t)(ceil(0.125 * iIdxNeedBits * nnz)); - char *buf = (char *)MNNMemoryAllocAlignZeroAlign(bufLen * sizeof(char)); - if (nullptr == buf) { - return nullptr; - } - StreamSizeRead(buf, 1, bufLen, myfile); - SplitBufToArray((uint8_t *)buf, (uint32_t)bufLen, (uint8_t *)arrIdx, (uint32_t)nnz, (uint32_t)iIdxNeedBits); - MNNMemoryFreeAlign(buf); - } - // 5. Avalable values Count(unsigned char) - StreamSizeRead(&ucMapSize, 1, 1, myfile); - if (0 == ucMapSize) { - ucMapSize = 256; - } - result->weightMap.resize(ucMapSize); - // 6. valueset(signed char * valueset_size) - for (int i = 0; i < ucMapSize; i++) { - int8_t tmp; - StreamSizeRead(&tmp, 1, 1, myfile); - InsertSimpleSet(setWeight, tmp); - result->weightMap[i] = tmp; - } - SimpleRank(setWeight->UniSet, setWeight->CurUniCnt, 1); - // map mapWeight; - PSIMPLE_MAP mapWeight = CreateSimpleMap(256); - if (mapWeight == nullptr) { - return nullptr; - } - std::shared_ptr __autoReleaseMapWeight(nullptr, [mapWeight](void *) { DestroySimpleMap(mapWeight); }); - - for (int i = 0; i < setWeight->CurUniCnt; i++) { - InsertMap(mapWeight, i, setWeight->UniSet[i]); - } - // unsigned char iIdx = 0; - // 7. none zero weights indexes(nnz*ceil(log2(Avalable_values_Count))/8) - AutoStorage arrWeightIdxBuffer(nnz); - unsigned char *arrWeightIdx = arrWeightIdxBuffer.get(); - if (nullptr == arrWeightIdx) { - return nullptr; - } - int iDataNeedBits = (int)ceil(_log2(ucMapSize)); - iDataNeedBits = iDataNeedBits < 1 ? 1 : iDataNeedBits; - { - size_t bufLen = (size_t)(ceil(0.125 * iDataNeedBits * nnz)); - char *buf = (char *)MNNMemoryAllocAlignZeroAlign(bufLen * sizeof(char)); - if (nullptr == buf) { - return nullptr; - } - StreamSizeRead(buf, 1, bufLen, myfile); - SplitBufToArray((uint8_t *)buf, (uint32_t)bufLen, (uint8_t *)arrWeightIdx, (uint32_t)nnz, - (uint32_t)iDataNeedBits); - MNNMemoryFreeAlign(buf); - } - // set blob data with idx and weight idx - { - if (alpha_size == 2 * shape[0]) { - const int min_value = -(1 << (iDataNeedBits - 1)); - auto alphaPtr = alpha_ptr; - int area = Size / shape[0]; - for (int i = 0; i < shape[0]; i++) { - float min = alphaPtr[2*i]; - float scale = alphaPtr[2*i+1]; - int zeroQuant = min_value; - if (scale > 1e-6) { - zeroQuant = round((0.0f - min) / scale) + min_value; - } - memset(blob+area*i, zeroQuant, area * sizeof(signed char)); - } - } else { - memset(blob, 0, Size * sizeof(signed char)); //backward compability with previous symmetric weight quant - } - int iPreIdx = 0; - for (int i = 0; i < nnz; i++) { - iPreIdx += arrIdx[i]; - int found = 0; - int8_t value = FindInMap(mapWeight, arrWeightIdx[i], &found); - if (!found) { - MNN_ERROR("Read quan weights error with idx:%d\n", arrWeightIdx[i]); - MNNMemoryFreeAlign(blob); - return nullptr; - } - blob[iPreIdx] = value; - } - } - *len = Size; - return blob; -} std::shared_ptr ConvolutionCommon::load(const Convolution2D *conv, Backend* backend, bool forceFloat, bool forceInt8) { auto quan = conv->quanParameter(); auto result = std::make_shared(); @@ -456,10 +54,10 @@ std::shared_ptr ConvolutionCommon::load(const Con int8_t *buffer = nullptr; auto originBuffer = (unsigned char *)buffer_ptr; if (1 == quan->type()) { - buffer = ReadQuanData_c(originBuffer, &weightLength, result.get(), quan->shapeInt32()); + buffer = IDSTDecoder::ReadQuanData_c(originBuffer, &weightLength, result.get(), quan->shapeInt32()); } if (2 == quan->type()) { - buffer = ReadSparseQuanData_c(originBuffer, &weightLength, alpha_ptr, alpha_size, result.get(), quan->shapeInt32()); + buffer = IDSTDecoder::ReadSparseQuanData_c(originBuffer, &weightLength, alpha_ptr, alpha_size, result.get(), quan->shapeInt32()); } if (result->weightMap.size() > 0) { result->canUseInt4 = true; diff --git a/source/core/IDSTDecoder.hpp b/source/core/IDSTDecoder.hpp new file mode 100644 index 000000000..14ddc60be --- /dev/null +++ b/source/core/IDSTDecoder.hpp @@ -0,0 +1,430 @@ +// +// IDSTDecoder.hpp +// MNN +// +// Created by MNN on 2024/03/18. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef IDSTDECODER_HPP +#define IDSTDECODER_HPP + +#include +#include +#include +#include "MNN_generated.h" +#include "core/ConvolutionCommon.hpp" + +using namespace MNN; + +namespace IDSTDecoder { + +static inline void *MNNMemoryAllocAlignZeroAlign(size_t size) { + return MNNMemoryCallocAlign(size, MNN_MEMORY_ALIGN_DEFAULT); +} + +static int ReadBlobDim(unsigned char *&myfile, unsigned int* shape, int shapeBufCnt, bool useInt32) { + int uSize = myfile[0]; + myfile++; + if (uSize > 4) { + printf("Read shape error!\n"); + return 0; + } + int copyLength = uSize; + if (copyLength > shapeBufCnt) { + copyLength = shapeBufCnt; + } + if (useInt32) { + ::memcpy(shape, myfile, sizeof(unsigned int) * copyLength); + myfile += copyLength * sizeof(unsigned int); + } else { + auto myfileint16 = (uint16_t*)myfile; + for (int i=0; i> (8 + uShift)) & cMask; + } else { + idx = (tmp[iOffset / 8] >> uShift) & cMask; + } + iOffset += iNeedBits; + if (iOffset % 8 == 0) { + tmp += iOffset / 8; + iOffset = 0; + } + arr[i] = idx; + } +} + +// fixme!!! not efficiency +typedef struct _SIMPLE_SET { + int8_t *UniSet; + uint32_t UniSetSize; + uint32_t CurUniCnt; +} SIMPLE_SET, *PSIMPLE_SET; + +static PSIMPLE_SET CreateSimpleSet(uint32_t maxSize) { + PSIMPLE_SET set = (PSIMPLE_SET)calloc(1, sizeof(SIMPLE_SET)); + if (set == nullptr) + return nullptr; + set->UniSet = (int8_t *)calloc(maxSize, sizeof(int8_t)); + set->UniSetSize = maxSize; + set->CurUniCnt = 0; + return set; +} + +static void SimpleRank(int8_t *data, uint32_t cnt, int up) { + if (up) { + for (uint32_t i = 0; i < cnt; i++) { + for (uint32_t j = i + 1; j < cnt; j++) { + if (data[i] > data[j]) { + int8_t tmp = data[i]; + data[i] = data[j]; + data[j] = tmp; + } + } + } + } else { + for (uint32_t i = 0; i < cnt; i++) { + for (uint32_t j = i + 1; j < cnt; j++) { + if (data[i] < data[j]) { + int8_t tmp = data[i]; + data[i] = data[j]; + data[j] = tmp; + } + } + } + } +} + +static void InsertSimpleSet(PSIMPLE_SET set, int8_t value) { + if (set->CurUniCnt >= set->UniSetSize) + return; + for (uint32_t i = 0; i < set->CurUniCnt; i++) { + if (set->UniSet[i] == value) + return; + } + set->UniSet[set->CurUniCnt++] = value; + // SimpleRank(set->UniSet, set->CurUniCnt, 1); +} + +void DestorySimpleSet(PSIMPLE_SET set) { + if (set->UniSet != nullptr) + free(set->UniSet); + free(set); +} + +typedef struct _SIMPLE_MAP { + int8_t *CharCharMap; + uint32_t CharMapSize; + uint32_t CurMapCnt; +} SIMPLE_MAP, *PSIMPLE_MAP; + +static PSIMPLE_MAP CreateSimpleMap(uint32_t MaxCnt) { + PSIMPLE_MAP map = (PSIMPLE_MAP)calloc(1, sizeof(SIMPLE_MAP)); + if (map == nullptr) + return nullptr; + map->CharMapSize = MaxCnt * sizeof(int8_t); + map->CurMapCnt = 0; + map->CharCharMap = (int8_t *)calloc(1, MaxCnt * 2); + return map; +} + +static void DestroySimpleMap(PSIMPLE_MAP map) { + if (map->CharCharMap) + free(map->CharCharMap); + free(map); +} + +static void InsertMap(PSIMPLE_MAP map, int8_t k, int8_t v) { + for (uint32_t i = 0; i < map->CurMapCnt; i++) { + if (map->CharCharMap[i * 2] == k) { + map->CharCharMap[i * 2 + 1] = v; + return; + } + } + if (map->CurMapCnt >= map->CharMapSize) + return; + map->CharCharMap[map->CurMapCnt * 2] = k; + map->CharCharMap[map->CurMapCnt * 2 + 1] = v; + map->CurMapCnt++; +} + +static int8_t FindInMap(PSIMPLE_MAP map, int8_t k, int *found) { + for (uint32_t i = 0; i < map->CurMapCnt; i++) { + if (map->CharCharMap[i * 2] == k) { + if (found != nullptr) + *found = 1; + return map->CharCharMap[i * 2 + 1]; + } + } + if (found != nullptr) + *found = 0; + return 0; +} + +static void StreamSizeRead(void *dst, int unit, size_t count, unsigned char *&file) { + ::memcpy(dst, file, unit * count); + file += (unit * count); +} + +static bool isLinearSample(const std::vector& sample, int bit) { + const int offset = 1 << (bit - 1); + const int size = 1 << bit; + if (sample.size() != size) { + return false; + } + for (int i = 0; i < sample.size(); i++) { + if (static_cast(sample[i]) != i - offset) { + return false; + } + } + return true; +} + +static int8_t *ReadQuanData_c(unsigned char *&s, size_t* len, ConvolutionCommon::Int8Common* result, bool shapeInt32) { + int8_t *blob = nullptr; + uint8_t *idxBuf = nullptr; + uint8_t *idxBytes = nullptr; + uint32_t dataCnt = 1; + + do { + // blob shape + unsigned int shape[32] = {0}; + uint32_t shapeDim = (uint32_t)ReadBlobDim(s, shape, 32, shapeInt32); + if (shapeDim == 0 || shapeDim > 32) + break; + for (uint32_t i = 0; i < shapeDim; i++) + dataCnt *= shape[i]; + + // sample + uint32_t sampleCnt = 0; + StreamSizeRead(&sampleCnt, 1, 1, s); + if (sampleCnt == 0) { + sampleCnt = 256; + } + result->weightMap.resize(sampleCnt); + auto samples = result->weightMap.data(); + if (samples == nullptr) + break; + StreamSizeRead(samples, 1, sampleCnt, s); + SimpleRank(samples, sampleCnt, 1); + uint32_t idxBitsCnt = atLestBitsCnt(sampleCnt); + idxBitsCnt = idxBitsCnt < 1 ? 1 : idxBitsCnt; + // index + size_t idxBufSize = ceil(idxBitsCnt * dataCnt * 0.125); + idxBuf = (uint8_t *)MNNMemoryAllocAlignZeroAlign(idxBufSize); + if (nullptr == idxBuf) { + MNN_ERROR("Not enought memory\n"); + break; + } + StreamSizeRead(idxBuf, 1, idxBufSize, s); + if (idxBitsCnt == 4) { + dataCnt = UP_DIV(dataCnt, 2) * 2; + } + blob = (int8_t *)MNNMemoryAllocAlignZeroAlign((size_t)dataCnt); + if (nullptr == blob) { + break; + } + + if (isLinearSample(result->weightMap, idxBitsCnt) && (idxBitsCnt == 4 || idxBitsCnt == 8)) { + // fast sample for bit = 4 or 8 + if (idxBitsCnt == 4) { + for (int i = 0; i < idxBufSize; i++) { + int val = idxBuf[i]; + int x1 = val / 16; + int x2 = val % 16; + blob[2 * i] = x1 - 8; + blob[2 * i + 1] = x2 - 8; + } + } + if (idxBitsCnt == 8) { + for (int i = 0; i < idxBufSize; i++) { + int val = idxBuf[i]; + blob[i] = val - 128; + } + } + } else { + // split index value into bytes + idxBytes = (uint8_t *)MNNMemoryAllocAlignZeroAlign(dataCnt * sizeof(uint8_t)); + if (idxBitsCnt == 0 || nullptr == idxBytes) { + break; + } + SplitBufToArray(idxBuf, (uint32_t)idxBufSize, idxBytes, (uint32_t)dataCnt, (uint32_t)idxBitsCnt); + int i = 0; + for (; i < dataCnt; i++) { + if (idxBytes[i] >= sampleCnt) { + MNN_PRINT("iNeedBits is %u\nRead quan weights error with idx:%d\n", idxBitsCnt, (int)idxBytes[i]); + break; + } + blob[i] = samples[idxBytes[i]]; + } + + if (i < dataCnt) { + MNNMemoryFreeAlign(blob); + blob = nullptr; + break; + } + MNNMemoryFreeAlign(idxBytes); + idxBytes = nullptr; + } + } while (0); + + if (idxBuf != nullptr) + MNNMemoryFreeAlign(idxBuf); + if (idxBytes != nullptr) + MNNMemoryFreeAlign(idxBytes); + if (len) + *len = blob ? dataCnt : 0; + return blob; +} + +static int8_t *ReadSparseQuanData_c(unsigned char *&myfile, size_t* len, const float* alpha_ptr, size_t alpha_size, ConvolutionCommon::Int8Common* result, bool useInt32) { // MNN_ERROR("sparse:%d\n", 1); + unsigned int shape[32]; + uint32_t ucMapSize = 0; + PSIMPLE_SET setWeight = CreateSimpleSet(256); + if (setWeight == nullptr) { + return nullptr; + } + std::shared_ptr __autoReleaseSetWeight(nullptr, [setWeight](void *) { DestorySimpleSet(setWeight); }); + unsigned int nnz; + unsigned char iIdxNeedBits; + int8_t *blob = nullptr; + // 1. weights blob shape(unsigned int32) + int ShapeDim = ReadBlobDim(myfile, shape, 32, useInt32); + size_t Size = sizeof(int8_t); + for (int i = 0; i < ShapeDim; i++) + Size *= shape[i]; + blob = (int8_t *)MNNMemoryAllocAlignZeroAlign((size_t)Size); + if (blob == nullptr) + return nullptr; + // 2. nnz + StreamSizeRead(&nnz, 4, 1, myfile); + // 3. max_step use # bits () (unsigned char) + StreamSizeRead(&iIdxNeedBits, 1, 1, myfile); + // read idx array + // 4. buf for steps ceil(nnz*step need bits/8) + AutoStorage arrIdxBuffer(nnz); + unsigned char *arrIdx = arrIdxBuffer.get(); + if (nullptr == arrIdx) { + return nullptr; + } + { + size_t bufLen = (size_t)(ceil(0.125 * iIdxNeedBits * nnz)); + char *buf = (char *)MNNMemoryAllocAlignZeroAlign(bufLen * sizeof(char)); + if (nullptr == buf) { + return nullptr; + } + StreamSizeRead(buf, 1, bufLen, myfile); + SplitBufToArray((uint8_t *)buf, (uint32_t)bufLen, (uint8_t *)arrIdx, (uint32_t)nnz, (uint32_t)iIdxNeedBits); + MNNMemoryFreeAlign(buf); + } + // 5. Avalable values Count(unsigned char) + StreamSizeRead(&ucMapSize, 1, 1, myfile); + if (0 == ucMapSize) { + ucMapSize = 256; + } + result->weightMap.resize(ucMapSize); + // 6. valueset(signed char * valueset_size) + for (int i = 0; i < ucMapSize; i++) { + int8_t tmp; + StreamSizeRead(&tmp, 1, 1, myfile); + InsertSimpleSet(setWeight, tmp); + result->weightMap[i] = tmp; + } + SimpleRank(setWeight->UniSet, setWeight->CurUniCnt, 1); + // map mapWeight; + PSIMPLE_MAP mapWeight = CreateSimpleMap(256); + if (mapWeight == nullptr) { + return nullptr; + } + std::shared_ptr __autoReleaseMapWeight(nullptr, [mapWeight](void *) { DestroySimpleMap(mapWeight); }); + + for (int i = 0; i < setWeight->CurUniCnt; i++) { + InsertMap(mapWeight, i, setWeight->UniSet[i]); + } + // unsigned char iIdx = 0; + // 7. none zero weights indexes(nnz*ceil(log2(Avalable_values_Count))/8) + AutoStorage arrWeightIdxBuffer(nnz); + unsigned char *arrWeightIdx = arrWeightIdxBuffer.get(); + if (nullptr == arrWeightIdx) { + return nullptr; + } + int iDataNeedBits = (int)ceil(_log2(ucMapSize)); + iDataNeedBits = iDataNeedBits < 1 ? 1 : iDataNeedBits; + { + size_t bufLen = (size_t)(ceil(0.125 * iDataNeedBits * nnz)); + char *buf = (char *)MNNMemoryAllocAlignZeroAlign(bufLen * sizeof(char)); + if (nullptr == buf) { + return nullptr; + } + StreamSizeRead(buf, 1, bufLen, myfile); + SplitBufToArray((uint8_t *)buf, (uint32_t)bufLen, (uint8_t *)arrWeightIdx, (uint32_t)nnz, + (uint32_t)iDataNeedBits); + MNNMemoryFreeAlign(buf); + } + // set blob data with idx and weight idx + { + if (alpha_size == 2 * shape[0]) { + const int min_value = -(1 << (iDataNeedBits - 1)); + auto alphaPtr = alpha_ptr; + int area = Size / shape[0]; + for (int i = 0; i < shape[0]; i++) { + float min = alphaPtr[2*i]; + float scale = alphaPtr[2*i+1]; + int zeroQuant = min_value; + if (scale > 1e-6) { + zeroQuant = round((0.0f - min) / scale) + min_value; + } + memset(blob+area*i, zeroQuant, area * sizeof(signed char)); + } + } else { + memset(blob, 0, Size * sizeof(signed char)); //backward compability with previous symmetric weight quant + } + int iPreIdx = 0; + for (int i = 0; i < nnz; i++) { + iPreIdx += arrIdx[i]; + int found = 0; + int8_t value = FindInMap(mapWeight, arrWeightIdx[i], &found); + if (!found) { + MNN_ERROR("Read quan weights error with idx:%d\n", arrWeightIdx[i]); + MNNMemoryFreeAlign(blob); + return nullptr; + } + blob[iPreIdx] = value; + } + } + *len = Size; + return blob; +} + + +} // namespace IDSTDecoder + +#endif // IDSTDECODER_HPP diff --git a/tools/cpp/IDSTEncoder.hpp b/source/core/IDSTEncoder.hpp similarity index 100% rename from tools/cpp/IDSTEncoder.hpp rename to source/core/IDSTEncoder.hpp diff --git a/source/common/MemoryFormater.h b/source/core/MemoryFormater.h similarity index 100% rename from source/common/MemoryFormater.h rename to source/core/MemoryFormater.h diff --git a/source/core/OpCommonUtils.cpp b/source/core/OpCommonUtils.cpp index c36635e45..405b9d9bb 100644 --- a/source/core/OpCommonUtils.cpp +++ b/source/core/OpCommonUtils.cpp @@ -572,6 +572,7 @@ bool OpCommonUtils::opCompabilityForLowp(const Op* op, int bytes) { case OpType_ROIPooling: case OpType_ROIAlign: case OpType_DynamicQuant: + case OpType_Attention: return true; default: break; diff --git a/source/core/Pipeline.cpp b/source/core/Pipeline.cpp index 3be39b13a..641120d2f 100644 --- a/source/core/Pipeline.cpp +++ b/source/core/Pipeline.cpp @@ -901,7 +901,8 @@ ErrorCode Pipeline::fixResizeCache() { if (info.type != Schedule::CONSTANT) { totalNumber += buffer.command.size(); } - if (info.computeCache.needComputeShape) { + if ((!info.computeCache.canCache()) && info.computeCache.needComputeShape) { + // If the session has been resized and the op is checked will change shape, set as shape mutable info.computeCache.close(false); continue; } diff --git a/source/core/Schedule.cpp b/source/core/Schedule.cpp index 74b949941..475708567 100644 --- a/source/core/Schedule.cpp +++ b/source/core/Schedule.cpp @@ -36,7 +36,7 @@ void Schedule::OpResizeCache::addContentIndex(int index) { mNeedCompareContent.emplace_back(index); } -bool Schedule::OpResizeCache::match(const std::vector& inputs) { +bool Schedule::OpResizeCache::match(const std::vector& inputs, bool& compared) { if (!mCanCache) { return mPass; } @@ -46,6 +46,7 @@ bool Schedule::OpResizeCache::match(const std::vector& inputs) { if (mInputInfos.size() != inputs.size()) { return false; } + compared = true; for (int u=0; udimensionFormat) { @@ -55,12 +56,10 @@ bool Schedule::OpResizeCache::match(const std::vector& inputs) { return false; } if (mInputInfos[u].dim.size() != inputs[u]->dimensions()) { - mCanCache = false; return false; } for (int v=0; vlength(v)) { - mCanCache = false; return false; } } @@ -72,7 +71,6 @@ bool Schedule::OpResizeCache::match(const std::vector& inputs) { auto t = inputs[dim]; auto& s = mInputInfos[dim]; if (0 != ::memcmp(s.buffer.data(), t->host(), s.buffer.size())) { - mCanCache = false; return false; } } diff --git a/source/core/Schedule.hpp b/source/core/Schedule.hpp index e05c3133c..83e5da7c2 100644 --- a/source/core/Schedule.hpp +++ b/source/core/Schedule.hpp @@ -35,7 +35,7 @@ class MNN_PUBLIC Schedule { }; class OpResizeCache { public: - bool match(const std::vector& inputs); + bool match(const std::vector& inputs, bool& compared); void insert(const std::vector& inputs); void close(bool pass = false); void open(); @@ -43,6 +43,9 @@ class MNN_PUBLIC Schedule { bool needExecuteConst = false; void addContentIndex(int index); void copyImmutable(const OpResizeCache& cache); + bool canCache() const { + return mCanCache; + } private: struct ShapeInfo { int order; diff --git a/source/common/WinogradInt8Attr.hpp b/source/core/WinogradInt8Attr.hpp similarity index 100% rename from source/common/WinogradInt8Attr.hpp rename to source/core/WinogradInt8Attr.hpp diff --git a/source/geometry/GeometryComputer.cpp b/source/geometry/GeometryComputer.cpp index dae9f9e56..29a34cade 100644 --- a/source/geometry/GeometryComputer.cpp +++ b/source/geometry/GeometryComputer.cpp @@ -84,6 +84,201 @@ static bool _virtualMemory(Tensor::InsideDescribe::NativeInsideDescribe* des) { return des->memoryType == Tensor::InsideDescribe::MEMORY_VIRTUAL && nullptr == des->rasterCommand.lock().get(); } +bool GeometryComputer::ComputePermuteRegion(Tensor* input, Tensor* output, int* newshape, int shapeDim) { + auto inputDes = TensorUtils::getDescribe(input); + auto outputDes = TensorUtils::getDescribe(output); + MNN_ASSERT(input->dimensions() >= 1); + MNN_ASSERT(output->dimensions() == input->dimensions()); + MNN_ASSERT(shapeDim == input->dimensions()); + auto originTensor = input; + int shape[MNN_MAX_TENSOR_DIM]; + if (nullptr != newshape) { + for (int i = 0; i < input->buffer().dimensions; ++i) { + shape[i] = newshape[i]; + } + } else { + for (int i = 0; i < input->buffer().dimensions; ++i) { + shape[i] = input->buffer().dimensions - i - 1; + } + } + + int inputShape[MNN_MAX_TENSOR_DIM]; + int inputStrides[MNN_MAX_TENSOR_DIM]; + int inputShapeSize = 0; + int preAxis = -2; + for (int i=0; ibuffer().dimensions; ++i) { + auto axis = shape[i]; + auto len = input->length(axis); + if (1 == len) { + continue; + } + if (axis - preAxis == 1) { + // Fuse dimension if possible + inputShape[inputShapeSize - 1] *= len; + } else { + if (preAxis >= 0) { + // Compute last stride + int stride = 1; + for (int v=preAxis+1; v < input->buffer().dimensions; ++v) { + stride *= input->length(v); + } + inputStrides[inputShapeSize - 1] = stride; + } + inputShapeSize+=1; + inputShape[inputShapeSize - 1] = len; + } + preAxis = shape[i]; + } + if (preAxis >= 0) { + // Compute last stride + int stride = 1; + for (int v=preAxis+1; v < input->buffer().dimensions; ++v) { + stride *= input->length(v); + } + inputStrides[inputShapeSize - 1] = stride; + } + if (0 == inputShapeSize) { + outputDes->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; + outputDes->regions = {TensorUtils::makeFullSlice(input)}; + return true; + } + int outputStrides[MNN_MAX_TENSOR_DIM]; + { + int stride = 1; + for (int i=inputShapeSize-1; i>=0; --i) { + outputStrides[i] = stride; + stride *= inputShape[i]; + } + } + /** Move max three inputShapeSize to last three location. + * Don't change max three number relative position + * */ +bool isReorderShape = false; +isReorderShape = (inputShapeSize > 4); + if (inputShapeSize == 4) { + // TODO: Opt this logic + isReorderShape = (inputShape[0] > inputShape[1] + inputShape[2] + inputShape[3]); + } + if (isReorderShape) { + int max1 = inputShape[0], max2 = -1, max3 = -1; + // Find Max Three Number + for (int i = 1; i < inputShapeSize; i++) { + if (inputShape[i] > max1) { + max3 = max2; + max2 = max1; + max1 = inputShape[i]; + } else if (inputShape[i] > max2) { + max3 = max2; + max2 = inputShape[i]; + } + else if (inputShape[i] > max3) { + max3 = inputShape[i]; + } + } + + // Move Max Three Number to Last Location + int lastIndex = inputShapeSize-1; + for (int i = inputShapeSize-1; i >= 0; i--) { + if (inputShape[i] == max1) { + if(i != lastIndex) { + std::swap(inputShape[i], inputShape[lastIndex]); + std::swap(inputStrides[i], inputStrides[lastIndex]); + std::swap(outputStrides[i], outputStrides[lastIndex]); + } + max1 = -1; + lastIndex--; + } else if (inputShape[i] == max2) { + if(i != lastIndex) { + std::swap(inputShape[i], inputShape[lastIndex]); + std::swap(inputStrides[i], inputStrides[lastIndex]); + std::swap(outputStrides[i], outputStrides[lastIndex]); + } + max2 = -1; + lastIndex--; + } else if (inputShape[i] == max3) { + if(i != lastIndex) { + std::swap(inputShape[i], inputShape[lastIndex]); + std::swap(inputStrides[i], inputStrides[lastIndex]); + std::swap(outputStrides[i], outputStrides[lastIndex]); + } + max3 = -1; + lastIndex--; + } + if(lastIndex < inputShapeSize-3) { + break; + } + } + } +// Compute inside, outside, axis + int inside = 1; + int insideStride = 0; + int outside = 1; + int outsideStride = 0; + int axis = 1; + int axisStride = 0; + int breakAxis = -1; + int remainSize = 1; + int outputInsideStride = 0; + int outputAxisStride = 0; + int outputOutsideStride = 0; + { + if (inputShapeSize >= 1) { + inside = inputShape[inputShapeSize-1]; + insideStride = inputStrides[inputShapeSize-1]; + outputInsideStride = outputStrides[inputShapeSize-1]; + } + if (inputShapeSize >= 2) { + axis = inputShape[inputShapeSize-2]; + axisStride = inputStrides[inputShapeSize-2]; + outputAxisStride = outputStrides[inputShapeSize-2]; + } + if (inputShapeSize >= 3) { + outside = inputShape[inputShapeSize-3]; + outsideStride = inputStrides[inputShapeSize-3]; + outputOutsideStride = outputStrides[inputShapeSize-3]; + breakAxis = inputShapeSize - 3; + for (int i = 0; i < inputShapeSize - 3; ++i) { + remainSize *= inputShape[i]; + } + } + } + outputDes->regions.resize(remainSize); + outputDes->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; + int32_t mod[MNN_MAX_TENSOR_DIM]; + for (int i = 0; i < breakAxis; ++i) { + int value = 1; + for (int j = i + 1; j < breakAxis; ++j) { + value *= inputShape[j]; + } + mod[i] = value; + } + for (int indice = 0; indice < remainSize; ++indice) { + int value = indice; + int inputOffset = 0; + int outputOffset = 0; + for (int i = 0; i < breakAxis; ++i) { + auto coordinate = value / mod[i]; + inputOffset += coordinate * inputStrides[i]; + outputOffset += coordinate * outputStrides[i]; + value = value % mod[i]; + } + Tensor::InsideDescribe::Region& slice = outputDes->regions[indice]; + slice.src.offset = inputOffset; + slice.src.stride[0] = outsideStride; + slice.size[0] = outside; + slice.src.stride[1] = axisStride; + slice.size[1] = axis; + slice.src.stride[2] = insideStride; + slice.size[2] = inside; + slice.origin = originTensor; + slice.dst.offset = outputOffset; + slice.dst.stride[0] = outputOutsideStride; + slice.dst.stride[1] = outputAxisStride; + slice.dst.stride[2] = outputInsideStride; + } + return true; +} + void GeometryComputer::Context::getRasterCacheCreateRecursive(Tensor* src, CommandBuffer& cmd) { auto srcDes = TensorUtils::getDescribe(src); if (!_virtualMemory(srcDes)) { diff --git a/source/geometry/GeometryComputer.hpp b/source/geometry/GeometryComputer.hpp index f603d361c..a049be684 100644 --- a/source/geometry/GeometryComputer.hpp +++ b/source/geometry/GeometryComputer.hpp @@ -61,6 +61,7 @@ class GeometryComputer { Context& context, CommandBuffer& cmd) const { return false; } + static bool ComputePermuteRegion(Tensor* input, Tensor* output, int* newshape, int shapeDim); }; class DefaultGeometryComputer : public GeometryComputer { diff --git a/source/geometry/GeometryComputerUtils.cpp b/source/geometry/GeometryComputerUtils.cpp index 0dc328691..54607eab6 100644 --- a/source/geometry/GeometryComputerUtils.cpp +++ b/source/geometry/GeometryComputerUtils.cpp @@ -182,7 +182,12 @@ ErrorCode GeometryComputerUtils::shapeComputeAndGeometryTransform( for (auto t : info.outputs) { TensorUtils::getDescribe(t)->stageMask &= (~Tensor::InsideDescribe::StageInfo::COMPUTE_SHAPE_STAGE); } - bool needCompute = !info.computeCache.match(info.inputs); + bool compared = false; + bool needCompute = !info.computeCache.match(info.inputs, compared); + if (needCompute && compared) { + // If not match, means the op's shape is mutable, close cache and don't compare + info.computeCache.close(false); + } if ((!skipShapeCompute) && needCompute) { auto res = SizeComputer::computeOutputSize(info.op, info.inputs, info.outputs); if (!res) { @@ -468,6 +473,44 @@ std::shared_ptr GeometryComputerUtils::makeReduce(ReductionType type, T cmd.op = flatbuffers::GetRoot(cmd.buffer->buffer()); return cmdP; } +std::shared_ptr GeometryComputerUtils::makeLayerNorm(Tensor* input0, Tensor* output, std::vector axis, float epsilon, std::vector gamma, std::vector beta, std::vector external, int group, bool useRMS) { + flatbuffers::FlatBufferBuilder builder(DEFAULT_ALLOCATE_SIZE); + std::vector g, b; + auto vecaxis = builder.CreateVector(axis); + auto vecgamma = builder.CreateVector(g); + auto vecbeta = builder.CreateVector(b); + if (gamma.size() > 0 && beta.size() > 0) { + vecgamma = builder.CreateVector(gamma.data(), gamma.size()); + vecbeta = builder.CreateVector(beta.data(), beta.size()); + } + + auto vecexternal = builder.CreateVector(external); + LayerNormBuilder builder_(builder); + builder_.add_axis(vecaxis); + builder_.add_group(group); + builder_.add_epsilon(epsilon); + if (gamma.size() > 0 && beta.size() > 0) { + builder_.add_gamma(vecgamma); + builder_.add_beta(vecbeta); + } + + builder_.add_useRMSNorm(useRMS); + builder_.add_external(vecexternal); + auto mainOffset = builder_.Finish().Union(); + OpBuilder opB(builder); + opB.add_type(OpType_LayerNorm); + opB.add_main(mainOffset); + opB.add_main_type(OpParameter_LayerNorm); + builder.Finish(opB.Finish()); + std::shared_ptr cmdP(new Command); + auto& cmd = *cmdP; + cmd.buffer.reset(new BufferStorage); + cmd.buffer->storage = builder.ReleaseRaw(cmd.buffer->allocated_size, cmd.buffer->offset); + cmd.inputs = {input0}; + cmd.outputs = {output}; + cmd.op = flatbuffers::GetRoot(cmd.buffer->buffer()); + return cmdP; +} std::shared_ptr GeometryComputerUtils::makeUnary(UnaryOpOperation type, Tensor* input0, Tensor* output) { flatbuffers::FlatBufferBuilder builder(DEFAULT_ALLOCATE_SIZE); UnaryOpBuilder builder_(builder); diff --git a/source/geometry/GeometryComputerUtils.hpp b/source/geometry/GeometryComputerUtils.hpp index 4fc26c449..4da7210ae 100644 --- a/source/geometry/GeometryComputerUtils.hpp +++ b/source/geometry/GeometryComputerUtils.hpp @@ -20,6 +20,7 @@ class GeometryComputerUtils { static std::shared_ptr makeBinary(int type, Tensor* input0, Tensor* input1, Tensor* output); static std::shared_ptr makeReduce(ReductionType type, Tensor* input0, Tensor* output); static std::shared_ptr makeUnary(UnaryOpOperation type, Tensor* input0, Tensor* output); + static std::shared_ptr makeLayerNorm(Tensor* input0, Tensor* output, std::vector axis, float epsilon, std::vector gamma, std::vector beta, std::vector external, int group = 1, bool useRMS = false); static std::shared_ptr makeMatMul(Tensor* input0, Tensor* input1, Tensor* output, Tensor* Bias = nullptr, bool transposeA = false, bool transposeB = false); static flatbuffers::Offset makePool(flatbuffers::FlatBufferBuilder& builder, std::pair kernel, std::pair stride, PoolType type, MNN::PoolPadType pad, std::pair pads, bool isglobal, AvgPoolCountType countType = AvgPoolCountType_DEFAULT); diff --git a/source/geometry/GeometryLayernorm.cpp b/source/geometry/GeometryLayernorm.cpp new file mode 100644 index 000000000..120a95c91 --- /dev/null +++ b/source/geometry/GeometryLayernorm.cpp @@ -0,0 +1,138 @@ +// +// GeometryLayerNorm.cpp +// MNN +// +// Created by MNN on 2020/06/09. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include "geometry/GeometryComputer.hpp" +#include "geometry/GeometryComputerUtils.hpp" +#include "core/OpCommonUtils.hpp" +namespace MNN { + +class GeometryLayerNorm : public GeometryComputer { +public: + virtual bool onCompute(const Op* op, const std::vector& inputs, const std::vector& outputs, + Context& context, CommandBuffer& res) const override { + /* Target: Ensure reduce dimensions must be a sequence subset [-rank,...,rank-1] */ + MNN_ASSERT(1 == outputs.size()); + MNN_ASSERT(1 == inputs.size()); + auto layernorm = op->main_as_LayerNorm(); + if (!layernorm->axis()) { + std::shared_ptr cmdP(new Command); + auto& cmd = *cmdP; + cmd.op = op; + cmd.inputs = {inputs[0]}; + cmd.outputs = std::move(outputs); + res.command.emplace_back(std::move(cmdP)); + return true; + } + auto reduceDims = layernorm->axis()->data(); + int reduceDimensionCount = layernorm->axis()->size(); + auto inputShape = inputs[0]->shape(); + auto outputShape = outputs[0]->shape(); + int rank = static_cast(inputShape.size()); + + // Case1: Do not need permute + bool needPermute = true; + if (reduceDims[0] < 0 && reduceDims[reduceDimensionCount - 1] == -1) { // [-r,-r+1...] + needPermute = false; + } + if (reduceDims[reduceDimensionCount - 1] > 0 && reduceDims[reduceDimensionCount - 1] == rank - 1 ) { // [...,r-2,r-1] + needPermute = false; + } + if (reduceDims[0] == 0 && rank == 1) { // reduce dim:[0], input dimensions=1 + needPermute = false; + } + std::vector lastdims(reduceDimensionCount); + for (int i = 0; i < reduceDimensionCount; ++i) { + lastdims[i] = (reduceDims[i] + rank) % rank; + } + if (false == needPermute) { + std::shared_ptr cmdP(new Command); + auto& cmd = *cmdP; + cmd.op = op; + cmd.inputs = {inputs[0]}; + cmd.outputs = std::move(outputs); + res.command.emplace_back(std::move(cmdP)); + return true; + } + // Case2 : Need permute + int oldorder[MNN_MAX_TENSOR_DIM]; + int neworder[MNN_MAX_TENSOR_DIM]; + + { + int di = 0; + int idx = 0; + while (di < rank) { + if (di < lastdims[0] || di > lastdims[reduceDimensionCount - 1]) { + neworder[idx++] = di; + } + di++; + } + for (int i = 0; i < reduceDimensionCount; ++i) { + neworder[idx++] = lastdims[i]; + } + } + { + int idx = 0; + for (int i = 0; i < rank; ++i) { + int j = 0; + while (i != neworder[j]) { + ++j; + } + oldorder[idx++] = j; + } + } + std::vector newshape; + for (int i = 0; i < rank; ++i) { + newshape.emplace_back(inputShape[neworder[i]]); + } + + std::shared_ptr outputTensorPermute(Tensor::createDevice(newshape, inputs[0]->getType(), inputs[0]->getDimensionType())); + res.extras.emplace_back(outputTensorPermute); + GeometryComputer::ComputePermuteRegion(inputs[0], outputTensorPermute.get(), neworder, rank); + + // Create LayerNorm command + auto currentInput = outputTensorPermute.get(); + float epislon = layernorm->epsilon(); + bool useRMS = layernorm->useRMSNorm(); + std::vector externalData; + if (layernorm->external()) { + externalData.resize(3); + externalData = {layernorm->external()->data()[0], layernorm->external()->data()[1], layernorm->external()->data()[2]}; + } + std::vector gamma, beta; + if (layernorm->gamma() && layernorm->beta()) { + int gammaSize = layernorm->gamma()->size(); + gamma.resize(gammaSize); + beta.resize(gammaSize); + int group = layernorm->group(); + ::memcpy(gamma.data(), layernorm->gamma()->data(), gammaSize * sizeof(float)); + ::memcpy(beta.data(), layernorm->beta()->data(), gammaSize * sizeof(float)); + } + std::shared_ptr inputTensorLayernorm(Tensor::createDevice(newshape, inputs[0]->getType(), inputs[0]->getDimensionType())); + auto des = TensorUtils::getDescribe(inputTensorLayernorm.get()); + des->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; + des->regions = {TensorUtils::makeFullSlice(currentInput)}; + res.extras.emplace_back(inputTensorLayernorm); + std::shared_ptr outputTensorLayerNorm(Tensor::createDevice(newshape, inputs[0]->getType(), inputs[0]->getDimensionType())); + res.extras.emplace_back(outputTensorLayerNorm); + { + auto cmd = GeometryComputerUtils::makeLayerNorm(inputTensorLayernorm.get(), outputTensorLayerNorm.get(), lastdims, epislon, gamma, beta, externalData, 1, useRMS); + res.command.emplace_back(std::move(cmd)); + } + + GeometryComputer::ComputePermuteRegion(outputTensorLayerNorm.get(), outputs[0], oldorder, rank); + return true; + } +}; +static void _create() { + std::shared_ptr comp(new GeometryLayerNorm); + GeometryComputer::registerGeometryComputer(comp, {OpType_LayerNorm}); +} + +REGISTER_GEOMETRY(GeometryLayerNorm, _create); + +} // namespace MNN diff --git a/source/geometry/GeometryOPRegister.cpp b/source/geometry/GeometryOPRegister.cpp index 023b08a50..91a743a1a 100644 --- a/source/geometry/GeometryOPRegister.cpp +++ b/source/geometry/GeometryOPRegister.cpp @@ -40,6 +40,7 @@ extern void ___GeometrySlice___create__(); extern void ___GeometryConcat___create__(); extern void ___GeometryUnary___create__(); extern void ___GeometryBinary___create__(); +extern void ___GeometryLayerNorm___create__(); void registerGeometryOps() { ___GeometryShape___create__(); @@ -81,5 +82,6 @@ ___GeometrySlice___create__(); ___GeometryConcat___create__(); ___GeometryUnary___create__(); ___GeometryBinary___create__(); +___GeometryLayerNorm___create__(); } } diff --git a/source/geometry/GeometryPermute.cpp b/source/geometry/GeometryPermute.cpp index a1e306793..0daa639cc 100644 --- a/source/geometry/GeometryPermute.cpp +++ b/source/geometry/GeometryPermute.cpp @@ -14,209 +14,30 @@ class GeometryPermute : public GeometryComputer { public: virtual bool onCompute(const Op* op, const std::vector& inputs, const std::vector& outputs, Context& context, CommandBuffer& res) const override { - auto input = inputs[0]; - auto output = outputs[0]; - auto inputDes = TensorUtils::getDescribe(input); - auto outputDes = TensorUtils::getDescribe(output); - auto inputSlice = inputDes->regions; - MNN_ASSERT(input->dimensions() >= 1); - MNN_ASSERT(output->dimensions() == input->dimensions()); - auto originTensor = input; - int shape[MNN_MAX_TENSOR_DIM]; + int dims = inputs[0]->buffer().dimensions; + int neworder[MNN_MAX_TENSOR_DIM]; + // get neworder if (op->type() == OpType_Permute) { auto shapeValue = op->main_as_Permute()->dims(); if (nullptr != shapeValue) { - for (int i = 0; i < input->buffer().dimensions; ++i) { - shape[i] = shapeValue->data()[i]; + for (int i = 0; i < dims; ++i) { + neworder[i] = shapeValue->data()[i]; } } else { - for (int i = 0; i < input->buffer().dimensions; ++i) { - shape[i] = input->buffer().dimensions - i - 1; + for (int i = 0; i < dims; ++i) { + neworder[i] = dims - i - 1; } } } else if (op->type() == OpType_Transpose) { + MNN_ASSERT(inputs.size() > 1); auto shapeValue = inputs[1]->host(); - for (int i = 0; i < input->buffer().dimensions; ++i) { - shape[i] = shapeValue[i]; + for (int i = 0; i < dims; ++i) { + neworder[i] = shapeValue[i]; } } else { MNN_ASSERT(false); } - int inputShape[MNN_MAX_TENSOR_DIM]; - int inputStrides[MNN_MAX_TENSOR_DIM]; - int inputShapeSize = 0; - int preAxis = -2; - for (int i=0; ibuffer().dimensions; ++i) { - auto axis = shape[i]; - auto len = input->length(axis); - if (1 == len) { - continue; - } - if (axis - preAxis == 1) { - // Fuse dimension if possible - inputShape[inputShapeSize - 1] *= len; - } else { - if (preAxis >= 0) { - // Compute last stride - int stride = 1; - for (int v=preAxis+1; v < input->buffer().dimensions; ++v) { - stride *= input->length(v); - } - inputStrides[inputShapeSize - 1] = stride; - } - inputShapeSize+=1; - inputShape[inputShapeSize - 1] = len; - } - preAxis = shape[i]; - } - if (preAxis >= 0) { - // Compute last stride - int stride = 1; - for (int v=preAxis+1; v < input->buffer().dimensions; ++v) { - stride *= input->length(v); - } - inputStrides[inputShapeSize - 1] = stride; - } - if (0 == inputShapeSize) { - outputDes->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; - outputDes->regions = {TensorUtils::makeFullSlice(input)}; - return true; - } - int outputStrides[MNN_MAX_TENSOR_DIM]; - { - int stride = 1; - for (int i=inputShapeSize-1; i>=0; --i) { - outputStrides[i] = stride; - stride *= inputShape[i]; - } - } - /** Move max three inputShapeSize to last three location. - * Don't change max three number relative position - * */ - bool isReorderShape = false; - isReorderShape = (inputShapeSize > 4); - if (inputShapeSize == 4) { - // TODO: Opt this logic - isReorderShape = (inputShape[0] > inputShape[1] + inputShape[2] + inputShape[3]); - } - if (isReorderShape) { - int max1 = inputShape[0], max2 = -1, max3 = -1; - // Find Max Three Number - for (int i = 1; i < inputShapeSize; i++) { - if (inputShape[i] > max1) { - max3 = max2; - max2 = max1; - max1 = inputShape[i]; - } else if (inputShape[i] > max2) { - max3 = max2; - max2 = inputShape[i]; - } - else if (inputShape[i] > max3) { - max3 = inputShape[i]; - } - } - - // Move Max Three Number to Last Location - int lastIndex = inputShapeSize-1; - for (int i = inputShapeSize-1; i >= 0; i--) { - if (inputShape[i] == max1) { - if(i != lastIndex) { - std::swap(inputShape[i], inputShape[lastIndex]); - std::swap(inputStrides[i], inputStrides[lastIndex]); - std::swap(outputStrides[i], outputStrides[lastIndex]); - } - max1 = -1; - lastIndex--; - } else if (inputShape[i] == max2) { - if(i != lastIndex) { - std::swap(inputShape[i], inputShape[lastIndex]); - std::swap(inputStrides[i], inputStrides[lastIndex]); - std::swap(outputStrides[i], outputStrides[lastIndex]); - } - max2 = -1; - lastIndex--; - } else if (inputShape[i] == max3) { - if(i != lastIndex) { - std::swap(inputShape[i], inputShape[lastIndex]); - std::swap(inputStrides[i], inputStrides[lastIndex]); - std::swap(outputStrides[i], outputStrides[lastIndex]); - } - max3 = -1; - lastIndex--; - } - if(lastIndex < inputShapeSize-3) { - break; - } - } - } - // Compute inside, outside, axis - int inside = 1; - int insideStride = 0; - int outside = 1; - int outsideStride = 0; - int axis = 1; - int axisStride = 0; - int breakAxis = -1; - int remainSize = 1; - int outputInsideStride = 0; - int outputAxisStride = 0; - int outputOutsideStride = 0; - { - if (inputShapeSize >= 1) { - inside = inputShape[inputShapeSize-1]; - insideStride = inputStrides[inputShapeSize-1]; - outputInsideStride = outputStrides[inputShapeSize-1]; - } - if (inputShapeSize >= 2) { - axis = inputShape[inputShapeSize-2]; - axisStride = inputStrides[inputShapeSize-2]; - outputAxisStride = outputStrides[inputShapeSize-2]; - } - if (inputShapeSize >= 3) { - outside = inputShape[inputShapeSize-3]; - outsideStride = inputStrides[inputShapeSize-3]; - outputOutsideStride = outputStrides[inputShapeSize-3]; - breakAxis = inputShapeSize - 3; - for (int i = 0; i < inputShapeSize - 3; ++i) { - remainSize *= inputShape[i]; - } - } - } - outputDes->regions.resize(remainSize); - outputDes->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; - int32_t mod[MNN_MAX_TENSOR_DIM]; - for (int i = 0; i < breakAxis; ++i) { - int value = 1; - for (int j = i + 1; j < breakAxis; ++j) { - value *= inputShape[j]; - } - mod[i] = value; - } - for (int indice = 0; indice < remainSize; ++indice) { - int value = indice; - int inputOffset = 0; - int outputOffset = 0; - for (int i = 0; i < breakAxis; ++i) { - auto coordinate = value / mod[i]; - inputOffset += coordinate * inputStrides[i]; - outputOffset += coordinate * outputStrides[i]; - value = value % mod[i]; - } - Tensor::InsideDescribe::Region& slice = outputDes->regions[indice]; - slice.src.offset = inputOffset; - slice.src.stride[0] = outsideStride; - slice.size[0] = outside; - slice.src.stride[1] = axisStride; - slice.size[1] = axis; - slice.src.stride[2] = insideStride; - slice.size[2] = inside; - slice.origin = originTensor; - slice.dst.offset = outputOffset; - slice.dst.stride[0] = outputOutsideStride; - slice.dst.stride[1] = outputAxisStride; - slice.dst.stride[2] = outputInsideStride; - } - return true; + return GeometryComputer::ComputePermuteRegion(inputs[0], outputs[0], neworder, dims); } }; static void _create() { diff --git a/source/math/Vec.hpp b/source/math/Vec.hpp index 8f992dffd..5e28154d5 100644 --- a/source/math/Vec.hpp +++ b/source/math/Vec.hpp @@ -296,10 +296,10 @@ struct Vec { vec1.value = m0m1.val[1]; vec2.value = m2m3.val[0]; vec3.value = m2m3.val[1]; - vec0.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64(m2m3.val[0], 0), vec0.value, 1)); - vec1.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64(m2m3.val[1], 0), vec1.value, 1)); - vec2.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64(m0m1.val[0], 1), vec2.value, 0)); - vec3.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64(m0m1.val[1], 1), vec3.value, 0)); + vec0.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64((int64x2_t)m2m3.val[0], 0), (int64x2_t)vec0.value, 1)); + vec1.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64((int64x2_t)m2m3.val[1], 0), (int64x2_t)vec1.value, 1)); + vec2.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64((int64x2_t)m0m1.val[0], 1), (int64x2_t)vec2.value, 0)); + vec3.value = (int32x4_t)(vsetq_lane_s64(vgetq_lane_s64((int64x2_t)m0m1.val[1], 1), (int64x2_t)vec3.value, 0)); #endif } diff --git a/source/shape/ShapeAttention.cpp b/source/shape/ShapeAttention.cpp index 525266449..9b59c1b38 100644 --- a/source/shape/ShapeAttention.cpp +++ b/source/shape/ShapeAttention.cpp @@ -5,12 +5,13 @@ // Created by MNN on 2023/09/10. // Copyright © 2018, Alibaba Group Holding Limited // -#ifdef MNN_SUPPORT_TRANSFORMER_FUSE #include "shape/SizeComputer.hpp" #include "core/Macro.h" #include "core/TensorUtils.hpp" + namespace MNN { +#ifdef MNN_SUPPORT_TRANSFORMER_FUSE class FmhaV2SizeComputer : public SizeComputer { virtual bool onComputeSize(const MNN::Op* op, const std::vector& inputs, @@ -18,7 +19,7 @@ class FmhaV2SizeComputer : public SizeComputer { auto input0 = inputs[0], output0 = outputs[0]; MNN_ASSERT(inputs.size() == 1); MNN_ASSERT(input0->buffer().dimensions == 3); - + output0->buffer().dim[0].extent = input0->buffer().dim[0].extent; output0->buffer().dim[1].extent = input0->buffer().dim[1].extent; output0->buffer().dim[2].extent = input0->buffer().dim[2].extent/3; @@ -42,7 +43,7 @@ class FmhcaSizeComputer : public SizeComputer { auto output0 = outputs[0]; MNN_ASSERT(input0->buffer().dimensions == 3); MNN_ASSERT(input1->buffer().dimensions == 3); - + output0->buffer().dim[0].extent = input0->buffer().dim[0].extent; output0->buffer().dim[1].extent = input0->buffer().dim[1].extent; output0->buffer().dim[2].extent = input0->buffer().dim[2].extent; @@ -56,8 +57,27 @@ class FmhcaSizeComputer : public SizeComputer { return true; } }; + +class AttentionSizeComputer : public SizeComputer { + virtual bool onComputeSize(const MNN::Op* op, const std::vector& inputs, + const std::vector& outputs) const override { + auto input = inputs[0], output = outputs[0]; + MNN_ASSERT(input->buffer().dimensions == 4); + output->buffer().dim[0].extent = input->buffer().dim[0].extent; + output->buffer().dim[1].extent = input->buffer().dim[1].extent; + output->buffer().dim[2].extent = input->buffer().dim[2].extent * input->buffer().dim[3].extent; + output->buffer().dimensions = 3; + output->buffer().type = input->buffer().type; + TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(input)->dimensionFormat; + return true; + } +}; + + REGISTER_SHAPE_INPUTS_TRANSFORMER_FUSE(FmhaV2SizeComputer, OpType_FmhaV2); REGISTER_SHAPE_INPUTS_TRANSFORMER_FUSE(FmhcaSizeComputer, OpType_Fmhca); +REGISTER_SHAPE_INPUTS_TRANSFORMER_FUSE(AttentionSizeComputer, OpType_Attention); +#endif } // namespace MNN -#endif + diff --git a/source/shape/ShapeRegister.cpp b/source/shape/ShapeRegister.cpp index 2c2615817..4763d7ab5 100644 --- a/source/shape/ShapeRegister.cpp +++ b/source/shape/ShapeRegister.cpp @@ -119,6 +119,7 @@ extern void ___SplitGeLUSizeComputer__OpType_SplitGeLU__(); extern void ___SeqLen2SpatialSizeComputer__OpType_SeqLen2Spatial__(); extern void ___FmhaV2SizeComputer__OpType_FmhaV2__(); extern void ___FmhcaSizeComputer__OpType_Fmhca__(); +extern void ___AttentionSizeComputer__OpType_Attention__(); #endif void registerShapeOps() { ___ShapeSizeComputer__OpType_Shape__(); @@ -239,6 +240,7 @@ ___SplitGeLUSizeComputer__OpType_SplitGeLU__(); ___SeqLen2SpatialSizeComputer__OpType_SeqLen2Spatial__(); ___FmhaV2SizeComputer__OpType_FmhaV2__(); ___FmhcaSizeComputer__OpType_Fmhca__(); +___AttentionSizeComputer__OpType_Attention__(); #endif } } diff --git a/test.sh b/test.sh index 5dc09f3f7..0d0e90e1b 100755 --- a/test.sh +++ b/test.sh @@ -42,7 +42,8 @@ SOURCE_CHANGE=$(git show --name-only | grep -E "^source/(internal|backend|core|c PYMNN_CHANGE=$(git show --name-only | grep -E "^pymnn/.*\.(cpp|cc|c|h|hpp|py)$") PY_CHANGE=$(git show --name-only | grep -E "^pymnn/pip_package/MNN/.*\.(py)$") OPENCV_CHANGE=$(git show --name-only | grep -E "^tools/cv/.*\.(cpp|cc|c|h|hpp)$") - +# OPENCL_CHANGE=$(git show --name-only | grep -E "^source/backend/opencl/.*\.(cpp|cc|c|h|hpp)$") +OPENCL_CHANGE=true failed() { printf "TEST_NAME_EXCEPTION: Exception\nTEST_CASE_AMOUNT_EXCEPTION: {\"blocked\":0,\"failed\":1,\"passed\":0,\"skipped\":0}\n" exit 1 @@ -272,11 +273,12 @@ unit_test() { echo '### 多线程单元测试失败，测试终止！' failed fi - - ./run_test.out op 3 1 4 - if [ $? -ne 0 ]; then - echo '### OpenCL单元测试失败，测试终止！' - failed + if [ "$OPENCL_CHANGE" ]; then + ./run_test.out op 3 1 4 + if [ $? -ne 0 ]; then + echo '### OpenCL单元测试失败，测试终止！' + failed + fi fi } @@ -293,10 +295,12 @@ model_test() { failed fi - ../tools/script/modelTest.py ~/AliNNModel 3 0.002 1 - if [ $? -ne 0 ]; then - echo '### OpenCL模型测试失败，测试终止！' - failed + if [ "$OPENCL_CHANGE" ]; then + ../tools/script/modelTest.py ~/AliNNModel 3 0.002 1 + if [ $? -ne 0 ]; then + echo '### OpenCL模型测试失败，测试终止！' + failed + fi fi } @@ -437,7 +441,7 @@ llm_test() { failed fi # 2. run llm model test - ./llm_demo ~/AliNNModel/qwen-1.8b-int4 0 ~/AliNNModel/qwen-1.8b-int4/prompt.txt + ./llm_demo ~/AliNNModel/qwen-1.8b-int4 0 10 ~/AliNNModel/qwen-1.8b-int4/prompt.txt if [ $? -gt 0 ]; then echo '### LLM模型测试失败，测试终止！' failed @@ -508,10 +512,12 @@ android_unit_test() { echo '### Android单元测试FP16-roipooling多线程失败，测试终止！' failed fi - adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./run_test.out op 3 1 4 $1" - if [ $? -ne 0 ]; then - echo '### Android单元测试OpenCL失败，测试终止！' - failed + if [ "$OPENCL_CHANGE" ]; then + adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./run_test.out op 3 1 4 $1" + if [ $? -ne 0 ]; then + echo '### Android单元测试OpenCL失败，测试终止！' + failed + fi fi } android_model_test() { @@ -528,11 +534,13 @@ android_model_test() { else pass_num=$[$pass_num+1] fi - adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./testModel.out ../AliNNModel/OpTestResource/$model/temp.bin ../AliNNModel/OpTestResource/$model/input_0.txt ../AliNNModel/OpTestResource/$model/output_0.txt 3 0.002 1" - if [ $? -ne 0 ]; then - fail_cl_num=$[$fail_cl_num+1] - else - pass_cl_num=$[$pass_cl_num+1] + if [ "$OPENCL_CHANGE" ]; then + adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./testModel.out ../AliNNModel/OpTestResource/$model/temp.bin ../AliNNModel/OpTestResource/$model/input_0.txt ../AliNNModel/OpTestResource/$model/output_0.txt 3 0.002 1" + if [ $? -ne 0 ]; then + fail_cl_num=$[$fail_cl_num+1] + else + pass_cl_num=$[$pass_cl_num+1] + fi fi done @@ -545,11 +553,13 @@ android_model_test() { else pass_num=$[$pass_num+1] fi + if [ "$OPENCL_CHANGE" ]; then adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./testModel.out ../AliNNModel/TestResource/$model/temp.bin ../AliNNModel/TestResource/$model/input_0.txt ../AliNNModel/TestResource/$model/output.txt 3 0.002 1" - if [ $? -ne 0 ]; then - fail_cl_num=$[$fail_cl_num+1] - else - pass_cl_num=$[$pass_cl_num+1] + if [ $? -ne 0 ]; then + fail_cl_num=$[$fail_cl_num+1] + else + pass_cl_num=$[$pass_cl_num+1] + fi fi done @@ -562,11 +572,13 @@ android_model_test() { else pass_num=$[$pass_num+1] fi - adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./testModelWithDescribe.out ../AliNNModel/TestWithDescribe/$model/temp.bin ../AliNNModel/TestWithDescribe/$model/config.txt 3 0.002 1" - if [ $? -ne 0 ]; then - fail_cl_num=$[$fail_cl_num+1] - else - pass_cl_num=$[$pass_cl_num+1] + if [ "$OPENCL_CHANGE" ]; then + adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./testModelWithDescribe.out ../AliNNModel/TestWithDescribe/$model/temp.bin ../AliNNModel/TestWithDescribe/$model/config.txt 3 0.002 1" + if [ $? -ne 0 ]; then + fail_cl_num=$[$fail_cl_num+1] + else + pass_cl_num=$[$pass_cl_num+1] + fi fi done printf "TEST_NAME_ANDROID_MODEL_TEST_$1: Android_$1模型测试\nTEST_CASE_AMOUNT_ANDROID_MODEL_TEST_$1: {\"blocked\":0,\"failed\":$fail_num,\"passed\":$pass_num,\"skipped\":0}\n" @@ -574,10 +586,12 @@ android_model_test() { echo '### Android模型测试失败，测试终止！' failed fi - printf "TEST_NAME_ANDROID_MODEL_OPENCL_TEST_$1: Android_$1模型测试\nTEST_CASE_AMOUNT_ANDROID_MODEL_TEST_$1: {\"blocked\":0,\"failed\":$fail_cl_num,\"passed\":$pass_cl_num,\"skipped\":0}\n" - if [ $fail_cl_num -ne 0 ]; then - echo '### Android OpenCL后端模型测试失败，测试终止！' - failed + if [ "$OPENCL_CHANGE" ]; then + printf "TEST_NAME_ANDROID_MODEL_OPENCL_TEST_$1: Android_$1模型测试\nTEST_CASE_AMOUNT_ANDROID_MODEL_TEST_$1: {\"blocked\":0,\"failed\":$fail_cl_num,\"passed\":$pass_cl_num,\"skipped\":0}\n" + if [ $fail_cl_num -ne 0 ]; then + echo '### Android OpenCL后端模型测试失败，测试终止！' + failed + fi fi } android_unit_test_low_memory() { diff --git a/test/CommonOpCreator.hpp b/test/CommonOpCreator.hpp index 64700a253..1097589cd 100644 --- a/test/CommonOpCreator.hpp +++ b/test/CommonOpCreator.hpp @@ -9,7 +9,7 @@ #ifndef CommonOpCreator_hpp #define CommonOpCreator_hpp #include "TestUtils.h" -#include "cpp/IDSTEncoder.hpp" +#include "core/IDSTEncoder.hpp" namespace MNN { static PadMode _convertPadMode(Express::PaddingMode mode) { switch (mode) { diff --git a/test/core/IDSTTest.cpp b/test/core/IDSTTest.cpp index 56d1a4cf7..62e620d60 100644 --- a/test/core/IDSTTest.cpp +++ b/test/core/IDSTTest.cpp @@ -8,7 +8,7 @@ #include "MNNTestSuite.h" #include -#include "cpp/IDSTEncoder.hpp" +#include "core/IDSTEncoder.hpp" #include "core/ConvolutionCommon.hpp" using namespace MNN; diff --git a/test/main.cpp b/test/main.cpp index 06462b152..862ecdc0c 100644 --- a/test/main.cpp +++ b/test/main.cpp @@ -27,8 +27,12 @@ int main(int argc, char* argv[]) { int memory = (int)MNN::BackendConfig::Memory_Normal; int thread = 1; const char* flag = ""; + MNN::BackendConfig config; + config.precision = (MNN::BackendConfig::PrecisionMode)precision; + config.memory = (MNN::BackendConfig::MemoryMode)memory; + auto type = MNN_FORWARD_CPU; if (argc > 2) { - auto type = (MNNForwardType)atoi(argv[2]); + type = (MNNForwardType)atoi(argv[2]); FUNC_PRINT(type); if (argc > 3) { precision = atoi(argv[3]); @@ -53,11 +57,16 @@ int main(int argc, char* argv[]) { MNN_ERROR("Invalid memory mode, use 0 instead\n"); memory = 0; } - MNN::BackendConfig config; config.precision = (MNN::BackendConfig::PrecisionMode)precision; config.memory = (MNN::BackendConfig::MemoryMode)memory; - MNN::Express::Executor::getGlobalExecutor()->setGlobalExecutorConfig(type, config, thread); } + auto exe = MNN::Express::Executor::newExecutor(type, config, thread); + if (exe == nullptr) { + MNN_ERROR("Can't create executor with type:%d, exit!\n", type); + return 0; + } + MNN::Express::ExecutorScope scope(exe); + exe->setGlobalExecutorConfig(type, config, thread); if (argc > 1) { auto name = argv[1]; if (strcmp(name, "all") == 0) { diff --git a/test/op/ConvInt8Test.cpp b/test/op/ConvInt8Test.cpp index 89fd3c617..68d25ad65 100644 --- a/test/op/ConvInt8Test.cpp +++ b/test/op/ConvInt8Test.cpp @@ -12,9 +12,9 @@ #include "MNN_generated.h" #include "MNNTestSuite.h" #include "TestUtils.h" -#include "common/CommonCompute.hpp" -#include "common/MemoryFormater.h" -#include "common/WinogradInt8Attr.hpp" +#include "core/CommonCompute.hpp" +#include "core/MemoryFormater.h" +#include "core/WinogradInt8Attr.hpp" #include "math/WingoradGenerater.hpp" #include @@ -271,7 +271,7 @@ class ConvInt8TestCommon : public MNNTestCase { // warm up, do onResize first for shapeDirty x->writeMap(); y->readMap(); - + MNN::Timer _t; const int LOOP = 100; for (int i = 0; i < LOOP; ++i) { @@ -442,10 +442,10 @@ class ConvInt8WinogradTestCommon : public MNNTestCase { public: static VARP referenceWinograd(const VARP xInt, const std::vector& weight, const std::vector& wScale, const std::vector& bias, INTS kernel, INTS channel, INTS pads, const WinogradInt8Attr::Attr& attr, float xScale, float yScale, int8_t xZeroPoint, int8_t yZeroPoint, bool relu) { auto clamp = [](VARP x) {return _Maximum(_Minimum(x, _Scalar(127)), _Scalar(-127));}; - + //auto round = [](VARP x) { return _Round(x); }; auto roundWithEps = [](VARP x) { return _Round(x + _Sign(x) * _Scalar(1e-6)); }; - + auto inDims = xInt->getInfo()->dim; int batch = inDims[0], inH = inDims[2], inW = inDims[3]; int outChannel = channel[1], inChannel = channel[0], kernelH = kernel[1], kernelW = kernel[0]; @@ -453,10 +453,10 @@ class ConvInt8WinogradTestCommon : public MNNTestCase { int outH = inH + 2 * padH - kernelH + 1, outW = inW + 2 * padW - kernelW + 1; int unitH = attr.unitY, unitW = attr.unitX, unitNumH = UP_DIV(outH, unitH), unitNumW = UP_DIV(outW, unitW); int alphaH = unitH + kernelH - 1, alphaW = unitW + kernelW - 1; - + int needH = unitNumH * unitH + kernelH - 1, needW = unitNumW * unitW + kernelW - 1; int paddings[] = {0, 0, 0, 0, padH, needH - inH - padH, padW, needW - inW - padW}; - + auto xx = _Int8ToFloat(xInt, _Scalar(xScale), xZeroPoint); xx = _Convert(xx, NCHW); xx = _Pad(xx, _Const(paddings, {8}, NCHW, halide_type_of())); @@ -470,12 +470,12 @@ class ConvInt8WinogradTestCommon : public MNNTestCase { xx = _MatMul(_MatMul(_Transpose(srcTransH, {1, 0}), xx), srcTransW); // [alphaH * alphaW, ic, N * h_unit_num * w_unit_num] xx = _Reshape(_Transpose(xx, {2, 3, 1, 0}), {alphaH * alphaW, inChannel, -1}); - + // simulate input asym quant auto xxScale = _Const(attr.inputScales.data(), {alphaH * alphaW, 1, 1}, NCHW); auto xxZeroPoint = _Cast(_Const(attr.inputZeroPoints.data(), {alphaH * alphaW, 1, 1}, NCHW, halide_type_of())); xx = (clamp(_Round(xx / xxScale + xxZeroPoint)) - xxZeroPoint) * xxScale; - + auto w = _Const(weight.data(), {outChannel, inChannel, kernelH, kernelW}, NCHW, halide_type_of()); w = _Cast(w) * _Const(wScale.data(), {outChannel, 1, 1, 1}, NCHW); auto wTransH = _Const(genH.G()->host(), {alphaH, kernelH}, NCHW); @@ -539,11 +539,11 @@ class ConvInt8WinogradTestCommon : public MNNTestCase { } } } - + x = _Convert(x, NC4HW4); // For sse we use uint8 instead of int8, use FloatToInt8 to hidden detail x = _FloatToInt8(x, _Scalar(1.0 / xScale), -127, 127, xZeroPoint); - + WinogradInt8Attr attrs; std::vector transInputScales(alpha2, 0.9), transWeightScales(alpha2 * oc, 1.1); std::vector transInputZeroPoint(alpha2, 1); @@ -553,7 +553,7 @@ class ConvInt8WinogradTestCommon : public MNNTestCase { kernel, CAFFE, {1, 1}, {1, 1}, 1, pads, relu, xScale, yScale, xZeroPoint, yZeroPoint, -127, 127, 127, false); y = attrs.turnToWinogradConv(y); - + yTarget = _Convert(_Cast(_Int8ToFloat(yTarget, _Scalar(1.0))), NCHW); y = _Convert(_Cast(_Int8ToFloat(y, _Scalar(1.0))), NCHW); auto yTargetInfo = yTarget->getInfo(), yInfo = y->getInfo(); @@ -575,7 +575,7 @@ class ConvInt8WinogradTestCommon : public MNNTestCase { // warm up, do onResize first for shapeDirty x->writeMap(); y->readMap(); - + MNN::Timer _t; const int LOOP = 20; for (int i = 0; i < LOOP; ++i) { @@ -626,7 +626,7 @@ class ConvSpeedInt8WinogradTest : public ConvInt8WinogradTestCommon { std::vector kernels = { {3, 3}//, {5, 5}, {7, 1}, {1, 7} // {w, h} }; - + std::vector titles = {"3x3", "5x5", "1x7", "7x1"}; for (int i = 0; i < kernels.size(); ++i) { auto res = testKernel(inputShape, kernels[i], channel, pad, {4, 4}, true, titles[i] + ",alpha=4"); diff --git a/test/op/ConvolutionTest.cpp b/test/op/ConvolutionTest.cpp index 8cc334624..f8245b0fe 100644 --- a/test/op/ConvolutionTest.cpp +++ b/test/op/ConvolutionTest.cpp @@ -17,8 +17,8 @@ #include "CommonOpCreator.hpp" #include "core/Session.hpp" #include "core/TensorUtils.hpp" -#include "common/MemoryFormater.h" -#include "common/CommonCompute.hpp" +#include "core/MemoryFormater.h" +#include "core/CommonCompute.hpp" #define TEST_RANDOM_SEED 100 @@ -610,7 +610,7 @@ class ConvolutionInt8CommonTest : public ConvolutionCommonTest { int beginIndex = k * kernel_size; auto absMax = findAbsMax(weightData.data() + beginIndex, kernel_size); wScale[k] = absMax / threshold; - + float* ptr = weightData.data() + beginIndex; for (int i = 0; i < kernel_size; ++i) { int8_t quantVal = (int8_t)(fmax(fmin(round(ptr[i] / wScale[k]), threshold), clampMin)); diff --git a/test/op/LayerNormTest.cpp b/test/op/LayerNormTest.cpp index 427f3dbea..fb3020112 100644 --- a/test/op/LayerNormTest.cpp +++ b/test/op/LayerNormTest.cpp @@ -14,7 +14,7 @@ using namespace MNN; using namespace MNN::Express; -VARP _LayerNorm(VARP x, std::vector axis, float epsilon, std::vector gamma, std::vector beta, int group = 1) { +static VARP _LayerNorm(VARP x, std::vector axis, float epsilon, std::vector gamma, std::vector beta, int group = 1, bool useRMS = false) { std::unique_ptr op(new OpT); op->main.type = OpParameter_LayerNorm; op->type = OpType_LayerNorm; @@ -28,450 +28,215 @@ VARP _LayerNorm(VARP x, std::vector axis, float epsilon, std::vectormain.AsLayerNorm()->epsilon = epsilon; op->main.AsLayerNorm()->axis = axis; op->main.AsLayerNorm()->group = group; + op->main.AsLayerNorm()->useRMSNorm = useRMS; return (Variable::create(Expr::create(std::move(op), {x}))); } -static void _refLayerNorm(float *dst, const float *src, const float *gamma, const float *beta, float epsilon, size_t in_size, size_t out_size) { - for(int i = 0; i < out_size; ++i){ - const float* input = src + i * in_size; - float* output = dst + i * in_size; - float sum = 0.f; - for (int j = 0; j < in_size; ++j) { - sum += input[j]; - } - float mean = sum / in_size; - float square_sum = 0.f; - for (int j = 0; j < in_size; ++j) { - square_sum += (input[j] - mean) * (input[j] - mean); - } - float variable = square_sum / in_size; - variable = 1.f / std::sqrt(variable + epsilon); - - if (gamma && beta) { - for (int j = 0; j < in_size; ++j) { - output[j] = (input[j] - mean) * variable * gamma[j] + beta[j]; - } - } else { - for (int j = 0; j < in_size; ++j) { - output[j] = (input[j] - mean) * variable; - } - } +std::vector inputdata_0 = {0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, + 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, + 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, + 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, + 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, + 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, + 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, + 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, + 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, + 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, + 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, + 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, + 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 0.6, 1.6, 2.6, 3.6, + 4.6, 5.6, 6.6, 7.6, 8.6, 9.6}; +std::vector tgdata_0 = {-1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, + -1.34164079, -0.4472136 , 0.4472136 , 1.34164079, -1.34164079, + -0.4472136 , 0.4472136 , 1.34164079, 0.86824314, 1.11631261, + -1.11631261, -0.86824314, -1.34164079, -0.4472136 , 0.4472136 , + 1.34164079, -1.34164079, -0.4472136 , 0.4472136 , 1.34164079}; +std::vector inputdata_1 = {0.7742, 0.5332, -0.8799, -1.0676, -0.7402, -1.5074, 0.2052, 0.3648, + 1.5056, -0.2804, 1.2785, 1.3410, 0.5395, 0.1665, -1.4594, 0.1158, + -1.8436, -0.1581, -1.5055, 0.3366, 0.4938, 0.0630, 0.5465, 0.9264, + -1.0491, 2.4297, 1.9942, 0.4256, 1.3902, -0.1021, -0.9883, 0.4500}; +std::vector tgdata_1 = {1.1381, 0.8445, -0.8770, -1.1056, -0.4238, -1.4374, 0.8252, 1.0360, + 0.7544, -1.7206, 0.4397, 0.5264, 0.9098, 0.4242, -1.6923, 0.3583, + -1.1587, 0.6996, -0.7859, 1.2451, -0.0446, -1.4518, 0.1277, 1.3688, + -1.4550, 1.0769, 0.7599, -0.3818, 1.3930, -0.3354, -1.3618, 0.3041}; +float eps = 0.000001f; + +static bool testKernel (std::vector inputdata, std::vector targetdata, std::vector dimensions, std::vector reduceAxis, float epsilon, std::vector gamma, std::vector beta, std::vector inputQuan, std::vector outputQuan, std::string testName, int precision, int group = 1) { + int size = 1; + for (int i = 0; i < dimensions.size(); ++i) { + size *= dimensions[i]; + } + int reducesize = 1; + for (int i = 0; i < reduceAxis.size(); ++i) { + reducesize *= dimensions[reduceAxis[i]]; } + MNN_ASSERT(gamma.size() == 0 || (gamma.size() >0 && reducesize == gamma.size())); + MNN_ASSERT(gamma.size() == beta.size()); + auto input = _Input(dimensions, NCHW); + if (inputQuan.size() > 0) { + input->writeScaleMap(inputQuan[0], inputQuan[1]); + } + auto inputPtr = input->writeMap(); + ::memcpy(inputPtr, inputdata.data(), input->getInfo()->size * sizeof(float)); + auto output = _LayerNorm(input, reduceAxis, epsilon, gamma, beta, group, false); + if (outputQuan.size() > 0) { + output->writeScaleMap(outputQuan[0], outputQuan[1]); + } + const float* outputPtr = output->readMap(); + float ratio = 0.02; + if (!checkVector(outputPtr, targetdata.data(), size, ratio)) { + MNN_ERROR("%s failed: data dimension=[", testName.c_str()); + for (int i = 0; i < dimensions.size(); ++i) { + if (i < dimensions.size() - 1) MNN_PRINT("%d, ", dimensions[i]); + else MNN_PRINT("%d], reduce axis=[", dimensions[i]); + } + for (int i = 0; i < reduceAxis.size(); ++i) { + if (i < reduceAxis.size() - 1) MNN_PRINT("%d, ", reduceAxis[i]); + else MNN_PRINT("%d]\n", reduceAxis[i]); + } + return false; + } + return true; } class LayerNormTest : public MNNTestCase { public: virtual ~LayerNormTest() = default; virtual bool run(int precision) { - { - std::vector dims = {1, 4, 1, 2}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector gammaData = {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f}; - std::vector betaData = {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f}; + { // test 1. std::vector axis = {0, 1, 2}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, gammaData, betaData); - std::vector expectedOutput(8); - - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, gammaData.data(), betaData.data(), eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 1e-5 * errorScale)) { - MNN_ERROR("Float LayerNormTest axis = %d test failed!