From 86c3116cf09ad5578aea4207d3450cec8af5be42 Mon Sep 17 00:00:00 2001 From: al_medved Date: Tue, 16 Jul 2019 00:17:44 +0500 Subject: [PATCH 01/35] snOperator -division into two modules: CPU/GPU and GPU only --- src/CMakeLists.txt | 58 +- .../CMakeLists.txt | 0 .../snOperator.h | 0 .../src/CPU/convolutionCPU.cpp | 0 .../src/CPU/deconvolutionCPU.cpp | 0 .../src/CPU/fullyConnectedCPU.cpp | 0 .../src/CPU/poolingCPU.cpp | 0 .../src/CUDA/convolutionCUDA.cu | 0 .../src/CUDA/deconvolutionCUDA.cu | 0 .../src/CUDA/fullyConnectedCUDA.cu | 0 .../src/CUDA/poolingCUDA.cu | 0 .../src/Operator/activation.cpp | 0 .../src/Operator/activation.h | 0 .../src/Operator/batchNorm.cpp | 0 .../src/Operator/batchNorm.h | 0 .../src/Operator/concat.cpp | 0 .../src/Operator/concat.h | 0 .../src/Operator/convolution.cpp | 0 .../src/Operator/convolution.h | 0 .../src/Operator/crop.cpp | 0 .../src/Operator/crop.h | 0 .../src/Operator/deconvolution.cpp | 0 .../src/Operator/deconvolution.h | 0 .../src/Operator/fullyConnected.cpp | 0 .../src/Operator/fullyConnected.h | 0 .../src/Operator/input.cpp | 0 .../src/Operator/input.h | 0 .../src/Operator/lock.cpp | 0 .../src/Operator/lock.h | 0 .../src/Operator/lossFunction.cpp | 0 .../src/Operator/lossFunction.h | 0 .../src/Operator/output.cpp | 0 .../src/Operator/output.h | 0 .../src/Operator/pooling.cpp | 0 .../src/Operator/pooling.h | 0 .../src/Operator/resize.cpp | 0 .../src/Operator/resize.h | 0 .../src/Operator/summator.cpp | 0 .../src/Operator/summator.h | 0 .../src/Operator/switch.cpp | 0 .../src/Operator/switch.h | 0 .../src/Operator/userLayer.cpp | 0 .../src/Operator/userLayer.h | 0 .../src/activeFunctions.cpp | 0 .../src/activeFunctions.h | 0 .../src/mathFunctions.cpp | 0 .../src/mathFunctions.h | 0 .../src/optimizer.cpp | 0 .../src/optimizer.h | 0 .../src/random.cpp | 0 .../src/random.h | 0 .../src/snOperator.cpp | 0 .../src/stdafx.h | 0 .../src/structurs.h | 0 .../src/weightInit.cpp | 0 .../src/weightInit.h | 0 src/snOperator_cuDNN/CMakeLists.txt | 12 + src/snOperator_cuDNN/snOperator.h | 35 + .../src/CUDA/convolutionCUDA.cu | 929 ++++++++++++++++++ .../src/CUDA/deconvolutionCUDA.cu | 915 +++++++++++++++++ .../src/CUDA/fullyConnectedCUDA.cu | 339 +++++++ src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu | 594 +++++++++++ .../src/Operator/activation.cpp | 84 ++ .../src/Operator/activation.h | 44 + .../src/Operator/batchNorm.cpp | 145 +++ src/snOperator_cuDNN/src/Operator/batchNorm.h | 52 + src/snOperator_cuDNN/src/Operator/concat.cpp | 123 +++ src/snOperator_cuDNN/src/Operator/concat.h | 46 + .../src/Operator/convolution.cpp | 460 +++++++++ .../src/Operator/convolution.h | 201 ++++ src/snOperator_cuDNN/src/Operator/crop.cpp | 147 +++ src/snOperator_cuDNN/src/Operator/crop.h | 54 + .../src/Operator/deconvolution.cpp | 392 ++++++++ .../src/Operator/deconvolution.h | 178 ++++ .../src/Operator/fullyConnected.cpp | 372 +++++++ .../src/Operator/fullyConnected.h | 171 ++++ src/snOperator_cuDNN/src/Operator/input.cpp | 46 + src/snOperator_cuDNN/src/Operator/input.h | 42 + src/snOperator_cuDNN/src/Operator/lock.cpp | 66 ++ src/snOperator_cuDNN/src/Operator/lock.h | 41 + .../src/Operator/lossFunction.cpp | 211 ++++ .../src/Operator/lossFunction.h | 57 ++ src/snOperator_cuDNN/src/Operator/output.cpp | 51 + src/snOperator_cuDNN/src/Operator/output.h | 41 + src/snOperator_cuDNN/src/Operator/pooling.cpp | 225 +++++ src/snOperator_cuDNN/src/Operator/pooling.h | 149 +++ src/snOperator_cuDNN/src/Operator/resize.cpp | 132 +++ src/snOperator_cuDNN/src/Operator/resize.h | 46 + .../src/Operator/summator.cpp | 94 ++ src/snOperator_cuDNN/src/Operator/summator.h | 53 + src/snOperator_cuDNN/src/Operator/switch.cpp | 69 ++ src/snOperator_cuDNN/src/Operator/switch.h | 41 + .../src/Operator/userLayer.cpp | 92 ++ src/snOperator_cuDNN/src/Operator/userLayer.h | 41 + src/snOperator_cuDNN/src/activeFunctions.cpp | 117 +++ src/snOperator_cuDNN/src/activeFunctions.h | 46 + src/snOperator_cuDNN/src/mathFunctions.cpp | 253 +++++ src/snOperator_cuDNN/src/mathFunctions.h | 45 + src/snOperator_cuDNN/src/optimizer.cpp | 90 ++ src/snOperator_cuDNN/src/optimizer.h | 55 ++ src/snOperator_cuDNN/src/random.cpp | 56 ++ src/snOperator_cuDNN/src/random.h | 35 + src/snOperator_cuDNN/src/snOperator.cpp | 92 ++ src/snOperator_cuDNN/src/stdafx.h | 44 + src/snOperator_cuDNN/src/structurs.h | 74 ++ src/snOperator_cuDNN/src/weightInit.cpp | 69 ++ src/snOperator_cuDNN/src/weightInit.h | 44 + 107 files changed, 7848 insertions(+), 20 deletions(-) rename src/{snOperator => snOperator_CPU_cuDNN}/CMakeLists.txt (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/snOperator.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CPU/convolutionCPU.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CPU/deconvolutionCPU.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CPU/fullyConnectedCPU.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CPU/poolingCPU.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CUDA/convolutionCUDA.cu (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CUDA/deconvolutionCUDA.cu (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CUDA/fullyConnectedCUDA.cu (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/CUDA/poolingCUDA.cu (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/activation.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/activation.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/batchNorm.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/batchNorm.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/concat.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/concat.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/convolution.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/convolution.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/crop.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/crop.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/deconvolution.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/deconvolution.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/fullyConnected.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/fullyConnected.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/input.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/input.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/lock.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/lock.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/lossFunction.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/lossFunction.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/output.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/output.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/pooling.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/pooling.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/resize.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/resize.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/summator.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/summator.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/switch.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/switch.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/userLayer.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/Operator/userLayer.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/activeFunctions.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/activeFunctions.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/mathFunctions.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/mathFunctions.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/optimizer.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/optimizer.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/random.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/random.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/snOperator.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/stdafx.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/structurs.h (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/weightInit.cpp (100%) rename src/{snOperator => snOperator_CPU_cuDNN}/src/weightInit.h (100%) create mode 100644 src/snOperator_cuDNN/CMakeLists.txt create mode 100644 src/snOperator_cuDNN/snOperator.h create mode 100644 src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu create mode 100644 src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu create mode 100644 src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu create mode 100644 src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu create mode 100644 src/snOperator_cuDNN/src/Operator/activation.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/activation.h create mode 100644 src/snOperator_cuDNN/src/Operator/batchNorm.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/batchNorm.h create mode 100644 src/snOperator_cuDNN/src/Operator/concat.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/concat.h create mode 100644 src/snOperator_cuDNN/src/Operator/convolution.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/convolution.h create mode 100644 src/snOperator_cuDNN/src/Operator/crop.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/crop.h create mode 100644 src/snOperator_cuDNN/src/Operator/deconvolution.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/deconvolution.h create mode 100644 src/snOperator_cuDNN/src/Operator/fullyConnected.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/fullyConnected.h create mode 100644 src/snOperator_cuDNN/src/Operator/input.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/input.h create mode 100644 src/snOperator_cuDNN/src/Operator/lock.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/lock.h create mode 100644 src/snOperator_cuDNN/src/Operator/lossFunction.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/lossFunction.h create mode 100644 src/snOperator_cuDNN/src/Operator/output.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/output.h create mode 100644 src/snOperator_cuDNN/src/Operator/pooling.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/pooling.h create mode 100644 src/snOperator_cuDNN/src/Operator/resize.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/resize.h create mode 100644 src/snOperator_cuDNN/src/Operator/summator.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/summator.h create mode 100644 src/snOperator_cuDNN/src/Operator/switch.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/switch.h create mode 100644 src/snOperator_cuDNN/src/Operator/userLayer.cpp create mode 100644 src/snOperator_cuDNN/src/Operator/userLayer.h create mode 100644 src/snOperator_cuDNN/src/activeFunctions.cpp create mode 100644 src/snOperator_cuDNN/src/activeFunctions.h create mode 100644 src/snOperator_cuDNN/src/mathFunctions.cpp create mode 100644 src/snOperator_cuDNN/src/mathFunctions.h create mode 100644 src/snOperator_cuDNN/src/optimizer.cpp create mode 100644 src/snOperator_cuDNN/src/optimizer.h create mode 100644 src/snOperator_cuDNN/src/random.cpp create mode 100644 src/snOperator_cuDNN/src/random.h create mode 100644 src/snOperator_cuDNN/src/snOperator.cpp create mode 100644 src/snOperator_cuDNN/src/stdafx.h create mode 100644 src/snOperator_cuDNN/src/structurs.h create mode 100644 src/snOperator_cuDNN/src/weightInit.cpp create mode 100644 src/snOperator_cuDNN/src/weightInit.h diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 0ea64eb..3bdacb5 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -7,39 +7,49 @@ set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/Builds/$/${CMAKE_ set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/Builds/$/${CMAKE_VS_PLATFORM_NAME}) set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/Builds/$/${CMAKE_VS_PLATFORM_NAME}) -set(GPU "NONE" CACHE STRING "Selected GPU library") -set_property(CACHE GPU PROPERTY STRINGS "NONE;CUDA;CUDNN") +set(GPU "NONE" CACHE STRING "Selected GPU mode") +set_property(CACHE GPU PROPERTY STRINGS "NONE;CUDNN_AND_CPU;CUDNN_ONLY") -option(USE_AVX "AVX library support" ON) +option(USE_AVX "AVX instr CPU support" ON) -if (GPU STREQUAL "CUDA" OR GPU STREQUAL "CUDNN") - set(USE_CUDA ON) +if (GPU STREQUAL "CUDNN_AND_CPU") + set(USE_CUDA_CPU ON) else() - set(USE_CUDA OFF) + set(USE_CUDA_CPU OFF) endif() -if(USE_CUDA) +if (GPU STREQUAL "CUDNN_ONLY") + set(USE_CUDA_ONLY ON) +else() + set(USE_CUDA_ONLY OFF) +endif() + +if(USE_CUDA_CPU OR USE_CUDA_ONLY) enable_language("CUDA") find_package(CUDA REQUIRED) include_directories( ${CUDA_INCLUDE_DIR}) link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib64) endif() -find_package(OpenMP REQUIRED) -if (OpenMP_CXX_FOUND) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") - set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}") +if(NOT USE_CUDA_ONLY) + find_package(OpenMP REQUIRED) + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}") endif() if (UNIX) - find_package(BLAS REQUIRED) - - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -pthread -fPIC") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -pthread -fPIC") - if (USE_AVX) + if (NOT USE_CUDA_ONLY) + find_package(BLAS REQUIRED) + + if (USE_AVX) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx") + endif() + endif() + endif() if(MSVC) @@ -54,9 +64,17 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}) add_subdirectory(snAux) add_subdirectory(snBase) add_subdirectory(snEngine) -add_subdirectory(snOperator) add_subdirectory(skynet) -if(USE_AVX) -add_subdirectory(snSIMD) + +if(NOT USE_CUDA_ONLY) + add_subdirectory(snOperator_CPU_cuDNN) + + if(USE_AVX) + add_subdirectory(snSIMD) + endif() + +else() + add_subdirectory(snOperator_cuDNN) + endif() -#add_subdirectory(snExample) + diff --git a/src/snOperator/CMakeLists.txt b/src/snOperator_CPU_cuDNN/CMakeLists.txt similarity index 100% rename from src/snOperator/CMakeLists.txt rename to src/snOperator_CPU_cuDNN/CMakeLists.txt diff --git a/src/snOperator/snOperator.h b/src/snOperator_CPU_cuDNN/snOperator.h similarity index 100% rename from src/snOperator/snOperator.h rename to src/snOperator_CPU_cuDNN/snOperator.h diff --git a/src/snOperator/src/CPU/convolutionCPU.cpp b/src/snOperator_CPU_cuDNN/src/CPU/convolutionCPU.cpp similarity index 100% rename from src/snOperator/src/CPU/convolutionCPU.cpp rename to src/snOperator_CPU_cuDNN/src/CPU/convolutionCPU.cpp diff --git a/src/snOperator/src/CPU/deconvolutionCPU.cpp b/src/snOperator_CPU_cuDNN/src/CPU/deconvolutionCPU.cpp similarity index 100% rename from src/snOperator/src/CPU/deconvolutionCPU.cpp rename to src/snOperator_CPU_cuDNN/src/CPU/deconvolutionCPU.cpp diff --git a/src/snOperator/src/CPU/fullyConnectedCPU.cpp b/src/snOperator_CPU_cuDNN/src/CPU/fullyConnectedCPU.cpp similarity index 100% rename from src/snOperator/src/CPU/fullyConnectedCPU.cpp rename to src/snOperator_CPU_cuDNN/src/CPU/fullyConnectedCPU.cpp diff --git a/src/snOperator/src/CPU/poolingCPU.cpp b/src/snOperator_CPU_cuDNN/src/CPU/poolingCPU.cpp similarity index 100% rename from src/snOperator/src/CPU/poolingCPU.cpp rename to src/snOperator_CPU_cuDNN/src/CPU/poolingCPU.cpp diff --git a/src/snOperator/src/CUDA/convolutionCUDA.cu b/src/snOperator_CPU_cuDNN/src/CUDA/convolutionCUDA.cu similarity index 100% rename from src/snOperator/src/CUDA/convolutionCUDA.cu rename to src/snOperator_CPU_cuDNN/src/CUDA/convolutionCUDA.cu diff --git a/src/snOperator/src/CUDA/deconvolutionCUDA.cu b/src/snOperator_CPU_cuDNN/src/CUDA/deconvolutionCUDA.cu similarity index 100% rename from src/snOperator/src/CUDA/deconvolutionCUDA.cu rename to src/snOperator_CPU_cuDNN/src/CUDA/deconvolutionCUDA.cu diff --git a/src/snOperator/src/CUDA/fullyConnectedCUDA.cu b/src/snOperator_CPU_cuDNN/src/CUDA/fullyConnectedCUDA.cu similarity index 100% rename from src/snOperator/src/CUDA/fullyConnectedCUDA.cu rename to src/snOperator_CPU_cuDNN/src/CUDA/fullyConnectedCUDA.cu diff --git a/src/snOperator/src/CUDA/poolingCUDA.cu b/src/snOperator_CPU_cuDNN/src/CUDA/poolingCUDA.cu similarity index 100% rename from src/snOperator/src/CUDA/poolingCUDA.cu rename to src/snOperator_CPU_cuDNN/src/CUDA/poolingCUDA.cu diff --git a/src/snOperator/src/Operator/activation.cpp b/src/snOperator_CPU_cuDNN/src/Operator/activation.cpp similarity index 100% rename from src/snOperator/src/Operator/activation.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/activation.cpp diff --git a/src/snOperator/src/Operator/activation.h b/src/snOperator_CPU_cuDNN/src/Operator/activation.h similarity index 100% rename from src/snOperator/src/Operator/activation.h rename to src/snOperator_CPU_cuDNN/src/Operator/activation.h diff --git a/src/snOperator/src/Operator/batchNorm.cpp b/src/snOperator_CPU_cuDNN/src/Operator/batchNorm.cpp similarity index 100% rename from src/snOperator/src/Operator/batchNorm.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/batchNorm.cpp diff --git a/src/snOperator/src/Operator/batchNorm.h b/src/snOperator_CPU_cuDNN/src/Operator/batchNorm.h similarity index 100% rename from src/snOperator/src/Operator/batchNorm.h rename to src/snOperator_CPU_cuDNN/src/Operator/batchNorm.h diff --git a/src/snOperator/src/Operator/concat.cpp b/src/snOperator_CPU_cuDNN/src/Operator/concat.cpp similarity index 100% rename from src/snOperator/src/Operator/concat.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/concat.cpp diff --git a/src/snOperator/src/Operator/concat.h b/src/snOperator_CPU_cuDNN/src/Operator/concat.h similarity index 100% rename from src/snOperator/src/Operator/concat.h rename to src/snOperator_CPU_cuDNN/src/Operator/concat.h diff --git a/src/snOperator/src/Operator/convolution.cpp b/src/snOperator_CPU_cuDNN/src/Operator/convolution.cpp similarity index 100% rename from src/snOperator/src/Operator/convolution.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/convolution.cpp diff --git a/src/snOperator/src/Operator/convolution.h b/src/snOperator_CPU_cuDNN/src/Operator/convolution.h similarity index 100% rename from src/snOperator/src/Operator/convolution.h rename to src/snOperator_CPU_cuDNN/src/Operator/convolution.h diff --git a/src/snOperator/src/Operator/crop.cpp b/src/snOperator_CPU_cuDNN/src/Operator/crop.cpp similarity index 100% rename from src/snOperator/src/Operator/crop.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/crop.cpp diff --git a/src/snOperator/src/Operator/crop.h b/src/snOperator_CPU_cuDNN/src/Operator/crop.h similarity index 100% rename from src/snOperator/src/Operator/crop.h rename to src/snOperator_CPU_cuDNN/src/Operator/crop.h diff --git a/src/snOperator/src/Operator/deconvolution.cpp b/src/snOperator_CPU_cuDNN/src/Operator/deconvolution.cpp similarity index 100% rename from src/snOperator/src/Operator/deconvolution.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/deconvolution.cpp diff --git a/src/snOperator/src/Operator/deconvolution.h b/src/snOperator_CPU_cuDNN/src/Operator/deconvolution.h similarity index 100% rename from src/snOperator/src/Operator/deconvolution.h rename to src/snOperator_CPU_cuDNN/src/Operator/deconvolution.h diff --git a/src/snOperator/src/Operator/fullyConnected.cpp b/src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.cpp similarity index 100% rename from src/snOperator/src/Operator/fullyConnected.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.cpp diff --git a/src/snOperator/src/Operator/fullyConnected.h b/src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.h similarity index 100% rename from src/snOperator/src/Operator/fullyConnected.h rename to src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.h diff --git a/src/snOperator/src/Operator/input.cpp b/src/snOperator_CPU_cuDNN/src/Operator/input.cpp similarity index 100% rename from src/snOperator/src/Operator/input.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/input.cpp diff --git a/src/snOperator/src/Operator/input.h b/src/snOperator_CPU_cuDNN/src/Operator/input.h similarity index 100% rename from src/snOperator/src/Operator/input.h rename to src/snOperator_CPU_cuDNN/src/Operator/input.h diff --git a/src/snOperator/src/Operator/lock.cpp b/src/snOperator_CPU_cuDNN/src/Operator/lock.cpp similarity index 100% rename from src/snOperator/src/Operator/lock.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/lock.cpp diff --git a/src/snOperator/src/Operator/lock.h b/src/snOperator_CPU_cuDNN/src/Operator/lock.h similarity index 100% rename from src/snOperator/src/Operator/lock.h rename to src/snOperator_CPU_cuDNN/src/Operator/lock.h diff --git a/src/snOperator/src/Operator/lossFunction.cpp b/src/snOperator_CPU_cuDNN/src/Operator/lossFunction.cpp similarity index 100% rename from src/snOperator/src/Operator/lossFunction.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/lossFunction.cpp diff --git a/src/snOperator/src/Operator/lossFunction.h b/src/snOperator_CPU_cuDNN/src/Operator/lossFunction.h similarity index 100% rename from src/snOperator/src/Operator/lossFunction.h rename to src/snOperator_CPU_cuDNN/src/Operator/lossFunction.h diff --git a/src/snOperator/src/Operator/output.cpp b/src/snOperator_CPU_cuDNN/src/Operator/output.cpp similarity index 100% rename from src/snOperator/src/Operator/output.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/output.cpp diff --git a/src/snOperator/src/Operator/output.h b/src/snOperator_CPU_cuDNN/src/Operator/output.h similarity index 100% rename from src/snOperator/src/Operator/output.h rename to src/snOperator_CPU_cuDNN/src/Operator/output.h diff --git a/src/snOperator/src/Operator/pooling.cpp b/src/snOperator_CPU_cuDNN/src/Operator/pooling.cpp similarity index 100% rename from src/snOperator/src/Operator/pooling.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/pooling.cpp diff --git a/src/snOperator/src/Operator/pooling.h b/src/snOperator_CPU_cuDNN/src/Operator/pooling.h similarity index 100% rename from src/snOperator/src/Operator/pooling.h rename to src/snOperator_CPU_cuDNN/src/Operator/pooling.h diff --git a/src/snOperator/src/Operator/resize.cpp b/src/snOperator_CPU_cuDNN/src/Operator/resize.cpp similarity index 100% rename from src/snOperator/src/Operator/resize.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/resize.cpp diff --git a/src/snOperator/src/Operator/resize.h b/src/snOperator_CPU_cuDNN/src/Operator/resize.h similarity index 100% rename from src/snOperator/src/Operator/resize.h rename to src/snOperator_CPU_cuDNN/src/Operator/resize.h diff --git a/src/snOperator/src/Operator/summator.cpp b/src/snOperator_CPU_cuDNN/src/Operator/summator.cpp similarity index 100% rename from src/snOperator/src/Operator/summator.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/summator.cpp diff --git a/src/snOperator/src/Operator/summator.h b/src/snOperator_CPU_cuDNN/src/Operator/summator.h similarity index 100% rename from src/snOperator/src/Operator/summator.h rename to src/snOperator_CPU_cuDNN/src/Operator/summator.h diff --git a/src/snOperator/src/Operator/switch.cpp b/src/snOperator_CPU_cuDNN/src/Operator/switch.cpp similarity index 100% rename from src/snOperator/src/Operator/switch.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/switch.cpp diff --git a/src/snOperator/src/Operator/switch.h b/src/snOperator_CPU_cuDNN/src/Operator/switch.h similarity index 100% rename from src/snOperator/src/Operator/switch.h rename to src/snOperator_CPU_cuDNN/src/Operator/switch.h diff --git a/src/snOperator/src/Operator/userLayer.cpp b/src/snOperator_CPU_cuDNN/src/Operator/userLayer.cpp similarity index 100% rename from src/snOperator/src/Operator/userLayer.cpp rename to src/snOperator_CPU_cuDNN/src/Operator/userLayer.cpp diff --git a/src/snOperator/src/Operator/userLayer.h b/src/snOperator_CPU_cuDNN/src/Operator/userLayer.h similarity index 100% rename from src/snOperator/src/Operator/userLayer.h rename to src/snOperator_CPU_cuDNN/src/Operator/userLayer.h diff --git a/src/snOperator/src/activeFunctions.cpp b/src/snOperator_CPU_cuDNN/src/activeFunctions.cpp similarity index 100% rename from src/snOperator/src/activeFunctions.cpp rename to src/snOperator_CPU_cuDNN/src/activeFunctions.cpp diff --git a/src/snOperator/src/activeFunctions.h b/src/snOperator_CPU_cuDNN/src/activeFunctions.h similarity index 100% rename from src/snOperator/src/activeFunctions.h rename to src/snOperator_CPU_cuDNN/src/activeFunctions.h diff --git a/src/snOperator/src/mathFunctions.cpp b/src/snOperator_CPU_cuDNN/src/mathFunctions.cpp similarity index 100% rename from src/snOperator/src/mathFunctions.cpp rename to src/snOperator_CPU_cuDNN/src/mathFunctions.cpp diff --git a/src/snOperator/src/mathFunctions.h b/src/snOperator_CPU_cuDNN/src/mathFunctions.h similarity index 100% rename from src/snOperator/src/mathFunctions.h rename to src/snOperator_CPU_cuDNN/src/mathFunctions.h diff --git a/src/snOperator/src/optimizer.cpp b/src/snOperator_CPU_cuDNN/src/optimizer.cpp similarity index 100% rename from src/snOperator/src/optimizer.cpp rename to src/snOperator_CPU_cuDNN/src/optimizer.cpp diff --git a/src/snOperator/src/optimizer.h b/src/snOperator_CPU_cuDNN/src/optimizer.h similarity index 100% rename from src/snOperator/src/optimizer.h rename to src/snOperator_CPU_cuDNN/src/optimizer.h diff --git a/src/snOperator/src/random.cpp b/src/snOperator_CPU_cuDNN/src/random.cpp similarity index 100% rename from src/snOperator/src/random.cpp rename to src/snOperator_CPU_cuDNN/src/random.cpp diff --git a/src/snOperator/src/random.h b/src/snOperator_CPU_cuDNN/src/random.h similarity index 100% rename from src/snOperator/src/random.h rename to src/snOperator_CPU_cuDNN/src/random.h diff --git a/src/snOperator/src/snOperator.cpp b/src/snOperator_CPU_cuDNN/src/snOperator.cpp similarity index 100% rename from src/snOperator/src/snOperator.cpp rename to src/snOperator_CPU_cuDNN/src/snOperator.cpp diff --git a/src/snOperator/src/stdafx.h b/src/snOperator_CPU_cuDNN/src/stdafx.h similarity index 100% rename from src/snOperator/src/stdafx.h rename to src/snOperator_CPU_cuDNN/src/stdafx.h diff --git a/src/snOperator/src/structurs.h b/src/snOperator_CPU_cuDNN/src/structurs.h similarity index 100% rename from src/snOperator/src/structurs.h rename to src/snOperator_CPU_cuDNN/src/structurs.h diff --git a/src/snOperator/src/weightInit.cpp b/src/snOperator_CPU_cuDNN/src/weightInit.cpp similarity index 100% rename from src/snOperator/src/weightInit.cpp rename to src/snOperator_CPU_cuDNN/src/weightInit.cpp diff --git a/src/snOperator/src/weightInit.h b/src/snOperator_CPU_cuDNN/src/weightInit.h similarity index 100% rename from src/snOperator/src/weightInit.h rename to src/snOperator_CPU_cuDNN/src/weightInit.h diff --git a/src/snOperator_cuDNN/CMakeLists.txt b/src/snOperator_cuDNN/CMakeLists.txt new file mode 100644 index 0000000..8f3d61e --- /dev/null +++ b/src/snOperator_cuDNN/CMakeLists.txt @@ -0,0 +1,12 @@ +cmake_minimum_required(VERSION 3.8 FATAL_ERROR) + +project(snOperator) + +FILE(GLOB_RECURSE SRC_snOperator "*.cpp" "*.h" "*.cu") + +add_library(snOperator STATIC ${SRC_snOperator}) + +set_target_properties(snOperator PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_compile_features(snOperator PUBLIC cxx_std_11) + +target_link_libraries(snOperator cublas cudart_static cudnn snAux snBase) \ No newline at end of file diff --git a/src/snOperator_cuDNN/snOperator.h b/src/snOperator_cuDNN/snOperator.h new file mode 100644 index 0000000..7414234 --- /dev/null +++ b/src/snOperator_cuDNN/snOperator.h @@ -0,0 +1,35 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + +namespace SN_Opr{ + + SN_Base::OperatorBase* createOperator(void* net, const std::string& fname, const std::string& node, + std::map& prms); + + void freeOperator(SN_Base::OperatorBase*, const std::string& fname); +} diff --git a/src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu b/src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu new file mode 100644 index 0000000..4c3eea8 --- /dev/null +++ b/src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu @@ -0,0 +1,929 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#ifdef SN_CUDNN + +#include +#include +#include "../stdafx.h" +#include "snOperator/src/Operator/convolution.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnConvolutionDescriptor_t conv_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + cudnnFilterDescriptor_t w_desc = 0; + cudnnFilterDescriptor_t dw_desc = 0; + cudnnTensorDescriptor_t bias_desc = 0; + + cudnnConvolutionFwdAlgo_t algoFwd; + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + + size_t wsFwdSz = 0; + size_t wsBwdDataSz = 0; + size_t wsBwdWSz = 0; + size_t inszMem = 0; + + snFloat* d_in = 0; + snFloat* d_w = 0; + snFloat* d_dw = 0; + snFloat* d_bias = 0; + snFloat* d_out = 0; + snFloat* d_grout = 0; + void* d_wsFwd = 0; + void* d_wsBwdData = 0; + void* d_wsBwdW = 0; +}; + +void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, + const convParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // w + cudnnFilterDescriptor_t w_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); + gpuPrm->w_desc = w_desc; + + // conv + cudnnConvolutionDescriptor_t conv_desc = nullptr; + cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); + cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), + CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); + if (!isFirst) + cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); + gpuPrm->conv_desc = conv_desc; + + // output + int out_n = 0, out_c = 0, out_h = 0, out_w = 0; + cuCHECK(cudnnGetConvolution2dForwardOutputDim( + conv_desc, in_desc, w_desc, + &out_n, &out_c, &out_h, &out_w)); + + if (outsz != snSize(out_w, out_h, out_c, out_n)){ + ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); + return; + } + + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + // bias + cudnnTensorDescriptor_t bias_desc; + cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + 1, out_c, 1, 1)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); + gpuPrm->bias_desc = bias_desc; + + // algorithm + cudnnConvolutionFwdAlgo_t algoFwd; + cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, + CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); + gpuPrm->algoFwd = algoFwd; + + // workspace + size_t wsFwdSz = 0; + cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, algoFwd, &wsFwdSz)); + gpuPrm->wsFwdSz = wsFwdSz; + + + size_t wsBwdDataSz = 0, wsBwdWSz = 0; + if (isLern){ + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // dw + cudnnFilterDescriptor_t dw_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); + gpuPrm->dw_desc = dw_desc; + + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + + // algorithm + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, + CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); + gpuPrm->algoBwdData = algoBwdData; + + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, + CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); + gpuPrm->algoBwdW = algoBwdW; + + // workspace + cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, algoBwdData, &wsBwdDataSz)); + gpuPrm->wsBwdDataSz = wsBwdDataSz; + + cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); + gpuPrm->wsBwdWSz = wsBwdWSz; + } + + if (isFirst && !gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + } + else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + gpuPrm->inszMem = insz.size(); + } +} + +void Convolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); + + cuCHECK(cudaFree(gpuPrm->d_in)); + cuCHECK(cudaFree(gpuPrm->d_w)); + cuCHECK(cudaFree(gpuPrm->d_bias)); + cuCHECK(cudaFree(gpuPrm->d_out)); + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); + + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); + + cuCHECK(cudaFree(gpuPrm->d_dw)); + cuCHECK(cudaFree(gpuPrm->d_grout)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); + } +} + +__global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ + + size_t osz = outsz.w * outsz.h; + + snFloat* pOut = output + osz * outsz.d * blockIdx.x; + unsigned int d = threadIdx.x; + while (d < outsz.d){ + + snFloat b = bias[d]; + for (size_t j = 0; j < osz; ++j) + pOut[j] += b; + + pOut += osz * blockDim.x; + + d += blockDim.x; + } +} + +void Convolution::forwardCUDA(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + size_t isz = insz.size(), osz = outsz.size(); + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, outsz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + gpuPrm->d_in, + gpuPrm->w_desc, + gpuPrm->d_w, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->out_desc, + gpuPrm->d_out)); + + // +bias + cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); + + // result + cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + } +} + +void Convolution::backwardCUDA_GW(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + void* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, gpuPrm->wsBwdWSz)); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // grin + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, outsz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + gpuPrm->d_w, + gpuPrm->grin_desc, + d_grin, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->grout_desc, + gpuPrm->d_grout)); + + cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + gpuPrm->d_in, + gpuPrm->grin_desc, + d_grin, + gpuPrm->conv_desc, + gpuPrm->algoBwdW, + gpuPrm->d_wsBwdW, + gpuPrm->wsBwdWSz, + &beta, + gpuPrm->dw_desc, + gpuPrm->d_dw)); + + cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + d_grin, + &beta, + gpuPrm->bias_desc, + gpuPrm->d_bias)); + + // результ + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + cuCHECK(cudaMemcpy(dWeightOut + wsz, gpuPrm->d_bias, outsz.d * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + } +} + +void Convolution::backwardCUDA_G(const convParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + void* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); + } + + // grin + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + gpuPrm->d_w, + gpuPrm->grin_desc, + d_grin, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->grout_desc, + gpuPrm->d_grout)); + + // результ + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + } +} + +#elif SN_CUDA + +#include +#include "../stdafx.h" +#include "snOperator/src/Operator/convolution.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + + +struct gpuParams{ + + cudaDeviceProp* cu_deviceProps = 0; + + snFloat* d_in = 0; + snFloat* d_out = 0; + snFloat* d_w = 0; + snFloat* d_dw = 0; + snFloat* d_grout = 0; + + size_t inszMem = 0; +}; + +void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, + const convParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + cudaDeviceProp* cu_deviceProps = new cudaDeviceProp(); + + cudaGetDeviceProperties(cu_deviceProps, 0); + if (cu_deviceProps->major < 2){ + ERROR_MESS("%s requires SM >= 2.0"); + delete cu_deviceProps; + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + gpuPrm->cu_deviceProps = cu_deviceProps; + + if (!gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); + } + } + } + else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ + + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); + } + + gpuPrm->inszMem = insz.size(); + } +} + +void Convolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + delete gpuPrm->cu_deviceProps; + + cuCHECK(cudaFree(gpuPrm->d_in)); + cuCHECK(cudaFree(gpuPrm->d_w)); + cuCHECK(cudaFree(gpuPrm->d_out)); + + if (gpuPrm->d_grout){ // isLern + cuCHECK(cudaFree(gpuPrm->d_grout)); + cuCHECK(cudaFree(gpuPrm->d_dw)); + } +} + +__global__ void cuConvFwd(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, + snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ + + size_t wStepByD = fWidth * fHeight, // step weight by input + wStepByK = wStepByD * insz.d, // step weight by output + outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d, // step out by batch + inStepByD = insz.w * insz.h, // step in by input + inStepByN = inStepByD * insz.d; // step in by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + weight += blockIdx.x * wStepByK; + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + input += blockIdx.y * inStepByN; + + + unsigned int oz = 0; + while (oz < insz.d){ + + unsigned int oy = threadIdx.y; + while (oy < outsz.h){ + + unsigned int ox = threadIdx.x; + while (ox < outsz.w){ + + if (oz == 0) + output[ox + oy * outsz.w] = weight[(outsz.d - blockIdx.x) * wStepByK + blockIdx.x]; // bias + + size_t posW = ox * stride, posH = oy * stride; + + // kernel + snFloat csum = 0; +#pragma unroll + for (size_t c = 0; c < wStepByD; ++c){ + + size_t cx = c % fWidth, cy = c / fWidth; + + csum += input[(cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w] * weight[cx + cy * fWidth]; + } + output[ox + oy * outsz.w] += csum; + + ox += blockDim.x; + } + oy += blockDim.y; + } + weight += wStepByD; + input += inStepByD; + ++oz; + } +} + +void Convolution::forwardCUDA(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + size_t isz = insz.size(), osz = outsz.size(), + wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(outsz.d), int(outsz.n)); + + cuConvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, + prms.fHeight, + prms.dilate, + prms.stride, + gpuPrm->d_w, + insz, + gpuPrm->d_in, + outsz, + gpuPrm->d_out); + + // result + cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + } +} + +__global__ void cuConvBwd_GW(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, + snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ + + size_t wStepByD = fWidth * fHeight, // step weight by input + wStepByK = wStepByD * insz.d, // step weight by output + wStepByN = wStepByK * outsz.d + outsz.d, // step weight by batch + outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d, // step out by batch + inStepByD = insz.w * insz.h, // step in by input + inStepByN = inStepByD * insz.d; // step in by batch + + // gridDim.x - number of input layers + // gridDim.y - batch size + + input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + weight += blockIdx.x * wStepByD; + dWeightOut += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + gradIn += blockIdx.y * outStepByN; + gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + + unsigned int oz = 0; + while (oz < outsz.d){ + + unsigned int oy = threadIdx.y; + while (oy < outsz.h){ + + unsigned int ox = threadIdx.x; + while (ox < outsz.w){ + + size_t posW = ox * stride, posH = oy * stride; + + // kernel conv + snFloat grIn = gradIn[ox + oy * outsz.w]; +#pragma unroll + for (size_t c = 0; c < wStepByD; ++c){ + + size_t cx = c % fWidth, cy = c / fWidth, + si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, + sw = cx + cy * fWidth; + + gradOut[si] += grIn * weight[sw]; + + dWeightOut[sw] += grIn * input[si]; + } + if (blockIdx.x == 0) + dWeightOut[wStepByK * (outsz.d - oz) + oz] += grIn; // bias + + ox += blockDim.x; + } + oy += blockDim.y; + } + weight += wStepByK; + dWeightOut += wStepByK; + gradIn += outStepByD; + ++oz; + } +} + +// weighted averaging over batch +__global__ void cuConvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ + + size_t wStepByD = fWidth * fHeight, // step weight by input + wStepByK = wStepByD * insz.d, // step weight by output + wStepByN = wStepByK * kernel + kernel; // step weight by batch + + unsigned int ox = threadIdx.x; + while (ox < wStepByN){ + + snFloat csum = weight[ox]; + for (size_t i = 1; i < insz.n; ++i) + csum += weight[ox + wStepByN * i]; + + weight[ox] = csum / insz.n; + + ox += blockDim.x; + } +} + +void Convolution::backwardCUDA_GW(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(), + wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; + snFloat* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // out + cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); + cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(insz.d), int(outsz.n)); + + cuConvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, + prms.fHeight, + prms.dilate, + prms.stride, + gpuPrm->d_w, + insz, + gpuPrm->d_in, + outsz, + d_grin, + gpuPrm->d_grout, + gpuPrm->d_dw); + + cuConvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); + + // result + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + } +} + +__global__ void cuConvBwd_G(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, + snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ + + size_t wStepByD = fWidth * fHeight, // step weight by input + wStepByK = wStepByD * insz.d, // step weight by output + outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d, // step out by batch + inStepByD = insz.w * insz.h, // step in by input + inStepByN = inStepByD * insz.d; // step in by batch + + + // gridDim.x - number of input layers + // gridDim.y - batch size + + weight += blockIdx.x * wStepByD; + gradIn += blockIdx.y * outStepByN; + gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + unsigned int oz = 0; + while (oz < outsz.d){ + + unsigned int oy = threadIdx.y; + while (oy < outsz.h){ + + unsigned int ox = threadIdx.x; + while (ox < outsz.w){ + + size_t posW = ox * stride, posH = oy * stride; + + // kernel conv + snFloat grIn = gradIn[ox + oy * outsz.w]; +#pragma unroll + for (size_t c = 0; c < wStepByD; ++c){ + + size_t cx = c % fWidth, cy = c / fWidth, + si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, + sw = cx + cy * fWidth; + + gradOut[si] += grIn * weight[sw]; + } + + ox += blockDim.x; + } + oy += blockDim.y; + } + weight += wStepByK; + gradIn += outStepByD; + ++oz; + } +} + +void Convolution::backwardCUDA_G(const convParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(), + wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; + snFloat* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(insz.d), int(outsz.n)); + + // out + cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); + + cuConvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, + prms.fHeight, + prms.dilate, + prms.stride, + gpuPrm->d_w, + insz, outsz, + d_grin, + gpuPrm->d_grout); + + // result + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + } +} + +#endif diff --git a/src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu b/src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu new file mode 100644 index 0000000..59d2b43 --- /dev/null +++ b/src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu @@ -0,0 +1,915 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#ifdef SN_CUDNN + +#include +#include +#include "../stdafx.h" +#include "snOperator/src/Operator/deconvolution.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnConvolutionDescriptor_t conv_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + cudnnFilterDescriptor_t w_desc = 0; + cudnnFilterDescriptor_t dw_desc = 0; + cudnnTensorDescriptor_t bias_desc = 0; + + cudnnConvolutionFwdAlgo_t algoFwd; + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + + size_t wsFwdSz = 0; + size_t wsBwdDataSz = 0; + size_t wsBwdWSz = 0; + size_t inszMem = 0; + + snFloat* d_in = 0; + snFloat* d_w = 0; + snFloat* d_dw = 0; + snFloat* d_bias = 0; + snFloat* d_out = 0; + snFloat* d_grout = 0; + void* d_wsFwd = 0; + void* d_wsBwdData = 0; + void* d_wsBwdW = 0; + +}; + +void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, + const deconvParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // w + cudnnFilterDescriptor_t w_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); + gpuPrm->w_desc = w_desc; + + // conv + cudnnConvolutionDescriptor_t conv_desc = nullptr; + cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); + cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), 1, 1, + CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); + if (!isFirst) + cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); + gpuPrm->conv_desc = conv_desc; + + // output + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + // algorithm + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, + CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); + gpuPrm->algoBwdData = algoBwdData; + + // workspace + size_t wsBwdDataSz = 0; + cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, algoBwdData, &wsBwdDataSz)); + gpuPrm->wsBwdDataSz = wsBwdDataSz; + + + size_t wsFwdSz = 0, wsBwdWSz = 0; + if (isLern){ + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // dw + cudnnFilterDescriptor_t dw_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); + gpuPrm->dw_desc = dw_desc; + + // bias + cudnnTensorDescriptor_t bias_desc; + cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + 1, int(insz.d), 1, 1)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); + gpuPrm->bias_desc = bias_desc; + + // algorithm + cudnnConvolutionFwdAlgo_t algoFwd; + cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, + CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); + gpuPrm->algoFwd = algoFwd; + + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, + CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); + gpuPrm->algoBwdW = algoBwdW; + + // workspace + cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, algoFwd, &wsFwdSz)); + gpuPrm->wsFwdSz = wsFwdSz; + + cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); + gpuPrm->wsBwdWSz = wsBwdWSz; + } + + if (isFirst && !gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); + } + } + else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + + if (isLern){ + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); + } + gpuPrm->inszMem = insz.size(); + } +} + +void Deconvolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); + + cuCHECK(cudaFree(gpuPrm->d_in)); + cuCHECK(cudaFree(gpuPrm->d_w)); + cuCHECK(cudaFree(gpuPrm->d_out)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); + + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); + + cuCHECK(cudaFree(gpuPrm->d_dw)); + cuCHECK(cudaFree(gpuPrm->d_bias)); + cuCHECK(cudaFree(gpuPrm->d_grout)); + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); + } +} + +void Deconvolution::forwardCUDA(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + gpuPrm->d_w, + gpuPrm->in_desc, + gpuPrm->d_in, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->out_desc, + gpuPrm->d_out)); + + // result + cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + } +} + +__global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ + + size_t isz = insz.w * insz.h; + + snFloat* pGrOut = grout + isz * insz.d * blockIdx.x; + unsigned int d = threadIdx.x; + while (d < insz.d){ + + snFloat b = bias[d]; + for (size_t j = 0; j < isz; ++j) + pGrOut[j] += b; + + pGrOut += isz * blockDim.x; + + d += blockDim.x; + } +} + +void Deconvolution::backwardCUDA_GW(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + void* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, gpuPrm->wsBwdWSz)); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // grin + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + d_grin, + gpuPrm->w_desc, + gpuPrm->d_w, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->grout_desc, + gpuPrm->d_grout)); + + cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + d_grin, + gpuPrm->in_desc, + gpuPrm->d_in, + gpuPrm->conv_desc, + gpuPrm->algoBwdW, + gpuPrm->d_wsBwdW, + gpuPrm->wsBwdWSz, + &beta, + gpuPrm->dw_desc, + gpuPrm->d_dw)); + + cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + gpuPrm->d_in, + &beta, + gpuPrm->bias_desc, + gpuPrm->d_bias)); + + // +bias + cuBwdBias <<< int(insz.n), 128 >>> (insz, gpuPrm->d_bias, gpuPrm->d_grout); + + // result + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + cuCHECK(cudaMemcpy(dWeightOut + wsz, gpuPrm->d_bias, insz.d * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + } +} + +void Deconvolution::backwardCUDA_G(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + void* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); + } + + // grin + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, insz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + d_grin, + gpuPrm->w_desc, + gpuPrm->d_w, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->grout_desc, + gpuPrm->d_grout)); + + // +bias + cuBwdBias <<< int(insz.n), 128 >>> (insz, gpuPrm->d_bias, gpuPrm->d_grout); + + // результ + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + } +} + + +#elif SN_CUDA + +#include +#include "../stdafx.h" +#include "snOperator/src/Operator/deconvolution.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + snFloat* d_in = 0; + snFloat* d_w = 0; + snFloat* d_dw = 0; + snFloat* d_out = 0; + snFloat* d_grout = 0; + + size_t inszMem = 0; +}; + +void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + if (!gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); + } + } + } + else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ + + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); + } + gpuPrm->inszMem = insz.size(); + } +} + +void Deconvolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudaFree(gpuPrm->d_in)); + cuCHECK(cudaFree(gpuPrm->d_w)); + cuCHECK(cudaFree(gpuPrm->d_out)); + + if (gpuPrm->d_grout){ // isLern + cuCHECK(cudaFree(gpuPrm->d_grout)); + cuCHECK(cudaFree(gpuPrm->d_dw)); + } +} + +__global__ void cuDeconvFwd(size_t fWidth, size_t fHeight, size_t stride, + snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ + + size_t wStepByD = fWidth * fHeight, + wStepByK = wStepByD * outsz.d, + outStepByD = outsz.w * outsz.h, + outStepByN = outStepByD * outsz.d, + inStepByD = insz.w * insz.h, + inStepByN = inStepByD * insz.d; + + // gridDim.x - number of output layers + // gridDim.y - batch sz + + weight += blockIdx.x * wStepByD; + input += blockIdx.y * inStepByN; + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + + unsigned int oz = 0; + while (oz < insz.d){ + + unsigned int oy = threadIdx.y; + while (oy < insz.h){ + + unsigned int ox = threadIdx.x; + while (ox < insz.w){ + + size_t posW = ox * stride, posH = oy * stride; + + // kernel + snFloat in = input[ox + oy * insz.w]; +#pragma unroll + for (size_t c = 0; c < wStepByD; ++c){ + + size_t cx = c % fWidth, cy = c / fWidth, + si = (cx + posW) + (cy + posH) * outsz.w, + sw = cx + cy * fWidth; + + output[si] += in * weight[sw]; + } + + ox += blockDim.x; + } + oy += blockDim.y; + } + weight += wStepByK; + input += inStepByD; + ++oz; + } +} + +void Deconvolution::forwardCUDA(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(), + wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // out + cuCHECK(cudaMemset(gpuPrm->d_out, 0, osz * sizeof(snFloat))); + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(outsz.d), int(outsz.n)); + + cuDeconvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, + prms.fHeight, + prms.stride, + gpuPrm->d_w, + insz, + gpuPrm->d_in, + outsz, + gpuPrm->d_out); + + // result + cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + } +} + +__global__ void cuDeconvBwd_GW(size_t fWidth, size_t fHeight, size_t stride, + snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ + + size_t wStepByD = fWidth * fHeight, + wStepByK = wStepByD * outsz.d, + wStepByN = wStepByK * insz.d + insz.d, + outStepByD = outsz.w * outsz.h, + outStepByN = outStepByD * outsz.d, + inStepByD = insz.w * insz.h, + inStepByN = inStepByD * insz.d; + + // gridDim.x - number of input layers + // gridDim.y - batch sz + + input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + weight += blockIdx.x * wStepByK; + dWeightOut += blockIdx.x * wStepByK + blockIdx.y * wStepByN; + gradIn += blockIdx.y * outStepByN; + gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + unsigned int oz = 0; + while (oz < outsz.d){ + + unsigned int oy = threadIdx.y; + while (oy < insz.h){ + + unsigned int ox = threadIdx.x; + while (ox < insz.w){ + + if (oz == 0) + gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias + + size_t posW = ox * stride, posH = oy * stride; + + // kernel + snFloat csum = 0, cin = input[ox + oy * insz.w]; +#pragma unroll + for (size_t c = 0; c < wStepByD; ++c){ + + size_t cx = c % fWidth, cy = c / fWidth, + si = (cx + posW) + (cy + posH) * outsz.w, + sw = cx + cy * fWidth; + + csum += gradIn[si] * weight[sw]; + + dWeightOut[sw] += gradIn[si] * cin; + } + gradOut[ox + oy * insz.w] += csum; + + if (oz == 0) + dWeightOut[wStepByK * (insz.d - blockIdx.x) + blockIdx.x] += cin; // bias + + ox += blockDim.x; + } + oy += blockDim.y; + } + weight += wStepByD; + dWeightOut += wStepByD; + gradIn += outStepByD; + ++oz; + } +} + +__global__ void cuDeconvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ + + size_t wStepByD = fWidth * fHeight, + wStepByK = wStepByD * kernel, + wStepByN = wStepByK * insz.d + insz.d; + + unsigned int ox = threadIdx.x; + while (ox < wStepByN){ + + snFloat csum = weight[ox]; + for (size_t i = 1; i < insz.n; ++i) + csum += weight[ox + wStepByN * i]; + + weight[ox] = csum / insz.n; + + ox += blockDim.x; + } +} + +void Deconvolution::backwardCUDA_GW(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(), + wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; + snFloat* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // out + cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(insz.d), int(outsz.n)); + + cuDeconvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, + prms.fHeight, + prms.stride, + gpuPrm->d_w, + insz, + gpuPrm->d_in, + outsz, d_grin, + gpuPrm->d_grout, + gpuPrm->d_dw); + + cuDeconvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); + + // result + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + } +} + +__global__ void cuDeconvBwd_G(size_t fWidth, size_t fHeight, size_t stride, + snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ + + size_t wStepByD = fWidth * fHeight, + wStepByK = wStepByD * outsz.d, + outStepByD = outsz.w * outsz.h, + outStepByN = outStepByD * outsz.d, + inStepByD = insz.w * insz.h, + inStepByN = inStepByD * insz.d; + + // gridDim.x - number of input layers + // gridDim.y - batch size + + weight += blockIdx.x * wStepByK; + gradIn += blockIdx.y * outStepByN; + gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + unsigned int oz = 0; + while (oz < outsz.d){ + + unsigned int oy = threadIdx.y; + while (oy < insz.h){ + + unsigned int ox = threadIdx.x; + while (ox < insz.w){ + + if (oz == 0) + gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias + + size_t posW = ox * stride, posH = oy * stride; + + // kernel + snFloat csum = 0; +#pragma unroll + for (size_t c = 0; c < wStepByD; ++c){ + + size_t cx = c % fWidth, cy = c / fWidth, + si = (cx + posW) + (cy + posH) * outsz.w, + sw = cx + cy * fWidth; + + csum += gradIn[si] * weight[sw]; + } + gradOut[ox + oy * insz.w] += csum; + + ox += blockDim.x; + } + oy += blockDim.y; + } + weight += wStepByD; + gradIn += outStepByD; + ++oz; + } +} + +void Deconvolution::backwardCUDA_G(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(), + wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; + snFloat* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // weight + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(insz.d), int(outsz.n)); + + cuDeconvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, + prms.fHeight, + prms.stride, + gpuPrm->d_w, + insz, + outsz, + d_grin, + gpuPrm->d_grout); + + // result + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + } +} + +#endif diff --git a/src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu b/src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu new file mode 100644 index 0000000..e9556e8 --- /dev/null +++ b/src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu @@ -0,0 +1,339 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#ifdef SN_CUDA + +#include +#include +#include "../stdafx.h" +#include "snOperator/src/Operator/fullyConnected.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + + +struct gpuParams{ + + cublasHandle_t cuBLAS = 0; + + snFloat* d_in = 0; + snFloat* d_w = 0; + snFloat* d_dw = 0; + snFloat* d_out = 0; + snFloat* d_grout = 0; + + size_t inszMem = 0; +}; + +void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + size_t ida = insz.w * insz.h * insz.d, bsz = insz.n; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cublasHandle_t cuHandle = nullptr; + cuCHECK(cublasCreate(&cuHandle)); + + gpuPrm->cuBLAS = cuHandle; + + if (!gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); + } + } + } + else if (!gpuClearMem_ && (gpuPrm->inszMem < ida * bsz)){ + + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); + } + + gpuPrm->inszMem = ida * bsz; + } +} + +void FullyConnected::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cublasDestroy(gpuPrm->cuBLAS); + cuCHECK(cudaFree(gpuPrm->d_in)); + cuCHECK(cudaFree(gpuPrm->d_out)); + cuCHECK(cudaFree(gpuPrm->d_w)); + + if (gpuPrm->d_grout){ // isLern + cuCHECK(cudaFree(gpuPrm->d_grout)); + cuCHECK(cudaFree(gpuPrm->d_dw)); + } +} + +__global__ void cuFwdBias(size_t kernel, snSize insz, snFloat* weight, snFloat* output){ + + weight += insz.w * insz.h * insz.d * kernel; + + snFloat* out = output + kernel * blockIdx.x; + unsigned int k = threadIdx.x; + while (k < kernel){ + + out[k] += weight[k]; + + k += blockDim.x; + } +} + +void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloat* input, const snFloat* weight, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); + + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); + } + + cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); + + cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // Out = α * W * In + βC + // In - data input matrix - values from the previous layer + // W - weights matrix + // Out - output matrix + float alpha = 1.0f, beta = 0.0f; + cuCHECK(cublasSgemm(gpuPrm->cuBLAS, + CUBLAS_OP_N, + CUBLAS_OP_N, + krn, // W, cols + bsz, // In, rows + ida, // In, cols, В М - rows + &alpha, // α + gpuPrm->d_w, // W + krn, // W, step to next W (W21 - W11) + gpuPrm->d_in, // In + ida, // In, step to next X (X21 - X11) + &beta, // β + gpuPrm->d_out, // Out + krn)); // Out, step to next Y (Y21 - Y11) + + // +bias + cuFwdBias <<< int(insz.n), 128 >>> (kernel, insz, gpuPrm->d_w, gpuPrm->d_out); + + // result + cuCHECK(cublasGetMatrix(bsz, krn, sizeof(snFloat), gpuPrm->d_out, bsz, output, bsz)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + } +} + +__global__ void cuBwdBias(size_t kernel, snSize insz, snFloat* gradIn, snFloat* dWOut){ + + dWOut += insz.w * insz.h * insz.d * kernel; + unsigned int k = threadIdx.x; + while (k < kernel){ + + snFloat* grin = gradIn + k, b = 0; + for (size_t j = 0; j < insz.n; ++j) + b += grin[kernel * j]; + + dWOut[k] = b / insz.n; + k += blockDim.x; + } +} + +void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, + const snSize& insz, const snFloat* input, const snFloat* gradIn, snFloat* gradOut, snFloat* dWOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); + + snFloat* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, bsz * kernel * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); + } + + cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); + + cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); + + // Weight gradient + // dW = αIn^T * GrIn + βdW + // In - data input matrix from previous layer + // GrIn - gradient matrix from the next layer + float alpha = 1.0F / insz.n, beta = 0.0f; + cuCHECK(cublasSgemm(gpuPrm->cuBLAS, + CUBLAS_OP_N, + CUBLAS_OP_T, + krn, // GrIn, cols + ida, // In, cols (+1 - X0) + bsz, // In, rows + &alpha, // α + d_grin, // GrIn + krn, // GrIn, step to next + gpuPrm->d_in, // In + ida, // In, step to next X (X21 - X11) + &beta, // β + gpuPrm->d_dw, // dW + krn)); // dW, step to next + + // bias + cuBwdBias <<< 1, 128 >>> (kernel, insz, d_grin, gpuPrm->d_dw); + + // result + cuCHECK(cudaMemcpy(dWOut, gpuPrm->d_dw, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + + cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); + + //// Gradient for previous layer + //// GrOut = αGrIn * W^T + βGrOut + //// GrIn - gradient matrix from the next layer + //// W - weight + alpha = 1.F; + cuCHECK(cublasSgemm(gpuPrm->cuBLAS, + CUBLAS_OP_T, + CUBLAS_OP_N, + ida, // W, cols + bsz, // W, rows + krn, // GrIn, cols + &alpha, // α + gpuPrm->d_w, // W + krn, // W, step to next + d_grin, // GrIn + krn, // GrIn, step to next + &beta, // β + gpuPrm->d_grout, // GrOut + ida)); // GrOut, step to next + + + // result + cuCHECK(cublasGetMatrix(bsz, ida, sizeof(snFloat), gpuPrm->d_grout, bsz, gradOut, bsz)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; + } +} + +void FullyConnected::backwardCUDA_G(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); + + snFloat* d_grin = gpuPrm->d_out; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_w, ida * kernel * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grin, bsz * kernel * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); + } + + cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); + + cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); + + //// Gradient for previous layer + //// GrOut = αGrIn * W^T + βGrOut + //// GrIn - gradient matrix from the next layer + //// W - weight + float alpha = 1.0F, beta = 0.0f; + cuCHECK(cublasSgemm(gpuPrm->cuBLAS, + CUBLAS_OP_T, + CUBLAS_OP_N, + ida, // W, cols + bsz, // W, rows + krn, // GrIn, cols + &alpha, // α + gpuPrm->d_w, // W + krn, // W, step to next + d_grin, // GrIn + krn, // GrIn, step to next + &beta, // β + gpuPrm->d_grout, // GrOut + ida)); // GrOut, step to next + + cuCHECK(cublasGetMatrix(bsz, ida, sizeof(snFloat), gpuPrm->d_grout, bsz, gradOut, bsz)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + } +} + +#endif \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu b/src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu new file mode 100644 index 0000000..f8cb48c --- /dev/null +++ b/src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu @@ -0,0 +1,594 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#ifdef SN_CUDNN + +#include +#include +#include "../stdafx.h" +#include "snOperator/src/Operator/pooling.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnPoolingDescriptor_t pool_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + + snFloat* d_in = 0; + snFloat* d_out = 0; + snFloat* d_grin = 0; + snFloat* d_grout = 0; + + size_t inszMem = 0; +}; + +void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // pool + cudnnPoolingDescriptor_t pool_desc = nullptr; + cuCHECK(cudnnCreatePoolingDescriptor(&pool_desc)); + + cudnnPoolingMode_t poolT = cudnnPoolingMode_t::CUDNN_POOLING_MAX; + if (poolPrms.type == poolType::avg) + poolT = cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; + + cuCHECK(cudnnSetPooling2dDescriptor(pool_desc, poolT, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, + int(poolPrms.kernel), int(poolPrms.kernel), 0, 0, int(poolPrms.stride), int(poolPrms.stride))); + if (!isFirst) + cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); + gpuPrm->pool_desc = pool_desc; + + // output + int out_n = 0, out_c = 0, out_h = 0, out_w = 0; + cuCHECK(cudnnGetPooling2dForwardOutputDim(pool_desc, in_desc, + &out_n, &out_c, &out_h, &out_w)); + + if (outsz != snSize(out_w, out_h, out_c, out_n)){ + ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); + return; + } + + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + if (isLern){ + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + } + + size_t isz = insz.size(), osz = outsz.size(); + if (isFirst && !gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + } + } + else if (!gpuClearMem_ && (gpuPrm->inszMem < isz)){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + + if (isLern){ + cuCHECK(cudaFree(gpuPrm->d_grin)); gpuPrm->d_grin = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + } + gpuPrm->inszMem = isz; + } +} + +void Pooling::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + + cuCHECK(cudaFree(gpuPrm->d_in)); + cuCHECK(cudaFree(gpuPrm->d_out)); + + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + + cuCHECK(cudaFree(gpuPrm->d_grin)); + cuCHECK(cudaFree(gpuPrm->d_grout)); + } +} + +__global__ void cuFiltrNegative(snSize outsz, snFloat* out){ + + out += blockIdx.x * outsz.w * outsz.h * outsz.d; + + unsigned int k = threadIdx.x; + while (k < outsz.d){ + + snFloat* pOut = out + outsz.w * outsz.h * k; + for (size_t j = 0; j < (outsz.w * outsz.h); ++j) + if (pOut[j] < 0) pOut[j] = 0.0; + + k += blockDim.x; + } +} + +void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, + const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnPoolingForward(gpuPrm->cudnn, + gpuPrm->pool_desc, + &alpha, + gpuPrm->in_desc, + gpuPrm->d_in, + &beta, + gpuPrm->out_desc, + gpuPrm->d_out)); + + // filtrNegative + dim3 dimBlock(256); + dim3 dimGrid(int(outsz.n)); + + cuFiltrNegative <<< dimGrid, dimBlock >>>(outsz, gpuPrm->d_out); + + // result + cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + } +} + +void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, + const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + size_t isz = insz.size(), osz = outsz.size(); + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // output + cuCHECK(cudaMemcpy(gpuPrm->d_out, output, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // grin + cuCHECK(cudaMemcpy(gpuPrm->d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnPoolingBackward(gpuPrm->cudnn, + gpuPrm->pool_desc, + &alpha, + gpuPrm->out_desc, + gpuPrm->d_out, + gpuPrm->grin_desc, + gpuPrm->d_grin, + gpuPrm->in_desc, + gpuPrm->d_in, + &beta, + gpuPrm->grout_desc, + gpuPrm->d_grout)); + + // result + cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_grin)); gpuPrm->d_grin = 0; + cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; + } +} + +#elif SN_CUDA + +#include +#include "../stdafx.h" +#include "snOperator/src/Operator/pooling.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + snFloat* d_in = 0; + snFloat* d_out = 0; + size_t* d_idx = 0; + + size_t inszMem = 0; +}; + +void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 2){ + ERROR_MESS("%s requires SM >= 2.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + if (!gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); + } + } + else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ + + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); + + gpuPrm->inszMem = insz.size(); + } +} + +void Pooling::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cudaFree(gpuPrm->d_in); + cudaFree(gpuPrm->d_out); + cudaFree(gpuPrm->d_idx); +} + +__global__ void cuPoolFwd(poolType type, size_t kernel, size_t stride, snSize insz, snFloat* input, snSize outsz, snFloat* output, size_t* outputInx){ + + size_t outStepByD = outsz.w * outsz.h, // step out by output + outStepByN = outStepByD * outsz.d, // step out by batch + inStepByD = insz.w * insz.h, // step in by input + inStepByN = inStepByD * insz.d, // step in by batch + kernelSz = kernel * kernel; + + // gridDim.x - number of input layers + // gridDim.y - batch sz + + input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + if (type == poolType::max){ // max + + unsigned int oy = threadIdx.y; + while (oy < outsz.h){ + + unsigned int ox = threadIdx.x; + while (ox < outsz.w){ + + size_t posW = ox * stride, posH = oy * stride; + + // kernel + snFloat valMax = 0; size_t idx = 0; +#pragma unroll + for (size_t c = 0; c < kernelSz; ++c){ + + size_t cx = c % kernel, cy = c / kernel; + + snFloat val = input[cx + posW + (cy + posH) * insz.w]; + if (val > valMax){ + valMax = val; + idx = c; + } + } + output[ox + oy * outsz.w] = valMax; + outputInx[ox + oy * outsz.w] = idx; + + ox += blockDim.x; + } + oy += blockDim.y; + } + } + else{ // mean + + unsigned int oy = threadIdx.y; + while (oy < outsz.h){ + + unsigned int ox = threadIdx.x; + while (ox < outsz.w){ + + size_t posW = ox * stride, posH = oy * stride; + + // kernel + snFloat valMean = 0; +#pragma unroll + for (size_t c = 0; c < kernelSz; ++c){ + + size_t cx = c % kernel, cy = c / kernel; + + valMean += input[cx + posW + (cy + posH) * insz.w]; + } + output[ox + oy * outsz.w] = valMean / kernelSz; + + ox += blockDim.x; + } + oy += blockDim.y; + } + } +} + +void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, + const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + size_t isz = insz.size(), osz = outsz.size(); + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&gpuPrm->d_idx, osz * sizeof(size_t))); + } + + // input + cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(outsz.d), int(outsz.n)); + + cuPoolFwd << < dimGrid, dimBlock >> >(poolPrms.type, + poolPrms.kernel, + poolPrms.stride, + insz, + gpuPrm->d_in, + outsz, + gpuPrm->d_out, + gpuPrm->d_idx); + + // result + cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + cuCHECK(cudaMemcpy(outputInx, gpuPrm->d_idx, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; + } +} + +__global__ void cuPoolBwd(poolType type, size_t kernel, size_t stride, snSize outsz, size_t* outputInx, snFloat* gradIn, snSize insz, snFloat* gradOut){ + + size_t outStepByD = outsz.w * outsz.h, // step out by output + outStepByN = outStepByD * outsz.d, // step out by batch + inStepByD = insz.w * insz.h, // step in by input + inStepByN = inStepByD * insz.d, // step in by batch + kernelSz = kernel * kernel; + + // gridDim.x - number of input layers + // gridDim.y - batch sz + + gradIn += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + if (type == poolType::max){ // max + + outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + unsigned int oy = threadIdx.y; + while (oy < outsz.h){ + + unsigned int ox = threadIdx.x; + while (ox < outsz.w){ + + size_t posW = ox * stride, posH = oy * stride; + + size_t c = outputInx[ox + oy * outsz.w], cx = c % kernel, cy = c / kernel; + + gradOut[cx + posW + (cy + posH) * insz.w] = gradIn[ox + oy * outsz.w]; + + ox += blockDim.x; + } + oy += blockDim.y; + } + } + else{ // mean + + unsigned int oy = threadIdx.y; + while (oy < outsz.h){ + + unsigned int ox = threadIdx.x; + while (ox < outsz.w){ + + size_t posW = ox * stride, posH = oy * stride; + + snFloat mean = gradIn[ox + oy * outsz.w] / kernel; +#pragma unroll + for (size_t c = 0; c < kernelSz; ++c){ + size_t cx = c % kernel, cy = c / kernel; + gradOut[(cx + posW) + (cy + posH) * insz.w] = mean; + } + + ox += blockDim.x; + } + oy += blockDim.y; + } + } +} + +void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, + const snSize& insz, const SN_Base::snFloat* input, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + snFloat* d_grin = gpuPrm->d_out, + * d_grout = gpuPrm->d_in; + size_t* d_idx = gpuPrm->d_idx; + + size_t isz = insz.size(), osz = outsz.size(); + if (gpuClearMem_){ + cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_grout, isz * sizeof(snFloat))); + cuCHECK(cudaMalloc(&d_idx, osz * sizeof(size_t))); + } + + // input + cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + cuCHECK(cudaMemcpy(d_idx, outputInx, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); + + // out + cuCHECK(cudaMemset(d_grout, 0, isz * sizeof(snFloat))); + + // run + dim3 dimBlock(16, 16); + dim3 dimGrid(int(outsz.d), int(outsz.n)); + + cuPoolBwd << < dimGrid, dimBlock >> >(poolPrms.type, + poolPrms.kernel, + poolPrms.stride, + outsz, + d_idx, + d_grin, + insz, + d_grout); + + // result + cuCHECK(cudaMemcpy(gradOut, d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); + + if (gpuClearMem_){ + cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; + cuCHECK(cudaFree(d_grout)); gpuPrm->d_in = 0; + cuCHECK(cudaFree(d_idx)); gpuPrm->d_idx = 0; + } +} + +#endif \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/activation.cpp b/src/snOperator_cuDNN/src/Operator/activation.cpp new file mode 100644 index 0000000..7e40504 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/activation.cpp @@ -0,0 +1,84 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/activation.h" +#include "snOperator/src/activeFunctions.h" +#include "snAux/auxFunc.h" + +using namespace std; +using namespace SN_Base; + + +Activation::Activation(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + + + if (prms.find("active") != prms.end()){ + + string atype = prms["active"]; + if (atype == "none") activeType_ = activeType::none; + else if (atype == "sigmoid") activeType_ = activeType::sigmoid; + else if (atype == "relu") activeType_ = activeType::relu; + else if (atype == "leakyRelu") activeType_ = activeType::leakyRelu; + else if (atype == "elu") activeType_ = activeType::elu; + else + ERROR_MESS("param 'active' = " + atype + " indefined"); + } +} + +std::vector Activation::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + baseOut_ = neighbOpr[0]->getOutput(); + + /// active func + activeFuncForward(baseOut_.size().size(), baseOut_.getData(), activeType_); + + } + else{ // backward + + baseGrad_ = neighbOpr[0]->getGradient(); + + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + + /// active func + activeFuncBackward(baseGrad_.size().size(), baseGrad_.getData(), activeType_); + } + + return vector(); +} \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/activation.h b/src/snOperator_cuDNN/src/Operator/activation.h new file mode 100644 index 0000000..6d1abbe --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/activation.h @@ -0,0 +1,44 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +#include "snOperator/src/structurs.h" + +class Activation final : SN_Base::OperatorBase{ + +public: + + Activation(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Activation() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +private: + + activeType activeType_ = activeType::relu; + +}; diff --git a/src/snOperator_cuDNN/src/Operator/batchNorm.cpp b/src/snOperator_cuDNN/src/Operator/batchNorm.cpp new file mode 100644 index 0000000..a968596 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/batchNorm.cpp @@ -0,0 +1,145 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/batchNorm.h" +#include "snOperator/src/mathFunctions.h" +#include "snAux/auxFunc.h" + +using namespace std; +using namespace SN_Base; + + +BatchNorm::BatchNorm(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + + if (basePrms_.find("bnType") != basePrms_.end()){ + + if (basePrms_["bnType"] == "byChannels") + bnType_ = batchNormType::byChannels; + else + bnType_ = batchNormType::byLayer; + } + + inSzMem_ = snSize(0); +} + +bool BatchNorm::setBatchNorm(const batchNorm& bn){ + + size_t osz = bn.sz.size(); + + auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + + memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); + + baseBatchNorm_.sz = bn.sz; + + return true; +} + +std::vector BatchNorm::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + baseOut_ = neighbOpr[0]->getOutput(); + + snFloat* out = baseOut_.getData(); + snSize outsz = baseOut_.size(); + + if (outsz != inSzMem_){ + inSzMem_ = outsz; + updateConfig(operPrm.isLerning, outsz); + } + + switch (bnType_){ + case batchNormType::byChannels: + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + break; + case batchNormType::byLayer: + layerBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + break; + default: + break; + } + } + else{ // backward + + baseGrad_ = neighbOpr[0]->getGradient(); + + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + + snFloat* out = baseGrad_.getData(); + snSize outsz = baseGrad_.size(); + + switch (bnType_){ + case batchNormType::byChannels: + channelBatchNorm(false, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + break; + case batchNormType::byLayer: + layerBatchNorm(false, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + break; + default: + break; + } + } + + return vector(); +} + +void BatchNorm::updateConfig(bool isLern, const snSize& newsz){ + + size_t osz = newsz.w * newsz.h * newsz.d; + + auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + + if (isLern){ + auxParams_["bn_norm"] = vector(osz * newsz.n); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); + auxParams_["bn_dScale"] = vector(osz, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); + auxParams_["bn_dSchift"] = vector(osz, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); + auxParams_["bn_onc"] = vector(newsz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); + } + baseBatchNorm_.sz = newsz; + baseBatchNorm_.sz.n = 1; +} \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/batchNorm.h b/src/snOperator_cuDNN/src/Operator/batchNorm.h new file mode 100644 index 0000000..8c75ca4 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/batchNorm.h @@ -0,0 +1,52 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +#include "snOperator/src/structurs.h" + +class BatchNorm final : SN_Base::OperatorBase{ + +public: + + BatchNorm(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~BatchNorm() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + + bool setBatchNorm(const SN_Base::batchNorm& bn) override; + +private: + + std::map> auxParams_; ///< aux data + + batchNormType bnType_ = batchNormType::byLayer; + + SN_Base::snSize inSzMem_; ///< insz mem + + + void updateConfig(bool isLern, const SN_Base::snSize& newsz); +}; diff --git a/src/snOperator_cuDNN/src/Operator/concat.cpp b/src/snOperator_cuDNN/src/Operator/concat.cpp new file mode 100644 index 0000000..af41ba9 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/concat.cpp @@ -0,0 +1,123 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/concat.h" +#include "snAux/auxFunc.h" + +using namespace std; +using namespace SN_Base; + + +/// Compound layers +Concat::Concat(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + + if (basePrms_.find("sequence") == basePrms_.end()) + ERROR_MESS("non set param 'sequence'"); +} + +std::vector Concat::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + auto seq = SN_Aux::split(basePrms_["sequence"], " "); + + OperatorBase* neighb = nullptr; + for (size_t j = 0; j < neighbOpr.size(); ++j){ + if (neighbOpr[j]->node() == seq[0]){ + neighb = neighbOpr[j]; + break; + } + } + + if (!neighb){ + ERROR_MESS("not found neighbor '" + seq[0] + "'"); + return vector{"noWay"}; + } + + baseOut_ = neighb->getOutput(); + + size_t ssz = seq.size(); + for (size_t i = 1; i < ssz; ++i){ + + OperatorBase* neighb = nullptr; + for (size_t j = 0; j < neighbOpr.size(); ++j){ + if (neighbOpr[j]->node() == seq[i]){ + neighb = neighbOpr[j]; + break; + } + } + + if (!neighb){ + ERROR_MESS("not found neighbor '" + seq[i] + "'"); + return vector{"noWay"}; + } + + snSize csz = baseOut_.size(); + + snSize nbsz = neighb->getOutput().size(); + + if ((csz.w != nbsz.w) || (csz.h != nbsz.h) || (csz.n != nbsz.n)){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + + Tensor buff = baseOut_; + + baseOut_.resize(snSize(csz.w, csz.h, csz.d + nbsz.d, csz.n)); + + size_t sz = csz.w * csz.h * (csz.d + nbsz.d), + cstp = csz.w * csz.h * csz.d, + nstp = nbsz.w * nbsz.h * nbsz.d; + for (size_t j = 0; j < csz.n; ++j){ + + snFloat* dst = baseOut_.getData() + sz * j, + *src = buff.getData() + cstp * j; + + memcpy(dst, src, cstp * sizeof(snFloat)); + + + dst += cstp; + src = neighb->getOutput().getData() + nstp * j; + + memcpy(dst, src, nstp * sizeof(snFloat)); + } + } + } + else{ // backward + + baseGrad_ = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + } + + return vector(); +} diff --git a/src/snOperator_cuDNN/src/Operator/concat.h b/src/snOperator_cuDNN/src/Operator/concat.h new file mode 100644 index 0000000..41f96d8 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/concat.h @@ -0,0 +1,46 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// Compound layers +class Concat final : SN_Base::OperatorBase{ + +public: + + Concat(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Concat() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +private: + + + SN_Base::snSize baseSz_; + +}; diff --git a/src/snOperator_cuDNN/src/Operator/convolution.cpp b/src/snOperator_cuDNN/src/Operator/convolution.cpp new file mode 100644 index 0000000..2961273 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/convolution.cpp @@ -0,0 +1,460 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/convolution.h" +#include "snAux/auxFunc.h" +#include "snOperator/src/weightInit.h" +#include "snOperator/src/activeFunctions.h" +#include "snOperator/src/optimizer.h" +#include "snOperator/src/structurs.h" +#include "snOperator/src/mathFunctions.h" + +using namespace std; +using namespace SN_Base; + +/// convolution layer + +Convolution::Convolution(void* net, const string& name, const string& node, std::map& prms) : + OperatorBase(net, name, node, prms){ + + load(prms); +} + +Convolution::~Convolution(){ + + switch (calcMode_){ + case calcMode::CUDA: freeParamCUDA(gpuParams_); break; + case calcMode::OpenCL: freeParamOCL(gpuParams_); break; + } +} + +void Convolution::load(std::map& prms){ + + auto setIntParam = [&prms, this](const string& name, bool isZero, bool checkExist, size_t& value){ + + if ((prms.find(name) != prms.end()) && SN_Aux::is_number(prms[name])){ + + size_t v = stoi(prms[name]); + if ((v > 0) || (isZero && (v == 0))) + value = v; + else + ERROR_MESS("param '" + name + (isZero ? "' < 0" : "' <= 0")); + } + else if (checkExist) + ERROR_MESS("not found (or not numder) param '" + name + "'"); + }; + + setIntParam("filters", false, true, convPrms_.kernel); + setIntParam("fWidth", false, false, convPrms_.fWidth); + setIntParam("fHeight", false, false, convPrms_.fHeight); + + if ((prms.find("padding") != prms.end()) && (prms["padding"] == "-1")) + isPaddingSame_ = true; + else + setIntParam("padding", true, false, convPrms_.paddingSet); + + if (prms.find("checkPadding") != prms.end()) + isCheckPadding_ = prms["checkPadding"] == "1"; + + if (prms.find("useBias") != prms.end()) + convPrms_.useBias_ = prms["useBias"] == "1"; + + setIntParam("stride", false, false, convPrms_.stride); + setIntParam("dilate", false, false, convPrms_.dilate); + + if (prms.find("gpuClearMem") != prms.end()) + gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; + + if (prms.find("gpuDeviceId") != prms.end()) + gpuDeviceId_ = stoi(prms["gpuDeviceId"]); + + if (prms.find("batchNorm") != prms.end()){ + + string bnType = prms["batchNorm"]; + if (bnType == "none") batchNormType_ = batchNormType::none; + else if (bnType == "beforeActive") batchNormType_ = batchNormType::beforeActive; + else if (bnType == "postActive") batchNormType_ = batchNormType::postActive; + else + ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); + } + + if (prms.find("mode") != prms.end()){ + + string mode = prms["mode"]; + if (mode == "CPU") calcMode_ = calcMode::CPU; + else if (mode == "CUDA") calcMode_ = calcMode::CUDA; + else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; + else + ERROR_MESS("param 'mode' = " + mode + " indefined"); + } + + // aux array + auxParams_["dWeight"] = vector(); + auxParams_["dWPrev"] = vector(); + auxParams_["dWGrad"] = vector(); + + if (batchNormType_ != batchNormType::none){ + auxParams_["bn_mean"] = vector(); + auxParams_["bn_varce"] = vector(); + auxParams_["bn_scale"] = vector(); + auxParams_["bn_schift"] = vector(); + auxParams_["bn_norm"] = vector(); + auxParams_["bn_dScale"] = vector(); + auxParams_["bn_dSchift"] = vector(); + auxParams_["bn_onc"] = vector(); + } + + setInternPrm(prms); +} + +bool Convolution::setInternPrm(std::map& prms){ + + basePrms_ = prms; + + if (prms.find("active") != prms.end()){ + + string atype = prms["active"]; + if (atype == "none") activeType_ = activeType::none; + else if (atype == "sigmoid") activeType_ = activeType::sigmoid; + else if (atype == "relu") activeType_ = activeType::relu; + else if (atype == "leakyRelu") activeType_ = activeType::leakyRelu; + else if (atype == "elu") activeType_ = activeType::elu; + else + ERROR_MESS("param 'active' = " + atype + " indefined"); + } + + if (prms.find("optimizer") != prms.end()){ + + string optType = prms["optimizer"]; + if (optType == "sgd") optimizerType_ = optimizerType::sgd; + else if (optType == "sgdMoment") optimizerType_ = optimizerType::sgdMoment; + else if (optType == "adagrad") optimizerType_ = optimizerType::adagrad; + else if (optType == "adam") optimizerType_ = optimizerType::adam; + else if (optType == "RMSprop") optimizerType_ = optimizerType::RMSprop; + else + ERROR_MESS("param 'optimizer' = " + optType + " indefined"); + } + + if (prms.find("weightInit") != prms.end()){ + + string wInit = prms["weightInit"]; + if (wInit == "uniform") weightInitType_ = weightInitType::uniform; + else if (wInit == "he") weightInitType_ = weightInitType::he; + else if (wInit == "lecun") weightInitType_ = weightInitType::lecun; + else if (wInit == "xavier") weightInitType_ = weightInitType::xavier; + else + ERROR_MESS("param 'weightInit' = " + wInit + " indefined"); + } + + if (prms.find("decayMomentDW") != prms.end()) + opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + + if (prms.find("decayMomentWGr") != prms.end()) + opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + + if (prms.find("lmbRegular") != prms.end()) + opt_lmbRegular_ = stof(prms["lmbRegular"]); + + if (prms.find("batchNormLr") != prms.end()) + baseBatchNorm_.lr = stof(prms["batchNormLr"]); + + if (prms.find("freeze") != prms.end()) + isFreeze_ = prms["freeze"] == "1"; + + if (prms.find("dropOut") != prms.end()){ + dropOut_ = stof(prms["dropOut"]); + if (dropOut_ > 1.F) dropOut_ = 1.F; + else if (dropOut_ < 0.F) dropOut_ = 0.F; + } + + return true; +} + +bool Convolution::setBatchNorm(const batchNorm& bn){ + + size_t osz = bn.sz.size(); + + auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + + memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); + + baseBatchNorm_.sz = bn.sz; + + return true; +} + +std::vector Convolution::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + forward(neighbOpr[0]->getOutput(), operPrm); + } + else{ + if (neighbOpr.size() == 1){ + backward(neighbOpr[0]->getGradient(), operPrm); + } + else{ + Tensor tns = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (tns != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + tns += neighbOpr[i]->getGradient(); + } + backward(tns, operPrm); + } + } + + return std::vector(); +} + +void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ + + snSize insz = inTns.size(); + inputMem_ = &inTns; + + /// Has the size of the data changed? + if (insz != inSzMem_){ + inSzMem_ = insz; + updateConfig(operPrm.isLerning, insz, inDataExpSz_); + } + + /// copy with offset padding for each image + snFloat* in = inputMem_->getData(); + bool isSame = (convPrms_.paddingW == 0) && (convPrms_.paddingH == 0); + if (!isSame){ + inTnsExp_.resize(inDataExpSz_); + paddingOffs(false, convPrms_.paddingW, convPrms_.paddingH, insz, inTnsExp_.getData(), in); + in = inTnsExp_.getData(); + } + + /// calculation of the output values + snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snSize outsz = baseOut_.size(); + + // +bias? + if (!convPrms_.useBias_){ + size_t kernel = convPrms_.kernel, + stepByN = convPrms_.fWidth * convPrms_.fHeight * insz.d * kernel; + memset(weight + stepByN, 0, kernel * sizeof(snFloat)); + } + + switch (calcMode_){ + case calcMode::CPU: forwardCPU(convPrms_, weight, inDataExpSz_, in, outsz, out); break; + case calcMode::CUDA: forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); break; + case calcMode::OpenCL: forwardOCL(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); break; + } + + /// dropOut + if (dropOut_ > 0.F) + dropOut(operPrm.isLerning, dropOut_, outsz, out); + + /// batchNorm + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + + /// active function + activeFuncForward(outsz.size(), out, activeType_); + + /// batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); +} + +void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ + + snFloat* gradIn = inTns.getData(); + + /// batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + + // active function + if (activeType_ != activeType::none){ + + snFloat* out = baseOut_.getData(); + + size_t osz = baseOut_.size().size(); + activeFuncBackward(osz, out, activeType_); + + // update grad + for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; + } + + /// batchNorm + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + + // calculation of the output gradient and weight correction + snFloat* gradOut = baseGrad_.getData(); + + bool isSame = (convPrms_.paddingW == 0) && (convPrms_.paddingH == 0); + if (!isSame){ + gradOutExp_.resize(inDataExpSz_); + gradOut = gradOutExp_.getData(); + } + + snFloat* weight = baseWeight_.getData(); + + if (!isFreeze_){ + snFloat* dWeight = auxParams_["dWeight"].data(); + + snFloat* in = inputMem_->getData(); + if (!isSame) + in = inTnsExp_.getData(); + + switch (calcMode_){ + case calcMode::CPU: backwardCPU_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight); break; + case calcMode::CUDA: backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); break; + case calcMode::OpenCL: backwardOCL_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); break; + } + + // correct weight + snFloat* dWPrev = auxParams_["dWPrev"].data(); + snFloat* dWGrad = auxParams_["dWGrad"].data(); + size_t wsz = baseWeight_.size().size(); + + optimizer(dWeight, + dWPrev, + dWGrad, + weight, + wsz, + operPrm.lr, + opt_lmbRegular_, + opt_decayMomentDW_, + opt_decayMomentWGr_, + optimizerType_); + } + else{ // isFreeze + switch (calcMode_){ + case calcMode::CPU: backwardCPU_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut); break; + case calcMode::CUDA: backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); break; + case calcMode::OpenCL: backwardOCL_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); break; + } + } + + if (!isSame) + paddingOffs(true, convPrms_.paddingW, convPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); + +} + +void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize& expSz){ + + size_t& kernel = convPrms_.kernel, + & fWidth = convPrms_.fWidth, + & fHeight = convPrms_.fHeight, + & paddingSet = convPrms_.paddingSet, + & paddingW = convPrms_.paddingW, + & paddingH = convPrms_.paddingH, + & stride = convPrms_.stride, + & dilate = convPrms_.dilate; + + size_t stp = fWidth * fHeight * newsz.d, ntp = (stp + 1) * kernel; // + 1 - bias + + // leave the existing weights as they are, initialize the remainder + size_t wcsz = baseWeight_.size().size(); + if (ntp > wcsz){ + + baseWeight_.resize(snSize(kernel, stp + 1)); + snFloat* wd = baseWeight_.getData(); + weightInit(wd + wcsz, ntp - wcsz, stp + 1, kernel, weightInitType_); + } + + snSize outSz(0, 0, kernel, newsz.n); + + if (isPaddingSame_){ + outSz.w = newsz.w; + outSz.h = newsz.h; + + paddingW = (newsz.w * (stride - 1) + fWidth + (fWidth - 1) * (dilate - 1) - stride) / 2; + paddingH = (newsz.h * (stride - 1) + fHeight + (fHeight - 1) * (dilate - 1) - stride) / 2; + } + else{ + paddingW = paddingH = paddingSet; + + outSz.w = (newsz.w + paddingW * 2 - fWidth - (fWidth - 1) * (dilate - 1)) / stride + 1; + outSz.h = (newsz.h + paddingH * 2 - fHeight - (fHeight - 1) * (dilate - 1)) / stride + 1; + } + + // check correct + if (isCheckPadding_){ + size_t res = (newsz.w + paddingW * 2 - fWidth - (fWidth - 1) * (dilate - 1)) % stride; + if (res != 0) + ERROR_MESS("not correct param 'stride' or 'fWidth'"); + + res = (newsz.h + paddingH * 2 - fHeight - (fHeight - 1) * (dilate - 1)) % stride; + if (res != 0) + ERROR_MESS("not correct param 'stride' or 'fHeight'"); + } + + expSz = snSize(newsz.w + paddingW * 2, newsz.h + paddingH * 2, newsz.d, newsz.n); + + baseOut_.resize(outSz); + + if (isLern){ + baseGrad_.resize(newsz); + + // aux array + auxParams_["dWeight"].resize(ntp, 0); + auxParams_["dWPrev"].resize(ntp, 0); + auxParams_["dWGrad"].resize(ntp, 0); + } + + size_t osz = outSz.w * outSz.h * outSz.d; + + if (batchNormType_ != batchNormType::none){ + auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"].resize(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"].resize(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"].resize(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + + if (isLern){ + auxParams_["bn_norm"].resize(osz * outSz.n); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); + auxParams_["bn_dScale"].resize(osz, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); + auxParams_["bn_dSchift"].resize(osz, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); + auxParams_["bn_onc"].resize(outSz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); + } + baseBatchNorm_.sz = outSz; + baseBatchNorm_.sz.n = 1; + } + + switch (calcMode_){ + case calcMode::CUDA: iniParamCUDA(isLern, expSz, outSz, convPrms_, &gpuParams_); break; + case calcMode::OpenCL: iniParamOCL(isLern, expSz, outSz, convPrms_, &gpuParams_); break; + } +} \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/convolution.h b/src/snOperator_cuDNN/src/Operator/convolution.h new file mode 100644 index 0000000..b12cb3c --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/convolution.h @@ -0,0 +1,201 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +#include"snOperator/src/structurs.h" +#include"snOperator/src/mathFunctions.h" + +/// convolution layer +class Convolution final : SN_Base::OperatorBase{ + +public: + + Convolution(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Convolution(); + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + + bool setInternPrm(std::map& prms) override; + + bool setBatchNorm(const SN_Base::batchNorm& bn) override; + +private: + + struct convParams{ + size_t kernel = 10; ///< number of output convolution layers + size_t fWidth = 3; ///< width mask + size_t fHeight = 3; ///< height mask + size_t dilate = 1; ///< expansion mask + size_t stride = 1; ///< step mask + size_t paddingSet = 0, paddingH = 0, paddingW = 0; ///< padding layer + bool useBias_ = true; + }; + + convParams convPrms_; + bool isPaddingSame_ = false, isCheckPadding_ = false; + + activeType activeType_ = activeType::relu; ///< active type + optimizerType optimizerType_ = optimizerType::adam; ///< optimizer type + weightInitType weightInitType_ = weightInitType::he; ///< init weight type + batchNormType batchNormType_ = batchNormType::none; ///< batchNorm + SN_Base::snSize inSzMem_; ///< insz mem + SN_Base::snSize inDataExpSz_; ///< insz expansion + + const SN_Base::Tensor* inputMem_ = nullptr; + + SN_Base::Tensor inTnsExp_; + SN_Base::Tensor gradOutExp_; + + bool isFreeze_ = false; ///< not change weight + bool gpuClearMem_ = false; ///< clear mem GPU + + uint32_t gpuDeviceId_ = 0; ///< gpu id + + calcMode calcMode_ = calcMode::CPU; ///< calc mode + + SN_Base::snFloat dropOut_ = 0.F; ///< random off out + + SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimiz weight + opt_decayMomentWGr_ = 0.99F, + opt_lmbRegular_ = 0.001F; + + std::map> auxParams_; ///< aux data + + void* gpuParams_ = nullptr; ///< gpu aux params + + + void load(std::map& prms); + + void updateConfig(bool isLern, const SN_Base::snSize& newSz, SN_Base::snSize& expSz); + + + void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + + /// CPU /////////////////////////// + + void forwardCPU(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output); + + // calc grad and weight + void backwardCPU_GW(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut); + + // calc grad + void backwardCPU_G(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut); + + + /// CUDA /////////////////////////// + + /// init aux params + void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, + const convParams&, void** gpuPrm); + + /// free aux params + void freeParamCUDA(void* gpuPrm); + + void forwardCUDA(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output, + void* gpuPrm); + + /// calc grad and weight + void backwardCUDA_GW(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut, + void* gpuPrm); + + /// calc grad + void backwardCUDA_G(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + void* gpuPrm); + + + /// OpenCL /////////////////////////// + + /// init aux params OpenCL + void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, + const convParams&, void** gpuPrm); + + /// free aux params OpenCL + void freeParamOCL(void* gpuPrm); + + void forwardOCL(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output, + void* gpuPrm); + + /// calc grad and weight + void backwardOCL_GW(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut, + void* gpuPrm); + + /// calc grad + void backwardOCL_G(const convParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + void* gpuPrm); +}; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/crop.cpp b/src/snOperator_cuDNN/src/Operator/crop.cpp new file mode 100644 index 0000000..a9d1d0d --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/crop.cpp @@ -0,0 +1,147 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/crop.h" +#include "snAux/auxFunc.h" + +using namespace std; +using namespace SN_Base; + + +/// Trimming data +Crop::Crop(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + + if (basePrms_.find("roi") != basePrms_.end()){ + + auto nsz = SN_Aux::split(basePrms_["roi"], " "); + + if (nsz.size() != 4) + ERROR_MESS("'roi' param no correct. Must be four arguments: x y w h"); + } + else + ERROR_MESS("no set param 'roi'"); +} + +std::vector Crop::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + baseOut_ = neighbOpr[0]->getOutput(); + + auto nsz = SN_Aux::split(basePrms_["roi"], " "); + + if (nsz.size() != 4){ + ERROR_MESS("'roi' param no correct. Must be four arguments: x y w h"); + return vector < string > {"noWay"}; + } + + baseSz_ = baseOut_.size(); + + size_t x = max(0, min(stoi(nsz[0]), baseSz_.w - 1)), + y = max(0, min(stoi(nsz[1]), baseSz_.h - 1)), + w = max(0, min(stoi(nsz[2]), baseSz_.w - x)), + h = max(0, min(stoi(nsz[3]), baseSz_.h - y)); + + roi_ = roi(x, y, w, h); + + Tensor tns(snSize(w, h, baseSz_.d, baseSz_.n)); + + snFloat* src = baseOut_.getData(), + * dst = tns.getData(); + + copyTo(true, roi_, baseSz_, src, dst); + + baseOut_ = tns; + } + else{ // backward + + baseGrad_ = neighbOpr[0]->getGradient(); + + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + + Tensor tns(baseSz_); + + snFloat* dst = tns.getData(), + * src = baseGrad_.getData(); + + copyTo(false, roi_, baseSz_, dst, src); + + baseGrad_ = tns; + } + + return vector(); +} + +void Crop::copyTo(bool inToOut, const roi& roi, const snSize& srcSz, snFloat* in, snFloat* out){ + + size_t bsz = srcSz.d * srcSz.n, srcStp = srcSz.w * srcSz.h, dstStp = roi.w * roi.h; + + if (inToOut){ + for (size_t i = 0; i < bsz; ++i){ + + snFloat* pIn = in + roi.x + roi.y * srcSz.w + srcStp * i; + snFloat* pOut = out + dstStp * i; + + for (size_t j = 0; j < roi.h; ++j){ + + for (size_t k = 0; k < roi.w; ++k) + pOut[k] = pIn[k]; + + pIn += srcSz.w; + pOut += roi.w; + } + } + } + else{ + for (size_t i = 0; i < bsz; ++i){ + + snFloat* pIn = in + roi.x + roi.y * srcSz.w + srcStp * i; + snFloat* pOut = out + dstStp * i; + + for (size_t j = 0; j < roi.h; ++j){ + + for (size_t k = 0; k < roi.w; ++k) + pIn[k] = pOut[k]; + + pIn += srcSz.w; + pOut += roi.w; + } + } + } +} + diff --git a/src/snOperator_cuDNN/src/Operator/crop.h b/src/snOperator_cuDNN/src/Operator/crop.h new file mode 100644 index 0000000..aaa3553 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/crop.h @@ -0,0 +1,54 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// Trimming data +class Crop final : SN_Base::OperatorBase{ + +public: + + Crop(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Crop() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +private: + + struct roi{ + size_t x, y, w, h; + + roi(size_t x_ = 0, size_t y_ = 0, size_t w_ = 0, size_t h_ = 0) : + x(x_), y(y_), w(w_), h(h_){} + }; + + roi roi_; + SN_Base::snSize baseSz_; + + void copyTo(bool inToOut, const roi& roi, const SN_Base::snSize& srcSz, SN_Base::snFloat* in, SN_Base::snFloat* out); +}; diff --git a/src/snOperator_cuDNN/src/Operator/deconvolution.cpp b/src/snOperator_cuDNN/src/Operator/deconvolution.cpp new file mode 100644 index 0000000..52734c4 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/deconvolution.cpp @@ -0,0 +1,392 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/deconvolution.h" +#include "snAux/auxFunc.h" +#include "snOperator/src/weightInit.h" +#include "snOperator/src/activeFunctions.h" +#include "snOperator/src/optimizer.h" +#include "snOperator/src/structurs.h" +#include "snOperator/src/mathFunctions.h" + +using namespace std; +using namespace SN_Base; + +/// deconvolution layer +Deconvolution::Deconvolution(void* net, const string& name, const string& node, std::map& prms) : + OperatorBase(net, name, node, prms){ + + load(prms); +} + +Deconvolution::~Deconvolution(){ + + if (calcMode_ == calcMode::CUDA) + freeParamCUDA(gpuParams_); + else if (calcMode_ == calcMode::OpenCL) + freeParamOCL(gpuParams_); +} + +void Deconvolution::load(std::map& prms){ + + auto setIntParam = [&prms, this](const string& name, bool isZero, bool checkExist, size_t& value){ + + if ((prms.find(name) != prms.end()) && SN_Aux::is_number(prms[name])){ + + size_t v = stoi(prms[name]); + if ((v > 0) || (isZero && (v == 0))) + value = v; + else + ERROR_MESS("param '" + name + (isZero ? "' < 0" : "' <= 0")); + } + else if (checkExist) + ERROR_MESS("not found (or not numder) param '" + name + "'"); + }; + + setIntParam("filters", false, true, deconvParams_.kernel); + setIntParam("fWidth", false, false, deconvParams_.fWidth); + setIntParam("fHeight", false, false, deconvParams_.fHeight); + + setIntParam("stride", false, false, deconvParams_.stride); + + if (prms.find("batchNorm") != prms.end()){ + + string bnType = prms["batchNorm"]; + if (bnType == "none") batchNormType_ = batchNormType::none; + else if (bnType == "beforeActive") batchNormType_ = batchNormType::beforeActive; + else if (bnType == "postActive") batchNormType_ = batchNormType::postActive; + else + ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); + } + + if (prms.find("gpuClearMem") != prms.end()) + gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; + + if (prms.find("gpuDeviceId") != prms.end()) + gpuDeviceId_ = stoi(prms["gpuDeviceId"]); + + if (prms.find("mode") != prms.end()){ + + string mode = prms["mode"]; + if (mode == "CPU") calcMode_ = calcMode::CPU; + else if (mode == "CUDA") calcMode_ = calcMode::CUDA; + else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; + else + ERROR_MESS("param 'mode' = " + mode + " indefined"); + } + + // aux arrays + auxParams_["dWeight"] = vector(); + auxParams_["dWPrev"] = vector(); + auxParams_["dWGrad"] = vector(); + + if (batchNormType_ != batchNormType::none){ + auxParams_["bn_mean"] = vector(); + auxParams_["bn_varce"] = vector(); + auxParams_["bn_scale"] = vector(); + auxParams_["bn_schift"] = vector(); + auxParams_["bn_norm"] = vector(); + auxParams_["bn_dScale"] = vector(); + auxParams_["bn_dSchift"] = vector(); + auxParams_["bn_onc"] = vector(); + } + + setInternPrm(prms); +} + +bool Deconvolution::setInternPrm(std::map& prms){ + + basePrms_ = prms; + + if (prms.find("active") != prms.end()){ + + string atype = prms["active"]; + if (atype == "none") activeType_ = activeType::none; + else if (atype == "sigmoid") activeType_ = activeType::sigmoid; + else if (atype == "relu") activeType_ = activeType::relu; + else if (atype == "leakyRelu") activeType_ = activeType::leakyRelu; + else if (atype == "elu") activeType_ = activeType::elu; + else + ERROR_MESS("param 'active' = " + atype + " indefined"); + } + + if (prms.find("optimizer") != prms.end()){ + + string optType = prms["optimizer"]; + if (optType == "sgd") optimizerType_ = optimizerType::sgd; + else if (optType == "sgdMoment") optimizerType_ = optimizerType::sgdMoment; + else if (optType == "adagrad") optimizerType_ = optimizerType::adagrad; + else if (optType == "adam") optimizerType_ = optimizerType::adam; + else if (optType == "RMSprop") optimizerType_ = optimizerType::RMSprop; + else + ERROR_MESS("param 'optimizer' = " + optType + " indefined"); + } + + if (prms.find("weightInit") != prms.end()){ + + string wInit = prms["weightInit"]; + if (wInit == "uniform") weightInitType_ = weightInitType::uniform; + else if (wInit == "he") weightInitType_ = weightInitType::he; + else if (wInit == "lecun") weightInitType_ = weightInitType::lecun; + else if (wInit == "xavier") weightInitType_ = weightInitType::xavier; + else + ERROR_MESS("param 'weightInit' = " + wInit + " indefined"); + } + + if (prms.find("decayMomentDW") != prms.end()) + opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + + if (prms.find("decayMomentWGr") != prms.end()) + opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + + if (prms.find("lmbRegular") != prms.end()) + opt_lmbRegular_ = stof(prms["lmbRegular"]); + + if (prms.find("batchNormLr") != prms.end()) + baseBatchNorm_.lr = stof(prms["batchNormLr"]); + + if (prms.find("freeze") != prms.end()) + isFreeze_ = prms["freeze"] == "1"; + + if (prms.find("dropOut") != prms.end()){ + dropOut_ = stof(prms["dropOut"]); + if (dropOut_ > 1.F) dropOut_ = 1.F; + else if (dropOut_ < 0.F) dropOut_ = 0.F; + } + + return true; +} + +bool Deconvolution::setBatchNorm(const batchNorm& bn){ + + size_t osz = bn.sz.size(); + + auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + + memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); + + baseBatchNorm_.sz = bn.sz; + + return true; +} + +std::vector Deconvolution::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + forward(neighbOpr[0]->getOutput(), operPrm); + } + else{ + if (neighbOpr.size() == 1){ + backward(neighbOpr[0]->getGradient(), operPrm); + } + else{ + Tensor tns = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (tns != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + tns += neighbOpr[i]->getGradient(); + } + backward(tns, operPrm); + } + } + + return std::vector(); +} + +void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ + + snSize insz = inTns.size(); + inputMem_ = &inTns; + + if (insz != inSzMem_){ + inSzMem_ = insz; + updateConfig(operPrm.isLerning, insz); + } + + snFloat* pInTns = inTns.getData(); + + snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snSize outsz = baseOut_.size(); + + switch (calcMode_){ + case calcMode::CPU: forwardCPU(deconvParams_, weight, insz, pInTns, outsz, out); break; + case calcMode::CUDA: forwardCUDA(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); break; + case calcMode::OpenCL: forwardOCL(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); break; + } + + /// dropOut + if (dropOut_ > 0.F) + dropOut(operPrm.isLerning, dropOut_, outsz, out); + + /// batchNorm + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + + /// active func + activeFuncForward(outsz.size(), out, activeType_); + + /// batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + +} + +void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ + + snFloat* gradIn = inTns.getData(); + + /// batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + + /// active func + if (activeType_ != activeType::none){ + + snFloat* out = baseOut_.getData(); + + size_t osz = baseOut_.size().size(); + activeFuncBackward(osz, out, activeType_); + + // update grad + for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; + } + + /// batchNorm + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + + // calculation of the output gradient and weight correction + snFloat* grOut = baseGrad_.getData(); + snFloat* weight = baseWeight_.getData(); + snFloat* in = inputMem_->getData(); + snFloat* out = baseOut_.getData(); + + snSize insz = inputMem_->size(), outsz = baseOut_.size(); + + if (!isFreeze_){ + snFloat* dWeight = auxParams_["dWeight"].data(); + + switch (calcMode_){ + case calcMode::CPU: backwardCPU_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight); break; + case calcMode::CUDA: backwardCUDA_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); break; + case calcMode::OpenCL: backwardOCL_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); break; + } + + snFloat* dWPrev = auxParams_["dWPrev"].data(); + snFloat* dWGrad = auxParams_["dWGrad"].data(); + size_t wsz = baseWeight_.size().size(); + + optimizer(dWeight, + dWPrev, + dWGrad, + weight, + wsz, + operPrm.lr, + opt_lmbRegular_, + opt_decayMomentDW_, + opt_decayMomentWGr_, + optimizerType_); + } + else{ // isFreeze + switch (calcMode_){ + case calcMode::CPU: backwardCPU_G(deconvParams_, weight, insz, outsz, gradIn, grOut); break; + case calcMode::CUDA: backwardCUDA_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); break; + case calcMode::OpenCL: backwardOCL_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); break; + } + } +} + +void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ + + size_t stp = deconvParams_.fWidth * deconvParams_.fHeight * deconvParams_.kernel, + ntp = (stp + 1) * newsz.d; + + // leave the existing weights as they are, initialize the remainder + size_t wcsz = baseWeight_.size().size(); + if (ntp > wcsz){ + + baseWeight_.resize(snSize(newsz.d, stp + 1)); + snFloat* wd = baseWeight_.getData(); + weightInit(wd + wcsz, ntp - wcsz, stp + 1, deconvParams_.kernel, weightInitType_); + } + + snSize outSz(0, 0, deconvParams_.kernel, newsz.n); + + + outSz.w = (newsz.w - 1) * deconvParams_.stride + deconvParams_.fWidth; + outSz.h = (newsz.h - 1) * deconvParams_.stride + deconvParams_.fHeight; + + baseOut_.resize(outSz); + + if (isLern){ + baseGrad_.resize(newsz); + + // aux array + auxParams_["dWeight"].resize(ntp, 0); + auxParams_["dWPrev"].resize(ntp, 0); + auxParams_["dWGrad"].resize(ntp, 0); + } + + size_t osz = outSz.w * outSz.h * outSz.d; + + if (batchNormType_ != batchNormType::none){ + auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"].resize(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"].resize(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"].resize(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + + if (isLern){ + auxParams_["bn_norm"].resize(osz * outSz.n); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); + auxParams_["bn_dScale"].resize(osz, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); + auxParams_["bn_dSchift"].resize(osz, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); + auxParams_["bn_onc"].resize(outSz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); + } + baseBatchNorm_.sz = outSz; + baseBatchNorm_.sz.n = 1; + } + + if (calcMode_ == calcMode::CUDA) + iniParamCUDA(isLern, newsz, outSz, deconvParams_, &gpuParams_); + else if (calcMode_ == calcMode::OpenCL) + iniParamOCL(isLern, newsz, outSz, deconvParams_, &gpuParams_); +} + + + diff --git a/src/snOperator_cuDNN/src/Operator/deconvolution.h b/src/snOperator_cuDNN/src/Operator/deconvolution.h new file mode 100644 index 0000000..ed33773 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/deconvolution.h @@ -0,0 +1,178 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +#include"snOperator/src/structurs.h" +#include"snOperator/src/mathFunctions.h" + +/// обратная свертка +class Deconvolution final : SN_Base::OperatorBase{ + +public: + + Deconvolution(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Deconvolution(); + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + + bool setInternPrm(std::map& prms) override; + + bool setBatchNorm(const SN_Base::batchNorm& bn) override; + +private: + + struct deconvParams{ + size_t kernel = 10; ///< number of output layers of convolution + size_t fWidth = 3; ///< mask width + size_t fHeight = 3; ///< mask height + size_t stride = 1; ///< mask step + }; + deconvParams deconvParams_; + + activeType activeType_ = activeType::relu; ///< activation type + optimizerType optimizerType_ = optimizerType::adam; ///< optimizer + weightInitType weightInitType_ = weightInitType::he; ///< weight init + batchNormType batchNormType_ = batchNormType::none; ///< batchNorm + SN_Base::snSize inSzMem_; ///< insz mem + + const SN_Base::Tensor* inputMem_ = nullptr; + + bool isFreeze_ = false; ///< not change weight + bool gpuClearMem_ = false; ///< clear mem GPU + + uint32_t gpuDeviceId_ = 0; ///< gpu id + + calcMode calcMode_ = calcMode::CPU; ///< calc mode + + SN_Base::snFloat dropOut_ = 0.F; ///< rand off output + + SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimizer weight coef + opt_decayMomentWGr_ = 0.99F, + opt_lmbRegular_ = 0.001F; + + std::map> auxParams_; ///< aux data + void* gpuParams_ = nullptr ; ///< aux GPU + + void load(std::map& prms); + + void updateConfig(bool isLern, const SN_Base::snSize& newSz); + + void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + + /// CPU /////////////////////////// + + void forwardCPU(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output); + + void backwardCPU_GW(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut); + + void backwardCPU_G(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut); + + + + /// CUDA /////////////////////////// + + void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const deconvParams&, void** gpuPrm); + + void freeParamCUDA(void* gpuPrm); + + void forwardCUDA(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output, + void* gpuPrm); + + void backwardCUDA_GW(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut, + void* gpuPrm); + + void backwardCUDA_G(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + void* gpuPrm); + + + /// OpenCL /////////////////////////// + + void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const deconvParams&, void** gpuPrm); + + void freeParamOCL(void* gpuPrm); + + void forwardOCL(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output, + void* gpuPrm); + + void backwardOCL_GW(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut, + void* gpuPrm); + + void backwardOCL_G(const deconvParams&, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snSize& outsz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + void* gpuPrm); +}; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/fullyConnected.cpp b/src/snOperator_cuDNN/src/Operator/fullyConnected.cpp new file mode 100644 index 0000000..fab2956 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/fullyConnected.cpp @@ -0,0 +1,372 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/fullyConnected.h" +#include "snAux/auxFunc.h" +#include "snOperator/src/weightInit.h" +#include "snOperator/src/activeFunctions.h" +#include "snOperator/src/optimizer.h" +#include "snOperator/src/structurs.h" +#include "snOperator/src/mathFunctions.h" + +using namespace std; +using namespace SN_Base; + +/// fullyConnected layer +FullyConnected::FullyConnected(void* net, const string& name, const string& node, std::map& prms) : + OperatorBase(net, name, node, prms){ + + load(prms); +} + +FullyConnected::~FullyConnected(){ + + if (calcMode_ == calcMode::CUDA) + freeParamCUDA(gpuParams_); + else if (calcMode_ == calcMode::OpenCL) + freeParamOCL(gpuParams_); +} + +void FullyConnected::load(std::map& prms){ + + if ((prms.find("units") != prms.end()) && SN_Aux::is_number(prms["units"])){ + + size_t kernel = stoi(prms["units"]); + if (kernel > 0) + kernel_ = kernel; + else + ERROR_MESS("param 'units' <= 0"); + } + else + ERROR_MESS("not found (or not numder) param 'units'"); + + if (prms.find("useBias") != prms.end()) + useBias_ = prms["useBias"] == "1"; + + if (prms.find("batchNorm") != prms.end()){ + + string bnType = prms["batchNorm"]; + if (bnType == "none") batchNormType_ = batchNormType::none; + else if (bnType == "beforeActive") batchNormType_ = batchNormType::beforeActive; + else if (bnType == "postActive") batchNormType_ = batchNormType::postActive; + else + ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); + } + + if (prms.find("gpuClearMem") != prms.end()) + gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; + + if (prms.find("mode") != prms.end()){ + + string mode = prms["mode"]; + if (mode == "CPU") calcMode_ = calcMode::CPU; + else if (mode == "CUDA") calcMode_ = calcMode::CUDA; + else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; + else + ERROR_MESS("param 'mode' = " + mode + " indefined"); + } + + if (prms.find("gpuDeviceId") != prms.end()) + gpuDeviceId_ = stoi(prms["gpuDeviceId"]); + + baseOut_.resize(snSize(kernel_)); + + // currrect params + setInternPrm(prms); + + // aux array + auxParams_["dWeight"] = vector(); + auxParams_["dWPrev"] = vector(); + auxParams_["dWGrad"] = vector(); + + if (batchNormType_ != batchNormType::none){ + auxParams_["bn_mean"] = vector(kernel_, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"] = vector(kernel_, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"] = vector(kernel_, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"] = vector(kernel_, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + auxParams_["bn_norm"] = vector(); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); + auxParams_["bn_dScale"] = vector(kernel_, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); + auxParams_["bn_dSchift"] = vector(kernel_, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); + auxParams_["bn_onc"] = vector(); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); + + baseBatchNorm_.sz = snSize(kernel_); + } +} + +bool FullyConnected::setInternPrm(std::map& prms){ + + basePrms_ = prms; + + if (prms.find("active") != prms.end()){ + + string atype = prms["active"]; + if (atype == "none") activeType_ = activeType::none; + else if (atype == "sigmoid") activeType_ = activeType::sigmoid; + else if (atype == "relu") activeType_ = activeType::relu; + else if (atype == "leakyRelu") activeType_ = activeType::leakyRelu; + else if (atype == "elu") activeType_ = activeType::elu; + else + ERROR_MESS("param 'active' = " + atype + " indefined"); + } + + if (prms.find("optimizer") != prms.end()){ + + string optType = prms["optimizer"]; + if (optType == "sgd") optimizerType_ = optimizerType::sgd; + else if (optType == "sgdMoment") optimizerType_ = optimizerType::sgdMoment; + else if (optType == "adagrad") optimizerType_ = optimizerType::adagrad; + else if (optType == "adam") optimizerType_ = optimizerType::adam; + else if (optType == "RMSprop") optimizerType_ = optimizerType::RMSprop; + else + ERROR_MESS("param 'optimizer' = " + optType + " indefined"); + } + + if (prms.find("weightInit") != prms.end()){ + + string wInit = prms["weightInit"]; + if (wInit == "uniform") weightInitType_ = weightInitType::uniform; + else if (wInit == "he") weightInitType_ = weightInitType::he; + else if (wInit == "lecun") weightInitType_ = weightInitType::lecun; + else if (wInit == "xavier") weightInitType_ = weightInitType::xavier; + else + ERROR_MESS("param 'weightInit' = " + wInit + " indefined"); + } + + if (prms.find("decayMomentDW") != prms.end()) + opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + + if (prms.find("decayMomentWGr") != prms.end()) + opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + + if (prms.find("lmbRegular") != prms.end()) + opt_lmbRegular_ = stof(prms["lmbRegular"]); + + if (prms.find("batchNormLr") != prms.end()) + baseBatchNorm_.lr = stof(prms["batchNormLr"]); + + if (prms.find("freeze") != prms.end()) + isFreeze_ = prms["freeze"] == "1"; + + if (prms.find("dropOut") != prms.end()){ + dropOut_ = stof(prms["dropOut"]); + if (dropOut_ > 1.F) dropOut_ = 1.F; + else if (dropOut_ < 0.F) dropOut_ = 0.F; + } + + return true; +} + +bool FullyConnected::setBatchNorm(const batchNorm& bn){ + + size_t osz = bn.sz.size(); + + auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + + memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); + memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); + + baseBatchNorm_.sz = bn.sz; + + return true; +} + +std::vector FullyConnected::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + forward(neighbOpr[0]->getOutput(), operPrm); + } + else{ + if (neighbOpr.size() == 1){ + backward(neighbOpr[0]->getGradient(), operPrm); + } + else{ + Tensor tns = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (tns != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + tns += neighbOpr[i]->getGradient(); + } + backward(tns, operPrm); + } + } + + return std::vector(); +} + +void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ + + snSize insz = inTns.size(); + inputMem_ = &inTns; + + if (insz != inSzMem_){ + inSzMem_ = insz; + updateConfig(operPrm.isLerning, insz); + } + + /// calculation of the output values of neurons + snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + + // +bias? + if (!useBias_){ + size_t stepByN = insz.w * insz.h * insz.d * kernel_; + memset(weight + stepByN, 0, kernel_ * sizeof(snFloat)); + } + + switch (calcMode_){ + case calcMode::CPU: forwardCPU(kernel_, insz, inTns.getData(), weight, out); break; + case calcMode::CUDA: forwardCUDA(kernel_, insz, inTns.getData(), weight, out, gpuParams_); break; + case calcMode::OpenCL: forwardOCL(kernel_, insz, inTns.getData(), weight, out, gpuParams_); break; + } + + /// dropOut + snSize outSz = baseOut_.size(); + if (dropOut_ > 0.F) + dropOut(operPrm.isLerning, dropOut_, outSz, out); + + /// batchNorm + if (batchNormType_ == batchNormType::beforeActive) + layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); + + /// active func + activeFuncForward(kernel_ * insz.n, out, activeType_); + + /// batchNorm + if (batchNormType_ == batchNormType::postActive) + layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); +} + +void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ + + snFloat* gradIn = inTns.getData(); + + /// batchNorm + snSize gsz = inTns.size(); + if (batchNormType_ == batchNormType::postActive) + layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); + + // active func + if (activeType_ != activeType::none){ + + snFloat* out = baseOut_.getData(); + + size_t osz = kernel_ * inSzMem_.n; + activeFuncBackward(osz, out, activeType_); + + // update grad + for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; + } + + /// batchNorm + if (batchNormType_ == batchNormType::beforeActive) + layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); + + // calculation of the output gradient and weight correction + snFloat* gradOut = baseGrad_.getData(); + snFloat* weight = baseWeight_.getData(); + + if (!isFreeze_){ + + snFloat* dWeight = auxParams_["dWeight"].data(); + + switch (calcMode_){ + case calcMode::CPU: backwardCPU_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight); break; + case calcMode::CUDA: backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); break; + case calcMode::OpenCL: backwardOCL_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); break; + } + + // correct weight + snFloat* dWPrev = auxParams_["dWPrev"].data(); + snFloat* dWGrad = auxParams_["dWGrad"].data(); + size_t wsz = baseWeight_.size().size(); + + optimizer(dWeight, + dWPrev, + dWGrad, + weight, + wsz, + operPrm.lr, + opt_lmbRegular_, + opt_decayMomentDW_, + opt_decayMomentWGr_, + optimizerType_); + + } + else{ // isFreeze + switch (calcMode_){ + case calcMode::CPU: backwardCPU_G(kernel_, weight, inSzMem_, gradIn, gradOut); break; + case calcMode::CUDA: backwardCUDA_G(kernel_, weight, inSzMem_, gradIn, gradOut, gpuParams_); break; + case calcMode::OpenCL: backwardOCL_G(kernel_, weight, inSzMem_, gradIn, gradOut, gpuParams_); break; + } + } +} + +void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ + + size_t stp = newsz.w * newsz.h * newsz.d, ntp = (stp + 1) * kernel_; + + // leave the existing weights as they are, initialize the remainder + size_t wcsz = baseWeight_.size().size(); + if (ntp > wcsz){ + + baseWeight_.resize(snSize(kernel_, stp + 1)); + snFloat* wd = baseWeight_.getData(); + weightInit(wd + wcsz, ntp - wcsz, stp + 1, kernel_, weightInitType_); + } + + baseOut_.resize(snSize(kernel_, 1, 1, newsz.n)); + + if (isLern){ + baseGrad_.resize(newsz); + + // aux array + auxParams_["dWeight"].resize(ntp, 0); + auxParams_["dWPrev"].resize(ntp, 0); + auxParams_["dWGrad"].resize(ntp, 0); + + if (batchNormType_ != batchNormType::none){ + auxParams_["bn_norm"].resize(newsz.n * kernel_, 0); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); + auxParams_["bn_onc"].resize(newsz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); + } + } + + if (calcMode_ == calcMode::CUDA) + iniParamCUDA(isLern, newsz, kernel_, &gpuParams_); + else if (calcMode_ == calcMode::OpenCL) + iniParamOCL(isLern, newsz, kernel_, &gpuParams_); +} \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/fullyConnected.h b/src/snOperator_cuDNN/src/Operator/fullyConnected.h new file mode 100644 index 0000000..3df0106 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/fullyConnected.h @@ -0,0 +1,171 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +#include"snOperator/src/structurs.h" + +/// fullyConnected layer +class FullyConnected final : SN_Base::OperatorBase{ + +public: + + FullyConnected(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~FullyConnected(); + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + + bool setInternPrm(std::map& prms) override; + + bool setBatchNorm(const SN_Base::batchNorm& bn) override; + +private: + + size_t kernel_ = 10; ///< number of hidden neurons + + activeType activeType_ = activeType::relu; ///< active func type + optimizerType optimizerType_ = optimizerType::adam; ///< optimizer weight type + weightInitType weightInitType_ = weightInitType::he; ///< init weight + batchNormType batchNormType_ = batchNormType::none; ///< batchNorm + SN_Base::snSize inSzMem_; ///< input sz mem + + const SN_Base::Tensor* inputMem_ = nullptr; + + bool useBias_ = true; + bool isFreeze_ = false; ///< not change weight + bool gpuClearMem_ = false; ///< free gpu mem + + uint32_t gpuDeviceId_ = 0; ///< gpu id + + calcMode calcMode_ = calcMode::CPU; ///< calc mode + + SN_Base::snFloat dropOut_ = 0.F; ///< rand off output + + SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimizer weight coef + opt_decayMomentWGr_ = 0.99F, + opt_lmbRegular_ = 0.001F; + + + std::map> auxParams_; ///< aux data + void* gpuParams_ = nullptr; ///< gpu data + + + + void load(std::map& prms); + + void updateConfig(bool isLern, const SN_Base::snSize& newSz); + + void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + + /// CPU /////////////////////////// + + void forwardCPU(size_t kernel, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snFloat* weight, + SN_Base::snFloat* output); + + // calc grad and dw + void backwardCPU_GW(size_t kernel, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut); + + // calc grad + void backwardCPU_G(size_t kernel, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut); + + + /// CUDA /////////////////////////// + + void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, size_t kernel, void** gpuPrm); + + void freeParamCUDA(void* gpuPrm); + + void forwardCUDA(size_t kernel, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snFloat* weight, + SN_Base::snFloat* output, + void* gpuPrm); + + // calc grad and dw + void backwardCUDA_GW(size_t kernel, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut, + void* gpuPrm); + + // calc grad + void backwardCUDA_G(size_t kernel, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + void* gpuPrm); + + + /// OpenCL /////////////////////////// + + void iniParamOCL(bool isLern, const SN_Base::snSize& insz, size_t kernel, void** gpuPrm); + + void freeParamOCL(void* gpuPrm); + + void forwardOCL(size_t kernel, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snFloat* weight, + SN_Base::snFloat* output, + void* gpuPrm); + + // calc grad and dw + void backwardOCL_GW(size_t kernel, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::snFloat* dWeightOut, + void* gpuPrm); + + // calc grad + void backwardOCL_G(size_t kernel, + const SN_Base::snFloat* weight, + const SN_Base::snSize& insz, + const SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + void* gpuPrm); +}; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/input.cpp b/src/snOperator_cuDNN/src/Operator/input.cpp new file mode 100644 index 0000000..cb18e51 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/input.cpp @@ -0,0 +1,46 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/input.h" + +using namespace std; +using namespace SN_Base; + +/// input layer +Input::Input(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + + +} + +bool Input::setInput(const snFloat* data, const snSize& dsz){ + baseOut_.setData(data, dsz); + return true; +} + +std::vector Input::Do(const operationParam& prm, const std::vector& neighbOpr){ + + return std::vector(); +} diff --git a/src/snOperator_cuDNN/src/Operator/input.h b/src/snOperator_cuDNN/src/Operator/input.h new file mode 100644 index 0000000..4ba2f6f --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/input.h @@ -0,0 +1,42 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// input layer +class Input final : SN_Base::OperatorBase{ + +public: + + Input(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Input() = default; + + bool setInput(const SN_Base::snFloat* data, const SN_Base::snSize& dsz) override; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; +}; diff --git a/src/snOperator_cuDNN/src/Operator/lock.cpp b/src/snOperator_cuDNN/src/Operator/lock.cpp new file mode 100644 index 0000000..e1cb9aa --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/lock.cpp @@ -0,0 +1,66 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/lock.h" + +using namespace std; +using namespace SN_Base; + +/// locks further passage along the graph +Lock::Lock(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + +} + +std::vector Lock::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if ((basePrms_.find("state") == basePrms_.end()) || + (basePrms_["state"] != "unlock")) + return std::vector < std::string > {"noWay"}; + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + baseOut_ = neighbOpr[0]->getOutput(); + } + else{ + + baseGrad_ = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + } + + return std::vector(); +} diff --git a/src/snOperator_cuDNN/src/Operator/lock.h b/src/snOperator_cuDNN/src/Operator/lock.h new file mode 100644 index 0000000..46e790f --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/lock.h @@ -0,0 +1,41 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// locks further passage along the graph +class Lock final : SN_Base::OperatorBase{ + +public: + + Lock(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Lock() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +}; diff --git a/src/snOperator_cuDNN/src/Operator/lossFunction.cpp b/src/snOperator_cuDNN/src/Operator/lossFunction.cpp new file mode 100644 index 0000000..a4072ee --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/lossFunction.cpp @@ -0,0 +1,211 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/lossFunction.h" +#include "snOperator/src/activeFunctions.h" + +using namespace std; +using namespace SN_Base; + + +void LossFunction::load(std::map& prms){ + + if (prms.find("loss") != prms.end()){ + + string stype = prms["loss"]; + if (stype == "softMaxToCrossEntropy") + lossType_ = lossType::softMaxACrossEntropy; + else if (stype == "binaryCrossEntropy") + lossType_ = lossType::binaryCrossEntropy; + else if (stype == "regressionMSE") + lossType_ = lossType::regressionMSE; + else if (stype == "userLoss") + lossType_ = lossType::userLoss; + else + ERROR_MESS("param 'loss' = " + stype + " indefined"); + } + else + ERROR_MESS("not found param 'loss'"); +} + +LossFunction::LossFunction(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + + load(prms); +} + + +std::vector LossFunction::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + if (lossType_ == LossFunction::lossType::userLoss){ + if (basePrms_.find("cbackName") == basePrms_.end()){ + ERROR_MESS("not set param 'cbackName'"); + return std::vector < std::string > {"noWay"}; + } + } + + if (operPrm.action == snAction::forward) + forward(neighbOpr[0]->getOutput()); + else + backward(neighbOpr[0]->getGradient(), operPrm); + + return std::vector(); +} + +void LossFunction::forward(const Tensor& inTns){ + + snSize tsz = inTns.size(); + baseOut_ = inTns; + + auto out = baseOut_.getData(); + + switch (lossType_){ + case LossFunction::lossType::softMaxACrossEntropy:{ + + if (auxParams_.find("sm_aux") == auxParams_.end()) + auxParams_["sm_aux"] = vector(tsz.w * tsz.h * tsz.d); + + // run through softMax each image separately + snFloat* aux = auxParams_["sm_aux"].data(); + size_t nsz = tsz.n, width = tsz.w * tsz.h * tsz.d; + for (size_t i = 0; i < nsz; ++i){ + + snFloat maxv = *std::max_element(out, out + width); + + snFloat denom = 0.F; + for (size_t j = 0; j < width; ++j){ + + aux[j] = (out[j] - maxv > -20) ? std::exp(out[j] - maxv) : 0.1E-8F; + denom += aux[j]; + } + + for (size_t j = 0; j < width; ++j) + out[j] = aux[j] / denom; + + out += width; + } + } + case LossFunction::lossType::binaryCrossEntropy:{ + + break; + } + case LossFunction::lossType::regressionMSE:{ + + + break; + } + case LossFunction::lossType::userLoss:{ + + snSize outSz; + snFloat* outData = nullptr; + g_userCBack(this, basePrms_["cbackName"], node_, + true, inTns.size(), inTns.getData(), outSz, &outData); + + if (outData){ + baseOut_.setData(outData, outSz); + } + else + ERROR_MESS("not set 'outData' in userCBack"); + + break; + } + + break; + default: + ERROR_MESS("param 'loss' indefined"); + break; + } + +} + +void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ + + snSize tsz = inTns.size(); + + snSize grsz = baseGrad_.size(); + if (grsz != tsz) + baseGrad_.resize(tsz); + + auto smOut = baseOut_.getData(); + auto target = inTns.getData(); + auto grad = baseGrad_.getData(); + + switch (lossType_){ + case LossFunction::lossType::softMaxACrossEntropy:{ + + size_t nsz = tsz.size(); + for (size_t i = 0; i < nsz; ++i) + grad[i] = smOut[i] - target[i]; + + break; + } + + case LossFunction::lossType::binaryCrossEntropy:{ + + size_t nsz = tsz.size(); + for (size_t i = 0; i < nsz; ++i) + grad[i] = (smOut[i] - target[i]) / (smOut[i] * (1.F - smOut[i])); + + break; + } + // Mean Square Error + case LossFunction::lossType::regressionMSE:{ + + size_t nsz = tsz.size(); + for (size_t i = 0; i < nsz; ++i){ + + grad[i] = 2 * (smOut[i] - target[i]) / nsz; + } + + break; + } + + case LossFunction::lossType::userLoss:{ + + snSize outSz; + snFloat* outData = nullptr; + g_userCBack(this, basePrms_["cbackName"], node_, + false, inTns.size(), inTns.getData(), outSz, &outData); + + if (outData){ + baseGrad_.setData(outData, outSz); + } + else + ERROR_MESS("not set 'outData' in userCBack"); + + break; + } + + + default: + ERROR_MESS("param 'lossType' indefined"); + break; + } +} diff --git a/src/snOperator_cuDNN/src/Operator/lossFunction.h b/src/snOperator_cuDNN/src/Operator/lossFunction.h new file mode 100644 index 0000000..57b1e57 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/lossFunction.h @@ -0,0 +1,57 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// оператор - расчет ошибки +class LossFunction final : SN_Base::OperatorBase{ + +public: + + LossFunction(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~LossFunction() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +private: + enum class lossType{ + softMaxACrossEntropy = 0, + binaryCrossEntropy = 1, + regressionMSE = 2, + userLoss = 3, + }; + + lossType lossType_ = lossType::softMaxACrossEntropy; + + std::map> auxParams_; ///< вспом данные для расчета + + void load(std::map& prms); + + void forward(const SN_Base::Tensor& inTns); + void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); +}; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/output.cpp b/src/snOperator_cuDNN/src/Operator/output.cpp new file mode 100644 index 0000000..e905ee5 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/output.cpp @@ -0,0 +1,51 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/output.h" + +using namespace std; +using namespace SN_Base; + + +/// output layer +Output::Output(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + +} + +std::vector Output::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + if (operPrm.action == snAction::forward) + baseOut_ = neighbOpr[0]->getOutput(); + + return std::vector(); +} + + diff --git a/src/snOperator_cuDNN/src/Operator/output.h b/src/snOperator_cuDNN/src/Operator/output.h new file mode 100644 index 0000000..a591113 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/output.h @@ -0,0 +1,41 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// output layer +class Output final : SN_Base::OperatorBase{ + +public: + + Output(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Output() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +}; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/pooling.cpp b/src/snOperator_cuDNN/src/Operator/pooling.cpp new file mode 100644 index 0000000..4bcbcc6 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/pooling.cpp @@ -0,0 +1,225 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/pooling.h" +#include "snAux/auxFunc.h" +#include "snOperator/src/structurs.h" + +using namespace std; +using namespace SN_Base; + +/// pooling layer +Pooling::Pooling(void* net, const string& name, const string& node, std::map& prms) : + OperatorBase(net, name, node, prms){ + + load(prms); +} + +Pooling::~Pooling(){ + + if (calcMode_ == calcMode::CUDA) + freeParamCUDA(gpuParams_); + else if (calcMode_ == calcMode::OpenCL) + freeParamOCL(gpuParams_); +} + +void Pooling::load(std::map& prms){ + + + auto setIntParam = [&prms, this](const string& name, bool isZero, bool checkExist, size_t& value){ + + if ((prms.find(name) != prms.end()) && SN_Aux::is_number(prms[name])){ + + size_t v = stoi(prms[name]); + if ((v > 0) || (isZero && (v == 0))) + value = v; + else + ERROR_MESS("param '" + name + (isZero ? "' < 0" : "' <= 0")); + } + else if (checkExist) + ERROR_MESS("not found (or not numder) param '" + name + "'"); + }; + + setIntParam("kernel", false, false, poolPrms_.kernel); + + if (prms.find("stride") != prms.end()) + setIntParam("stride", false, false, poolPrms_.stride); + else + poolPrms_.stride = poolPrms_.kernel; + + if (prms.find("gpuClearMem") != prms.end()) + gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; + + if (prms.find("gpuDeviceId") != prms.end()) + gpuDeviceId_ = stoi(prms["gpuDeviceId"]); + + if (prms.find("pool") != prms.end()){ + + string atype = prms["pool"]; + if (atype == "max") poolPrms_.type = poolType::max; + else if (atype == "avg") poolPrms_.type = poolType::avg; + else + ERROR_MESS("param 'pool' = " + atype + " indefined"); + } + + if (prms.find("mode") != prms.end()){ + + string mode = prms["mode"]; + if (mode == "CPU") calcMode_ = calcMode::CPU; + else if (mode == "CUDA") calcMode_ = calcMode::CUDA; + else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; + else + ERROR_MESS("param 'mode' = " + mode + " indefined"); + } + + basePrms_ = prms; +} + +std::vector Pooling::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + forward(neighbOpr[0]->getOutput(), operPrm); + } + else{ + if (neighbOpr.size() == 1){ + backward(neighbOpr[0]->getGradient(), operPrm); + } + else{ + Tensor tns = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (tns != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + tns += neighbOpr[i]->getGradient(); + } + backward(tns, operPrm); + } + } + return std::vector(); +} + +void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm){ + + snSize insz = inTns.size(); + inputMem_ = &inTns; + + if (insz != inSzMem_){ + inSzMem_ = insz; + updateConfig(operPrm.isLerning, insz); + } + + /// copy with offset padding for each image + snFloat* in = inputMem_->getData(); + if (isPadding_){ + inTnsExp_.resize(inDataExpSz_); + paddingOffs(false, poolPrms_.paddingW, poolPrms_.paddingH, insz, inTnsExp_.getData(), inputMem_->getData()); + insz = inDataExpSz_; + in = inTnsExp_.getData(); + } + + /// output calculation + snFloat* out = baseOut_.getData(); + + switch (calcMode_){ + case calcMode::CPU: forwardCPU(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data()); break; + case calcMode::CUDA: forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); break; + case calcMode::OpenCL: forwardOCL(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); break; + } +} + +void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ + + snFloat* gradIn = inTns.getData(); + + snFloat* input = inputMem_->getData(); + snFloat* gradOut = baseGrad_.getData(); + if (isPadding_){ + gradOutExp_.resize(inDataExpSz_); + gradOut = gradOutExp_.getData(); + input = inTnsExp_.getData(); + } + + /// grad calculation + switch (calcMode_){ + case calcMode::CPU: backwardCPU(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut); break; + case calcMode::CUDA: backwardCUDA(poolPrms_, baseOut_.size(), outInx_.data(), baseOut_.getData(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); break; + case calcMode::OpenCL: backwardOCL(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut, gpuParams_); break; + } + + if (isPadding_) + paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); +} + +void Pooling::updateConfig(bool isLern, const snSize& newsz){ + + size_t& kernel = poolPrms_.kernel, + & paddingW = poolPrms_.paddingW, + & paddingH = poolPrms_.paddingH, + & stride = poolPrms_.stride; + + snSize outSz(0, 0, newsz.d, newsz.n); + + outSz.w = (newsz.w - kernel) / stride + 1; + outSz.h = (newsz.h - kernel) / stride + 1; + + // check correct + size_t resW = (newsz.w - kernel) % stride, + resH = (newsz.h - kernel) % stride; + + isPadding_ = (resW != 0) || (resH != 0); + + inDataExpSz_ = newsz; + + if (isPadding_){ + + paddingW = 1; + paddingH = 1; + + outSz.w = (newsz.w + paddingW * 2 - kernel) / stride + 1; + outSz.h = (newsz.h + paddingH * 2 - kernel) / stride + 1; + + inDataExpSz_ = snSize(newsz.w + paddingW * 2, newsz.h + paddingH * 2, newsz.d, newsz.n); + } + + baseOut_.resize(outSz); + + outInx_.resize(outSz.size(), 0); + + if (isLern) + baseGrad_.resize(newsz); + + if (calcMode_ == calcMode::CUDA) + iniParamCUDA(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); + else if (calcMode_ == calcMode::OpenCL) + iniParamOCL(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); +} \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/pooling.h b/src/snOperator_cuDNN/src/Operator/pooling.h new file mode 100644 index 0000000..c4623a7 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/pooling.h @@ -0,0 +1,149 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +#include"snOperator/src/structurs.h" +#include"snOperator/src/mathFunctions.h" + + +/// pooling layer +class Pooling final : SN_Base::OperatorBase{ + +public: + + Pooling(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Pooling(); + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + + +private: + + struct poolParams{ + poolType type = poolType::max; ///< type + size_t kernel = 2; ///< mask size + size_t stride = 2; ///< step mask + size_t paddingH = 0, paddingW = 0; + }; + + poolParams poolPrms_; + + SN_Base::snSize inSzMem_; ///< input size mem + SN_Base::snSize inDataExpSz_; ///< input size expand + std::vector outInx_; ///< index select elem + + SN_Base::Tensor inTnsExp_; + SN_Base::Tensor gradOutExp_; + + const SN_Base::Tensor* inputMem_ = nullptr; + + + bool isPadding_ = false; + + bool gpuClearMem_ = false; ///< freee gpu mem + + uint32_t gpuDeviceId_ = 0; ///< gpu id + + calcMode calcMode_ = calcMode::CPU; ///< calc mode + + + std::map> auxParams_; ///< aux data + void* gpuParams_ = nullptr; ///< gpu data + + void load(std::map& prms); + + void updateConfig(bool isLern, const SN_Base::snSize& newSz); + + void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); + + /// CPU /////////////////////////// + + void forwardCPU(const poolParams& poolPrms, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output, + size_t* outputInx); + + void backwardCPU(const poolParams& poolPrms, + const SN_Base::snSize& outsz, + const size_t* outputInx, + const SN_Base::snFloat* gradIn, + const SN_Base::snSize& insz, + SN_Base::snFloat* gradOut); + + + /// CUDA /////////////////////////// + + void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const poolParams&, void** gpuPrm); + + void freeParamCUDA(void* gpuPrm); + + void forwardCUDA(const poolParams& poolPrms, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output, + size_t* outputInx, + void* gpuParams); + + /// обратный проход CUDA + void backwardCUDA(const poolParams& poolPrms, + const SN_Base::snSize& outsz, + const size_t* outputInx, + const SN_Base::snFloat* output, + const SN_Base::snFloat* gradIn, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + SN_Base::snFloat* gradOut, + void* gpuParams); + + + /// OpenCL /////////////////////////// + + void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const poolParams&, void** gpuPrm); + + void freeParamOCL(void* gpuPrm); + + void forwardOCL(const poolParams& poolPrms, + const SN_Base::snSize& insz, + const SN_Base::snFloat* input, + const SN_Base::snSize& outsz, + SN_Base::snFloat* output, + size_t* outputInx, + void* gpuParams); + + void backwardOCL(const poolParams& poolPrms, + const SN_Base::snSize& outsz, + const size_t* outputInx, + const SN_Base::snFloat* gradIn, + const SN_Base::snSize& insz, + SN_Base::snFloat* gradOut, + void* gpuParams); + +}; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/resize.cpp b/src/snOperator_cuDNN/src/Operator/resize.cpp new file mode 100644 index 0000000..2b60c09 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/resize.cpp @@ -0,0 +1,132 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/resize.h" +#include "snAux/auxFunc.h" + +using namespace std; +using namespace SN_Base; + + +/// Trimming the number of layers +Resize::Resize(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + +} + +std::vector Resize::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + baseOut_ = neighbOpr[0]->getOutput(); + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + if (basePrms_.find("fwdDiap") != basePrms_.end()){ + + snSize csz = baseOut_.size(); + + auto ls = SN_Aux::split(basePrms_["fwdDiap"], " "); + + size_t begin = min(stol(ls[0]), csz.d - 1), end = csz.d; + + if (ls.size() > 1) end = min(stol(ls[1]), csz.d); + + if (begin >= end){ + ERROR_MESS("fwdDiap begin >= end"); + return std::vector < std::string > {"noWay"}; + } + + if ((end - begin) < csz.d){ + + Tensor buff = baseOut_; + + baseOut_.resize(snSize(csz.w, csz.h, (end - begin), csz.n)); + + size_t sz = csz.w * csz.h * (end - begin), + offset = csz.w * csz.h * begin, + cstp = csz.w * csz.h * csz.d; + for (size_t j = 0; j < csz.n; ++j){ + + snFloat* dst = baseOut_.getData() + sz * j, + *src = buff.getData() + cstp * j + offset; + + memcpy(dst, src, sz * sizeof(snFloat)); + } + } + } + } + else{ // backward + + baseGrad_ = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + + if (basePrms_.find("bwdDiap") != basePrms_.end()){ + + snSize csz = baseGrad_.size(); + + auto ls = SN_Aux::split(basePrms_["bwdDiap"], " "); + + size_t begin = min(stol(ls[0]), csz.d - 1), end = csz.d; + + if (ls.size() > 1) end = min(stol(ls[1]), csz.d); + + if (begin >= end){ + ERROR_MESS("bwdDiap begin >= end"); + return std::vector < std::string > {"noWay"}; + } + + if ((end - begin) < csz.d){ + + Tensor buff = baseGrad_; + + baseGrad_.resize(snSize(csz.w, csz.h, (end - begin), csz.n)); + + size_t sz = csz.w * csz.h * (end - begin), + offset = csz.w * csz.h * begin, + cstp = csz.w * csz.h * csz.d; + for (size_t j = 0; j < csz.n; ++j){ + + snFloat* dst = baseGrad_.getData() + sz * j, + *src = buff.getData() + cstp * j + offset; + + memcpy(dst, src, sz * sizeof(snFloat)); + } + } + } + } + + return vector(); +} diff --git a/src/snOperator_cuDNN/src/Operator/resize.h b/src/snOperator_cuDNN/src/Operator/resize.h new file mode 100644 index 0000000..0296ec5 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/resize.h @@ -0,0 +1,46 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// Trimming the number of layers +class Resize final : SN_Base::OperatorBase{ + +public: + + Resize(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Resize() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +private: + + + SN_Base::snSize baseSz_; + +}; diff --git a/src/snOperator_cuDNN/src/Operator/summator.cpp b/src/snOperator_cuDNN/src/Operator/summator.cpp new file mode 100644 index 0000000..cac5306 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/summator.cpp @@ -0,0 +1,94 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/summator.h" + +using namespace std; +using namespace SN_Base; + +/// adder of 2 and more layers +Summator::Summator(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + + if (basePrms_.find("type") != basePrms_.end()){ + if (basePrms_["type"] == "summ") sType_ = sType::summ; + else if (basePrms_["type"] == "diff") sType_ = sType::diff; + else if (basePrms_["type"] == "mean") sType_ = sType::mean; + else + ERROR_MESS("param 'type' indefined"); + } +} + +std::vector Summator::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + + if (operPrm.action == snAction::forward){ + + baseOut_ = neighbOpr[0]->getOutput(); + + size_t sz = neighbOpr.size(); + for (size_t i = 1; i < sz; ++i){ + + if (baseOut_ != neighbOpr[i]->getOutput()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + switch (sType_){ + case Summator::sType::summ: baseOut_ += neighbOpr[i]->getOutput(); break; + case Summator::sType::diff: baseOut_ -= neighbOpr[i]->getOutput(); break; + case Summator::sType::mean: mean(baseOut_, neighbOpr[i]->getOutput(), baseOut_); break; + } + } + } + else{ + + baseGrad_ = neighbOpr[0]->getGradient(); + + size_t sz = neighbOpr.size(); + for (size_t i = 1; i < sz; ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + } + + + return std::vector(); +} + +void Summator::mean(const Tensor& one, const Tensor& two, Tensor& out){ + + snFloat* done = one.getData(), + * dtwo = two.getData(), + * dout = out.getData(); + + size_t sz = one.size().size(); + for (size_t i = 0; i < sz; ++i){ + dout[i] = (done[i] + dtwo[i]) / 2; + } +} \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/summator.h b/src/snOperator_cuDNN/src/Operator/summator.h new file mode 100644 index 0000000..8013d49 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/summator.h @@ -0,0 +1,53 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// adder of 2 and more layers +class Summator final : SN_Base::OperatorBase{ + +public: + + Summator(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Summator() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +private: + + enum class sType{ + summ = 0, + diff = 1, + mean = 2, + }; + + sType sType_ = sType::summ; + + + void mean(const SN_Base::Tensor& one, const SN_Base::Tensor& two, SN_Base::Tensor& out); +}; diff --git a/src/snOperator_cuDNN/src/Operator/switch.cpp b/src/snOperator_cuDNN/src/Operator/switch.cpp new file mode 100644 index 0000000..06026d0 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/switch.cpp @@ -0,0 +1,69 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/switch.h" +#include "snAux/auxFunc.h" + +using namespace std; +using namespace SN_Base; + +/// The choice of the path +Switch::Switch(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ +} + +std::vector Switch::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + + if (operPrm.action == snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + baseOut_ = neighbOpr[0]->getOutput(); + } + else{ + + baseGrad_ = neighbOpr[0]->getGradient(); + + size_t sz = neighbOpr.size(); + for (size_t i = 1; i < sz; ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + } + + vector nw; + if (basePrms_.find("nextWay") != basePrms_.end()){ + nw = SN_Aux::split(basePrms_["nextWay"], " "); + } + + return nw; +} diff --git a/src/snOperator_cuDNN/src/Operator/switch.h b/src/snOperator_cuDNN/src/Operator/switch.h new file mode 100644 index 0000000..607d4c8 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/switch.h @@ -0,0 +1,41 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// The choice of the path +class Switch final : SN_Base::OperatorBase{ + +public: + + Switch(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~Switch() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +}; diff --git a/src/snOperator_cuDNN/src/Operator/userLayer.cpp b/src/snOperator_cuDNN/src/Operator/userLayer.cpp new file mode 100644 index 0000000..8f38af9 --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/userLayer.cpp @@ -0,0 +1,92 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperator/src/Operator/userLayer.h" + +using namespace std; +using namespace SN_Base; + +/// custom layer +UserLayer::UserLayer(void* net, const string& name, const string& node, std::map& prms) : +OperatorBase(net, name, node, prms){ + +} + +std::vector UserLayer::Do(const operationParam& opr, const std::vector& neighbOpr){ + + + if (basePrms_.find("cbackName") == basePrms_.end()){ + ERROR_MESS("not set param 'cbackName'"); + return std::vector < std::string > {"noWay"}; + } + + if (opr.action == SN_Base::snAction::forward){ + + if (neighbOpr.size() > 1){ + ERROR_MESS("neighbOpr.size() > 1"); + return std::vector < std::string > {"noWay"}; + } + + baseOut_ = neighbOpr[0]->getOutput(); + + snSize outSz; + snFloat* outData = nullptr; + + g_userCBack(this, basePrms_["cbackName"], node_, + true, baseOut_.size(), baseOut_.getData(), outSz, &outData); + + if (outData) + baseOut_.setData(outData, outSz); + else{ + ERROR_MESS("not set 'outData' in userCBack"); + } + } + else{ + + baseGrad_ = neighbOpr[0]->getGradient(); + for (size_t i = 1; i < neighbOpr.size(); ++i){ + + if (baseGrad_ != neighbOpr[i]->getGradient()){ + ERROR_MESS("operators size is not equals"); + return std::vector < std::string > {"noWay"}; + } + baseGrad_ += neighbOpr[i]->getGradient(); + } + + snSize outSz; + snFloat* outData = nullptr; + + g_userCBack(this, basePrms_["cbackName"], node_, + false, baseGrad_.size(), baseGrad_.getData(), outSz, &outData); + + if (outData) + baseGrad_.setData(outData, outSz); + else{ + ERROR_MESS("not set 'outData' in userCBack"); + } + } + + return std::vector(); +} diff --git a/src/snOperator_cuDNN/src/Operator/userLayer.h b/src/snOperator_cuDNN/src/Operator/userLayer.h new file mode 100644 index 0000000..23399dd --- /dev/null +++ b/src/snOperator_cuDNN/src/Operator/userLayer.h @@ -0,0 +1,41 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + + +/// custom layer +class UserLayer final : SN_Base::OperatorBase{ + +public: + + UserLayer(void* net, const std::string& name, const std::string& node, std::map& prms); + + ~UserLayer() = default; + + std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; + +}; diff --git a/src/snOperator_cuDNN/src/activeFunctions.cpp b/src/snOperator_cuDNN/src/activeFunctions.cpp new file mode 100644 index 0000000..386d25a --- /dev/null +++ b/src/snOperator_cuDNN/src/activeFunctions.cpp @@ -0,0 +1,117 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "stdafx.h" +#include "structurs.h" +#include "activeFunctions.h" + +using namespace std; +using namespace SN_Base; + +// fv - value, df - deriv + +void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType active){ + + switch (active){ + case activeType::sigmoid: fv_sigmoid(data, sz); break; + case activeType::relu: fv_relu(data, sz); break; + case activeType::leakyRelu: fv_leakyRelu(data, sz); break; + case activeType::elu: fv_elu(data, sz); break; + default: break; + } +} + +void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType active){ + + switch (active){ + case activeType::sigmoid: df_sigmoid(data, sz); break; + case activeType::relu: df_relu(data, sz); break; + case activeType::leakyRelu: df_leakyRelu(data, sz); break; + case activeType::elu: df_elu(data, sz); break; + default: break; + } +} + +void fv_sigmoid(snFloat* ioVal, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = 1.F / (1.F + std::exp(-ioVal[i])); + + ioVal[i] = (ioVal[i] < 0.99999F) ? ioVal[i] : 0.99999F; + ioVal[i] = (ioVal[i] > 0.00001F) ? ioVal[i] : 0.00001F; + } +} +void df_sigmoid(snFloat* ioSigm, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioSigm[i] = ioSigm[i] * (1.F - ioSigm[i]); + } +} + +void fv_relu(snFloat* ioVal, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : 0; + } +}; +void df_relu(snFloat* ioRelu, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioRelu[i] = ioRelu[i] >= 0 ? 1.F : 0.F; + } +}; + +void fv_leakyRelu(snFloat* ioVal, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * ioVal[i]; + } +} +void df_leakyRelu(snFloat* ioRelu, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioRelu[i] = ioRelu[i] >= 0 ? 1 : minv; + } +} + +void fv_elu(snFloat* ioVal, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * (exp(ioVal[i]) - 1.F); + } +} +void df_elu(snFloat* ioElu, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioElu[i] = ioElu[i] >= 0 ? 1 : ioElu[i] + minv; + } +} diff --git a/src/snOperator_cuDNN/src/activeFunctions.h b/src/snOperator_cuDNN/src/activeFunctions.h new file mode 100644 index 0000000..347c4aa --- /dev/null +++ b/src/snOperator_cuDNN/src/activeFunctions.h @@ -0,0 +1,46 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "stdafx.h" +#include "structurs.h" + +// fv - value, df - deriv + +void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType); + +void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType); + +void fv_sigmoid(SN_Base::snFloat* ioVal, size_t sz); +void df_sigmoid(SN_Base::snFloat* inSigm, size_t sz); + +void fv_relu(SN_Base::snFloat* ioVal, size_t sz); +void df_relu(SN_Base::snFloat* inRelu, size_t sz); + +void fv_leakyRelu(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat minv = 0.01F); +void df_leakyRelu(SN_Base::snFloat* inRelu, size_t sz, SN_Base::snFloat minv = 0.01F); + +void fv_elu(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat minv = 0.01F); +void df_elu(SN_Base::snFloat* inElu, size_t sz, SN_Base::snFloat minv = 0.01F); diff --git a/src/snOperator_cuDNN/src/mathFunctions.cpp b/src/snOperator_cuDNN/src/mathFunctions.cpp new file mode 100644 index 0000000..ef5bc24 --- /dev/null +++ b/src/snOperator_cuDNN/src/mathFunctions.cpp @@ -0,0 +1,253 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include "stdafx.h" +#include "Lib/OpenBLAS/cblas.h" +#include "snOperator/src/mathFunctions.h" +#include "snOperator/src/random.h" + +using namespace std; +using namespace SN_Base; + + +void channelBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ + + /* Select 1 output layer from each image in the batch and normalize */ + + size_t stepD = insz.w * insz.h, stepN = stepD * insz.d, bsz = insz.n; + + if (!isLern){ + + for (size_t i = 0; i < insz.d; ++i){ + + /// y = ^x * γ + β + for (size_t j = 0; j < bsz; ++j){ + + snFloat* cin = in + stepN * j + stepD * i, + *cout = out + stepN * j + stepD * i; + for (size_t k = 0; k < stepD; ++k) + cout[k] = (cin[k] - prm.mean[k]) * prm.scale[k] / prm.varce[k] + prm.schift[k]; + } + prm.offset(stepD); + } + } + else{ + + snFloat* share = (snFloat*)calloc(stepD * bsz, sizeof(snFloat)); + snSize sz(insz.w, insz.h, 1, insz.n); + + for (size_t i = 0; i < insz.d; ++i){ + + snFloat* pSh = share; + snFloat* pIn = in + stepD * i; + for (size_t j = 0; j < bsz; ++j){ + + memcpy(pSh, pIn, stepD * sizeof(snFloat)); + pSh += stepD; + pIn += stepN; + } + + if (fwBw) + batchNormForward(sz, share, share, prm); + else + batchNormBackward(sz, share, share, prm); + + pSh = share; + snFloat* pOut = out + stepD * i; + for (size_t j = 0; j < bsz; ++j){ + memcpy(pOut, pSh, stepD * sizeof(snFloat)); + pSh += stepD; + pOut += stepN; + } + + prm.offset(stepD); + prm.norm += stepD * bsz; + } + free(share); + } +} + +void layerBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, const batchNorm& prm){ + + if (!isLern){ + size_t sz = insz.w * insz.h * insz.d, bsz = insz.n; + + /// y = ^x * γ + β + for (size_t j = 0; j < bsz; ++j){ + + snFloat* cin = in + j * sz, *cout = out + j * sz; + for (size_t i = 0; i < sz; ++i) + cout[i] = (cin[i] - prm.mean[i]) * prm.scale[i] / prm.varce[i] + prm.schift[i]; + } + } + else{ // isLerning + if (fwBw) + batchNormForward(insz, in, out, prm); + else + batchNormBackward(insz, in, out, prm); + } +} + +void batchNormForward(const SN_Base::snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ + + size_t inSz = insz.w * insz.h * insz.d, bsz = insz.n; + + /// μ = 1/n * ∑x + cblas_sgemv(CBLAS_ORDER::CblasRowMajor, + CBLAS_TRANSPOSE::CblasTrans, + blasint(bsz), + blasint(inSz), + 1.F / bsz, + in, + blasint(inSz), + prm.onc, + blasint(1), + 0.F, + prm.mean, + blasint(1)); + + /// varce = sqrt(∑xx - mean^2 + e) + for (size_t i = 0; i < inSz; ++i){ + + snFloat* cin = in + i, srq = 0.F; + for (size_t j = 0; j < bsz; ++j){ + srq += cin[0] * cin[0]; + cin += inSz; + } + prm.varce[i] = sqrt(srq / bsz - prm.mean[i] * prm.mean[i] + 0.00001F); + } + + /// norm = (in - mean) / varce + /// y = ^x * γ + β + for (size_t j = 0; j < bsz; ++j){ + + snFloat* cin = in + j * inSz, *cout = out + j * inSz, *norm = prm.norm + j * inSz; + + for (size_t i = 0; i < inSz; ++i){ + norm[i] = (cin[i] - prm.mean[i]) / prm.varce[i]; + cout[i] = norm[i] * prm.scale[i] + prm.schift[i]; + } + } + +} + +void batchNormBackward(const SN_Base::snSize& insz, snFloat* gradIn, snFloat* gradOut, batchNorm prm){ + // https://kevinzakka.github.io/2016/09/14/batch_normalization/ + + size_t inSz = insz.w * insz.h * insz.d, bsz = insz.n; + + /// ∂f/∂β = ∑∂f/∂y + cblas_sgemv(CBLAS_ORDER::CblasRowMajor, + CBLAS_TRANSPOSE::CblasTrans, + blasint(bsz), + blasint(inSz), + 1.F, + gradIn, + blasint(inSz), + prm.onc, + blasint(1), + 0.F, + prm.dSchift, + blasint(1)); + + /// ∂f/∂γ = ∑∂f/∂y ⋅ ^x + for (size_t i = 0; i < inSz; ++i){ + + snFloat* igr = gradIn + i, *norm = prm.norm + i, dScale = 0.F; + for (size_t j = 0; j < bsz; ++j){ + + dScale += igr[0] * norm[0]; + + norm += inSz; + igr += inSz; + } + prm.dScale[i] = dScale; + } + + /// ∂f/∂x = (m⋅γ⋅∂f/∂y − γ⋅∂f/∂β − ^x⋅γ⋅∂f/∂γ) / m⋅σ2 + for (size_t j = 0; j < bsz; ++j){ + + snFloat* igr = gradIn + j * inSz, *ogr = gradOut + j * inSz, *norm = prm.norm + j * inSz; + for (size_t i = 0; i < inSz; ++i) + ogr[i] = prm.scale[i] * (igr[i] * bsz - prm.dSchift[i] - norm[i] * prm.dScale[i]) / (prm.varce[i] * bsz); + } + for (size_t i = 0; i < inSz; ++i){ + prm.schift[i] -= prm.dSchift[i] * prm.lr; + prm.scale[i] -= prm.dScale[i] * prm.lr; + } + +} + +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ + + if (isLern){ + size_t sz = size_t(outsz.size() * dropOut); + vector rnd(sz); + rnd_uniformInt(rnd.data(), sz, 0, int(outsz.size())); + + for (auto i : rnd) out[i] = 0; + } + else{ + size_t sz = outsz.size(); + for (size_t i = 0; i < sz; ++i) + out[i] *= (1.F - dropOut); + } +} + +void paddingOffs(bool in2out, size_t paddW, size_t paddH, const snSize& insz, snFloat* in, snFloat* out){ + + /// copy with offset padding for each image + size_t sz = insz.h * insz.d * insz.n, stW = insz.w, stH = insz.h; + if (in2out){ + in += (stW + paddW * 2) * paddH; + for (size_t i = 0; i < sz; ++i){ + + if ((i % stH == 0) && (i > 0)) + in += (stW + paddW * 2) * paddH * 2; + + in += paddW; + for (size_t j = 0; j < stW; ++j) + out[j] = in[j]; + in += paddW + stW; + + out += stW; + } + } + else{ + in += (stW + paddW * 2) * paddH; + for (size_t i = 0; i < sz; ++i){ + + if ((i % stH == 0) && (i > 0)) + in += (stW + paddW * 2) * paddH * 2; + + in += paddW; + for (size_t j = 0; j < stW; ++j) + in[j] = out[j]; + in += paddW + stW; + + out += stW; + } + } +} diff --git a/src/snOperator_cuDNN/src/mathFunctions.h b/src/snOperator_cuDNN/src/mathFunctions.h new file mode 100644 index 0000000..16eaadd --- /dev/null +++ b/src/snOperator_cuDNN/src/mathFunctions.h @@ -0,0 +1,45 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + +void channelBatchNorm(bool fwBw, bool isLern, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out, SN_Base::batchNorm prm); + +void layerBatchNorm(bool fwBw, bool isLern, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out, const SN_Base::batchNorm& prm); + +void batchNormForward(const SN_Base::snSize& insz, + SN_Base::snFloat* in, + SN_Base::snFloat* out, + SN_Base::batchNorm); + +void batchNormBackward(const SN_Base::snSize& insz, + SN_Base::snFloat* gradIn, + SN_Base::snFloat* gradOut, + SN_Base::batchNorm); + +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out); + +void paddingOffs(bool in2out, size_t paddingW, size_t paddingH, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out); \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/optimizer.cpp b/src/snOperator_cuDNN/src/optimizer.cpp new file mode 100644 index 0000000..dac070e --- /dev/null +++ b/src/snOperator_cuDNN/src/optimizer.cpp @@ -0,0 +1,90 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "stdafx.h" +#include "optimizer.h" +#include "structurs.h" + + +using namespace std; +using namespace SN_Base; + +void optimizer(snFloat* dWeight, snFloat* dWPrev, snFloat* dWGrad, snFloat* weight, size_t wsz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, optimizerType otype){ + + switch (otype){ + case optimizerType::sgd: opt_sgd(dWeight, weight, wsz, alpha, lambda); break; + case optimizerType::sgdMoment: opt_sgdMoment(dWeight, dWPrev, weight, wsz, alpha, lambda, mudW); break; + case optimizerType::RMSprop: opt_RMSprop(dWeight, dWGrad, weight, wsz, alpha, lambda, muGr); break; + case optimizerType::adagrad: opt_adagrad(dWeight, dWGrad, weight, wsz, alpha, lambda); break; + case optimizerType::adam: opt_adam(dWeight, dWPrev, dWGrad, weight, wsz, alpha, lambda, mudW, muGr); break; + default: break; + } +} + +/// adaptive gradient method +void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat eps){ + + for (size_t i = 0; i < sz; ++i){ + ioWGr[i] += dW[i] * dW[i]; + ioW[i] -= alpha * (dW[i] + ioW[i] * lambda) / (std::sqrt(ioWGr[i]) + eps); + } +} + +/// RMSprop +void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat mu, snFloat eps){ + + for (size_t i = 0; i < sz; ++i){ + ioWGr[i] = ioWGr[i] * mu + (1.F - mu) * dW[i] * dW[i]; + ioW[i] -= alpha * (dW[i] + ioW[i] * lambda) / std::sqrt(ioWGr[i] + eps); + } +} + +/// adam +void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, snFloat eps){ + + for (size_t i = 0; i < sz; ++i){ + + iodWPrev[i] = iodWPrev[i] * mudW - (1.F - mudW) * alpha * (dW[i] + ioW[i] * lambda); + + ioWGr[i] = ioWGr[i] * muGr + (1.F - muGr) * dW[i] * dW[i]; + + ioW[i] += iodWPrev[i] / std::sqrt(ioWGr[i] + eps); + } +} + +/// SGD without momentum +void opt_sgd(snFloat* dW, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda){ + for(size_t i = 0; i < sz; ++i){ + ioW[i] -= alpha * (dW[i] + lambda * ioW[i]); + } +} + +/// SGD with momentum +void opt_sgdMoment(snFloat* dW, snFloat* iodWPrev, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat mu){ + + for(size_t i = 0; i < sz; ++i){ + iodWPrev[i] = mu * iodWPrev[i] - alpha * (dW[i] + ioW[i] * lambda); + ioW[i] += iodWPrev[i]; + } +} \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/optimizer.h b/src/snOperator_cuDNN/src/optimizer.h new file mode 100644 index 0000000..4f00d84 --- /dev/null +++ b/src/snOperator_cuDNN/src/optimizer.h @@ -0,0 +1,55 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#pragma once + +#include "stdafx.h" +#include "structurs.h" + +void optimizer(SN_Base::snFloat* dWeight, + SN_Base::snFloat* dWPrev, + SN_Base::snFloat* dWGrad, + SN_Base::snFloat* weight, + size_t wsz, + SN_Base::snFloat alpha, + SN_Base::snFloat lambda, + SN_Base::snFloat mudW, + SN_Base::snFloat muGr, + optimizerType otype); + +/// adaptive gradient method +void opt_adagrad(SN_Base::snFloat* dW, SN_Base::snFloat* ioWGr, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat eps = 1e-8F); + +/// RMSprop +void opt_RMSprop(SN_Base::snFloat* dW, SN_Base::snFloat* ioWGr, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat mu = 0.9F, SN_Base::snFloat eps = 1e-8F); + +/// adam +void opt_adam(SN_Base::snFloat* dW, SN_Base::snFloat* iodWPrev, SN_Base::snFloat* ioWGr, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat muWd = 0.9F, SN_Base::snFloat muGr = 0.9F, SN_Base::snFloat eps = 1e-8F); + +/// SGD +void opt_sgd(SN_Base::snFloat* dW, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F); + +/// SGD with momentum +void opt_sgdMoment(SN_Base::snFloat* dW, SN_Base::snFloat* iodWPrev, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.01F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat mu = 0.9F); diff --git a/src/snOperator_cuDNN/src/random.cpp b/src/snOperator_cuDNN/src/random.cpp new file mode 100644 index 0000000..aee087e --- /dev/null +++ b/src/snOperator_cuDNN/src/random.cpp @@ -0,0 +1,56 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include +#include +#include "snBase/snBase.h" + +using namespace std; +using namespace SN_Base; + + +void rnd_uniformReal(snFloat* ioVal, size_t sz, snFloat min, snFloat max) { + std::uniform_real_distribution dst(min, max); + + std::mt19937 rnd_generator(clock()); + for (size_t i = 0; i < sz; ++i) + ioVal[i] = dst(rnd_generator); +} + +void rnd_uniformInt(int* ioVal, size_t sz, int min, int max) { + std::uniform_int_distribution dst(min, max); + + std::mt19937 rnd_generator(clock()); + for (size_t i = 0; i < sz; ++i) + ioVal[i] = dst(rnd_generator); +} + +void rnd_gaussian(snFloat* ioVal, size_t sz, snFloat mean, snFloat sigma) { + + std::mt19937 rnd_generator(clock()); + std::normal_distribution dst(mean, sigma); + for (size_t i = 0; i < sz; ++i) + ioVal[i] = dst(rnd_generator); +} diff --git a/src/snOperator_cuDNN/src/random.h b/src/snOperator_cuDNN/src/random.h new file mode 100644 index 0000000..8a8c674 --- /dev/null +++ b/src/snOperator_cuDNN/src/random.h @@ -0,0 +1,35 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include +#include +#include "snBase/snBase.h" + +void rnd_uniformReal(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat min, SN_Base::snFloat max); + +void rnd_uniformInt(int* ioVal, size_t sz, int min, int max); + +void rnd_gaussian(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat mean, SN_Base::snFloat sigma); diff --git a/src/snOperator_cuDNN/src/snOperator.cpp b/src/snOperator_cuDNN/src/snOperator.cpp new file mode 100644 index 0000000..c43fd23 --- /dev/null +++ b/src/snOperator_cuDNN/src/snOperator.cpp @@ -0,0 +1,92 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "snOperator/snOperator.h" +#include "snBase/snBase.h" +#include "Operator/fullyConnected.h" +#include "Operator/convolution.h" +#include "Operator/deconvolution.h" +#include "Operator/pooling.h" +#include "Operator/input.h" +#include "Operator/output.h" +#include "Operator/lossFunction.h" +#include "Operator/lock.h" +#include "Operator/summator.h" +#include "Operator/userLayer.h" +#include "Operator/switch.h" +#include "Operator/crop.h" +#include "Operator/concat.h" +#include "Operator/resize.h" +#include "Operator/batchNorm.h" +#include "Operator/activation.h" + +namespace SN_Opr{ + + SN_Base::OperatorBase* createOperator(void* net, const std::string& fname, const std::string& node, + std::map& prms){ + + SN_Base::OperatorBase* ret = nullptr; + + if (fname == "Input") ret = (SN_Base::OperatorBase*)new Input(net, fname, node, prms); + else if (fname == "Output") ret = (SN_Base::OperatorBase*)new Output(net, fname, node, prms); + else if (fname == "FullyConnected") ret = (SN_Base::OperatorBase*)new FullyConnected(net, fname, node, prms); + else if (fname == "LossFunction") ret = (SN_Base::OperatorBase*)new LossFunction(net, fname, node, prms); + else if (fname == "Convolution") ret = (SN_Base::OperatorBase*)new Convolution(net, fname, node, prms); + else if (fname == "Deconvolution") ret = (SN_Base::OperatorBase*)new Deconvolution(net, fname, node, prms); + else if (fname == "Pooling") ret = (SN_Base::OperatorBase*)new Pooling(net, fname, node, prms); + else if (fname == "Lock") ret = (SN_Base::OperatorBase*)new Lock(net, fname, node, prms); + else if (fname == "Summator") ret = (SN_Base::OperatorBase*)new Summator(net, fname, node, prms); + else if (fname == "Switch") ret = (SN_Base::OperatorBase*)new Switch(net, fname, node, prms); + else if (fname == "UserLayer") ret = (SN_Base::OperatorBase*)new UserLayer(net, fname, node, prms); + else if (fname == "Crop") ret = (SN_Base::OperatorBase*)new Crop(net, fname, node, prms); + else if (fname == "Concat") ret = (SN_Base::OperatorBase*)new Concat(net, fname, node, prms); + else if (fname == "Resize") ret = (SN_Base::OperatorBase*)new Resize(net, fname, node, prms); + else if (fname == "BatchNorm") ret = (SN_Base::OperatorBase*)new BatchNorm(net, fname, node, prms); + else if (fname == "Activation") ret = (SN_Base::OperatorBase*)new Activation(net, fname, node, prms); + + return ret; + } + + void freeOperator(SN_Base::OperatorBase* opr, const std::string& fname){ + + if (opr){ + if (fname == "Input") delete (Input*)opr; + else if (fname == "Output") delete (Output*)opr; + else if (fname == "FullyConnected") delete (FullyConnected*)opr; + else if (fname == "LossFunction") delete (LossFunction*)opr; + else if (fname == "Convolution") delete (Convolution*)opr; + else if (fname == "Deconvolution") delete (Deconvolution*)opr; + else if (fname == "Pooling") delete (Pooling*)opr; + else if (fname == "Lock") delete (Lock*)opr; + else if (fname == "Summator") delete (Summator*)opr; + else if (fname == "Switch") delete (Switch*)opr; + else if (fname == "UserLayer") delete (UserLayer*)opr; + else if (fname == "Crop") delete (Crop*)opr; + else if (fname == "Concat") delete (Concat*)opr; + else if (fname == "Resize") delete (Resize*)opr; + else if (fname == "BatchNorm") delete (BatchNorm*)opr; + else if (fname == "Activation") delete (Activation*)opr; + } + } +} diff --git a/src/snOperator_cuDNN/src/stdafx.h b/src/snOperator_cuDNN/src/stdafx.h new file mode 100644 index 0000000..90af050 --- /dev/null +++ b/src/snOperator_cuDNN/src/stdafx.h @@ -0,0 +1,44 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#pragma once + +#include +#include +#include +#include +#include +#include "snBase/snBase.h" +#include "snOperator/snOperator.h" + +#define PROFILE_START double ctm = omp_get_wtime(); +#define PROFILE_END(func) g_statusMess(this, name_ + " " + node_ + " " + func + " " + std::to_string(omp_get_wtime() - ctm)); ctm = omp_get_wtime(); + +#define ERROR_MESS(mess) g_statusMess(this, name_ + " '" + node_ + "' error: " + mess); + +void g_statusMess(SN_Base::OperatorBase* opr, const std::string& mess); + +void g_userCBack(SN_Base::OperatorBase* opr, const std::string& cbname, const std::string& node, + bool fwBw, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snSize& outsz, SN_Base::snFloat** out); diff --git a/src/snOperator_cuDNN/src/structurs.h b/src/snOperator_cuDNN/src/structurs.h new file mode 100644 index 0000000..897eec2 --- /dev/null +++ b/src/snOperator_cuDNN/src/structurs.h @@ -0,0 +1,74 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + +/// тип ф-ии активации +enum class activeType{ + none = -1, + sigmoid = 0, + relu = 1, + leakyRelu = 2, + elu = 3, +}; + +/// тип инициализации весов +enum class weightInitType{ + uniform = 0, + he = 1, + lecun = 2, + xavier = 3, +}; + +/// тип оптимизации весов +enum class optimizerType{ + sgd = 0, + sgdMoment = 1, + adagrad = 2, + RMSprop = 3, + adam = 4, +}; + +/// batchNorm +enum class batchNormType{ + none = -1, + beforeActive = 0, + postActive = 1, + byChannels = 2, + byLayer = 3, +}; + +/// pooling +enum class poolType{ + max = 0, + avg = 1, +}; + +enum class calcMode{ + CPU = 0, + CUDA = 1, + OpenCL = 2, +}; diff --git a/src/snOperator_cuDNN/src/weightInit.cpp b/src/snOperator_cuDNN/src/weightInit.cpp new file mode 100644 index 0000000..b4acead --- /dev/null +++ b/src/snOperator_cuDNN/src/weightInit.cpp @@ -0,0 +1,69 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "snBase/snBase.h" +#include "random.h" +#include "structurs.h" +#include "weightInit.h" + +using namespace std; +using namespace SN_Base; + +// инициализация весов + +void weightInit(snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype){ + + switch (wtype){ + case weightInitType::uniform: wi_uniform(ioW, sz); break; + case weightInitType::he: wi_he(ioW, sz, fan_in); break; + case weightInitType::lecun:wi_lecun(ioW, sz, fan_out); break; + case weightInitType::xavier:wi_xavier(ioW, sz, fan_in, fan_out); break; + } +} + +void wi_uniform(snFloat* ioW, size_t sz){ + + rnd_uniformReal(ioW, sz, -1.F, 1.F); +}; + +void wi_xavier(snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out){ + snFloat wbase = std::sqrt(6.F / (fan_in + fan_out)); + + rnd_uniformReal(ioW, sz, -wbase, wbase); +}; + +void wi_lecun(snFloat* ioW, size_t sz, size_t fan_out){ + + snFloat wbase = 1.F / std::sqrt(snFloat(fan_out)); + + rnd_uniformReal(ioW, sz, -wbase, wbase); +} + +void wi_he(snFloat* ioW, size_t sz, size_t fan_in){ + + snFloat sigma = std::sqrt(2.F / fan_in); + + rnd_gaussian(ioW, sz, 0.0F, sigma); +} + diff --git a/src/snOperator_cuDNN/src/weightInit.h b/src/snOperator_cuDNN/src/weightInit.h new file mode 100644 index 0000000..2c05375 --- /dev/null +++ b/src/snOperator_cuDNN/src/weightInit.h @@ -0,0 +1,44 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +// инициализация весов + +#include "snBase/snBase.h" +#include "structurs.h" +#include "random.h" + +void weightInit(SN_Base::snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype); + +void wi_uniform(SN_Base::snFloat* ioW, size_t sz); + +// 2010 Xavier Glorot +void wi_xavier(SN_Base::snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out); + +// 1998 Yann LeCun +void wi_lecun(SN_Base::snFloat* ioW, size_t sz, size_t fan_out); + +// 2015 Kaiming He +void wi_he(SN_Base::snFloat* ioW, size_t sz, size_t fan_in); From ff7db67ebd57b31db3df67023c1919f2c141a1b2 Mon Sep 17 00:00:00 2001 From: Tyill Date: Tue, 16 Jul 2019 11:52:10 +0500 Subject: [PATCH 02/35] -develop --- mswin/VS12/skyNet.sln | 61 +- mswin/VS12/skyNet.vcxproj | 4 +- mswin/VS12/snOperator.vcxproj.filters | 197 ---- mswin/VS12/snOperatorCPU.vcxproj | 407 ++++++++ mswin/VS12/snOperatorCPU.vcxproj.filters | 180 ++++ ...perator.vcxproj => snOperatorCUDA.vcxproj} | 123 ++- mswin/VS12/snOperatorCUDA.vcxproj.filters | 182 ++++ src/CMakeLists.txt | 46 +- src/skynet/CMakeLists.txt | 8 +- src/skynet/src/snet.cpp | 1 + src/snBase/snBase.h | 95 +- src/snEngine/snEngine.h | 1 - src/snEngine/src/snEngine.cpp | 2 - src/snExample/main.cpp | 190 ++-- .../CMakeLists.txt | 15 - .../snOperator.h | 0 .../src/CPU/convolutionCPU.cpp | 66 +- .../src/CPU/deconvolutionCPU.cpp | 57 +- .../src/CPU/fullyConnectedCPU.cpp | 56 +- .../src/CPU/poolingCPU.cpp | 50 +- src/snOperatorCPU/src/CPU/tensor.cpp | 100 ++ .../src/Operator/activation.cpp | 4 +- .../src/Operator/activation.h | 2 +- .../src/Operator/batchNorm.cpp | 4 +- .../src/Operator/batchNorm.h | 2 +- .../src/Operator/concat.cpp | 2 +- .../src/Operator/concat.h | 0 .../src/Operator/convolution.cpp | 79 +- .../src/Operator/convolution.h | 130 +-- .../src/Operator/crop.cpp | 2 +- .../src/Operator/crop.h | 0 .../src/Operator/deconvolution.cpp | 78 +- .../src/Operator/deconvolution.h | 86 +- .../src/Operator/fullyConnected.cpp | 76 +- .../src/Operator/fullyConnected.h | 84 +- .../src/Operator/input.cpp | 2 +- .../src/Operator/input.h | 0 .../src/Operator/lock.cpp | 2 +- .../src/Operator/lock.h | 0 .../src/Operator/lossFunction.cpp | 4 +- .../src/Operator/lossFunction.h | 0 .../src/Operator/output.cpp | 2 +- .../src/Operator/output.h | 0 .../src/Operator/pooling.cpp | 52 +- .../src/Operator/pooling.h | 69 +- .../src/Operator/resize.cpp | 2 +- .../src/Operator/resize.h | 0 .../src/Operator/summator.cpp | 2 +- .../src/Operator/summator.h | 0 .../src/Operator/switch.cpp | 2 +- .../src/Operator/switch.h | 0 .../src/Operator/userLayer.cpp | 2 +- .../src/Operator/userLayer.h | 0 .../src/activeFunctions.cpp | 0 .../src/activeFunctions.h | 0 .../src/mathFunctions.cpp | 4 +- .../src/mathFunctions.h | 0 .../src/optimizer.cpp | 0 .../src/optimizer.h | 0 .../src/random.cpp | 0 .../src/random.h | 0 .../src/snOperator.cpp | 2 +- .../src/stdafx.h | 2 +- .../src/structurs.h | 0 .../src/weightInit.cpp | 0 .../src/weightInit.h | 0 .../CMakeLists.txt | 0 .../snOperator.h | 0 .../src/CUDA/convolutionCUDA.cu | 811 +++++++++++++++ .../src/CUDA/deconvolutionCUDA.cu | 795 +++++++++++++++ .../src/CUDA/fullyConnectedCUDA.cu | 168 +--- src/snOperatorCUDA/src/CUDA/poolingCUDA.cu | 510 ++++++++++ src/snOperatorCUDA/src/CUDA/tensor.cu | 128 +++ .../src/Operator/activation.cpp | 4 +- .../src/Operator/activation.h | 2 +- .../src/Operator/batchNorm.cpp | 4 +- .../src/Operator/batchNorm.h | 2 +- .../src/Operator/concat.cpp | 2 +- .../src/Operator/concat.h | 0 .../src/Operator/convolution.cpp | 76 +- .../src/Operator/convolution.h | 82 +- .../src/Operator/crop.cpp | 2 +- .../src/Operator/crop.h | 0 .../src/Operator/deconvolution.cpp | 75 +- .../src/Operator/deconvolution.h | 80 +- .../src/Operator/fullyConnected.cpp | 75 +- .../src/Operator/fullyConnected.h | 71 +- .../src/Operator/input.cpp | 2 +- .../src/Operator/input.h | 0 .../src/Operator/lock.cpp | 2 +- .../src/Operator/lock.h | 0 .../src/Operator/lossFunction.cpp | 4 +- .../src/Operator/lossFunction.h | 0 .../src/Operator/output.cpp | 2 +- .../src/Operator/output.h | 0 .../src/Operator/pooling.cpp | 45 +- .../src/Operator/pooling.h | 61 +- .../src/Operator/resize.cpp | 2 +- .../src/Operator/resize.h | 0 .../src/Operator/summator.cpp | 2 +- .../src/Operator/summator.h | 0 .../src/Operator/switch.cpp | 2 +- .../src/Operator/switch.h | 0 .../src/Operator/userLayer.cpp | 2 +- .../src/Operator/userLayer.h | 0 .../src/activeFunctions.cpp | 0 .../src/activeFunctions.h | 0 .../src/mathFunctions.cpp | 4 +- .../src/mathFunctions.h | 0 .../src/optimizer.cpp | 0 .../src/optimizer.h | 0 .../src/random.cpp | 0 .../src/random.h | 0 .../src/snOperator.cpp | 2 +- .../src/stdafx.h | 2 +- .../src/structurs.h | 0 .../src/weightInit.cpp | 0 .../src/weightInit.h | 0 .../src/CUDA/convolutionCUDA.cu | 929 ------------------ .../src/CUDA/deconvolutionCUDA.cu | 915 ----------------- .../src/CUDA/poolingCUDA.cu | 594 ----------- .../src/CUDA/convolutionCUDA.cu | 929 ------------------ .../src/CUDA/deconvolutionCUDA.cu | 915 ----------------- .../src/CUDA/fullyConnectedCUDA.cu | 339 ------- src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu | 594 ----------- 125 files changed, 3668 insertions(+), 7103 deletions(-) delete mode 100644 mswin/VS12/snOperator.vcxproj.filters create mode 100644 mswin/VS12/snOperatorCPU.vcxproj create mode 100644 mswin/VS12/snOperatorCPU.vcxproj.filters rename mswin/VS12/{snOperator.vcxproj => snOperatorCUDA.vcxproj} (78%) create mode 100644 mswin/VS12/snOperatorCUDA.vcxproj.filters rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/CMakeLists.txt (54%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/snOperator.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/CPU/convolutionCPU.cpp (84%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/CPU/deconvolutionCPU.cpp (83%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/CPU/fullyConnectedCPU.cpp (77%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/CPU/poolingCPU.cpp (84%) create mode 100644 src/snOperatorCPU/src/CPU/tensor.cpp rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/activation.cpp (96%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/activation.h (97%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/batchNorm.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/batchNorm.h (97%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/concat.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/concat.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/convolution.cpp (84%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/convolution.h (55%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/crop.cpp (99%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/crop.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/deconvolution.cpp (82%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/deconvolution.h (59%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/fullyConnected.cpp (81%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/fullyConnected.h (60%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/input.cpp (97%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/input.h (100%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/lock.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/lock.h (100%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/lossFunction.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/lossFunction.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/output.cpp (97%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/output.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/pooling.cpp (75%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/pooling.h (60%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/resize.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/resize.h (100%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/summator.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/summator.h (100%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/Operator/switch.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/switch.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/userLayer.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/Operator/userLayer.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/activeFunctions.cpp (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/activeFunctions.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/mathFunctions.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/mathFunctions.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/optimizer.cpp (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/optimizer.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/random.cpp (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/random.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/snOperator.cpp (99%) rename src/{snOperator_cuDNN => snOperatorCPU}/src/stdafx.h (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/structurs.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/weightInit.cpp (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCPU}/src/weightInit.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/CMakeLists.txt (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/snOperator.h (100%) create mode 100644 src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu create mode 100644 src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/CUDA/fullyConnectedCUDA.cu (60%) create mode 100644 src/snOperatorCUDA/src/CUDA/poolingCUDA.cu create mode 100644 src/snOperatorCUDA/src/CUDA/tensor.cu rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/activation.cpp (96%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/activation.h (97%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/batchNorm.cpp (98%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/batchNorm.h (97%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/concat.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/concat.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/convolution.cpp (85%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/convolution.h (66%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/crop.cpp (99%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/crop.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/deconvolution.cpp (83%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/deconvolution.h (64%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/fullyConnected.cpp (82%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/fullyConnected.h (65%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/input.cpp (97%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/input.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/lock.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/lock.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/lossFunction.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/lossFunction.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/output.cpp (97%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/output.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/pooling.cpp (77%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/pooling.h (68%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/resize.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/resize.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/summator.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/summator.h (100%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/Operator/switch.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/switch.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/userLayer.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/Operator/userLayer.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/activeFunctions.cpp (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/activeFunctions.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/mathFunctions.cpp (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/mathFunctions.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/optimizer.cpp (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/optimizer.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/random.cpp (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/random.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/snOperator.cpp (99%) rename src/{snOperator_CPU_cuDNN => snOperatorCUDA}/src/stdafx.h (98%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/structurs.h (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/weightInit.cpp (100%) rename src/{snOperator_cuDNN => snOperatorCUDA}/src/weightInit.h (100%) delete mode 100644 src/snOperator_CPU_cuDNN/src/CUDA/convolutionCUDA.cu delete mode 100644 src/snOperator_CPU_cuDNN/src/CUDA/deconvolutionCUDA.cu delete mode 100644 src/snOperator_CPU_cuDNN/src/CUDA/poolingCUDA.cu delete mode 100644 src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu delete mode 100644 src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu delete mode 100644 src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu delete mode 100644 src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu diff --git a/mswin/VS12/skyNet.sln b/mswin/VS12/skyNet.sln index fa031f1..19f3d1f 100644 --- a/mswin/VS12/skyNet.sln +++ b/mswin/VS12/skyNet.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio 2013 -VisualStudioVersion = 12.0.31101.0 +VisualStudioVersion = 12.0.40629.0 MinimumVisualStudioVersion = 10.0.40219.1 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snEngine", "SNEngine.vcxproj", "{87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}" ProjectSection(ProjectDependencies) = postProject @@ -24,13 +24,6 @@ Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snExample", "SNExample.vcxp {342D19FA-7A64-4009-A9AD-BE781192038E} = {342D19FA-7A64-4009-A9AD-BE781192038E} EndProjectSection EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snOperator", "SNOperator.vcxproj", "{F3E981BD-4BA4-4538-9D21-6ACD252F4895}" - ProjectSection(ProjectDependencies) = postProject - {C993F645-29F0-4079-ACBA-033BFED8B8F6} = {C993F645-29F0-4079-ACBA-033BFED8B8F6} - {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6} = {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6} - {342D19FA-7A64-4009-A9AD-BE781192038E} = {342D19FA-7A64-4009-A9AD-BE781192038E} - EndProjectSection -EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snAux", "SNAux.vcxproj", "{C993F645-29F0-4079-ACBA-033BFED8B8F6}" EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "skynet", "SkyNet.vcxproj", "{507A9B3C-E5E6-4CEC-A99D-729D2171DF81}" @@ -45,43 +38,87 @@ Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "snSharpClient", "snSharpCli EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snSIMD", "snSIMD.vcxproj", "{30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}" EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snOperatorCPU", "snOperatorCPU.vcxproj", "{F3E981BD-4BA4-4538-9D21-6ACD252F4895}" +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snOperatorCUDA", "snOperatorCUDA.vcxproj", "{2C7FA033-361B-4429-97E9-64F2A0A20CBE}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution + Debug|Win32 = Debug|Win32 Debug|x64 = Debug|x64 + Release|Win32 = Release|Win32 Release|x64 = Release|x64 EndGlobalSection GlobalSection(ProjectConfigurationPlatforms) = postSolution + {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|Win32.ActiveCfg = Debug|Win32 + {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|Win32.Build.0 = Debug|Win32 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|x64.ActiveCfg = Debug|x64 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|x64.Build.0 = Debug|x64 + {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|Win32.ActiveCfg = Release|Win32 + {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|Win32.Build.0 = Release|Win32 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|x64.ActiveCfg = Release|x64 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|x64.Build.0 = Release|x64 + {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|Win32.ActiveCfg = Debug|Win32 + {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|Win32.Build.0 = Debug|Win32 {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|x64.ActiveCfg = Debug|x64 {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|x64.Build.0 = Debug|x64 + {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|Win32.ActiveCfg = Release|Win32 + {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|Win32.Build.0 = Release|Win32 {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|x64.ActiveCfg = Release|x64 {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|x64.Build.0 = Release|x64 + {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|Win32.ActiveCfg = Debug|Win32 + {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|Win32.Build.0 = Debug|Win32 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|x64.ActiveCfg = Debug|x64 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|x64.Build.0 = Debug|x64 + {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|Win32.ActiveCfg = Release|Win32 + {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|Win32.Build.0 = Release|Win32 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|x64.ActiveCfg = Release|x64 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|x64.Build.0 = Release|x64 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|x64.ActiveCfg = Debug|x64 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|x64.Build.0 = Debug|x64 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|x64.ActiveCfg = Release|x64 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|x64.Build.0 = Release|x64 + {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|Win32.ActiveCfg = Debug|Win32 + {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|Win32.Build.0 = Debug|Win32 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|x64.ActiveCfg = Debug|x64 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|x64.Build.0 = Debug|x64 + {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|Win32.ActiveCfg = Release|Win32 + {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|Win32.Build.0 = Release|Win32 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|x64.ActiveCfg = Release|x64 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|x64.Build.0 = Release|x64 + {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|Win32.ActiveCfg = Debug|Win32 + {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|Win32.Build.0 = Debug|Win32 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|x64.ActiveCfg = Debug|x64 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|x64.Build.0 = Debug|x64 + {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|Win32.ActiveCfg = Release|Win32 + {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|Win32.Build.0 = Release|Win32 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|x64.ActiveCfg = Release|x64 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|x64.Build.0 = Release|x64 + {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Debug|Win32.ActiveCfg = Debug|Any CPU {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Debug|x64.ActiveCfg = Debug|Any CPU {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Debug|x64.Build.0 = Debug|Any CPU + {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Release|Win32.ActiveCfg = Release|Any CPU {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Release|x64.ActiveCfg = Release|Any CPU + {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|Win32.ActiveCfg = Debug|Win32 + {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|Win32.Build.0 = Debug|Win32 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|x64.ActiveCfg = Debug|x64 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|x64.Build.0 = Debug|x64 + {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|Win32.ActiveCfg = Release|Win32 + {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|Win32.Build.0 = Release|Win32 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|x64.ActiveCfg = Release|x64 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|x64.Build.0 = Release|x64 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|Win32.ActiveCfg = Debug|Win32 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|Win32.Build.0 = Debug|Win32 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|x64.ActiveCfg = Debug|x64 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|x64.Build.0 = Debug|x64 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|Win32.ActiveCfg = Release|Win32 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|Win32.Build.0 = Release|Win32 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|x64.ActiveCfg = Release|x64 + {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|x64.Build.0 = Release|x64 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|Win32.ActiveCfg = Debug|Win32 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|Win32.Build.0 = Debug|Win32 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|x64.ActiveCfg = Debug|x64 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|x64.Build.0 = Debug|x64 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|Win32.ActiveCfg = Release|Win32 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|Win32.Build.0 = Release|Win32 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|x64.ActiveCfg = Release|x64 + {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|x64.Build.0 = Release|x64 EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/mswin/VS12/skyNet.vcxproj b/mswin/VS12/skyNet.vcxproj index 9d53674..cd9b039 100644 --- a/mswin/VS12/skyNet.vcxproj +++ b/mswin/VS12/skyNet.vcxproj @@ -133,7 +133,7 @@ Windows true $(OutDir) - snOperator.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) @@ -175,7 +175,7 @@ true true $(OutDir) - snOperator.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) diff --git a/mswin/VS12/snOperator.vcxproj.filters b/mswin/VS12/snOperator.vcxproj.filters deleted file mode 100644 index 157ddec..0000000 --- a/mswin/VS12/snOperator.vcxproj.filters +++ /dev/null @@ -1,197 +0,0 @@ - - - - - {4FC737F1-C7A5-4376-A066-2A32D752A2FF} - cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx - - - {93995380-89BD-4b04-88EB-625FBE52EBFB} - h;hh;hpp;hxx;hm;inl;inc;xsd - - - {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} - rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms - - - {00f476b6-2a3d-4e67-b892-078ed803aad8} - - - {6246e236-99f7-45f9-9657-b848ba19ed5f} - - - {45042fac-ebea-43c9-8dfe-21144c5116cc} - - - {2b7c4e93-c6f2-4c9f-a972-ba3698c3306f} - - - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - - - CPU - - - CPU - - - CPU - - - CPU - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Operator - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Operator - - - Operator - - - - - CUDA - - - CUDA - - - CUDA - - - CUDA - - - \ No newline at end of file diff --git a/mswin/VS12/snOperatorCPU.vcxproj b/mswin/VS12/snOperatorCPU.vcxproj new file mode 100644 index 0000000..77a48d0 --- /dev/null +++ b/mswin/VS12/snOperatorCPU.vcxproj @@ -0,0 +1,407 @@ + + + + + Debug + Win32 + + + Debug + x64 + + + Release + Win32 + + + Release + x64 + + + + {F3E981BD-4BA4-4538-9D21-6ACD252F4895} + Win32Proj + SNOperatorCPU + snOperatorCPU + + + + StaticLibrary + true + v120 + Unicode + + + StaticLibrary + true + v120 + Unicode + + + StaticLibrary + false + v120 + true + Unicode + + + StaticLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + + + + + true + $(Platform)\$(Configuration)\$(ProjectName)\ + $(SolutionDir)$(Configuration)\$(Platform)\ + .lib + + + true + $(SolutionDir)$(Configuration)\$(Platform)\ + .lib + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + $(Platform)\$(Configuration)\$(ProjectName)\ + $(SolutionDir)$(Configuration)\$(Platform)\ + .lib + + + false + $(SolutionDir)$(Configuration)\$(Platform)\ + .lib + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + NotUsing + Level3 + Disabled + WIN32;SN_DEBUG; SN_AVX; SN_CUDA;SN_CUDNN;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src + + + true + + + Windows + true + SNAux.lib;%(AdditionalDependencies) + $(OutDir);%(AdditionalLibraryDirectories) + + + $(IntDir)%(Filename)%(Extension).obj + true + false + $(CudaIntDir) + false + compile + Static + 32 + false + compute_30,sm_30 + false + 0 + false + true + false + + + false + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + + + + + true + $(IntDir)$(TargetName).device-link.obj + true + true + InheritFromProject + InheritFromProject + + + %(AdditionalLibraryDirectories);$(OutDir); + libopenblas.lib; + + + + + + + + + NotUsing + Level3 + Disabled + WIN32;SN_DEBUG;SN_AVX;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src + + + true + true + /arch:AVX + + + Windows + true + libopenblas.dll.a;SNAux.lib;%(AdditionalDependencies) + $(OutDir);%(AdditionalLibraryDirectories) + + + 64 + $(IntDir)%(Filename)%(Extension).obj + true + false + $(CudaIntDir) + false + compile + Static + false + compute_30,sm_30 + false + 0 + false + true + false + + + false + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + + + + + + + %(AdditionalLibraryDirectories);$(OutDir); + libopenblas.lib; snSimd.lib + + + true + $(IntDir)$(TargetName).device-link.obj + true + true + InheritFromProject + InheritFromProject + + + + + + + + + + + Level3 + NotUsing + MaxSpeed + true + true + WIN32;SN_DEBUG; SN_AVX; SN_CUDA;SN_CUDNN;NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src + + + true + + + Windows + true + true + true + SNAux.lib;%(AdditionalDependencies) + $(OutDir);%(AdditionalLibraryDirectories) + + + $(IntDir)%(Filename)%(Extension).obj + true + false + $(CudaIntDir) + false + compile + Static + 32 + false + compute_30,sm_30 + false + 0 + false + true + false + + + false + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + + + + + true + $(IntDir)$(TargetName).device-link.obj + true + true + InheritFromProject + InheritFromProject + + + %(AdditionalLibraryDirectories);$(OutDir); + libopenblas.lib; + + + + + + + + + Level3 + NotUsing + MaxSpeed + true + true + WIN32;SN_DEBUG;SN_AVX; NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src + + + true + /arch:AVX + + + Windows + true + true + true + libopenblas.dll.a;SNAux.lib;%(AdditionalDependencies) + $(OutDir);%(AdditionalLibraryDirectories) + + + 64 + $(IntDir)%(Filename)%(Extension).obj + true + false + $(CudaIntDir) + false + compile + Static + false + compute_30,sm_30 + false + 0 + false + true + false + + + false + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + + + + + %(AdditionalLibraryDirectories);$(OutDir); + libopenblas.lib; snSimd.lib + + + true + $(IntDir)$(TargetName).device-link.obj + true + true + InheritFromProject + InheritFromProject + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/mswin/VS12/snOperatorCPU.vcxproj.filters b/mswin/VS12/snOperatorCPU.vcxproj.filters new file mode 100644 index 0000000..2dcbcbc --- /dev/null +++ b/mswin/VS12/snOperatorCPU.vcxproj.filters @@ -0,0 +1,180 @@ + + + + + {4FC737F1-C7A5-4376-A066-2A32D752A2FF} + cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx + + + {93995380-89BD-4b04-88EB-625FBE52EBFB} + h;hh;hpp;hxx;hm;inl;inc;xsd + + + {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} + rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms + + + {45042fac-ebea-43c9-8dfe-21144c5116cc} + + + {2b7c4e93-c6f2-4c9f-a972-ba3698c3306f} + + + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + CPU + + + CPU + + + CPU + + + CPU + + + CPU + + + \ No newline at end of file diff --git a/mswin/VS12/snOperator.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj similarity index 78% rename from mswin/VS12/snOperator.vcxproj rename to mswin/VS12/snOperatorCUDA.vcxproj index 4c07468..8b62bb7 100644 --- a/mswin/VS12/snOperator.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -19,10 +19,10 @@ - {F3E981BD-4BA4-4538-9D21-6ACD252F4895} + {2C7FA033-361B-4429-97E9-64F2A0A20CBE} Win32Proj - SNOperator - snOperator + snOperatorCUDA + snOperatorCUDA @@ -146,7 +146,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cublas.lib;cudart_static.lib;cudnn.lib;libopenblas.lib; + cublas.lib;cudart_static.lib;cudnn.lib; @@ -158,7 +158,7 @@ NotUsing Level3 Disabled - WIN32;SN_DEBUG;SN_AVX;SN_CUDA; SN_CUDNN;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + WIN32;SN_DEBUG;SN_AVX;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src @@ -203,7 +203,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - libopenblas.lib; cublas.lib;cudart_static.lib;cudnn.lib;snSimd.lib + cublas.lib;cudart_static.lib;cudnn.lib; true @@ -278,7 +278,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cublas.lib;cudart_static.lib;cudnn.lib;libopenblas.lib; + cublas.lib;cudart_static.lib;cudnn.lib; @@ -292,7 +292,7 @@ MaxSpeed true true - WIN32;SN_DEBUG;SN_AVX;SN_CUDA; SN_CUDNN; NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + WIN32;SN_DEBUG;SN_AVX; NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src @@ -336,7 +336,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - libopenblas.lib; cublas.lib;cudart_static.lib;cudnn.lib;snSimd.lib + cublas.lib;cudart_static.lib;cudnn.lib; true @@ -352,64 +352,61 @@ - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + - - - - + + + + + diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters new file mode 100644 index 0000000..95b1389 --- /dev/null +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -0,0 +1,182 @@ + + + + + {4FC737F1-C7A5-4376-A066-2A32D752A2FF} + cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx + + + {93995380-89BD-4b04-88EB-625FBE52EBFB} + h;hh;hpp;hxx;hm;inl;inc;xsd + + + {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} + rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms + + + {45042fac-ebea-43c9-8dfe-21144c5116cc} + + + {8200b800-2d8d-4fe5-91d2-2ce6f623b316} + + + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Header Files + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Operator + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + + + CUDA + + + CUDA + + + CUDA + + + CUDA + + + CUDA + + + \ No newline at end of file diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 3bdacb5..3b3476c 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -7,31 +7,15 @@ set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/Builds/$/${CMAKE_ set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/Builds/$/${CMAKE_VS_PLATFORM_NAME}) set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/Builds/$/${CMAKE_VS_PLATFORM_NAME}) -set(GPU "NONE" CACHE STRING "Selected GPU mode") -set_property(CACHE GPU PROPERTY STRINGS "NONE;CUDNN_AND_CPU;CUDNN_ONLY") - option(USE_AVX "AVX instr CPU support" ON) +option(USE_CUDA "CUDA instr GPU support" OFF) -if (GPU STREQUAL "CUDNN_AND_CPU") - set(USE_CUDA_CPU ON) -else() - set(USE_CUDA_CPU OFF) -endif() - -if (GPU STREQUAL "CUDNN_ONLY") - set(USE_CUDA_ONLY ON) +if(USE_CUDA) + enable_language("CUDA") + find_package(CUDA REQUIRED) + include_directories( ${CUDA_INCLUDE_DIR}) + link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib64) else() - set(USE_CUDA_ONLY OFF) -endif() - -if(USE_CUDA_CPU OR USE_CUDA_ONLY) - enable_language("CUDA") - find_package(CUDA REQUIRED) - include_directories( ${CUDA_INCLUDE_DIR}) - link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib64) -endif() - -if(NOT USE_CUDA_ONLY) find_package(OpenMP REQUIRED) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") @@ -41,15 +25,14 @@ endif() if (UNIX) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -pthread -fPIC") - if (NOT USE_CUDA_ONLY) + if (NOT USE_CUDA) find_package(BLAS REQUIRED) if (USE_AVX) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx") endif() - - endif() - + + endif() endif() if(MSVC) @@ -66,15 +49,12 @@ add_subdirectory(snBase) add_subdirectory(snEngine) add_subdirectory(skynet) -if(NOT USE_CUDA_ONLY) - add_subdirectory(snOperator_CPU_cuDNN) - +if(USE_CUDA) + add_subdirectory(snOperatorCUDA) +else() + add_subdirectory(snOperatorCPU) if(USE_AVX) add_subdirectory(snSIMD) endif() - -else() - add_subdirectory(snOperator_cuDNN) - endif() diff --git a/src/skynet/CMakeLists.txt b/src/skynet/CMakeLists.txt index e118e78..02e3d82 100644 --- a/src/skynet/CMakeLists.txt +++ b/src/skynet/CMakeLists.txt @@ -19,4 +19,10 @@ if(WIN32) COMMAND ${CMAKE_COMMAND} -E copy_directory ${DEPEND_WIN_DIR} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}) endif() -target_link_libraries(skynet snAux snBase snEngine snOperator) \ No newline at end of file +target_link_libraries(skynet snAux snBase snEngine) + +if(USE_CUDA) + target_link_libraries(skynet snOperatorCUDA) +else() + target_link_libraries(skynet snOperatorCPU) +endif() \ No newline at end of file diff --git a/src/skynet/src/snet.cpp b/src/skynet/src/snet.cpp index dec904e..0406c41 100644 --- a/src/skynet/src/snet.cpp +++ b/src/skynet/src/snet.cpp @@ -30,6 +30,7 @@ #include "snAux/auxFunc.h" #include "snBase/snBase.h" #include "snet.h" +#include "snOperatorCPU/snOperator.h" using namespace std; using namespace SN_Base; diff --git a/src/snBase/snBase.h b/src/snBase/snBase.h index 85e3a86..a839d22 100644 --- a/src/snBase/snBase.h +++ b/src/snBase/snBase.h @@ -72,23 +72,15 @@ namespace SN_Base{ }; /// tensor - input data and output data of each node of the network. + /// snOperatorCPU/tensor.cpp - implementation CPU + /// snOperatorCUDA/tensor.cu - implementation CUDA struct Tensor{ - - explicit Tensor(const snSize& sz = snSize(0,0,0,0,0)) : sz_(sz){ - - size_t ssz = sz.size(); - if (ssz > 0) - data_ = (snFloat*)calloc(ssz, sizeof(snFloat)); - } + explicit Tensor(const snSize& sz = snSize(0, 0, 0, 0, 0)); - ~Tensor(){ - if (data_) free(data_); - } + ~Tensor(); - Tensor(const Tensor& other){ - setData(other.getData(), other.size()); - } + Tensor(const Tensor& other); friend bool operator==(const Tensor& left, const Tensor& right){ @@ -100,81 +92,28 @@ namespace SN_Base{ return left.sz_ != right.sz_; } - Tensor& operator=(const Tensor& other){ - - setData(other.getData(), other.size()); - - return *this; - } - - Tensor& operator+=(const Tensor& other){ - - assert(other == *this); - - auto od = other.getData(); - - size_t sz = this->size().size(); - for (size_t i = 0; i < sz; ++i){ - data_[i] += od[i]; - } - - return *this; - } - - Tensor& operator-=(const Tensor& other){ - - assert(other == *this); - - auto od = other.getData(); - - size_t sz = this->size().size(); - for (size_t i = 0; i < sz; ++i){ - data_[i] -= od[i]; - } - - return *this; - } - - snFloat* getData() const{ - - return data_; - } - - void setData(const snFloat* data, const snSize& nsz){ - - size_t nnsz = nsz.size(); - assert(data && (nnsz > 0)); - - if (sz_.size() < nnsz) - data_ = (snFloat*)realloc(data_, nnsz * sizeof(snFloat)); + Tensor& operator=(const Tensor& other); - memcpy(data_, data, nnsz * sizeof(snFloat)); - sz_ = nsz; - } + Tensor& operator+=(const Tensor& other); - void resize(const snSize& nsz){ + Tensor& operator-=(const Tensor& other); + + // !!! do not use directly !!! + snFloat* getData() const; + + // !!! do not use directly !!! + void setData(const snFloat* data, const snSize& nsz); - size_t nnsz = nsz.size(), csz = sz_.size(); - assert(nnsz > 0); + void getDataForCPU(snFloat* out, const snSize& osz) const; - if (csz < nnsz){ - data_ = (snFloat*)realloc(data_, nnsz * sizeof(snFloat)); - memset(data_ + csz, 0, (nnsz - csz) * sizeof(snFloat)); - } - - sz_ = nsz; - } + void resize(const snSize& nsz); snSize size() const{ return sz_; } - void tfree(){ - if (data_) free(data_); - data_ = nullptr; - sz_ = snSize(0, 0, 0, 0, 0); - } + void tfree(); private: snFloat* data_ = nullptr; diff --git a/src/snEngine/snEngine.h b/src/snEngine/snEngine.h index b32a801..926842c 100644 --- a/src/snEngine/snEngine.h +++ b/src/snEngine/snEngine.h @@ -26,7 +26,6 @@ #include #include "snBase/snBase.h" -#include "snOperator/snOperator.h" #include "src/threadPool.h" namespace SN_Eng{ diff --git a/src/snEngine/src/snEngine.cpp b/src/snEngine/src/snEngine.cpp index dedef35..738d5f9 100644 --- a/src/snEngine/src/snEngine.cpp +++ b/src/snEngine/src/snEngine.cpp @@ -26,8 +26,6 @@ #include #include "snBase/snBase.h" #include "snEngine/snEngine.h" -#include "snOperator/snOperator.h" -#include "snAux/auxFunc.h" using namespace std; using namespace SN_Base; diff --git a/src/snExample/main.cpp b/src/snExample/main.cpp index fc9ff5b..9e128f3 100644 --- a/src/snExample/main.cpp +++ b/src/snExample/main.cpp @@ -63,115 +63,109 @@ int main(int argc, char* argv[]){ sn::Net snet; - snet.addNode("Input", sn::Input(), "C1") - .addNode("C1", sn::Convolution(5, 0, sn::calcMode::CPU, sn::batchNormType::beforeActive), "C2") - .addNode("C2", sn::Convolution(10, -1, sn::calcMode::CPU, sn::batchNormType::beforeActive), "P1") - .addNode("P1", sn::Pooling(sn::calcMode::CPU), "FC2") - - .addNode("FC2", sn::FullyConnected(256, sn::calcMode::CPU, sn::batchNormType::beforeActive), "FC3") - .addNode("FC3", sn::FullyConnected(10, sn::calcMode::CPU), "LS") - .addNode("LS", sn::LossFunction(sn::lossType::softMaxToCrossEntropy), "Output"); + sn::Convolution cnv(1, 7, 0, 2); + cnv.act = sn::active::none; + cnv.useBias = false; + + snet.addNode("Input", sn::Input(), "C1") + .addNode("C1", cnv, "Output"); + #ifdef WIN32 string imgPath = "c:/cpp/other/skyNet/example/mnist/images/"; #else string imgPath = "/home/alex/CLionProjects/skynet/example/mnist/images/"; #endif - int batchSz = 100, classCnt = 10, w = 28, h = 28; float lr = 0.001F; - vector> imgName(classCnt); - vector imgCntDir(classCnt); - map images; - - if (!loadImage(imgPath, classCnt, imgName, imgCntDir, images)){ - cout << "Error 'loadImage' path: " << imgPath << endl; - system("pause"); - return -1; - } - - snet.loadAllWeightFromFile("c:/cpp/w.dat"); - - sn::Tensor inLayer(sn::snLSize(w, h, 1, batchSz)); - sn::Tensor targetLayer(sn::snLSize(classCnt, 1, 1, batchSz)); - sn::Tensor outLayer(sn::snLSize(classCnt, 1, 1, batchSz)); - + sn::Tensor inLayer(sn::snLSize(9, 9, 3, 1)); + sn::Tensor outLayer(sn::snLSize(2, 2, 1, 1)); - size_t sum_metric = 0; - size_t num_inst = 0; - float accuratSumm = 0; - for (int k = 0; k < 10; ++k){ + float* refData = inLayer.data(); + /*srand(clock()); + _sleep(100); + srand(clock()); + string ff; + for (int i = 0; i < 49; ++i) + ff += to_string(rand() % 100) + ", "*/; - targetLayer.clear(); - srand(clock()); - - for (int i = 0; i < batchSz; ++i){ - - // directory - int ndir = rand() % classCnt; - while (imgCntDir[ndir] == 0) ndir = rand() % classCnt; - - // image - int nimg = rand() % imgCntDir[ndir]; - - // read - cv::Mat img; string nm = imgName[ndir][nimg]; - if (images.find(nm) != images.end()) - img = images[nm]; - else{ - img = cv::imread(imgPath + to_string(ndir) + "/" + nm, CV_LOAD_IMAGE_UNCHANGED); - - if ((img.cols != w) || (img.rows != h)) - cv::resize(img, img, cv::Size(w, h)); - - images[nm] = img; - } - - float* refData = inLayer.data() + i * w * h; - - double mean = cv::mean(img)[0]; - size_t nr = img.rows, nc = img.cols; - for (size_t r = 0; r < nr; ++r){ - uchar* pt = img.ptr(r); - for (size_t c = 0; c < nc; ++c) - refData[r * nc + c] = pt[c]; - } - - float* tarData = targetLayer.data() + classCnt * i; - - tarData[ndir] = 1; - } - - // training - float accurat = 0; - snet.training(lr, - inLayer, - outLayer, - targetLayer, - accurat); - - // calc error - sn::snFloat* targetData = targetLayer.data(); - sn::snFloat* outData = outLayer.data(); - size_t accCnt = 0, bsz = batchSz; - for (int i = 0; i < bsz; ++i){ - - float* refTarget = targetData + i * classCnt; - float* refOutput = outData + i * classCnt; - - int maxOutInx = distance(refOutput, max_element(refOutput, refOutput + classCnt)), - maxTargInx = distance(refTarget, max_element(refTarget, refTarget + classCnt)); - - if (maxTargInx == maxOutInx) - ++accCnt; - } - - accuratSumm += (accCnt * 1.F) / bsz; - - cout << k << " accurate " << accuratSumm / k << " " << snet.getLastErrorStr() << endl; - } - snet.saveAllWeightToFile("c:/cpp/w.dat"); + float b1[81]{ 41, 67, 34, 0, 69, 24, 78, 58, 62, + 64, 5, 45, 81, 27, 61, 91, 95, 42, + 27, 36, 91, 4, 2, 53, 92, 82, 21, + 16, 18, 95, 47, 26, 71, 38, 69, 12, + 67, 99, 35, 94, 3, 11, 22, 33, 73, + 64, 41, 11, 53, 68, 47, 44, 62, 57, + 37, 59, 23, 41, 29, 78, 16, 35, 90, + 42, 88, 6, 40, 42, 64, 48, 46, 5, + 90, 29, 70, 50, 6, 1, 93, 48, 29}; + + float b2[81]{ 57, 80, 25, 16, 72, 55, 25, 1, 53, + 41, 4, 36, 48, 7, 47, 65, 65, 75, + 37, 50, 33, 92, 49, 15, 2, 65, 68, + 34, 42, 71, 44, 96, 19, 24, 41, 63, + 95, 42, 23, 41, 41, 0, 73, 61, 94, + 19, 57, 89, 6, 24, 15, 20, 92, 76, + 10, 36, 7, 0, 0, 54, 81, 6, 41, + 16, 56, 90, 84, 36, 32, 26, 41, 8, + 24, 54, 83, 13, 32, 31, 24, 72, 30}; + + float b3[81]{ 24, 54, 88, 58, 62, 44, 35, 71, 39, + 52, 90, 11, 74, 17, 38, 52, 42, 47, + 44, 98, 0, 68, 32, 22, 27, 81, 52, + 50, 68, 79, 60, 36, 77, 16, 13, 81, + 17, 14, 16, 88, 29, 70, 4, 63, 81, + 41, 56, 29, 70, 7, 62, 92, 47, 99, + 58, 85, 33, 47, 87, 18, 32, 56, 3, + 99, 22, 37, 90, 8, 6, 4, 17, 10, + 90, 25, 86, 64, 88, 62, 11, 16, 27}; + + memcpy(refData, b1, 81 * sizeof(float)); + memcpy(refData + 81, b2, 81 * sizeof(float)); + memcpy(refData + 162, b3, 81 * sizeof(float)); + + sn::Tensor weight(sn::snLSize(7, 7, 3, 1)); + + float w1[49] { 41, 67, 34, 0, 69, 24, 78, + 58, 62, 64, 5, 45, 81, 27, + 61, 91, 95, 42, 27, 36, 91, + 4, 2, 53, 92, 82, 21, 16, + 18, 95, 47, 26, 71, 38, 69, + 12, 67, 99, 35, 94, 3, 11, + 22, 33, 73, 64, 41, 11, 53}; + + float w2[49] { 28, 35, 84, 53, 76, 71, 47, + 43, 50, 4, 43, 55, 45, 96, + 31, 22, 92, 4, 74, 55, 15, + 81, 79, 69, 62, 40, 93, 58, + 61, 96, 61, 5, 8, 67, 43, + 57, 73, 95, 95, 47, 0, 34, + 4, 10, 58, 20, 85, 97, 45}; + + float w3[49] { 57, 80, 25, 16, 72, 55, 25, + 1, 53, 41, 4, 36, 48, 7, + 47, 65, 65, 75, 37, 50, 33, + 92, 49, 15, 2, 65, 68, 34, + 42, 71, 44, 96, 19, 24, 41, + 63, 95, 42, 23, 41, 41, 0, + 73, 61, 94, 19, 57, 89, 6}; + + + float* refWeight = weight.data(); + + memcpy(refWeight, w1, 49 * sizeof(float)); + memcpy(refWeight + 49, w2, 49 * sizeof(float)); + memcpy(refWeight + 98, w3, 49 * sizeof(float)); + + snet.setWeightNode("C1", weight); + + snet.forward(false, inLayer, outLayer); + + for (int i = 0; i < outLayer.size().w * outLayer.size().h * outLayer.size().ch; ++i) + cout << outLayer.data()[i] << " "; + + cout << endl << "get last error: " << snet.getLastErrorStr() << endl; + system("pause"); return 0; } diff --git a/src/snOperator_CPU_cuDNN/CMakeLists.txt b/src/snOperatorCPU/CMakeLists.txt similarity index 54% rename from src/snOperator_CPU_cuDNN/CMakeLists.txt rename to src/snOperatorCPU/CMakeLists.txt index 13fcc94..113802c 100644 --- a/src/snOperator_CPU_cuDNN/CMakeLists.txt +++ b/src/snOperatorCPU/CMakeLists.txt @@ -6,21 +6,6 @@ FILE(GLOB_RECURSE SRC_snOperator "*.cpp" "*.h" "*.cu") add_library(snOperator STATIC ${SRC_snOperator}) -if(USE_CUDA) - - set_target_properties(snOperator PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_compile_features(snOperator PUBLIC cxx_std_11) - - add_definitions(-DSN_CUDA) - - target_link_libraries(snOperator cublas cudart_static) - - if(GPU STREQUAL "CUDNN") - add_definitions(-DSN_CUDNN) - target_link_libraries(snOperator cudnn) - endif() -endif() - if(USE_AVX) add_definitions(-DSN_AVX) diff --git a/src/snOperator_CPU_cuDNN/snOperator.h b/src/snOperatorCPU/snOperator.h similarity index 100% rename from src/snOperator_CPU_cuDNN/snOperator.h rename to src/snOperatorCPU/snOperator.h diff --git a/src/snOperator_CPU_cuDNN/src/CPU/convolutionCPU.cpp b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp similarity index 84% rename from src/snOperator_CPU_cuDNN/src/CPU/convolutionCPU.cpp rename to src/snOperatorCPU/src/CPU/convolutionCPU.cpp index 72a918a..b12e651 100644 --- a/src/snOperator_CPU_cuDNN/src/CPU/convolutionCPU.cpp +++ b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp @@ -24,7 +24,7 @@ // #include "../stdafx.h" -#include "snOperator/src/Operator/convolution.h" +#include "snOperatorCPU/src/Operator/convolution.h" #include #include #include @@ -314,66 +314,4 @@ void Convolution::backwardCPU_G(const convParams& prms, backwardG_Base(prms.kernel, prms.fWidth, prms.fHeight, prms.stride, prms.dilate, weight, insz, outsz, gradIn, gradOut); -} - -#ifndef SN_CUDA - -/// init aux params CUDA -void Convolution::iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, - const convParams&, void** gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -/// free aux params CUDA -void Convolution::freeParamCUDA(void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Convolution::forwardCUDA(const convParams&, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Convolution::backwardCUDA_GW(const convParams&, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); - -} - -void Convolution::backwardCUDA_G(const convParams&, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -#endif - -#ifndef SN_OpenCL - -/// init aux params OpenCL -void Convolution::iniParamOCL(bool isLern, const snSize& insz, const snSize& outsz, - const convParams&, void** gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -/// free aux params OpenCL -void Convolution::freeParamOCL(void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void Convolution::forwardOCL(const convParams&, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void Convolution::backwardOCL_GW(const convParams&, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); - -} - -void Convolution::backwardOCL_G(const convParams&, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -#endif \ No newline at end of file +} \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/CPU/deconvolutionCPU.cpp b/src/snOperatorCPU/src/CPU/deconvolutionCPU.cpp similarity index 83% rename from src/snOperator_CPU_cuDNN/src/CPU/deconvolutionCPU.cpp rename to src/snOperatorCPU/src/CPU/deconvolutionCPU.cpp index a616dad..d512108 100644 --- a/src/snOperator_CPU_cuDNN/src/CPU/deconvolutionCPU.cpp +++ b/src/snOperatorCPU/src/CPU/deconvolutionCPU.cpp @@ -24,7 +24,7 @@ // #include "../stdafx.h" -#include "snOperator/src/Operator/deconvolution.h" +#include "snOperatorCPU/src/Operator/deconvolution.h" #include #include @@ -292,58 +292,3 @@ void Deconvolution::backwardCPU_G(const deconvParams& prms, free(share); } - - -#ifndef SN_CUDA - -void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Deconvolution::freeParamCUDA(void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Deconvolution::forwardCUDA(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Deconvolution::backwardCUDA_GW(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Deconvolution::backwardCUDA_G(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -#endif - -#ifndef SN_OpenCL - -void Deconvolution::iniParamOCL(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void Deconvolution::freeParamOCL(void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void Deconvolution::forwardOCL(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void Deconvolution::backwardOCL_GW(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void Deconvolution::backwardOCL_G(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -#endif \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/CPU/fullyConnectedCPU.cpp b/src/snOperatorCPU/src/CPU/fullyConnectedCPU.cpp similarity index 77% rename from src/snOperator_CPU_cuDNN/src/CPU/fullyConnectedCPU.cpp rename to src/snOperatorCPU/src/CPU/fullyConnectedCPU.cpp index d586c01..e06fad8 100644 --- a/src/snOperator_CPU_cuDNN/src/CPU/fullyConnectedCPU.cpp +++ b/src/snOperatorCPU/src/CPU/fullyConnectedCPU.cpp @@ -25,7 +25,7 @@ #include "../stdafx.h" #include "Lib/OpenBLAS/cblas.h" -#include "snOperator/src/Operator/fullyConnected.h" +#include "snOperatorCPU/src/Operator/fullyConnected.h" #include using namespace std; @@ -144,57 +144,3 @@ void FullyConnected::backwardCPU_G(size_t kernel, const snFloat* weight, const s gradOut, // GrOut blasint(imSz)); // GrOut, step to next Y (Y21 - Y11) } - - -#ifndef SN_CUDA - -void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel, void** gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void FullyConnected::freeParamCUDA(void* auxPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloat* input, const snFloat* weight, snFloat* output, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, - const snSize& insz, const snFloat* input, const snFloat* gradIn, snFloat* gradOut, snFloat* dWOut, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void FullyConnected::backwardCUDA_G(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - - -#endif - - -#ifndef SN_OpenCL - -void FullyConnected::iniParamOCL(bool isLern, const SN_Base::snSize& insz, size_t kernel, void** gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void FullyConnected::freeParamOCL(void* auxPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void FullyConnected::forwardOCL(size_t kernel, const snSize& insz, const snFloat* input, const snFloat* weight, snFloat* output, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void FullyConnected::backwardOCL_GW(size_t kernel, const snFloat* weight, - const snSize& insz, const snFloat* input, const snFloat* gradIn, snFloat* gradOut, snFloat* dWOut, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void FullyConnected::backwardOCL_G(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - - -#endif \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/CPU/poolingCPU.cpp b/src/snOperatorCPU/src/CPU/poolingCPU.cpp similarity index 84% rename from src/snOperator_CPU_cuDNN/src/CPU/poolingCPU.cpp rename to src/snOperatorCPU/src/CPU/poolingCPU.cpp index 7eedc41..63aa10d 100644 --- a/src/snOperator_CPU_cuDNN/src/CPU/poolingCPU.cpp +++ b/src/snOperatorCPU/src/CPU/poolingCPU.cpp @@ -24,7 +24,7 @@ // #include "../stdafx.h" -#include "snOperator/src/Operator/pooling.h" +#include "snOperatorCPU/src/Operator/pooling.h" #include #include @@ -224,50 +224,4 @@ void Pooling::backwardCPU(const poolParams& poolPrms, const snSize& outsz, const free(share); } -} - - -#ifndef SN_CUDA - -void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Pooling::freeParamCUDA(void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, - const snFloat* gradIn, const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -#endif - - -#ifndef SN_OpenCL - -void Pooling::iniParamOCL(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Pooling::freeParamOCL(void* gpuPrm){ - ERROR_MESS("CUDA non compiler"); -} - -void Pooling::forwardOCL(const poolParams& poolPrms, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -void Pooling::backwardOCL(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, - const snFloat* gradIn, const snSize& insz, snFloat* gradOut, void* gpuPrm){ - ERROR_MESS("OpenCL non compiler"); -} - -#endif \ No newline at end of file +} \ No newline at end of file diff --git a/src/snOperatorCPU/src/CPU/tensor.cpp b/src/snOperatorCPU/src/CPU/tensor.cpp new file mode 100644 index 0000000..8f5b7b3 --- /dev/null +++ b/src/snOperatorCPU/src/CPU/tensor.cpp @@ -0,0 +1,100 @@ + +#include "snBase/snBase.h" + +using namespace SN_Base; + +/// tensor - input data and output data of each node of the network. + +Tensor::Tensor(const snSize& sz) : sz_(sz){ + + size_t ssz = sz.size(); + + if (ssz > 0) + data_ = (snFloat*)calloc(ssz, sizeof(snFloat)); +} + +Tensor::~Tensor(){ + if (data_) free(data_); +} + +Tensor::Tensor(const Tensor& other){ + setData(other.getData(), other.size()); +} + +Tensor& Tensor::operator=(const Tensor& other){ + + setData(other.getData(), other.size()); + + return *this; +} + +Tensor& Tensor::operator+=(const Tensor& other){ + + assert(other == *this); + + auto od = other.getData(); + + size_t sz = this->size().size(); + for (size_t i = 0; i < sz; ++i){ + data_[i] += od[i]; + } + + return *this; +} + +Tensor& Tensor::operator-=(const Tensor& other){ + + assert(other == *this); + + auto od = other.getData(); + + size_t sz = this->size().size(); + for (size_t i = 0; i < sz; ++i){ + data_[i] -= od[i]; + } + + return *this; +} + +void Tensor::setData(const snFloat* data, const snSize& nsz){ + + size_t nnsz = nsz.size(); + assert(data && (nnsz > 0)); + + if (sz_.size() < nnsz) + data_ = (snFloat*)realloc(data_, nnsz * sizeof(snFloat)); + + memcpy(data_, data, nnsz * sizeof(snFloat)); + sz_ = nsz; +} + +snFloat* Tensor::getData() const{ + + return data_; +} + +void Tensor::getDataForCPU(snFloat* out, const snSize& osz) const{ + + assert(sz_ == osz); + + memcpy(out, data_, sz_.size() * sizeof(snFloat)); +} + +void Tensor::resize(const snSize& nsz){ + + size_t nnsz = nsz.size(), csz = sz_.size(); + assert(nnsz > 0); + + if (csz < nnsz){ + data_ = (snFloat*)realloc(data_, nnsz * sizeof(snFloat)); + memset(data_ + csz, 0, (nnsz - csz) * sizeof(snFloat)); + } + + sz_ = nsz; +} + +void Tensor::tfree(){ + if (data_) free(data_); + data_ = nullptr; + sz_ = snSize(0, 0, 0, 0, 0); +} diff --git a/src/snOperator_CPU_cuDNN/src/Operator/activation.cpp b/src/snOperatorCPU/src/Operator/activation.cpp similarity index 96% rename from src/snOperator_CPU_cuDNN/src/Operator/activation.cpp rename to src/snOperatorCPU/src/Operator/activation.cpp index 7e40504..c861f11 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/activation.cpp +++ b/src/snOperatorCPU/src/Operator/activation.cpp @@ -23,8 +23,8 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/activation.h" -#include "snOperator/src/activeFunctions.h" +#include "snOperatorCPU/src/Operator/activation.h" +#include "snOperatorCPU/src/activeFunctions.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_cuDNN/src/Operator/activation.h b/src/snOperatorCPU/src/Operator/activation.h similarity index 97% rename from src/snOperator_cuDNN/src/Operator/activation.h rename to src/snOperatorCPU/src/Operator/activation.h index 6d1abbe..16c41d1 100644 --- a/src/snOperator_cuDNN/src/Operator/activation.h +++ b/src/snOperatorCPU/src/Operator/activation.h @@ -25,7 +25,7 @@ #pragma once #include "snBase/snBase.h" -#include "snOperator/src/structurs.h" +#include "snOperatorCPU/src/structurs.h" class Activation final : SN_Base::OperatorBase{ diff --git a/src/snOperator_cuDNN/src/Operator/batchNorm.cpp b/src/snOperatorCPU/src/Operator/batchNorm.