\n", axis_size); - return false; - } - } - // LayerNorm Int8 test. - { std::vector dims = {1, 4, 1, 2}; - auto input = _Input(dims, NCHW); // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - input->writeScaleMap(0.063745, 2.0); - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector gammaData = {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f}; - std::vector betaData = {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f}; - std::vector axis = {0, 1, 2}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, gammaData, betaData); - output->writeScaleMap(0.0095, 0.0); - std::vector expectedOutput(8); + std::vector inputdata = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; + std::vector tgdata = {0.03527864, 0.0242914, 0.14944272, 0.1714172, -0.24249224, -0.22996021, 0.68665631, 0.66994695}; + std::vector gammaData = {0.1f, 0.2f, 0.3f, 0.4f}; + std::vector betaData = {0.08f, 0.06f, 0.16f, 0.15f}; - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, gammaData.data(), betaData.data(), eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 0.01)) { - MNN_ERROR("Int8 LayerNormTest axis = %d test failed!\n", axis_size); + bool testSuc = testKernel(inputdata, tgdata, dims, axis, eps, gammaData, betaData, {}, {}, "Float LayerNorm Test", 1); + if (!testSuc) { return false; } } - { - std::vector dims = {1, 4, 1, 2}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); + { // test 2. std::vector axis = {0, 1, 2}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, {}, {}); - std::vector expectedOutput(8); - - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, nullptr, nullptr, eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 1e-5 * errorScale)) { - MNN_ERROR("Float LayerNormTest without gamma beta axis = %d test failed!\n", axis_size); - return false; - } - } - { std::vector dims = {1, 4, 1, 2}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - input->writeScaleMap(0.063745, 2.0); - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector axis = {0, 1, 2}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, {}, {}); - std::vector expectedOutput(8); - - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, nullptr, nullptr, eps, inner_size, outter_size); - output->writeScaleMap(0.01087, 0.0); - auto gotOutput = output->readMap(); - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 0.01)) { - MNN_ERROR("Int8 LayerNormTest without gamma beta axis = %d test failed!\n", axis_size); - return false; - } - } - { - std::vector dims = {1, 2, 2, 2}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector gammaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector betaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector axis = {0, 1}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, gammaData, betaData); - std::vector expectedOutput(8); - - int axis_size = axis.size(); - int rank = dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, gammaData.data(), betaData.data(), eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 1e-5 * errorScale)) { - MNN_ERROR("Float LayerNormTest axis = %d test failed!\n", axis_size); - return false; - } - } - { - std::vector dims = {1, 2, 2, 2}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - input->writeScaleMap(0.063745, 2.0); - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector gammaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector betaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector axis = {0, 1}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, gammaData, betaData); - std::vector expectedOutput(8); - - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, gammaData.data(), betaData.data(), eps, inner_size, outter_size); - output->writeScaleMap(0.0084, 0.0); - auto gotOutput = output->readMap(); - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 0.01)) { - MNN_ERROR("Int8 LayerNormTest axis = %d test failed!\n", axis_size); - return false; - } - } - { - std::vector dims = {1, 2, 2, 2}; - auto input = _Input(dims, NCHW); // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector axis = {0, 1}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, {}, {}); - std::vector expectedOutput(8); + std::vector inputdata = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; + std::vector tgdata = {0.03527864, 0.0242914, 0.14944272, 0.1714172, -0.24249224, -0.22996021, 0.68665631, 0.66994695}; + std::vector inputQuan = {0.063745, 2.0}; + std::vector outputQuan = {0.0095, 0.0}; + std::vector gammaData = {0.1f, 0.2f, 0.3f, 0.4f}; + std::vector betaData = {0.08f, 0.06f, 0.16f, 0.15f}; - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, nullptr, nullptr, eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 1e-5 * errorScale)) { - MNN_ERROR("Float LayerNormTest without gamma beta axis = %d test failed!\n", axis_size); + bool testSuc = testKernel(inputdata, tgdata, dims, axis, eps, gammaData, betaData, inputQuan, outputQuan, "Int8 LayerNorm Test", 1); + if (!testSuc) { return false; } } - { - std::vector dims = {1, 2, 2, 2}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - input->writeScaleMap(0.064, 2.0); - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector axis = {0, 1}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, {}, {}); - std::vector expectedOutput(8); - - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, nullptr, nullptr, eps, inner_size, outter_size); - output->writeScaleMap(0.0089, 0.0); - auto gotOutput = output->readMap(); - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 0.01)) { - MNN_ERROR("Int8 LayerNormTest without gamma beta axis = %d test failed!\n", axis_size); - return false; - } - } - { - std::vector dims = {1, 2, 1, 4}; - auto input = _Input(dims, NCHW); + { // test 3. + std::vector axis = {0, 1, 2}; + std::vector dims = {1, 4, 1, 2}; + std::vector gammaData = {}; + std::vector betaData = {}; // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector gammaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector betaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector axis = {0}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, gammaData, betaData); - std::vector expectedOutput(8); + std::vector inputdata = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; + std::vector tgdata = {-0.4472136, -0.55708601, 0.4472136, 0.55708601, -1.34164079, -1.29986737, 1.34164079, 1.29986737}; - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, gammaData.data(), betaData.data(), eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 1e-5 * errorScale)) { - MNN_ERROR("Float LayerNormTest axis = %d test failed!\n", axis_size); + bool testSuc = testKernel(inputdata, tgdata, dims, axis, eps, gammaData, betaData, {}, {}, "Float LayerNorm Test", 1); + if (!testSuc) { return false; } } - { - std::vector dims = {1, 2, 1, 4}; - auto input = _Input(dims, NCHW); + { // test 4. + std::vector axis = {0, 1, 2}; + std::vector dims = {1, 4, 1, 2}; + std::vector gammaData = {}; + std::vector betaData = {}; // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - input->writeScaleMap(0.064, 2.0); - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector gammaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector betaData = {0.5f, 0.5f, 0.5f, 0.5f}; - std::vector axis = {0}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, gammaData, betaData); - std::vector expectedOutput(8); + std::vector inputdata = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; + std::vector tgdata = {-0.4472136, -0.55708601, 0.4472136, 0.55708601, -1.34164079, -1.29986737, 1.34164079, 1.29986737}; + std::vector inputQuan = {0.063745, 2.0}; + std::vector outputQuan = {0.0105, 0.0}; - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, gammaData.data(), betaData.data(), eps, inner_size, outter_size); - output->writeScaleMap(0.0099, 0.0); - auto gotOutput = output->readMap(); - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 0.01)) { - MNN_ERROR("Int8 LayerNormTest axis = %d test failed!\n", axis_size); + bool testSuc = testKernel(inputdata, tgdata, dims, axis, eps, gammaData, betaData, inputQuan, outputQuan, "Int8 LayerNorm Test", 1); + if (!testSuc) { return false; } } - { - std::vector dims = {1, 2, 1, 4}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector axis = {0}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, {}, {}); - std::vector expectedOutput(8); - - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, nullptr,nullptr, eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 1e-5 * errorScale)) { - MNN_ERROR("Float LayerNormTest without gamma beta axis = %d test failed!\n", axis_size); + { // test 5. + std::vector axis = {2, 3}; + std::vector dims = {6, 10, 2, 2}; + std::vector gammaData = {}; + std::vector betaData = {}; + + bool testSuc = testKernel(inputdata_0, tgdata_0, dims, axis, eps, gammaData, betaData, {}, {}, "Float LayerNorm Test", 1); + if (!testSuc) { return false; } } { - std::vector dims = {1, 2, 1, 4}; - auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - input->writeScaleMap(0.06375, 2.0); - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector axis = {0}; - float eps = 1.00f; - auto output = _LayerNorm(input, axis, eps, {}, {}); - std::vector expectedOutput(8); + std::vector axis = {2, 3}; + std::vector dims = {6, 10, 2, 2}; + std::vector gammaData = {}; + std::vector betaData = {}; + std::vector inputQuan = {0.0752, 0.f}; + std::vector outputQuan = {0.0105, 0.f}; - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = 1; - int inner_size = 1; - for (int i = 0; i < rank - axis_size; ++i) { - outter_size *= dims[i]; - } - for (int i = rank - axis_size; i < rank; ++i) { - inner_size *= dims[i]; - } - _refLayerNorm(expectedOutput.data(), inpudata, nullptr,nullptr, eps, inner_size, outter_size); - output->writeScaleMap(0.00858, 0.f); - auto gotOutput = output->readMap(); - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 0.01)) { - MNN_ERROR("Int8 LayerNormTest without gamma beta axis = %d test failed!\n", axis_size); + bool testSuc = testKernel(inputdata_0, tgdata_0, dims, axis, eps, gammaData, betaData, inputQuan, outputQuan, "Int8 LayerNorm Test", 1); + if (!testSuc) { return false; } } - { - std::vector dims = {1, 2, 1, 4}; + { // Group Norm without axis + std::vector dims = {2, 4, 2, 2}; auto input = _Input(dims, NCHW); - // set input data - const float inpudata[] = {-1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0}; - auto inputPtr = input->writeMap(); - memcpy(inputPtr, inpudata, 8 * sizeof(float)); - std::vector axis = {}; - float eps = 1.00f; - int group = 2; - auto output = _LayerNorm(input, axis, eps, {}, {}, group); - std::vector expectedOutput(8); - - int axis_size = (int)axis.size(); - int rank = (int)dims.size(); - int outter_size = group * dims[0]; - int inner_size = 1; - for (int i = 1; i < rank; ++i) { - inner_size *= dims[i]; - } - inner_size /= group; - _refLayerNorm(expectedOutput.data(), inpudata, nullptr,nullptr, eps, inner_size, outter_size); - auto gotOutput = output->readMap(); - float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000; - if (!checkVectorByRelativeError(gotOutput, expectedOutput.data(), 8, 1e-5 * errorScale)) { - MNN_ERROR("Float LayerNormTest without gamma beta axis = %d test failed!\n", axis_size); + bool testSuc = testKernel(inputdata_1, tgdata_1, dims, {}, eps, {}, {}, {}, {}, "Flaot GroupNorm Test", 1, 4); + if (!testSuc) { return false; } } - return true; } }; diff --git a/test/op/SequenceGRUTest.cpp b/test/op/SequenceGRUTest.cpp index c1478b5e3..e64394ea8 100644 --- a/test/op/SequenceGRUTest.cpp +++ b/test/op/SequenceGRUTest.cpp @@ -8,7 +8,7 @@ // #include #include -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" #include "MNNTestSuite.h" #include "TestUtils.h" diff --git a/tools/converter/source/common/AddSparseInfo.cpp b/tools/converter/source/common/AddSparseInfo.cpp index 7097bc7a9..2ae05111d 100644 --- a/tools/converter/source/common/AddSparseInfo.cpp +++ b/tools/converter/source/common/AddSparseInfo.cpp @@ -8,7 +8,7 @@ #include #include "CommonUtils.hpp" -#include "common/CommonCompute.hpp" +#include "core/CommonCompute.hpp" #include "backend/cpu/compute/SparseConvolutionTiledExecutor.hpp" using namespace MNN; diff --git a/tools/converter/source/common/FullQuantAndCoding.cpp b/tools/converter/source/common/FullQuantAndCoding.cpp index 2f31db6e7..a9e8b61ce 100644 --- a/tools/converter/source/common/FullQuantAndCoding.cpp +++ b/tools/converter/source/common/FullQuantAndCoding.cpp @@ -9,7 +9,7 @@ #include #include "CommonUtils.hpp" #include "MNN/expr/ExprCreator.hpp" -#include "cpp/IDSTEncoder.hpp" +#include "core/IDSTEncoder.hpp" using namespace MNN; using namespace MNN::Express; diff --git a/tools/converter/source/common/WeightQuantAndCoding.cpp b/tools/converter/source/common/WeightQuantAndCoding.cpp index 9e29bcd14..3fd718dfb 100644 --- a/tools/converter/source/common/WeightQuantAndCoding.cpp +++ b/tools/converter/source/common/WeightQuantAndCoding.cpp @@ -7,8 +7,8 @@ // #include "CommonUtils.hpp" -#include "common/CommonCompute.hpp" -#include "cpp/IDSTEncoder.hpp" +#include "core/CommonCompute.hpp" +#include "core/IDSTEncoder.hpp" static float findAbsMax(const float *weights, const int count) { float absMax = fabs(weights[0]); diff --git a/tools/converter/source/common/cli.cpp b/tools/converter/source/common/cli.cpp index 33660788e..c2a64a8ee 100644 --- a/tools/converter/source/common/cli.cpp +++ b/tools/converter/source/common/cli.cpp @@ -37,7 +37,7 @@ #include #include #include -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" namespace MNN { @@ -465,7 +465,7 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv if (result.count("convertMatmulToConv")) { modelPath.convertMatmulToConv = result["convertMatmulToConv"].as(); } - + if (result.count("testdir")) { modelPath.testDir = result["testdir"].as(); } @@ -1234,7 +1234,7 @@ bool CommonKit::json2protobuf(const char* jsonFile, const char* protoFile, MNN:: if (layerInfo.HasMember("method")) { newLayer->set_method((MNN::Compression::LayerQuantizeParams_QuantMethod)layerInfo["method"].GetInt()); } - + // Weight. auto weights_ = layerInfo["weight"].GetArray(); for (auto w = weights_.begin(); w != weights_.end(); ++w) { @@ -1255,7 +1255,7 @@ bool CommonKit::json2protobuf(const char* jsonFile, const char* protoFile, MNN:: weight->add_scales(scale[k].GetFloat()); } } - + // Input. auto inputs_ = layerInfo["input"].GetArray(); for (auto w = inputs_.begin(); w != inputs_.end(); ++w) { @@ -1276,7 +1276,7 @@ bool CommonKit::json2protobuf(const char* jsonFile, const char* protoFile, MNN:: input->add_scales(scale[k].GetFloat()); } } - + // Output. auto outputs_ = layerInfo["output"].GetArray(); for (auto w = outputs_.begin(); w != outputs_.end(); ++w) { diff --git a/tools/converter/source/onnx/SigmoidOnnx.cpp b/tools/converter/source/onnx/SigmoidOnnx.cpp index de3515ac3..33a36e430 100644 --- a/tools/converter/source/onnx/SigmoidOnnx.cpp +++ b/tools/converter/source/onnx/SigmoidOnnx.cpp @@ -11,15 +11,18 @@ DECLARE_OP_CONVERTER(SigmoidOnnx); MNN::OpType SigmoidOnnx::opType() { - return MNN::OpType_Sigmoid; + return MNN::OpType_UnaryOp; } MNN::OpParameter SigmoidOnnx::type() { - return MNN::OpParameter_NONE; + return MNN::OpParameter_UnaryOp; } void SigmoidOnnx::run(MNN::OpT *dstOp, const onnx::NodeProto *onnxNode, OnnxScope* scope) { + auto res = new MNN::UnaryOpT; + res->opType = MNN::UnaryOpOperation_SIGMOID;; + dstOp->main.value = res; return; } diff --git a/tools/converter/source/onnx/TanhOnnx.cpp b/tools/converter/source/onnx/TanhOnnx.cpp index 069d0d724..21b9be672 100644 --- a/tools/converter/source/onnx/TanhOnnx.cpp +++ b/tools/converter/source/onnx/TanhOnnx.cpp @@ -12,15 +12,17 @@ DECLARE_OP_CONVERTER(TanhOnnx); MNN::OpType TanhOnnx::opType() { - return MNN::OpType_TanH; + return MNN::OpType_UnaryOp; } MNN::OpParameter TanhOnnx::type() { - return MNN::OpParameter_NONE; + return MNN::OpParameter_UnaryOp; } void TanhOnnx::run(MNN::OpT* dstOp, const onnx::NodeProto* onnxNode, OnnxScope* scope) { - dstOp->main.value = nullptr; + auto res = new MNN::UnaryOpT; + res->opType = MNN::UnaryOpOperation_TANH; + dstOp->main.value = res; } REGISTER_CONVERTER(TanhOnnx, Tanh); diff --git a/tools/converter/source/optimizer/merge/FuseAttention.cpp b/tools/converter/source/optimizer/merge/FuseAttention.cpp new file mode 100644 index 000000000..b13132ac5 --- /dev/null +++ b/tools/converter/source/optimizer/merge/FuseAttention.cpp @@ -0,0 +1,226 @@ +// +// FuseAttention.cpp +// MNNConverter +// +// Created by MNN on 2024/03/18. +// Copyright © 2018, Alibaba Group Holding Limited +// +#include + +#include "../TemplateMerge.hpp" +#include "MNN/expr/ExprCreator.hpp" +#include "MNN_generated.h" +#include "MergeHelpers.hpp" + +namespace MNN { +namespace Express { + +class FuseAttention { +public: + FuseAttention(); +private: + VARP query, key, value, mask; +}; + +FuseAttention::FuseAttention() { + auto match = [this](EXPRP expr) -> bool { + auto config = Global::Get(); + if(!config->transformerFuse) { + return false; + } + // transpose + if (!helpers::IsReshape(expr)) { + return false; + } + + expr = expr->inputs().at(0)->expr().first; + if (!helpers::IsTranspose(expr)) { + return false; + } + + EXPRP x, y; + // softmax @ v + auto matmul = expr->inputs().at(0)->expr().first; + if (!helpers::IsMatMul(matmul)) { + return false; + } + + // transpose + y = matmul->inputs().at(1)->expr().first; + if (!helpers::IsTranspose(y)) { + return false; + } + // concat + y = y->inputs().at(0)->expr().first; + if (!helpers::IsConcat(y)) { + return false; + } + // value + value = y->inputs().at(1); + + // softmax + x = matmul->inputs().at(0)->expr().first; + if (helpers::IsCast(x)) { + x = x->inputs().at(0)->expr().first; + } + if (!helpers::IsSoftmax(x)) { + return false; + } + // where + x = x->inputs().at(0)->expr().first; + if (!helpers::IsSelect(x)) { + return false; + } + // mask + mask = x->inputs().at(0); + // div + x = x->inputs().at(1)->expr().first; + if (helpers::IsCast(x)) { + x = x->inputs().at(0)->expr().first; + } + if (!helpers::IsBinaryOp(x)) { + return false; + } + // q @ k + x = x->inputs().at(0)->expr().first; + if (!helpers::IsMatMul(x)) { + return false; + } + // transpose + auto z = x->inputs().at(0)->expr().first; + if (!helpers::IsTranspose(z)) { + return false; + } + // query + query = z->inputs().at(0); + + // transpose + y = x->inputs().at(1)->expr().first; + if (!helpers::IsTranspose(y)) { + return false; + } + // concat + y = y->inputs().at(0)->expr().first; + if (!helpers::IsConcat(y)) { + return false; + } + // key + key = y->inputs().at(1); + return true; + }; + + auto fold = [this](EXPRP expr) -> bool { + auto config = Global::Get(); + auto version = config->targetVersion; + if (version < 2.8f) { + // For target version < 2.8 , don't support fmha_v2 + return false; + } + + std::unique_ptr attention(new OpT); + attention->name = "Attention" + expr->name(); + attention->type = OpType_Attention; + attention->main.type = OpParameter_AttentionParam; + attention->main.value = new AttentionParamT; + + auto attention_expr = Variable::create(Expr::create(attention.get(), {query, key, value, mask}, 1)); + + attention_expr->setName(expr->name()); + Expr::replace(expr, attention_expr->expr().first); + return true /*modified*/; + }; + TemplateMerge::getInstance("Merge").insertTemplate("FuseAttention", match, fold); +} + +class RemovePastKeyValue { +public: + RemovePastKeyValue(); +private: + VARP kv_in; +}; + +RemovePastKeyValue::RemovePastKeyValue() { + auto match = [this](EXPRP expr) -> bool { + auto config = Global::Get(); + if(!config->transformerFuse) { + return false; + } + /* + llm: out <- stack [concat + unsqueeze] <- stack [concat + unsqueeze] <- concat <- gatherv2 <- gatherv2 <- in + block: out <------------------------------- stack [concat + unsqueeze] <- concat <- gatherv2 <------------- in + */ + if (!helpers::IsConcat(expr)) { + return false; + } + expr = expr->inputs().at(0)->expr().first; + if (!helpers::IsUnsqueeze(expr)) { + return false; + } + expr = expr->inputs().at(0)->expr().first; + if (!helpers::IsConcat(expr) && expr->inputs().size() == 2) { + return false; + } + expr = expr->inputs().at(0)->expr().first; + // llm model + if (helpers::IsUnsqueeze(expr)) { + // concat [past_k, k] + expr = expr->inputs().at(0)->expr().first; + if (!helpers::IsConcat(expr) && expr->inputs().size() == 2) { + return false; + } + // gatherv2 + expr = expr->inputs().at(0)->expr().first; + if (!helpers::IsGatherV2(expr)) { + return false; + } + // gatherv2 + expr = expr->inputs().at(0)->expr().first; + if (!helpers::IsGatherV2(expr)) { + return false; + } + kv_in = expr->inputs().at(0); + if (!kv_in->expr().first->inputs().empty()) { + return false; + } + return true; + } + // block model + if (helpers::IsGatherV2(expr)) { + kv_in = expr->inputs().at(0); + if (!kv_in->expr().first->inputs().empty()) { + return false; + } + return true; + } + return false; + }; + + auto fold = [this](EXPRP expr) -> bool { + auto config = Global::Get(); + auto version = config->targetVersion; + if (version < 2.8f) { + // For target version < 2.8 , don't support fmha_v2 + return false; + } + + // past-kv remove + std::unique_ptr reshape(new OpT); + reshape->name = expr->name(); + reshape->type = OpType_Reshape; + reshape->main.type = OpParameter_Reshape; + auto reshape_t = new ReshapeT; + reshape_t->dims = {-1}; + reshape->main.value = reshape_t; + auto copy_expr = Variable::create(Expr::create(reshape.get(), {kv_in}, 1)); + Expr::replace(expr, copy_expr->expr().first); + return true /*modified*/; + }; + TemplateMerge::getInstance("Merge").insertTemplate("RemovePastKeyValue", match, fold); +} + + +static FuseAttention g_fuse_attenion; +static RemovePastKeyValue g_remove_kv; + +} // namespace Express +} // namespace MNN diff --git a/tools/converter/source/optimizer/merge/MergeHelpers.cpp b/tools/converter/source/optimizer/merge/MergeHelpers.cpp index 3327b7dac..b49201c0e 100644 --- a/tools/converter/source/optimizer/merge/MergeHelpers.cpp +++ b/tools/converter/source/optimizer/merge/MergeHelpers.cpp @@ -71,6 +71,16 @@ bool IsSoftmax(EXPRP expr) { return op && op->type() == OpType_Softmax; } +bool IsSelect(EXPRP expr) { + const Op* op = expr->get(); + return op && op->type() == OpType_Select; +} + +bool IsGatherV2(EXPRP expr) { + const Op* op = expr->get(); + return op && op->type() == OpType_GatherV2; +} + bool IsSlice(EXPRP expr) { const Op* op = expr->get(); return op && (op->type() == OpType_Slice || op->type() == OpType_StridedSlice || op->type() == OpType_SliceTf); diff --git a/tools/converter/source/optimizer/merge/MergeHelpers.hpp b/tools/converter/source/optimizer/merge/MergeHelpers.hpp index e2fea0a4a..bfc720c97 100644 --- a/tools/converter/source/optimizer/merge/MergeHelpers.hpp +++ b/tools/converter/source/optimizer/merge/MergeHelpers.hpp @@ -28,6 +28,8 @@ bool IsScatterNd(Express::EXPRP expr); bool IsMatMul(Express::EXPRP expr); bool IsSoftmax(Express::EXPRP expr); bool IsSlice(Express::EXPRP expr); +bool IsSelect(Express::EXPRP expr); +bool IsGatherV2(Express::EXPRP expr); bool IsBinaryAdd(Express::EXPRP expr); bool IsBinarySub(Express::EXPRP expr); diff --git a/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp b/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp index 648c11d2a..f902a64f0 100644 --- a/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp +++ b/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp @@ -183,7 +183,7 @@ class OnnxInstanceNormalTransform : public OnnxExtraManager::Transform { layerNormOp->main.type = OpParameter_LayerNorm; { auto param = layerNormOp->main.AsLayerNorm(); - param->axis = {2}; // Layernorm only need axis's size as 1 + param->axis = {-1}; // Layernorm only need axis's size as 1 param->epsilon = epsilon; param->group = 1; } diff --git a/tools/converter/source/tflite/liteConverter.cpp b/tools/converter/source/tflite/liteConverter.cpp index 33faab79c..49d307e28 100644 --- a/tools/converter/source/tflite/liteConverter.cpp +++ b/tools/converter/source/tflite/liteConverter.cpp @@ -7,7 +7,7 @@ // #include - +#include #include "logkit.h" #include "liteConverter.hpp" @@ -69,21 +69,61 @@ static void _converteConstantDataToMNNConstantNode( MNNNetT->tensorName.emplace_back(mnnConstantOp->name); MNNNetT->oplists.emplace_back(std::move(mnnConstantOp)); } +template +void convert(const SRC* s, DST* d, size_t sizeInBytes) { + auto size = sizeInBytes / sizeof(SRC); + for (size_t i=0; i _getConvertFunction(tflite::TensorType type) { + switch (type) { + case tflite::TensorType_FLOAT64: + return [](const void* s, void* d, size_t size) { + convert((double*)s, (float*)d, size); + }; + case tflite::TensorType_UINT64: + return [](const void* s, void* d, size_t size) { + convert((uint64_t*)s, (int32_t*)d, size); + }; + case tflite::TensorType_INT16: + return [](const void* s, void* d, size_t size) { + convert((int16_t*)s, (int32_t*)d, size); + }; + case tflite::TensorType_INT64: + return [](const void* s, void* d, size_t size) { + convert((int64_t*)s, (int32_t*)d, size); + }; + default: + break; + } + return nullptr; +} static MNN::DataType _convertType(tflite::TensorType type) { if (type == tflite::TensorType_FLOAT32) { return MNN::DataType_DT_FLOAT; } + if (type == tflite::TensorType_FLOAT64) { + return MNN::DataType_DT_FLOAT; + } if (type == tflite::TensorType_INT8) { return MNN::DataType_DT_INT8; } if (type == tflite::TensorType_INT16) { - return MNN::DataType_DT_INT16; + return MNN::DataType_DT_INT32; + } + if (type == tflite::TensorType_INT32) { + return MNN::DataType_DT_INT32; + } + if (type == tflite::TensorType_INT64) { + return MNN::DataType_DT_INT32; } if (type == tflite::TensorType_UINT8) { return MNN::DataType_DT_UINT8; } - if (type == tflite::TensorType_INT32) { + if (type == tflite::TensorType_UINT64) { return MNN::DataType_DT_INT32; } if (type == tflite::TensorType_FLOAT16) { @@ -234,7 +274,12 @@ int tflite2MNNNet(const std::string inputModel, const std::string bizCode, default: break; } - ::memcpy(dst, buffer->data.data(), buffer->data.size()); + auto func = _getConvertFunction(tensor->type); + if (nullptr == func) { + ::memcpy(dst, buffer->data.data(), buffer->data.size()); + } else { + func(buffer->data.data(), dst, buffer->data.size()); + } MNNNetT->oplists.emplace_back(std::move(newOp)); } } diff --git a/tools/converter/source/torch/LayerNormTorch.cpp b/tools/converter/source/torch/LayerNormTorch.cpp index 02193553a..f6ed99976 100644 --- a/tools/converter/source/torch/LayerNormTorch.cpp +++ b/tools/converter/source/torch/LayerNormTorch.cpp @@ -32,7 +32,7 @@ void LayerNormTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchSco std::string opType = getRealOpType(node); if (opType == "group_norm") { param->group = getValue(inputs[1]); - param->axis = {1,2,3}; + param->axis = {-1}; // add scale op after layernorm { auto scaleName = dstOp->name + "/scale"; @@ -54,7 +54,7 @@ void LayerNormTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchSco } else { auto norm_shape = getValue>(inputs[1]); // TODO: convert norm_shape to axis - param->axis = {1}; + param->axis = {-1}; param->gamma = getValue(weight, shape); param->beta = getValue(bias, shape); } diff --git a/tools/cpp/CMakeLists.txt b/tools/cpp/CMakeLists.txt index 29461d638..eca3a884a 100644 --- a/tools/cpp/CMakeLists.txt +++ b/tools/cpp/CMakeLists.txt @@ -52,6 +52,9 @@ list(APPEND MNN_CPP_TOOLS testTrain.out) add_executable(fuseTest ${CMAKE_CURRENT_LIST_DIR}/fuseTest.cpp) list(APPEND MNN_CPP_TOOLS fuseTest) +add_executable(LoRA ${CMAKE_CURRENT_LIST_DIR}/LoRA.cpp) +list(APPEND MNN_CPP_TOOLS LoRA) + foreach(TARGET ${MNN_CPP_TOOLS}) target_link_libraries(${TARGET} ${MNN_DEPS}) if (MSVC) diff --git a/tools/cpp/ConvertToFullQuant.hpp b/tools/cpp/ConvertToFullQuant.hpp index a7bd45ce3..4d996756d 100644 --- a/tools/cpp/ConvertToFullQuant.hpp +++ b/tools/cpp/ConvertToFullQuant.hpp @@ -15,7 +15,7 @@ #include #include #include "MNN_generated.h" -#include "cpp/IDSTEncoder.hpp" +#include "core/IDSTEncoder.hpp" using namespace MNN; @@ -175,7 +175,7 @@ void convert(std::string modelFile) { outputOs << input.rdbuf(); netT = MNN::UnPackNet(outputOs.str().c_str()); auto net = netT.get(); - + std::vector netNeedEraseIndices; for (int i = 0; i < net->oplists.size(); i++) { auto& op = net->oplists[i]; diff --git a/tools/cpp/GpuInterTest.cpp b/tools/cpp/GpuInterTest.cpp index 0ac9d6e14..c92b698ce 100644 --- a/tools/cpp/GpuInterTest.cpp +++ b/tools/cpp/GpuInterTest.cpp @@ -13,7 +13,7 @@ #define MNN_OPEN_TIME_TRACE #include #include "rapidjson/document.h" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" #include #include #include @@ -208,7 +208,7 @@ int main(int argc, char *argv[]) { testMode = atoi(argv[3]); MNN_PRINT("Use extra forward type: %d(0:OpenCL 1:OpenGL)\n", testMode); } - + auto type = MNN_FORWARD_CPU; if (argc > 4) { type = (MNNForwardType)atoi(argv[4]); @@ -267,7 +267,7 @@ int main(int argc, char *argv[]) { } backendConfig.sharedContext = &DeviceContext; - + std::shared_ptr rtmgr(Executor::RuntimeManager::createRuntimeManager(config)); rtmgr->setCache(cacheFileName); std::shared_ptr net; diff --git a/tools/cpp/LoRA.cpp b/tools/cpp/LoRA.cpp new file mode 100644 index 000000000..c2fc7137f --- /dev/null +++ b/tools/cpp/LoRA.cpp @@ -0,0 +1,271 @@ +// +// LoRA.cpp +// MNN +// +// Created by MNN on 2024/03/15. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include "LoRA.hpp" +#include "core/CommonCompute.hpp" +#include "core/MemoryFormater.h" +#include "core/IDSTDecoder.hpp" +#include "core/IDSTEncoder.hpp" +#include "core/ConvolutionCommon.hpp" + +#include +#include +#include + +int SymmetricQuantizeWeight(const float* weight, const int size, int8_t* quantizedWeight, float* scale, + const int channels, float weightClampValue) { + const int channelStride = size / channels; + const int quantizedMaxValue = weightClampValue; + + for (int c = 0; c < channels; ++c) { + const auto weightChannelStart = weight + c * channelStride; + auto quantizedWeightChannelStart = quantizedWeight + c * channelStride; + auto minmaxValue = std::minmax_element(weightChannelStart, weightChannelStart + channelStride); + const float dataAbsMax = std::fmax(std::fabs(*minmaxValue.first), std::fabs(*minmaxValue.second)); + + float scaleDataToInt8 = 1.0f; + if (dataAbsMax == 0) { + scale[c] = 0.0f; + } else { + scale[c] = dataAbsMax / quantizedMaxValue; + scaleDataToInt8 = quantizedMaxValue / dataAbsMax; + } + + for (int i = 0; i < channelStride; ++i) { + const int32_t quantizedInt8Value = static_cast(roundf(weightChannelStart[i] * scaleDataToInt8)); + quantizedWeightChannelStart[i] = + std::min(quantizedMaxValue, std::max(-quantizedMaxValue, quantizedInt8Value)); + } + } + + return 0; +} + +std::unique_ptr LoRA::load_model(const char* name) { + std::ifstream inputFile(name, std::ios::binary); + inputFile.seekg(0, std::ios::end); + const auto size = inputFile.tellg(); + inputFile.seekg(0, std::ios::beg); + + char* buffer = new char[size]; + inputFile.read(buffer, size); + inputFile.close(); + auto net = MNN::UnPackNet(buffer); + delete[] buffer; + MNN_ASSERT(net->oplists.size() > 0); + return net; +} + +static float findAbsMax(const float *weights, const int count) { + float absMax = fabs(weights[0]); + for (int i = 1; i < count; i++) { + float value = fabs(weights[i]); + if (value > absMax) { + absMax = value; + } + } + + return absMax; +} + +static std::vector findMinMax(const float *weights, const int count) { + float min = weights[0]; + float max = weights[0]; + + for (int i = 1; i < count; i++) { + float value = weights[i]; + if (value > max) { + max = value; + } + if (value < min) { + min = value; + } + } + + return {min, max}; +} + + +LoRA::LoRA(const char* origin_model, const char* lora_model) { + mMNNNet = std::move(load_model(origin_model)); + mLoRANet = std::move(load_model(lora_model)); + mExternalFile.reset(new std::fstream(std::string(origin_model) + ".weight", std::ios::in | std::ios::out | std::ios::binary)); + if (mExternalFile->bad()) { + mExternalFile.reset(nullptr); + } +} + +LoRA::~LoRA() { +} + +std::vector split(const std::string& name, char delimiter) { + std::vector tokens; + std::string token; + std::istringstream tokenStream(name); + while (std::getline(tokenStream, token, delimiter)) { + tokens.push_back(token); + } + return tokens; +} + +inline MNN::Express::VARP OpT2Const(MNN::OpT* op) { + return MNN::Express::Variable::create(MNN::Express::Expr::create(op, {}, 1)); +} + +inline MNN::Express::VARP computeLoRA(MNN::OpT *lora_A, MNN::OpT *lora_B) { + auto A = MNN::Express::_Cast(OpT2Const(lora_A), halide_type_of()); + auto B = MNN::Express::_Cast(OpT2Const(lora_B), halide_type_of()); + auto scale = MNN::Express::_Scalar(4.0 * 5); + auto lora = MNN::Express::_Multiply(MNN::Express::_MatMul(B, A), scale); + // lora = MNN::Express::_Transpose(lora, {1, 0}); + return lora; +} + +void LoRA::apply_external(MNN::OpT* op, MNN::OpT* lora_A, MNN::OpT* lora_B) { + // lora origin weight + auto result = std::make_shared(); + auto param = op->main.AsConvolution2D(); + int ic = param->common->inputCount; + int oc = param->common->outputCount; + auto buffer_size = param->external[1]; + auto alpha_size = param->external[2]; + result->weight.reset(buffer_size); + result->alpha.reset(alpha_size / sizeof(float)); + mExternalFile->seekg(param->external[0]); + mExternalFile->read(reinterpret_cast(result->weight.get()), buffer_size); + mExternalFile->read(reinterpret_cast(result->alpha.get()), alpha_size); + auto& quan = param->quanParameter; + size_t weightLength = 0; + auto ptr = reinterpret_cast(result->weight.get()); + auto new_ptr = IDSTDecoder::ReadQuanData_c(ptr, &weightLength, result.get(), quan->shapeInt32); + result->weight.set(new_ptr, weightLength); + result->weightFloat.reset(weightLength); + // dequant to float + bool oldType4 = (quan->type == 4 && quan->aMin == 0 && std::abs(quan->quantScale) < 1e-6); + if (quan->readType != 0 || oldType4) { + result->asymmetric = true; + float clampMin = quan->aMin == 0 ? -128 : quan->aMin; + for (int o = 0; o < oc; ++o) { + float min = result->alpha.get()[2 * o]; + float alpha = result->alpha.get()[2 * o + 1]; + min = min - clampMin * alpha; + auto dstW = result->weightFloat.get() + o * ic; + auto srcW = result->weight.get() + o * ic; + for (int v=0; v < ic; ++v) { + dstW[v] = (float)srcW[v] * alpha + min; + } + } + } else { + result->asymmetric = false; + for (int o = 0; o < oc; ++o) { + float alpha = result->alpha.get()[o]; + auto dstW = result->weightFloat.get() + o * ic; + auto srcW = result->weight.get() + o * ic; + for (int v=0; v < ic; ++v) { + dstW[v] = (float)srcW[v] * alpha; + } + } + } + result->weight.release(); + result->alpha.release(); + auto weight = Express::_Const(result->weightFloat.get(), {oc, ic}); + auto lora = computeLoRA(lora_A, lora_B); + result->weightFloat.release(); + weight = Express::_Add(weight, lora); + // weight = Express::_Subtract(weight, lora); + // quant + int bits = 4; + float threshold = (float)(1 << (bits - 1)) - 1.0f; + auto clampMin = quan->aMin; + std::vector scales; + std::vector quantWeights; + if (result->asymmetric) { + scales.resize(oc*2); + for (int o = 0; o < oc; ++o) { + const float* ptr = weight->readMap() + o * ic; + auto minAndMax = findMinMax(ptr, ic); + float min = minAndMax[0]; + float max = minAndMax[1]; + float scale = (max - min) / (threshold - clampMin); + + scales[2*o] = min; + scales[2*o+1] = scale; + /* + for (int ii = 0; ii < partWeightSize; ii++) { + int8_t quantValue = int8_t(std::round((ptr[ii] - min) / scale + clampMin)); + quantWeights.emplace_back(quantValue); + } + */ + } + } + auto res = IDSTEncoder::encode(weight->readMap(), scales, ic, oc, result->asymmetric, /*quantWeights.data()*/nullptr, int(clampMin), bits, false); + mExternalFile->seekp(param->external[0]); + mExternalFile->write(reinterpret_cast(res->buffer.data()), buffer_size); + mExternalFile->write(reinterpret_cast(res->alpha.data()), alpha_size); +} + +void LoRA::apply_lora() { + std::set lora_keys; + std::map> loras; + for (int i = 0; i < mLoRANet->oplists.size(); i+= 2) { + auto& op_A = mLoRANet->oplists[i]; + auto& op_B = mLoRANet->oplists[i + 1]; + auto tokens = split(op_A->name, '/'); + auto layer = tokens[4]; + auto key = tokens[6]; + lora_keys.insert(key); + loras[layer + key] = std::make_pair(op_A.get(), op_B.get()); + } + for (auto& op : mMNNNet->oplists) { + if (op->type == MNN::OpType_Convolution) { + bool has_lora = false; + for (auto key : lora_keys) { + if (op->name.find(key) != std::string::npos) { + has_lora = true; + break; + } + } + if (!has_lora) continue; + auto tokens = split(op->name, '/'); + auto layer = split(tokens[1], '.')[1]; + auto key = tokens[3]; + auto lora = loras[layer + key]; + apply_external(op.get(), lora.first, lora.second); + } + } + mExternalFile->flush(); + mExternalFile->close(); +} + +int main(int argc, char *argv[]) { + if (argc < 3) { + MNN_ERROR("Usage: ./LoRA ${origin.mnn} ${lora.mnn}\n"); + return 0; + } + const char* origin_model = argv[1]; + const char* lora_model = argv[2]; + auto lora = std::unique_ptr(new LoRA(origin_model, lora_model)); + auto st = std::chrono::system_clock::now(); + lora->apply_lora(); + auto et = std::chrono::system_clock::now(); + auto lora_during = std::chrono::duration_cast(et - st).count() * 1e-6; + printf("### total time = %.2f s\n", lora_during); + return 0; +} diff --git a/tools/cpp/LoRA.hpp b/tools/cpp/LoRA.hpp new file mode 100644 index 000000000..2fecd587e --- /dev/null +++ b/tools/cpp/LoRA.hpp @@ -0,0 +1,32 @@ +// +// LoRA.hpp +// MNN +// +// Created by MNN on 2024/03/15. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef LORA_HPP +#define LORA_HPP + +#include "MNN_generated.h" + +class LoRA { +public: + LoRA(const char* originalModelFileName, const char* loraModelFileName); + ~LoRA(); + void* getBuffer() const; + const size_t getBufferSize() const; + void apply_lora(); + void revert_lora(); +private: + std::unique_ptr load_model(const char* name); + void apply_external(MNN::OpT* conv, MNN::OpT* lora_A, MNN::OpT* lora_B); +private: + LoRA(); + std::unique_ptr mMNNNet, mLoRANet; + std::unique_ptr mExternalFile; + void packMNNNet(); +}; + +#endif // LORA_HPP diff --git a/tools/cpp/ModuleBasic.cpp b/tools/cpp/ModuleBasic.cpp index e13f39b47..9dafd4c7e 100644 --- a/tools/cpp/ModuleBasic.cpp +++ b/tools/cpp/ModuleBasic.cpp @@ -13,7 +13,7 @@ #define MNN_OPEN_TIME_TRACE #include #include "rapidjson/document.h" -#include "common/MemoryFormater.h" +#include "core/MemoryFormater.h" #include #include #include diff --git a/tools/cpp/revertMNNModel.cpp b/tools/cpp/revertMNNModel.cpp index 3663267dc..fd3e17064 100644 --- a/tools/cpp/revertMNNModel.cpp +++ b/tools/cpp/revertMNNModel.cpp @@ -16,9 +16,9 @@ #include #include #include "revertMNNModel.hpp" -#include "common/CommonCompute.hpp" -#include "common/MemoryFormater.h" -#include "IDSTEncoder.hpp" +#include "core/CommonCompute.hpp" +#include "core/MemoryFormater.h" +#include "core/IDSTEncoder.hpp" #include "core/ConvolutionCommon.hpp" int SymmetricQuantizeWeight(const float* weight, const int size, int8_t* quantizedWeight, float* scale, @@ -98,7 +98,7 @@ void Revert::writeExtraDescribeTensor(float* scale, float* offset) { const float inputScale = *scale; const float outputScale = *scale; const int outputChannel = static_cast(op->outputIndexes.size()); - + auto param = op->main.AsConvolution2D(); float* originWeight = param->weight.data(); const int channels = param->common->outputCount; @@ -108,7 +108,7 @@ void Revert::writeExtraDescribeTensor(float* scale, float* offset) { param->common->inputCount = weightSize / (channels * param->common->kernelX * param->common->kernelY); std::vector quantizedWeight(weightSize); std::vector quantizedWeightScale(channels); - + if (originWeight[0] == 0.f && originWeight[1] == 0.f) { // Process weight is null. // Initialize originWeight std::uniform_real_distribution u(-200, 200); @@ -117,7 +117,7 @@ void Revert::writeExtraDescribeTensor(float* scale, float* offset) { originWeight[i] = u(e); } } - + SymmetricQuantizeWeight(originWeight, weightSize, quantizedWeight.data(), quantizedWeightScale.data(), channels, 127.0f); param->quanParameter = IDSTEncoder::encode(param->weight.data(), quantizedWeightScale, weightSize/channels, channels, false, quantizedWeight.data(), -127.0f); param->quanParameter->scaleIn = *scale; diff --git a/tools/quantization/calibration.cpp b/tools/quantization/calibration.cpp index 4c9046d00..32d13aaad 100644 --- a/tools/quantization/calibration.cpp +++ b/tools/quantization/calibration.cpp @@ -25,7 +25,7 @@ #include #include "Helper.hpp" #include "core/TensorUtils.hpp" -#include "cpp/IDSTEncoder.hpp" +#include "core/IDSTEncoder.hpp" #include #include @@ -298,7 +298,7 @@ std::vector Calibration::_getInputShape(std::string filename) { void Calibration::_resizeIfNeeded(std::string filename, bool force) { std::vector inputShape = _getInputShape(filename); - + if ((inputShape != _inputTensorDims && _featureQuantizeMethod == "KL") || force) { _inputTensorDims = inputShape; _interpreter->resizeTensor(_inputTensor, _inputTensorDims); @@ -689,7 +689,7 @@ void Calibration::_insertScale() { if (iter != _skip_quant_ops.end()) { continue; } - + if (opType != MNN::OpType_Convolution && opType != MNN::OpType_ConvolutionDepthwise && opType != MNN::OpType_Deconvolution) { continue; } @@ -981,7 +981,7 @@ void Calibration::_quantizeModelEMA() { } Variable::save(predicts, _destModelFile.c_str()); ConvertToFullQuant::convert(_destModelFile); - + std::unique_ptr netT; { std::ifstream input(_destModelFile, std::ifstream::in | std::ifstream::binary); @@ -1037,7 +1037,7 @@ void Calibration::dumpTensorScales(const std::string& modelFile) { auto op = iter->get(); const auto opType = op->type; const auto name = op->name; - + if (opType == MNN::OpType_Raster) { continue; } @@ -1055,10 +1055,10 @@ void Calibration::dumpTensorScales(const std::string& modelFile) { writer.StartArray(); for (int i = 0; i < inputSize; ++i) { const auto curInputIndex = inputIndexes[i]; - + auto input = _tensorMap[curInputIndex]; auto inputOpScale = _scales[input]; - + writer.StartObject(); writer.Key("tensorIndex"); writer.Int(curInputIndex); @@ -1077,7 +1077,7 @@ void Calibration::dumpTensorScales(const std::string& modelFile) { } writer.EndArray(); } - + auto& outputIndexes = op->outputIndexes; const int outputSize = static_cast(outputIndexes.size()); @@ -1086,10 +1086,10 @@ void Calibration::dumpTensorScales(const std::string& modelFile) { writer.StartArray(); for (int i = 0; i < outputSize; ++i) { const auto curOutputIndex = outputIndexes[i]; - + auto output = _tensorMap[curOutputIndex]; auto outputOpScale = _scales[output]; - + writer.StartObject(); writer.Key("tensorIndex"); writer.Int(curOutputIndex); diff --git a/tools/script/convertTfliteTest.py b/tools/script/convertTfliteTest.py index b06dc486f..f25257c1c 100755 --- a/tools/script/convertTfliteTest.py +++ b/tools/script/convertTfliteTest.py @@ -7,8 +7,10 @@ import os def run_cmd(args): - from subprocess import Popen, PIPE, STDOUT - stdout, _ = Popen(args, stdout=PIPE, stderr=STDOUT).communicate() + cmd = args[0] + for i in range(1, len(args)): + cmd += ' ' + args[i] + stdout = os.popen(cmd).read() global total_num total_num += 1 return stdout diff --git a/tools/script/register.py b/tools/script/register.py index e2dc6710e..3e679648c 100644 --- a/tools/script/register.py +++ b/tools/script/register.py @@ -96,6 +96,7 @@ def generateCPUFile(rootDir): cpuRegFile = os.path.join(cpuDir, "CPUOPRegister.cpp") funcNames = [] renderNames = [] + transformerNamse = [] def collectFile(fileNames, dirname): for fi in fileNames: f = os.path.join(dirname, fi) @@ -113,6 +114,8 @@ def collectFile(fileNames, dirname): funcName = '___' + l[0] + '__' + l[1] + '__' if lo.find('REGISTER_CPU_OP_CREATOR_RENDER') >=0: renderNames.append(funcName) + elif lo.find('REGISTER_CPU_OP_CREATOR_TRANSFORMER') >= 0: + transformerNamse.append(funcName) else: funcNames.append(funcName) fileNames = os.listdir(cpuDir) @@ -132,6 +135,10 @@ def collectFile(fileNames, dirname): for l in renderNames: f.write("extern void " + l + '();\n') f.write('#endif\n') + f.write('#ifdef ' + 'MNN_SUPPORT_TRANSFORMER_FUSE' + '\n') + for l in transformerNamse: + f.write("extern void " + l + '();\n') + f.write('#endif\n') f.write('void registerCPUOps() {\n') for l in funcNames: f.write(l+'();\n') @@ -139,7 +146,70 @@ def collectFile(fileNames, dirname): for l in renderNames: f.write(l+'();\n') f.write('#endif\n') + f.write('#ifdef ' + 'MNN_SUPPORT_TRANSFORMER_FUSE' + '\n') + for l in transformerNamse: + f.write(l+'();\n') + f.write('#endif\n') f.write("}\n}\n") + +def generateOPENCLFile(rootDir): + openclDir = os.path.join(rootDir, "source", "backend", "opencl") + openclBufferDir = os.path.join(rootDir, "source", "backend", "opencl", "execution", "buffer") + openclImageDir = os.path.join(rootDir, "source", "backend", "opencl", "execution", "image") + openclRegFile = os.path.join(openclDir, "core", "OpenCLOPRegister.cpp") + opNames = [] + transformerNamse = [] + def collectFile(fileNames, dirname): + end = '__IMAGE__' + if "buffer" in dirname: + end = '__BUFFER__' + for fi in fileNames: + f = os.path.join(dirname, fi) + if os.path.isdir(f): + continue + with open(f) as fileC: + c = fileC.read().split('\n') + c = list(filter(lambda l:l.find('REGISTER_OPENCL_OP_CREATOR')>=0, c)) + c = list(filter(lambda l:l.find('OpType')>=0, c)) + for lo in c: + l = lo.split('(')[1] + l = l.split(')')[0] + l = l.replace(' ', '') + l = l.split(',') + funcName = '___' + 'OpenCL' + l[0] + '__' + l[1] + end + if lo.find('REGISTER_OPENCL_OP_CREATOR_TRANSFORMER') >=0: + transformerNamse.append(funcName) + else: + opNames.append(funcName) + bufferFileNames = os.listdir(openclBufferDir) + print(bufferFileNames) + collectFile(bufferFileNames, openclBufferDir) + + imageFileNames = os.listdir(openclImageDir) + print(imageFileNames) + collectFile(imageFileNames, openclImageDir) + + with open(openclRegFile, 'w') as f: + f.write('// This file is generated by Shell for ops register\n') + f.write('#ifndef MNN_OPENCL_SEP_BUILD\n') + f.write('namespace MNN {\n') + f.write('namespace OpenCL {\n') + for l in opNames: + f.write("extern void " + l + '();\n') + f.write('\n') + f.write('#ifdef ' + 'MNN_SUPPORT_TRANSFORMER_FUSE' + '\n') + for l in transformerNamse: + f.write("extern void " + l + '();\n') + f.write('#endif\n') + f.write('void registerOpenCLOps() {\n') + for l in opNames: + f.write(l+'();\n') + f.write('#ifdef ' + 'MNN_SUPPORT_TRANSFORMER_FUSE' + '\n') + for l in transformerNamse: + f.write(l+'();\n') + f.write('#endif\n') + f.write("}\n}\n}\n") + f.write('#endif\n') def generateGeoFile(rootDir): geoDir = os.path.join(rootDir, "source", "geometry") @@ -257,3 +327,4 @@ def generateNNAPIFile(rootDir): generateGeoFile(sys.argv[1]) generateCoreMLFile(sys.argv[1]) generateNNAPIFile(sys.argv[1]) +generateOPENCLFile(sys.argv[1]) diff --git a/tools/train/source/nn/NN.cpp b/tools/train/source/nn/NN.cpp index 71941fba4..7594e7197 100644 --- a/tools/train/source/nn/NN.cpp +++ b/tools/train/source/nn/NN.cpp @@ -17,7 +17,7 @@ #include "RandomGenerator.hpp" #include "core/Macro.h" #include "math/WingoradGenerater.hpp" -#include "common/WinogradInt8Attr.hpp" +#include "core/WinogradInt8Attr.hpp" #include using namespace MNN::Express; @@ -592,11 +592,11 @@ class ConvBNReluFusedModule : public Module { mWeightClampValue = _Scalar(mLimit); // mInputClampValue = _Scalar(mLimit); // mOutputClampValue = _Scalar(mLimit); - + // lower bits only apply to weights mInputClampValue = _Scalar((float)(1 << (8 - 1)) - 1.0f); mOutputClampValue = _Scalar((float)(1 << (8 - 1)) - 1.0f); - + mInputMinPos = addParameter(mInputMin); mInputMaxPos = addParameter(mInputMax); mOutputMinPos = addParameter(mOutputMin); @@ -761,13 +761,13 @@ class ConvBNReluFusedModule : public Module { xx = _MatMul(_MatMul(_Transpose(srcTransH, {1, 0}), xx), srcTransW); // [alphaH * alphaW, ic, N * h_unit_num * w_unit_num] xx = _Reshape(_Transpose(xx, {2, 3, 1, 0}), {alphaH * alphaW, inChannel, -1}); - + auto inputPair = fakeQuantFeatureWithMinMax(xx, nullptr, nullptr, mInputClampValue, {1, 2, 3}); mWinogradTransInputMin = updateParameter(mWinogradTransInputMin, inputPair[1]); mWinogradTransInputMax = updateParameter(mWinogradTransInputMax, inputPair[2]); setParameter(mWinogradTransInputMin, mWinogradTransInputMinPos); setParameter(mWinogradTransInputMax, mWinogradTransInputMaxPos); - + auto wTransH = _Const(genH.G()->host(), {alphaH, kernelH}, NCHW); auto wTransW = _Const(genW.G()->host(), {alphaW, kernelW}, NCHW); // [oc, ic, alphaH, alphaW] @@ -775,12 +775,12 @@ class ConvBNReluFusedModule : public Module { // [alphaH * alphaW, oc, ic] ww = _Transpose(_Reshape(ww, {outChannel, inChannel, -1}), {2, 0, 1}); auto wwInfo = ww->getInfo(); - + // simulate weight quant auto weightScale = _Maximum(_ReduceMax(_Abs(ww), {2}, true), _Scalar(1E-6)) * _Reciprocal(mWeightClampValue); // ww = clamp(_Round(ww * _Reciprocal(weightScale)), mWeightClampValue) * weightScale; setParameter(weightScale, mWinogradTransWeightScalePos); - + // [alphaH * alphaW, oc, N * h_unit_num * w_unit_num] auto yy = _MatMul(ww, xx); // [oc, N * h_unit_num * w_unit_num, alphaH, alphaW] @@ -963,7 +963,7 @@ class ConvBNReluFusedModule : public Module { bias.resize(biasinfo->size); auto ptr = fusedBias->readMap(); ::memcpy(bias.data(), ptr, bias.size() * sizeof(float)); - + auto info = weightScale->getInfo(); weightScaleVector.resize(info->size); MNN_ASSERT(weightScaleVector.size() == bias.size()); @@ -973,7 +973,7 @@ class ConvBNReluFusedModule : public Module { } bool relu = mActivation == NN::None ? false : true; res = _Conv(std::move(weight), std::move(bias), std::move(weightScaleVector), _Convert(x, NC4HW4), mOption.channel, - mOption.kernelSize, mOption.padMode, mOption.stride, mOption.dilate, mGroup, mOption.pads, relu, + mOption.kernelSize, mOption.padMode, mOption.stride, mOption.dilate, mGroup, mOption.pads, relu, mInputScale->readMap()[0], mOutputScale->readMap()[0], inputZeroPoint, outputZeroPoint, -int8_t(mOutputClampValue->readMap()[0]), int8_t(mOutputClampValue->readMap()[0]), mWeightClampValue->readMap()[0], mAccumulateToInt16); @@ -982,7 +982,7 @@ class ConvBNReluFusedModule : public Module { auto inputScaleVar = scaleAndZeroPoint.first; auto inputZeroPointVar = scaleAndZeroPoint.second; auto weightScaleVar = parameters()[mWinogradTransWeightScalePos]; - + // Winograd Transformed input scale auto inputScaleInfo = inputScaleVar->getInfo(); auto inputScaleData = inputScaleVar->readMap(); @@ -1008,11 +1008,11 @@ class ConvBNReluFusedModule : public Module { return {}; } std::vector weightScales(weightScaleData, weightScaleData + weightScaleInfo->size); - + mWinogradAttr->attrs[0].inputScales = inputScales; mWinogradAttr->attrs[0].inputZeroPoints = inputZeroPoints; mWinogradAttr->attrs[0].weightScales = weightScales; - + res = mWinogradAttr->turnToWinogradConv(res); } res->setName(name()); diff --git a/transformers/diffusion/CMakeLists.txt b/transformers/diffusion/CMakeLists.txt new file mode 100644 index 000000000..b8b7460cb --- /dev/null +++ b/transformers/diffusion/CMakeLists.txt @@ -0,0 +1,14 @@ +cmake_minimum_required(VERSION 3.10) + +# source files +FILE(GLOB SRCS ${CMAKE_CURRENT_LIST_DIR}/*.cpp) +include_directories(${CMAKE_CURRENT_LIST_DIR}/../../tools/cv/include) +add_executable(diffusion_demo ${SRCS}) +IF(MNN_SEP_BUILD) +target_link_libraries(diffusion_demo MNN MNN_Express MNNOpenCV) +IF(MNN_OPENCL) + target_link_libraries(diffusion_demo MNN_CL) +ENDIF() +ELSE() +target_link_libraries(diffusion_demo MNN) +ENDIF() diff --git a/transformers/diffusion/main.cpp b/transformers/diffusion/main.cpp new file mode 100644 index 000000000..2b6fbf25b --- /dev/null +++ b/transformers/diffusion/main.cpp @@ -0,0 +1,19 @@ +#include +#include "pipeline.hpp" + +int main(int argc, const char* argv[]) { + if (argc < 3) { + printf("Usage: ./diffusion_demo "); + return 1; + } + auto sentence = argv[1]; + auto img_name = argv[2]; + auto resource_path = argv[3]; + + printf("input setnetce: %s\n", sentence); + printf("output img_name: %s\n", img_name); + printf("model resource path: %s\n", resource_path); + diffusion::Pipeline pipeline(resource_path); + pipeline.run(sentence, img_name); + return 0; +} diff --git a/transformers/diffusion/pipeline.cpp b/transformers/diffusion/pipeline.cpp new file mode 100644 index 000000000..3cd64e5f3 --- /dev/null +++ b/transformers/diffusion/pipeline.cpp @@ -0,0 +1,318 @@ +#include +#include +#include +#include "pipeline.hpp" +#include "tokenizer.hpp" +#define MNN_OPEN_TIME_TRACE +#include +#include +#include +#include +#include + +#if defined(_MSC_VER) +#include +#undef min +#undef max +#else +#include +#endif + +//#define MNN_DUMP_DATA + +using namespace CV; + +namespace diffusion { + +static inline int64_t getTime() { + uint64_t time; +#if defined(_MSC_VER) + LARGE_INTEGER now, freq; + QueryPerformanceCounter(&now); + QueryPerformanceFrequency(&freq); + uint64_t sec = now.QuadPart / freq.QuadPart; + uint64_t usec = (now.QuadPart % freq.QuadPart) * 1000000 / freq.QuadPart; + time = sec * 1000000 + usec; +#else + struct timeval tv; + gettimeofday(&tv, nullptr); + time = static_cast(tv.tv_sec) * 1000000 + tv.tv_usec; +#endif + return time; +} + +void display_progress(int cur, int total){ + putchar('\r'); + printf("["); + for (int i = 0; i < cur; i++) putchar('#'); + for (int i = 0; i < total - cur; i++) putchar('-'); + printf("]"); + fprintf(stdout, " [%3d%%]", cur * 100 / total); + if (cur == total) putchar('\n'); + fflush(stdout); +} + +Pipeline::Pipeline(std::string modelPath) : mModelPath(modelPath) { + std::ifstream alphaFile(modelPath + "/alphas.txt"); + int index = 0; + float alpha; + while (alphaFile >> alpha) { + mAlphas.push_back(alpha); + } +#if 0 // Different steps setting + mTimeSteps = { // 50 steps + 981, 961, 961, 941, 921, 901, 881, 861, 841, 821, 801, 781, 761, 741, + 721, 701, 681, 661, 641, 621, 601, 581, 561, 541, 521, 501, 481, 461, + 441, 421, 401, 381, 361, 341, 321, 301, 281, 261, 241, 221, 201, 181, + 161, 141, 121, 101, 81, 61, 41, 21, 1 + }; + mTimeSteps = { // 20 steps + 951, 901, 901, 851, 801, 751, 701, 651, 601, 551, 501, 451, + 401, 351, 301, 251, 201, 151, 101, 51, 1 + }; +#endif + mTimeSteps = { + 801, 601, 601, + 401, 201, 1 + }; +} + +bool Pipeline::load_modules(std::string modelPath) { + AUTOTIME; + + ScheduleConfig config; + BackendConfig backendConfig; +// config.type = MNN_FORWARD_CPU; + config.type = MNN_FORWARD_OPENCL; + config.numThread = 65; + backendConfig.precision = BackendConfig::Precision_Normal; + backendConfig.memory = BackendConfig::Memory_Low; + config.backendConfig = &backendConfig; + + + Module::Config module_config; + module_config.shapeMutable = false; + // module_config.rearrange = true; + runtime_manager_.reset(Executor::RuntimeManager::createRuntimeManager(config)); + + // if (config.type == MNN_FORWARD_OPENCL) { + // const char* cacheFileName = ".tempcache"; + // runtime_manager_->setCache(cacheFileName); + // } + + mModules.resize(3); + // load text_encoder model + { + std::string model_path = modelPath + "/text_encoder.mnn"; + mModules[0].reset(Module::load( + {"input_ids"}, {"last_hidden_state", "pooler_output"}, model_path.c_str(), runtime_manager_, &module_config)); + + } + // load unet model + { + std::string model_path = modelPath + "/unet.mnn"; + mModules[1].reset(Module::load( + {"sample", "timestep", "encoder_hidden_states"}, {"out_sample"}, model_path.c_str(), runtime_manager_, &module_config)); + } + // load vae_decoder model + { + std::string model_path = modelPath + "/vae_decoder.mnn"; + mModules[2].reset(Module::load( + {"latent_sample"}, {"sample"}, model_path.c_str(), runtime_manager_, &module_config)); + } + + auto exe = ExecutorScope::Current(); + exe->lazyEval = false; + exe->setGlobalExecutorConfig(config.type, backendConfig, config.numThread); + + return true; +} + +VARP Pipeline::text_encoder(const std::vector& ids) { + auto inputs_ids_ = _Const(ids.data(), {2, 512}, NCHW, halide_type_of()); + + auto outputs = mModules[0]->onForward({inputs_ids_}); + auto output = _Convert(outputs[0], NCHW); + output.fix(VARP::CONSTANT); + +#ifdef MNN_DUMP_DATA + auto xx = output->readMap(); + for(int i=0; i<10; i+=2) { + printf("%f %f ", xx[i], xx[i+512*768]); + } + printf("\n\n"); +#endif + return output; +} + +VARP Pipeline::step_plms(VARP sample, VARP model_output, int index) { + int timestep = mTimeSteps[index]; + int prev_timestep = 0; + if (index + 1 < mTimeSteps.size()) { + prev_timestep = mTimeSteps[index + 1]; + } + if (index != 1) { + if (mEts.size() >= 4) { + mEts[mEts.size() - 4] = nullptr; + } + mEts.push_back(model_output); + } else { + timestep = mTimeSteps[0]; + prev_timestep = mTimeSteps[1]; + } + int ets = mEts.size() - 1; + if (index == 0) { + mSample = sample; + } else if (index == 1) { + model_output = (model_output + mEts[ets]) * _Const(0.5); + sample = mSample; + } else if (ets == 1) { + model_output = (_Const(3.0) * mEts[ets] - mEts[ets-1]) * _Const(0.5); + } else if (ets == 2) { + model_output = (_Const(23.0) * mEts[ets] - _Const(16.0) * mEts[ets-1] + _Const(5.0) * mEts[ets-2]) * _Const(1.0 / 12.0); + } else if (ets >= 3) { + model_output = _Const(1. / 24.) * (_Const(55.0) * mEts[ets] - _Const(59.0) * mEts[ets-1] + _Const(37.0) * mEts[ets-2] - _Const(9.0) * mEts[ets-3]); + } + auto alpha_prod_t = mAlphas[timestep]; + auto alpha_prod_t_prev = mAlphas[prev_timestep]; + auto beta_prod_t = 1 - alpha_prod_t; + auto beta_prod_t_prev = 1 - alpha_prod_t_prev; + auto sample_coeff = std::sqrt(alpha_prod_t_prev / alpha_prod_t); + auto model_output_denom_coeff = alpha_prod_t * std::sqrt(beta_prod_t_prev) + std::sqrt(alpha_prod_t * beta_prod_t * alpha_prod_t_prev); + auto prev_sample = _Scalar(sample_coeff) * sample - _Scalar((alpha_prod_t_prev - alpha_prod_t)/model_output_denom_coeff) * model_output; + return prev_sample; +} + +VARP Pipeline::unet(VARP text_embeddings) { + mModules[0].reset(); + std::mt19937 rng; + rng.seed(std::random_device()()); + std::normal_distribution normal(0, 1); + std::vector initVal(16384); + +#ifdef MNN_DUMP_DATA + std::ostringstream fileName; + fileName << "random.txt"; + std::ifstream input(fileName.str().c_str()); + for (int i = 0; i < 16384; ++i) { + input >> initVal[i]; + } +#else + for (int i = 0; i < 16384; i++) { + initVal[i] = normal(rng); + } +#endif + + // VARP latentvar = _Const(initVal.data(), {1, 4, 64, 64}, NCHW); + VARP latentvar = _Input({1, 4, 64, 64}, NCHW, halide_type_of()); + memcpy((void *)latentvar->writeMap(), initVal.data(), 16384*sizeof(float)); + + VARP scalevar = _Input({1}, NCHW, halide_type_of()); + auto scaleptr = scalevar->writeMap(); + scaleptr[0] = 7.5; + + + VARP timestepvar = _Input({1}, NCHW, halide_type_of()); + auto floatVar = _Input({1}, NCHW, halide_type_of()); + auto ptr = floatVar->writeMap(); + + for (int i = 0; i < mTimeSteps.size(); i++) { + AUTOTIME; + display_progress(i, mTimeSteps.size()); + + auto t0 = getTime(); + + int timestep = mTimeSteps[i]; + + ptr[0] = timestep; + auto temp = _Cast(floatVar, halide_type_of()); + timestepvar->input(temp); + + VARP samplevar = _Concat({latentvar, latentvar}, 0); + auto outputs = mModules[1]->onForward({samplevar, timestepvar, text_embeddings}); + + auto output = _Convert(outputs[0], NCHW); + auto t1 = getTime(); + + auto noise_pred = output; + + auto splitvar = _Split(noise_pred, {2}, 0); + auto noise_pred_uncond = splitvar[0]; + auto noise_pred_text = splitvar[1]; + + noise_pred = scalevar * (noise_pred_text - noise_pred_uncond) + noise_pred_uncond; + auto t2 = getTime(); + + latentvar = step_plms(latentvar, noise_pred, i); + auto t3 = getTime(); + MNN_PRINT("Times: %f %f %f ms\n", (t1-t0)/ 1000.0f, (t2-t1)/ 1000.0f, (t3-t2)/ 1000.0f); + // latentvar.fix(VARP::CONSTANT); +#ifdef MNN_DUMP_DATA + auto xx = output->readMap(); + auto yy = samplevar->readMap(); + auto zz = text_embeddings->readMap(); + + for(int i=0; i<6; i+=2) { + printf("%f %f %f ", xx[i], yy[i], zz[i]); + } + for(int i=0; i<6; i+=2) { + printf("%f %f %f ", xx[16384+i], yy[16384+i], zz[512*768+i]); + } + printf("\n\n"); +#endif + } + latentvar.fix(VARP::CONSTANT); + +#ifdef MNN_DUMP_DATA + auto xx = latentvar->readMap(); + for(int i=0; i<10; i+=2) { + printf("%f ", xx[i]); + } + printf("\n\n"); +#endif + return latentvar; +} + +VARP Pipeline::vae_decoder(VARP latent) { + mModules[1].reset(); + latent = latent * _Const(1 / 0.18215); + + auto outputs = mModules[2]->onForward({latent}); + auto output = _Convert(outputs[0], NCHW); + +#ifdef MNN_DUMP_DATA + auto xx = output->readMap(); + for(int i=0; i<320; i+=32) { + printf("%f ", xx[i]); + } + printf("\n\n"); +#endif + + auto image = output; + image = _Relu6(image * _Const(0.5) + _Const(0.5), 0, 1); + image = _Squeeze(_Transpose(image, {0, 2, 3, 1})); + image = _Cast(_Round(image * _Const(255.0)), halide_type_of()); + image = cvtColor(image, COLOR_BGR2RGB); + image.fix(VARP::CONSTANT); + return image; +} + +bool Pipeline::run(const std::string& sentence, const std::string& img_name) { + diffusion::tokenizer tok(mModelPath + "/vocab.txt"); + load_modules(mModelPath); + + AUTOTIME; + auto ids = tok.sentence(sentence, 512); + auto text_embeddings = text_encoder(ids); + + auto latent = unet(text_embeddings); + auto image = vae_decoder(latent); + bool res = imwrite(img_name, image); + if (res) { + printf("SUCCESS! write to %s\n", img_name.c_str()); + } + // runtime_manager_->updateCache(); + return res; +} + +} diff --git a/transformers/diffusion/pipeline.hpp b/transformers/diffusion/pipeline.hpp new file mode 100644 index 000000000..b00cec0fb --- /dev/null +++ b/transformers/diffusion/pipeline.hpp @@ -0,0 +1,37 @@ +#include +#include +#include +#include +#include +#include +#include + +using namespace MNN; +using namespace MNN::Express; + +namespace diffusion { + +class Pipeline { +public: + Pipeline(std::string modelPath); + ~Pipeline() = default; + bool run(const std::string& sentence, const std::string& img_name); +private: + bool load_modules(std::string modelPath); + VARP step_plms(VARP sample, VARP model_output, int index); + VARP text_encoder(const std::vector& ids); + VARP unet(VARP text_embeddings); + VARP vae_decoder(VARP latent); +private: + std::shared_ptr runtime_manager_; + std::vector> mModules; + + std::string mModelPath; + // step_plms + std::vector mTimeSteps; + std::vector mAlphas; + std::vector mEts; + VARP mSample; +}; + +} diff --git a/transformers/diffusion/tokenizer.cpp b/transformers/diffusion/tokenizer.cpp new file mode 100644 index 000000000..02400e487 --- /dev/null +++ b/transformers/diffusion/tokenizer.cpp @@ -0,0 +1,56 @@ + +#include +#include +#include +#include "tokenizer.hpp" + +namespace diffusion { + +tokenizer::tokenizer(std::string dictPath) { + std::ifstream dictFile(dictPath); + int index = 0; + std::string word; + while (dictFile >> word) { + mWordDict.insert(std::make_pair(std::move(word), index++)); + } + mStartIdx = this->word("[CLS]"); + mEndIdx = this->word("[SEP]"); +} + +int tokenizer::word(std::string word) { + const auto& iter = mWordDict.find(word); + if (iter != mWordDict.end()) { + return iter->second; + } + return -1; +} + +std::vector tokenizer::sentence(std::string sentence, int maxlen) { + std::vector ids(maxlen * 2, 0); + // uncond + ids[0] = mStartIdx; + ids[1] = mEndIdx; + // ids + int idx = maxlen; + ids[idx++] = mStartIdx; + for (size_t i = 0; i < sentence.length();) { + int wordlen = 1; + if ((sentence[i] & 0xf8) == 0xf0) { + wordlen = 4; + } else if ((sentence[i] & 0xf0) == 0xe0) { + wordlen = 3; + } else if ((sentence[i] & 0xe0) == 0xc0) { + wordlen = 2; + } + if ((i + wordlen) > sentence.length()) { + wordlen = 1; + } + std::string word = sentence.substr(i, wordlen); + ids[idx++] = this->word(word); + i += wordlen; + } + ids[idx++] = mEndIdx; + return ids; +} + +} // diffusion \ No newline at end of file diff --git a/transformers/diffusion/tokenizer.hpp b/transformers/diffusion/tokenizer.hpp new file mode 100644 index 000000000..529aa05bf --- /dev/null +++ b/transformers/diffusion/tokenizer.hpp @@ -0,0 +1,18 @@ +#include +#include +#include + +namespace diffusion { + +class tokenizer { +public: + tokenizer(std::string dictPath); + ~tokenizer() {} + int word(std::string word); + std::vector sentence(std::string sentence, int maxlen = 0); +private: + int mStartIdx, mEndIdx; + std::unordered_map mWordDict; +}; + +} // diffusion \ No newline at end of file diff --git a/llm/CMakeLists.txt b/transformers/llm/engine/CMakeLists.txt similarity index 100% rename from llm/CMakeLists.txt rename to transformers/llm/engine/CMakeLists.txt diff --git a/llm/include/llm.hpp b/transformers/llm/engine/include/llm.hpp similarity index 98% rename from llm/include/llm.hpp rename to transformers/llm/engine/include/llm.hpp index d003ed106..3053b0f0c 100644 --- a/llm/include/llm.hpp +++ b/transformers/llm/engine/include/llm.hpp @@ -59,7 +59,8 @@ class MNN_PUBLIC Llm { visual_module_.reset(); runtime_manager_.reset(); } - static Llm* createLLM(const std::string& path, std::string model_type = "auto", int forwardType = 0); + // Default memory is low, precision is low + static Llm* createLLM(const std::string& path, std::string model_type = "auto", int forwardType = 0, int memoryprecison = 10); void load(const std::string& model_dir); void chat(); void trace(bool start); @@ -89,6 +90,7 @@ class MNN_PUBLIC Llm { std::vector tokenizer_encode(const std::string& input_str); std::string decode(int id); protected: + VARP inputs_embeds_, attention_mask_, position_ids_; // model configs bool is_single_ = false; bool is_disk_embedding_ = false; @@ -119,6 +121,7 @@ class MNN_PUBLIC Llm { // model dir std::string model_dir_; int mForwardType = 0; + int mPrecisionMemory = 0; }; // some llm models @@ -369,6 +372,7 @@ class Bge : public Embedding { virtual VARP gen_attention_mask(int seq_len) override; virtual VARP gen_position_ids(int seq_len) override; }; + // Embedding end #endif // LLM_hpp diff --git a/llm/include/tokenizer.hpp b/transformers/llm/engine/include/tokenizer.hpp similarity index 100% rename from llm/include/tokenizer.hpp rename to transformers/llm/engine/include/tokenizer.hpp diff --git a/llm/llm_demo.cpp b/transformers/llm/engine/llm_demo.cpp similarity index 89% rename from llm/llm_demo.cpp rename to transformers/llm/engine/llm_demo.cpp index 4a59954ae..37b7b44c4 100644 --- a/llm/llm_demo.cpp +++ b/transformers/llm/engine/llm_demo.cpp @@ -75,7 +75,7 @@ static void benchmark(Llm* llm, std::string prompt_file) { int main(int argc, const char* argv[]) { if (argc < 2) { - std::cout << "Usage: " << argv[0] << " model_dir " << std::endl; + std::cout << "Usage: " << argv[0] << " model_dir " << std::endl; return 0; } std::string model_dir = argv[1]; @@ -84,8 +84,13 @@ int main(int argc, const char* argv[]) { std::istringstream os(argv[2]); os >> forwardType; } + int memoryprecision = 10; + if (argc >= 4) { + std::istringstream os(argv[3]); + os >> memoryprecision; + } std::cout << "model path is " << model_dir << std::endl; - std::unique_ptr llm(Llm::createLLM(model_dir, "auto", forwardType)); + std::unique_ptr llm(Llm::createLLM(model_dir, "auto", forwardType, memoryprecision)); { AUTOTIME; llm->load(model_dir); @@ -94,10 +99,10 @@ int main(int argc, const char* argv[]) { AUTOTIME; trace_prepare(llm.get()); } - if (argc < 4) { + if (argc < 5) { llm->chat(); } - std::string prompt_file = argv[3]; + std::string prompt_file = argv[4]; benchmark(llm.get(), prompt_file); return 0; } diff --git a/llm/src/llm.cpp b/transformers/llm/engine/src/llm.cpp similarity index 96% rename from llm/src/llm.cpp rename to transformers/llm/engine/src/llm.cpp index 89c38fa1b..466f4ffa5 100644 --- a/llm/src/llm.cpp +++ b/transformers/llm/engine/src/llm.cpp @@ -23,7 +23,7 @@ #endif // Llm start -Llm* Llm::createLLM(const std::string& path, std::string model_type, int forwardType) { +Llm* Llm::createLLM(const std::string& path, std::string model_type, int forwardType, int preicsionmemory) { auto size = path.size(); // end with '.mnn' is single model file, otherwise split block models @@ -97,6 +97,7 @@ Llm* Llm::createLLM(const std::string& path, std::string model_type, int forward } llm->mForwardType = forwardType; llm->is_single_ = is_single; + llm->mPrecisionMemory = preicsionmemory; std::cout << "### model name : "<< llm->model_name_ << std::endl; return llm; } @@ -228,8 +229,8 @@ void Llm::load(const std::string& model_dir) { } ExecutorScope::Current()->setGlobalExecutorConfig(MNN_FORWARD_CPU, cpuBackendConfig, config.numThread); - cpuBackendConfig.precision = BackendConfig::Precision_Low; - cpuBackendConfig.memory = BackendConfig::Memory_Low; + cpuBackendConfig.precision = (BackendConfig::PrecisionMode)(mPrecisionMemory % 4); + cpuBackendConfig.memory = (BackendConfig::MemoryMode)((mPrecisionMemory / 4) % 4); config.backendConfig = &cpuBackendConfig; runtime_manager_.reset(Executor::RuntimeManager::createRuntimeManager(config)); runtime_manager_->setHint(MNN::Interpreter::MEM_ALLOCATOR_TYPE, 0); @@ -318,6 +319,7 @@ void Llm::load(const std::string& model_dir) { decode_modules_.resize(modules_.size()); for (int v=0; v& input_ids) { return id; } +static inline bool needNewVar(VARP var, int axis, int seq_len) { + if (var == nullptr) { + return true; + } + if (var->getInfo()->dim[axis] != seq_len) { + return true; + } + return false; +} + VARP Llm::txt_embedding(const std::vector& input_ids) { if (!is_disk_embedding_) { // using model forward @@ -385,23 +397,26 @@ VARP Llm::txt_embedding(const std::vector& input_ids) { } AUTOTIME; // disk embedding to save memory - size_t seq_len = input_ids.size(); - auto embedding = _Input({static_cast(seq_len), 1, hidden_size_}, NCHW); + int seq_len = static_cast(input_ids.size()); + if (needNewVar(inputs_embeds_, 0, seq_len)) { + inputs_embeds_ = _Input({seq_len, 1, hidden_size_}, NCHW); + } size_t size = hidden_size_ * sizeof(int16_t); FILE* file = fopen(disk_embedding_file_.c_str(), "rb"); std::unique_ptr buffer(new int16_t[hidden_size_]); for (size_t i = 0; i < seq_len; i++) { fseek(file, input_ids[i] * size, SEEK_SET); fread(buffer.get(), 1, size, file); - auto ptr = embedding->writeMap() + i * hidden_size_ * 2; + auto ptr = inputs_embeds_->writeMap() + i * hidden_size_ * 2; for (int j = 0; j < hidden_size_; j++) { ptr[j * 2] = 0; ptr[j * 2 + 1] = buffer[j]; } } fclose(file); - return embedding; + return inputs_embeds_; } + void Llm::trace(bool start) { auto status = MNN::Interpreter::Session_Resize_Check; if (start) { @@ -544,19 +559,25 @@ std::vector Qwen_7b::tokenizer(const std::string& query) { } VARP Qwen_7b::gen_attention_mask(int seq_len) { - auto attention_mask = _Input({1, 1, seq_len, seq_len}, NCHW, halide_type_of()); - auto ptr = attention_mask->writeMap(); + if (needNewVar(attention_mask_, 2, seq_len)) { + attention_mask_ = _Input({1, 1, seq_len, seq_len}, NCHW, halide_type_of()); + } else { + return attention_mask_; + } + auto ptr = attention_mask_->writeMap(); for (int i = 0; i < seq_len; i++) { for (int j = 0; j < seq_len; j++) { ptr[seq_len * i + j] = j <= i; } } - return attention_mask; + return attention_mask_; } VARP Qwen_7b::gen_position_ids(int seq_len) { - auto position_ids = _Input({seq_len}, NCHW, halide_type_of()); - auto ptr = position_ids->writeMap(); + if (needNewVar(position_ids_, 0, seq_len)) { + position_ids_ = _Input({seq_len}, NCHW, halide_type_of()); + } + auto ptr = position_ids_->writeMap(); if (seq_len == 1) { ptr[0] = all_seq_len_; } else { @@ -564,7 +585,7 @@ VARP Qwen_7b::gen_position_ids(int seq_len) { ptr[i] = i; } } - return position_ids; + return position_ids_; } bool Qwen_7b::is_stop(int token_id) { diff --git a/llm/src/tokenizer.cpp b/transformers/llm/engine/src/tokenizer.cpp similarity index 100% rename from llm/src/tokenizer.cpp rename to transformers/llm/engine/src/tokenizer.cpp diff --git a/transformers/llm/export/README.md b/transformers/llm/export/README.md new file mode 100644 index 000000000..bc72b39da --- /dev/null +++ b/transformers/llm/export/README.md @@ -0,0 +1,159 @@ +# llm-export + +[English](./README_en.md) + +llm-export是一个llm模型导出工具，能够将llm模型导出为onnx和mnn模型。 + +- 🚀 均完成`onnxruntime`正确性测试 +- 🚀 优化原始代码，支持动态形状 +- 🚀 优化原始代码，减少常量部分 +- 🚀 使用[OnnxSlim](https://github.com/WeLoveAI/OnnxSlim)优化onnx模型，性能提升约5%; by [@inisis](https://github.com/inisis) +- 🚀 支持将lora权重导出为onnx和mnn + +## 模型支持与下载 +- [![Download][download-chatglm-6b-onnx]][release-chatglm-6b-onnx] +- [![Download][download-chatglm2-6b-onnx]][release-chatglm2-6b-onnx] +- [![Download][download-chatglm3-6b-onnx]][release-chatglm3-6b-onnx] +- [![Download][download-codegeex2-6b-onnx]][release-codegeex2-6b-onnx] +- [![Download][download-qwen-7b-chat-onnx]][release-qwen-7b-chat-onnx] +- [![Download][download-baichuan2-7b-chat-onnx]][release-baichuan2-7b-chat-onnx] +- [![Download][download-llama2-7b-chat-onnx]][release-llama2-7b-chat-onnx] +- [![Download][download-qwen-1.8b-chat-onnx]][release-qwen-1.8b-chat-onnx] +- [![Download][download-phi-2-onnx]][release-phi-2-onnx] +- [![Download][download-internlm-7b-onnx]][release-internlm-7b-onnx] +- [![Download][download-qwen-vl-onnx]][release-qwen-vl-onnx] +- [![Download][download-bge-large-zh-onnx]][release-bge-large-zh-onnx] +- [![Download][download-tinyllama-1.1b-chat-onnx]][release-tinyllama-1.1b-chat-onnx] +- [![Download][download-yi-6b-chat-onnx]][release-yi-6b-chat-onnx] +- [![Download][download-deepseek-7b-chat-onnx]][release-deepseek-7b-chat-onnx] +- [![Download][download-qwen1.5-0.5b-chat-onnx]][release-qwen1.5-0.5b-chat-onnx] +- [![Download][download-qwen1.5-1.8b-chat-onnx]][release-qwen1.5-1.8b-chat-onnx] +- [![Download][download-qwen1.5-4b-chat-onnx]][release-qwen1.5-4b-chat-onnx] +- [![Download][download-qwen1.5-7b-chat-onnx]][release-qwen1.5-7b-chat-onnx] +- [![Download][download-llama3-8b-instruct-onnx]][release-llama3-8b-instruct-onnx] + +[download-chatglm-6b-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/chatglm-6b-onnx/total +[download-chatglm2-6b-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/chatglm2-6b-onnx/total +[download-chatglm3-6b-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/chatglm3-6b-onnx/total +[download-codegeex2-6b-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/codegeex2-6b-onnx/total +[download-qwen-7b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/qwen-7b-chat-onnx/total +[download-baichuan2-7b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/baichuan2-7b-chat-onnx/total +[download-llama2-7b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/llama2-7b-chat-onnx/total +[download-qwen-1.8b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/qwen-1.8b-onnx/total +[download-phi-2-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/phi-2-onnx/total +[download-internlm-7b-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/internlm-7b-onnx/total +[download-qwen-vl-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/qwen-vl-onnx/total +[download-bge-large-zh-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/bge-large-zh-onnx/total +[download-tinyllama-1.1b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/tinyllama-1.1b-chat-onnx/total +[download-yi-6b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/yi-6b-chat-onnx/total +[download-deepseek-7b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/deepseek-7b-chat-onnx/total +[download-qwen1.5-0.5b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/qwen1.5-0.5b-chat-onnx/total +[download-qwen1.5-1.8b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/qwen1.5-1.8b-chat-onnx/total +[download-qwen1.5-4b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/qwen1.5-4b-chat-onnx/total +[download-qwen1.5-7b-chat-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/qwen1.5-7b-chat-onnx/total +[download-llama3-8b-instruct-onnx]: https://img.shields.io/github/downloads/wangzhaode/llm-export/llama3-8b-instruct-onnx/total +[release-chatglm-6b-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/chatglm-6b-onnx +[release-chatglm2-6b-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/chatglm2-6b-onnx +[release-chatglm3-6b-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/chatglm3-6b-onnx +[release-codegeex2-6b-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/codegeex2-6b-onnx +[release-qwen-7b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/qwen-7b-chat-onnx +[release-baichuan2-7b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/baichuan2-7b-chat-onnx +[release-llama2-7b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/llama2-7b-chat-onnx +[release-qwen-1.8b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/qwen-1.8b-onnx +[release-phi-2-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/phi-2-onnx +[release-internlm-7b-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/internlm-7b-onnx +[release-qwen-vl-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/qwen-vl-onnx +[release-bge-large-zh-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/bge-large-zh-onnx +[release-tinyllama-1.1b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/tinyllama-1.1b-chat-onnx +[release-yi-6b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/yi-6b-chat-onnx +[release-deepseek-7b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/deepseek-7b-chat-onnx +[release-qwen1.5-0.5b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/qwen1.5-0.5b-chat-onnx +[release-qwen1.5-1.8b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/qwen1.5-1.8b-chat-onnx +[release-qwen1.5-4b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/qwen1.5-4b-chat-onnx +[release-qwen1.5-7b-chat-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/qwen1.5-7b-chat-onnx +[release-llama3-8b-instruct-onnx]: https://github.com/wangzhaode/llm-export/releases/tag/llama3-8b-instruct-onnx + +## 用法 +1. 将该项目clone到本地 +```sh +git clone git@github.com:wangzhaode/llm-export.git +``` +2. 将需要导出的LLM项目clone到本地，如：chatglm2-6b +```sh +git clone https://huggingface.co/THUDM/chatglm2-6b +# 如果huggingface下载慢可以使用modelscope +git clone https://modelscope.cn/ZhipuAI/chatglm2-6b.git +``` +3. 执行LLMExporter导出模型 +```sh +cd mnn-llm +# 将chatglm2-6b分为embedding, blocks, lm分别导出为onnx并转换为mnn, 并导出tokenizer.txt +python llm_export.py \ + --path ../chatglm2-6b \ + --export_split \ + --export_token \ + --export_mnn \ + --onnx_path ./chatglm2-6b-onnx \ + --mnn_path ./chatglm2-6b-mnn +``` + +## 功能 +- 支持将模型完整导出为一个onnx模型，使用`--export` +- 支持将模型分段导出为多个模型，使用`--export_split` +- 支持导出模型的词表到一个文本文件，每行代表一个token；其中token使用base64编码；使用`--export_verbose` +- 支持导出模型的Embedding层为一个onnx模型，使用`--export_embed`，同时支持bf16格式，使用`--embed_bf16` +- 支持分层导出模型的block，使用`--export_blocks`导出全部层；使用`--export_block $id`导出指定层 +- 支持导出模型的lm_head层为一个onnx模型，使用`--export_lm` +- 支持导出多模态模型的visual模型为一个onnx模型，使用`--export_visual` +- 支持对模型进行对话测试，使用`--test $query`会返回llm的回复内容 +- 支持在导出onnx模型后使用onnxruntime对结果一致性进行校验，使用`--export_test` +- 支持将tokenizer导出为文本文件，使用`--export_token` +- 支持将导出的onnx模型转换为mnn模型，默认转换为非对称4bit量化，使用`--export_mnn` +- 指定导出路径使用`--onnx_path`和`--mnn_path` +- 默认会使用onnx-slim对onnx模型进行优化，跳过该步骤使用`--skip_slim` +- 支持合并lora权重后导出，指定lora权重的目录使用`--lora_path` + +## 参数 +``` +usage: llm_export.py [-h] --path PATH + [--type {chatglm-6b,chatglm2-6b,chatglm3-6b,codegeex2-6b,Qwen-7B-Chat,Qwen-1_8B-Chat,Qwen-1_8B,Qwen-VL-Chat,Qwen1_5-0_5B-Chat,Qwen1_5-1_8B-Chat,Qwen1_5-4B-Chat,Qwen1_5-7B-Chat,Baichuan2-7B-Chat,Llama-2-7b-chat-ms,Llama-3-8B-Instruct,internlm-chat-7b,TinyLlama-1_1B-Chat,Yi-6B-Chat,deepseek-llm-7b-chat,phi-2,bge-large-zh,lora}] + [--lora_path LORA_PATH] [--onnx_path ONNX_PATH] [--mnn_path MNN_PATH] [--export_mnn] [--export_verbose] [--export_test] [--test TEST] [--export] + [--export_split] [--export_token] [--export_embed] [--export_visual] [--export_lm] [--export_block EXPORT_BLOCK] [--export_blocks] [--embed_bin] + [--embed_bf16] [--skip_slim] + +llm_exporter + +optional arguments: + -h, --help show this help message and exit + --path PATH path(`str` or `os.PathLike`): + Can be either: + - A string, the *model id* of a pretrained model like `THUDM/chatglm-6b`. [TODO] + - A path to a *directory* clone from repo like `../chatglm-6b`. + --type {chatglm-6b,chatglm2-6b,chatglm3-6b,codegeex2-6b,Qwen-7B-Chat,Qwen-1_8B-Chat,Qwen-1_8B,Qwen-VL-Chat,Qwen1_5-0_5B-Chat,Qwen1_5-1_8B-Chat,Qwen1_5-4B-Chat,Qwen1_5-7B-Chat,Baichuan2-7B-Chat,Llama-2-7b-chat-ms,Llama-3-8B-Instruct,internlm-chat-7b,TinyLlama-1_1B-Chat,Yi-6B-Chat,deepseek-llm-7b-chat,phi-2,bge-large-zh,lora} + type(`str`, *optional*): + The pretrain llm model type. + --lora_path LORA_PATH + lora path, defaut is `None` mean not apply lora. + --onnx_path ONNX_PATH + export onnx model path, defaut is `./onnx`. + --mnn_path MNN_PATH export mnn model path, defaut is `./mnn`. + --export_mnn Whether or not to export mnn model after onnx. + --export_verbose Whether or not to export onnx with verbose. + --export_test Whether or not to export onnx with test using onnxruntime. + --test TEST test model inference with query `TEST`. + --export export model to an `onnx` model. + --export_split export model split to some `onnx` models: + - embedding model. + - block models. + - lm_head model. + --export_token export llm tokenizer to a txt file. + --export_embed export llm embedding to an `onnx` model. + --export_visual export llm visual model to an `onnx` model. + --export_lm export llm lm_head to an `onnx` model. + --export_block EXPORT_BLOCK + export llm block [id] to an `onnx` model. + --export_blocks export llm all blocks to `onnx` models. + --embed_bin export embedding weight as bin file with dtype `bfloat16` + --embed_bf16 using `bfloat16` replace `float32` in embedding. + --skip_slim Whether or not to skip onnx-slim. +``` diff --git a/transformers/llm/export/README_en.md b/transformers/llm/export/README_en.md new file mode 100644 index 000000000..9942c23f1 --- /dev/null +++ b/transformers/llm/export/README_en.md @@ -0,0 +1,92 @@ +# llm-export + +[中文](./README_en.md) + +llm-export is a tool for exporting llm models, capable of converting llm models into ONNX or MNN models. +- 🚀 All passed `onnxruntime` correctness tests +- 🚀 Optimized the original code to support dynamic shapes +- 🚀 Optimized the original code to reduce the constant portion +- 🚀 Using [OnnxSlim](https://github.com/WeLoveAI/OnnxSlim) slim onnx model，speed up 5%; by [@inisis](https://github.com/inisis) +- 🚀 Support export lora weight to onnx or MNN model + +## Model Support and Downloads + +## Usage +1. Clone this project locally +```sh +git clnoe git@github.com:wangzhaode/llm-export.git +``` +2. Clone the LLM project that you want to export locally, such as: chatglm2-6b +```sh +git clone https://huggingface.co/THUDM/chatglm2-6b +# If downloading from Hugging Face is slow, you can use ModelScope +git clone https://modelscope.cn/ZhipuAI/chatglm2-6b.git +``` +3. Execute LLMExporter to export the model +```sh +cd mnn-llm +# Divide chatglm2-6b into embedding, blocks, lm, export each as ONNX and convert to MNN, and also export tokenizer.txt +python llm_export.py \ + --path ../chatglm2-6b \ + --export_split \ + --export_token \ + --export_mnn \ + --onnx_path ./chatglm2-6b-onnx \ + --mnn_path ./chatglm2-6b-mnn +``` + +## Features +- Supports exporting the entire model as a single ONNX model, use --export +- Supports exporting the model in segments as multiple models, use --export_split +- Supports exporting the model's vocabulary to a text file, each line representing a token; tokens are encoded using base64, use --export_verbose +- Supports exporting the model's Embedding layer as an ONNX model, use --export_embed, also supports bf16 format, use --embed_bf16 +- Supports layered export of the model's blocks, use --export_blocks to export all layers; use --export_block $id to export a specified layer +- Supports exporting the model's lm_head layer as an ONNX model, use --export_lm +- Supports exporting the VL model's visual model as an ONNX model, use --export_visual +- Supports conducting a dialogue test on the model, using --test $query will return the llm's response +- Supports verifying the consistency of results using onnxruntime after exporting the ONNX model, use --export_test +- Supports exporting the tokenizer as a text file, use --export_token +- Supports converting the exported ONNX model to an MNN model, with default conversion to non-symmetric 4bit quantization, use --export_mnn +- Specify export paths using --onnx_path and --mnn_path +- Default using onnx-slim, skip using --skip_slim + +## Commad Args +``` +usage: llm_export.py [-h] --path PATH + [--type {chatglm-6b,chatglm2-6b,chatglm3-6b,codegeex2-6b,Qwen-7B-Chat,Qwen-1_8B-Chat,Qwen-VL-Chat,Baichuan2-7B-Chat,Llama-2-7b-chat-ms,internlm-chat-7b,TinyLlama-1_1B-Chat,Yi-6B-Chat,deepseek-llm-7b-chat,phi-2,bge-large-zh}] + [--onnx_path ONNX_PATH] [--mnn_path MNN_PATH] [--export_mnn] [--export_verbose] [--export_test] [--test TEST] [--export] [--export_split] [--export_token] [--export_embed] [--export_visual] [--export_lm] + [--export_block EXPORT_BLOCK] [--export_blocks] [--embed_bf16] [--skip_slim] + +llm_exporter + +optional arguments: + -h, --help show this help message and exit + --path PATH path(`str` or `os.PathLike`): + Can be either: + - A string, the *model id* of a pretrained model like `THUDM/chatglm-6b`. [TODO] + - A path to a *directory* clone from repo like `../chatglm-6b`. + --type {chatglm-6b,chatglm2-6b,chatglm3-6b,codegeex2-6b,Qwen-7B-Chat,Qwen-1_8B-Chat,Qwen-VL-Chat,Baichuan2-7B-Chat,Llama-2-7b-chat-ms,internlm-chat-7b,TinyLlama-1_1B-Chat,Yi-6B-Chat,deepseek-llm-7b-chat,phi-2,bge-large-zh} + type(`str`, *optional*): + The pretrain llm model type. + --onnx_path ONNX_PATH + export onnx model path, defaut is `./onnx`. + --mnn_path MNN_PATH export mnn model path, defaut is `./mnn`. + --export_mnn Whether or not to export mnn model after onnx. + --export_verbose Whether or not to export onnx with verbose. + --export_test Whether or not to export onnx with test using onnxruntime. + --test TEST test model inference with query `TEST`. + --export export model to an `onnx` model. + --export_split export model split to some `onnx` models: + - embedding model. + - block models. + - lm_head model. + --export_token export llm tokenizer to a txt file. + --export_embed export llm embedding to an `onnx` model. + --export_visual export llm visual model to an `onnx` model. + --export_lm export llm lm_head to an `onnx` model. + --export_block EXPORT_BLOCK + export llm block [id] to an `onnx` model. + --export_blocks export llm all blocks to `onnx` models. + --embed_bf16 using `bfloat16` replace `float32` in embedding. + --skip_slim Whether or not to skip onnx-slim. +``` diff --git a/transformers/llm/export/llm_export.py b/transformers/llm/export/llm_export.py new file mode 100644 index 000000000..3ee64ca77 --- /dev/null +++ b/transformers/llm/export/llm_export.py @@ -0,0 +1,1280 @@ +import os +import base64 +import glob +import shutil +import argparse +import torch +import numpy as np +from onnxslim import slim +import onnxruntime as ort +import sentencepiece as spm +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer +from peft import LoraConfig, TaskType, get_peft_model, PeftModel +try: + import _tools as MNNTools +except: + MNNTools = None + +def onnx2mnn(onnx_path, mnn_dir, quant_bit = 4, asymmetric = True, external_data = False, bizCode : str= None): + model_name, model_extension = os.path.splitext(os.path.basename(onnx_path)) + if model_extension != '.onnx': + return + mnn_name = model_name + '.mnn' + mnn_path = os.path.join(mnn_dir, mnn_name) + convert_args = [ + '', + '-f', + 'ONNX', + '--modelFile', + str(onnx_path), + '--MNNModel', + str(mnn_path), + '--weightQuantBits', + str(quant_bit), + ] + if asymmetric: + convert_args.append("--weightQuantAsymmetric") + if external_data: + convert_args.append("--saveExternalData") + if bizCode is not None: + convert_args.append("--bizCode") + convert_args.append(str(bizCode)) + MNNTools.mnnconvert(convert_args) + +# some wrapper class for export +class Embedding(torch.nn.Module): + def __init__(self, embed, using_bf16: bool = False): + super().__init__() + self.bf16 = using_bf16 + self.embed_dim = embed.weight.shape[-1] + if using_bf16: + # using bf16 embedding weight + self.embed = embed.bfloat16() + else: + self.embed = embed + + def forward(self, input_ids): + res = self.embed(input_ids) + if self.bf16: + res = res.float() + return res.view(-1, 1, self.embed_dim) + +class Lm(torch.nn.Module): + def __init__(self, lm): + super().__init__() + self.lm = lm + + def forward(self, hidden_states): + m_logits = self.lm(hidden_states) + token = torch.argmax(m_logits) + return token + +class LLM(torch.nn.Module): + ''' + Base class for all llm model. Inherits from [`torch.nn.Module`]. + ''' + + def __init__(self, args): + super().__init__() + self.quant_bit = 4 + self.asymmetric = True + self.onnx_path = args.onnx_path + self.mnn_path = args.mnn_path + if not os.path.exists(self.onnx_path): + os.makedirs(self.onnx_path) + if not os.path.exists(self.mnn_path): + os.makedirs(self.mnn_path) + self.export_mnn = args.export_mnn + self.export_verbose = args.export_verbose + self.export_test = args.export_test + # default is False, just set True when using below command: + # `python llm_export ../path --export --embed_bin` to export single model without embedding + self.without_embed = False + self.embed_bin = args.embed_bin + if self.embed_bin: + self.embed_bf16 = True + else: + self.embed_bf16 = args.embed_bf16 + self.skip_slim = args.skip_slim + tokenizer_model = os.path.join(args.path, 'tokenizer.model') + if os.path.exists(tokenizer_model): + self.sp_model = spm.SentencePieceProcessor(tokenizer_model) + else: + self.sp_model = None + merge_file = os.path.join(args.path, 'merges.txt') + if os.path.exists(merge_file): + self.merge_txt = merge_file + else: + self.merge_txt = None + self.stop_ids = [] + self.max_length = 1024 + self.hidden_size = 4096 + self.visual = None # defualt is not visual + self.lora_path = args.lora_path + self.load_hf(args.path) + self.load_model() + + def load_hf(self, model_path: str): + self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + self.model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).float().eval() + self.config = self.model.config + if self.lora_path is not None: + adapter = PeftModel.from_pretrained(self.model, model_id=self.lora_path) + self.model = adapter.merge_and_unload(progressbar=True) + + def load_model(self): + raise NotImplementedError + + def get_attention_mask(self) -> torch.Tensor: + raise NotImplementedError + + def get_position_ids(self) -> torch.Tensor: + raise NotImplementedError + + def export_vocab(self): + raise NotImplementedError + + def visual_embed(self, input_ids): + raise NotImplementedError + + def __embedding(self, input_ids): + if self.visual is not None and self.token_len == 0: + input_embeds = self.visual_embed(input_ids) + else: + input_embeds = self.embed(input_ids) + return input_embeds + + def __decode(self, hidden_states, attention_mask, position_ids, past_key_values): + presents = [] + for i in range(self.block_nums): + hidden_states, kv = self.blocks[i](hidden_states, attention_mask, position_ids, past_key_values[i]) + presents.append(kv) + token_id = self.lm(hidden_states).view(1) + presents = torch.stack(presents) + self.seq_len += 1 + self.token_len += 1 + return token_id, presents + + def forward(self, input_ids, attention_mask, position_ids, past_key_values): + if self.without_embed: + return self.__decode(input_ids, attention_mask, position_ids, past_key_values) + return self.__decode(self.__embedding(input_ids), attention_mask, position_ids, past_key_values) + + # some test functions + def build_prompt(self, query): + if hasattr(self.tokenizer, 'build_prompt'): + prompt = self.tokenizer.build_prompt(query) + else: + prompt = query + return prompt + + def str_to_ids(self, prompt): + input_ids = self.tokenizer(prompt, return_tensors="pt")['input_ids'] + return input_ids + + def id_to_str(self, token_id): + word = self.tokenizer._convert_id_to_token(int(token_id)) + word = self.tokenizer.convert_tokens_to_string([word]) + return word + + def response(self, query): + prompt = self.build_prompt(query) + input_ids = self.str_to_ids(prompt) + self.seq_len = input_ids.numel() + self.context_len = self.seq_len - 2 + self.token_len = 0 + past_key_values = [None for i in range(self.block_nums)] + token_id = input_ids + while self.token_len < self.max_length: + attention_mask = self.get_attention_mask() + position_ids = self.get_position_ids() + token_id, past_key_values = self.forward(token_id, attention_mask, position_ids, past_key_values) + if token_id == self.stop_id or token_id in self.stop_ids: + print("", end='\n') + break + word = self.id_to_str(token_id) + print(word, end="", flush=True) + + # some export functions + def assert_equal(self, torch_outs, onnx_outs): + if type(torch_outs) not in (list, tuple): + torch_outs = (torch_outs, ) + onnx_outs = (onnx_outs, ) + same = True + for orig, onnx in zip(torch_outs, onnx_outs): + orig = orig.detach().numpy() + if not np.allclose(orig, onnx, rtol=1e-3, atol=1e-3): + print('Error: onnx outputs dont match original. [shape = {}] onnx: {}, original: {}'.format(onnx.shape, onnx, orig)) + same = False + break + if same: + print('onnx test SUCCESS') + + def export_lm(self): + model = self.lm + hidden_states = torch.randn(1, self.hidden_size) + onnx_model = f'./{self.onnx_path}/lm.onnx' + torch.onnx.export(model, (hidden_states), + onnx_model, + verbose=self.export_verbose, + input_names=['hidden_states'], + output_names=['token_id'], + do_constant_folding=True, + opset_version=15) + if not self.skip_slim: + slim(onnx_model, output_model=onnx_model) + # test lm + if self.export_test: + original_outs = model(hidden_states) + ort_session = ort.InferenceSession(onnx_model, providers=['CPUExecutionProvider']) + inputs = { + 'hidden_states' : hidden_states.numpy(), + } + onnx_outs = ort_session.run(None, inputs) + self.assert_equal(original_outs, onnx_outs) + if self.export_mnn: + onnx2mnn(onnx_model, self.mnn_path, self.quant_bit, self.asymmetric) + + def export_visual(self): + if self.visual is None: + return + input_images = torch.randn((1, 3, self.image_size, self.image_size)) + model = self.visual + onnx_model = f'./{self.onnx_path}/visual.onnx' + torch.onnx.export(model, (input_images), + onnx_model, + verbose=self.export_verbose, + input_names=['input_images'], + output_names=['image_embeds'], + dynamic_axes={"input_images": { + 0: "size" + }}, + do_constant_folding=True, + opset_version=15) + if not self.skip_slim: + slim(onnx_model, output_model=onnx_model) + # test + if self.export_test: + original_outs = model(input_images) + ort_session = ort.InferenceSession(onnx_model, providers=['CPUExecutionProvider']) + inputs = { + 'input_images' : input_images.numpy(), + } + onnx_outs = ort_session.run(None, inputs)[0] + self.assert_equal(original_outs, onnx_outs) + if self.export_mnn: + onnx2mnn(onnx_model, self.mnn_path) + + def export_embed(self): + model = self.embed + if self.embed_bin: + import ctypes + tensor_data = model.embed.weight.data + data_ptr = tensor_data.untyped_storage().data_ptr() + buffer = (ctypes.c_byte * (tensor_data.numel() * 2)).from_address(data_ptr) + with open(f'./{self.mnn_path}/embeddings_bf16.bin', 'wb') as f: + f.write(buffer) + return + input_ids = torch.arange(3, dtype=torch.long) + onnx_model = f'./{self.onnx_path}/embedding.onnx' + torch.onnx.export(model, (input_ids), + onnx_model, + verbose=self.export_verbose, + input_names=['input_ids'], + output_names=['inputs_embeds'], + dynamic_axes={"input_ids": { + 0: "length" + }}, + do_constant_folding=True, + opset_version=15) + if not self.skip_slim: + slim(onnx_model, output_model=onnx_model) + # test + if self.export_test: + original_outs = model(input_ids) + ort_session = ort.InferenceSession(onnx_model, providers=['CPUExecutionProvider']) + inputs = { + 'input_ids' : input_ids.numpy(), + } + onnx_outs = ort_session.run(None, inputs) + self.assert_equal(original_outs, onnx_outs) + if self.export_mnn: + onnx2mnn(onnx_model, self.mnn_path) + + def export_block(self, block_id: int): + self.seq_len = 3 + self.token_len = 0 + inputs_embeds = torch.randn((self.seq_len, 1, self.hidden_size)) + attention_mask = self.get_attention_mask() + position_ids = self.get_position_ids() + past_key_values = torch.zeros(self.past_kv_shape[1:]) + model = self.blocks[block_id] + onnx_model = f'./{self.onnx_path}/block_{block_id}.onnx' + torch.onnx.export( + model, (inputs_embeds, attention_mask, position_ids, past_key_values), + onnx_model, + verbose=self.export_verbose, + input_names=[ + 'inputs_embeds', 'attention_mask', 'position_ids', 'past_key_values' + ], + output_names=['hidden_states', 'presents'], + dynamic_axes=self.block_dynamic_axes, + do_constant_folding=True, + opset_version=15) + if not self.skip_slim: + slim(onnx_model, output_model=onnx_model) + if self.export_test: + original_outs = model(inputs_embeds, attention_mask, position_ids, past_key_values) + ort_session = ort.InferenceSession(onnx_model, providers=['CPUExecutionProvider']) + inputs = { + 'inputs_embeds' : inputs_embeds.detach().numpy(), + 'attention_mask' : attention_mask.numpy(), + 'position_ids' : position_ids.numpy(), + 'past_key_values' : past_key_values.numpy() + } + onnx_outs = ort_session.run(None, inputs) + self.assert_equal(original_outs, onnx_outs) + if self.export_mnn: + onnx2mnn(onnx_model, self.mnn_path, self.quant_bit, self.asymmetric) + + def export_blocks(self): + for i in range(self.block_nums): + self.export_block(i) + + def export(self): + model = self + self.seq_len = 3 + self.token_len = 0 + input_ids = torch.arange(3, dtype=torch.long) + attention_mask = self.get_attention_mask() + position_ids = self.get_position_ids() + past_key_values = torch.zeros(self.past_kv_shape) + onnx_model = f'./{self.onnx_path}/llm.onnx' + if self.embed_bin: + self.without_embed = True + input_ids = self.__embedding(input_ids) + print('export start ...') + torch.onnx.export( + model, (input_ids, attention_mask, position_ids, past_key_values), + onnx_model, + verbose=self.export_verbose, + input_names=[ + 'input_ids', 'attention_mask', 'position_ids', 'past_key_values' + ], + output_names=['token_id', 'presents'], + dynamic_axes=self.model_dynamic_axes, + do_constant_folding=True, + opset_version=15) + print('export done!') + if not self.skip_slim: + slim(onnx_model, output_model=onnx_model) + if self.export_test: + # test + original_outs = model(input_ids, attention_mask, position_ids, past_key_values) + ort_session = ort.InferenceSession(onnx_model, providers=['CPUExecutionProvider']) + inputs = { + 'input_ids' : input_ids.detach().numpy(), + 'attention_mask' : attention_mask.numpy(), + 'position_ids' : position_ids.numpy(), + 'past_key_values' : past_key_values.numpy() + } + onnx_outs = ort_session.run(None, inputs) + self.assert_equal(original_outs, onnx_outs) + if self.export_mnn: + # single model is > 2G, using external_data + onnx2mnn(onnx_model, self.mnn_path, self.quant_bit, self.asymmetric, True) + if self.without_embed: + self.without_embed = False + + def export_tokenizer(self): + file_path = os.path.join(self.onnx_path, "tokenizer.txt") + if self.sp_model is not None: + # senetencepiece + print('# senetencepiece tokenier') + NORMAL = 1; UNKNOWN = 2; CONTROL = 3 + USER_DEFINED = 4; UNUSED = 5; BYTE = 6 + fp = open(file_path, "w", encoding="utf8") + for i in range(self.sp_model.GetPieceSize()): + token = self.sp_model.IdToPiece(i) + score = self.sp_model.GetScore(i) + type = NORMAL + if self.sp_model.IsUnknown(i): + type = UNKNOWN + elif self.sp_model.IsControl(i): + type = CONTROL + elif self.sp_model.IsUnused(i): + type = UNUSED + elif self.sp_model.IsByte(i): + type = BYTE + if self.model_name == 'Chatglm_6b': + if '' in token: token = '\n' + if '<|tab|>' in token: token = '\t' + if '<|blank_' in token: token = ' ' * int(token[8:token.find('|>')]) + if '▁' in token: token = token.replace('▁', ' ') + token_encode = base64.b64encode(token.encode("utf-8")).decode("utf8") + fp.write(f'{token_encode} {score} {type}\n') + fp.close() + elif hasattr(self.tokenizer, 'mergeable_ranks'): + print('# tiktoken tokenier') + # tikton + with open(file_path, "w", encoding="utf8") as fp: + for k, v in self.tokenizer.mergeable_ranks.items(): + line = base64.b64encode(k).decode("utf8") + "\n" + fp.write(line) + if hasattr(self.tokenizer, 'special_tokens'): + for k, v in self.tokenizer.special_tokens.items(): + line = base64.b64encode(k.encode("utf-8")).decode("utf8") + "\n" + fp.write(line) + elif self.merge_txt is not None: + # huggingface tokenizer + merge_list = [] + vocab = self.tokenizer.get_vocab() + vocab_list = ['' for i in range(len(vocab))] + # load vocab + for k, v in vocab.items(): + vocab_list[int(v)] = k + # load merge + with open(self.merge_txt, 'rt') as merge: + for line in merge.readlines(): + merge_list.append(line) + # write to tokenizer.txt + with open(file_path, "w", encoding="utf8") as fp: + fp.write(f'{len(vocab_list)} {len(merge_list)}\n') + for v in vocab_list: + fp.write(v + '\n') + for m in merge_list: + fp.write(m) + else: + # huggingface tokenizer + def unicode_to_byte(u: int): + if u >= 256 and u <= 288: + return u - 256 + if u >= 289 and u <= 322: + return u - 162 + if u == 323: + return 173 + if u == 65372: # | + return 124 + if u == 9601: # _ + return 95 + return u + with open(file_path, "w", encoding="utf8") as fp: + vocab = self.tokenizer.get_vocab() + vocab_list = ['' for i in range(len(vocab))] + for k, v in vocab.items(): + try: + vocab_list[int(v)] = bytes([unicode_to_byte(ord(c)) for c in k]).decode('utf-8', errors='ignore') + except: + vocab_list[int(v)] = k + for v in vocab_list: + line = base64.b64encode(v.encode('utf-8')).decode("utf8") + "\n" + fp.write(line) + + +# chatglm +class GLMBlock(torch.nn.Module): + def __init__(self, block, block_id, final_layernorm = None): + super().__init__() + self.block = block + self.block_id = block_id + self.final_layernorm = final_layernorm + + def forward(self, hidden_states, attention_mask, position_ids, past_kv): + hidden_states, presents = self.block(hidden_states, + position_ids, + attention_mask, + self.block_id, + past_kv, + use_cache=True) + if self.final_layernorm is not None: + hidden_states = self.final_layernorm(hidden_states) + hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) + if isinstance(presents, tuple): + presents = torch.stack(presents) + return hidden_states, presents + +class Chatglm_6b(LLM): + def __init__(self, args): + super().__init__(args) + self.model_name = 'Chatglm_6b' + + def load_model(self): + transformer = self.model.transformer + self.lm_ = self.model.lm_head + self.embed_ = transformer.word_embeddings + self.blocks_ = transformer.layers + self.final_layernorm_ = transformer.final_layernorm + # some wrapper + self.stop_id = self.tokenizer._convert_token_to_id(self.tokenizer.eos_token) + self.block_nums = len(self.blocks_) + self.lm = Lm(self.lm_) + # chatglm embedding and lm using same param, copy embedding when using bf16 + if self.embed_bf16: + import copy + embed_copy = copy.deepcopy(self.embed_) + self.embed = Embedding(embed_copy, self.embed_bf16) + else: + self.embed = Embedding(self.embed_, self.embed_bf16) + self.blocks = [GLMBlock(self.blocks_[i], i, self.final_layernorm_ if i == len(self.blocks_) - 1 else None) for i in range(self.block_nums)] + # some config for export + self.past_kv_shape = [28, 2, 0, 1, 32, 128] + self.block_dynamic_axes = { + "inputs_embeds" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 2: "seq_len" }, + "past_key_values" : { 1: "history_len" } + } + self.model_dynamic_axes = { + "input_ids" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 2: "seq_len" }, + "past_key_values" : { 2: "history_len" } + } + + def get_attention_mask(self) -> torch.Tensor: + if self.token_len: + return torch.zeros([1]).bool().reshape([1, 1, 1, 1]) + attention_mask = torch.zeros([self.seq_len, self.seq_len], dtype=torch.bool) + for i in range(self.seq_len): + attention_mask[i][-1] = True + attention_mask = attention_mask.reshape([1, 1, self.seq_len, self.seq_len]) + return attention_mask + + def get_position_ids(self) -> torch.Tensor: + if self.token_len: + return torch.tensor([1, self.seq_len - self.context_len]).reshape([1, 2, 1]) + position_ids_0 = torch.arange(self.seq_len, dtype=torch.long) + position_ids_1 = torch.zeros(self.seq_len, dtype=torch.long) + position_ids_1[-1] = 1 + position_ids = torch.stack([position_ids_0, position_ids_1]).view(1, 2, -1) + return position_ids + +# chatglm2 +class GLM2Block(torch.nn.Module): + def __init__(self, block, block_id, final_layernorm = None): + super().__init__() + self.block = block + self.block_id = block_id + self.final_layernorm = final_layernorm + self.hidden_size = 4096 + + def forward(self, hidden_states, attention_mask, position_ids, past_kv): + theta = 1.0 / (10000 ** (torch.arange(0, 64, 2, dtype=torch.float32) / 64)) + position_ids = position_ids.float().reshape(-1, 1) + idx_theta = position_ids * theta + rotary_pos_emb = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1).unsqueeze(0).contiguous() + hidden_states, presents = self.block(hidden_states, + attention_mask, + kv_cache=past_kv, + rotary_pos_emb=rotary_pos_emb) + if self.final_layernorm is not None: + hidden_states = self.final_layernorm(hidden_states) + hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) + if isinstance(presents, tuple): + presents = torch.stack(presents) + return hidden_states, presents + +class Chatglm2_6b(LLM): + def __init__(self, args): + super().__init__(args) + self.model_name = 'Chatglm2_6b' + if 'codegeex2-6b' in args.path: + self.model_name = 'Codegeex2_6b' + + def load_model(self): + transformer = self.model.transformer + self.lm_ = transformer.output_layer + self.embed_ = transformer.embedding.word_embeddings + self.blocks_ = transformer.encoder.layers + self.final_layernorm_ = transformer.encoder.final_layernorm + # some wrapper + self.stop_id = self.tokenizer.eos_token_id + if self.stop_id is None: + # codegeex2-6b + self.stop_id = self.tokenizer.tokenizer.eos_id + self.block_nums = len(self.blocks_) + self.embed = Embedding(self.embed_, self.embed_bf16) + self.lm = Lm(self.lm_) + self.blocks = [GLM2Block(self.blocks_[i], i, self.final_layernorm_ if i == len(self.blocks_) - 1 else None) for i in range(self.block_nums)] + # some config for export + self.past_kv_shape = [28, 2, 0, 1, 2, 128] + self.block_dynamic_axes = { + "inputs_embeds" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 1: "history_len" } + } + self.model_dynamic_axes = { + "input_ids" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 2: "history_len" } + } + + def get_attention_mask(self) -> torch.Tensor: + if self.token_len: + return torch.zeros([1, 1, 1, 1]).bool() + attention_mask = ~torch.tril(torch.ones([1, 1, self.seq_len, self.seq_len]).bool()) + return attention_mask + + def get_position_ids(self) -> torch.Tensor: + if self.token_len: + return torch.tensor([self.token_len], dtype=torch.long) + return torch.arange(self.seq_len, dtype=torch.long) + +# chatglm3 +class Chatglm3_6b(Chatglm2_6b): + def __init__(self, args): + super().__init__(args) + self.model_name = 'Chatglm3_6b' + + def build_prompt(self, query): + return f'<|user|>\n{query}\n<|assistant|>\n' + +# qwen +class QWENBlock(torch.nn.Module): + def __init__(self, name, block, block_id, hidden_size, final_layernorm = None): + super().__init__() + self.name = name + self.block = block + self.block_id = block_id + self.final_layernorm = final_layernorm + self.hidden_size = hidden_size + + def forward(self, hidden_states, attention_mask, position_ids, past_kv): + theta = 1.0 / (10000.0 ** (torch.arange(0, 128, 2, dtype=torch.float32) / 128)) + position_ids = position_ids.float().reshape(-1, 1) + idx_theta = position_ids * theta + rotary_pos_emb = torch.cat((idx_theta, idx_theta), dim=-1) + rotary_pos_emb = rotary_pos_emb.unsqueeze(1).unsqueeze(0) + if self.name != 'Qwen-7B': + rotary_pos_emb = torch.stack([torch.cos(rotary_pos_emb), torch.sin(rotary_pos_emb)]) + hidden_states = hidden_states.view(1, -1, self.hidden_size) + hidden_states, presents = self.block(hidden_states=hidden_states, + layer_past=past_kv, + attention_mask=attention_mask, + rotary_pos_emb=rotary_pos_emb, + use_cache=True) + if self.final_layernorm is not None: + hidden_states = self.final_layernorm(hidden_states) + hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) + if isinstance(presents, tuple): + presents = torch.stack(presents) + return hidden_states, presents + +class QWEN18Block(torch.nn.Module): + def __init__(self, block, block_id, hidden_size, final_layernorm = None): + super().__init__() + self.block = block + self.block_id = block_id + self.final_layernorm = final_layernorm + self.hidden_size = hidden_size + + def forward(self, hidden_states, attention_mask, position_ids, past_kv): + theta = 1.0 / (10000.0 ** (torch.arange(0, 128, 2, dtype=torch.float32) / 128)) + position_ids = position_ids.float().reshape(-1, 1) + idx_theta = position_ids * theta + rotary_pos_emb = torch.cat((idx_theta, idx_theta), dim=-1).unsqueeze(1).unsqueeze(0) + rotary_pos_emb = torch.stack([torch.cos(rotary_pos_emb), torch.sin(rotary_pos_emb)]) + hidden_states = hidden_states.view(1, -1, self.hidden_size) + hidden_states, presents = self.block(hidden_states, + rotary_pos_emb, + past_kv, + attention_mask, + use_cache=True) + if self.final_layernorm is not None: + hidden_states = self.final_layernorm(hidden_states) + hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) + if isinstance(presents, tuple): + presents = torch.stack(presents) + return hidden_states, presents + +class Qwen_Chat(LLM): + def __init__(self, args): + super().__init__(args) + + def load_model(self): + # Qwen models + self.model_name = 'Qwen-7B' + if '1_8' in model_path: + self.model_name = 'Qwen-1_8b' + if 'VL' in model_path: + self.model_name = 'Qwen-VL' + transformer = self.model.transformer + self.lm_ = self.model.lm_head + self.embed_ = transformer.wte + self.blocks_ = transformer.h + self.final_layernorm_ = transformer.ln_f + if hasattr(transformer, 'visual'): + self.visual = transformer.visual + self.image_start_id = transformer.config.visual['image_start_id'] + self.image_size = transformer.config.visual['image_size'] + # some wrapper + self.stop_id = self.tokenizer.im_end_id + self.block_nums = len(self.blocks_) + self.hidden_size = transformer.embed_dim + self.embed = Embedding(self.embed_, self.embed_bf16) + self.lm = Lm(self.lm_) + self.blocks = [QWENBlock(self.model_name, self.blocks_[i], i, self.hidden_size, self.final_layernorm_ if i == len(self.blocks_) - 1 else None) for i in range(self.block_nums)] + if self.block_nums == 32: + # qwen-7b, qwen-vl + self.past_kv_shape = [32, 2, 1, 0, 32, 128] + elif self.block_nums == 24: + # qwen-1.8b + self.past_kv_shape = [24, 2, 1, 0, 16, 128] + # some config for export + self.block_dynamic_axes = { + "inputs_embeds" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 2: "history_len" } + } + self.model_dynamic_axes = { + "input_ids" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 3: "history_len" } + } + + def build_prompt(self, query): + return f'\n<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n' + + def get_attention_mask(self) -> torch.Tensor: + if self.model_name == 'Qwen-VL': + if self.token_len: + return torch.zeros([1, 1, 1, self.seq_len], dtype=torch.float32) + return (1 - torch.tril(torch.ones([1, 1, self.seq_len, self.seq_len]))) * torch.finfo(torch.float32).min + if self.token_len: + return torch.ones([1, 1, 1, 1]).bool() + return torch.tril(torch.ones([1, 1, self.seq_len, self.seq_len]).bool()) + + def get_position_ids(self) -> torch.Tensor: + if self.token_len: + return torch.tensor([self.seq_len - 1], dtype=torch.long) + return torch.arange(self.seq_len, dtype=torch.long) + + def visual_embed(self, input_ids): + if not torch.any(input_ids == self.image_start_id): + return self.embed(input_ids) + bos_pos = torch.where(input_ids == self.image_start_id) + eos_pos = torch.where(input_ids == self.image_start_id + 1) + img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1) + images = [] + for i, a, b in img_pos: + image = input_ids[i][a + 1 : b - 1].tolist() + image = image[ : image.index(self.image_start_id + 2)] + images.append(bytes(image).decode('utf-8')) + images = self.visual.encode(images) + hidden_states = self.embed(input_ids).view(1, -1, self.hidden_size) + for idx, (i, a, b) in enumerate(img_pos): + hidden_states[i][a + 1 : b] = images[idx] + return hidden_states.view(-1, 1, self.hidden_size) + +class QWEN2Block(torch.nn.Module): + def __init__(self, name, block, block_id, hidden_size, head_dim, final_layernorm = None): + super().__init__() + self.name = name + self.block = block + self.block_id = block_id + self.final_layernorm = final_layernorm + self.hidden_size = hidden_size + self.head_dim = head_dim + + def forward(self, hidden_states, attention_mask, position_ids, past_kv): + theta = 1.0 / (10000.0 ** (torch.arange(0, self.head_dim, 2, dtype=torch.float32) / self.head_dim)) + position_ids = position_ids.float().reshape(-1, 1) + idx_theta = position_ids * theta + rotary_pos_emb = torch.cat((idx_theta, idx_theta), dim=-1) + rotary_pos_emb = rotary_pos_emb.unsqueeze(0).unsqueeze(0) + rotary_pos_emb = torch.stack([torch.cos(rotary_pos_emb), torch.sin(rotary_pos_emb)]) + hidden_states = hidden_states.view(1, -1, self.hidden_size) + hidden_states, presents = self.block(hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_value=past_kv, + rotary_pos_emb=rotary_pos_emb, + use_cache=True) + if self.final_layernorm is not None: + hidden_states = self.final_layernorm(hidden_states) + hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) + if isinstance(presents, tuple): + presents = torch.stack(presents) + # print('###', presents.shape) + return hidden_states, presents + +class Qwen2_Chat(LLM): + def __init__(self, args): + super().