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/batchNorm.cpp rename to src/snOperatorCPU/src/Operator/batchNorm.cpp index a968596..2142106 100644 --- a/src/snOperator_cuDNN/src/Operator/batchNorm.cpp +++ b/src/snOperatorCPU/src/Operator/batchNorm.cpp @@ -23,9 +23,9 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/batchNorm.h" -#include "snOperator/src/mathFunctions.h" #include "snAux/auxFunc.h" +#include "snOperatorCPU/src/Operator/batchNorm.h" +#include "snOperatorCPU/src/mathFunctions.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/Operator/batchNorm.h b/src/snOperatorCPU/src/Operator/batchNorm.h similarity index 97% rename from src/snOperator_cuDNN/src/Operator/batchNorm.h rename to src/snOperatorCPU/src/Operator/batchNorm.h index 8c75ca4..109edbf 100644 --- a/src/snOperator_cuDNN/src/Operator/batchNorm.h +++ b/src/snOperatorCPU/src/Operator/batchNorm.h @@ -25,7 +25,7 @@ #pragma once #include "snBase/snBase.h" -#include "snOperator/src/structurs.h" +#include "snOperatorCPU/src/structurs.h" class BatchNorm final : SN_Base::OperatorBase{ diff --git a/src/snOperator_cuDNN/src/Operator/concat.cpp b/src/snOperatorCPU/src/Operator/concat.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/concat.cpp rename to src/snOperatorCPU/src/Operator/concat.cpp index af41ba9..73b06b8 100644 --- a/src/snOperator_cuDNN/src/Operator/concat.cpp +++ b/src/snOperatorCPU/src/Operator/concat.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/concat.h" +#include "snOperatorCPU/src/Operator/concat.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/concat.h b/src/snOperatorCPU/src/Operator/concat.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/concat.h rename to src/snOperatorCPU/src/Operator/concat.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp similarity index 84% rename from src/snOperator_CPU_cuDNN/src/Operator/convolution.cpp rename to src/snOperatorCPU/src/Operator/convolution.cpp index 2961273..be93cb1 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -23,13 +23,13 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/convolution.h" #include "snAux/auxFunc.h" -#include "snOperator/src/weightInit.h" -#include "snOperator/src/activeFunctions.h" -#include "snOperator/src/optimizer.h" -#include "snOperator/src/structurs.h" -#include "snOperator/src/mathFunctions.h" +#include "snOperatorCPU/src/Operator/convolution.h" +#include "snOperatorCPU/src/weightInit.h" +#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/optimizer.h" +#include "snOperatorCPU/src/structurs.h" +#include "snOperatorCPU/src/mathFunctions.h" using namespace std; using namespace SN_Base; @@ -43,11 +43,7 @@ Convolution::Convolution(void* net, const string& name, const string& node, std: } Convolution::~Convolution(){ - - switch (calcMode_){ - case calcMode::CUDA: freeParamCUDA(gpuParams_); break; - case calcMode::OpenCL: freeParamOCL(gpuParams_); break; - } + } void Convolution::load(std::map& prms){ @@ -83,13 +79,7 @@ void Convolution::load(std::map& prms){ setIntParam("stride", false, false, convPrms_.stride); setIntParam("dilate", false, false, convPrms_.dilate); - - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - - if (prms.find("gpuDeviceId") != prms.end()) - gpuDeviceId_ = stoi(prms["gpuDeviceId"]); - + if (prms.find("batchNorm") != prms.end()){ string bnType = prms["batchNorm"]; @@ -99,17 +89,7 @@ void Convolution::load(std::map& prms){ else ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); } - - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - + // aux array auxParams_["dWeight"] = vector(); auxParams_["dWPrev"] = vector(); @@ -169,13 +149,13 @@ bool Convolution::setInternPrm(std::map& prms){ } if (prms.find("decayMomentDW") != prms.end()) - opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + optDecayMomentDW_ = stof(prms["decayMomentDW"]); if (prms.find("decayMomentWGr") != prms.end()) - opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + optDecayMomentWGr_ = stof(prms["decayMomentWGr"]); if (prms.find("lmbRegular") != prms.end()) - opt_lmbRegular_ = stof(prms["lmbRegular"]); + optLmbRegular_ = stof(prms["lmbRegular"]); if (prms.find("batchNormLr") != prms.end()) baseBatchNorm_.lr = stof(prms["batchNormLr"]); @@ -274,12 +254,9 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op memset(weight + stepByN, 0, kernel * sizeof(snFloat)); } - switch (calcMode_){ - case calcMode::CPU: forwardCPU(convPrms_, weight, inDataExpSz_, in, outsz, out); break; - case calcMode::CUDA: forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); break; - case calcMode::OpenCL: forwardOCL(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); break; - } - + // calculation + forwardCPU(convPrms_, weight, inDataExpSz_, in, outsz, out); + /// dropOut if (dropOut_ > 0.F) dropOut(operPrm.isLerning, dropOut_, outsz, out); @@ -338,12 +315,9 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o if (!isSame) in = inTnsExp_.getData(); - switch (calcMode_){ - case calcMode::CPU: backwardCPU_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight); break; - case calcMode::CUDA: backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); break; - } - + // calculation + backwardCPU_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight); + // correct weight snFloat* dWPrev = auxParams_["dWPrev"].data(); snFloat* dWGrad = auxParams_["dWGrad"].data(); @@ -355,17 +329,13 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o weight, wsz, operPrm.lr, - opt_lmbRegular_, - opt_decayMomentDW_, - opt_decayMomentWGr_, + optLmbRegular_, + optDecayMomentDW_, + optDecayMomentWGr_, optimizerType_); } else{ // isFreeze - switch (calcMode_){ - case calcMode::CPU: backwardCPU_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut); break; - case calcMode::CUDA: backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); break; - } + backwardCPU_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut); } if (!isSame) @@ -452,9 +422,4 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseBatchNorm_.sz = outSz; baseBatchNorm_.sz.n = 1; } - - switch (calcMode_){ - case calcMode::CUDA: iniParamCUDA(isLern, expSz, outSz, convPrms_, &gpuParams_); break; - case calcMode::OpenCL: iniParamOCL(isLern, expSz, outSz, convPrms_, &gpuParams_); break; - } } \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/Operator/convolution.h b/src/snOperatorCPU/src/Operator/convolution.h similarity index 55% rename from src/snOperator_CPU_cuDNN/src/Operator/convolution.h rename to src/snOperatorCPU/src/Operator/convolution.h index b12cb3c..56e8c82 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/convolution.h +++ b/src/snOperatorCPU/src/Operator/convolution.h @@ -25,8 +25,8 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" -#include"snOperator/src/mathFunctions.h" +#include"snOperatorCPU/src/structurs.h" +#include"snOperatorCPU/src/mathFunctions.h" /// convolution layer class Convolution final : SN_Base::OperatorBase{ @@ -56,37 +56,32 @@ class Convolution final : SN_Base::OperatorBase{ }; convParams convPrms_; - bool isPaddingSame_ = false, isCheckPadding_ = false; - - activeType activeType_ = activeType::relu; ///< active type - optimizerType optimizerType_ = optimizerType::adam; ///< optimizer type - weightInitType weightInitType_ = weightInitType::he; ///< init weight type - batchNormType batchNormType_ = batchNormType::none; ///< batchNorm - SN_Base::snSize inSzMem_; ///< insz mem - SN_Base::snSize inDataExpSz_; ///< insz expansion - - const SN_Base::Tensor* inputMem_ = nullptr; - - SN_Base::Tensor inTnsExp_; - SN_Base::Tensor gradOutExp_; - - bool isFreeze_ = false; ///< not change weight - bool gpuClearMem_ = false; ///< clear mem GPU - - uint32_t gpuDeviceId_ = 0; ///< gpu id - calcMode calcMode_ = calcMode::CPU; ///< calc mode - - SN_Base::snFloat dropOut_ = 0.F; ///< random off out - - SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimiz weight - opt_decayMomentWGr_ = 0.99F, - opt_lmbRegular_ = 0.001F; + bool isPaddingSame_ = false, + isCheckPadding_ = false; + + activeType activeType_ = activeType::relu; ///< active type + optimizerType optimizerType_ = optimizerType::adam; ///< optimizer type + weightInitType weightInitType_ = weightInitType::he; ///< init weight type + batchNormType batchNormType_ = batchNormType::none; ///< batchNorm + SN_Base::snSize inSzMem_; ///< insz mem + SN_Base::snSize inDataExpSz_; ///< insz expansion + + const SN_Base::Tensor* inputMem_ = nullptr; ///< input tensor mem + + SN_Base::Tensor inTnsExp_; + SN_Base::Tensor gradOutExp_; + + bool isFreeze_ = false; ///< not change weight + + SN_Base::snFloat dropOut_ = 0.F; ///< random off out + + SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimiz weight + optDecayMomentWGr_ = 0.99F, + optLmbRegular_ = 0.001F; std::map> auxParams_; ///< aux data - - void* gpuParams_ = nullptr; ///< gpu aux params - + void load(std::map& prms); @@ -123,79 +118,4 @@ class Convolution final : SN_Base::OperatorBase{ const SN_Base::snFloat* gradIn, SN_Base::snFloat* gradOut); - - /// CUDA /////////////////////////// - - /// init aux params - void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, - const convParams&, void** gpuPrm); - - /// free aux params - void freeParamCUDA(void* gpuPrm); - - void forwardCUDA(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - void* gpuPrm); - - /// calc grad and weight - void backwardCUDA_GW(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - /// calc grad - void backwardCUDA_G(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); - - - /// OpenCL /////////////////////////// - - /// init aux params OpenCL - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, - const convParams&, void** gpuPrm); - - /// free aux params OpenCL - void freeParamOCL(void* gpuPrm); - - void forwardOCL(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - void* gpuPrm); - - /// calc grad and weight - void backwardOCL_GW(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - /// calc grad - void backwardOCL_G(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); }; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/crop.cpp b/src/snOperatorCPU/src/Operator/crop.cpp similarity index 99% rename from src/snOperator_cuDNN/src/Operator/crop.cpp rename to src/snOperatorCPU/src/Operator/crop.cpp index a9d1d0d..17c8434 100644 --- a/src/snOperator_cuDNN/src/Operator/crop.cpp +++ b/src/snOperatorCPU/src/Operator/crop.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/crop.h" +#include "snOperatorCPU/src/Operator/crop.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/crop.h b/src/snOperatorCPU/src/Operator/crop.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/crop.h rename to src/snOperatorCPU/src/Operator/crop.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/deconvolution.cpp b/src/snOperatorCPU/src/Operator/deconvolution.cpp similarity index 82% rename from src/snOperator_CPU_cuDNN/src/Operator/deconvolution.cpp rename to src/snOperatorCPU/src/Operator/deconvolution.cpp index 52734c4..7df9687 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/deconvolution.cpp +++ b/src/snOperatorCPU/src/Operator/deconvolution.cpp @@ -23,13 +23,13 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/deconvolution.h" #include "snAux/auxFunc.h" -#include "snOperator/src/weightInit.h" -#include "snOperator/src/activeFunctions.h" -#include "snOperator/src/optimizer.h" -#include "snOperator/src/structurs.h" -#include "snOperator/src/mathFunctions.h" +#include "snOperatorCPU/src/Operator/deconvolution.h" +#include "snOperatorCPU/src/weightInit.h" +#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/optimizer.h" +#include "snOperatorCPU/src/structurs.h" +#include "snOperatorCPU/src/mathFunctions.h" using namespace std; using namespace SN_Base; @@ -42,11 +42,8 @@ Deconvolution::Deconvolution(void* net, const string& name, const string& node, } Deconvolution::~Deconvolution(){ - - if (calcMode_ == calcMode::CUDA) - freeParamCUDA(gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - freeParamOCL(gpuParams_); + + } void Deconvolution::load(std::map& prms){ @@ -80,23 +77,7 @@ void Deconvolution::load(std::map& prms){ else ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); } - - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - - if (prms.find("gpuDeviceId") != prms.end()) - gpuDeviceId_ = stoi(prms["gpuDeviceId"]); - - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - + // aux arrays auxParams_["dWeight"] = vector(); auxParams_["dWPrev"] = vector(); @@ -156,13 +137,13 @@ bool Deconvolution::setInternPrm(std::map& prms){ } if (prms.find("decayMomentDW") != prms.end()) - opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + optDecayMomentDW_ = stof(prms["decayMomentDW"]); if (prms.find("decayMomentWGr") != prms.end()) - opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + optDecayMomentWGr_ = stof(prms["decayMomentWGr"]); if (prms.find("lmbRegular") != prms.end()) - opt_lmbRegular_ = stof(prms["lmbRegular"]); + optLmbRegular_ = stof(prms["lmbRegular"]); if (prms.find("batchNormLr") != prms.end()) baseBatchNorm_.lr = stof(prms["batchNormLr"]); @@ -245,12 +226,9 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); snSize outsz = baseOut_.size(); - switch (calcMode_){ - case calcMode::CPU: forwardCPU(deconvParams_, weight, insz, pInTns, outsz, out); break; - case calcMode::CUDA: forwardCUDA(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); break; - case calcMode::OpenCL: forwardOCL(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); break; - } - + // calculation + forwardCPU(deconvParams_, weight, insz, pInTns, outsz, out); + /// dropOut if (dropOut_ > 0.F) dropOut(operPrm.isLerning, dropOut_, outsz, out); @@ -303,12 +281,9 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); - switch (calcMode_){ - case calcMode::CPU: backwardCPU_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight); break; - case calcMode::CUDA: backwardCUDA_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); break; - } - + // calculation + backwardCPU_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight); + snFloat* dWPrev = auxParams_["dWPrev"].data(); snFloat* dWGrad = auxParams_["dWGrad"].data(); size_t wsz = baseWeight_.size().size(); @@ -319,17 +294,13 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& weight, wsz, operPrm.lr, - opt_lmbRegular_, - opt_decayMomentDW_, - opt_decayMomentWGr_, + optLmbRegular_, + optDecayMomentDW_, + optDecayMomentWGr_, optimizerType_); } else{ // isFreeze - switch (calcMode_){ - case calcMode::CPU: backwardCPU_G(deconvParams_, weight, insz, outsz, gradIn, grOut); break; - case calcMode::CUDA: backwardCUDA_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); break; - } + backwardCPU_G(deconvParams_, weight, insz, outsz, gradIn, grOut); } } @@ -381,11 +352,6 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ baseBatchNorm_.sz = outSz; baseBatchNorm_.sz.n = 1; } - - if (calcMode_ == calcMode::CUDA) - iniParamCUDA(isLern, newsz, outSz, deconvParams_, &gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - iniParamOCL(isLern, newsz, outSz, deconvParams_, &gpuParams_); } diff --git a/src/snOperator_CPU_cuDNN/src/Operator/deconvolution.h b/src/snOperatorCPU/src/Operator/deconvolution.h similarity index 59% rename from src/snOperator_CPU_cuDNN/src/Operator/deconvolution.h rename to src/snOperatorCPU/src/Operator/deconvolution.h index ed33773..f765ad9 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/deconvolution.h +++ b/src/snOperatorCPU/src/Operator/deconvolution.h @@ -25,8 +25,8 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" -#include"snOperator/src/mathFunctions.h" +#include"snOperatorCPU/src/structurs.h" +#include"snOperatorCPU/src/mathFunctions.h" /// обратная свертка class Deconvolution final : SN_Base::OperatorBase{ @@ -62,21 +62,15 @@ class Deconvolution final : SN_Base::OperatorBase{ const SN_Base::Tensor* inputMem_ = nullptr; bool isFreeze_ = false; ///< not change weight - bool gpuClearMem_ = false; ///< clear mem GPU - - uint32_t gpuDeviceId_ = 0; ///< gpu id - - calcMode calcMode_ = calcMode::CPU; ///< calc mode - + SN_Base::snFloat dropOut_ = 0.F; ///< rand off output - SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimizer weight coef - opt_decayMomentWGr_ = 0.99F, - opt_lmbRegular_ = 0.001F; + SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimizer weight coef + optDecayMomentWGr_ = 0.99F, + optLmbRegular_ = 0.001F; std::map> auxParams_; ///< aux data - void* gpuParams_ = nullptr ; ///< aux GPU - + void load(std::map& prms); void updateConfig(bool isLern, const SN_Base::snSize& newSz); @@ -109,70 +103,4 @@ class Deconvolution final : SN_Base::OperatorBase{ const SN_Base::snFloat* gradIn, SN_Base::snFloat* gradOut); - - - /// CUDA /////////////////////////// - - void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const deconvParams&, void** gpuPrm); - - void freeParamCUDA(void* gpuPrm); - - void forwardCUDA(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - void* gpuPrm); - - void backwardCUDA_GW(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - void backwardCUDA_G(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); - - - /// OpenCL /////////////////////////// - - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const deconvParams&, void** gpuPrm); - - void freeParamOCL(void* gpuPrm); - - void forwardOCL(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - void* gpuPrm); - - void backwardOCL_GW(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - void backwardOCL_G(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); }; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/fullyConnected.cpp b/src/snOperatorCPU/src/Operator/fullyConnected.cpp similarity index 81% rename from src/snOperator_cuDNN/src/Operator/fullyConnected.cpp rename to src/snOperatorCPU/src/Operator/fullyConnected.cpp index fab2956..0a80338 100644 --- a/src/snOperator_cuDNN/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCPU/src/Operator/fullyConnected.cpp @@ -23,13 +23,13 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/fullyConnected.h" #include "snAux/auxFunc.h" -#include "snOperator/src/weightInit.h" -#include "snOperator/src/activeFunctions.h" -#include "snOperator/src/optimizer.h" -#include "snOperator/src/structurs.h" -#include "snOperator/src/mathFunctions.h" +#include "snOperatorCPU/src/Operator/fullyConnected.h" +#include "snOperatorCPU/src/weightInit.h" +#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/optimizer.h" +#include "snOperatorCPU/src/structurs.h" +#include "snOperatorCPU/src/mathFunctions.h" using namespace std; using namespace SN_Base; @@ -43,10 +43,7 @@ FullyConnected::FullyConnected(void* net, const string& name, const string& node FullyConnected::~FullyConnected(){ - if (calcMode_ == calcMode::CUDA) - freeParamCUDA(gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - freeParamOCL(gpuParams_); + } void FullyConnected::load(std::map& prms){ @@ -75,22 +72,6 @@ void FullyConnected::load(std::map& prms){ ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); } - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - - if (prms.find("gpuDeviceId") != prms.end()) - gpuDeviceId_ = stoi(prms["gpuDeviceId"]); - baseOut_.resize(snSize(kernel_)); // currrect params @@ -155,13 +136,13 @@ bool FullyConnected::setInternPrm(std::map& prms){ } if (prms.find("decayMomentDW") != prms.end()) - opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + optDecayMomentDW_ = stof(prms["decayMomentDW"]); if (prms.find("decayMomentWGr") != prms.end()) - opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + optDecayMomentWGr_ = stof(prms["decayMomentWGr"]); if (prms.find("lmbRegular") != prms.end()) - opt_lmbRegular_ = stof(prms["lmbRegular"]); + optLmbRegular_ = stof(prms["lmbRegular"]); if (prms.find("batchNormLr") != prms.end()) baseBatchNorm_.lr = stof(prms["batchNormLr"]); @@ -248,11 +229,8 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& memset(weight + stepByN, 0, kernel_ * sizeof(snFloat)); } - switch (calcMode_){ - case calcMode::CPU: forwardCPU(kernel_, insz, inTns.getData(), weight, out); break; - case calcMode::CUDA: forwardCUDA(kernel_, insz, inTns.getData(), weight, out, gpuParams_); break; - case calcMode::OpenCL: forwardOCL(kernel_, insz, inTns.getData(), weight, out, gpuParams_); break; - } + // calculation + forwardCPU(kernel_, insz, inTns.getData(), weight, out); /// dropOut snSize outSz = baseOut_.size(); @@ -303,13 +281,10 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); - - switch (calcMode_){ - case calcMode::CPU: backwardCPU_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight); break; - case calcMode::CUDA: backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); break; - } - + + // calculation + backwardCPU_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight); + // correct weight snFloat* dWPrev = auxParams_["dWPrev"].data(); snFloat* dWGrad = auxParams_["dWGrad"].data(); @@ -321,18 +296,14 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam weight, wsz, operPrm.lr, - opt_lmbRegular_, - opt_decayMomentDW_, - opt_decayMomentWGr_, + optLmbRegular_, + optDecayMomentDW_, + optDecayMomentWGr_, optimizerType_); } else{ // isFreeze - switch (calcMode_){ - case calcMode::CPU: backwardCPU_G(kernel_, weight, inSzMem_, gradIn, gradOut); break; - case calcMode::CUDA: backwardCUDA_G(kernel_, weight, inSzMem_, gradIn, gradOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_G(kernel_, weight, inSzMem_, gradIn, gradOut, gpuParams_); break; - } + backwardCPU_G(kernel_, weight, inSzMem_, gradIn, gradOut); } } @@ -363,10 +334,5 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ auxParams_["bn_norm"].resize(newsz.n * kernel_, 0); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); auxParams_["bn_onc"].resize(newsz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); } - } - - if (calcMode_ == calcMode::CUDA) - iniParamCUDA(isLern, newsz, kernel_, &gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - iniParamOCL(isLern, newsz, kernel_, &gpuParams_); + } } \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.h b/src/snOperatorCPU/src/Operator/fullyConnected.h similarity index 60% rename from src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.h rename to src/snOperatorCPU/src/Operator/fullyConnected.h index 3df0106..bb31f6d 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.h +++ b/src/snOperatorCPU/src/Operator/fullyConnected.h @@ -25,7 +25,7 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" +#include"snOperatorCPU/src/structurs.h" /// fullyConnected layer class FullyConnected final : SN_Base::OperatorBase{ @@ -56,23 +56,16 @@ class FullyConnected final : SN_Base::OperatorBase{ bool useBias_ = true; bool isFreeze_ = false; ///< not change weight - bool gpuClearMem_ = false; ///< free gpu mem - - uint32_t gpuDeviceId_ = 0; ///< gpu id - - calcMode calcMode_ = calcMode::CPU; ///< calc mode - + SN_Base::snFloat dropOut_ = 0.F; ///< rand off output - SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimizer weight coef - opt_decayMomentWGr_ = 0.99F, - opt_lmbRegular_ = 0.001F; + SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimizer weight coef + optDecayMomentWGr_ = 0.99F, + optLmbRegular_ = 0.001F; std::map> auxParams_; ///< aux data - void* gpuParams_ = nullptr; ///< gpu data - - + void load(std::map& prms); @@ -104,68 +97,5 @@ class FullyConnected final : SN_Base::OperatorBase{ const SN_Base::snSize& insz, const SN_Base::snFloat* gradIn, SN_Base::snFloat* gradOut); - - - /// CUDA /////////////////////////// - - void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, size_t kernel, void** gpuPrm); - - void freeParamCUDA(void* gpuPrm); - - void forwardCUDA(size_t kernel, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* weight, - SN_Base::snFloat* output, - void* gpuPrm); - - // calc grad and dw - void backwardCUDA_GW(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - // calc grad - void backwardCUDA_G(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); - - - /// OpenCL /////////////////////////// - - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, size_t kernel, void** gpuPrm); - - void freeParamOCL(void* gpuPrm); - - void forwardOCL(size_t kernel, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* weight, - SN_Base::snFloat* output, - void* gpuPrm); - - // calc grad and dw - void backwardOCL_GW(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - // calc grad - void backwardOCL_G(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); + }; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/input.cpp b/src/snOperatorCPU/src/Operator/input.cpp similarity index 97% rename from src/snOperator_cuDNN/src/Operator/input.cpp rename to src/snOperatorCPU/src/Operator/input.cpp index cb18e51..01f56b0 100644 --- a/src/snOperator_cuDNN/src/Operator/input.cpp +++ b/src/snOperatorCPU/src/Operator/input.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/input.h" +#include "snOperatorCPU/src/Operator/input.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/input.h b/src/snOperatorCPU/src/Operator/input.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/input.h rename to src/snOperatorCPU/src/Operator/input.h diff --git a/src/snOperator_cuDNN/src/Operator/lock.cpp b/src/snOperatorCPU/src/Operator/lock.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/lock.cpp rename to src/snOperatorCPU/src/Operator/lock.cpp index e1cb9aa..085608a 100644 --- a/src/snOperator_cuDNN/src/Operator/lock.cpp +++ b/src/snOperatorCPU/src/Operator/lock.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/lock.h" +#include "snOperatorCPU/src/Operator/lock.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/lock.h b/src/snOperatorCPU/src/Operator/lock.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/lock.h rename to src/snOperatorCPU/src/Operator/lock.h diff --git a/src/snOperator_cuDNN/src/Operator/lossFunction.cpp b/src/snOperatorCPU/src/Operator/lossFunction.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/lossFunction.cpp rename to src/snOperatorCPU/src/Operator/lossFunction.cpp index a4072ee..2bdab30 100644 --- a/src/snOperator_cuDNN/src/Operator/lossFunction.cpp +++ b/src/snOperatorCPU/src/Operator/lossFunction.cpp @@ -23,8 +23,8 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/lossFunction.h" -#include "snOperator/src/activeFunctions.h" +#include "snOperatorCPU/src/Operator/lossFunction.h" +#include "snOperatorCPU/src/activeFunctions.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/lossFunction.h b/src/snOperatorCPU/src/Operator/lossFunction.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/lossFunction.h rename to src/snOperatorCPU/src/Operator/lossFunction.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/output.cpp b/src/snOperatorCPU/src/Operator/output.cpp similarity index 97% rename from src/snOperator_CPU_cuDNN/src/Operator/output.cpp rename to src/snOperatorCPU/src/Operator/output.cpp index e905ee5..d655c81 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/output.cpp +++ b/src/snOperatorCPU/src/Operator/output.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/output.h" +#include "snOperatorCPU/src/Operator/output.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/output.h b/src/snOperatorCPU/src/Operator/output.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/output.h rename to src/snOperatorCPU/src/Operator/output.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/pooling.cpp b/src/snOperatorCPU/src/Operator/pooling.cpp similarity index 75% rename from src/snOperator_CPU_cuDNN/src/Operator/pooling.cpp rename to src/snOperatorCPU/src/Operator/pooling.cpp index 4bcbcc6..bf7d846 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/pooling.cpp +++ b/src/snOperatorCPU/src/Operator/pooling.cpp @@ -23,9 +23,9 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/pooling.h" #include "snAux/auxFunc.h" -#include "snOperator/src/structurs.h" +#include "snOperatorCPU/src/Operator/pooling.h" +#include "snOperatorCPU/src/structurs.h" using namespace std; using namespace SN_Base; @@ -38,11 +38,7 @@ Pooling::Pooling(void* net, const string& name, const string& node, std::map& prms){ @@ -68,13 +64,7 @@ void Pooling::load(std::map& prms){ setIntParam("stride", false, false, poolPrms_.stride); else poolPrms_.stride = poolPrms_.kernel; - - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - - if (prms.find("gpuDeviceId") != prms.end()) - gpuDeviceId_ = stoi(prms["gpuDeviceId"]); - + if (prms.find("pool") != prms.end()){ string atype = prms["pool"]; @@ -83,17 +73,7 @@ void Pooling::load(std::map& prms){ else ERROR_MESS("param 'pool' = " + atype + " indefined"); } - - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - + basePrms_ = prms; } @@ -150,11 +130,9 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara /// output calculation snFloat* out = baseOut_.getData(); - switch (calcMode_){ - case calcMode::CPU: forwardCPU(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data()); break; - case calcMode::CUDA: forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); break; - case calcMode::OpenCL: forwardOCL(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); break; - } + // calculation + forwardCPU(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data()); + } void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ @@ -169,13 +147,9 @@ void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operP input = inTnsExp_.getData(); } - /// grad calculation - switch (calcMode_){ - case calcMode::CPU: backwardCPU(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut); break; - case calcMode::CUDA: backwardCUDA(poolPrms_, baseOut_.size(), outInx_.data(), baseOut_.getData(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut, gpuParams_); break; - } - + /// calculation + backwardCPU(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut); + if (isPadding_) paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); } @@ -218,8 +192,4 @@ void Pooling::updateConfig(bool isLern, const snSize& newsz){ if (isLern) baseGrad_.resize(newsz); - if (calcMode_ == calcMode::CUDA) - iniParamCUDA(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - iniParamOCL(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); } \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/pooling.h b/src/snOperatorCPU/src/Operator/pooling.h similarity index 60% rename from src/snOperator_cuDNN/src/Operator/pooling.h rename to src/snOperatorCPU/src/Operator/pooling.h index c4623a7..cc32a77 100644 --- a/src/snOperator_cuDNN/src/Operator/pooling.h +++ b/src/snOperatorCPU/src/Operator/pooling.h @@ -25,8 +25,8 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" -#include"snOperator/src/mathFunctions.h" +#include"snOperatorCPU/src/structurs.h" +#include"snOperatorCPU/src/mathFunctions.h" /// pooling layer @@ -60,19 +60,10 @@ class Pooling final : SN_Base::OperatorBase{ SN_Base::Tensor gradOutExp_; const SN_Base::Tensor* inputMem_ = nullptr; - - + bool isPadding_ = false; - - bool gpuClearMem_ = false; ///< freee gpu mem - - uint32_t gpuDeviceId_ = 0; ///< gpu id - - calcMode calcMode_ = calcMode::CPU; ///< calc mode - - + std::map> auxParams_; ///< aux data - void* gpuParams_ = nullptr; ///< gpu data void load(std::map& prms); @@ -95,55 +86,5 @@ class Pooling final : SN_Base::OperatorBase{ const size_t* outputInx, const SN_Base::snFloat* gradIn, const SN_Base::snSize& insz, - SN_Base::snFloat* gradOut); - - - /// CUDA /////////////////////////// - - void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const poolParams&, void** gpuPrm); - - void freeParamCUDA(void* gpuPrm); - - void forwardCUDA(const poolParams& poolPrms, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - size_t* outputInx, - void* gpuParams); - - /// обратный проход CUDA - void backwardCUDA(const poolParams& poolPrms, - const SN_Base::snSize& outsz, - const size_t* outputInx, - const SN_Base::snFloat* output, - const SN_Base::snFloat* gradIn, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - SN_Base::snFloat* gradOut, - void* gpuParams); - - - /// OpenCL /////////////////////////// - - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const poolParams&, void** gpuPrm); - - void freeParamOCL(void* gpuPrm); - - void forwardOCL(const poolParams& poolPrms, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - size_t* outputInx, - void* gpuParams); - - void backwardOCL(const poolParams& poolPrms, - const SN_Base::snSize& outsz, - const size_t* outputInx, - const SN_Base::snFloat* gradIn, - const SN_Base::snSize& insz, - SN_Base::snFloat* gradOut, - void* gpuParams); - + SN_Base::snFloat* gradOut); }; \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/resize.cpp b/src/snOperatorCPU/src/Operator/resize.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/resize.cpp rename to src/snOperatorCPU/src/Operator/resize.cpp index 2b60c09..a414825 100644 --- a/src/snOperator_cuDNN/src/Operator/resize.cpp +++ b/src/snOperatorCPU/src/Operator/resize.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/resize.h" +#include "snOperatorCPU/src/Operator/resize.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/resize.h b/src/snOperatorCPU/src/Operator/resize.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/resize.h rename to src/snOperatorCPU/src/Operator/resize.h diff --git a/src/snOperator_cuDNN/src/Operator/summator.cpp b/src/snOperatorCPU/src/Operator/summator.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/summator.cpp rename to src/snOperatorCPU/src/Operator/summator.cpp index cac5306..58219b9 100644 --- a/src/snOperator_cuDNN/src/Operator/summator.cpp +++ b/src/snOperatorCPU/src/Operator/summator.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/summator.h" +#include "snOperatorCPU/src/Operator/summator.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/summator.h b/src/snOperatorCPU/src/Operator/summator.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/summator.h rename to src/snOperatorCPU/src/Operator/summator.h diff --git a/src/snOperator_cuDNN/src/Operator/switch.cpp b/src/snOperatorCPU/src/Operator/switch.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/switch.cpp rename to src/snOperatorCPU/src/Operator/switch.cpp index 06026d0..bc61c56 100644 --- a/src/snOperator_cuDNN/src/Operator/switch.cpp +++ b/src/snOperatorCPU/src/Operator/switch.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/switch.h" +#include "snOperatorCPU/src/Operator/switch.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/switch.h b/src/snOperatorCPU/src/Operator/switch.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/switch.h rename to src/snOperatorCPU/src/Operator/switch.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/userLayer.cpp b/src/snOperatorCPU/src/Operator/userLayer.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/userLayer.cpp rename to src/snOperatorCPU/src/Operator/userLayer.cpp index 8f38af9..dc1bc6e 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/userLayer.cpp +++ b/src/snOperatorCPU/src/Operator/userLayer.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/userLayer.h" +#include "snOperatorCPU/src/Operator/userLayer.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/userLayer.h b/src/snOperatorCPU/src/Operator/userLayer.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/Operator/userLayer.h rename to src/snOperatorCPU/src/Operator/userLayer.h diff --git a/src/snOperator_CPU_cuDNN/src/activeFunctions.cpp b/src/snOperatorCPU/src/activeFunctions.cpp similarity index 100% rename from src/snOperator_CPU_cuDNN/src/activeFunctions.cpp rename to src/snOperatorCPU/src/activeFunctions.cpp diff --git a/src/snOperator_CPU_cuDNN/src/activeFunctions.h b/src/snOperatorCPU/src/activeFunctions.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/activeFunctions.h rename to src/snOperatorCPU/src/activeFunctions.h diff --git a/src/snOperator_CPU_cuDNN/src/mathFunctions.cpp b/src/snOperatorCPU/src/mathFunctions.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/mathFunctions.cpp rename to src/snOperatorCPU/src/mathFunctions.cpp index ef5bc24..bab4a61 100644 --- a/src/snOperator_CPU_cuDNN/src/mathFunctions.cpp +++ b/src/snOperatorCPU/src/mathFunctions.cpp @@ -25,8 +25,8 @@ #include "stdafx.h" #include "Lib/OpenBLAS/cblas.h" -#include "snOperator/src/mathFunctions.h" -#include "snOperator/src/random.h" +#include "snOperatorCPU/src/mathFunctions.h" +#include "snOperatorCPU/src/random.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_CPU_cuDNN/src/mathFunctions.h b/src/snOperatorCPU/src/mathFunctions.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/mathFunctions.h rename to src/snOperatorCPU/src/mathFunctions.h diff --git a/src/snOperator_CPU_cuDNN/src/optimizer.cpp b/src/snOperatorCPU/src/optimizer.cpp similarity index 100% rename from src/snOperator_CPU_cuDNN/src/optimizer.cpp rename to src/snOperatorCPU/src/optimizer.cpp diff --git a/src/snOperator_CPU_cuDNN/src/optimizer.h b/src/snOperatorCPU/src/optimizer.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/optimizer.h rename to src/snOperatorCPU/src/optimizer.h diff --git a/src/snOperator_CPU_cuDNN/src/random.cpp b/src/snOperatorCPU/src/random.cpp similarity index 100% rename from src/snOperator_CPU_cuDNN/src/random.cpp rename to src/snOperatorCPU/src/random.cpp diff --git a/src/snOperator_CPU_cuDNN/src/random.h b/src/snOperatorCPU/src/random.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/random.h rename to src/snOperatorCPU/src/random.h diff --git a/src/snOperator_CPU_cuDNN/src/snOperator.cpp b/src/snOperatorCPU/src/snOperator.cpp similarity index 99% rename from src/snOperator_CPU_cuDNN/src/snOperator.cpp rename to src/snOperatorCPU/src/snOperator.cpp index c43fd23..711781f 100644 --- a/src/snOperator_CPU_cuDNN/src/snOperator.cpp +++ b/src/snOperatorCPU/src/snOperator.cpp @@ -22,7 +22,7 @@ // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN // THE SOFTWARE. // -#include "snOperator/snOperator.h" +#include "snOperatorCPU/snOperator.h" #include "snBase/snBase.h" #include "Operator/fullyConnected.h" #include "Operator/convolution.h" diff --git a/src/snOperator_cuDNN/src/stdafx.h b/src/snOperatorCPU/src/stdafx.h similarity index 98% rename from src/snOperator_cuDNN/src/stdafx.h rename to src/snOperatorCPU/src/stdafx.h index 90af050..7647edb 100644 --- a/src/snOperator_cuDNN/src/stdafx.h +++ b/src/snOperatorCPU/src/stdafx.h @@ -31,7 +31,7 @@ #include #include #include "snBase/snBase.h" -#include "snOperator/snOperator.h" +#include "snOperatorCPU/snOperator.h" #define PROFILE_START double ctm = omp_get_wtime(); #define PROFILE_END(func) g_statusMess(this, name_ + " " + node_ + " " + func + " " + std::to_string(omp_get_wtime() - ctm)); ctm = omp_get_wtime(); diff --git a/src/snOperator_CPU_cuDNN/src/structurs.h b/src/snOperatorCPU/src/structurs.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/structurs.h rename to src/snOperatorCPU/src/structurs.h diff --git a/src/snOperator_CPU_cuDNN/src/weightInit.cpp b/src/snOperatorCPU/src/weightInit.cpp similarity index 100% rename from src/snOperator_CPU_cuDNN/src/weightInit.cpp rename to src/snOperatorCPU/src/weightInit.cpp diff --git a/src/snOperator_CPU_cuDNN/src/weightInit.h b/src/snOperatorCPU/src/weightInit.h similarity index 100% rename from src/snOperator_CPU_cuDNN/src/weightInit.h rename to src/snOperatorCPU/src/weightInit.h diff --git a/src/snOperator_cuDNN/CMakeLists.txt b/src/snOperatorCUDA/CMakeLists.txt similarity index 100% rename from src/snOperator_cuDNN/CMakeLists.txt rename to src/snOperatorCUDA/CMakeLists.txt diff --git a/src/snOperator_cuDNN/snOperator.h b/src/snOperatorCUDA/snOperator.h similarity index 100% rename from src/snOperator_cuDNN/snOperator.h rename to src/snOperatorCUDA/snOperator.h diff --git a/src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu b/src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu new file mode 100644 index 0000000..b270a21 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu @@ -0,0 +1,811 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include +#include +#include "../stdafx.h" +#include "snOperatorCUDA/src/Operator/convolution.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnConvolutionDescriptor_t conv_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + cudnnFilterDescriptor_t w_desc = 0; + cudnnFilterDescriptor_t dw_desc = 0; + cudnnTensorDescriptor_t bias_desc = 0; + + cudnnConvolutionFwdAlgo_t algoFwd; + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + + size_t wsFwdSz = 0; + size_t wsBwdDataSz = 0; + size_t wsBwdWSz = 0; + size_t inszMem = 0; + + void* d_wsFwd = 0; + void* d_wsBwdData = 0; + void* d_wsBwdW = 0; +}; + +void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, + const convParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // w + cudnnFilterDescriptor_t w_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); + gpuPrm->w_desc = w_desc; + + // conv + cudnnConvolutionDescriptor_t conv_desc = nullptr; + cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); + cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), + CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); + if (!isFirst) + cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); + gpuPrm->conv_desc = conv_desc; + + // output + int out_n = 0, out_c = 0, out_h = 0, out_w = 0; + cuCHECK(cudnnGetConvolution2dForwardOutputDim( + conv_desc, in_desc, w_desc, + &out_n, &out_c, &out_h, &out_w)); + + if (outsz != snSize(out_w, out_h, out_c, out_n)){ + ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); + return; + } + + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + // bias + cudnnTensorDescriptor_t bias_desc; + cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + 1, out_c, 1, 1)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); + gpuPrm->bias_desc = bias_desc; + + // algorithm + cudnnConvolutionFwdAlgo_t algoFwd; + cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, + CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); + gpuPrm->algoFwd = algoFwd; + + // workspace + size_t wsFwdSz = 0; + cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, algoFwd, &wsFwdSz)); + gpuPrm->wsFwdSz = wsFwdSz; + + + size_t wsBwdDataSz = 0, wsBwdWSz = 0; + if (isLern){ + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // dw + cudnnFilterDescriptor_t dw_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); + gpuPrm->dw_desc = dw_desc; + + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + + // algorithm + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, + CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); + gpuPrm->algoBwdData = algoBwdData; + + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, + CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); + gpuPrm->algoBwdW = algoBwdW; + + // workspace + cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, algoBwdData, &wsBwdDataSz)); + gpuPrm->wsBwdDataSz = wsBwdDataSz; + + cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); + gpuPrm->wsBwdWSz = wsBwdWSz; + } + + if (isFirst){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + } + else if (gpuPrm->inszMem < insz.size()){ + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + + if (isLern){ + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + gpuPrm->inszMem = insz.size(); + } +} + +void Convolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); + + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); + } +} + +__global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ + + size_t osz = outsz.w * outsz.h; + + snFloat* pOut = output + osz * outsz.d * blockIdx.x; + unsigned int d = threadIdx.x; + while (d < outsz.d){ + + snFloat b = bias[d]; + for (size_t j = 0; j < osz; ++j) + pOut[j] += b; + + pOut += osz * blockDim.x; + + d += blockDim.x; + } +} + +void Convolution::forwardCUDA(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + size_t isz = insz.size(), osz = outsz.size(); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + input, + gpuPrm->w_desc, + weight, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->out_desc, + output)); + + // +bias + cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); +} + +void Convolution::backwardCUDA_GW(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + weight, + gpuPrm->grin_desc, + gradIn, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + + cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + input, + gpuPrm->grin_desc, + gradIn, + gpuPrm->conv_desc, + gpuPrm->algoBwdW, + gpuPrm->d_wsBwdW, + gpuPrm->wsBwdWSz, + &beta, + gpuPrm->dw_desc, + dWeightOut)); + + /*cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + &beta, + gpuPrm->bias_desc, + gpuPrm->d_bias));*/ + +} + +void Convolution::backwardCUDA_G(const convParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + weight, + gpuPrm->grin_desc, + gradIn, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + +} + + +// CUDA implementation. Deprecated +// +//#include +//#include "../stdafx.h" +//#include "snOperatorCUDA/src/Operator/convolution.h" +// +//using namespace std; +//using namespace SN_Base; +// +//#ifndef cuCHECK +//#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +//#endif +// +// +//struct gpuParams{ +// +// cudaDeviceProp* cu_deviceProps = 0; +// +// snFloat* d_in = 0; +// snFloat* d_out = 0; +// snFloat* d_w = 0; +// snFloat* d_dw = 0; +// snFloat* d_grout = 0; +// +// size_t inszMem = 0; +//}; +// +//void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, +// const convParams& prms, void** pGpuPrm){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; +// if (!gpuPrm){ +// cudaDeviceProp* cu_deviceProps = new cudaDeviceProp(); +// +// cudaGetDeviceProperties(cu_deviceProps, 0); +// if (cu_deviceProps->major < 2){ +// ERROR_MESS("%s requires SM >= 2.0"); +// delete cu_deviceProps; +// return; +// } +// gpuPrm = new gpuParams(); +// memset(gpuPrm, 0, sizeof(gpuParams)); +// *pGpuPrm = gpuPrm; +// +// gpuPrm->cu_deviceProps = cu_deviceProps; +// +// if (!gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); +// +// if (isLern){ +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); +// } +// } +// } +// else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ +// +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; +// +// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); +// +// if (isLern){ +// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; +// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; +// +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); +// } +// +// gpuPrm->inszMem = insz.size(); +// } +//} +// +//void Convolution::freeParamCUDA(void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// +// if (!gpuPrm) return; +// +// delete gpuPrm->cu_deviceProps; +// +// cuCHECK(cudaFree(gpuPrm->d_in)); +// cuCHECK(cudaFree(gpuPrm->d_w)); +// cuCHECK(cudaFree(gpuPrm->d_out)); +// +// if (gpuPrm->d_grout){ // isLern +// cuCHECK(cudaFree(gpuPrm->d_grout)); +// cuCHECK(cudaFree(gpuPrm->d_dw)); +// } +//} +// +//__global__ void cuConvFwd(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, +// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ +// +// size_t wStepByD = fWidth * fHeight, // step weight by input +// wStepByK = wStepByD * insz.d, // step weight by output +// outStepByD = outsz.w * outsz.h, // step out by input +// outStepByN = outStepByD * outsz.d, // step out by batch +// inStepByD = insz.w * insz.h, // step in by input +// inStepByN = inStepByD * insz.d; // step in by batch +// +// // gridDim.x - number of out layers +// // gridDim.y - batch size +// +// weight += blockIdx.x * wStepByK; +// output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; +// input += blockIdx.y * inStepByN; +// +// +// unsigned int oz = 0; +// while (oz < insz.d){ +// +// unsigned int oy = threadIdx.y; +// while (oy < outsz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < outsz.w){ +// +// if (oz == 0) +// output[ox + oy * outsz.w] = weight[(outsz.d - blockIdx.x) * wStepByK + blockIdx.x]; // bias +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel +// snFloat csum = 0; +//#pragma unroll +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % fWidth, cy = c / fWidth; +// +// csum += input[(cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w] * weight[cx + cy * fWidth]; +// } +// output[ox + oy * outsz.w] += csum; +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// weight += wStepByD; +// input += inStepByD; +// ++oz; +// } +//} +// +//void Convolution::forwardCUDA(const convParams& prms, +// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// size_t isz = insz.size(), osz = outsz.size(), +// wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); +// } +// +// // input +// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // weight +// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(outsz.d), int(outsz.n)); +// +// cuConvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, +// prms.fHeight, +// prms.dilate, +// prms.stride, +// gpuPrm->d_w, +// insz, +// gpuPrm->d_in, +// outsz, +// gpuPrm->d_out); +// +// // result +// cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; +// } +//} +// +//__global__ void cuConvBwd_GW(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, +// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ +// +// size_t wStepByD = fWidth * fHeight, // step weight by input +// wStepByK = wStepByD * insz.d, // step weight by output +// wStepByN = wStepByK * outsz.d + outsz.d, // step weight by batch +// outStepByD = outsz.w * outsz.h, // step out by input +// outStepByN = outStepByD * outsz.d, // step out by batch +// inStepByD = insz.w * insz.h, // step in by input +// inStepByN = inStepByD * insz.d; // step in by batch +// +// // gridDim.x - number of input layers +// // gridDim.y - batch size +// +// input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// weight += blockIdx.x * wStepByD; +// dWeightOut += blockIdx.x * wStepByD + blockIdx.y * wStepByN; +// gradIn += blockIdx.y * outStepByN; +// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// +// +// unsigned int oz = 0; +// while (oz < outsz.d){ +// +// unsigned int oy = threadIdx.y; +// while (oy < outsz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < outsz.w){ +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel conv +// snFloat grIn = gradIn[ox + oy * outsz.w]; +//#pragma unroll +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % fWidth, cy = c / fWidth, +// si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, +// sw = cx + cy * fWidth; +// +// gradOut[si] += grIn * weight[sw]; +// +// dWeightOut[sw] += grIn * input[si]; +// } +// if (blockIdx.x == 0) +// dWeightOut[wStepByK * (outsz.d - oz) + oz] += grIn; // bias +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// weight += wStepByK; +// dWeightOut += wStepByK; +// gradIn += outStepByD; +// ++oz; +// } +//} +// +//// weighted averaging over batch +//__global__ void cuConvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ +// +// size_t wStepByD = fWidth * fHeight, // step weight by input +// wStepByK = wStepByD * insz.d, // step weight by output +// wStepByN = wStepByK * kernel + kernel; // step weight by batch +// +// unsigned int ox = threadIdx.x; +// while (ox < wStepByN){ +// +// snFloat csum = weight[ox]; +// for (size_t i = 1; i < insz.n; ++i) +// csum += weight[ox + wStepByN * i]; +// +// weight[ox] = csum / insz.n; +// +// ox += blockDim.x; +// } +//} +// +//void Convolution::backwardCUDA_GW(const convParams& prms, +// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// size_t isz = insz.size(), osz = outsz.size(), +// wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; +// snFloat* d_grin = gpuPrm->d_out; +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); +// } +// +// // input +// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // weight +// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // out +// cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); +// cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(insz.d), int(outsz.n)); +// +// cuConvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, +// prms.fHeight, +// prms.dilate, +// prms.stride, +// gpuPrm->d_w, +// insz, +// gpuPrm->d_in, +// outsz, +// d_grin, +// gpuPrm->d_grout, +// gpuPrm->d_dw); +// +// cuConvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); +// +// // result +// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; +// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; +// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; +// } +//} +// +//__global__ void cuConvBwd_G(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, +// snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ +// +// size_t wStepByD = fWidth * fHeight, // step weight by input +// wStepByK = wStepByD * insz.d, // step weight by output +// outStepByD = outsz.w * outsz.h, // step out by input +// outStepByN = outStepByD * outsz.d, // step out by batch +// inStepByD = insz.w * insz.h, // step in by input +// inStepByN = inStepByD * insz.d; // step in by batch +// +// +// // gridDim.x - number of input layers +// // gridDim.y - batch size +// +// weight += blockIdx.x * wStepByD; +// gradIn += blockIdx.y * outStepByN; +// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// +// unsigned int oz = 0; +// while (oz < outsz.d){ +// +// unsigned int oy = threadIdx.y; +// while (oy < outsz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < outsz.w){ +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel conv +// snFloat grIn = gradIn[ox + oy * outsz.w]; +//#pragma unroll +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % fWidth, cy = c / fWidth, +// si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, +// sw = cx + cy * fWidth; +// +// gradOut[si] += grIn * weight[sw]; +// } +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// weight += wStepByK; +// gradIn += outStepByD; +// ++oz; +// } +//} +// +//void Convolution::backwardCUDA_G(const convParams& prms, +// const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// size_t isz = insz.size(), osz = outsz.size(), +// wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; +// snFloat* d_grin = gpuPrm->d_out; +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); +// } +// +// // input +// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // weight +// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(insz.d), int(outsz.n)); +// +// // out +// cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); +// +// cuConvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, +// prms.fHeight, +// prms.dilate, +// prms.stride, +// gpuPrm->d_w, +// insz, outsz, +// d_grin, +// gpuPrm->d_grout); +// +// // result +// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; +// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; +// } +//} diff --git a/src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu b/src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu new file mode 100644 index 0000000..71056b1 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu @@ -0,0 +1,795 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include +#include +#include "../stdafx.h" +#include "snOperatorCUDA/src/Operator/deconvolution.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnConvolutionDescriptor_t conv_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + cudnnFilterDescriptor_t w_desc = 0; + cudnnFilterDescriptor_t dw_desc = 0; + cudnnTensorDescriptor_t bias_desc = 0; + + cudnnConvolutionFwdAlgo_t algoFwd; + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + + size_t wsFwdSz = 0; + size_t wsBwdDataSz = 0; + size_t wsBwdWSz = 0; + size_t inszMem = 0; + + void* d_wsFwd = 0; + void* d_wsBwdData = 0; + void* d_wsBwdW = 0; + +}; + +void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, + const deconvParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // w + cudnnFilterDescriptor_t w_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); + gpuPrm->w_desc = w_desc; + + // conv + cudnnConvolutionDescriptor_t conv_desc = nullptr; + cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); + cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), 1, 1, + CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); + if (!isFirst) + cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); + gpuPrm->conv_desc = conv_desc; + + // output + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + // algorithm + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, + CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); + gpuPrm->algoBwdData = algoBwdData; + + // workspace + size_t wsBwdDataSz = 0; + cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, algoBwdData, &wsBwdDataSz)); + gpuPrm->wsBwdDataSz = wsBwdDataSz; + + + size_t wsFwdSz = 0, wsBwdWSz = 0; + if (isLern){ + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // dw + cudnnFilterDescriptor_t dw_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); + gpuPrm->dw_desc = dw_desc; + + // bias + cudnnTensorDescriptor_t bias_desc; + cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + 1, int(insz.d), 1, 1)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); + gpuPrm->bias_desc = bias_desc; + + // algorithm + cudnnConvolutionFwdAlgo_t algoFwd; + cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, + CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); + gpuPrm->algoFwd = algoFwd; + + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, + CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); + gpuPrm->algoBwdW = algoBwdW; + + // workspace + cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, algoFwd, &wsFwdSz)); + gpuPrm->wsFwdSz = wsFwdSz; + + cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); + gpuPrm->wsBwdWSz = wsBwdWSz; + } + + if (isFirst){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + } + else if (gpuPrm->inszMem < insz.size()){ + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + + if (isLern){ + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + gpuPrm->inszMem = insz.size(); + } +} + +void Deconvolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); + + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); + } +} + +void Deconvolution::forwardCUDA(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + weight, + gpuPrm->in_desc, + input, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->out_desc, + output)); + +} + +__global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ + + size_t isz = insz.w * insz.h; + + snFloat* pGrOut = grout + isz * insz.d * blockIdx.x; + unsigned int d = threadIdx.x; + while (d < insz.d){ + + snFloat b = bias[d]; + for (size_t j = 0; j < isz; ++j) + pGrOut[j] += b; + + pGrOut += isz * blockDim.x; + + d += blockDim.x; + } +} + +void Deconvolution::backwardCUDA_GW(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + gpuPrm->w_desc, + weight, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + + cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + gpuPrm->in_desc, + input, + gpuPrm->conv_desc, + gpuPrm->algoBwdW, + gpuPrm->d_wsBwdW, + gpuPrm->wsBwdWSz, + &beta, + gpuPrm->dw_desc, + dWeightOut)); + + /* cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + input, + &beta, + gpuPrm->bias_desc, + gpuPrm->d_bias));*/ + + // +bias + cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + +} + +void Deconvolution::backwardCUDA_G(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + gpuPrm->w_desc, + weight, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + + // +bias + cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + +} + + +// CUDA implementation. Deprecated +// +//#include +//#include "../stdafx.h" +//#include "snOperatorCUDA/src/Operator/deconvolution.h" +// +//using namespace std; +//using namespace SN_Base; +// +//#ifndef cuCHECK +//#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +//#endif +// +//struct gpuParams{ +// +// snFloat* d_in = 0; +// snFloat* d_w = 0; +// snFloat* d_dw = 0; +// snFloat* d_out = 0; +// snFloat* d_grout = 0; +// +// size_t inszMem = 0; +//}; +// +//void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** pGpuPrm){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; +// if (!gpuPrm){ +// +// cudaDeviceProp cu_deviceProps; +// cudaGetDeviceProperties(&cu_deviceProps, 0); +// if (cu_deviceProps.major < 3){ +// ERROR_MESS("%s requires SM >= 3.0"); +// return; +// } +// gpuPrm = new gpuParams(); +// memset(gpuPrm, 0, sizeof(gpuParams)); +// *pGpuPrm = gpuPrm; +// +// if (!gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); +// +// if (isLern){ +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); +// } +// } +// } +// else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ +// +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; +// +// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); +// +// if (isLern){ +// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; +// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; +// +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); +// } +// gpuPrm->inszMem = insz.size(); +// } +//} +// +//void Deconvolution::freeParamCUDA(void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// +// if (!gpuPrm) return; +// +// cuCHECK(cudaFree(gpuPrm->d_in)); +// cuCHECK(cudaFree(gpuPrm->d_w)); +// cuCHECK(cudaFree(gpuPrm->d_out)); +// +// if (gpuPrm->d_grout){ // isLern +// cuCHECK(cudaFree(gpuPrm->d_grout)); +// cuCHECK(cudaFree(gpuPrm->d_dw)); +// } +//} +// +//__global__ void cuDeconvFwd(size_t fWidth, size_t fHeight, size_t stride, +// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ +// +// size_t wStepByD = fWidth * fHeight, +// wStepByK = wStepByD * outsz.d, +// outStepByD = outsz.w * outsz.h, +// outStepByN = outStepByD * outsz.d, +// inStepByD = insz.w * insz.h, +// inStepByN = inStepByD * insz.d; +// +// // gridDim.x - number of output layers +// // gridDim.y - batch sz +// +// weight += blockIdx.x * wStepByD; +// input += blockIdx.y * inStepByN; +// output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; +// +// +// unsigned int oz = 0; +// while (oz < insz.d){ +// +// unsigned int oy = threadIdx.y; +// while (oy < insz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < insz.w){ +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel +// snFloat in = input[ox + oy * insz.w]; +//#pragma unroll +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % fWidth, cy = c / fWidth, +// si = (cx + posW) + (cy + posH) * outsz.w, +// sw = cx + cy * fWidth; +// +// output[si] += in * weight[sw]; +// } +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// weight += wStepByK; +// input += inStepByD; +// ++oz; +// } +//} +// +//void Deconvolution::forwardCUDA(const deconvParams& prms, +// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// size_t isz = insz.size(), osz = outsz.size(), +// wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); +// } +// +// // input +// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // weight +// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // out +// cuCHECK(cudaMemset(gpuPrm->d_out, 0, osz * sizeof(snFloat))); +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(outsz.d), int(outsz.n)); +// +// cuDeconvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, +// prms.fHeight, +// prms.stride, +// gpuPrm->d_w, +// insz, +// gpuPrm->d_in, +// outsz, +// gpuPrm->d_out); +// +// // result +// cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; +// } +//} +// +//__global__ void cuDeconvBwd_GW(size_t fWidth, size_t fHeight, size_t stride, +// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ +// +// size_t wStepByD = fWidth * fHeight, +// wStepByK = wStepByD * outsz.d, +// wStepByN = wStepByK * insz.d + insz.d, +// outStepByD = outsz.w * outsz.h, +// outStepByN = outStepByD * outsz.d, +// inStepByD = insz.w * insz.h, +// inStepByN = inStepByD * insz.d; +// +// // gridDim.x - number of input layers +// // gridDim.y - batch sz +// +// input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// weight += blockIdx.x * wStepByK; +// dWeightOut += blockIdx.x * wStepByK + blockIdx.y * wStepByN; +// gradIn += blockIdx.y * outStepByN; +// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// +// unsigned int oz = 0; +// while (oz < outsz.d){ +// +// unsigned int oy = threadIdx.y; +// while (oy < insz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < insz.w){ +// +// if (oz == 0) +// gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel +// snFloat csum = 0, cin = input[ox + oy * insz.w]; +//#pragma unroll +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % fWidth, cy = c / fWidth, +// si = (cx + posW) + (cy + posH) * outsz.w, +// sw = cx + cy * fWidth; +// +// csum += gradIn[si] * weight[sw]; +// +// dWeightOut[sw] += gradIn[si] * cin; +// } +// gradOut[ox + oy * insz.w] += csum; +// +// if (oz == 0) +// dWeightOut[wStepByK * (insz.d - blockIdx.x) + blockIdx.x] += cin; // bias +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// weight += wStepByD; +// dWeightOut += wStepByD; +// gradIn += outStepByD; +// ++oz; +// } +//} +// +//__global__ void cuDeconvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ +// +// size_t wStepByD = fWidth * fHeight, +// wStepByK = wStepByD * kernel, +// wStepByN = wStepByK * insz.d + insz.d; +// +// unsigned int ox = threadIdx.x; +// while (ox < wStepByN){ +// +// snFloat csum = weight[ox]; +// for (size_t i = 1; i < insz.n; ++i) +// csum += weight[ox + wStepByN * i]; +// +// weight[ox] = csum / insz.n; +// +// ox += blockDim.x; +// } +//} +// +//void Deconvolution::backwardCUDA_GW(const deconvParams& prms, +// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// size_t isz = insz.size(), osz = outsz.size(), +// wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; +// snFloat* d_grin = gpuPrm->d_out; +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); +// } +// +// // input +// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // weight +// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // out +// cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(insz.d), int(outsz.n)); +// +// cuDeconvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, +// prms.fHeight, +// prms.stride, +// gpuPrm->d_w, +// insz, +// gpuPrm->d_in, +// outsz, d_grin, +// gpuPrm->d_grout, +// gpuPrm->d_dw); +// +// cuDeconvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); +// +// // result +// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; +// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; +// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; +// } +//} +// +//__global__ void cuDeconvBwd_G(size_t fWidth, size_t fHeight, size_t stride, +// snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ +// +// size_t wStepByD = fWidth * fHeight, +// wStepByK = wStepByD * outsz.d, +// outStepByD = outsz.w * outsz.h, +// outStepByN = outStepByD * outsz.d, +// inStepByD = insz.w * insz.h, +// inStepByN = inStepByD * insz.d; +// +// // gridDim.x - number of input layers +// // gridDim.y - batch size +// +// weight += blockIdx.x * wStepByK; +// gradIn += blockIdx.y * outStepByN; +// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// +// unsigned int oz = 0; +// while (oz < outsz.d){ +// +// unsigned int oy = threadIdx.y; +// while (oy < insz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < insz.w){ +// +// if (oz == 0) +// gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel +// snFloat csum = 0; +//#pragma unroll +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % fWidth, cy = c / fWidth, +// si = (cx + posW) + (cy + posH) * outsz.w, +// sw = cx + cy * fWidth; +// +// csum += gradIn[si] * weight[sw]; +// } +// gradOut[ox + oy * insz.w] += csum; +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// weight += wStepByD; +// gradIn += outStepByD; +// ++oz; +// } +//} +// +//void Deconvolution::backwardCUDA_G(const deconvParams& prms, +// const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// size_t isz = insz.size(), osz = outsz.size(), +// wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; +// snFloat* d_grin = gpuPrm->d_out; +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); +// } +// +// // input +// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // weight +// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(insz.d), int(outsz.n)); +// +// cuDeconvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, +// prms.fHeight, +// prms.stride, +// gpuPrm->d_w, +// insz, +// outsz, +// d_grin, +// gpuPrm->d_grout); +// +// // result +// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; +// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; +// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; +// } +//} diff --git a/src/snOperator_CPU_cuDNN/src/CUDA/fullyConnectedCUDA.cu b/src/snOperatorCUDA/src/CUDA/fullyConnectedCUDA.cu similarity index 60% rename from src/snOperator_CPU_cuDNN/src/CUDA/fullyConnectedCUDA.cu rename to src/snOperatorCUDA/src/CUDA/fullyConnectedCUDA.cu index e9556e8..5825b3e 100644 --- a/src/snOperator_CPU_cuDNN/src/CUDA/fullyConnectedCUDA.cu +++ b/src/snOperatorCUDA/src/CUDA/fullyConnectedCUDA.cu @@ -23,12 +23,10 @@ // THE SOFTWARE. // -#ifdef SN_CUDA - #include #include #include "../stdafx.h" -#include "snOperator/src/Operator/fullyConnected.h" +#include "snOperatorCUDA/src/Operator/fullyConnected.h" using namespace std; using namespace SN_Base; @@ -41,14 +39,7 @@ using namespace SN_Base; struct gpuParams{ cublasHandle_t cuBLAS = 0; - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; + }; void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel, void** pGpuPrm){ @@ -74,42 +65,11 @@ void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel cuCHECK(cublasCreate(&cuHandle)); gpuPrm->cuBLAS = cuHandle; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); - } - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < ida * bsz)){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); - } - - gpuPrm->inszMem = ida * bsz; } } void FullyConnected::freeParamCUDA(void* gpuPrms){ - + cudaSetDevice(gpuDeviceId_); gpuParams* gpuPrm = (gpuParams*)gpuPrms; @@ -117,14 +77,6 @@ void FullyConnected::freeParamCUDA(void* gpuPrms){ if (!gpuPrm) return; cublasDestroy(gpuPrm->cuBLAS); - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_out)); - cuCHECK(cudaFree(gpuPrm->d_w)); - - if (gpuPrm->d_grout){ // isLern - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_dw)); - } } __global__ void cuFwdBias(size_t kernel, snSize insz, snFloat* weight, snFloat* output){ @@ -142,21 +94,15 @@ __global__ void cuFwdBias(size_t kernel, snSize insz, snFloat* weight, snFloat* } void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloat* input, const snFloat* weight, snFloat* output, void* gpuPrms){ - + cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); - } - - cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); + + // cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyHostToDevice)); + // cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyHostToDevice)); // Out = α * W * In + βC // In - data input matrix - values from the previous layer @@ -170,25 +116,17 @@ void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloa bsz, // In, rows ida, // In, cols, В М - rows &alpha, // α - gpuPrm->d_w, // W + weight, // W krn, // W, step to next W (W21 - W11) - gpuPrm->d_in, // In + input, // In ida, // In, step to next X (X21 - X11) &beta, // β - gpuPrm->d_out, // Out + output, // Out krn)); // Out, step to next Y (Y21 - Y11) // +bias cuFwdBias <<< int(insz.n), 128 >>> (kernel, insz, gpuPrm->d_w, gpuPrm->d_out); - - // result - cuCHECK(cublasGetMatrix(bsz, krn, sizeof(snFloat), gpuPrm->d_out, bsz, output, bsz)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } + } __global__ void cuBwdBias(size_t kernel, snSize insz, snFloat* gradIn, snFloat* dWOut){ @@ -208,24 +146,16 @@ __global__ void cuBwdBias(size_t kernel, snSize insz, snFloat* gradIn, snFloat* void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* input, const snFloat* gradIn, snFloat* gradOut, snFloat* dWOut, void* gpuPrms){ - + cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, bsz * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); - } - + /* cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); +*/ // Weight gradient // dW = αIn^T * GrIn + βdW @@ -239,22 +169,18 @@ void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, ida, // In, cols (+1 - X0) bsz, // In, rows &alpha, // α - d_grin, // GrIn + gradIn, // GrIn krn, // GrIn, step to next - gpuPrm->d_in, // In + input, // In ida, // In, step to next X (X21 - X11) &beta, // β - gpuPrm->d_dw, // dW + dWOut, // dW krn)); // dW, step to next // bias cuBwdBias <<< 1, 128 >>> (kernel, insz, d_grin, gpuPrm->d_dw); - - // result - cuCHECK(cudaMemcpy(dWOut, gpuPrm->d_dw, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - - cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); + +// cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); //// Gradient for previous layer //// GrOut = αGrIn * W^T + βGrOut @@ -268,44 +194,27 @@ void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, bsz, // W, rows krn, // GrIn, cols &alpha, // α - gpuPrm->d_w, // W + weight, // W krn, // W, step to next - d_grin, // GrIn + gradIn, // GrIn krn, // GrIn, step to next &beta, // β - gpuPrm->d_grout, // GrOut + gradOut, // GrOut ida)); // GrOut, step to next - - - // result - cuCHECK(cublasGetMatrix(bsz, ida, sizeof(snFloat), gpuPrm->d_grout, bsz, gradOut, bsz)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - } + } void FullyConnected::backwardCUDA_G(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - + cudaSetDevice(gpuDeviceId_); gpuParams* gpuPrm = (gpuParams*)gpuPrms; int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_w, ida * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, bsz * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - } - - cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); + + /*cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); +*/ //// Gradient for previous layer //// GrOut = αGrIn * W^T + βGrOut @@ -319,21 +228,12 @@ void FullyConnected::backwardCUDA_G(size_t kernel, const snFloat* weight, const bsz, // W, rows krn, // GrIn, cols &alpha, // α - gpuPrm->d_w, // W + weight, // W krn, // W, step to next - d_grin, // GrIn + gradIn, // GrIn krn, // GrIn, step to next &beta, // β - gpuPrm->d_grout, // GrOut + gradOut, // GrOut ida)); // GrOut, step to next - - cuCHECK(cublasGetMatrix(bsz, ida, sizeof(snFloat), gpuPrm->d_grout, bsz, gradOut, bsz)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#endif \ No newline at end of file + +} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/poolingCUDA.cu b/src/snOperatorCUDA/src/CUDA/poolingCUDA.cu new file mode 100644 index 0000000..7d1cefb --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/poolingCUDA.cu @@ -0,0 +1,510 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include +#include +#include "../stdafx.h" +#include "snOperatorCUDA/src/Operator/pooling.h" + +using namespace std; +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#endif + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnPoolingDescriptor_t pool_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + +}; + +void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // pool + cudnnPoolingDescriptor_t pool_desc = nullptr; + cuCHECK(cudnnCreatePoolingDescriptor(&pool_desc)); + + cudnnPoolingMode_t poolT = cudnnPoolingMode_t::CUDNN_POOLING_MAX; + if (poolPrms.type == poolType::avg) + poolT = cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; + + cuCHECK(cudnnSetPooling2dDescriptor(pool_desc, poolT, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, + int(poolPrms.kernel), int(poolPrms.kernel), 0, 0, int(poolPrms.stride), int(poolPrms.stride))); + if (!isFirst) + cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); + gpuPrm->pool_desc = pool_desc; + + // output + int out_n = 0, out_c = 0, out_h = 0, out_w = 0; + cuCHECK(cudnnGetPooling2dForwardOutputDim(pool_desc, in_desc, + &out_n, &out_c, &out_h, &out_w)); + + if (outsz != snSize(out_w, out_h, out_c, out_n)){ + ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); + return; + } + + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + if (isLern){ + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + } + +} + +void Pooling::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + +} + +__global__ void cuFiltrNegative(snSize outsz, snFloat* out){ + + out += blockIdx.x * outsz.w * outsz.h * outsz.d; + + unsigned int k = threadIdx.x; + while (k < outsz.d){ + + snFloat* pOut = out + outsz.w * outsz.h * k; + for (size_t j = 0; j < (outsz.w * outsz.h); ++j) + if (pOut[j] < 0) pOut[j] = 0.0; + + k += blockDim.x; + } +} + +void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, + const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnPoolingForward(gpuPrm->cudnn, + gpuPrm->pool_desc, + &alpha, + gpuPrm->in_desc, + input, + &beta, + gpuPrm->out_desc, + output)); + + // filtrNegative + dim3 dimBlock(256); + dim3 dimGrid(int(outsz.n)); + + cuFiltrNegative << < dimGrid, dimBlock >> >(outsz, output); + +} + +void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const snFloat* output, const snFloat* gradIn, + const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnPoolingBackward(gpuPrm->cudnn, + gpuPrm->pool_desc, + &alpha, + gpuPrm->out_desc, + output, + gpuPrm->grin_desc, + gradIn, + gpuPrm->in_desc, + input, + &beta, + gpuPrm->grout_desc, + gradOut)); + +} + + + +// CUDA implementation. Deprecated +// +//#include +//#include "../stdafx.h" +//#include "snOperatorCUDA/src/Operator/pooling.h" +// +//using namespace std; +//using namespace SN_Base; +// +//#ifndef cuCHECK +//#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +//#endif +// +//struct gpuParams{ +// +// snFloat* d_in = 0; +// snFloat* d_out = 0; +// size_t* d_idx = 0; +// +// size_t inszMem = 0; +//}; +// +//void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; +// if (!gpuPrm){ +// +// cudaDeviceProp cu_deviceProps; +// cudaGetDeviceProperties(&cu_deviceProps, 0); +// if (cu_deviceProps.major < 2){ +// ERROR_MESS("%s requires SM >= 2.0"); +// return; +// } +// gpuPrm = new gpuParams(); +// memset(gpuPrm, 0, sizeof(gpuParams)); +// *pGpuPrm = gpuPrm; +// +// if (!gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); +// } +// } +// else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ +// +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; +// cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; +// +// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); +// +// gpuPrm->inszMem = insz.size(); +// } +//} +// +//void Pooling::freeParamCUDA(void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// +// if (!gpuPrm) return; +// +// cudaFree(gpuPrm->d_in); +// cudaFree(gpuPrm->d_out); +// cudaFree(gpuPrm->d_idx); +//} +// +//__global__ void cuPoolFwd(poolType type, size_t kernel, size_t stride, snSize insz, snFloat* input, snSize outsz, snFloat* output, size_t* outputInx){ +// +// size_t outStepByD = outsz.w * outsz.h, // step out by output +// outStepByN = outStepByD * outsz.d, // step out by batch +// inStepByD = insz.w * insz.h, // step in by input +// inStepByN = inStepByD * insz.d, // step in by batch +// kernelSz = kernel * kernel; +// +// // gridDim.x - number of input layers +// // gridDim.y - batch sz +// +// input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; +// outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; +// +// if (type == poolType::max){ // max +// +// unsigned int oy = threadIdx.y; +// while (oy < outsz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < outsz.w){ +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel +// snFloat valMax = 0; size_t idx = 0; +//#pragma unroll +// for (size_t c = 0; c < kernelSz; ++c){ +// +// size_t cx = c % kernel, cy = c / kernel; +// +// snFloat val = input[cx + posW + (cy + posH) * insz.w]; +// if (val > valMax){ +// valMax = val; +// idx = c; +// } +// } +// output[ox + oy * outsz.w] = valMax; +// outputInx[ox + oy * outsz.w] = idx; +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// } +// else{ // mean +// +// unsigned int oy = threadIdx.y; +// while (oy < outsz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < outsz.w){ +// +// size_t posW = ox * stride, posH = oy * stride; +// +// // kernel +// snFloat valMean = 0; +//#pragma unroll +// for (size_t c = 0; c < kernelSz; ++c){ +// +// size_t cx = c % kernel, cy = c / kernel; +// +// valMean += input[cx + posW + (cy + posH) * insz.w]; +// } +// output[ox + oy * outsz.w] = valMean / kernelSz; +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// } +//} +// +//void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, +// const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// size_t isz = insz.size(), osz = outsz.size(); +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&gpuPrm->d_idx, osz * sizeof(size_t))); +// } +// +// // input +// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(outsz.d), int(outsz.n)); +// +// cuPoolFwd << < dimGrid, dimBlock >> >(poolPrms.type, +// poolPrms.kernel, +// poolPrms.stride, +// insz, +// gpuPrm->d_in, +// outsz, +// gpuPrm->d_out, +// gpuPrm->d_idx); +// +// // result +// cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// cuCHECK(cudaMemcpy(outputInx, gpuPrm->d_idx, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; +// cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; +// } +//} +// +//__global__ void cuPoolBwd(poolType type, size_t kernel, size_t stride, snSize outsz, size_t* outputInx, snFloat* gradIn, snSize insz, snFloat* gradOut){ +// +// size_t outStepByD = outsz.w * outsz.h, // step out by output +// outStepByN = outStepByD * outsz.d, // step out by batch +// inStepByD = insz.w * insz.h, // step in by input +// inStepByN = inStepByD * insz.d, // step in by batch +// kernelSz = kernel * kernel; +// +// // gridDim.x - number of input layers +// // gridDim.y - batch sz +// +// gradIn += blockIdx.x * outStepByD + blockIdx.y * outStepByN; +// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; +// +// if (type == poolType::max){ // max +// +// outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; +// +// unsigned int oy = threadIdx.y; +// while (oy < outsz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < outsz.w){ +// +// size_t posW = ox * stride, posH = oy * stride; +// +// size_t c = outputInx[ox + oy * outsz.w], cx = c % kernel, cy = c / kernel; +// +// gradOut[cx + posW + (cy + posH) * insz.w] = gradIn[ox + oy * outsz.w]; +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// } +// else{ // mean +// +// unsigned int oy = threadIdx.y; +// while (oy < outsz.h){ +// +// unsigned int ox = threadIdx.x; +// while (ox < outsz.w){ +// +// size_t posW = ox * stride, posH = oy * stride; +// +// snFloat mean = gradIn[ox + oy * outsz.w] / kernel; +//#pragma unroll +// for (size_t c = 0; c < kernelSz; ++c){ +// size_t cx = c % kernel, cy = c / kernel; +// gradOut[(cx + posW) + (cy + posH) * insz.w] = mean; +// } +// +// ox += blockDim.x; +// } +// oy += blockDim.y; +// } +// } +//} +// +//void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, +// const snSize& insz, const SN_Base::snFloat* input, snFloat* gradOut, void* gpuPrms){ +// +// cudaSetDevice(gpuDeviceId_); +// +// gpuParams* gpuPrm = (gpuParams*)gpuPrms; +// +// snFloat* d_grin = gpuPrm->d_out, +// * d_grout = gpuPrm->d_in; +// size_t* d_idx = gpuPrm->d_idx; +// +// size_t isz = insz.size(), osz = outsz.size(); +// if (gpuClearMem_){ +// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&d_grout, isz * sizeof(snFloat))); +// cuCHECK(cudaMalloc(&d_idx, osz * sizeof(size_t))); +// } +// +// // input +// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// cuCHECK(cudaMemcpy(d_idx, outputInx, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); +// +// // out +// cuCHECK(cudaMemset(d_grout, 0, isz * sizeof(snFloat))); +// +// // run +// dim3 dimBlock(16, 16); +// dim3 dimGrid(int(outsz.d), int(outsz.n)); +// +// cuPoolBwd << < dimGrid, dimBlock >> >(poolPrms.type, +// poolPrms.kernel, +// poolPrms.stride, +// outsz, +// d_idx, +// d_grin, +// insz, +// d_grout); +// +// // result +// cuCHECK(cudaMemcpy(gradOut, d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); +// +// if (gpuClearMem_){ +// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; +// cuCHECK(cudaFree(d_grout)); gpuPrm->d_in = 0; +// cuCHECK(cudaFree(d_idx)); gpuPrm->d_idx = 0; +// } +//} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/tensor.cu b/src/snOperatorCUDA/src/CUDA/tensor.cu new file mode 100644 index 0000000..b3d8e9e --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/tensor.cu @@ -0,0 +1,128 @@ + +#include + +#include +#include + +#include "../stdafx.h" + +using namespace SN_Base; + +#ifndef cuCHECK +#define cuCHECK(func) if (func != 0){ std::cout << "CUDA error: " << cudaGetErrorString(cudaGetLastError()) << std::endl; return;} +#endif + +/// tensor - input data and output data of each node of the network. + +Tensor::Tensor(const snSize& sz) : sz_(sz){ + + size_t ssz = sz.size(); + + if (ssz > 0){ + cuCHECK(cudaMalloc(&data_, ssz * sizeof(snFloat))); + cuCHECK(cudaMemset(data_, 0, ssz * sizeof(snFloat))); + } +} + +Tensor::~Tensor(){ + if (data_) + cuCHECK(cudaFree(data_)); +} + +Tensor::Tensor(const Tensor& other){ + setData(other.getData(), other.size()); +} + +Tensor& Tensor::operator=(const Tensor& other){ + + setData(other.getData(), other.size()); + + return *this; +} + +Tensor& Tensor::operator+=(const Tensor& other){ + + assert(other == *this); + + auto od = other.getData(); + + //size_t sz = this->size().size(); + //for (size_t i = 0; i < sz; ++i){ + // data_[i] += od[i]; + //} + + return *this; +} + +Tensor& Tensor::operator-=(const Tensor& other){ + + assert(other == *this); + + auto od = other.getData(); + + /* size_t sz = this->size().size(); + for (size_t i = 0; i < sz; ++i){ + data_[i] -= od[i]; + }*/ + + return *this; +} + +void Tensor::setData(const snFloat* data, const snSize& nsz){ + + size_t nnsz = nsz.size(); + assert(data && (nnsz > 0)); + + if (sz_.size() < nnsz){ + + if (data_) + cuCHECK(cudaFree(data_)); + + cuCHECK(cudaMalloc(&data_, nnsz * sizeof(snFloat))); + } + + cuCHECK(cudaMemcpy(data_, data, nnsz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + sz_ = nsz; +} + +snFloat* Tensor::getData() const{ + + return data_; +} + +void Tensor::getDataForCPU(snFloat* out, const snSize& osz) const{ + + assert(sz_ == osz); + + cuCHECK(cudaMemcpy(out, data_, sz_.size() * sizeof(snFloat), cudaMemcpyDeviceToHost)); +} + +void Tensor::resize(const snSize& nsz){ + + size_t nnsz = nsz.size(), csz = sz_.size(); + assert(nnsz > 0); + + if (csz < nnsz){ + + snFloat* mem = nullptr; + cuCHECK(cudaMalloc(&mem, nnsz * sizeof(snFloat))); + + if (data_){ + cuCHECK(cudaMemcpy(mem, data_, csz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + cuCHECK(cudaFree(data_)); + } + data_ = mem; + + cuCHECK(cudaMemset(data_ + csz, 0, (nnsz - csz) * sizeof(snFloat))); + } + + sz_ = nsz; +} + +void Tensor::tfree(){ + if (data_) + cuCHECK(cudaFree(data_)); + + data_ = nullptr; + sz_ = snSize(0, 0, 0, 0, 0); +} diff --git a/src/snOperator_cuDNN/src/Operator/activation.cpp b/src/snOperatorCUDA/src/Operator/activation.cpp similarity index 96% rename from src/snOperator_cuDNN/src/Operator/activation.cpp rename to src/snOperatorCUDA/src/Operator/activation.cpp index 7e40504..48fdfac 100644 --- a/src/snOperator_cuDNN/src/Operator/activation.cpp +++ b/src/snOperatorCUDA/src/Operator/activation.cpp @@ -23,8 +23,8 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/activation.h" -#include "snOperator/src/activeFunctions.h" +#include "snOperatorCUDA/src/Operator/activation.h" +#include "snOperatorCUDA/src/activeFunctions.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/activation.h b/src/snOperatorCUDA/src/Operator/activation.h similarity index 97% rename from src/snOperator_CPU_cuDNN/src/Operator/activation.h rename to src/snOperatorCUDA/src/Operator/activation.h index 6d1abbe..e64e9be 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/activation.h +++ b/src/snOperatorCUDA/src/Operator/activation.h @@ -25,7 +25,7 @@ #pragma once #include "snBase/snBase.h" -#include "snOperator/src/structurs.h" +#include "snOperatorCUDA/src/structurs.h" class Activation final : SN_Base::OperatorBase{ diff --git a/src/snOperator_CPU_cuDNN/src/Operator/batchNorm.cpp b/src/snOperatorCUDA/src/Operator/batchNorm.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/batchNorm.cpp rename to src/snOperatorCUDA/src/Operator/batchNorm.cpp index a968596..9ffcbdf 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/batchNorm.cpp +++ b/src/snOperatorCUDA/src/Operator/batchNorm.cpp @@ -23,8 +23,8 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/batchNorm.h" -#include "snOperator/src/mathFunctions.h" +#include "snOperatorCUDA/src/Operator/batchNorm.h" +#include "snOperatorCUDA/src/mathFunctions.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_CPU_cuDNN/src/Operator/batchNorm.h b/src/snOperatorCUDA/src/Operator/batchNorm.h similarity index 97% rename from src/snOperator_CPU_cuDNN/src/Operator/batchNorm.h rename to src/snOperatorCUDA/src/Operator/batchNorm.h index 8c75ca4..49a2600 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/batchNorm.h +++ b/src/snOperatorCUDA/src/Operator/batchNorm.h @@ -25,7 +25,7 @@ #pragma once #include "snBase/snBase.h" -#include "snOperator/src/structurs.h" +#include "snOperatorCUDA/src/structurs.h" class BatchNorm final : SN_Base::OperatorBase{ diff --git a/src/snOperator_CPU_cuDNN/src/Operator/concat.cpp b/src/snOperatorCUDA/src/Operator/concat.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/concat.cpp rename to src/snOperatorCUDA/src/Operator/concat.cpp index af41ba9..6cd301c 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/concat.cpp +++ b/src/snOperatorCUDA/src/Operator/concat.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/concat.h" +#include "snOperatorCUDA/src/Operator/concat.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_cuDNN/src/Operator/concat.h b/src/snOperatorCUDA/src/Operator/concat.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/concat.h rename to src/snOperatorCUDA/src/Operator/concat.h diff --git a/src/snOperator_cuDNN/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp similarity index 85% rename from src/snOperator_cuDNN/src/Operator/convolution.cpp rename to src/snOperatorCUDA/src/Operator/convolution.cpp index 2961273..71ca923 100644 --- a/src/snOperator_cuDNN/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -23,13 +23,13 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/convolution.h" +#include "snOperatorCUDA/src/Operator/convolution.h" #include "snAux/auxFunc.h" -#include "snOperator/src/weightInit.h" -#include "snOperator/src/activeFunctions.h" -#include "snOperator/src/optimizer.h" -#include "snOperator/src/structurs.h" -#include "snOperator/src/mathFunctions.h" +#include "snOperatorCUDA/src/weightInit.h" +#include "snOperatorCUDA/src/activeFunctions.h" +#include "snOperatorCUDA/src/optimizer.h" +#include "snOperatorCUDA/src/structurs.h" +#include "snOperatorCUDA/src/mathFunctions.h" using namespace std; using namespace SN_Base; @@ -44,10 +44,7 @@ Convolution::Convolution(void* net, const string& name, const string& node, std: Convolution::~Convolution(){ - switch (calcMode_){ - case calcMode::CUDA: freeParamCUDA(gpuParams_); break; - case calcMode::OpenCL: freeParamOCL(gpuParams_); break; - } + freeParamCUDA(gpuParams_); } void Convolution::load(std::map& prms){ @@ -83,10 +80,7 @@ void Convolution::load(std::map& prms){ setIntParam("stride", false, false, convPrms_.stride); setIntParam("dilate", false, false, convPrms_.dilate); - - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - + if (prms.find("gpuDeviceId") != prms.end()) gpuDeviceId_ = stoi(prms["gpuDeviceId"]); @@ -99,17 +93,7 @@ void Convolution::load(std::map& prms){ else ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); } - - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - + // aux array auxParams_["dWeight"] = vector(); auxParams_["dWPrev"] = vector(); @@ -169,13 +153,13 @@ bool Convolution::setInternPrm(std::map& prms){ } if (prms.find("decayMomentDW") != prms.end()) - opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + optDecayMomentDW_ = stof(prms["decayMomentDW"]); if (prms.find("decayMomentWGr") != prms.end()) - opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + optDecayMomentWGr_ = stof(prms["decayMomentWGr"]); if (prms.find("lmbRegular") != prms.end()) - opt_lmbRegular_ = stof(prms["lmbRegular"]); + optLmbRegular_ = stof(prms["lmbRegular"]); if (prms.find("batchNormLr") != prms.end()) baseBatchNorm_.lr = stof(prms["batchNormLr"]); @@ -274,12 +258,9 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op memset(weight + stepByN, 0, kernel * sizeof(snFloat)); } - switch (calcMode_){ - case calcMode::CPU: forwardCPU(convPrms_, weight, inDataExpSz_, in, outsz, out); break; - case calcMode::CUDA: forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); break; - case calcMode::OpenCL: forwardOCL(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); break; - } - + // calculation + forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); + /// dropOut if (dropOut_ > 0.F) dropOut(operPrm.isLerning, dropOut_, outsz, out); @@ -338,12 +319,9 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o if (!isSame) in = inTnsExp_.getData(); - switch (calcMode_){ - case calcMode::CPU: backwardCPU_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight); break; - case calcMode::CUDA: backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); break; - } - + // calculation + backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); + // correct weight snFloat* dWPrev = auxParams_["dWPrev"].data(); snFloat* dWGrad = auxParams_["dWGrad"].data(); @@ -355,17 +333,13 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o weight, wsz, operPrm.lr, - opt_lmbRegular_, - opt_decayMomentDW_, - opt_decayMomentWGr_, + optLmbRegular_, + optDecayMomentDW_, + optDecayMomentWGr_, optimizerType_); } else{ // isFreeze - switch (calcMode_){ - case calcMode::CPU: backwardCPU_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut); break; - case calcMode::CUDA: backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); break; - } + backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); } if (!isSame) @@ -453,8 +427,6 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseBatchNorm_.sz.n = 1; } - switch (calcMode_){ - case calcMode::CUDA: iniParamCUDA(isLern, expSz, outSz, convPrms_, &gpuParams_); break; - case calcMode::OpenCL: iniParamOCL(isLern, expSz, outSz, convPrms_, &gpuParams_); break; - } + iniParamCUDA(isLern, expSz, outSz, convPrms_, &gpuParams_); + } \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/convolution.h b/src/snOperatorCUDA/src/Operator/convolution.h similarity index 66% rename from src/snOperator_cuDNN/src/Operator/convolution.h rename to src/snOperatorCUDA/src/Operator/convolution.h index b12cb3c..9a33782 100644 --- a/src/snOperator_cuDNN/src/Operator/convolution.h +++ b/src/snOperatorCUDA/src/Operator/convolution.h @@ -25,8 +25,8 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" -#include"snOperator/src/mathFunctions.h" +#include "snOperatorCUDA/src/structurs.h" +#include "snOperatorCUDA/src/mathFunctions.h" /// convolution layer class Convolution final : SN_Base::OperatorBase{ @@ -71,17 +71,14 @@ class Convolution final : SN_Base::OperatorBase{ SN_Base::Tensor gradOutExp_; bool isFreeze_ = false; ///< not change weight - bool gpuClearMem_ = false; ///< clear mem GPU uint32_t gpuDeviceId_ = 0; ///< gpu id - - calcMode calcMode_ = calcMode::CPU; ///< calc mode - + SN_Base::snFloat dropOut_ = 0.F; ///< random off out - SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimiz weight - opt_decayMomentWGr_ = 0.99F, - opt_lmbRegular_ = 0.001F; + SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimiz weight + optDecayMomentWGr_ = 0.99F, + optLmbRegular_ = 0.001F; std::map> auxParams_; ///< aux data @@ -96,34 +93,7 @@ class Convolution final : SN_Base::OperatorBase{ void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); - /// CPU /////////////////////////// - - void forwardCPU(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output); - - // calc grad and weight - void backwardCPU_GW(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut); - - // calc grad - void backwardCPU_G(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut); - - + /// CUDA /////////////////////////// /// init aux params @@ -160,42 +130,4 @@ class Convolution final : SN_Base::OperatorBase{ const SN_Base::snFloat* gradIn, SN_Base::snFloat* gradOut, void* gpuPrm); - - - /// OpenCL /////////////////////////// - - /// init aux params OpenCL - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, - const convParams&, void** gpuPrm); - - /// free aux params OpenCL - void freeParamOCL(void* gpuPrm); - - void forwardOCL(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - void* gpuPrm); - - /// calc grad and weight - void backwardOCL_GW(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - /// calc grad - void backwardOCL_G(const convParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); }; \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/Operator/crop.cpp b/src/snOperatorCUDA/src/Operator/crop.cpp similarity index 99% rename from src/snOperator_CPU_cuDNN/src/Operator/crop.cpp rename to src/snOperatorCUDA/src/Operator/crop.cpp index a9d1d0d..e89c399 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/crop.cpp +++ b/src/snOperatorCUDA/src/Operator/crop.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/crop.h" +#include "snOperatorCUDA/src/Operator/crop.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_cuDNN/src/Operator/crop.h b/src/snOperatorCUDA/src/Operator/crop.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/crop.h rename to src/snOperatorCUDA/src/Operator/crop.h diff --git a/src/snOperator_cuDNN/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp similarity index 83% rename from src/snOperator_cuDNN/src/Operator/deconvolution.cpp rename to src/snOperatorCUDA/src/Operator/deconvolution.cpp index 52734c4..f1ce4c1 100644 --- a/src/snOperator_cuDNN/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -23,13 +23,13 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/deconvolution.h" +#include "snOperatorCUDA/src/Operator/deconvolution.h" #include "snAux/auxFunc.h" -#include "snOperator/src/weightInit.h" -#include "snOperator/src/activeFunctions.h" -#include "snOperator/src/optimizer.h" -#include "snOperator/src/structurs.h" -#include "snOperator/src/mathFunctions.h" +#include "snOperatorCUDA/src/weightInit.h" +#include "snOperatorCUDA/src/activeFunctions.h" +#include "snOperatorCUDA/src/optimizer.h" +#include "snOperatorCUDA/src/structurs.h" +#include "snOperatorCUDA/src/mathFunctions.h" using namespace std; using namespace SN_Base; @@ -43,10 +43,8 @@ Deconvolution::Deconvolution(void* net, const string& name, const string& node, Deconvolution::~Deconvolution(){ - if (calcMode_ == calcMode::CUDA) - freeParamCUDA(gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - freeParamOCL(gpuParams_); + freeParamCUDA(gpuParams_); + } void Deconvolution::load(std::map& prms){ @@ -80,23 +78,11 @@ void Deconvolution::load(std::map& prms){ else ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); } - - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - + if (prms.find("gpuDeviceId") != prms.end()) gpuDeviceId_ = stoi(prms["gpuDeviceId"]); - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - + // aux arrays auxParams_["dWeight"] = vector(); auxParams_["dWPrev"] = vector(); @@ -156,13 +142,13 @@ bool Deconvolution::setInternPrm(std::map& prms){ } if (prms.find("decayMomentDW") != prms.end()) - opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + optDecayMomentDW_ = stof(prms["decayMomentDW"]); if (prms.find("decayMomentWGr") != prms.end()) - opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + optDecayMomentWGr_ = stof(prms["decayMomentWGr"]); if (prms.find("lmbRegular") != prms.end()) - opt_lmbRegular_ = stof(prms["lmbRegular"]); + optLmbRegular_ = stof(prms["lmbRegular"]); if (prms.find("batchNormLr") != prms.end()) baseBatchNorm_.lr = stof(prms["batchNormLr"]); @@ -245,12 +231,9 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); snSize outsz = baseOut_.size(); - switch (calcMode_){ - case calcMode::CPU: forwardCPU(deconvParams_, weight, insz, pInTns, outsz, out); break; - case calcMode::CUDA: forwardCUDA(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); break; - case calcMode::OpenCL: forwardOCL(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); break; - } - + // calculation + forwardCUDA(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); + /// dropOut if (dropOut_ > 0.F) dropOut(operPrm.isLerning, dropOut_, outsz, out); @@ -303,11 +286,8 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); - switch (calcMode_){ - case calcMode::CPU: backwardCPU_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight); break; - case calcMode::CUDA: backwardCUDA_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); break; - } + // calculation + backwardCUDA_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); snFloat* dWPrev = auxParams_["dWPrev"].data(); snFloat* dWGrad = auxParams_["dWGrad"].data(); @@ -319,17 +299,14 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& weight, wsz, operPrm.lr, - opt_lmbRegular_, - opt_decayMomentDW_, - opt_decayMomentWGr_, + optLmbRegular_, + optDecayMomentDW_, + optDecayMomentWGr_, optimizerType_); } else{ // isFreeze - switch (calcMode_){ - case calcMode::CPU: backwardCPU_G(deconvParams_, weight, insz, outsz, gradIn, grOut); break; - case calcMode::CUDA: backwardCUDA_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); break; - } + backwardCUDA_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); + } } @@ -382,10 +359,8 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ baseBatchNorm_.sz.n = 1; } - if (calcMode_ == calcMode::CUDA) - iniParamCUDA(isLern, newsz, outSz, deconvParams_, &gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - iniParamOCL(isLern, newsz, outSz, deconvParams_, &gpuParams_); + iniParamCUDA(isLern, newsz, outSz, deconvParams_, &gpuParams_); + } diff --git a/src/snOperator_cuDNN/src/Operator/deconvolution.h b/src/snOperatorCUDA/src/Operator/deconvolution.h similarity index 64% rename from src/snOperator_cuDNN/src/Operator/deconvolution.h rename to src/snOperatorCUDA/src/Operator/deconvolution.h index ed33773..2ae3dc4 100644 --- a/src/snOperator_cuDNN/src/Operator/deconvolution.h +++ b/src/snOperatorCUDA/src/Operator/deconvolution.h @@ -25,10 +25,10 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" -#include"snOperator/src/mathFunctions.h" +#include"snOperatorCUDA/src/structurs.h" +#include"snOperatorCUDA/src/mathFunctions.h" -/// обратная свертка +/// deconvolution layer class Deconvolution final : SN_Base::OperatorBase{ public: @@ -62,20 +62,17 @@ class Deconvolution final : SN_Base::OperatorBase{ const SN_Base::Tensor* inputMem_ = nullptr; bool isFreeze_ = false; ///< not change weight - bool gpuClearMem_ = false; ///< clear mem GPU uint32_t gpuDeviceId_ = 0; ///< gpu id - - calcMode calcMode_ = calcMode::CPU; ///< calc mode - + SN_Base::snFloat dropOut_ = 0.F; ///< rand off output - SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimizer weight coef - opt_decayMomentWGr_ = 0.99F, - opt_lmbRegular_ = 0.001F; + SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimizer weight coef + optDecayMomentWGr_ = 0.99F, + optLmbRegular_ = 0.001F; std::map> auxParams_; ///< aux data - void* gpuParams_ = nullptr ; ///< aux GPU + void* gpuParams_ = nullptr; ///< aux GPU void load(std::map& prms); @@ -84,33 +81,7 @@ class Deconvolution final : SN_Base::OperatorBase{ void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); - /// CPU /////////////////////////// - - void forwardCPU(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output); - - void backwardCPU_GW(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut); - - void backwardCPU_G(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut); - - - + /// CUDA /////////////////////////// void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const deconvParams&, void** gpuPrm); @@ -142,37 +113,4 @@ class Deconvolution final : SN_Base::OperatorBase{ const SN_Base::snFloat* gradIn, SN_Base::snFloat* gradOut, void* gpuPrm); - - - /// OpenCL /////////////////////////// - - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const deconvParams&, void** gpuPrm); - - void freeParamOCL(void* gpuPrm); - - void forwardOCL(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - void* gpuPrm); - - void backwardOCL_GW(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - void backwardOCL_G(const deconvParams&, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snSize& outsz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); }; \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp similarity index 82% rename from src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.cpp rename to src/snOperatorCUDA/src/Operator/fullyConnected.cpp index fab2956..d655fae 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -23,13 +23,13 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/fullyConnected.h" +#include "snOperatorCUDA/src/Operator/fullyConnected.h" #include "snAux/auxFunc.h" -#include "snOperator/src/weightInit.h" -#include "snOperator/src/activeFunctions.h" -#include "snOperator/src/optimizer.h" -#include "snOperator/src/structurs.h" -#include "snOperator/src/mathFunctions.h" +#include "snOperatorCUDA/src/weightInit.h" +#include "snOperatorCUDA/src/activeFunctions.h" +#include "snOperatorCUDA/src/optimizer.h" +#include "snOperatorCUDA/src/structurs.h" +#include "snOperatorCUDA/src/mathFunctions.h" using namespace std; using namespace SN_Base; @@ -43,10 +43,7 @@ FullyConnected::FullyConnected(void* net, const string& name, const string& node FullyConnected::~FullyConnected(){ - if (calcMode_ == calcMode::CUDA) - freeParamCUDA(gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - freeParamOCL(gpuParams_); + freeParamCUDA(gpuParams_); } void FullyConnected::load(std::map& prms){ @@ -73,21 +70,8 @@ void FullyConnected::load(std::map& prms){ else if (bnType == "postActive") batchNormType_ = batchNormType::postActive; else ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); - } - - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - + } + if (prms.find("gpuDeviceId") != prms.end()) gpuDeviceId_ = stoi(prms["gpuDeviceId"]); @@ -155,13 +139,13 @@ bool FullyConnected::setInternPrm(std::map& prms){ } if (prms.find("decayMomentDW") != prms.end()) - opt_decayMomentDW_ = stof(prms["decayMomentDW"]); + optDecayMomentDW_ = stof(prms["decayMomentDW"]); if (prms.find("decayMomentWGr") != prms.end()) - opt_decayMomentWGr_ = stof(prms["decayMomentWGr"]); + optDecayMomentWGr_ = stof(prms["decayMomentWGr"]); if (prms.find("lmbRegular") != prms.end()) - opt_lmbRegular_ = stof(prms["lmbRegular"]); + optLmbRegular_ = stof(prms["lmbRegular"]); if (prms.find("batchNormLr") != prms.end()) baseBatchNorm_.lr = stof(prms["batchNormLr"]); @@ -248,12 +232,9 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& memset(weight + stepByN, 0, kernel_ * sizeof(snFloat)); } - switch (calcMode_){ - case calcMode::CPU: forwardCPU(kernel_, insz, inTns.getData(), weight, out); break; - case calcMode::CUDA: forwardCUDA(kernel_, insz, inTns.getData(), weight, out, gpuParams_); break; - case calcMode::OpenCL: forwardOCL(kernel_, insz, inTns.getData(), weight, out, gpuParams_); break; - } - + // calculation + forwardCUDA(kernel_, insz, inTns.getData(), weight, out, gpuParams_); + /// dropOut snSize outSz = baseOut_.size(); if (dropOut_ > 0.F) @@ -304,12 +285,9 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam snFloat* dWeight = auxParams_["dWeight"].data(); - switch (calcMode_){ - case calcMode::CPU: backwardCPU_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight); break; - case calcMode::CUDA: backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); break; - } - + // calculation + backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); + // correct weight snFloat* dWPrev = auxParams_["dWPrev"].data(); snFloat* dWGrad = auxParams_["dWGrad"].data(); @@ -321,18 +299,14 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam weight, wsz, operPrm.lr, - opt_lmbRegular_, - opt_decayMomentDW_, - opt_decayMomentWGr_, + optLmbRegular_, + optDecayMomentDW_, + optDecayMomentWGr_, optimizerType_); } else{ // isFreeze - switch (calcMode_){ - case calcMode::CPU: backwardCPU_G(kernel_, weight, inSzMem_, gradIn, gradOut); break; - case calcMode::CUDA: backwardCUDA_G(kernel_, weight, inSzMem_, gradIn, gradOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL_G(kernel_, weight, inSzMem_, gradIn, gradOut, gpuParams_); break; - } + backwardCUDA_G(kernel_, weight, inSzMem_, gradIn, gradOut, gpuParams_); } } @@ -365,8 +339,5 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ } } - if (calcMode_ == calcMode::CUDA) - iniParamCUDA(isLern, newsz, kernel_, &gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - iniParamOCL(isLern, newsz, kernel_, &gpuParams_); + iniParamCUDA(isLern, newsz, kernel_, &gpuParams_); } \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/Operator/fullyConnected.h b/src/snOperatorCUDA/src/Operator/fullyConnected.h similarity index 65% rename from src/snOperator_cuDNN/src/Operator/fullyConnected.h rename to src/snOperatorCUDA/src/Operator/fullyConnected.h index 3df0106..eac8718 100644 --- a/src/snOperator_cuDNN/src/Operator/fullyConnected.h +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.h @@ -25,7 +25,7 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" +#include"snOperatorCUDA/src/structurs.h" /// fullyConnected layer class FullyConnected final : SN_Base::OperatorBase{ @@ -56,17 +56,15 @@ class FullyConnected final : SN_Base::OperatorBase{ bool useBias_ = true; bool isFreeze_ = false; ///< not change weight - bool gpuClearMem_ = false; ///< free gpu mem - + uint32_t gpuDeviceId_ = 0; ///< gpu id - calcMode calcMode_ = calcMode::CPU; ///< calc mode - + SN_Base::snFloat dropOut_ = 0.F; ///< rand off output - SN_Base::snFloat opt_decayMomentDW_ = 0.9F, ///< optimizer weight coef - opt_decayMomentWGr_ = 0.99F, - opt_lmbRegular_ = 0.001F; + SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimizer weight coef + optDecayMomentWGr_ = 0.99F, + optLmbRegular_ = 0.001F; std::map> auxParams_; ///< aux data @@ -80,31 +78,7 @@ class FullyConnected final : SN_Base::OperatorBase{ void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); - - /// CPU /////////////////////////// - - void forwardCPU(size_t kernel, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* weight, - SN_Base::snFloat* output); - - // calc grad and dw - void backwardCPU_GW(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut); - - // calc grad - void backwardCPU_G(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut); - + /// CUDA /////////////////////////// @@ -137,35 +111,4 @@ class FullyConnected final : SN_Base::OperatorBase{ SN_Base::snFloat* gradOut, void* gpuPrm); - - /// OpenCL /////////////////////////// - - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, size_t kernel, void** gpuPrm); - - void freeParamOCL(void* gpuPrm); - - void forwardOCL(size_t kernel, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* weight, - SN_Base::snFloat* output, - void* gpuPrm); - - // calc grad and dw - void backwardOCL_GW(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::snFloat* dWeightOut, - void* gpuPrm); - - // calc grad - void backwardOCL_G(size_t kernel, - const SN_Base::snFloat* weight, - const SN_Base::snSize& insz, - const SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - void* gpuPrm); }; \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/Operator/input.cpp b/src/snOperatorCUDA/src/Operator/input.cpp similarity index 97% rename from src/snOperator_CPU_cuDNN/src/Operator/input.cpp rename to src/snOperatorCUDA/src/Operator/input.cpp index cb18e51..c8e7053 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/input.cpp +++ b/src/snOperatorCUDA/src/Operator/input.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/input.h" +#include "snOperatorCUDA/src/Operator/input.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/Operator/input.h b/src/snOperatorCUDA/src/Operator/input.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/input.h rename to src/snOperatorCUDA/src/Operator/input.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/lock.cpp b/src/snOperatorCUDA/src/Operator/lock.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/lock.cpp rename to src/snOperatorCUDA/src/Operator/lock.cpp index e1cb9aa..2806231 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/lock.cpp +++ b/src/snOperatorCUDA/src/Operator/lock.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/lock.h" +#include "snOperatorCUDA/src/Operator/lock.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/Operator/lock.h b/src/snOperatorCUDA/src/Operator/lock.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/lock.h rename to src/snOperatorCUDA/src/Operator/lock.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/lossFunction.cpp rename to src/snOperatorCUDA/src/Operator/lossFunction.cpp index a4072ee..fc3fc50 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -23,8 +23,8 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/lossFunction.h" -#include "snOperator/src/activeFunctions.h" +#include "snOperatorCUDA/src/Operator/lossFunction.h" +#include "snOperatorCUDA/src/activeFunctions.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/Operator/lossFunction.h b/src/snOperatorCUDA/src/Operator/lossFunction.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/lossFunction.h rename to src/snOperatorCUDA/src/Operator/lossFunction.h diff --git a/src/snOperator_cuDNN/src/Operator/output.cpp b/src/snOperatorCUDA/src/Operator/output.cpp similarity index 97% rename from src/snOperator_cuDNN/src/Operator/output.cpp rename to src/snOperatorCUDA/src/Operator/output.cpp index e905ee5..2baced4 100644 --- a/src/snOperator_cuDNN/src/Operator/output.cpp +++ b/src/snOperatorCUDA/src/Operator/output.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/output.h" +#include "snOperatorCUDA/src/Operator/output.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/Operator/output.h b/src/snOperatorCUDA/src/Operator/output.