__init__(args) + + def load_model(self): + # Qwen2 models + self.model_name = 'Qwen2' + transformer = self.model.model + self.lm_ = self.model.lm_head + self.embed_ = transformer.embed_tokens + self.blocks_ = transformer.layers + self.final_layernorm_ = transformer.norm + # some wrapper + self.stop_id = self.tokenizer.eos_token_id + if hasattr(model, 'generation_config'): + self.stop_ids.append(self.stop_id) + for id in self.model.generation_config.eos_token_id: + self.stop_ids.append(id) + self.block_nums = self.config.num_hidden_layers + self.hidden_size = self.config.hidden_size + self.num_heads = self.config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.embed = Embedding(self.embed_, self.embed_bf16) + self.lm = Lm(self.lm_) + self.past_kv_shape = [self.block_nums, 2, 1, self.num_heads, 0, self.head_dim] + self.blocks = [QWEN2Block(self.model_name, self.blocks_[i], i, self.hidden_size, self.head_dim, self.final_layernorm_ if i == len(self.blocks_) - 1 else None) for i in range(self.block_nums)] + # some config for export + self.block_dynamic_axes = { + "inputs_embeds" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 2: "history_len" } + } + self.model_dynamic_axes = { + "input_ids" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 3: "history_len" } + } + + def build_prompt(self, query): + return f'<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n' + + def get_attention_mask(self) -> torch.Tensor: + if self.token_len: + return torch.zeros([1, 1, 1, self.seq_len], dtype=torch.float32) + return (1 - torch.tril(torch.ones([1, 1, self.seq_len, self.seq_len]))) * torch.finfo(torch.float32).min + + + def get_position_ids(self) -> torch.Tensor: + if self.token_len: + return torch.tensor([[self.seq_len - 1]], dtype=torch.long) + return torch.arange(self.seq_len, dtype=torch.long).unsqueeze(0) + + def visual_embed(self, input_ids): + if not torch.any(input_ids == self.image_start_id): + return self.embed(input_ids) + bos_pos = torch.where(input_ids == self.image_start_id) + eos_pos = torch.where(input_ids == self.image_start_id + 1) + img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1) + images = [] + for i, a, b in img_pos: + image = input_ids[i][a + 1 : b - 1].tolist() + image = image[ : image.index(self.image_start_id + 2)] + images.append(bytes(image).decode('utf-8')) + images = self.visual.encode(images) + hidden_states = self.embed(input_ids).view(1, -1, self.hidden_size) + for idx, (i, a, b) in enumerate(img_pos): + hidden_states[i][a + 1 : b] = images[idx] + return hidden_states.view(-1, 1, self.hidden_size) + +# llama2 +class LLAMA2Block(torch.nn.Module): + def __init__(self, block, block_id, hidden_size, final_layernorm = None): + super().__init__() + self.block = block + self.block_id = block_id + self.final_layernorm = final_layernorm + self.hidden_size = hidden_size + + def forward(self, hidden_states, attention_mask, position_ids, past_kv): + hidden_states = hidden_states.view(1, -1, self.hidden_size) + hidden_states, presents = self.block(hidden_states, + attention_mask, + position_ids, + past_kv, + use_cache=True) + if self.final_layernorm is not None: + hidden_states = self.final_layernorm(hidden_states) + hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) + if isinstance(presents, tuple): + presents = torch.stack(presents) + return hidden_states, presents + +class Llama2_7b_Chat(LLM): + def __init__(self, args): + self.model_name = 'Llama2_7b' + if 'Baichuan2' in args.path: + self.model_name = 'Baichuan2_7B' + if 'internlm' in args.path: + self.model_name = 'Internlm_7b' + if 'TinyLlama' in args.path: + self.model_name = 'TinyLlama' + if 'Yi' in args.path: + self.model_name = 'Yi' + if 'deepseek' in args.path: + self.model_name = 'deepseek' + if 'Llama-3' in args.path: + self.model_name = 'Llama3_8B' + super().__init__(args) + + def load_model(self): + self.config = self.model.config + transformer = self.model.model + self.lm_ = self.model.lm_head + self.embed_ = transformer.embed_tokens + self.blocks_ = transformer.layers + self.final_layernorm_ = transformer.norm + # some wrapper + self.hidden_size = self.embed_.weight.shape[-1] + self.stop_id = self.tokenizer.eos_token_id + if hasattr(model, 'generation_config'): + self.stop_ids.append(self.stop_id) + self.stop_ids.append(self.model.generation_config.eos_token_id) + if self.model_name == 'Llama3_8B': + self.stop_ids.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>")) + self.block_nums = len(self.blocks_) + self.embed = Embedding(self.embed_, self.embed_bf16) + self.lm = Lm(self.lm_) + self.blocks = [LLAMA2Block(self.blocks_[i], i, self.hidden_size, self.final_layernorm_ if i == len(self.blocks_) - 1 else None) for i in range(self.block_nums)] + self.block_nums = self.config.num_hidden_layers + self.hidden_size = self.config.hidden_size + self.num_attention_heads = self.config.num_attention_heads + self.head_dim = self.hidden_size // self.num_attention_heads + self.num_key_value_heads = self.config.num_key_value_heads + self.past_kv_shape = [self.block_nums, 2, 1, self.num_key_value_heads, 0, self.head_dim] + self.block_dynamic_axes = { + "inputs_embeds" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 3: "history_len" } + } + self.model_dynamic_axes = { + "input_ids" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 4: "history_len" } + } + + def build_prompt(self, query): + if 'Baichuan2' in self.model_name: + return f'{query}' + if 'Internlm_7b' in self.model_name: + return f'<|User|>:{query}\n<|Bot|>:' + if 'TinyLlama' in self.model_name: + return f'<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate\n<|user|>\n{query}\n<|assistant|>\n' + if 'Yi' in self.model_name: + return f'<|im_start|> user\n{query}<|im_end|>\n<|im_start|> assistant\n' + if 'deepseek' in self.model_name: + return f'<|begin▁of▁sentence|>User: {query}\nAssistant:' + if 'Llama3' in self.model_name: + return f'<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{query}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' + return f'[INST]{query}[/INST]' + + def get_attention_mask(self) -> torch.Tensor: + if self.token_len: + return torch.zeros([1, 1, 1, self.seq_len], dtype=torch.float32) + return (1 - torch.tril(torch.ones([1, 1, self.seq_len, self.seq_len]))) * torch.finfo(torch.float32).min + + def get_position_ids(self) -> torch.Tensor: + if self.token_len: + return torch.tensor([[self.seq_len - 1]], dtype=torch.long) + return torch.arange(self.seq_len, dtype=torch.long).unsqueeze(0) + +# phi-2 +class PHI2Block(torch.nn.Module): + def __init__(self, block, block_id, hidden_size): + super().__init__() + self.block = block + self.block_id = block_id + self.hidden_size = hidden_size + + def forward(self, hidden_states, attention_mask, position_ids, past_kv): + theta = 1.0 / (10000 ** (torch.arange(0, 32, 2, dtype=torch.float32) / 32)) + position_ids = position_ids.float().reshape(-1, 1) + idx_theta = position_ids * theta + rotary_pos_emb = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=0).contiguous() + hidden_states = hidden_states.view(1, -1, self.hidden_size) + hidden_states, presents = self.block(hidden_states, + past_kv, + rotary_pos_emb=rotary_pos_emb, + causal_mask=attention_mask + ) + if self.block_id == 31: + hidden_states = hidden_states[:, -1, :] + return hidden_states, presents + +class phi_2(LLM): + def __init__(self, args): + super().__init__(args) + self.model_name = 'phi-2' + self.asymmetric = False # TODO: some precision bug when using asymmetric + + def load_model(self): + transformer = self.model.transformer + self.lm_ = self.model.lm_head + self.embed_ = transformer.embd.wte + self.hidden_size = self.embed_.weight.shape[-1] + self.blocks_ = transformer.h + # self.final_layernorm_ = transformer.final_layernorm + # some wrapper + self.stop_id = self.tokenizer.eos_token_id + self.block_nums = len(self.blocks_) + self.embed = Embedding(self.embed_, self.embed_bf16) + self.lm = Lm(self.lm_) + self.blocks = [PHI2Block(self.blocks_[i], i, self.hidden_size) for i in range(self.block_nums)] + # some config for export + self.past_kv_shape = [len(self.blocks), 1, 0, 2, 32, 80] + self.block_dynamic_axes = { + "inputs_embeds" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 1: "history_len" } + } + self.model_dynamic_axes = { + "input_ids" : { 0: "seq_len" }, + "attention_mask" : { 2: "seq_len", 3: "seq_len" }, + "position_ids" : { 0: "seq_len" }, + "past_key_values" : { 2: "history_len" } + } + + def build_prompt(self, query): + return f'Instruct: {query}\nOutput:' + + def get_attention_mask(self) -> torch.Tensor: + if self.token_len: + return torch.zeros([1, 1, 1, 1]).bool() + attention_mask = ~torch.tril(torch.ones([1, 1, self.seq_len, self.seq_len]).bool()) + return attention_mask + + def get_position_ids(self) -> torch.Tensor: + if self.token_len: + return torch.tensor([[self.seq_len - 1]], dtype=torch.long) + return torch.arange(self.seq_len, dtype=torch.long).unsqueeze(0) + +# BGE is Embedding Model based Bert +class BGEBlock(torch.nn.Module): + def __init__(self, block, block_id, hidden_size): + super().__init__() + self.block = block + self.block_id = block_id + self.hidden_size = hidden_size + + def forward(self, hidden_states, attention_mask): + hidden_states = self.block(hidden_states, attention_mask)[0] + return hidden_states + +class bge(LLM): + def __init__(self, args): + super().__init__(args) + self.model_name = 'bge-large-zh' + + def forward(self, input_ids, position_ids, attention_mask): + input_ids = input_ids.view(1, -1) + token_type_ids = (1 - attention_mask).view(1, -1) + hidden_states = self.embed(input_ids, token_type_ids, position_ids)[0].unsqueeze(0) + for i in range(self.block_nums): + hidden_states = self.blocks[i](hidden_states, attention_mask) + # hidden_states = self.lm(hidden_states) # sentence_embeddings not need + sentence_embeddings = hidden_states[:, 0] + sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) + return sentence_embeddings + + def response(self, query): + self.eval() + input_ids = self.tokenizer(query)['input_ids'] + self.seq_len = len(input_ids) + input_ids = torch.tensor(input_ids) + position_ids = self.get_position_ids() + attention_mask = self.get_attention_mask() + res = self.forward(input_ids, position_ids, attention_mask) + return res + + def load_model(self): + transformer = self.model.encoder + self.lm_ = self.model.pooler + self.embed_ = self.model.embeddings + self.hidden_size = self.embed_.word_embeddings.weight.shape[-1] + self.blocks_ = transformer.layer + # some wrapper + self.stop_id = self.tokenizer.eos_token_id + self.block_nums = len(self.blocks_) + self.embed = self.embed_ + self.lm = self.lm_ + self.blocks = [BGEBlock(self.blocks_[i], i, self.hidden_size) for i in range(self.block_nums)] + # some config for export + self.model_dynamic_axes = { + "input_ids" : { 0: "seq_len" }, + "position_ids" : { 1: "seq_len" }, + "attention_mask" : { 3: "seq_len" } + } + + def export(self): + model = self.eval() + self.seq_len = 3 + input_ids = torch.arange(3, dtype=torch.long) + position_ids = self.get_position_ids() + attention_mask = self.get_attention_mask() + onnx_model = f'./{self.onnx_path}/bge.onnx' + torch.onnx.export( + model, (input_ids, position_ids, attention_mask), + onnx_model, + verbose=self.export_verbose, + input_names=[ + 'input_ids', + 'position_ids', + 'attention_mask' + ], + output_names=['sentence_embeddings'], + dynamic_axes=self.model_dynamic_axes, + do_constant_folding=True, + opset_version=15) + if not self.skip_slim: + slim(onnx_model, output_model=onnx_model) + if self.export_test: + self.seq_len = 4 + position_ids = self.get_position_ids() + input_ids = torch.tensor([ 101, 872, 1962, 102 ], dtype=torch.long) + attention_mask = self.get_attention_mask() + # test + original_outs = model(input_ids, position_ids, attention_mask) + ort_session = ort.InferenceSession(onnx_model, providers=['CPUExecutionProvider']) + inputs = { + 'input_ids' : input_ids.detach().numpy(), + 'position_ids' : position_ids.detach().numpy(), + 'attention_mask' : attention_mask.detach().numpy() + } + onnx_outs = ort_session.run(None, inputs)[0] + self.assert_equal(original_outs, onnx_outs) + + token_str = None + if False: # save tokenizer in mnn + self.export_tokenizer() + token_path = os.path.join(self.onnx_path, "tokenizer.txt") + token_str = open(token_path, 'rt').read() + + if self.export_mnn: + onnx2mnn(onnx_model, self.mnn_path, 8, True, bizCode=token_str) + + def get_position_ids(self) -> torch.Tensor: + return torch.arange(self.seq_len, dtype=torch.long).unsqueeze(0) + + def get_attention_mask(self) -> torch.Tensor: + return torch.ones([1, 1, 1, self.seq_len], dtype=torch.long) + +class LoraModule(torch.nn.Module): + def __init__(self, args): + super().__init__() + self.onnx_path = args.onnx_path + self.mnn_path = args.mnn_path + self.export_mnn = args.export_mnn + import peft + lora_weight = peft.load_peft_weights(args.path) + for k, v in lora_weight.items(): + k = k.replace('.', '/') + self.register_buffer(k, v.cpu()) + + def forward(self, dummpy): + return self._buffers + + def export(self): + onnx_model = f'./{self.onnx_path}/lora.onnx' + torch.onnx.export(self.eval(), torch.tensor([]), onnx_model) + if self.export_mnn: + onnx2mnn(onnx_model, self.mnn_path) + + +if __name__ == '__main__': + llm_models = { + 'chatglm-6b': Chatglm_6b, + 'chatglm2-6b': Chatglm2_6b, + 'chatglm3-6b': Chatglm3_6b, + 'codegeex2-6b': Chatglm2_6b, + 'Qwen-7B-Chat': Qwen_Chat, + 'Qwen-1_8B-Chat': Qwen_Chat, + 'Qwen-1_8B': Qwen_Chat, + 'Qwen-VL-Chat': Qwen_Chat, + 'Qwen1_5-0_5B-Chat': Qwen2_Chat, + 'Qwen1_5-1_8B-Chat': Qwen2_Chat, + 'Qwen1_5-4B-Chat': Qwen2_Chat, + 'Qwen1_5-7B-Chat': Qwen2_Chat, + 'Baichuan2-7B-Chat': Llama2_7b_Chat, + 'Llama-2-7b-chat-ms': Llama2_7b_Chat, + 'Llama-3-8B-Instruct': Llama2_7b_Chat, + 'internlm-chat-7b': Llama2_7b_Chat, + 'TinyLlama-1_1B-Chat': Llama2_7b_Chat, + 'Yi-6B-Chat': Llama2_7b_Chat, + 'deepseek-llm-7b-chat': Llama2_7b_Chat, + 'phi-2': phi_2, + 'bge-large-zh': bge, + 'lora': LoraModule + } + parser = argparse.ArgumentParser(description='llm_exporter', formatter_class=argparse.RawTextHelpFormatter) + parser.add_argument('--path', type=str, default='THUDM/chatglm-6b', required=True, + help='path(`str` or `os.PathLike`):\nCan be either:' + '\n\t- A string, the *model id* of a pretrained model like `THUDM/chatglm-6b`. [TODO]' + '\n\t- A path to a *directory* clone from repo like `../chatglm-6b`.') + parser.add_argument('--type', type=str, choices=llm_models.keys(), default=None, + help='type(`str`, *optional*):' + '\n\tThe pretrain llm model type.' + ) + parser.add_argument('--lora_path', type=str, default=None, help='lora path, defaut is `None` mean not apply lora.') + parser.add_argument('--onnx_path', type=str, default='./onnx', help='export onnx model path, defaut is `./onnx`.') + parser.add_argument('--mnn_path', type=str, default='./mnn', help='export mnn model path, defaut is `./mnn`.') + parser.add_argument('--export_mnn', action='store_true', default=False, help='Whether or not to export mnn model after onnx.') + parser.add_argument('--export_verbose', action='store_true', default=False, help='Whether or not to export onnx with verbose.') + parser.add_argument('--export_test', action='store_true', help='Whether or not to export onnx with test using onnxruntime.') + parser.add_argument('--test', type=str, help='test model inference with query `TEST`.') + parser.add_argument('--export', action='store_true', help='export model to an `onnx` model.') + parser.add_argument('--export_split', action='store_true', + help='export model split to some `onnx` models:' + '\n\t- embedding model.' + '\n\t- block models.' + '\n\t- lm_head model.' + ) + parser.add_argument('--export_token', action='store_true', help='export llm tokenizer to a txt file.') + parser.add_argument('--export_embed', action='store_true', help='export llm embedding to an `onnx` model.') + parser.add_argument('--export_visual', action='store_true', help='export llm visual model to an `onnx` model.') + parser.add_argument('--export_lm', action='store_true', help='export llm lm_head to an `onnx` model.') + parser.add_argument('--export_block', type=int, help='export llm block [id] to an `onnx` model.') + parser.add_argument('--export_blocks', action='store_true', help='export llm all blocks to `onnx` models.') + parser.add_argument('--embed_bin', action='store_true', help='export embedding weight as bin file with dtype `bfloat16`') + parser.add_argument('--embed_bf16', action='store_true', help='using `bfloat16` replace `float32` in embedding.') + parser.add_argument('--skip_slim', action='store_true', help='Whether or not to skip onnx-slim.') + + + args = parser.parse_args() + model_path = args.path + model_type = args.type + # not sepcify model type, using path + if model_type is None: + for model in llm_models: + if model in model_path: + model_type = model + if model_type is None: + raise RuntimeError('Please specify model type.') + + # copy modeling py file to pretrain model for export + for file in glob.glob(f'./llm_models/{model_type}/*'): + shutil.copy2(file, model_path) + + llm_exporter = llm_models[model_type](args) + + # some actions + if args.test is not None: + llm_exporter.response(args.test) + + if args.export: + llm_exporter.export() + + if args.export_token: + llm_exporter.export_tokenizer() + + if args.export_embed or args.export_split: + llm_exporter.export_embed() + + if args.export_visual or args.export_split: + llm_exporter.export_visual() + + if args.export_lm or args.export_split: + llm_exporter.export_lm() + + if args.export_blocks or args.export_split: + llm_exporter.export_blocks() + + if args.export_block is not None: + llm_exporter.export_block(args.export_block) diff --git a/transformers/llm/export/llm_models/Baichuan2-7B-Chat/modeling_baichuan.py b/transformers/llm/export/llm_models/Baichuan2-7B-Chat/modeling_baichuan.py new file mode 100755 index 000000000..9f2968cc4 --- /dev/null +++ b/transformers/llm/export/llm_models/Baichuan2-7B-Chat/modeling_baichuan.py @@ -0,0 +1,796 @@ +# Copyright 2023 Baichuan Inc. All Rights Reserved. + +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from .configuration_baichuan import BaichuanConfig +from .generation_utils import build_chat_input, TextIterStreamer + +import math +from typing import List, Optional, Tuple, Union +from threading import Thread + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from torch.nn import functional as F +from transformers import PreTrainedModel, PretrainedConfig +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.generation.utils import GenerationConfig +from transformers.utils import logging, ContextManagers + +import os +from contextlib import contextmanager +logger = logging.get_logger(__name__) + +try: + from xformers import ops as xops +except ImportError: + xops = None + logger.warning( + "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers." + ) + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + if len(mask.size()) == 3: + bsz, src_len, _ = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + expanded_mask = mask[:,None,:,:].expand(bsz, 1, tgt_len, src_len).to(dtype) + else: + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states + + +class RotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) + self.max_seq_len_cached = max_position_embeddings + t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32) + freqs = torch.outer(t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1) + self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32) + self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32) + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. + if seq_len > self.max_seq_len_cached: + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32) + freqs = torch.outer(t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1) + self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32).to(x.device) + self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32).to(x.device) + elif self.cos_cached.device != x.device: + self.cos_cached = self.cos_cached.to(x.device) + self.sin_cached = self.sin_cached.to(x.device) + return ( + self.cos_cached[:, :, :seq_len, ...], + self.sin_cached[:, :, :seq_len, ...], + ) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2:] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids): + cos = torch.squeeze(cos_) # [seq_len, dim] + sin = torch.squeeze(sin_) # [seq_len, dim] + # print(f'### cos.shape = {cos.shape}, position_ids.shape = {position_ids.shape}, cos[position_ids].shape = {cos[position_ids].shape}') + # cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + # sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + # print(f'### q.shape = {q.shape}, cos.shape = {cos.shape}') + cos = cos[position_ids] + sin = sin[position_ids] + q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin) + k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin) + return q_embed.to(q.dtype), k_embed.to(k.dtype) + + +class MLP(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + ): + super().__init__() + self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) + self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.act_fn = ACT2FN[hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +class Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + def __init__(self, config: BaichuanConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.max_position_embeddings = config.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def raw_atten(self, query_layer, key_layer, value_layer, attention_mask): + attn_weight = torch.softmax((query_layer @ key_layer.transpose(-2, -1) / math.sqrt(query_layer.size(-1))) + attention_mask, dim=-1) + return attn_weight @ value_layer + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + proj = self.W_pack(hidden_states) + proj = proj.reshape([1, -1, 3, 4096]).permute([2, 0, 1, 3]) + # proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2) + query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + if xops is not None and self.training: + attn_weights = None + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + attn_output = xops.memory_efficient_attention( + query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask() + ) + else: + ''' + with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True): + attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask) + ''' + attn_output = self.raw_atten(query_states, key_states, value_states, attention_mask) + attn_output = attn_output.transpose(1, 2) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class DecoderLayer(nn.Module): + def __init__(self, config: BaichuanConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = Attention(config=config) + self.mlp = MLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + ) + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +class BaichuanPreTrainedModel(PreTrainedModel): + config_class = BaichuanConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["DecoderLayer"] + _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, BaichuanModel): + module.gradient_checkpointing = value + + +class BaichuanModel(BaichuanPreTrainedModel): + def __init__(self, config: BaichuanConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class NormHead(nn.Module): + def __init__(self, hidden_size, vocab_size, bias=False): + super().__init__() + self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size))) + nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + self.first_flag = True + + def forward(self, hidden_states): + if self.training: + norm_weight = nn.functional.normalize(self.weight) + elif self.first_flag: + self.first_flag = False + self.weight = nn.Parameter(nn.functional.normalize(self.weight)) + norm_weight = self.weight + else: + norm_weight = self.weight + return nn.functional.linear(hidden_states, norm_weight) + +_init_weights = True +@contextmanager +def no_init_weights(_enable=True): + global _init_weights + old_init_weights = _init_weights + if _enable: + _init_weights = False + try: + yield + finally: + _init_weights = old_init_weights + +class BaichuanForCausalLM(BaichuanPreTrainedModel): + def __init__(self, config, *model_args, **model_kwargs): + super().__init__(config, *model_args, **model_kwargs) + self.model = BaichuanModel(config) + + self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False) + if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']: + try: + from .quantizer import quantize_offline, init_model_weight_int4 + except ImportError: + raise ImportError(f"Needs QLinear to run quantize.") + quantize_offline(self, 4) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @classmethod + def from_pretrained( + cls, + pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], + *model_args, + config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None, + cache_dir: Optional[Union[str, os.PathLike]] = None, + ignore_mismatched_sizes: bool = False, + force_download: bool = False, + local_files_only: bool = False, + token: Optional[Union[str, bool]] = None, + revision: str = "main", + use_safetensors: bool = None, + **kwargs, + ): + # Load config if we don't provide a configuration + if not isinstance(config, PretrainedConfig): + config_path = config if config is not None else pretrained_model_name_or_path + config, model_kwargs = cls.config_class.from_pretrained( + config_path, + cache_dir=cache_dir, + return_unused_kwargs=True, + force_download=force_download, + resume_download=False, + proxies=None, + local_files_only=local_files_only, + token=token, + revision=revision, + subfolder="", + _from_auto=False, + _from_pipeline=None, + **kwargs, + ) + else: + model_kwargs = kwargs + + if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']: + try: + from .quantizer import init_model_weight_int4 + from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map + from accelerate.utils import CustomDtype + from accelerate.utils import get_balanced_memory + except ImportError: + raise ImportError(f"Needs import model weight init func to run quantize.") + # Instantiate model. + init_contexts = [no_init_weights(_enable=True)] + init_contexts.append(init_empty_weights()) + with ContextManagers(init_contexts): + model = cls(config) + + model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin') + state_dict = torch.load(model_file, map_location="cpu") + model.is_quantized = True + + device_map = kwargs.pop("device_map", None) + torch_dtype = kwargs.pop("torch_dtype", None) + + kwargs = {"no_split_module_classes": model._no_split_modules} + target_dtype = CustomDtype.INT4 + max_memory = get_balanced_memory( + model, + dtype=target_dtype, + low_zero=(device_map == "balanced_low_0"), + max_memory=None, + **kwargs, + ) + kwargs["max_memory"] = max_memory + + device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs) + model = init_model_weight_int4(config, model, state_dict) + + # Set model in evaluation mode to deactivate DropOut modules by default + model.eval() + # If it is a model with generation capabilities, attempt to load the generation config + if model.can_generate(): + try: + model.generation_config = GenerationConfig.from_pretrained( + pretrained_model_name_or_path, + cache_dir=cache_dir, + force_download=force_download, + resume_download=False, + proxies=None, + local_files_only=local_files_only, + token=token, + revision=revision, + subfolder="", + _from_auto=False, + _from_pipeline=None, + **kwargs, + ) + except (OSError, TypeError): + logger.info( + "Generation config file not found, using a generation config created from the model config." + ) + pass + + if device_map is not None: + dispatch_model(model, device_map=device_map) + + return model + return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args, + config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, + force_download=force_download, local_files_only=local_files_only, token=token, revision=revision, + use_safetensors=use_safetensors, **kwargs) + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + softmax_normalizer = shift_logits.max(-1).values ** 2 + z_loss = self.config.z_loss_weight * softmax_normalizer.mean() + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + z_loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past + + def quantize(self, bits: int): + try: + from .quantizer import quantize_online + except ImportError: + raise ImportError(f"Needs QLinear to run quantize.") + return quantize_online(self, bits) + + def chat(self, tokenizer, messages: List[dict], stream=False, + generation_config: Optional[GenerationConfig]=None): + generation_config = generation_config or self.generation_config + input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens) + if stream: + streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) + Thread(target=self.generate, kwargs=dict( + inputs=input_ids, streamer=streamer, + generation_config=generation_config, + )).start() + return streamer + else: + outputs = self.generate(input_ids, generation_config=generation_config) + print(outputs[0]) + response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True) + return response diff --git a/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/config.json b/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/config.json new file mode 100755 index 000000000..e2ba91313 --- /dev/null +++ b/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/config.json @@ -0,0 +1,28 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "auto_map": { + "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM" + }, + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 11008, + "max_position_embeddings": 4096, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 32, + "pad_token_id": 0, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "tie_word_embeddings": false, + "torch_dtype": "float16", + "transformers_version": "4.31.0.dev0", + "use_cache": true, + "vocab_size": 32000 +} diff --git a/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/configuration_llama.py b/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/configuration_llama.py new file mode 100644 index 000000000..1b0e9c357 --- /dev/null +++ b/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/configuration_llama.py @@ -0,0 +1,174 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" LLaMA model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {} + + +class LlamaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the LLaMA-7B. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`LlamaModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + pretraining_tp (`int`, *optional*, defaults to `1`): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is + necessary to ensure exact reproducibility of the pretraining results. Please refer to [this + issue](https://github.com/pytorch/pytorch/issues/76232). + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings(`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling + strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format + is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + + Example: + + ```python + >>> from transformers import LlamaModel, LlamaConfig + + >>> # Initializing a LLaMA llama-7b style configuration + >>> configuration = LlamaConfig() + + >>> # Initializing a model from the llama-7b style configuration + >>> model = LlamaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + model_type = "llama" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=False, + rope_scaling=None, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_scaling = rope_scaling + self._rope_scaling_validation() + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, " + f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: + raise ValueError( + f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" + ) + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}") diff --git a/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/modeling_llama.py b/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/modeling_llama.py new file mode 100644 index 000000000..8c562c604 --- /dev/null +++ b/transformers/llm/export/llm_models/Llama-2-7b-chat-ms/modeling_llama.py @@ -0,0 +1,1010 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch LLaMA model.""" +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_llama import LlamaConfig + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "LlamaConfig" + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class LlamaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + LlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +class LlamaRotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. + # cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] + # sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] + cos = torch.squeeze(cos) # [seq_len, dim] + sin = torch.squeeze(sin) # [seq_len, dim] + cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class LlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.pretraining_tp = config.pretraining_tp + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + if self.pretraining_tp > 1: + slice = self.intermediate_size // self.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class LlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: LlamaConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.pretraining_tp = config.pretraining_tp + self.max_position_embeddings = config.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = LlamaLinearScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + elif scaling_type == "dynamic": + self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + if self.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp + query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + if self.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaDecoderLayer(nn.Module): + def __init__(self, config: LlamaConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = LlamaAttention(config=config) + self.mlp = LlamaMLP(config) + self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +LLAMA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`LlamaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaPreTrainedModel(PreTrainedModel): + config_class = LlamaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["LlamaDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, LlamaModel): + module.gradient_checkpointing = value + + +LLAMA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaModel(LlamaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] + + Args: + config: LlamaConfig + """ + + def __init__(self, config: LlamaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class LlamaForCausalLM(LlamaPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = LlamaModel(config) + self.pretraining_tp = config.pretraining_tp + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The LLaMa Model transformer with a sequence classification head on top (linear layer). + + [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + LLAMA_START_DOCSTRING, +) +class LlamaForSequenceClassification(LlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = LlamaModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/Llama-3-8B-Instruct/config.json b/transformers/llm/export/llm_models/Llama-3-8B-Instruct/config.json new file mode 100755 index 000000000..d9c36dfca --- /dev/null +++ b/transformers/llm/export/llm_models/Llama-3-8B-Instruct/config.json @@ -0,0 +1,31 @@ +{ + "_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct", + "architectures": [ + "LlamaForCausalLM" + ], + "auto_map": { + "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM" + }, + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 8192, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 500000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.38.2", + "use_cache": true, + "vocab_size": 128256 +} diff --git a/transformers/llm/export/llm_models/Llama-3-8B-Instruct/configuration_llama.py b/transformers/llm/export/llm_models/Llama-3-8B-Instruct/configuration_llama.py new file mode 100644 index 000000000..1b0e9c357 --- /dev/null +++ b/transformers/llm/export/llm_models/Llama-3-8B-Instruct/configuration_llama.py @@ -0,0 +1,174 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" LLaMA model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {} + + +class LlamaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the LLaMA-7B. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`LlamaModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + pretraining_tp (`int`, *optional*, defaults to `1`): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is + necessary to ensure exact reproducibility of the pretraining results. Please refer to [this + issue](https://github.com/pytorch/pytorch/issues/76232). + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings(`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling + strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format + is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + + Example: + + ```python + >>> from transformers import LlamaModel, LlamaConfig + + >>> # Initializing a LLaMA llama-7b style configuration + >>> configuration = LlamaConfig() + + >>> # Initializing a model from the llama-7b style configuration + >>> model = LlamaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + model_type = "llama" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=False, + rope_scaling=None, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_scaling = rope_scaling + self._rope_scaling_validation() + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, " + f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: + raise ValueError( + f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" + ) + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}") diff --git a/transformers/llm/export/llm_models/Llama-3-8B-Instruct/modeling_llama.py b/transformers/llm/export/llm_models/Llama-3-8B-Instruct/modeling_llama.py new file mode 100644 index 000000000..8c562c604 --- /dev/null +++ b/transformers/llm/export/llm_models/Llama-3-8B-Instruct/modeling_llama.py @@ -0,0 +1,1010 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch LLaMA model.""" +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_llama import LlamaConfig + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "LlamaConfig" + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class LlamaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + LlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +class LlamaRotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. + # cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] + # sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] + cos = torch.squeeze(cos) # [seq_len, dim] + sin = torch.squeeze(sin) # [seq_len, dim] + cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class LlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.pretraining_tp = config.pretraining_tp + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + if self.pretraining_tp > 1: + slice = self.intermediate_size // self.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class LlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: LlamaConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.pretraining_tp = config.pretraining_tp + self.max_position_embeddings = config.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = LlamaLinearScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + elif scaling_type == "dynamic": + self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + if self.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp + query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + if self.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaDecoderLayer(nn.Module): + def __init__(self, config: LlamaConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = LlamaAttention(config=config) + self.mlp = LlamaMLP(config) + self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +LLAMA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`LlamaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaPreTrainedModel(PreTrainedModel): + config_class = LlamaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["LlamaDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, LlamaModel): + module.gradient_checkpointing = value + + +LLAMA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaModel(LlamaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] + + Args: + config: LlamaConfig + """ + + def __init__(self, config: LlamaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class LlamaForCausalLM(LlamaPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = LlamaModel(config) + self.pretraining_tp = config.pretraining_tp + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The LLaMa Model transformer with a sequence classification head on top (linear layer). + + [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + LLAMA_START_DOCSTRING, +) +class LlamaForSequenceClassification(LlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = LlamaModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/Qwen-1_8B-Chat/modeling_qwen.py b/transformers/llm/export/llm_models/Qwen-1_8B-Chat/modeling_qwen.py new file mode 100755 index 000000000..5138eea76 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen-1_8B-Chat/modeling_qwen.py @@ -0,0 +1,1406 @@ +# Copyright (c) Alibaba Cloud. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import importlib +import math +import pathlib +from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +import warnings + +from torch.nn import CrossEntropyLoss +from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList +from transformers.generation.logits_process import LogitsProcessorList + +if TYPE_CHECKING: + from transformers.generation.streamers import BaseStreamer +from transformers.generation.utils import GenerateOutput +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +try: + from einops import rearrange +except ImportError: + rearrange = None +from torch import nn + +SUPPORT_CUDA = torch.cuda.is_available() +SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported() +SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7 +SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2 + + +from .configuration_qwen import QWenConfig +from .qwen_generation_utils import ( + HistoryType, + make_context, + decode_tokens, + get_stop_words_ids, + StopWordsLogitsProcessor, +) + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "qwen" +_CONFIG_FOR_DOC = "QWenConfig" + +QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"] + +_ERROR_BAD_CHAT_FORMAT = """\ +We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml". +If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat(). +我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。 +如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。 +""" + +_SENTINEL = object() +_ERROR_STREAM_IN_CHAT = """\ +Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True). +向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。 +""" + +_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED = """\ +We detect you have activated flash attention support, but running model computation on CPU. Please make sure that your input data has been placed on GPU. If you actually want to run CPU computation, please following the readme and set device_map="cpu" to disable flash attention when loading the model (calling AutoModelForCausalLM.from_pretrained). +检测到您的模型已激活了flash attention支持，但正在执行CPU运算任务。如使用flash attention，请您确认模型输入已经传到GPU上。如果您确认要执行CPU运算，请您在载入模型（调用AutoModelForCausalLM.from_pretrained）时，按照readme说法，指定device_map="cpu"以禁用flash attention。 +""" + +apply_rotary_emb_func = None +rms_norm = None +flash_attn_unpadded_func = None +flash_attn_func = None + +def _import_flash_attn(): + global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func, flash_attn_func + try: + from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func + apply_rotary_emb_func = __apply_rotary_emb_func + except ImportError: + logger.warn( + "Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary" + ) + + try: + from flash_attn.ops.rms_norm import rms_norm as __rms_norm + rms_norm = __rms_norm + except ImportError: + logger.warn( + "Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm" + ) + + try: + import flash_attn + _flash_attn_func = None + if not hasattr(flash_attn, '__version__'): + from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func + else: + if int(flash_attn.__version__.split(".")[0]) >= 2: + if int(flash_attn.__version__.split(".")[1]) >= 1: + from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func + from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func + else: + from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func + flash_attn_unpadded_func = __flash_attn_unpadded_func + flash_attn_func = _flash_attn_func + except ImportError: + logger.warn( + "Warning: import flash_attn fail, please install FlashAttention to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention" + ) + +def quantize_cache_v(fdata, bits, qmax, qmin): + # b, s, head, h-dim->b, head, s, h-dim + qtype = torch.uint8 + device = fdata.device + shape = fdata.shape + + fdata_cal = torch.flatten(fdata, 2) + fmax = torch.amax(fdata_cal, dim=-1, keepdim=True) + fmin = torch.amin(fdata_cal, dim=-1, keepdim=True) + # Compute params + if qmax.device != fmax.device: + qmax = qmax.to(device) + qmin = qmin.to(device) + scale = (fmax - fmin) / (qmax - qmin) + zero = qmin - fmin / scale + scale = scale.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous() + zero = zero.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous() + # Quantize + res_data = fdata / scale + zero + qdata = torch.clamp(res_data, qmin, qmax).to(qtype) + return qdata.contiguous(), scale, zero + +def dequantize_cache_torch(qdata, scale, zero): + data = scale * (qdata - zero) + return data + +class FlashSelfAttention(torch.nn.Module): + def __init__( + self, + causal=False, + softmax_scale=None, + attention_dropout=0.0, + ): + super().__init__() + assert flash_attn_unpadded_func is not None, ( + "Please install FlashAttention first, " "e.g., with pip install flash-attn" + ) + assert ( + rearrange is not None + ), "Please install einops first, e.g., with pip install einops" + self.causal = causal + self.softmax_scale = softmax_scale + self.dropout_p = attention_dropout + + def unpad_input(self, hidden_states, attention_mask): + valid_mask = attention_mask.squeeze(1).squeeze(1).eq(0) + seqlens_in_batch = valid_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(valid_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + hidden_states = hidden_states[indices] + return hidden_states, indices, cu_seqlens, max_seqlen_in_batch + + def pad_input(self, hidden_states, indices, batch, seqlen): + output = torch.zeros(batch * seqlen, *hidden_states.shape[1:], device=hidden_states.device, + dtype=hidden_states.dtype) + output[indices] = hidden_states + return rearrange(output, '(b s) ... -> b s ...', b=batch) + + def forward(self, q, k, v, attention_mask=None): + assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v))) + assert all((i.is_cuda for i in (q, k, v))) + batch_size, seqlen_q = q.shape[0], q.shape[1] + seqlen_k = k.shape[1] + seqlen_out = seqlen_q + + if flash_attn_func is not None and batch_size == 1: + dropout_p = self.dropout_p if self.training else 0 + output = flash_attn_func(q, k, v, dropout_p, softmax_scale=self.softmax_scale, causal=self.causal) + return output + + q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]] + cu_seqlens_q = torch.arange( + 0, + (batch_size + 1) * seqlen_q, + step=seqlen_q, + dtype=torch.int32, + device=q.device, + ) + + if batch_size > 1 and attention_mask is not None: + k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask) + if q.size(0) == v.size(0): + q = q[indices_k] + cu_seqlens_q = cu_seqlens_k + seqlen_q = seqlen_k + v = v[indices_k] + else: + cu_seqlens_k = torch.arange( + 0, + (batch_size + 1) * seqlen_k, + step=seqlen_k, + dtype=torch.int32, + device=q.device, + ) + + if self.training: + assert seqlen_k == seqlen_q + is_causal = self.causal + dropout_p = self.dropout_p + else: + is_causal = seqlen_q == seqlen_k + dropout_p = 0 + + output = flash_attn_unpadded_func( + q, + k, + v, + cu_seqlens_q, + cu_seqlens_k, + seqlen_q, + seqlen_k, + dropout_p, + softmax_scale=self.softmax_scale, + causal=is_causal, + ) + if batch_size > 1 and attention_mask is not None and seqlen_q == seqlen_k: + output = self.pad_input(output, indices_k, batch_size, seqlen_out) + else: + new_shape = (batch_size, output.shape[0] // batch_size) + output.shape[1:] + output = output.view(new_shape) + return output + + +class QWenAttention(nn.Module): + def __init__(self, config): + super().__init__() + + self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) + self.seq_length = config.seq_length + + self.hidden_size = config.hidden_size + self.split_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + + self.use_flash_attn = config.use_flash_attn + self.scale_attn_weights = True + + self.projection_size = config.kv_channels * config.num_attention_heads + + assert self.projection_size % config.num_attention_heads == 0 + self.hidden_size_per_attention_head = ( + self.projection_size // config.num_attention_heads + ) + + self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size) + + self.c_proj = nn.Linear( + config.hidden_size, self.projection_size, bias=not config.no_bias + ) + + self.is_fp32 = not (config.bf16 or config.fp16) + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + ): + self.core_attention_flash = FlashSelfAttention( + causal=True, attention_dropout=config.attn_dropout_prob + ) + self.bf16 = config.bf16 + + self.use_dynamic_ntk = config.use_dynamic_ntk + self.use_logn_attn = config.use_logn_attn + + logn_list = [ + math.log(i, self.seq_length) if i > self.seq_length else 1 + for i in range(1, 32768) + ] + logn_tensor = torch.tensor(logn_list)[None, :, None, None] + self.register_buffer("logn_tensor", logn_tensor, persistent=False) + + self.attn_dropout = nn.Dropout(config.attn_dropout_prob) + self.softmax_in_fp32 = config.softmax_in_fp32 if hasattr(config, 'softmax_in_fp32') else False + self.use_cache_quantization = config.use_cache_quantization if hasattr(config, 'use_cache_quantization') else False + self.use_cache_kernel = config.use_cache_kernel if hasattr(config,'use_cache_kernel') else False + cache_dtype = torch.float + if self.bf16: + cache_dtype=torch.bfloat16 + elif config.fp16: + cache_dtype = torch.float16 + self.cache_qmax = torch.tensor(torch.iinfo(torch.uint8).max, dtype=cache_dtype) + self.cache_qmin = torch.tensor(torch.iinfo(torch.uint8).min, dtype=cache_dtype) + + if config.use_cache_quantization and config.use_cache_kernel: + # pre check if the support files existing + module_root = pathlib.Path(__file__).parent + src_files = ("cache_autogptq_cuda_256.cpp", "cache_autogptq_cuda_kernel_256.cu") + if any(not (module_root/src).is_file() for src in src_files): + warnings.warn("KV cache kernel source files (.cpp and .cu) not found.") + self.cache_kernels = None + else: + try: + from .cpp_kernels import cache_autogptq_cuda_256 + self.cache_kernels = cache_autogptq_cuda_256 + except ImportError: + warnings.warn("Failed to import KV cache kernels.") + self.cache_kernels = None + + def _attn(self, query, key, value, no_use_mask, attention_mask=None, head_mask=None): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + attn_weights = attn_weights / torch.full( + [], + value.size(-1) ** 0.5, + dtype=attn_weights.dtype, + device=attn_weights.device, + ) + + query_length, key_length = query.size(-2), key.size(-2) + if attention_mask is None: + causal_mask = self.bias[ + :, :, key_length - query_length : key_length, :key_length + ] + else: + causal_mask = attention_mask + mask_value = torch.finfo(attn_weights.dtype).min + mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to( + attn_weights.device + ) + attn_weights = torch.where( + causal_mask, attn_weights.to(attn_weights.dtype), mask_value + ) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_weights = attn_weights.type(value.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2) + + return attn_output, attn_weights + + def __attn(self, query, key, value, causal_mask=None, attention_mask=None, head_mask=None): + device = query.device + if self.use_cache_quantization: + qk, qk_scale, qk_zero = key + if self.use_cache_kernel and self.cache_kernels is not None: + shape = query.shape[:-1] + (qk.shape[-2],) + attn_weights = torch.zeros(shape, dtype=torch.float16, device=device) + self.cache_kernels.vecquant8matmul_batched_faster_old( + query.contiguous() if query.dtype == torch.float16 else query.to(torch.float16).contiguous(), + qk.transpose(-1, -2).contiguous(), + attn_weights, + qk_scale.contiguous() if qk_scale.dtype == torch.float16 else qk_scale.to(torch.float16).contiguous(), + qk_zero.contiguous()if qk_zero.dtype == torch.float16 else qk_zero.to(torch.float16).contiguous()) + # attn_weights = attn_weights.to(query.dtype).contiguous() + else: + key = dequantize_cache_torch(qk, qk_scale, qk_zero) + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + else: + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + if self.use_cache_quantization: + size_temp = value[0].size(-1) + else: + size_temp = value.size(-1) + attn_weights = attn_weights / (size_temp ** 0.5) + + mask_value = torch.finfo(attn_weights.dtype).min + if causal_mask is not None: + attn_weights = torch.where( + causal_mask, attn_weights.to(attn_weights.dtype), mask_value + ) + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + if self.softmax_in_fp32: + attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1) + else: + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_weights = attn_weights.type(query.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + if self.use_cache_quantization: + qv, qv_scale, qv_zero = value + if self.use_cache_kernel and self.cache_kernels is not None: + shape = attn_weights.shape[:-1] + (query.shape[-1],) + attn_output = torch.zeros(shape, dtype=torch.float16, device=device) + self.cache_kernels.vecquant8matmul_batched_column_compression_faster_old( + attn_weights.contiguous() if attn_weights.dtype == torch.float16 else attn_weights.to(torch.float16).contiguous(), + qv.contiguous(), # dtype: int32 + attn_output, + qv_scale.contiguous() if qv_scale.dtype == torch.float16 else qv_scale.to(torch.float16).contiguous(), + qv_zero.contiguous() if qv_zero.dtype == torch.float16 else qv_zero.to(torch.float16).contiguous()) + if attn_output.dtype != query.dtype: + attn_output = attn_output.to(query.dtype) + attn_weights = attn_weights.to(query.dtype) + else: + value = dequantize_cache_torch(qv, qv_scale, qv_zero) + attn_output = torch.matmul(attn_weights, value) + else: + attn_output = torch.matmul(attn_weights, value) + + attn_output = attn_output.transpose(1, 2) + + return attn_output, attn_weights + + def _split_heads(self, tensor, num_heads, attn_head_size): + new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) + tensor = tensor.view(new_shape) + return tensor + + def _merge_heads(self, tensor, num_heads, attn_head_size): + tensor = tensor.contiguous() + new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) + return tensor.view(new_shape) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + rotary_pos_emb_list: Optional[List[torch.Tensor]] = None, + layer_past: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ): + mixed_x_layer = self.c_attn(hidden_states) + + query, key, value = mixed_x_layer.split(self.split_size, dim=2) + + query = self._split_heads(query, self.num_heads, self.head_dim) + key = self._split_heads(key, self.num_heads, self.head_dim) + value = self._split_heads(value, self.num_heads, self.head_dim) + + if rotary_pos_emb_list is not None: + cur_len = query.shape[1] + if True: + rotary_pos_emb = rotary_pos_emb_list + rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb] + rotary_pos_emb = (rotary_pos_emb,) * 2 + q_pos_emb, k_pos_emb = rotary_pos_emb + # Slice the pos emb for current inference + query = apply_rotary_pos_emb(query, q_pos_emb) + key = apply_rotary_pos_emb(key, k_pos_emb) + else: + query_list = [] + key_list = [] + for i, rotary_pos_emb in enumerate(rotary_pos_emb_list): + rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb] + rotary_pos_emb = (rotary_pos_emb,) * 2 + q_pos_emb, k_pos_emb = rotary_pos_emb + # Slice the pos emb for current inference + query_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)] + key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)] + query = torch.cat(query_list, dim=0) + key = torch.cat(key_list, dim=0) + + if self.use_cache_quantization: + key = quantize_cache_v(key.permute(0, 2, 1, 3), + bits=8, + qmin=self.cache_qmin, + qmax=self.cache_qmax) + value = quantize_cache_v(value.permute(0, 2, 1, 3), + bits=8, + qmin=self.cache_qmin, + qmax=self.cache_qmax) + + + if layer_past is not None: + past_key, past_value = layer_past[0], layer_past[1] + if self.use_cache_quantization: + # use_cache_quantization: + # present=((q_key,key_scale,key_zero_point), + # (q_value,value_scale,value_zero_point)) + key = (torch.cat((past_key[0], key[0]), dim=2), + torch.cat((past_key[1], key[1]), dim=2), + torch.cat((past_key[2], key[2]), dim=2)) + value = (torch.cat((past_value[0], value[0]), dim=2), + torch.cat((past_value[1], value[1]), dim=2), + torch.cat((past_value[2], value[2]), dim=2)) + else: + # not use_cache_quantization: + # present=(key,value) + key = torch.cat((past_key, key), dim=1) + value = torch.cat((past_value, value), dim=1) + + if use_cache: + present = (key, value) + else: + present = None + + key_size = key[0].size(2) if self.use_cache_quantization else key.size(1) + if key_size > self.seq_length and self.use_logn_attn and not self.training: + if self.use_cache_quantization: + seq_start = key[0].size(2) - query.size(1) + seq_end = key[0].size(2) + else: + seq_start = key.size(1) - query.size(1) + seq_end = key.size(1) + logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query) + query = query * logn_tensor.expand_as(query) + + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + and query.is_cuda + ): + q, k, v = query, key, value + attn_output = self.core_attention_flash(q, k, v, attention_mask=attention_mask) + else: + key_size = key[0].size(2) if self.use_cache_quantization else key.size(1) + if query.size(1) == key_size: + causal_mask = torch.tril( + torch.ones((key_size, key_size), dtype=torch.bool, device=query.device) + ).view(1, 1, key_size, key_size) + else: + causal_mask = None + query = query.permute(0, 2, 1, 3) + if not self.use_cache_quantization: + key = key.permute(0, 2, 1, 3) + value = value.permute(0, 2, 1, 3) + if ( + causal_mask is None + and self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + and not query.is_cuda + ): + raise Exception(_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED) + + if not self.use_cache_quantization and SUPPORT_TORCH2 and False: + if attention_mask is not None: + # attention_mask = attention_mask.expand( + # -1, -1, causal_mask.size(2), -1 + # ) + # if causal_mask is not None: + # attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min) + causal_mask = attention_mask + else: + attention_mask = causal_mask + attn_output = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask + ).transpose(1, 2) + attn_weight = None + else: + attn_output, attn_weight = self._attn( + # query, key, value, causal_mask, attention_mask, head_mask + query, key, value, attention_mask, attention_mask, head_mask + ) + context_layer = self._merge_heads( + attn_output, self.num_heads, self.head_dim + ) + + attn_output = self.c_proj(context_layer) + + outputs = (attn_output, present) + if output_attentions: + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + ): + raise ValueError("Cannot output attentions while using flash-attn") + elif not self.use_cache_quantization and SUPPORT_TORCH2: + raise ValueError("Cannot output attentions while using scaled_dot_product_attention") + else: + outputs += (attn_weight,) + + return outputs + + +class QWenMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.w1 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + self.w2 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + ff_dim_in = config.intermediate_size // 2 + self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias) + + def forward(self, hidden_states): + a1 = self.w1(hidden_states) + a2 = self.w2(hidden_states) + intermediate_parallel = a1 * F.silu(a2) + output = self.c_proj(intermediate_parallel) + return output + + +class QWenBlock(nn.Module): + def __init__(self, config): + super().