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/output.h rename to src/snOperatorCUDA/src/Operator/output.h diff --git a/src/snOperator_cuDNN/src/Operator/pooling.cpp b/src/snOperatorCUDA/src/Operator/pooling.cpp similarity index 77% rename from src/snOperator_cuDNN/src/Operator/pooling.cpp rename to src/snOperatorCUDA/src/Operator/pooling.cpp index 4bcbcc6..0c065cb 100644 --- a/src/snOperator_cuDNN/src/Operator/pooling.cpp +++ b/src/snOperatorCUDA/src/Operator/pooling.cpp @@ -23,9 +23,9 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/pooling.h" +#include "snOperatorCUDA/src/Operator/pooling.h" #include "snAux/auxFunc.h" -#include "snOperator/src/structurs.h" +#include "snOperatorCUDA/src/structurs.h" using namespace std; using namespace SN_Base; @@ -39,10 +39,7 @@ Pooling::Pooling(void* net, const string& name, const string& node, std::map& prms){ @@ -68,10 +65,7 @@ void Pooling::load(std::map& prms){ setIntParam("stride", false, false, poolPrms_.stride); else poolPrms_.stride = poolPrms_.kernel; - - if (prms.find("gpuClearMem") != prms.end()) - gpuClearMem_ = stoi(prms["gpuClearMem"]) == 1; - + if (prms.find("gpuDeviceId") != prms.end()) gpuDeviceId_ = stoi(prms["gpuDeviceId"]); @@ -83,17 +77,7 @@ void Pooling::load(std::map& prms){ else ERROR_MESS("param 'pool' = " + atype + " indefined"); } - - if (prms.find("mode") != prms.end()){ - - string mode = prms["mode"]; - if (mode == "CPU") calcMode_ = calcMode::CPU; - else if (mode == "CUDA") calcMode_ = calcMode::CUDA; - else if (mode == "OpenCL") calcMode_ = calcMode::OpenCL; - else - ERROR_MESS("param 'mode' = " + mode + " indefined"); - } - + basePrms_ = prms; } @@ -150,11 +134,9 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara /// output calculation snFloat* out = baseOut_.getData(); - switch (calcMode_){ - case calcMode::CPU: forwardCPU(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data()); break; - case calcMode::CUDA: forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); break; - case calcMode::OpenCL: forwardOCL(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); break; - } + // calculation + forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); + } void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ @@ -170,11 +152,7 @@ void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operP } /// grad calculation - switch (calcMode_){ - case calcMode::CPU: backwardCPU(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut); break; - case calcMode::CUDA: backwardCUDA(poolPrms_, baseOut_.size(), outInx_.data(), baseOut_.getData(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); break; - case calcMode::OpenCL: backwardOCL(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut, gpuParams_); break; - } + backwardCUDA(poolPrms_, baseOut_.size(), outInx_.data(), baseOut_.getData(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); if (isPadding_) paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); @@ -218,8 +196,5 @@ void Pooling::updateConfig(bool isLern, const snSize& newsz){ if (isLern) baseGrad_.resize(newsz); - if (calcMode_ == calcMode::CUDA) - iniParamCUDA(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); - else if (calcMode_ == calcMode::OpenCL) - iniParamOCL(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); + iniParamCUDA(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); } \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/Operator/pooling.h b/src/snOperatorCUDA/src/Operator/pooling.h similarity index 68% rename from src/snOperator_CPU_cuDNN/src/Operator/pooling.h rename to src/snOperatorCUDA/src/Operator/pooling.h index c4623a7..4c5b8ee 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/pooling.h +++ b/src/snOperatorCUDA/src/Operator/pooling.h @@ -25,8 +25,8 @@ #pragma once #include "snBase/snBase.h" -#include"snOperator/src/structurs.h" -#include"snOperator/src/mathFunctions.h" +#include"snOperatorCUDA/src/structurs.h" +#include"snOperatorCUDA/src/mathFunctions.h" /// pooling layer @@ -54,8 +54,7 @@ class Pooling final : SN_Base::OperatorBase{ SN_Base::snSize inSzMem_; ///< input size mem SN_Base::snSize inDataExpSz_; ///< input size expand - std::vector outInx_; ///< index select elem - + SN_Base::Tensor inTnsExp_; SN_Base::Tensor gradOutExp_; @@ -63,14 +62,10 @@ class Pooling final : SN_Base::OperatorBase{ bool isPadding_ = false; - - bool gpuClearMem_ = false; ///< freee gpu mem - + uint32_t gpuDeviceId_ = 0; ///< gpu id - calcMode calcMode_ = calcMode::CPU; ///< calc mode - - + std::map> auxParams_; ///< aux data void* gpuParams_ = nullptr; ///< gpu data @@ -81,23 +76,7 @@ class Pooling final : SN_Base::OperatorBase{ void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); void backward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); - /// CPU /////////////////////////// - - void forwardCPU(const poolParams& poolPrms, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - size_t* outputInx); - - void backwardCPU(const poolParams& poolPrms, - const SN_Base::snSize& outsz, - const size_t* outputInx, - const SN_Base::snFloat* gradIn, - const SN_Base::snSize& insz, - SN_Base::snFloat* gradOut); - - + /// CUDA /////////////////////////// void iniParamCUDA(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const poolParams&, void** gpuPrm); @@ -109,41 +88,15 @@ class Pooling final : SN_Base::OperatorBase{ const SN_Base::snFloat* input, const SN_Base::snSize& outsz, SN_Base::snFloat* output, - size_t* outputInx, void* gpuParams); /// обратный проход CUDA void backwardCUDA(const poolParams& poolPrms, - const SN_Base::snSize& outsz, - const size_t* outputInx, + const SN_Base::snSize& outsz, const SN_Base::snFloat* output, const SN_Base::snFloat* gradIn, const SN_Base::snSize& insz, const SN_Base::snFloat* input, SN_Base::snFloat* gradOut, void* gpuParams); - - - /// OpenCL /////////////////////////// - - void iniParamOCL(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, const poolParams&, void** gpuPrm); - - void freeParamOCL(void* gpuPrm); - - void forwardOCL(const poolParams& poolPrms, - const SN_Base::snSize& insz, - const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, - SN_Base::snFloat* output, - size_t* outputInx, - void* gpuParams); - - void backwardOCL(const poolParams& poolPrms, - const SN_Base::snSize& outsz, - const size_t* outputInx, - const SN_Base::snFloat* gradIn, - const SN_Base::snSize& insz, - SN_Base::snFloat* gradOut, - void* gpuParams); - }; \ No newline at end of file diff --git a/src/snOperator_CPU_cuDNN/src/Operator/resize.cpp b/src/snOperatorCUDA/src/Operator/resize.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/resize.cpp rename to src/snOperatorCUDA/src/Operator/resize.cpp index 2b60c09..22db594 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/resize.cpp +++ b/src/snOperatorCUDA/src/Operator/resize.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/resize.h" +#include "snOperatorCUDA/src/Operator/resize.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_cuDNN/src/Operator/resize.h b/src/snOperatorCUDA/src/Operator/resize.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/resize.h rename to src/snOperatorCUDA/src/Operator/resize.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/summator.cpp b/src/snOperatorCUDA/src/Operator/summator.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/summator.cpp rename to src/snOperatorCUDA/src/Operator/summator.cpp index cac5306..1cb4e91 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/summator.cpp +++ b/src/snOperatorCUDA/src/Operator/summator.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/summator.h" +#include "snOperatorCUDA/src/Operator/summator.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/Operator/summator.h b/src/snOperatorCUDA/src/Operator/summator.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/summator.h rename to src/snOperatorCUDA/src/Operator/summator.h diff --git a/src/snOperator_CPU_cuDNN/src/Operator/switch.cpp b/src/snOperatorCUDA/src/Operator/switch.cpp similarity index 98% rename from src/snOperator_CPU_cuDNN/src/Operator/switch.cpp rename to src/snOperatorCUDA/src/Operator/switch.cpp index 06026d0..02b96d1 100644 --- a/src/snOperator_CPU_cuDNN/src/Operator/switch.cpp +++ b/src/snOperatorCUDA/src/Operator/switch.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/switch.h" +#include "snOperatorCUDA/src/Operator/switch.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperator_cuDNN/src/Operator/switch.h b/src/snOperatorCUDA/src/Operator/switch.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/switch.h rename to src/snOperatorCUDA/src/Operator/switch.h diff --git a/src/snOperator_cuDNN/src/Operator/userLayer.cpp b/src/snOperatorCUDA/src/Operator/userLayer.cpp similarity index 98% rename from src/snOperator_cuDNN/src/Operator/userLayer.cpp rename to src/snOperatorCUDA/src/Operator/userLayer.cpp index 8f38af9..58e1ae9 100644 --- a/src/snOperator_cuDNN/src/Operator/userLayer.cpp +++ b/src/snOperatorCUDA/src/Operator/userLayer.cpp @@ -23,7 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "snOperator/src/Operator/userLayer.h" +#include "snOperatorCUDA/src/Operator/userLayer.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/Operator/userLayer.h b/src/snOperatorCUDA/src/Operator/userLayer.h similarity index 100% rename from src/snOperator_cuDNN/src/Operator/userLayer.h rename to src/snOperatorCUDA/src/Operator/userLayer.h diff --git a/src/snOperator_cuDNN/src/activeFunctions.cpp b/src/snOperatorCUDA/src/activeFunctions.cpp similarity index 100% rename from src/snOperator_cuDNN/src/activeFunctions.cpp rename to src/snOperatorCUDA/src/activeFunctions.cpp diff --git a/src/snOperator_cuDNN/src/activeFunctions.h b/src/snOperatorCUDA/src/activeFunctions.h similarity index 100% rename from src/snOperator_cuDNN/src/activeFunctions.h rename to src/snOperatorCUDA/src/activeFunctions.h diff --git a/src/snOperator_cuDNN/src/mathFunctions.cpp b/src/snOperatorCUDA/src/mathFunctions.cpp similarity index 98% rename from src/snOperator_cuDNN/src/mathFunctions.cpp rename to src/snOperatorCUDA/src/mathFunctions.cpp index ef5bc24..eed3701 100644 --- a/src/snOperator_cuDNN/src/mathFunctions.cpp +++ b/src/snOperatorCUDA/src/mathFunctions.cpp @@ -25,8 +25,8 @@ #include "stdafx.h" #include "Lib/OpenBLAS/cblas.h" -#include "snOperator/src/mathFunctions.h" -#include "snOperator/src/random.h" +#include "snOperatorCUDA/src/mathFunctions.h" +#include "snOperatorCUDA/src/random.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperator_cuDNN/src/mathFunctions.h b/src/snOperatorCUDA/src/mathFunctions.h similarity index 100% rename from src/snOperator_cuDNN/src/mathFunctions.h rename to src/snOperatorCUDA/src/mathFunctions.h diff --git a/src/snOperator_cuDNN/src/optimizer.cpp b/src/snOperatorCUDA/src/optimizer.cpp similarity index 100% rename from src/snOperator_cuDNN/src/optimizer.cpp rename to src/snOperatorCUDA/src/optimizer.cpp diff --git a/src/snOperator_cuDNN/src/optimizer.h b/src/snOperatorCUDA/src/optimizer.h similarity index 100% rename from src/snOperator_cuDNN/src/optimizer.h rename to src/snOperatorCUDA/src/optimizer.h diff --git a/src/snOperator_cuDNN/src/random.cpp b/src/snOperatorCUDA/src/random.cpp similarity index 100% rename from src/snOperator_cuDNN/src/random.cpp rename to src/snOperatorCUDA/src/random.cpp diff --git a/src/snOperator_cuDNN/src/random.h b/src/snOperatorCUDA/src/random.h similarity index 100% rename from src/snOperator_cuDNN/src/random.h rename to src/snOperatorCUDA/src/random.h diff --git a/src/snOperator_cuDNN/src/snOperator.cpp b/src/snOperatorCUDA/src/snOperator.cpp similarity index 99% rename from src/snOperator_cuDNN/src/snOperator.cpp rename to src/snOperatorCUDA/src/snOperator.cpp index c43fd23..b43bb5d 100644 --- a/src/snOperator_cuDNN/src/snOperator.cpp +++ b/src/snOperatorCUDA/src/snOperator.cpp @@ -22,7 +22,7 @@ // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN // THE SOFTWARE. // -#include "snOperator/snOperator.h" +#include "snOperatorCUDA/snOperator.h" #include "snBase/snBase.h" #include "Operator/fullyConnected.h" #include "Operator/convolution.h" diff --git a/src/snOperator_CPU_cuDNN/src/stdafx.h b/src/snOperatorCUDA/src/stdafx.h similarity index 98% rename from src/snOperator_CPU_cuDNN/src/stdafx.h rename to src/snOperatorCUDA/src/stdafx.h index 90af050..650295e 100644 --- a/src/snOperator_CPU_cuDNN/src/stdafx.h +++ b/src/snOperatorCUDA/src/stdafx.h @@ -31,7 +31,7 @@ #include #include #include "snBase/snBase.h" -#include "snOperator/snOperator.h" +#include "snOperatorCUDA/snOperator.h" #define PROFILE_START double ctm = omp_get_wtime(); #define PROFILE_END(func) g_statusMess(this, name_ + " " + node_ + " " + func + " " + std::to_string(omp_get_wtime() - ctm)); ctm = omp_get_wtime(); diff --git a/src/snOperator_cuDNN/src/structurs.h b/src/snOperatorCUDA/src/structurs.h similarity index 100% rename from src/snOperator_cuDNN/src/structurs.h rename to src/snOperatorCUDA/src/structurs.h diff --git a/src/snOperator_cuDNN/src/weightInit.cpp b/src/snOperatorCUDA/src/weightInit.cpp similarity index 100% rename from src/snOperator_cuDNN/src/weightInit.cpp rename to src/snOperatorCUDA/src/weightInit.cpp diff --git a/src/snOperator_cuDNN/src/weightInit.h b/src/snOperatorCUDA/src/weightInit.h similarity index 100% rename from src/snOperator_cuDNN/src/weightInit.h rename to src/snOperatorCUDA/src/weightInit.h diff --git a/src/snOperator_CPU_cuDNN/src/CUDA/convolutionCUDA.cu b/src/snOperator_CPU_cuDNN/src/CUDA/convolutionCUDA.cu deleted file mode 100644 index 4c3eea8..0000000 --- a/src/snOperator_CPU_cuDNN/src/CUDA/convolutionCUDA.cu +++ /dev/null @@ -1,929 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#ifdef SN_CUDNN - -#include -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/convolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnConvolutionDescriptor_t conv_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - cudnnFilterDescriptor_t w_desc = 0; - cudnnFilterDescriptor_t dw_desc = 0; - cudnnTensorDescriptor_t bias_desc = 0; - - cudnnConvolutionFwdAlgo_t algoFwd; - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - - size_t wsFwdSz = 0; - size_t wsBwdDataSz = 0; - size_t wsBwdWSz = 0; - size_t inszMem = 0; - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_bias = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - void* d_wsFwd = 0; - void* d_wsBwdData = 0; - void* d_wsBwdW = 0; -}; - -void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const convParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // w - cudnnFilterDescriptor_t w_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); - gpuPrm->w_desc = w_desc; - - // conv - cudnnConvolutionDescriptor_t conv_desc = nullptr; - cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); - cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), - CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); - if (!isFirst) - cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); - gpuPrm->conv_desc = conv_desc; - - // output - int out_n = 0, out_c = 0, out_h = 0, out_w = 0; - cuCHECK(cudnnGetConvolution2dForwardOutputDim( - conv_desc, in_desc, w_desc, - &out_n, &out_c, &out_h, &out_w)); - - if (outsz != snSize(out_w, out_h, out_c, out_n)){ - ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); - return; - } - - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - // bias - cudnnTensorDescriptor_t bias_desc; - cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - 1, out_c, 1, 1)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); - gpuPrm->bias_desc = bias_desc; - - // algorithm - cudnnConvolutionFwdAlgo_t algoFwd; - cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); - gpuPrm->algoFwd = algoFwd; - - // workspace - size_t wsFwdSz = 0; - cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, algoFwd, &wsFwdSz)); - gpuPrm->wsFwdSz = wsFwdSz; - - - size_t wsBwdDataSz = 0, wsBwdWSz = 0; - if (isLern){ - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // dw - cudnnFilterDescriptor_t dw_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); - gpuPrm->dw_desc = dw_desc; - - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - - // algorithm - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); - gpuPrm->algoBwdData = algoBwdData; - - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); - gpuPrm->algoBwdW = algoBwdW; - - // workspace - cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, algoBwdData, &wsBwdDataSz)); - gpuPrm->wsBwdDataSz = wsBwdDataSz; - - cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); - gpuPrm->wsBwdWSz = wsBwdWSz; - } - - if (isFirst && !gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Convolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_bias)); - cuCHECK(cudaFree(gpuPrm->d_out)); - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); - - cuCHECK(cudaFree(gpuPrm->d_dw)); - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); - } -} - -__global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ - - size_t osz = outsz.w * outsz.h; - - snFloat* pOut = output + osz * outsz.d * blockIdx.x; - unsigned int d = threadIdx.x; - while (d < outsz.d){ - - snFloat b = bias[d]; - for (size_t j = 0; j < osz; ++j) - pOut[j] += b; - - pOut += osz * blockDim.x; - - d += blockDim.x; - } -} - -void Convolution::forwardCUDA(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, outsz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->out_desc, - gpuPrm->d_out)); - - // +bias - cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -void Convolution::backwardCUDA_GW(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, gpuPrm->wsBwdWSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, outsz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->grin_desc, - d_grin, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->grin_desc, - d_grin, - gpuPrm->conv_desc, - gpuPrm->algoBwdW, - gpuPrm->d_wsBwdW, - gpuPrm->wsBwdWSz, - &beta, - gpuPrm->dw_desc, - gpuPrm->d_dw)); - - cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - &beta, - gpuPrm->bias_desc, - gpuPrm->d_bias)); - - // результ - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut + wsz, gpuPrm->d_bias, outsz.d * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - } -} - -void Convolution::backwardCUDA_G(const convParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); - } - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->grin_desc, - d_grin, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - // результ - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - } -} - -#elif SN_CUDA - -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/convolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - - -struct gpuParams{ - - cudaDeviceProp* cu_deviceProps = 0; - - snFloat* d_in = 0; - snFloat* d_out = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; -}; - -void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const convParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - cudaDeviceProp* cu_deviceProps = new cudaDeviceProp(); - - cudaGetDeviceProperties(cu_deviceProps, 0); - if (cu_deviceProps->major < 2){ - ERROR_MESS("%s requires SM >= 2.0"); - delete cu_deviceProps; - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - gpuPrm->cu_deviceProps = cu_deviceProps; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); - } - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); - } - - gpuPrm->inszMem = insz.size(); - } -} - -void Convolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - delete gpuPrm->cu_deviceProps; - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_out)); - - if (gpuPrm->d_grout){ // isLern - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_dw)); - } -} - -__global__ void cuConvFwd(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - outStepByD = outsz.w * outsz.h, // step out by input - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d; // step in by batch - - // gridDim.x - number of out layers - // gridDim.y - batch size - - weight += blockIdx.x * wStepByK; - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - input += blockIdx.y * inStepByN; - - - unsigned int oz = 0; - while (oz < insz.d){ - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - if (oz == 0) - output[ox + oy * outsz.w] = weight[(outsz.d - blockIdx.x) * wStepByK + blockIdx.x]; // bias - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat csum = 0; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth; - - csum += input[(cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w] * weight[cx + cy * fWidth]; - } - output[ox + oy * outsz.w] += csum; - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByD; - input += inStepByD; - ++oz; - } -} - -void Convolution::forwardCUDA(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuConvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, - prms.fHeight, - prms.dilate, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, - gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -__global__ void cuConvBwd_GW(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - wStepByN = wStepByK * outsz.d + outsz.d, // step weight by batch - outStepByD = outsz.w * outsz.h, // step out by input - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d; // step in by batch - - // gridDim.x - number of input layers - // gridDim.y - batch size - - input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - weight += blockIdx.x * wStepByD; - dWeightOut += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel conv - snFloat grIn = gradIn[ox + oy * outsz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, - sw = cx + cy * fWidth; - - gradOut[si] += grIn * weight[sw]; - - dWeightOut[sw] += grIn * input[si]; - } - if (blockIdx.x == 0) - dWeightOut[wStepByK * (outsz.d - oz) + oz] += grIn; // bias - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByK; - dWeightOut += wStepByK; - gradIn += outStepByD; - ++oz; - } -} - -// weighted averaging over batch -__global__ void cuConvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - wStepByN = wStepByK * kernel + kernel; // step weight by batch - - unsigned int ox = threadIdx.x; - while (ox < wStepByN){ - - snFloat csum = weight[ox]; - for (size_t i = 1; i < insz.n; ++i) - csum += weight[ox + wStepByN * i]; - - weight[ox] = csum / insz.n; - - ox += blockDim.x; - } -} - -void Convolution::backwardCUDA_GW(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); - cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - cuConvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.dilate, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, - d_grin, - gpuPrm->d_grout, - gpuPrm->d_dw); - - cuConvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - } -} - -__global__ void cuConvBwd_G(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, - snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - outStepByD = outsz.w * outsz.h, // step out by input - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d; // step in by batch - - - // gridDim.x - number of input layers - // gridDim.y - batch size - - weight += blockIdx.x * wStepByD; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel conv - snFloat grIn = gradIn[ox + oy * outsz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, - sw = cx + cy * fWidth; - - gradOut[si] += grIn * weight[sw]; - } - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByK; - gradIn += outStepByD; - ++oz; - } -} - -void Convolution::backwardCUDA_G(const convParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); - - cuConvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.dilate, - prms.stride, - gpuPrm->d_w, - insz, outsz, - d_grin, - gpuPrm->d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#endif diff --git a/src/snOperator_CPU_cuDNN/src/CUDA/deconvolutionCUDA.cu b/src/snOperator_CPU_cuDNN/src/CUDA/deconvolutionCUDA.cu deleted file mode 100644 index 59d2b43..0000000 --- a/src/snOperator_CPU_cuDNN/src/CUDA/deconvolutionCUDA.cu +++ /dev/null @@ -1,915 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#ifdef SN_CUDNN - -#include -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/deconvolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnConvolutionDescriptor_t conv_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - cudnnFilterDescriptor_t w_desc = 0; - cudnnFilterDescriptor_t dw_desc = 0; - cudnnTensorDescriptor_t bias_desc = 0; - - cudnnConvolutionFwdAlgo_t algoFwd; - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - - size_t wsFwdSz = 0; - size_t wsBwdDataSz = 0; - size_t wsBwdWSz = 0; - size_t inszMem = 0; - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_bias = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - void* d_wsFwd = 0; - void* d_wsBwdData = 0; - void* d_wsBwdW = 0; - -}; - -void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const deconvParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // w - cudnnFilterDescriptor_t w_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); - gpuPrm->w_desc = w_desc; - - // conv - cudnnConvolutionDescriptor_t conv_desc = nullptr; - cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); - cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), 1, 1, - CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); - if (!isFirst) - cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); - gpuPrm->conv_desc = conv_desc; - - // output - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - // algorithm - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); - gpuPrm->algoBwdData = algoBwdData; - - // workspace - size_t wsBwdDataSz = 0; - cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, algoBwdData, &wsBwdDataSz)); - gpuPrm->wsBwdDataSz = wsBwdDataSz; - - - size_t wsFwdSz = 0, wsBwdWSz = 0; - if (isLern){ - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // dw - cudnnFilterDescriptor_t dw_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); - gpuPrm->dw_desc = dw_desc; - - // bias - cudnnTensorDescriptor_t bias_desc; - cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - 1, int(insz.d), 1, 1)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); - gpuPrm->bias_desc = bias_desc; - - // algorithm - cudnnConvolutionFwdAlgo_t algoFwd; - cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); - gpuPrm->algoFwd = algoFwd; - - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); - gpuPrm->algoBwdW = algoBwdW; - - // workspace - cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, algoFwd, &wsFwdSz)); - gpuPrm->wsFwdSz = wsFwdSz; - - cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); - gpuPrm->wsBwdWSz = wsBwdWSz; - } - - if (isFirst && !gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Deconvolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_out)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); - - cuCHECK(cudaFree(gpuPrm->d_dw)); - cuCHECK(cudaFree(gpuPrm->d_bias)); - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); - } -} - -void Deconvolution::forwardCUDA(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->out_desc, - gpuPrm->d_out)); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -__global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ - - size_t isz = insz.w * insz.h; - - snFloat* pGrOut = grout + isz * insz.d * blockIdx.x; - unsigned int d = threadIdx.x; - while (d < insz.d){ - - snFloat b = bias[d]; - for (size_t j = 0; j < isz; ++j) - pGrOut[j] += b; - - pGrOut += isz * blockDim.x; - - d += blockDim.x; - } -} - -void Deconvolution::backwardCUDA_GW(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, gpuPrm->wsBwdWSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->conv_desc, - gpuPrm->algoBwdW, - gpuPrm->d_wsBwdW, - gpuPrm->wsBwdWSz, - &beta, - gpuPrm->dw_desc, - gpuPrm->d_dw)); - - cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - &beta, - gpuPrm->bias_desc, - gpuPrm->d_bias)); - - // +bias - cuBwdBias <<< int(insz.n), 128 >>> (insz, gpuPrm->d_bias, gpuPrm->d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut + wsz, gpuPrm->d_bias, insz.d * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - } -} - -void Deconvolution::backwardCUDA_G(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - } - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, insz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - // +bias - cuBwdBias <<< int(insz.n), 128 >>> (insz, gpuPrm->d_bias, gpuPrm->d_grout); - - // результ - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - } -} - - -#elif SN_CUDA - -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/deconvolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; -}; - -void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); - } - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Deconvolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_out)); - - if (gpuPrm->d_grout){ // isLern - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_dw)); - } -} - -__global__ void cuDeconvFwd(size_t fWidth, size_t fHeight, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * outsz.d, - outStepByD = outsz.w * outsz.h, - outStepByN = outStepByD * outsz.d, - inStepByD = insz.w * insz.h, - inStepByN = inStepByD * insz.d; - - // gridDim.x - number of output layers - // gridDim.y - batch sz - - weight += blockIdx.x * wStepByD; - input += blockIdx.y * inStepByN; - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - - - unsigned int oz = 0; - while (oz < insz.d){ - - unsigned int oy = threadIdx.y; - while (oy < insz.h){ - - unsigned int ox = threadIdx.x; - while (ox < insz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat in = input[ox + oy * insz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW) + (cy + posH) * outsz.w, - sw = cx + cy * fWidth; - - output[si] += in * weight[sw]; - } - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByK; - input += inStepByD; - ++oz; - } -} - -void Deconvolution::forwardCUDA(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_out, 0, osz * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuDeconvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, - prms.fHeight, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, - gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -__global__ void cuDeconvBwd_GW(size_t fWidth, size_t fHeight, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * outsz.d, - wStepByN = wStepByK * insz.d + insz.d, - outStepByD = outsz.w * outsz.h, - outStepByN = outStepByD * outsz.d, - inStepByD = insz.w * insz.h, - inStepByN = inStepByD * insz.d; - - // gridDim.x - number of input layers - // gridDim.y - batch sz - - input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - weight += blockIdx.x * wStepByK; - dWeightOut += blockIdx.x * wStepByK + blockIdx.y * wStepByN; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < insz.h){ - - unsigned int ox = threadIdx.x; - while (ox < insz.w){ - - if (oz == 0) - gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat csum = 0, cin = input[ox + oy * insz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW) + (cy + posH) * outsz.w, - sw = cx + cy * fWidth; - - csum += gradIn[si] * weight[sw]; - - dWeightOut[sw] += gradIn[si] * cin; - } - gradOut[ox + oy * insz.w] += csum; - - if (oz == 0) - dWeightOut[wStepByK * (insz.d - blockIdx.x) + blockIdx.x] += cin; // bias - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByD; - dWeightOut += wStepByD; - gradIn += outStepByD; - ++oz; - } -} - -__global__ void cuDeconvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * kernel, - wStepByN = wStepByK * insz.d + insz.d; - - unsigned int ox = threadIdx.x; - while (ox < wStepByN){ - - snFloat csum = weight[ox]; - for (size_t i = 1; i < insz.n; ++i) - csum += weight[ox + wStepByN * i]; - - weight[ox] = csum / insz.n; - - ox += blockDim.x; - } -} - -void Deconvolution::backwardCUDA_GW(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - cuDeconvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, d_grin, - gpuPrm->d_grout, - gpuPrm->d_dw); - - cuDeconvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - } -} - -__global__ void cuDeconvBwd_G(size_t fWidth, size_t fHeight, size_t stride, - snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * outsz.d, - outStepByD = outsz.w * outsz.h, - outStepByN = outStepByD * outsz.d, - inStepByD = insz.w * insz.h, - inStepByN = inStepByD * insz.d; - - // gridDim.x - number of input layers - // gridDim.y - batch size - - weight += blockIdx.x * wStepByK; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < insz.h){ - - unsigned int ox = threadIdx.x; - while (ox < insz.w){ - - if (oz == 0) - gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat csum = 0; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW) + (cy + posH) * outsz.w, - sw = cx + cy * fWidth; - - csum += gradIn[si] * weight[sw]; - } - gradOut[ox + oy * insz.w] += csum; - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByD; - gradIn += outStepByD; - ++oz; - } -} - -void Deconvolution::backwardCUDA_G(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - cuDeconvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.stride, - gpuPrm->d_w, - insz, - outsz, - d_grin, - gpuPrm->d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#endif diff --git a/src/snOperator_CPU_cuDNN/src/CUDA/poolingCUDA.cu b/src/snOperator_CPU_cuDNN/src/CUDA/poolingCUDA.cu deleted file mode 100644 index f8cb48c..0000000 --- a/src/snOperator_CPU_cuDNN/src/CUDA/poolingCUDA.cu +++ /dev/null @@ -1,594 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#ifdef SN_CUDNN - -#include -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/pooling.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnPoolingDescriptor_t pool_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - - snFloat* d_in = 0; - snFloat* d_out = 0; - snFloat* d_grin = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; -}; - -void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // pool - cudnnPoolingDescriptor_t pool_desc = nullptr; - cuCHECK(cudnnCreatePoolingDescriptor(&pool_desc)); - - cudnnPoolingMode_t poolT = cudnnPoolingMode_t::CUDNN_POOLING_MAX; - if (poolPrms.type == poolType::avg) - poolT = cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; - - cuCHECK(cudnnSetPooling2dDescriptor(pool_desc, poolT, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, - int(poolPrms.kernel), int(poolPrms.kernel), 0, 0, int(poolPrms.stride), int(poolPrms.stride))); - if (!isFirst) - cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); - gpuPrm->pool_desc = pool_desc; - - // output - int out_n = 0, out_c = 0, out_h = 0, out_w = 0; - cuCHECK(cudnnGetPooling2dForwardOutputDim(pool_desc, in_desc, - &out_n, &out_c, &out_h, &out_w)); - - if (outsz != snSize(out_w, out_h, out_c, out_n)){ - ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); - return; - } - - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - if (isLern){ - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - } - - size_t isz = insz.size(), osz = outsz.size(); - if (isFirst && !gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < isz)){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grin)); gpuPrm->d_grin = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - gpuPrm->inszMem = isz; - } -} - -void Pooling::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_out)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - - cuCHECK(cudaFree(gpuPrm->d_grin)); - cuCHECK(cudaFree(gpuPrm->d_grout)); - } -} - -__global__ void cuFiltrNegative(snSize outsz, snFloat* out){ - - out += blockIdx.x * outsz.w * outsz.h * outsz.d; - - unsigned int k = threadIdx.x; - while (k < outsz.d){ - - snFloat* pOut = out + outsz.w * outsz.h * k; - for (size_t j = 0; j < (outsz.w * outsz.h); ++j) - if (pOut[j] < 0) pOut[j] = 0.0; - - k += blockDim.x; - } -} - -void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnPoolingForward(gpuPrm->cudnn, - gpuPrm->pool_desc, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - &beta, - gpuPrm->out_desc, - gpuPrm->d_out)); - - // filtrNegative - dim3 dimBlock(256); - dim3 dimGrid(int(outsz.n)); - - cuFiltrNegative <<< dimGrid, dimBlock >>>(outsz, gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, - const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - size_t isz = insz.size(), osz = outsz.size(); - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // output - cuCHECK(cudaMemcpy(gpuPrm->d_out, output, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // grin - cuCHECK(cudaMemcpy(gpuPrm->d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnPoolingBackward(gpuPrm->cudnn, - gpuPrm->pool_desc, - &alpha, - gpuPrm->out_desc, - gpuPrm->d_out, - gpuPrm->grin_desc, - gpuPrm->d_grin, - gpuPrm->in_desc, - gpuPrm->d_in, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grin)); gpuPrm->d_grin = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#elif SN_CUDA - -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/pooling.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - snFloat* d_in = 0; - snFloat* d_out = 0; - size_t* d_idx = 0; - - size_t inszMem = 0; -}; - -void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 2){ - ERROR_MESS("%s requires SM >= 2.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); - - gpuPrm->inszMem = insz.size(); - } -} - -void Pooling::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cudaFree(gpuPrm->d_in); - cudaFree(gpuPrm->d_out); - cudaFree(gpuPrm->d_idx); -} - -__global__ void cuPoolFwd(poolType type, size_t kernel, size_t stride, snSize insz, snFloat* input, snSize outsz, snFloat* output, size_t* outputInx){ - - size_t outStepByD = outsz.w * outsz.h, // step out by output - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d, // step in by batch - kernelSz = kernel * kernel; - - // gridDim.x - number of input layers - // gridDim.y - batch sz - - input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - - if (type == poolType::max){ // max - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat valMax = 0; size_t idx = 0; -#pragma unroll - for (size_t c = 0; c < kernelSz; ++c){ - - size_t cx = c % kernel, cy = c / kernel; - - snFloat val = input[cx + posW + (cy + posH) * insz.w]; - if (val > valMax){ - valMax = val; - idx = c; - } - } - output[ox + oy * outsz.w] = valMax; - outputInx[ox + oy * outsz.w] = idx; - - ox += blockDim.x; - } - oy += blockDim.y; - } - } - else{ // mean - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat valMean = 0; -#pragma unroll - for (size_t c = 0; c < kernelSz; ++c){ - - size_t cx = c % kernel, cy = c / kernel; - - valMean += input[cx + posW + (cy + posH) * insz.w]; - } - output[ox + oy * outsz.w] = valMean / kernelSz; - - ox += blockDim.x; - } - oy += blockDim.y; - } - } -} - -void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - size_t isz = insz.size(), osz = outsz.size(); - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_idx, osz * sizeof(size_t))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuPoolFwd << < dimGrid, dimBlock >> >(poolPrms.type, - poolPrms.kernel, - poolPrms.stride, - insz, - gpuPrm->d_in, - outsz, - gpuPrm->d_out, - gpuPrm->d_idx); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(outputInx, gpuPrm->d_idx, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; - } -} - -__global__ void cuPoolBwd(poolType type, size_t kernel, size_t stride, snSize outsz, size_t* outputInx, snFloat* gradIn, snSize insz, snFloat* gradOut){ - - size_t outStepByD = outsz.w * outsz.h, // step out by output - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d, // step in by batch - kernelSz = kernel * kernel; - - // gridDim.x - number of input layers - // gridDim.y - batch sz - - gradIn += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - if (type == poolType::max){ // max - - outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - size_t c = outputInx[ox + oy * outsz.w], cx = c % kernel, cy = c / kernel; - - gradOut[cx + posW + (cy + posH) * insz.w] = gradIn[ox + oy * outsz.w]; - - ox += blockDim.x; - } - oy += blockDim.y; - } - } - else{ // mean - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - snFloat mean = gradIn[ox + oy * outsz.w] / kernel; -#pragma unroll - for (size_t c = 0; c < kernelSz; ++c){ - size_t cx = c % kernel, cy = c / kernel; - gradOut[(cx + posW) + (cy + posH) * insz.w] = mean; - } - - ox += blockDim.x; - } - oy += blockDim.y; - } - } -} - -void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, - const snSize& insz, const SN_Base::snFloat* input, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - snFloat* d_grin = gpuPrm->d_out, - * d_grout = gpuPrm->d_in; - size_t* d_idx = gpuPrm->d_idx; - - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_idx, osz * sizeof(size_t))); - } - - // input - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - cuCHECK(cudaMemcpy(d_idx, outputInx, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(d_grout, 0, isz * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuPoolBwd << < dimGrid, dimBlock >> >(poolPrms.type, - poolPrms.kernel, - poolPrms.stride, - outsz, - d_idx, - d_grin, - insz, - d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(d_grout)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(d_idx)); gpuPrm->d_idx = 0; - } -} - -#endif \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu b/src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu deleted file mode 100644 index 4c3eea8..0000000 --- a/src/snOperator_cuDNN/src/CUDA/convolutionCUDA.cu +++ /dev/null @@ -1,929 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#ifdef SN_CUDNN - -#include -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/convolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnConvolutionDescriptor_t conv_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - cudnnFilterDescriptor_t w_desc = 0; - cudnnFilterDescriptor_t dw_desc = 0; - cudnnTensorDescriptor_t bias_desc = 0; - - cudnnConvolutionFwdAlgo_t algoFwd; - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - - size_t wsFwdSz = 0; - size_t wsBwdDataSz = 0; - size_t wsBwdWSz = 0; - size_t inszMem = 0; - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_bias = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - void* d_wsFwd = 0; - void* d_wsBwdData = 0; - void* d_wsBwdW = 0; -}; - -void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const convParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // w - cudnnFilterDescriptor_t w_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); - gpuPrm->w_desc = w_desc; - - // conv - cudnnConvolutionDescriptor_t conv_desc = nullptr; - cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); - cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), - CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); - if (!isFirst) - cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); - gpuPrm->conv_desc = conv_desc; - - // output - int out_n = 0, out_c = 0, out_h = 0, out_w = 0; - cuCHECK(cudnnGetConvolution2dForwardOutputDim( - conv_desc, in_desc, w_desc, - &out_n, &out_c, &out_h, &out_w)); - - if (outsz != snSize(out_w, out_h, out_c, out_n)){ - ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); - return; - } - - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - // bias - cudnnTensorDescriptor_t bias_desc; - cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - 1, out_c, 1, 1)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); - gpuPrm->bias_desc = bias_desc; - - // algorithm - cudnnConvolutionFwdAlgo_t algoFwd; - cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); - gpuPrm->algoFwd = algoFwd; - - // workspace - size_t wsFwdSz = 0; - cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, algoFwd, &wsFwdSz)); - gpuPrm->wsFwdSz = wsFwdSz; - - - size_t wsBwdDataSz = 0, wsBwdWSz = 0; - if (isLern){ - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // dw - cudnnFilterDescriptor_t dw_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); - gpuPrm->dw_desc = dw_desc; - - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - - // algorithm - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); - gpuPrm->algoBwdData = algoBwdData; - - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); - gpuPrm->algoBwdW = algoBwdW; - - // workspace - cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, algoBwdData, &wsBwdDataSz)); - gpuPrm->wsBwdDataSz = wsBwdDataSz; - - cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); - gpuPrm->wsBwdWSz = wsBwdWSz; - } - - if (isFirst && !gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Convolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_bias)); - cuCHECK(cudaFree(gpuPrm->d_out)); - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); - - cuCHECK(cudaFree(gpuPrm->d_dw)); - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); - } -} - -__global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ - - size_t osz = outsz.w * outsz.h; - - snFloat* pOut = output + osz * outsz.d * blockIdx.x; - unsigned int d = threadIdx.x; - while (d < outsz.d){ - - snFloat b = bias[d]; - for (size_t j = 0; j < osz; ++j) - pOut[j] += b; - - pOut += osz * blockDim.x; - - d += blockDim.x; - } -} - -void Convolution::forwardCUDA(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, outsz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->out_desc, - gpuPrm->d_out)); - - // +bias - cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -void Convolution::backwardCUDA_GW(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, gpuPrm->wsBwdWSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, outsz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->grin_desc, - d_grin, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->grin_desc, - d_grin, - gpuPrm->conv_desc, - gpuPrm->algoBwdW, - gpuPrm->d_wsBwdW, - gpuPrm->wsBwdWSz, - &beta, - gpuPrm->dw_desc, - gpuPrm->d_dw)); - - cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - &beta, - gpuPrm->bias_desc, - gpuPrm->d_bias)); - - // результ - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut + wsz, gpuPrm->d_bias, outsz.d * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - } -} - -void Convolution::backwardCUDA_G(const convParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); - } - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->grin_desc, - d_grin, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - // результ - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - } -} - -#elif SN_CUDA - -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/convolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - - -struct gpuParams{ - - cudaDeviceProp* cu_deviceProps = 0; - - snFloat* d_in = 0; - snFloat* d_out = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; -}; - -void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const convParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - cudaDeviceProp* cu_deviceProps = new cudaDeviceProp(); - - cudaGetDeviceProperties(cu_deviceProps, 0); - if (cu_deviceProps->major < 2){ - ERROR_MESS("%s requires SM >= 2.0"); - delete cu_deviceProps; - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - gpuPrm->cu_deviceProps = cu_deviceProps; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); - } - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); - } - - gpuPrm->inszMem = insz.size(); - } -} - -void Convolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - delete gpuPrm->cu_deviceProps; - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_out)); - - if (gpuPrm->d_grout){ // isLern - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_dw)); - } -} - -__global__ void cuConvFwd(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - outStepByD = outsz.w * outsz.h, // step out by input - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d; // step in by batch - - // gridDim.x - number of out layers - // gridDim.y - batch size - - weight += blockIdx.x * wStepByK; - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - input += blockIdx.y * inStepByN; - - - unsigned int oz = 0; - while (oz < insz.d){ - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - if (oz == 0) - output[ox + oy * outsz.w] = weight[(outsz.d - blockIdx.x) * wStepByK + blockIdx.x]; // bias - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat csum = 0; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth; - - csum += input[(cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w] * weight[cx + cy * fWidth]; - } - output[ox + oy * outsz.w] += csum; - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByD; - input += inStepByD; - ++oz; - } -} - -void Convolution::forwardCUDA(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuConvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, - prms.fHeight, - prms.dilate, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, - gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -__global__ void cuConvBwd_GW(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - wStepByN = wStepByK * outsz.d + outsz.d, // step weight by batch - outStepByD = outsz.w * outsz.h, // step out by input - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d; // step in by batch - - // gridDim.x - number of input layers - // gridDim.y - batch size - - input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - weight += blockIdx.x * wStepByD; - dWeightOut += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel conv - snFloat grIn = gradIn[ox + oy * outsz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, - sw = cx + cy * fWidth; - - gradOut[si] += grIn * weight[sw]; - - dWeightOut[sw] += grIn * input[si]; - } - if (blockIdx.x == 0) - dWeightOut[wStepByK * (outsz.d - oz) + oz] += grIn; // bias - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByK; - dWeightOut += wStepByK; - gradIn += outStepByD; - ++oz; - } -} - -// weighted averaging over batch -__global__ void cuConvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - wStepByN = wStepByK * kernel + kernel; // step weight by batch - - unsigned int ox = threadIdx.x; - while (ox < wStepByN){ - - snFloat csum = weight[ox]; - for (size_t i = 1; i < insz.n; ++i) - csum += weight[ox + wStepByN * i]; - - weight[ox] = csum / insz.n; - - ox += blockDim.x; - } -} - -void Convolution::backwardCUDA_GW(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); - cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - cuConvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.dilate, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, - d_grin, - gpuPrm->d_grout, - gpuPrm->d_dw); - - cuConvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - } -} - -__global__ void cuConvBwd_G(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, - snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ - - size_t wStepByD = fWidth * fHeight, // step weight by input - wStepByK = wStepByD * insz.d, // step weight by output - outStepByD = outsz.w * outsz.h, // step out by input - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d; // step in by batch - - - // gridDim.x - number of input layers - // gridDim.y - batch size - - weight += blockIdx.x * wStepByD; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel conv - snFloat grIn = gradIn[ox + oy * outsz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, - sw = cx + cy * fWidth; - - gradOut[si] += grIn * weight[sw]; - } - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByK; - gradIn += outStepByD; - ++oz; - } -} - -void Convolution::backwardCUDA_G(const convParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); - - cuConvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.dilate, - prms.stride, - gpuPrm->d_w, - insz, outsz, - d_grin, - gpuPrm->d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#endif diff --git a/src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu b/src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu deleted file mode 100644 index 59d2b43..0000000 --- a/src/snOperator_cuDNN/src/CUDA/deconvolutionCUDA.cu +++ /dev/null @@ -1,915 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#ifdef SN_CUDNN - -#include -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/deconvolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnConvolutionDescriptor_t conv_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - cudnnFilterDescriptor_t w_desc = 0; - cudnnFilterDescriptor_t dw_desc = 0; - cudnnTensorDescriptor_t bias_desc = 0; - - cudnnConvolutionFwdAlgo_t algoFwd; - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - - size_t wsFwdSz = 0; - size_t wsBwdDataSz = 0; - size_t wsBwdWSz = 0; - size_t inszMem = 0; - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_bias = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - void* d_wsFwd = 0; - void* d_wsBwdData = 0; - void* d_wsBwdW = 0; - -}; - -void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const deconvParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // w - cudnnFilterDescriptor_t w_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); - gpuPrm->w_desc = w_desc; - - // conv - cudnnConvolutionDescriptor_t conv_desc = nullptr; - cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); - cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), 1, 1, - CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); - if (!isFirst) - cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); - gpuPrm->conv_desc = conv_desc; - - // output - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - // algorithm - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); - gpuPrm->algoBwdData = algoBwdData; - - // workspace - size_t wsBwdDataSz = 0; - cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, algoBwdData, &wsBwdDataSz)); - gpuPrm->wsBwdDataSz = wsBwdDataSz; - - - size_t wsFwdSz = 0, wsBwdWSz = 0; - if (isLern){ - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // dw - cudnnFilterDescriptor_t dw_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); - gpuPrm->dw_desc = dw_desc; - - // bias - cudnnTensorDescriptor_t bias_desc; - cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - 1, int(insz.d), 1, 1)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); - gpuPrm->bias_desc = bias_desc; - - // algorithm - cudnnConvolutionFwdAlgo_t algoFwd; - cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); - gpuPrm->algoFwd = algoFwd; - - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); - gpuPrm->algoBwdW = algoBwdW; - - // workspace - cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, algoFwd, &wsFwdSz)); - gpuPrm->wsFwdSz = wsFwdSz; - - cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); - gpuPrm->wsBwdWSz = wsBwdWSz; - } - - if (isFirst && !gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Deconvolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_out)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); - - cuCHECK(cudaFree(gpuPrm->d_dw)); - cuCHECK(cudaFree(gpuPrm->d_bias)); - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); - } -} - -void Deconvolution::forwardCUDA(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, gpuPrm->wsBwdDataSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->out_desc, - gpuPrm->d_out)); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -__global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ - - size_t isz = insz.w * insz.h; - - snFloat* pGrOut = grout + isz * insz.d * blockIdx.x; - unsigned int d = threadIdx.x; - while (d < insz.d){ - - snFloat b = bias[d]; - for (size_t j = 0; j < isz; ++j) - pGrOut[j] += b; - - pGrOut += isz * blockDim.x; - - d += blockDim.x; - } -} - -void Deconvolution::backwardCUDA_GW(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, gpuPrm->wsBwdWSz)); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - gpuPrm->in_desc, - gpuPrm->d_in, - gpuPrm->conv_desc, - gpuPrm->algoBwdW, - gpuPrm->d_wsBwdW, - gpuPrm->wsBwdWSz, - &beta, - gpuPrm->dw_desc, - gpuPrm->d_dw)); - - cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - &beta, - gpuPrm->bias_desc, - gpuPrm->d_bias)); - - // +bias - cuBwdBias <<< int(insz.n), 128 >>> (insz, gpuPrm->d_bias, gpuPrm->d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut + wsz, gpuPrm->d_bias, insz.d * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - } -} - -void Deconvolution::backwardCUDA_G(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - void* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_w, prms.fWidth * prms.fHeight * insz.d * outsz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, gpuPrm->wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_bias, insz.d * sizeof(snFloat))); - } - - // grin - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - size_t wsz = outsz.d * insz.d * prms.fHeight * prms.fWidth; - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - cuCHECK(cudaMemcpy(gpuPrm->d_bias, weight + wsz, insz.d * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - d_grin, - gpuPrm->w_desc, - gpuPrm->d_w, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - // +bias - cuBwdBias <<< int(insz.n), 128 >>> (insz, gpuPrm->d_bias, gpuPrm->d_grout); - - // результ - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_bias)); gpuPrm->d_bias = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - } -} - - -#elif SN_CUDA - -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/deconvolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; -}; - -void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); - } - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Deconvolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_w)); - cuCHECK(cudaFree(gpuPrm->d_out)); - - if (gpuPrm->d_grout){ // isLern - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_dw)); - } -} - -__global__ void cuDeconvFwd(size_t fWidth, size_t fHeight, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * outsz.d, - outStepByD = outsz.w * outsz.h, - outStepByN = outStepByD * outsz.d, - inStepByD = insz.w * insz.h, - inStepByN = inStepByD * insz.d; - - // gridDim.x - number of output layers - // gridDim.y - batch sz - - weight += blockIdx.x * wStepByD; - input += blockIdx.y * inStepByN; - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - - - unsigned int oz = 0; - while (oz < insz.d){ - - unsigned int oy = threadIdx.y; - while (oy < insz.h){ - - unsigned int ox = threadIdx.x; - while (ox < insz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat in = input[ox + oy * insz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW) + (cy + posH) * outsz.w, - sw = cx + cy * fWidth; - - output[si] += in * weight[sw]; - } - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByK; - input += inStepByD; - ++oz; - } -} - -void Deconvolution::forwardCUDA(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_out, 0, osz * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuDeconvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, - prms.fHeight, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, - gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -__global__ void cuDeconvBwd_GW(size_t fWidth, size_t fHeight, size_t stride, - snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * outsz.d, - wStepByN = wStepByK * insz.d + insz.d, - outStepByD = outsz.w * outsz.h, - outStepByN = outStepByD * outsz.d, - inStepByD = insz.w * insz.h, - inStepByN = inStepByD * insz.d; - - // gridDim.x - number of input layers - // gridDim.y - batch sz - - input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - weight += blockIdx.x * wStepByK; - dWeightOut += blockIdx.x * wStepByK + blockIdx.y * wStepByN; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < insz.h){ - - unsigned int ox = threadIdx.x; - while (ox < insz.w){ - - if (oz == 0) - gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat csum = 0, cin = input[ox + oy * insz.w]; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW) + (cy + posH) * outsz.w, - sw = cx + cy * fWidth; - - csum += gradIn[si] * weight[sw]; - - dWeightOut[sw] += gradIn[si] * cin; - } - gradOut[ox + oy * insz.w] += csum; - - if (oz == 0) - dWeightOut[wStepByK * (insz.d - blockIdx.x) + blockIdx.x] += cin; // bias - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByD; - dWeightOut += wStepByD; - gradIn += outStepByD; - ++oz; - } -} - -__global__ void cuDeconvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * kernel, - wStepByN = wStepByK * insz.d + insz.d; - - unsigned int ox = threadIdx.x; - while (ox < wStepByN){ - - snFloat csum = weight[ox]; - for (size_t i = 1; i < insz.n; ++i) - csum += weight[ox + wStepByN * i]; - - weight[ox] = csum / insz.n; - - ox += blockDim.x; - } -} - -void Deconvolution::backwardCUDA_GW(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - cuDeconvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.stride, - gpuPrm->d_w, - insz, - gpuPrm->d_in, - outsz, d_grin, - gpuPrm->d_grout, - gpuPrm->d_dw); - - cuDeconvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - } -} - -__global__ void cuDeconvBwd_G(size_t fWidth, size_t fHeight, size_t stride, - snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ - - size_t wStepByD = fWidth * fHeight, - wStepByK = wStepByD * outsz.d, - outStepByD = outsz.w * outsz.h, - outStepByN = outStepByD * outsz.d, - inStepByD = insz.w * insz.h, - inStepByN = inStepByD * insz.d; - - // gridDim.x - number of input layers - // gridDim.y - batch size - - weight += blockIdx.x * wStepByK; - gradIn += blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - unsigned int oz = 0; - while (oz < outsz.d){ - - unsigned int oy = threadIdx.y; - while (oy < insz.h){ - - unsigned int ox = threadIdx.x; - while (ox < insz.w){ - - if (oz == 0) - gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat csum = 0; -#pragma unroll - for (size_t c = 0; c < wStepByD; ++c){ - - size_t cx = c % fWidth, cy = c / fWidth, - si = (cx + posW) + (cy + posH) * outsz.w, - sw = cx + cy * fWidth; - - csum += gradIn[si] * weight[sw]; - } - gradOut[ox + oy * insz.w] += csum; - - ox += blockDim.x; - } - oy += blockDim.y; - } - weight += wStepByD; - gradIn += outStepByD; - ++oz; - } -} - -void Deconvolution::backwardCUDA_G(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(), - wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // weight - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(insz.d), int(outsz.n)); - - cuDeconvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, - prms.fHeight, - prms.stride, - gpuPrm->d_w, - insz, - outsz, - d_grin, - gpuPrm->d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#endif diff --git a/src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu b/src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu deleted file mode 100644 index e9556e8..0000000 --- a/src/snOperator_cuDNN/src/CUDA/fullyConnectedCUDA.cu +++ /dev/null @@ -1,339 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#ifdef SN_CUDA - -#include -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/fullyConnected.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - - -struct gpuParams{ - - cublasHandle_t cuBLAS = 0; - - snFloat* d_in = 0; - snFloat* d_w = 0; - snFloat* d_dw = 0; - snFloat* d_out = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; -}; - -void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - size_t ida = insz.w * insz.h * insz.d, bsz = insz.n; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cublasHandle_t cuHandle = nullptr; - cuCHECK(cublasCreate(&cuHandle)); - - gpuPrm->cuBLAS = cuHandle; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); - } - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < ida * bsz)){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); - } - - gpuPrm->inszMem = ida * bsz; - } -} - -void FullyConnected::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cublasDestroy(gpuPrm->cuBLAS); - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_out)); - cuCHECK(cudaFree(gpuPrm->d_w)); - - if (gpuPrm->d_grout){ // isLern - cuCHECK(cudaFree(gpuPrm->d_grout)); - cuCHECK(cudaFree(gpuPrm->d_dw)); - } -} - -__global__ void cuFwdBias(size_t kernel, snSize insz, snFloat* weight, snFloat* output){ - - weight += insz.w * insz.h * insz.d * kernel; - - snFloat* out = output + kernel * blockIdx.x; - unsigned int k = threadIdx.x; - while (k < kernel){ - - out[k] += weight[k]; - - k += blockDim.x; - } -} - -void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloat* input, const snFloat* weight, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, bsz * kernel * sizeof(snFloat))); - } - - cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); - - cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // Out = α * W * In + βC - // In - data input matrix - values from the previous layer - // W - weights matrix - // Out - output matrix - float alpha = 1.0f, beta = 0.0f; - cuCHECK(cublasSgemm(gpuPrm->cuBLAS, - CUBLAS_OP_N, - CUBLAS_OP_N, - krn, // W, cols - bsz, // In, rows - ida, // In, cols, В М - rows - &alpha, // α - gpuPrm->d_w, // W - krn, // W, step to next W (W21 - W11) - gpuPrm->d_in, // In - ida, // In, step to next X (X21 - X11) - &beta, // β - gpuPrm->d_out, // Out - krn)); // Out, step to next Y (Y21 - Y11) - - // +bias - cuFwdBias <<< int(insz.n), 128 >>> (kernel, insz, gpuPrm->d_w, gpuPrm->d_out); - - // result - cuCHECK(cublasGetMatrix(bsz, krn, sizeof(snFloat), gpuPrm->d_out, bsz, output, bsz)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -__global__ void cuBwdBias(size_t kernel, snSize insz, snFloat* gradIn, snFloat* dWOut){ - - dWOut += insz.w * insz.h * insz.d * kernel; - unsigned int k = threadIdx.x; - while (k < kernel){ - - snFloat* grin = gradIn + k, b = 0; - for (size_t j = 0; j < insz.n; ++j) - b += grin[kernel * j]; - - dWOut[k] = b / insz.n; - k += blockDim.x; - } -} - -void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, - const snSize& insz, const snFloat* input, const snFloat* gradIn, snFloat* gradOut, snFloat* dWOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_w, (ida + 1) * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, bsz * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_dw, (ida + 1) * kernel * sizeof(snFloat))); - } - - cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); - - cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); - - // Weight gradient - // dW = αIn^T * GrIn + βdW - // In - data input matrix from previous layer - // GrIn - gradient matrix from the next layer - float alpha = 1.0F / insz.n, beta = 0.0f; - cuCHECK(cublasSgemm(gpuPrm->cuBLAS, - CUBLAS_OP_N, - CUBLAS_OP_T, - krn, // GrIn, cols - ida, // In, cols (+1 - X0) - bsz, // In, rows - &alpha, // α - d_grin, // GrIn - krn, // GrIn, step to next - gpuPrm->d_in, // In - ida, // In, step to next X (X21 - X11) - &beta, // β - gpuPrm->d_dw, // dW - krn)); // dW, step to next - - // bias - cuBwdBias <<< 1, 128 >>> (kernel, insz, d_grin, gpuPrm->d_dw); - - // result - cuCHECK(cudaMemcpy(dWOut, gpuPrm->d_dw, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - - cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); - - //// Gradient for previous layer - //// GrOut = αGrIn * W^T + βGrOut - //// GrIn - gradient matrix from the next layer - //// W - weight - alpha = 1.F; - cuCHECK(cublasSgemm(gpuPrm->cuBLAS, - CUBLAS_OP_T, - CUBLAS_OP_N, - ida, // W, cols - bsz, // W, rows - krn, // GrIn, cols - &alpha, // α - gpuPrm->d_w, // W - krn, // W, step to next - d_grin, // GrIn - krn, // GrIn, step to next - &beta, // β - gpuPrm->d_grout, // GrOut - ida)); // GrOut, step to next - - - // result - cuCHECK(cublasGetMatrix(bsz, ida, sizeof(snFloat), gpuPrm->d_grout, bsz, gradOut, bsz)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; - } -} - -void FullyConnected::backwardCUDA_G(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - snFloat* d_grin = gpuPrm->d_out; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_w, ida * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grin, bsz * kernel * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, bsz * ida * sizeof(snFloat))); - } - - cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); - - cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); - - //// Gradient for previous layer - //// GrOut = αGrIn * W^T + βGrOut - //// GrIn - gradient matrix from the next layer - //// W - weight - float alpha = 1.0F, beta = 0.0f; - cuCHECK(cublasSgemm(gpuPrm->cuBLAS, - CUBLAS_OP_T, - CUBLAS_OP_N, - ida, // W, cols - bsz, // W, rows - krn, // GrIn, cols - &alpha, // α - gpuPrm->d_w, // W - krn, // W, step to next - d_grin, // GrIn - krn, // GrIn, step to next - &beta, // β - gpuPrm->d_grout, // GrOut - ida)); // GrOut, step to next - - cuCHECK(cublasGetMatrix(bsz, ida, sizeof(snFloat), gpuPrm->d_grout, bsz, gradOut, bsz)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#endif \ No newline at end of file diff --git a/src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu b/src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu deleted file mode 100644 index f8cb48c..0000000 --- a/src/snOperator_cuDNN/src/CUDA/poolingCUDA.cu +++ /dev/null @@ -1,594 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#ifdef SN_CUDNN - -#include -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/pooling.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnPoolingDescriptor_t pool_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - - snFloat* d_in = 0; - snFloat* d_out = 0; - snFloat* d_grin = 0; - snFloat* d_grout = 0; - - size_t inszMem = 0; -}; - -void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // pool - cudnnPoolingDescriptor_t pool_desc = nullptr; - cuCHECK(cudnnCreatePoolingDescriptor(&pool_desc)); - - cudnnPoolingMode_t poolT = cudnnPoolingMode_t::CUDNN_POOLING_MAX; - if (poolPrms.type == poolType::avg) - poolT = cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; - - cuCHECK(cudnnSetPooling2dDescriptor(pool_desc, poolT, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, - int(poolPrms.kernel), int(poolPrms.kernel), 0, 0, int(poolPrms.stride), int(poolPrms.stride))); - if (!isFirst) - cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); - gpuPrm->pool_desc = pool_desc; - - // output - int out_n = 0, out_c = 0, out_h = 0, out_w = 0; - cuCHECK(cudnnGetPooling2dForwardOutputDim(pool_desc, in_desc, - &out_n, &out_c, &out_h, &out_w)); - - if (outsz != snSize(out_w, out_h, out_c, out_n)){ - ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); - return; - } - - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - if (isLern){ - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - } - - size_t isz = insz.size(), osz = outsz.size(); - if (isFirst && !gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < isz)){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - - if (isLern){ - cuCHECK(cudaFree(gpuPrm->d_grin)); gpuPrm->d_grin = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - gpuPrm->inszMem = isz; - } -} - -void Pooling::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - - cuCHECK(cudaFree(gpuPrm->d_in)); - cuCHECK(cudaFree(gpuPrm->d_out)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - - cuCHECK(cudaFree(gpuPrm->d_grin)); - cuCHECK(cudaFree(gpuPrm->d_grout)); - } -} - -__global__ void cuFiltrNegative(snSize outsz, snFloat* out){ - - out += blockIdx.x * outsz.w * outsz.h * outsz.d; - - unsigned int k = threadIdx.x; - while (k < outsz.d){ - - snFloat* pOut = out + outsz.w * outsz.h * k; - for (size_t j = 0; j < (outsz.w * outsz.h); ++j) - if (pOut[j] < 0) pOut[j] = 0.0; - - k += blockDim.x; - } -} - -void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnPoolingForward(gpuPrm->cudnn, - gpuPrm->pool_desc, - &alpha, - gpuPrm->in_desc, - gpuPrm->d_in, - &beta, - gpuPrm->out_desc, - gpuPrm->d_out)); - - // filtrNegative - dim3 dimBlock(256); - dim3 dimGrid(int(outsz.n)); - - cuFiltrNegative <<< dimGrid, dimBlock >>>(outsz, gpuPrm->d_out); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - } -} - -void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, - const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - size_t isz = insz.size(), osz = outsz.size(); - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // output - cuCHECK(cudaMemcpy(gpuPrm->d_out, output, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // grin - cuCHECK(cudaMemcpy(gpuPrm->d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnPoolingBackward(gpuPrm->cudnn, - gpuPrm->pool_desc, - &alpha, - gpuPrm->out_desc, - gpuPrm->d_out, - gpuPrm->grin_desc, - gpuPrm->d_grin, - gpuPrm->in_desc, - gpuPrm->d_in, - &beta, - gpuPrm->grout_desc, - gpuPrm->d_grout)); - - // result - cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_grin)); gpuPrm->d_grin = 0; - cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; - } -} - -#elif SN_CUDA - -#include -#include "../stdafx.h" -#include "snOperator/src/Operator/pooling.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - snFloat* d_in = 0; - snFloat* d_out = 0; - size_t* d_idx = 0; - - size_t inszMem = 0; -}; - -void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 2){ - ERROR_MESS("%s requires SM >= 2.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - if (!gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); - } - } - else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ - - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); - - gpuPrm->inszMem = insz.size(); - } -} - -void Pooling::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cudaFree(gpuPrm->d_in); - cudaFree(gpuPrm->d_out); - cudaFree(gpuPrm->d_idx); -} - -__global__ void cuPoolFwd(poolType type, size_t kernel, size_t stride, snSize insz, snFloat* input, snSize outsz, snFloat* output, size_t* outputInx){ - - size_t outStepByD = outsz.w * outsz.h, // step out by output - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d, // step in by batch - kernelSz = kernel * kernel; - - // gridDim.x - number of input layers - // gridDim.y - batch sz - - input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - - if (type == poolType::max){ // max - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat valMax = 0; size_t idx = 0; -#pragma unroll - for (size_t c = 0; c < kernelSz; ++c){ - - size_t cx = c % kernel, cy = c / kernel; - - snFloat val = input[cx + posW + (cy + posH) * insz.w]; - if (val > valMax){ - valMax = val; - idx = c; - } - } - output[ox + oy * outsz.w] = valMax; - outputInx[ox + oy * outsz.w] = idx; - - ox += blockDim.x; - } - oy += blockDim.y; - } - } - else{ // mean - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - // kernel - snFloat valMean = 0; -#pragma unroll - for (size_t c = 0; c < kernelSz; ++c){ - - size_t cx = c % kernel, cy = c / kernel; - - valMean += input[cx + posW + (cy + posH) * insz.w]; - } - output[ox + oy * outsz.w] = valMean / kernelSz; - - ox += blockDim.x; - } - oy += blockDim.y; - } - } -} - -void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - size_t isz = insz.size(), osz = outsz.size(); - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&gpuPrm->d_idx, osz * sizeof(size_t))); - } - - // input - cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuPoolFwd << < dimGrid, dimBlock >> >(poolPrms.type, - poolPrms.kernel, - poolPrms.stride, - insz, - gpuPrm->d_in, - outsz, - gpuPrm->d_out, - gpuPrm->d_idx); - - // result - cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - cuCHECK(cudaMemcpy(outputInx, gpuPrm->d_idx, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; - } -} - -__global__ void cuPoolBwd(poolType type, size_t kernel, size_t stride, snSize outsz, size_t* outputInx, snFloat* gradIn, snSize insz, snFloat* gradOut){ - - size_t outStepByD = outsz.w * outsz.h, // step out by output - outStepByN = outStepByD * outsz.d, // step out by batch - inStepByD = insz.w * insz.h, // step in by input - inStepByN = inStepByD * insz.d, // step in by batch - kernelSz = kernel * kernel; - - // gridDim.x - number of input layers - // gridDim.y - batch sz - - gradIn += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; - - if (type == poolType::max){ // max - - outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - size_t c = outputInx[ox + oy * outsz.w], cx = c % kernel, cy = c / kernel; - - gradOut[cx + posW + (cy + posH) * insz.w] = gradIn[ox + oy * outsz.w]; - - ox += blockDim.x; - } - oy += blockDim.y; - } - } - else{ // mean - - unsigned int oy = threadIdx.y; - while (oy < outsz.h){ - - unsigned int ox = threadIdx.x; - while (ox < outsz.w){ - - size_t posW = ox * stride, posH = oy * stride; - - snFloat mean = gradIn[ox + oy * outsz.w] / kernel; -#pragma unroll - for (size_t c = 0; c < kernelSz; ++c){ - size_t cx = c % kernel, cy = c / kernel; - gradOut[(cx + posW) + (cy + posH) * insz.w] = mean; - } - - ox += blockDim.x; - } - oy += blockDim.y; - } - } -} - -void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, - const snSize& insz, const SN_Base::snFloat* input, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - snFloat* d_grin = gpuPrm->d_out, - * d_grout = gpuPrm->d_in; - size_t* d_idx = gpuPrm->d_idx; - - size_t isz = insz.size(), osz = outsz.size(); - if (gpuClearMem_){ - cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_grout, isz * sizeof(snFloat))); - cuCHECK(cudaMalloc(&d_idx, osz * sizeof(size_t))); - } - - // input - cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - cuCHECK(cudaMemcpy(d_idx, outputInx, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); - - // out - cuCHECK(cudaMemset(d_grout, 0, isz * sizeof(snFloat))); - - // run - dim3 dimBlock(16, 16); - dim3 dimGrid(int(outsz.d), int(outsz.n)); - - cuPoolBwd << < dimGrid, dimBlock >> >(poolPrms.type, - poolPrms.kernel, - poolPrms.stride, - outsz, - d_idx, - d_grin, - insz, - d_grout); - - // result - cuCHECK(cudaMemcpy(gradOut, d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); - - if (gpuClearMem_){ - cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; - cuCHECK(cudaFree(d_grout)); gpuPrm->d_in = 0; - cuCHECK(cudaFree(d_idx)); gpuPrm->d_idx = 0; - } -} - -#endif \ No newline at end of file From d364ddd7db195ab819068d76443b5d76986b7a7c Mon Sep 17 00:00:00 2001 From: Tyill Date: Tue, 16 Jul 2019 13:04:26 +0500 Subject: [PATCH 03/35] -develop --- src/snBase/snBase.h | 5 +- src/snOperatorCUDA/src/CUDA/tensor.cu | 9 +++- .../src/Operator/convolution.cpp | 50 +++++-------------- src/snOperatorCUDA/src/Operator/convolution.h | 5 +- src/snOperatorCUDA/src/mathFunctions.cpp | 36 ------------- src/snOperatorCUDA/src/weightInit.cpp | 16 ++++-- src/snOperatorCUDA/src/weightInit.h | 2 +- 7 files changed, 36 insertions(+), 87 deletions(-) diff --git a/src/snBase/snBase.h b/src/snBase/snBase.h index a839d22..4ff98f8 100644 --- a/src/snBase/snBase.h +++ b/src/snBase/snBase.h @@ -103,8 +103,10 @@ namespace SN_Base{ // !!! do not use directly !!! void setData(const snFloat* data, const snSize& nsz); + + void setDataCPU2GPU(const snFloat* data, const snSize& nsz, const size_t& offset); - void getDataForCPU(snFloat* out, const snSize& osz) const; + void getDataGPU2CPU(snFloat* out, const snSize& osz) const; void resize(const snSize& nsz); @@ -246,7 +248,6 @@ namespace SN_Base{ /// node in the symbol structure of the NN struct Node{ - std::string name; ///< the name of the node is to be unique within the branch, without the '' and '-'. "Begin", "End" are reserved as the beginning, the end of the network std::string oprName; ///< the node operator that is executed at the node. One operator per node. std::map oprPrms; ///< parameters of the operator (specifies the user when creating the net) diff --git a/src/snOperatorCUDA/src/CUDA/tensor.cu b/src/snOperatorCUDA/src/CUDA/tensor.cu index b3d8e9e..01eb57e 100644 --- a/src/snOperatorCUDA/src/CUDA/tensor.cu +++ b/src/snOperatorCUDA/src/CUDA/tensor.cu @@ -85,12 +85,19 @@ void Tensor::setData(const snFloat* data, const snSize& nsz){ sz_ = nsz; } +void Tensor::setDataCPU2GPU(const snFloat* data, const snSize& nsz, const size_t& offset){ + + assert(sz_.size() == (nsz.size() + offset)); + + cuCHECK(cudaMemcpy(data_ + offset, data, nsz.size() * sizeof(snFloat), cudaMemcpyHostToDevice)); +} + snFloat* Tensor::getData() const{ return data_; } -void Tensor::getDataForCPU(snFloat* out, const snSize& osz) const{ +void Tensor::getDataGPU2CPU(snFloat* out, const snSize& osz) const{ assert(sz_ == osz); diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 71ca923..7652d23 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -240,24 +240,11 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op /// copy with offset padding for each image snFloat* in = inputMem_->getData(); - bool isSame = (convPrms_.paddingW == 0) && (convPrms_.paddingH == 0); - if (!isSame){ - inTnsExp_.resize(inDataExpSz_); - paddingOffs(false, convPrms_.paddingW, convPrms_.paddingH, insz, inTnsExp_.getData(), in); - in = inTnsExp_.getData(); - } - + /// calculation of the output values snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); snSize outsz = baseOut_.size(); - - // +bias? - if (!convPrms_.useBias_){ - size_t kernel = convPrms_.kernel, - stepByN = convPrms_.fWidth * convPrms_.fHeight * insz.d * kernel; - memset(weight + stepByN, 0, kernel * sizeof(snFloat)); - } - + // calculation forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); @@ -303,22 +290,14 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o // calculation of the output gradient and weight correction snFloat* gradOut = baseGrad_.getData(); - - bool isSame = (convPrms_.paddingW == 0) && (convPrms_.paddingH == 0); - if (!isSame){ - gradOutExp_.resize(inDataExpSz_); - gradOut = gradOutExp_.getData(); - } - + snFloat* weight = baseWeight_.getData(); if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); snFloat* in = inputMem_->getData(); - if (!isSame) - in = inTnsExp_.getData(); - + // calculation backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); @@ -340,11 +319,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o } else{ // isFreeze backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); - } - - if (!isSame) - paddingOffs(true, convPrms_.paddingW, convPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); - + } } void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize& expSz){ @@ -365,8 +340,8 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize if (ntp > wcsz){ baseWeight_.resize(snSize(kernel, stp + 1)); - snFloat* wd = baseWeight_.getData(); - weightInit(wd + wcsz, ntp - wcsz, stp + 1, kernel, weightInitType_); + + weightInit(baseWeight_, ntp - wcsz, stp + 1, kernel, weightInitType_); } snSize outSz(0, 0, kernel, newsz.n); @@ -412,10 +387,10 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize size_t osz = outSz.w * outSz.h * outSz.d; if (batchNormType_ != batchNormType::none){ - auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); - auxParams_["bn_varce"].resize(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); - auxParams_["bn_scale"].resize(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); - auxParams_["bn_schift"].resize(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + auxParams_["bn_varce"].resize(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); + auxParams_["bn_scale"].resize(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); + auxParams_["bn_schift"].resize(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); if (isLern){ auxParams_["bn_norm"].resize(osz * outSz.n); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); @@ -427,6 +402,5 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseBatchNorm_.sz.n = 1; } - iniParamCUDA(isLern, expSz, outSz, convPrms_, &gpuParams_); - + iniParamCUDA(isLern, expSz, outSz, convPrms_, &gpuParams_); } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/convolution.h b/src/snOperatorCUDA/src/Operator/convolution.h index 9a33782..c18118a 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.h +++ b/src/snOperatorCUDA/src/Operator/convolution.h @@ -66,10 +66,7 @@ class Convolution final : SN_Base::OperatorBase{ SN_Base::snSize inDataExpSz_; ///< insz expansion const SN_Base::Tensor* inputMem_ = nullptr; - - SN_Base::Tensor inTnsExp_; - SN_Base::Tensor gradOutExp_; - + bool isFreeze_ = false; ///< not change weight uint32_t gpuDeviceId_ = 0; ///< gpu id diff --git a/src/snOperatorCUDA/src/mathFunctions.cpp b/src/snOperatorCUDA/src/mathFunctions.cpp index eed3701..8198f03 100644 --- a/src/snOperatorCUDA/src/mathFunctions.cpp +++ b/src/snOperatorCUDA/src/mathFunctions.cpp @@ -215,39 +215,3 @@ void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz out[i] *= (1.F - dropOut); } } - -void paddingOffs(bool in2out, size_t paddW, size_t paddH, const snSize& insz, snFloat* in, snFloat* out){ - - /// copy with offset padding for each image - size_t sz = insz.h * insz.d * insz.n, stW = insz.w, stH = insz.h; - if (in2out){ - in += (stW + paddW * 2) * paddH; - for (size_t i = 0; i < sz; ++i){ - - if ((i % stH == 0) && (i > 0)) - in += (stW + paddW * 2) * paddH * 2; - - in += paddW; - for (size_t j = 0; j < stW; ++j) - out[j] = in[j]; - in += paddW + stW; - - out += stW; - } - } - else{ - in += (stW + paddW * 2) * paddH; - for (size_t i = 0; i < sz; ++i){ - - if ((i % stH == 0) && (i > 0)) - in += (stW + paddW * 2) * paddH * 2; - - in += paddW; - for (size_t j = 0; j < stW; ++j) - in[j] = out[j]; - in += paddW + stW; - - out += stW; - } - } -} diff --git a/src/snOperatorCUDA/src/weightInit.cpp b/src/snOperatorCUDA/src/weightInit.cpp index b4acead..019bd92 100644 --- a/src/snOperatorCUDA/src/weightInit.cpp +++ b/src/snOperatorCUDA/src/weightInit.cpp @@ -22,6 +22,8 @@ // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN // THE SOFTWARE. // +#include + #include "snBase/snBase.h" #include "random.h" #include "structurs.h" @@ -32,14 +34,18 @@ using namespace SN_Base; // инициализация весов -void weightInit(snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype){ +void weightInit(SN_Base::Tensor& ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype){ + + vector buff(sz); switch (wtype){ - case weightInitType::uniform: wi_uniform(ioW, sz); break; - case weightInitType::he: wi_he(ioW, sz, fan_in); break; - case weightInitType::lecun:wi_lecun(ioW, sz, fan_out); break; - case weightInitType::xavier:wi_xavier(ioW, sz, fan_in, fan_out); break; + case weightInitType::uniform: wi_uniform(buff.data(), sz); break; + case weightInitType::he: wi_he(buff.data(), sz, fan_in); break; + case weightInitType::lecun:wi_lecun(buff.data(), sz, fan_out); break; + case weightInitType::xavier:wi_xavier(buff.data(), sz, fan_in, fan_out); break; } + + ioW.setDataCPU2GPU(buff.data(), sz, ioW.size().size()); } void wi_uniform(snFloat* ioW, size_t sz){ diff --git a/src/snOperatorCUDA/src/weightInit.h b/src/snOperatorCUDA/src/weightInit.h index 2c05375..a541dc9 100644 --- a/src/snOperatorCUDA/src/weightInit.h +++ b/src/snOperatorCUDA/src/weightInit.h @@ -30,7 +30,7 @@ #include "structurs.h" #include "random.h" -void weightInit(SN_Base::snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype); +void weightInit(SN_Base::Tensor& ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype); void wi_uniform(SN_Base::snFloat* ioW, size_t sz); From f2801a8da63cacdb4c485e8e023099e3f9bf529a Mon Sep 17 00:00:00 2001 From: al_medved Date: Tue, 16 Jul 2019 23:50:25 +0500 Subject: [PATCH 04/35] -develop --- mswin/VS12/snOperatorCUDA.vcxproj | 15 ++++++++----- mswin/VS12/snOperatorCUDA.vcxproj.filters | 5 +++++ src/snOperatorCUDA/src/CUDA/dropOut.cu | 27 +++++++++++++++++++++++ src/snOperatorCUDA/src/mathFunctions.cpp | 16 +------------- src/snOperatorCUDA/src/mathFunctions.h | 4 +--- src/snOperatorCUDA/src/stdafx.h | 4 ++++ 6 files changed, 47 insertions(+), 24 deletions(-) create mode 100644 src/snOperatorCUDA/src/CUDA/dropOut.cu diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index 8b62bb7..37129cf 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -52,9 +52,9 @@ Unicode - - - + + + @@ -408,8 +408,11 @@ + + + - - - + + + \ No newline at end of file diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index 95b1389..22a985f 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -179,4 +179,9 @@ CUDA + + + CUDA + + \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/dropOut.cu b/src/snOperatorCUDA/src/CUDA/dropOut.cu new file mode 100644 index 0000000..9ae795f --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/dropOut.cu @@ -0,0 +1,27 @@ + +#include + +#include +#include + +#include "../stdafx.h" + +using namespace SN_Base; + + +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ + + if (isLern){ + size_t sz = size_t(outsz.size() * dropOut); + vector rnd(sz); + rnd_uniformInt(rnd.data(), sz, 0, int(outsz.size())); + + for (auto i : rnd) out[i] = 0; + } + else{ + size_t sz = outsz.size(); + for (size_t i = 0; i < sz; ++i) + out[i] *= (1.F - dropOut); + } + +} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/mathFunctions.cpp b/src/snOperatorCUDA/src/mathFunctions.cpp index 8198f03..8ebaf97 100644 --- a/src/snOperatorCUDA/src/mathFunctions.cpp +++ b/src/snOperatorCUDA/src/mathFunctions.cpp @@ -200,18 +200,4 @@ void batchNormBackward(const SN_Base::snSize& insz, snFloat* gradIn, snFloat* gr } -void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ - - if (isLern){ - size_t sz = size_t(outsz.size() * dropOut); - vector rnd(sz); - rnd_uniformInt(rnd.data(), sz, 0, int(outsz.size())); - - for (auto i : rnd) out[i] = 0; - } - else{ - size_t sz = outsz.size(); - for (size_t i = 0; i < sz; ++i) - out[i] *= (1.F - dropOut); - } -} + diff --git a/src/snOperatorCUDA/src/mathFunctions.h b/src/snOperatorCUDA/src/mathFunctions.h index 16eaadd..b2d7999 100644 --- a/src/snOperatorCUDA/src/mathFunctions.h +++ b/src/snOperatorCUDA/src/mathFunctions.h @@ -40,6 +40,4 @@ void batchNormBackward(const SN_Base::snSize& insz, SN_Base::snFloat* gradOut, SN_Base::batchNorm); -void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out); - -void paddingOffs(bool in2out, size_t paddingW, size_t paddingH, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out); \ No newline at end of file +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out, void* gpuPrms); \ No newline at end of file diff --git a/src/snOperatorCUDA/src/stdafx.h b/src/snOperatorCUDA/src/stdafx.h index 650295e..fad7aef 100644 --- a/src/snOperatorCUDA/src/stdafx.h +++ b/src/snOperatorCUDA/src/stdafx.h @@ -42,3 +42,7 @@ void g_statusMess(SN_Base::OperatorBase* opr, const std::string& mess); void g_userCBack(SN_Base::OperatorBase* opr, const std::string& cbname, const std::string& node, bool fwBw, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snSize& outsz, SN_Base::snFloat** out); + +#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} + +#define cuCHECKFree(func) if (func != 0){ g_statusMess(nullptr, "CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} From 5693661dc8e3f102be5dc6b563191319713e25b9 Mon Sep 17 00:00:00 2001 From: Tyill Date: Wed, 17 Jul 2019 13:05:48 +0500 Subject: [PATCH 05/35] -develop --- src/skynet/src/snet.cpp | 14 +- src/snBase/snBase.h | 27 ++-- src/snOperatorCPU/src/CPU/convolutionCPU.cpp | 2 - .../src/CPU/deconvolutionCPU.cpp | 1 - .../src/CPU/fullyConnectedCPU.cpp | 1 - src/snOperatorCPU/src/CPU/poolingCPU.cpp | 1 - src/snOperatorCPU/src/CPU/tensor.cpp | 47 +++--- src/snOperatorCPU/src/Operator/activation.cpp | 4 +- src/snOperatorCPU/src/Operator/batchNorm.cpp | 4 +- src/snOperatorCPU/src/Operator/concat.cpp | 6 +- .../src/Operator/convolution.cpp | 26 ++-- src/snOperatorCPU/src/Operator/crop.cpp | 8 +- .../src/Operator/deconvolution.cpp | 18 +-- .../src/Operator/fullyConnected.cpp | 16 +- src/snOperatorCPU/src/Operator/input.cpp | 2 +- .../src/Operator/lossFunction.cpp | 16 +- src/snOperatorCPU/src/Operator/pooling.cpp | 20 +-- src/snOperatorCPU/src/Operator/resize.cpp | 8 +- src/snOperatorCPU/src/Operator/summator.cpp | 6 +- src/snOperatorCPU/src/Operator/userLayer.cpp | 8 +- src/snOperatorCUDA/src/CUDA/dropOut.cu | 141 ++++++++++++++++-- src/snOperatorCUDA/src/CUDA/tensor.cu | 105 ++++++------- .../src/Operator/activation.cpp | 4 +- src/snOperatorCUDA/src/Operator/batchNorm.cpp | 4 +- src/snOperatorCUDA/src/Operator/concat.cpp | 6 +- .../src/Operator/convolution.cpp | 43 +++--- src/snOperatorCUDA/src/Operator/convolution.h | 3 +- src/snOperatorCUDA/src/Operator/crop.cpp | 8 +- .../src/Operator/deconvolution.cpp | 21 +-- .../src/Operator/fullyConnected.cpp | 19 +-- .../src/Operator/lossFunction.cpp | 12 +- src/snOperatorCUDA/src/Operator/pooling.cpp | 22 +-- src/snOperatorCUDA/src/Operator/resize.cpp | 8 +- src/snOperatorCUDA/src/Operator/summator.cpp | 6 +- src/snOperatorCUDA/src/Operator/userLayer.cpp | 4 +- src/snOperatorCUDA/src/mathFunctions.h | 7 +- src/snOperatorCUDA/src/stdafx.h | 6 +- src/snOperatorCUDA/src/weightInit.cpp | 2 +- 38 files changed, 383 insertions(+), 273 deletions(-) diff --git a/src/skynet/src/snet.cpp b/src/skynet/src/snet.cpp index 0406c41..a1a45db 100644 --- a/src/skynet/src/snet.cpp +++ b/src/skynet/src/snet.cpp @@ -281,7 +281,7 @@ bool SNet::forward(bool isLern, const snSize& isz, const snFloat* iLayer, const return false; } - memcpy(outData, tnsOut.getData(), tnsOutSz.size() * sizeof(snFloat)); + memcpy(outData, tnsOut.getDataCPU(), tnsOutSz.size() * sizeof(snFloat)); } return true; @@ -318,8 +318,8 @@ bool SNet::backward(snFloat lr, const snSize& gsz, const snFloat* gradErr){ SN_Base::snFloat SNet::calcAccurate(const Tensor& targetTens, const Tensor& outTens){ - snFloat* targetData = targetTens.getData(); - snFloat* outData = outTens.getData(); + snFloat* targetData = targetTens.getDataCPU(); + snFloat* outData = outTens.getDataCPU(); size_t accCnt = 0, osz = outTens.size().size(); for (size_t i = 0; i < osz; ++i){ @@ -358,7 +358,7 @@ bool SNet::getWeightNode(const char* nodeName, SN_Base::snSize& wsz, SN_Base::sn *wData = (snFloat*)realloc(*wData, wsz.size() * sizeof(snFloat)); - memcpy(*wData, weight.getData(), wsz.size() * sizeof(snFloat)); + memcpy(*wData, weight.getDataCPU(), wsz.size() * sizeof(snFloat)); return true; } @@ -423,7 +423,7 @@ bool SNet::getOutputNode(const char* nodeName, SN_Base::snSize& osz, SN_Base::sn *outData = (snFloat*)realloc(*outData, osz.size() * sizeof(snFloat)); - memcpy(*outData, outTns.getData(), osz.size() * sizeof(snFloat)); + memcpy(*outData, outTns.getDataCPU(), osz.size() * sizeof(snFloat)); return true; } @@ -469,7 +469,7 @@ bool SNet::getGradientNode(const char* nodeName, SN_Base::snSize& gsz, SN_Base:: *gData = (snFloat*)realloc(*gData, gsz.size() * sizeof(snFloat)); - memcpy(*gData, grad.getData(), gsz.size() * sizeof(snFloat)); + memcpy(*gData, grad.getDataCPU(), gsz.size() * sizeof(snFloat)); return true; } @@ -496,7 +496,7 @@ bool SNet::saveAllWeightToFile(const char* filePath){ snSize lSize; for (auto opr : operats_){ - data = opr.second->getWeight().getData(); + data = opr.second->getWeight().getDataCPU(); lSize = opr.second->getWeight().size(); if (data){ diff --git a/src/snBase/snBase.h b/src/snBase/snBase.h index 4ff98f8..5490f24 100644 --- a/src/snBase/snBase.h +++ b/src/snBase/snBase.h @@ -97,16 +97,14 @@ namespace SN_Base{ Tensor& operator+=(const Tensor& other); Tensor& operator-=(const Tensor& other); - - // !!! do not use directly !!! - snFloat* getData() const; - - // !!! do not use directly !!! - void setData(const snFloat* data, const snSize& nsz); - void setDataCPU2GPU(const snFloat* data, const snSize& nsz, const size_t& offset); + void setDataGPU(const snFloat* data, const snSize& nsz); + + snFloat* getDataGPU() const; + + void setDataCPU(const snFloat* data, const snSize& nsz); - void getDataGPU2CPU(snFloat* out, const snSize& osz) const; + snFloat* getDataCPU() const; void resize(const snSize& nsz); @@ -114,11 +112,10 @@ namespace SN_Base{ return sz_; } - - void tfree(); - + private: - snFloat* data_ = nullptr; + mutable snFloat* dataCPU_, + * dataGPU_ = nullptr; snSize sz_; }; @@ -180,17 +177,17 @@ namespace SN_Base{ } virtual bool setInput(const snFloat* data, const snSize& dsz){ - baseInput_.setData(data, dsz); + baseInput_.setDataCPU(data, dsz); return true; } virtual bool setGradient(const snFloat* data, const snSize& dsz){ - baseGrad_.setData(data, dsz); + baseGrad_.setDataCPU(data, dsz); return true; } virtual bool setWeight(const snFloat* data, const snSize& dsz){ - baseWeight_.setData(data, dsz); + baseWeight_.setDataCPU(data, dsz); return true; } diff --git a/src/snOperatorCPU/src/CPU/convolutionCPU.cpp b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp index b12e651..61226ba 100644 --- a/src/snOperatorCPU/src/CPU/convolutionCPU.cpp +++ b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp @@ -25,9 +25,7 @@ #include "../stdafx.h" #include "snOperatorCPU/src/Operator/convolution.h" -#include #include -#include #ifdef SN_AVX #include "snSIMD/snSIMD.h" diff --git a/src/snOperatorCPU/src/CPU/deconvolutionCPU.cpp b/src/snOperatorCPU/src/CPU/deconvolutionCPU.cpp index d512108..369f3ae 100644 --- a/src/snOperatorCPU/src/CPU/deconvolutionCPU.cpp +++ b/src/snOperatorCPU/src/CPU/deconvolutionCPU.cpp @@ -25,7 +25,6 @@ #include "../stdafx.h" #include "snOperatorCPU/src/Operator/deconvolution.h" -#include #include using namespace std; diff --git a/src/snOperatorCPU/src/CPU/fullyConnectedCPU.cpp b/src/snOperatorCPU/src/CPU/fullyConnectedCPU.cpp index e06fad8..d929976 100644 --- a/src/snOperatorCPU/src/CPU/fullyConnectedCPU.cpp +++ b/src/snOperatorCPU/src/CPU/fullyConnectedCPU.cpp @@ -26,7 +26,6 @@ #include "../stdafx.h" #include "Lib/OpenBLAS/cblas.h" #include "snOperatorCPU/src/Operator/fullyConnected.h" -#include using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/CPU/poolingCPU.cpp b/src/snOperatorCPU/src/CPU/poolingCPU.cpp index 63aa10d..96beb81 100644 --- a/src/snOperatorCPU/src/CPU/poolingCPU.cpp +++ b/src/snOperatorCPU/src/CPU/poolingCPU.cpp @@ -25,7 +25,6 @@ #include "../stdafx.h" #include "snOperatorCPU/src/Operator/pooling.h" -#include #include using namespace std; diff --git a/src/snOperatorCPU/src/CPU/tensor.cpp b/src/snOperatorCPU/src/CPU/tensor.cpp index 8f5b7b3..1ce730f 100644 --- a/src/snOperatorCPU/src/CPU/tensor.cpp +++ b/src/snOperatorCPU/src/CPU/tensor.cpp @@ -10,20 +10,22 @@ Tensor::Tensor(const snSize& sz) : sz_(sz){ size_t ssz = sz.size(); if (ssz > 0) - data_ = (snFloat*)calloc(ssz, sizeof(snFloat)); + dataCPU_ = (snFloat*)calloc(ssz, sizeof(snFloat)); } Tensor::~Tensor(){ - if (data_) free(data_); + + if (dataCPU_) + free(dataCPU_); } Tensor::Tensor(const Tensor& other){ - setData(other.getData(), other.size()); + setDataCPU(other.getDataCPU(), other.size()); } Tensor& Tensor::operator=(const Tensor& other){ - setData(other.getData(), other.size()); + setDataCPU(other.getDataCPU(), other.size()); return *this; } @@ -32,11 +34,11 @@ Tensor& Tensor::operator+=(const Tensor& other){ assert(other == *this); - auto od = other.getData(); + auto od = other.getDataCPU(); size_t sz = this->size().size(); for (size_t i = 0; i < sz; ++i){ - data_[i] += od[i]; + dataCPU_[i] += od[i]; } return *this; @@ -46,38 +48,31 @@ Tensor& Tensor::operator-=(const Tensor& other){ assert(other == *this); - auto od = other.getData(); + auto od = other.getDataCPU(); size_t sz = this->size().size(); for (size_t i = 0; i < sz; ++i){ - data_[i] -= od[i]; + dataCPU_[i] -= od[i]; } return *this; } -void Tensor::setData(const snFloat* data, const snSize& nsz){ +void Tensor::setDataCPU(const snFloat* data, const snSize& nsz){ size_t nnsz = nsz.size(); assert(data && (nnsz > 0)); if (sz_.size() < nnsz) - data_ = (snFloat*)realloc(data_, nnsz * sizeof(snFloat)); + dataCPU_ = (snFloat*)realloc(dataCPU_, nnsz * sizeof(snFloat)); - memcpy(data_, data, nnsz * sizeof(snFloat)); + memcpy(dataCPU_, data, nnsz * sizeof(snFloat)); sz_ = nsz; } -snFloat* Tensor::getData() const{ - - return data_; -} - -void Tensor::getDataForCPU(snFloat* out, const snSize& osz) const{ +snFloat* Tensor::getDataCPU() const{ - assert(sz_ == osz); - - memcpy(out, data_, sz_.size() * sizeof(snFloat)); + return dataCPU_; } void Tensor::resize(const snSize& nsz){ @@ -86,15 +81,9 @@ void Tensor::resize(const snSize& nsz){ assert(nnsz > 0); if (csz < nnsz){ - data_ = (snFloat*)realloc(data_, nnsz * sizeof(snFloat)); - memset(data_ + csz, 0, (nnsz - csz) * sizeof(snFloat)); + dataCPU_ = (snFloat*)realloc(dataCPU_, nnsz * sizeof(snFloat)); + memset(dataCPU_ + csz, 0, (nnsz - csz) * sizeof(snFloat)); } sz_ = nsz; -} - -void Tensor::tfree(){ - if (data_) free(data_); - data_ = nullptr; - sz_ = snSize(0, 0, 0, 0, 0); -} +} \ No newline at end of file diff --git a/src/snOperatorCPU/src/Operator/activation.cpp b/src/snOperatorCPU/src/Operator/activation.cpp index c861f11..0a63da4 100644 --- a/src/snOperatorCPU/src/Operator/activation.cpp +++ b/src/snOperatorCPU/src/Operator/activation.cpp @@ -60,7 +60,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std baseOut_ = neighbOpr[0]->getOutput(); /// active func - activeFuncForward(baseOut_.size().size(), baseOut_.getData(), activeType_); + activeFuncForward(baseOut_.size().size(), baseOut_.getDataCPU(), activeType_); } else{ // backward @@ -77,7 +77,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std } /// active func - activeFuncBackward(baseGrad_.size().size(), baseGrad_.getData(), activeType_); + activeFuncBackward(baseGrad_.size().size(), baseGrad_.getDataCPU(), activeType_); } return vector(); diff --git a/src/snOperatorCPU/src/Operator/batchNorm.cpp b/src/snOperatorCPU/src/Operator/batchNorm.cpp index 2142106..6a68211 100644 --- a/src/snOperatorCPU/src/Operator/batchNorm.cpp +++ b/src/snOperatorCPU/src/Operator/batchNorm.cpp @@ -75,7 +75,7 @@ std::vector BatchNorm::Do(const operationParam& operPrm, const std: baseOut_ = neighbOpr[0]->getOutput(); - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataCPU(); snSize outsz = baseOut_.size(); if (outsz != inSzMem_){ @@ -107,7 +107,7 @@ std::vector BatchNorm::Do(const operationParam& operPrm, const std: baseGrad_ += neighbOpr[i]->getGradient(); } - snFloat* out = baseGrad_.getData(); + snFloat* out = baseGrad_.getDataCPU(); snSize outsz = baseGrad_.size(); switch (bnType_){ diff --git a/src/snOperatorCPU/src/Operator/concat.cpp b/src/snOperatorCPU/src/Operator/concat.cpp index 73b06b8..7359d45 100644 --- a/src/snOperatorCPU/src/Operator/concat.cpp +++ b/src/snOperatorCPU/src/Operator/concat.cpp @@ -93,14 +93,14 @@ std::vector Concat::Do(const operationParam& operPrm, const std::ve nstp = nbsz.w * nbsz.h * nbsz.d; for (size_t j = 0; j < csz.n; ++j){ - snFloat* dst = baseOut_.getData() + sz * j, - *src = buff.getData() + cstp * j; + snFloat* dst = baseOut_.getDataCPU() + sz * j, + *src = buff.getDataCPU() + cstp * j; memcpy(dst, src, cstp * sizeof(snFloat)); dst += cstp; - src = neighb->getOutput().getData() + nstp * j; + src = neighb->getOutput().getDataCPU() + nstp * j; memcpy(dst, src, nstp * sizeof(snFloat)); } diff --git a/src/snOperatorCPU/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp index be93cb1..d3a9844 100644 --- a/src/snOperatorCPU/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -235,16 +235,16 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op } /// copy with offset padding for each image - snFloat* in = inputMem_->getData(); + snFloat* in = inputMem_->getDataCPU(); bool isSame = (convPrms_.paddingW == 0) && (convPrms_.paddingH == 0); if (!isSame){ inTnsExp_.resize(inDataExpSz_); - paddingOffs(false, convPrms_.paddingW, convPrms_.paddingH, insz, inTnsExp_.getData(), in); - in = inTnsExp_.getData(); + paddingOffs(false, convPrms_.paddingW, convPrms_.paddingH, insz, inTnsExp_.getDataCPU(), in); + in = inTnsExp_.getDataCPU(); } /// calculation of the output values - snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snFloat* out = baseOut_.getDataCPU(), *weight = baseWeight_.getDataCPU(); snSize outsz = baseOut_.size(); // +bias? @@ -275,7 +275,7 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); + snFloat* gradIn = inTns.getDataCPU(); /// batchNorm if (batchNormType_ == batchNormType::postActive) @@ -284,7 +284,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o // active function if (activeType_ != activeType::none){ - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataCPU(); size_t osz = baseOut_.size().size(); activeFuncBackward(osz, out, activeType_); @@ -298,22 +298,22 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction - snFloat* gradOut = baseGrad_.getData(); + snFloat* gradOut = baseGrad_.getDataCPU(); bool isSame = (convPrms_.paddingW == 0) && (convPrms_.paddingH == 0); if (!isSame){ gradOutExp_.resize(inDataExpSz_); - gradOut = gradOutExp_.getData(); + gradOut = gradOutExp_.getDataCPU(); } - snFloat* weight = baseWeight_.getData(); + snFloat* weight = baseWeight_.getDataCPU(); if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); - snFloat* in = inputMem_->getData(); + snFloat* in = inputMem_->getDataCPU(); if (!isSame) - in = inTnsExp_.getData(); + in = inTnsExp_.getDataCPU(); // calculation backwardCPU_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight); @@ -339,7 +339,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o } if (!isSame) - paddingOffs(true, convPrms_.paddingW, convPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); + paddingOffs(true, convPrms_.paddingW, convPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getDataCPU()); } @@ -361,7 +361,7 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize if (ntp > wcsz){ baseWeight_.resize(snSize(kernel, stp + 1)); - snFloat* wd = baseWeight_.getData(); + snFloat* wd = baseWeight_.getDataCPU(); weightInit(wd + wcsz, ntp - wcsz, stp + 1, kernel, weightInitType_); } diff --git a/src/snOperatorCPU/src/Operator/crop.cpp b/src/snOperatorCPU/src/Operator/crop.cpp index 17c8434..7a6deb2 100644 --- a/src/snOperatorCPU/src/Operator/crop.cpp +++ b/src/snOperatorCPU/src/Operator/crop.cpp @@ -74,8 +74,8 @@ std::vector Crop::Do(const operationParam& operPrm, const std::vect Tensor tns(snSize(w, h, baseSz_.d, baseSz_.n)); - snFloat* src = baseOut_.getData(), - * dst = tns.getData(); + snFloat* src = baseOut_.getDataCPU(), + * dst = tns.getDataCPU(); copyTo(true, roi_, baseSz_, src, dst); @@ -96,8 +96,8 @@ std::vector Crop::Do(const operationParam& operPrm, const std::vect Tensor tns(baseSz_); - snFloat* dst = tns.getData(), - * src = baseGrad_.getData(); + snFloat* dst = tns.getDataCPU(), + * src = baseGrad_.getDataCPU(); copyTo(false, roi_, baseSz_, dst, src); diff --git a/src/snOperatorCPU/src/Operator/deconvolution.cpp b/src/snOperatorCPU/src/Operator/deconvolution.cpp index 7df9687..a9489f5 100644 --- a/src/snOperatorCPU/src/Operator/deconvolution.cpp +++ b/src/snOperatorCPU/src/Operator/deconvolution.cpp @@ -221,9 +221,9 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& updateConfig(operPrm.isLerning, insz); } - snFloat* pInTns = inTns.getData(); + snFloat* pInTns = inTns.getDataCPU(); - snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snFloat* out = baseOut_.getDataCPU(), *weight = baseWeight_.getDataCPU(); snSize outsz = baseOut_.size(); // calculation @@ -248,7 +248,7 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); + snFloat* gradIn = inTns.getDataCPU(); /// batchNorm if (batchNormType_ == batchNormType::postActive) @@ -257,7 +257,7 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& /// active func if (activeType_ != activeType::none){ - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataCPU(); size_t osz = baseOut_.size().size(); activeFuncBackward(osz, out, activeType_); @@ -271,10 +271,10 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction - snFloat* grOut = baseGrad_.getData(); - snFloat* weight = baseWeight_.getData(); - snFloat* in = inputMem_->getData(); - snFloat* out = baseOut_.getData(); + snFloat* grOut = baseGrad_.getDataCPU(); + snFloat* weight = baseWeight_.getDataCPU(); + snFloat* in = inputMem_->getDataCPU(); + snFloat* out = baseOut_.getDataCPU(); snSize insz = inputMem_->size(), outsz = baseOut_.size(); @@ -314,7 +314,7 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ if (ntp > wcsz){ baseWeight_.resize(snSize(newsz.d, stp + 1)); - snFloat* wd = baseWeight_.getData(); + snFloat* wd = baseWeight_.getDataCPU(); weightInit(wd + wcsz, ntp - wcsz, stp + 1, deconvParams_.kernel, weightInitType_); } diff --git a/src/snOperatorCPU/src/Operator/fullyConnected.cpp b/src/snOperatorCPU/src/Operator/fullyConnected.cpp index 0a80338..8378587 100644 --- a/src/snOperatorCPU/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCPU/src/Operator/fullyConnected.cpp @@ -221,7 +221,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& } /// calculation of the output values of neurons - snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snFloat* out = baseOut_.getDataCPU(), *weight = baseWeight_.getDataCPU(); // +bias? if (!useBias_){ @@ -230,7 +230,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& } // calculation - forwardCPU(kernel_, insz, inTns.getData(), weight, out); + forwardCPU(kernel_, insz, inTns.getDataCPU(), weight, out); /// dropOut snSize outSz = baseOut_.size(); @@ -251,7 +251,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); + snFloat* gradIn = inTns.getDataCPU(); /// batchNorm snSize gsz = inTns.size(); @@ -261,7 +261,7 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam // active func if (activeType_ != activeType::none){ - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataCPU(); size_t osz = kernel_ * inSzMem_.n; activeFuncBackward(osz, out, activeType_); @@ -275,15 +275,15 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction - snFloat* gradOut = baseGrad_.getData(); - snFloat* weight = baseWeight_.getData(); + snFloat* gradOut = baseGrad_.getDataCPU(); + snFloat* weight = baseWeight_.getDataCPU(); if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); // calculation - backwardCPU_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight); + backwardCPU_GW(kernel_, weight, inSzMem_, inputMem_->getDataCPU(), gradIn, gradOut, dWeight); // correct weight snFloat* dWPrev = auxParams_["dWPrev"].data(); @@ -316,7 +316,7 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ if (ntp > wcsz){ baseWeight_.resize(snSize(kernel_, stp + 1)); - snFloat* wd = baseWeight_.getData(); + snFloat* wd = baseWeight_.getDataCPU(); weightInit(wd + wcsz, ntp - wcsz, stp + 1, kernel_, weightInitType_); } diff --git a/src/snOperatorCPU/src/Operator/input.cpp b/src/snOperatorCPU/src/Operator/input.cpp index 01f56b0..865ce86 100644 --- a/src/snOperatorCPU/src/Operator/input.cpp +++ b/src/snOperatorCPU/src/Operator/input.cpp @@ -36,7 +36,7 @@ OperatorBase(net, name, node, prms){ } bool Input::setInput(const snFloat* data, const snSize& dsz){ - baseOut_.setData(data, dsz); + baseOut_.setDataCPU(data, dsz); return true; } diff --git a/src/snOperatorCPU/src/Operator/lossFunction.cpp b/src/snOperatorCPU/src/Operator/lossFunction.cpp index 2bdab30..21d93eb 100644 --- a/src/snOperatorCPU/src/Operator/lossFunction.cpp +++ b/src/snOperatorCPU/src/Operator/lossFunction.cpp @@ -84,7 +84,7 @@ void LossFunction::forward(const Tensor& inTns){ snSize tsz = inTns.size(); baseOut_ = inTns; - auto out = baseOut_.getData(); + auto out = baseOut_.getDataCPU(); switch (lossType_){ case LossFunction::lossType::softMaxACrossEntropy:{ @@ -126,10 +126,10 @@ void LossFunction::forward(const Tensor& inTns){ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - true, inTns.size(), inTns.getData(), outSz, &outData); + true, inTns.size(), inTns.getDataCPU(), outSz, &outData); if (outData){ - baseOut_.setData(outData, outSz); + baseOut_.setDataCPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); @@ -153,9 +153,9 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ if (grsz != tsz) baseGrad_.resize(tsz); - auto smOut = baseOut_.getData(); - auto target = inTns.getData(); - auto grad = baseGrad_.getData(); + auto smOut = baseOut_.getDataCPU(); + auto target = inTns.getDataCPU(); + auto grad = baseGrad_.getDataCPU(); switch (lossType_){ case LossFunction::lossType::softMaxACrossEntropy:{ @@ -192,10 +192,10 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - false, inTns.size(), inTns.getData(), outSz, &outData); + false, inTns.size(), inTns.getDataCPU(), outSz, &outData); if (outData){ - baseGrad_.setData(outData, outSz); + baseGrad_.setDataCPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); diff --git a/src/snOperatorCPU/src/Operator/pooling.cpp b/src/snOperatorCPU/src/Operator/pooling.cpp index bf7d846..01a001e 100644 --- a/src/snOperatorCPU/src/Operator/pooling.cpp +++ b/src/snOperatorCPU/src/Operator/pooling.cpp @@ -119,16 +119,16 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara } /// copy with offset padding for each image - snFloat* in = inputMem_->getData(); + snFloat* in = inputMem_->getDataCPU(); if (isPadding_){ inTnsExp_.resize(inDataExpSz_); - paddingOffs(false, poolPrms_.paddingW, poolPrms_.paddingH, insz, inTnsExp_.getData(), inputMem_->getData()); + paddingOffs(false, poolPrms_.paddingW, poolPrms_.paddingH, insz, inTnsExp_.getDataCPU(), inputMem_->getDataCPU()); insz = inDataExpSz_; - in = inTnsExp_.getData(); + in = inTnsExp_.getDataCPU(); } /// output calculation - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataCPU(); // calculation forwardCPU(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data()); @@ -137,21 +137,21 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); + snFloat* gradIn = inTns.getDataCPU(); - snFloat* input = inputMem_->getData(); - snFloat* gradOut = baseGrad_.getData(); + snFloat* input = inputMem_->getDataCPU(); + snFloat* gradOut = baseGrad_.getDataCPU(); if (isPadding_){ gradOutExp_.resize(inDataExpSz_); - gradOut = gradOutExp_.getData(); - input = inTnsExp_.getData(); + gradOut = gradOutExp_.getDataCPU(); + input = inTnsExp_.getDataCPU(); } /// calculation backwardCPU(poolPrms_, baseOut_.size(), outInx_.data(), gradIn, inDataExpSz_, gradOut); if (isPadding_) - paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); + paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getDataCPU()); } void Pooling::updateConfig(bool isLern, const snSize& newsz){ diff --git a/src/snOperatorCPU/src/Operator/resize.cpp b/src/snOperatorCPU/src/Operator/resize.cpp index a414825..b7671cd 100644 --- a/src/snOperatorCPU/src/Operator/resize.cpp +++ b/src/snOperatorCPU/src/Operator/resize.cpp @@ -73,8 +73,8 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve cstp = csz.w * csz.h * csz.d; for (size_t j = 0; j < csz.n; ++j){ - snFloat* dst = baseOut_.getData() + sz * j, - *src = buff.getData() + cstp * j + offset; + snFloat* dst = baseOut_.getDataCPU() + sz * j, + *src = buff.getDataCPU() + cstp * j + offset; memcpy(dst, src, sz * sizeof(snFloat)); } @@ -119,8 +119,8 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve cstp = csz.w * csz.h * csz.d; for (size_t j = 0; j < csz.n; ++j){ - snFloat* dst = baseGrad_.getData() + sz * j, - *src = buff.getData() + cstp * j + offset; + snFloat* dst = baseGrad_.getDataCPU() + sz * j, + *src = buff.getDataCPU() + cstp * j + offset; memcpy(dst, src, sz * sizeof(snFloat)); } diff --git a/src/snOperatorCPU/src/Operator/summator.cpp b/src/snOperatorCPU/src/Operator/summator.cpp index 58219b9..a5f0585 100644 --- a/src/snOperatorCPU/src/Operator/summator.cpp +++ b/src/snOperatorCPU/src/Operator/summator.cpp @@ -83,9 +83,9 @@ std::vector Summator::Do(const operationParam& operPrm, const std:: void Summator::mean(const Tensor& one, const Tensor& two, Tensor& out){ - snFloat* done = one.getData(), - * dtwo = two.getData(), - * dout = out.getData(); + snFloat* done = one.getDataCPU(), + * dtwo = two.getDataCPU(), + * dout = out.getDataCPU(); size_t sz = one.size().size(); for (size_t i = 0; i < sz; ++i){ diff --git a/src/snOperatorCPU/src/Operator/userLayer.cpp b/src/snOperatorCPU/src/Operator/userLayer.cpp index dc1bc6e..4a4d5c3 100644 --- a/src/snOperatorCPU/src/Operator/userLayer.cpp +++ b/src/snOperatorCPU/src/Operator/userLayer.cpp @@ -55,10 +55,10 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - true, baseOut_.size(), baseOut_.getData(), outSz, &outData); + true, baseOut_.size(), baseOut_.getDataCPU(), outSz, &outData); if (outData) - baseOut_.setData(outData, outSz); + baseOut_.setDataCPU(outData, outSz); else{ ERROR_MESS("not set 'outData' in userCBack"); } @@ -79,10 +79,10 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - false, baseGrad_.size(), baseGrad_.getData(), outSz, &outData); + false, baseGrad_.size(), baseGrad_.getDataCPU(), outSz, &outData); if (outData) - baseGrad_.setData(outData, outSz); + baseGrad_.setDataCPU(outData, outSz); else{ ERROR_MESS("not set 'outData' in userCBack"); } diff --git a/src/snOperatorCUDA/src/CUDA/dropOut.cu b/src/snOperatorCUDA/src/CUDA/dropOut.cu index 9ae795f..b556594 100644 --- a/src/snOperatorCUDA/src/CUDA/dropOut.cu +++ b/src/snOperatorCUDA/src/CUDA/dropOut.cu @@ -1,6 +1,4 @@ -#include - #include #include @@ -8,20 +6,139 @@ using namespace SN_Base; +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnDropoutDescriptor_t drop_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + + size_t wsSz = 0; + size_t state_sizes = 0; + SN_Base::snSize inszMem = 0; + + snFloat* in_mem = nullptr; + + void* state_memory = 0; + void* d_ws = 0; +}; + +void dropOutInit(void** pGpuPrm, SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz){ + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + + if (!gpuPrm){ + + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; -void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ + cudnnHandle_t cudnn = nullptr; + cuAssert(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; - if (isLern){ - size_t sz = size_t(outsz.size() * dropOut); - vector rnd(sz); - rnd_uniformInt(rnd.data(), sz, 0, int(outsz.size())); + cuAssert(cudnnDropoutGetStatesSize(gpuPrm->cudnn, &gpuPrm->state_sizes)); + cuAssert(cudaMalloc(&gpuPrm->state_memory, gpuPrm->state_sizes)); - for (auto i : rnd) out[i] = 0; + isFirst = true; } - else{ - size_t sz = outsz.size(); - for (size_t i = 0; i < sz; ++i) - out[i] *= (1.F - dropOut); + + if (gpuPrm->inszMem != outsz){ + + // drop_desc + cudnnDropoutDescriptor_t drop_desc = nullptr; + cuAssert(cudnnCreateDropoutDescriptor(&drop_desc)); + cuAssert(cudnnSetDropoutDescriptor(drop_desc, gpuPrm->cudnn, dropOut, gpuPrm->state_memory, gpuPrm->state_sizes, 1234ULL)); + if (!isFirst) + cuAssert(cudnnDestroyDropoutDescriptor(gpuPrm->drop_desc)); + gpuPrm->drop_desc = drop_desc; + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuAssert(cudnnCreateTensorDescriptor(&in_desc)); + cuAssert(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // output + cudnnTensorDescriptor_t out_desc; + cuAssert(cudnnCreateTensorDescriptor(&out_desc)); + cuAssert(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + // ws + cuAssert(cudnnDropoutGetReserveSpaceSize(in_desc, &gpuPrm->wsSz)); + cuAssert(cudaMalloc(&gpuPrm->d_ws, gpuPrm->wsSz)); + + // in mem + if (!isFirst) + cuAssert(cudaFree(gpuPrm->in_mem)); + cuAssert(cudaMalloc(&gpuPrm->in_mem, outsz.size() * sizeof(snFloat))); + + gpuPrm->inszMem = outsz; } +} + +void dropOutFree(uint32_t deviceId, void* pGpuPrm){ + + cudaSetDevice(deviceId); + + gpuParams* gpuPrm = (gpuParams*)pGpuPrm; + + if (!gpuPrm) return; + + cuAssert(cudnnDestroy(gpuPrm->cudnn)); + cuAssert(cudnnDestroyDropoutDescriptor(gpuPrm->drop_desc)); + cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + + cuAssert(cudaFree(gpuPrm->d_ws)); + cuAssert(cudaFree(gpuPrm->state_memory)); +} + +void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm){ + + cudaSetDevice(deviceId); + + dropOutInit(pGpuPrm, dropOut, inout, outsz); + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + + cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, outsz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + + cuAssert(cudnnDropoutForward(gpuPrm->cudnn, + gpuPrm->drop_desc, + gpuPrm->in_desc, + gpuPrm->in_mem, + gpuPrm->out_desc, + inout, + gpuPrm->d_ws, + gpuPrm->wsSz)); + +} + +void dropOutBackward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm){ + + cudaSetDevice(deviceId); + + dropOutInit(pGpuPrm, dropOut, inout, outsz); + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + + cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, outsz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + + cuAssert(cudnnDropoutBackward(gpuPrm->cudnn, + gpuPrm->drop_desc, + gpuPrm->in_desc, + gpuPrm->in_mem, + gpuPrm->out_desc, + inout, + gpuPrm->d_ws, + gpuPrm->wsSz)); } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/tensor.cu b/src/snOperatorCUDA/src/CUDA/tensor.cu index 01eb57e..6803de3 100644 --- a/src/snOperatorCUDA/src/CUDA/tensor.cu +++ b/src/snOperatorCUDA/src/CUDA/tensor.cu @@ -1,6 +1,4 @@ -#include - #include #include @@ -8,9 +6,6 @@ using namespace SN_Base; -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ std::cout << "CUDA error: " << cudaGetErrorString(cudaGetLastError()) << std::endl; return;} -#endif /// tensor - input data and output data of each node of the network. @@ -18,24 +13,28 @@ Tensor::Tensor(const snSize& sz) : sz_(sz){ size_t ssz = sz.size(); - if (ssz > 0){ - cuCHECK(cudaMalloc(&data_, ssz * sizeof(snFloat))); - cuCHECK(cudaMemset(data_, 0, ssz * sizeof(snFloat))); + if (ssz > 0){ + cuAssert(cudaMalloc(&dataGPU_, ssz * sizeof(snFloat))); + cuAssert(cudaMemset(dataGPU_, 0, ssz * sizeof(snFloat))); } } Tensor::~Tensor(){ - if (data_) - cuCHECK(cudaFree(data_)); + if (dataGPU_) + cuAssert(cudaFree(dataGPU_)); + + if (dataCPU_) + free(dataCPU_); } Tensor::Tensor(const Tensor& other){ - setData(other.getData(), other.size()); + + setDataGPU(other.getDataGPU(), other.size()); } Tensor& Tensor::operator=(const Tensor& other){ - setData(other.getData(), other.size()); + setDataGPU(other.getDataGPU(), other.size()); return *this; } @@ -43,14 +42,8 @@ Tensor& Tensor::operator=(const Tensor& other){ Tensor& Tensor::operator+=(const Tensor& other){ assert(other == *this); - - auto od = other.getData(); - - //size_t sz = this->size().size(); - //for (size_t i = 0; i < sz; ++i){ - // data_[i] += od[i]; - //} - + + return *this; } @@ -58,50 +51,58 @@ Tensor& Tensor::operator-=(const Tensor& other){ assert(other == *this); - auto od = other.getData(); - - /* size_t sz = this->size().size(); - for (size_t i = 0; i < sz; ++i){ - data_[i] -= od[i]; - }*/ - + return *this; } -void Tensor::setData(const snFloat* data, const snSize& nsz){ +void Tensor::setDataGPU(const snFloat* data, const snSize& nsz){ size_t nnsz = nsz.size(); assert(data && (nnsz > 0)); if (sz_.size() < nnsz){ - if (data_) - cuCHECK(cudaFree(data_)); + if (dataGPU_) + cuAssert(cudaFree(dataGPU_)); - cuCHECK(cudaMalloc(&data_, nnsz * sizeof(snFloat))); + cuAssert(cudaMalloc(&dataGPU_, nnsz * sizeof(snFloat))); } - cuCHECK(cudaMemcpy(data_, data, nnsz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + cuAssert(cudaMemcpy(dataGPU_, data, nnsz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); sz_ = nsz; } -void Tensor::setDataCPU2GPU(const snFloat* data, const snSize& nsz, const size_t& offset){ +void Tensor::setDataCPU(const snFloat* data, const snSize& nsz){ + + size_t nnsz = nsz.size(); + assert(data && (nnsz > 0)); - assert(sz_.size() == (nsz.size() + offset)); + if (sz_.size() < nnsz){ - cuCHECK(cudaMemcpy(data_ + offset, data, nsz.size() * sizeof(snFloat), cudaMemcpyHostToDevice)); + if (dataGPU_) + cuAssert(cudaFree(dataGPU_)); + + cuAssert(cudaMalloc(&dataGPU_, nnsz * sizeof(snFloat))); + } + + cuAssert(cudaMemcpy(dataGPU_, data, nnsz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyHostToDevice)); + sz_ = nsz; } -snFloat* Tensor::getData() const{ +snFloat* Tensor::getDataGPU() const{ - return data_; + return dataGPU_; } -void Tensor::getDataGPU2CPU(snFloat* out, const snSize& osz) const{ - - assert(sz_ == osz); +snFloat* Tensor::getDataCPU() const{ + + size_t csz = sz_.size(); - cuCHECK(cudaMemcpy(out, data_, sz_.size() * sizeof(snFloat), cudaMemcpyDeviceToHost)); + dataCPU_ = (snFloat*)realloc(dataCPU_, csz * sizeof(snFloat)); + + cuAssert(cudaMemcpy(dataCPU_, dataGPU_, csz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToHost)); + + return dataCPU_; } void Tensor::resize(const snSize& nsz){ @@ -112,24 +113,16 @@ void Tensor::resize(const snSize& nsz){ if (csz < nnsz){ snFloat* mem = nullptr; - cuCHECK(cudaMalloc(&mem, nnsz * sizeof(snFloat))); + cuAssert(cudaMalloc(&mem, nnsz * sizeof(snFloat))); - if (data_){ - cuCHECK(cudaMemcpy(mem, data_, csz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); - cuCHECK(cudaFree(data_)); + if (dataGPU_){ + cuAssert(cudaMemcpy(mem, dataGPU_, csz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + cuAssert(cudaFree(dataGPU_)); } - data_ = mem; + dataGPU_ = mem; - cuCHECK(cudaMemset(data_ + csz, 0, (nnsz - csz) * sizeof(snFloat))); + cuAssert(cudaMemset(dataGPU_ + csz, 0, (nnsz - csz) * sizeof(snFloat))); } sz_ = nsz; -} - -void Tensor::tfree(){ - if (data_) - cuCHECK(cudaFree(data_)); - - data_ = nullptr; - sz_ = snSize(0, 0, 0, 0, 0); -} +} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/activation.cpp b/src/snOperatorCUDA/src/Operator/activation.cpp index 48fdfac..0f3a882 100644 --- a/src/snOperatorCUDA/src/Operator/activation.cpp +++ b/src/snOperatorCUDA/src/Operator/activation.cpp @@ -60,7 +60,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std baseOut_ = neighbOpr[0]->getOutput(); /// active func - activeFuncForward(baseOut_.size().size(), baseOut_.getData(), activeType_); + activeFuncForward(baseOut_.size().size(), baseOut_.getDataGPU(), activeType_); } else{ // backward @@ -77,7 +77,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std } /// active func - activeFuncBackward(baseGrad_.size().size(), baseGrad_.getData(), activeType_); + activeFuncBackward(baseGrad_.size().size(), baseGrad_.getDataGPU(), activeType_); } return vector(); diff --git a/src/snOperatorCUDA/src/Operator/batchNorm.cpp b/src/snOperatorCUDA/src/Operator/batchNorm.cpp index 9ffcbdf..be420d7 100644 --- a/src/snOperatorCUDA/src/Operator/batchNorm.cpp +++ b/src/snOperatorCUDA/src/Operator/batchNorm.cpp @@ -75,7 +75,7 @@ std::vector BatchNorm::Do(const operationParam& operPrm, const std: baseOut_ = neighbOpr[0]->getOutput(); - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataGPU(); snSize outsz = baseOut_.size(); if (outsz != inSzMem_){ @@ -107,7 +107,7 @@ std::vector BatchNorm::Do(const operationParam& operPrm, const std: baseGrad_ += neighbOpr[i]->getGradient(); } - snFloat* out = baseGrad_.getData(); + snFloat* out = baseGrad_.getDataGPU(); snSize outsz = baseGrad_.size(); switch (bnType_){ diff --git a/src/snOperatorCUDA/src/Operator/concat.cpp b/src/snOperatorCUDA/src/Operator/concat.cpp index 6cd301c..77e6bfc 100644 --- a/src/snOperatorCUDA/src/Operator/concat.cpp +++ b/src/snOperatorCUDA/src/Operator/concat.cpp @@ -93,14 +93,14 @@ std::vector Concat::Do(const operationParam& operPrm, const std::ve nstp = nbsz.w * nbsz.h * nbsz.d; for (size_t j = 0; j < csz.n; ++j){ - snFloat* dst = baseOut_.getData() + sz * j, - *src = buff.getData() + cstp * j; + snFloat* dst = baseOut_.getDataGPU() + sz * j, + *src = buff.getDataGPU() + cstp * j; memcpy(dst, src, cstp * sizeof(snFloat)); dst += cstp; - src = neighb->getOutput().getData() + nstp * j; + src = neighb->getOutput().getDataGPU() + nstp * j; memcpy(dst, src, nstp * sizeof(snFloat)); } diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 7652d23..6afbe8d 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -44,7 +44,8 @@ Convolution::Convolution(void* net, const string& name, const string& node, std: Convolution::~Convolution(){ - freeParamCUDA(gpuParams_); + freeParamCUDA(convGPUParams_); + dropOutFree(gpuDeviceId_, dropGPUParams_); } void Convolution::load(std::map& prms){ @@ -239,18 +240,18 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op } /// copy with offset padding for each image - snFloat* in = inputMem_->getData(); + snFloat* in = inputMem_->getDataGPU(); /// calculation of the output values - snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snFloat* out = baseOut_.getDataGPU(), *weight = baseWeight_.getDataGPU(); snSize outsz = baseOut_.size(); // calculation - forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, gpuParams_); - + forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, convGPUParams_); + /// dropOut if (dropOut_ > 0.F) - dropOut(operPrm.isLerning, dropOut_, outsz, out); + dropOutForward(dropOut_, out, outsz, gpuDeviceId_, &dropGPUParams_); /// batchNorm if (batchNormType_ == batchNormType::beforeActive) @@ -266,16 +267,17 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); - + snFloat* gradIn = inTns.getDataGPU(); + snSize insz = inTns.size(); + /// batchNorm if (batchNormType_ == batchNormType::postActive) - channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); // active function if (activeType_ != activeType::none){ - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataGPU(); size_t osz = baseOut_.size().size(); activeFuncBackward(osz, out, activeType_); @@ -286,20 +288,24 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o /// batchNorm if (batchNormType_ == batchNormType::beforeActive) - channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); + /// dropOut + if (dropOut_ > 0.F) + dropOutBackward(dropOut_, gradIn, insz, gpuDeviceId_, &dropGPUParams_); + // calculation of the output gradient and weight correction - snFloat* gradOut = baseGrad_.getData(); + snFloat* gradOut = baseGrad_.getDataGPU(); - snFloat* weight = baseWeight_.getData(); + snFloat* weight = baseWeight_.getDataGPU(); if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); - snFloat* in = inputMem_->getData(); + snFloat* in = inputMem_->getDataGPU(); // calculation - backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, gpuParams_); + backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, convGPUParams_); // correct weight snFloat* dWPrev = auxParams_["dWPrev"].data(); @@ -318,7 +324,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o optimizerType_); } else{ // isFreeze - backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, gpuParams_); + backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, convGPUParams_); } } @@ -341,7 +347,8 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseWeight_.resize(snSize(kernel, stp + 1)); - weightInit(baseWeight_, ntp - wcsz, stp + 1, kernel, weightInitType_); + if (wcsz == 0) + weightInit(baseWeight_, ntp, stp + 1, kernel, weightInitType_); } snSize outSz(0, 0, kernel, newsz.n); @@ -402,5 +409,5 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseBatchNorm_.sz.n = 1; } - iniParamCUDA(isLern, expSz, outSz, convPrms_, &gpuParams_); + iniParamCUDA(isLern, expSz, outSz, convPrms_, &convGPUParams_); } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/convolution.h b/src/snOperatorCUDA/src/Operator/convolution.h index c18118a..d59a4e1 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.h +++ b/src/snOperatorCUDA/src/Operator/convolution.h @@ -79,7 +79,8 @@ class Convolution final : SN_Base::OperatorBase{ std::map> auxParams_; ///< aux data - void* gpuParams_ = nullptr; ///< gpu aux params + void* convGPUParams_ = nullptr, ///< gpu aux params + * dropGPUParams_ = nullptr; void load(std::map& prms); diff --git a/src/snOperatorCUDA/src/Operator/crop.cpp b/src/snOperatorCUDA/src/Operator/crop.cpp index e89c399..a5d0d6d 100644 --- a/src/snOperatorCUDA/src/Operator/crop.cpp +++ b/src/snOperatorCUDA/src/Operator/crop.cpp @@ -74,8 +74,8 @@ std::vector Crop::Do(const operationParam& operPrm, const std::vect Tensor tns(snSize(w, h, baseSz_.d, baseSz_.n)); - snFloat* src = baseOut_.getData(), - * dst = tns.getData(); + snFloat* src = baseOut_.getDataGPU(), + * dst = tns.getDataGPU(); copyTo(true, roi_, baseSz_, src, dst); @@ -96,8 +96,8 @@ std::vector Crop::Do(const operationParam& operPrm, const std::vect Tensor tns(baseSz_); - snFloat* dst = tns.getData(), - * src = baseGrad_.getData(); + snFloat* dst = tns.getDataGPU(), + * src = baseGrad_.getDataGPU(); copyTo(false, roi_, baseSz_, dst, src); diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp index f1ce4c1..81738d6 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -226,9 +226,9 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& updateConfig(operPrm.isLerning, insz); } - snFloat* pInTns = inTns.getData(); + snFloat* pInTns = inTns.getDataGPU(); - snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snFloat* out = baseOut_.getDataGPU(), *weight = baseWeight_.getDataGPU(); snSize outsz = baseOut_.size(); // calculation @@ -253,7 +253,7 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); + snFloat* gradIn = inTns.getDataGPU(); /// batchNorm if (batchNormType_ == batchNormType::postActive) @@ -262,7 +262,7 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& /// active func if (activeType_ != activeType::none){ - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataGPU(); size_t osz = baseOut_.size().size(); activeFuncBackward(osz, out, activeType_); @@ -276,10 +276,10 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction - snFloat* grOut = baseGrad_.getData(); - snFloat* weight = baseWeight_.getData(); - snFloat* in = inputMem_->getData(); - snFloat* out = baseOut_.getData(); + snFloat* grOut = baseGrad_.getDataGPU(); + snFloat* weight = baseWeight_.getDataGPU(); + snFloat* in = inputMem_->getDataGPU(); + snFloat* out = baseOut_.getDataGPU(); snSize insz = inputMem_->size(), outsz = baseOut_.size(); @@ -320,8 +320,9 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ if (ntp > wcsz){ baseWeight_.resize(snSize(newsz.d, stp + 1)); - snFloat* wd = baseWeight_.getData(); - weightInit(wd + wcsz, ntp - wcsz, stp + 1, deconvParams_.kernel, weightInitType_); + + if (wcsz == 0) + weightInit(baseWeight_, ntp - wcsz, stp + 1, deconvParams_.kernel, weightInitType_); } snSize outSz(0, 0, deconvParams_.kernel, newsz.n); diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index d655fae..4c75007 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -224,7 +224,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& } /// calculation of the output values of neurons - snFloat* out = baseOut_.getData(), *weight = baseWeight_.getData(); + snFloat* out = baseOut_.getDataGPU(), *weight = baseWeight_.getDataGPU(); // +bias? if (!useBias_){ @@ -233,7 +233,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& } // calculation - forwardCUDA(kernel_, insz, inTns.getData(), weight, out, gpuParams_); + forwardCUDA(kernel_, insz, inTns.getDataGPU(), weight, out, gpuParams_); /// dropOut snSize outSz = baseOut_.size(); @@ -254,7 +254,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); + snFloat* gradIn = inTns.getDataGPU(); /// batchNorm snSize gsz = inTns.size(); @@ -264,7 +264,7 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam // active func if (activeType_ != activeType::none){ - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataGPU(); size_t osz = kernel_ * inSzMem_.n; activeFuncBackward(osz, out, activeType_); @@ -278,15 +278,15 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction - snFloat* gradOut = baseGrad_.getData(); - snFloat* weight = baseWeight_.getData(); + snFloat* gradOut = baseGrad_.getDataGPU(); + snFloat* weight = baseWeight_.getDataGPU(); if (!isFreeze_){ snFloat* dWeight = auxParams_["dWeight"].data(); // calculation - backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getData(), gradIn, gradOut, dWeight, gpuParams_); + backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getDataGPU(), gradIn, gradOut, dWeight, gpuParams_); // correct weight snFloat* dWPrev = auxParams_["dWPrev"].data(); @@ -319,8 +319,9 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ if (ntp > wcsz){ baseWeight_.resize(snSize(kernel_, stp + 1)); - snFloat* wd = baseWeight_.getData(); - weightInit(wd + wcsz, ntp - wcsz, stp + 1, kernel_, weightInitType_); + + if (wcsz == 0) + weightInit(baseWeight_, ntp - wcsz, stp + 1, kernel_, weightInitType_); } baseOut_.resize(snSize(kernel_, 1, 1, newsz.n)); diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp index fc3fc50..8f46e36 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -84,7 +84,7 @@ void LossFunction::forward(const Tensor& inTns){ snSize tsz = inTns.size(); baseOut_ = inTns; - auto out = baseOut_.getData(); + auto out = baseOut_.getDataGPU(); switch (lossType_){ case LossFunction::lossType::softMaxACrossEntropy:{ @@ -126,7 +126,7 @@ void LossFunction::forward(const Tensor& inTns){ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - true, inTns.size(), inTns.getData(), outSz, &outData); + true, inTns.size(), inTns.getDataGPU(), outSz, &outData); if (outData){ baseOut_.setData(outData, outSz); @@ -153,9 +153,9 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ if (grsz != tsz) baseGrad_.resize(tsz); - auto smOut = baseOut_.getData(); - auto target = inTns.getData(); - auto grad = baseGrad_.getData(); + auto smOut = baseOut_.getDataGPU(); + auto target = inTns.getDataGPU(); + auto grad = baseGrad_.getDataGPU(); switch (lossType_){ case LossFunction::lossType::softMaxACrossEntropy:{ @@ -192,7 +192,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - false, inTns.size(), inTns.getData(), outSz, &outData); + false, inTns.size(), inTns.getDataGPU(), outSz, &outData); if (outData){ baseGrad_.setData(outData, outSz); diff --git a/src/snOperatorCUDA/src/Operator/pooling.cpp b/src/snOperatorCUDA/src/Operator/pooling.cpp index 0c065cb..f3da593 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.cpp +++ b/src/snOperatorCUDA/src/Operator/pooling.cpp @@ -123,16 +123,16 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara } /// copy with offset padding for each image - snFloat* in = inputMem_->getData(); + snFloat* in = inputMem_->getDataGPU(); if (isPadding_){ inTnsExp_.resize(inDataExpSz_); - paddingOffs(false, poolPrms_.paddingW, poolPrms_.paddingH, insz, inTnsExp_.getData(), inputMem_->getData()); + paddingOffs(false, poolPrms_.paddingW, poolPrms_.paddingH, insz, inTnsExp_.getDataGPU(), inputMem_->getDataGPU()); insz = inDataExpSz_; - in = inTnsExp_.getData(); + in = inTnsExp_.getDataGPU(); } /// output calculation - snFloat* out = baseOut_.getData(); + snFloat* out = baseOut_.getDataGPU(); // calculation forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); @@ -141,21 +141,21 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snFloat* gradIn = inTns.getData(); + snFloat* gradIn = inTns.getDataGPU(); - snFloat* input = inputMem_->getData(); - snFloat* gradOut = baseGrad_.getData(); + snFloat* input = inputMem_->getDataGPU(); + snFloat* gradOut = baseGrad_.getDataGPU(); if (isPadding_){ gradOutExp_.resize(inDataExpSz_); - gradOut = gradOutExp_.getData(); - input = inTnsExp_.getData(); + gradOut = gradOutExp_.getDataGPU(); + input = inTnsExp_.getDataGPU(); } /// grad calculation - backwardCUDA(poolPrms_, baseOut_.size(), outInx_.data(), baseOut_.getData(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); + backwardCUDA(poolPrms_, baseOut_.size(), outInx_.data(), baseOut_.getDataGPU(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); if (isPadding_) - paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getData()); + paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getDataGPU()); } void Pooling::updateConfig(bool isLern, const snSize& newsz){ diff --git a/src/snOperatorCUDA/src/Operator/resize.cpp b/src/snOperatorCUDA/src/Operator/resize.cpp index 22db594..1c23d0d 100644 --- a/src/snOperatorCUDA/src/Operator/resize.cpp +++ b/src/snOperatorCUDA/src/Operator/resize.cpp @@ -73,8 +73,8 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve cstp = csz.w * csz.h * csz.d; for (size_t j = 0; j < csz.n; ++j){ - snFloat* dst = baseOut_.getData() + sz * j, - *src = buff.getData() + cstp * j + offset; + snFloat* dst = baseOut_.getDataGPU() + sz * j, + *src = buff.getDataGPU() + cstp * j + offset; memcpy(dst, src, sz * sizeof(snFloat)); } @@ -119,8 +119,8 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve cstp = csz.w * csz.h * csz.d; for (size_t j = 0; j < csz.n; ++j){ - snFloat* dst = baseGrad_.getData() + sz * j, - *src = buff.getData() + cstp * j + offset; + snFloat* dst = baseGrad_.getDataGPU() + sz * j, + *src = buff.getDataGPU() + cstp * j + offset; memcpy(dst, src, sz * sizeof(snFloat)); } diff --git a/src/snOperatorCUDA/src/Operator/summator.cpp b/src/snOperatorCUDA/src/Operator/summator.cpp index 1cb4e91..c7fa7dd 100644 --- a/src/snOperatorCUDA/src/Operator/summator.cpp +++ b/src/snOperatorCUDA/src/Operator/summator.cpp @@ -83,9 +83,9 @@ std::vector Summator::Do(const operationParam& operPrm, const std:: void Summator::mean(const Tensor& one, const Tensor& two, Tensor& out){ - snFloat* done = one.getData(), - * dtwo = two.getData(), - * dout = out.getData(); + snFloat* done = one.getDataGPU(), + * dtwo = two.getDataGPU(), + * dout = out.getDataGPU(); size_t sz = one.size().size(); for (size_t i = 0; i < sz; ++i){ diff --git a/src/snOperatorCUDA/src/Operator/userLayer.cpp b/src/snOperatorCUDA/src/Operator/userLayer.cpp index 58e1ae9..c5abb2b 100644 --- a/src/snOperatorCUDA/src/Operator/userLayer.cpp +++ b/src/snOperatorCUDA/src/Operator/userLayer.cpp @@ -55,7 +55,7 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - true, baseOut_.size(), baseOut_.getData(), outSz, &outData); + true, baseOut_.size(), baseOut_.getDataGPU(), outSz, &outData); if (outData) baseOut_.setData(outData, outSz); @@ -79,7 +79,7 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - false, baseGrad_.size(), baseGrad_.getData(), outSz, &outData); + false, baseGrad_.size(), baseGrad_.getDataGPU(), outSz, &outData); if (outData) baseGrad_.setData(outData, outSz); diff --git a/src/snOperatorCUDA/src/mathFunctions.h b/src/snOperatorCUDA/src/mathFunctions.h index b2d7999..8070cc9 100644 --- a/src/snOperatorCUDA/src/mathFunctions.h +++ b/src/snOperatorCUDA/src/mathFunctions.h @@ -40,4 +40,9 @@ void batchNormBackward(const SN_Base::snSize& insz, SN_Base::snFloat* gradOut, SN_Base::batchNorm); -void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out, void* gpuPrms); \ No newline at end of file + +void dropOutFree(uint32_t deviceId, void* pGpuPrm); + +void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm); + +void dropOutBackward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm); \ No newline at end of file diff --git a/src/snOperatorCUDA/src/stdafx.h b/src/snOperatorCUDA/src/stdafx.h index fad7aef..15b5b9b 100644 --- a/src/snOperatorCUDA/src/stdafx.h +++ b/src/snOperatorCUDA/src/stdafx.h @@ -45,4 +45,8 @@ void g_userCBack(SN_Base::OperatorBase* opr, const std::string& cbname, const st #define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#define cuCHECKFree(func) if (func != 0){ g_statusMess(nullptr, "CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} +#define cuAssert(func) \ + if (func != 0){ \ + std::string mess = std::string("CUDA error: ") + cudaGetErrorString(cudaGetLastError()); \ + assert(!mess.c_str()); \ + } diff --git a/src/snOperatorCUDA/src/weightInit.cpp b/src/snOperatorCUDA/src/weightInit.cpp index 019bd92..446a4c0 100644 --- a/src/snOperatorCUDA/src/weightInit.cpp +++ b/src/snOperatorCUDA/src/weightInit.cpp @@ -45,7 +45,7 @@ void weightInit(SN_Base::Tensor& ioW, size_t sz, size_t fan_in, size_t fan_out, case weightInitType::xavier:wi_xavier(buff.data(), sz, fan_in, fan_out); break; } - ioW.setDataCPU2GPU(buff.data(), sz, ioW.size().size()); + ioW.setDataCPU(buff.data(), sz); } void wi_uniform(snFloat* ioW, size_t sz){ From c9572cc6064040d70b847453e959731a89047554 Mon Sep 17 00:00:00 2001 From: al_medved Date: Thu, 18 Jul 2019 00:49:01 +0500 Subject: [PATCH 06/35] -develop --- src/snBase/snBase.h | 2 +- src/snSIMD/src/convolutionFWDTest.cpp | 399 ++++++++++++++++++++++++++ 2 files changed, 400 insertions(+), 1 deletion(-) create mode 100644 src/snSIMD/src/convolutionFWDTest.cpp diff --git a/src/snBase/snBase.h b/src/snBase/snBase.h index 5490f24..e893775 100644 --- a/src/snBase/snBase.h +++ b/src/snBase/snBase.h @@ -114,7 +114,7 @@ namespace SN_Base{ } private: - mutable snFloat* dataCPU_, + mutable snFloat* dataCPU_ = nullptr, * dataGPU_ = nullptr; snSize sz_; diff --git a/src/snSIMD/src/convolutionFWDTest.cpp b/src/snSIMD/src/convolutionFWDTest.cpp new file mode 100644 index 0000000..e3688da --- /dev/null +++ b/src/snSIMD/src/convolutionFWDTest.cpp @@ -0,0 +1,399 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include "snBase/snBase.h" +#include "Lib/OpenBLAS/cblas.h" + +using namespace std; +using namespace SN_Base; + +namespace SN_SIMD{ + + struct buf_t{ + + SN_Base::snFloat* p = nullptr; + SN_Base::snSize sz; + + buf_t(const SN_Base::snSize& size = SN_Base::snSize(0, 0, 0, 0, 0), size_t add = 0) : sz(size) { + + if (size.size() > 0) + //p = (SN_Base::snFloat*)_mm_malloc((size.size() + add) * sizeof(SN_Base::snFloat), 64); + p = (SN_Base::snFloat*)malloc((size.size() + add) * sizeof(SN_Base::snFloat)); + } + + ~buf_t() { + if (p) free(p); + } + + }; + + template + void reorderInputCHW2HCW(const SN_Base::snSize& insz, const SN_Base::snFloat* input, const SN_Base::snSize& outsz, SN_Base::snFloat* output){ + + SN_Base::snFloat* pOut = output; + + if (M == 1){ + + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ + + for (size_t j = 0; j < insz.d; ++j){ + + size_t ci = i % outsz.w, cr = i / outsz.w; + + *pOut = *(input + S * insz.w * cr + S * ci + insz.w * insz.h * j); + + ++pOut; + } + } + } + + ///////////////////////////////////////////// + + else if ((M == 3) && (D == 1)){ + + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ + + for (size_t j = 0; j < insz.d; ++j){ + + size_t ci = i % outsz.w, cr = i / outsz.w; + + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; + + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); + + pOut += M * M; + } + } + } + + ///////////////////////////////////////////// + + else if ((M == 5) && (D == 1)){ + + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ + + for (size_t j = 0; j < insz.d; ++j){ + + size_t ci = i % outsz.w, cr = i / outsz.w; + + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; + + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); + _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); + _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); + + pOut += M * M; + + } + } + } + + ///////////////////////////////////////////// + + else if ((M == 7) && (D == 1)){ + + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ + + for (size_t j = 0; j < insz.d; ++j){ + + size_t ci = i % outsz.w, cr = i / outsz.w; + + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; + + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); + _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); + _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); + _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); + _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); + + pOut += M * M; + } + } + } + + ///////////////////////////////////////////// + + else if ((M == 9) && (D == 1)){ + + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ + + for (size_t j = 0; j < insz.d; ++j){ + + size_t ci = i % outsz.w, cr = i / outsz.w; + + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; + + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); + _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); + _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); + _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); + _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); + _mm256_storeu_ps(pOut + 7 * M, _mm256_loadu_ps(pIn + 7 * insz.w)); + _mm256_storeu_ps(pOut + 8 * M, _mm256_loadu_ps(pIn + 8 * insz.w)); + + pIn += insz.w * insz.h; + pOut += M * M; + } + } + } + + /////////////////////////////////////////////////// + // TODO + + /*else if ((M == 3) && (D == 2)){ + + } + + else if ((M == 5) && (D == 2)){ + + } + + else if ((M == 7) && (D == 2)){ + + } + + else if ((M == 9) && (D == 2)){ + + + }*/ + }; + + template + void convolutionFWD(const snFloat* weight, + const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ + + /// Reorder input + buf_t inHCWBuff(snSize(M * M * insz.d, outsz.w, outsz.h), 8); + + reorderInputCHW2HCW(insz, input, outsz, inHCWBuff.p); + + + /////////////////////////////////// + + const size_t wStepByD = M * M, + wStepByK = wStepByD * insz.d, + wStepByN = wStepByK * outsz.d, + imSz = inHCWBuff.sz.size(); + + // Out = α * In * W + βC + // In - data input matrix - values from the previous layer + // W - weights matrix + // Out - data output matrix + cblas_sgemm(CBLAS_ORDER::CblasRowMajor, + CBLAS_TRANSPOSE::CblasNoTrans, + CBLAS_TRANSPOSE::CblasNoTrans, + blasint(outsz.d), // In, rows + blasint(outsz.w * outsz.h), // W, cols + blasint(M * M * insz.d), // In, cols, W, rows + 1.0F, // α + inHCWBuff.p, // In + blasint(M * M * insz.d), // In, step to next In + weight, // W + blasint(outsz.w * outsz.h), // W, step to next W (W21 - W11) + 0.0, // β + output, // Out + blasint(outsz.w * outsz.h)); // Out, step to next Out (Y21 - Y11) + + /*auto core = std::thread::hardware_concurrency(); + if (core == 0) core = 4; + +#pragma omp parallel for num_threads(core) + for (int od = 0; od < int(outsz.d); ++od){ + + const snFloat bias = *(weight + wStepByN + od); + + for (size_t oi = 0; oi < (outsz.w * outsz.h) / RO; ++oi){ + + const snFloat* pW = weight + wStepByK * od, + * pIn = inHCWBuff.p + (oi * RO) * M * M * insz.d; + + snFloat* pOut = output + (oi * RO) + od * (outsz.w * outsz.h); + + kernel(core, pW, bias, insz, pIn, outsz, pOut); + } + + if (peak){ + + const size_t offs = ((outsz.w * outsz.h) / RO) * RO; + + const snFloat* pW = weight + wStepByK * od, + * pIn = inHCWBuff.p + offs * M * M * insz.d; + + snFloat* pOut = output + offs + od * (outsz.w * outsz.h); + + kernelPeak(peak, pW, bias, insz, pIn, outsz, pOut); + } + } */ + } + +// template +// void defaultFWD(size_t S, size_t D, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ +// +// const size_t wStepByD = M * M, // step weight by input +// kernel = outsz.d, +// wStepByK = wStepByD * insz.d, // step weight by output +// wStepByN = wStepByK * kernel, // step weight by batch +// inStepByD = insz.w * insz.h, // step in by input +// inStepByN = inStepByD * insz.d, // step in by batch +// outStepByD = outsz.w * outsz.h, // step out by input +// outStepByN = outStepByD * outsz.d; // step out by batch +// +// size_t shareStepByN = kernel; // for local mem +// snFloat* share = (snFloat*)calloc(shareStepByN * insz.n, sizeof(snFloat)); +// +// auto core = std::thread::hardware_concurrency(); +// if (core == 0) core = 4; +// +// // by batch +//#pragma omp parallel for num_threads(core) +// for (int n = 0; n < int(insz.n); ++n){ +// +// snFloat* outBuff = share + shareStepByN * n; +// snFloat In[wStepByD], W[wStepByD]; +// +// for (size_t p = 0; p < outStepByD; ++p){ +// +// size_t ox = p % outsz.w, oy = p / outsz.w, +// posW = ox * S, posH = oy * S; +// +// memset(outBuff, 0, kernel * sizeof(snFloat)); +// +// const snFloat* pIn = input + inStepByN * n, +// * pW = weight; +// +// // on all in layers +// for (size_t d = 0; d < insz.d; ++d){ +// +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % M, cy = c / M; +// In[c] = *(pIn + (cx + posW + cx * (D - 1)) + (cy + posH + cy * (D - 1)) * insz.w); +// } +// +// pW = weight + wStepByD * d; +// +// // on all out layers +// for (size_t k = 0; k < kernel; ++k){ +// +// for (size_t c = 0; c < wStepByD; ++c){ +// +// size_t cx = c % M, cy = c / M; +// W[c] = *(pW + cx + cy * M); +// } +// +// __m256 arOut = _mm256_setzero_ps(); +// +// for (int z = 0; z < wStepByD / 8; ++z){ +// +// __m256 arIn = _mm256_loadu_ps(In + z * 8); +// +// __m256 arW = _mm256_loadu_ps(W + z * 8); +// +// arOut = _mm256_add_ps(arOut, _mm256_mul_ps(arIn, arW)); +// } +// +// outBuff[k] += horSummReg<__m256>(arOut); +// +// outBuff[k] += In[wStepByD - 1] * W[wStepByD - 1]; +// +// pW += wStepByK; +// } +// +// pIn += inStepByD; +// +// } +// +// snFloat* pOut = output + ox + oy * outsz.w + n * outStepByN; +// pW = weight + wStepByN; +// +// // on all out layers +// for (size_t k = 0; k < kernel; ++k){ +// +// *pOut = outBuff[k] + *(pW + k); // + bias +// +// pOut += outStepByD; +// } +// } +// } +// +// free(share); +// } +// + + bool convolutionFWD(size_t M, size_t S, size_t D, + const snFloat* weight, + const snSize& insz, const snFloat* input, + const snSize& outsz, snFloat* output){ + + + /*if ((insz.n > 1) || (S > 2) || (D > 1)){ + +#define dfwd(MS) \ + if (M == MS){ \ + defaultFWD(S, D, weight, insz, input, outsz, output); return true; }; + + dfwd(1) + dfwd(3) + dfwd(5) + dfwd(7) + dfwd(9) + + return false; + } +#undef dfwd*/ + + + +#define cfwd(MS, SS, DS) \ + if ((M == MS) && (S == SS) && (D == DS)){ \ + convolutionFWD(weight, insz, input, outsz, output); return true; }; + + cfwd(1, 1, 1) + cfwd(3, 1, 1) + cfwd(5, 1, 1) + cfwd(7, 1, 1) + cfwd(9, 1, 1) + + cfwd(1, 2, 1) + cfwd(3, 2, 1) + cfwd(5, 2, 1) + cfwd(7, 2, 1) + cfwd(9, 2, 1) + + return false; + +#undef cfwd + + }; +}; + From 5b49fdd156757ab0ca5f577ab250e6bebd7ee4dd Mon Sep 17 00:00:00 2001 From: Tyill Date: Thu, 18 Jul 2019 13:03:22 +0500 Subject: [PATCH 07/35] -develop --- mswin/VS12/snSIMD.vcxproj | 13 +- mswin/VS12/snSIMD.vcxproj.filters | 7 +- src/snOperatorCPU/src/CPU/convolutionCPU.cpp | 26 +- .../src/Operator/convolution.cpp | 19 +- src/snOperatorCPU/src/Operator/convolution.h | 19 +- src/snSIMD/snSIMD.h | 7 +- src/snSIMD/src/base.h | 962 ------------------ src/snSIMD/src/convolutionBWD.cpp | 1 - src/snSIMD/src/convolutionFWD.cpp | 473 +++------ src/snSIMD/src/convolutionFWDTest.cpp | 399 -------- 10 files changed, 208 insertions(+), 1718 deletions(-) delete mode 100644 src/snSIMD/src/base.h delete mode 100644 src/snSIMD/src/convolutionFWDTest.cpp diff --git a/mswin/VS12/snSIMD.vcxproj b/mswin/VS12/snSIMD.vcxproj index c33251b..9bad21b 100644 --- a/mswin/VS12/snSIMD.vcxproj +++ b/mswin/VS12/snSIMD.vcxproj @@ -20,7 +20,6 @@ - @@ -128,6 +127,12 @@ true $(OutDir) + + + + + + @@ -166,6 +171,12 @@ true $(OutDir) + + + + + + diff --git a/mswin/VS12/snSIMD.vcxproj.filters b/mswin/VS12/snSIMD.vcxproj.filters index 8080af6..6249bfd 100644 --- a/mswin/VS12/snSIMD.vcxproj.filters +++ b/mswin/VS12/snSIMD.vcxproj.filters @@ -18,15 +18,12 @@ Header Files - - Header Files - - + Source Files - + Source Files diff --git a/src/snOperatorCPU/src/CPU/convolutionCPU.cpp b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp index 61226ba..599daea 100644 --- a/src/snOperatorCPU/src/CPU/convolutionCPU.cpp +++ b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp @@ -35,6 +35,27 @@ using namespace std; using namespace SN_Base; + +void Convolution::iniParamCPU(bool isLern, const snSize& insz, const snSize& outsz, + const convParams& prms, CPUParams& cpuPrm){ + + if (!isLern && (insz.n == 1)){ + + // M * M * insz.d * outsz.w * outsz.h + size_t sz = prms.fWidth * prms.fHeight * insz.d * outsz.w * outsz.h; + + cpuPrm.buffMemFWD = (snFloat*)realloc(cpuPrm.buffMemFWD, sz * sizeof(snFloat)); + } +} + +void Convolution::freeParamCPU(CPUParams& cpuPrm){ + + if (cpuPrm.buffMemFWD){ + + free(cpuPrm.buffMemFWD); + } +} + void forwardBASE(size_t kernel, size_t fWidth, size_t fHeight, size_t stride, size_t dilate, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ @@ -108,11 +129,12 @@ void forwardBASE(size_t kernel, size_t fWidth, size_t fHeight, size_t stride, si } void Convolution::forwardCPU(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, CPUParams& cpuPrm){ #ifdef SN_AVX + if ((prms.fWidth != prms.fHeight) || !SN_SIMD::convolutionFWD(prms.fWidth, prms.stride, prms.dilate, - weight, insz, input, outsz, output)) + weight, insz, input, outsz, output, cpuPrm.buffMemFWD)) #endif forwardBASE(prms.kernel, prms.fWidth, prms.fHeight, prms.stride, prms.dilate, weight, insz, input, outsz, output); diff --git a/src/snOperatorCPU/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp index d3a9844..6fc4eb1 100644 --- a/src/snOperatorCPU/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -44,6 +44,7 @@ Convolution::Convolution(void* net, const string& name, const string& node, std: Convolution::~Convolution(){ + freeParamCPU(cpuParams_); } void Convolution::load(std::map& prms){ @@ -246,16 +247,9 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op /// calculation of the output values snFloat* out = baseOut_.getDataCPU(), *weight = baseWeight_.getDataCPU(); snSize outsz = baseOut_.size(); - - // +bias? - if (!convPrms_.useBias_){ - size_t kernel = convPrms_.kernel, - stepByN = convPrms_.fWidth * convPrms_.fHeight * insz.d * kernel; - memset(weight + stepByN, 0, kernel * sizeof(snFloat)); - } - + // calculation - forwardCPU(convPrms_, weight, inDataExpSz_, in, outsz, out); + forwardCPU(convPrms_, weight, inDataExpSz_, in, outsz, out, cpuParams_); /// dropOut if (dropOut_ > 0.F) @@ -365,6 +359,11 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize weightInit(wd + wcsz, ntp - wcsz, stp + 1, kernel, weightInitType_); } + // +bias? + if (!convPrms_.useBias_){ + memset(baseWeight_.getDataCPU() + stp * kernel, 0, kernel * sizeof(snFloat)); + } + snSize outSz(0, 0, kernel, newsz.n); if (isPaddingSame_){ @@ -422,4 +421,6 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseBatchNorm_.sz = outSz; baseBatchNorm_.sz.n = 1; } + + iniParamCPU(isLern, expSz, outSz, convPrms_, cpuParams_); } \ No newline at end of file diff --git a/src/snOperatorCPU/src/Operator/convolution.h b/src/snOperatorCPU/src/Operator/convolution.h index 56e8c82..4024d03 100644 --- a/src/snOperatorCPU/src/Operator/convolution.h +++ b/src/snOperatorCPU/src/Operator/convolution.h @@ -55,8 +55,15 @@ class Convolution final : SN_Base::OperatorBase{ bool useBias_ = true; }; + struct CPUParams{ + + SN_Base::snFloat* buffMemFWD = nullptr; + }; + convParams convPrms_; + CPUParams cpuParams_; ///< cpu aux params + bool isPaddingSame_ = false, isCheckPadding_ = false; @@ -79,7 +86,7 @@ class Convolution final : SN_Base::OperatorBase{ SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimiz weight optDecayMomentWGr_ = 0.99F, optLmbRegular_ = 0.001F; - + std::map> auxParams_; ///< aux data @@ -93,12 +100,20 @@ class Convolution final : SN_Base::OperatorBase{ /// CPU /////////////////////////// + /// init aux params + void iniParamCPU(bool isLern, const SN_Base::snSize& insz, const SN_Base::snSize& outsz, + const convParams&, CPUParams& cpuPrm); + + /// free aux params + void freeParamCPU(CPUParams& cpuPrm); + void forwardCPU(const convParams&, const SN_Base::snFloat* weight, const SN_Base::snSize& insz, const SN_Base::snFloat* input, const SN_Base::snSize& outsz, - SN_Base::snFloat* output); + SN_Base::snFloat* output, + CPUParams& cpuPrm); // calc grad and weight void backwardCPU_GW(const convParams&, diff --git a/src/snSIMD/snSIMD.h b/src/snSIMD/snSIMD.h index fdec97a..a91a769 100644 --- a/src/snSIMD/snSIMD.h +++ b/src/snSIMD/snSIMD.h @@ -26,13 +26,16 @@ #include "snBase/snBase.h" namespace SN_SIMD{ + /// @param[in] Mask := [1, 3..9], Stride := [1, ..), Dilate := [1, ..) + /// @param[in] buffMem size := M * M * insz.d * outsz.w * outsz.h /// @return true - ok - bool convolutionFWD(size_t M, size_t S, size_t D, + bool convolutionFWD(size_t M, size_t S, size_t D, const SN_Base::snFloat* weight, const SN_Base::snSize& insz, const SN_Base::snFloat* input, - const SN_Base::snSize& outsz, SN_Base::snFloat* output); + const SN_Base::snSize& outsz, SN_Base::snFloat* output, + SN_Base::snFloat* buffMem); /// @param[in] Mask := [1, 3..9], Stride := [1, ..), Dilate := [1, ..) /// @return true - ok diff --git a/src/snSIMD/src/base.h b/src/snSIMD/src/base.h deleted file mode 100644 index 11fd33e..0000000 --- a/src/snSIMD/src/base.h +++ /dev/null @@ -1,962 +0,0 @@ - -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// -#pragma once - -#include "snBase/snBase.h" -#include -#include - -namespace SN_SIMD{ - - -#define CREATE_REG(reg) \ - __m256 reg = _mm256_setzero_ps(); - -#define CREATE_2REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); - -#define CREATE_3REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); - -#define CREATE_4REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); - -#define CREATE_5REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); - -#define CREATE_6REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); - -#define CREATE_7REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); - -#define CREATE_8REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); \ - __m256 reg ## 7 = _mm256_setzero_ps(); - -#define CREATE_9REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); \ - __m256 reg ## 7 = _mm256_setzero_ps(); \ - __m256 reg ## 8 = _mm256_setzero_ps(); - -#define CREATE_10REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); \ - __m256 reg ## 7 = _mm256_setzero_ps(); \ - __m256 reg ## 8 = _mm256_setzero_ps(); \ - __m256 reg ## 9 = _mm256_setzero_ps(); - -#define CREATE_11REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); \ - __m256 reg ## 7 = _mm256_setzero_ps(); \ - __m256 reg ## 8 = _mm256_setzero_ps(); \ - __m256 reg ## 9 = _mm256_setzero_ps(); \ - __m256 reg ## 10 = _mm256_setzero_ps(); - -#define CREATE_12REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); \ - __m256 reg ## 7 = _mm256_setzero_ps(); \ - __m256 reg ## 8 = _mm256_setzero_ps(); \ - __m256 reg ## 9 = _mm256_setzero_ps(); \ - __m256 reg ## 10 = _mm256_setzero_ps(); \ - __m256 reg ## 11 = _mm256_setzero_ps(); - -#define CREATE_13REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); \ - __m256 reg ## 7 = _mm256_setzero_ps(); \ - __m256 reg ## 8 = _mm256_setzero_ps(); \ - __m256 reg ## 9 = _mm256_setzero_ps(); \ - __m256 reg ## 10 = _mm256_setzero_ps(); \ - __m256 reg ## 11 = _mm256_setzero_ps(); \ - __m256 reg ## 12 = _mm256_setzero_ps(); - -#define CREATE_14REG(reg) \ - __m256 reg ## 0 = _mm256_setzero_ps(); \ - __m256 reg ## 1 = _mm256_setzero_ps(); \ - __m256 reg ## 2 = _mm256_setzero_ps(); \ - __m256 reg ## 3 = _mm256_setzero_ps(); \ - __m256 reg ## 4 = _mm256_setzero_ps(); \ - __m256 reg ## 5 = _mm256_setzero_ps(); \ - __m256 reg ## 6 = _mm256_setzero_ps(); \ - __m256 reg ## 7 = _mm256_setzero_ps(); \ - __m256 reg ## 8 = _mm256_setzero_ps(); \ - __m256 reg ## 9 = _mm256_setzero_ps(); \ - __m256 reg ## 10 = _mm256_setzero_ps(); \ - __m256 reg ## 11 = _mm256_setzero_ps(); \ - __m256 reg ## 12 = _mm256_setzero_ps(); \ - __m256 reg ## 13 = _mm256_setzero_ps(); - -#define LOAD_REG(in, offs, reg) \ - reg = _mm256_loadu_ps(in + (offs)); - -#define LOAD_3REG(in, offs, reg) \ - reg ## 0 = _mm256_loadu_ps(in + 0 * (offs)); \ - reg ## 1 = _mm256_loadu_ps(in + 1 * (offs)); \ - reg ## 2 = _mm256_loadu_ps(in + 2 * (offs)); - -#define LOAD_5REG(in, offs, reg) \ - reg ## 0 = _mm256_loadu_ps(in + 0 * (offs)); \ - reg ## 1 = _mm256_loadu_ps(in + 1 * (offs)); \ - reg ## 2 = _mm256_loadu_ps(in + 2 * (offs)); \ - reg ## 3 = _mm256_loadu_ps(in + 3 * (offs)); \ - reg ## 4 = _mm256_loadu_ps(in + 4 * (offs)); - -#define LOAD_6REG(in, offs, reg) \ - reg ## 0 = _mm256_loadu_ps(in + 0 * (offs)); \ - reg ## 1 = _mm256_loadu_ps(in + 1 * (offs)); \ - reg ## 2 = _mm256_loadu_ps(in + 2 * (offs)); \ - reg ## 3 = _mm256_loadu_ps(in + 3 * (offs)); \ - reg ## 4 = _mm256_loadu_ps(in + 4 * (offs)); \ - reg ## 5 = _mm256_loadu_ps(in + 5 * (offs)); - -#define SUMM_3x3REG_1OUT(arIn, arW, arO) \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 0, arW ## 0), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 1, arW ## 1), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 2, arW ## 2), arO); - -#define SUMM_3x3REG_2OUT(pIn, inOffs, arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); - -#define SUMM_3x3REG_3OUT(pIn, inOffs,arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); - -#define SUMM_3x3REG_4OUT(pIn, inOffs,arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_3REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 3); - -#define SUMM_3x3REG_5OUT(pIn, inOffs,arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_3REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 3); \ - LOAD_3REG(pIn + 4 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 4); - -#define SUMM_3x3REG_6OUT(pIn, inOffs,arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_3REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 3); \ - LOAD_3REG(pIn + 4 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 4); \ - LOAD_3REG(pIn + 5 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 5); - -#define SUMM_3x3REG_7OUT(pIn, inOffs,arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_3REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 3); \ - LOAD_3REG(pIn + 4 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 4); \ - LOAD_3REG(pIn + 5 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 5); \ - LOAD_3REG(pIn + 6 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 6); - -#define SUMM_3x3REG_8OUT(pIn, inOffs,arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_3REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 3); \ - LOAD_3REG(pIn + 4 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 4); \ - LOAD_3REG(pIn + 5 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 5); \ - LOAD_3REG(pIn + 6 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 6); \ - LOAD_3REG(pIn + 7 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 7); - -#define SUMM_3x3REG_9OUT(pIn, inOffs,arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_3REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 3); \ - LOAD_3REG(pIn + 4 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 4); \ - LOAD_3REG(pIn + 5 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 5); \ - LOAD_3REG(pIn + 6 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 6); \ - LOAD_3REG(pIn + 7 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 7); \ - LOAD_3REG(pIn + 8 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 8); - -#define SUMM_5x5REG_1OUT(arIn, arW, arO) \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 0, arW ## 0), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 1, arW ## 1), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 2, arW ## 2), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 3, arW ## 3), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 4, arW ## 4), arO); - -#define SUMM_6x6REG_1OUT(arIn, arW, arO) \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 0, arW ## 0), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 1, arW ## 1), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 2, arW ## 2), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 3, arW ## 3), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 4, arW ## 4), arO); \ - arO = _mm256_add_ps(_mm256_mul_ps(arIn ## 5, arW ## 5), arO); - -#define SUMM_6x6REG_2OUT(pIn, inOffs, arIn, arW, arO) \ - LOAD_6REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_6REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 1); - -#define SUMM_6x6REG_3OUT(pIn, inOffs, arIn, arW, arO) \ - LOAD_6REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_6REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_6REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 2); - -#define SUMM_6x6REG_4OUT(pIn, inOffs, arIn, arW, arO) \ - LOAD_6REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_6REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_6REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_6REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_6x6REG_1OUT(arIn, arW, arO ## 3); - -#define SUMM_3x3REG_10OUT(pIn, inOffs, arIn, arW, arO) \ - LOAD_3REG(pIn + 0 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 0); \ - LOAD_3REG(pIn + 1 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 1); \ - LOAD_3REG(pIn + 2 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 2); \ - LOAD_3REG(pIn + 3 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 3); \ - LOAD_3REG(pIn + 4 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 4); \ - LOAD_3REG(pIn + 5 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 5); \ - LOAD_3REG(pIn + 6 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 6); \ - LOAD_3REG(pIn + 7 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 7); \ - LOAD_3REG(pIn + 8 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 8); \ - LOAD_3REG(pIn + 9 * (inOffs), 8, arIn); \ - SUMM_3x3REG_1OUT(arIn, arW, arO ## 9); - - -#define SUMM_REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, inOffs, arIn); arO = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO); - -#define SUMM_1REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, inOffs, arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); - -#define SUMM_2REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); - -#define SUMM_3REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); - -#define SUMM_4REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); - -#define SUMM_5REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); - -#define SUMM_6REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); - -#define SUMM_7REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); - -#define SUMM_8REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); \ - LOAD_REG(in, 7 * (inOffs), arIn); arO ## 7 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 7); - -#define SUMM_9REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); \ - LOAD_REG(in, 7 * (inOffs), arIn); arO ## 7 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 7); \ - LOAD_REG(in, 8 * (inOffs), arIn); arO ## 8 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 8); - -#define SUMM_10REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); \ - LOAD_REG(in, 7 * (inOffs), arIn); arO ## 7 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 7); \ - LOAD_REG(in, 8 * (inOffs), arIn); arO ## 8 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 8); \ - LOAD_REG(in, 9 * (inOffs), arIn); arO ## 9 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 9); - -#define SUMM_11REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); \ - LOAD_REG(in, 7 * (inOffs), arIn); arO ## 7 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 7); \ - LOAD_REG(in, 8 * (inOffs), arIn); arO ## 8 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 8); \ - LOAD_REG(in, 9 * (inOffs), arIn); arO ## 9 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 9); \ - LOAD_REG(in, 10 * (inOffs), arIn); arO ## 10 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 10); - -#define SUMM_12REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); \ - LOAD_REG(in, 7 * (inOffs), arIn); arO ## 7 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 7); \ - LOAD_REG(in, 8 * (inOffs), arIn); arO ## 8 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 8); \ - LOAD_REG(in, 9 * (inOffs), arIn); arO ## 9 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 9); \ - LOAD_REG(in, 10 * (inOffs), arIn); arO ## 10 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 10); \ - LOAD_REG(in, 11 * (inOffs), arIn); arO ## 11 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 11); - -#define SUMM_13REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); \ - LOAD_REG(in, 7 * (inOffs), arIn); arO ## 7 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 7); \ - LOAD_REG(in, 8 * (inOffs), arIn); arO ## 8 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 8); \ - LOAD_REG(in, 9 * (inOffs), arIn); arO ## 9 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 9); \ - LOAD_REG(in, 10 * (inOffs), arIn); arO ## 10 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 10); \ - LOAD_REG(in, 11 * (inOffs), arIn); arO ## 11 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 11); \ - LOAD_REG(in, 12 * (inOffs), arIn); arO ## 12 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 12); - -#define SUMM_14REG(in, inOffs, arIn, arW, arO) \ - LOAD_REG(in, 0 * (inOffs), arIn); arO ## 0 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 0); \ - LOAD_REG(in, 1 * (inOffs), arIn); arO ## 1 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 1); \ - LOAD_REG(in, 2 * (inOffs), arIn); arO ## 2 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 2); \ - LOAD_REG(in, 3 * (inOffs), arIn); arO ## 3 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 3); \ - LOAD_REG(in, 4 * (inOffs), arIn); arO ## 4 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 4); \ - LOAD_REG(in, 5 * (inOffs), arIn); arO ## 5 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 5); \ - LOAD_REG(in, 6 * (inOffs), arIn); arO ## 6 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 6); \ - LOAD_REG(in, 7 * (inOffs), arIn); arO ## 7 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 7); \ - LOAD_REG(in, 8 * (inOffs), arIn); arO ## 8 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 8); \ - LOAD_REG(in, 9 * (inOffs), arIn); arO ## 9 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 9); \ - LOAD_REG(in, 10 * (inOffs), arIn); arO ## 10 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 10); \ - LOAD_REG(in, 11 * (inOffs), arIn); arO ## 11 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 11); \ - LOAD_REG(in, 12 * (inOffs), arIn); arO ## 12 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 12); \ - LOAD_REG(in, 13 * (inOffs), arIn); arO ## 13 = _mm256_add_ps(_mm256_mul_ps(arIn, arW), arO ## 13); - - -#define SET_OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO); - -#define SET_1OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); - -#define SET_2OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); - -#define SET_3OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); - -#define SET_4OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); - -#define SET_5OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); - -#define SET_6OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); - -#define SET_7OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); - -#define SET_8OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); \ - pOut[7] = bias + horSummReg<__m256>(arO ## 7); - -#define SET_9OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); \ - pOut[7] = bias + horSummReg<__m256>(arO ## 7); \ - pOut[8] = bias + horSummReg<__m256>(arO ## 8); - -#define SET_10OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); \ - pOut[7] = bias + horSummReg<__m256>(arO ## 7); \ - pOut[8] = bias + horSummReg<__m256>(arO ## 8); \ - pOut[9] = bias + horSummReg<__m256>(arO ## 9); - -#define SET_11OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); \ - pOut[7] = bias + horSummReg<__m256>(arO ## 7); \ - pOut[8] = bias + horSummReg<__m256>(arO ## 8); \ - pOut[9] = bias + horSummReg<__m256>(arO ## 9); \ - pOut[10] = bias + horSummReg<__m256>(arO ## 10); - -#define SET_12OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); \ - pOut[7] = bias + horSummReg<__m256>(arO ## 7); \ - pOut[8] = bias + horSummReg<__m256>(arO ## 8); \ - pOut[9] = bias + horSummReg<__m256>(arO ## 9); \ - pOut[10] = bias + horSummReg<__m256>(arO ## 10); \ - pOut[11] = bias + horSummReg<__m256>(arO ## 11); - -#define SET_13OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); \ - pOut[7] = bias + horSummReg<__m256>(arO ## 7); \ - pOut[8] = bias + horSummReg<__m256>(arO ## 8); \ - pOut[9] = bias + horSummReg<__m256>(arO ## 9); \ - pOut[10] = bias + horSummReg<__m256>(arO ## 10); \ - pOut[11] = bias + horSummReg<__m256>(arO ## 11); \ - pOut[12] = bias + horSummReg<__m256>(arO ## 12); - -#define SET_14OUT(arO, pOut) \ - pOut[0] = bias + horSummReg<__m256>(arO ## 0); \ - pOut[1] = bias + horSummReg<__m256>(arO ## 1); \ - pOut[2] = bias + horSummReg<__m256>(arO ## 2); \ - pOut[3] = bias + horSummReg<__m256>(arO ## 3); \ - pOut[4] = bias + horSummReg<__m256>(arO ## 4); \ - pOut[5] = bias + horSummReg<__m256>(arO ## 5); \ - pOut[6] = bias + horSummReg<__m256>(arO ## 6); \ - pOut[7] = bias + horSummReg<__m256>(arO ## 7); \ - pOut[8] = bias + horSummReg<__m256>(arO ## 8); \ - pOut[9] = bias + horSummReg<__m256>(arO ## 9); \ - pOut[10] = bias + horSummReg<__m256>(arO ## 10); \ - pOut[11] = bias + horSummReg<__m256>(arO ## 11); \ - pOut[12] = bias + horSummReg<__m256>(arO ## 12); \ - pOut[13] = bias + horSummReg<__m256>(arO ## 13); - - - struct buf_t{ - - SN_Base::snFloat* p = nullptr; - SN_Base::snSize sz; - - buf_t(const SN_Base::snSize& size = SN_Base::snSize(0,0,0,0,0), size_t add = 0) : sz(size) { - - if (size.size() > 0) - p = (SN_Base::snFloat*)_mm_malloc((size.size() + add) * sizeof(SN_Base::snFloat), 64); - } - - ~buf_t() { - if (p) _mm_free(p); - } - - }; - - - template - float horSummReg(T a); - - template<> - inline float horSummReg<__m256>(__m256 a){ - - __m128 hi = _mm256_extractf128_ps(a, 1); - __m128 lo = _mm256_extractf128_ps(a, 0); - lo = _mm_add_ps(hi, lo); - hi = _mm_movehl_ps(hi, lo); - lo = _mm_add_ps(hi, lo); - hi = _mm_shuffle_ps(lo, lo, 1); - lo = _mm_add_ss(hi, lo); - return _mm_cvtss_f32(lo); - }; - - template - void reorderInputCHW2HCW(const SN_Base::snSize& insz, const SN_Base::snFloat* input, const SN_Base::snSize& outsz, SN_Base::snFloat* output){ - - SN_Base::snFloat* pOut = output; - - if (M == 1){ - - for (size_t i = 0; i < (outsz.w * outsz.h) / RO; ++i){ - - for (size_t j = 0; j < insz.d / 8; ++j){ - - for (size_t k = 0; k < RO; ++k){ - - for (size_t t = 0; t < 8; ++t){ - - size_t ci = (i * RO + k) % outsz.w, cr = (i * RO + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * (t + j * 8); - - *pOut = *pIn; - - pOut += M * M; - } - } - } - - if (insz.d % 8){ - for (size_t k = 0; k < RO; ++k){ - - for (size_t t = 0; t < insz.d % 8; ++t){ - - size_t ci = (i * RO + k) % outsz.w, cr = (i * RO + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * (t + (insz.d / 8) * 8); - - *pOut = *pIn; - - pOut += M * M; - } - } - } - } - - const size_t rmr = (outsz.w * outsz.h) % RO; - if (rmr){ - - const size_t offs = ((outsz.w * outsz.h) / RO) * RO; - - for (size_t j = 0; j < insz.d / 8; ++j){ - - for (size_t k = 0; k < rmr; ++k){ - - for (size_t t = 0; t < 8; ++t){ - - size_t ci = (offs + k) % outsz.w, cr = (offs + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * (t + j * 8); - - *pOut = *pIn; - - pOut += M * M; - } - } - } - - if (insz.d % 8){ - for (size_t k = 0; k < rmr; ++k){ - - for (size_t t = 0; t < insz.d % 8; ++t){ - - size_t ci = (offs + k) % outsz.w, cr = (offs + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * (t + (insz.d / 8) * 8); - - *pOut = *pIn; - - pOut += M * M; - } - } - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 3) && (D == 1)){ - - for (size_t i = 0; i < (outsz.w * outsz.h) / RO; ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - for (size_t k = 0; k < RO; ++k){ - - size_t ci = (i * RO + k) % outsz.w, cr = (i * RO + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - - pOut += M * M; - } - } - } - - const size_t rmr = (outsz.w * outsz.h) % RO; - if (rmr){ - - const size_t offs = ((outsz.w * outsz.h) / RO) * RO; - - for (size_t j = 0; j < insz.d; ++j){ - - for (size_t k = 0; k < rmr; ++k){ - - size_t ci = (offs + k) % outsz.w, cr = (offs + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - - pOut += M * M; - } - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 5) && (D == 1)){ - - for (size_t i = 0; i < (outsz.w * outsz.h) / RO; ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - for (size_t k = 0; k < RO; ++k){ - - size_t ci = (i * RO + k) % outsz.w, cr = (i * RO + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - - pOut += M * M; - } - } - } - - const size_t rmr = (outsz.w * outsz.h) % RO; - if (rmr){ - - const size_t offs = ((outsz.w * outsz.h) / RO) * RO; - - for (size_t j = 0; j < insz.d; ++j){ - - for (size_t k = 0; k < rmr; ++k){ - - size_t ci = (offs + k) % outsz.w, cr = (offs + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - - pOut += M * M; - } - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 7) && (D == 1)){ - - for (size_t i = 0; i < (outsz.w * outsz.h) / RO; ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - for (size_t k = 0; k < RO; ++k){ - - size_t ci = (i * RO + k) % outsz.w, cr = (i * RO + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); - _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); - - pOut += M * M; - } - } - } - - const size_t rmr = (outsz.w * outsz.h) % RO; - if (rmr){ - - const size_t offs = ((outsz.w * outsz.h) / RO) * RO; - - for (size_t j = 0; j < insz.d; ++j){ - - for (size_t k = 0; k < rmr; ++k){ - - size_t ci = (offs + k) % outsz.w, cr = (offs + k) / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); - _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); - - pOut += M * M; - } - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 9) && (D == 1)){ // RO == 1 - - for (size_t i = 0; i < outsz.h; ++i){ - - for (size_t j = 0; j < outsz.w; ++j){ - - const SN_Base::snFloat* pIn = input + S * insz.w * i + S * j; - - for (size_t k = 0; k < insz.d; ++k){ - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); - _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); - _mm256_storeu_ps(pOut + 7 * M, _mm256_loadu_ps(pIn + 7 * insz.w)); - _mm256_storeu_ps(pOut + 8 * M, _mm256_loadu_ps(pIn + 8 * insz.w)); - - pIn += insz.w * insz.h; - pOut += M * M; - } - } - } - } - - /////////////////////////////////////////////////// - // TODO - - /*else if ((M == 3) && (D == 2)){ - - } - - else if ((M == 5) && (D == 2)){ - - } - - else if ((M == 7) && (D == 2)){ - - } - - else if ((M == 9) && (D == 2)){ - - - }*/ - }; - - template - void getPeakOutput(size_t W, const SN_Base::snFloat* pIn, const SN_Base::snFloat* pW, SN_Base::snFloat* pOut){ - - for (size_t i = 0; i < W; ++i){ - - for (size_t j = 0; j < RO; ++j) - pOut[j] += pIn[M * M * j + (M * M - 1) + i * RO * M * M] * pW[M * M * i + (M * M - 1)]; - } - } -}; \ No newline at end of file diff --git a/src/snSIMD/src/convolutionBWD.cpp b/src/snSIMD/src/convolutionBWD.cpp index 885d442..fa8cf26 100644 --- a/src/snSIMD/src/convolutionBWD.cpp +++ b/src/snSIMD/src/convolutionBWD.cpp @@ -27,7 +27,6 @@ #include #include #include "snBase/snBase.h" -#include "base.h" using namespace std; using namespace SN_Base; diff --git a/src/snSIMD/src/convolutionFWD.cpp b/src/snSIMD/src/convolutionFWD.cpp index 7981d51..4f5f875 100644 --- a/src/snSIMD/src/convolutionFWD.cpp +++ b/src/snSIMD/src/convolutionFWD.cpp @@ -1,4 +1,4 @@ -// +// // SkyNet Project // Copyright (C) 2018 by Contributors // @@ -23,376 +23,188 @@ // THE SOFTWARE. // - #include #include -#include + #include "snBase/snBase.h" -#include "base.h" +#include "Lib/OpenBLAS/cblas.h" using namespace std; using namespace SN_Base; namespace SN_SIMD{ - - - template - void kernel(unsigned int core, const snFloat* weight, snFloat bias, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ - - const snFloat* pIn = input, - * pW = weight; - - snFloat* pOut = output; + + template + float horSummReg(T a); + + template<> + inline float horSummReg<__m256>(__m256 a){ + + __m128 hi = _mm256_extractf128_ps(a, 1); + __m128 lo = _mm256_extractf128_ps(a, 0); + lo = _mm_add_ps(hi, lo); + hi = _mm_movehl_ps(hi, lo); + lo = _mm_add_ps(hi, lo); + hi = _mm_shuffle_ps(lo, lo, 1); + lo = _mm_add_ss(hi, lo); + return _mm_cvtss_f32(lo); + }; - if (M == 1){ // RO == 14 + template + void reorderInputCHW2HCW(size_t S, const SN_Base::snSize& insz, const SN_Base::snFloat* input, const SN_Base::snSize& outsz, SN_Base::snFloat* output){ - CREATE_14REG(arO); - CREATE_REG(arW); - CREATE_REG(arIn); - - for (size_t i = 0; i < insz.d / 8; ++i){ + SN_Base::snFloat* pOut = output; - LOAD_REG(pW, 0, arW); + if (M == 1){ - SUMM_14REG(pIn, 8, arIn, arW, arO); + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - pIn += 8 * RO; - pW += 8; - } + for (size_t j = 0; j < insz.d; ++j){ - SET_14OUT(arO, pOut); + size_t ci = i % outsz.w, cr = i / outsz.w; - if (insz.d % 8){ - for (size_t i = 0; i < RO; ++i){ + *pOut = *(input + S * insz.w * cr + S * ci + insz.w * insz.h * j); - for (size_t j = 0; j < insz.d % 8; ++j) - pOut[i] += pIn[j + i * (insz.d % 8)] * pW[j]; + ++pOut; } } } - else if (M == 3){ // RO == 14 + ///////////////////////////////////////////// - CREATE_14REG(arO); - CREATE_REG(arW); - CREATE_REG(arIn); - - for (size_t i = 0; i < insz.d; ++i){ + else if (M == 3){ - LOAD_REG(pW, 0, arW); + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - SUMM_14REG(pIn, M * M, arIn, arW, arO); + for (size_t j = 0; j < insz.d; ++j){ - pIn += M * M * RO; - pW += M * M; - } - - SET_14OUT(arO, pOut); - - getPeakOutput(insz.d, input, weight, pOut); - } + size_t ci = i % outsz.w, cr = i / outsz.w; - else if (M == 5){ // RO == 10 + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - CREATE_10REG(arO); - CREATE_3REG(arW); - CREATE_3REG(arIn); - - for (size_t i = 0; i < insz.d; ++i){ + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - LOAD_3REG(pW, 8, arW); + pOut += M * M; + } + } + } - SUMM_3x3REG_10OUT(pIn, M * M, arIn, arW, arO); + ///////////////////////////////////////////// - pIn += M * M * RO; - pW += M * M; - } - - SET_10OUT(arO, pOut); + else if (M == 5){ - getPeakOutput(insz.d, input, weight, pOut); - } + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - else if (M == 7){ // RO == 4 + for (size_t j = 0; j < insz.d; ++j){ - CREATE_4REG(arO); - CREATE_6REG(arW); - CREATE_6REG(arIn); - - for (size_t i = 0; i < insz.d; ++i){ + size_t ci = i % outsz.w, cr = i / outsz.w; - LOAD_6REG(pW, 8, arW); + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - SUMM_6x6REG_4OUT(pIn, M * M, arIn, arW, arO); + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); + _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); + _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - pIn += M * M * RO; - pW += M * M; + pOut += M * M; + + } } - - SET_4OUT(arO, pOut); - - getPeakOutput(insz.d, input, weight, pOut); } - else if (M == 9){ // RO == 1 + ///////////////////////////////////////////// - CREATE_REG(arO); - CREATE_5REG(arW); - CREATE_5REG(arIn); - - for (size_t i = 0; i < insz.d; ++i){ + else if (M == 7){ - LOAD_5REG(pW, 8, arW); - LOAD_5REG(pIn, 8, arIn); + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - SUMM_5x5REG_1OUT(arIn, arW, arO); + for (size_t j = 0; j < insz.d; ++j){ - LOAD_5REG(pW + 8 * 5, 8, arW); - LOAD_5REG(pIn + 8 * 5, 8, arIn); + size_t ci = i % outsz.w, cr = i / outsz.w; - SUMM_5x5REG_1OUT(arIn, arW, arO); + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - pIn += M * M * RO; - pW += M * M; - } - - SET_OUT(arO, pOut); - - getPeakOutput(insz.d, input, weight, pOut); - } - } + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); + _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); + _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); + _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); + _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); - template - void kernelPeak(size_t peak, const snFloat* weight, snFloat bias, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ - - const snFloat* pIn = input, - * pW = weight; - - snFloat* pOut = output; - - if (M == 1){ // RO == 14 - - CREATE_13REG(arO); - CREATE_REG(arW); - CREATE_REG(arIn); - - for (size_t i = 0; i < (insz.d / 8); ++i){ - - LOAD_REG(pW, 0, arW); - - switch (peak){ - case 1: { SUMM_1REG(pIn, 0, arIn, arW, arO); } break; - case 2: { SUMM_2REG(pIn, 8, arIn, arW, arO); } break; - case 3: { SUMM_3REG(pIn, 8, arIn, arW, arO); } break; - case 4: { SUMM_4REG(pIn, 8, arIn, arW, arO); } break; - case 5: { SUMM_5REG(pIn, 8, arIn, arW, arO); } break; - case 6: { SUMM_6REG(pIn, 8, arIn, arW, arO); } break; - case 7: { SUMM_7REG(pIn, 8, arIn, arW, arO); } break; - case 8: { SUMM_8REG(pIn, 8, arIn, arW, arO); } break; - case 9: { SUMM_9REG(pIn, 8, arIn, arW, arO); } break; - case 10: { SUMM_10REG(pIn, 8, arIn, arW, arO); } break; - case 11: { SUMM_11REG(pIn, 8, arIn, arW, arO); } break; - case 12: { SUMM_12REG(pIn, 8, arIn, arW, arO); } break; - case 13: { SUMM_13REG(pIn, 8, arIn, arW, arO); } break; - default: break; + pOut += M * M; } - - pIn += 8 * peak; - pW += 8; - } - - switch (peak){ - case 1: SET_1OUT(arO, pOut); break; - case 2: SET_2OUT(arO, pOut); break; - case 3: SET_3OUT(arO, pOut); break; - case 4: SET_4OUT(arO, pOut); break; - case 5: SET_5OUT(arO, pOut); break; - case 6: SET_6OUT(arO, pOut); break; - case 7: SET_7OUT(arO, pOut); break; - case 8: SET_8OUT(arO, pOut); break; - case 9: SET_9OUT(arO, pOut); break; - case 10: SET_10OUT(arO, pOut); break; - case 11: SET_11OUT(arO, pOut); break; - case 12: SET_12OUT(arO, pOut); break; - case 13: SET_13OUT(arO, pOut); break; - default: break; - } - - for (size_t i = 0; i < peak; ++i){ - - for (size_t j = 0; j < insz.d % 8; ++j) - - pOut[i] += pIn[j + i * (insz.d % 8)] * pW[j]; } } - else if (M == 3){ // RO == 14 - - CREATE_13REG(arO); - CREATE_REG(arW); - CREATE_REG(arIn); - - for (size_t i = 0; i < insz.d; ++i){ - - LOAD_REG(pW, 0, arW); - - switch (peak){ - case 1: { SUMM_1REG(pIn, 0, arIn, arW, arO); } break; - case 2: { SUMM_2REG(pIn, M * M, arIn, arW, arO); } break; - case 3: { SUMM_3REG(pIn, M * M, arIn, arW, arO); } break; - case 4: { SUMM_4REG(pIn, M * M, arIn, arW, arO); } break; - case 5: { SUMM_5REG(pIn, M * M, arIn, arW, arO); } break; - case 6: { SUMM_6REG(pIn, M * M, arIn, arW, arO); } break; - case 7: { SUMM_7REG(pIn, M * M, arIn, arW, arO); } break; - case 8: { SUMM_8REG(pIn, M * M, arIn, arW, arO); } break; - case 9: { SUMM_9REG(pIn, M * M, arIn, arW, arO); } break; - case 10: { SUMM_10REG(pIn, M * M, arIn, arW, arO); } break; - case 11: { SUMM_11REG(pIn, M * M, arIn, arW, arO); } break; - case 12: { SUMM_12REG(pIn, M * M, arIn, arW, arO); } break; - case 13: { SUMM_13REG(pIn, M * M, arIn, arW, arO); } break; - default: break; - } + ///////////////////////////////////////////// - pIn += M * M * peak; - pW += M * M; - } - - switch (peak){ - case 1:{ SET_1OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 2:{ SET_2OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 3:{ SET_3OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 4:{ SET_4OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 5:{ SET_5OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 6:{ SET_6OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 7:{ SET_7OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 8:{ SET_8OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 9:{ SET_9OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 10:{ SET_10OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 11:{ SET_11OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 12:{ SET_12OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 13:{ SET_13OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - default: break; - } - } + else if (M == 9){ - else if (M == 5){ // RO == 10 - - CREATE_10REG(arO); - CREATE_3REG(arW); - CREATE_3REG(arIn); - - for (size_t i = 0; i < insz.d; ++i){ - - LOAD_3REG(pW, 8, arW); - - switch (peak){ - case 1: SUMM_3x3REG_1OUT(arIn, arW, arO0); break; - case 2: SUMM_3x3REG_2OUT(pIn, M * M, arIn, arW, arO); break; - case 3: SUMM_3x3REG_3OUT(pIn, M * M, arIn, arW, arO); break; - case 4: SUMM_3x3REG_4OUT(pIn, M * M, arIn, arW, arO); break; - case 5: SUMM_3x3REG_5OUT(pIn, M * M, arIn, arW, arO); break; - case 6: SUMM_3x3REG_6OUT(pIn, M * M, arIn, arW, arO); break; - case 7: SUMM_3x3REG_7OUT(pIn, M * M, arIn, arW, arO); break; - case 8: SUMM_3x3REG_8OUT(pIn, M * M, arIn, arW, arO); break; - case 9: SUMM_3x3REG_9OUT(pIn, M * M, arIn, arW, arO); break; - default: break; - } + for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - pIn += M * M * peak; - pW += M * M; - } - - switch (peak){ - case 1:{ SET_1OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 2:{ SET_2OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 3:{ SET_3OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 4:{ SET_4OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 5:{ SET_5OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 6:{ SET_6OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 7:{ SET_7OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 8:{ SET_8OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 9:{ SET_9OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - default: break; - } - } + for (size_t j = 0; j < insz.d; ++j){ - else if (M == 7){ // RO == 4 + size_t ci = i % outsz.w, cr = i / outsz.w; - CREATE_4REG(arO); - CREATE_6REG(arW); - CREATE_6REG(arIn); - - for (size_t i = 0; i < insz.d; ++i){ + const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - LOAD_6REG(pW, 8, arW); + _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); + _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); + _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); + _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); + _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); + _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); + _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); + _mm256_storeu_ps(pOut + 7 * M, _mm256_loadu_ps(pIn + 7 * insz.w)); + _mm256_storeu_ps(pOut + 8 * M, _mm256_loadu_ps(pIn + 8 * insz.w)); - switch (peak){ - case 1: SUMM_6x6REG_1OUT(arIn, arW, arO0); break; - case 2: SUMM_6x6REG_2OUT(pIn, M * M, arIn, arW, arO); break; - case 3: SUMM_6x6REG_3OUT(pIn, M * M, arIn, arW, arO); break; - default: break; + pIn += insz.w * insz.h; + pOut += M * M; } - - pIn += M * M * peak; - pW += M * M; - } - - switch (peak){ - case 1:{ SET_1OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 2:{ SET_2OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - case 3:{ SET_3OUT(arO, pOut); getPeakOutput(insz.d, input, weight, pOut); } break; - default: break; } } - } - - template - void convolutionFWD(const snFloat* weight, - const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ - - /// Reorder input - buf_t inHCWBuff(snSize(M * M * insz.d, outsz.w, outsz.h), 8); - - reorderInputCHW2HCW(insz, input, outsz, inHCWBuff.p); + }; + - - /////////////////////////////////// + template + void convolutionFWD(size_t S, const snFloat* weight, + const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, snFloat* buff){ + /// Reorder input + reorderInputCHW2HCW(S, insz, input, outsz, buff); + const size_t wStepByD = M * M, wStepByK = wStepByD * insz.d, - wStepByN = wStepByK * outsz.d, - peak = (outsz.w * outsz.h) % RO; - - auto core = std::thread::hardware_concurrency(); - if (core == 0) core = 4; - -#pragma omp parallel for num_threads(core) - for (int od = 0; od < int(outsz.d); ++od){ - - const snFloat bias = *(weight + wStepByN + od); - - for (size_t oi = 0; oi < (outsz.w * outsz.h) / RO; ++oi){ - - const snFloat* pW = weight + wStepByK * od, - * pIn = inHCWBuff.p + (oi * RO) * M * M * insz.d; - - snFloat* pOut = output + (oi * RO) + od * (outsz.w * outsz.h); - - kernel(core, pW, bias, insz, pIn, outsz, pOut); - } - - if (peak){ - - const size_t offs = ((outsz.w * outsz.h) / RO) * RO; - - const snFloat* pW = weight + wStepByK * od, - * pIn = inHCWBuff.p + offs * M * M * insz.d; - - snFloat* pOut = output + offs + od * (outsz.w * outsz.h); - - kernelPeak(peak, pW, bias, insz, pIn, outsz, pOut); - } - } + wStepByN = wStepByK * outsz.d; + + cblas_sgemm(CBLAS_ORDER::CblasRowMajor, + CBLAS_TRANSPOSE::CblasNoTrans, + CBLAS_TRANSPOSE::CblasTrans, + blasint(outsz.d), // W, rows + blasint(outsz.w * outsz.h), // In, cols + blasint(wStepByK), // W, cols, In, rows + 1.0F, // α + weight, // W + blasint(wStepByK), // W, step to next W + buff, // In + blasint(wStepByK), // In, step to next In (In21 - In11) + 0.0, // β + output, // Out + blasint(outsz.w * outsz.h)); // Out, step to next Out (Y21 - Y11) + + // +bias on all out layers + weight += wStepByN; + for (size_t i = 0; i < outsz.d; ++i){ + + snFloat* pOut = output + (outsz.w * outsz.h) * i; + float bias = *(weight + i); + for (size_t j = 0; j < (outsz.w * outsz.h); ++j) + pOut[j] += bias; + } } template @@ -428,7 +240,7 @@ namespace SN_SIMD{ memset(outBuff, 0, kernel * sizeof(snFloat)); const snFloat* pIn = input + inStepByN * n, - * pW = weight; + *pW = weight; // on all in layers for (size_t d = 0; d < insz.d; ++d){ @@ -487,16 +299,15 @@ namespace SN_SIMD{ free(share); } - + bool convolutionFWD(size_t M, size_t S, size_t D, const snFloat* weight, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output){ + const snSize& outsz, snFloat* output, snFloat* buff){ + + if ((insz.n > 1) || (D > 1)){ - - if ((insz.n > 1) || (S > 2) || (D > 1)){ - #define dfwd(MS) \ if (M == MS){ \ defaultFWD(S, D, weight, insz, input, outsz, output); return true; }; @@ -512,27 +323,19 @@ namespace SN_SIMD{ #undef dfwd - -#define cfwd(MS, SS, DS, RO) \ - if ((M == MS) && (S == SS) && (D == DS)){ \ - convolutionFWD(weight, insz, input, outsz, output); return true; }; - - cfwd(1, 1, 1, 14) - cfwd(3, 1, 1, 14) - cfwd(5, 1, 1, 10) - cfwd(7, 1, 1, 4) - cfwd(9, 1, 1, 1) - - cfwd(1, 2, 1, 14) - cfwd(3, 2, 1, 14) - cfwd(5, 2, 1, 10) - cfwd(7, 2, 1, 4) - cfwd(9, 2, 1, 1) - - return false; - +#define cfwd(MS) \ + if (M == MS){ \ + convolutionFWD(S, weight, insz, input, outsz, output, buff); return true; }; + + cfwd(1) + cfwd(3) + cfwd(5) + cfwd(7) + cfwd(9) + + return false; + #undef cfwd }; -}; - +} \ No newline at end of file diff --git a/src/snSIMD/src/convolutionFWDTest.cpp b/src/snSIMD/src/convolutionFWDTest.cpp deleted file mode 100644 index e3688da..0000000 --- a/src/snSIMD/src/convolutionFWDTest.cpp +++ /dev/null @@ -1,399 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#include "snBase/snBase.h" -#include "Lib/OpenBLAS/cblas.h" - -using namespace std; -using namespace SN_Base; - -namespace SN_SIMD{ - - struct buf_t{ - - SN_Base::snFloat* p = nullptr; - SN_Base::snSize sz; - - buf_t(const SN_Base::snSize& size = SN_Base::snSize(0, 0, 0, 0, 0), size_t add = 0) : sz(size) { - - if (size.size() > 0) - //p = (SN_Base::snFloat*)_mm_malloc((size.size() + add) * sizeof(SN_Base::snFloat), 64); - p = (SN_Base::snFloat*)malloc((size.size() + add) * sizeof(SN_Base::snFloat)); - } - - ~buf_t() { - if (p) free(p); - } - - }; - - template - void reorderInputCHW2HCW(const SN_Base::snSize& insz, const SN_Base::snFloat* input, const SN_Base::snSize& outsz, SN_Base::snFloat* output){ - - SN_Base::snFloat* pOut = output; - - if (M == 1){ - - for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - size_t ci = i % outsz.w, cr = i / outsz.w; - - *pOut = *(input + S * insz.w * cr + S * ci + insz.w * insz.h * j); - - ++pOut; - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 3) && (D == 1)){ - - for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - size_t ci = i % outsz.w, cr = i / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - - pOut += M * M; - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 5) && (D == 1)){ - - for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - size_t ci = i % outsz.w, cr = i / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - - pOut += M * M; - - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 7) && (D == 1)){ - - for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - size_t ci = i % outsz.w, cr = i / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); - _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); - - pOut += M * M; - } - } - } - - ///////////////////////////////////////////// - - else if ((M == 9) && (D == 1)){ - - for (size_t i = 0; i < (outsz.w * outsz.h); ++i){ - - for (size_t j = 0; j < insz.d; ++j){ - - size_t ci = i % outsz.w, cr = i / outsz.w; - - const SN_Base::snFloat* pIn = input + S * insz.w * cr + S * ci + insz.w * insz.h * j; - - _mm256_storeu_ps(pOut, _mm256_loadu_ps(pIn)); - _mm256_storeu_ps(pOut + M, _mm256_loadu_ps(pIn + insz.w)); - _mm256_storeu_ps(pOut + 2 * M, _mm256_loadu_ps(pIn + 2 * insz.w)); - _mm256_storeu_ps(pOut + 3 * M, _mm256_loadu_ps(pIn + 3 * insz.w)); - _mm256_storeu_ps(pOut + 4 * M, _mm256_loadu_ps(pIn + 4 * insz.w)); - _mm256_storeu_ps(pOut + 5 * M, _mm256_loadu_ps(pIn + 5 * insz.w)); - _mm256_storeu_ps(pOut + 6 * M, _mm256_loadu_ps(pIn + 6 * insz.w)); - _mm256_storeu_ps(pOut + 7 * M, _mm256_loadu_ps(pIn + 7 * insz.w)); - _mm256_storeu_ps(pOut + 8 * M, _mm256_loadu_ps(pIn + 8 * insz.w)); - - pIn += insz.w * insz.h; - pOut += M * M; - } - } - } - - /////////////////////////////////////////////////// - // TODO - - /*else if ((M == 3) && (D == 2)){ - - } - - else if ((M == 5) && (D == 2)){ - - } - - else if ((M == 7) && (D == 2)){ - - } - - else if ((M == 9) && (D == 2)){ - - - }*/ - }; - - template - void convolutionFWD(const snFloat* weight, - const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ - - /// Reorder input - buf_t inHCWBuff(snSize(M * M * insz.d, outsz.w, outsz.h), 8); - - reorderInputCHW2HCW(insz, input, outsz, inHCWBuff.p); - - - /////////////////////////////////// - - const size_t wStepByD = M * M, - wStepByK = wStepByD * insz.d, - wStepByN = wStepByK * outsz.d, - imSz = inHCWBuff.sz.size(); - - // Out = α * In * W + βC - // In - data input matrix - values from the previous layer - // W - weights matrix - // Out - data output matrix - cblas_sgemm(CBLAS_ORDER::CblasRowMajor, - CBLAS_TRANSPOSE::CblasNoTrans, - CBLAS_TRANSPOSE::CblasNoTrans, - blasint(outsz.d), // In, rows - blasint(outsz.w * outsz.h), // W, cols - blasint(M * M * insz.d), // In, cols, W, rows - 1.0F, // α - inHCWBuff.p, // In - blasint(M * M * insz.d), // In, step to next In - weight, // W - blasint(outsz.w * outsz.h), // W, step to next W (W21 - W11) - 0.0, // β - output, // Out - blasint(outsz.w * outsz.h)); // Out, step to next Out (Y21 - Y11) - - /*auto core = std::thread::hardware_concurrency(); - if (core == 0) core = 4; - -#pragma omp parallel for num_threads(core) - for (int od = 0; od < int(outsz.d); ++od){ - - const snFloat bias = *(weight + wStepByN + od); - - for (size_t oi = 0; oi < (outsz.w * outsz.h) / RO; ++oi){ - - const snFloat* pW = weight + wStepByK * od, - * pIn = inHCWBuff.p + (oi * RO) * M * M * insz.d; - - snFloat* pOut = output + (oi * RO) + od * (outsz.w * outsz.h); - - kernel(core, pW, bias, insz, pIn, outsz, pOut); - } - - if (peak){ - - const size_t offs = ((outsz.w * outsz.h) / RO) * RO; - - const snFloat* pW = weight + wStepByK * od, - * pIn = inHCWBuff.p + offs * M * M * insz.d; - - snFloat* pOut = output + offs + od * (outsz.w * outsz.h); - - kernelPeak(peak, pW, bias, insz, pIn, outsz, pOut); - } - } */ - } - -// template -// void defaultFWD(size_t S, size_t D, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output){ -// -// const size_t wStepByD = M * M, // step weight by input -// kernel = outsz.d, -// wStepByK = wStepByD * insz.d, // step weight by output -// wStepByN = wStepByK * kernel, // step weight by batch -// inStepByD = insz.w * insz.h, // step in by input -// inStepByN = inStepByD * insz.d, // step in by batch -// outStepByD = outsz.w * outsz.h, // step out by input -// outStepByN = outStepByD * outsz.d; // step out by batch -// -// size_t shareStepByN = kernel; // for local mem -// snFloat* share = (snFloat*)calloc(shareStepByN * insz.n, sizeof(snFloat)); -// -// auto core = std::thread::hardware_concurrency(); -// if (core == 0) core = 4; -// -// // by batch -//#pragma omp parallel for num_threads(core) -// for (int n = 0; n < int(insz.n); ++n){ -// -// snFloat* outBuff = share + shareStepByN * n; -// snFloat In[wStepByD], W[wStepByD]; -// -// for (size_t p = 0; p < outStepByD; ++p){ -// -// size_t ox = p % outsz.w, oy = p / outsz.w, -// posW = ox * S, posH = oy * S; -// -// memset(outBuff, 0, kernel * sizeof(snFloat)); -// -// const snFloat* pIn = input + inStepByN * n, -// * pW = weight; -// -// // on all in layers -// for (size_t d = 0; d < insz.d; ++d){ -// -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % M, cy = c / M; -// In[c] = *(pIn + (cx + posW + cx * (D - 1)) + (cy + posH + cy * (D - 1)) * insz.w); -// } -// -// pW = weight + wStepByD * d; -// -// // on all out layers -// for (size_t k = 0; k < kernel; ++k){ -// -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % M, cy = c / M; -// W[c] = *(pW + cx + cy * M); -// } -// -// __m256 arOut = _mm256_setzero_ps(); -// -// for (int z = 0; z < wStepByD / 8; ++z){ -// -// __m256 arIn = _mm256_loadu_ps(In + z * 8); -// -// __m256 arW = _mm256_loadu_ps(W + z * 8); -// -// arOut = _mm256_add_ps(arOut, _mm256_mul_ps(arIn, arW)); -// } -// -// outBuff[k] += horSummReg<__m256>(arOut); -// -// outBuff[k] += In[wStepByD - 1] * W[wStepByD - 1]; -// -// pW += wStepByK; -// } -// -// pIn += inStepByD; -// -// } -// -// snFloat* pOut = output + ox + oy * outsz.w + n * outStepByN; -// pW = weight + wStepByN; -// -// // on all out layers -// for (size_t k = 0; k < kernel; ++k){ -// -// *pOut = outBuff[k] + *(pW + k); // + bias -// -// pOut += outStepByD; -// } -// } -// } -// -// free(share); -// } -// - - bool convolutionFWD(size_t M, size_t S, size_t D, - const snFloat* weight, - const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output){ - - - /*if ((insz.n > 1) || (S > 2) || (D > 1)){ - -#define dfwd(MS) \ - if (M == MS){ \ - defaultFWD(S, D, weight, insz, input, outsz, output); return true; }; - - dfwd(1) - dfwd(3) - dfwd(5) - dfwd(7) - dfwd(9) - - return false; - } -#undef dfwd*/ - - - -#define cfwd(MS, SS, DS) \ - if ((M == MS) && (S == SS) && (D == DS)){ \ - convolutionFWD(weight, insz, input, outsz, output); return true; }; - - cfwd(1, 1, 1) - cfwd(3, 1, 1) - cfwd(5, 1, 1) - cfwd(7, 1, 1) - cfwd(9, 1, 1) - - cfwd(1, 2, 1) - cfwd(3, 2, 1) - cfwd(5, 2, 1) - cfwd(7, 2, 1) - cfwd(9, 2, 1) - - return false; - -#undef cfwd - - }; -}; - From cbc733e508ea511e2eaadd9c4355de031e50b561 Mon Sep 17 00:00:00 2001 From: Tyill Date: Thu, 18 Jul 2019 17:11:26 +0500 Subject: [PATCH 08/35] -develop --- mswin/VS12/snOperatorCUDA.vcxproj | 25 +- mswin/VS12/snOperatorCUDA.vcxproj.filters | 25 +- .../src/Operator/convolution.cpp | 3 +- src/snOperatorCUDA/src/CUDA/activation.cu | 102 +++ src/snOperatorCUDA/src/CUDA/batchNorm.cu | 144 ++++ src/snOperatorCUDA/src/CUDA/convolution.cu | 373 ++++++++ .../src/CUDA/convolutionCUDA.cu | 811 ------------------ src/snOperatorCUDA/src/CUDA/deconvolution.cu | 365 ++++++++ .../src/CUDA/deconvolutionCUDA.cu | 795 ----------------- src/snOperatorCUDA/src/CUDA/dropOut.cu | 132 +-- ...ullyConnectedCUDA.cu => fullyConnected.cu} | 7 +- src/snOperatorCUDA/src/CUDA/pooling.cu | 211 +++++ src/snOperatorCUDA/src/CUDA/poolingCUDA.cu | 510 ----------- .../src/Operator/convolution.cpp | 36 +- src/snOperatorCUDA/src/Operator/convolution.h | 11 +- src/snOperatorCUDA/src/activeFunctions.cpp | 117 --- src/snOperatorCUDA/src/activeFunctions.h | 6 +- .../src/{mathFunctions.h => dropOut.h} | 21 +- 18 files changed, 1255 insertions(+), 2439 deletions(-) create mode 100644 src/snOperatorCUDA/src/CUDA/activation.cu create mode 100644 src/snOperatorCUDA/src/CUDA/batchNorm.cu create mode 100644 src/snOperatorCUDA/src/CUDA/convolution.cu delete mode 100644 src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu create mode 100644 src/snOperatorCUDA/src/CUDA/deconvolution.cu delete mode 100644 src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu rename src/snOperatorCUDA/src/CUDA/{fullyConnectedCUDA.cu => fullyConnected.cu} (98%) create mode 100644 src/snOperatorCUDA/src/CUDA/pooling.cu delete mode 100644 src/snOperatorCUDA/src/CUDA/poolingCUDA.cu delete mode 100644 src/snOperatorCUDA/src/activeFunctions.cpp rename src/snOperatorCUDA/src/{mathFunctions.h => dropOut.h} (64%) diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index 37129cf..eaa4bcf 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -52,9 +52,9 @@ Unicode - - - + + + @@ -354,7 +354,7 @@ - + @@ -378,7 +378,6 @@ - @@ -402,17 +401,19 @@ - - - - + + + + + + - - - + + + \ No newline at end of file diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index 22a985f..415a7e5 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -24,9 +24,6 @@ Header Files - - Header Files - Header Files @@ -93,6 +90,9 @@ Operator + + Header Files + @@ -143,9 +143,6 @@ Operator - - Source Files - Source Files @@ -163,19 +160,25 @@ - + CUDA - + CUDA - + CUDA - + CUDA - + + CUDA + + + CUDA + + CUDA diff --git a/src/snOperatorCPU/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp index 6fc4eb1..cff3c60 100644 --- a/src/snOperatorCPU/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -284,7 +284,8 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o activeFuncBackward(osz, out, activeType_); // update grad - for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; + for (size_t i = 0; i < osz; ++i) + gradIn[i] *= out[i]; } /// batchNorm diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu new file mode 100644 index 0000000..5110746 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -0,0 +1,102 @@ + +#include +#include + +#include +#include + +#include "../stdafx.h" +#include "../structurs.h" +#include "../activeFunctions.h" + +using namespace SN_Base; + + +void activationForward(size_t sz, SN_Base::snFloat* data, activeType active, uint32_t deviceId){ + + cudaSetDevice(deviceId); + + switch (active){ + case activeType::sigmoid: fv_sigmoid(data, sz); break; + case activeType::relu: fv_relu(data, sz); break; + case activeType::leakyRelu: fv_leakyRelu(data, sz); break; + case activeType::elu: fv_elu(data, sz); break; + default: break; + } +} + +void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType active, uint32_t deviceId){ + + cudaSetDevice(deviceId); + + switch (active){ + case activeType::sigmoid: df_sigmoid(data, sz); break; + case activeType::relu: df_relu(data, sz); break; + case activeType::leakyRelu: df_leakyRelu(data, sz); break; + case activeType::elu: df_elu(data, sz); break; + default: break; + } +} + +void fv_sigmoid(snFloat* ioVal, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = 1.F / (1.F + std::exp(-ioVal[i])); + + ioVal[i] = (ioVal[i] < 0.99999F) ? ioVal[i] : 0.99999F; + ioVal[i] = (ioVal[i] > 0.00001F) ? ioVal[i] : 0.00001F; + } +} +void df_sigmoid(snFloat* ioSigm, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioSigm[i] = ioSigm[i] * (1.F - ioSigm[i]); + } +} + +void fv_relu(snFloat* ioVal, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : 0; + } +}; +void df_relu(snFloat* ioRelu, size_t sz){ + + for (size_t i = 0; i < sz; ++i){ + + ioRelu[i] = ioRelu[i] >= 0 ? 1.F : 0.F; + } +}; + +void fv_leakyRelu(snFloat* ioVal, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * ioVal[i]; + } +} +void df_leakyRelu(snFloat* ioRelu, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioRelu[i] = ioRelu[i] >= 0 ? 1 : minv; + } +} + +void fv_elu(snFloat* ioVal, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * (exp(ioVal[i]) - 1.F); + } +} +void df_elu(snFloat* ioElu, size_t sz, snFloat minv){ + + for (size_t i = 0; i < sz; ++i){ + + ioElu[i] = ioElu[i] >= 0 ? 1 : ioElu[i] + minv; + } +} diff --git a/src/snOperatorCUDA/src/CUDA/batchNorm.cu b/src/snOperatorCUDA/src/CUDA/batchNorm.cu new file mode 100644 index 0000000..6cf9b8c --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/batchNorm.cu @@ -0,0 +1,144 @@ + +#include +#include + +#include "../stdafx.h" +#include "../structurs.h" + +using namespace SN_Base; + + +void batchNormInit(SN_Base::snFloat* inout, const SN_Base::snSize& iosz, void** pGpuPrm){ + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + + if (!gpuPrm){ + + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuAssert(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + if (gpuPrm->inszMem != iosz){ + + cudnnActivationMode_t actMode; + + switch (atype){ + case activeType::sigmoid: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_SIGMOID; break; + case activeType::relu: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_RELU; break; + case activeType::leakyRelu: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_CLIPPED_RELU; break; + case activeType::elu: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_ELU; break; + default: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_RELU; break; + } + + // activ_desc + cudnnActivationDescriptor_t activ_desc = nullptr; + cuAssert(cudnnCreateActivationDescriptor(&activ_desc)); + cuAssert(cudnnSetActivationDescriptor(activ_desc, actMode, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, 0.0)); + if (!isFirst) + cuAssert(cudnnDestroyActivationDescriptor(gpuPrm->activ_desc)); + gpuPrm->activ_desc = activ_desc; + + // input + cudnnTensorDescriptor_t x_desc = nullptr; + cuAssert(cudnnCreateTensorDescriptor(&x_desc)); + cuAssert(cudnnSetTensor4dDescriptor(x_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); + if (!isFirst) + cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->x_desc)); + gpuPrm->x_desc = x_desc; + + // dinput + cudnnTensorDescriptor_t dx_desc = nullptr; + cuAssert(cudnnCreateTensorDescriptor(&dx_desc)); + cuAssert(cudnnSetTensor4dDescriptor(dx_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); + if (!isFirst) + cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->dx_desc)); + gpuPrm->dx_desc = dx_desc; + + // output + cudnnTensorDescriptor_t y_desc; + cuAssert(cudnnCreateTensorDescriptor(&y_desc)); + cuAssert(cudnnSetTensor4dDescriptor(y_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); + if (!isFirst) + cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->y_desc)); + gpuPrm->y_desc = y_desc; + + // doutput + cudnnTensorDescriptor_t dy_desc; + cuAssert(cudnnCreateTensorDescriptor(&dy_desc)); + cuAssert(cudnnSetTensor4dDescriptor(dy_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); + if (!isFirst) + cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->dy_desc)); + gpuPrm->dy_desc = dy_desc; + + gpuPrm->inszMem = iosz; + } +} + +void batchNormFree(uint32_t deviceId, void* pGpuPrm){ + + cudaSetDevice(deviceId); + + gpuParams* gpuPrm = (gpuParams*)pGpuPrm; + + if (!gpuPrm) return; + + cuAssert(cudnnDestroy(gpuPrm->cudnn)); + cuAssert(cudnnDestroyActivationDescriptor(gpuPrm->activ_desc)); + cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->x_desc)); + cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->y_desc)); +} + +void batchNormForward(activeType atype, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId, void** pGpuPrm){ + + cudaSetDevice(deviceId); + + activationInit(atype, inout, iosz, pGpuPrm); + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + + cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, iosz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + + float alpha = 1.0, beta = 0.0; + + cuAssert(cudnnActivationForward(gpuPrm->cudnn, + gpuPrm->activ_desc, + &alpha, + gpuPrm->x_desc, + gpuPrm->in_mem, + &beta, + gpuPrm->y_desc, + inout)); +} + +void batchNormBackward(SN_Base::snFloat* inprev, SN_Base::snFloat* outprev, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId, void* pGpuPrm){ + + cudaSetDevice(deviceId); + + gpuParams* gpuPrm = (gpuParams*)pGpuPrm; + + cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, iosz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + + float alpha = 1.0, beta = 0.0; + + cuAssert(cudnnActivationBackward(gpuPrm->cudnn, + gpuPrm->activ_desc, + &alpha, + gpuPrm->y_desc, + outprev, + gpuPrm->dy_desc, + gpuPrm->in_mem, + gpuPrm->x_desc, + inprev, + &beta, + gpuPrm->dx_desc, + inout)); +} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/convolution.cu b/src/snOperatorCUDA/src/CUDA/convolution.cu new file mode 100644 index 0000000..9aa9324 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/convolution.cu @@ -0,0 +1,373 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include +#include +#include "../stdafx.h" +#include "snOperatorCUDA/src/Operator/convolution.h" + +using namespace std; +using namespace SN_Base; + + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnConvolutionDescriptor_t conv_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + cudnnFilterDescriptor_t w_desc = 0; + cudnnFilterDescriptor_t dw_desc = 0; + cudnnTensorDescriptor_t bias_desc = 0; + + cudnnConvolutionFwdAlgo_t algoFwd; + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + + size_t wsFwdSz = 0; + size_t wsBwdDataSz = 0; + size_t wsBwdWSz = 0; + size_t inszMem = 0; + + void* d_wsFwd = 0; + void* d_wsBwdData = 0; + void* d_wsBwdW = 0; +}; + +void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, + const convParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // w + cudnnFilterDescriptor_t w_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); + gpuPrm->w_desc = w_desc; + + // conv + cudnnConvolutionDescriptor_t conv_desc = nullptr; + cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); + cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), + CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); + if (!isFirst) + cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); + gpuPrm->conv_desc = conv_desc; + + // output + int out_n = 0, out_c = 0, out_h = 0, out_w = 0; + cuCHECK(cudnnGetConvolution2dForwardOutputDim( + conv_desc, in_desc, w_desc, + &out_n, &out_c, &out_h, &out_w)); + + if (outsz != snSize(out_w, out_h, out_c, out_n)){ + ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); + return; + } + + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + // bias + cudnnTensorDescriptor_t bias_desc; + cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + 1, out_c, 1, 1)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); + gpuPrm->bias_desc = bias_desc; + + // algorithm + cudnnConvolutionFwdAlgo_t algoFwd; + cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, + CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); + gpuPrm->algoFwd = algoFwd; + + // workspace + size_t wsFwdSz = 0; + cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, algoFwd, &wsFwdSz)); + gpuPrm->wsFwdSz = wsFwdSz; + + + size_t wsBwdDataSz = 0, wsBwdWSz = 0; + if (isLern){ + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // dw + cudnnFilterDescriptor_t dw_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); + gpuPrm->dw_desc = dw_desc; + + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + + // algorithm + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, + CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); + gpuPrm->algoBwdData = algoBwdData; + + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, + CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); + gpuPrm->algoBwdW = algoBwdW; + + // workspace + cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, algoBwdData, &wsBwdDataSz)); + gpuPrm->wsBwdDataSz = wsBwdDataSz; + + cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); + gpuPrm->wsBwdWSz = wsBwdWSz; + } + + if (isFirst){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + } + else if (gpuPrm->inszMem < insz.size()){ + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + + if (isLern){ + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + gpuPrm->inszMem = insz.size(); + } +} + +void Convolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); + + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); + } +} + +__global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ + + size_t osz = outsz.w * outsz.h; + + snFloat* pOut = output + osz * outsz.d * blockIdx.x; + unsigned int d = threadIdx.x; + while (d < outsz.d){ + + snFloat b = bias[d]; + for (size_t j = 0; j < osz; ++j) + pOut[j] += b; + + pOut += osz * blockDim.x; + + d += blockDim.x; + } +} + +void Convolution::forwardCUDA(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + size_t isz = insz.size(), osz = outsz.size(); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + input, + gpuPrm->w_desc, + weight, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->out_desc, + output)); + + // +bias + cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); +} + +void Convolution::backwardCUDA_GW(const convParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + weight, + gpuPrm->grin_desc, + gradIn, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + + cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + input, + gpuPrm->grin_desc, + gradIn, + gpuPrm->conv_desc, + gpuPrm->algoBwdW, + gpuPrm->d_wsBwdW, + gpuPrm->wsBwdWSz, + &beta, + gpuPrm->dw_desc, + dWeightOut)); + + /*cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + &beta, + gpuPrm->bias_desc, + gpuPrm->d_bias));*/ + +} + +void Convolution::backwardCUDA_G(const convParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + size_t isz = insz.size(), osz = outsz.size(); + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + weight, + gpuPrm->grin_desc, + gradIn, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + +} diff --git a/src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu b/src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu deleted file mode 100644 index b270a21..0000000 --- a/src/snOperatorCUDA/src/CUDA/convolutionCUDA.cu +++ /dev/null @@ -1,811 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#include -#include -#include "../stdafx.h" -#include "snOperatorCUDA/src/Operator/convolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnConvolutionDescriptor_t conv_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - cudnnFilterDescriptor_t w_desc = 0; - cudnnFilterDescriptor_t dw_desc = 0; - cudnnTensorDescriptor_t bias_desc = 0; - - cudnnConvolutionFwdAlgo_t algoFwd; - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - - size_t wsFwdSz = 0; - size_t wsBwdDataSz = 0; - size_t wsBwdWSz = 0; - size_t inszMem = 0; - - void* d_wsFwd = 0; - void* d_wsBwdData = 0; - void* d_wsBwdW = 0; -}; - -void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const convParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // w - cudnnFilterDescriptor_t w_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); - gpuPrm->w_desc = w_desc; - - // conv - cudnnConvolutionDescriptor_t conv_desc = nullptr; - cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); - cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), - CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); - if (!isFirst) - cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); - gpuPrm->conv_desc = conv_desc; - - // output - int out_n = 0, out_c = 0, out_h = 0, out_w = 0; - cuCHECK(cudnnGetConvolution2dForwardOutputDim( - conv_desc, in_desc, w_desc, - &out_n, &out_c, &out_h, &out_w)); - - if (outsz != snSize(out_w, out_h, out_c, out_n)){ - ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); - return; - } - - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - // bias - cudnnTensorDescriptor_t bias_desc; - cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - 1, out_c, 1, 1)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); - gpuPrm->bias_desc = bias_desc; - - // algorithm - cudnnConvolutionFwdAlgo_t algoFwd; - cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); - gpuPrm->algoFwd = algoFwd; - - // workspace - size_t wsFwdSz = 0; - cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, in_desc, w_desc, conv_desc, out_desc, algoFwd, &wsFwdSz)); - gpuPrm->wsFwdSz = wsFwdSz; - - - size_t wsBwdDataSz = 0, wsBwdWSz = 0; - if (isLern){ - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // dw - cudnnFilterDescriptor_t dw_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(outsz.d), int(insz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); - gpuPrm->dw_desc = dw_desc; - - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - - // algorithm - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); - gpuPrm->algoBwdData = algoBwdData; - - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); - gpuPrm->algoBwdW = algoBwdW; - - // workspace - cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, grin_desc, conv_desc, grout_desc, algoBwdData, &wsBwdDataSz)); - gpuPrm->wsBwdDataSz = wsBwdDataSz; - - cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, in_desc, grin_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); - gpuPrm->wsBwdWSz = wsBwdWSz; - } - - if (isFirst){ - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - } - else if (gpuPrm->inszMem < insz.size()){ - - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - - if (isLern){ - - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Convolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); - - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); - - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); - } -} - -__global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ - - size_t osz = outsz.w * outsz.h; - - snFloat* pOut = output + osz * outsz.d * blockIdx.x; - unsigned int d = threadIdx.x; - while (d < outsz.d){ - - snFloat b = bias[d]; - for (size_t j = 0; j < osz; ++j) - pOut[j] += b; - - pOut += osz * blockDim.x; - - d += blockDim.x; - } -} - -void Convolution::forwardCUDA(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - size_t isz = insz.size(), osz = outsz.size(); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - input, - gpuPrm->w_desc, - weight, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->out_desc, - output)); - - // +bias - cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); -} - -void Convolution::backwardCUDA_GW(const convParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - weight, - gpuPrm->grin_desc, - gradIn, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->grout_desc, - gradOut)); - - cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - input, - gpuPrm->grin_desc, - gradIn, - gpuPrm->conv_desc, - gpuPrm->algoBwdW, - gpuPrm->d_wsBwdW, - gpuPrm->wsBwdWSz, - &beta, - gpuPrm->dw_desc, - dWeightOut)); - - /*cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - gradIn, - &beta, - gpuPrm->bias_desc, - gpuPrm->d_bias));*/ - -} - -void Convolution::backwardCUDA_G(const convParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - weight, - gpuPrm->grin_desc, - gradIn, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->grout_desc, - gradOut)); - -} - - -// CUDA implementation. Deprecated -// -//#include -//#include "../stdafx.h" -//#include "snOperatorCUDA/src/Operator/convolution.h" -// -//using namespace std; -//using namespace SN_Base; -// -//#ifndef cuCHECK -//#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -//#endif -// -// -//struct gpuParams{ -// -// cudaDeviceProp* cu_deviceProps = 0; -// -// snFloat* d_in = 0; -// snFloat* d_out = 0; -// snFloat* d_w = 0; -// snFloat* d_dw = 0; -// snFloat* d_grout = 0; -// -// size_t inszMem = 0; -//}; -// -//void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, -// const convParams& prms, void** pGpuPrm){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; -// if (!gpuPrm){ -// cudaDeviceProp* cu_deviceProps = new cudaDeviceProp(); -// -// cudaGetDeviceProperties(cu_deviceProps, 0); -// if (cu_deviceProps->major < 2){ -// ERROR_MESS("%s requires SM >= 2.0"); -// delete cu_deviceProps; -// return; -// } -// gpuPrm = new gpuParams(); -// memset(gpuPrm, 0, sizeof(gpuParams)); -// *pGpuPrm = gpuPrm; -// -// gpuPrm->cu_deviceProps = cu_deviceProps; -// -// if (!gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); -// -// if (isLern){ -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); -// } -// } -// } -// else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ -// -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; -// -// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); -// -// if (isLern){ -// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; -// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; -// -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d * outsz.n * sizeof(snFloat))); -// } -// -// gpuPrm->inszMem = insz.size(); -// } -//} -// -//void Convolution::freeParamCUDA(void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// -// if (!gpuPrm) return; -// -// delete gpuPrm->cu_deviceProps; -// -// cuCHECK(cudaFree(gpuPrm->d_in)); -// cuCHECK(cudaFree(gpuPrm->d_w)); -// cuCHECK(cudaFree(gpuPrm->d_out)); -// -// if (gpuPrm->d_grout){ // isLern -// cuCHECK(cudaFree(gpuPrm->d_grout)); -// cuCHECK(cudaFree(gpuPrm->d_dw)); -// } -//} -// -//__global__ void cuConvFwd(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, -// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ -// -// size_t wStepByD = fWidth * fHeight, // step weight by input -// wStepByK = wStepByD * insz.d, // step weight by output -// outStepByD = outsz.w * outsz.h, // step out by input -// outStepByN = outStepByD * outsz.d, // step out by batch -// inStepByD = insz.w * insz.h, // step in by input -// inStepByN = inStepByD * insz.d; // step in by batch -// -// // gridDim.x - number of out layers -// // gridDim.y - batch size -// -// weight += blockIdx.x * wStepByK; -// output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -// input += blockIdx.y * inStepByN; -// -// -// unsigned int oz = 0; -// while (oz < insz.d){ -// -// unsigned int oy = threadIdx.y; -// while (oy < outsz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < outsz.w){ -// -// if (oz == 0) -// output[ox + oy * outsz.w] = weight[(outsz.d - blockIdx.x) * wStepByK + blockIdx.x]; // bias -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel -// snFloat csum = 0; -//#pragma unroll -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % fWidth, cy = c / fWidth; -// -// csum += input[(cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w] * weight[cx + cy * fWidth]; -// } -// output[ox + oy * outsz.w] += csum; -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// weight += wStepByD; -// input += inStepByD; -// ++oz; -// } -//} -// -//void Convolution::forwardCUDA(const convParams& prms, -// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// size_t isz = insz.size(), osz = outsz.size(), -// wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); -// } -// -// // input -// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // weight -// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(outsz.d), int(outsz.n)); -// -// cuConvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, -// prms.fHeight, -// prms.dilate, -// prms.stride, -// gpuPrm->d_w, -// insz, -// gpuPrm->d_in, -// outsz, -// gpuPrm->d_out); -// -// // result -// cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; -// } -//} -// -//__global__ void cuConvBwd_GW(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, -// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ -// -// size_t wStepByD = fWidth * fHeight, // step weight by input -// wStepByK = wStepByD * insz.d, // step weight by output -// wStepByN = wStepByK * outsz.d + outsz.d, // step weight by batch -// outStepByD = outsz.w * outsz.h, // step out by input -// outStepByN = outStepByD * outsz.d, // step out by batch -// inStepByD = insz.w * insz.h, // step in by input -// inStepByN = inStepByD * insz.d; // step in by batch -// -// // gridDim.x - number of input layers -// // gridDim.y - batch size -// -// input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// weight += blockIdx.x * wStepByD; -// dWeightOut += blockIdx.x * wStepByD + blockIdx.y * wStepByN; -// gradIn += blockIdx.y * outStepByN; -// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// -// -// unsigned int oz = 0; -// while (oz < outsz.d){ -// -// unsigned int oy = threadIdx.y; -// while (oy < outsz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < outsz.w){ -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel conv -// snFloat grIn = gradIn[ox + oy * outsz.w]; -//#pragma unroll -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % fWidth, cy = c / fWidth, -// si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, -// sw = cx + cy * fWidth; -// -// gradOut[si] += grIn * weight[sw]; -// -// dWeightOut[sw] += grIn * input[si]; -// } -// if (blockIdx.x == 0) -// dWeightOut[wStepByK * (outsz.d - oz) + oz] += grIn; // bias -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// weight += wStepByK; -// dWeightOut += wStepByK; -// gradIn += outStepByD; -// ++oz; -// } -//} -// -//// weighted averaging over batch -//__global__ void cuConvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ -// -// size_t wStepByD = fWidth * fHeight, // step weight by input -// wStepByK = wStepByD * insz.d, // step weight by output -// wStepByN = wStepByK * kernel + kernel; // step weight by batch -// -// unsigned int ox = threadIdx.x; -// while (ox < wStepByN){ -// -// snFloat csum = weight[ox]; -// for (size_t i = 1; i < insz.n; ++i) -// csum += weight[ox + wStepByN * i]; -// -// weight[ox] = csum / insz.n; -// -// ox += blockDim.x; -// } -//} -// -//void Convolution::backwardCUDA_GW(const convParams& prms, -// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// size_t isz = insz.size(), osz = outsz.size(), -// wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; -// snFloat* d_grin = gpuPrm->d_out; -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); -// } -// -// // input -// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // weight -// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // out -// cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); -// cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(insz.d), int(outsz.n)); -// -// cuConvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, -// prms.fHeight, -// prms.dilate, -// prms.stride, -// gpuPrm->d_w, -// insz, -// gpuPrm->d_in, -// outsz, -// d_grin, -// gpuPrm->d_grout, -// gpuPrm->d_dw); -// -// cuConvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); -// -// // result -// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; -// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; -// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; -// } -//} -// -//__global__ void cuConvBwd_G(size_t fWidth, size_t fHeight, size_t dilate, size_t stride, -// snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ -// -// size_t wStepByD = fWidth * fHeight, // step weight by input -// wStepByK = wStepByD * insz.d, // step weight by output -// outStepByD = outsz.w * outsz.h, // step out by input -// outStepByN = outStepByD * outsz.d, // step out by batch -// inStepByD = insz.w * insz.h, // step in by input -// inStepByN = inStepByD * insz.d; // step in by batch -// -// -// // gridDim.x - number of input layers -// // gridDim.y - batch size -// -// weight += blockIdx.x * wStepByD; -// gradIn += blockIdx.y * outStepByN; -// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// -// unsigned int oz = 0; -// while (oz < outsz.d){ -// -// unsigned int oy = threadIdx.y; -// while (oy < outsz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < outsz.w){ -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel conv -// snFloat grIn = gradIn[ox + oy * outsz.w]; -//#pragma unroll -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % fWidth, cy = c / fWidth, -// si = (cx + posW + cx * (dilate - 1)) + (cy + posH + cy * (dilate - 1)) * insz.w, -// sw = cx + cy * fWidth; -// -// gradOut[si] += grIn * weight[sw]; -// } -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// weight += wStepByK; -// gradIn += outStepByD; -// ++oz; -// } -//} -// -//void Convolution::backwardCUDA_G(const convParams& prms, -// const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// size_t isz = insz.size(), osz = outsz.size(), -// wsz = (prms.fWidth * prms.fHeight * insz.d + 1) * outsz.d; -// snFloat* d_grin = gpuPrm->d_out; -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); -// } -// -// // input -// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // weight -// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(insz.d), int(outsz.n)); -// -// // out -// cuCHECK(cudaMemset(gpuPrm->d_grout, 0, isz * sizeof(snFloat))); -// -// cuConvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, -// prms.fHeight, -// prms.dilate, -// prms.stride, -// gpuPrm->d_w, -// insz, outsz, -// d_grin, -// gpuPrm->d_grout); -// -// // result -// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; -// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; -// } -//} diff --git a/src/snOperatorCUDA/src/CUDA/deconvolution.cu b/src/snOperatorCUDA/src/CUDA/deconvolution.cu new file mode 100644 index 0000000..5db3b2b --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/deconvolution.cu @@ -0,0 +1,365 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include +#include +#include "../stdafx.h" +#include "snOperatorCUDA/src/Operator/deconvolution.h" + +using namespace std; +using namespace SN_Base; + + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnConvolutionDescriptor_t conv_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + cudnnFilterDescriptor_t w_desc = 0; + cudnnFilterDescriptor_t dw_desc = 0; + cudnnTensorDescriptor_t bias_desc = 0; + + cudnnConvolutionFwdAlgo_t algoFwd; + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + + size_t wsFwdSz = 0; + size_t wsBwdDataSz = 0; + size_t wsBwdWSz = 0; + size_t inszMem = 0; + + void* d_wsFwd = 0; + void* d_wsBwdData = 0; + void* d_wsBwdW = 0; + +}; + +void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, + const deconvParams& prms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // w + cudnnFilterDescriptor_t w_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); + gpuPrm->w_desc = w_desc; + + // conv + cudnnConvolutionDescriptor_t conv_desc = nullptr; + cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); + cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), 1, 1, + CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); + if (!isFirst) + cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); + gpuPrm->conv_desc = conv_desc; + + // output + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + // algorithm + cudnnConvolutionBwdDataAlgo_t algoBwdData; + cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, + CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); + gpuPrm->algoBwdData = algoBwdData; + + // workspace + size_t wsBwdDataSz = 0; + cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, algoBwdData, &wsBwdDataSz)); + gpuPrm->wsBwdDataSz = wsBwdDataSz; + + + size_t wsFwdSz = 0, wsBwdWSz = 0; + if (isLern){ + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // dw + cudnnFilterDescriptor_t dw_desc = nullptr; + cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); + cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, + int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); + if (!isFirst) + cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); + gpuPrm->dw_desc = dw_desc; + + // bias + cudnnTensorDescriptor_t bias_desc; + cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + 1, int(insz.d), 1, 1)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); + gpuPrm->bias_desc = bias_desc; + + // algorithm + cudnnConvolutionFwdAlgo_t algoFwd; + cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, + CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); + gpuPrm->algoFwd = algoFwd; + + cudnnConvolutionBwdFilterAlgo_t algoBwdW; + cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, + CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); + gpuPrm->algoBwdW = algoBwdW; + + // workspace + cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, algoFwd, &wsFwdSz)); + gpuPrm->wsFwdSz = wsFwdSz; + + cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); + gpuPrm->wsBwdWSz = wsBwdWSz; + } + + if (isFirst){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + + if (isLern){ + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + } + else if (gpuPrm->inszMem < insz.size()){ + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); + + if (isLern){ + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; + + cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); + cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); + } + gpuPrm->inszMem = insz.size(); + } +} + +void Deconvolution::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); + + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); + + cuCHECK(cudaFree(gpuPrm->d_wsFwd)); + cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); + } +} + +void Deconvolution::forwardCUDA(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, + &alpha, + gpuPrm->w_desc, + weight, + gpuPrm->in_desc, + input, + gpuPrm->conv_desc, + gpuPrm->algoBwdData, + gpuPrm->d_wsBwdData, + gpuPrm->wsBwdDataSz, + &beta, + gpuPrm->out_desc, + output)); + +} + +__global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ + + size_t isz = insz.w * insz.h; + + snFloat* pGrOut = grout + isz * insz.d * blockIdx.x; + unsigned int d = threadIdx.x; + while (d < insz.d){ + + snFloat b = bias[d]; + for (size_t j = 0; j < isz; ++j) + pGrOut[j] += b; + + pGrOut += isz * blockDim.x; + + d += blockDim.x; + } +} + +void Deconvolution::backwardCUDA_GW(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + gpuPrm->w_desc, + weight, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + + cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + gpuPrm->in_desc, + input, + gpuPrm->conv_desc, + gpuPrm->algoBwdW, + gpuPrm->d_wsBwdW, + gpuPrm->wsBwdWSz, + &beta, + gpuPrm->dw_desc, + dWeightOut)); + + /* cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + &alpha, + gpuPrm->in_desc, + input, + &beta, + gpuPrm->bias_desc, + gpuPrm->d_bias));*/ + + // +bias + cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + +} + +void Deconvolution::backwardCUDA_G(const deconvParams& prms, + const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, + &alpha, + gpuPrm->grin_desc, + gradIn, + gpuPrm->w_desc, + weight, + gpuPrm->conv_desc, + gpuPrm->algoFwd, + gpuPrm->d_wsFwd, + gpuPrm->wsFwdSz, + &beta, + gpuPrm->grout_desc, + gradOut)); + + // +bias + cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + +} diff --git a/src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu b/src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu deleted file mode 100644 index 71056b1..0000000 --- a/src/snOperatorCUDA/src/CUDA/deconvolutionCUDA.cu +++ /dev/null @@ -1,795 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#include -#include -#include "../stdafx.h" -#include "snOperatorCUDA/src/Operator/deconvolution.