__init__() + hidden_size = config.hidden_size + self.bf16 = config.bf16 + + self.ln_1 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + self.attn = QWenAttention(config) + self.ln_2 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + + self.mlp = QWenMLP(config) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + rotary_pos_emb: Optional[List[List[torch.Tensor]]] = None, + layer_past: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ): + layernorm_output = self.ln_1(hidden_states) + + attn_outputs = self.attn( + layernorm_output, + rotary_pos_emb, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + attn_output = attn_outputs[0] + + outputs = attn_outputs[1:] + + residual = hidden_states + layernorm_input = attn_output + residual + + layernorm_output = self.ln_2(layernorm_input) + + residual = layernorm_input + mlp_output = self.mlp(layernorm_output) + hidden_states = residual + mlp_output + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs + + +class QWenPreTrainedModel(PreTrainedModel): + config_class = QWenConfig + base_model_prefix = "transformer" + is_parallelizable = False + supports_gradient_checkpointing = True + _no_split_modules = ["QWenBlock"] + _skip_keys_device_placement = "past_key_values" + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, RMSNorm): + module.weight.data.fill_(1.0) + + for name, p in module.named_parameters(): + if name == "c_proj.weight": + p.data.normal_( + mean=0.0, + std=( + self.config.initializer_range + / math.sqrt(2 * self.config.num_hidden_layers) + ), + ) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, QWenModel): + module.gradient_checkpointing = value + + +class QWenModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = ["attn.masked_bias"] + + def __init__(self, config): + super().__init__(config) + self.vocab_size = config.vocab_size + self.num_hidden_layers = config.num_hidden_layers + self.embed_dim = config.hidden_size + self.use_cache_quantization = self.config.use_cache_quantization if hasattr(self.config, 'use_cache_quantization') else False + + self.gradient_checkpointing = False + self.use_dynamic_ntk = config.use_dynamic_ntk + self.seq_length = config.seq_length + + self.wte = nn.Embedding(self.vocab_size, self.embed_dim) + + self.drop = nn.Dropout(config.emb_dropout_prob) + + if config.rotary_pct == 1.0: + self.rotary_ndims = None + else: + assert config.rotary_pct < 1 + self.rotary_ndims = int( + config.kv_channels * config.rotary_pct + ) + dim = ( + self.rotary_ndims + if self.rotary_ndims is not None + else config.kv_channels + ) + self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base) + + self.use_flash_attn = config.use_flash_attn + self.is_fp32 = not (config.bf16 or config.fp16) + + self.h = nn.ModuleList( + [ + QWenBlock( + config + ) + for i in range(config.num_hidden_layers) + ] + ) + self.ln_f = RMSNorm( + self.embed_dim, + eps=config.layer_norm_epsilon, + ) + + self.post_init() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + def get_ntk_alpha(self, true_seq_len): + context_value = math.log(true_seq_len / self.seq_length, 2) + 1 + ntk_alpha = 2 ** math.ceil(context_value) - 1 + ntk_alpha = max(ntk_alpha, 1) + return ntk_alpha + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, input_shape[-1]) + if position_ids is not None: + position_ids = position_ids.view(-1, input_shape[-1]) + + if past_key_values is None: + past_length = 0 + past_key_values = tuple([None] * len(self.h)) + else: + if self.use_cache_quantization: + past_length = past_key_values[0][0][0].size(2) + else: + past_length = past_key_values[0][0].size(-2) + if position_ids is None: + position_ids = torch.arange( + past_length, + input_shape[-1] + past_length, + dtype=torch.long, + device=device, + ) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + if attention_mask is not None: + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = attention_mask.view(batch_size, -1) + attention_mask = attention_mask[:, None, None, :] + attention_mask = attention_mask.to(dtype=self.dtype) + attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min + + encoder_attention_mask = None + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + hidden_states = inputs_embeds + + kv_seq_len = hidden_states.size()[1] + if past_key_values[0] is not None: + # past key values[0][0] shape: bs * seq_len * head_num * dim + if self.use_cache_quantization: + kv_seq_len += past_key_values[0][0][0].shape[2] + else: + kv_seq_len += past_key_values[0][0].shape[1] + + if self.training or not self.use_dynamic_ntk: + ntk_alpha_list = [1.0] + elif kv_seq_len != hidden_states.size()[1]: + ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list + else: + ntk_alpha_list = [] + if attention_mask is not None and kv_seq_len > self.seq_length: + true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32) + for i in range(hidden_states.size()[0]): + true_seq_len = true_seq_lens[i].item() + ntk_alpha = self.get_ntk_alpha(true_seq_len) + ntk_alpha_list.append(ntk_alpha) + else: + ntk_alpha = self.get_ntk_alpha(kv_seq_len) + ntk_alpha_list.append(ntk_alpha) + self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list + rotary_pos_emb_list = [ + self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list + ] + + hidden_states = self.drop(hidden_states) + output_shape = input_shape + (hidden_states.size(-1),) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + rotary_pos_emb_list, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + rotary_pos_emb=rotary_pos_emb_list, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + + hidden_states = self.ln_f(hidden_states) + hidden_states = hidden_states.view(output_shape) + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states] if v is not None + ) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class QWenLMHeadModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"] + _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"] + + def __init__(self, config): + super().__init__(config) + assert ( + config.bf16 + config.fp16 + config.fp32 <= 1 + ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true" + + autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0 + + if autoset_precision: + if SUPPORT_BF16: + logger.warn( + "The model is automatically converting to bf16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.bf16 = True + elif SUPPORT_FP16: + logger.warn( + "The model is automatically converting to fp16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.fp16 = True + else: + config.fp32 = True + + if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16: + logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".") + if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16: + logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster") + if config.fp32: + if SUPPORT_BF16: + logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".") + elif SUPPORT_FP16: + logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".") + + if config.use_flash_attn == "auto": + if config.bf16 or config.fp16: + logger.warn("Try importing flash-attention for faster inference...") + config.use_flash_attn = True + else: + config.use_flash_attn = False + if config.use_flash_attn and config.fp32: + logger.warn("Flash attention will be disabled because it does NOT support fp32.") + + if config.use_flash_attn: + _import_flash_attn() + + self.transformer = QWenModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + if config.bf16: + self.transformer.bfloat16() + self.lm_head.bfloat16() + if config.fp16: + self.transformer.half() + self.lm_head.half() + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1].unsqueeze(-1) + + if input_ids.size(0) == 1: + attention_mask = None + else: + attention_mask = kwargs.get("attention_mask", None) + + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + loss = loss_fct( + shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1) + ) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor + ) -> Tuple[Tuple[torch.Tensor]]: + + return tuple( + tuple( + past_state.index_select(0, beam_idx.to(past_state.device)) + for past_state in layer_past + ) + for layer_past in past_key_values + ) + + def chat( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + stream: Optional[bool] = _SENTINEL, + stop_words_ids: Optional[List[List[int]]] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Tuple[str, HistoryType]: + generation_config = generation_config if generation_config is not None else self.generation_config + + assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + else: + # make a copy of the user's input such that is is left untouched + history = copy.deepcopy(history) + + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + input_ids = torch.tensor([context_tokens]).to(self.device) + outputs = self.generate( + input_ids, + stop_words_ids=stop_words_ids, + return_dict_in_generate=False, + generation_config=generation_config, + **kwargs, + ) + + response = decode_tokens( + outputs[0], + tokenizer, + raw_text_len=len(raw_text), + context_length=len(context_tokens), + chat_format=generation_config.chat_format, + verbose=False, + errors='replace' + ) + + # as history is a copy of the user inputs, + # we can always return the new turn to the user. + # separating input history and output history also enables the user + # to implement more complex history management + history.append((query, response)) + + return response, history + + def chat_stream( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + stop_words_ids: Optional[List[List[int]]] = None, + logits_processor: Optional[LogitsProcessorList] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Generator[str, Any, None]: + generation_config = generation_config if generation_config is not None else self.generation_config + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + input_ids = torch.tensor([context_tokens]).to(self.device) + + from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig + self.__class__.generate_stream = NewGenerationMixin.generate + self.__class__.sample_stream = NewGenerationMixin.sample_stream + stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True) + + def stream_generator(): + outputs = [] + for token in self.generate_stream( + input_ids, + return_dict_in_generate=False, + generation_config=stream_config, + logits_processor=logits_processor, + seed=-1, + **kwargs): + outputs.append(token.item()) + yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore') + + return stream_generator() + + def generate( + self, + inputs: Optional[torch.Tensor] = None, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[ + Callable[[int, torch.Tensor], List[int]] + ] = None, + synced_gpus: Optional[bool] = None, + assistant_model: Optional["PreTrainedModel"] = None, + streamer: Optional["BaseStreamer"] = None, + **kwargs, + ) -> Union[GenerateOutput, torch.LongTensor]: + generation_config = generation_config if generation_config is not None else self.generation_config + + # Process stop_words_ids. + stop_words_ids = kwargs.pop("stop_words_ids", None) + if stop_words_ids is None and generation_config is not None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + if stop_words_ids is None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + + return super().generate( + inputs, + generation_config=generation_config, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + synced_gpus=synced_gpus, + assistant_model=assistant_model, + streamer=streamer, + **kwargs, + ) + + +class RotaryEmbedding(torch.nn.Module): + def __init__(self, dim, base=10000): + super().__init__() + self.dim = dim + self.base = base + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + if importlib.util.find_spec("einops") is None: + raise RuntimeError("einops is required for Rotary Embedding") + + self._rotary_pos_emb_cache = None + self._seq_len_cached = 0 + self._ntk_alpha_cached = 1.0 + self._ntk_alpha_cached_list = [1.0] + + def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0): + if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached: + base = self.base * ntk_alpha ** (self.dim / (self.dim - 2)) + self.inv_freq = 1.0 / ( + base + ** ( + torch.arange(0, self.dim, 2, device=self.inv_freq.device).float() + / self.dim + ) + ) + self._seq_len_cached = max(2 * seqlen, 16) + self._ntk_alpha_cached = ntk_alpha + seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device) + freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq) + + emb = torch.cat((freqs, freqs), dim=-1) + from einops import rearrange + + emb = rearrange(emb, "n d -> 1 n 1 d") + + cos, sin = emb.cos(), emb.sin() + self._rotary_pos_emb_cache = [cos, sin] + + def forward(self, max_seq_len, ntk_alpha=1.0): + self.update_rotary_pos_emb_cache(max_seq_len, ntk_alpha) + cos, sin = self._rotary_pos_emb_cache + return [cos[:, :max_seq_len], sin[:, :max_seq_len]] + + +def _rotate_half(x): + from einops import rearrange + + x = rearrange(x, "... (j d) -> ... j d", j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(t, freqs): + """ Apply rotary embedding to the first rotary_dim of the iput + + Arguments: + t (tensor(batch_size, seq_len, n_head, head_dim)): + the input embedding/hidden states + freqs (list[tensor(1, seq_len, 1, rotary_dim), tensor(1, seq_len, 1, rotary_dim)]): + the cached cos/sin position embeddings + """ + rot_dim = freqs[0].shape[-1] + cos, sin = freqs + t_float = t.float() + if apply_rotary_emb_func is not None and t.is_cuda: + # apply_rotary_emb in flash_attn requires cos/sin to be of + # shape (seqlen, rotary_dim / 2) and apply rotary embedding + # to the first rotary_dim of the input + cos = cos.squeeze(0).squeeze(1)[:, : rot_dim // 2] + sin = sin.squeeze(0).squeeze(1)[:, : rot_dim // 2] + return apply_rotary_emb_func(t_float, cos, sin).type_as(t) + else: + t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:] + t_rot = (t_rot * cos) + (_rotate_half(t_rot) * sin) + return torch.cat((t_rot, t_pass), dim=-1).type_as(t) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + if rms_norm is not None and x.is_cuda: + return rms_norm(x, self.weight, self.eps) + else: + output = self._norm(x.float()).type_as(x) + return output * self.weight diff --git a/transformers/llm/export/llm_models/Qwen-1_8B/modeling_qwen.py b/transformers/llm/export/llm_models/Qwen-1_8B/modeling_qwen.py new file mode 100755 index 000000000..5138eea76 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen-1_8B/modeling_qwen.py @@ -0,0 +1,1406 @@ +# Copyright (c) Alibaba Cloud. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import importlib +import math +import pathlib +from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +import warnings + +from torch.nn import CrossEntropyLoss +from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList +from transformers.generation.logits_process import LogitsProcessorList + +if TYPE_CHECKING: + from transformers.generation.streamers import BaseStreamer +from transformers.generation.utils import GenerateOutput +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +try: + from einops import rearrange +except ImportError: + rearrange = None +from torch import nn + +SUPPORT_CUDA = torch.cuda.is_available() +SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported() +SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7 +SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2 + + +from .configuration_qwen import QWenConfig +from .qwen_generation_utils import ( + HistoryType, + make_context, + decode_tokens, + get_stop_words_ids, + StopWordsLogitsProcessor, +) + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "qwen" +_CONFIG_FOR_DOC = "QWenConfig" + +QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"] + +_ERROR_BAD_CHAT_FORMAT = """\ +We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml". +If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat(). +我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。 +如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。 +""" + +_SENTINEL = object() +_ERROR_STREAM_IN_CHAT = """\ +Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True). +向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。 +""" + +_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED = """\ +We detect you have activated flash attention support, but running model computation on CPU. Please make sure that your input data has been placed on GPU. If you actually want to run CPU computation, please following the readme and set device_map="cpu" to disable flash attention when loading the model (calling AutoModelForCausalLM.from_pretrained). +检测到您的模型已激活了flash attention支持，但正在执行CPU运算任务。如使用flash attention，请您确认模型输入已经传到GPU上。如果您确认要执行CPU运算，请您在载入模型（调用AutoModelForCausalLM.from_pretrained）时，按照readme说法，指定device_map="cpu"以禁用flash attention。 +""" + +apply_rotary_emb_func = None +rms_norm = None +flash_attn_unpadded_func = None +flash_attn_func = None + +def _import_flash_attn(): + global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func, flash_attn_func + try: + from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func + apply_rotary_emb_func = __apply_rotary_emb_func + except ImportError: + logger.warn( + "Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary" + ) + + try: + from flash_attn.ops.rms_norm import rms_norm as __rms_norm + rms_norm = __rms_norm + except ImportError: + logger.warn( + "Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm" + ) + + try: + import flash_attn + _flash_attn_func = None + if not hasattr(flash_attn, '__version__'): + from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func + else: + if int(flash_attn.__version__.split(".")[0]) >= 2: + if int(flash_attn.__version__.split(".")[1]) >= 1: + from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func + from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func + else: + from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func + flash_attn_unpadded_func = __flash_attn_unpadded_func + flash_attn_func = _flash_attn_func + except ImportError: + logger.warn( + "Warning: import flash_attn fail, please install FlashAttention to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention" + ) + +def quantize_cache_v(fdata, bits, qmax, qmin): + # b, s, head, h-dim->b, head, s, h-dim + qtype = torch.uint8 + device = fdata.device + shape = fdata.shape + + fdata_cal = torch.flatten(fdata, 2) + fmax = torch.amax(fdata_cal, dim=-1, keepdim=True) + fmin = torch.amin(fdata_cal, dim=-1, keepdim=True) + # Compute params + if qmax.device != fmax.device: + qmax = qmax.to(device) + qmin = qmin.to(device) + scale = (fmax - fmin) / (qmax - qmin) + zero = qmin - fmin / scale + scale = scale.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous() + zero = zero.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous() + # Quantize + res_data = fdata / scale + zero + qdata = torch.clamp(res_data, qmin, qmax).to(qtype) + return qdata.contiguous(), scale, zero + +def dequantize_cache_torch(qdata, scale, zero): + data = scale * (qdata - zero) + return data + +class FlashSelfAttention(torch.nn.Module): + def __init__( + self, + causal=False, + softmax_scale=None, + attention_dropout=0.0, + ): + super().__init__() + assert flash_attn_unpadded_func is not None, ( + "Please install FlashAttention first, " "e.g., with pip install flash-attn" + ) + assert ( + rearrange is not None + ), "Please install einops first, e.g., with pip install einops" + self.causal = causal + self.softmax_scale = softmax_scale + self.dropout_p = attention_dropout + + def unpad_input(self, hidden_states, attention_mask): + valid_mask = attention_mask.squeeze(1).squeeze(1).eq(0) + seqlens_in_batch = valid_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(valid_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + hidden_states = hidden_states[indices] + return hidden_states, indices, cu_seqlens, max_seqlen_in_batch + + def pad_input(self, hidden_states, indices, batch, seqlen): + output = torch.zeros(batch * seqlen, *hidden_states.shape[1:], device=hidden_states.device, + dtype=hidden_states.dtype) + output[indices] = hidden_states + return rearrange(output, '(b s) ... -> b s ...', b=batch) + + def forward(self, q, k, v, attention_mask=None): + assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v))) + assert all((i.is_cuda for i in (q, k, v))) + batch_size, seqlen_q = q.shape[0], q.shape[1] + seqlen_k = k.shape[1] + seqlen_out = seqlen_q + + if flash_attn_func is not None and batch_size == 1: + dropout_p = self.dropout_p if self.training else 0 + output = flash_attn_func(q, k, v, dropout_p, softmax_scale=self.softmax_scale, causal=self.causal) + return output + + q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]] + cu_seqlens_q = torch.arange( + 0, + (batch_size + 1) * seqlen_q, + step=seqlen_q, + dtype=torch.int32, + device=q.device, + ) + + if batch_size > 1 and attention_mask is not None: + k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask) + if q.size(0) == v.size(0): + q = q[indices_k] + cu_seqlens_q = cu_seqlens_k + seqlen_q = seqlen_k + v = v[indices_k] + else: + cu_seqlens_k = torch.arange( + 0, + (batch_size + 1) * seqlen_k, + step=seqlen_k, + dtype=torch.int32, + device=q.device, + ) + + if self.training: + assert seqlen_k == seqlen_q + is_causal = self.causal + dropout_p = self.dropout_p + else: + is_causal = seqlen_q == seqlen_k + dropout_p = 0 + + output = flash_attn_unpadded_func( + q, + k, + v, + cu_seqlens_q, + cu_seqlens_k, + seqlen_q, + seqlen_k, + dropout_p, + softmax_scale=self.softmax_scale, + causal=is_causal, + ) + if batch_size > 1 and attention_mask is not None and seqlen_q == seqlen_k: + output = self.pad_input(output, indices_k, batch_size, seqlen_out) + else: + new_shape = (batch_size, output.shape[0] // batch_size) + output.shape[1:] + output = output.view(new_shape) + return output + + +class QWenAttention(nn.Module): + def __init__(self, config): + super().__init__() + + self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) + self.seq_length = config.seq_length + + self.hidden_size = config.hidden_size + self.split_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + + self.use_flash_attn = config.use_flash_attn + self.scale_attn_weights = True + + self.projection_size = config.kv_channels * config.num_attention_heads + + assert self.projection_size % config.num_attention_heads == 0 + self.hidden_size_per_attention_head = ( + self.projection_size // config.num_attention_heads + ) + + self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size) + + self.c_proj = nn.Linear( + config.hidden_size, self.projection_size, bias=not config.no_bias + ) + + self.is_fp32 = not (config.bf16 or config.fp16) + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + ): + self.core_attention_flash = FlashSelfAttention( + causal=True, attention_dropout=config.attn_dropout_prob + ) + self.bf16 = config.bf16 + + self.use_dynamic_ntk = config.use_dynamic_ntk + self.use_logn_attn = config.use_logn_attn + + logn_list = [ + math.log(i, self.seq_length) if i > self.seq_length else 1 + for i in range(1, 32768) + ] + logn_tensor = torch.tensor(logn_list)[None, :, None, None] + self.register_buffer("logn_tensor", logn_tensor, persistent=False) + + self.attn_dropout = nn.Dropout(config.attn_dropout_prob) + self.softmax_in_fp32 = config.softmax_in_fp32 if hasattr(config, 'softmax_in_fp32') else False + self.use_cache_quantization = config.use_cache_quantization if hasattr(config, 'use_cache_quantization') else False + self.use_cache_kernel = config.use_cache_kernel if hasattr(config,'use_cache_kernel') else False + cache_dtype = torch.float + if self.bf16: + cache_dtype=torch.bfloat16 + elif config.fp16: + cache_dtype = torch.float16 + self.cache_qmax = torch.tensor(torch.iinfo(torch.uint8).max, dtype=cache_dtype) + self.cache_qmin = torch.tensor(torch.iinfo(torch.uint8).min, dtype=cache_dtype) + + if config.use_cache_quantization and config.use_cache_kernel: + # pre check if the support files existing + module_root = pathlib.Path(__file__).parent + src_files = ("cache_autogptq_cuda_256.cpp", "cache_autogptq_cuda_kernel_256.cu") + if any(not (module_root/src).is_file() for src in src_files): + warnings.warn("KV cache kernel source files (.cpp and .cu) not found.") + self.cache_kernels = None + else: + try: + from .cpp_kernels import cache_autogptq_cuda_256 + self.cache_kernels = cache_autogptq_cuda_256 + except ImportError: + warnings.warn("Failed to import KV cache kernels.") + self.cache_kernels = None + + def _attn(self, query, key, value, no_use_mask, attention_mask=None, head_mask=None): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + attn_weights = attn_weights / torch.full( + [], + value.size(-1) ** 0.5, + dtype=attn_weights.dtype, + device=attn_weights.device, + ) + + query_length, key_length = query.size(-2), key.size(-2) + if attention_mask is None: + causal_mask = self.bias[ + :, :, key_length - query_length : key_length, :key_length + ] + else: + causal_mask = attention_mask + mask_value = torch.finfo(attn_weights.dtype).min + mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to( + attn_weights.device + ) + attn_weights = torch.where( + causal_mask, attn_weights.to(attn_weights.dtype), mask_value + ) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_weights = attn_weights.type(value.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2) + + return attn_output, attn_weights + + def __attn(self, query, key, value, causal_mask=None, attention_mask=None, head_mask=None): + device = query.device + if self.use_cache_quantization: + qk, qk_scale, qk_zero = key + if self.use_cache_kernel and self.cache_kernels is not None: + shape = query.shape[:-1] + (qk.shape[-2],) + attn_weights = torch.zeros(shape, dtype=torch.float16, device=device) + self.cache_kernels.vecquant8matmul_batched_faster_old( + query.contiguous() if query.dtype == torch.float16 else query.to(torch.float16).contiguous(), + qk.transpose(-1, -2).contiguous(), + attn_weights, + qk_scale.contiguous() if qk_scale.dtype == torch.float16 else qk_scale.to(torch.float16).contiguous(), + qk_zero.contiguous()if qk_zero.dtype == torch.float16 else qk_zero.to(torch.float16).contiguous()) + # attn_weights = attn_weights.to(query.dtype).contiguous() + else: + key = dequantize_cache_torch(qk, qk_scale, qk_zero) + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + else: + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + if self.use_cache_quantization: + size_temp = value[0].size(-1) + else: + size_temp = value.size(-1) + attn_weights = attn_weights / (size_temp ** 0.5) + + mask_value = torch.finfo(attn_weights.dtype).min + if causal_mask is not None: + attn_weights = torch.where( + causal_mask, attn_weights.to(attn_weights.dtype), mask_value + ) + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + if self.softmax_in_fp32: + attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1) + else: + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_weights = attn_weights.type(query.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + if self.use_cache_quantization: + qv, qv_scale, qv_zero = value + if self.use_cache_kernel and self.cache_kernels is not None: + shape = attn_weights.shape[:-1] + (query.shape[-1],) + attn_output = torch.zeros(shape, dtype=torch.float16, device=device) + self.cache_kernels.vecquant8matmul_batched_column_compression_faster_old( + attn_weights.contiguous() if attn_weights.dtype == torch.float16 else attn_weights.to(torch.float16).contiguous(), + qv.contiguous(), # dtype: int32 + attn_output, + qv_scale.contiguous() if qv_scale.dtype == torch.float16 else qv_scale.to(torch.float16).contiguous(), + qv_zero.contiguous() if qv_zero.dtype == torch.float16 else qv_zero.to(torch.float16).contiguous()) + if attn_output.dtype != query.dtype: + attn_output = attn_output.to(query.dtype) + attn_weights = attn_weights.to(query.dtype) + else: + value = dequantize_cache_torch(qv, qv_scale, qv_zero) + attn_output = torch.matmul(attn_weights, value) + else: + attn_output = torch.matmul(attn_weights, value) + + attn_output = attn_output.transpose(1, 2) + + return attn_output, attn_weights + + def _split_heads(self, tensor, num_heads, attn_head_size): + new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) + tensor = tensor.view(new_shape) + return tensor + + def _merge_heads(self, tensor, num_heads, attn_head_size): + tensor = tensor.contiguous() + new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) + return tensor.view(new_shape) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + rotary_pos_emb_list: Optional[List[torch.Tensor]] = None, + layer_past: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ): + mixed_x_layer = self.c_attn(hidden_states) + + query, key, value = mixed_x_layer.split(self.split_size, dim=2) + + query = self._split_heads(query, self.num_heads, self.head_dim) + key = self._split_heads(key, self.num_heads, self.head_dim) + value = self._split_heads(value, self.num_heads, self.head_dim) + + if rotary_pos_emb_list is not None: + cur_len = query.shape[1] + if True: + rotary_pos_emb = rotary_pos_emb_list + rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb] + rotary_pos_emb = (rotary_pos_emb,) * 2 + q_pos_emb, k_pos_emb = rotary_pos_emb + # Slice the pos emb for current inference + query = apply_rotary_pos_emb(query, q_pos_emb) + key = apply_rotary_pos_emb(key, k_pos_emb) + else: + query_list = [] + key_list = [] + for i, rotary_pos_emb in enumerate(rotary_pos_emb_list): + rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb] + rotary_pos_emb = (rotary_pos_emb,) * 2 + q_pos_emb, k_pos_emb = rotary_pos_emb + # Slice the pos emb for current inference + query_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)] + key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)] + query = torch.cat(query_list, dim=0) + key = torch.cat(key_list, dim=0) + + if self.use_cache_quantization: + key = quantize_cache_v(key.permute(0, 2, 1, 3), + bits=8, + qmin=self.cache_qmin, + qmax=self.cache_qmax) + value = quantize_cache_v(value.permute(0, 2, 1, 3), + bits=8, + qmin=self.cache_qmin, + qmax=self.cache_qmax) + + + if layer_past is not None: + past_key, past_value = layer_past[0], layer_past[1] + if self.use_cache_quantization: + # use_cache_quantization: + # present=((q_key,key_scale,key_zero_point), + # (q_value,value_scale,value_zero_point)) + key = (torch.cat((past_key[0], key[0]), dim=2), + torch.cat((past_key[1], key[1]), dim=2), + torch.cat((past_key[2], key[2]), dim=2)) + value = (torch.cat((past_value[0], value[0]), dim=2), + torch.cat((past_value[1], value[1]), dim=2), + torch.cat((past_value[2], value[2]), dim=2)) + else: + # not use_cache_quantization: + # present=(key,value) + key = torch.cat((past_key, key), dim=1) + value = torch.cat((past_value, value), dim=1) + + if use_cache: + present = (key, value) + else: + present = None + + key_size = key[0].size(2) if self.use_cache_quantization else key.size(1) + if key_size > self.seq_length and self.use_logn_attn and not self.training: + if self.use_cache_quantization: + seq_start = key[0].size(2) - query.size(1) + seq_end = key[0].size(2) + else: + seq_start = key.size(1) - query.size(1) + seq_end = key.size(1) + logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query) + query = query * logn_tensor.expand_as(query) + + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + and query.is_cuda + ): + q, k, v = query, key, value + attn_output = self.core_attention_flash(q, k, v, attention_mask=attention_mask) + else: + key_size = key[0].size(2) if self.use_cache_quantization else key.size(1) + if query.size(1) == key_size: + causal_mask = torch.tril( + torch.ones((key_size, key_size), dtype=torch.bool, device=query.device) + ).view(1, 1, key_size, key_size) + else: + causal_mask = None + query = query.permute(0, 2, 1, 3) + if not self.use_cache_quantization: + key = key.permute(0, 2, 1, 3) + value = value.permute(0, 2, 1, 3) + if ( + causal_mask is None + and self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + and not query.is_cuda + ): + raise Exception(_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED) + + if not self.use_cache_quantization and SUPPORT_TORCH2 and False: + if attention_mask is not None: + # attention_mask = attention_mask.expand( + # -1, -1, causal_mask.size(2), -1 + # ) + # if causal_mask is not None: + # attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min) + causal_mask = attention_mask + else: + attention_mask = causal_mask + attn_output = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask + ).transpose(1, 2) + attn_weight = None + else: + attn_output, attn_weight = self._attn( + # query, key, value, causal_mask, attention_mask, head_mask + query, key, value, attention_mask, attention_mask, head_mask + ) + context_layer = self._merge_heads( + attn_output, self.num_heads, self.head_dim + ) + + attn_output = self.c_proj(context_layer) + + outputs = (attn_output, present) + if output_attentions: + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + ): + raise ValueError("Cannot output attentions while using flash-attn") + elif not self.use_cache_quantization and SUPPORT_TORCH2: + raise ValueError("Cannot output attentions while using scaled_dot_product_attention") + else: + outputs += (attn_weight,) + + return outputs + + +class QWenMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.w1 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + self.w2 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + ff_dim_in = config.intermediate_size // 2 + self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias) + + def forward(self, hidden_states): + a1 = self.w1(hidden_states) + a2 = self.w2(hidden_states) + intermediate_parallel = a1 * F.silu(a2) + output = self.c_proj(intermediate_parallel) + return output + + +class QWenBlock(nn.Module): + def __init__(self, config): + super().__init__() + hidden_size = config.hidden_size + self.bf16 = config.bf16 + + self.ln_1 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + self.attn = QWenAttention(config) + self.ln_2 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + + self.mlp = QWenMLP(config) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + rotary_pos_emb: Optional[List[List[torch.Tensor]]] = None, + layer_past: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ): + layernorm_output = self.ln_1(hidden_states) + + attn_outputs = self.attn( + layernorm_output, + rotary_pos_emb, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + attn_output = attn_outputs[0] + + outputs = attn_outputs[1:] + + residual = hidden_states + layernorm_input = attn_output + residual + + layernorm_output = self.ln_2(layernorm_input) + + residual = layernorm_input + mlp_output = self.mlp(layernorm_output) + hidden_states = residual + mlp_output + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs + + +class QWenPreTrainedModel(PreTrainedModel): + config_class = QWenConfig + base_model_prefix = "transformer" + is_parallelizable = False + supports_gradient_checkpointing = True + _no_split_modules = ["QWenBlock"] + _skip_keys_device_placement = "past_key_values" + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, RMSNorm): + module.weight.data.fill_(1.0) + + for name, p in module.named_parameters(): + if name == "c_proj.weight": + p.data.normal_( + mean=0.0, + std=( + self.config.initializer_range + / math.sqrt(2 * self.config.num_hidden_layers) + ), + ) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, QWenModel): + module.gradient_checkpointing = value + + +class QWenModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = ["attn.masked_bias"] + + def __init__(self, config): + super().__init__(config) + self.vocab_size = config.vocab_size + self.num_hidden_layers = config.num_hidden_layers + self.embed_dim = config.hidden_size + self.use_cache_quantization = self.config.use_cache_quantization if hasattr(self.config, 'use_cache_quantization') else False + + self.gradient_checkpointing = False + self.use_dynamic_ntk = config.use_dynamic_ntk + self.seq_length = config.seq_length + + self.wte = nn.Embedding(self.vocab_size, self.embed_dim) + + self.drop = nn.Dropout(config.emb_dropout_prob) + + if config.rotary_pct == 1.0: + self.rotary_ndims = None + else: + assert config.rotary_pct < 1 + self.rotary_ndims = int( + config.kv_channels * config.rotary_pct + ) + dim = ( + self.rotary_ndims + if self.rotary_ndims is not None + else config.kv_channels + ) + self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base) + + self.use_flash_attn = config.use_flash_attn + self.is_fp32 = not (config.bf16 or config.fp16) + + self.h = nn.ModuleList( + [ + QWenBlock( + config + ) + for i in range(config.num_hidden_layers) + ] + ) + self.ln_f = RMSNorm( + self.embed_dim, + eps=config.layer_norm_epsilon, + ) + + self.post_init() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + def get_ntk_alpha(self, true_seq_len): + context_value = math.log(true_seq_len / self.seq_length, 2) + 1 + ntk_alpha = 2 ** math.ceil(context_value) - 1 + ntk_alpha = max(ntk_alpha, 1) + return ntk_alpha + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, input_shape[-1]) + if position_ids is not None: + position_ids = position_ids.view(-1, input_shape[-1]) + + if past_key_values is None: + past_length = 0 + past_key_values = tuple([None] * len(self.h)) + else: + if self.use_cache_quantization: + past_length = past_key_values[0][0][0].size(2) + else: + past_length = past_key_values[0][0].size(-2) + if position_ids is None: + position_ids = torch.arange( + past_length, + input_shape[-1] + past_length, + dtype=torch.long, + device=device, + ) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + if attention_mask is not None: + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = attention_mask.view(batch_size, -1) + attention_mask = attention_mask[:, None, None, :] + attention_mask = attention_mask.to(dtype=self.dtype) + attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min + + encoder_attention_mask = None + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + hidden_states = inputs_embeds + + kv_seq_len = hidden_states.size()[1] + if past_key_values[0] is not None: + # past key values[0][0] shape: bs * seq_len * head_num * dim + if self.use_cache_quantization: + kv_seq_len += past_key_values[0][0][0].shape[2] + else: + kv_seq_len += past_key_values[0][0].shape[1] + + if self.training or not self.use_dynamic_ntk: + ntk_alpha_list = [1.0] + elif kv_seq_len != hidden_states.size()[1]: + ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list + else: + ntk_alpha_list = [] + if attention_mask is not None and kv_seq_len > self.seq_length: + true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32) + for i in range(hidden_states.size()[0]): + true_seq_len = true_seq_lens[i].item() + ntk_alpha = self.get_ntk_alpha(true_seq_len) + ntk_alpha_list.append(ntk_alpha) + else: + ntk_alpha = self.get_ntk_alpha(kv_seq_len) + ntk_alpha_list.append(ntk_alpha) + self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list + rotary_pos_emb_list = [ + self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list + ] + + hidden_states = self.drop(hidden_states) + output_shape = input_shape + (hidden_states.size(-1),) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + rotary_pos_emb_list, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + rotary_pos_emb=rotary_pos_emb_list, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + + hidden_states = self.ln_f(hidden_states) + hidden_states = hidden_states.view(output_shape) + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states] if v is not None + ) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class QWenLMHeadModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"] + _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"] + + def __init__(self, config): + super().__init__(config) + assert ( + config.bf16 + config.fp16 + config.fp32 <= 1 + ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true" + + autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0 + + if autoset_precision: + if SUPPORT_BF16: + logger.warn( + "The model is automatically converting to bf16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.bf16 = True + elif SUPPORT_FP16: + logger.warn( + "The model is automatically converting to fp16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.fp16 = True + else: + config.fp32 = True + + if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16: + logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".") + if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16: + logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster") + if config.fp32: + if SUPPORT_BF16: + logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".") + elif SUPPORT_FP16: + logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".") + + if config.use_flash_attn == "auto": + if config.bf16 or config.fp16: + logger.warn("Try importing flash-attention for faster inference...") + config.use_flash_attn = True + else: + config.use_flash_attn = False + if config.use_flash_attn and config.fp32: + logger.warn("Flash attention will be disabled because it does NOT support fp32.") + + if config.use_flash_attn: + _import_flash_attn() + + self.transformer = QWenModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + if config.bf16: + self.transformer.bfloat16() + self.lm_head.bfloat16() + if config.fp16: + self.transformer.half() + self.lm_head.half() + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1].unsqueeze(-1) + + if input_ids.size(0) == 1: + attention_mask = None + else: + attention_mask = kwargs.get("attention_mask", None) + + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + loss = loss_fct( + shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1) + ) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor + ) -> Tuple[Tuple[torch.Tensor]]: + + return tuple( + tuple( + past_state.index_select(0, beam_idx.to(past_state.device)) + for past_state in layer_past + ) + for layer_past in past_key_values + ) + + def chat( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + stream: Optional[bool] = _SENTINEL, + stop_words_ids: Optional[List[List[int]]] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Tuple[str, HistoryType]: + generation_config = generation_config if generation_config is not None else self.generation_config + + assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + else: + # make a copy of the user's input such that is is left untouched + history = copy.deepcopy(history) + + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + input_ids = torch.tensor([context_tokens]).to(self.device) + outputs = self.generate( + input_ids, + stop_words_ids=stop_words_ids, + return_dict_in_generate=False, + generation_config=generation_config, + **kwargs, + ) + + response = decode_tokens( + outputs[0], + tokenizer, + raw_text_len=len(raw_text), + context_length=len(context_tokens), + chat_format=generation_config.chat_format, + verbose=False, + errors='replace' + ) + + # as history is a copy of the user inputs, + # we can always return the new turn to the user. + # separating input history and output history also enables the user + # to implement more complex history management + history.append((query, response)) + + return response, history + + def chat_stream( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + stop_words_ids: Optional[List[List[int]]] = None, + logits_processor: Optional[LogitsProcessorList] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Generator[str, Any, None]: + generation_config = generation_config if generation_config is not None else self.generation_config + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + input_ids = torch.tensor([context_tokens]).to(self.device) + + from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig + self.__class__.generate_stream = NewGenerationMixin.generate + self.__class__.sample_stream = NewGenerationMixin.sample_stream + stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True) + + def stream_generator(): + outputs = [] + for token in self.generate_stream( + input_ids, + return_dict_in_generate=False, + generation_config=stream_config, + logits_processor=logits_processor, + seed=-1, + **kwargs): + outputs.append(token.item()) + yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore') + + return stream_generator() + + def generate( + self, + inputs: Optional[torch.Tensor] = None, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[ + Callable[[int, torch.Tensor], List[int]] + ] = None, + synced_gpus: Optional[bool] = None, + assistant_model: Optional["PreTrainedModel"] = None, + streamer: Optional["BaseStreamer"] = None, + **kwargs, + ) -> Union[GenerateOutput, torch.LongTensor]: + generation_config = generation_config if generation_config is not None else self.generation_config + + # Process stop_words_ids. + stop_words_ids = kwargs.pop("stop_words_ids", None) + if stop_words_ids is None and generation_config is not None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + if stop_words_ids is None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + + return super().generate( + inputs, + generation_config=generation_config, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + synced_gpus=synced_gpus, + assistant_model=assistant_model, + streamer=streamer, + **kwargs, + ) + + +class RotaryEmbedding(torch.nn.Module): + def __init__(self, dim, base=10000): + super().__init__() + self.dim = dim + self.base = base + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + if importlib.util.find_spec("einops") is None: + raise RuntimeError("einops is required for Rotary Embedding") + + self._rotary_pos_emb_cache = None + self._seq_len_cached = 0 + self._ntk_alpha_cached = 1.0 + self._ntk_alpha_cached_list = [1.0] + + def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0): + if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached: + base = self.base * ntk_alpha ** (self.dim / (self.dim - 2)) + self.inv_freq = 1.0 / ( + base + ** ( + torch.arange(0, self.dim, 2, device=self.inv_freq.device).float() + / self.dim + ) + ) + self._seq_len_cached = max(2 * seqlen, 16) + self._ntk_alpha_cached = ntk_alpha + seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device) + freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq) + + emb = torch.cat((freqs, freqs), dim=-1) + from einops import rearrange + + emb = rearrange(emb, "n d -> 1 n 1 d") + + cos, sin = emb.cos(), emb.sin() + self._rotary_pos_emb_cache = [cos, sin] + + def forward(self, max_seq_len, ntk_alpha=1.0): + self.update_rotary_pos_emb_cache(max_seq_len, ntk_alpha) + cos, sin = self._rotary_pos_emb_cache + return [cos[:, :max_seq_len], sin[:, :max_seq_len]] + + +def _rotate_half(x): + from einops import rearrange + + x = rearrange(x, "... (j d) -> ... j d", j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(t, freqs): + """ Apply rotary embedding to the first rotary_dim of the iput + + Arguments: + t (tensor(batch_size, seq_len, n_head, head_dim)): + the input embedding/hidden states + freqs (list[tensor(1, seq_len, 1, rotary_dim), tensor(1, seq_len, 1, rotary_dim)]): + the cached cos/sin position embeddings + """ + rot_dim = freqs[0].shape[-1] + cos, sin = freqs + t_float = t.float() + if apply_rotary_emb_func is not None and t.is_cuda: + # apply_rotary_emb in flash_attn requires cos/sin to be of + # shape (seqlen, rotary_dim / 2) and apply rotary embedding + # to the first rotary_dim of the input + cos = cos.squeeze(0).squeeze(1)[:, : rot_dim // 2] + sin = sin.squeeze(0).squeeze(1)[:, : rot_dim // 2] + return apply_rotary_emb_func(t_float, cos, sin).type_as(t) + else: + t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:] + t_rot = (t_rot * cos) + (_rotate_half(t_rot) * sin) + return torch.cat((t_rot, t_pass), dim=-1).type_as(t) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + if rms_norm is not None and x.is_cuda: + return rms_norm(x, self.weight, self.eps) + else: + output = self._norm(x.float()).type_as(x) + return output * self.weight diff --git a/transformers/llm/export/llm_models/Qwen-7B-Chat/config.json b/transformers/llm/export/llm_models/Qwen-7B-Chat/config.json new file mode 100644 index 000000000..2a794d958 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen-7B-Chat/config.json @@ -0,0 +1,37 @@ +{ + "architectures": [ + "QWenLMHeadModel" + ], + "auto_map": { + "AutoConfig": "configuration_qwen.QWenConfig", + "AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel" + }, + "attn_dropout_prob": 0.0, + "bf16": false, + "fp16": false, + "fp32": false, + "emb_dropout_prob": 0.0, + "intermediate_size": 22016, + "initializer_range": 0.02, + "kv_channels": 128, + "layer_norm_epsilon": 1e-06, + "model_type": "qwen", + "hidden_size": 4096, + "num_attention_heads": 32, + "num_hidden_layers": 32, + "max_position_embeddings": 8192, + "no_bias": true, + "onnx_safe": null, + "rotary_emb_base": 10000, + "rotary_pct": 1.0, + "scale_attn_weights": true, + "seq_length": 2048, + "tie_word_embeddings": false, + "tokenizer_type": "QWenTokenizer", + "transformers_version": "4.31.0", + "use_cache": true, + "use_flash_attn": "auto", + "vocab_size": 151936, + "use_dynamic_ntk": true, + "use_logn_attn": false +} diff --git a/transformers/llm/export/llm_models/Qwen-7B-Chat/modeling_qwen.py b/transformers/llm/export/llm_models/Qwen-7B-Chat/modeling_qwen.py new file mode 100644 index 000000000..94437bcd2 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen-7B-Chat/modeling_qwen.py @@ -0,0 +1,1204 @@ +# Copyright (c) Alibaba Cloud. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import math +from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch.cuda.amp import autocast + +from torch.nn import CrossEntropyLoss +from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList +from transformers.generation.logits_process import LogitsProcessorList + +if TYPE_CHECKING: + from transformers.generation.streamers import BaseStreamer +from transformers.generation.utils import GenerateOutput +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +try: + from einops import rearrange +except ImportError: + rearrange = None +from torch import nn + +SUPPORT_CUDA = torch.cuda.is_available() +SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported() +SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7 + +from .configuration_qwen import QWenConfig +from .qwen_generation_utils import ( + HistoryType, + make_context, + decode_tokens, + get_stop_words_ids, + StopWordsLogitsProcessor, +) + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "qwen" +_CONFIG_FOR_DOC = "QWenConfig" + +QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"] + +_ERROR_BAD_CHAT_FORMAT = """\ +We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml". +If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat(). +我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。 +如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。 +""" + +_SENTINEL = object() +_ERROR_STREAM_IN_CHAT = """\ +Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True). +向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。 +""" + +apply_rotary_emb_func = None +rms_norm = None +flash_attn_unpadded_func = None + + +def _import_flash_attn(): + global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func + try: + from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func + apply_rotary_emb_func = __apply_rotary_emb_func + except ImportError: + logger.warn( + "Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary" + ) + + try: + from flash_attn.ops.rms_norm import rms_norm as __rms_norm + rms_norm = __rms_norm + except ImportError: + logger.warn( + "Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm" + ) + + try: + import flash_attn + if not hasattr(flash_attn, '__version__'): + from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func + else: + if int(flash_attn.__version__.split(".")[0]) >= 2: + from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func + else: + from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func + flash_attn_unpadded_func = __flash_attn_unpadded_func + except ImportError: + logger.warn( + "Warning: import flash_attn fail, please install FlashAttention to get higher efficiency " + "https://github.com/Dao-AILab/flash-attention" + ) + + +class FlashSelfAttention(torch.nn.Module): + def __init__( + self, + causal=False, + softmax_scale=None, + attention_dropout=0.0, + ): + super().__init__() + assert flash_attn_unpadded_func is not None, ( + "Please install FlashAttention first, " "e.g., with pip install flash-attn" + ) + assert ( + rearrange is not None + ), "Please install einops first, e.g., with pip install einops" + self.causal = causal + self.softmax_scale = softmax_scale + self.dropout_p = attention_dropout + + def forward(self, q, k, v): + assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v))) + assert all((i.is_cuda for i in (q, k, v))) + batch_size, seqlen_q = q.shape[0], q.shape[1] + seqlen_k = k.shape[1] + q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]] + cu_seqlens_q = torch.arange( + 0, + (batch_size + 1) * seqlen_q, + step=seqlen_q, + dtype=torch.int32, + device=q.device, + ) + + if self.training: + assert seqlen_k == seqlen_q + + is_causal = self.causal + cu_seqlens_k = cu_seqlens_q + else: + is_causal = seqlen_q == seqlen_k + cu_seqlens_k = torch.arange( + 0, + (batch_size + 1) * seqlen_k, + step=seqlen_k, + dtype=torch.int32, + device=q.device, + ) + self.dropout_p = 0 + output = flash_attn_unpadded_func( + q, + k, + v, + cu_seqlens_q, + cu_seqlens_k, + seqlen_q, + seqlen_k, + self.dropout_p, + softmax_scale=self.softmax_scale, + causal=is_causal, + ) + + output = rearrange(output, "(b s) ... -> b s ...", b=batch_size) + return output + + +class QWenAttention(nn.Module): + def __init__(self, config): + super().__init__() + + max_positions = config.max_position_embeddings + self.register_buffer( + "bias", + torch.tril( + torch.ones((max_positions, max_positions), dtype=torch.bool) + ).view(1, 1, max_positions, max_positions), + persistent=False, + ) + self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) + self.seq_length = config.seq_length + + self.hidden_size = config.hidden_size + self.split_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + + self.use_flash_attn = config.use_flash_attn + self.scale_attn_weights = True + + self.projection_size = config.kv_channels * config.num_attention_heads + + assert self.projection_size % config.num_attention_heads == 0 + self.hidden_size_per_attention_head = ( + self.projection_size // config.num_attention_heads + ) + + self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size) + + self.c_proj = nn.Linear( + config.hidden_size, self.projection_size, bias=not config.no_bias + ) + + self.is_fp32 = not (config.bf16 or config.fp16) + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + ): + self.core_attention_flash = FlashSelfAttention( + causal=True, attention_dropout=config.attn_dropout_prob + ) + + self.bf16 = config.bf16 + + if config.rotary_pct == 1.0: + self.rotary_ndims = None + else: + assert config.rotary_pct < 1 + self.rotary_ndims = int( + self.hidden_size_per_attention_head * config.rotary_pct + ) + dim = ( + self.rotary_ndims + if self.rotary_ndims is not None + else self.hidden_size_per_attention_head + ) + self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base) + + self.use_dynamic_ntk = config.use_dynamic_ntk + self.use_logn_attn = config.use_logn_attn + + logn_list = [ + math.log(i, self.seq_length) if i > self.seq_length else 1 + for i in range(1, 32768) + ] + self.logn_tensor = torch.tensor(logn_list)[None, :, None, None] + self._ntk_cached = 1.0 + + self.attn_dropout = nn.Dropout(config.attn_dropout_prob) + + def _attn(self, query, key, value, attention_mask=None, head_mask=None): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + attn_weights = attn_weights / torch.full( + [], + value.size(-1) ** 0.5, + dtype=attn_weights.dtype, + device=attn_weights.device, + ) + + query_length, key_length = query.size(-2), key.size(-2) + if attention_mask is None: + causal_mask = self.bias[ + :, :, key_length - query_length : key_length, :key_length + ] + else: + causal_mask = attention_mask + mask_value = torch.finfo(attn_weights.dtype).min + mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to( + attn_weights.device + ) + attn_weights = torch.where( + causal_mask, attn_weights.to(attn_weights.dtype), mask_value + ) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_weights = attn_weights.type(value.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2) + + return attn_output, attn_weights + + def _upcast_and_reordered_attn( + self, query, key, value, attention_mask=None, head_mask=None + ): + bsz, num_heads, q_seq_len, dk = query.size() + _, _, k_seq_len, _ = key.size() + + attn_weights = torch.empty( + bsz * num_heads, + q_seq_len, + k_seq_len, + dtype=torch.float32, + device=query.device, + ) + + scale_factor = 1.0 + if self.scale_attn_weights: + scale_factor /= float(value.size(-1)) ** 0.5 + + with autocast(enabled=False): + q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape( + -1, dk, k_seq_len + ) + attn_weights = torch.baddbmm( + attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor + ) + attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) + + query_length, key_length = query.size(-2), key.size(-2) + causal_mask = self.bias[ + :, :, key_length - query_length : key_length, :key_length + ] + mask_value = torch.finfo(attn_weights.dtype).min + mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to( + attn_weights.device + ) + attn_weights = torch.where(causal_mask, attn_weights, mask_value) + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if attn_weights.dtype != torch.float32: + raise RuntimeError( + "Error with upcasting, attn_weights does not have dtype torch.float32" + ) + attn_weights = attn_weights.type(value.