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnConvolutionDescriptor_t conv_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - cudnnFilterDescriptor_t w_desc = 0; - cudnnFilterDescriptor_t dw_desc = 0; - cudnnTensorDescriptor_t bias_desc = 0; - - cudnnConvolutionFwdAlgo_t algoFwd; - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - - size_t wsFwdSz = 0; - size_t wsBwdDataSz = 0; - size_t wsBwdWSz = 0; - size_t inszMem = 0; - - void* d_wsFwd = 0; - void* d_wsBwdData = 0; - void* d_wsBwdW = 0; - -}; - -void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, - const deconvParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // w - cudnnFilterDescriptor_t w_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&w_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(w_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->w_desc)); - gpuPrm->w_desc = w_desc; - - // conv - cudnnConvolutionDescriptor_t conv_desc = nullptr; - cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); - cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), 1, 1, - CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); - if (!isFirst) - cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); - gpuPrm->conv_desc = conv_desc; - - // output - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - // algorithm - cudnnConvolutionBwdDataAlgo_t algoBwdData; - cuCHECK(cudnnGetConvolutionBackwardDataAlgorithm(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoBwdData)); - gpuPrm->algoBwdData = algoBwdData; - - // workspace - size_t wsBwdDataSz = 0; - cuCHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(gpuPrm->cudnn, w_desc, in_desc, conv_desc, out_desc, algoBwdData, &wsBwdDataSz)); - gpuPrm->wsBwdDataSz = wsBwdDataSz; - - - size_t wsFwdSz = 0, wsBwdWSz = 0; - if (isLern){ - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // dw - cudnnFilterDescriptor_t dw_desc = nullptr; - cuCHECK(cudnnCreateFilterDescriptor(&dw_desc)); - cuCHECK(cudnnSetFilter4dDescriptor(dw_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, - int(insz.d), int(outsz.d), int(prms.fHeight), int(prms.fWidth))); - if (!isFirst) - cuCHECK(cudnnDestroyFilterDescriptor((cudnnFilterDescriptor_t)gpuPrm->dw_desc)); - gpuPrm->dw_desc = dw_desc; - - // bias - cudnnTensorDescriptor_t bias_desc; - cuCHECK(cudnnCreateTensorDescriptor(&bias_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - 1, int(insz.d), 1, 1)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->bias_desc)); - gpuPrm->bias_desc = bias_desc; - - // algorithm - cudnnConvolutionFwdAlgo_t algoFwd; - cuCHECK(cudnnGetConvolutionForwardAlgorithm(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algoFwd)); - gpuPrm->algoFwd = algoFwd; - - cudnnConvolutionBwdFilterAlgo_t algoBwdW; - cuCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoBwdW)); - gpuPrm->algoBwdW = algoBwdW; - - // workspace - cuCHECK(cudnnGetConvolutionForwardWorkspaceSize(gpuPrm->cudnn, grin_desc, w_desc, conv_desc, grout_desc, algoFwd, &wsFwdSz)); - gpuPrm->wsFwdSz = wsFwdSz; - - cuCHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(gpuPrm->cudnn, grin_desc, in_desc, conv_desc, dw_desc, algoBwdW, &wsBwdWSz)); - gpuPrm->wsBwdWSz = wsBwdWSz; - } - - if (isFirst){ - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - - if (isLern){ - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - } - else if (gpuPrm->inszMem < insz.size()){ - - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); gpuPrm->d_wsBwdData = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdData, wsBwdDataSz)); - - if (isLern){ - - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); gpuPrm->d_wsFwd = 0; - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); gpuPrm->d_wsBwdW = 0; - - cuCHECK(cudaMalloc(&gpuPrm->d_wsFwd, wsFwdSz)); - cuCHECK(cudaMalloc(&gpuPrm->d_wsBwdW, wsBwdWSz)); - } - gpuPrm->inszMem = insz.size(); - } -} - -void Deconvolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyConvolutionDescriptor(gpuPrm->conv_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->w_desc)); - - cuCHECK(cudaFree(gpuPrm->d_wsBwdData)); - - if (gpuPrm->grin_desc){ // isLern - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - cuCHECK(cudnnDestroyFilterDescriptor(gpuPrm->dw_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->bias_desc)); - - cuCHECK(cudaFree(gpuPrm->d_wsFwd)); - cuCHECK(cudaFree(gpuPrm->d_wsBwdW)); - } -} - -void Deconvolution::forwardCUDA(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, - &alpha, - gpuPrm->w_desc, - weight, - gpuPrm->in_desc, - input, - gpuPrm->conv_desc, - gpuPrm->algoBwdData, - gpuPrm->d_wsBwdData, - gpuPrm->wsBwdDataSz, - &beta, - gpuPrm->out_desc, - output)); - -} - -__global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ - - size_t isz = insz.w * insz.h; - - snFloat* pGrOut = grout + isz * insz.d * blockIdx.x; - unsigned int d = threadIdx.x; - while (d < insz.d){ - - snFloat b = bias[d]; - for (size_t j = 0; j < isz; ++j) - pGrOut[j] += b; - - pGrOut += isz * blockDim.x; - - d += blockDim.x; - } -} - -void Deconvolution::backwardCUDA_GW(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - gradIn, - gpuPrm->w_desc, - weight, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->grout_desc, - gradOut)); - - cuCHECK(cudnnConvolutionBackwardFilter(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - gradIn, - gpuPrm->in_desc, - input, - gpuPrm->conv_desc, - gpuPrm->algoBwdW, - gpuPrm->d_wsBwdW, - gpuPrm->wsBwdWSz, - &beta, - gpuPrm->dw_desc, - dWeightOut)); - - /* cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, - &alpha, - gpuPrm->in_desc, - input, - &beta, - gpuPrm->bias_desc, - gpuPrm->d_bias));*/ - - // +bias - cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); - -} - -void Deconvolution::backwardCUDA_G(const deconvParams& prms, - const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, - &alpha, - gpuPrm->grin_desc, - gradIn, - gpuPrm->w_desc, - weight, - gpuPrm->conv_desc, - gpuPrm->algoFwd, - gpuPrm->d_wsFwd, - gpuPrm->wsFwdSz, - &beta, - gpuPrm->grout_desc, - gradOut)); - - // +bias - cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); - -} - - -// CUDA implementation. Deprecated -// -//#include -//#include "../stdafx.h" -//#include "snOperatorCUDA/src/Operator/deconvolution.h" -// -//using namespace std; -//using namespace SN_Base; -// -//#ifndef cuCHECK -//#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -//#endif -// -//struct gpuParams{ -// -// snFloat* d_in = 0; -// snFloat* d_w = 0; -// snFloat* d_dw = 0; -// snFloat* d_out = 0; -// snFloat* d_grout = 0; -// -// size_t inszMem = 0; -//}; -// -//void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** pGpuPrm){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; -// if (!gpuPrm){ -// -// cudaDeviceProp cu_deviceProps; -// cudaGetDeviceProperties(&cu_deviceProps, 0); -// if (cu_deviceProps.major < 3){ -// ERROR_MESS("%s requires SM >= 3.0"); -// return; -// } -// gpuPrm = new gpuParams(); -// memset(gpuPrm, 0, sizeof(gpuParams)); -// *pGpuPrm = gpuPrm; -// -// if (!gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); -// -// if (isLern){ -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); -// } -// } -// } -// else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ -// -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; -// -// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); -// -// if (isLern){ -// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; -// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; -// -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_dw, (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d * outsz.n * sizeof(snFloat))); -// } -// gpuPrm->inszMem = insz.size(); -// } -//} -// -//void Deconvolution::freeParamCUDA(void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// -// if (!gpuPrm) return; -// -// cuCHECK(cudaFree(gpuPrm->d_in)); -// cuCHECK(cudaFree(gpuPrm->d_w)); -// cuCHECK(cudaFree(gpuPrm->d_out)); -// -// if (gpuPrm->d_grout){ // isLern -// cuCHECK(cudaFree(gpuPrm->d_grout)); -// cuCHECK(cudaFree(gpuPrm->d_dw)); -// } -//} -// -//__global__ void cuDeconvFwd(size_t fWidth, size_t fHeight, size_t stride, -// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* output){ -// -// size_t wStepByD = fWidth * fHeight, -// wStepByK = wStepByD * outsz.d, -// outStepByD = outsz.w * outsz.h, -// outStepByN = outStepByD * outsz.d, -// inStepByD = insz.w * insz.h, -// inStepByN = inStepByD * insz.d; -// -// // gridDim.x - number of output layers -// // gridDim.y - batch sz -// -// weight += blockIdx.x * wStepByD; -// input += blockIdx.y * inStepByN; -// output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -// -// -// unsigned int oz = 0; -// while (oz < insz.d){ -// -// unsigned int oy = threadIdx.y; -// while (oy < insz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < insz.w){ -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel -// snFloat in = input[ox + oy * insz.w]; -//#pragma unroll -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % fWidth, cy = c / fWidth, -// si = (cx + posW) + (cy + posH) * outsz.w, -// sw = cx + cy * fWidth; -// -// output[si] += in * weight[sw]; -// } -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// weight += wStepByK; -// input += inStepByD; -// ++oz; -// } -//} -// -//void Deconvolution::forwardCUDA(const deconvParams& prms, -// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// size_t isz = insz.size(), osz = outsz.size(), -// wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); -// } -// -// // input -// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // weight -// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // out -// cuCHECK(cudaMemset(gpuPrm->d_out, 0, osz * sizeof(snFloat))); -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(outsz.d), int(outsz.n)); -// -// cuDeconvFwd <<< dimGrid, dimBlock >>>(prms.fWidth, -// prms.fHeight, -// prms.stride, -// gpuPrm->d_w, -// insz, -// gpuPrm->d_in, -// outsz, -// gpuPrm->d_out); -// -// // result -// cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; -// } -//} -// -//__global__ void cuDeconvBwd_GW(size_t fWidth, size_t fHeight, size_t stride, -// snFloat* weight, snSize insz, snFloat* input, snSize outsz, snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut){ -// -// size_t wStepByD = fWidth * fHeight, -// wStepByK = wStepByD * outsz.d, -// wStepByN = wStepByK * insz.d + insz.d, -// outStepByD = outsz.w * outsz.h, -// outStepByN = outStepByD * outsz.d, -// inStepByD = insz.w * insz.h, -// inStepByN = inStepByD * insz.d; -// -// // gridDim.x - number of input layers -// // gridDim.y - batch sz -// -// input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// weight += blockIdx.x * wStepByK; -// dWeightOut += blockIdx.x * wStepByK + blockIdx.y * wStepByN; -// gradIn += blockIdx.y * outStepByN; -// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// -// unsigned int oz = 0; -// while (oz < outsz.d){ -// -// unsigned int oy = threadIdx.y; -// while (oy < insz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < insz.w){ -// -// if (oz == 0) -// gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel -// snFloat csum = 0, cin = input[ox + oy * insz.w]; -//#pragma unroll -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % fWidth, cy = c / fWidth, -// si = (cx + posW) + (cy + posH) * outsz.w, -// sw = cx + cy * fWidth; -// -// csum += gradIn[si] * weight[sw]; -// -// dWeightOut[sw] += gradIn[si] * cin; -// } -// gradOut[ox + oy * insz.w] += csum; -// -// if (oz == 0) -// dWeightOut[wStepByK * (insz.d - blockIdx.x) + blockIdx.x] += cin; // bias -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// weight += wStepByD; -// dWeightOut += wStepByD; -// gradIn += outStepByD; -// ++oz; -// } -//} -// -//__global__ void cuDeconvWeightMean(size_t kernel, size_t fWidth, size_t fHeight, snSize insz, snFloat* weight){ -// -// size_t wStepByD = fWidth * fHeight, -// wStepByK = wStepByD * kernel, -// wStepByN = wStepByK * insz.d + insz.d; -// -// unsigned int ox = threadIdx.x; -// while (ox < wStepByN){ -// -// snFloat csum = weight[ox]; -// for (size_t i = 1; i < insz.n; ++i) -// csum += weight[ox + wStepByN * i]; -// -// weight[ox] = csum / insz.n; -// -// ox += blockDim.x; -// } -//} -// -//void Deconvolution::backwardCUDA_GW(const deconvParams& prms, -// const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// size_t isz = insz.size(), osz = outsz.size(), -// wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; -// snFloat* d_grin = gpuPrm->d_out; -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_dw, wsz * outsz.n * sizeof(snFloat))); -// } -// -// // input -// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // weight -// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // out -// cuCHECK(cudaMemset(gpuPrm->d_dw, 0, wsz * outsz.n * sizeof(snFloat))); -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(insz.d), int(outsz.n)); -// -// cuDeconvBwd_GW <<< dimGrid, dimBlock >>> (prms.fWidth, -// prms.fHeight, -// prms.stride, -// gpuPrm->d_w, -// insz, -// gpuPrm->d_in, -// outsz, d_grin, -// gpuPrm->d_grout, -// gpuPrm->d_dw); -// -// cuDeconvWeightMean <<< 1, 32 >>> (prms.kernel, prms.fWidth, prms.fHeight, insz, gpuPrm->d_dw); -// -// // result -// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// cuCHECK(cudaMemcpy(dWeightOut, gpuPrm->d_dw, wsz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; -// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; -// cuCHECK(cudaFree(gpuPrm->d_dw)); gpuPrm->d_dw = 0; -// } -//} -// -//__global__ void cuDeconvBwd_G(size_t fWidth, size_t fHeight, size_t stride, -// snFloat* weight, snSize insz, snSize outsz, snFloat* gradIn, snFloat* gradOut){ -// -// size_t wStepByD = fWidth * fHeight, -// wStepByK = wStepByD * outsz.d, -// outStepByD = outsz.w * outsz.h, -// outStepByN = outStepByD * outsz.d, -// inStepByD = insz.w * insz.h, -// inStepByN = inStepByD * insz.d; -// -// // gridDim.x - number of input layers -// // gridDim.y - batch size -// -// weight += blockIdx.x * wStepByK; -// gradIn += blockIdx.y * outStepByN; -// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// -// unsigned int oz = 0; -// while (oz < outsz.d){ -// -// unsigned int oy = threadIdx.y; -// while (oy < insz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < insz.w){ -// -// if (oz == 0) -// gradOut[ox + oy * insz.w] = weight[wStepByK * (insz.d - blockIdx.x) + blockIdx.x]; // bias -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel -// snFloat csum = 0; -//#pragma unroll -// for (size_t c = 0; c < wStepByD; ++c){ -// -// size_t cx = c % fWidth, cy = c / fWidth, -// si = (cx + posW) + (cy + posH) * outsz.w, -// sw = cx + cy * fWidth; -// -// csum += gradIn[si] * weight[sw]; -// } -// gradOut[ox + oy * insz.w] += csum; -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// weight += wStepByD; -// gradIn += outStepByD; -// ++oz; -// } -//} -// -//void Deconvolution::backwardCUDA_G(const deconvParams& prms, -// const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// size_t isz = insz.size(), osz = outsz.size(), -// wsz = (prms.fWidth * prms.fHeight * outsz.d + 1) * insz.d; -// snFloat* d_grin = gpuPrm->d_out; -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_w, wsz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_grout, isz * sizeof(snFloat))); -// } -// -// // input -// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // weight -// cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, wsz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(insz.d), int(outsz.n)); -// -// cuDeconvBwd_G <<< dimGrid, dimBlock >>> (prms.fWidth, -// prms.fHeight, -// prms.stride, -// gpuPrm->d_w, -// insz, -// outsz, -// d_grin, -// gpuPrm->d_grout); -// -// // result -// cuCHECK(cudaMemcpy(gradOut, gpuPrm->d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; -// cuCHECK(cudaFree(gpuPrm->d_w)); gpuPrm->d_w = 0; -// cuCHECK(cudaFree(gpuPrm->d_grout)); gpuPrm->d_grout = 0; -// } -//} diff --git a/src/snOperatorCUDA/src/CUDA/dropOut.cu b/src/snOperatorCUDA/src/CUDA/dropOut.cu index b556594..8a40e33 100644 --- a/src/snOperatorCUDA/src/CUDA/dropOut.cu +++ b/src/snOperatorCUDA/src/CUDA/dropOut.cu @@ -6,139 +6,17 @@ using namespace SN_Base; -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnDropoutDescriptor_t drop_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - - size_t wsSz = 0; - size_t state_sizes = 0; - SN_Base::snSize inszMem = 0; - - snFloat* in_mem = nullptr; - - void* state_memory = 0; - void* d_ws = 0; -}; - -void dropOutInit(void** pGpuPrm, SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz){ - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - - if (!gpuPrm){ - - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuAssert(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - cuAssert(cudnnDropoutGetStatesSize(gpuPrm->cudnn, &gpuPrm->state_sizes)); - cuAssert(cudaMalloc(&gpuPrm->state_memory, gpuPrm->state_sizes)); - - isFirst = true; - } - - if (gpuPrm->inszMem != outsz){ - - // drop_desc - cudnnDropoutDescriptor_t drop_desc = nullptr; - cuAssert(cudnnCreateDropoutDescriptor(&drop_desc)); - cuAssert(cudnnSetDropoutDescriptor(drop_desc, gpuPrm->cudnn, dropOut, gpuPrm->state_memory, gpuPrm->state_sizes, 1234ULL)); - if (!isFirst) - cuAssert(cudnnDestroyDropoutDescriptor(gpuPrm->drop_desc)); - gpuPrm->drop_desc = drop_desc; - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuAssert(cudnnCreateTensorDescriptor(&in_desc)); - cuAssert(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // output - cudnnTensorDescriptor_t out_desc; - cuAssert(cudnnCreateTensorDescriptor(&out_desc)); - cuAssert(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(outsz.n), int(outsz.d), int(outsz.h), int(outsz.w))); - if (!isFirst) - cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - // ws - cuAssert(cudnnDropoutGetReserveSpaceSize(in_desc, &gpuPrm->wsSz)); - cuAssert(cudaMalloc(&gpuPrm->d_ws, gpuPrm->wsSz)); - - // in mem - if (!isFirst) - cuAssert(cudaFree(gpuPrm->in_mem)); - cuAssert(cudaMalloc(&gpuPrm->in_mem, outsz.size() * sizeof(snFloat))); - - gpuPrm->inszMem = outsz; - } -} - -void dropOutFree(uint32_t deviceId, void* pGpuPrm){ - - cudaSetDevice(deviceId); - - gpuParams* gpuPrm = (gpuParams*)pGpuPrm; - - if (!gpuPrm) return; - - cuAssert(cudnnDestroy(gpuPrm->cudnn)); - cuAssert(cudnnDestroyDropoutDescriptor(gpuPrm->drop_desc)); - cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - - cuAssert(cudaFree(gpuPrm->d_ws)); - cuAssert(cudaFree(gpuPrm->state_memory)); -} - -void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm){ +void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId){ cudaSetDevice(deviceId); - dropOutInit(pGpuPrm, dropOut, inout, outsz); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - - cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, outsz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); - - cuAssert(cudnnDropoutForward(gpuPrm->cudnn, - gpuPrm->drop_desc, - gpuPrm->in_desc, - gpuPrm->in_mem, - gpuPrm->out_desc, - inout, - gpuPrm->d_ws, - gpuPrm->wsSz)); - + } -void dropOutBackward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm){ +void dropOutBackward(SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId){ cudaSetDevice(deviceId); - - dropOutInit(pGpuPrm, dropOut, inout, outsz); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - - cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, outsz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); - - cuAssert(cudnnDropoutBackward(gpuPrm->cudnn, - gpuPrm->drop_desc, - gpuPrm->in_desc, - gpuPrm->in_mem, - gpuPrm->out_desc, - inout, - gpuPrm->d_ws, - gpuPrm->wsSz)); + + } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/fullyConnectedCUDA.cu b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu similarity index 98% rename from src/snOperatorCUDA/src/CUDA/fullyConnectedCUDA.cu rename to src/snOperatorCUDA/src/CUDA/fullyConnected.cu index 5825b3e..e33594a 100644 --- a/src/snOperatorCUDA/src/CUDA/fullyConnectedCUDA.cu +++ b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu @@ -30,12 +30,7 @@ using namespace std; using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - - + struct gpuParams{ cublasHandle_t cuBLAS = 0; diff --git a/src/snOperatorCUDA/src/CUDA/pooling.cu b/src/snOperatorCUDA/src/CUDA/pooling.cu new file mode 100644 index 0000000..a42d82a --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/pooling.cu @@ -0,0 +1,211 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include +#include +#include "../stdafx.h" +#include "snOperatorCUDA/src/Operator/pooling.h" + +using namespace std; +using namespace SN_Base; + + +struct gpuParams{ + + cudnnHandle_t cudnn = 0; + cudnnPoolingDescriptor_t pool_desc = 0; + cudnnTensorDescriptor_t in_desc = 0; + cudnnTensorDescriptor_t out_desc = 0; + cudnnTensorDescriptor_t grin_desc = 0; + cudnnTensorDescriptor_t grout_desc = 0; + +}; + +void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ + + cudaSetDevice(gpuDeviceId_); + + bool isFirst = false; + + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; + if (!gpuPrm){ + + cudaDeviceProp cu_deviceProps; + cudaGetDeviceProperties(&cu_deviceProps, 0); + if (cu_deviceProps.major < 3){ + ERROR_MESS("%s requires SM >= 3.0"); + return; + } + gpuPrm = new gpuParams(); + memset(gpuPrm, 0, sizeof(gpuParams)); + *pGpuPrm = gpuPrm; + + cudnnHandle_t cudnn = nullptr; + cuCHECK(cudnnCreate(&cudnn)); + gpuPrm->cudnn = cudnn; + + isFirst = true; + } + + // input + cudnnTensorDescriptor_t in_desc = nullptr; + cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + gpuPrm->in_desc = in_desc; + + // pool + cudnnPoolingDescriptor_t pool_desc = nullptr; + cuCHECK(cudnnCreatePoolingDescriptor(&pool_desc)); + + cudnnPoolingMode_t poolT = cudnnPoolingMode_t::CUDNN_POOLING_MAX; + if (poolPrms.type == poolType::avg) + poolT = cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; + + cuCHECK(cudnnSetPooling2dDescriptor(pool_desc, poolT, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, + int(poolPrms.kernel), int(poolPrms.kernel), 0, 0, int(poolPrms.stride), int(poolPrms.stride))); + if (!isFirst) + cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); + gpuPrm->pool_desc = pool_desc; + + // output + int out_n = 0, out_c = 0, out_h = 0, out_w = 0; + cuCHECK(cudnnGetPooling2dForwardOutputDim(pool_desc, in_desc, + &out_n, &out_c, &out_h, &out_w)); + + if (outsz != snSize(out_w, out_h, out_c, out_n)){ + ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); + return; + } + + cudnnTensorDescriptor_t out_desc; + cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + gpuPrm->out_desc = out_desc; + + if (isLern){ + // grout + cudnnTensorDescriptor_t grout_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + gpuPrm->grout_desc = grout_desc; + + // grin + cudnnTensorDescriptor_t grin_desc; + cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); + cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, + out_n, out_c, out_h, out_w)); + if (!isFirst) + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + gpuPrm->grin_desc = grin_desc; + } + +} + +void Pooling::freeParamCUDA(void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + if (!gpuPrm) return; + + cuCHECK(cudnnDestroy(gpuPrm->cudnn)); + cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + +} + +__global__ void cuFiltrNegative(snSize outsz, snFloat* out){ + + out += blockIdx.x * outsz.w * outsz.h * outsz.d; + + unsigned int k = threadIdx.x; + while (k < outsz.d){ + + snFloat* pOut = out + outsz.w * outsz.h * k; + for (size_t j = 0; j < (outsz.w * outsz.h); ++j) + if (pOut[j] < 0) pOut[j] = 0.0; + + k += blockDim.x; + } +} + +void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, + const snSize& outsz, snFloat* output, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnPoolingForward(gpuPrm->cudnn, + gpuPrm->pool_desc, + &alpha, + gpuPrm->in_desc, + input, + &beta, + gpuPrm->out_desc, + output)); + + // filtrNegative + dim3 dimBlock(256); + dim3 dimGrid(int(outsz.n)); + + cuFiltrNegative << < dimGrid, dimBlock >> >(outsz, output); + +} + +void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const snFloat* output, const snFloat* gradIn, + const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrms){ + + cudaSetDevice(gpuDeviceId_); + + gpuParams* gpuPrm = (gpuParams*)gpuPrms; + + // run + snFloat alpha = 1.f, beta = 0.f; + cuCHECK(cudnnPoolingBackward(gpuPrm->cudnn, + gpuPrm->pool_desc, + &alpha, + gpuPrm->out_desc, + output, + gpuPrm->grin_desc, + gradIn, + gpuPrm->in_desc, + input, + &beta, + gpuPrm->grout_desc, + gradOut)); + +} diff --git a/src/snOperatorCUDA/src/CUDA/poolingCUDA.cu b/src/snOperatorCUDA/src/CUDA/poolingCUDA.cu deleted file mode 100644 index 7d1cefb..0000000 --- a/src/snOperatorCUDA/src/CUDA/poolingCUDA.cu +++ /dev/null @@ -1,510 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#include -#include -#include "../stdafx.h" -#include "snOperatorCUDA/src/Operator/pooling.h" - -using namespace std; -using namespace SN_Base; - -#ifndef cuCHECK -#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#endif - -struct gpuParams{ - - cudnnHandle_t cudnn = 0; - cudnnPoolingDescriptor_t pool_desc = 0; - cudnnTensorDescriptor_t in_desc = 0; - cudnnTensorDescriptor_t out_desc = 0; - cudnnTensorDescriptor_t grin_desc = 0; - cudnnTensorDescriptor_t grout_desc = 0; - -}; - -void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - if (!gpuPrm){ - - cudaDeviceProp cu_deviceProps; - cudaGetDeviceProperties(&cu_deviceProps, 0); - if (cu_deviceProps.major < 3){ - ERROR_MESS("%s requires SM >= 3.0"); - return; - } - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuCHECK(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - // input - cudnnTensorDescriptor_t in_desc = nullptr; - cuCHECK(cudnnCreateTensorDescriptor(&in_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(in_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - gpuPrm->in_desc = in_desc; - - // pool - cudnnPoolingDescriptor_t pool_desc = nullptr; - cuCHECK(cudnnCreatePoolingDescriptor(&pool_desc)); - - cudnnPoolingMode_t poolT = cudnnPoolingMode_t::CUDNN_POOLING_MAX; - if (poolPrms.type == poolType::avg) - poolT = cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; - - cuCHECK(cudnnSetPooling2dDescriptor(pool_desc, poolT, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, - int(poolPrms.kernel), int(poolPrms.kernel), 0, 0, int(poolPrms.stride), int(poolPrms.stride))); - if (!isFirst) - cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); - gpuPrm->pool_desc = pool_desc; - - // output - int out_n = 0, out_c = 0, out_h = 0, out_w = 0; - cuCHECK(cudnnGetPooling2dForwardOutputDim(pool_desc, in_desc, - &out_n, &out_c, &out_h, &out_w)); - - if (outsz != snSize(out_w, out_h, out_c, out_n)){ - ERROR_MESS("CUDA error: outsz != snSize(out_w, out_h, out_c, out_n)"); - return; - } - - cudnnTensorDescriptor_t out_desc; - cuCHECK(cudnnCreateTensorDescriptor(&out_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(out_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - gpuPrm->out_desc = out_desc; - - if (isLern){ - // grout - cudnnTensorDescriptor_t grout_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grout_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grout_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(insz.n), int(insz.d), int(insz.h), int(insz.w))); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); - gpuPrm->grout_desc = grout_desc; - - // grin - cudnnTensorDescriptor_t grin_desc; - cuCHECK(cudnnCreateTensorDescriptor(&grin_desc)); - cuCHECK(cudnnSetTensor4dDescriptor(grin_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, - out_n, out_c, out_h, out_w)); - if (!isFirst) - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); - gpuPrm->grin_desc = grin_desc; - } - -} - -void Pooling::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - if (!gpuPrm) return; - - cuCHECK(cudnnDestroy(gpuPrm->cudnn)); - cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); - cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); - -} - -__global__ void cuFiltrNegative(snSize outsz, snFloat* out){ - - out += blockIdx.x * outsz.w * outsz.h * outsz.d; - - unsigned int k = threadIdx.x; - while (k < outsz.d){ - - snFloat* pOut = out + outsz.w * outsz.h * k; - for (size_t j = 0; j < (outsz.w * outsz.h); ++j) - if (pOut[j] < 0) pOut[j] = 0.0; - - k += blockDim.x; - } -} - -void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, - const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnPoolingForward(gpuPrm->cudnn, - gpuPrm->pool_desc, - &alpha, - gpuPrm->in_desc, - input, - &beta, - gpuPrm->out_desc, - output)); - - // filtrNegative - dim3 dimBlock(256); - dim3 dimGrid(int(outsz.n)); - - cuFiltrNegative << < dimGrid, dimBlock >> >(outsz, output); - -} - -void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const snFloat* output, const snFloat* gradIn, - const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - - gpuParams* gpuPrm = (gpuParams*)gpuPrms; - - // run - snFloat alpha = 1.f, beta = 0.f; - cuCHECK(cudnnPoolingBackward(gpuPrm->cudnn, - gpuPrm->pool_desc, - &alpha, - gpuPrm->out_desc, - output, - gpuPrm->grin_desc, - gradIn, - gpuPrm->in_desc, - input, - &beta, - gpuPrm->grout_desc, - gradOut)); - -} - - - -// CUDA implementation. Deprecated -// -//#include -//#include "../stdafx.h" -//#include "snOperatorCUDA/src/Operator/pooling.h" -// -//using namespace std; -//using namespace SN_Base; -// -//#ifndef cuCHECK -//#define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -//#endif -// -//struct gpuParams{ -// -// snFloat* d_in = 0; -// snFloat* d_out = 0; -// size_t* d_idx = 0; -// -// size_t inszMem = 0; -//}; -// -//void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; -// if (!gpuPrm){ -// -// cudaDeviceProp cu_deviceProps; -// cudaGetDeviceProperties(&cu_deviceProps, 0); -// if (cu_deviceProps.major < 2){ -// ERROR_MESS("%s requires SM >= 2.0"); -// return; -// } -// gpuPrm = new gpuParams(); -// memset(gpuPrm, 0, sizeof(gpuParams)); -// *pGpuPrm = gpuPrm; -// -// if (!gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); -// } -// } -// else if (!gpuClearMem_ && (gpuPrm->inszMem < insz.size())){ -// -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; -// cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; -// -// cuCHECK(cudaMalloc(&gpuPrm->d_in, insz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, outsz.size() * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_idx, outsz.size() * sizeof(size_t))); -// -// gpuPrm->inszMem = insz.size(); -// } -//} -// -//void Pooling::freeParamCUDA(void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// -// if (!gpuPrm) return; -// -// cudaFree(gpuPrm->d_in); -// cudaFree(gpuPrm->d_out); -// cudaFree(gpuPrm->d_idx); -//} -// -//__global__ void cuPoolFwd(poolType type, size_t kernel, size_t stride, snSize insz, snFloat* input, snSize outsz, snFloat* output, size_t* outputInx){ -// -// size_t outStepByD = outsz.w * outsz.h, // step out by output -// outStepByN = outStepByD * outsz.d, // step out by batch -// inStepByD = insz.w * insz.h, // step in by input -// inStepByN = inStepByD * insz.d, // step in by batch -// kernelSz = kernel * kernel; -// -// // gridDim.x - number of input layers -// // gridDim.y - batch sz -// -// input += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -// outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -// -// if (type == poolType::max){ // max -// -// unsigned int oy = threadIdx.y; -// while (oy < outsz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < outsz.w){ -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel -// snFloat valMax = 0; size_t idx = 0; -//#pragma unroll -// for (size_t c = 0; c < kernelSz; ++c){ -// -// size_t cx = c % kernel, cy = c / kernel; -// -// snFloat val = input[cx + posW + (cy + posH) * insz.w]; -// if (val > valMax){ -// valMax = val; -// idx = c; -// } -// } -// output[ox + oy * outsz.w] = valMax; -// outputInx[ox + oy * outsz.w] = idx; -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// } -// else{ // mean -// -// unsigned int oy = threadIdx.y; -// while (oy < outsz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < outsz.w){ -// -// size_t posW = ox * stride, posH = oy * stride; -// -// // kernel -// snFloat valMean = 0; -//#pragma unroll -// for (size_t c = 0; c < kernelSz; ++c){ -// -// size_t cx = c % kernel, cy = c / kernel; -// -// valMean += input[cx + posW + (cy + posH) * insz.w]; -// } -// output[ox + oy * outsz.w] = valMean / kernelSz; -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// } -//} -// -//void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, -// const snSize& outsz, snFloat* output, size_t* outputInx, void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// size_t isz = insz.size(), osz = outsz.size(); -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&gpuPrm->d_in, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_out, osz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&gpuPrm->d_idx, osz * sizeof(size_t))); -// } -// -// // input -// cuCHECK(cudaMemcpy(gpuPrm->d_in, input, isz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(outsz.d), int(outsz.n)); -// -// cuPoolFwd << < dimGrid, dimBlock >> >(poolPrms.type, -// poolPrms.kernel, -// poolPrms.stride, -// insz, -// gpuPrm->d_in, -// outsz, -// gpuPrm->d_out, -// gpuPrm->d_idx); -// -// // result -// cuCHECK(cudaMemcpy(output, gpuPrm->d_out, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// cuCHECK(cudaMemcpy(outputInx, gpuPrm->d_idx, osz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(gpuPrm->d_in)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(gpuPrm->d_out)); gpuPrm->d_out = 0; -// cuCHECK(cudaFree(gpuPrm->d_idx)); gpuPrm->d_idx = 0; -// } -//} -// -//__global__ void cuPoolBwd(poolType type, size_t kernel, size_t stride, snSize outsz, size_t* outputInx, snFloat* gradIn, snSize insz, snFloat* gradOut){ -// -// size_t outStepByD = outsz.w * outsz.h, // step out by output -// outStepByN = outStepByD * outsz.d, // step out by batch -// inStepByD = insz.w * insz.h, // step in by input -// inStepByN = inStepByD * insz.d, // step in by batch -// kernelSz = kernel * kernel; -// -// // gridDim.x - number of input layers -// // gridDim.y - batch sz -// -// gradIn += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -// gradOut += blockIdx.x * inStepByD + blockIdx.y * inStepByN; -// -// if (type == poolType::max){ // max -// -// outputInx += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -// -// unsigned int oy = threadIdx.y; -// while (oy < outsz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < outsz.w){ -// -// size_t posW = ox * stride, posH = oy * stride; -// -// size_t c = outputInx[ox + oy * outsz.w], cx = c % kernel, cy = c / kernel; -// -// gradOut[cx + posW + (cy + posH) * insz.w] = gradIn[ox + oy * outsz.w]; -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// } -// else{ // mean -// -// unsigned int oy = threadIdx.y; -// while (oy < outsz.h){ -// -// unsigned int ox = threadIdx.x; -// while (ox < outsz.w){ -// -// size_t posW = ox * stride, posH = oy * stride; -// -// snFloat mean = gradIn[ox + oy * outsz.w] / kernel; -//#pragma unroll -// for (size_t c = 0; c < kernelSz; ++c){ -// size_t cx = c % kernel, cy = c / kernel; -// gradOut[(cx + posW) + (cy + posH) * insz.w] = mean; -// } -// -// ox += blockDim.x; -// } -// oy += blockDim.y; -// } -// } -//} -// -//void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const size_t* outputInx, const snFloat* output, const snFloat* gradIn, -// const snSize& insz, const SN_Base::snFloat* input, snFloat* gradOut, void* gpuPrms){ -// -// cudaSetDevice(gpuDeviceId_); -// -// gpuParams* gpuPrm = (gpuParams*)gpuPrms; -// -// snFloat* d_grin = gpuPrm->d_out, -// * d_grout = gpuPrm->d_in; -// size_t* d_idx = gpuPrm->d_idx; -// -// size_t isz = insz.size(), osz = outsz.size(); -// if (gpuClearMem_){ -// cuCHECK(cudaMalloc(&d_grin, osz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&d_grout, isz * sizeof(snFloat))); -// cuCHECK(cudaMalloc(&d_idx, osz * sizeof(size_t))); -// } -// -// // input -// cuCHECK(cudaMemcpy(d_grin, gradIn, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// cuCHECK(cudaMemcpy(d_idx, outputInx, osz * sizeof(snFloat), cudaMemcpyHostToDevice)); -// -// // out -// cuCHECK(cudaMemset(d_grout, 0, isz * sizeof(snFloat))); -// -// // run -// dim3 dimBlock(16, 16); -// dim3 dimGrid(int(outsz.d), int(outsz.n)); -// -// cuPoolBwd << < dimGrid, dimBlock >> >(poolPrms.type, -// poolPrms.kernel, -// poolPrms.stride, -// outsz, -// d_idx, -// d_grin, -// insz, -// d_grout); -// -// // result -// cuCHECK(cudaMemcpy(gradOut, d_grout, isz * sizeof(snFloat), cudaMemcpyDeviceToHost)); -// -// if (gpuClearMem_){ -// cuCHECK(cudaFree(d_grin)); gpuPrm->d_out = 0; -// cuCHECK(cudaFree(d_grout)); gpuPrm->d_in = 0; -// cuCHECK(cudaFree(d_idx)); gpuPrm->d_idx = 0; -// } -//} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 6afbe8d..477a4fc 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -29,7 +29,8 @@ #include "snOperatorCUDA/src/activeFunctions.h" #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" -#include "snOperatorCUDA/src/mathFunctions.h" +#include "snOperatorCUDA/src/dropOut.h" +#include "snOperatorCUDA/src/activeFunctions.h" using namespace std; using namespace SN_Base; @@ -46,6 +47,7 @@ Convolution::~Convolution(){ freeParamCUDA(convGPUParams_); dropOutFree(gpuDeviceId_, dropGPUParams_); + activationFree(gpuDeviceId_, activGPUParams_); } void Convolution::load(std::map& prms){ @@ -258,7 +260,7 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active function - activeFuncForward(outsz.size(), out, activeType_); + activationForward(activeType_, out, outsz.size(), gpuDeviceId_, &activGPUParams_); /// batchNorm if (batchNormType_ == batchNormType::postActive) @@ -276,14 +278,10 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o // active function if (activeType_ != activeType::none){ - - snFloat* out = baseOut_.getDataGPU(); - + size_t osz = baseOut_.size().size(); - activeFuncBackward(osz, out, activeType_); - - // update grad - for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; + activationBackward(inputMem_->getDataGPU(), baseOut_.getDataGPU(), gradIn, osz, gpuDeviceId_, activGPUParams_); + } /// batchNorm @@ -292,7 +290,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o /// dropOut if (dropOut_ > 0.F) - dropOutBackward(dropOut_, gradIn, insz, gpuDeviceId_, &dropGPUParams_); + dropOutBackward(gradIn, insz, gpuDeviceId_, dropGPUParams_); // calculation of the output gradient and weight correction snFloat* gradOut = baseGrad_.getDataGPU(); @@ -313,15 +311,15 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o size_t wsz = baseWeight_.size().size(); optimizer(dWeight, - dWPrev, - dWGrad, - weight, - wsz, - operPrm.lr, - optLmbRegular_, - optDecayMomentDW_, - optDecayMomentWGr_, - optimizerType_); + dWPrev, + dWGrad, + weight, + wsz, + operPrm.lr, + optLmbRegular_, + optDecayMomentDW_, + optDecayMomentWGr_, + optimizerType_); } else{ // isFreeze backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, convGPUParams_); diff --git a/src/snOperatorCUDA/src/Operator/convolution.h b/src/snOperatorCUDA/src/Operator/convolution.h index d59a4e1..f58a0d2 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.h +++ b/src/snOperatorCUDA/src/Operator/convolution.h @@ -26,7 +26,6 @@ #include "snBase/snBase.h" #include "snOperatorCUDA/src/structurs.h" -#include "snOperatorCUDA/src/mathFunctions.h" /// convolution layer class Convolution final : SN_Base::OperatorBase{ @@ -76,13 +75,9 @@ class Convolution final : SN_Base::OperatorBase{ SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimiz weight optDecayMomentWGr_ = 0.99F, optLmbRegular_ = 0.001F; - - std::map> auxParams_; ///< aux data - - void* convGPUParams_ = nullptr, ///< gpu aux params - * dropGPUParams_ = nullptr; - - + + void* convGPUParams_ = nullptr; ///< gpu aux params + void load(std::map& prms); void updateConfig(bool isLern, const SN_Base::snSize& newSz, SN_Base::snSize& expSz); diff --git a/src/snOperatorCUDA/src/activeFunctions.cpp b/src/snOperatorCUDA/src/activeFunctions.cpp deleted file mode 100644 index 386d25a..0000000 --- a/src/snOperatorCUDA/src/activeFunctions.cpp +++ /dev/null @@ -1,117 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// -#include "stdafx.h" -#include "structurs.h" -#include "activeFunctions.h" - -using namespace std; -using namespace SN_Base; - -// fv - value, df - deriv - -void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType active){ - - switch (active){ - case activeType::sigmoid: fv_sigmoid(data, sz); break; - case activeType::relu: fv_relu(data, sz); break; - case activeType::leakyRelu: fv_leakyRelu(data, sz); break; - case activeType::elu: fv_elu(data, sz); break; - default: break; - } -} - -void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType active){ - - switch (active){ - case activeType::sigmoid: df_sigmoid(data, sz); break; - case activeType::relu: df_relu(data, sz); break; - case activeType::leakyRelu: df_leakyRelu(data, sz); break; - case activeType::elu: df_elu(data, sz); break; - default: break; - } -} - -void fv_sigmoid(snFloat* ioVal, size_t sz){ - - for (size_t i = 0; i < sz; ++i){ - - ioVal[i] = 1.F / (1.F + std::exp(-ioVal[i])); - - ioVal[i] = (ioVal[i] < 0.99999F) ? ioVal[i] : 0.99999F; - ioVal[i] = (ioVal[i] > 0.00001F) ? ioVal[i] : 0.00001F; - } -} -void df_sigmoid(snFloat* ioSigm, size_t sz){ - - for (size_t i = 0; i < sz; ++i){ - - ioSigm[i] = ioSigm[i] * (1.F - ioSigm[i]); - } -} - -void fv_relu(snFloat* ioVal, size_t sz){ - - for (size_t i = 0; i < sz; ++i){ - - ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : 0; - } -}; -void df_relu(snFloat* ioRelu, size_t sz){ - - for (size_t i = 0; i < sz; ++i){ - - ioRelu[i] = ioRelu[i] >= 0 ? 1.F : 0.F; - } -}; - -void fv_leakyRelu(snFloat* ioVal, size_t sz, snFloat minv){ - - for (size_t i = 0; i < sz; ++i){ - - ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * ioVal[i]; - } -} -void df_leakyRelu(snFloat* ioRelu, size_t sz, snFloat minv){ - - for (size_t i = 0; i < sz; ++i){ - - ioRelu[i] = ioRelu[i] >= 0 ? 1 : minv; - } -} - -void fv_elu(snFloat* ioVal, size_t sz, snFloat minv){ - - for (size_t i = 0; i < sz; ++i){ - - ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * (exp(ioVal[i]) - 1.F); - } -} -void df_elu(snFloat* ioElu, size_t sz, snFloat minv){ - - for (size_t i = 0; i < sz; ++i){ - - ioElu[i] = ioElu[i] >= 0 ? 1 : ioElu[i] + minv; - } -} diff --git a/src/snOperatorCUDA/src/activeFunctions.h b/src/snOperatorCUDA/src/activeFunctions.h index 347c4aa..d8d8620 100644 --- a/src/snOperatorCUDA/src/activeFunctions.h +++ b/src/snOperatorCUDA/src/activeFunctions.h @@ -29,9 +29,9 @@ // fv - value, df - deriv -void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType); +void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); -void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType); +void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); void fv_sigmoid(SN_Base::snFloat* ioVal, size_t sz); void df_sigmoid(SN_Base::snFloat* inSigm, size_t sz); @@ -43,4 +43,4 @@ void fv_leakyRelu(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat minv = 0. void df_leakyRelu(SN_Base::snFloat* inRelu, size_t sz, SN_Base::snFloat minv = 0.01F); void fv_elu(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat minv = 0.01F); -void df_elu(SN_Base::snFloat* inElu, size_t sz, SN_Base::snFloat minv = 0.01F); +void df_elu(SN_Base::snFloat* inElu, size_t sz, SN_Base::snFloat minv = 0.01F); \ No newline at end of file diff --git a/src/snOperatorCUDA/src/mathFunctions.h b/src/snOperatorCUDA/src/dropOut.h similarity index 64% rename from src/snOperatorCUDA/src/mathFunctions.h rename to src/snOperatorCUDA/src/dropOut.h index 8070cc9..245ba21 100644 --- a/src/snOperatorCUDA/src/mathFunctions.h +++ b/src/snOperatorCUDA/src/dropOut.h @@ -26,23 +26,6 @@ #include "snBase/snBase.h" -void channelBatchNorm(bool fwBw, bool isLern, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out, SN_Base::batchNorm prm); +void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId); -void layerBatchNorm(bool fwBw, bool isLern, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out, const SN_Base::batchNorm& prm); - -void batchNormForward(const SN_Base::snSize& insz, - SN_Base::snFloat* in, - SN_Base::snFloat* out, - SN_Base::batchNorm); - -void batchNormBackward(const SN_Base::snSize& insz, - SN_Base::snFloat* gradIn, - SN_Base::snFloat* gradOut, - SN_Base::batchNorm); - - -void dropOutFree(uint32_t deviceId, void* pGpuPrm); - -void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm); - -void dropOutBackward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& outsz, uint32_t deviceId, void** pGpuPrm); \ No newline at end of file +void dropOutBackward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId); \ No newline at end of file From bd80e123d5e93f54ed7d66594606325d37c9d9c9 Mon Sep 17 00:00:00 2001 From: al_medved Date: Thu, 18 Jul 2019 20:48:11 +0500 Subject: [PATCH 09/35] -develop --- mswin/VS12/skyNet.sln | 38 +-- mswin/VS12/skyNet.vcxproj | 81 +----- mswin/VS12/snAux.vcxproj | 63 ----- mswin/VS12/snBase.vcxproj | 69 ----- mswin/VS12/snEngine.vcxproj | 71 ----- mswin/VS12/snExample.vcxproj | 77 +----- mswin/VS12/snExample.vcxproj.filters | 2 +- mswin/VS12/snOperatorCPU.vcxproj | 173 +----------- mswin/VS12/snOperatorCPU.vcxproj.filters | 24 +- mswin/VS12/snOperatorCUDA.vcxproj | 170 +----------- mswin/VS12/snOperatorCUDA.vcxproj.filters | 3 - mswin/VS12/snSIMD.vcxproj | 71 ----- src/snOperatorCPU/CMakeLists.txt | 12 +- src/snOperatorCPU/src/Operator/batchNorm.cpp | 2 +- .../src/Operator/convolution.cpp | 4 +- src/snOperatorCPU/src/Operator/convolution.h | 1 - .../src/Operator/deconvolution.cpp | 4 +- .../src/Operator/deconvolution.h | 1 - .../src/Operator/fullyConnected.cpp | 4 +- src/snOperatorCPU/src/Operator/pooling.cpp | 1 + src/snOperatorCPU/src/Operator/pooling.h | 2 - src/snOperatorCPU/src/activeFunctions.h | 2 +- .../src/batchNormFunctions.cpp} | 7 +- .../{mathFunctions.h => batchNormFunctions.h} | 4 - src/snOperatorCPU/src/dropOut.cpp | 46 ++++ src/snOperatorCPU/src/dropOut.h | 29 ++ src/snOperatorCPU/src/mathFunctions.cpp | 253 ------------------ src/snOperatorCPU/src/optimizer.h | 2 +- src/snOperatorCPU/src/paddingOffs.cpp | 66 +++++ src/snOperatorCPU/src/paddingOffs.h | 29 ++ src/snOperatorCPU/src/random.h | 2 - src/snOperatorCPU/src/stdafx.h | 1 - src/snOperatorCPU/src/structurs.h | 2 - src/snOperatorCPU/src/weightInit.h | 1 - src/snOperatorCUDA/CMakeLists.txt | 10 +- src/snOperatorCUDA/src/CUDA/activation.cu | 2 +- src/snOperatorCUDA/src/CUDA/batchNorm.cu | 125 +-------- src/snOperatorCUDA/src/CUDA/convolution.cu | 2 +- src/snOperatorCUDA/src/CUDA/deconvolution.cu | 4 +- src/snOperatorCUDA/src/CUDA/fullyConnected.cu | 4 +- .../src/Operator/activation.cpp | 6 +- src/snOperatorCUDA/src/Operator/batchNorm.cpp | 9 +- .../src/Operator/convolution.cpp | 88 ++---- .../src/Operator/deconvolution.cpp | 27 +- .../src/Operator/deconvolution.h | 1 - .../src/Operator/fullyConnected.cpp | 28 +- src/snOperatorCUDA/src/Operator/input.cpp | 2 +- .../src/Operator/lossFunction.cpp | 5 +- src/snOperatorCUDA/src/Operator/pooling.cpp | 16 +- src/snOperatorCUDA/src/Operator/pooling.h | 2 - src/snOperatorCUDA/src/Operator/userLayer.cpp | 4 +- ...ctiveFunctions.h => activationFunctions.h} | 0 src/snOperatorCUDA/src/batchNornFunctions.h | 35 +++ 53 files changed, 344 insertions(+), 1343 deletions(-) rename src/{snOperatorCUDA/src/mathFunctions.cpp => snOperatorCPU/src/batchNormFunctions.cpp} (98%) rename src/snOperatorCPU/src/{mathFunctions.h => batchNormFunctions.h} (88%) create mode 100644 src/snOperatorCPU/src/dropOut.cpp create mode 100644 src/snOperatorCPU/src/dropOut.h delete mode 100644 src/snOperatorCPU/src/mathFunctions.cpp create mode 100644 src/snOperatorCPU/src/paddingOffs.cpp create mode 100644 src/snOperatorCPU/src/paddingOffs.h rename src/snOperatorCUDA/src/{activeFunctions.h => activationFunctions.h} (100%) create mode 100644 src/snOperatorCUDA/src/batchNornFunctions.h diff --git a/mswin/VS12/skyNet.sln b/mswin/VS12/skyNet.sln index 19f3d1f..490c007 100644 --- a/mswin/VS12/skyNet.sln +++ b/mswin/VS12/skyNet.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio 2013 -VisualStudioVersion = 12.0.40629.0 +VisualStudioVersion = 12.0.31101.0 MinimumVisualStudioVersion = 10.0.40219.1 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snEngine", "SNEngine.vcxproj", "{87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}" ProjectSection(ProjectDependencies) = postProject @@ -44,79 +44,43 @@ Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "snOperatorCUDA", "snOperato EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution - Debug|Win32 = Debug|Win32 Debug|x64 = Debug|x64 - Release|Win32 = Release|Win32 Release|x64 = Release|x64 EndGlobalSection GlobalSection(ProjectConfigurationPlatforms) = postSolution - {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|Win32.ActiveCfg = Debug|Win32 - {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|Win32.Build.0 = Debug|Win32 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|x64.ActiveCfg = Debug|x64 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Debug|x64.Build.0 = Debug|x64 - {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|Win32.ActiveCfg = Release|Win32 - {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|Win32.Build.0 = Release|Win32 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|x64.ActiveCfg = Release|x64 {87E622F8-2436-40B6-A5A6-3F5DDE14BAC5}.Release|x64.Build.0 = Release|x64 - {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|Win32.ActiveCfg = Debug|Win32 - {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|Win32.Build.0 = Debug|Win32 {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|x64.ActiveCfg = Debug|x64 {342D19FA-7A64-4009-A9AD-BE781192038E}.Debug|x64.Build.0 = Debug|x64 - {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|Win32.ActiveCfg = Release|Win32 - {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|Win32.Build.0 = Release|Win32 {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|x64.ActiveCfg = Release|x64 {342D19FA-7A64-4009-A9AD-BE781192038E}.Release|x64.Build.0 = Release|x64 - {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|Win32.ActiveCfg = Debug|Win32 - {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|Win32.Build.0 = Debug|Win32 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|x64.ActiveCfg = Debug|x64 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Debug|x64.Build.0 = Debug|x64 - {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|Win32.ActiveCfg = Release|Win32 - {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|Win32.Build.0 = Release|Win32 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|x64.ActiveCfg = Release|x64 {CDDE5523-2322-4C88-B4DE-C42942F2A957}.Release|x64.Build.0 = Release|x64 - {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|Win32.ActiveCfg = Debug|Win32 - {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|Win32.Build.0 = Debug|Win32 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|x64.ActiveCfg = Debug|x64 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Debug|x64.Build.0 = Debug|x64 - {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|Win32.ActiveCfg = Release|Win32 - {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|Win32.Build.0 = Release|Win32 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|x64.ActiveCfg = Release|x64 {C993F645-29F0-4079-ACBA-033BFED8B8F6}.Release|x64.Build.0 = Release|x64 - {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|Win32.ActiveCfg = Debug|Win32 - {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|Win32.Build.0 = Debug|Win32 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|x64.ActiveCfg = Debug|x64 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Debug|x64.Build.0 = Debug|x64 - {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|Win32.ActiveCfg = Release|Win32 - {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|Win32.Build.0 = Release|Win32 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|x64.ActiveCfg = Release|x64 {507A9B3C-E5E6-4CEC-A99D-729D2171DF81}.Release|x64.Build.0 = Release|x64 - {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Debug|Win32.ActiveCfg = Debug|Any CPU {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Debug|x64.ActiveCfg = Debug|Any CPU {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Debug|x64.Build.0 = Debug|Any CPU - {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Release|Win32.ActiveCfg = Release|Any CPU {6EDFE831-7D48-4811-81E9-FECDD4C4C845}.Release|x64.ActiveCfg = Release|Any CPU - {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|Win32.ActiveCfg = Debug|Win32 - {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|Win32.Build.0 = Debug|Win32 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|x64.ActiveCfg = Debug|x64 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Debug|x64.Build.0 = Debug|x64 - {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|Win32.ActiveCfg = Release|Win32 - {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|Win32.Build.0 = Release|Win32 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|x64.ActiveCfg = Release|x64 {30FAF753-DBAE-4701-B5F4-29C7FBCBF9F6}.Release|x64.Build.0 = Release|x64 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|Win32.ActiveCfg = Debug|Win32 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|Win32.Build.0 = Debug|Win32 {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|x64.ActiveCfg = Debug|x64 {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Debug|x64.Build.0 = Debug|x64 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|Win32.ActiveCfg = Release|Win32 - {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|Win32.Build.0 = Release|Win32 {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|x64.ActiveCfg = Release|x64 {F3E981BD-4BA4-4538-9D21-6ACD252F4895}.Release|x64.Build.0 = Release|x64 - {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|Win32.ActiveCfg = Debug|Win32 - {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|Win32.Build.0 = Debug|Win32 {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|x64.ActiveCfg = Debug|x64 {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Debug|x64.Build.0 = Debug|x64 - {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|Win32.ActiveCfg = Release|Win32 - {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|Win32.Build.0 = Release|Win32 {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|x64.ActiveCfg = Release|x64 {2C7FA033-361B-4429-97E9-64F2A0A20CBE}.Release|x64.Build.0 = Release|x64 EndGlobalSection diff --git a/mswin/VS12/skyNet.vcxproj b/mswin/VS12/skyNet.vcxproj index cd9b039..05625b3 100644 --- a/mswin/VS12/skyNet.vcxproj +++ b/mswin/VS12/skyNet.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -36,25 +28,12 @@ skynet - - DynamicLibrary - true - v120 - Unicode - DynamicLibrary true v120 Unicode - - DynamicLibrary - false - v120 - true - Unicode - DynamicLibrary false @@ -65,60 +44,25 @@ - - - - - - - - true - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - libskynet - true $(SolutionDir)$(Configuration)\$(Platform)\ libskynet $(Platform)\$(Configuration)\$(ProjectName)\ - - false - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - libskynet - false $(SolutionDir)$(Configuration)\$(Platform)\ libskynet $(Platform)\$(Configuration)\$(ProjectName)\ - - - NotUsing - Level3 - Disabled - WIN32;_CRT_SECURE_NO_WARNINGS;_DEBUG;_WINDOWS;_USRDLL;SKYNETDLL_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir)../../src - - - - - Windows - true - $(OutDir) - snOperator.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) - - NotUsing @@ -133,28 +77,7 @@ Windows true $(OutDir) - snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) - - - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;_CRT_SECURE_NO_WARNINGS;NDEBUG;_WINDOWS;_USRDLL;SKYNETDLL_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir)../../src - - - - - Windows - true - true - true - $(OutDir) - snOperator.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCUDA.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) @@ -175,7 +98,7 @@ true true $(OutDir) - snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCUDA.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) diff --git a/mswin/VS12/snAux.vcxproj b/mswin/VS12/snAux.vcxproj index 9f2e43d..e9c31ec 100644 --- a/mswin/VS12/snAux.vcxproj +++ b/mswin/VS12/snAux.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -32,25 +24,12 @@ snAux - - StaticLibrary - true - v120 - Unicode - StaticLibrary true v120 Unicode - - StaticLibrary - false - v120 - true - Unicode - StaticLibrary false @@ -61,47 +40,21 @@ - - - - - - - - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - - NotUsing - Level3 - Disabled - WIN32;_CRT_SECURE_NO_WARNINGS;_DEBUG;_LIB;%(PreprocessorDefinitions) - - - Windows - true - - NotUsing @@ -114,22 +67,6 @@ true - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;_CRT_SECURE_NO_WARNINGS;NDEBUG;_LIB;%(PreprocessorDefinitions) - - - Windows - true - true - true - - Level3 diff --git a/mswin/VS12/snBase.vcxproj b/mswin/VS12/snBase.vcxproj index e9c143b..d9ba4ce 100644 --- a/mswin/VS12/snBase.vcxproj +++ b/mswin/VS12/snBase.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -28,25 +20,12 @@ snBase - - StaticLibrary - true - v120 - Unicode - StaticLibrary true v120 Unicode - - StaticLibrary - false - v120 - true - Unicode - StaticLibrary false @@ -57,56 +36,25 @@ - - - - - - - - true - .lib - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - true .lib $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - false - .lib - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - false .lib $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - - NotUsing - Level3 - Disabled - WIN32;_DEBUG;_WINDOWS;_USRDLL;SNBASE_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir) - - - Windows - true - - NotUsing @@ -120,23 +68,6 @@ true - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;NDEBUG;_WINDOWS;_USRDLL;SNBASE_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir) - - - Windows - true - true - true - - Level3 diff --git a/mswin/VS12/snEngine.vcxproj b/mswin/VS12/snEngine.vcxproj index 691174c..9d822d6 100644 --- a/mswin/VS12/snEngine.vcxproj +++ b/mswin/VS12/snEngine.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -32,25 +24,12 @@ snEngine - - StaticLibrary - true - v120 - Unicode - StaticLibrary true v120 Unicode - - StaticLibrary - false - v120 - true - Unicode - StaticLibrary false @@ -61,57 +40,25 @@ - - - - - - - - true - .lib - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - true .lib $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - false - .lib - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - false .lib $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - - NotUsing - Level3 - Disabled - WIN32;_DEBUG;_LIB;SNENGINE_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir)../../src - - - Windows - true - $(OutDir) - - NotUsing @@ -126,24 +73,6 @@ $(OutDir) - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;NDEBUG;_LIB;SNENGINE_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir)../../src - - - Windows - true - true - true - $(OutDir) - - Level3 diff --git a/mswin/VS12/snExample.vcxproj b/mswin/VS12/snExample.vcxproj index 285d1c8..8a3c040 100644 --- a/mswin/VS12/snExample.vcxproj +++ b/mswin/VS12/snExample.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -25,7 +17,7 @@ - + {CDDE5523-2322-4C88-B4DE-C42942F2A957} @@ -34,25 +26,12 @@ snExample - - Application - true - v120 - Unicode - Application true v120 Unicode - - Application - false - v120 - true - Unicode - Application false @@ -63,56 +42,23 @@ - - - - - - - - true - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - true $(Platform)\$(Configuration)\$(ProjectName)\ $(SolutionDir)$(Configuration)\$(Platform)\ - - false - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - false $(Platform)\$(Configuration)\$(ProjectName)\ $(SolutionDir)$(Configuration)\$(Platform)\ - - - NotUsing - Level3 - Disabled - WIN32;_CRT_SECURE_NO_WARNINGS;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) - $(SolutionDir)../../src;$(SolutionDir)../../src/Lib/OpenCV_3.3.0 - - - - - Console - true - $(OutDir) - libskynet.lib;snAux.lib;opencv_world330d.lib;%(AdditionalDependencies) - - NotUsing @@ -130,27 +76,6 @@ libskynet.lib;snAux.lib;opencv_world330d.lib;%(AdditionalDependencies) - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;_CRT_SECURE_NO_WARNINGS;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) - $(SolutionDir)../../src;$(SolutionDir)../../src/Lib/OpenCV_3.3.0 - - - - - Console - true - true - true - $(OutDir) - libskynet.lib;snAux.lib;opencv_world330.lib;%(AdditionalDependencies) - - Level3 diff --git a/mswin/VS12/snExample.vcxproj.filters b/mswin/VS12/snExample.vcxproj.filters index f4eabc0..bf8954f 100644 --- a/mswin/VS12/snExample.vcxproj.filters +++ b/mswin/VS12/snExample.vcxproj.filters @@ -29,7 +29,7 @@ - + Source Files diff --git a/mswin/VS12/snOperatorCPU.vcxproj b/mswin/VS12/snOperatorCPU.vcxproj index 77a48d0..582a2eb 100644 --- a/mswin/VS12/snOperatorCPU.vcxproj +++ b/mswin/VS12/snOperatorCPU.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -25,25 +17,12 @@ snOperatorCPU - - StaticLibrary - true - v120 - Unicode - StaticLibrary true v120 Unicode - - StaticLibrary - false - v120 - true - Unicode - StaticLibrary false @@ -52,104 +31,25 @@ Unicode - - - - - - - - true - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - .lib - true $(SolutionDir)$(Configuration)\$(Platform)\ .lib $(Platform)\$(Configuration)\$(ProjectName)\ - - false - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - .lib - false $(SolutionDir)$(Configuration)\$(Platform)\ .lib $(Platform)\$(Configuration)\$(ProjectName)\ - - - NotUsing - Level3 - Disabled - WIN32;SN_DEBUG; SN_AVX; SN_CUDA;SN_CUDNN;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) - %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src - - - true - - - Windows - true - SNAux.lib;%(AdditionalDependencies) - $(OutDir);%(AdditionalLibraryDirectories) - - - $(IntDir)%(Filename)%(Extension).obj - true - false - $(CudaIntDir) - false - compile - Static - 32 - false - compute_30,sm_30 - false - 0 - false - true - false - - - false - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - - - - - true - $(IntDir)$(TargetName).device-link.obj - true - true - InheritFromProject - InheritFromProject - - - %(AdditionalLibraryDirectories);$(OutDir); - libopenblas.lib; - - - - - - NotUsing @@ -217,71 +117,6 @@ - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;SN_DEBUG; SN_AVX; SN_CUDA;SN_CUDNN;NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) - %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src - - - true - - - Windows - true - true - true - SNAux.lib;%(AdditionalDependencies) - $(OutDir);%(AdditionalLibraryDirectories) - - - $(IntDir)%(Filename)%(Extension).obj - true - false - $(CudaIntDir) - false - compile - Static - 32 - false - compute_30,sm_30 - false - 0 - false - true - false - - - false - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - - - - - true - $(IntDir)$(TargetName).device-link.obj - true - true - InheritFromProject - InheritFromProject - - - %(AdditionalLibraryDirectories);$(OutDir); - libopenblas.lib; - - - - - - Level3 @@ -351,7 +186,8 @@ - + + @@ -369,6 +205,7 @@ + @@ -376,12 +213,13 @@ + - + @@ -399,6 +237,7 @@ + diff --git a/mswin/VS12/snOperatorCPU.vcxproj.filters b/mswin/VS12/snOperatorCPU.vcxproj.filters index 2dcbcbc..29268b9 100644 --- a/mswin/VS12/snOperatorCPU.vcxproj.filters +++ b/mswin/VS12/snOperatorCPU.vcxproj.filters @@ -24,9 +24,6 @@ Header Files - - Header Files - Header Files @@ -93,6 +90,15 @@ Operator + + Header Files + + + Header Files + + + Header Files + @@ -146,9 +152,6 @@ Source Files - - Source Files - Source Files @@ -176,5 +179,14 @@ CPU + + Source Files + + + Source Files + + + Source Files + \ No newline at end of file diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index eaa4bcf..8fbc7d9 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -25,25 +17,12 @@ snOperatorCUDA - - StaticLibrary - true - v120 - Unicode - StaticLibrary true v120 Unicode - - StaticLibrary - false - v120 - true - Unicode - StaticLibrary false @@ -55,104 +34,25 @@ - - - - - - - - true - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - .lib - true $(SolutionDir)$(Configuration)\$(Platform)\ .lib $(Platform)\$(Configuration)\$(ProjectName)\ - - false - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - .lib - false $(SolutionDir)$(Configuration)\$(Platform)\ .lib $(Platform)\$(Configuration)\$(ProjectName)\ - - - NotUsing - Level3 - Disabled - WIN32;SN_DEBUG; SN_AVX; SN_CUDA;SN_CUDNN;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) - %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src - - - true - - - Windows - true - SNAux.lib;%(AdditionalDependencies) - $(OutDir);%(AdditionalLibraryDirectories) - - - $(IntDir)%(Filename)%(Extension).obj - true - false - $(CudaIntDir) - false - compile - Static - 32 - false - compute_30,sm_30 - false - 0 - false - true - false - - - false - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - - - - - true - $(IntDir)$(TargetName).device-link.obj - true - true - InheritFromProject - InheritFromProject - - - %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cublas.lib;cudart_static.lib;cudnn.lib; - - - - - - NotUsing @@ -203,7 +103,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cublas.lib;cudart_static.lib;cudnn.lib; + cudart_static.lib;cublas.lib;cudnn.lib; true @@ -220,71 +120,6 @@ - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;SN_DEBUG; SN_AVX; SN_CUDA;SN_CUDNN;NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) - %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src - - - true - - - Windows - true - true - true - SNAux.lib;%(AdditionalDependencies) - $(OutDir);%(AdditionalLibraryDirectories) - - - $(IntDir)%(Filename)%(Extension).obj - true - false - $(CudaIntDir) - false - compile - Static - 32 - false - compute_30,sm_30 - false - 0 - false - true - false - - - false - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - InheritFromHost - - - - - true - $(IntDir)$(TargetName).device-link.obj - true - true - InheritFromProject - InheritFromProject - - - %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cublas.lib;cudart_static.lib;cudnn.lib; - - - - - - Level3 @@ -336,7 +171,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cublas.lib;cudart_static.lib;cudnn.lib; + cudart_static.lib;cublas.lib;cudnn.lib; true @@ -378,7 +213,6 @@ - diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index 415a7e5..cc5fc63 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -143,9 +143,6 @@ Operator - - Source Files - Source Files diff --git a/mswin/VS12/snSIMD.vcxproj b/mswin/VS12/snSIMD.vcxproj index 9bad21b..ca29284 100644 --- a/mswin/VS12/snSIMD.vcxproj +++ b/mswin/VS12/snSIMD.vcxproj @@ -1,18 +1,10 @@  - - Debug - Win32 - Debug x64 - - Release - Win32 - Release x64 @@ -32,25 +24,12 @@ snSIMD - - StaticLibrary - true - v120 - Unicode - StaticLibrary true v120 Unicode - - StaticLibrary - false - v120 - true - Unicode - StaticLibrary false @@ -61,57 +40,25 @@ - - - - - - - - true - .lib - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - true .lib $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - false - .lib - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Configuration)\$(Platform)\ - false .lib $(SolutionDir)$(Configuration)\$(Platform)\ $(Platform)\$(Configuration)\$(ProjectName)\ - - - NotUsing - Level3 - Disabled - WIN32;_DEBUG;_LIB;SNSIMD_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir)../../src - - - Windows - true - $(OutDir) - - NotUsing @@ -134,24 +81,6 @@ - - - Level3 - NotUsing - MaxSpeed - true - true - WIN32;NDEBUG;_LIB;SNSIMD_EXPORTS;%(PreprocessorDefinitions) - $(SolutionDir)../../src - - - Windows - true - true - true - $(OutDir) - - Level3 diff --git a/src/snOperatorCPU/CMakeLists.txt b/src/snOperatorCPU/CMakeLists.txt index 113802c..3fdb664 100644 --- a/src/snOperatorCPU/CMakeLists.txt +++ b/src/snOperatorCPU/CMakeLists.txt @@ -1,21 +1,21 @@ cmake_minimum_required(VERSION 3.8 FATAL_ERROR) -project(snOperator) +project(snOperatorCPU) FILE(GLOB_RECURSE SRC_snOperator "*.cpp" "*.h" "*.cu") -add_library(snOperator STATIC ${SRC_snOperator}) +add_library(snOperatorCPU STATIC ${SRC_snOperator}) if(USE_AVX) add_definitions(-DSN_AVX) - target_link_libraries(snOperator snSIMD) + target_link_libraries(snOperatorCPU snSIMD) endif() -target_link_libraries(snOperator snAux snBase) +target_link_libraries(snOperatorCPU snAux snBase) if(WIN32) - target_link_libraries(snOperator libopenblas) + target_link_libraries(snOperatorCPU libopenblas) else() - target_link_libraries(snOperator openblas) + target_link_libraries(snOperatorCPU openblas) endif() \ No newline at end of file diff --git a/src/snOperatorCPU/src/Operator/batchNorm.cpp b/src/snOperatorCPU/src/Operator/batchNorm.cpp index 6a68211..ba54e0c 100644 --- a/src/snOperatorCPU/src/Operator/batchNorm.cpp +++ b/src/snOperatorCPU/src/Operator/batchNorm.cpp @@ -25,7 +25,7 @@ #include "../stdafx.h" #include "snAux/auxFunc.h" #include "snOperatorCPU/src/Operator/batchNorm.h" -#include "snOperatorCPU/src/mathFunctions.h" +#include "snOperatorCPU/src/batchNormFunctions.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp index cff3c60..64eb433 100644 --- a/src/snOperatorCPU/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -29,7 +29,9 @@ #include "snOperatorCPU/src/activeFunctions.h" #include "snOperatorCPU/src/optimizer.h" #include "snOperatorCPU/src/structurs.h" -#include "snOperatorCPU/src/mathFunctions.h" +#include "snOperatorCPU/src/batchNormFunctions.h" +#include "snOperatorCPU/src/dropOut.h" +#include "snOperatorCPU/src/paddingOffs.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/Operator/convolution.h b/src/snOperatorCPU/src/Operator/convolution.h index 4024d03..6d3faf3 100644 --- a/src/snOperatorCPU/src/Operator/convolution.h +++ b/src/snOperatorCPU/src/Operator/convolution.h @@ -26,7 +26,6 @@ #include "snBase/snBase.h" #include"snOperatorCPU/src/structurs.h" -#include"snOperatorCPU/src/mathFunctions.h" /// convolution layer class Convolution final : SN_Base::OperatorBase{ diff --git a/src/snOperatorCPU/src/Operator/deconvolution.cpp b/src/snOperatorCPU/src/Operator/deconvolution.cpp index a9489f5..a56ad6e 100644 --- a/src/snOperatorCPU/src/Operator/deconvolution.cpp +++ b/src/snOperatorCPU/src/Operator/deconvolution.cpp @@ -29,7 +29,9 @@ #include "snOperatorCPU/src/activeFunctions.h" #include "snOperatorCPU/src/optimizer.h" #include "snOperatorCPU/src/structurs.h" -#include "snOperatorCPU/src/mathFunctions.h" +#include "snOperatorCPU/src/batchNormFunctions.h" +#include "snOperatorCPU/src/dropOut.h" + using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/Operator/deconvolution.h b/src/snOperatorCPU/src/Operator/deconvolution.h index f765ad9..d3e697d 100644 --- a/src/snOperatorCPU/src/Operator/deconvolution.h +++ b/src/snOperatorCPU/src/Operator/deconvolution.h @@ -26,7 +26,6 @@ #include "snBase/snBase.h" #include"snOperatorCPU/src/structurs.h" -#include"snOperatorCPU/src/mathFunctions.h" /// обратная свертка class Deconvolution final : SN_Base::OperatorBase{ diff --git a/src/snOperatorCPU/src/Operator/fullyConnected.cpp b/src/snOperatorCPU/src/Operator/fullyConnected.cpp index 8378587..c397f97 100644 --- a/src/snOperatorCPU/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCPU/src/Operator/fullyConnected.cpp @@ -29,7 +29,9 @@ #include "snOperatorCPU/src/activeFunctions.h" #include "snOperatorCPU/src/optimizer.h" #include "snOperatorCPU/src/structurs.h" -#include "snOperatorCPU/src/mathFunctions.h" +#include "snOperatorCPU/src/batchNormFunctions.h" +#include "snOperatorCPU/src/dropOut.h" + using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/Operator/pooling.cpp b/src/snOperatorCPU/src/Operator/pooling.cpp index 01a001e..6bc7301 100644 --- a/src/snOperatorCPU/src/Operator/pooling.cpp +++ b/src/snOperatorCPU/src/Operator/pooling.cpp @@ -26,6 +26,7 @@ #include "snAux/auxFunc.h" #include "snOperatorCPU/src/Operator/pooling.h" #include "snOperatorCPU/src/structurs.h" +#include "snOperatorCPU/src/paddingOffs.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/Operator/pooling.h b/src/snOperatorCPU/src/Operator/pooling.h index cc32a77..a4a3363 100644 --- a/src/snOperatorCPU/src/Operator/pooling.h +++ b/src/snOperatorCPU/src/Operator/pooling.h @@ -26,8 +26,6 @@ #include "snBase/snBase.h" #include"snOperatorCPU/src/structurs.h" -#include"snOperatorCPU/src/mathFunctions.h" - /// pooling layer class Pooling final : SN_Base::OperatorBase{ diff --git a/src/snOperatorCPU/src/activeFunctions.h b/src/snOperatorCPU/src/activeFunctions.h index 347c4aa..0f74f1b 100644 --- a/src/snOperatorCPU/src/activeFunctions.h +++ b/src/snOperatorCPU/src/activeFunctions.h @@ -24,7 +24,7 @@ // #pragma once -#include "stdafx.h" +#include "snBase/snBase.h" #include "structurs.h" // fv - value, df - deriv diff --git a/src/snOperatorCUDA/src/mathFunctions.cpp b/src/snOperatorCPU/src/batchNormFunctions.cpp similarity index 98% rename from src/snOperatorCUDA/src/mathFunctions.cpp rename to src/snOperatorCPU/src/batchNormFunctions.cpp index 8ebaf97..0bb7d35 100644 --- a/src/snOperatorCUDA/src/mathFunctions.cpp +++ b/src/snOperatorCPU/src/batchNormFunctions.cpp @@ -25,8 +25,7 @@ #include "stdafx.h" #include "Lib/OpenBLAS/cblas.h" -#include "snOperatorCUDA/src/mathFunctions.h" -#include "snOperatorCUDA/src/random.h" +#include "batchNormFunctions.h" using namespace std; using namespace SN_Base; @@ -198,6 +197,4 @@ void batchNormBackward(const SN_Base::snSize& insz, snFloat* gradIn, snFloat* gr prm.scale[i] -= prm.dScale[i] * prm.lr; } -} - - +} \ No newline at end of file diff --git a/src/snOperatorCPU/src/mathFunctions.h b/src/snOperatorCPU/src/batchNormFunctions.h similarity index 88% rename from src/snOperatorCPU/src/mathFunctions.h rename to src/snOperatorCPU/src/batchNormFunctions.h index 16eaadd..7dfd828 100644 --- a/src/snOperatorCPU/src/mathFunctions.h +++ b/src/snOperatorCPU/src/batchNormFunctions.h @@ -39,7 +39,3 @@ void batchNormBackward(const SN_Base::snSize& insz, SN_Base::snFloat* gradIn, SN_Base::snFloat* gradOut, SN_Base::batchNorm); - -void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out); - -void paddingOffs(bool in2out, size_t paddingW, size_t paddingH, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out); \ No newline at end of file diff --git a/src/snOperatorCPU/src/dropOut.cpp b/src/snOperatorCPU/src/dropOut.cpp new file mode 100644 index 0000000..51e56d5 --- /dev/null +++ b/src/snOperatorCPU/src/dropOut.cpp @@ -0,0 +1,46 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include "stdafx.h" +#include "snOperatorCPU/src/random.h" + +using namespace std; +using namespace SN_Base; + +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ + + if (isLern){ + size_t sz = size_t(outsz.size() * dropOut); + vector rnd(sz); + rnd_uniformInt(rnd.data(), sz, 0, int(outsz.size())); + + for (auto i : rnd) out[i] = 0; + } + else{ + size_t sz = outsz.size(); + for (size_t i = 0; i < sz; ++i) + out[i] *= (1.F - dropOut); + } +} diff --git a/src/snOperatorCPU/src/dropOut.h b/src/snOperatorCPU/src/dropOut.h new file mode 100644 index 0000000..5868341 --- /dev/null +++ b/src/snOperatorCPU/src/dropOut.h @@ -0,0 +1,29 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out); diff --git a/src/snOperatorCPU/src/mathFunctions.cpp b/src/snOperatorCPU/src/mathFunctions.cpp deleted file mode 100644 index bab4a61..0000000 --- a/src/snOperatorCPU/src/mathFunctions.cpp +++ /dev/null @@ -1,253 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// - -#include "stdafx.h" -#include "Lib/OpenBLAS/cblas.h" -#include "snOperatorCPU/src/mathFunctions.h" -#include "snOperatorCPU/src/random.h" - -using namespace std; -using namespace SN_Base; - - -void channelBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ - - /* Select 1 output layer from each image in the batch and normalize */ - - size_t stepD = insz.w * insz.h, stepN = stepD * insz.d, bsz = insz.n; - - if (!isLern){ - - for (size_t i = 0; i < insz.d; ++i){ - - /// y = ^x * γ + β - for (size_t j = 0; j < bsz; ++j){ - - snFloat* cin = in + stepN * j + stepD * i, - *cout = out + stepN * j + stepD * i; - for (size_t k = 0; k < stepD; ++k) - cout[k] = (cin[k] - prm.mean[k]) * prm.scale[k] / prm.varce[k] + prm.schift[k]; - } - prm.offset(stepD); - } - } - else{ - - snFloat* share = (snFloat*)calloc(stepD * bsz, sizeof(snFloat)); - snSize sz(insz.w, insz.h, 1, insz.n); - - for (size_t i = 0; i < insz.d; ++i){ - - snFloat* pSh = share; - snFloat* pIn = in + stepD * i; - for (size_t j = 0; j < bsz; ++j){ - - memcpy(pSh, pIn, stepD * sizeof(snFloat)); - pSh += stepD; - pIn += stepN; - } - - if (fwBw) - batchNormForward(sz, share, share, prm); - else - batchNormBackward(sz, share, share, prm); - - pSh = share; - snFloat* pOut = out + stepD * i; - for (size_t j = 0; j < bsz; ++j){ - memcpy(pOut, pSh, stepD * sizeof(snFloat)); - pSh += stepD; - pOut += stepN; - } - - prm.offset(stepD); - prm.norm += stepD * bsz; - } - free(share); - } -} - -void layerBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, const batchNorm& prm){ - - if (!isLern){ - size_t sz = insz.w * insz.h * insz.d, bsz = insz.n; - - /// y = ^x * γ + β - for (size_t j = 0; j < bsz; ++j){ - - snFloat* cin = in + j * sz, *cout = out + j * sz; - for (size_t i = 0; i < sz; ++i) - cout[i] = (cin[i] - prm.mean[i]) * prm.scale[i] / prm.varce[i] + prm.schift[i]; - } - } - else{ // isLerning - if (fwBw) - batchNormForward(insz, in, out, prm); - else - batchNormBackward(insz, in, out, prm); - } -} - -void batchNormForward(const SN_Base::snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ - - size_t inSz = insz.w * insz.h * insz.d, bsz = insz.n; - - /// μ = 1/n * ∑x - cblas_sgemv(CBLAS_ORDER::CblasRowMajor, - CBLAS_TRANSPOSE::CblasTrans, - blasint(bsz), - blasint(inSz), - 1.F / bsz, - in, - blasint(inSz), - prm.onc, - blasint(1), - 0.F, - prm.mean, - blasint(1)); - - /// varce = sqrt(∑xx - mean^2 + e) - for (size_t i = 0; i < inSz; ++i){ - - snFloat* cin = in + i, srq = 0.F; - for (size_t j = 0; j < bsz; ++j){ - srq += cin[0] * cin[0]; - cin += inSz; - } - prm.varce[i] = sqrt(srq / bsz - prm.mean[i] * prm.mean[i] + 0.00001F); - } - - /// norm = (in - mean) / varce - /// y = ^x * γ + β - for (size_t j = 0; j < bsz; ++j){ - - snFloat* cin = in + j * inSz, *cout = out + j * inSz, *norm = prm.norm + j * inSz; - - for (size_t i = 0; i < inSz; ++i){ - norm[i] = (cin[i] - prm.mean[i]) / prm.varce[i]; - cout[i] = norm[i] * prm.scale[i] + prm.schift[i]; - } - } - -} - -void batchNormBackward(const SN_Base::snSize& insz, snFloat* gradIn, snFloat* gradOut, batchNorm prm){ - // https://kevinzakka.github.io/2016/09/14/batch_normalization/ - - size_t inSz = insz.w * insz.h * insz.d, bsz = insz.n; - - /// ∂f/∂β = ∑∂f/∂y - cblas_sgemv(CBLAS_ORDER::CblasRowMajor, - CBLAS_TRANSPOSE::CblasTrans, - blasint(bsz), - blasint(inSz), - 1.F, - gradIn, - blasint(inSz), - prm.onc, - blasint(1), - 0.F, - prm.dSchift, - blasint(1)); - - /// ∂f/∂γ = ∑∂f/∂y ⋅ ^x - for (size_t i = 0; i < inSz; ++i){ - - snFloat* igr = gradIn + i, *norm = prm.norm + i, dScale = 0.F; - for (size_t j = 0; j < bsz; ++j){ - - dScale += igr[0] * norm[0]; - - norm += inSz; - igr += inSz; - } - prm.dScale[i] = dScale; - } - - /// ∂f/∂x = (m⋅γ⋅∂f/∂y − γ⋅∂f/∂β − ^x⋅γ⋅∂f/∂γ) / m⋅σ2 - for (size_t j = 0; j < bsz; ++j){ - - snFloat* igr = gradIn + j * inSz, *ogr = gradOut + j * inSz, *norm = prm.norm + j * inSz; - for (size_t i = 0; i < inSz; ++i) - ogr[i] = prm.scale[i] * (igr[i] * bsz - prm.dSchift[i] - norm[i] * prm.dScale[i]) / (prm.varce[i] * bsz); - } - for (size_t i = 0; i < inSz; ++i){ - prm.schift[i] -= prm.dSchift[i] * prm.lr; - prm.scale[i] -= prm.dScale[i] * prm.lr; - } - -} - -void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ - - if (isLern){ - size_t sz = size_t(outsz.size() * dropOut); - vector rnd(sz); - rnd_uniformInt(rnd.data(), sz, 0, int(outsz.size())); - - for (auto i : rnd) out[i] = 0; - } - else{ - size_t sz = outsz.size(); - for (size_t i = 0; i < sz; ++i) - out[i] *= (1.F - dropOut); - } -} - -void paddingOffs(bool in2out, size_t paddW, size_t paddH, const snSize& insz, snFloat* in, snFloat* out){ - - /// copy with offset padding for each image - size_t sz = insz.h * insz.d * insz.n, stW = insz.w, stH = insz.h; - if (in2out){ - in += (stW + paddW * 2) * paddH; - for (size_t i = 0; i < sz; ++i){ - - if ((i % stH == 0) && (i > 0)) - in += (stW + paddW * 2) * paddH * 2; - - in += paddW; - for (size_t j = 0; j < stW; ++j) - out[j] = in[j]; - in += paddW + stW; - - out += stW; - } - } - else{ - in += (stW + paddW * 2) * paddH; - for (size_t i = 0; i < sz; ++i){ - - if ((i % stH == 0) && (i > 0)) - in += (stW + paddW * 2) * paddH * 2; - - in += paddW; - for (size_t j = 0; j < stW; ++j) - in[j] = out[j]; - in += paddW + stW; - - out += stW; - } - } -} diff --git a/src/snOperatorCPU/src/optimizer.h b/src/snOperatorCPU/src/optimizer.h index 4f00d84..43d630b 100644 --- a/src/snOperatorCPU/src/optimizer.h +++ b/src/snOperatorCPU/src/optimizer.h @@ -25,7 +25,7 @@ #pragma once -#include "stdafx.h" +#include "snBase/snBase.h" #include "structurs.h" void optimizer(SN_Base::snFloat* dWeight, diff --git a/src/snOperatorCPU/src/paddingOffs.cpp b/src/snOperatorCPU/src/paddingOffs.cpp new file mode 100644 index 0000000..fe1ef55 --- /dev/null +++ b/src/snOperatorCPU/src/paddingOffs.cpp @@ -0,0 +1,66 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// + +#include "stdafx.h" + +using namespace std; +using namespace SN_Base; + + +void paddingOffs(bool in2out, size_t paddW, size_t paddH, const snSize& insz, snFloat* in, snFloat* out){ + + /// copy with offset padding for each image + size_t sz = insz.h * insz.d * insz.n, stW = insz.w, stH = insz.h; + if (in2out){ + in += (stW + paddW * 2) * paddH; + for (size_t i = 0; i < sz; ++i){ + + if ((i % stH == 0) && (i > 0)) + in += (stW + paddW * 2) * paddH * 2; + + in += paddW; + for (size_t j = 0; j < stW; ++j) + out[j] = in[j]; + in += paddW + stW; + + out += stW; + } + } + else{ + in += (stW + paddW * 2) * paddH; + for (size_t i = 0; i < sz; ++i){ + + if ((i % stH == 0) && (i > 0)) + in += (stW + paddW * 2) * paddH * 2; + + in += paddW; + for (size_t j = 0; j < stW; ++j) + in[j] = out[j]; + in += paddW + stW; + + out += stW; + } + } +} diff --git a/src/snOperatorCPU/src/paddingOffs.h b/src/snOperatorCPU/src/paddingOffs.h new file mode 100644 index 0000000..eb3ee40 --- /dev/null +++ b/src/snOperatorCPU/src/paddingOffs.h @@ -0,0 +1,29 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + +void paddingOffs(bool in2out, size_t paddingW, size_t paddingH, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out); \ No newline at end of file diff --git a/src/snOperatorCPU/src/random.h b/src/snOperatorCPU/src/random.h index 8a8c674..5587e41 100644 --- a/src/snOperatorCPU/src/random.h +++ b/src/snOperatorCPU/src/random.h @@ -24,8 +24,6 @@ // #pragma once -#include -#include #include "snBase/snBase.h" void rnd_uniformReal(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat min, SN_Base::snFloat max); diff --git a/src/snOperatorCPU/src/stdafx.h b/src/snOperatorCPU/src/stdafx.h index 7647edb..00aea73 100644 --- a/src/snOperatorCPU/src/stdafx.h +++ b/src/snOperatorCPU/src/stdafx.h @@ -31,7 +31,6 @@ #include #include #include "snBase/snBase.h" -#include "snOperatorCPU/snOperator.h" #define PROFILE_START double ctm = omp_get_wtime(); #define PROFILE_END(func) g_statusMess(this, name_ + " " + node_ + " " + func + " " + std::to_string(omp_get_wtime() - ctm)); ctm = omp_get_wtime(); diff --git a/src/snOperatorCPU/src/structurs.h b/src/snOperatorCPU/src/structurs.h index 897eec2..028d7a5 100644 --- a/src/snOperatorCPU/src/structurs.h +++ b/src/snOperatorCPU/src/structurs.h @@ -24,8 +24,6 @@ // #pragma once -#include "snBase/snBase.h" - /// тип ф-ии активации enum class activeType{ none = -1, diff --git a/src/snOperatorCPU/src/weightInit.h b/src/snOperatorCPU/src/weightInit.h index 2c05375..66c5b11 100644 --- a/src/snOperatorCPU/src/weightInit.h +++ b/src/snOperatorCPU/src/weightInit.h @@ -28,7 +28,6 @@ #include "snBase/snBase.h" #include "structurs.h" -#include "random.h" void weightInit(SN_Base::snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype); diff --git a/src/snOperatorCUDA/CMakeLists.txt b/src/snOperatorCUDA/CMakeLists.txt index 8f3d61e..c5c8e8f 100644 --- a/src/snOperatorCUDA/CMakeLists.txt +++ b/src/snOperatorCUDA/CMakeLists.txt @@ -1,12 +1,12 @@ cmake_minimum_required(VERSION 3.8 FATAL_ERROR) -project(snOperator) +project(snOperatorCUDA) FILE(GLOB_RECURSE SRC_snOperator "*.cpp" "*.h" "*.cu") -add_library(snOperator STATIC ${SRC_snOperator}) +add_library(snOperatorCUDA STATIC ${SRC_snOperator}) -set_target_properties(snOperator PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_compile_features(snOperator PUBLIC cxx_std_11) +set_target_properties(snOperatorCUDA PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_compile_features(snOperatorCUDA PUBLIC cxx_std_11) -target_link_libraries(snOperator cublas cudart_static cudnn snAux snBase) \ No newline at end of file +target_link_libraries(snOperatorCUDA cublas cudart_static cudnn snAux snBase) \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu index 5110746..e8f9ac6 100644 --- a/src/snOperatorCUDA/src/CUDA/activation.cu +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -7,7 +7,7 @@ #include "../stdafx.h" #include "../structurs.h" -#include "../activeFunctions.h" +#include "../activationFunctions.h" using namespace SN_Base; diff --git a/src/snOperatorCUDA/src/CUDA/batchNorm.cu b/src/snOperatorCUDA/src/CUDA/batchNorm.cu index 6cf9b8c..a96ef7d 100644 --- a/src/snOperatorCUDA/src/CUDA/batchNorm.cu +++ b/src/snOperatorCUDA/src/CUDA/batchNorm.cu @@ -8,137 +8,16 @@ using namespace SN_Base; -void batchNormInit(SN_Base::snFloat* inout, const SN_Base::snSize& iosz, void** pGpuPrm){ - - bool isFirst = false; - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - - if (!gpuPrm){ - - gpuPrm = new gpuParams(); - memset(gpuPrm, 0, sizeof(gpuParams)); - *pGpuPrm = gpuPrm; - - cudnnHandle_t cudnn = nullptr; - cuAssert(cudnnCreate(&cudnn)); - gpuPrm->cudnn = cudnn; - - isFirst = true; - } - - if (gpuPrm->inszMem != iosz){ - - cudnnActivationMode_t actMode; - - switch (atype){ - case activeType::sigmoid: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_SIGMOID; break; - case activeType::relu: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_RELU; break; - case activeType::leakyRelu: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_CLIPPED_RELU; break; - case activeType::elu: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_ELU; break; - default: actMode = cudnnActivationMode_t::CUDNN_ACTIVATION_RELU; break; - } - - // activ_desc - cudnnActivationDescriptor_t activ_desc = nullptr; - cuAssert(cudnnCreateActivationDescriptor(&activ_desc)); - cuAssert(cudnnSetActivationDescriptor(activ_desc, actMode, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, 0.0)); - if (!isFirst) - cuAssert(cudnnDestroyActivationDescriptor(gpuPrm->activ_desc)); - gpuPrm->activ_desc = activ_desc; - - // input - cudnnTensorDescriptor_t x_desc = nullptr; - cuAssert(cudnnCreateTensorDescriptor(&x_desc)); - cuAssert(cudnnSetTensor4dDescriptor(x_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); - if (!isFirst) - cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->x_desc)); - gpuPrm->x_desc = x_desc; - - // dinput - cudnnTensorDescriptor_t dx_desc = nullptr; - cuAssert(cudnnCreateTensorDescriptor(&dx_desc)); - cuAssert(cudnnSetTensor4dDescriptor(dx_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); - if (!isFirst) - cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->dx_desc)); - gpuPrm->dx_desc = dx_desc; - - // output - cudnnTensorDescriptor_t y_desc; - cuAssert(cudnnCreateTensorDescriptor(&y_desc)); - cuAssert(cudnnSetTensor4dDescriptor(y_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); - if (!isFirst) - cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->y_desc)); - gpuPrm->y_desc = y_desc; - - // doutput - cudnnTensorDescriptor_t dy_desc; - cuAssert(cudnnCreateTensorDescriptor(&dy_desc)); - cuAssert(cudnnSetTensor4dDescriptor(dy_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, int(iosz.n), int(iosz.d), int(iosz.h), int(iosz.w))); - if (!isFirst) - cuAssert(cudnnDestroyTensorDescriptor((cudnnTensorDescriptor_t)gpuPrm->dy_desc)); - gpuPrm->dy_desc = dy_desc; - - gpuPrm->inszMem = iosz; - } -} - -void batchNormFree(uint32_t deviceId, void* pGpuPrm){ - - cudaSetDevice(deviceId); - - gpuParams* gpuPrm = (gpuParams*)pGpuPrm; - - if (!gpuPrm) return; - - cuAssert(cudnnDestroy(gpuPrm->cudnn)); - cuAssert(cudnnDestroyActivationDescriptor(gpuPrm->activ_desc)); - cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->x_desc)); - cuAssert(cudnnDestroyTensorDescriptor(gpuPrm->y_desc)); -} - void batchNormForward(activeType atype, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId, void** pGpuPrm){ cudaSetDevice(deviceId); - activationInit(atype, inout, iosz, pGpuPrm); - - gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; - - cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, iosz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); - - float alpha = 1.0, beta = 0.0; - - cuAssert(cudnnActivationForward(gpuPrm->cudnn, - gpuPrm->activ_desc, - &alpha, - gpuPrm->x_desc, - gpuPrm->in_mem, - &beta, - gpuPrm->y_desc, - inout)); + } void batchNormBackward(SN_Base::snFloat* inprev, SN_Base::snFloat* outprev, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId, void* pGpuPrm){ cudaSetDevice(deviceId); - gpuParams* gpuPrm = (gpuParams*)pGpuPrm; - - cuAssert(cudaMemcpy(gpuPrm->in_mem, inout, iosz.size() * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); - - float alpha = 1.0, beta = 0.0; - - cuAssert(cudnnActivationBackward(gpuPrm->cudnn, - gpuPrm->activ_desc, - &alpha, - gpuPrm->y_desc, - outprev, - gpuPrm->dy_desc, - gpuPrm->in_mem, - gpuPrm->x_desc, - inprev, - &beta, - gpuPrm->dx_desc, - inout)); + } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/convolution.cu b/src/snOperatorCUDA/src/CUDA/convolution.cu index 9aa9324..766a933 100644 --- a/src/snOperatorCUDA/src/CUDA/convolution.cu +++ b/src/snOperatorCUDA/src/CUDA/convolution.cu @@ -295,7 +295,7 @@ void Convolution::forwardCUDA(const convParams& prms, output)); // +bias - cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); +// cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); } void Convolution::backwardCUDA_GW(const convParams& prms, diff --git a/src/snOperatorCUDA/src/CUDA/deconvolution.cu b/src/snOperatorCUDA/src/CUDA/deconvolution.cu index 5db3b2b..bcf0aef 100644 --- a/src/snOperatorCUDA/src/CUDA/deconvolution.cu +++ b/src/snOperatorCUDA/src/CUDA/deconvolution.cu @@ -332,7 +332,7 @@ void Deconvolution::backwardCUDA_GW(const deconvParams& prms, gpuPrm->d_bias));*/ // +bias - cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + // cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); } @@ -360,6 +360,6 @@ void Deconvolution::backwardCUDA_G(const deconvParams& prms, gradOut)); // +bias - cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + // cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); } diff --git a/src/snOperatorCUDA/src/CUDA/fullyConnected.cu b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu index e33594a..846ba4b 100644 --- a/src/snOperatorCUDA/src/CUDA/fullyConnected.cu +++ b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu @@ -120,7 +120,7 @@ void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloa krn)); // Out, step to next Y (Y21 - Y11) // +bias - cuFwdBias <<< int(insz.n), 128 >>> (kernel, insz, gpuPrm->d_w, gpuPrm->d_out); +// cuFwdBias <<< int(insz.n), 128 >>> (kernel, insz, gpuPrm->d_w, gpuPrm->d_out); } @@ -173,7 +173,7 @@ void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, krn)); // dW, step to next // bias - cuBwdBias <<< 1, 128 >>> (kernel, insz, d_grin, gpuPrm->d_dw); +// cuBwdBias <<< 1, 128 >>> (kernel, insz, d_grin, gpuPrm->d_dw); // cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); diff --git a/src/snOperatorCUDA/src/Operator/activation.cpp b/src/snOperatorCUDA/src/Operator/activation.cpp index 0f3a882..4b92cb2 100644 --- a/src/snOperatorCUDA/src/Operator/activation.cpp +++ b/src/snOperatorCUDA/src/Operator/activation.cpp @@ -24,7 +24,7 @@ // #include "../stdafx.h" #include "snOperatorCUDA/src/Operator/activation.h" -#include "snOperatorCUDA/src/activeFunctions.h" +#include "snOperatorCUDA/src/activationFunctions.h" #include "snAux/auxFunc.h" using namespace std; @@ -60,7 +60,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std baseOut_ = neighbOpr[0]->getOutput(); /// active func - activeFuncForward(baseOut_.size().size(), baseOut_.getDataGPU(), activeType_); + // activeFuncForward(baseOut_.size().size(), baseOut_.getDataGPU(), activeType_); } else{ // backward @@ -77,7 +77,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std } /// active func - activeFuncBackward(baseGrad_.size().size(), baseGrad_.getDataGPU(), activeType_); + // activeFuncBackward(baseGrad_.size().size(), baseGrad_.getDataGPU(), activeType_); } return vector(); diff --git a/src/snOperatorCUDA/src/Operator/batchNorm.cpp b/src/snOperatorCUDA/src/Operator/batchNorm.cpp index be420d7..ab95e75 100644 --- a/src/snOperatorCUDA/src/Operator/batchNorm.cpp +++ b/src/snOperatorCUDA/src/Operator/batchNorm.cpp @@ -24,7 +24,6 @@ // #include "../stdafx.h" #include "snOperatorCUDA/src/Operator/batchNorm.h" -#include "snOperatorCUDA/src/mathFunctions.h" #include "snAux/auxFunc.h" using namespace std; @@ -85,10 +84,10 @@ std::vector BatchNorm::Do(const operationParam& operPrm, const std: switch (bnType_){ case batchNormType::byChannels: - channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); break; case batchNormType::byLayer: - layerBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // layerBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); break; default: break; @@ -112,10 +111,10 @@ std::vector BatchNorm::Do(const operationParam& operPrm, const std: switch (bnType_){ case batchNormType::byChannels: - channelBatchNorm(false, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // channelBatchNorm(false, operPrm.isLerning, outsz, out, out, baseBatchNorm_); break; case batchNormType::byLayer: - layerBatchNorm(false, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // layerBatchNorm(false, operPrm.isLerning, outsz, out, out, baseBatchNorm_); break; default: break; diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 477a4fc..236f487 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -26,11 +26,10 @@ #include "snOperatorCUDA/src/Operator/convolution.h" #include "snAux/auxFunc.h" #include "snOperatorCUDA/src/weightInit.h" -#include "snOperatorCUDA/src/activeFunctions.h" +#include "snOperatorCUDA/src/activationFunctions.h" #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" #include "snOperatorCUDA/src/dropOut.h" -#include "snOperatorCUDA/src/activeFunctions.h" using namespace std; using namespace SN_Base; @@ -45,9 +44,7 @@ Convolution::Convolution(void* net, const string& name, const string& node, std: Convolution::~Convolution(){ - freeParamCUDA(convGPUParams_); - dropOutFree(gpuDeviceId_, dropGPUParams_); - activationFree(gpuDeviceId_, activGPUParams_); + freeParamCUDA(convGPUParams_); } void Convolution::load(std::map& prms){ @@ -96,23 +93,7 @@ void Convolution::load(std::map& prms){ else ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); } - - // aux array - auxParams_["dWeight"] = vector(); - auxParams_["dWPrev"] = vector(); - auxParams_["dWGrad"] = vector(); - - if (batchNormType_ != batchNormType::none){ - auxParams_["bn_mean"] = vector(); - auxParams_["bn_varce"] = vector(); - auxParams_["bn_scale"] = vector(); - auxParams_["bn_schift"] = vector(); - auxParams_["bn_norm"] = vector(); - auxParams_["bn_dScale"] = vector(); - auxParams_["bn_dSchift"] = vector(); - auxParams_["bn_onc"] = vector(); - } - + setInternPrm(prms); } @@ -182,17 +163,12 @@ bool Convolution::setInternPrm(std::map& prms){ bool Convolution::setBatchNorm(const batchNorm& bn){ size_t osz = bn.sz.size(); - - auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); - auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); - auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); - auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); - - memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); + + /* memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); - +*/ baseBatchNorm_.sz = bn.sz; return true; @@ -252,19 +228,19 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, convGPUParams_); /// dropOut - if (dropOut_ > 0.F) - dropOutForward(dropOut_, out, outsz, gpuDeviceId_, &dropGPUParams_); + //if (dropOut_ > 0.F) + // dropOutForward(dropOut_, out, outsz, gpuDeviceId_, &dropGPUParams_); /// batchNorm - if (batchNormType_ == batchNormType::beforeActive) - channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // if (batchNormType_ == batchNormType::beforeActive) + // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active function - activationForward(activeType_, out, outsz.size(), gpuDeviceId_, &activGPUParams_); + // activationForward(activeType_, out, outsz.size(), gpuDeviceId_, &activGPUParams_); /// batchNorm - if (batchNormType_ == batchNormType::postActive) - channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // if (batchNormType_ == batchNormType::postActive) + // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); } void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ @@ -273,24 +249,24 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o snSize insz = inTns.size(); /// batchNorm - if (batchNormType_ == batchNormType::postActive) - channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); + // if (batchNormType_ == batchNormType::postActive) + // channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); // active function if (activeType_ != activeType::none){ size_t osz = baseOut_.size().size(); - activationBackward(inputMem_->getDataGPU(), baseOut_.getDataGPU(), gradIn, osz, gpuDeviceId_, activGPUParams_); + // activationBackward(inputMem_->getDataGPU(), baseOut_.getDataGPU(), gradIn, osz, gpuDeviceId_, activGPUParams_); } /// batchNorm - if (batchNormType_ == batchNormType::beforeActive) - channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); + // if (batchNormType_ == batchNormType::beforeActive) + // channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); /// dropOut - if (dropOut_ > 0.F) - dropOutBackward(gradIn, insz, gpuDeviceId_, dropGPUParams_); + // if (dropOut_ > 0.F) + // dropOutBackward(gradIn, insz, gpuDeviceId_, dropGPUParams_); // calculation of the output gradient and weight correction snFloat* gradOut = baseGrad_.getDataGPU(); @@ -298,19 +274,19 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o snFloat* weight = baseWeight_.getDataGPU(); if (!isFreeze_){ - snFloat* dWeight = auxParams_["dWeight"].data(); + // snFloat* dWeight = auxParams_["dWeight"].data(); snFloat* in = inputMem_->getDataGPU(); // calculation - backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, convGPUParams_); + // backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, convGPUParams_); // correct weight - snFloat* dWPrev = auxParams_["dWPrev"].data(); - snFloat* dWGrad = auxParams_["dWGrad"].data(); + // snFloat* dWPrev = auxParams_["dWPrev"].data(); + // snFloat* dWGrad = auxParams_["dWGrad"].data(); size_t wsz = baseWeight_.size().size(); - optimizer(dWeight, + /* optimizer(dWeight, dWPrev, dWGrad, weight, @@ -319,7 +295,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o optLmbRegular_, optDecayMomentDW_, optDecayMomentWGr_, - optimizerType_); + optimizerType_);*/ } else{ // isFreeze backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, convGPUParams_); @@ -380,19 +356,13 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseOut_.resize(outSz); - if (isLern){ + if (isLern) baseGrad_.resize(newsz); - // aux array - auxParams_["dWeight"].resize(ntp, 0); - auxParams_["dWPrev"].resize(ntp, 0); - auxParams_["dWGrad"].resize(ntp, 0); - } - size_t osz = outSz.w * outSz.h * outSz.d; if (batchNormType_ != batchNormType::none){ - auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); + /* auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); auxParams_["bn_varce"].resize(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); auxParams_["bn_scale"].resize(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); auxParams_["bn_schift"].resize(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); @@ -402,7 +372,7 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize auxParams_["bn_dScale"].resize(osz, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); auxParams_["bn_dSchift"].resize(osz, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); auxParams_["bn_onc"].resize(outSz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); - } + }*/ baseBatchNorm_.sz = outSz; baseBatchNorm_.sz.n = 1; } diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp index 81738d6..8675fd9 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -26,10 +26,9 @@ #include "snOperatorCUDA/src/Operator/deconvolution.h" #include "snAux/auxFunc.h" #include "snOperatorCUDA/src/weightInit.h" -#include "snOperatorCUDA/src/activeFunctions.h" +#include "snOperatorCUDA/src/activationFunctions.h" #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" -#include "snOperatorCUDA/src/mathFunctions.h" using namespace std; using namespace SN_Base; @@ -235,19 +234,19 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& forwardCUDA(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); /// dropOut - if (dropOut_ > 0.F) - dropOut(operPrm.isLerning, dropOut_, outsz, out); + // if (dropOut_ > 0.F) + // dropOut(operPrm.isLerning, dropOut_, outsz, out); /// batchNorm - if (batchNormType_ == batchNormType::beforeActive) - channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // if (batchNormType_ == batchNormType::beforeActive) + // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active func - activeFuncForward(outsz.size(), out, activeType_); + // activeFuncForward(outsz.size(), out, activeType_); /// batchNorm - if (batchNormType_ == batchNormType::postActive) - channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + //if (batchNormType_ == batchNormType::postActive) + // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); } @@ -256,8 +255,8 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& snFloat* gradIn = inTns.getDataGPU(); /// batchNorm - if (batchNormType_ == batchNormType::postActive) - channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + // if (batchNormType_ == batchNormType::postActive) + // channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); /// active func if (activeType_ != activeType::none){ @@ -265,15 +264,15 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& snFloat* out = baseOut_.getDataGPU(); size_t osz = baseOut_.size().size(); - activeFuncBackward(osz, out, activeType_); + // activeFuncBackward(osz, out, activeType_); // update grad for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; } /// batchNorm - if (batchNormType_ == batchNormType::beforeActive) - channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + // if (batchNormType_ == batchNormType::beforeActive) + // channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction snFloat* grOut = baseGrad_.getDataGPU(); diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.h b/src/snOperatorCUDA/src/Operator/deconvolution.h index 2ae3dc4..3f208cb 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.h +++ b/src/snOperatorCUDA/src/Operator/deconvolution.h @@ -26,7 +26,6 @@ #include "snBase/snBase.h" #include"snOperatorCUDA/src/structurs.h" -#include"snOperatorCUDA/src/mathFunctions.h" /// deconvolution layer class Deconvolution final : SN_Base::OperatorBase{ diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index 4c75007..0ff3dce 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -26,10 +26,10 @@ #include "snOperatorCUDA/src/Operator/fullyConnected.h" #include "snAux/auxFunc.h" #include "snOperatorCUDA/src/weightInit.h" -#include "snOperatorCUDA/src/activeFunctions.h" +#include "snOperatorCUDA/src/activationFunctions.h" #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" -#include "snOperatorCUDA/src/mathFunctions.h" +#include "snOperatorCUDA/src/batchNornFunctions.h" using namespace std; using namespace SN_Base; @@ -237,19 +237,19 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& /// dropOut snSize outSz = baseOut_.size(); - if (dropOut_ > 0.F) - dropOut(operPrm.isLerning, dropOut_, outSz, out); + // if (dropOut_ > 0.F) + // dropOut(operPrm.isLerning, dropOut_, outSz, out); /// batchNorm - if (batchNormType_ == batchNormType::beforeActive) - layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); + // if (batchNormType_ == batchNormType::beforeActive) + // layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); /// active func - activeFuncForward(kernel_ * insz.n, out, activeType_); + // activeFuncForward(kernel_ * insz.n, out, activeType_); /// batchNorm - if (batchNormType_ == batchNormType::postActive) - layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); + // if (batchNormType_ == batchNormType::postActive) + // layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); } void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ @@ -258,8 +258,8 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam /// batchNorm snSize gsz = inTns.size(); - if (batchNormType_ == batchNormType::postActive) - layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); + // if (batchNormType_ == batchNormType::postActive) + // layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); // active func if (activeType_ != activeType::none){ @@ -267,15 +267,15 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam snFloat* out = baseOut_.getDataGPU(); size_t osz = kernel_ * inSzMem_.n; - activeFuncBackward(osz, out, activeType_); + // activeFuncBackward(osz, out, activeType_); // update grad for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; } /// batchNorm - if (batchNormType_ == batchNormType::beforeActive) - layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); + // if (batchNormType_ == batchNormType::beforeActive) + // layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction snFloat* gradOut = baseGrad_.getDataGPU(); diff --git a/src/snOperatorCUDA/src/Operator/input.cpp b/src/snOperatorCUDA/src/Operator/input.cpp index c8e7053..069cbad 100644 --- a/src/snOperatorCUDA/src/Operator/input.cpp +++ b/src/snOperatorCUDA/src/Operator/input.cpp @@ -36,7 +36,7 @@ OperatorBase(net, name, node, prms){ } bool Input::setInput(const snFloat* data, const snSize& dsz){ - baseOut_.setData(data, dsz); + baseOut_.setDataGPU(data, dsz); return true; } diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp index 8f46e36..8f032be 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -24,7 +24,6 @@ // #include "../stdafx.h" #include "snOperatorCUDA/src/Operator/lossFunction.h" -#include "snOperatorCUDA/src/activeFunctions.h" using namespace std; using namespace SN_Base; @@ -129,7 +128,7 @@ void LossFunction::forward(const Tensor& inTns){ true, inTns.size(), inTns.getDataGPU(), outSz, &outData); if (outData){ - baseOut_.setData(outData, outSz); + baseOut_.setDataGPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); @@ -195,7 +194,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ false, inTns.size(), inTns.getDataGPU(), outSz, &outData); if (outData){ - baseGrad_.setData(outData, outSz); + baseGrad_.setDataGPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); diff --git a/src/snOperatorCUDA/src/Operator/pooling.cpp b/src/snOperatorCUDA/src/Operator/pooling.cpp index f3da593..da9ff85 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.cpp +++ b/src/snOperatorCUDA/src/Operator/pooling.cpp @@ -124,18 +124,18 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara /// copy with offset padding for each image snFloat* in = inputMem_->getDataGPU(); - if (isPadding_){ + /* if (isPadding_){ inTnsExp_.resize(inDataExpSz_); paddingOffs(false, poolPrms_.paddingW, poolPrms_.paddingH, insz, inTnsExp_.getDataGPU(), inputMem_->getDataGPU()); insz = inDataExpSz_; in = inTnsExp_.getDataGPU(); - } + }*/ /// output calculation snFloat* out = baseOut_.getDataGPU(); // calculation - forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, outInx_.data(), gpuParams_); + forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, gpuParams_); } @@ -152,10 +152,8 @@ void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operP } /// grad calculation - backwardCUDA(poolPrms_, baseOut_.size(), outInx_.data(), baseOut_.getDataGPU(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); - - if (isPadding_) - paddingOffs(true, poolPrms_.paddingW, poolPrms_.paddingH, inSzMem_, gradOut, baseGrad_.getDataGPU()); + backwardCUDA(poolPrms_, baseOut_.size(), baseOut_.getDataGPU(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); + } void Pooling::updateConfig(bool isLern, const snSize& newsz){ @@ -190,9 +188,7 @@ void Pooling::updateConfig(bool isLern, const snSize& newsz){ } baseOut_.resize(outSz); - - outInx_.resize(outSz.size(), 0); - + if (isLern) baseGrad_.resize(newsz); diff --git a/src/snOperatorCUDA/src/Operator/pooling.h b/src/snOperatorCUDA/src/Operator/pooling.h index 4c5b8ee..69251f5 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.h +++ b/src/snOperatorCUDA/src/Operator/pooling.h @@ -26,8 +26,6 @@ #include "snBase/snBase.h" #include"snOperatorCUDA/src/structurs.h" -#include"snOperatorCUDA/src/mathFunctions.h" - /// pooling layer class Pooling final : SN_Base::OperatorBase{ diff --git a/src/snOperatorCUDA/src/Operator/userLayer.cpp b/src/snOperatorCUDA/src/Operator/userLayer.cpp index c5abb2b..48eed76 100644 --- a/src/snOperatorCUDA/src/Operator/userLayer.cpp +++ b/src/snOperatorCUDA/src/Operator/userLayer.cpp @@ -58,7 +58,7 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec true, baseOut_.size(), baseOut_.getDataGPU(), outSz, &outData); if (outData) - baseOut_.setData(outData, outSz); + baseOut_.setDataGPU(outData, outSz); else{ ERROR_MESS("not set 'outData' in userCBack"); } @@ -82,7 +82,7 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec false, baseGrad_.size(), baseGrad_.getDataGPU(), outSz, &outData); if (outData) - baseGrad_.setData(outData, outSz); + baseGrad_.setDataGPU(outData, outSz); else{ ERROR_MESS("not set 'outData' in userCBack"); } diff --git a/src/snOperatorCUDA/src/activeFunctions.h b/src/snOperatorCUDA/src/activationFunctions.h similarity index 100% rename from src/snOperatorCUDA/src/activeFunctions.h rename to src/snOperatorCUDA/src/activationFunctions.h diff --git a/src/snOperatorCUDA/src/batchNornFunctions.h b/src/snOperatorCUDA/src/batchNornFunctions.h new file mode 100644 index 0000000..82360f4 --- /dev/null +++ b/src/snOperatorCUDA/src/batchNornFunctions.h @@ -0,0 +1,35 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +// +//void channelBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, batchNorm prm); +// +//void layerBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, const batchNorm& prm); +// +//void batchNormForward(const SN_Base::snSize& insz, snFloat* in, snFloat* out, batchNorm prm); +// +//void batchNormBackward(const SN_Base::snSize& insz, snFloat* gradIn, snFloat* gradOut, batchNorm prm); \ No newline at end of file From 223f939112541db85d6d55105da9a34add609db0 Mon Sep 17 00:00:00 2001 From: al_medved Date: Thu, 18 Jul 2019 22:08:39 +0500 Subject: [PATCH 10/35] -develop --- mswin/VS12/snOperatorCUDA.vcxproj | 2 +- mswin/VS12/snOperatorCUDA.vcxproj.filters | 6 +- src/snOperatorCUDA/src/CUDA/activation.cu | 188 +++++++++++++----- .../src/Operator/convolution.cpp | 32 +-- src/snOperatorCUDA/src/Operator/input.cpp | 2 +- src/snOperatorCUDA/src/activationFunctions.h | 18 +- 6 files changed, 161 insertions(+), 87 deletions(-) diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index 8fbc7d9..be8dbbe 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -188,7 +188,7 @@ - + diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index cc5fc63..596dcb1 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -21,9 +21,6 @@ - - Header Files - Header Files @@ -93,6 +90,9 @@ Header Files + + Header Files + diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu index e8f9ac6..a59feaa 100644 --- a/src/snOperatorCUDA/src/CUDA/activation.cu +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -1,9 +1,5 @@ #include -#include - -#include -#include #include "../stdafx.h" #include "../structurs.h" @@ -11,92 +7,180 @@ using namespace SN_Base; +__global__ void fv_sigmoid(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch -void activationForward(size_t sz, SN_Base::snFloat* data, activeType active, uint32_t deviceId){ - - cudaSetDevice(deviceId); + // gridDim.x - number of out layers + // gridDim.y - batch size - switch (active){ - case activeType::sigmoid: fv_sigmoid(data, sz); break; - case activeType::relu: fv_relu(data, sz); break; - case activeType::leakyRelu: fv_leakyRelu(data, sz); break; - case activeType::elu: fv_elu(data, sz); break; - default: break; - } -} + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType active, uint32_t deviceId){ + unsigned int i = threadIdx.x; + while (i < outStepByD){ + + output[i] = 1.F / (1.F + std::exp(-output[i])); - cudaSetDevice(deviceId); - - switch (active){ - case activeType::sigmoid: df_sigmoid(data, sz); break; - case activeType::relu: df_relu(data, sz); break; - case activeType::leakyRelu: df_leakyRelu(data, sz); break; - case activeType::elu: df_elu(data, sz); break; - default: break; + output[i] = (output[i] < 0.99999F) ? output[i] : 0.99999F; + output[i] = (output[i] > 0.00001F) ? output[i] : 0.00001F; + + i += blockDim.x; } } +__global__ void df_sigmoid(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch -void fv_sigmoid(snFloat* ioVal, size_t sz){ + // gridDim.x - number of out layers + // gridDim.y - batch size - for (size_t i = 0; i < sz; ++i){ + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - ioVal[i] = 1.F / (1.F + std::exp(-ioVal[i])); + unsigned int i = threadIdx.x; + while (i < outStepByD){ - ioVal[i] = (ioVal[i] < 0.99999F) ? ioVal[i] : 0.99999F; - ioVal[i] = (ioVal[i] > 0.00001F) ? ioVal[i] : 0.00001F; + output[i] = output[i] * (1.F - output[i]); + + i += blockDim.x; } } -void df_sigmoid(snFloat* ioSigm, size_t sz){ - for (size_t i = 0; i < sz; ++i){ +__global__ void fv_relu(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size - ioSigm[i] = ioSigm[i] * (1.F - ioSigm[i]); - } -} + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; -void fv_relu(snFloat* ioVal, size_t sz){ + unsigned int i = threadIdx.x; + while (i < outStepByD){ - for (size_t i = 0; i < sz; ++i){ + output[i] = output[i] >= 0 ? output[i] : 0; - ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : 0; + i += blockDim.x; } }; -void df_relu(snFloat* ioRelu, size_t sz){ +__global__ void df_relu(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size - for (size_t i = 0; i < sz; ++i){ + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - ioRelu[i] = ioRelu[i] >= 0 ? 