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + + return attn_output, attn_weights + + def _split_heads(self, tensor, num_heads, attn_head_size): + new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) + tensor = tensor.view(new_shape) + return tensor + + def _merge_heads(self, tensor, num_heads, attn_head_size): + tensor = tensor.contiguous() + new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) + return tensor.view(new_shape) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + layer_past: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ): + mixed_x_layer = self.c_attn(hidden_states) + query, key, value = mixed_x_layer.split(self.split_size, dim=2) + + query = self._split_heads(query, self.num_heads, self.head_dim) + key = self._split_heads(key, self.num_heads, self.head_dim) + value = self._split_heads(value, self.num_heads, self.head_dim) + + kv_seq_len = hidden_states.size()[1] + if layer_past is not None: + # layer past[0] shape: bs * seq_len * head_num * dim + kv_seq_len += layer_past[0].shape[1] + if ( + self.use_dynamic_ntk + and kv_seq_len == hidden_states.size()[1] + and not self.training + ): + context_value = math.log(kv_seq_len / self.seq_length, 2) + 1 + ntk_alpha = 2 ** math.ceil(context_value) - 1 + ntk_alpha = max(ntk_alpha, 1) + self._ntk_cached = ntk_alpha + else: + ntk_alpha = self._ntk_cached + if rotary_pos_emb is None: + rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha).to( + hidden_states.device + ) + cur_len = query.shape[1] + rotary_pos_emb = rotary_pos_emb[:, -cur_len:, :, :] + + if rotary_pos_emb is not None and False: + if isinstance(rotary_pos_emb, tuple): + rotary_pos_emb = rotary_pos_emb + else: + rotary_pos_emb = (rotary_pos_emb,) * 2 + + if rotary_pos_emb is not None: + ''' + q_pos_emb, k_pos_emb = rotary_pos_emb + # Slice the pos emb for current inference + cur_len = query.shape[1] + q_pos_emb = q_pos_emb[:, -cur_len:, :, :] + k_pos_emb = k_pos_emb[:, -cur_len:, :, :] + ''' + query = apply_rotary_pos_emb(query, rotary_pos_emb) + key = apply_rotary_pos_emb(key, rotary_pos_emb) + + if layer_past is not None: + past_key, past_value = layer_past[0], layer_past[1] + key = torch.cat((past_key, key), dim=1) + value = torch.cat((past_value, value), dim=1) + + if use_cache: + present = torch.stack((key, value)) + else: + present = None + + if self.use_logn_attn and not self.training: + if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype: + self.logn_tensor = self.logn_tensor.to(query.device).type_as(query) + seq_start = key.size(1) - query.size(1) + seq_end = key.size(1) + logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :] + query = query * logn_tensor.expand_as(query) + + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + and query.is_cuda + ): + q, k, v = query, key, value + context_layer = self.core_attention_flash(q, k, v) + + context_layer = rearrange( + context_layer, "b s h d -> b s (h d)" + ).contiguous() + else: + query = query.permute(0, 2, 1, 3) + key = key.permute(0, 2, 1, 3) + value = value.permute(0, 2, 1, 3) + attn_output, attn_weight = self._attn( + query, key, value, attention_mask, head_mask + ) + context_layer = self._merge_heads( + attn_output, self.num_heads, self.head_dim + ) + + attn_output = self.c_proj(context_layer) + outputs = (attn_output, present) + if output_attentions: + if ( + self.use_flash_attn + and flash_attn_unpadded_func is not None + and not self.is_fp32 + ): + raise ValueError("Cannot output attentions while using flash-attn") + else: + outputs += (attn_weight,) + + return outputs + + +class QWenMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.w1 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + self.w2 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + ff_dim_in = config.intermediate_size // 2 + self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias) + + def forward(self, hidden_states): + a1 = self.w1(hidden_states) + a2 = self.w2(hidden_states) + intermediate_parallel = a1 * F.silu(a2) + output = self.c_proj(intermediate_parallel) + return output + + +class QWenBlock(nn.Module): + def __init__(self, config): + super().__init__() + hidden_size = config.hidden_size + self.bf16 = config.bf16 + + self.ln_1 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + self.attn = QWenAttention(config) + self.ln_2 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + + self.mlp = QWenMLP(config) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + layer_past: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ): + layernorm_output = self.ln_1(hidden_states) + + attn_outputs = self.attn( + layernorm_output, + layer_past=layer_past, + attention_mask=attention_mask, + rotary_pos_emb=rotary_pos_emb, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + attn_output = attn_outputs[0] + + outputs = attn_outputs[1:] + + residual = hidden_states + layernorm_input = attn_output + residual + + layernorm_output = self.ln_2(layernorm_input) + + residual = layernorm_input + mlp_output = self.mlp(layernorm_output) + hidden_states = residual + mlp_output + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs + + +class QWenPreTrainedModel(PreTrainedModel): + config_class = QWenConfig + base_model_prefix = "transformer" + is_parallelizable = False + supports_gradient_checkpointing = True + _no_split_modules = ["QWenBlock"] + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, RMSNorm): + module.weight.data.fill_(1.0) + + for name, p in module.named_parameters(): + if name == "c_proj.weight": + p.data.normal_( + mean=0.0, + std=( + self.config.initializer_range + / math.sqrt(2 * self.config.num_hidden_layers) + ), + ) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, QWenModel): + module.gradient_checkpointing = value + + +class QWenModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = ["attn.masked_bias"] + + def __init__(self, config): + super().__init__(config) + self.vocab_size = config.vocab_size + self.num_hidden_layers = config.num_hidden_layers + self.embed_dim = config.hidden_size + + self.gradient_checkpointing = False + + self.wte = nn.Embedding(self.vocab_size, self.embed_dim) + + self.drop = nn.Dropout(config.emb_dropout_prob) + self.h = nn.ModuleList( + [ + QWenBlock( + config, + ) + for i in range(config.num_hidden_layers) + ] + ) + self.ln_f = RMSNorm( + self.embed_dim, + eps=config.layer_norm_epsilon, + ) + + self.post_init() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, input_shape[-1]) + if position_ids is not None: + position_ids = position_ids.view(-1, input_shape[-1]) + + if past_key_values is None: + past_length = 0 + past_key_values = tuple([None] * len(self.h)) + else: + past_length = past_key_values[0][0].size(-2) + + if position_ids is None: + position_ids = torch.arange( + past_length, + input_shape[-1] + past_length, + dtype=torch.long, + device=device, + ) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + if attention_mask is not None: + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = attention_mask.view(batch_size, -1) + attention_mask = attention_mask[:, None, None, :] + attention_mask = attention_mask.to(dtype=self.dtype) + attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min + + encoder_attention_mask = None + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + hidden_states = inputs_embeds + + hidden_states = self.drop(hidden_states) + output_shape = input_shape + (hidden_states.size(-1),) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[1],) + + hidden_states = self.ln_f(hidden_states) + hidden_states = hidden_states.view(output_shape) + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states] if v is not None + ) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class QWenLMHeadModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"] + _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"] + + def __init__(self, config): + super().__init__(config) + assert ( + config.bf16 + config.fp16 + config.fp32 <= 1 + ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true" + + autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0 + + if autoset_precision: + if SUPPORT_BF16: + logger.warn( + "The model is automatically converting to bf16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.bf16 = True + elif SUPPORT_FP16: + logger.warn( + "The model is automatically converting to fp16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.fp16 = True + else: + config.fp32 = True + + if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16: + logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".") + if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16: + logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster") + if config.fp32: + if SUPPORT_BF16: + logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".") + elif SUPPORT_FP16: + logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".") + + if config.use_flash_attn == "auto": + if config.bf16 or config.fp16: + logger.warn("Try importing flash-attention for faster inference...") + config.use_flash_attn = True + else: + config.use_flash_attn = False + if config.use_flash_attn and config.fp32: + logger.warn("Flash attention will be disabled because it does NOT support fp32.") + + if config.use_flash_attn: + _import_flash_attn() + + self.transformer = QWenModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + if config.bf16: + self.transformer.bfloat16() + self.lm_head.bfloat16() + if config.fp16: + self.transformer.half() + self.lm_head.half() + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs + ): + token_type_ids = kwargs.get("token_type_ids", None) + if past_key_values: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + ) + return model_inputs + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + loss = loss_fct( + shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1) + ) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor + ) -> Tuple[Tuple[torch.Tensor]]: + + return tuple( + tuple( + past_state.index_select(0, beam_idx.to(past_state.device)) + for past_state in layer_past + ) + for layer_past in past_key_values + ) + + def chat( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + append_history: bool = True, + stream: Optional[bool] = _SENTINEL, + stop_words_ids: Optional[List[List[int]]] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Tuple[str, HistoryType]: + generation_config = generation_config if generation_config is not None else self.generation_config + + assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + input_ids = torch.tensor([context_tokens]).to(self.device) + outputs = self.generate( + input_ids, + stop_words_ids=stop_words_ids, + return_dict_in_generate=False, + generation_config=generation_config, + **kwargs, + ) + + response = decode_tokens( + outputs[0], + tokenizer, + raw_text_len=len(raw_text), + context_length=len(context_tokens), + chat_format=generation_config.chat_format, + verbose=False, + errors='replace' + ) + + if append_history: + history.append((query, response)) + + return response, history + + def chat_stream( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + stop_words_ids: Optional[List[List[int]]] = None, + logits_processor: Optional[LogitsProcessorList] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Generator[str, Any, None]: + generation_config = generation_config if generation_config is not None else self.generation_config + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + input_ids = torch.tensor([context_tokens]).to(self.device) + + from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig + self.__class__.generate_stream = NewGenerationMixin.generate + self.__class__.sample_stream = NewGenerationMixin.sample_stream + stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True) + + def stream_generator(): + outputs = [] + for token in self.generate_stream( + input_ids, + return_dict_in_generate=False, + generation_config=stream_config, + logits_processor=logits_processor, + seed=-1, + **kwargs): + outputs.append(token.item()) + yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore') + + return stream_generator() + + def generate( + self, + inputs: Optional[torch.Tensor] = None, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[ + Callable[[int, torch.Tensor], List[int]] + ] = None, + synced_gpus: Optional[bool] = None, + assistant_model: Optional["PreTrainedModel"] = None, + streamer: Optional["BaseStreamer"] = None, + **kwargs, + ) -> Union[GenerateOutput, torch.LongTensor]: + generation_config = generation_config if generation_config is not None else self.generation_config + + # Process stop_words_ids. + stop_words_ids = kwargs.pop("stop_words_ids", None) + if stop_words_ids is None and generation_config is not None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + if stop_words_ids is None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + + return super().generate( + inputs, + generation_config=generation_config, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + synced_gpus=synced_gpus, + assistant_model=assistant_model, + streamer=streamer, + **kwargs, + ) + + +class RotaryEmbedding(torch.nn.Module): + def __init__(self, dim, base=10000): + super().__init__() + self.dim = dim + self.base = base + self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + if importlib.util.find_spec("einops") is None: + raise RuntimeError("einops is required for Rotary Embedding") + + self._rotary_pos_emb_cache = None + self._seq_len_cached = 0 + self._ntk_alpha_cached = 1.0 + + def update_rotary_pos_emb_cache(self, max_seq_len, offset=0, ntk_alpha=1.0): + seqlen = max_seq_len + offset + if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached: + base = self.base * ntk_alpha ** (self.dim / (self.dim - 2)) + self.inv_freq = 1.0 / ( + base + ** ( + torch.arange(0, self.dim, 2, device=self.inv_freq.device).float() + / self.dim + ) + ) + self._seq_len_cached = max(2 * seqlen, 16) + self._ntk_alpha_cached = ntk_alpha + seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device) + freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1) + from einops import rearrange + + self._rotary_pos_emb_cache = rearrange(emb, "n d -> 1 n 1 d") + + def forward(self, max_seq_len, offset=0, ntk_alpha=1.0): + self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha) + return self._rotary_pos_emb_cache[:, offset : offset + max_seq_len] + + +def _rotate_half(x): + from einops import rearrange + + x = rearrange(x, "... (j d) -> ... j d", j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(t, freqs): + if apply_rotary_emb_func is not None and t.is_cuda: + t_ = t.float() + freqs = freqs.squeeze(0).squeeze(1) + cos = freqs[:, : freqs.shape[-1] // 2].cos() + sin = freqs[:, : freqs.shape[-1] // 2].sin() + output = apply_rotary_emb_func(t_, cos, sin).type_as(t) + return output + else: + rot_dim = freqs.shape[-1] + t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:] + t_ = t_.float() + t_pass_ = t_pass_.float() + t_ = (t_ * freqs.cos()) + (_rotate_half(t_) * freqs.sin()) + return torch.cat((t_, t_pass_), dim=-1).type_as(t) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + if rms_norm is not None and x.is_cuda: + return rms_norm(x, self.weight, self.eps) + else: + output = self._norm(x.float()).type_as(x) + return output * self.weight diff --git a/transformers/llm/export/llm_models/Qwen-VL-Chat/modeling_qwen.py b/transformers/llm/export/llm_models/Qwen-VL-Chat/modeling_qwen.py new file mode 100755 index 000000000..94c7453b1 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen-VL-Chat/modeling_qwen.py @@ -0,0 +1,1168 @@ +# Copyright (c) Alibaba Cloud. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import math +from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch.cuda.amp import autocast + +from torch.nn import CrossEntropyLoss +from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList +from transformers.generation.logits_process import LogitsProcessorList + +if TYPE_CHECKING: + from transformers.generation.streamers import BaseStreamer +from transformers.generation.utils import GenerateOutput +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +try: + from einops import rearrange +except ImportError: + rearrange = None +from torch import nn + +SUPPORT_CUDA = torch.cuda.is_available() +SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported() +SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7 + +from .configuration_qwen import QWenConfig +from .qwen_generation_utils import ( + HistoryType, + make_context, + decode_tokens, + get_stop_words_ids, + StopWordsLogitsProcessor, +) +from .visual import VisionTransformer + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "qwen" +_CONFIG_FOR_DOC = "QWenConfig" + +QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"] + +_ERROR_BAD_CHAT_FORMAT = """\ +We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml". +If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat(). +我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。 +如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。 +""" + +_SENTINEL = object() +_ERROR_STREAM_IN_CHAT = """\ +Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True). +向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。 +""" + +apply_rotary_emb_func = None +rms_norm = None + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class QWenAttention(nn.Module): + def __init__(self, config): + super().__init__() + + self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) + self.seq_length = config.seq_length + + self.hidden_size = config.hidden_size + self.split_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + + self.scale_attn_weights = True + + self.projection_size = config.kv_channels * config.num_attention_heads + + assert self.projection_size % config.num_attention_heads == 0 + self.hidden_size_per_attention_head = ( + self.projection_size // config.num_attention_heads + ) + + self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size) + + self.c_proj = nn.Linear( + config.hidden_size, self.projection_size, bias=not config.no_bias + ) + + self.is_fp32 = not (config.bf16 or config.fp16) + self.bf16 = config.bf16 + + self.use_dynamic_ntk = config.use_dynamic_ntk + self.use_logn_attn = config.use_logn_attn + + logn_list = [ + math.log(i, self.seq_length) if i > self.seq_length else 1 + for i in range(1, 32768) + ] + self.logn_tensor = torch.tensor(logn_list)[None, :, None, None] + + self.attn_dropout = nn.Dropout(config.attn_dropout_prob) + + def _attn(self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + attn_weights = attn_weights / torch.full( + [], + value.size(-1) ** 0.5, + dtype=attn_weights.dtype, + device=attn_weights.device, + ) + + query_length, key_length = query.size(-2), key.size(-2) + # causal_mask = self.bias[ + # :, :, key_length - query_length : key_length, :key_length + # ] + # mask_value = torch.finfo(attn_weights.dtype).min + # mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to( + # attn_weights.device + # ) + # attn_weights = torch.where( + # causal_mask, attn_weights.to(attn_weights.dtype), mask_value + # ) + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_weights = attn_weights.type(value.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2) + + return attn_output, attn_weights + + def _upcast_and_reordered_attn( + self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None + ): + bsz, num_heads, q_seq_len, dk = query.size() + _, _, k_seq_len, _ = key.size() + + attn_weights = torch.empty( + bsz * num_heads, + q_seq_len, + k_seq_len, + dtype=torch.float32, + device=query.device, + ) + + scale_factor = 1.0 + if self.scale_attn_weights: + scale_factor /= float(value.size(-1)) ** 0.5 + + with autocast(enabled=False): + q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape( + -1, dk, k_seq_len + ) + attn_weights = torch.baddbmm( + attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor + ) + attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) + + query_length, key_length = query.size(-2), key.size(-2) + causal_mask = registered_causal_mask[ + :, :, key_length - query_length : key_length, :key_length + ] + mask_value = torch.finfo(attn_weights.dtype).min + mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to( + attn_weights.device + ) + attn_weights = torch.where(causal_mask, attn_weights, mask_value) + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if attn_weights.dtype != torch.float32: + raise RuntimeError( + "Error with upcasting, attn_weights does not have dtype torch.float32" + ) + attn_weights = attn_weights.type(value.dtype) + attn_weights = self.attn_dropout(attn_weights) + + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + + return attn_output, attn_weights + + def _split_heads(self, tensor, num_heads, attn_head_size): + new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) + tensor = tensor.view(new_shape) + return tensor + + def _merge_heads(self, tensor, num_heads, attn_head_size): + tensor = tensor.contiguous() + new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) + return tensor.view(new_shape) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + rotary_pos_emb: Optional[List[torch.Tensor]] = None, + registered_causal_mask: Optional[torch.Tensor] = None, + layer_past: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ): + + mixed_x_layer = self.c_attn(hidden_states) + + query, key, value = mixed_x_layer.split(self.split_size, dim=2) + + query = self._split_heads(query, self.num_heads, self.head_dim) + key = self._split_heads(key, self.num_heads, self.head_dim) + value = self._split_heads(value, self.num_heads, self.head_dim) + + if rotary_pos_emb is not None: + ''' + cur_len = query.shape[1] + rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb] + rotary_pos_emb = (rotary_pos_emb,) * 2 + q_pos_emb, k_pos_emb = rotary_pos_emb + # Slice the pos emb for current inference + print('len(q_pos_emb) = ', len(q_pos_emb)) # 2 + print('q_pos_emb[0].shape = ', q_pos_emb[0].shape) # 1, 20, 1, 128 + query = apply_rotary_pos_emb(query, q_pos_emb) + key = apply_rotary_pos_emb(key, k_pos_emb) + ''' + query = apply_rotary_pos_emb(query, rotary_pos_emb) + key = apply_rotary_pos_emb(key, rotary_pos_emb) + + if layer_past is not None: + past_key, past_value = layer_past[0], layer_past[1] + key = torch.cat((past_key, key), dim=1) + value = torch.cat((past_value, value), dim=1) + + if use_cache: + present = torch.stack([key, value]) + else: + present = None + + if self.use_logn_attn and not self.training: + if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype: + self.logn_tensor = self.logn_tensor.to(query.device).type_as(query) + seq_start = key.size(1) - query.size(1) + seq_end = key.size(1) + logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :] + query = query * logn_tensor.expand_as(query) + + query = query.permute(0, 2, 1, 3) + key = key.permute(0, 2, 1, 3) + value = value.permute(0, 2, 1, 3) + attn_output, attn_weight = self._attn( + query, key, value, registered_causal_mask, attention_mask, head_mask + ) + context_layer = self._merge_heads( + attn_output, self.num_heads, self.head_dim + ) + + attn_output = self.c_proj(context_layer) + + outputs = (attn_output, present) + if output_attentions: + outputs += (attn_weight,) + + return outputs + + +class QWenMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.w1 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + self.w2 = nn.Linear( + config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias + ) + ff_dim_in = config.intermediate_size // 2 + self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias) + + def forward(self, hidden_states): + a1 = self.w1(hidden_states) + a2 = self.w2(hidden_states) + intermediate_parallel = a1 * F.silu(a2) + output = self.c_proj(intermediate_parallel) + return output + +class QWenBlock(nn.Module): + def __init__(self, config): + super().__init__() + hidden_size = config.hidden_size + self.bf16 = config.bf16 + + self.ln_1 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + self.attn = QWenAttention(config) + self.ln_2 = RMSNorm( + hidden_size, + eps=config.layer_norm_epsilon, + ) + + self.mlp = QWenMLP(config) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + rotary_pos_emb: Optional[List[torch.Tensor]] = None, + registered_causal_mask: Optional[torch.Tensor] = None, + layer_past: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ): + layernorm_output = self.ln_1(hidden_states) + + attn_outputs = self.attn( + layernorm_output, + rotary_pos_emb, + registered_causal_mask=registered_causal_mask, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + attn_output = attn_outputs[0] + + outputs = attn_outputs[1:] + + residual = hidden_states + layernorm_input = attn_output + residual + + layernorm_output = self.ln_2(layernorm_input) + + residual = layernorm_input + mlp_output = self.mlp(layernorm_output) + hidden_states = residual + mlp_output + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs + + +class QWenPreTrainedModel(PreTrainedModel): + config_class = QWenConfig + base_model_prefix = "transformer" + is_parallelizable = False + supports_gradient_checkpointing = True + _no_split_modules = ["QWenBlock"] + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, RMSNorm): + module.weight.data.fill_(1.0) + + for name, p in module.named_parameters(): + if name == "c_proj.weight": + p.data.normal_( + mean=0.0, + std=( + self.config.initializer_range + / math.sqrt(2 * self.config.num_hidden_layers) + ), + ) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, QWenModel): + module.gradient_checkpointing = value + + +class QWenModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = ["attn.masked_bias"] + + def __init__(self, config): + super().__init__(config) + self.vocab_size = config.vocab_size + self.num_hidden_layers = config.num_hidden_layers + self.embed_dim = config.hidden_size + + self.gradient_checkpointing = False + self.use_dynamic_ntk = config.use_dynamic_ntk + self.seq_length = config.seq_length + + self.wte = nn.Embedding(self.vocab_size, self.embed_dim) + + self.drop = nn.Dropout(config.emb_dropout_prob) + + if config.rotary_pct == 1.0: + self.rotary_ndims = None + else: + assert config.rotary_pct < 1 + self.rotary_ndims = int( + config.kv_channels * config.rotary_pct + ) + dim = ( + self.rotary_ndims + if self.rotary_ndims is not None + else config.kv_channels + ) + self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base) + + self.use_flash_attn = config.use_flash_attn + self.is_fp32 = not (config.bf16 or config.fp16) + self.registered_causal_mask = None + # if ( + # self.use_flash_attn + # and flash_attn_unpadded_func is not None + # and not self.is_fp32 + # ): + # self.registered_causal_mask = None + # else: + # max_positions = config.max_position_embeddings + # self.register_buffer( + # "registered_causal_mask", + # torch.tril( + # torch.ones((max_positions, max_positions), dtype=torch.bool) + # ).view(1, 1, max_positions, max_positions), + # persistent=False, + # ) + + self.h = nn.ModuleList( + [ + QWenBlock( + config + ) + for i in range(config.num_hidden_layers) + ] + ) + self.ln_f = RMSNorm( + self.embed_dim, + eps=config.layer_norm_epsilon, + ) + + self.visual = VisionTransformer(**config.visual) + + self.post_init() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']): + bos_pos = torch.where(input_ids == self.config.visual['image_start_id']) + eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1) + assert (bos_pos[0] == eos_pos[0]).all() + img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1) + images = [] + for i, a, b in img_pos: + image = input_ids[i][a + 1 : b - 1].tolist() + image = image[ : image.index(self.config.visual['image_start_id'] + 2)] + images.append(bytes(image).decode('utf-8')) + + images = self.visual.encode(images) + assert images.shape[0] == len(images) + fake_images = None + elif self.training: + fake_images=torch.zeros(1,3,224,224).to( + dtype=self.visual.conv1.weight.dtype, device=self.visual.conv1.weight.device) + images = self.visual(fake_images) + else: + fake_images = None + images = None + + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, input_shape[-1]) + if position_ids is not None: + position_ids = position_ids.view(-1, input_shape[-1]) + + if past_key_values is None: + past_length = 0 + past_key_values = tuple([None] * len(self.h)) + else: + past_length = past_key_values[0][0].size(-2) + + if position_ids is None: + position_ids = torch.arange( + past_length, + input_shape[-1] + past_length, + dtype=torch.long, + device=device, + ) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + encoder_attention_mask = None + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, input_shape, inputs_embeds, past_length + ) + + hidden_states = inputs_embeds + + kv_seq_len = hidden_states.size()[1] + if past_key_values[0] is not None: + # past key values[0][0] shape: bs * seq_len * head_num * dim + kv_seq_len += past_key_values[0][0].shape[1] + if ( + self.use_dynamic_ntk + and kv_seq_len == hidden_states.size()[1] + and not self.training + ): + context_value = math.log(kv_seq_len / self.seq_length, 2) + 1 + ntk_alpha = 2 ** math.ceil(context_value) - 1 + ntk_alpha = max(ntk_alpha, 1) + else: + ntk_alpha = self.rotary_emb._ntk_alpha_cached + + rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) + for idx in range(len(rotary_pos_emb)): + rotary_pos_emb[idx] = rotary_pos_emb[idx].to(hidden_states.device) + + hidden_states = self.drop(hidden_states).clone() + if fake_images is not None: + hidden_states = hidden_states + images.mean()*0 + elif images is not None: + for idx, (i, a, b) in enumerate(img_pos): + hidden_states[i][a + 1 : b] = images[idx] + output_shape = input_shape + (hidden_states.size(-1),) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + rotary_pos_emb, + self.registered_causal_mask, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + rotary_pos_emb=rotary_pos_emb, + registered_causal_mask=self.registered_causal_mask, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + + hidden_states = self.ln_f(hidden_states) + hidden_states = hidden_states.view(output_shape) + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states] if v is not None + ) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class QWenLMHeadModel(QWenPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"] + _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"] + + def __init__(self, config): + super().__init__(config) + assert ( + config.bf16 + config.fp16 + config.fp32 <= 1 + ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true" + + autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0 + + if autoset_precision: + if SUPPORT_BF16: + logger.warn( + "The model is automatically converting to bf16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.bf16 = True + elif SUPPORT_FP16: + logger.warn( + "The model is automatically converting to fp16 for faster inference. " + "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." + ) + config.fp16 = True + else: + config.fp32 = True + + if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16: + logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".") + if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16: + logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster") + if config.fp32: + if SUPPORT_BF16: + logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".") + elif SUPPORT_FP16: + logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".") + + self.transformer = QWenModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + if config.bf16: + self.transformer.bfloat16() + self.lm_head.bfloat16() + if config.fp16: + self.transformer.half() + self.lm_head.half() + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs + ): + token_type_ids = kwargs.get("token_type_ids", None) + if past_key_values: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + ) + return model_inputs + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + loss = loss_fct( + shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1) + ) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor + ) -> Tuple[Tuple[torch.Tensor]]: + + return tuple( + tuple( + past_state.index_select(0, beam_idx.to(past_state.device)) + for past_state in layer_past + ) + for layer_past in past_key_values + ) + + def chat( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + append_history: bool = True, + stream: Optional[bool] = _SENTINEL, + stop_words_ids: Optional[List[List[int]]] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Tuple[str, HistoryType]: + generation_config = generation_config if generation_config is not None else self.generation_config + + assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + input_ids = torch.tensor([context_tokens]).to(self.device) + outputs = self.generate( + input_ids, + stop_words_ids=stop_words_ids, + return_dict_in_generate=False, + generation_config=generation_config, + **kwargs, + ) + + response = decode_tokens( + outputs[0], + tokenizer, + raw_text_len=len(raw_text), + context_length=len(context_tokens), + chat_format=generation_config.chat_format, + verbose=False, + errors='replace' + ) + + if append_history: + history.append((query, response)) + + return response, history + + def chat_stream( + self, + tokenizer: PreTrainedTokenizer, + query: str, + history: Optional[HistoryType], + system: str = "You are a helpful assistant.", + stop_words_ids: Optional[List[List[int]]] = None, + logits_processor: Optional[LogitsProcessorList] = None, + generation_config: Optional[GenerationConfig] = None, + **kwargs, + ) -> Generator[str, Any, None]: + generation_config = generation_config if generation_config is not None else self.generation_config + assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT + if history is None: + history = [] + if stop_words_ids is None: + stop_words_ids = [] + + max_window_size = kwargs.get('max_window_size', None) + if max_window_size is None: + max_window_size = generation_config.max_window_size + raw_text, context_tokens = make_context( + tokenizer, + query, + history=history, + system=system, + max_window_size=max_window_size, + chat_format=generation_config.chat_format, + ) + + stop_words_ids.extend(get_stop_words_ids( + generation_config.chat_format, tokenizer + )) + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + input_ids = torch.tensor([context_tokens]).to(self.device) + + from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig + self.__class__.generate_stream = NewGenerationMixin.generate + self.__class__.sample_stream = NewGenerationMixin.sample_stream + stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True) + + def stream_generator(): + outputs = [] + for token in self.generate_stream( + input_ids, + return_dict_in_generate=False, + generation_config=stream_config, + logits_processor=logits_processor, + seed=-1, + **kwargs): + outputs.append(token.item()) + yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore', keep_image_special=True) + + return stream_generator() + + def generate( + self, + inputs: Optional[torch.Tensor] = None, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[ + Callable[[int, torch.Tensor], List[int]] + ] = None, + synced_gpus: Optional[bool] = None, + assistant_model: Optional["PreTrainedModel"] = None, + streamer: Optional["BaseStreamer"] = None, + **kwargs, + ) -> Union[GenerateOutput, torch.LongTensor]: + generation_config = generation_config if generation_config is not None else self.generation_config + + # Process stop_words_ids. + stop_words_ids = kwargs.pop("stop_words_ids", None) + if stop_words_ids is None and generation_config is not None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + if stop_words_ids is None: + stop_words_ids = getattr(generation_config, "stop_words_ids", None) + + if stop_words_ids is not None: + stop_words_logits_processor = StopWordsLogitsProcessor( + stop_words_ids=stop_words_ids, + eos_token_id=generation_config.eos_token_id, + ) + if logits_processor is None: + logits_processor = LogitsProcessorList([stop_words_logits_processor]) + else: + logits_processor.append(stop_words_logits_processor) + + return super().generate( + inputs, + generation_config=generation_config, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + synced_gpus=synced_gpus, + assistant_model=assistant_model, + streamer=streamer, + **kwargs, + ) + + +class RotaryEmbedding(torch.nn.Module): + def __init__(self, dim, base=10000): + super().__init__() + self.dim = dim + self.base = base + self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + if importlib.util.find_spec("einops") is None: + raise RuntimeError("einops is required for Rotary Embedding") + + self._rotary_pos_emb_cache = None + self._seq_len_cached = 0 + self._ntk_alpha_cached = 1.0 + + def update_rotary_pos_emb_cache(self, max_seq_len, offset=0, ntk_alpha=1.0): + seqlen = max_seq_len + offset + if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached: + base = self.base * ntk_alpha ** (self.dim / (self.dim - 2)) + self.inv_freq = 1.0 / ( + base + ** ( + torch.arange(0, self.dim, 2, device=self.inv_freq.device).float() + / self.dim + ) + ) + self._seq_len_cached = max(2 * seqlen, 16) + self._ntk_alpha_cached = ntk_alpha + seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device) + freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq) + + emb = torch.cat((freqs, freqs), dim=-1) + from einops import rearrange + + emb = rearrange(emb, "n d -> 1 n 1 d") + + cos, sin = emb.cos(), emb.sin() + self._rotary_pos_emb_cache = [cos, sin] + + def forward(self, max_seq_len, offset=0, ntk_alpha=1.0): + self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha) + cos, sin = self._rotary_pos_emb_cache + return [cos[:, offset : offset + max_seq_len], sin[:, offset : offset + max_seq_len]] + + +def _rotate_half(x): + from einops import rearrange + + x = rearrange(x, "... (j d) -> ... j d", j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(t, freqs): + cos, sin = freqs + if apply_rotary_emb_func is not None and t.is_cuda: + t_ = t.float() + cos = cos.squeeze(0).squeeze(1)[:, : cos.shape[-1] // 2] + sin = sin.squeeze(0).squeeze(1)[:, : sin.shape[-1] // 2] + output = apply_rotary_emb_func(t_, cos, sin).type_as(t) + return output + else: + rot_dim = freqs[0].shape[-1] + cos, sin = freqs + t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:] + t_ = t_.float() + t_pass_ = t_pass_.float() + t_ = (t_ * cos) + (_rotate_half(t_) * sin) + return torch.cat((t_, t_pass_), dim=-1).type_as(t) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + if rms_norm is not None and x.is_cuda: + return rms_norm(x, self.weight, self.eps) + else: + output = self._norm(x.float()).type_as(x) + return output * self.weight diff --git a/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/config.json b/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/config.json new file mode 100755 index 000000000..ea93bc66a --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/config.json @@ -0,0 +1,32 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_qwen2.Qwen2Config", + "AutoModelForCausalLM": "modeling_qwen2.Qwen2ForCausalLM" + }, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 2816, + "max_position_embeddings": 32768, + "max_window_layers": 21, + "model_type": "qwen2", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "num_key_value_heads": 16, + "rms_norm_eps": 1e-06, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "transformers_version": "4.37.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 151936 +} + diff --git a/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/configuration_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/configuration_qwen2.py new file mode 100644 index 000000000..b6ca1ed43 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/configuration_qwen2.py @@ -0,0 +1,144 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Qwen2 model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json", +} + + +class Qwen2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a + Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of + Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta). + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 151936): + Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Qwen2Model`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 22016): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*, defaults to 32): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 32768): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether the model's input and output word embeddings should be tied. + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + use_sliding_window (`bool`, *optional*, defaults to `False`): + Whether to use sliding window attention. + sliding_window (`int`, *optional*, defaults to 4096): + Sliding window attention (SWA) window size. If not specified, will default to `4096`. + max_window_layers (`int`, *optional*, defaults to 28): + The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + + ```python + >>> from transformers import Qwen2Model, Qwen2Config + + >>> # Initializing a Qwen2 style configuration + >>> configuration = Qwen2Config() + + >>> # Initializing a model from the Qwen2-7B style configuration + >>> model = Qwen2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=151936, + hidden_size=4096, + intermediate_size=22016, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=32, + hidden_act="silu", + max_position_embeddings=32768, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + tie_word_embeddings=False, + rope_theta=10000.0, + use_sliding_window=False, + sliding_window=4096, + max_window_layers=28, + attention_dropout=0.0, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.use_sliding_window = use_sliding_window + self.sliding_window = sliding_window + self.max_window_layers = max_window_layers + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_dropout = attention_dropout + + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/modeling_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/modeling_qwen2.py new file mode 100644 index 000000000..23b740356 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-0_5B-Chat/modeling_qwen2.py @@ -0,0 +1,1412 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Qwen2 model.""" +import inspect +import math +import warnings +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache +from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from .configuration_qwen2 import Qwen2Config + + +# if is_flash_attn_2_available(): + #from flash_attn import flash_attn_func, flash_attn_varlen_func + #from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + #_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) + + +logger = logging.get_logger(__name__) + + +_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta" +_CONFIG_FOR_DOC = "Qwen2Config" + +QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "Qwen/Qwen2-7B-beta", + # See all Qwen2 models at https://huggingface.co/models?filter=qwen2 +] + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2 +class Qwen2RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Qwen2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Qwen2 +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2 +class Qwen2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class Qwen2Attention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + self.attention_dropout = config.attention_dropout + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + + self.rotary_emb = Qwen2RotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[torch.Tensor] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + kv_seq_len += past_key_value[0].shape[2] + if rotary_pos_emb is None: + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + else: + cos, sin = rotary_pos_emb + query_states = (query_states * cos) + (rotate_half(query_states) * sin) + key_states = (key_states * cos) + (rotate_half(key_states) * sin) + + if past_key_value is not None: + past_key, past_value = past_key_value[0], past_key_value[1] + key_states = torch.cat((past_key, key_states), dim=2) + value_states = torch.cat((past_value, value_states), dim=2) + # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + past_key_value = torch.stack((key_states, value_states)) + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class Qwen2FlashAttention2(Qwen2Attention): + """ + Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention` + as the weights of the module stays untouched. The only required change would be on the forward pass + where it needs to correctly call the public API of flash attention and deal with padding tokens + in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom + config.max_window_layers layers. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ): + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and self.config.use_sliding_window + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + if past_key_value is not None: + # Activate slicing cache only if the config has a value `sliding_windows` attribute + cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0 + if ( + getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and cache_has_contents + ): + slicing_tokens = 1 - self.config.sliding_window + + past_key = past_key_value[self.layer_idx][0] + past_value = past_key_value[self.layer_idx][1] + + past_key = past_key[:, :, slicing_tokens:, :].contiguous() + past_value = past_value[:, :, slicing_tokens:, :].contiguous() + + if past_key.shape[-2] != self.config.sliding_window - 1: + raise ValueError( + f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" + f" {past_key.shape}" + ) + + if attention_mask is not None: + attention_mask = attention_mask[:, slicing_tokens:] + attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) + + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + use_sliding_windows=False, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + use_sliding_windows (`bool`, *optional*): + Whether to activate sliding window attention. + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Decide whether to use SWA or not by layer index. + if use_sliding_windows and self.layer_idx >= self.config.max_window_layers: + use_sliding_windows = False + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + if not use_sliding_windows: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + return attn_output + + # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape + + # On the first iteration we need to properly re-create the padding mask + # by slicing it on the proper place + if kv_seq_len != attention_mask.shape[-1]: + attention_mask_num_tokens = attention_mask.shape[-1] + attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Qwen2 +class Qwen2SdpaAttention(Qwen2Attention): + """ + Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from Qwen2Attention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +QWEN2_ATTENTION_CLASSES = { + "eager": Qwen2Attention, + "flash_attention_2": Qwen2FlashAttention2, + "sdpa": Qwen2SdpaAttention, +} + + +class Qwen2DecoderLayer(nn.Module): + def __init__(self, config: Qwen2Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + if config.use_sliding_window and config._attn_implementation != "flash_attention_2": + logger.warning_once( + f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " + "unexpected results may be encountered." + ) + # self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) + self.self_attn = Qwen2Attention(config, layer_idx) + + self.mlp = Qwen2MLP(config) + self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. " + "Please make sure use `attention_mask` instead.`" + ) + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + rotary_pos_emb=rotary_pos_emb, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +QWEN2_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`Qwen2Config`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2PreTrainedModel(PreTrainedModel): + config_class = Qwen2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +QWEN2_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2Model(Qwen2PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] + + Args: + config: Qwen2Config + """ + + def __init__(self, config: Qwen2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + past_key_values_length = 0 + + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_length) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + + if self._attn_implementation == "flash_attention_2": + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa" and not output_attentions: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class Qwen2ForCausalLM(Qwen2PreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = Qwen2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Qwen2ForCausalLM + + >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + # Omit tokens covered by past_key_values + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The Qwen2 Model transformer with a sequence classification head on top (linear layer). + + [`Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + QWEN2_START_DOCSTRING, +) +class Qwen2ForSequenceClassification(Qwen2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = Qwen2Model(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/config.json b/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/config.json new file mode 100755 index 000000000..26ce493f6 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/config.json @@ -0,0 +1,31 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_qwen2.Qwen2Config", + "AutoModelForCausalLM": "modeling_qwen2.Qwen2ForCausalLM" + }, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 5504, + "max_position_embeddings": 32768, + "max_window_layers": 21, + "model_type": "qwen2", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "num_key_value_heads": 16, + "rms_norm_eps": 1e-06, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.37.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 151936 +} diff --git a/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/configuration_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/configuration_qwen2.py new file mode 100644 index 000000000..b6ca1ed43 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/configuration_qwen2.py @@ -0,0 +1,144 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Qwen2 model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json", +} + + +class Qwen2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a + Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of + Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta). + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 151936): + Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Qwen2Model`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 22016): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*, defaults to 32): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 32768): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether the model's input and output word embeddings should be tied. + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + use_sliding_window (`bool`, *optional*, defaults to `False`): + Whether to use sliding window attention. + sliding_window (`int`, *optional*, defaults to 4096): + Sliding window attention (SWA) window size. If not specified, will default to `4096`. + max_window_layers (`int`, *optional*, defaults to 28): + The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + + ```python + >>> from transformers import Qwen2Model, Qwen2Config + + >>> # Initializing a Qwen2 style configuration + >>> configuration = Qwen2Config() + + >>> # Initializing a model from the Qwen2-7B style configuration + >>> model = Qwen2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=151936, + hidden_size=4096, + intermediate_size=22016, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=32, + hidden_act="silu", + max_position_embeddings=32768, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + tie_word_embeddings=False, + rope_theta=10000.0, + use_sliding_window=False, + sliding_window=4096, + max_window_layers=28, + attention_dropout=0.0, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.use_sliding_window = use_sliding_window + self.sliding_window = sliding_window + self.max_window_layers = max_window_layers + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_dropout = attention_dropout + + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/modeling_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/modeling_qwen2.py new file mode 100644 index 000000000..23b740356 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-1_8B-Chat/modeling_qwen2.py @@ -0,0 +1,1412 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Qwen2 model.""" +import inspect +import math +import warnings +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache +from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from .configuration_qwen2 import Qwen2Config + + +# if is_flash_attn_2_available(): + #from flash_attn import flash_attn_func, flash_attn_varlen_func + #from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + #_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) + + +logger = logging.get_logger(__name__) + + +_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta" +_CONFIG_FOR_DOC = "Qwen2Config" + +QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "Qwen/Qwen2-7B-beta", + # See all Qwen2 models at https://huggingface.co/models?filter=qwen2 +] + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2 +class Qwen2RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Qwen2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Qwen2 +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2 +class Qwen2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class Qwen2Attention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + self.attention_dropout = config.attention_dropout + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + + self.rotary_emb = Qwen2RotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[torch.Tensor] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + kv_seq_len += past_key_value[0].shape[2] + if rotary_pos_emb is None: + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + else: + cos, sin = rotary_pos_emb + query_states = (query_states * cos) + (rotate_half(query_states) * sin) + key_states = (key_states * cos) + (rotate_half(key_states) * sin) + + if past_key_value is not None: + past_key, past_value = past_key_value[0], past_key_value[1] + key_states = torch.cat((past_key, key_states), dim=2) + value_states = torch.cat((past_value, value_states), dim=2) + # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + past_key_value = torch.stack((key_states, value_states)) + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class Qwen2FlashAttention2(Qwen2Attention): + """ + Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention` + as the weights of the module stays untouched. The only required change would be on the forward pass + where it needs to correctly call the public API of flash attention and deal with padding tokens + in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom + config.max_window_layers layers. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ): + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and self.config.use_sliding_window + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + if past_key_value is not None: + # Activate slicing cache only if the config has a value `sliding_windows` attribute + cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0 + if ( + getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and cache_has_contents + ): + slicing_tokens = 1 - self.config.sliding_window + + past_key = past_key_value[self.layer_idx][0] + past_value = past_key_value[self.layer_idx][1] + + past_key = past_key[:, :, slicing_tokens:, :].contiguous() + past_value = past_value[:, :, slicing_tokens:, :].contiguous() + + if past_key.shape[-2] != self.config.sliding_window - 1: + raise ValueError( + f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" + f" {past_key.shape}" + ) + + if attention_mask is not None: + attention_mask = attention_mask[:, slicing_tokens:] + attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) + + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + use_sliding_windows=False, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + use_sliding_windows (`bool`, *optional*): + Whether to activate sliding window attention. + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Decide whether to use SWA or not by layer index. + if use_sliding_windows and self.layer_idx >= self.config.max_window_layers: + use_sliding_windows = False + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + if not use_sliding_windows: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + return attn_output + + # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape + + # On the first iteration we need to properly re-create the padding mask + # by slicing it on the proper place + if kv_seq_len != attention_mask.shape[-1]: + attention_mask_num_tokens = attention_mask.shape[-1] + attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Qwen2 +class Qwen2SdpaAttention(Qwen2Attention): + """ + Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from Qwen2Attention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +QWEN2_ATTENTION_CLASSES = { + "eager": Qwen2Attention, + "flash_attention_2": Qwen2FlashAttention2, + "sdpa": Qwen2SdpaAttention, +} + + +class Qwen2DecoderLayer(nn.Module): + def __init__(self, config: Qwen2Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + if config.use_sliding_window and config._attn_implementation != "flash_attention_2": + logger.warning_once( + f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " + "unexpected results may be encountered." + ) + # self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) + self.self_attn = Qwen2Attention(config, layer_idx) + + self.mlp = Qwen2MLP(config) + self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. " + "Please make sure use `attention_mask` instead.`" + ) + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + rotary_pos_emb=rotary_pos_emb, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +QWEN2_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`Qwen2Config`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2PreTrainedModel(PreTrainedModel): + config_class = Qwen2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +QWEN2_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2Model(Qwen2PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] + + Args: + config: Qwen2Config + """ + + def __init__(self, config: Qwen2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + past_key_values_length = 0 + + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_length) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + + if self._attn_implementation == "flash_attention_2": + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa" and not output_attentions: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class Qwen2ForCausalLM(Qwen2PreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = Qwen2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Qwen2ForCausalLM + + >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + # Omit tokens covered by past_key_values + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The Qwen2 Model transformer with a sequence classification head on top (linear layer). + + [`Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + QWEN2_START_DOCSTRING, +) +class Qwen2ForSequenceClassification(Qwen2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = Qwen2Model(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/config.json b/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/config.json new file mode 100755 index 000000000..9f2be4f60 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/config.json @@ -0,0 +1,31 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_qwen2.Qwen2Config", + "AutoModelForCausalLM": "modeling_qwen2.Qwen2ForCausalLM" + }, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 2560, + "initializer_range": 0.02, + "intermediate_size": 6912, + "max_position_embeddings": 32768, + "max_window_layers": 21, + "model_type": "qwen2", + "num_attention_heads": 20, + "num_hidden_layers": 40, + "num_key_value_heads": 20, + "rms_norm_eps": 1e-06, + "rope_theta": 5000000.0, + "sliding_window": 32768, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.37.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 151936 +} diff --git a/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/configuration_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/configuration_qwen2.py new file mode 100644 index 000000000..b6ca1ed43 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/configuration_qwen2.py @@ -0,0 +1,144 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Qwen2 model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json", +} + + +class Qwen2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a + Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of + Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta). + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 151936): + Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Qwen2Model`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 22016): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*, defaults to 32): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 32768): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether the model's input and output word embeddings should be tied. + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + use_sliding_window (`bool`, *optional*, defaults to `False`): + Whether to use sliding window attention. + sliding_window (`int`, *optional*, defaults to 4096): + Sliding window attention (SWA) window size. If not specified, will default to `4096`. + max_window_layers (`int`, *optional*, defaults to 28): + The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + + ```python + >>> from transformers import Qwen2Model, Qwen2Config + + >>> # Initializing a Qwen2 style configuration + >>> configuration = Qwen2Config() + + >>> # Initializing a model from the Qwen2-7B style configuration + >>> model = Qwen2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=151936, + hidden_size=4096, + intermediate_size=22016, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=32, + hidden_act="silu", + max_position_embeddings=32768, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + tie_word_embeddings=False, + rope_theta=10000.0, + use_sliding_window=False, + sliding_window=4096, + max_window_layers=28, + attention_dropout=0.0, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.use_sliding_window = use_sliding_window + self.sliding_window = sliding_window + self.max_window_layers = max_window_layers + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_dropout = attention_dropout + + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/modeling_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/modeling_qwen2.py new file mode 100644 index 000000000..23b740356 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-4B-Chat/modeling_qwen2.py @@ -0,0 +1,1412 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Qwen2 model.""" +import inspect +import math +import warnings +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache +from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from .configuration_qwen2 import Qwen2Config + + +# if is_flash_attn_2_available(): + #from flash_attn import flash_attn_func, flash_attn_varlen_func + #from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + #_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) + + +logger = logging.get_logger(__name__) + + +_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta" +_CONFIG_FOR_DOC = "Qwen2Config" + +QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "Qwen/Qwen2-7B-beta", + # See all Qwen2 models at https://huggingface.co/models?filter=qwen2 +] + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2 +class Qwen2RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Qwen2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Qwen2 +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2 +class Qwen2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class Qwen2Attention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + self.attention_dropout = config.attention_dropout + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + + self.rotary_emb = Qwen2RotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[torch.Tensor] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + kv_seq_len += past_key_value[0].shape[2] + if rotary_pos_emb is None: + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + else: + cos, sin = rotary_pos_emb + query_states = (query_states * cos) + (rotate_half(query_states) * sin) + key_states = (key_states * cos) + (rotate_half(key_states) * sin) + + if past_key_value is not None: + past_key, past_value = past_key_value[0], past_key_value[1] + key_states = torch.cat((past_key, key_states), dim=2) + value_states = torch.cat((past_value, value_states), dim=2) + # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + past_key_value = torch.stack((key_states, value_states)) + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class Qwen2FlashAttention2(Qwen2Attention): + """ + Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention` + as the weights of the module stays untouched. The only required change would be on the forward pass + where it needs to correctly call the public API of flash attention and deal with padding tokens + in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom + config.max_window_layers layers. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ): + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and self.config.use_sliding_window + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + if past_key_value is not None: + # Activate slicing cache only if the config has a value `sliding_windows` attribute + cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0 + if ( + getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and cache_has_contents + ): + slicing_tokens = 1 - self.config.sliding_window + + past_key = past_key_value[self.layer_idx][0] + past_value = past_key_value[self.layer_idx][1] + + past_key = past_key[:, :, slicing_tokens:, :].contiguous() + past_value = past_value[:, :, slicing_tokens:, :].contiguous() + + if past_key.shape[-2] != self.config.sliding_window - 1: + raise ValueError( + f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" + f" {past_key.shape}" + ) + + if attention_mask is not None: + attention_mask = attention_mask[:, slicing_tokens:] + attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) + + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + use_sliding_windows=False, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + use_sliding_windows (`bool`, *optional*): + Whether to activate sliding window attention. + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Decide whether to use SWA or not by layer index. + if use_sliding_windows and self.layer_idx >= self.config.max_window_layers: + use_sliding_windows = False + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + if not use_sliding_windows: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + return attn_output + + # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape + + # On the first iteration we need to properly re-create the padding mask + # by slicing it on the proper place + if kv_seq_len != attention_mask.shape[-1]: + attention_mask_num_tokens = attention_mask.shape[-1] + attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Qwen2 +class Qwen2SdpaAttention(Qwen2Attention): + """ + Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from Qwen2Attention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +QWEN2_ATTENTION_CLASSES = { + "eager": Qwen2Attention, + "flash_attention_2": Qwen2FlashAttention2, + "sdpa": Qwen2SdpaAttention, +} + + +class Qwen2DecoderLayer(nn.Module): + def __init__(self, config: Qwen2Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + if config.use_sliding_window and config._attn_implementation != "flash_attention_2": + logger.warning_once( + f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " + "unexpected results may be encountered." + ) + # self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) + self.self_attn = Qwen2Attention(config, layer_idx) + + self.mlp = Qwen2MLP(config) + self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. " + "Please make sure use `attention_mask` instead.`" + ) + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + rotary_pos_emb=rotary_pos_emb, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +QWEN2_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`Qwen2Config`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2PreTrainedModel(PreTrainedModel): + config_class = Qwen2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +QWEN2_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2Model(Qwen2PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] + + Args: + config: Qwen2Config + """ + + def __init__(self, config: Qwen2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + past_key_values_length = 0 + + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_length) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + + if self._attn_implementation == "flash_attention_2": + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa" and not output_attentions: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class Qwen2ForCausalLM(Qwen2PreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = Qwen2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Qwen2ForCausalLM + + >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + # Omit tokens covered by past_key_values + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The Qwen2 Model transformer with a sequence classification head on top (linear layer). + + [`Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + QWEN2_START_DOCSTRING, +) +class Qwen2ForSequenceClassification(Qwen2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = Qwen2Model(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/config.json b/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/config.json new file mode 100755 index 000000000..6b0cfb9b4 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/config.json @@ -0,0 +1,31 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_qwen2.Qwen2Config", + "AutoModelForCausalLM": "modeling_qwen2.Qwen2ForCausalLM" + }, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 11008, + "max_position_embeddings": 32768, + "max_window_layers": 28, + "model_type": "qwen2", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 32, + "rms_norm_eps": 1e-06, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.37.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 151936 +} diff --git a/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/configuration_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/configuration_qwen2.py new file mode 100644 index 000000000..b6ca1ed43 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/configuration_qwen2.py @@ -0,0 +1,144 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Qwen2 model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json", +} + + +class Qwen2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a + Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of + Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta). + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 151936): + Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Qwen2Model`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 22016): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*, defaults to 32): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 32768): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether the model's input and output word embeddings should be tied. + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + use_sliding_window (`bool`, *optional*, defaults to `False`): + Whether to use sliding window attention. + sliding_window (`int`, *optional*, defaults to 4096): + Sliding window attention (SWA) window size. If not specified, will default to `4096`. + max_window_layers (`int`, *optional*, defaults to 28): + The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + + ```python + >>> from transformers import Qwen2Model, Qwen2Config + + >>> # Initializing a Qwen2 style configuration + >>> configuration = Qwen2Config() + + >>> # Initializing a model from the Qwen2-7B style configuration + >>> model = Qwen2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=151936, + hidden_size=4096, + intermediate_size=22016, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=32, + hidden_act="silu", + max_position_embeddings=32768, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + tie_word_embeddings=False, + rope_theta=10000.0, + use_sliding_window=False, + sliding_window=4096, + max_window_layers=28, + attention_dropout=0.0, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.use_sliding_window = use_sliding_window + self.sliding_window = sliding_window + self.max_window_layers = max_window_layers + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_dropout = attention_dropout + + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/modeling_qwen2.py b/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/modeling_qwen2.py new file mode 100644 index 000000000..23b740356 --- /dev/null +++ b/transformers/llm/export/llm_models/Qwen1_5-7B-Chat/modeling_qwen2.py @@ -0,0 +1,1412 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Qwen2 model.""" +import inspect +import math +import warnings +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache +from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from .configuration_qwen2 import Qwen2Config + + +# if is_flash_attn_2_available(): + #from flash_attn import flash_attn_func, flash_attn_varlen_func + #from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + #_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) + + +logger = logging.get_logger(__name__) + + +_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta" +_CONFIG_FOR_DOC = "Qwen2Config" + +QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "Qwen/Qwen2-7B-beta", + # See all Qwen2 models at https://huggingface.co/models?filter=qwen2 +] + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2 +class Qwen2RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Qwen2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Qwen2 +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2 +class Qwen2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class Qwen2Attention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + self.attention_dropout = config.attention_dropout + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + + self.rotary_emb = Qwen2RotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[torch.Tensor] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + kv_seq_len += past_key_value[0].shape[2] + if rotary_pos_emb is None: + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + else: + cos, sin = rotary_pos_emb + query_states = (query_states * cos) + (rotate_half(query_states) * sin) + key_states = (key_states * cos) + (rotate_half(key_states) * sin) + + if past_key_value is not None: + past_key, past_value = past_key_value[0], past_key_value[1] + key_states = torch.cat((past_key, key_states), dim=2) + value_states = torch.cat((past_value, value_states), dim=2) + # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + past_key_value = torch.stack((key_states, value_states)) + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class Qwen2FlashAttention2(Qwen2Attention): + """ + Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention` + as the weights of the module stays untouched. The only required change would be on the forward pass + where it needs to correctly call the public API of flash attention and deal with padding tokens + in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom + config.max_window_layers layers. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ): + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and self.config.use_sliding_window + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + if past_key_value is not None: + # Activate slicing cache only if the config has a value `sliding_windows` attribute + cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0 + if ( + getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and cache_has_contents + ): + slicing_tokens = 1 - self.config.sliding_window + + past_key = past_key_value[self.layer_idx][0] + past_value = past_key_value[self.layer_idx][1] + + past_key = past_key[:, :, slicing_tokens:, :].contiguous() + past_value = past_value[:, :, slicing_tokens:, :].contiguous() + + if past_key.shape[-2] != self.config.sliding_window - 1: + raise ValueError( + f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" + f" {past_key.shape}" + ) + + if attention_mask is not None: + attention_mask = attention_mask[:, slicing_tokens:] + attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) + + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + use_sliding_windows=False, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + use_sliding_windows (`bool`, *optional*): + Whether to activate sliding window attention. + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Decide whether to use SWA or not by layer index. + if use_sliding_windows and self.layer_idx >= self.config.max_window_layers: + use_sliding_windows = False + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + if not use_sliding_windows: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + return attn_output + + # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape + + # On the first iteration we need to properly re-create the padding mask + # by slicing it on the proper place + if kv_seq_len != attention_mask.shape[-1]: + attention_mask_num_tokens = attention_mask.shape[-1] + attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Qwen2 +class Qwen2SdpaAttention(Qwen2Attention): + """ + Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from Qwen2Attention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +QWEN2_ATTENTION_CLASSES = { + "eager": Qwen2Attention, + "flash_attention_2": Qwen2FlashAttention2, + "sdpa": Qwen2SdpaAttention, +} + + +class Qwen2DecoderLayer(nn.Module): + def __init__(self, config: Qwen2Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + if config.use_sliding_window and config._attn_implementation != "flash_attention_2": + logger.warning_once( + f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " + "unexpected results may be encountered." + ) + # self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) + self.self_attn = Qwen2Attention(config, layer_idx) + + self.mlp = Qwen2MLP(config) + self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_pos_emb: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. " + "Please make sure use `attention_mask` instead.`" + ) + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + rotary_pos_emb=rotary_pos_emb, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +QWEN2_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`Qwen2Config`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2PreTrainedModel(PreTrainedModel): + config_class = Qwen2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +QWEN2_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.", + QWEN2_START_DOCSTRING, +) +class Qwen2Model(Qwen2PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] + + Args: + config: Qwen2Config + """ + + def __init__(self, config: Qwen2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + past_key_values_length = 0 + + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_length) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + + if self._attn_implementation == "flash_attention_2": + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa" and not output_attentions: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class Qwen2ForCausalLM(Qwen2PreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = Qwen2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Qwen2ForCausalLM + + >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + # Omit tokens covered by past_key_values + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The Qwen2 Model transformer with a sequence classification head on top (linear layer). + + [`Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + QWEN2_START_DOCSTRING, +) +class Qwen2ForSequenceClassification(Qwen2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = Qwen2Model(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/config.json b/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/config.json new file mode 100755 index 000000000..117c9e5d6 --- /dev/null +++ b/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/config.json @@ -0,0 +1,30 @@ +{ + "_name_or_path": "/mnt/petrelfs/libo1.p/alignment-handbook/data/tinyllama-2T-sft-full", + "architectures": [ + "LlamaForCausalLM" + ], + "auto_map": { + "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM" + }, + "attention_bias": false, + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 5632, + "max_position_embeddings": 2048, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 22, + "num_key_value_heads": 4, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 10000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.35.0", + "use_cache": false, + "vocab_size": 32000 +} diff --git a/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/configuration_llama.py b/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/configuration_llama.py new file mode 100644 index 000000000..1b0e9c357 --- /dev/null +++ b/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/configuration_llama.py @@ -0,0 +1,174 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" LLaMA model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {} + + +class LlamaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the LLaMA-7B. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`LlamaModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + pretraining_tp (`int`, *optional*, defaults to `1`): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is + necessary to ensure exact reproducibility of the pretraining results. Please refer to [this + issue](https://github.com/pytorch/pytorch/issues/76232). + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings(`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling + strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format + is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + + Example: + + ```python + >>> from transformers import LlamaModel, LlamaConfig + + >>> # Initializing a LLaMA llama-7b style configuration + >>> configuration = LlamaConfig() + + >>> # Initializing a model from the llama-7b style configuration + >>> model = LlamaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + model_type = "llama" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=False, + rope_scaling=None, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_scaling = rope_scaling + self._rope_scaling_validation() + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, " + f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: + raise ValueError( + f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" + ) + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}") diff --git a/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/modeling_llama.py b/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/modeling_llama.py new file mode 100644 index 000000000..8c562c604 --- /dev/null +++ b/transformers/llm/export/llm_models/TinyLlama-1.1B-Chat/modeling_llama.py @@ -0,0 +1,1010 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch LLaMA model.""" +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_llama import LlamaConfig + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "LlamaConfig" + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class LlamaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + LlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +class LlamaRotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. + # cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] + # sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] + cos = torch.squeeze(cos) # [seq_len, dim] + sin = torch.squeeze(sin) # [seq_len, dim] + cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class LlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.pretraining_tp = config.pretraining_tp + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + if self.pretraining_tp > 1: + slice = self.intermediate_size // self.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class LlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: LlamaConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.pretraining_tp = config.pretraining_tp + self.max_position_embeddings = config.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = LlamaLinearScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + elif scaling_type == "dynamic": + self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + if self.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp + query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + if self.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaDecoderLayer(nn.Module): + def __init__(self, config: LlamaConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = LlamaAttention(config=config) + self.mlp = LlamaMLP(config) + self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +LLAMA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`LlamaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaPreTrainedModel(PreTrainedModel): + config_class = LlamaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["LlamaDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, LlamaModel): + module.gradient_checkpointing = value + + +LLAMA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaModel(LlamaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] + + Args: + config: LlamaConfig + """ + + def __init__(self, config: LlamaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class LlamaForCausalLM(LlamaPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = LlamaModel(config) + self.pretraining_tp = config.pretraining_tp + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The LLaMa Model transformer with a sequence classification head on top (linear layer). + + [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + LLAMA_START_DOCSTRING, +) +class LlamaForSequenceClassification(LlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = LlamaModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/Yi-6B-Chat/config.json b/transformers/llm/export/llm_models/Yi-6B-Chat/config.json new file mode 100755 index 000000000..aad6b1d39 --- /dev/null +++ b/transformers/llm/export/llm_models/Yi-6B-Chat/config.json @@ -0,0 +1,29 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "auto_map": { + "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM" + }, + "attention_bias": false, + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 11008, + "max_position_embeddings": 4096, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 4, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 5000000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.35.0", + "use_cache": true, + "vocab_size": 64000 +} diff --git a/transformers/llm/export/llm_models/Yi-6B-Chat/configuration_llama.py b/transformers/llm/export/llm_models/Yi-6B-Chat/configuration_llama.py new file mode 100644 index 000000000..1b0e9c357 --- /dev/null +++ b/transformers/llm/export/llm_models/Yi-6B-Chat/configuration_llama.py @@ -0,0 +1,174 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" LLaMA model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {} + + +class LlamaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the LLaMA-7B. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`LlamaModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + pretraining_tp (`int`, *optional*, defaults to `1`): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is + necessary to ensure exact reproducibility of the pretraining results. Please refer to [this + issue](https://github.com/pytorch/pytorch/issues/76232). + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings(`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling + strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format + is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + + Example: + + ```python + >>> from transformers import LlamaModel, LlamaConfig + + >>> # Initializing a LLaMA llama-7b style configuration + >>> configuration = LlamaConfig() + + >>> # Initializing a model from the llama-7b style configuration + >>> model = LlamaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + model_type = "llama" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=False, + rope_scaling=None, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_scaling = rope_scaling + self._rope_scaling_validation() + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, " + f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: + raise ValueError( + f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" + ) + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}") diff --git a/transformers/llm/export/llm_models/Yi-6B-Chat/modeling_llama.py b/transformers/llm/export/llm_models/Yi-6B-Chat/modeling_llama.py new file mode 100644 index 000000000..8c562c604 --- /dev/null +++ b/transformers/llm/export/llm_models/Yi-6B-Chat/modeling_llama.py @@ -0,0 +1,1010 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch LLaMA model.""" +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_llama import LlamaConfig + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "LlamaConfig" + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class LlamaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + LlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +class LlamaRotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. + # cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] + # sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] + cos = torch.squeeze(cos) # [seq_len, dim] + sin = torch.squeeze(sin) # [seq_len, dim] + cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class LlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.pretraining_tp = config.pretraining_tp + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + if self.pretraining_tp > 1: + slice = self.intermediate_size // self.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class LlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: LlamaConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.pretraining_tp = config.pretraining_tp + self.max_position_embeddings = config.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = LlamaLinearScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + elif scaling_type == "dynamic": + self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + if self.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp + query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + if self.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaDecoderLayer(nn.Module): + def __init__(self, config: LlamaConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = LlamaAttention(config=config) + self.mlp = LlamaMLP(config) + self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +LLAMA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`LlamaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaPreTrainedModel(PreTrainedModel): + config_class = LlamaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["LlamaDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, LlamaModel): + module.gradient_checkpointing = value + + +LLAMA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaModel(LlamaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] + + Args: + config: LlamaConfig + """ + + def __init__(self, config: LlamaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class LlamaForCausalLM(LlamaPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = LlamaModel(config) + self.pretraining_tp = config.pretraining_tp + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The LLaMa Model transformer with a sequence classification head on top (linear layer). + + [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + LLAMA_START_DOCSTRING, +) +class LlamaForSequenceClassification(LlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = LlamaModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/chatglm-6b/modeling_chatglm.py b/transformers/llm/export/llm_models/chatglm-6b/modeling_chatglm.py new file mode 100644 index 000000000..82effe877 --- /dev/null +++ b/transformers/llm/export/llm_models/chatglm-6b/modeling_chatglm.py @@ -0,0 +1,1441 @@ +""" PyTorch ChatGLM model. """ + +import math +import copy +import os +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any + +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + BaseModelOutputWithPastAndCrossAttentions, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput + +from .configuration_chatglm import ChatGLMConfig + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm-6b", + # See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm +] + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path): + """Load tf checkpoints in a pytorch model.""" + try: + import re + + import numpy as np + import tensorflow as tf + except ImportError: + logger.error( + "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions." + ) + raise + tf_path = os.path.abspath(tf_checkpoint_path) + logger.info(f"Converting TensorFlow checkpoint from {tf_path}") + # Load weights from TF model + init_vars = tf.train.list_variables(tf_path) + names = [] + arrays = [] + for name, shape in init_vars: + logger.info(f"Loading TF weight {name} with shape {shape}") + array = tf.train.load_variable(tf_path, name) + names.append(name) + arrays.append(array) + + for name, array in zip(names, arrays): + name = name.split("/") + # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v + # which are not required for using pretrained model + if any( + n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] + for n in name + ): + logger.info(f"Skipping {'/'.join(name)}") + continue + pointer = model + for m_name in name: + if re.fullmatch(r"[A-Za-z]+_\d+", m_name): + scope_names = re.split(r"_(\d+)", m_name) + else: + scope_names = [m_name] + if scope_names[0] == "kernel" or scope_names[0] == "gamma": + pointer = getattr(pointer, "weight") + elif scope_names[0] == "output_bias" or scope_names[0] == "beta": + pointer = getattr(pointer, "bias") + elif scope_names[0] == "output_weights": + pointer = getattr(pointer, "weight") + elif scope_names[0] == "squad": + pointer = getattr(pointer, "classifier") + else: + try: + pointer = getattr(pointer, scope_names[0]) + except AttributeError: + logger.info(f"Skipping {'/'.join(name)}") + continue + if len(scope_names) >= 2: + num = int(scope_names[1]) + pointer = pointer[num] + if m_name[-11:] == "_embeddings": + pointer = getattr(pointer, "weight") + elif m_name == "kernel": + array = np.transpose(array) + try: + assert ( + pointer.shape == array.shape + ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + logger.info(f"Initialize PyTorch weight {name}") + pointer.data = torch.from_numpy(array) + return model + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(config.hidden_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2) + ) + else: + self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +@torch.jit.script +def gelu_impl(x): + """OpenAI's gelu implementation.""" + return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * + (1.0 + 0.044715 * x * x))) + + +def gelu(x): + return gelu_impl(x) + + +class RotaryEmbedding(torch.nn.Module): + def __init__(self, dim, base=10000, precision=torch.half, learnable=False): + super().__init__() + inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) + inv_freq = inv_freq.half() + self.learnable = learnable + if learnable: + self.inv_freq = torch.nn.Parameter(inv_freq) + self.max_seq_len_cached = None + else: + self.register_buffer('inv_freq', inv_freq) + self.max_seq_len_cached = None + self.cos_cached = None + self.sin_cached = None + self.precision = precision + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, + error_msgs): + pass + + def forward(self, x, seq_dim=1, seq_len=None): + if seq_len is None: + seq_len = x.shape[seq_dim] + if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached): + self.max_seq_len_cached = None if self.learnable else seq_len + t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum('i,j->ij', t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + if self.precision == torch.bfloat16: + emb = emb.float() + + # [sx, 1 (b * np), hn] + cos_cached = emb.cos()[:, None, :] + sin_cached = emb.sin()[:, None, :] + if self.precision == torch.bfloat16: + cos_cached = cos_cached.bfloat16() + sin_cached = sin_cached.bfloat16() + if self.learnable: + return cos_cached, sin_cached + self.cos_cached, self.sin_cached = cos_cached, sin_cached + return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...] + + def _apply(self, fn): + if self.cos_cached is not None: + self.cos_cached = fn(self.cos_cached) + if self.sin_cached is not None: + self.sin_cached = fn(self.sin_cached) + return super()._apply(fn) + + +def rotate_half(x): + x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:] + return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions + + +@torch.jit.script +def apply_rotary_pos_emb_index(q, k, cos, sin, position_id): + # position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn] + cos, sin = F.embedding(position_id, torch.squeeze(cos)).unsqueeze(2), \ + F.embedding(position_id, torch.squeeze(sin)).unsqueeze(2) + q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin) + return q, k + + +def attention_fn( + self, + query_layer, + key_layer, + value_layer, + attention_mask, + hidden_size_per_partition, + layer_id, + layer_past=None, + scaling_attention_score=True, + use_cache=False, +): + if layer_past is not None: + past_key, past_value = layer_past[0], layer_past[1] + key_layer = torch.cat((past_key, key_layer), dim=0) + value_layer = torch.cat((past_value, value_layer), dim=0) + + # seqlen, batch, num_attention_heads, hidden_size_per_attention_head + seq_len, b, nh, hidden_size = key_layer.shape + + if use_cache: + present = (key_layer, value_layer) + else: + present = None + + query_key_layer_scaling_coeff = float(layer_id + 1) + if scaling_attention_score: + query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff) + + # =================================== + # Raw attention scores. [b, np, s, s] + # =================================== + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + #query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + #key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + query_layer = query_layer.squeeze(1) + key_layer = key_layer.squeeze(1) + + matmul_result = torch.zeros( + 1, 1, 1, + dtype=query_layer.dtype, + device=query_layer.device, + ) + + matmul_result = torch.baddbmm( + matmul_result, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=1.0, + ) + + # change view to [b, np, sq, sk] + # attention_scores = matmul_result.view(*output_size) + attention_scores = matmul_result.unsqueeze(0) + + if self.scale_mask_softmax: + self.scale_mask_softmax.scale = query_key_layer_scaling_coeff + attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous()) + else: + if not (attention_mask == 0).all(): + # if auto-regressive, skip + attention_scores.masked_fill_(attention_mask, -10000.0) + dtype = attention_scores.dtype + attention_scores = attention_scores.float() + attention_scores = attention_scores * query_key_layer_scaling_coeff + + attention_probs = F.softmax(attention_scores, dim=-1) + + attention_probs = attention_probs.type(dtype) + + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + + # change view [sk, b * np, hn] + # value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + value_layer = value_layer.squeeze(1) + + # change view [b * np, sq, sk] + # attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + attention_probs = attention_probs.squeeze(0) + + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + + # change view [b, np, sq, hn] + # context_layer = context_layer.view(*output_size) + context_layer = context_layer.unsqueeze(0) + + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,) + # context_layer = context_layer.view(*new_context_layer_shape) + context_layer = context_layer.view([-1, 1, hidden_size_per_partition]) + outputs = (context_layer, present, attention_probs) + + return outputs + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class SelfAttention(torch.nn.Module): + def __init__(self, hidden_size, num_attention_heads, + layer_id, hidden_size_per_attention_head=None, bias=True, + params_dtype=torch.float, position_encoding_2d=True, empty_init=True): + if empty_init: + init_method = skip_init + else: + init_method = default_init + super(SelfAttention, self).__init__() + + self.layer_id = layer_id + self.hidden_size = hidden_size + self.hidden_size_per_partition = hidden_size + self.num_attention_heads = num_attention_heads + self.num_attention_heads_per_partition = num_attention_heads + self.position_encoding_2d = position_encoding_2d + self.rotary_emb = RotaryEmbedding( + self.hidden_size // (self.num_attention_heads * 2) + if position_encoding_2d + else self.hidden_size // self.num_attention_heads, + base=10000, + precision=torch.half, + learnable=False, + ) + + self.scale_mask_softmax = None + + if hidden_size_per_attention_head is None: + self.hidden_size_per_attention_head = hidden_size // num_attention_heads + else: + self.hidden_size_per_attention_head = hidden_size_per_attention_head + + self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head + + # Strided linear layer. + self.query_key_value = init_method( + torch.nn.Linear, + hidden_size, + 3 * self.inner_hidden_size, + bias=bias, + dtype=params_dtype, + ) + + self.dense = init_method( + torch.nn.Linear, + self.inner_hidden_size, + hidden_size, + bias=bias, + dtype=params_dtype, + ) + + @staticmethod + def attention_mask_func(attention_scores, attention_mask): + attention_scores.masked_fill_(attention_mask, -10000.0) + return attention_scores + + def split_tensor_along_last_dim(self, tensor, num_partitions, + contiguous_split_chunks=False): + """Split a tensor along its last dimension. + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + def forward( + self, + hidden_states: torch.Tensor, + position_ids, + attention_mask: torch.Tensor, + layer_id, + layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + use_cache: bool = False, + output_attentions: bool = False, + ): + """ + hidden_states: [seq_len, batch, hidden_size] + attention_mask: [(1, 1), seq_len, seq_len] + """ + + # [seq_len, batch, 3 * hidden_size] + mixed_raw_layer = self.query_key_value(hidden_states) + + # [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head] + new_tensor_shape = mixed_raw_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head, + ) + mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape) + + # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head] + (query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3) + + if self.position_encoding_2d: + q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1)) + k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1)) + cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1) + position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \ + position_ids[:, 1, :].transpose(0, 1).contiguous() + q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids) + q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids) + query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1)) + key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1)) + else: + position_ids = position_ids.transpose(0, 1) + cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1) + # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head] + query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids) + + # [seq_len, batch, hidden_size] + context_layer, present, attention_probs = attention_fn( + self=self, + query_layer=query_layer, + key_layer=key_layer, + value_layer=value_layer, + attention_mask=attention_mask, + hidden_size_per_partition=self.hidden_size_per_partition, + layer_id=layer_id, + layer_past=layer_past, + use_cache=use_cache + ) + + output = self.dense(context_layer) + + outputs = (output, present) + + if output_attentions: + outputs += (attention_probs,) + + return outputs # output, present, attention_probs + + +class GEGLU(torch.nn.Module): + def __init__(self): + super().__init__() + self.activation_fn = F.gelu + + def forward(self, x): + # dim=-1 breaks in jit for pt<1.10 + x1, x2 = x.chunk(2, dim=(x.ndim - 1)) + return x1 * self.activation_fn(x2) + + +class GLU(torch.nn.Module): + def __init__(self, hidden_size, inner_hidden_size=None, + layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float, empty_init=True): + super(GLU, self).__init__() + if empty_init: + init_method = skip_init + else: + init_method = default_init + self.layer_id = layer_id + self.activation_func = activation_func + + # Project to 4h. + self.hidden_size = hidden_size + if inner_hidden_size is None: + inner_hidden_size = 4 * hidden_size + self.inner_hidden_size = inner_hidden_size + self.dense_h_to_4h = init_method( + torch.nn.Linear, + self.hidden_size, + self.inner_hidden_size, + bias=bias, + dtype=params_dtype, + ) + # Project back to h. + self.dense_4h_to_h = init_method( + torch.nn.Linear, + self.inner_hidden_size, + self.hidden_size, + bias=bias, + dtype=params_dtype, + ) + + def forward(self, hidden_states): + """ + hidden_states: [seq_len, batch, hidden_size] + """ + + # [seq_len, batch, inner_hidden_size] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + + intermediate_parallel = self.activation_func(intermediate_parallel) + + output = self.dense_4h_to_h(intermediate_parallel) + + return output + + +class GLMBlock(torch.nn.Module): + def __init__( + self, + hidden_size, + num_attention_heads, + layernorm_epsilon, + layer_id, + inner_hidden_size=None, + hidden_size_per_attention_head=None, + layernorm=LayerNorm, + use_bias=True, + params_dtype=torch.float, + num_layers=28, + position_encoding_2d=True, + empty_init=True + ): + super(GLMBlock, self).__init__() + # Set output layer initialization if not provided. + + self.layer_id = layer_id + + # Layernorm on the input data. + self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon) + + self.position_encoding_2d = position_encoding_2d + + # Self attention. + self.attention = SelfAttention( + hidden_size, + num_attention_heads, + layer_id, + hidden_size_per_attention_head=hidden_size_per_attention_head, + bias=use_bias, + params_dtype=params_dtype, + position_encoding_2d=self.position_encoding_2d, + empty_init=empty_init + ) + + # Layernorm on the input data. + self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon) + + self.num_layers = num_layers + + # GLU + self.mlp = GLU( + hidden_size, + inner_hidden_size=inner_hidden_size, + bias=use_bias, + layer_id=layer_id, + params_dtype=params_dtype, + empty_init=empty_init + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_ids, + attention_mask: torch.Tensor, + layer_id, + layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + use_cache: bool = False, + output_attentions: bool = False, + ): + """ + hidden_states: [seq_len, batch, hidden_size] + attention_mask: [(1, 1), seq_len, seq_len] + """ + + # Layer norm at the begining of the transformer layer. + # [seq_len, batch, hidden_size] + attention_input = self.input_layernorm(hidden_states) + + # Self attention. + attention_outputs = self.attention( + attention_input, + position_ids, + attention_mask=attention_mask, + layer_id=layer_id, + layer_past=layer_past, + use_cache=use_cache, + output_attentions=output_attentions + ) + + attention_output = attention_outputs[0] + + outputs = attention_outputs[1:] + + # Residual connection. + alpha = (2 * self.num_layers) ** 0.5 + hidden_states = attention_input * alpha + attention_output + + mlp_input = self.post_attention_layernorm(hidden_states) + + # MLP. + mlp_output = self.mlp(mlp_input) + + # Second residual connection. + output = mlp_input * alpha + mlp_output + + if use_cache: + outputs = (output,) + outputs + else: + outputs = (output,) + outputs[1:] + + return outputs # hidden_states, present, attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, device): + batch_size, seq_length = input_ids.shape + context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids] + attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device) + attention_mask.tril_() + for i, context_length in enumerate(context_lengths): + attention_mask[i, :, :context_length] = 1 + attention_mask.unsqueeze_(1) + attention_mask = (attention_mask < 0.5).bool() + + return attention_mask + + def get_position_ids(self, input_ids, mask_positions, device, use_gmasks=None): + batch_size, seq_length = input_ids.shape + if use_gmasks is None: + use_gmasks = [False] * batch_size + context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids] + if self.position_encoding_2d: + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + for i, context_length in enumerate(context_lengths): + position_ids[i, context_length:] = mask_positions[i] + block_position_ids = [torch.cat(( + torch.zeros(context_length, dtype=torch.long, device=device), + torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1 + )) for context_length in context_lengths] + block_position_ids = torch.stack(block_position_ids, dim=0) + position_ids = torch.stack((position_ids, block_position_ids), dim=1) + else: + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + for i, context_length in enumerate(context_lengths): + if not use_gmasks[i]: + position_ids[i, context_length:] = mask_positions[i] + + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, ChatGLMModel): + module.gradient_checkpointing = value + + +CHATGLM_6B_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general + usage and behavior. + + Parameters: + config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the configuration. + Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +CHATGLM_6B_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`ChatGLM6BTokenizer`]. + See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. + Selected in the range `[0, config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert *input_ids* indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.", + CHATGLM_6B_START_DOCSTRING, +) +class ChatGLMModel(ChatGLMPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well + as a decoder, in which case a layer of cross-attention is added between + the self-attention layers, following the architecture described in [Attention is + all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, + Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the + `is_decoder` argument of the configuration set to `True`. + To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` + argument and `add_cross_attention` set to `True`; an + `encoder_hidden_states` is then expected as an input to the forward pass. + """ + + def __init__(self, config: ChatGLMConfig, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + # recording parameters + self.max_sequence_length = config.max_sequence_length + self.hidden_size = config.hidden_size + self.params_dtype = torch.half + self.num_attention_heads = config.num_attention_heads + self.vocab_size = config.vocab_size + self.num_layers = config.num_layers + self.layernorm_epsilon = config.layernorm_epsilon + self.inner_hidden_size = config.inner_hidden_size + self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads + self.position_encoding_2d = config.position_encoding_2d + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + + self.word_embeddings = init_method( + torch.nn.Embedding, + num_embeddings=self.vocab_size, embedding_dim=self.hidden_size, + dtype=self.params_dtype + ) + self.gradient_checkpointing = False + + def get_layer(layer_id): + return GLMBlock( + self.hidden_size, + self.num_attention_heads, + self.layernorm_epsilon, + layer_id, + inner_hidden_size=self.inner_hidden_size, + hidden_size_per_attention_head=self.hidden_size_per_attention_head, + layernorm=LayerNorm, + use_bias=True, + params_dtype=self.params_dtype, + position_encoding_2d=self.position_encoding_2d, + empty_init=empty_init + ) + + self.layers = torch.nn.ModuleList( + [get_layer(layer_id) for layer_id in range(self.num_layers)] + ) + + # Final layer norm before output. + self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon) + + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + # total_params = sum(p.numel() for p in self.parameters()) + # trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad) + # print("Using p-tuning v2: # trainable_params = {} / {}".format(trainable_params, total_params)) + + def get_input_embeddings(self): + return self.word_embeddings + + def set_input_embeddings(self, new_embeddings: torch.Tensor): + self.word_embeddings = new_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.num_attention_heads, + self.hidden_size // self.num_attention_heads + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + # past_key_values = [(v[0], v[1]) for v in past_key_values] + return past_key_values + + @add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPastAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPast]: + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape[:2] + elif inputs_embeds is not None: + batch_size, seq_length = inputs_embeds.shape[:2] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + if past_key_values is None: + if self.pre_seq_len is not None: + past_key_values = self.get_prompt(batch_size=input_ids.shape[0], device=input_ids.device, + dtype=inputs_embeds.dtype) + else: + past_key_values = tuple([None] * len(self.layers)) + + if attention_mask is None: + attention_mask = self.get_masks( + input_ids, + device=input_ids.device + ) + + + if position_ids is None: + MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id + seqs = input_ids.tolist() + + mask_positions, use_gmasks = [], [] + for seq in seqs: + mask_token = gMASK if gMASK in seq else MASK + use_gmask = mask_token == gMASK + mask_positions.append(seq.index(mask_token)) + use_gmasks.append(use_gmask) + + position_ids = self.get_position_ids( + input_ids, + mask_positions=mask_positions, + device=input_ids.device, + use_gmasks=use_gmasks + ) + + if self.pre_seq_len is not None and attention_mask is not None: + prefix_attention_mask = torch.ones(batch_size, 1, input_ids.size(-1), self.pre_seq_len).to( + attention_mask.device) + prefix_attention_mask = (prefix_attention_mask < 0.5).bool() + attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3) + + # [seq_len, batch, hidden_size] + hidden_states = inputs_embeds.transpose(0, 1) + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + if attention_mask is None: + attention_mask = torch.zeros(1, 1, device=input_ids.device).bool() + else: + attention_mask = attention_mask.to(hidden_states.device) + + for i, layer in enumerate(self.layers): + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + layer_past = past_key_values[i] + + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + position_ids, + attention_mask, + torch.tensor(i), + layer_past, + use_cache, + output_attentions + ) + else: + layer_ret = layer( + hidden_states, + position_ids=position_ids, + attention_mask=attention_mask, + layer_id=torch.tensor(i), + layer_past=layer_past, + use_cache=use_cache, + output_attentions=output_attentions + ) + + hidden_states = layer_ret[0] + + if use_cache: + presents = presents + (layer_ret[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],) + + # Final layer norm. + hidden_states = self.final_layernorm(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + + # self.hidden_size = config.hidden_size + # self.params_dtype = torch.half + # self.vocab_size = config.vocab_size + self.max_sequence_length = config.max_sequence_length + + self.position_encoding_2d = config.position_encoding_2d + + self.transformer = ChatGLMModel(config, empty_init=empty_init) + + self.lm_head = init_method( + nn.Linear, + config.hidden_size, + config.vocab_size, + bias=False, + dtype=torch.half + ) + + self.config = config + + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + if attention_mask is not None and attention_mask.dtype == torch.bool: + attention_mask = torch.cat( + [attention_mask, attention_mask.new_ones((*attention_mask.shape[:3], 1))], dim=3) + new_attention_mask = attention_mask[:, :, -1:].clone() + new_attention_mask[..., -1] = False + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, new_attention_mask], dim=2 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id[:, 1, :] += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past: Optional[torch.Tensor] = None, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + **kwargs + ) -> dict: + batch_size, seq_length = input_ids.shape + MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id + seqs = input_ids.tolist() + mask_positions, use_gmasks = [], [] + for seq in seqs: + mask_token = gMASK if gMASK in seq else MASK + use_gmask = mask_token == gMASK + mask_positions.append(seq.index(mask_token)) + use_gmasks.append(use_gmask) + + # only last token for input_ids if past is not None + if past is not None or past_key_values is not None: + last_token = input_ids[:, -1].unsqueeze(-1) + if attention_mask is not None and attention_mask.dtype == torch.bool: + attention_mask = attention_mask[:, :, -1:] + else: + attention_mask = None + if position_ids is not None: + position_ids = position_ids[..., -1:] + else: + context_lengths = [seq.index(self.config.bos_token_id) for seq in seqs] + if self.position_encoding_2d: + position_ids = torch.tensor( + [[mask_position, seq_length - context_length] for mask_position, context_length in + zip(mask_positions, context_lengths)], dtype=torch.long, device=input_ids.device).unsqueeze(-1) + else: + position_ids = torch.tensor([mask_position for mask_position in mask_positions], dtype=torch.long, + device=input_ids.device).unsqueeze(-1) + + if past is None: + past = past_key_values + return { + "input_ids": last_token, + "past_key_values": past, + "position_ids": position_ids, + "attention_mask": attention_mask + } + else: + if attention_mask is not None and attention_mask.dtype != torch.bool: + logger.warning_once(f"The dtype of attention mask ({attention_mask.dtype}) is not bool") + attention_mask = None + if attention_mask is None: + attention_mask = self.get_masks( + input_ids, + device=input_ids.device + ) + if position_ids is None: + position_ids = self.get_position_ids( + input_ids, + device=input_ids.device, + mask_positions=mask_positions, + use_gmasks=use_gmasks + ) + + return { + "input_ids": input_ids, + "past_key_values": past, + "position_ids": position_ids, + "attention_mask": attention_mask + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + + lm_logits = self.lm_head(hidden_states).permute(1, 0, 2).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, response): + response = response.strip() + response = response.replace("[[训练时间]]", "2023年") + punkts = [ + [",", "，"], + ["!", "！"], + [":", "："], + [";", "；"], + ["\?", "？"], + ] + for item in punkts: + response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response) + response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response) + return response + + @torch.no_grad() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, num_beams=1, + do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if not history: + prompt = query + else: + prompt = "" + for i, (old_query, response) in enumerate(history): + prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response) + prompt += "[Round {}]\n问：{}\n答：".format(len(history), query) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + history = history + [(query, response)] + return response, history + + @torch.no_grad() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, + do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if not history: + prompt = query + else: + prompt = "" + for i, (old_query, response) in enumerate(history): + prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response) + prompt += "[Round {}]\n问：{}\n答：".format(len(history), query) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + for outputs in self.stream_generate(**inputs, **gen_kwargs): + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + new_history = history + [(query, response)] + yield response, new_history + + @torch.no_grad() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) + + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + yield input_ids + + def quantize(self, bits: int, empty_init=False, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer = quantize(self.transformer, bits, empty_init=empty_init, **kwargs) + return self diff --git a/transformers/llm/export/llm_models/chatglm2-6b/modeling_chatglm.py b/transformers/llm/export/llm_models/chatglm2-6b/modeling_chatglm.py new file mode 100644 index 000000000..e9b5ca258 --- /dev/null +++ b/transformers/llm/export/llm_models/chatglm2-6b/modeling_chatglm.py @@ -0,0 +1,1193 @@ +""" PyTorch ChatGLM model. """ + +import math +import copy +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any + +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput + +from .configuration_chatglm import ChatGLMConfig + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm2-6b", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size) + ) + else: + self.embedding = torch.nn.Embedding(config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=dtype, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split('.')[0]) + if pytorch_major_version >= 2 and False: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, + device=query_layer.device + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: + attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], + device=attention_scores.device, dtype=torch.bool) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = ( + self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + ) + self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, **_config_to_kwargs(config) + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, + device=device, **_config_to_kwargs(config) + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view( + query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + key_layer = key_layer.view( + key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.view( + value_layer.size()[:-1] + + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + key_layer = key_layer.contiguous().view( + key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + value_layer = value_layer.contiguous().view( + value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, + device=input_ids.device), full_attention_mask), dim=-1) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = ( + config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + ) + + self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, + dtype=config.torch_dtype) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, + dtype=config.torch_dtype, **init_kwargs) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, + dtype=inputs_embeds.dtype) + if attention_mask is not None: + attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask], dim=-1) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states + ) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + is_first_forward: bool = True, + **kwargs + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, response): + response = response.strip() + response = response.replace("[[训练时间]]", "2023年") + return response + + def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + prompt = tokenizer.build_prompt(query, history=history) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + if history: + prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + input_ids = tokenizer.encode(prompt, add_special_tokens=False) + input_ids = input_ids[1:] + inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) + else: + prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + @torch.inference_mode() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, num_beams=1, + do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = self.build_inputs(tokenizer, query, history=history) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + history = history + [(query, response)] + return response, history + + @torch.inference_mode() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values=None, + max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, + return_past_key_values=False, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if past_key_values is None and not return_past_key_values: + inputs = self.build_inputs(tokenizer, query, history=history) + else: + inputs = self.build_stream_inputs(tokenizer, query, history=history) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs['attention_mask'] = attention_mask + for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, + return_past_key_values=return_past_key_values, **gen_kwargs): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + if response and response[-1] != "�": + response = self.process_response(response) + new_history = history + [(query, response)] + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.inference_mode() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, + **kwargs) + return self diff --git a/transformers/llm/export/llm_models/chatglm3-6b/modeling_chatglm.py b/transformers/llm/export/llm_models/chatglm3-6b/modeling_chatglm.py new file mode 100755 index 000000000..f887c44ce --- /dev/null +++ b/transformers/llm/export/llm_models/chatglm3-6b/modeling_chatglm.py @@ -0,0 +1,1293 @@ +""" PyTorch ChatGLM model. """ + +import math +import copy +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any +from copy import deepcopy + +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput + +from .configuration_chatglm import ChatGLMConfig + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM" +_CONFIG_FOR_DOC = "ChatGLMConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm3-6b", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size) + ) + else: + self.embedding = torch.nn.Embedding(config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=torch.float, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split('.')[0]) + if pytorch_major_version >= 2 and False: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, + device=query_layer.device + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: + attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], + device=attention_scores.device, dtype=torch.bool) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = ( + self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + ) + self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, **_config_to_kwargs(config) + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, + device=device, **_config_to_kwargs(config) + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view( + query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + key_layer = key_layer.view( + key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.view( + value_layer.size()[:-1] + + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + key_layer = key_layer.contiguous().view( + key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + value_layer = value_layer.contiguous().view( + value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, + device=input_ids.device), full_attention_mask), dim=-1) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = ( + config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + ) + + self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, + dtype=config.torch_dtype) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, + dtype=config.torch_dtype, **init_kwargs) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, + dtype=inputs_embeds.dtype) + if attention_mask is not None: + attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask], dim=-1) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states + ) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + is_first_forward: bool = True, + **kwargs + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + if past_key_values is not None: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True, + "use_cache": use_cache + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, output, history): + content = "" + history = deepcopy(history) + for response in output.split("<|assistant|>"): + metadata, content = response.split("\n", maxsplit=1) + if not metadata.strip(): + content = content.strip() + history.append({"role": "assistant", "metadata": metadata, "content": content}) + content = content.replace("[[训练时间]]", "2023年") + else: + history.append({"role": "assistant", "metadata": metadata, "content": content}) + if history[0]["role"] == "system" and "tools" in history[0]: + content = "\n".join(content.split("\n")[1:-1]) + def tool_call(**kwargs): + return kwargs + parameters = eval(content) + content = {"name": metadata.strip(), "parameters": parameters} + else: + content = {"name": metadata.strip(), "content": content} + return content, history + + @torch.inference_mode() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, + **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = tokenizer.build_chat_input(query, history=history, role=role) + inputs = inputs.to(self.device) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + history.append({"role": role, "content": query}) + response, history = self.process_response(response, history) + return response, history + + @torch.inference_mode() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, + logits_processor=None, return_past_key_values=False, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if past_key_values is None: + inputs = tokenizer.build_chat_input(query, history=history, role=role) + else: + inputs = tokenizer.build_chat_input(query, role=role) + inputs = inputs.to(self.device) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs['attention_mask'] = attention_mask + history.append({"role": role, "content": query}) + for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, + eos_token_id=eos_token_id, return_past_key_values=return_past_key_values, + **gen_kwargs): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + if response and response[-1] != "�": + response, new_history = self.process_response(response, history) + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.inference_mode() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + model_kwargs["use_cache"] = generation_config.use_cache + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul( + next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0) + ) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, + **kwargs) + return self + + +class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.num_labels = config.num_labels + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + + self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half) + if config.classifier_dropout is not None: + self.dropout = nn.Dropout(config.classifier_dropout) + else: + self.dropout = None + self.config = config + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + full_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + full_attention_mask=full_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + pooled_hidden_states = hidden_states[-1] + if self.dropout is not None: + pooled_hidden_states = self.dropout(pooled_hidden_states) + logits = self.classifier_head(pooled_hidden_states) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze().float(), labels.squeeze()) + else: + loss = loss_fct(logits.float(), labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits.float(), labels.view(-1, self.num_labels)) + + if not return_dict: + output = (logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/codegeex2-6b/modeling_chatglm.py b/transformers/llm/export/llm_models/codegeex2-6b/modeling_chatglm.py new file mode 100755 index 000000000..fdc619f81 --- /dev/null +++ b/transformers/llm/export/llm_models/codegeex2-6b/modeling_chatglm.py @@ -0,0 +1,1092 @@ +""" PyTorch ChatGLM model. """ + +import math +import copy +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any + +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput + +from .configuration_chatglm import ChatGLMConfig + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm-6b", + # See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device, dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_original_impl( + self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=dtype, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + if self.original_impl: + return self.forward_original_impl( + max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device + ) + + +@torch.jit.script +def apply_rotary_pos_emb_original(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, input: torch.Tensor): + norm_x = torch.mean(input * input, dim=-1, keepdim=True) + x_normed = input * torch.rsqrt(norm_x + self.eps) + return self.weight * x_normed + + +class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split('.')[0]) + if pytorch_major_version >= 2 and False: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, + device=query_layer.device + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: + attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], + device=attention_scores.device, dtype=torch.bool) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = ( + self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + ) + self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, **_config_to_kwargs(config) + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, + device=device, **_config_to_kwargs(config) + ) + + self.interleaved_qkv = config.interleaved_qkv + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view( + query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + key_layer = key_layer.view( + key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.view( + value_layer.size()[:-1] + + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + else: + if self.interleaved_qkv: + new_tensor_shape = mixed_x_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + if not self.interleaved_qkv: + query_layer = query_layer.view( + query_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ).contiguous() + key_layer = key_layer.view( + key_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ).contiguous() + value_layer = value_layer.view( + value_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ).contiguous() + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb_original(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb_original(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if use_cache: + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + key_layer = key_layer.contiguous().view( + key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + value_layer = value_layer.contiguous().view( + value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + + hidden_states, kv_cache = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache + ) + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, + device=input_ids.device), full_attention_mask), dim=-1) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, ChatGLMModel): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = ( + config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + ) + + if config.rotary_percent < 1.0: + rotary_dim = int(rotary_dim * config.rotary_percent) + + # partial rotary embeddings, which is better than full rotary + # Wang and Komatsuzaki et al + # https://github.com/kingoflolz/mesh-transformer-jax/ + self.rotary_pos_emb = RotaryEmbedding(rotary_dim, original_impl=config.original_rope, device=device, + dtype=config.torch_dtype) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, + dtype=config.torch_dtype, **init_kwargs) + self.gradient_checkpointing = False + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if full_attention_mask is None and attention_mask is not None and not attention_mask.all(): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states + ) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + input_pos: int = None, + **kwargs + ) -> dict: + # only last token for input_ids if past is not None + if past_key_values is not None: + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, response): + response = response.strip() + response = response.replace("[[训练时间]]", "2023年") + return response + + def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + prompt = "" + for i, (old_query, response) in enumerate(history): + prompt += "[Round {}]\n\n问：{}\n\n答：{}\n\n".format(i + 1, old_query, response) + prompt += "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + @torch.no_grad() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, num_beams=1, + do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = self.build_inputs(tokenizer, query, history=history) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + history = history + [(query, response)] + return response, history + + @torch.no_grad() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, + do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = self.build_inputs(tokenizer, query, history=history) + for outputs in self.stream_generate(**inputs, **gen_kwargs): + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + new_history = history + [(query, response)] + yield response, new_history + + @torch.no_grad() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) + + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + yield input_ids + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, + **kwargs) + return self diff --git a/transformers/llm/export/llm_models/deepseek-llm-7b-chat/config.json b/transformers/llm/export/llm_models/deepseek-llm-7b-chat/config.json new file mode 100755 index 000000000..67a803b6a --- /dev/null +++ b/transformers/llm/export/llm_models/deepseek-llm-7b-chat/config.json @@ -0,0 +1,28 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "auto_map": { + "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM" + }, + "bos_token_id": 100000, + "eos_token_id": 100001, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 11008, + "max_position_embeddings": 4096, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 30, + "num_key_value_heads": 32, + "pretraining_tp": 1, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 10000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.33.1", + "use_cache": true, + "vocab_size": 102400 +} diff --git a/transformers/llm/export/llm_models/deepseek-llm-7b-chat/configuration_llama.py b/transformers/llm/export/llm_models/deepseek-llm-7b-chat/configuration_llama.py new file mode 100644 index 000000000..1b0e9c357 --- /dev/null +++ b/transformers/llm/export/llm_models/deepseek-llm-7b-chat/configuration_llama.py @@ -0,0 +1,174 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" LLaMA model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {} + + +class LlamaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the LLaMA-7B. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`LlamaModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + pretraining_tp (`int`, *optional*, defaults to `1`): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is + necessary to ensure exact reproducibility of the pretraining results. Please refer to [this + issue](https://github.com/pytorch/pytorch/issues/76232). + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + tie_word_embeddings(`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling + strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format + is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + + Example: + + ```python + >>> from transformers import LlamaModel, LlamaConfig + + >>> # Initializing a LLaMA llama-7b style configuration + >>> configuration = LlamaConfig() + + >>> # Initializing a model from the llama-7b style configuration + >>> model = LlamaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + model_type = "llama" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=False, + rope_scaling=None, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_scaling = rope_scaling + self._rope_scaling_validation() + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, " + f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: + raise ValueError( + f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" + ) + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}") diff --git a/transformers/llm/export/llm_models/deepseek-llm-7b-chat/modeling_llama.py b/transformers/llm/export/llm_models/deepseek-llm-7b-chat/modeling_llama.py new file mode 100644 index 000000000..8c562c604 --- /dev/null +++ b/transformers/llm/export/llm_models/deepseek-llm-7b-chat/modeling_llama.py @@ -0,0 +1,1010 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch LLaMA model.""" +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_llama import LlamaConfig + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "LlamaConfig" + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class LlamaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + LlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +class LlamaRotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. + # cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] + # sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] + cos = torch.squeeze(cos) # [seq_len, dim] + sin = torch.squeeze(sin) # [seq_len, dim] + cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class LlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.pretraining_tp = config.pretraining_tp + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + if self.pretraining_tp > 1: + slice = self.intermediate_size // self.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class LlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: LlamaConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.pretraining_tp = config.pretraining_tp + self.max_position_embeddings = config.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = LlamaLinearScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + elif scaling_type == "dynamic": + self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + if self.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp + query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + if self.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaDecoderLayer(nn.Module): + def __init__(self, config: LlamaConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = LlamaAttention(config=config) + self.mlp = LlamaMLP(config) + self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +LLAMA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`LlamaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaPreTrainedModel(PreTrainedModel): + config_class = LlamaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["LlamaDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, LlamaModel): + module.gradient_checkpointing = value + + +LLAMA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class LlamaModel(LlamaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] + + Args: + config: LlamaConfig + """ + + def __init__(self, config: LlamaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class LlamaForCausalLM(LlamaPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = LlamaModel(config) + self.pretraining_tp = config.pretraining_tp + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The LLaMa Model transformer with a sequence classification head on top (linear layer). + + [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + LLAMA_START_DOCSTRING, +) +class LlamaForSequenceClassification(LlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = LlamaModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/internlm-chat-7b/modeling_internlm.py b/transformers/llm/export/llm_models/internlm-chat-7b/modeling_internlm.py new file mode 100755 index 000000000..af9f5842c --- /dev/null +++ b/transformers/llm/export/llm_models/internlm-chat-7b/modeling_internlm.py @@ -0,0 +1,1017 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch InternLM model.""" +import math +from typing import List, Optional, Tuple, Union +import threading, queue + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.generation.streamers import BaseStreamer +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_internlm import InternLMConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "InternLMConfig" + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class InternLMRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + InternLMRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states + + +class InternLMRotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + self.max_seq_len_cached = max_position_embeddings + t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. + if seq_len > self.max_seq_len_cached: + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. + # cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] + # sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] + cos = torch.squeeze(cos) # [seq_len, dim] + sin = torch.squeeze(sin) # [seq_len, dim] + cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class InternLMMLP(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + ): + super().__init__() + self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) + self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.act_fn = ACT2FN[hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +class InternLMAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: InternLMConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.max_position_embeddings = config.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias) + self.rotary_emb = InternLMRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class InternLMDecoderLayer(nn.Module): + def __init__(self, config: InternLMConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = InternLMAttention(config=config) + self.mlp = InternLMMLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + ) + self.input_layernorm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +INTERNLM_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`InternLMConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare InternLM Model outputting raw hidden-states without any specific head on top.", + INTERNLM_START_DOCSTRING, +) +class InternLMPreTrainedModel(PreTrainedModel): + config_class = InternLMConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["InternLMDecoderLayer"] + _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, InternLMModel): + module.gradient_checkpointing = value + + +INTERNLM_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare InternLM Model outputting raw hidden-states without any specific head on top.", + INTERNLM_START_DOCSTRING, +) +class InternLMModel(InternLMPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLMDecoderLayer`] + + Args: + config: InternLMConfig + """ + + _auto_class = "AutoModel" + + def __init__(self, config: InternLMConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([InternLMDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + @add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class InternLMForCausalLM(InternLMPreTrainedModel): + _auto_class = "AutoModelForCausalLM" + + def __init__(self, config): + super().__init__(config) + self.model = InternLMModel(config) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, InternLMForCausalLM + + >>> model = InternLMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you consciours? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past + + def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = []): + prompt = "" + for record in history: + prompt += f"""<|User|>:{record[0]}\n<|Bot|>:{record[1]}\n""" + if len(prompt) == 0: + prompt += "" + prompt += f"""<|User|>:{query}\n<|Bot|>:""" + return tokenizer([prompt], return_tensors="pt") + + @torch.no_grad() + def chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = [], + streamer: Optional[BaseStreamer] = None, + max_new_tokens: int = 1024, + do_sample: bool = True, + temperature: float = 0.8, + top_p: float = 0.8, + **kwargs, + ): + inputs = self.build_inputs(tokenizer, query, history) + inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)} + outputs = self.generate( + **inputs, + streamer=streamer, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + temperature=temperature, + top_p=top_p, + **kwargs, + ) + outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :] + response = tokenizer.decode(outputs, skip_special_tokens=True) + response = response.split("")[0] + history = history + [(query, response)] + return response, history + + @torch.no_grad() + def stream_chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = [], + max_new_tokens: int = 1024, + do_sample: bool = True, + temperature: float = 0.8, + top_p: float = 0.8, + **kwargs, + ): + """ + Return a generator in format: (response, history) + Eg. + ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')]) + ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')]) + """ + + response_queue = queue.Queue(maxsize=20) + + class ChatStreamer(BaseStreamer): + def __init__(self, tokenizer) -> None: + super().__init__() + self.tokenizer = tokenizer + self.queue = response_queue + self.query = query + self.history = history + self.response = "" + self.received_inputs = False + self.queue.put((self.response, history + [(self.query, self.response)])) + + def put(self, value): + if len(value.shape) > 1 and value.shape[0] > 1: + raise ValueError("ChatStreamer only supports batch size 1") + elif len(value.shape) > 1: + value = value[0] + + if not self.received_inputs: + # The first received value is input_ids, ignore here + self.received_inputs = True + return + + token = self.tokenizer.decode([value[-1]], skip_special_tokens=True) + if token.strip() != "": + self.response = self.response + token + history = self.history + [(self.query, self.response)] + self.queue.put((self.response, history)) + + def end(self): + self.queue.put(None) + + def stream_producer(): + return self.chat( + tokenizer=tokenizer, + query=query, + streamer=ChatStreamer(tokenizer=tokenizer), + history=history, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + temperature=temperature, + top_p=top_p, + **kwargs, + ) + + def consumer(): + producer = threading.Thread(target=stream_producer) + producer.start() + while True: + res = response_queue.get() + if res is not None: + return + yield res + + return consumer() + + +@add_start_docstrings( + """ + The InternLM Model transformer with a sequence classification head on top (linear layer). + + [`InternLMForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + INTERNLM_START_DOCSTRING, +) +class InternLMForSequenceClassification(InternLMPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = InternLMModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/transformers/llm/export/llm_models/phi-2/modeling_phi.py b/transformers/llm/export/llm_models/phi-2/modeling_phi.py new file mode 100644 index 000000000..30b7fc8fd --- /dev/null +++ b/transformers/llm/export/llm_models/phi-2/modeling_phi.py @@ -0,0 +1,989 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. +# +# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu. +# Licensed under the BSD 3-Clause License. + +from __future__ import annotations + +import math +from dataclasses import dataclass, field +from typing import Any, Dict, Optional, Tuple, Union + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from transformers import PretrainedConfig, PreTrainedModel +from transformers.activations import ACT2FN +from transformers.modeling_outputs import CausalLMOutputWithPast + +from .configuration_phi import PhiConfig + +try: + from flash_attn.bert_padding import pad_input, unpad_input + from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding + from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention + from flash_attn.ops.fused_dense import FusedDense +except: + pad_input, unpad_input = None, None + FlashRotaryEmbedding = None + FlashSelfAttention, FlashCrossAttention = None, None + FusedDense = None + + +@dataclass +class InferenceParams: + """Inference parameters passed to model to efficiently calculate + and store context during inference. + + Reference: + https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py. + + Args: + max_seqlen: Maximum sequence length. + max_batch_size: Maximum batch size. + seqlen_offset: Sequence length offset. + batch_size_offset: Batch size offset. + key_value_memory_dict: Key value memory dictionary. + lengths_per_sample: Lengths per sample. + + """ + + max_seqlen: int = field(metadata={"help": "Maximum sequence length."}) + + max_batch_size: int = field(metadata={"help": "Maximum batch size."}) + + seqlen_offset: int = field(default=0, metadata={"help": "Sequence length offset."}) + + batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."}) + + key_value_memory_dict: Dict[str, Any] = field( + default_factory=dict, metadata={"help": "Key value memory dictionary."} + ) + + lengths_per_sample: torch.Tensor = field(default=None, metadata={"help": "Lengths per sample."}) + + +class Embedding(nn.Module): + """Token embedding with dropout.""" + + def __init__(self, config: PretrainedConfig) -> None: + super().__init__() + + self.wte = nn.Embedding(config.vocab_size, config.n_embd) + self.drop = nn.Dropout(config.embd_pdrop) + + def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + hidden_states = self.wte(input_ids) + hidden_states = self.drop(hidden_states) + + return hidden_states + + +def _apply_rotary_emb( + x: torch.FloatTensor, + cos: torch.FloatTensor, + sin: torch.FloatTensor, +) -> torch.FloatTensor: + _, seqlen, _, _ = x.shape + _, rotary_dim = cos.shape + rotary_dim *= 2 + + x_rot = x[:, :, :, :rotary_dim] + x_pass = x[:, :, :, rotary_dim:] + + x1, x2 = x_rot.chunk(2, dim=-1) + c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d") + x1, x2, c, s = [t.to(dtype=torch.float32) for t in [x1, x2, c, s]] + + x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], axis=-1).to(x.dtype) + + return torch.cat([x_rot, x_pass], axis=-1) + + +def _apply_rotary_emb_kv( + kv: torch.FloatTensor, + cos: torch.FloatTensor, + sin: torch.FloatTensor, + cos_k: Optional[torch.FloatTensor] = None, + sin_k: Optional[torch.FloatTensor] = None, +) -> torch.FloatTensor: + _, seqlen, _, _, _ = kv.shape + _, rotary_dim = cos.shape + rotary_dim *= 2 + + k_rot = kv[:, :, 0, :, :rotary_dim] + k_pass = kv[:, :, 0, :, rotary_dim:] + + k1, k2 = k_rot.chunk(2, dim=-1) + c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d") + k1, k2, c, s = [t.to(dtype=torch.float32) for t in [k1, k2, c, s]] + + k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(kv.dtype) + + return torch.cat( + [ + torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2), + kv[:, :, 1:2, :, :], + ], + axis=2, + ) + + +def _apply_rotary_emb_qkv( + qkv: torch.FloatTensor, + cos: torch.FloatTensor, + sin: torch.FloatTensor, + cos_k: Optional[torch.FloatTensor] = None, + sin_k: Optional[torch.FloatTensor] = None, +) -> torch.FloatTensor: + _, seqlen, _, _, _ = qkv.shape + _, rotary_dim = cos.shape + rotary_dim *= 2 + + q_rot = qkv[:, :, 0, :, :rotary_dim] + q_pass = qkv[:, :, 0, :, rotary_dim:] + + k_rot = qkv[:, :, 1, :, :rotary_dim] + k_pass = qkv[:, :, 1, :, rotary_dim:] + + q1, q2 = q_rot.chunk(2, dim=-1) + k1, k2 = k_rot.chunk(2, dim=-1) + c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d") + q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]] + + q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype) + k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype) + + return torch.cat( + [ + torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2), + torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2), + qkv[:, :, 2:3, :, :], + ], + axis=2, + ) + + +class RotaryEmbedding(nn.Module): + """Rotary positional embedding (RoPE). + + Reference: + RoFormer: Enhanced Transformer with Rotary Position Embedding. + https://arxiv.org/pdf/2104.09864.pdf. + + """ + + def __init__( + self, + dim: int, + base: int = 10000, + scale_base: Optional[float] = None, + pos_idx_in_fp32: bool = True, + max_position_embeddings: int = 2048, + device: Optional[str] = None, + **kwargs, + ) -> None: + super().__init__() + + if scale_base is not None: + raise NotImplementedError + + self.dim = dim + self.base = float(base) + self.scale_base = scale_base + self.pos_idx_in_fp32 = pos_idx_in_fp32 + self.max_position_embeddings = max_position_embeddings + self.device = device + + # Generate and save the inverse frequency buffer (non-trainable) + inv_freq = self._compute_inv_freq(device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Generate and save the scale buffer (non-trainable) + scale = ( + (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim) + if scale_base is not None + else None + ) + self.register_buffer("scale", scale, persistent=False) + + # Initialize cached attributes since ONNX can't rely on dynamic initialization + self._update_cos_sin_cache(max_position_embeddings, device=device, dtype=torch.float32) + + def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor: + return 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim)) + + def _update_cos_sin_cache( + self, + seqlen: int, + device: Optional[str] = None, + dtype: Optional[torch.dtype] = None, + ) -> None: + self._seq_len_cached = seqlen + + # fp32 is preferred since the output of `torch.arange` can be quite large + # and bf16 would lose a lot of precision + if self.pos_idx_in_fp32: + t = torch.arange(seqlen, device=device, dtype=torch.float32) + if self.inv_freq.dtype != torch.float32: + inv_freq = self._compute_inv_freq(device=device) + else: + inv_freq = self.inv_freq + else: + t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) + inv_freq = self.inv_freq + + # `torch.outer` is preferred since `torch.einsum` converts from fp32 to fp16 if used with AMP + freqs = torch.outer(t, inv_freq) + if self.scale is None: + self._cos_cached = torch.cos(freqs).to(dtype) + self._sin_cached = torch.sin(freqs).to(dtype) + else: + power = ( + torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2 + ) / self.scale_base + scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1") + + # Force the scale multiplication to happen in fp32 + self._cos_cached = (torch.cos(freqs) * scale).to(dtype) + self._sin_cached = (torch.sin(freqs) * scale).to(dtype) + self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype) + self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype) + + def forward( + self, + qkv: torch.Tensor, + kv: Optional[torch.Tensor] = None, + seqlen_offset: int = 0, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + if ( + self._seq_len_cached < qkv.shape[1] + seqlen_offset + or self._cos_cached.device != qkv.device + or self._cos_cached.dtype != qkv.dtype + or (self.training and self._cos_cached.is_inference()) + ): + self._update_cos_sin_cache(qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype) + + if kv is None: + return _apply_rotary_emb_qkv( + qkv, + self._cos_cached[seqlen_offset:], + self._sin_cached[seqlen_offset:], + ) + else: + q = _apply_rotary_emb( + qkv, + self._cos_cached[seqlen_offset:], + self._sin_cached[seqlen_offset:], + ) + kv = _apply_rotary_emb_kv( + kv, + self._cos_cached[seqlen_offset:], + self._sin_cached[seqlen_offset:], + ) + + return q, kv + + +class MLP(nn.Module): + """Multi-Layer Perceptron. + + Reference: + Attention Is All You Need. + https://arxiv.org/pdf/1706.03762.pdf. + + """ + + def __init__( + self, + config: PretrainedConfig, + n_inner: Optional[int] = None, + act_fn: Optional[str] = None, + ) -> None: + super().__init__() + + act_fn = config.activation_function if act_fn is None else act_fn + + n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner + n_inner = n_inner if n_inner is not None else 4 * config.n_embd + + self.fc1 = nn.Linear(config.n_embd, n_inner) + self.fc2 = nn.Linear(n_inner, config.n_embd) + self.act = ACT2FN[act_fn] + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.fc2(hidden_states) + + return hidden_states + + +class SelfAttention(nn.Module): + """Self-attention layer (compatible with PyTorch). + + Reference: + https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py. + + """ + + def __init__( + self, + causal: bool = True, + softmax_scale: Optional[float] = None, + attention_dropout: float = 0.0, + ) -> None: + super().__init__() + + self.causal = causal + self.softmax_scale = softmax_scale + self.drop = nn.Dropout(attention_dropout) + + @torch.autocast("cpu", enabled=False) + @torch.autocast("cuda", enabled=False) + def forward( + self, + qkv: torch.FloatTensor, + causal: bool = None, + key_padding_mask: Optional[torch.BoolTensor] = None, + **kwargs, + ) -> torch.FloatTensor: + batch_size, seqlen = qkv.shape[0], qkv.shape[1] + q, k, v = qkv.unbind(dim=2) + + q = q.to(torch.float32) + k = k.to(torch.float32) + + causal = self.causal if causal is None else causal + softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1]) + + # Autocast is manually disabled to avoid `torch.einsum` performing the operation + # using float16, which might lead to overflow + scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale) + + if key_padding_mask is not None: + padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device) + padding_mask.masked_fill_(key_padding_mask, 0.0) + + scores = scores + rearrange(padding_mask, "b s -> b 1 1 s") + + if causal: + causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1) + scores = scores + causal_mask.to(dtype=scores.dtype) + + attention = torch.softmax(scores, dim=-1).to(v.dtype) + attention = self.drop(attention) + + output = torch.einsum("bhts,bshd->bthd", attention, v) + + return output + + +class CrossAttention(nn.Module): + """Cross-attention layer (compatible with PyTorch). + + Reference: + https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py. + + """ + + def __init__( + self, + causal: bool = True, + softmax_scale: Optional[float] = None, + attention_dropout: float = 0.0, + ) -> None: + super().__init__() + + self.causal = causal + self.softmax_scale = softmax_scale + self.drop = nn.Dropout(attention_dropout) + + @torch.autocast("cpu", enabled=False) + @torch.autocast("cuda", enabled=False) + def forward( + self, + q: torch.FloatTensor, + kv: torch.FloatTensor, + causal: bool = None, + key_padding_mask: Optional[torch.BoolTensor] = None, + causal_mask: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.FloatTensor: + batch_size, seqlen_q = q.shape[0], q.shape[1] + seqlen_k = kv.shape[1] + + if kv.shape[3] != q.shape[2]: + kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3]) + k, v = kv.unbind(dim=2) + + q = q.to(torch.float32) + k = k.to(torch.float32) + + causal = self.causal if causal is None else causal + softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1]) + + # Autocast is manually disabled to avoid `torch.einsum` performing the operation + # using float16, which might lead to overflow + # scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale) + q = q.permute(0, 2, 1, 3) + k_ = (k * softmax_scale).permute(0, 2, 3, 1) + scores = torch.matmul(q, k_) + + if key_padding_mask is not None: + padding_mask = torch.full( + (batch_size, seqlen_k), + -10000.0, + dtype=scores.dtype, + device=scores.device, + ) + padding_mask.masked_fill_(key_padding_mask, 0.0) + + scores = scores + rearrange(padding_mask, "b s -> b 1 1 s") + + if causal_mask is not None: + scores = scores.masked_fill(causal_mask, -10000.0) + elif causal: + rows = rearrange(torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1") + cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long) + causal_mask = cols > rows + seqlen_k - seqlen_q + + scores = scores.masked_fill(causal_mask, -10000.0) + + attention = torch.softmax(scores, dim=-1).to(v.dtype) + attention = self.drop(attention) + + # output = torch.einsum("bhts,bshd->bthd", attention, v) + v = v.permute(0, 2, 1, 3) + output = torch.matmul(attention, v).permute(0, 2, 1, 3) + return output + + +def _find_mha_dims( + config: PretrainedConfig, + n_head: Optional[int] = None, + n_head_kv: Optional[int] = None, + head_dim: Optional[int] = None, +) -> Tuple[int, int]: + if n_head is None and head_dim is None: + head_dim = config.n_embd // config.n_head + n_head = config.n_head + elif n_head is None or head_dim is None: + raise ValueError("`n_head` and `head_dim` must be both specified or `None`.") + + if n_head_kv is None: + n_head_kv = getattr(config, "n_head_kv", None) or n_head + + return n_head, n_head_kv, head_dim + + +def _update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor: + num_heads, head_dim = kv.shape[-2:] + + if layer_idx not in inference_params.key_value_memory_dict: + inference_params.key_value_memory_dict[layer_idx] = torch.empty( + inference_params.max_batch_size, + inference_params.max_seqlen, + 2, + num_heads, + head_dim, + dtype=kv.dtype, + device=kv.device, + ) + + batch_start = inference_params.batch_size_offset + batch_end = batch_start + kv.shape[0] + + sequence_start = inference_params.seqlen_offset + sequence_end = sequence_start + kv.shape[1] + + # When the current sequence length is equal to or larger than the maximum sequence length, + # we need to concatenate the current `kv` with the cached `kv` to expand its length + if sequence_end >= inference_params.max_seqlen: + inference_params.key_value_memory_dict[layer_idx] = torch.concatenate((inference_params.key_value_memory_dict[layer_idx], kv), dim=1) + + inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, sequence_start:sequence_end, ...] = kv + kv = inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, :sequence_end, ...] + + return kv + + +class MHA(nn.Module): + """Multi-head attention layer.""" + + def __init__( + self, + config: PretrainedConfig, + dtype: Optional[torch.dtype] = None, + device: Optional[str] = None, + rotary_dim: Optional[int] = None, + rotary_base: float = 10000.0, + rotary_scale_base: Optional[float] = None, + n_head: Optional[int] = None, + n_head_kv: Optional[int] = None, + head_dim: Optional[int] = None, + bias: bool = True, + causal: bool = True, + softmax_scale: Optional[float] = None, + layer_idx: Optional[int] = None, + return_residual: bool = False, + checkpointing: bool = False, + ) -> None: + super().__init__() + + # Rotary embedding + self.rotary_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0) + if self.rotary_dim > 0: + rotary_cls = FlashRotaryEmbedding if config.flash_rotary else RotaryEmbedding + if rotary_cls is None: + rotary_cls = RotaryEmbedding + + rotary_kwargs = {} + if rotary_cls is RotaryEmbedding: + rotary_kwargs["max_position_embeddings"] = config.n_positions + + self.rotary_emb = rotary_cls( + self.rotary_dim, + base=rotary_base, + scale_base=rotary_scale_base, + device=device, + **rotary_kwargs, + ) + + # MLP + self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims( + config, n_head=n_head, n_head_kv=n_head_kv, head_dim=head_dim + ) + op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv) + hidden_size = config.n_embd + + linear_cls = FusedDense if config.fused_dense else nn.Linear + if linear_cls is None: + linear_cls = nn.Linear + + self.Wqkv = linear_cls(hidden_size, op_size, bias=bias, device=device, dtype=dtype) + self.out_proj = linear_cls(hidden_size, hidden_size, bias=bias, device=device, dtype=dtype) + + # Attention + attn_cls = FlashSelfAttention if config.flash_attn else SelfAttention + if attn_cls is None: + attn_cls = SelfAttention + + cross_attn_cls = FlashCrossAttention if config.flash_attn else CrossAttention + if cross_attn_cls is None: + cross_attn_cls = CrossAttention + + self.inner_attn = attn_cls( + causal=causal, + softmax_scale=softmax_scale, + attention_dropout=config.attn_pdrop, + ) + self.inner_cross_attn = cross_attn_cls( + causal=causal, + softmax_scale=softmax_scale, + attention_dropout=config.attn_pdrop, + ) + + self.flash_attn = config.flash_attn and attn_cls is FlashSelfAttention + self.layer_idx = layer_idx + self.return_residual = return_residual + self.checkpointing = checkpointing + + def _forward_self_attn( + self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor] + ) -> torch.FloatTensor: + qkv = self.Wqkv(x) + qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim) + + if self.rotary_dim > 0: + qkv = self.rotary_emb(qkv) + + if self.flash_attn: + batch_size, seqlen = qkv.shape[0], qkv.shape[1] + + cu_seqlens, max_seqlen = None, None + if key_padding_mask is not None: + # If `key_padding_mask` is supplied, we need to unpad the input and retrieve + # the `cu_seqlens` and `max_seqlen` to be used by `flash-attn` + qkv, indices, cu_seqlens, max_seqlen = unpad_input(qkv, key_padding_mask) + + if self.checkpointing: + attn_output = torch.utils.checkpoint.checkpoint( + self.inner_attn, qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen + ) + else: + attn_output = self.inner_attn(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen).to(qkv.device) + + # If `key_padding_mask` is supplied, we need to pad the output back to the original shape + return pad_input(attn_output, indices, batch_size, seqlen) if key_padding_mask is not None else attn_output + + if self.checkpointing: + return torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, key_padding_mask=key_padding_mask) + + return self.inner_attn(qkv, key_padding_mask=key_padding_mask) + + def _forward_cross_attn( + self, + x: torch.FloatTensor, + past_key_values: Optional[Union[torch.Tensor, InferenceParams]], + key_padding_mask: Optional[torch.BoolTensor], + rotary_pos_emb: Optional[torch.Tensor] = None, + causal_mask: Optional[torch.Tensor] = None, + ) -> torch.FloatTensor: + batch_size = x.shape[0] + + qkv = self.Wqkv(x) + + q = qkv[..., : self.n_head * self.head_dim] + q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim) + + kv = qkv[..., self.n_head * self.head_dim :] + kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim) + + if rotary_pos_emb is None: + seqlen_offset = past_key_values.seqlen_offset if past_key_values is not None else 0 + causal = None if seqlen_offset == 0 else False + if self.rotary_dim > 0: + q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset) + else: + causal = False + cos_pos, sin_pos = rotary_pos_emb + q = _apply_rotary_emb(q, cos_pos, sin_pos) + kv = _apply_rotary_emb_kv(kv, cos_pos, sin_pos) + + if past_key_values is not None: + if type(past_key_values) is InferenceParams: + kv = _update_kv_cache(kv, past_key_values, self.layer_idx) + else: + # kv.shape is [1, past + 1, 2, 32, 80] + #print('kv.shape', kv.shape) + #print('past_key_values.shape', past_key_values.shape) + kv = torch.cat((past_key_values, kv), dim=1) + + if self.flash_attn: + batch_size, seqlen_q = q.shape[0], q.shape[1] + seqlen_k = kv.shape[1] + + cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = ( + None, + None, + None, + None, + ) + if key_padding_mask is not None: + kv, _, cu_seqlens_k, max_seqlen_k = unpad_input(kv, key_padding_mask) + + if seqlen_q == 1: + key_padding_mask = torch.ones(batch_size, 1, device=q.device) + elif seqlen_q != seqlen_k: + key_padding_mask = key_padding_mask[:, -seqlen_q:] + + q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, key_padding_mask) + + if self.checkpointing: + attn_output = torch.utils.checkpoint.checkpoint( + self.inner_cross_attn, + q, + kv, + causal=causal, + cu_seqlens=cu_seqlens_q, + max_seqlen=max_seqlen_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_k=max_seqlen_k, + ) + else: + attn_output = self.inner_cross_attn( + q, + kv, + causal=causal, + cu_seqlens=cu_seqlens_q, + max_seqlen=max_seqlen_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_k=max_seqlen_k, + ) + + return ( + pad_input(attn_output, indices_q, batch_size, max_seqlen_q) + if key_padding_mask is not None + else attn_output + ) + + if self.checkpointing: + return torch.utils.checkpoint.checkpoint( + self.inner_cross_attn, + q, + kv, + key_padding_mask=key_padding_mask, + causal=causal, + ) + output = self.inner_cross_attn(q, kv, key_padding_mask=key_padding_mask, causal=causal, causal_mask=causal_mask) + return output, kv + + def forward( + self, + x: torch.FloatTensor, + past_key_values: Optional[InferenceParams] = None, + attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None, + rotary_pos_emb: Optional[torch.Tensor] = None, + causal_mask: Optional[torch.Tensor] = None, + **kwargs, + ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: + if attention_mask is not None: + attention_mask = attention_mask.bool() + else: + attention_mask = None + + attention_mask = None + kv = None + # MHA + if self.n_head == self.n_head_kv: + if past_key_values is None and False: + # If `past_key_values` are not supplied, we run self-attention + attn_output = self._forward_self_attn(x, attention_mask) + else: + # If `past_key_values` are supplied, it means that we might have cached values and + # could take advantage of cross-attention + attn_output, kv = self._forward_cross_attn(x, past_key_values, attention_mask, rotary_pos_emb, causal_mask) + # MQA / GQA + else: + # Regardless of `past_key_values` being supplied or not, it always use cross-attention + # because `q` and `kv` lengths might be different + attn_output = self._forward_cross_attn(x, past_key_values, attention_mask) + + output = rearrange(attn_output, "... h d -> ... (h d)") + output = self.out_proj(output) + + # return output if not self.return_residual else (output, x) + return output, kv + + +class ParallelBlock(nn.Module): + """Parallel block. + + This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen). + + """ + + def __init__( + self, + config: PretrainedConfig, + block_idx: Optional[int] = None, + ) -> None: + super().__init__() + + self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) + self.resid_dropout = nn.Dropout(config.resid_pdrop) + self.block_idx = block_idx + + self.mixer = MHA(config, layer_idx=block_idx) + self.mlp = MLP(config) + + def forward( + self, + hidden_states: torch.FloatTensor, + past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None, + attention_mask: Optional[torch.BoolTensor] = None, + rotary_pos_emb: Optional[torch.Tensor] = None, + causal_mask: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.FloatTensor: + residual = hidden_states + hidden_states = self.ln(hidden_states) + + attn_outputs, kv = self.mixer( + hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + rotary_pos_emb=rotary_pos_emb, + causal_mask=causal_mask, + ) + + if isinstance(attn_outputs, tuple): + attn_outputs = attn_outputs[0] + + # attn_outputs = self.resid_dropout(attn_outputs) + # feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states)) + feed_forward_hidden_states = self.mlp(hidden_states) + hidden_states = attn_outputs + feed_forward_hidden_states + residual + return hidden_states, kv + + +class CausalLMHead(nn.Module): + """Causal Language Modeling head. + + Reference: + Improving Language Understanding by Generative Pre-Training. + https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf. + + """ + + def __init__(self, config: PretrainedConfig) -> None: + super().__init__() + + self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) + self.linear = nn.Linear(config.n_embd, config.vocab_size) + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + hidden_states = self.ln(hidden_states) + logits = self.linear(hidden_states).to(torch.float32) + return logits + + +class CausalLMLoss(nn.Module): + """Causal Language Modeling loss. + + Reference: + Improving Language Understanding by Generative Pre-Training. + https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf. + + """ + + def __init__(self, shift_labels: bool = True) -> None: + super().__init__() + + self.shift_labels = shift_labels + self.loss_fct = nn.CrossEntropyLoss() + + def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor) -> torch.FloatTensor: + if self.shift_labels: + logits = logits[..., :-1, :].contiguous() + labels = labels[..., 1:].contiguous() + + loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1)) + + return loss + + +class PhiPreTrainedModel(PreTrainedModel): + """Phi pre-trained model.""" + + config_class = PhiConfig + base_model_prefix = "transformer" + supports_gradient_checkpointing = False + _no_split_modules = ["ParallelBlock"] + + def __init__(self, *inputs, **kwargs) -> None: + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module: nn.Module) -> None: + if isinstance(module, (nn.Linear,)): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None, + attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None, + **kwargs, + ) -> Dict[str, Any]: + if past_key_values is None or not (isinstance(past_key_values, InferenceParams)): + past_key_values = InferenceParams( + max_seqlen=self.config.n_positions, + max_batch_size=input_ids.shape[0], + seqlen_offset=0, + batch_size_offset=0, + key_value_memory_dict={}, + lengths_per_sample=None, + ) + else: + # Assume that `past_key_values` has cached all tokens up to the last token in `input_ids` + past_key_values.seqlen_offset = input_ids.shape[1] - 1 + input_ids = input_ids[:, -1].unsqueeze(-1) + + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "attention_mask": attention_mask, + } + + +class PhiModel(PhiPreTrainedModel): + """Phi model.""" + + _keys_to_ignore_on_load_missing = [""] + _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"] + + def __init__(self, config: PhiConfig) -> None: + super().__init__(config) + + self.embd = Embedding(config) + self.h = nn.ModuleList([ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]) + self.gradient_checkpointing = False + self.post_init() + + def get_input_embeddings(self) -> nn.Embedding: + return self.embd.wte + + def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None: + self.embd.wte = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None, + attention_mask: Optional[torch.BoolTensor] = None, + ) -> torch.FloatTensor: + hidden_states = self.embd(input_ids) + + for layer in self.h: + hidden_states, _ = layer( + hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + ) + + return hidden_states + + +class PhiForCausalLM(PhiPreTrainedModel): + """Phi for Causal Language Modeling.""" + + _keys_to_ignore_on_load_missing = [""] + _keys_to_ignore_on_load_unexpected = [r"transformer\.h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"] + + def __init__(self, config: PhiConfig) -> None: + super().__init__(config) + + self.transformer = PhiModel(config) + self.lm_head = CausalLMHead(config) + self.loss = CausalLMLoss() + + self.post_init() + + def get_output_embeddings(self) -> nn.Linear: + return self.lm_head.linear + + def set_output_embeddings(self, new_embeddings: nn.Linear) -> None: + self.lm_head.linear = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None, + attention_mask: Optional[torch.BoolTensor] = None, + labels: Optional[torch.LongTensor] = None, + **kwargs, + ) -> CausalLMOutputWithPast: + hidden_states = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask) + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + loss = self.loss(lm_logits, labels) + + return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)