1.F : 0.F; + unsigned int i = threadIdx.x; + while (i < outStepByD){ + + output[i] = output[i] >= 0 ? 1.F : 0.F; + + i += blockDim.x; } }; -void fv_leakyRelu(snFloat* ioVal, size_t sz, snFloat minv){ +__global__ void fv_leakyRelu(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + unsigned int i = threadIdx.x; + while (i < outStepByD){ + + output[i] = output[i] >= 0 ? output[i] : 0.01F * output[i]; + + i += blockDim.x; + } +} +__global__ void df_leakyRelu(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size - for (size_t i = 0; i < sz; ++i){ + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * ioVal[i]; + unsigned int i = threadIdx.x; + while (i < outStepByD){ + + output[i] = output[i] >= 0 ? 1 : 0.01F; + + i += blockDim.x; } } -void df_leakyRelu(snFloat* ioRelu, size_t sz, snFloat minv){ - for (size_t i = 0; i < sz; ++i){ +__global__ void fv_elu(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - ioRelu[i] = ioRelu[i] >= 0 ? 1 : minv; + unsigned int i = threadIdx.x; + while (i < outStepByD){ + + output[i] = output[i] >= 0 ? output[i] : 0.01F * (exp(output[i]) - 1.F); + + i += blockDim.x; } } +__global__ void df_elu(const snSize& outsz, snFloat* output){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size -void fv_elu(snFloat* ioVal, size_t sz, snFloat minv){ + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; - for (size_t i = 0; i < sz; ++i){ + unsigned int i = threadIdx.x; + while (i < outStepByD){ + + output[i] = output[i] >= 0 ? 1 : output[i] + 0.01F; + + i += blockDim.x; + } +} - ioVal[i] = ioVal[i] >= 0 ? ioVal[i] : minv * (exp(ioVal[i]) - 1.F); +void activationForward(const snSize& sz, snFloat* data, activeType active, uint32_t deviceId){ + + cudaSetDevice(deviceId); + + switch (active){ + case activeType::sigmoid: fv_sigmoid(sz, data); break; + case activeType::relu: fv_relu(data, sz); break; + case activeType::leakyRelu: fv_leakyRelu(data, sz); break; + case activeType::elu: fv_elu(data, sz); break; + default: break; } } -void df_elu(snFloat* ioElu, size_t sz, snFloat minv){ - for (size_t i = 0; i < sz; ++i){ +void activeFuncBackward(const snSize& sz, snFloat* data, activeType active, uint32_t deviceId){ - ioElu[i] = ioElu[i] >= 0 ? 1 : ioElu[i] + minv; + cudaSetDevice(deviceId); + + switch (active){ + case activeType::sigmoid: df_sigmoid(data, sz); break; + case activeType::relu: df_relu(data, sz); break; + case activeType::leakyRelu: df_leakyRelu(data, sz); break; + case activeType::elu: df_elu(data, sz); break; + default: break; } } + diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 236f487..3d3bb5b 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -208,24 +208,26 @@ std::vector Convolution::Do(const operationParam& operPrm, const st void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - snSize insz = inTns.size(); - inputMem_ = &inTns; + //snSize insz = inTns.size(); + //inputMem_ = &inTns; - /// Has the size of the data changed? - if (insz != inSzMem_){ - inSzMem_ = insz; - updateConfig(operPrm.isLerning, insz, inDataExpSz_); - } + ///// Has the size of the data changed? + //if (insz != inSzMem_){ + // inSzMem_ = insz; + // updateConfig(operPrm.isLerning, insz, inDataExpSz_); + //} - /// copy with offset padding for each image - snFloat* in = inputMem_->getDataGPU(); + ///// copy with offset padding for each image + //snFloat* in = inputMem_->getDataGPU(); - /// calculation of the output values - snFloat* out = baseOut_.getDataGPU(), *weight = baseWeight_.getDataGPU(); + ///// calculation of the output values + snFloat* out = baseOut_.getDataGPU(), + * weight = baseWeight_.getDataGPU(); + snSize outsz = baseOut_.size(); - - // calculation - forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, convGPUParams_); + // + //// calculation + //forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, convGPUParams_); /// dropOut //if (dropOut_ > 0.F) @@ -236,7 +238,7 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active function - // activationForward(activeType_, out, outsz.size(), gpuDeviceId_, &activGPUParams_); + activationForward(outsz, out, activeType_, gpuDeviceId_); /// batchNorm // if (batchNormType_ == batchNormType::postActive) diff --git a/src/snOperatorCUDA/src/Operator/input.cpp b/src/snOperatorCUDA/src/Operator/input.cpp index 069cbad..10bd20b 100644 --- a/src/snOperatorCUDA/src/Operator/input.cpp +++ b/src/snOperatorCUDA/src/Operator/input.cpp @@ -36,7 +36,7 @@ OperatorBase(net, name, node, prms){ } bool Input::setInput(const snFloat* data, const snSize& dsz){ - baseOut_.setDataGPU(data, dsz); + baseOut_.setDataCPU(data, dsz); return true; } diff --git a/src/snOperatorCUDA/src/activationFunctions.h b/src/snOperatorCUDA/src/activationFunctions.h index d8d8620..e937a08 100644 --- a/src/snOperatorCUDA/src/activationFunctions.h +++ b/src/snOperatorCUDA/src/activationFunctions.h @@ -24,23 +24,11 @@ // #pragma once -#include "stdafx.h" +#include "snBase/snBase.h" #include "structurs.h" // fv - value, df - deriv -void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); +void activationForward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); -void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); - -void fv_sigmoid(SN_Base::snFloat* ioVal, size_t sz); -void df_sigmoid(SN_Base::snFloat* inSigm, size_t sz); - -void fv_relu(SN_Base::snFloat* ioVal, size_t sz); -void df_relu(SN_Base::snFloat* inRelu, size_t sz); - -void fv_leakyRelu(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat minv = 0.01F); -void df_leakyRelu(SN_Base::snFloat* inRelu, size_t sz, SN_Base::snFloat minv = 0.01F); - -void fv_elu(SN_Base::snFloat* ioVal, size_t sz, SN_Base::snFloat minv = 0.01F); -void df_elu(SN_Base::snFloat* inElu, size_t sz, SN_Base::snFloat minv = 0.01F); \ No newline at end of file +void activationBackward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); From 48033751b8110bd5de096daec360f5eb6a20e63b Mon Sep 17 00:00:00 2001 From: Tyill Date: Fri, 19 Jul 2019 09:25:26 +0500 Subject: [PATCH 11/35] -develop --- mswin/VS12/snOperatorCUDA.vcxproj | 4 +- src/snOperatorCPU/src/CPU/convolutionCPU.cpp | 15 ++- .../src/Operator/convolution.cpp | 3 +- src/snSIMD/src/convolutionFWD.cpp | 104 ++++++++++-------- 4 files changed, 68 insertions(+), 58 deletions(-) diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index be8dbbe..1e2adb8 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -58,7 +58,7 @@ NotUsing Level3 Disabled - WIN32;SN_DEBUG;SN_AVX;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + WIN32;SN_DEBUG;_DEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src @@ -127,7 +127,7 @@ MaxSpeed true true - WIN32;SN_DEBUG;SN_AVX; NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) + WIN32;SN_DEBUG; NDEBUG;_CRT_SECURE_NO_WARNINGS;_LIB;%(PreprocessorDefinitions) %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(SolutionDir)../../src diff --git a/src/snOperatorCPU/src/CPU/convolutionCPU.cpp b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp index 599daea..809dd57 100644 --- a/src/snOperatorCPU/src/CPU/convolutionCPU.cpp +++ b/src/snOperatorCPU/src/CPU/convolutionCPU.cpp @@ -38,14 +38,13 @@ using namespace SN_Base; void Convolution::iniParamCPU(bool isLern, const snSize& insz, const snSize& outsz, const convParams& prms, CPUParams& cpuPrm){ + +#ifdef SN_AVX + // M * M * insz.d * outsz.w * outsz.h + size_t sz = prms.fWidth * prms.fHeight * insz.d * outsz.w * outsz.h; - if (!isLern && (insz.n == 1)){ - - // M * M * insz.d * outsz.w * outsz.h - size_t sz = prms.fWidth * prms.fHeight * insz.d * outsz.w * outsz.h; - - cpuPrm.buffMemFWD = (snFloat*)realloc(cpuPrm.buffMemFWD, sz * sizeof(snFloat)); - } + cpuPrm.buffMemFWD = (snFloat*)realloc(cpuPrm.buffMemFWD, sz * sizeof(snFloat)); +#endif } void Convolution::freeParamCPU(CPUParams& cpuPrm){ @@ -247,7 +246,7 @@ void Convolution::backwardCPU_GW(const convParams& prms, #ifdef SN_AVX - if ((prms.fWidth != prms.fHeight) || (prms.fWidth != 3) || !SN_SIMD::convolutionBWD_GW(prms.fWidth, prms.stride, prms.dilate, + if ((prms.fWidth != prms.fHeight) || !SN_SIMD::convolutionBWD_GW(prms.fWidth, prms.stride, prms.dilate, weight, insz, input, outsz, gradIn, gradOut, dWeightOut)) #endif backwardGW_BASE(prms.kernel, prms.fWidth, prms.fHeight, prms.stride, prms.dilate, diff --git a/src/snOperatorCPU/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp index 64eb433..51364b8 100644 --- a/src/snOperatorCPU/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -247,7 +247,8 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op } /// calculation of the output values - snFloat* out = baseOut_.getDataCPU(), *weight = baseWeight_.getDataCPU(); + snFloat* out = baseOut_.getDataCPU(), + * weight = baseWeight_.getDataCPU(); snSize outsz = baseOut_.size(); // calculation diff --git a/src/snSIMD/src/convolutionFWD.cpp b/src/snSIMD/src/convolutionFWD.cpp index 4f5f875..bfb74e6 100644 --- a/src/snSIMD/src/convolutionFWD.cpp +++ b/src/snSIMD/src/convolutionFWD.cpp @@ -25,13 +25,14 @@ #include #include - +#include #include "snBase/snBase.h" #include "Lib/OpenBLAS/cblas.h" using namespace std; using namespace SN_Base; + namespace SN_SIMD{ template @@ -173,38 +174,48 @@ namespace SN_SIMD{ template void convolutionFWD(size_t S, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, snFloat* buff){ - - /// Reorder input - reorderInputCHW2HCW(S, insz, input, outsz, buff); const size_t wStepByD = M * M, wStepByK = wStepByD * insz.d, - wStepByN = wStepByK * outsz.d; - - cblas_sgemm(CBLAS_ORDER::CblasRowMajor, - CBLAS_TRANSPOSE::CblasNoTrans, - CBLAS_TRANSPOSE::CblasTrans, - blasint(outsz.d), // W, rows - blasint(outsz.w * outsz.h), // In, cols - blasint(wStepByK), // W, cols, In, rows - 1.0F, // α - weight, // W - blasint(wStepByK), // W, step to next W - buff, // In - blasint(wStepByK), // In, step to next In (In21 - In11) - 0.0, // β - output, // Out - blasint(outsz.w * outsz.h)); // Out, step to next Out (Y21 - Y11) - - // +bias on all out layers - weight += wStepByN; - for (size_t i = 0; i < outsz.d; ++i){ - - snFloat* pOut = output + (outsz.w * outsz.h) * i; - float bias = *(weight + i); - for (size_t j = 0; j < (outsz.w * outsz.h); ++j) - pOut[j] += bias; - } + wStepByN = wStepByK * outsz.d, + inStepByD = insz.w * insz.h, + inStepByN = inStepByD * insz.d, + outStepByD = outsz.w * outsz.h, + outStepByN = outStepByD * outsz.d; + + for (size_t i = 0; i < insz.n; ++i){ + + /// Reorder input + reorderInputCHW2HCW(S, insz, input, outsz, buff); + + cblas_sgemm(CBLAS_ORDER::CblasRowMajor, + CBLAS_TRANSPOSE::CblasNoTrans, + CBLAS_TRANSPOSE::CblasTrans, + blasint(outsz.d), // W, rows + blasint(outsz.w * outsz.h), // In, cols + blasint(wStepByK), // W, cols, In, rows + 1.0F, // α + weight, // W + blasint(wStepByK), // W, step to next W + buff, // In + blasint(wStepByK), // In, step to next In (In21 - In11) + 0.0, // β + output, // Out + blasint(outsz.w * outsz.h)); // Out, step to next Out (Y21 - Y11) + + // +bias on all out layers + const snFloat* pW = weight + wStepByN; + for (size_t i = 0; i < outsz.d; ++i){ + + snFloat* pOut = output + (outsz.w * outsz.h) * i; + float bias = *(pW + i); + for (size_t j = 0; j < (outsz.w * outsz.h); ++j) + pOut[j] += bias; + } + + input += inStepByN; + output += outStepByN; + } } template @@ -306,7 +317,21 @@ namespace SN_SIMD{ const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, snFloat* buff){ - if ((insz.n > 1) || (D > 1)){ + if (D == 1){ + +#define cfwd(MS) \ + if (M == MS){ \ + convolutionFWD(S, weight, insz, input, outsz, output, buff); return true; }; + + cfwd(1) + cfwd(3) + cfwd(5) + cfwd(7) + cfwd(9) + + return false; + } + else{ #define dfwd(MS) \ if (M == MS){ \ @@ -320,22 +345,7 @@ namespace SN_SIMD{ return false; } -#undef dfwd - - -#define cfwd(MS) \ - if (M == MS){ \ - convolutionFWD(S, weight, insz, input, outsz, output, buff); return true; }; - - cfwd(1) - cfwd(3) - cfwd(5) - cfwd(7) - cfwd(9) - - return false; - #undef cfwd - +#undef dfwd }; } \ No newline at end of file From 2132a395fda051b12cda787407a9c8b486459e32 Mon Sep 17 00:00:00 2001 From: Tyill Date: Fri, 19 Jul 2019 19:17:23 +0500 Subject: [PATCH 12/35] -develop --- mswin/VS12/skyNet.vcxproj | 5 +- mswin/VS12/skyNet.vcxproj.filters | 3 - mswin/VS12/snAux.vcxproj | 1 - mswin/VS12/snAux.vcxproj.filters | 3 - mswin/VS12/snOperatorCPU.vcxproj | 4 +- mswin/VS12/snOperatorCPU.vcxproj.filters | 12 +- mswin/VS12/snOperatorCUDA.vcxproj | 5 +- mswin/VS12/snOperatorCUDA.vcxproj.filters | 15 +- src/skynet/src/dllmain.cpp | 1 - src/skynet/src/skynet.cpp | 1 - src/skynet/src/snet.cpp | 1 - src/skynet/src/stdafx.h | 9 - src/snAux/src/auxFunc.cpp | 7 +- src/snAux/src/stdafx.h | 11 - src/snBase/snBase.h | 17 +- src/snOperatorCPU/src/CPU/tensor.cpp | 8 +- src/snOperatorCPU/src/Operator/activation.cpp | 2 +- .../src/Operator/convolution.cpp | 2 +- .../src/Operator/deconvolution.cpp | 2 +- .../src/Operator/fullyConnected.cpp | 2 +- .../src/Operator/lossFunction.cpp | 2 +- ...eFunctions.cpp => activationFunctions.cpp} | 2 +- ...ctiveFunctions.h => activationFunctions.h} | 0 src/snOperatorCPU/src/batchNormFunctions.cpp | 2 +- src/snOperatorCPU/src/dropOut.cpp | 3 +- src/snOperatorCPU/src/stdafx.h | 3 - src/snOperatorCUDA/src/CUDA/activation.cu | 33 +-- src/snOperatorCUDA/src/CUDA/batchNorm.cu | 206 +++++++++++++++++- src/snOperatorCUDA/src/CUDA/common.cu | 86 ++++++++ src/snOperatorCUDA/src/CUDA/convolution.cu | 20 +- src/snOperatorCUDA/src/CUDA/deconvolution.cu | 18 +- src/snOperatorCUDA/src/CUDA/dropOut.cu | 79 ++++++- src/snOperatorCUDA/src/CUDA/fullyConnected.cu | 20 +- src/snOperatorCUDA/src/CUDA/optimizer.cu | 125 +++++++++++ src/snOperatorCUDA/src/CUDA/pooling.cu | 16 +- src/snOperatorCUDA/src/CUDA/tensor.cu | 13 +- .../src/Operator/convolution.cpp | 173 +++++++++------ src/snOperatorCUDA/src/Operator/convolution.h | 27 ++- .../src/Operator/deconvolution.cpp | 13 +- .../src/Operator/fullyConnected.cpp | 12 +- src/snOperatorCUDA/src/Operator/pooling.cpp | 5 + src/snOperatorCUDA/src/activationFunctions.h | 4 +- ...chNornFunctions.h => batchNormFunctions.h} | 12 +- src/snOperatorCUDA/src/cudaCommon.h | 37 ++++ src/snOperatorCUDA/src/dropOut.h | 4 +- src/snOperatorCUDA/src/optimizer.cpp | 90 -------- src/snOperatorCUDA/src/optimizer.h | 18 +- src/snOperatorCUDA/src/stdafx.h | 10 +- src/snOperatorCUDA/src/structurs.h | 2 - 49 files changed, 762 insertions(+), 384 deletions(-) delete mode 100644 src/skynet/src/stdafx.h delete mode 100644 src/snAux/src/stdafx.h rename src/snOperatorCPU/src/{activeFunctions.cpp => activationFunctions.cpp} (99%) rename src/snOperatorCPU/src/{activeFunctions.h => activationFunctions.h} (100%) create mode 100644 src/snOperatorCUDA/src/CUDA/common.cu create mode 100644 src/snOperatorCUDA/src/CUDA/optimizer.cu rename src/snOperatorCUDA/src/{batchNornFunctions.h => batchNormFunctions.h} (74%) create mode 100644 src/snOperatorCUDA/src/cudaCommon.h delete mode 100644 src/snOperatorCUDA/src/optimizer.cpp diff --git a/mswin/VS12/skyNet.vcxproj b/mswin/VS12/skyNet.vcxproj index 05625b3..2f0ec4c 100644 --- a/mswin/VS12/skyNet.vcxproj +++ b/mswin/VS12/skyNet.vcxproj @@ -19,7 +19,6 @@ - {507A9B3C-E5E6-4CEC-A99D-729D2171DF81} @@ -77,7 +76,7 @@ Windows true $(OutDir) - snOperatorCUDA.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) @@ -98,7 +97,7 @@ true true $(OutDir) - snOperatorCUDA.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) diff --git a/mswin/VS12/skyNet.vcxproj.filters b/mswin/VS12/skyNet.vcxproj.filters index 1bfc094..81ebd6e 100644 --- a/mswin/VS12/skyNet.vcxproj.filters +++ b/mswin/VS12/skyNet.vcxproj.filters @@ -32,9 +32,6 @@ Header Files - - Header Files - Header Files diff --git a/mswin/VS12/snAux.vcxproj b/mswin/VS12/snAux.vcxproj index e9c31ec..5835eb3 100644 --- a/mswin/VS12/snAux.vcxproj +++ b/mswin/VS12/snAux.vcxproj @@ -12,7 +12,6 @@ - diff --git a/mswin/VS12/snAux.vcxproj.filters b/mswin/VS12/snAux.vcxproj.filters index 07b0244..c3fe8a8 100644 --- a/mswin/VS12/snAux.vcxproj.filters +++ b/mswin/VS12/snAux.vcxproj.filters @@ -18,9 +18,6 @@ Header Files - - Header Files - diff --git a/mswin/VS12/snOperatorCPU.vcxproj b/mswin/VS12/snOperatorCPU.vcxproj index 582a2eb..8c662a5 100644 --- a/mswin/VS12/snOperatorCPU.vcxproj +++ b/mswin/VS12/snOperatorCPU.vcxproj @@ -185,7 +185,7 @@ - + @@ -212,7 +212,7 @@ - + diff --git a/mswin/VS12/snOperatorCPU.vcxproj.filters b/mswin/VS12/snOperatorCPU.vcxproj.filters index 29268b9..62a0696 100644 --- a/mswin/VS12/snOperatorCPU.vcxproj.filters +++ b/mswin/VS12/snOperatorCPU.vcxproj.filters @@ -21,9 +21,6 @@ - - Header Files - Header Files @@ -99,6 +96,9 @@ Header Files + + Header Files + @@ -149,9 +149,6 @@ Operator - - Source Files - Source Files @@ -188,5 +185,8 @@ Source Files + + Source Files + \ No newline at end of file diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index 1e2adb8..b253ad3 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -189,6 +189,8 @@ + + @@ -229,7 +231,6 @@ - @@ -237,9 +238,11 @@ + + diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index 596dcb1..86ea4b7 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -93,6 +93,12 @@ Header Files + + Header Files + + + Header Files + @@ -143,9 +149,6 @@ Operator - - Source Files - Source Files @@ -178,6 +181,12 @@ CUDA + + CUDA + + + CUDA + diff --git a/src/skynet/src/dllmain.cpp b/src/skynet/src/dllmain.cpp index bd8361f..6606e52 100644 --- a/src/skynet/src/dllmain.cpp +++ b/src/skynet/src/dllmain.cpp @@ -1,5 +1,4 @@ // dllmain.cpp : Defines the entry point for the DLL application. -#include "stdafx.h" #ifdef WIN32 diff --git a/src/skynet/src/skynet.cpp b/src/skynet/src/skynet.cpp index 3a81e92..ad0adc8 100644 --- a/src/skynet/src/skynet.cpp +++ b/src/skynet/src/skynet.cpp @@ -23,7 +23,6 @@ // THE SOFTWARE. // -#include "stdafx.h" #include "snBase/snBase.h" #include "skynet/skyNet.h" #include "skynet/src/snet.h" diff --git a/src/skynet/src/snet.cpp b/src/skynet/src/snet.cpp index a1a45db..c83c5b2 100644 --- a/src/skynet/src/snet.cpp +++ b/src/skynet/src/snet.cpp @@ -26,7 +26,6 @@ #include #include #include -#include "stdafx.h" #include "snAux/auxFunc.h" #include "snBase/snBase.h" #include "snet.h" diff --git a/src/skynet/src/stdafx.h b/src/skynet/src/stdafx.h deleted file mode 100644 index c4b560d..0000000 --- a/src/skynet/src/stdafx.h +++ /dev/null @@ -1,9 +0,0 @@ -// stdafx.h : include file for standard system include files, -// or project specific include files that are used frequently, but -// are changed infrequently -// - -#pragma once - -#include -#include diff --git a/src/snAux/src/auxFunc.cpp b/src/snAux/src/auxFunc.cpp index aec2f62..36510fe 100644 --- a/src/snAux/src/auxFunc.cpp +++ b/src/snAux/src/auxFunc.cpp @@ -24,11 +24,12 @@ // #include #include -#include -#include +#include +#include +#include +#include #include #include -#include "stdafx.h" #ifdef WIN32 #include diff --git a/src/snAux/src/stdafx.h b/src/snAux/src/stdafx.h deleted file mode 100644 index 49d6242..0000000 --- a/src/snAux/src/stdafx.h +++ /dev/null @@ -1,11 +0,0 @@ -// stdafx.h : include file for standard system include files, -// or project specific include files that are used frequently, but -// are changed infrequently -// - -#pragma once - -#include -#include -#include -#include diff --git a/src/snBase/snBase.h b/src/snBase/snBase.h index e893775..a289995 100644 --- a/src/snBase/snBase.h +++ b/src/snBase/snBase.h @@ -25,16 +25,17 @@ #pragma once #include -#include -#include +#include #include +#include #include -#ifdef SN_DEBUG -#define SN_PRINTMESS(mess) printf("%s \n", (mess).c_str()); -#else -#define SN_PRINTMESS(mess); -#endif +#define ASSERT_MESS(condition, message) \ + if (!(condition)) { \ + std::cerr << "Assertion `" #condition "` failed in " << __FILE__ \ + << " line " << __LINE__ << ": " << (message) << std::endl; \ + std::abort(); \ + } namespace SN_Base{ @@ -204,7 +205,7 @@ namespace SN_Base{ return baseWeight_; } - virtual batchNorm getBatchNorm() const final{ + virtual batchNorm getBatchNorm(){ return baseBatchNorm_; } diff --git a/src/snOperatorCPU/src/CPU/tensor.cpp b/src/snOperatorCPU/src/CPU/tensor.cpp index 1ce730f..aaa9929 100644 --- a/src/snOperatorCPU/src/CPU/tensor.cpp +++ b/src/snOperatorCPU/src/CPU/tensor.cpp @@ -32,7 +32,7 @@ Tensor& Tensor::operator=(const Tensor& other){ Tensor& Tensor::operator+=(const Tensor& other){ - assert(other == *this); + ASSERT_MESS(other == *this, ""); auto od = other.getDataCPU(); @@ -46,7 +46,7 @@ Tensor& Tensor::operator+=(const Tensor& other){ Tensor& Tensor::operator-=(const Tensor& other){ - assert(other == *this); + ASSERT_MESS(other == *this, ""); auto od = other.getDataCPU(); @@ -61,7 +61,7 @@ Tensor& Tensor::operator-=(const Tensor& other){ void Tensor::setDataCPU(const snFloat* data, const snSize& nsz){ size_t nnsz = nsz.size(); - assert(data && (nnsz > 0)); + ASSERT_MESS(data && (nnsz > 0), ""); if (sz_.size() < nnsz) dataCPU_ = (snFloat*)realloc(dataCPU_, nnsz * sizeof(snFloat)); @@ -78,7 +78,7 @@ snFloat* Tensor::getDataCPU() const{ void Tensor::resize(const snSize& nsz){ size_t nnsz = nsz.size(), csz = sz_.size(); - assert(nnsz > 0); + ASSERT_MESS(nnsz > 0, ""); if (csz < nnsz){ dataCPU_ = (snFloat*)realloc(dataCPU_, nnsz * sizeof(snFloat)); diff --git a/src/snOperatorCPU/src/Operator/activation.cpp b/src/snOperatorCPU/src/Operator/activation.cpp index 0a63da4..8850006 100644 --- a/src/snOperatorCPU/src/Operator/activation.cpp +++ b/src/snOperatorCPU/src/Operator/activation.cpp @@ -24,7 +24,7 @@ // #include "../stdafx.h" #include "snOperatorCPU/src/Operator/activation.h" -#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/activationFunctions.h" #include "snAux/auxFunc.h" using namespace std; diff --git a/src/snOperatorCPU/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp index 51364b8..bbdc7bf 100644 --- a/src/snOperatorCPU/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -26,7 +26,7 @@ #include "snAux/auxFunc.h" #include "snOperatorCPU/src/Operator/convolution.h" #include "snOperatorCPU/src/weightInit.h" -#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/activationFunctions.h" #include "snOperatorCPU/src/optimizer.h" #include "snOperatorCPU/src/structurs.h" #include "snOperatorCPU/src/batchNormFunctions.h" diff --git a/src/snOperatorCPU/src/Operator/deconvolution.cpp b/src/snOperatorCPU/src/Operator/deconvolution.cpp index a56ad6e..ce79fc2 100644 --- a/src/snOperatorCPU/src/Operator/deconvolution.cpp +++ b/src/snOperatorCPU/src/Operator/deconvolution.cpp @@ -26,7 +26,7 @@ #include "snAux/auxFunc.h" #include "snOperatorCPU/src/Operator/deconvolution.h" #include "snOperatorCPU/src/weightInit.h" -#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/activationFunctions.h" #include "snOperatorCPU/src/optimizer.h" #include "snOperatorCPU/src/structurs.h" #include "snOperatorCPU/src/batchNormFunctions.h" diff --git a/src/snOperatorCPU/src/Operator/fullyConnected.cpp b/src/snOperatorCPU/src/Operator/fullyConnected.cpp index c397f97..2a1a1e5 100644 --- a/src/snOperatorCPU/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCPU/src/Operator/fullyConnected.cpp @@ -26,7 +26,7 @@ #include "snAux/auxFunc.h" #include "snOperatorCPU/src/Operator/fullyConnected.h" #include "snOperatorCPU/src/weightInit.h" -#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/activationFunctions.h" #include "snOperatorCPU/src/optimizer.h" #include "snOperatorCPU/src/structurs.h" #include "snOperatorCPU/src/batchNormFunctions.h" diff --git a/src/snOperatorCPU/src/Operator/lossFunction.cpp b/src/snOperatorCPU/src/Operator/lossFunction.cpp index 21d93eb..3243dbc 100644 --- a/src/snOperatorCPU/src/Operator/lossFunction.cpp +++ b/src/snOperatorCPU/src/Operator/lossFunction.cpp @@ -24,7 +24,7 @@ // #include "../stdafx.h" #include "snOperatorCPU/src/Operator/lossFunction.h" -#include "snOperatorCPU/src/activeFunctions.h" +#include "snOperatorCPU/src/activationFunctions.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/activeFunctions.cpp b/src/snOperatorCPU/src/activationFunctions.cpp similarity index 99% rename from src/snOperatorCPU/src/activeFunctions.cpp rename to src/snOperatorCPU/src/activationFunctions.cpp index 386d25a..fc7c5b4 100644 --- a/src/snOperatorCPU/src/activeFunctions.cpp +++ b/src/snOperatorCPU/src/activationFunctions.cpp @@ -24,7 +24,7 @@ // #include "stdafx.h" #include "structurs.h" -#include "activeFunctions.h" +#include "activationFunctions.h" using namespace std; using namespace SN_Base; diff --git a/src/snOperatorCPU/src/activeFunctions.h b/src/snOperatorCPU/src/activationFunctions.h similarity index 100% rename from src/snOperatorCPU/src/activeFunctions.h rename to src/snOperatorCPU/src/activationFunctions.h diff --git a/src/snOperatorCPU/src/batchNormFunctions.cpp b/src/snOperatorCPU/src/batchNormFunctions.cpp index 0bb7d35..b4c888c 100644 --- a/src/snOperatorCPU/src/batchNormFunctions.cpp +++ b/src/snOperatorCPU/src/batchNormFunctions.cpp @@ -45,7 +45,7 @@ void channelBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, s for (size_t j = 0; j < bsz; ++j){ snFloat* cin = in + stepN * j + stepD * i, - *cout = out + stepN * j + stepD * i; + * cout = out + stepN * j + stepD * i; for (size_t k = 0; k < stepD; ++k) cout[k] = (cin[k] - prm.mean[k]) * prm.scale[k] / prm.varce[k] + prm.schift[k]; } diff --git a/src/snOperatorCPU/src/dropOut.cpp b/src/snOperatorCPU/src/dropOut.cpp index 51e56d5..f80f76a 100644 --- a/src/snOperatorCPU/src/dropOut.cpp +++ b/src/snOperatorCPU/src/dropOut.cpp @@ -36,7 +36,8 @@ void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz vector rnd(sz); rnd_uniformInt(rnd.data(), sz, 0, int(outsz.size())); - for (auto i : rnd) out[i] = 0; + for (auto i : rnd) + out[i] = 0; } else{ size_t sz = outsz.size(); diff --git a/src/snOperatorCPU/src/stdafx.h b/src/snOperatorCPU/src/stdafx.h index 00aea73..553b18a 100644 --- a/src/snOperatorCPU/src/stdafx.h +++ b/src/snOperatorCPU/src/stdafx.h @@ -25,9 +25,6 @@ #pragma once -#include -#include -#include #include #include #include "snBase/snBase.h" diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu index a59feaa..da6ccd7 100644 --- a/src/snOperatorCUDA/src/CUDA/activation.cu +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -5,6 +5,7 @@ #include "../structurs.h" #include "../activationFunctions.h" + using namespace SN_Base; __global__ void fv_sigmoid(const snSize& outsz, snFloat* output){ @@ -20,7 +21,7 @@ __global__ void fv_sigmoid(const snSize& outsz, snFloat* output){ unsigned int i = threadIdx.x; while (i < outStepByD){ - output[i] = 1.F / (1.F + std::exp(-output[i])); + output[i] = 1.F / (1.F + exp(-output[i])); output[i] = (output[i] < 0.99999F) ? output[i] : 0.99999F; output[i] = (output[i] > 0.00001F) ? output[i] : 0.00001F; @@ -158,28 +159,30 @@ __global__ void df_elu(const snSize& outsz, snFloat* output){ } } -void activationForward(const snSize& sz, snFloat* data, activeType active, uint32_t deviceId){ +void activationForward(const snSize& sz, snFloat* data, activeType active){ + + dim3 dimBlock(128); + dim3 dimGrid(int(sz.d), int(sz.n)); - cudaSetDevice(deviceId); - switch (active){ - case activeType::sigmoid: fv_sigmoid(sz, data); break; - case activeType::relu: fv_relu(data, sz); break; - case activeType::leakyRelu: fv_leakyRelu(data, sz); break; - case activeType::elu: fv_elu(data, sz); break; + case activeType::sigmoid: fv_sigmoid <<< dimGrid, dimBlock >>>(sz, data); break; + case activeType::relu: fv_relu <<< dimGrid, dimBlock >>>(sz, data); break; + case activeType::leakyRelu: fv_leakyRelu <<< dimGrid, dimBlock >>>(sz, data); break; + case activeType::elu: fv_elu <<< dimGrid, dimBlock >>>(sz, data); break; default: break; } } -void activeFuncBackward(const snSize& sz, snFloat* data, activeType active, uint32_t deviceId){ +void activeFuncBackward(const snSize& sz, snFloat* data, activeType active){ + + dim3 dimBlock(128); + dim3 dimGrid(int(sz.d), int(sz.n)); - cudaSetDevice(deviceId); - switch (active){ - case activeType::sigmoid: df_sigmoid(data, sz); break; - case activeType::relu: df_relu(data, sz); break; - case activeType::leakyRelu: df_leakyRelu(data, sz); break; - case activeType::elu: df_elu(data, sz); break; + case activeType::sigmoid: df_sigmoid <<< dimGrid, dimBlock >>>(sz, data); break; + case activeType::relu: df_relu <<< dimGrid, dimBlock >>>(sz, data); break; + case activeType::leakyRelu: df_leakyRelu <<< dimGrid, dimBlock >>>(sz, data); break; + case activeType::elu: df_elu <<< dimGrid, dimBlock >>>(sz, data); break; default: break; } } diff --git a/src/snOperatorCUDA/src/CUDA/batchNorm.cu b/src/snOperatorCUDA/src/CUDA/batchNorm.cu index a96ef7d..94175d9 100644 --- a/src/snOperatorCUDA/src/CUDA/batchNorm.cu +++ b/src/snOperatorCUDA/src/CUDA/batchNorm.cu @@ -1,23 +1,207 @@ - + #include -#include #include "../stdafx.h" -#include "../structurs.h" using namespace SN_Base; +__global__ void channelBatchNormInf(const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ + + size_t inStepByD = insz.w * insz.h, // step out by input + inStepByN = inStepByD * insz.d; // step out by batch -void batchNormForward(activeType atype, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId, void** pGpuPrm){ - - cudaSetDevice(deviceId); + // gridDim.x - number of out layers + // gridDim.y - batch size + + snFloat* cin = in + inStepByN * blockIdx.y + inStepByD * blockIdx.x, + * cout = out + inStepByN * blockIdx.y + inStepByD * blockIdx.x; + + prm.offset(inStepByD * blockIdx.x); + + unsigned int i = threadIdx.x; + while (i < inStepByD){ + + cout[i] = (cin[i] - prm.mean[i]) * prm.scale[i] / prm.varce[i] + prm.schift[i]; - + i += blockDim.x; + } } -void batchNormBackward(SN_Base::snFloat* inprev, SN_Base::snFloat* outprev, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId, void* pGpuPrm){ +__global__ void layerBatchNormInf(const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ + + size_t inStepByD = insz.w * insz.h, // step out by input + inStepByN = inStepByD * insz.d; // step out by batch - cudaSetDevice(deviceId); + // gridDim.x - number of out layers + // gridDim.y - batch size + + snFloat* cin = in + inStepByN * blockIdx.y + inStepByD * blockIdx.x, + * cout = out + inStepByN * blockIdx.y + inStepByD * blockIdx.x; - -} \ No newline at end of file + unsigned int i = threadIdx.x; + while (i < inStepByD){ + + cout[i] = (cin[i] - prm.mean[i]) * prm.scale[i] / prm.varce[i] + prm.schift[i]; + + i += blockDim.x; + } +} + +// +//void batchNormForward(const SN_Base::snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ +// +// size_t inSz = insz.w * insz.h * insz.d, bsz = insz.n; +// +// /// μ = 1/n * ∑x +// cblas_sgemv(CBLAS_ORDER::CblasRowMajor, +// CBLAS_TRANSPOSE::CblasTrans, +// blasint(bsz), +// blasint(inSz), +// 1.F / bsz, +// in, +// blasint(inSz), +// prm.onc, +// blasint(1), +// 0.F, +// prm.mean, +// blasint(1)); +// +// /// varce = sqrt(∑xx - mean^2 + e) +// for (size_t i = 0; i < inSz; ++i){ +// +// snFloat* cin = in + i, srq = 0.F; +// for (size_t j = 0; j < bsz; ++j){ +// srq += cin[0] * cin[0]; +// cin += inSz; +// } +// prm.varce[i] = sqrt(srq / bsz - prm.mean[i] * prm.mean[i] + 0.00001F); +// } +// +// /// norm = (in - mean) / varce +// /// y = ^x * γ + β +// for (size_t j = 0; j < bsz; ++j){ +// +// snFloat* cin = in + j * inSz, *cout = out + j * inSz, *norm = prm.norm + j * inSz; +// +// for (size_t i = 0; i < inSz; ++i){ +// norm[i] = (cin[i] - prm.mean[i]) / prm.varce[i]; +// cout[i] = norm[i] * prm.scale[i] + prm.schift[i]; +// } +// } +// +//} +// +//void batchNormBackward(const SN_Base::snSize& insz, snFloat* gradIn, snFloat* gradOut, batchNorm prm){ +// // https://kevinzakka.github.io/2016/09/14/batch_normalization/ +// +// size_t inSz = insz.w * insz.h * insz.d, bsz = insz.n; +// +// /// ∂f/∂β = ∑∂f/∂y +// cblas_sgemv(CBLAS_ORDER::CblasRowMajor, +// CBLAS_TRANSPOSE::CblasTrans, +// blasint(bsz), +// blasint(inSz), +// 1.F, +// gradIn, +// blasint(inSz), +// prm.onc, +// blasint(1), +// 0.F, +// prm.dSchift, +// blasint(1)); +// +// /// ∂f/∂γ = ∑∂f/∂y ⋅ ^x +// for (size_t i = 0; i < inSz; ++i){ +// +// snFloat* igr = gradIn + i, *norm = prm.norm + i, dScale = 0.F; +// for (size_t j = 0; j < bsz; ++j){ +// +// dScale += igr[0] * norm[0]; +// +// norm += inSz; +// igr += inSz; +// } +// prm.dScale[i] = dScale; +// } +// +// /// ∂f/∂x = (m⋅γ⋅∂f/∂y − γ⋅∂f/∂β − ^x⋅γ⋅∂f/∂γ) / m⋅σ2 +// for (size_t j = 0; j < bsz; ++j){ +// +// snFloat* igr = gradIn + j * inSz, *ogr = gradOut + j * inSz, *norm = prm.norm + j * inSz; +// for (size_t i = 0; i < inSz; ++i) +// ogr[i] = prm.scale[i] * (igr[i] * bsz - prm.dSchift[i] - norm[i] * prm.dScale[i]) / (prm.varce[i] * bsz); +// } +// for (size_t i = 0; i < inSz; ++i){ +// prm.schift[i] -= prm.dSchift[i] * prm.lr; +// prm.scale[i] -= prm.dScale[i] * prm.lr; +// } +// +//} + + +void channelBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ + + if (!isLern){ + + dim3 dimBlock(128); + dim3 dimGrid(int(insz.d), int(insz.n)); + + channelBatchNormInf << > > (insz, in, out, prm); + } + /* else{ + + size_t stepD = insz.w * insz.h, + stepN = stepD * insz.d, + bsz = insz.n; + + snFloat* share = (snFloat*)calloc(stepD * bsz, sizeof(snFloat)); + snSize sz(insz.w, insz.h, 1, insz.n); + + for (size_t i = 0; i < insz.d; ++i){ + + snFloat* pSh = share; + snFloat* pIn = in + stepD * i; + for (size_t j = 0; j < bsz; ++j){ + + memcpy(pSh, pIn, stepD * sizeof(snFloat)); + pSh += stepD; + pIn += stepN; + } + + if (fwBw) + batchNormForward(sz, share, share, prm); + else + batchNormBackward(sz, share, share, prm); + + pSh = share; + snFloat* pOut = out + stepD * i; + for (size_t j = 0; j < bsz; ++j){ + memcpy(pOut, pSh, stepD * sizeof(snFloat)); + pSh += stepD; + pOut += stepN; + } + + prm.offset(stepD); + prm.norm += stepD * bsz; + } + free(share); + }*/ +} + +void layerBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, const batchNorm& prm){ + + if (!isLern){ + + dim3 dimBlock(128); + dim3 dimGrid(int(insz.d), int(insz.n)); + + layerBatchNormInf << > > (insz, in, out, prm); + } + //else{ // isLerning + // if (fwBw) + // batchNormForward(insz, in, out, prm); + // else + // batchNormBackward(insz, in, out, prm); + //} +} + diff --git a/src/snOperatorCUDA/src/CUDA/common.cu b/src/snOperatorCUDA/src/CUDA/common.cu new file mode 100644 index 0000000..23dbca8 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/common.cu @@ -0,0 +1,86 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include +#include "../stdafx.h" + +using namespace SN_Base; + +void setDeviceId(int id){ + + cuAssert(cudaSetDevice(id)); +} + +snFloat* memAlloc(size_t sz, int initVal){ + + snFloat* mem = nullptr; + cuAssert(cudaMalloc(&mem, sz * sizeof(snFloat))); + + cuAssert(cudaMemset(mem, initVal, sz * sizeof(snFloat))); + + return mem; +} + +snFloat* memRealloc(size_t csz, size_t nsz, snFloat* data, int initVal){ + + ASSERT_MESS(nsz > 0, ""); + + if (csz < nsz){ + + snFloat* mem = nullptr; + cuAssert(cudaMalloc(&mem, nsz * sizeof(snFloat))); + + cuAssert(cudaMemset(mem, initVal, nsz * sizeof(snFloat))); + + if (data){ + if (csz > 0) + cuAssert(cudaMemcpy(mem, data, csz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + cuAssert(cudaFree(data)); + } + data = mem; + } + + return data; +} + +void memCpyCPU2GPU(size_t dstSz, SN_Base::snFloat* dstGPU, size_t srcSz, SN_Base::snFloat* srcCPU){ + + ASSERT_MESS(dstSz == srcSz, ""); + + cuAssert(cudaMemcpy(dstGPU, srcCPU, srcSz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyHostToDevice)); +} + +void memCpyGPU2CPU(size_t dstSz, SN_Base::snFloat* dstCPU, size_t srcSz, SN_Base::snFloat* srcGPU){ + + ASSERT_MESS(dstSz == srcSz, ""); + + cuAssert(cudaMemcpy(dstCPU, srcGPU, srcSz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToHost)); +} + +void memFree(snFloat* data){ + + cuAssert(cudaFree(data)); +} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/convolution.cu b/src/snOperatorCUDA/src/CUDA/convolution.cu index 766a933..8ecc63e 100644 --- a/src/snOperatorCUDA/src/CUDA/convolution.cu +++ b/src/snOperatorCUDA/src/CUDA/convolution.cu @@ -60,9 +60,7 @@ struct gpuParams{ void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const convParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - + bool isFirst = false; gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; @@ -225,9 +223,7 @@ void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& ou } void Convolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; if (!gpuPrm) return; @@ -271,9 +267,7 @@ __global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ void Convolution::forwardCUDA(const convParams& prms, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; size_t isz = insz.size(), osz = outsz.size(); @@ -300,9 +294,7 @@ void Convolution::forwardCUDA(const convParams& prms, void Convolution::backwardCUDA_GW(const convParams& prms, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; size_t isz = insz.size(), osz = outsz.size(); @@ -348,9 +340,7 @@ void Convolution::backwardCUDA_GW(const convParams& prms, void Convolution::backwardCUDA_G(const convParams& prms, const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; size_t isz = insz.size(), osz = outsz.size(); diff --git a/src/snOperatorCUDA/src/CUDA/deconvolution.cu b/src/snOperatorCUDA/src/CUDA/deconvolution.cu index bcf0aef..fb31b20 100644 --- a/src/snOperatorCUDA/src/CUDA/deconvolution.cu +++ b/src/snOperatorCUDA/src/CUDA/deconvolution.cu @@ -61,9 +61,7 @@ struct gpuParams{ void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const deconvParams& prms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - + bool isFirst = false; gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; @@ -217,9 +215,7 @@ void Deconvolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& } void Deconvolution::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; if (!gpuPrm) return; @@ -245,9 +241,7 @@ void Deconvolution::freeParamCUDA(void* gpuPrms){ void Deconvolution::forwardCUDA(const deconvParams& prms, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; // run @@ -288,9 +282,7 @@ __global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ void Deconvolution::backwardCUDA_GW(const deconvParams& prms, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; // run @@ -338,8 +330,6 @@ void Deconvolution::backwardCUDA_GW(const deconvParams& prms, void Deconvolution::backwardCUDA_G(const deconvParams& prms, const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); gpuParams* gpuPrm = (gpuParams*)gpuPrms; diff --git a/src/snOperatorCUDA/src/CUDA/dropOut.cu b/src/snOperatorCUDA/src/CUDA/dropOut.cu index 8a40e33..8ac44f2 100644 --- a/src/snOperatorCUDA/src/CUDA/dropOut.cu +++ b/src/snOperatorCUDA/src/CUDA/dropOut.cu @@ -1,22 +1,83 @@ #include -#include +#include #include "../stdafx.h" using namespace SN_Base; -void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId){ - - cudaSetDevice(deviceId); +__global__ dropOutLern(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* rnd, SN_Base::snFloat* out){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + unsigned int i = threadIdx.x; + + if (rnd[i] < dropOut){ + + while (i < outStepByD){ + + out[i] = 0.F; + + i += blockDim.x; + } + } +} + +__global__ dropOutInf(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ + + size_t outStepByD = outsz.w * outsz.h, // step out by input + outStepByN = outStepByD * outsz.d; // step out by batch - + // gridDim.x - number of out layers + // gridDim.y - batch size + + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + unsigned int i = threadIdx.x; + + while (i < outStepByD){ + + out[i] *= (1.F - dropOut); + + i += blockDim.x; + } } -void dropOutBackward(SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId){ - cudaSetDevice(deviceId); - - +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* inout){ + + if (isLern){ + int blockSz = 128; + + float* rndData = nullptr; + cuAssert(cudaMalloc((void**)&rndData, blockSz * sizeof(float))); + + curandGenerator_t gen; + cuAssert(curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_DEFAULT)); + + cuAssert(curandSetPseudoRandomGeneratorSeed(gen, 1234ULL)); + + cuAssert(curandGenerateUniform(gen, rndData, blockSz)); + dim3 dimBlock(blockSz); + dim3 dimGrid(int(outsz.d), int(outsz.n)); + + dropOutLern <<< dimGrid, dimBlock >>>(dropOut, outsz, rndData, out); + + cuAssert(curandDestroyGenerator(gen)); + cuAssert(cudaFree(rndData)); + } + else{ + + dim3 dimBlock(128); + dim3 dimGrid(int(outsz.d), int(outsz.n)); + + dropOutInf << > >(dropOut, outsz, out); + } } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/fullyConnected.cu b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu index 846ba4b..b0725db 100644 --- a/src/snOperatorCUDA/src/CUDA/fullyConnected.cu +++ b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu @@ -38,9 +38,7 @@ struct gpuParams{ }; void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - + size_t ida = insz.w * insz.h * insz.d, bsz = insz.n; gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; @@ -64,9 +62,7 @@ void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel } void FullyConnected::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; if (!gpuPrm) return; @@ -89,9 +85,7 @@ __global__ void cuFwdBias(size_t kernel, snSize insz, snFloat* weight, snFloat* } void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloat* input, const snFloat* weight, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); @@ -141,9 +135,7 @@ __global__ void cuBwdBias(size_t kernel, snSize insz, snFloat* gradIn, snFloat* void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* input, const snFloat* gradIn, snFloat* gradOut, snFloat* dWOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); /* @@ -200,9 +192,7 @@ void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, } void FullyConnected::backwardCUDA_G(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); diff --git a/src/snOperatorCUDA/src/CUDA/optimizer.cu b/src/snOperatorCUDA/src/CUDA/optimizer.cu new file mode 100644 index 0000000..1b2ac4b --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/optimizer.cu @@ -0,0 +1,125 @@ + +#include + +#include "../stdafx.h" +#include "../structurs.h" + +using namespace SN_Base; + +/// adaptive gradient method +__global__ void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat eps){ + + size_t wStepByD = sz.w * sz.h, + wStepByN = wStepByD * sz.d; + + ioWGr += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + + unsigned int i = threadIdx.x; + while (i < wStepByD){ + + ioWGr[i] += dW[i] * dW[i]; + ioW[i] -= alpha * (dW[i] + ioW[i] * lambda) / (sqrt(ioWGr[i]) + eps); + + i += blockDim.x; + } +} + +/// RMSprop +__global__ void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mu, snFloat eps){ + + size_t wStepByD = sz.w * sz.h, + wStepByN = wStepByD * sz.d; + + ioWGr += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + + unsigned int i = threadIdx.x; + while (i < wStepByD){ + + ioWGr[i] = ioWGr[i] * mu + (1.F - mu) * dW[i] * dW[i]; + ioW[i] -= alpha * (dW[i] + ioW[i] * lambda) / std::sqrt(ioWGr[i] + eps); + + i += blockDim.x; + } +} + +/// adam +__global__ void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, snFloat eps){ + + size_t wStepByD = sz.w * sz.h, + wStepByN = wStepByD * sz.d; + + iodWPrev += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioWGr += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + + unsigned int i = threadIdx.x; + while (i < wStepByD){ + + iodWPrev[i] = iodWPrev[i] * mudW - (1.F - mudW) * alpha * (dW[i] + ioW[i] * lambda); + + ioWGr[i] = ioWGr[i] * muGr + (1.F - muGr) * dW[i] * dW[i]; + + ioW[i] += iodWPrev[i] / std::sqrt(ioWGr[i] + eps); + + i += blockDim.x; + } + +} + +/// SGD without momentum +__global__ void opt_sgd(snFloat* dW, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda){ + + size_t wStepByD = sz.w * sz.h, + wStepByN = wStepByD * sz.d; + + ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + + unsigned int i = threadIdx.x; + while (i < wStepByD){ + + ioW[i] -= alpha * (dW[i] + lambda * ioW[i]); + + i += blockDim.x; + } +} + +/// SGD with momentum +__global__ void opt_sgdMoment(snFloat* dW, snFloat* iodWPrev, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mu){ + + size_t wStepByD = sz.w * sz.h, + wStepByN = wStepByD * sz.d; + + iodWPrev += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + + unsigned int i = threadIdx.x; + while (i < wStepByD){ + + iodWPrev[i] = mu * iodWPrev[i] - alpha * (dW[i] + ioW[i] * lambda); + ioW[i] += iodWPrev[i]; + + i += blockDim.x; + } +} + +void optimizer(snFloat* dWeight, snFloat* dWPrev, snFloat* dWGrad, snFloat* weight, const SN_Base::snSize& wsz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, optimizerType otype){ + + dim3 dimBlock(128); + dim3 dimGrid(int(wsz.d), int(wsz.n)); + + switch (otype){ + case optimizerType::sgd: opt_sgd <<>> (dWeight, weight, wsz, alpha, lambda); break; + case optimizerType::sgdMoment: opt_sgdMoment <<>> (dWeight, dWPrev, weight, wsz, alpha, lambda, mudW); break; + case optimizerType::RMSprop: opt_RMSprop <<>> (dWeight, dWGrad, weight, wsz, alpha, lambda, muGr, 1e-8F); break; + case optimizerType::adagrad: opt_adagrad <<>> (dWeight, dWGrad, weight, wsz, alpha, lambda, 1e-8F); break; + case optimizerType::adam: opt_adam <<>> (dWeight, dWPrev, dWGrad, weight, wsz, alpha, lambda, mudW, muGr, 1e-8F); break; + default: break; + } +} diff --git a/src/snOperatorCUDA/src/CUDA/pooling.cu b/src/snOperatorCUDA/src/CUDA/pooling.cu index a42d82a..fc4240e 100644 --- a/src/snOperatorCUDA/src/CUDA/pooling.cu +++ b/src/snOperatorCUDA/src/CUDA/pooling.cu @@ -44,9 +44,7 @@ struct gpuParams{ }; void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ - - cudaSetDevice(gpuDeviceId_); - + bool isFirst = false; gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; @@ -131,9 +129,7 @@ void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, } void Pooling::freeParamCUDA(void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; if (!gpuPrm) return; @@ -162,9 +158,7 @@ __global__ void cuFiltrNegative(snSize outsz, snFloat* out){ void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const snFloat* input, const snSize& outsz, snFloat* output, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; // run @@ -188,9 +182,7 @@ void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const void Pooling::backwardCUDA(const poolParams& poolPrms, const snSize& outsz, const snFloat* output, const snFloat* gradIn, const snSize& insz, const snFloat* input, snFloat* gradOut, void* gpuPrms){ - - cudaSetDevice(gpuDeviceId_); - + gpuParams* gpuPrm = (gpuParams*)gpuPrms; // run diff --git a/src/snOperatorCUDA/src/CUDA/tensor.cu b/src/snOperatorCUDA/src/CUDA/tensor.cu index 6803de3..93c88ea 100644 --- a/src/snOperatorCUDA/src/CUDA/tensor.cu +++ b/src/snOperatorCUDA/src/CUDA/tensor.cu @@ -41,7 +41,7 @@ Tensor& Tensor::operator=(const Tensor& other){ Tensor& Tensor::operator+=(const Tensor& other){ - assert(other == *this); + ASSERT_MESS(other == *this, ""); return *this; @@ -49,7 +49,7 @@ Tensor& Tensor::operator+=(const Tensor& other){ Tensor& Tensor::operator-=(const Tensor& other){ - assert(other == *this); + ASSERT_MESS(other == *this, ""); return *this; @@ -58,7 +58,7 @@ Tensor& Tensor::operator-=(const Tensor& other){ void Tensor::setDataGPU(const snFloat* data, const snSize& nsz){ size_t nnsz = nsz.size(); - assert(data && (nnsz > 0)); + ASSERT_MESS(data && (nnsz > 0), ""); if (sz_.size() < nnsz){ @@ -75,7 +75,7 @@ void Tensor::setDataGPU(const snFloat* data, const snSize& nsz){ void Tensor::setDataCPU(const snFloat* data, const snSize& nsz){ size_t nnsz = nsz.size(); - assert(data && (nnsz > 0)); + ASSERT_MESS(data && (nnsz > 0), ""); if (sz_.size() < nnsz){ @@ -108,7 +108,7 @@ snFloat* Tensor::getDataCPU() const{ void Tensor::resize(const snSize& nsz){ size_t nnsz = nsz.size(), csz = sz_.size(); - assert(nnsz > 0); + ASSERT_MESS(nnsz > 0, ""); if (csz < nnsz){ @@ -116,7 +116,8 @@ void Tensor::resize(const snSize& nsz){ cuAssert(cudaMalloc(&mem, nnsz * sizeof(snFloat))); if (dataGPU_){ - cuAssert(cudaMemcpy(mem, dataGPU_, csz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); + if (csz > 0) + cuAssert(cudaMemcpy(mem, dataGPU_, csz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); cuAssert(cudaFree(dataGPU_)); } dataGPU_ = mem; diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 3d3bb5b..8ea5373 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -27,9 +27,11 @@ #include "snAux/auxFunc.h" #include "snOperatorCUDA/src/weightInit.h" #include "snOperatorCUDA/src/activationFunctions.h" +#include "snOperatorCUDA/src/batchNormFunctions.h" #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" #include "snOperatorCUDA/src/dropOut.h" +#include "snOperatorCUDA/src/cudaCommon.h" using namespace std; using namespace SN_Base; @@ -44,6 +46,8 @@ Convolution::Convolution(void* net, const string& name, const string& node, std: Convolution::~Convolution(){ + setDeviceId(gpuDeviceId_); + freeParamCUDA(convGPUParams_); } @@ -93,7 +97,7 @@ void Convolution::load(std::map& prms){ else ERROR_MESS("param 'batchNorm' = " + bnType + " indefined"); } - + setInternPrm(prms); } @@ -162,20 +166,53 @@ bool Convolution::setInternPrm(std::map& prms){ bool Convolution::setBatchNorm(const batchNorm& bn){ - size_t osz = bn.sz.size(); - - /* memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); -*/ + size_t csz = baseBatchNorm_.sz.size(), + osz = bn.sz.size(); + + baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); + + memCpyCPU2GPU(osz, baseBatchNorm_.mean, osz, bn.mean); + memCpyCPU2GPU(osz, baseBatchNorm_.varce, osz, bn.varce); + memCpyCPU2GPU(osz, baseBatchNorm_.scale, osz, bn.scale); + memCpyCPU2GPU(osz, baseBatchNorm_.schift, osz, bn.schift); + baseBatchNorm_.sz = bn.sz; return true; } +batchNorm Convolution::getBatchNorm(){ + + size_t csz = baseBatchNorm_.sz.size(); + + auxCPUParams_["bn_mean"] = vector(csz); + auxCPUParams_["bn_varce"] = vector(csz); + auxCPUParams_["bn_scale"] = vector(csz); + auxCPUParams_["bn_schift"] = vector(csz); + + memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), csz, baseBatchNorm_.mean); + memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), csz, baseBatchNorm_.varce); + memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), csz, baseBatchNorm_.scale); + memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), csz, baseBatchNorm_.schift); + + batchNorm bn; + bn.mean = auxCPUParams_["bn_mean"].data(); + bn.varce = auxCPUParams_["bn_varce"].data(); + bn.scale = auxCPUParams_["bn_scale"].data(); + bn.schift = auxCPUParams_["bn_schift"].data(); + + bn.sz = csz; + + return bn; +} + std::vector Convolution::Do(const operationParam& operPrm, const std::vector& neighbOpr){ - + + setDeviceId(gpuDeviceId_); + if (operPrm.action == snAction::forward){ if (neighbOpr.size() > 1){ @@ -202,47 +239,48 @@ std::vector Convolution::Do(const operationParam& operPrm, const st backward(tns, operPrm); } } - + return std::vector(); } void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ - //snSize insz = inTns.size(); - //inputMem_ = &inTns; + snSize insz = inTns.size(); + inputMem_ = &inTns; - ///// Has the size of the data changed? - //if (insz != inSzMem_){ - // inSzMem_ = insz; - // updateConfig(operPrm.isLerning, insz, inDataExpSz_); - //} + // Has the size of the data changed? + if (insz != inSzMem_){ + inSzMem_ = insz; + updateConfig(operPrm.isLerning, insz, inDataExpSz_); + } - ///// copy with offset padding for each image - //snFloat* in = inputMem_->getDataGPU(); + // copy with offset padding for each image + snFloat* in = inputMem_->getDataGPU(); - ///// calculation of the output values + // calculation of the output values snFloat* out = baseOut_.getDataGPU(), * weight = baseWeight_.getDataGPU(); snSize outsz = baseOut_.size(); - // - //// calculation - //forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, convGPUParams_); + + // calculation + forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, convGPUParams_); /// dropOut - //if (dropOut_ > 0.F) - // dropOutForward(dropOut_, out, outsz, gpuDeviceId_, &dropGPUParams_); + if (dropOut_ > 0.F) + dropOut(operPrm.isLerning, dropOut_, outsz, out); - /// batchNorm - // if (batchNormType_ == batchNormType::beforeActive) - // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // batchNorm + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active function - activationForward(outsz, out, activeType_, gpuDeviceId_); + if (activeType_ != activeType::none) + activationForward(outsz, out, activeType_); - /// batchNorm - // if (batchNormType_ == batchNormType::postActive) - // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); } void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ @@ -250,54 +288,46 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o snFloat* gradIn = inTns.getDataGPU(); snSize insz = inTns.size(); - /// batchNorm - // if (batchNormType_ == batchNormType::postActive) - // channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); + // batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); // active function - if (activeType_ != activeType::none){ - - size_t osz = baseOut_.size().size(); - // activationBackward(inputMem_->getDataGPU(), baseOut_.getDataGPU(), gradIn, osz, gpuDeviceId_, activGPUParams_); - - } - - /// batchNorm - // if (batchNormType_ == batchNormType::beforeActive) - // channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); + if (activeType_ != activeType::none) + activationBackward(baseOut_.size(), baseOut_.getDataGPU(), activeType_); - /// dropOut - // if (dropOut_ > 0.F) - // dropOutBackward(gradIn, insz, gpuDeviceId_, dropGPUParams_); - + // batchNorm + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); + // calculation of the output gradient and weight correction snFloat* gradOut = baseGrad_.getDataGPU(); snFloat* weight = baseWeight_.getDataGPU(); if (!isFreeze_){ - // snFloat* dWeight = auxParams_["dWeight"].data(); + snFloat* dWeight = auxGPUParams_["dWeight"]; snFloat* in = inputMem_->getDataGPU(); // calculation - // backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, convGPUParams_); + backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, convGPUParams_); // correct weight - // snFloat* dWPrev = auxParams_["dWPrev"].data(); - // snFloat* dWGrad = auxParams_["dWGrad"].data(); + snFloat* dWPrev = auxGPUParams_["dWPrev"]; + snFloat* dWGrad = auxGPUParams_["dWGrad"]; size_t wsz = baseWeight_.size().size(); - /* optimizer(dWeight, + optimizer(dWeight, dWPrev, dWGrad, weight, - wsz, + snSize(convPrms_.fWidth, convPrms_.fHeight, insz.d, convPrms_.kernel), operPrm.lr, optLmbRegular_, optDecayMomentDW_, optDecayMomentWGr_, - optimizerType_);*/ + optimizerType_); } else{ // isFreeze backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, convGPUParams_); @@ -357,24 +387,31 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize expSz = snSize(newsz.w + paddingW * 2, newsz.h + paddingH * 2, newsz.d, newsz.n); baseOut_.resize(outSz); - - if (isLern) + + if (isLern){ baseGrad_.resize(newsz); - size_t osz = outSz.w * outSz.h * outSz.d; + // aux array + auxGPUParams_["dWeight"] = memRealloc(0, ntp, auxGPUParams_["dWeight"], 0); + auxGPUParams_["dWPrev"] = memRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); + auxGPUParams_["dWGrad"] = memRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); + } + + size_t csz = baseBatchNorm_.sz.w * baseBatchNorm_.sz.h * baseBatchNorm_.sz.d, + osz = outSz.w * outSz.h * outSz.d; if (batchNormType_ != batchNormType::none){ - /* auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); - auxParams_["bn_varce"].resize(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); - auxParams_["bn_scale"].resize(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); - auxParams_["bn_schift"].resize(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); if (isLern){ - auxParams_["bn_norm"].resize(osz * outSz.n); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); - auxParams_["bn_dScale"].resize(osz, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); - auxParams_["bn_dSchift"].resize(osz, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); - auxParams_["bn_onc"].resize(outSz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); - }*/ + baseBatchNorm_.norm = memRealloc(csz * outSz.n, osz * outSz.n, baseBatchNorm_.mean, 0); + baseBatchNorm_.dScale = memRealloc(csz, osz, baseBatchNorm_.dScale, 0); + baseBatchNorm_.dSchift = memRealloc(csz, osz, baseBatchNorm_.dSchift, 0); + } + baseBatchNorm_.sz = outSz; baseBatchNorm_.sz.n = 1; } diff --git a/src/snOperatorCUDA/src/Operator/convolution.h b/src/snOperatorCUDA/src/Operator/convolution.h index f58a0d2..6f2581b 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.h +++ b/src/snOperatorCUDA/src/Operator/convolution.h @@ -42,6 +42,8 @@ class Convolution final : SN_Base::OperatorBase{ bool setBatchNorm(const SN_Base::batchNorm& bn) override; + batchNorm getBatchNorm() override; + private: struct convParams{ @@ -57,27 +59,30 @@ class Convolution final : SN_Base::OperatorBase{ convParams convPrms_; bool isPaddingSame_ = false, isCheckPadding_ = false; - activeType activeType_ = activeType::relu; ///< active type - optimizerType optimizerType_ = optimizerType::adam; ///< optimizer type - weightInitType weightInitType_ = weightInitType::he; ///< init weight type - batchNormType batchNormType_ = batchNormType::none; ///< batchNorm - SN_Base::snSize inSzMem_; ///< insz mem - SN_Base::snSize inDataExpSz_; ///< insz expansion + activeType activeType_ = activeType::relu; ///< active type + optimizerType optimizerType_ = optimizerType::adam; ///< optimizer type + weightInitType weightInitType_ = weightInitType::he; ///< init weight type + batchNormType batchNormType_ = batchNormType::none; ///< batchNorm + SN_Base::snSize inSzMem_; ///< insz mem + SN_Base::snSize inDataExpSz_; ///< insz expansion const SN_Base::Tensor* inputMem_ = nullptr; - bool isFreeze_ = false; ///< not change weight + bool isFreeze_ = false; ///< not change weight - uint32_t gpuDeviceId_ = 0; ///< gpu id + uint32_t gpuDeviceId_ = 0; ///< gpu id - SN_Base::snFloat dropOut_ = 0.F; ///< random off out + SN_Base::snFloat dropOut_ = 0.F; ///< random off out - SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimiz weight + SN_Base::snFloat optDecayMomentDW_ = 0.9F, ///< optimiz weight optDecayMomentWGr_ = 0.99F, optLmbRegular_ = 0.001F; - void* convGPUParams_ = nullptr; ///< gpu aux params + void* convGPUParams_ = nullptr; ///< gpu aux params + std::map auxGPUParams_; ///< aux data + std::map> auxCPUParams_; + void load(std::map& prms); void updateConfig(bool isLern, const SN_Base::snSize& newSz, SN_Base::snSize& expSz); diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp index 8675fd9..71f8a41 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -29,6 +29,8 @@ #include "snOperatorCUDA/src/activationFunctions.h" #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" +#include "snOperatorCUDA/src/cudaCommon.h" +#include "snOperatorCUDA/src/dropOut.h" using namespace std; using namespace SN_Base; @@ -42,8 +44,9 @@ Deconvolution::Deconvolution(void* net, const string& name, const string& node, Deconvolution::~Deconvolution(){ - freeParamCUDA(gpuParams_); - + setDeviceId(gpuDeviceId_); + + freeParamCUDA(gpuParams_); } void Deconvolution::load(std::map& prms){ @@ -185,6 +188,8 @@ bool Deconvolution::setBatchNorm(const batchNorm& bn){ std::vector Deconvolution::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + setDeviceId(gpuDeviceId_); + if (operPrm.action == snAction::forward){ if (neighbOpr.size() > 1){ @@ -234,8 +239,8 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& forwardCUDA(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); /// dropOut - // if (dropOut_ > 0.F) - // dropOut(operPrm.isLerning, dropOut_, outsz, out); + if (dropOut_ > 0.F) + dropOut(operPrm.isLerning, dropOut_, outsz, out); /// batchNorm // if (batchNormType_ == batchNormType::beforeActive) diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index 0ff3dce..fb06f62 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -30,6 +30,8 @@ #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" #include "snOperatorCUDA/src/batchNornFunctions.h" +#include "snOperatorCUDA/src/cudaCommon.h" +#include "snOperatorCUDA/src/dropOut.h" using namespace std; using namespace SN_Base; @@ -43,6 +45,8 @@ FullyConnected::FullyConnected(void* net, const string& name, const string& node FullyConnected::~FullyConnected(){ + setDeviceId(gpuDeviceId_); + freeParamCUDA(gpuParams_); } @@ -183,6 +187,8 @@ bool FullyConnected::setBatchNorm(const batchNorm& bn){ std::vector FullyConnected::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + setDeviceId(gpuDeviceId_); + if (operPrm.action == snAction::forward){ if (neighbOpr.size() > 1){ @@ -236,9 +242,9 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& forwardCUDA(kernel_, insz, inTns.getDataGPU(), weight, out, gpuParams_); /// dropOut - snSize outSz = baseOut_.size(); - // if (dropOut_ > 0.F) - // dropOut(operPrm.isLerning, dropOut_, outSz, out); + snSize outsz = baseOut_.size(); + if (dropOut_ > 0.F) + dropOut(operPrm.isLerning, dropOut_, outsz, out); /// batchNorm // if (batchNormType_ == batchNormType::beforeActive) diff --git a/src/snOperatorCUDA/src/Operator/pooling.cpp b/src/snOperatorCUDA/src/Operator/pooling.cpp index da9ff85..344a0e1 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.cpp +++ b/src/snOperatorCUDA/src/Operator/pooling.cpp @@ -26,6 +26,7 @@ #include "snOperatorCUDA/src/Operator/pooling.h" #include "snAux/auxFunc.h" #include "snOperatorCUDA/src/structurs.h" +#include "snOperatorCUDA/src/cudaCommon.h" using namespace std; using namespace SN_Base; @@ -39,6 +40,8 @@ Pooling::Pooling(void* net, const string& name, const string& node, std::map& prms){ std::vector Pooling::Do(const operationParam& operPrm, const std::vector& neighbOpr){ + setDeviceId(gpuDeviceId_); + if (operPrm.action == snAction::forward){ if (neighbOpr.size() > 1){ diff --git a/src/snOperatorCUDA/src/activationFunctions.h b/src/snOperatorCUDA/src/activationFunctions.h index e937a08..83dcadd 100644 --- a/src/snOperatorCUDA/src/activationFunctions.h +++ b/src/snOperatorCUDA/src/activationFunctions.h @@ -29,6 +29,6 @@ // fv - value, df - deriv -void activationForward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); +void activationForward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType); -void activationBackward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType, uint32_t deviceId); +void activationBackward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType); diff --git a/src/snOperatorCUDA/src/batchNornFunctions.h b/src/snOperatorCUDA/src/batchNormFunctions.h similarity index 74% rename from src/snOperatorCUDA/src/batchNornFunctions.h rename to src/snOperatorCUDA/src/batchNormFunctions.h index 82360f4..0fb4fd7 100644 --- a/src/snOperatorCUDA/src/batchNornFunctions.h +++ b/src/snOperatorCUDA/src/batchNormFunctions.h @@ -25,11 +25,7 @@ #pragma once #include "snBase/snBase.h" -// -//void channelBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, batchNorm prm); -// -//void layerBatchNorm(bool fwBw, bool isLern, const snSize& insz, snFloat* in, snFloat* out, const batchNorm& prm); -// -//void batchNormForward(const SN_Base::snSize& insz, snFloat* in, snFloat* out, batchNorm prm); -// -//void batchNormBackward(const SN_Base::snSize& insz, snFloat* gradIn, snFloat* gradOut, batchNorm prm); \ No newline at end of file + +void channelBatchNorm(bool fwBw, bool isLern, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out, SN_Base::batchNorm prm); + +void layerBatchNorm(bool fwBw, bool isLern, const SN_Base::snSize& insz, SN_Base::snFloat* in, SN_Base::snFloat* out, const SN_Base::batchNorm& prm); diff --git a/src/snOperatorCUDA/src/cudaCommon.h b/src/snOperatorCUDA/src/cudaCommon.h new file mode 100644 index 0000000..4ea416a --- /dev/null +++ b/src/snOperatorCUDA/src/cudaCommon.h @@ -0,0 +1,37 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +void setDeviceId(int); + +SN_Base::snFloat* memAlloc(size_t sz, int initVal); + +SN_Base::snFloat* memRealloc(size_t csz, size_t nsz, SN_Base::snFloat*, int initVal); + +void memCpyCPU2GPU(size_t dstSz, SN_Base::snFloat* dstGPU, size_t srcSz, SN_Base::snFloat* srcCPU); + +void memCpyGPU2CPU(size_t dstSz, SN_Base::snFloat* dstCPU, size_t srcSz, SN_Base::snFloat* srcGPU); + +void memFree(SN_Base::snFloat*); diff --git a/src/snOperatorCUDA/src/dropOut.h b/src/snOperatorCUDA/src/dropOut.h index 245ba21..91cf03c 100644 --- a/src/snOperatorCUDA/src/dropOut.h +++ b/src/snOperatorCUDA/src/dropOut.h @@ -26,6 +26,4 @@ #include "snBase/snBase.h" -void dropOutForward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId); - -void dropOutBackward(SN_Base::snFloat dropOut, SN_Base::snFloat* inout, const SN_Base::snSize& iosz, uint32_t deviceId); \ No newline at end of file +void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& iosz, SN_Base::snFloat* inout); \ No newline at end of file diff --git a/src/snOperatorCUDA/src/optimizer.cpp b/src/snOperatorCUDA/src/optimizer.cpp deleted file mode 100644 index dac070e..0000000 --- a/src/snOperatorCUDA/src/optimizer.cpp +++ /dev/null @@ -1,90 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// -#include "stdafx.h" -#include "optimizer.h" -#include "structurs.h" - - -using namespace std; -using namespace SN_Base; - -void optimizer(snFloat* dWeight, snFloat* dWPrev, snFloat* dWGrad, snFloat* weight, size_t wsz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, optimizerType otype){ - - switch (otype){ - case optimizerType::sgd: opt_sgd(dWeight, weight, wsz, alpha, lambda); break; - case optimizerType::sgdMoment: opt_sgdMoment(dWeight, dWPrev, weight, wsz, alpha, lambda, mudW); break; - case optimizerType::RMSprop: opt_RMSprop(dWeight, dWGrad, weight, wsz, alpha, lambda, muGr); break; - case optimizerType::adagrad: opt_adagrad(dWeight, dWGrad, weight, wsz, alpha, lambda); break; - case optimizerType::adam: opt_adam(dWeight, dWPrev, dWGrad, weight, wsz, alpha, lambda, mudW, muGr); break; - default: break; - } -} - -/// adaptive gradient method -void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat eps){ - - for (size_t i = 0; i < sz; ++i){ - ioWGr[i] += dW[i] * dW[i]; - ioW[i] -= alpha * (dW[i] + ioW[i] * lambda) / (std::sqrt(ioWGr[i]) + eps); - } -} - -/// RMSprop -void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat mu, snFloat eps){ - - for (size_t i = 0; i < sz; ++i){ - ioWGr[i] = ioWGr[i] * mu + (1.F - mu) * dW[i] * dW[i]; - ioW[i] -= alpha * (dW[i] + ioW[i] * lambda) / std::sqrt(ioWGr[i] + eps); - } -} - -/// adam -void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, snFloat eps){ - - for (size_t i = 0; i < sz; ++i){ - - iodWPrev[i] = iodWPrev[i] * mudW - (1.F - mudW) * alpha * (dW[i] + ioW[i] * lambda); - - ioWGr[i] = ioWGr[i] * muGr + (1.F - muGr) * dW[i] * dW[i]; - - ioW[i] += iodWPrev[i] / std::sqrt(ioWGr[i] + eps); - } -} - -/// SGD without momentum -void opt_sgd(snFloat* dW, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda){ - for(size_t i = 0; i < sz; ++i){ - ioW[i] -= alpha * (dW[i] + lambda * ioW[i]); - } -} - -/// SGD with momentum -void opt_sgdMoment(snFloat* dW, snFloat* iodWPrev, snFloat* ioW, size_t sz, snFloat alpha, snFloat lambda, snFloat mu){ - - for(size_t i = 0; i < sz; ++i){ - iodWPrev[i] = mu * iodWPrev[i] - alpha * (dW[i] + ioW[i] * lambda); - ioW[i] += iodWPrev[i]; - } -} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/optimizer.h b/src/snOperatorCUDA/src/optimizer.h index 4f00d84..b2b0d9b 100644 --- a/src/snOperatorCUDA/src/optimizer.h +++ b/src/snOperatorCUDA/src/optimizer.h @@ -25,31 +25,17 @@ #pragma once -#include "stdafx.h" +#include "snBase/snBase.h" #include "structurs.h" void optimizer(SN_Base::snFloat* dWeight, SN_Base::snFloat* dWPrev, SN_Base::snFloat* dWGrad, SN_Base::snFloat* weight, - size_t wsz, + const SN_Base::snSize& wsz, SN_Base::snFloat alpha, SN_Base::snFloat lambda, SN_Base::snFloat mudW, SN_Base::snFloat muGr, optimizerType otype); -/// adaptive gradient method -void opt_adagrad(SN_Base::snFloat* dW, SN_Base::snFloat* ioWGr, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat eps = 1e-8F); - -/// RMSprop -void opt_RMSprop(SN_Base::snFloat* dW, SN_Base::snFloat* ioWGr, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat mu = 0.9F, SN_Base::snFloat eps = 1e-8F); - -/// adam -void opt_adam(SN_Base::snFloat* dW, SN_Base::snFloat* iodWPrev, SN_Base::snFloat* ioWGr, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat muWd = 0.9F, SN_Base::snFloat muGr = 0.9F, SN_Base::snFloat eps = 1e-8F); - -/// SGD -void opt_sgd(SN_Base::snFloat* dW, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.001F, SN_Base::snFloat lambda = 0.F); - -/// SGD with momentum -void opt_sgdMoment(SN_Base::snFloat* dW, SN_Base::snFloat* iodWPrev, SN_Base::snFloat* ioW, size_t sz, SN_Base::snFloat alpha = 0.01F, SN_Base::snFloat lambda = 0.F, SN_Base::snFloat mu = 0.9F); diff --git a/src/snOperatorCUDA/src/stdafx.h b/src/snOperatorCUDA/src/stdafx.h index 15b5b9b..f853e28 100644 --- a/src/snOperatorCUDA/src/stdafx.h +++ b/src/snOperatorCUDA/src/stdafx.h @@ -25,13 +25,9 @@ #pragma once -#include -#include -#include #include #include #include "snBase/snBase.h" -#include "snOperatorCUDA/snOperator.h" #define PROFILE_START double ctm = omp_get_wtime(); #define PROFILE_END(func) g_statusMess(this, name_ + " " + node_ + " " + func + " " + std::to_string(omp_get_wtime() - ctm)); ctm = omp_get_wtime(); @@ -45,8 +41,4 @@ void g_userCBack(SN_Base::OperatorBase* opr, const std::string& cbname, const st #define cuCHECK(func) if (func != 0){ ERROR_MESS("CUDA error: " + cudaGetErrorString(cudaGetLastError())); return;} -#define cuAssert(func) \ - if (func != 0){ \ - std::string mess = std::string("CUDA error: ") + cudaGetErrorString(cudaGetLastError()); \ - assert(!mess.c_str()); \ - } +#define cuAssert(func) ASSERT_MESS(func == 0, std::string("CUDA error: ") + cudaGetErrorString(cudaGetLastError())); diff --git a/src/snOperatorCUDA/src/structurs.h b/src/snOperatorCUDA/src/structurs.h index 897eec2..028d7a5 100644 --- a/src/snOperatorCUDA/src/structurs.h +++ b/src/snOperatorCUDA/src/structurs.h @@ -24,8 +24,6 @@ // #pragma once -#include "snBase/snBase.h" - /// тип ф-ии активации enum class activeType{ none = -1, From 7a28eac8af8cb1738c18b5aaa6c47d4f4d972bf6 Mon Sep 17 00:00:00 2001 From: al_medved Date: Fri, 19 Jul 2019 21:59:58 +0500 Subject: [PATCH 13/35] -develop --- src/snOperatorCPU/src/Operator/activation.cpp | 11 +- src/snOperatorCUDA/src/CUDA/activation.cu | 26 ++-- src/snOperatorCUDA/src/CUDA/optimizer.cu | 49 +++---- .../src/Operator/activation.cpp | 8 +- .../src/Operator/convolution.cpp | 5 +- .../src/Operator/deconvolution.cpp | 134 +++++++++--------- .../src/Operator/deconvolution.h | 6 +- .../src/Operator/fullyConnected.cpp | 127 +++++++++-------- .../src/Operator/fullyConnected.h | 9 +- src/snOperatorCUDA/src/Operator/pooling.cpp | 25 +--- src/snOperatorCUDA/src/Operator/userLayer.cpp | 8 +- src/snOperatorCUDA/src/activationFunctions.h | 2 +- src/snOperatorCUDA/src/weightInit.cpp | 30 ++-- src/snOperatorCUDA/src/weightInit.h | 11 -- 14 files changed, 232 insertions(+), 219 deletions(-) diff --git a/src/snOperatorCPU/src/Operator/activation.cpp b/src/snOperatorCPU/src/Operator/activation.cpp index 8850006..b0d0086 100644 --- a/src/snOperatorCPU/src/Operator/activation.cpp +++ b/src/snOperatorCPU/src/Operator/activation.cpp @@ -77,7 +77,16 @@ std::vector Activation::Do(const operationParam& operPrm, const std } /// active func - activeFuncBackward(baseGrad_.size().size(), baseGrad_.getDataCPU(), activeType_); + snFloat* gradIn = baseGrad_.getDataCPU(), + * out = baseOut_.getDataCPU(); + + size_t osz = baseGrad_.size().size(); + + activeFuncBackward(osz, out, activeType_); + + // update grad + for (size_t i = 0; i < osz; ++i) + gradIn[i] *= out[i]; } return vector(); diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu index da6ccd7..085af6a 100644 --- a/src/snOperatorCUDA/src/CUDA/activation.cu +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -29,7 +29,7 @@ __global__ void fv_sigmoid(const snSize& outsz, snFloat* output){ i += blockDim.x; } } -__global__ void df_sigmoid(const snSize& outsz, snFloat* output){ +__global__ void df_sigmoid(const snSize& outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -44,6 +44,8 @@ __global__ void df_sigmoid(const snSize& outsz, snFloat* output){ output[i] = output[i] * (1.F - output[i]); + grad[i] *= output[i]; + i += blockDim.x; } } @@ -66,7 +68,7 @@ __global__ void fv_relu(const snSize& outsz, snFloat* output){ i += blockDim.x; } }; -__global__ void df_relu(const snSize& outsz, snFloat* output){ +__global__ void df_relu(const snSize& outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -81,6 +83,8 @@ __global__ void df_relu(const snSize& outsz, snFloat* output){ output[i] = output[i] >= 0 ? 1.F : 0.F; + grad[i] *= output[i]; + i += blockDim.x; } }; @@ -103,7 +107,7 @@ __global__ void fv_leakyRelu(const snSize& outsz, snFloat* output){ i += blockDim.x; } } -__global__ void df_leakyRelu(const snSize& outsz, snFloat* output){ +__global__ void df_leakyRelu(const snSize& outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -118,6 +122,8 @@ __global__ void df_leakyRelu(const snSize& outsz, snFloat* output){ output[i] = output[i] >= 0 ? 1 : 0.01F; + grad[i] *= output[i]; + i += blockDim.x; } } @@ -140,7 +146,7 @@ __global__ void fv_elu(const snSize& outsz, snFloat* output){ i += blockDim.x; } } -__global__ void df_elu(const snSize& outsz, snFloat* output){ +__global__ void df_elu(const snSize& outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -155,6 +161,8 @@ __global__ void df_elu(const snSize& outsz, snFloat* output){ output[i] = output[i] >= 0 ? 1 : output[i] + 0.01F; + grad[i] *= output[i]; + i += blockDim.x; } } @@ -173,16 +181,16 @@ void activationForward(const snSize& sz, snFloat* data, activeType active){ } } -void activeFuncBackward(const snSize& sz, snFloat* data, activeType active){ +void activationBackward(const snSize& sz, snFloat* data, snFloat* gradIn, activeType active){ dim3 dimBlock(128); dim3 dimGrid(int(sz.d), int(sz.n)); switch (active){ - case activeType::sigmoid: df_sigmoid <<< dimGrid, dimBlock >>>(sz, data); break; - case activeType::relu: df_relu <<< dimGrid, dimBlock >>>(sz, data); break; - case activeType::leakyRelu: df_leakyRelu <<< dimGrid, dimBlock >>>(sz, data); break; - case activeType::elu: df_elu <<< dimGrid, dimBlock >>>(sz, data); break; + case activeType::sigmoid: df_sigmoid <<< dimGrid, dimBlock >>>(sz, data, gradIn); break; + case activeType::relu: df_relu <<< dimGrid, dimBlock >>>(sz, data, gradIn); break; + case activeType::leakyRelu: df_leakyRelu <<< dimGrid, dimBlock >>>(sz, data, gradIn); break; + case activeType::elu: df_elu <<< dimGrid, dimBlock >>>(sz, data, gradIn); break; default: break; } } diff --git a/src/snOperatorCUDA/src/CUDA/optimizer.cu b/src/snOperatorCUDA/src/CUDA/optimizer.cu index 1b2ac4b..7346822 100644 --- a/src/snOperatorCUDA/src/CUDA/optimizer.cu +++ b/src/snOperatorCUDA/src/CUDA/optimizer.cu @@ -9,12 +9,11 @@ using namespace SN_Base; /// adaptive gradient method __global__ void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat eps){ - size_t wStepByD = sz.w * sz.h, - wStepByN = wStepByD * sz.d; + size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 - ioWGr += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioWGr += blockIdx.x * wStepByD; + ioW += blockIdx.x * wStepByD; + dW += blockIdx.x * wStepByD; unsigned int i = threadIdx.x; while (i < wStepByD){ @@ -29,12 +28,11 @@ __global__ void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snS /// RMSprop __global__ void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mu, snFloat eps){ - size_t wStepByD = sz.w * sz.h, - wStepByN = wStepByD * sz.d; + size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 - ioWGr += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioWGr += blockIdx.x * wStepByD; + ioW += blockIdx.x * wStepByD; + dW += blockIdx.x * wStepByD; unsigned int i = threadIdx.x; while (i < wStepByD){ @@ -49,13 +47,12 @@ __global__ void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snS /// adam __global__ void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, snFloat eps){ - size_t wStepByD = sz.w * sz.h, - wStepByN = wStepByD * sz.d; + size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 - iodWPrev += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - ioWGr += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + iodWPrev += blockIdx.x * wStepByD; + ioWGr += blockIdx.x * wStepByD; + ioW += blockIdx.x * wStepByD; + dW += blockIdx.x * wStepByD; unsigned int i = threadIdx.x; while (i < wStepByD){ @@ -74,11 +71,10 @@ __global__ void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat /// SGD without momentum __global__ void opt_sgd(snFloat* dW, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda){ - size_t wStepByD = sz.w * sz.h, - wStepByN = wStepByD * sz.d; + size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 - ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + ioW += blockIdx.x * wStepByD; + dW += blockIdx.x * wStepByD; unsigned int i = threadIdx.x; while (i < wStepByD){ @@ -92,12 +88,11 @@ __global__ void opt_sgd(snFloat* dW, snFloat* ioW, const snSize& sz, snFloat alp /// SGD with momentum __global__ void opt_sgdMoment(snFloat* dW, snFloat* iodWPrev, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mu){ - size_t wStepByD = sz.w * sz.h, - wStepByN = wStepByD * sz.d; + size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 - iodWPrev += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - ioW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; - dW += blockIdx.x * wStepByD + blockIdx.y * wStepByN; + iodWPrev += blockIdx.x * wStepByD; + ioW += blockIdx.x * wStepByD; + dW += blockIdx.x * wStepByD; unsigned int i = threadIdx.x; while (i < wStepByD){ @@ -111,8 +106,8 @@ __global__ void opt_sgdMoment(snFloat* dW, snFloat* iodWPrev, snFloat* ioW, cons void optimizer(snFloat* dWeight, snFloat* dWPrev, snFloat* dWGrad, snFloat* weight, const SN_Base::snSize& wsz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, optimizerType otype){ - dim3 dimBlock(128); - dim3 dimGrid(int(wsz.d), int(wsz.n)); + dim3 dimBlock(256); + dim3 dimGrid(int(wsz.w)); // wsz.w := kernel switch (otype){ case optimizerType::sgd: opt_sgd <<>> (dWeight, weight, wsz, alpha, lambda); break; diff --git a/src/snOperatorCUDA/src/Operator/activation.cpp b/src/snOperatorCUDA/src/Operator/activation.cpp index 4b92cb2..38abb8f 100644 --- a/src/snOperatorCUDA/src/Operator/activation.cpp +++ b/src/snOperatorCUDA/src/Operator/activation.cpp @@ -59,8 +59,8 @@ std::vector Activation::Do(const operationParam& operPrm, const std baseOut_ = neighbOpr[0]->getOutput(); - /// active func - // activeFuncForward(baseOut_.size().size(), baseOut_.getDataGPU(), activeType_); + // active func + activationForward(baseOut_.size(), baseOut_.getDataGPU(), activeType_); } else{ // backward @@ -76,8 +76,8 @@ std::vector Activation::Do(const operationParam& operPrm, const std baseGrad_ += neighbOpr[i]->getGradient(); } - /// active func - // activeFuncBackward(baseGrad_.size().size(), baseGrad_.getDataGPU(), activeType_); + // active func + activationBackward(baseOut_.size(), baseOut_.getDataGPU(), baseGrad_.getDataGPU(), activeType_); } return vector(); diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 8ea5373..a2f9ff4 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -294,7 +294,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o // active function if (activeType_ != activeType::none) - activationBackward(baseOut_.size(), baseOut_.getDataGPU(), activeType_); + activationBackward(baseOut_.size(), baseOut_.getDataGPU(), gradIn, activeType_); // batchNorm if (batchNormType_ == batchNormType::beforeActive) @@ -316,13 +316,12 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o // correct weight snFloat* dWPrev = auxGPUParams_["dWPrev"]; snFloat* dWGrad = auxGPUParams_["dWGrad"]; - size_t wsz = baseWeight_.size().size(); optimizer(dWeight, dWPrev, dWGrad, weight, - snSize(convPrms_.fWidth, convPrms_.fHeight, insz.d, convPrms_.kernel), + baseWeight_.size(), operPrm.lr, optLmbRegular_, optDecayMomentDW_, diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp index 71f8a41..c841d70 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -31,6 +31,7 @@ #include "snOperatorCUDA/src/structurs.h" #include "snOperatorCUDA/src/cudaCommon.h" #include "snOperatorCUDA/src/dropOut.h" +#include "snOperatorCUDA/src/batchNormFunctions.h" using namespace std; using namespace SN_Base; @@ -83,24 +84,8 @@ void Deconvolution::load(std::map& prms){ if (prms.find("gpuDeviceId") != prms.end()) gpuDeviceId_ = stoi(prms["gpuDeviceId"]); - - - // aux arrays - auxParams_["dWeight"] = vector(); - auxParams_["dWPrev"] = vector(); - auxParams_["dWGrad"] = vector(); - - if (batchNormType_ != batchNormType::none){ - auxParams_["bn_mean"] = vector(); - auxParams_["bn_varce"] = vector(); - auxParams_["bn_scale"] = vector(); - auxParams_["bn_schift"] = vector(); - auxParams_["bn_norm"] = vector(); - auxParams_["bn_dScale"] = vector(); - auxParams_["bn_dSchift"] = vector(); - auxParams_["bn_onc"] = vector(); - } - + + setInternPrm(prms); } @@ -169,23 +154,49 @@ bool Deconvolution::setInternPrm(std::map& prms){ bool Deconvolution::setBatchNorm(const batchNorm& bn){ - size_t osz = bn.sz.size(); + size_t csz = baseBatchNorm_.sz.size(), + osz = bn.sz.size(); - auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); - auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); - auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); - auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); - memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); + memCpyCPU2GPU(osz, baseBatchNorm_.mean, osz, bn.mean); + memCpyCPU2GPU(osz, baseBatchNorm_.varce, osz, bn.varce); + memCpyCPU2GPU(osz, baseBatchNorm_.scale, osz, bn.scale); + memCpyCPU2GPU(osz, baseBatchNorm_.schift, osz, bn.schift); baseBatchNorm_.sz = bn.sz; return true; } +SN_Base::batchNorm Deconvolution::getBatchNorm(){ + + size_t csz = baseBatchNorm_.sz.size(); + + auxCPUParams_["bn_mean"] = vector(csz); + auxCPUParams_["bn_varce"] = vector(csz); + auxCPUParams_["bn_scale"] = vector(csz); + auxCPUParams_["bn_schift"] = vector(csz); + + memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), csz, baseBatchNorm_.mean); + memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), csz, baseBatchNorm_.varce); + memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), csz, baseBatchNorm_.scale); + memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), csz, baseBatchNorm_.schift); + + batchNorm bn; + bn.mean = auxCPUParams_["bn_mean"].data(); + bn.varce = auxCPUParams_["bn_varce"].data(); + bn.scale = auxCPUParams_["bn_scale"].data(); + bn.schift = auxCPUParams_["bn_schift"].data(); + + bn.sz = csz; + + return bn; +} + std::vector Deconvolution::Do(const operationParam& operPrm, const std::vector& neighbOpr){ setDeviceId(gpuDeviceId_); @@ -243,15 +254,15 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& dropOut(operPrm.isLerning, dropOut_, outsz, out); /// batchNorm - // if (batchNormType_ == batchNormType::beforeActive) - // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); - /// active func - // activeFuncForward(outsz.size(), out, activeType_); + // active func + activationForward(outsz, out, activeType_); - /// batchNorm - //if (batchNormType_ == batchNormType::postActive) - // channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); + // batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); } @@ -259,25 +270,19 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& snFloat* gradIn = inTns.getDataGPU(); - /// batchNorm - // if (batchNormType_ == batchNormType::postActive) - // channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + // batchNorm + if (batchNormType_ == batchNormType::postActive) + channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); /// active func if (activeType_ != activeType::none){ - - snFloat* out = baseOut_.getDataGPU(); - - size_t osz = baseOut_.size().size(); - // activeFuncBackward(osz, out, activeType_); - - // update grad - for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; + + activationBackward(baseOut_.size(), baseOut_.getDataGPU(), gradIn, activeType_); } - /// batchNorm - // if (batchNormType_ == batchNormType::beforeActive) - // channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); + // batchNorm + if (batchNormType_ == batchNormType::beforeActive) + channelBatchNorm(false, true, inTns.size(), gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction snFloat* grOut = baseGrad_.getDataGPU(); @@ -288,13 +293,13 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& snSize insz = inputMem_->size(), outsz = baseOut_.size(); if (!isFreeze_){ - snFloat* dWeight = auxParams_["dWeight"].data(); + snFloat* dWeight = auxGPUParams_["dWeight"]; // calculation backwardCUDA_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); - snFloat* dWPrev = auxParams_["dWPrev"].data(); - snFloat* dWGrad = auxParams_["dWGrad"].data(); + snFloat* dWPrev = auxGPUParams_["dWPrev"]; + snFloat* dWGrad = auxGPUParams_["dWGrad"]; size_t wsz = baseWeight_.size().size(); optimizer(dWeight, @@ -341,25 +346,26 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ baseGrad_.resize(newsz); // aux array - auxParams_["dWeight"].resize(ntp, 0); - auxParams_["dWPrev"].resize(ntp, 0); - auxParams_["dWGrad"].resize(ntp, 0); + auxGPUParams_["dWeight"] = memRealloc(0, ntp, auxGPUParams_["dWeight"], 0); + auxGPUParams_["dWPrev"] = memRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); + auxGPUParams_["dWGrad"] = memRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); } - size_t osz = outSz.w * outSz.h * outSz.d; + size_t csz = baseBatchNorm_.sz.w * baseBatchNorm_.sz.h * baseBatchNorm_.sz.d, + osz = outSz.w * outSz.h * outSz.d; if (batchNormType_ != batchNormType::none){ - auxParams_["bn_mean"].resize(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); - auxParams_["bn_varce"].resize(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); - auxParams_["bn_scale"].resize(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); - auxParams_["bn_schift"].resize(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); - + baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); + if (isLern){ - auxParams_["bn_norm"].resize(osz * outSz.n); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); - auxParams_["bn_dScale"].resize(osz, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); - auxParams_["bn_dSchift"].resize(osz, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); - auxParams_["bn_onc"].resize(outSz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); + baseBatchNorm_.norm = memRealloc(csz * outSz.n, osz * outSz.n, baseBatchNorm_.mean, 0); + baseBatchNorm_.dScale = memRealloc(csz, osz, baseBatchNorm_.dScale, 0); + baseBatchNorm_.dSchift = memRealloc(csz, osz, baseBatchNorm_.dSchift, 0); } + baseBatchNorm_.sz = outSz; baseBatchNorm_.sz.n = 1; } diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.h b/src/snOperatorCUDA/src/Operator/deconvolution.h index 3f208cb..14afa27 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.h +++ b/src/snOperatorCUDA/src/Operator/deconvolution.h @@ -41,6 +41,8 @@ class Deconvolution final : SN_Base::OperatorBase{ bool setInternPrm(std::map& prms) override; bool setBatchNorm(const SN_Base::batchNorm& bn) override; + + SN_Base::batchNorm getBatchNorm() override; private: @@ -70,7 +72,9 @@ class Deconvolution final : SN_Base::OperatorBase{ optDecayMomentWGr_ = 0.99F, optLmbRegular_ = 0.001F; - std::map> auxParams_; ///< aux data + std::map auxGPUParams_; ///< aux data + std::map> auxCPUParams_; + void* gpuParams_ = nullptr; ///< aux GPU void load(std::map& prms); diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index fb06f62..0e7c036 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -29,7 +29,7 @@ #include "snOperatorCUDA/src/activationFunctions.h" #include "snOperatorCUDA/src/optimizer.h" #include "snOperatorCUDA/src/structurs.h" -#include "snOperatorCUDA/src/batchNornFunctions.h" +#include "snOperatorCUDA/src/batchNormFunctions.h" #include "snOperatorCUDA/src/cudaCommon.h" #include "snOperatorCUDA/src/dropOut.h" @@ -82,23 +82,15 @@ void FullyConnected::load(std::map& prms){ baseOut_.resize(snSize(kernel_)); // currrect params - setInternPrm(prms); - - // aux array - auxParams_["dWeight"] = vector(); - auxParams_["dWPrev"] = vector(); - auxParams_["dWGrad"] = vector(); + setInternPrm(prms); if (batchNormType_ != batchNormType::none){ - auxParams_["bn_mean"] = vector(kernel_, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); - auxParams_["bn_varce"] = vector(kernel_, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); - auxParams_["bn_scale"] = vector(kernel_, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); - auxParams_["bn_schift"] = vector(kernel_, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); - auxParams_["bn_norm"] = vector(); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); - auxParams_["bn_dScale"] = vector(kernel_, 0); baseBatchNorm_.dScale = auxParams_["bn_dScale"].data(); - auxParams_["bn_dSchift"] = vector(kernel_, 0); baseBatchNorm_.dSchift = auxParams_["bn_dSchift"].data(); - auxParams_["bn_onc"] = vector(); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); - + + baseBatchNorm_.mean = memRealloc(0, kernel_, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = memRealloc(0, kernel_, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = memRealloc(0, kernel_, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = memRealloc(0, kernel_, baseBatchNorm_.schift, 0); + baseBatchNorm_.sz = snSize(kernel_); } } @@ -168,23 +160,49 @@ bool FullyConnected::setInternPrm(std::map& prms){ bool FullyConnected::setBatchNorm(const batchNorm& bn){ - size_t osz = bn.sz.size(); + size_t csz = baseBatchNorm_.sz.size(), + osz = bn.sz.size(); - auxParams_["bn_mean"] = vector(osz, 0); baseBatchNorm_.mean = auxParams_["bn_mean"].data(); - auxParams_["bn_varce"] = vector(osz, 1); baseBatchNorm_.varce = auxParams_["bn_varce"].data(); - auxParams_["bn_scale"] = vector(osz, 1); baseBatchNorm_.scale = auxParams_["bn_scale"].data(); - auxParams_["bn_schift"] = vector(osz, 0); baseBatchNorm_.schift = auxParams_["bn_schift"].data(); + baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); - memcpy(baseBatchNorm_.mean, bn.mean, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.varce, bn.varce, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.scale, bn.scale, osz * sizeof(snFloat)); - memcpy(baseBatchNorm_.schift, bn.schift, osz * sizeof(snFloat)); + memCpyCPU2GPU(osz, baseBatchNorm_.mean, osz, bn.mean); + memCpyCPU2GPU(osz, baseBatchNorm_.varce, osz, bn.varce); + memCpyCPU2GPU(osz, baseBatchNorm_.scale, osz, bn.scale); + memCpyCPU2GPU(osz, baseBatchNorm_.schift, osz, bn.schift); baseBatchNorm_.sz = bn.sz; - + return true; } +batchNorm FullyConnected::getBatchNorm(){ + + size_t csz = baseBatchNorm_.sz.size(); + + auxCPUParams_["bn_mean"] = vector(csz); + auxCPUParams_["bn_varce"] = vector(csz); + auxCPUParams_["bn_scale"] = vector(csz); + auxCPUParams_["bn_schift"] = vector(csz); + + memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), csz, baseBatchNorm_.mean); + memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), csz, baseBatchNorm_.varce); + memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), csz, baseBatchNorm_.scale); + memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), csz, baseBatchNorm_.schift); + + batchNorm bn; + bn.mean = auxCPUParams_["bn_mean"].data(); + bn.varce = auxCPUParams_["bn_varce"].data(); + bn.scale = auxCPUParams_["bn_scale"].data(); + bn.schift = auxCPUParams_["bn_schift"].data(); + + bn.sz = csz; + + return bn; +} + std::vector FullyConnected::Do(const operationParam& operPrm, const std::vector& neighbOpr){ setDeviceId(gpuDeviceId_); @@ -247,15 +265,15 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& dropOut(operPrm.isLerning, dropOut_, outsz, out); /// batchNorm - // if (batchNormType_ == batchNormType::beforeActive) - // layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); + if (batchNormType_ == batchNormType::beforeActive) + layerBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active func - // activeFuncForward(kernel_ * insz.n, out, activeType_); + activationForward(kernel_ * insz.n, out, activeType_); /// batchNorm - // if (batchNormType_ == batchNormType::postActive) - // layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); + if (batchNormType_ == batchNormType::postActive) + layerBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); } void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ @@ -264,24 +282,16 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam /// batchNorm snSize gsz = inTns.size(); - // if (batchNormType_ == batchNormType::postActive) - // layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); + if (batchNormType_ == batchNormType::postActive) + layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); // active func - if (activeType_ != activeType::none){ - - snFloat* out = baseOut_.getDataGPU(); - - size_t osz = kernel_ * inSzMem_.n; - // activeFuncBackward(osz, out, activeType_); - - // update grad - for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; - } + if (activeType_ != activeType::none) + activationBackward(baseOut_.size(), baseOut_.getDataGPU(), gradIn, activeType_); - /// batchNorm - // if (batchNormType_ == batchNormType::beforeActive) - // layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); + // batchNorm + if (batchNormType_ == batchNormType::beforeActive) + layerBatchNorm(false, true, gsz, gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction snFloat* gradOut = baseGrad_.getDataGPU(); @@ -289,21 +299,20 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam if (!isFreeze_){ - snFloat* dWeight = auxParams_["dWeight"].data(); + snFloat* dWeight = auxGPUParams_["dWeight"]; // calculation backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getDataGPU(), gradIn, gradOut, dWeight, gpuParams_); // correct weight - snFloat* dWPrev = auxParams_["dWPrev"].data(); - snFloat* dWGrad = auxParams_["dWGrad"].data(); - size_t wsz = baseWeight_.size().size(); + snFloat* dWPrev = auxGPUParams_["dWPrev"]; + snFloat* dWGrad = auxGPUParams_["dWGrad"]; optimizer(dWeight, dWPrev, dWGrad, weight, - wsz, + baseWeight_.size(), operPrm.lr, optLmbRegular_, optDecayMomentDW_, @@ -336,13 +345,19 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ baseGrad_.resize(newsz); // aux array - auxParams_["dWeight"].resize(ntp, 0); - auxParams_["dWPrev"].resize(ntp, 0); - auxParams_["dWGrad"].resize(ntp, 0); + auxGPUParams_["dWeight"] = memRealloc(0, ntp, auxGPUParams_["dWeight"], 0); + auxGPUParams_["dWPrev"] = memRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); + auxGPUParams_["dWGrad"] = memRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); if (batchNormType_ != batchNormType::none){ - auxParams_["bn_norm"].resize(newsz.n * kernel_, 0); baseBatchNorm_.norm = auxParams_["bn_norm"].data(); - auxParams_["bn_onc"].resize(newsz.n, 1.F); baseBatchNorm_.onc = auxParams_["bn_onc"].data(); + + auto bcsz = baseBatchNorm_.sz.size(); + + baseBatchNorm_.norm = memRealloc(bcsz, newsz.n * kernel_, baseBatchNorm_.norm, 0); + baseBatchNorm_.dScale = memRealloc(bcsz, kernel_, baseBatchNorm_.dScale, 0); + baseBatchNorm_.dSchift = memRealloc(bcsz, kernel_, baseBatchNorm_.dSchift, 0); + + baseBatchNorm_.sz = snSize(kernel_); } } diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.h b/src/snOperatorCUDA/src/Operator/fullyConnected.h index eac8718..f82fe98 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.h +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.h @@ -42,6 +42,8 @@ class FullyConnected final : SN_Base::OperatorBase{ bool setBatchNorm(const SN_Base::batchNorm& bn) override; + SN_Base::batchNorm getBatchNorm() override; + private: size_t kernel_ = 10; ///< number of hidden neurons @@ -67,10 +69,11 @@ class FullyConnected final : SN_Base::OperatorBase{ optLmbRegular_ = 0.001F; - std::map> auxParams_; ///< aux data - void* gpuParams_ = nullptr; ///< gpu data + std::map auxGPUParams_; ///< aux data + std::map> auxCPUParams_; - + void* gpuParams_ = nullptr; ///< gpu data + void load(std::map& prms); diff --git a/src/snOperatorCUDA/src/Operator/pooling.cpp b/src/snOperatorCUDA/src/Operator/pooling.cpp index 344a0e1..367a95c 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.cpp +++ b/src/snOperatorCUDA/src/Operator/pooling.cpp @@ -127,17 +127,8 @@ void Pooling::forward(const SN_Base::Tensor& inTns, const SN_Base::operationPara updateConfig(operPrm.isLerning, insz); } - /// copy with offset padding for each image - snFloat* in = inputMem_->getDataGPU(); - /* if (isPadding_){ - inTnsExp_.resize(inDataExpSz_); - paddingOffs(false, poolPrms_.paddingW, poolPrms_.paddingH, insz, inTnsExp_.getDataGPU(), inputMem_->getDataGPU()); - insz = inDataExpSz_; - in = inTnsExp_.getDataGPU(); - }*/ - - /// output calculation - snFloat* out = baseOut_.getDataGPU(); + snFloat* in = inputMem_->getDataGPU(), + * out = baseOut_.getDataGPU(); // calculation forwardCUDA(poolPrms_, insz, in, baseOut_.size(), out, gpuParams_); @@ -148,17 +139,11 @@ void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operP snFloat* gradIn = inTns.getDataGPU(); - snFloat* input = inputMem_->getDataGPU(); - snFloat* gradOut = baseGrad_.getDataGPU(); - if (isPadding_){ - gradOutExp_.resize(inDataExpSz_); - gradOut = gradOutExp_.getDataGPU(); - input = inTnsExp_.getDataGPU(); - } + snFloat* input = inputMem_->getDataGPU(), + * gradOut = baseGrad_.getDataGPU(); /// grad calculation - backwardCUDA(poolPrms_, baseOut_.size(), baseOut_.getDataGPU(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); - + backwardCUDA(poolPrms_, baseOut_.size(), baseOut_.getDataGPU(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); } void Pooling::updateConfig(bool isLern, const snSize& newsz){ diff --git a/src/snOperatorCUDA/src/Operator/userLayer.cpp b/src/snOperatorCUDA/src/Operator/userLayer.cpp index 48eed76..8c758af 100644 --- a/src/snOperatorCUDA/src/Operator/userLayer.cpp +++ b/src/snOperatorCUDA/src/Operator/userLayer.cpp @@ -55,10 +55,10 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - true, baseOut_.size(), baseOut_.getDataGPU(), outSz, &outData); + true, baseOut_.size(), baseOut_.getDataCPU(), outSz, &outData); if (outData) - baseOut_.setDataGPU(outData, outSz); + baseOut_.setDataCPU(outData, outSz); else{ ERROR_MESS("not set 'outData' in userCBack"); } @@ -79,10 +79,10 @@ std::vector UserLayer::Do(const operationParam& opr, const std::vec snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - false, baseGrad_.size(), baseGrad_.getDataGPU(), outSz, &outData); + false, baseGrad_.size(), baseGrad_.getDataCPU(), outSz, &outData); if (outData) - baseGrad_.setDataGPU(outData, outSz); + baseGrad_.setDataCPU(outData, outSz); else{ ERROR_MESS("not set 'outData' in userCBack"); } diff --git a/src/snOperatorCUDA/src/activationFunctions.h b/src/snOperatorCUDA/src/activationFunctions.h index 83dcadd..1ff0733 100644 --- a/src/snOperatorCUDA/src/activationFunctions.h +++ b/src/snOperatorCUDA/src/activationFunctions.h @@ -31,4 +31,4 @@ void activationForward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType); -void activationBackward(const SN_Base::snSize& sz, SN_Base::snFloat* data, activeType); +void activationBackward(const SN_Base::snSize& sz, SN_Base::snFloat* data, SN_Base::snFloat* gradIn, activeType); diff --git a/src/snOperatorCUDA/src/weightInit.cpp b/src/snOperatorCUDA/src/weightInit.cpp index 446a4c0..73d57e4 100644 --- a/src/snOperatorCUDA/src/weightInit.cpp +++ b/src/snOperatorCUDA/src/weightInit.cpp @@ -34,22 +34,9 @@ using namespace SN_Base; // инициализация весов -void weightInit(SN_Base::Tensor& ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype){ - - vector buff(sz); - - switch (wtype){ - case weightInitType::uniform: wi_uniform(buff.data(), sz); break; - case weightInitType::he: wi_he(buff.data(), sz, fan_in); break; - case weightInitType::lecun:wi_lecun(buff.data(), sz, fan_out); break; - case weightInitType::xavier:wi_xavier(buff.data(), sz, fan_in, fan_out); break; - } - - ioW.setDataCPU(buff.data(), sz); -} void wi_uniform(snFloat* ioW, size_t sz){ - + rnd_uniformReal(ioW, sz, -1.F, 1.F); }; @@ -60,7 +47,7 @@ void wi_xavier(snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out){ }; void wi_lecun(snFloat* ioW, size_t sz, size_t fan_out){ - + snFloat wbase = 1.F / std::sqrt(snFloat(fan_out)); rnd_uniformReal(ioW, sz, -wbase, wbase); @@ -73,3 +60,16 @@ void wi_he(snFloat* ioW, size_t sz, size_t fan_in){ rnd_gaussian(ioW, sz, 0.0F, sigma); } +void weightInit(SN_Base::Tensor& ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype){ + + vector buff(sz); + + switch (wtype){ + case weightInitType::uniform: wi_uniform(buff.data(), sz); break; + case weightInitType::he: wi_he(buff.data(), sz, fan_in); break; + case weightInitType::lecun:wi_lecun(buff.data(), sz, fan_out); break; + case weightInitType::xavier:wi_xavier(buff.data(), sz, fan_in, fan_out); break; + } + + ioW.setDataCPU(buff.data(), sz); +} diff --git a/src/snOperatorCUDA/src/weightInit.h b/src/snOperatorCUDA/src/weightInit.h index a541dc9..c151f4e 100644 --- a/src/snOperatorCUDA/src/weightInit.h +++ b/src/snOperatorCUDA/src/weightInit.h @@ -31,14 +31,3 @@ #include "random.h" void weightInit(SN_Base::Tensor& ioW, size_t sz, size_t fan_in, size_t fan_out, weightInitType wtype); - -void wi_uniform(SN_Base::snFloat* ioW, size_t sz); - -// 2010 Xavier Glorot -void wi_xavier(SN_Base::snFloat* ioW, size_t sz, size_t fan_in, size_t fan_out); - -// 1998 Yann LeCun -void wi_lecun(SN_Base::snFloat* ioW, size_t sz, size_t fan_out); - -// 2015 Kaiming He -void wi_he(SN_Base::snFloat* ioW, size_t sz, size_t fan_in); From 55503ec6b46143494df360043f388584db822e33 Mon Sep 17 00:00:00 2001 From: al_medved Date: Sat, 20 Jul 2019 00:38:21 +0500 Subject: [PATCH 14/35] -develop --- mswin/VS12/snOperatorCUDA.vcxproj | 6 +- mswin/VS12/snOperatorCUDA.vcxproj.filters | 6 ++ src/snOperatorCPU/src/Operator/activation.cpp | 4 +- .../src/Operator/convolution.cpp | 4 +- .../src/Operator/deconvolution.cpp | 4 +- .../src/Operator/fullyConnected.cpp | 4 +- src/snOperatorCPU/src/Operator/summator.cpp | 13 ++- src/snOperatorCPU/src/Operator/summator.h | 2 +- src/snOperatorCPU/src/activationFunctions.cpp | 4 +- src/snOperatorCPU/src/activationFunctions.h | 4 +- src/snOperatorCUDA/src/CUDA/arithmetic.cu | 93 +++++++++++++++++++ src/snOperatorCUDA/src/CUDA/tensor.cu | 3 + src/snOperatorCUDA/src/Operator/summator.cpp | 17 +--- src/snOperatorCUDA/src/Operator/summator.h | 3 - src/snOperatorCUDA/src/arithmetic.h | 33 +++++++ 15 files changed, 162 insertions(+), 38 deletions(-) create mode 100644 src/snOperatorCUDA/src/CUDA/arithmetic.cu create mode 100644 src/snOperatorCUDA/src/arithmetic.h diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index b253ad3..17e641a 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -32,7 +32,7 @@ - + @@ -189,6 +189,7 @@ + @@ -247,10 +248,11 @@ + - + \ No newline at end of file diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index 86ea4b7..2b44515 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -99,6 +99,9 @@ Header Files + + Header Files + @@ -192,5 +195,8 @@ CUDA + + CUDA + \ No newline at end of file diff --git a/src/snOperatorCPU/src/Operator/activation.cpp b/src/snOperatorCPU/src/Operator/activation.cpp index b0d0086..c6db596 100644 --- a/src/snOperatorCPU/src/Operator/activation.cpp +++ b/src/snOperatorCPU/src/Operator/activation.cpp @@ -60,7 +60,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std baseOut_ = neighbOpr[0]->getOutput(); /// active func - activeFuncForward(baseOut_.size().size(), baseOut_.getDataCPU(), activeType_); + activationForward(baseOut_.size().size(), baseOut_.getDataCPU(), activeType_); } else{ // backward @@ -82,7 +82,7 @@ std::vector Activation::Do(const operationParam& operPrm, const std size_t osz = baseGrad_.size().size(); - activeFuncBackward(osz, out, activeType_); + activationBackward(osz, out, activeType_); // update grad for (size_t i = 0; i < osz; ++i) diff --git a/src/snOperatorCPU/src/Operator/convolution.cpp b/src/snOperatorCPU/src/Operator/convolution.cpp index bbdc7bf..0c22081 100644 --- a/src/snOperatorCPU/src/Operator/convolution.cpp +++ b/src/snOperatorCPU/src/Operator/convolution.cpp @@ -263,7 +263,7 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active function - activeFuncForward(outsz.size(), out, activeType_); + activationForward(outsz.size(), out, activeType_); /// batchNorm if (batchNormType_ == batchNormType::postActive) @@ -284,7 +284,7 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o snFloat* out = baseOut_.getDataCPU(); size_t osz = baseOut_.size().size(); - activeFuncBackward(osz, out, activeType_); + activationBackward(osz, out, activeType_); // update grad for (size_t i = 0; i < osz; ++i) diff --git a/src/snOperatorCPU/src/Operator/deconvolution.cpp b/src/snOperatorCPU/src/Operator/deconvolution.cpp index ce79fc2..cc5486c 100644 --- a/src/snOperatorCPU/src/Operator/deconvolution.cpp +++ b/src/snOperatorCPU/src/Operator/deconvolution.cpp @@ -240,7 +240,7 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& channelBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active func - activeFuncForward(outsz.size(), out, activeType_); + activationForward(outsz.size(), out, activeType_); /// batchNorm if (batchNormType_ == batchNormType::postActive) @@ -262,7 +262,7 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& snFloat* out = baseOut_.getDataCPU(); size_t osz = baseOut_.size().size(); - activeFuncBackward(osz, out, activeType_); + activationBackward(osz, out, activeType_); // update grad for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; diff --git a/src/snOperatorCPU/src/Operator/fullyConnected.cpp b/src/snOperatorCPU/src/Operator/fullyConnected.cpp index 2a1a1e5..565e851 100644 --- a/src/snOperatorCPU/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCPU/src/Operator/fullyConnected.cpp @@ -244,7 +244,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& layerBatchNorm(true, operPrm.isLerning, outSz, out, out, baseBatchNorm_); /// active func - activeFuncForward(kernel_ * insz.n, out, activeType_); + activationForward(kernel_ * insz.n, out, activeType_); /// batchNorm if (batchNormType_ == batchNormType::postActive) @@ -266,7 +266,7 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam snFloat* out = baseOut_.getDataCPU(); size_t osz = kernel_ * inSzMem_.n; - activeFuncBackward(osz, out, activeType_); + activationBackward(osz, out, activeType_); // update grad for (size_t i = 0; i < osz; ++i) gradIn[i] *= out[i]; diff --git a/src/snOperatorCPU/src/Operator/summator.cpp b/src/snOperatorCPU/src/Operator/summator.cpp index a5f0585..6781935 100644 --- a/src/snOperatorCPU/src/Operator/summator.cpp +++ b/src/snOperatorCPU/src/Operator/summator.cpp @@ -58,7 +58,7 @@ std::vector Summator::Do(const operationParam& operPrm, const std:: switch (sType_){ case Summator::sType::summ: baseOut_ += neighbOpr[i]->getOutput(); break; case Summator::sType::diff: baseOut_ -= neighbOpr[i]->getOutput(); break; - case Summator::sType::mean: mean(baseOut_, neighbOpr[i]->getOutput(), baseOut_); break; + case Summator::sType::mean: mean(baseOut_, neighbOpr[i]->getOutput()); break; } } } @@ -81,14 +81,13 @@ std::vector Summator::Do(const operationParam& operPrm, const std:: return std::vector(); } -void Summator::mean(const Tensor& one, const Tensor& two, Tensor& out){ +void Summator::mean(SN_Base::Tensor& inout, const SN_Base::Tensor& two){ - snFloat* done = one.getDataCPU(), - * dtwo = two.getDataCPU(), - * dout = out.getDataCPU(); + snFloat* done = inout.getDataCPU(), + * dtwo = two.getDataCPU(); - size_t sz = one.size().size(); + size_t sz = inout.size().size(); for (size_t i = 0; i < sz; ++i){ - dout[i] = (done[i] + dtwo[i]) / 2; + done[i] = (done[i] + dtwo[i]) / 2; } } \ No newline at end of file diff --git a/src/snOperatorCPU/src/Operator/summator.h b/src/snOperatorCPU/src/Operator/summator.h index 8013d49..fd5b335 100644 --- a/src/snOperatorCPU/src/Operator/summator.h +++ b/src/snOperatorCPU/src/Operator/summator.h @@ -49,5 +49,5 @@ class Summator final : SN_Base::OperatorBase{ sType sType_ = sType::summ; - void mean(const SN_Base::Tensor& one, const SN_Base::Tensor& two, SN_Base::Tensor& out); + void mean(SN_Base::Tensor& inout, const SN_Base::Tensor& two); }; diff --git a/src/snOperatorCPU/src/activationFunctions.cpp b/src/snOperatorCPU/src/activationFunctions.cpp index fc7c5b4..b318a5b 100644 --- a/src/snOperatorCPU/src/activationFunctions.cpp +++ b/src/snOperatorCPU/src/activationFunctions.cpp @@ -31,7 +31,7 @@ using namespace SN_Base; // fv - value, df - deriv -void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType active){ +void activationForward(size_t sz, SN_Base::snFloat* data, activeType active){ switch (active){ case activeType::sigmoid: fv_sigmoid(data, sz); break; @@ -42,7 +42,7 @@ void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType active){ } } -void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType active){ +void activationBackward(size_t sz, SN_Base::snFloat* data, activeType active){ switch (active){ case activeType::sigmoid: df_sigmoid(data, sz); break; diff --git a/src/snOperatorCPU/src/activationFunctions.h b/src/snOperatorCPU/src/activationFunctions.h index 0f74f1b..fcdb89d 100644 --- a/src/snOperatorCPU/src/activationFunctions.h +++ b/src/snOperatorCPU/src/activationFunctions.h @@ -29,9 +29,9 @@ // fv - value, df - deriv -void activeFuncForward(size_t sz, SN_Base::snFloat* data, activeType); +void activationForward(size_t sz, SN_Base::snFloat* data, activeType); -void activeFuncBackward(size_t sz, SN_Base::snFloat* data, activeType); +void activationBackward(size_t sz, SN_Base::snFloat* data, activeType); void fv_sigmoid(SN_Base::snFloat* ioVal, size_t sz); void df_sigmoid(SN_Base::snFloat* inSigm, size_t sz); diff --git a/src/snOperatorCUDA/src/CUDA/arithmetic.cu b/src/snOperatorCUDA/src/CUDA/arithmetic.cu new file mode 100644 index 0000000..7a37b37 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/arithmetic.cu @@ -0,0 +1,93 @@ + +#include + +#include "../stdafx.h" + +using namespace SN_Base; + +__global__ void summInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ + + size_t outStepByD = sz.w * sz.h, // step out by input + outStepByN = outStepByD * sz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + one += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + two += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + unsigned int i = threadIdx.x; + + while (i < outStepByD){ + + one[i] += two[i]; + + i += blockDim.x; + } +} + +__global__ void differenceInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ + + size_t outStepByD = sz.w * sz.h, // step out by input + outStepByN = outStepByD * sz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + one += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + two += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + unsigned int i = threadIdx.x; + + while (i < outStepByD){ + + one[i] -= two[i]; + + i += blockDim.x; + } +} + +__global__ void meanInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ + + size_t outStepByD = sz.w * sz.h, // step out by input + outStepByN = outStepByD * sz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + one += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + two += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + + unsigned int i = threadIdx.x; + + while (i < outStepByD){ + + one[i] = (one[i] + two[i]) / 2; + + i += blockDim.x; + } +} + +void summ(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two){ + + dim3 dimBlock(128); + dim3 dimGrid(int(sz.d), int(sz.n)); + + summInf <<< dimGrid, dimBlock >>>(sz, inout, two); +} + +void difference(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two){ + + dim3 dimBlock(128); + dim3 dimGrid(int(sz.d), int(sz.n)); + + differenceInf <<< dimGrid, dimBlock >>>(sz, inout, two); +} + +void mean(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two){ + + dim3 dimBlock(128); + dim3 dimGrid(int(sz.d), int(sz.n)); + + meanInf <<< dimGrid, dimBlock >>>(sz, inout, two); +} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/tensor.cu b/src/snOperatorCUDA/src/CUDA/tensor.cu index 93c88ea..bee26e8 100644 --- a/src/snOperatorCUDA/src/CUDA/tensor.cu +++ b/src/snOperatorCUDA/src/CUDA/tensor.cu @@ -3,6 +3,7 @@ #include #include "../stdafx.h" +#include "../arithmetic.h" using namespace SN_Base; @@ -43,6 +44,7 @@ Tensor& Tensor::operator+=(const Tensor& other){ ASSERT_MESS(other == *this, ""); + summ(sz_, dataGPU_, other->getDataGPU()); return *this; } @@ -51,6 +53,7 @@ Tensor& Tensor::operator-=(const Tensor& other){ ASSERT_MESS(other == *this, ""); + difference(sz_, dataGPU_, other->getDataGPU()); return *this; } diff --git a/src/snOperatorCUDA/src/Operator/summator.cpp b/src/snOperatorCUDA/src/Operator/summator.cpp index c7fa7dd..9e31da6 100644 --- a/src/snOperatorCUDA/src/Operator/summator.cpp +++ b/src/snOperatorCUDA/src/Operator/summator.cpp @@ -23,6 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" +#include "../arithmetic.h" #include "snOperatorCUDA/src/Operator/summator.h" using namespace std; @@ -58,7 +59,9 @@ std::vector Summator::Do(const operationParam& operPrm, const std:: switch (sType_){ case Summator::sType::summ: baseOut_ += neighbOpr[i]->getOutput(); break; case Summator::sType::diff: baseOut_ -= neighbOpr[i]->getOutput(); break; - case Summator::sType::mean: mean(baseOut_, neighbOpr[i]->getOutput(), baseOut_); break; + case Summator::sType::mean: mean(baseOut_.size(), + baseOut_.getDataGPU(), + neighbOpr[i]->getOutput().getDataGPU()); break; } } } @@ -80,15 +83,3 @@ std::vector Summator::Do(const operationParam& operPrm, const std:: return std::vector(); } - -void Summator::mean(const Tensor& one, const Tensor& two, Tensor& out){ - - snFloat* done = one.getDataGPU(), - * dtwo = two.getDataGPU(), - * dout = out.getDataGPU(); - - size_t sz = one.size().size(); - for (size_t i = 0; i < sz; ++i){ - dout[i] = (done[i] + dtwo[i]) / 2; - } -} \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/summator.h b/src/snOperatorCUDA/src/Operator/summator.h index 8013d49..0a582f7 100644 --- a/src/snOperatorCUDA/src/Operator/summator.h +++ b/src/snOperatorCUDA/src/Operator/summator.h @@ -47,7 +47,4 @@ class Summator final : SN_Base::OperatorBase{ }; sType sType_ = sType::summ; - - - void mean(const SN_Base::Tensor& one, const SN_Base::Tensor& two, SN_Base::Tensor& out); }; diff --git a/src/snOperatorCUDA/src/arithmetic.h b/src/snOperatorCUDA/src/arithmetic.h new file mode 100644 index 0000000..146f026 --- /dev/null +++ b/src/snOperatorCUDA/src/arithmetic.h @@ -0,0 +1,33 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" + +void summ(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two); + +void difference(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two); + +void mean(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two); \ No newline at end of file From 7c33239624a955ea32510bcd3da88ec28ffb494b Mon Sep 17 00:00:00 2001 From: al_medved Date: Sat, 20 Jul 2019 22:04:25 +0500 Subject: [PATCH 15/35] -develop --- mswin/VS12/skyNet.vcxproj | 4 +- mswin/VS12/snOperatorCUDA.vcxproj | 16 ++-- mswin/VS12/snOperatorCUDA.vcxproj.filters | 16 ++-- src/snBase/snBase.h | 2 +- .../src/Operator/lossFunction.cpp | 18 ++-- src/snOperatorCPU/src/Operator/lossFunction.h | 9 +- src/snOperatorCPU/src/structurs.h | 10 +- src/snOperatorCUDA/src/CUDA/arithmetic.cu | 41 ++++---- src/snOperatorCUDA/src/CUDA/batchNorm.cu | 11 ++- src/snOperatorCUDA/src/CUDA/convolution.cu | 17 ++-- src/snOperatorCUDA/src/CUDA/deconvolution.cu | 16 ++-- src/snOperatorCUDA/src/CUDA/dropOut.cu | 14 +-- src/snOperatorCUDA/src/CUDA/fullyConnected.cu | 49 ++++------ src/snOperatorCUDA/src/CUDA/lossFunctions.cu | 96 +++++++++++++++++++ src/snOperatorCUDA/src/CUDA/tensor.cu | 8 +- .../src/Operator/convolution.cpp | 9 +- src/snOperatorCUDA/src/Operator/convolution.h | 4 +- .../src/Operator/deconvolution.cpp | 9 +- .../src/Operator/deconvolution.h | 4 +- .../src/Operator/fullyConnected.cpp | 18 ++-- .../src/Operator/fullyConnected.h | 4 +- .../src/Operator/lossFunction.cpp | 48 +++------- .../src/Operator/lossFunction.h | 16 +--- src/snOperatorCUDA/src/lossFunctions.h | 32 +++++++ src/snOperatorCUDA/src/structurs.h | 12 ++- 25 files changed, 298 insertions(+), 185 deletions(-) create mode 100644 src/snOperatorCUDA/src/CUDA/lossFunctions.cu create mode 100644 src/snOperatorCUDA/src/lossFunctions.h diff --git a/mswin/VS12/skyNet.vcxproj b/mswin/VS12/skyNet.vcxproj index 2f0ec4c..deb2624 100644 --- a/mswin/VS12/skyNet.vcxproj +++ b/mswin/VS12/skyNet.vcxproj @@ -76,7 +76,7 @@ Windows true $(OutDir) - snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCUDA.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) @@ -97,7 +97,7 @@ true true $(OutDir) - snOperatorCPU.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) + snOperatorCUDA.lib;snAux.lib;snEngine.lib;%(AdditionalDependencies) diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index 17e641a..0c954cb 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -32,7 +32,7 @@ - + @@ -103,7 +103,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cudart_static.lib;cublas.lib;cudnn.lib; + cudart_static.lib;cublas.lib;cudnn.lib;curand.lib; true @@ -171,7 +171,7 @@ %(AdditionalLibraryDirectories);$(OutDir);$(CudaToolkitLibDir); - cudart_static.lib;cublas.lib;cudnn.lib; + cudart_static.lib;cublas.lib;cudnn.lib;curand.lib; true @@ -193,6 +193,7 @@ + @@ -238,21 +239,20 @@ + + + - - - - - + \ No newline at end of file diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index 2b44515..dc1d4a2 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -102,6 +102,9 @@ Header Files + + Header Files + @@ -190,13 +193,14 @@ CUDA - - - + CUDA - - + + + CUDA + + CUDA - + \ No newline at end of file diff --git a/src/snBase/snBase.h b/src/snBase/snBase.h index a289995..44f8617 100644 --- a/src/snBase/snBase.h +++ b/src/snBase/snBase.h @@ -205,7 +205,7 @@ namespace SN_Base{ return baseWeight_; } - virtual batchNorm getBatchNorm(){ + virtual batchNorm getBatchNorm() const{ return baseBatchNorm_; } diff --git a/src/snOperatorCPU/src/Operator/lossFunction.cpp b/src/snOperatorCPU/src/Operator/lossFunction.cpp index 3243dbc..c2f56f6 100644 --- a/src/snOperatorCPU/src/Operator/lossFunction.cpp +++ b/src/snOperatorCPU/src/Operator/lossFunction.cpp @@ -64,7 +64,7 @@ std::vector LossFunction::Do(const operationParam& operPrm, const s return std::vector < std::string > {"noWay"}; } - if (lossType_ == LossFunction::lossType::userLoss){ + if (lossType_ == lossType::userLoss){ if (basePrms_.find("cbackName") == basePrms_.end()){ ERROR_MESS("not set param 'cbackName'"); return std::vector < std::string > {"noWay"}; @@ -87,7 +87,7 @@ void LossFunction::forward(const Tensor& inTns){ auto out = baseOut_.getDataCPU(); switch (lossType_){ - case LossFunction::lossType::softMaxACrossEntropy:{ + case lossType::softMaxACrossEntropy:{ if (auxParams_.find("sm_aux") == auxParams_.end()) auxParams_["sm_aux"] = vector(tsz.w * tsz.h * tsz.d); @@ -112,16 +112,16 @@ void LossFunction::forward(const Tensor& inTns){ out += width; } } - case LossFunction::lossType::binaryCrossEntropy:{ + case lossType::binaryCrossEntropy:{ break; } - case LossFunction::lossType::regressionMSE:{ + case lossType::regressionMSE:{ break; } - case LossFunction::lossType::userLoss:{ + case lossType::userLoss:{ snSize outSz; snFloat* outData = nullptr; @@ -158,7 +158,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ auto grad = baseGrad_.getDataCPU(); switch (lossType_){ - case LossFunction::lossType::softMaxACrossEntropy:{ + case lossType::softMaxACrossEntropy:{ size_t nsz = tsz.size(); for (size_t i = 0; i < nsz; ++i) @@ -167,7 +167,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ break; } - case LossFunction::lossType::binaryCrossEntropy:{ + case lossType::binaryCrossEntropy:{ size_t nsz = tsz.size(); for (size_t i = 0; i < nsz; ++i) @@ -176,7 +176,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ break; } // Mean Square Error - case LossFunction::lossType::regressionMSE:{ + case lossType::regressionMSE:{ size_t nsz = tsz.size(); for (size_t i = 0; i < nsz; ++i){ @@ -187,7 +187,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ break; } - case LossFunction::lossType::userLoss:{ + case lossType::userLoss:{ snSize outSz; snFloat* outData = nullptr; diff --git a/src/snOperatorCPU/src/Operator/lossFunction.h b/src/snOperatorCPU/src/Operator/lossFunction.h index 57b1e57..8b94ba6 100644 --- a/src/snOperatorCPU/src/Operator/lossFunction.h +++ b/src/snOperatorCPU/src/Operator/lossFunction.h @@ -25,6 +25,7 @@ #pragma once #include "snBase/snBase.h" +#include "../structurs.h" /// оператор - расчет ошибки @@ -39,13 +40,7 @@ class LossFunction final : SN_Base::OperatorBase{ std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; private: - enum class lossType{ - softMaxACrossEntropy = 0, - binaryCrossEntropy = 1, - regressionMSE = 2, - userLoss = 3, - }; - + lossType lossType_ = lossType::softMaxACrossEntropy; std::map> auxParams_; ///< вспом данные для расчета diff --git a/src/snOperatorCPU/src/structurs.h b/src/snOperatorCPU/src/structurs.h index 028d7a5..e63755a 100644 --- a/src/snOperatorCPU/src/structurs.h +++ b/src/snOperatorCPU/src/structurs.h @@ -65,8 +65,10 @@ enum class poolType{ avg = 1, }; -enum class calcMode{ - CPU = 0, - CUDA = 1, - OpenCL = 2, +// loss +enum class lossType{ + softMaxACrossEntropy = 0, + binaryCrossEntropy = 1, + regressionMSE = 2, + userLoss = 3, }; diff --git a/src/snOperatorCUDA/src/CUDA/arithmetic.cu b/src/snOperatorCUDA/src/CUDA/arithmetic.cu index 7a37b37..f0c92ed 100644 --- a/src/snOperatorCUDA/src/CUDA/arithmetic.cu +++ b/src/snOperatorCUDA/src/CUDA/arithmetic.cu @@ -5,10 +5,12 @@ using namespace SN_Base; + + __global__ void summInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ - + size_t outStepByD = sz.w * sz.h, // step out by input - outStepByN = outStepByD * sz.d; // step out by batch + outStepByN = outStepByD * sz.d; // step out by batch // gridDim.x - number of out layers // gridDim.y - batch size @@ -17,7 +19,7 @@ __global__ void summInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const two += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; - + while (i < outStepByD){ one[i] += two[i]; @@ -27,9 +29,9 @@ __global__ void summInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const } __global__ void differenceInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ - + size_t outStepByD = sz.w * sz.h, // step out by input - outStepByN = outStepByD * sz.d; // step out by batch + outStepByN = outStepByD * sz.d; // step out by batch // gridDim.x - number of out layers // gridDim.y - batch size @@ -38,7 +40,7 @@ __global__ void differenceInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, two += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; - + while (i < outStepByD){ one[i] -= two[i]; @@ -48,9 +50,9 @@ __global__ void differenceInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, } __global__ void meanInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ - + size_t outStepByD = sz.w * sz.h, // step out by input - outStepByN = outStepByD * sz.d; // step out by batch + outStepByN = outStepByD * sz.d; // step out by batch // gridDim.x - number of out layers // gridDim.y - batch size @@ -59,7 +61,7 @@ __global__ void meanInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const two += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; - + while (i < outStepByD){ one[i] = (one[i] + two[i]) / 2; @@ -69,25 +71,26 @@ __global__ void meanInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const } void summ(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two){ - + dim3 dimBlock(128); dim3 dimGrid(int(sz.d), int(sz.n)); - - summInf <<< dimGrid, dimBlock >>>(sz, inout, two); + + summInf << < dimGrid, dimBlock >> >(sz, inout, two); } void difference(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two){ - + dim3 dimBlock(128); dim3 dimGrid(int(sz.d), int(sz.n)); - - differenceInf <<< dimGrid, dimBlock >>>(sz, inout, two); + + differenceInf << < dimGrid, dimBlock >> >(sz, inout, two); } void mean(const SN_Base::snSize& sz, SN_Base::snFloat* inout, const SN_Base::snFloat* two){ - + dim3 dimBlock(128); dim3 dimGrid(int(sz.d), int(sz.n)); - - meanInf <<< dimGrid, dimBlock >>>(sz, inout, two); -} \ No newline at end of file + + meanInf << < dimGrid, dimBlock >> >(sz, inout, two); +} + diff --git a/src/snOperatorCUDA/src/CUDA/batchNorm.cu b/src/snOperatorCUDA/src/CUDA/batchNorm.cu index 94175d9..9cb3987 100644 --- a/src/snOperatorCUDA/src/CUDA/batchNorm.cu +++ b/src/snOperatorCUDA/src/CUDA/batchNorm.cu @@ -5,6 +5,15 @@ using namespace SN_Base; +__device__ void bnOffset(batchNorm& bn, size_t offs){ + bn.mean += offs; + bn.varce += offs; + bn.scale += offs; + bn.dScale += offs; + bn.schift += offs; + bn.dSchift += offs; +} + __global__ void channelBatchNormInf(const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ size_t inStepByD = insz.w * insz.h, // step out by input @@ -16,7 +25,7 @@ __global__ void channelBatchNormInf(const snSize& insz, snFloat* in, snFloat* ou snFloat* cin = in + inStepByN * blockIdx.y + inStepByD * blockIdx.x, * cout = out + inStepByN * blockIdx.y + inStepByD * blockIdx.x; - prm.offset(inStepByD * blockIdx.x); + bnOffset(prm, inStepByD * blockIdx.x); unsigned int i = threadIdx.x; while (i < inStepByD){ diff --git a/src/snOperatorCUDA/src/CUDA/convolution.cu b/src/snOperatorCUDA/src/CUDA/convolution.cu index 8ecc63e..483b1e4 100644 --- a/src/snOperatorCUDA/src/CUDA/convolution.cu +++ b/src/snOperatorCUDA/src/CUDA/convolution.cu @@ -247,7 +247,7 @@ void Convolution::freeParamCUDA(void* gpuPrms){ } } -__global__ void cuFwdBias(snSize outsz, snFloat* bias, snFloat* output){ +__global__ void cuFwdBias(snSize outsz, const snFloat* bias, snFloat* output){ size_t osz = outsz.w * outsz.h; @@ -270,8 +270,8 @@ void Convolution::forwardCUDA(const convParams& prms, gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - + size_t wStepByN = prms.fWidth * prms.fHeight * insz.d * outsz.d; + // run snFloat alpha = 1.f, beta = 0.f; cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, @@ -289,15 +289,15 @@ void Convolution::forwardCUDA(const convParams& prms, output)); // +bias -// cuFwdBias <<< int(insz.n), 128 >>> (outsz, gpuPrm->d_bias, gpuPrm->d_out); + cuFwdBias << < int(insz.n), 128 >> > (outsz, weight + wStepByN, output); } void Convolution::backwardCUDA_GW(const convParams& prms, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); - + size_t wStepByN = prms.fWidth * prms.fHeight * insz.d * outsz.d; + // run snFloat alpha = 1.f, beta = 0.f; cuCHECK(cudnnConvolutionBackwardData(gpuPrm->cudnn, @@ -328,13 +328,13 @@ void Convolution::backwardCUDA_GW(const convParams& prms, gpuPrm->dw_desc, dWeightOut)); - /*cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, &alpha, gpuPrm->grin_desc, gradIn, &beta, gpuPrm->bias_desc, - gpuPrm->d_bias));*/ + dWeightOut + wStepByN)); } @@ -342,7 +342,6 @@ void Convolution::backwardCUDA_G(const convParams& prms, const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ gpuParams* gpuPrm = (gpuParams*)gpuPrms; - size_t isz = insz.size(), osz = outsz.size(); // run snFloat alpha = 1.f, beta = 0.f; diff --git a/src/snOperatorCUDA/src/CUDA/deconvolution.cu b/src/snOperatorCUDA/src/CUDA/deconvolution.cu index fb31b20..3ef9fcc 100644 --- a/src/snOperatorCUDA/src/CUDA/deconvolution.cu +++ b/src/snOperatorCUDA/src/CUDA/deconvolution.cu @@ -262,7 +262,7 @@ void Deconvolution::forwardCUDA(const deconvParams& prms, } -__global__ void cuBwdBias(snSize insz, snFloat* bias, snFloat* grout){ +__global__ void cuBwdBias(snSize insz, const snFloat* bias, snFloat* grout){ size_t isz = insz.w * insz.h; @@ -284,7 +284,8 @@ void Deconvolution::backwardCUDA_GW(const deconvParams& prms, const snFloat* weight, const snSize& insz, const snFloat* input, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, snFloat* dWeightOut, void* gpuPrms){ gpuParams* gpuPrm = (gpuParams*)gpuPrms; - + size_t wStepByN = prms.fWidth * prms.fHeight * insz.d * outsz.d; + // run snFloat alpha = 1.f, beta = 0.f; cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, @@ -315,16 +316,16 @@ void Deconvolution::backwardCUDA_GW(const deconvParams& prms, gpuPrm->dw_desc, dWeightOut)); - /* cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, + cuCHECK(cudnnConvolutionBackwardBias(gpuPrm->cudnn, &alpha, gpuPrm->in_desc, input, &beta, gpuPrm->bias_desc, - gpuPrm->d_bias));*/ + dWeightOut + wStepByN)); // +bias - // cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + cuBwdBias << < int(insz.n), 128 >> > (insz, weight + wStepByN, gradOut); } @@ -332,7 +333,8 @@ void Deconvolution::backwardCUDA_G(const deconvParams& prms, const snFloat* weight, const snSize& insz, const snSize& outsz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ gpuParams* gpuPrm = (gpuParams*)gpuPrms; - + size_t wStepByN = prms.fWidth * prms.fHeight * insz.d * outsz.d; + // run snFloat alpha = 1.f, beta = 0.f; cuCHECK(cudnnConvolutionForward(gpuPrm->cudnn, @@ -350,6 +352,6 @@ void Deconvolution::backwardCUDA_G(const deconvParams& prms, gradOut)); // +bias - // cuBwdBias << < int(insz.n), 128 >> > (insz, gpuPrm->d_bias, gradOut); + cuBwdBias << < int(insz.n), 128 >> > (insz, weight + wStepByN, gradOut); } diff --git a/src/snOperatorCUDA/src/CUDA/dropOut.cu b/src/snOperatorCUDA/src/CUDA/dropOut.cu index 8ac44f2..294ad8c 100644 --- a/src/snOperatorCUDA/src/CUDA/dropOut.cu +++ b/src/snOperatorCUDA/src/CUDA/dropOut.cu @@ -6,7 +6,7 @@ using namespace SN_Base; -__global__ dropOutLern(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* rnd, SN_Base::snFloat* out){ +__global__ void dropOutLern(SN_Base::snFloat dropOut, const snSize& outsz, snFloat* rnd, SN_Base::snFloat* out){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -14,7 +14,7 @@ __global__ dropOutLern(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, S // gridDim.x - number of out layers // gridDim.y - batch size - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + out += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; @@ -29,7 +29,7 @@ __global__ dropOutLern(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, S } } -__global__ dropOutInf(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* out){ +__global__ void dropOutInf(SN_Base::snFloat dropOut, const snSize& outsz, snFloat* out){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -37,7 +37,7 @@ __global__ dropOutInf(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN // gridDim.x - number of out layers // gridDim.y - batch size - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + out += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; @@ -50,7 +50,7 @@ __global__ dropOutInf(SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN } -void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz, SN_Base::snFloat* inout){ +void dropOut(bool isLern, snFloat dropOut, const snSize& outsz, snFloat* inout){ if (isLern){ int blockSz = 128; @@ -68,7 +68,7 @@ void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz dim3 dimBlock(blockSz); dim3 dimGrid(int(outsz.d), int(outsz.n)); - dropOutLern <<< dimGrid, dimBlock >>>(dropOut, outsz, rndData, out); + dropOutLern << < dimGrid, dimBlock >> >(dropOut, outsz, rndData, inout); cuAssert(curandDestroyGenerator(gen)); cuAssert(cudaFree(rndData)); @@ -78,6 +78,6 @@ void dropOut(bool isLern, SN_Base::snFloat dropOut, const SN_Base::snSize& outsz dim3 dimBlock(128); dim3 dimGrid(int(outsz.d), int(outsz.n)); - dropOutInf << > >(dropOut, outsz, out); + dropOutInf << > >(dropOut, outsz, inout); } } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/CUDA/fullyConnected.cu b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu index b0725db..fddd5ab 100644 --- a/src/snOperatorCUDA/src/CUDA/fullyConnected.cu +++ b/src/snOperatorCUDA/src/CUDA/fullyConnected.cu @@ -38,9 +38,7 @@ struct gpuParams{ }; void FullyConnected::iniParamCUDA(bool isLern, const snSize& insz, size_t kernel, void** pGpuPrm){ - - size_t ida = insz.w * insz.h * insz.d, bsz = insz.n; - + gpuParams* gpuPrm = (gpuParams*)*pGpuPrm; if (!gpuPrm){ @@ -70,7 +68,7 @@ void FullyConnected::freeParamCUDA(void* gpuPrms){ cublasDestroy(gpuPrm->cuBLAS); } -__global__ void cuFwdBias(size_t kernel, snSize insz, snFloat* weight, snFloat* output){ +__global__ void cuFwdBias(size_t kernel, snSize insz, const snFloat* weight, snFloat* output){ weight += insz.w * insz.h * insz.d * kernel; @@ -87,12 +85,10 @@ __global__ void cuFwdBias(size_t kernel, snSize insz, snFloat* weight, snFloat* void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloat* input, const snFloat* weight, snFloat* output, void* gpuPrms){ gpuParams* gpuPrm = (gpuParams*)gpuPrms; - int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - // cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); - - // cuCHECK(cudaMemcpy(gpuPrm->d_w, weight, (ida + 1) * krn * sizeof(snFloat), cudaMemcpyHostToDevice)); - + int ida = int(insz.w * insz.h * insz.d), + bsz = int(insz.n), + krn = int(kernel); + // Out = α * W * In + βC // In - data input matrix - values from the previous layer // W - weights matrix @@ -114,17 +110,18 @@ void FullyConnected::forwardCUDA(size_t kernel, const snSize& insz, const snFloa krn)); // Out, step to next Y (Y21 - Y11) // +bias -// cuFwdBias <<< int(insz.n), 128 >>> (kernel, insz, gpuPrm->d_w, gpuPrm->d_out); + cuFwdBias << < int(insz.n), 128 >> > (kernel, insz, weight, output); } -__global__ void cuBwdBias(size_t kernel, snSize insz, snFloat* gradIn, snFloat* dWOut){ +__global__ void cuBwdBias(size_t kernel, snSize insz, const snFloat* gradIn, snFloat* dWOut){ dWOut += insz.w * insz.h * insz.d * kernel; unsigned int k = threadIdx.x; while (k < kernel){ - snFloat* grin = gradIn + k, b = 0; + const snFloat* grin = gradIn + k; + snFloat b = 0; for (size_t j = 0; j < insz.n; ++j) b += grin[kernel * j]; @@ -137,12 +134,9 @@ void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* input, const snFloat* gradIn, snFloat* gradOut, snFloat* dWOut, void* gpuPrms){ gpuParams* gpuPrm = (gpuParams*)gpuPrms; - int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - /* - cuCHECK(cublasSetMatrix(bsz, ida, sizeof(snFloat), input, bsz, gpuPrm->d_in, bsz)); - - cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); -*/ + int ida = int(insz.w * insz.h * insz.d), + bsz = int(insz.n), + krn = int(kernel); // Weight gradient // dW = αIn^T * GrIn + βdW @@ -165,10 +159,8 @@ void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, krn)); // dW, step to next // bias -// cuBwdBias <<< 1, 128 >>> (kernel, insz, d_grin, gpuPrm->d_dw); - -// cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); - + cuBwdBias << < 1, 128 >> > (kernel, insz, gradIn, dWOut); + //// Gradient for previous layer //// GrOut = αGrIn * W^T + βGrOut //// GrIn - gradient matrix from the next layer @@ -194,13 +186,10 @@ void FullyConnected::backwardCUDA_GW(size_t kernel, const snFloat* weight, void FullyConnected::backwardCUDA_G(size_t kernel, const snFloat* weight, const snSize& insz, const snFloat* gradIn, snFloat* gradOut, void* gpuPrms){ gpuParams* gpuPrm = (gpuParams*)gpuPrms; - int ida = int(insz.w * insz.h * insz.d), bsz = int(insz.n), krn = int(kernel); - - /*cuCHECK(cublasSetMatrix(bsz, krn, sizeof(snFloat), gradIn, bsz, d_grin, bsz)); - - cuCHECK(cublasSetMatrix(ida, krn, sizeof(snFloat), weight, ida, gpuPrm->d_w, ida)); -*/ - + int ida = int(insz.w * insz.h * insz.d), + bsz = int(insz.n), + krn = int(kernel); + //// Gradient for previous layer //// GrOut = αGrIn * W^T + βGrOut //// GrIn - gradient matrix from the next layer diff --git a/src/snOperatorCUDA/src/CUDA/lossFunctions.cu b/src/snOperatorCUDA/src/CUDA/lossFunctions.cu new file mode 100644 index 0000000..1544f73 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/lossFunctions.cu @@ -0,0 +1,96 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#include "../stdafx.h" +#include "snOperatorCUDA/src/Operator/lossFunction.h" + +using namespace std; +using namespace SN_Base; + + +__global__ void softMaxACrossEntropy(const snSize& iosz, snFloat* inout){ + + size_t inStepByD = iosz.w * iosz.h, // step out by input + inStepByN = inStepByD * iosz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + inout += blockIdx.x * inStepByN; + + __shared__ int tmax; + __shared__ snFloat tsumm; + + tmax = 0; + tsumm = 0; + + __syncthreads(); + + unsigned int i = threadIdx.x; + + while (i < inStepByN){ + + atomicMax(&tmax, int(inout[i])); + + i += blockDim.x; + } + + __syncthreads(); + + while (i < inStepByN){ + + inout[i] = (inout[i] - tmax > -20) ? exp(inout[i] - tmax) : 0.1E-8F; + + atomicAdd(&tsumm, inout[i]); + + i += blockDim.x; + } + + __syncthreads(); + + while (i < inStepByN){ + + inout[i] /= tsumm; + + i += blockDim.x; + } +} + +void lossForward(const snSize& insz, snFloat* inout, lossType loss){ + + dim3 dimBlock(256); + dim3 dimGrid(int(insz.n)); + + switch (loss){ + case lossType::softMaxACrossEntropy: + softMaxACrossEntropy <<>>(insz, inout); + break; + } +} + +void lossBackward(const Tensor& inTns, snFloat* out, snFloat* targ, snFloat* grad, lossType loss){ + + + +} diff --git a/src/snOperatorCUDA/src/CUDA/tensor.cu b/src/snOperatorCUDA/src/CUDA/tensor.cu index bee26e8..33d70d1 100644 --- a/src/snOperatorCUDA/src/CUDA/tensor.cu +++ b/src/snOperatorCUDA/src/CUDA/tensor.cu @@ -7,7 +7,6 @@ using namespace SN_Base; - /// tensor - input data and output data of each node of the network. Tensor::Tensor(const snSize& sz) : sz_(sz){ @@ -44,7 +43,7 @@ Tensor& Tensor::operator+=(const Tensor& other){ ASSERT_MESS(other == *this, ""); - summ(sz_, dataGPU_, other->getDataGPU()); + summ(sz_, dataGPU_, other.getDataGPU()); return *this; } @@ -53,7 +52,7 @@ Tensor& Tensor::operator-=(const Tensor& other){ ASSERT_MESS(other == *this, ""); - difference(sz_, dataGPU_, other->getDataGPU()); + difference(sz_, dataGPU_, other.getDataGPU()); return *this; } @@ -115,7 +114,8 @@ void Tensor::resize(const snSize& nsz){ if (csz < nnsz){ - snFloat* mem = nullptr; + std::cout << nnsz << std::endl; + snFloat* mem; cuAssert(cudaMalloc(&mem, nnsz * sizeof(snFloat))); if (dataGPU_){ diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index a2f9ff4..4601420 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -184,7 +184,7 @@ bool Convolution::setBatchNorm(const batchNorm& bn){ return true; } -batchNorm Convolution::getBatchNorm(){ +batchNorm Convolution::getBatchNorm()const{ size_t csz = baseBatchNorm_.sz.size(); @@ -355,7 +355,12 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize if (wcsz == 0) weightInit(baseWeight_, ntp, stp + 1, kernel, weightInitType_); } - + + // +bias? + /* if (!useBias_){ + memset(baseWeight_.getDataCPU() + stp * kernel, 0, kernel * sizeof(snFloat)); + }*/ + snSize outSz(0, 0, kernel, newsz.n); if (isPaddingSame_){ diff --git a/src/snOperatorCUDA/src/Operator/convolution.h b/src/snOperatorCUDA/src/Operator/convolution.h index 6f2581b..ca3cca9 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.h +++ b/src/snOperatorCUDA/src/Operator/convolution.h @@ -42,7 +42,7 @@ class Convolution final : SN_Base::OperatorBase{ bool setBatchNorm(const SN_Base::batchNorm& bn) override; - batchNorm getBatchNorm() override; + SN_Base::batchNorm getBatchNorm()const override; private: @@ -81,7 +81,7 @@ class Convolution final : SN_Base::OperatorBase{ void* convGPUParams_ = nullptr; ///< gpu aux params std::map auxGPUParams_; ///< aux data - std::map> auxCPUParams_; + mutable std::map> auxCPUParams_; void load(std::map& prms); diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp index c841d70..c6857c2 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -172,7 +172,7 @@ bool Deconvolution::setBatchNorm(const batchNorm& bn){ return true; } -SN_Base::batchNorm Deconvolution::getBatchNorm(){ +SN_Base::batchNorm Deconvolution::getBatchNorm()const{ size_t csz = baseBatchNorm_.sz.size(); @@ -333,7 +333,12 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ if (wcsz == 0) weightInit(baseWeight_, ntp - wcsz, stp + 1, deconvParams_.kernel, weightInitType_); } - + + // +bias? + /* if (!useBias_){ + memset(baseWeight_.getDataCPU() + stp * kernel, 0, kernel * sizeof(snFloat)); + }*/ + snSize outSz(0, 0, deconvParams_.kernel, newsz.n); diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.h b/src/snOperatorCUDA/src/Operator/deconvolution.h index 14afa27..6f99d29 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.h +++ b/src/snOperatorCUDA/src/Operator/deconvolution.h @@ -42,7 +42,7 @@ class Deconvolution final : SN_Base::OperatorBase{ bool setBatchNorm(const SN_Base::batchNorm& bn) override; - SN_Base::batchNorm getBatchNorm() override; + SN_Base::batchNorm getBatchNorm()const override; private: @@ -73,7 +73,7 @@ class Deconvolution final : SN_Base::OperatorBase{ optLmbRegular_ = 0.001F; std::map auxGPUParams_; ///< aux data - std::map> auxCPUParams_; + mutable std::map> auxCPUParams_; void* gpuParams_ = nullptr; ///< aux GPU diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index 0e7c036..15c6d0a 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -178,7 +178,7 @@ bool FullyConnected::setBatchNorm(const batchNorm& bn){ return true; } -batchNorm FullyConnected::getBatchNorm(){ +batchNorm FullyConnected::getBatchNorm()const{ size_t csz = baseBatchNorm_.sz.size(); @@ -248,14 +248,9 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& } /// calculation of the output values of neurons - snFloat* out = baseOut_.getDataGPU(), *weight = baseWeight_.getDataGPU(); - - // +bias? - if (!useBias_){ - size_t stepByN = insz.w * insz.h * insz.d * kernel_; - memset(weight + stepByN, 0, kernel_ * sizeof(snFloat)); - } - + snFloat* out = baseOut_.getDataGPU(), + * weight = baseWeight_.getDataGPU(); + // calculation forwardCUDA(kernel_, insz, inTns.getDataGPU(), weight, out, gpuParams_); @@ -339,6 +334,11 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ weightInit(baseWeight_, ntp - wcsz, stp + 1, kernel_, weightInitType_); } + // +bias? + /* if (!useBias_){ + memset(baseWeight_.getDataCPU() + stp * kernel, 0, kernel * sizeof(snFloat)); + }*/ + baseOut_.resize(snSize(kernel_, 1, 1, newsz.n)); if (isLern){ diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.h b/src/snOperatorCUDA/src/Operator/fullyConnected.h index f82fe98..523d314 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.h +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.h @@ -42,7 +42,7 @@ class FullyConnected final : SN_Base::OperatorBase{ bool setBatchNorm(const SN_Base::batchNorm& bn) override; - SN_Base::batchNorm getBatchNorm() override; + SN_Base::batchNorm getBatchNorm()const override; private: @@ -70,7 +70,7 @@ class FullyConnected final : SN_Base::OperatorBase{ std::map auxGPUParams_; ///< aux data - std::map> auxCPUParams_; + mutable std::map> auxCPUParams_; void* gpuParams_ = nullptr; ///< gpu data diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp index 8f032be..0bf79c8 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -63,7 +63,7 @@ std::vector LossFunction::Do(const operationParam& operPrm, const s return std::vector < std::string > {"noWay"}; } - if (lossType_ == LossFunction::lossType::userLoss){ + if (lossType_ == lossType::userLoss){ if (basePrms_.find("cbackName") == basePrms_.end()){ ERROR_MESS("not set param 'cbackName'"); return std::vector < std::string > {"noWay"}; @@ -86,49 +86,27 @@ void LossFunction::forward(const Tensor& inTns){ auto out = baseOut_.getDataGPU(); switch (lossType_){ - case LossFunction::lossType::softMaxACrossEntropy:{ + case lossType::softMaxACrossEntropy:{ - if (auxParams_.find("sm_aux") == auxParams_.end()) - auxParams_["sm_aux"] = vector(tsz.w * tsz.h * tsz.d); - - // run through softMax each image separately - snFloat* aux = auxParams_["sm_aux"].data(); - size_t nsz = tsz.n, width = tsz.w * tsz.h * tsz.d; - for (size_t i = 0; i < nsz; ++i){ - - snFloat maxv = *std::max_element(out, out + width); - - snFloat denom = 0.F; - for (size_t j = 0; j < width; ++j){ - - aux[j] = (out[j] - maxv > -20) ? std::exp(out[j] - maxv) : 0.1E-8F; - denom += aux[j]; - } - - for (size_t j = 0; j < width; ++j) - out[j] = aux[j] / denom; - - out += width; - } } - case LossFunction::lossType::binaryCrossEntropy:{ + case lossType::binaryCrossEntropy:{ break; } - case LossFunction::lossType::regressionMSE:{ + case lossType::regressionMSE:{ break; } - case LossFunction::lossType::userLoss:{ + case lossType::userLoss:{ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - true, inTns.size(), inTns.getDataGPU(), outSz, &outData); + true, inTns.size(), inTns.getDataCPU(), outSz, &outData); if (outData){ - baseOut_.setDataGPU(outData, outSz); + baseOut_.setDataCPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); @@ -157,7 +135,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ auto grad = baseGrad_.getDataGPU(); switch (lossType_){ - case LossFunction::lossType::softMaxACrossEntropy:{ + case lossType::softMaxACrossEntropy:{ size_t nsz = tsz.size(); for (size_t i = 0; i < nsz; ++i) @@ -166,7 +144,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ break; } - case LossFunction::lossType::binaryCrossEntropy:{ + case lossType::binaryCrossEntropy:{ size_t nsz = tsz.size(); for (size_t i = 0; i < nsz; ++i) @@ -175,7 +153,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ break; } // Mean Square Error - case LossFunction::lossType::regressionMSE:{ + case lossType::regressionMSE:{ size_t nsz = tsz.size(); for (size_t i = 0; i < nsz; ++i){ @@ -186,15 +164,15 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ break; } - case LossFunction::lossType::userLoss:{ + case lossType::userLoss:{ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - false, inTns.size(), inTns.getDataGPU(), outSz, &outData); + false, inTns.size(), inTns.getDataCPU(), outSz, &outData); if (outData){ - baseGrad_.setDataGPU(outData, outSz); + baseGrad_.setDataCPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.h b/src/snOperatorCUDA/src/Operator/lossFunction.h index 57b1e57..67ce6c0 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.h +++ b/src/snOperatorCUDA/src/Operator/lossFunction.h @@ -25,7 +25,7 @@ #pragma once #include "snBase/snBase.h" - +#include "../structurs.h" /// оператор - расчет ошибки class LossFunction final : SN_Base::OperatorBase{ @@ -39,17 +39,9 @@ class LossFunction final : SN_Base::OperatorBase{ std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; private: - enum class lossType{ - softMaxACrossEntropy = 0, - binaryCrossEntropy = 1, - regressionMSE = 2, - userLoss = 3, - }; - - lossType lossType_ = lossType::softMaxACrossEntropy; - - std::map> auxParams_; ///< вспом данные для расчета - + + lossType lossType_ = lossType::softMaxACrossEntropy; + void load(std::map& prms); void forward(const SN_Base::Tensor& inTns); diff --git a/src/snOperatorCUDA/src/lossFunctions.h b/src/snOperatorCUDA/src/lossFunctions.h new file mode 100644 index 0000000..d4f171d --- /dev/null +++ b/src/snOperatorCUDA/src/lossFunctions.h @@ -0,0 +1,32 @@ +// +// SkyNet Project +// Copyright (C) 2018 by Contributors +// +// This code is licensed under the MIT License. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files(the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions : +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. +// +#pragma once + +#include "snBase/snBase.h" +#include "structurs.h" + +void lossForward(const SN_Base::snSize& insz, SN_Base::snFloat* inout, lossType loss); + +void lossBackward(const SN_Base::Tensor& inTns, SN_Base::snFloat* out, SN_Base::snFloat* targ, SN_Base::snFloat* grad, lossType loss); diff --git a/src/snOperatorCUDA/src/structurs.h b/src/snOperatorCUDA/src/structurs.h index 028d7a5..4efc570 100644 --- a/src/snOperatorCUDA/src/structurs.h +++ b/src/snOperatorCUDA/src/structurs.h @@ -65,8 +65,10 @@ enum class poolType{ avg = 1, }; -enum class calcMode{ - CPU = 0, - CUDA = 1, - OpenCL = 2, -}; +// loss +enum class lossType{ + softMaxACrossEntropy = 0, + binaryCrossEntropy = 1, + regressionMSE = 2, + userLoss = 3, +}; \ No newline at end of file From d6dab9685ed56e36c3466ccd8c963e0bd815d3bc Mon Sep 17 00:00:00 2001 From: al_medved Date: Tue, 23 Jul 2019 21:24:16 +0500 Subject: [PATCH 16/35] -develop --- src/skynet/src/snet.cpp | 10 +++++----- src/snOperatorCUDA/src/CUDA/activation.cu | 9 +++++---- src/snOperatorCUDA/src/CUDA/tensor.cu | 5 ++--- src/snOperatorCUDA/src/Operator/fullyConnected.cpp | 4 ++-- src/snOperatorCUDA/src/Operator/input.cpp | 1 + src/snOperatorCUDA/src/Operator/lossFunction.cpp | 2 +- src/snOperatorCUDA/src/Operator/output.cpp | 2 +- 7 files changed, 17 insertions(+), 16 deletions(-) diff --git a/src/skynet/src/snet.cpp b/src/skynet/src/snet.cpp index c83c5b2..6063811 100644 --- a/src/skynet/src/snet.cpp +++ b/src/skynet/src/snet.cpp @@ -247,8 +247,8 @@ bool SNet::training(snFloat lr, const snSize& isz, const snFloat* iLayer, const // metrics if (outAccurate){ - auto setGrad = operats_["EndNet"]->getGradient(); - auto outTensor = operats_["EndNet"]->getOutput(); + auto& setGrad = operats_["EndNet"]->getGradient(); + auto& outTensor = operats_["EndNet"]->getOutput(); *outAccurate = calcAccurate(setGrad, outTensor); } @@ -271,7 +271,7 @@ bool SNet::forward(bool isLern, const snSize& isz, const snFloat* iLayer, const engine_->forward(operPrm_); if (isEndNet_){ - auto tnsOut = operats_["EndNet"]->getOutput(); + auto& tnsOut = operats_["EndNet"]->getOutput(); auto tnsOutSz = tnsOut.size(); if (tnsOutSz != osz){ @@ -295,7 +295,7 @@ bool SNet::backward(snFloat lr, const snSize& gsz, const snFloat* gradErr){ } if (isEndNet_){ - auto outTensor = operats_["EndNet"]->getOutput(); + auto& outTensor = operats_["EndNet"]->getOutput(); auto tsz = outTensor.size(); if (tsz != gsz){ @@ -416,7 +416,7 @@ bool SNet::getOutputNode(const char* nodeName, SN_Base::snSize& osz, SN_Base::sn return false; } - auto outTns = operats_[nodeName]->getOutput(); + auto& outTns = operats_[nodeName]->getOutput(); osz = outTns.size(); diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu index 085af6a..b04f779 100644 --- a/src/snOperatorCUDA/src/CUDA/activation.cu +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -58,12 +58,13 @@ __global__ void fv_relu(const snSize& outsz, snFloat* output){ // gridDim.x - number of out layers // gridDim.y - batch size - output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + // output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; while (i < outStepByD){ - output[i] = output[i] >= 0 ? output[i] : 0; + // if (output[i] < 0) + // output[i] = 0; i += blockDim.x; } @@ -171,14 +172,14 @@ void activationForward(const snSize& sz, snFloat* data, activeType active){ dim3 dimBlock(128); dim3 dimGrid(int(sz.d), int(sz.n)); - + switch (active){ case activeType::sigmoid: fv_sigmoid <<< dimGrid, dimBlock >>>(sz, data); break; case activeType::relu: fv_relu <<< dimGrid, dimBlock >>>(sz, data); break; case activeType::leakyRelu: fv_leakyRelu <<< dimGrid, dimBlock >>>(sz, data); break; case activeType::elu: fv_elu <<< dimGrid, dimBlock >>>(sz, data); break; default: break; - } + } } void activationBackward(const snSize& sz, snFloat* data, snFloat* gradIn, activeType active){ diff --git a/src/snOperatorCUDA/src/CUDA/tensor.cu b/src/snOperatorCUDA/src/CUDA/tensor.cu index 33d70d1..4a7bfaf 100644 --- a/src/snOperatorCUDA/src/CUDA/tensor.cu +++ b/src/snOperatorCUDA/src/CUDA/tensor.cu @@ -113,9 +113,8 @@ void Tensor::resize(const snSize& nsz){ ASSERT_MESS(nnsz > 0, ""); if (csz < nnsz){ - - std::cout << nnsz << std::endl; - snFloat* mem; + + snFloat* mem = nullptr; cuAssert(cudaMalloc(&mem, nnsz * sizeof(snFloat))); if (dataGPU_){ diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index 15c6d0a..36de186 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -252,7 +252,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& * weight = baseWeight_.getDataGPU(); // calculation - forwardCUDA(kernel_, insz, inTns.getDataGPU(), weight, out, gpuParams_); + // forwardCUDA(kernel_, insz, inTns.getDataGPU(), weight, out, gpuParams_); /// dropOut snSize outsz = baseOut_.size(); @@ -264,7 +264,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& layerBatchNorm(true, operPrm.isLerning, outsz, out, out, baseBatchNorm_); /// active func - activationForward(kernel_ * insz.n, out, activeType_); + activationForward(outsz, out, activeType_); /// batchNorm if (batchNormType_ == batchNormType::postActive) diff --git a/src/snOperatorCUDA/src/Operator/input.cpp b/src/snOperatorCUDA/src/Operator/input.cpp index 10bd20b..03cac81 100644 --- a/src/snOperatorCUDA/src/Operator/input.cpp +++ b/src/snOperatorCUDA/src/Operator/input.cpp @@ -37,6 +37,7 @@ OperatorBase(net, name, node, prms){ bool Input::setInput(const snFloat* data, const snSize& dsz){ baseOut_.setDataCPU(data, dsz); + return true; } diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp index 0bf79c8..89ad77b 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -87,7 +87,7 @@ void LossFunction::forward(const Tensor& inTns){ switch (lossType_){ case lossType::softMaxACrossEntropy:{ - + break; } case lossType::binaryCrossEntropy:{ diff --git a/src/snOperatorCUDA/src/Operator/output.cpp b/src/snOperatorCUDA/src/Operator/output.cpp index 2baced4..97c8364 100644 --- a/src/snOperatorCUDA/src/Operator/output.cpp +++ b/src/snOperatorCUDA/src/Operator/output.cpp @@ -44,7 +44,7 @@ std::vector Output::Do(const operationParam& operPrm, const std::ve if (operPrm.action == snAction::forward) baseOut_ = neighbOpr[0]->getOutput(); - + return std::vector(); } From 059b87174eea210f97ebb1886db373b4f98c9af3 Mon Sep 17 00:00:00 2001 From: al_medved Date: Wed, 24 Jul 2019 22:35:53 +0500 Subject: [PATCH 17/35] -develop --- src/snOperatorCUDA/src/CUDA/activation.cu | 22 ++++----- src/snOperatorCUDA/src/CUDA/arithmetic.cu | 6 +-- src/snOperatorCUDA/src/CUDA/batchNorm.cu | 20 ++++---- src/snOperatorCUDA/src/CUDA/convolution.cu | 2 +- src/snOperatorCUDA/src/CUDA/dropOut.cu | 4 +- src/snOperatorCUDA/src/CUDA/lossFunctions.cu | 2 +- src/snOperatorCUDA/src/CUDA/optimizer.cu | 10 ++-- src/snOperatorCUDA/src/CUDA/pooling.cu | 12 ++--- .../src/Operator/convolution.cpp | 49 ++++++++----------- src/snOperatorCUDA/src/Operator/convolution.h | 3 +- .../src/Operator/fullyConnected.cpp | 11 ++--- .../src/Operator/lossFunction.cpp | 37 +++----------- src/snOperatorCUDA/src/Operator/pooling.cpp | 9 ++-- src/snOperatorCUDA/src/Operator/pooling.h | 4 -- 14 files changed, 75 insertions(+), 116 deletions(-) diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu index b04f779..140622d 100644 --- a/src/snOperatorCUDA/src/CUDA/activation.cu +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -8,7 +8,7 @@ using namespace SN_Base; -__global__ void fv_sigmoid(const snSize& outsz, snFloat* output){ +__global__ void fv_sigmoid(snSize outsz, snFloat* output){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -29,7 +29,7 @@ __global__ void fv_sigmoid(const snSize& outsz, snFloat* output){ i += blockDim.x; } } -__global__ void df_sigmoid(const snSize& outsz, snFloat* output, snFloat* grad){ +__global__ void df_sigmoid(snSize outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -50,7 +50,7 @@ __global__ void df_sigmoid(const snSize& outsz, snFloat* output, snFloat* grad){ } } -__global__ void fv_relu(const snSize& outsz, snFloat* output){ +__global__ void fv_relu(snSize outsz, snFloat* output){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -58,18 +58,18 @@ __global__ void fv_relu(const snSize& outsz, snFloat* output){ // gridDim.x - number of out layers // gridDim.y - batch size - // output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; while (i < outStepByD){ - // if (output[i] < 0) - // output[i] = 0; + if (output[i] < 0) + output[i] = 0; i += blockDim.x; } }; -__global__ void df_relu(const snSize& outsz, snFloat* output, snFloat* grad){ +__global__ void df_relu(snSize outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -90,7 +90,7 @@ __global__ void df_relu(const snSize& outsz, snFloat* output, snFloat* grad){ } }; -__global__ void fv_leakyRelu(const snSize& outsz, snFloat* output){ +__global__ void fv_leakyRelu(snSize outsz, snFloat* output){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -108,7 +108,7 @@ __global__ void fv_leakyRelu(const snSize& outsz, snFloat* output){ i += blockDim.x; } } -__global__ void df_leakyRelu(const snSize& outsz, snFloat* output, snFloat* grad){ +__global__ void df_leakyRelu(snSize outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -129,7 +129,7 @@ __global__ void df_leakyRelu(const snSize& outsz, snFloat* output, snFloat* grad } } -__global__ void fv_elu(const snSize& outsz, snFloat* output){ +__global__ void fv_elu(snSize outsz, snFloat* output){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -147,7 +147,7 @@ __global__ void fv_elu(const snSize& outsz, snFloat* output){ i += blockDim.x; } } -__global__ void df_elu(const snSize& outsz, snFloat* output, snFloat* grad){ +__global__ void df_elu(snSize outsz, snFloat* output, snFloat* grad){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch diff --git a/src/snOperatorCUDA/src/CUDA/arithmetic.cu b/src/snOperatorCUDA/src/CUDA/arithmetic.cu index f0c92ed..7aeccdd 100644 --- a/src/snOperatorCUDA/src/CUDA/arithmetic.cu +++ b/src/snOperatorCUDA/src/CUDA/arithmetic.cu @@ -7,7 +7,7 @@ using namespace SN_Base; -__global__ void summInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ +__global__ void summInf(SN_Base::snSize sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ size_t outStepByD = sz.w * sz.h, // step out by input outStepByN = outStepByD * sz.d; // step out by batch @@ -28,7 +28,7 @@ __global__ void summInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const } } -__global__ void differenceInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ +__global__ void differenceInf(SN_Base::snSize sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ size_t outStepByD = sz.w * sz.h, // step out by input outStepByN = outStepByD * sz.d; // step out by batch @@ -49,7 +49,7 @@ __global__ void differenceInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, } } -__global__ void meanInf(const SN_Base::snSize& sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ +__global__ void meanInf(SN_Base::snSize sz, SN_Base::snFloat* one, const SN_Base::snFloat* two){ size_t outStepByD = sz.w * sz.h, // step out by input outStepByN = outStepByD * sz.d; // step out by batch diff --git a/src/snOperatorCUDA/src/CUDA/batchNorm.cu b/src/snOperatorCUDA/src/CUDA/batchNorm.cu index 9cb3987..94deeb7 100644 --- a/src/snOperatorCUDA/src/CUDA/batchNorm.cu +++ b/src/snOperatorCUDA/src/CUDA/batchNorm.cu @@ -5,16 +5,16 @@ using namespace SN_Base; -__device__ void bnOffset(batchNorm& bn, size_t offs){ - bn.mean += offs; - bn.varce += offs; - bn.scale += offs; - bn.dScale += offs; - bn.schift += offs; - bn.dSchift += offs; +__device__ void bnOffset(batchNorm* bn, size_t offs){ + bn->mean += offs; + bn->varce += offs; + bn->scale += offs; + bn->dScale += offs; + bn->schift += offs; + bn->dSchift += offs; } -__global__ void channelBatchNormInf(const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ +__global__ void channelBatchNormInf(snSize insz, snFloat* in, snFloat* out, batchNorm prm){ size_t inStepByD = insz.w * insz.h, // step out by input inStepByN = inStepByD * insz.d; // step out by batch @@ -25,7 +25,7 @@ __global__ void channelBatchNormInf(const snSize& insz, snFloat* in, snFloat* ou snFloat* cin = in + inStepByN * blockIdx.y + inStepByD * blockIdx.x, * cout = out + inStepByN * blockIdx.y + inStepByD * blockIdx.x; - bnOffset(prm, inStepByD * blockIdx.x); + bnOffset(&prm, inStepByD * blockIdx.x); unsigned int i = threadIdx.x; while (i < inStepByD){ @@ -36,7 +36,7 @@ __global__ void channelBatchNormInf(const snSize& insz, snFloat* in, snFloat* ou } } -__global__ void layerBatchNormInf(const snSize& insz, snFloat* in, snFloat* out, batchNorm prm){ +__global__ void layerBatchNormInf(snSize insz, snFloat* in, snFloat* out, batchNorm prm){ size_t inStepByD = insz.w * insz.h, // step out by input inStepByN = inStepByD * insz.d; // step out by batch diff --git a/src/snOperatorCUDA/src/CUDA/convolution.cu b/src/snOperatorCUDA/src/CUDA/convolution.cu index 483b1e4..d26c2c6 100644 --- a/src/snOperatorCUDA/src/CUDA/convolution.cu +++ b/src/snOperatorCUDA/src/CUDA/convolution.cu @@ -104,7 +104,7 @@ void Convolution::iniParamCUDA(bool isLern, const snSize& insz, const snSize& ou // conv cudnnConvolutionDescriptor_t conv_desc = nullptr; cuCHECK(cudnnCreateConvolutionDescriptor(&conv_desc)); - cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, 0, 0, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), + cuCHECK(cudnnSetConvolution2dDescriptor(conv_desc, prms.paddingH, prms.paddingW, int(prms.stride), int(prms.stride), int(prms.dilate), int(prms.dilate), CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); if (!isFirst) cuCHECK(cudnnDestroyConvolutionDescriptor((cudnnConvolutionDescriptor_t)gpuPrm->conv_desc)); diff --git a/src/snOperatorCUDA/src/CUDA/dropOut.cu b/src/snOperatorCUDA/src/CUDA/dropOut.cu index 294ad8c..50f0727 100644 --- a/src/snOperatorCUDA/src/CUDA/dropOut.cu +++ b/src/snOperatorCUDA/src/CUDA/dropOut.cu @@ -6,7 +6,7 @@ using namespace SN_Base; -__global__ void dropOutLern(SN_Base::snFloat dropOut, const snSize& outsz, snFloat* rnd, SN_Base::snFloat* out){ +__global__ void dropOutLern(SN_Base::snFloat dropOut, snSize outsz, snFloat* rnd, SN_Base::snFloat* out){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch @@ -29,7 +29,7 @@ __global__ void dropOutLern(SN_Base::snFloat dropOut, const snSize& outsz, snFlo } } -__global__ void dropOutInf(SN_Base::snFloat dropOut, const snSize& outsz, snFloat* out){ +__global__ void dropOutInf(SN_Base::snFloat dropOut, snSize outsz, snFloat* out){ size_t outStepByD = outsz.w * outsz.h, // step out by input outStepByN = outStepByD * outsz.d; // step out by batch diff --git a/src/snOperatorCUDA/src/CUDA/lossFunctions.cu b/src/snOperatorCUDA/src/CUDA/lossFunctions.cu index 1544f73..e933d5a 100644 --- a/src/snOperatorCUDA/src/CUDA/lossFunctions.cu +++ b/src/snOperatorCUDA/src/CUDA/lossFunctions.cu @@ -29,7 +29,7 @@ using namespace std; using namespace SN_Base; -__global__ void softMaxACrossEntropy(const snSize& iosz, snFloat* inout){ +__global__ void softMaxACrossEntropy(snSize iosz, snFloat* inout){ size_t inStepByD = iosz.w * iosz.h, // step out by input inStepByN = inStepByD * iosz.d; // step out by batch diff --git a/src/snOperatorCUDA/src/CUDA/optimizer.cu b/src/snOperatorCUDA/src/CUDA/optimizer.cu index 7346822..86f5f06 100644 --- a/src/snOperatorCUDA/src/CUDA/optimizer.cu +++ b/src/snOperatorCUDA/src/CUDA/optimizer.cu @@ -7,7 +7,7 @@ using namespace SN_Base; /// adaptive gradient method -__global__ void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat eps){ +__global__ void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, snSize sz, snFloat alpha, snFloat lambda, snFloat eps){ size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 @@ -26,7 +26,7 @@ __global__ void opt_adagrad(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snS } /// RMSprop -__global__ void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mu, snFloat eps){ +__global__ void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, snSize sz, snFloat alpha, snFloat lambda, snFloat mu, snFloat eps){ size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 @@ -45,7 +45,7 @@ __global__ void opt_RMSprop(snFloat* dW, snFloat* ioWGr, snFloat* ioW, const snS } /// adam -__global__ void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, snFloat eps){ +__global__ void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat* ioW, snSize sz, snFloat alpha, snFloat lambda, snFloat mudW, snFloat muGr, snFloat eps){ size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 @@ -69,7 +69,7 @@ __global__ void opt_adam(snFloat* dW, snFloat* iodWPrev, snFloat* ioWGr, snFloat } /// SGD without momentum -__global__ void opt_sgd(snFloat* dW, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda){ +__global__ void opt_sgd(snFloat* dW, snFloat* ioW, snSize sz, snFloat alpha, snFloat lambda){ size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 @@ -86,7 +86,7 @@ __global__ void opt_sgd(snFloat* dW, snFloat* ioW, const snSize& sz, snFloat alp } /// SGD with momentum -__global__ void opt_sgdMoment(snFloat* dW, snFloat* iodWPrev, snFloat* ioW, const snSize& sz, snFloat alpha, snFloat lambda, snFloat mu){ +__global__ void opt_sgdMoment(snFloat* dW, snFloat* iodWPrev, snFloat* ioW, snSize sz, snFloat alpha, snFloat lambda, snFloat mu){ size_t wStepByD = sz.h; // wsz.h := fWidth * fHeight * d + 1 diff --git a/src/snOperatorCUDA/src/CUDA/pooling.cu b/src/snOperatorCUDA/src/CUDA/pooling.cu index fc4240e..5af2366 100644 --- a/src/snOperatorCUDA/src/CUDA/pooling.cu +++ b/src/snOperatorCUDA/src/CUDA/pooling.cu @@ -43,7 +43,7 @@ struct gpuParams{ }; -void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& poolPrms, void** pGpuPrm){ +void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, const poolParams& prms, void** pGpuPrm){ bool isFirst = false; @@ -80,11 +80,11 @@ void Pooling::iniParamCUDA(bool isLern, const snSize& insz, const snSize& outsz, cuCHECK(cudnnCreatePoolingDescriptor(&pool_desc)); cudnnPoolingMode_t poolT = cudnnPoolingMode_t::CUDNN_POOLING_MAX; - if (poolPrms.type == poolType::avg) + if (prms.type == poolType::avg) poolT = cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; cuCHECK(cudnnSetPooling2dDescriptor(pool_desc, poolT, cudnnNanPropagation_t::CUDNN_NOT_PROPAGATE_NAN, - int(poolPrms.kernel), int(poolPrms.kernel), 0, 0, int(poolPrms.stride), int(poolPrms.stride))); + int(prms.kernel), int(prms.kernel), prms.paddingH, prms.paddingW, int(prms.stride), int(prms.stride))); if (!isFirst) cuCHECK(cudnnDestroyPoolingDescriptor(gpuPrm->pool_desc)); gpuPrm->pool_desc = pool_desc; @@ -173,10 +173,10 @@ void Pooling::forwardCUDA(const poolParams& poolPrms, const snSize& insz, const output)); // filtrNegative - dim3 dimBlock(256); - dim3 dimGrid(int(outsz.n)); + // dim3 dimBlock(256); + // dim3 dimGrid(int(outsz.n)); - cuFiltrNegative << < dimGrid, dimBlock >> >(outsz, output); + // cuFiltrNegative << < dimGrid, dimBlock >> >(outsz, output); } diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 4601420..a45bf95 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -251,20 +251,17 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op // Has the size of the data changed? if (insz != inSzMem_){ inSzMem_ = insz; - updateConfig(operPrm.isLerning, insz, inDataExpSz_); + updateConfig(operPrm.isLerning, insz); } - - // copy with offset padding for each image - snFloat* in = inputMem_->getDataGPU(); - - // calculation of the output values - snFloat* out = baseOut_.getDataGPU(), + + snFloat* in = inputMem_->getDataGPU(), + * out = baseOut_.getDataGPU(), * weight = baseWeight_.getDataGPU(); snSize outsz = baseOut_.size(); // calculation - forwardCUDA(convPrms_, weight, inDataExpSz_, in, outsz, out, convGPUParams_); + forwardCUDA(convPrms_, weight, insz, in, outsz, out, convGPUParams_); /// dropOut if (dropOut_ > 0.F) @@ -286,7 +283,8 @@ void Convolution::forward(const SN_Base::Tensor& inTns, const operationParam& op void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& operPrm){ snFloat* gradIn = inTns.getDataGPU(); - snSize insz = inTns.size(); + snSize insz = inTns.size(), + outsz = baseOut_.size(); // batchNorm if (batchNormType_ == batchNormType::postActive) @@ -294,32 +292,27 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o // active function if (activeType_ != activeType::none) - activationBackward(baseOut_.size(), baseOut_.getDataGPU(), gradIn, activeType_); + activationBackward(outsz, baseOut_.getDataGPU(), gradIn, activeType_); // batchNorm if (batchNormType_ == batchNormType::beforeActive) channelBatchNorm(false, true, insz, gradIn, gradIn, baseBatchNorm_); // calculation of the output gradient and weight correction - snFloat* gradOut = baseGrad_.getDataGPU(); - - snFloat* weight = baseWeight_.getDataGPU(); + snFloat* gradOut = baseGrad_.getDataGPU(), + * weight = baseWeight_.getDataGPU(); if (!isFreeze_){ - snFloat* dWeight = auxGPUParams_["dWeight"]; - - snFloat* in = inputMem_->getDataGPU(); + snFloat* dWeight = auxGPUParams_["dWeight"], + * in = inputMem_->getDataGPU(); // calculation - backwardCUDA_GW(convPrms_, weight, inDataExpSz_, in, baseOut_.size(), gradIn, gradOut, dWeight, convGPUParams_); + backwardCUDA_GW(convPrms_, weight, insz, in, outsz, gradIn, gradOut, dWeight, convGPUParams_); - // correct weight - snFloat* dWPrev = auxGPUParams_["dWPrev"]; - snFloat* dWGrad = auxGPUParams_["dWGrad"]; - + // correct weight optimizer(dWeight, - dWPrev, - dWGrad, + auxGPUParams_["dWPrev"], + auxGPUParams_["dWGrad"], weight, baseWeight_.size(), operPrm.lr, @@ -329,11 +322,11 @@ void Convolution::backward(const SN_Base::Tensor& inTns, const operationParam& o optimizerType_); } else{ // isFreeze - backwardCUDA_G(convPrms_, weight, inDataExpSz_, baseOut_.size(), gradIn, gradOut, convGPUParams_); + backwardCUDA_G(convPrms_, weight, insz, outsz, gradIn, gradOut, convGPUParams_); } } -void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize& expSz){ +void Convolution::updateConfig(bool isLern, const snSize& newsz){ size_t& kernel = convPrms_.kernel, & fWidth = convPrms_.fWidth, @@ -387,9 +380,7 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize if (res != 0) ERROR_MESS("not correct param 'stride' or 'fHeight'"); } - - expSz = snSize(newsz.w + paddingW * 2, newsz.h + paddingH * 2, newsz.d, newsz.n); - + baseOut_.resize(outSz); if (isLern){ @@ -420,5 +411,5 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz, SN_Base::snSize baseBatchNorm_.sz.n = 1; } - iniParamCUDA(isLern, expSz, outSz, convPrms_, &convGPUParams_); + iniParamCUDA(isLern, newsz, outSz, convPrms_, &convGPUParams_); } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/convolution.h b/src/snOperatorCUDA/src/Operator/convolution.h index ca3cca9..9ba4673 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.h +++ b/src/snOperatorCUDA/src/Operator/convolution.h @@ -64,7 +64,6 @@ class Convolution final : SN_Base::OperatorBase{ weightInitType weightInitType_ = weightInitType::he; ///< init weight type batchNormType batchNormType_ = batchNormType::none; ///< batchNorm SN_Base::snSize inSzMem_; ///< insz mem - SN_Base::snSize inDataExpSz_; ///< insz expansion const SN_Base::Tensor* inputMem_ = nullptr; @@ -85,7 +84,7 @@ class Convolution final : SN_Base::OperatorBase{ void load(std::map& prms); - void updateConfig(bool isLern, const SN_Base::snSize& newSz, SN_Base::snSize& expSz); + void updateConfig(bool isLern, const SN_Base::snSize& newSz); void forward(const SN_Base::Tensor& inTns, const SN_Base::operationParam& operPrm); diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index 36de186..79d9f5d 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -252,7 +252,7 @@ void FullyConnected::forward(const SN_Base::Tensor& inTns, const operationParam& * weight = baseWeight_.getDataGPU(); // calculation - // forwardCUDA(kernel_, insz, inTns.getDataGPU(), weight, out, gpuParams_); + forwardCUDA(kernel_, insz, inTns.getDataGPU(), weight, out, gpuParams_); /// dropOut snSize outsz = baseOut_.size(); @@ -299,13 +299,10 @@ void FullyConnected::backward(const SN_Base::Tensor& inTns, const operationParam // calculation backwardCUDA_GW(kernel_, weight, inSzMem_, inputMem_->getDataGPU(), gradIn, gradOut, dWeight, gpuParams_); - // correct weight - snFloat* dWPrev = auxGPUParams_["dWPrev"]; - snFloat* dWGrad = auxGPUParams_["dWGrad"]; - + // correct weight optimizer(dWeight, - dWPrev, - dWGrad, + auxGPUParams_["dWPrev"], + auxGPUParams_["dWGrad"], weight, baseWeight_.size(), operPrm.lr, diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp index 89ad77b..2efee50 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -23,6 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" +#include "../lossFunctions.h" #include "snOperatorCUDA/src/Operator/lossFunction.h" using namespace std; @@ -80,46 +81,24 @@ std::vector LossFunction::Do(const operationParam& operPrm, const s void LossFunction::forward(const Tensor& inTns){ - snSize tsz = inTns.size(); - baseOut_ = inTns; - - auto out = baseOut_.getDataGPU(); - - switch (lossType_){ - case lossType::softMaxACrossEntropy:{ - break; - } - case lossType::binaryCrossEntropy:{ + baseOut_ = inTns; + + if (lossType_ != lossType::userLoss){ - break; - } - case lossType::regressionMSE:{ - - - break; + lossForward(baseOut_.size(), baseOut_.getDataGPU(), lossType_); } - case lossType::userLoss:{ - + else{ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - true, inTns.size(), inTns.getDataCPU(), outSz, &outData); + true, inTns.size(), inTns.getDataCPU(), outSz, &outData); if (outData){ baseOut_.setDataCPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); - - break; - } - - break; - default: - ERROR_MESS("param 'loss' indefined"); - break; - } - + } } void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ diff --git a/src/snOperatorCUDA/src/Operator/pooling.cpp b/src/snOperatorCUDA/src/Operator/pooling.cpp index 367a95c..29c262a 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.cpp +++ b/src/snOperatorCUDA/src/Operator/pooling.cpp @@ -143,7 +143,7 @@ void Pooling::backward(const SN_Base::Tensor& inTns, const operationParam& operP * gradOut = baseGrad_.getDataGPU(); /// grad calculation - backwardCUDA(poolPrms_, baseOut_.size(), baseOut_.getDataGPU(), gradIn, inDataExpSz_, input, gradOut, gpuParams_); + backwardCUDA(poolPrms_, baseOut_.size(), baseOut_.getDataGPU(), gradIn, inSzMem_, input, gradOut, gpuParams_); } void Pooling::updateConfig(bool isLern, const snSize& newsz){ @@ -163,9 +163,7 @@ void Pooling::updateConfig(bool isLern, const snSize& newsz){ resH = (newsz.h - kernel) % stride; isPadding_ = (resW != 0) || (resH != 0); - - inDataExpSz_ = newsz; - + if (isPadding_){ paddingW = 1; @@ -174,7 +172,6 @@ void Pooling::updateConfig(bool isLern, const snSize& newsz){ outSz.w = (newsz.w + paddingW * 2 - kernel) / stride + 1; outSz.h = (newsz.h + paddingH * 2 - kernel) / stride + 1; - inDataExpSz_ = snSize(newsz.w + paddingW * 2, newsz.h + paddingH * 2, newsz.d, newsz.n); } baseOut_.resize(outSz); @@ -182,5 +179,5 @@ void Pooling::updateConfig(bool isLern, const snSize& newsz){ if (isLern) baseGrad_.resize(newsz); - iniParamCUDA(isLern, inDataExpSz_, outSz, poolPrms_, &gpuParams_); + iniParamCUDA(isLern, newsz, outSz, poolPrms_, &gpuParams_); } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/pooling.h b/src/snOperatorCUDA/src/Operator/pooling.h index 69251f5..9ec2cc3 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.h +++ b/src/snOperatorCUDA/src/Operator/pooling.h @@ -51,11 +51,7 @@ class Pooling final : SN_Base::OperatorBase{ poolParams poolPrms_; SN_Base::snSize inSzMem_; ///< input size mem - SN_Base::snSize inDataExpSz_; ///< input size expand - SN_Base::Tensor inTnsExp_; - SN_Base::Tensor gradOutExp_; - const SN_Base::Tensor* inputMem_ = nullptr; From ee7ddc5b6a20c1a8b2284755d84b277c430449ba Mon Sep 17 00:00:00 2001 From: al_medved Date: Thu, 25 Jul 2019 00:38:37 +0500 Subject: [PATCH 18/35] -develop --- src/snOperatorCUDA/src/CUDA/activation.cu | 4 ++++ src/snOperatorCUDA/src/CUDA/pooling.cu | 4 ++++ 2 files changed, 8 insertions(+) diff --git a/src/snOperatorCUDA/src/CUDA/activation.cu b/src/snOperatorCUDA/src/CUDA/activation.cu index 140622d..193eaf7 100644 --- a/src/snOperatorCUDA/src/CUDA/activation.cu +++ b/src/snOperatorCUDA/src/CUDA/activation.cu @@ -38,6 +38,7 @@ __global__ void df_sigmoid(snSize outsz, snFloat* output, snFloat* grad){ // gridDim.y - batch size output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + grad += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; while (i < outStepByD){ @@ -78,6 +79,7 @@ __global__ void df_relu(snSize outsz, snFloat* output, snFloat* grad){ // gridDim.y - batch size output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + grad += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; while (i < outStepByD){ @@ -117,6 +119,7 @@ __global__ void df_leakyRelu(snSize outsz, snFloat* output, snFloat* grad){ // gridDim.y - batch size output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + grad += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; while (i < outStepByD){ @@ -156,6 +159,7 @@ __global__ void df_elu(snSize outsz, snFloat* output, snFloat* grad){ // gridDim.y - batch size output += blockIdx.x * outStepByD + blockIdx.y * outStepByN; + grad += blockIdx.x * outStepByD + blockIdx.y * outStepByN; unsigned int i = threadIdx.x; while (i < outStepByD){ diff --git a/src/snOperatorCUDA/src/CUDA/pooling.cu b/src/snOperatorCUDA/src/CUDA/pooling.cu index 5af2366..0e5e6bd 100644 --- a/src/snOperatorCUDA/src/CUDA/pooling.cu +++ b/src/snOperatorCUDA/src/CUDA/pooling.cu @@ -139,6 +139,10 @@ void Pooling::freeParamCUDA(void* gpuPrms){ cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->in_desc)); cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->out_desc)); + if (gpuPrm->grin_desc){ // isLern + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grin_desc)); + cuCHECK(cudnnDestroyTensorDescriptor(gpuPrm->grout_desc)); + } } __global__ void cuFiltrNegative(snSize outsz, snFloat* out){ From 4496f71d1b3e2e93decac1350ed0b7ed22a9045f Mon Sep 17 00:00:00 2001 From: Tyill Date: Thu, 25 Jul 2019 16:18:38 +0500 Subject: [PATCH 19/35] -develop --- c_sharp/example.cs | 10 +- c_sharp/snOperator.cs | 73 +++-------- c_sharp/snType.cs | 31 +---- cpp/snOperator.h | 81 +++++-------- cpp/snType.h | 17 +-- example/cifar10/c#_example.cs | 28 ++--- example/cifar10/cpp_example.cpp | 26 ++-- example/cifar10/python_example.py | 24 ++-- example/mnist/c#_example.cs | 14 +-- example/mnist/c_example.cpp | 12 +- example/mnist/compare_PyTorch/Skynet.py | 18 +-- example/mnist/cpp_example.cpp | 18 +-- example/mnist/python_example.py | 12 +- example/resnet50/cpp_example.cpp | 91 +++++++------- example/resnet50/createNet.py | 2 +- example/resnet50/python_example.py | 39 +----- example/unet/c#_example.cs | 34 +++--- example/unet/cpp_example.cpp | 32 ++--- example/unet/python_example.py | 35 +++--- mswin/VS12/snOperatorCUDA.vcxproj | 2 +- mswin/VS12/snOperatorCUDA.vcxproj.filters | 6 +- python/libskynet/snOperator.py | 41 ++----- python/libskynet/snResNet50.py | 62 +++++----- python/libskynet/snType.py | 6 - .../src/Operator/lossFunction.cpp | 3 +- src/snOperatorCUDA/src/CUDA/crop.cu | 77 ++++++++++++ src/snOperatorCUDA/src/CUDA/lossFunctions.cu | 113 ++++++++++++++++-- src/snOperatorCUDA/src/Operator/crop.cpp | 44 +------ src/snOperatorCUDA/src/Operator/crop.h | 12 +- .../src/Operator/lossFunction.cpp | 61 +++------- src/snOperatorCUDA/src/lossFunctions.h | 3 - src/snOperatorCUDA/src/structurs.h | 7 ++ 32 files changed, 488 insertions(+), 546 deletions(-) create mode 100644 src/snOperatorCUDA/src/CUDA/crop.cu diff --git a/c_sharp/example.cs b/c_sharp/example.cs index 7f38ae6..d8d189a 100644 --- a/c_sharp/example.cs +++ b/c_sharp/example.cs @@ -67,11 +67,11 @@ static void Main(string[] args) Console.WriteLine("Version snlib " + ver); snet.addNode("Input", new sn.Input(), "C1") - .addNode("C1", new sn.Convolution(15, 0, sn.calcMode.type.CUDA), "C2") - .addNode("C2", new sn.Convolution(15, 0, sn.calcMode.type.CUDA), "P1") - .addNode("P1", new sn.Pooling(sn.calcMode.type.CUDA), "FC1") - .addNode("FC1", new sn.FullyConnected(128, sn.calcMode.type.CUDA), "FC2") - .addNode("FC2", new sn.FullyConnected(10, sn::calcMode.type.CUDA), "LS") + .addNode("C1", new sn.Convolution(15, 0), "C2") + .addNode("C2", new sn.Convolution(15, 0), "P1") + .addNode("P1", new sn.Pooling(), "FC1") + .addNode("FC1", new sn.FullyConnected(128), "FC2") + .addNode("FC2", new sn.FullyConnected(10), "LS") .addNode("LS", new sn.LossFunction(sn.lossType.type.softMaxToCrossEntropy), "Output"); string imgPath = "c://C++//skyNet//example//mnist//images//"; diff --git a/c_sharp/snOperator.cs b/c_sharp/snOperator.cs index 8b6ca05..8739a61 100644 --- a/c_sharp/snOperator.cs +++ b/c_sharp/snOperator.cs @@ -66,9 +66,7 @@ public class FullyConnected : IOperator public optimizer opt = new optimizer(optimizer.type.adam); ///< Optimizer of weights. Optional parameter public float dropOut = 0.0f; ///< Random disconnection of neurons. Optional parameter [0..1.F] public batchNormType bnorm = new batchNormType(batchNormType.type.none); ///< Type of batch norm. Optional parameter - public calcMode mode = new calcMode(calcMode.type.CPU); ///< Сalculation mode. Optional parameter public uint gpuDeviceId = 0; ///< GPU Id. Optional parameter - public bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter public bool freeze = false; ///< Do not change weights. Optional parameter public bool useBias = true; ///< +bias. Optional parameter public weightInit wini = new weightInit(weightInit.type.he); ///< Type of initialization of weights. Optional parameter @@ -81,27 +79,18 @@ public FullyConnected(uint units_, active.type act_ = active.type.relu, optimizer.type opt_ = optimizer.type.adam, float dropOut_ = 0.0f, - batchNormType.type bnorm_ = batchNormType.type.none, - calcMode.type mode_ = calcMode.type.CPU, + batchNormType.type bnorm_ = batchNormType.type.none, uint gpuDeviceId_ = 0) { units = units_; act = new active(act_); opt = new optimizer(opt_); dropOut = dropOut_; - bnorm = new batchNormType(bnorm_); - mode = new calcMode(mode_); + bnorm = new batchNormType(bnorm_); gpuDeviceId = gpuDeviceId_; } - public FullyConnected(uint units_, calcMode.type mode_ = calcMode.type.CPU, - batchNormType.type bnorm_ = batchNormType.type.none) - { - units = units_; - bnorm = new batchNormType(bnorm_); - mode = new calcMode(mode_); - } - + public string getParamsJn() { @@ -114,12 +103,10 @@ public string getParamsJn() "\"decayMomentDW\":\"" + decayMomentDW.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"decayMomentWGr\":\"" + decayMomentWGr.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"lmbRegular\":\"" + lmbRegular.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + - "\"dropOut\":\"" + dropOut.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + - "\"mode\":\"" + mode.str() + "\"," + + "\"dropOut\":\"" + dropOut.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"gpuDeviceId\":\"" + gpuDeviceId.ToString() + "\"," + "\"freeze\":\"" + (freeze ? "1" : "0") + "\"," + - "\"useBias\":\"" + (useBias ? "1" : "0") + "\"," + - "\"gpuClearMem\":\"" + (gpuClearMem ? "1" : "0") + "\"" + + "\"useBias\":\"" + (useBias ? "1" : "0") + "\"" + "}"; return ss; @@ -147,9 +134,7 @@ public class Convolution : IOperator public int padding = 0; ///< Padding around the edges. Optional parameter public uint stride = 1; ///< Mask movement step. Optional parameter(> 0) public uint dilate = 1; ///< Expansion mask. Optional parameter(> 0) - public calcMode mode = new calcMode(calcMode.type.CPU); ///< Сalculation mode. Optional parameter public uint gpuDeviceId = 0; ///< GPU Id. Optional parameter - public bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter public bool freeze = false; ///< Do not change weights. Optional parameter public bool useBias = true; ///< +bias. Optional parameter public weightInit wini = new weightInit(weightInit.type.he); ///< Type of initialization of weights. Optional parameter @@ -168,7 +153,6 @@ public Convolution(uint filters_, int padding_ = 0, uint stride_ = 1, uint dilate_ = 1, - calcMode.type mode_ = calcMode.type.CPU, uint gpuDeviceId_ = 0) { @@ -182,16 +166,14 @@ public Convolution(uint filters_, padding = padding_; stride = stride_; dilate = dilate_; - mode = new calcMode(mode_); gpuDeviceId = gpuDeviceId_; } - public Convolution(uint filters_, int padding_ = 0, calcMode.type mode_ = calcMode.type.CPU, + public Convolution(uint filters_, int padding_ = 0, batchNormType.type bnorm_ = batchNormType.type.none) { filters = filters_; - padding = padding_; - mode = new calcMode(mode_); + padding = padding_; bnorm = new batchNormType(bnorm_); } @@ -212,12 +194,10 @@ public string getParamsJn() "\"decayMomentDW\":\"" + decayMomentDW.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"decayMomentWGr\":\"" + decayMomentWGr.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"lmbRegular\":\"" + lmbRegular.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + - "\"dropOut\":\"" + dropOut.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + - "\"mode\":\"" + mode.str() + "\"," + + "\"dropOut\":\"" + dropOut.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"gpuDeviceId\":\"" + gpuDeviceId.ToString() + "\"," + "\"freeze\":\"" + (freeze ? "1" : "0") + "\"," + - "\"useBias\":\"" + (useBias ? "1" : "0") + "\"," + - "\"gpuClearMem\":\"" + (gpuClearMem ? "1" : "0") + "\"" + + "\"useBias\":\"" + (useBias ? "1" : "0") + "\"" + "}"; return ss; @@ -244,9 +224,7 @@ public class Deconvolution : IOperator public uint fWidth = 3; ///< Width of mask. Optional parameter(> 0) public uint fHeight = 3; ///< Height of mask. Optional parameter(> 0) public uint stride = 2; ///< Mask movement step. Optional parameter(> 0) - public calcMode mode = new calcMode(calcMode.type.CPU); ///< Сalculation mode. Optional parameter public uint gpuDeviceId = 0; ///< GPU Id. Optional parameter - public bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter public bool freeze = false; ///< Do not change weights. Optional parameter public weightInit wini = new weightInit(weightInit.type.he); ///< Type of initialization of weights. Optional parameter public float decayMomentDW = 0.9F; ///< Optimizer of weights moment change. Optional parameter [0..1.F] @@ -262,7 +240,6 @@ public Deconvolution(uint filters_, uint fWidth_ = 3, uint fHeight_ = 3, uint stride_ = 1, - calcMode.type mode_ = calcMode.type.CPU, uint gpuDeviceId_ = 0) { @@ -274,15 +251,13 @@ public Deconvolution(uint filters_, fWidth = fWidth_; fHeight = fHeight_; stride = stride_; - mode = new calcMode(mode_); gpuDeviceId = gpuDeviceId_; } - public Deconvolution(uint filters_, calcMode.type mode_ = calcMode.type.CPU, + public Deconvolution(uint filters_, batchNormType.type bnorm_ = batchNormType.type.none) { - filters = filters_; - mode = new calcMode(mode_); + filters = filters_; bnorm = new batchNormType(bnorm_); } @@ -302,10 +277,8 @@ public string getParamsJn() "\"decayMomentWGr\":\"" + decayMomentWGr.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"lmbRegular\":\"" + lmbRegular.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + "\"dropOut\":\"" + dropOut.ToString("0.000", System.Globalization.CultureInfo.InvariantCulture) + "\"," + - "\"mode\":\"" + mode.str() + "\"," + - "\"gpuDeviceId\":\"" + gpuDeviceId.ToString() + "\"," + - "\"freeze\":\"" + (freeze ? "1" : "0") + "\"," + - "\"gpuClearMem\":\"" + (gpuClearMem ? "1" : "0") + "\"" + + "\"gpuDeviceId\":\"" + gpuDeviceId.ToString() + "\"," + + "\"freeze\":\"" + (freeze ? "1" : "0") + "\"" + "}"; return ss; @@ -327,17 +300,11 @@ public class Pooling : IOperator public uint kernel = 2; ///< Square Mask Size. Optional parameter (> 0) public uint stride = 2; ///< Mask movement step. Optional parameter(> 0) public pooling pool = new pooling(pooling.type.max); ///< Operator Type. Optional parameter - public calcMode mode = new calcMode(calcMode.type.CPU); ///< Сalculation mode. Optional parameter - public uint gpuDeviceId = 0; ///< GPU Id. Optional parameter - public bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter - - public Pooling(calcMode.type mode_ = calcMode.type.CPU, - uint gpuDeviceId_ = 0, - bool gpuClearMem_ = false) + public uint gpuDeviceId = 0; ///< GPU Id. Optional parameter + + public Pooling(uint gpuDeviceId_ = 0) { - mode = new calcMode(mode_); gpuDeviceId = gpuDeviceId_; - gpuClearMem = gpuClearMem_; } public string getParamsJn() @@ -345,11 +312,9 @@ public string getParamsJn() string ss = "{\"kernel\":\"" + kernel.ToString() + "\"," + "\"stride\":\"" + stride.ToString() + "\"," + - "\"pool\":\"" + pool.str() + "\"," + - "\"mode\":\"" + mode.str() + "\"," + - "\"gpuDeviceId\":\"" + gpuDeviceId.ToString() + "\"," + - "\"gpuClearMem\":\"" + (gpuClearMem ? "1" : "0") + "\"" + - "}"; + "\"pool\":\"" + pool.str() + "\"," + + "\"gpuDeviceId\":\"" + gpuDeviceId.ToString() + "\"" + + "}"; return ss; } diff --git a/c_sharp/snType.cs b/c_sharp/snType.cs index aa11171..b9569dd 100644 --- a/c_sharp/snType.cs +++ b/c_sharp/snType.cs @@ -195,36 +195,7 @@ public string str() private type type_; } - /// - /// CPU, CUDA or OpenCL - /// - public class calcMode - { - - public calcMode(type tp) - { - type_ = tp; - } - - public enum type - { - CPU = 0, - CUDA = 1, - OpenCL = 2, - }; - public string str() - { - switch (type_) - { - case type.CPU: return "CPU"; - case type.CUDA: return "CUDA"; - case type.OpenCL: return "OpenCL"; - default: return "CPU"; - } - } - private type type_; - } - + /// /// lockType /// diff --git a/cpp/snOperator.h b/cpp/snOperator.h index ae81160..026c324 100644 --- a/cpp/snOperator.h +++ b/cpp/snOperator.h @@ -70,9 +70,7 @@ namespace SN_API{ optimizer opt = optimizer::adam; ///< Optimizer of weights. Optional parameter snFloat dropOut = 0.0; ///< Random disconnection of neurons. Optional parameter [0..1.F] batchNormType bnorm = batchNormType::none; ///< Type of batch norm. Optional parameter - calcMode mode = calcMode::CPU; ///< Сalculation mode. Optional parameter uint32_t gpuDeviceId = 0; ///< GPU Id. Optional parameter - bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter bool freeze = false; ///< Do not change weights. Optional parameter bool useBias = true; ///< +bias. Optional parameter weightInit wini = weightInit::he; ///< Type of initialization of weights. Optional parameter @@ -86,17 +84,13 @@ namespace SN_API{ optimizer opt_ = optimizer::adam, snFloat dropOut_ = 0.0, batchNormType bnorm_ = batchNormType::none, - calcMode mode_ = calcMode::CPU, uint32_t gpuDeviceId_ = 0): units(units_), act(act_), opt(opt_), - dropOut(dropOut_), bnorm(bnorm_), mode(mode_), gpuDeviceId(gpuDeviceId_){}; + dropOut(dropOut_), bnorm(bnorm_), gpuDeviceId(gpuDeviceId_){}; - FullyConnected(uint32_t units_, calcMode mode_ = calcMode::CPU, batchNormType bnorm_ = batchNormType::none) : - units(units_), mode(mode_), bnorm(bnorm_){} - - FullyConnected(uint32_t units_, active act_, calcMode mode_) : - units(units_), act(act_), mode(mode_){} + FullyConnected(uint32_t units_, batchNormType bnorm_ = batchNormType::none) : + units(units_), bnorm(bnorm_){} ~FullyConnected(){}; @@ -112,12 +106,10 @@ namespace SN_API{ "\"decayMomentDW\":\"" << decayMomentDW << "\"," "\"decayMomentWGr\":\"" << decayMomentWGr << "\"," "\"lmbRegular\":\"" << lmbRegular << "\"," - "\"dropOut\":\"" << dropOut << "\"," - "\"mode\":\"" << calcModeStr(mode) << "\"," + "\"dropOut\":\"" << dropOut << "\"," "\"gpuDeviceId\":\"" << gpuDeviceId << "\"," "\"freeze\":\"" << (freeze ? 1 : 0) << "\"," - "\"useBias\":\"" << (useBias ? 1 : 0) << "\"," - "\"gpuClearMem\":\"" << (gpuClearMem ? 1 : 0) << "\"" + "\"useBias\":\"" << (useBias ? 1 : 0) << "\"" "}"; return ss.str(); @@ -145,9 +137,7 @@ namespace SN_API{ int padding = 0; ///< Padding around the edges. Optional parameter uint32_t stride = 1; ///< Mask movement step. Optional parameter(> 0) uint32_t dilate = 1; ///< Expansion mask. Optional parameter(> 0) - calcMode mode = calcMode::CPU; ///< Сalculation mode. Optional parameter uint32_t gpuDeviceId = 0; ///< GPU Id. Optional parameter - bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter bool freeze = false; ///< Do not change weights. Optional parameter bool useBias = true; ///< +bias. Optional parameter weightInit wini = weightInit::he; ///< Type of initialization of weights. Optional parameter @@ -166,21 +156,20 @@ namespace SN_API{ uint32_t fHeight_ = 3, int padding_ = 0, uint32_t stride_ = 1, - uint32_t dilate_ = 1, - calcMode mode_ = calcMode::CPU, + uint32_t dilate_ = 1, uint32_t gpuDeviceId_ = 0): filters(filters_), act(act_), opt(opt_), dropOut(dropOut_), bnorm(bnorm_), fWidth(fWidth_), fHeight(fHeight_), padding(padding_), stride(stride_), - dilate(dilate_), mode(mode_), gpuDeviceId(gpuDeviceId_){} + dilate(dilate_), gpuDeviceId(gpuDeviceId_){} Convolution(uint32_t filters_, uint32_t kernelSz, int padding_ = 0, uint32_t stride_ = 1, - batchNormType bnorm_ = batchNormType::none, active act_ = active::relu, calcMode mode_ = calcMode::CPU) : + batchNormType bnorm_ = batchNormType::none, active act_ = active::relu) : filters(filters_), fWidth(kernelSz), fHeight(kernelSz), padding(padding_), stride(stride_), - bnorm(bnorm_), act(act_), mode(mode_){} + bnorm(bnorm_), act(act_){} - Convolution(uint32_t filters_, int padding_ = 0, calcMode mode_ = calcMode::CPU) : - filters(filters_), padding(padding_), mode(mode_){} + Convolution(uint32_t filters_, int padding_ = 0) : + filters(filters_), padding(padding_){} ~Convolution(){}; @@ -201,12 +190,10 @@ namespace SN_API{ "\"decayMomentDW\":\"" << decayMomentDW << "\"," "\"decayMomentWGr\":\"" << decayMomentWGr << "\"," "\"lmbRegular\":\"" << lmbRegular << "\"," - "\"dropOut\":\"" << dropOut << "\"," - "\"mode\":\"" << calcModeStr(mode) << "\"," + "\"dropOut\":\"" << dropOut << "\"," "\"gpuDeviceId\":\"" << gpuDeviceId << "\"," "\"freeze\":\"" << (freeze ? 1 : 0) << "\"," - "\"useBias\":\"" << (useBias ? 1 : 0) << "\"," - "\"gpuClearMem\":\"" << (gpuClearMem ? 1 : 0) << "\"" + "\"useBias\":\"" << (useBias ? 1 : 0) << "\"" "}"; return ss.str(); @@ -232,10 +219,8 @@ namespace SN_API{ batchNormType bnorm = batchNormType::none; ///< Type of batch norm. Optional parameter uint32_t fWidth = 3; ///< Width of mask. Optional parameter(> 0) uint32_t fHeight = 3; ///< Height of mask. Optional parameter(> 0) - uint32_t stride = 2; ///< Mask movement step. Optional parameter(> 0) - calcMode mode = calcMode::CPU; ///< Сalculation mode. Optional parameter - uint32_t gpuDeviceId = 0; ///< GPU Id. Optional parameter - bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter + uint32_t stride = 2; ///< Mask movement step. Optional parameter(> 0) + uint32_t gpuDeviceId = 0; ///< GPU Id. Optional parameter bool freeze = false; ///< Do not change weights. Optional parameter weightInit wini = weightInit::he; ///< Type of initialization of weights. Optional parameter snFloat decayMomentDW = 0.9F; ///< Optimizer of weights moment change. Optional parameter [0..1.F] @@ -250,16 +235,15 @@ namespace SN_API{ batchNormType bnorm_ = batchNormType::none, uint32_t fWidth_ = 3, uint32_t fHeight_ = 3, - uint32_t stride_ = 2, - calcMode mode_ = calcMode::CPU, + uint32_t stride_ = 2, uint32_t gpuDeviceId_ = 0): filters(filters_), act(act_), opt(opt_), dropOut(dropOut_), bnorm(bnorm_), fWidth(fWidth_), fHeight(fHeight_), stride(stride_), - mode(mode_), gpuDeviceId(gpuDeviceId_){} + gpuDeviceId(gpuDeviceId_){} - Deconvolution(uint32_t filters_, calcMode mode_ = calcMode::CPU) : - filters(filters_), mode(mode_){} + Deconvolution(uint32_t filters_) : + filters(filters_){} ~Deconvolution(){}; @@ -278,11 +262,9 @@ namespace SN_API{ "\"decayMomentDW\":\"" << decayMomentDW << "\"," "\"decayMomentWGr\":\"" << decayMomentWGr << "\"," "\"lmbRegular\":\"" << lmbRegular << "\"," - "\"dropOut\":\"" << dropOut << "\"," - "\"mode\":\"" << calcModeStr(mode) << "\"," + "\"dropOut\":\"" << dropOut << "\"," "\"gpuDeviceId\":\"" << gpuDeviceId << "\"," - "\"freeze\":\"" << (freeze ? 1 : 0) << "\"," - "\"gpuClearMem\":\"" << (gpuClearMem ? 1 : 0) << "\"" + "\"freeze\":\"" << (freeze ? 1 : 0) << "\"" "}"; return ss.str(); @@ -304,18 +286,13 @@ namespace SN_API{ uint32_t kernel = 2; ///< Square Mask Size. Optional parameter (> 0) uint32_t stride = 2; ///< Mask movement step. Optional parameter(> 0) poolType pool = poolType::max; ///< Operator Type. Optional parameter - calcMode mode = calcMode::CPU; ///< Сalculation mode. Optional parameter uint32_t gpuDeviceId = 0; ///< GPU Id. Optional parameter - bool gpuClearMem = false; ///< Clear memory GPU. Optional parameter - - Pooling(calcMode mode_ = calcMode::CPU, - uint32_t gpuDeviceId_ = 0, - bool gpuClearMem_ = false): - mode(mode_), - gpuDeviceId(gpuDeviceId_), gpuClearMem(gpuClearMem_){} + + Pooling(uint32_t gpuDeviceId_ = 0): + gpuDeviceId(gpuDeviceId_){} - Pooling(uint32_t kernel_, uint32_t stride_, poolType pool_ = poolType::max, calcMode mode_ = calcMode::CPU) : - kernel(kernel_), stride(stride_), pool(pool_), mode(mode_){} + Pooling(uint32_t kernel_, uint32_t stride_, poolType pool_ = poolType::max) : + kernel(kernel_), stride(stride_), pool(pool_){} ~Pooling(){}; @@ -324,10 +301,8 @@ namespace SN_API{ std::stringstream ss; ss << "{\"kernel\":\"" << kernel << "\"," "\"stride\":\"" << stride << "\"," - "\"pool\":\"" << poolTypeStr(pool) << "\"," - "\"mode\":\"" << calcModeStr(mode) << "\"," - "\"gpuDeviceId\":\"" << gpuDeviceId << "\"," - "\"gpuClearMem\":\"" << (gpuClearMem ? 1 : 0) << "\"" + "\"pool\":\"" << poolTypeStr(pool) << "\"," + "\"gpuDeviceId\":\"" << gpuDeviceId << "\"" "}"; return ss.str(); diff --git a/cpp/snType.h b/cpp/snType.h index 796f42e..668e337 100644 --- a/cpp/snType.h +++ b/cpp/snType.h @@ -117,22 +117,7 @@ namespace SN_API{ default: return "max"; } } - - /// CPU, CUDA or OpenCL(for the future) - enum class calcMode{ - CPU = 0, - CUDA = 1, - //OpenCL = 2, - }; - std::string calcModeStr(calcMode mode){ - - switch (mode){ - case calcMode::CPU: return "CPU"; - case calcMode::CUDA: return "CUDA"; - default: return "CPU"; - } - } - + enum class lockType{ lock = 0, unlock = 1, diff --git a/example/cifar10/c#_example.cs b/example/cifar10/c#_example.cs index af9ee74..728a5fe 100644 --- a/example/cifar10/c#_example.cs +++ b/example/cifar10/c#_example.cs @@ -67,24 +67,24 @@ static void Main(string[] args) Console.WriteLine("Version snlib " + ver); snet.addNode("Input", new sn.Input(), "C1") - .addNode("C1", new sn.Convolution(15, -1, sn.calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "C2") - .addNode("C2", new sn.Convolution(15, 0, sn.calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "P1") - .addNode("P1", new sn.Pooling(sn.calcMode.type.CUDA), "C3") + .addNode("C1", new sn.Convolution(15, -1, sn.batchNormType.type.beforeActive), "C2") + .addNode("C2", new sn.Convolution(15, 0, sn.batchNormType.type.beforeActive), "P1") + .addNode("P1", new sn.Pooling(), "C3") - .addNode("C3", new sn.Convolution(25, -1, sn.calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "C4") - .addNode("C4", new sn.Convolution(25, 0, sn.calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "P2") - .addNode("P2", new sn.Pooling(sn.calcMode.type.CUDA), "C5") + .addNode("C3", new sn.Convolution(25, -1, sn.batchNormType.type.beforeActive), "C4") + .addNode("C4", new sn.Convolution(25, 0, sn.batchNormType.type.beforeActive), "P2") + .addNode("P2", new sn.Pooling(), "C5") - .addNode("C5", new sn.Convolution(25, -1, sn.calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "C6") - .addNode("C6", new sn.Convolution(25, 0, sn.calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "P3") - .addNode("P3", new sn.Pooling(sn.calcMode.type.CUDA), "FC1") + .addNode("C5", new sn.Convolution(25, -1, sn.batchNormType.type.beforeActive), "C6") + .addNode("C6", new sn.Convolution(25, 0, sn.batchNormType.type.beforeActive), "P3") + .addNode("P3", new sn.Pooling(), "FC1") - .addNode("FC1", new sn.FullyConnected(2048, sn.calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "FC2") - .addNode("FC2", new sn.FullyConnected(128, sn::calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "FC3") - .addNode("FC3", new sn.FullyConnected(10, sn::calcMode.type.CUDA, sn.batchNormType.type.beforeActive), "LS") + .addNode("FC1", new sn.FullyConnected(2048, sn.batchNormType.type.beforeActive), "FC2") + .addNode("FC2", new sn.FullyConnected(128, sn.batchNormType.type.beforeActive), "FC3") + .addNode("FC3", new sn.FullyConnected(10, sn.batchNormType.type.beforeActive), "LS") .addNode("LS", new sn.LossFunction(sn.lossType.type.softMaxToCrossEntropy), "Output"); - string imgPath = "c://C++//skyNet//example//cifar10//images//"; + string imgPath = "c://cpp//skyNet//example//cifar10//images//"; uint batchSz = 100, classCnt = 10, w = 32, h = 32, d = 3; float lr = 0.001F; @@ -99,7 +99,7 @@ static void Main(string[] args) return; } - string wpath = "c:/C++/w.dat"; + string wpath = "c:/cpp/w.dat"; // if (snet.loadAllWeightFromFile(wpath)) // Console.WriteLine("Load weight ok path: " + wpath); // else diff --git a/example/cifar10/cpp_example.cpp b/example/cifar10/cpp_example.cpp index 7449b2e..65c340c 100644 --- a/example/cifar10/cpp_example.cpp +++ b/example/cifar10/cpp_example.cpp @@ -50,24 +50,24 @@ int main(int argc, char* argv[]){ sn::Net snet; snet.addNode("Input", sn::Input(), "C1") - .addNode("C1", sn::Convolution(15, -1, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "C2") - .addNode("C2", sn::Convolution(15, 0, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "P1") - .addNode("P1", sn::Pooling(sn::calcMode::CUDA), "C3") + .addNode("C1", sn::Convolution(15, -1, sn::batchNormType::beforeActive), "C2") + .addNode("C2", sn::Convolution(15, 0, sn::batchNormType::beforeActive), "P1") + .addNode("P1", sn::Pooling(), "C3") - .addNode("C3", sn::Convolution(25, -1, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "C4") - .addNode("C4", sn::Convolution(25, 0, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "P2") - .addNode("P2", sn::Pooling(sn::calcMode::CUDA), "C5") + .addNode("C3", sn::Convolution(25, -1, sn::batchNormType::beforeActive), "C4") + .addNode("C4", sn::Convolution(25, 0, sn::batchNormType::beforeActive), "P2") + .addNode("P2", sn::Pooling(), "C5") - .addNode("C5", sn::Convolution(40, -1, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "C6") - .addNode("C6", sn::Convolution(40, 0, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "P3") - .addNode("P3", sn::Pooling(sn::calcMode::CUDA), "FC1") + .addNode("C5", sn::Convolution(40, -1, sn::batchNormType::beforeActive), "C6") + .addNode("C6", sn::Convolution(40, 0, sn::batchNormType::beforeActive), "P3") + .addNode("P3", sn::Pooling(), "FC1") - .addNode("FC1", sn::FullyConnected(2048, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "FC2") - .addNode("FC2", sn::FullyConnected(128, sn::calcMode::CUDA, sn::batchNormType::beforeActive), "FC3") - .addNode("FC3", sn::FullyConnected(10, sn::calcMode::CUDA), "LS") + .addNode("FC1", sn::FullyConnected(2048, sn::batchNormType::beforeActive), "FC2") + .addNode("FC2", sn::FullyConnected(128, sn::batchNormType::beforeActive), "FC3") + .addNode("FC3", sn::FullyConnected(10), "LS") .addNode("LS", sn::LossFunction(sn::lossType::softMaxToCrossEntropy), "Output"); - string imgPath = "c://C++//skyNet//example//cifar10//images//"; + string imgPath = "c://cpp//skyNet//example//cifar10//images//"; int batchSz = 100, classCnt = 10, w = 28, h = 28, d = 3; float lr = 0.001F; vector> imgName(classCnt); diff --git a/example/cifar10/python_example.py b/example/cifar10/python_example.py index 86af022..62746a0 100644 --- a/example/cifar10/python_example.py +++ b/example/cifar10/python_example.py @@ -11,18 +11,18 @@ # create net net = snNet.Net() net.addNode('In', snOperator.Input(), 'C1') \ - .addNode('C1', snOperator.Convolution(15, -1, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'C2') \ - .addNode('C2', snOperator.Convolution(15, 0, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'P1') \ - .addNode('P1', snOperator.Pooling(snType.poolType.max, snType.calcMode.CPU), 'C3') \ - .addNode('C3', snOperator.Convolution(25, -1, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'C4') \ - .addNode('C4', snOperator.Convolution(25, 0, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'P2') \ - .addNode('P2', snOperator.Pooling(snType.poolType.max, snType.calcMode.CPU), 'C5') \ - .addNode('C5', snOperator.Convolution(40, -1, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'C6') \ - .addNode('C6', snOperator.Convolution(40, 0, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'P3') \ - .addNode('P3', snOperator.Pooling(snType.poolType.max, snType.calcMode.CPU), 'F1') \ - .addNode('F1', snOperator.FullyConnected(2048, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'F2') \ - .addNode('F2', snOperator.FullyConnected(128, snType.calcMode.CPU, snType.batchNormType.beforeActive), 'F3') \ - .addNode('F3', snOperator.FullyConnected(10, snType.calcMode.CPU), 'LS') \ + .addNode('C1', snOperator.Convolution(15, -1, snType.batchNormType.beforeActive), 'C2') \ + .addNode('C2', snOperator.Convolution(15, 0, snType.batchNormType.beforeActive), 'P1') \ + .addNode('P1', snOperator.Pooling(snType.poolType.max), 'C3') \ + .addNode('C3', snOperator.Convolution(25, -1, snType.batchNormType.beforeActive), 'C4') \ + .addNode('C4', snOperator.Convolution(25, 0, snType.batchNormType.beforeActive), 'P2') \ + .addNode('P2', snOperator.Pooling(snType.poolType.max), 'C5') \ + .addNode('C5', snOperator.Convolution(40, -1, snType.batchNormType.beforeActive), 'C6') \ + .addNode('C6', snOperator.Convolution(40, 0, snType.batchNormType.beforeActive), 'P3') \ + .addNode('P3', snOperator.Pooling(snType.poolType.max), 'F1') \ + .addNode('F1', snOperator.FullyConnected(2048, snType.batchNormType.beforeActive), 'F2') \ + .addNode('F2', snOperator.FullyConnected(128, snType.batchNormType.beforeActive), 'F3') \ + .addNode('F3', snOperator.FullyConnected(10), 'LS') \ .addNode('LS', snOperator.LossFunction(snType.lossType.softMaxToCrossEntropy), 'Output') # loadImg diff --git a/example/mnist/c#_example.cs b/example/mnist/c#_example.cs index 7f38ae6..38e777d 100644 --- a/example/mnist/c#_example.cs +++ b/example/mnist/c#_example.cs @@ -67,14 +67,14 @@ static void Main(string[] args) Console.WriteLine("Version snlib " + ver); snet.addNode("Input", new sn.Input(), "C1") - .addNode("C1", new sn.Convolution(15, 0, sn.calcMode.type.CUDA), "C2") - .addNode("C2", new sn.Convolution(15, 0, sn.calcMode.type.CUDA), "P1") - .addNode("P1", new sn.Pooling(sn.calcMode.type.CUDA), "FC1") - .addNode("FC1", new sn.FullyConnected(128, sn.calcMode.type.CUDA), "FC2") - .addNode("FC2", new sn.FullyConnected(10, sn::calcMode.type.CUDA), "LS") + .addNode("C1", new sn.Convolution(15, 0), "C2") + .addNode("C2", new sn.Convolution(15, 0), "P1") + .addNode("P1", new sn.Pooling(), "FC1") + .addNode("FC1", new sn.FullyConnected(128), "FC2") + .addNode("FC2", new sn.FullyConnected(10), "LS") .addNode("LS", new sn.LossFunction(sn.lossType.type.softMaxToCrossEntropy), "Output"); - string imgPath = "c://C++//skyNet//example//mnist//images//"; + string imgPath = "c://cpp//skyNet//example//mnist//images//"; uint batchSz = 100, classCnt = 10, w = 28, h = 28; float lr = 0.001F; @@ -89,7 +89,7 @@ static void Main(string[] args) return; } - string wpath = "c:/C++/w.dat"; + string wpath = "c:/cpp/w.dat"; if (snet.loadAllWeightFromFile(wpath)) Console.WriteLine("Load weight ok path: " + wpath); else diff --git a/example/mnist/c_example.cpp b/example/mnist/c_example.cpp index 4fbc062..766833f 100644 --- a/example/mnist/c_example.cpp +++ b/example/mnist/c_example.cpp @@ -62,35 +62,35 @@ int main(int argc, char* argv[]) "\"NodeName\":\"C1\"," "\"NextNodes\":\"C2\"," "\"OperatorName\":\"Convolution\"," - "\"OperatorParams\":{\"kernel\":\"15\",\"mode\":\"CUDA\"}" + "\"OperatorParams\":{\"kernel\":\"15\"}" "}," "{" "\"NodeName\":\"C2\"," "\"NextNodes\":\"P1\"," "\"OperatorName\":\"Convolution\"," - "\"OperatorParams\":{\"kernel\":\"15\",\"mode\":\"CUDA\"}" + "\"OperatorParams\":{\"kernel\":\"15\"}" "}," "{" "\"NodeName\":\"P1\"," "\"NextNodes\":\"FC1\"," "\"OperatorName\":\"Pooling\"," - "\"OperatorParams\":{\"mode\":\"CUDA\"}" + "\"OperatorParams\":{}" "}," "{" "\"NodeName\":\"FC1\"," "\"NextNodes\":\"FC2\"," "\"OperatorName\":\"FullyConnected\"," - "\"OperatorParams\":{\"kernel\":\"128\",\"mode\":\"CUDA\"}" + "\"OperatorParams\":{\"kernel\":\"128\"}" "}," "{" "\"NodeName\":\"FC2\"," "\"NextNodes\":\"LS\"," "\"OperatorName\":\"FullyConnected\"," - "\"OperatorParams\":{\"kernel\":\"10\",\"mode\":\"CUDA\"}" + "\"OperatorParams\":{\"kernel\":\"10\"}" "}," "{" @@ -115,7 +115,7 @@ int main(int argc, char* argv[]) return -1; } - string imgPath = "c:\\C++\\skyNet\\example\\mnist\\images\\"; + string imgPath = "c:\\cpp\\skyNet\\example\\mnist\\images\\"; int batchSz = 100, classCnt = 10, w = 28, h = 28; float lr = 0.001F; vector> imgName(classCnt); diff --git a/example/mnist/compare_PyTorch/Skynet.py b/example/mnist/compare_PyTorch/Skynet.py index ec4271a..a6010eb 100644 --- a/example/mnist/compare_PyTorch/Skynet.py +++ b/example/mnist/compare_PyTorch/Skynet.py @@ -9,23 +9,23 @@ # create net net = snNet.Net() net.addNode('In', snOperator.Input(), 'C1') \ - .addNode('C1', snOperator.Convolution(10, 0, snType.calcMode.CUDA), 'P1') \ - .addNode('P1', snOperator.Pooling(snType.calcMode.CUDA), 'C2') \ - .addNode('C2', snOperator.Convolution(20, 0, snType.calcMode.CUDA), 'P2') \ - .addNode('P2', snOperator.Pooling(snType.calcMode.CUDA), 'F1') \ - .addNode('F1', snOperator.FullyConnected(50, snType.calcMode.CUDA), 'F2') \ - .addNode('F2', snOperator.FullyConnected(10, snType.calcMode.CUDA), 'LS') \ + .addNode('C1', snOperator.Convolution(10, 0), 'P1') \ + .addNode('P1', snOperator.Pooling(), 'C2') \ + .addNode('C2', snOperator.Convolution(20, 0), 'P2') \ + .addNode('P2', snOperator.Pooling(), 'F1') \ + .addNode('F1', snOperator.FullyConnected(50), 'F2') \ + .addNode('F2', snOperator.FullyConnected(10), 'LS') \ .addNode('LS', snOperator.LossFunction(snType.lossType.softMaxToCrossEntropy), 'Output') # load of weight -# if (net.loadAllWeightFromFile('c:/C++/w.dat')): +# if (net.loadAllWeightFromFile('c:/cpp/w.dat')): # print('weight is load') # else: # print('error load weight') # loadImg imgList = [] -pathImg = 'c:/C++/skyNet/example/mnist/images/' +pathImg = 'c:/cpp/skyNet/example/mnist/images/' for i in range(10): imgList.append(os.listdir(pathImg + str(i))) @@ -70,7 +70,7 @@ print(datetime.datetime.now().strftime('%H:%M:%S'), n, "accurate", accuratSumm / (n + 1)) # save weight -if (net.saveAllWeightToFile('c:/C++/w.dat')): +if (net.saveAllWeightToFile('c:/cpp/w.dat')): print('weight is save') else: print('error save weight') \ No newline at end of file diff --git a/example/mnist/cpp_example.cpp b/example/mnist/cpp_example.cpp index 3bd7047..b0dc685 100644 --- a/example/mnist/cpp_example.cpp +++ b/example/mnist/cpp_example.cpp @@ -51,14 +51,14 @@ int main(int argc, char* argv[]){ sn::Net snet; snet.addNode("Input", sn::Input(), "C1") - .addNode("C1", sn::Convolution(15, 3, 0, 1, sn::batchNormType::none, sn::active::relu, sn::calcMode::CUDA), "C2") - .addNode("C2", sn::Convolution(15, 3, 0, 1, sn::batchNormType::none, sn::active::relu, sn::calcMode::CUDA), "P1") - .addNode("P1", sn::Pooling(sn::calcMode::CUDA), "FC1") - .addNode("FC1", sn::FullyConnected(128, sn::calcMode::CUDA), "FC2") - .addNode("FC2", sn::FullyConnected(10, sn::calcMode::CUDA), "LS") + .addNode("C1", sn::Convolution(15, 3, 0, 1, sn::batchNormType::none, sn::active::relu), "C2") + .addNode("C2", sn::Convolution(15, 3, 0, 1, sn::batchNormType::none, sn::active::relu), "P1") + .addNode("P1", sn::Pooling(), "FC1") + .addNode("FC1", sn::FullyConnected(128), "FC2") + .addNode("FC2", sn::FullyConnected(10), "LS") .addNode("LS", sn::LossFunction(sn::lossType::softMaxToCrossEntropy), "Output"); - string imgPath = "c://cpp//skyNet//example//mnist//images//"; + string imgPath = "c://cpp//other//skyNet//example//mnist//images//"; int batchSz = 100, classCnt = 10, w = 28, h = 28; float lr = 0.001F; vector> imgName(classCnt); @@ -71,7 +71,7 @@ int main(int argc, char* argv[]){ return -1; } - snet.loadAllWeightFromFile("c:\\cpp\\w.dat"); + //snet.loadAllWeightFromFile("c:\\cpp\\w.dat"); sn::Tensor inLayer(sn::snLSize(w, h, 1, batchSz)); @@ -81,7 +81,7 @@ int main(int argc, char* argv[]){ size_t sum_metric = 0; size_t num_inst = 0; float accuratSumm = 0; - for (int k = 0; k < 1000; ++k){ + for (int k = 0; k < 100; ++k){ targetLayer.clear(); @@ -151,6 +151,6 @@ int main(int argc, char* argv[]){ snet.saveAllWeightToFile("c:\\cpp\\w.dat"); - system("pause"); + // system("pause"); return 0; } diff --git a/example/mnist/python_example.py b/example/mnist/python_example.py index cf8ecf8..d861579 100644 --- a/example/mnist/python_example.py +++ b/example/mnist/python_example.py @@ -12,15 +12,15 @@ # create net net = snNet.Net() net.addNode('In', snOperator.Input(), 'C1') \ - .addNode('C1', snOperator.Convolution(15, 0, snType.calcMode.CPU), 'C2') \ - .addNode('C2', snOperator.Convolution(25, 0, snType.calcMode.CPU), 'P1') \ - .addNode('P1', snOperator.Pooling(snType.poolType.max, snType.calcMode.CPU), 'F1') \ - .addNode('F1', snOperator.FullyConnected(256, snType.calcMode.CPU), 'F2') \ - .addNode('F2', snOperator.FullyConnected(10, snType.calcMode.CPU), 'LS') \ + .addNode('C1', snOperator.Convolution(15, 0), 'C2') \ + .addNode('C2', snOperator.Convolution(25, 0), 'P1') \ + .addNode('P1', snOperator.Pooling(snType.poolType.max), 'F1') \ + .addNode('F1', snOperator.FullyConnected(256), 'F2') \ + .addNode('F2', snOperator.FullyConnected(10), 'LS') \ .addNode('LS', snOperator.LossFunction(snType.lossType.softMaxToCrossEntropy), 'Output') # load of weight -#if (net.loadAllWeightFromFile('c:/C++/w.dat')): +#if (net.loadAllWeightFromFile('c:/cpp/w.dat')): # print('weight is load') #else: # print('error load weight') diff --git a/example/resnet50/cpp_example.cpp b/example/resnet50/cpp_example.cpp index 7de9098..84215f2 100644 --- a/example/resnet50/cpp_example.cpp +++ b/example/resnet50/cpp_example.cpp @@ -6,6 +6,8 @@ #include #include + +#include #include "../cpp/snNet.h" #include "../cpp/snTensor.h" #include "../cpp/snOperator.h" @@ -21,21 +23,21 @@ using namespace std; namespace sn = SN_API; -void idntBlock(sn::Net& net, vector&& filters, uint32_t kernelSize, string oprName, string nextOprName, sn::calcMode mode){ +void idntBlock(sn::Net& net, vector&& filters, uint32_t kernelSize, string oprName, string nextOprName){ - net.addNode(oprName + "2a", sn::Convolution(filters[0], 1, 0, 1, sn::batchNormType::beforeActive, sn::active::relu, mode), oprName + "2b") - .addNode(oprName + "2b", sn::Convolution(filters[1], kernelSize, -1, 1, sn::batchNormType::beforeActive, sn::active::relu, mode), oprName + "2c") - .addNode(oprName + "2c", sn::Convolution(filters[2], 1, 0, 1, sn::batchNormType::beforeActive, sn::active::none, mode), oprName + "Sum"); + net.addNode(oprName + "2a", sn::Convolution(filters[0], 1, 0, 1, sn::batchNormType::beforeActive, sn::active::relu), oprName + "2b") + .addNode(oprName + "2b", sn::Convolution(filters[1], kernelSize, -1, 1, sn::batchNormType::beforeActive, sn::active::relu), oprName + "2c") + .addNode(oprName + "2c", sn::Convolution(filters[2], 1, 0, 1, sn::batchNormType::beforeActive, sn::active::none), oprName + "Sum"); net.addNode(oprName + "Sum", sn::Summator(sn::summatorType::summ), oprName + "Act") .addNode(oprName + "Act", sn::Activation(sn::active::relu), nextOprName); } -void convBlock(sn::Net& net, vector&& filters, uint32_t kernelSize, uint32_t stride, string oprName, string nextOprName, sn::calcMode mode){ +void convBlock(sn::Net& net, vector&& filters, uint32_t kernelSize, uint32_t stride, string oprName, string nextOprName){ - net.addNode(oprName + "2a", sn::Convolution(filters[0], 1, 0, stride, sn::batchNormType::beforeActive, sn::active::relu, mode), oprName + "2b") - .addNode(oprName + "2b", sn::Convolution(filters[1], kernelSize, -1, 1, sn::batchNormType::beforeActive, sn::active::relu, mode), oprName + "2c") - .addNode(oprName + "2c", sn::Convolution(filters[2], 1, 0, 1, sn::batchNormType::beforeActive, sn::active::none, mode), oprName + "Sum"); + net.addNode(oprName + "2a", sn::Convolution(filters[0], 1, 0, stride, sn::batchNormType::beforeActive, sn::active::relu), oprName + "2b") + .addNode(oprName + "2b", sn::Convolution(filters[1], kernelSize, -1, 1, sn::batchNormType::beforeActive, sn::active::relu), oprName + "2c") + .addNode(oprName + "2c", sn::Convolution(filters[2], 1, 0, 1, sn::batchNormType::beforeActive, sn::active::none), oprName + "Sum"); // shortcut net.addNode(oprName + "1", sn::Convolution(filters[2], 1, 0, stride, sn::batchNormType::beforeActive, sn::active::none), oprName + "Sum"); @@ -45,36 +47,36 @@ void convBlock(sn::Net& net, vector&& filters, uint32_t kernelSize, ui .addNode(oprName + "Act", sn::Activation(sn::active::relu), nextOprName); } -sn::Net createNet(sn::calcMode mode){ +sn::Net createNet(){ auto net = sn::Net(); net.addNode("In", sn::Input(), "conv1") - .addNode("conv1", sn::Convolution(64, 7, 3, 2, sn::batchNormType::beforeActive, sn::active::none, mode), "pool1_pad") - .addNode("pool1_pad", sn::Pooling(3, 2, sn::poolType::max, mode), "res2a_branch1 res2a_branch2a"); + .addNode("conv1", sn::Convolution(64, 7, 3, 2, sn::batchNormType::beforeActive, sn::active::none), "pool1_pad") + .addNode("pool1_pad", sn::Pooling(3, 2, sn::poolType::max), "res2a_branch1 res2a_branch2a"); - convBlock(net, vector{ 64, 64, 256 }, 3, 1, "res2a_branch", "res2b_branch2a res2b_branchSum", mode); - idntBlock(net, vector{ 64, 64, 256 }, 3, "res2b_branch", "res2c_branch2a res2c_branchSum", mode); - idntBlock(net, vector{ 64, 64, 256}, 3, "res2c_branch", "res3a_branch1 res3a_branch2a", mode); - - convBlock(net, vector{ 128, 128, 512 }, 3, 2, "res3a_branch", "res3b_branch2a res3b_branchSum", mode); - idntBlock(net, vector{ 128, 128, 512 }, 3, "res3b_branch", "res3c_branch2a res3c_branchSum", mode); - idntBlock(net, vector{ 128, 128, 512 }, 3, "res3c_branch", "res3d_branch2a res3d_branchSum", mode); - idntBlock(net, vector{ 128, 128, 512 }, 3, "res3d_branch", "res4a_branch1 res4a_branch2a", mode); - - convBlock(net, vector{ 256, 256, 1024 }, 3, 2, "res4a_branch", "res4b_branch2a res4b_branchSum", mode); - idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4b_branch", "res4c_branch2a res4c_branchSum", mode); - idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4c_branch", "res4d_branch2a res4d_branchSum", mode); - idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4d_branch", "res4e_branch2a res4e_branchSum", mode); - idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4e_branch", "res4f_branch2a res4f_branchSum", mode); - idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4f_branch", "res5a_branch1 res5a_branch2a", mode); - - convBlock(net, vector{ 512, 512, 2048 }, 3, 2, "res5a_branch", "res5b_branch2a res5b_branchSum", mode); - idntBlock(net, vector{ 512, 512, 2048 }, 3, "res5b_branch", "res5c_branch2a res5c_branchSum", mode); - idntBlock(net, vector{ 512, 512, 2048 }, 3, "res5c_branch", "avg_pool", mode); - - net.addNode("avg_pool", sn::Pooling(7, 7, sn::poolType::avg, mode), "fc1000") - .addNode("fc1000", sn::FullyConnected(1000, sn::active::none, mode), "LS") + convBlock(net, vector{ 64, 64, 256 }, 3, 1, "res2a_branch", "res2b_branch2a res2b_branchSum"); + idntBlock(net, vector{ 64, 64, 256 }, 3, "res2b_branch", "res2c_branch2a res2c_branchSum"); + idntBlock(net, vector{ 64, 64, 256}, 3, "res2c_branch", "res3a_branch1 res3a_branch2a"); + + convBlock(net, vector{ 128, 128, 512 }, 3, 2, "res3a_branch", "res3b_branch2a res3b_branchSum"); + idntBlock(net, vector{ 128, 128, 512 }, 3, "res3b_branch", "res3c_branch2a res3c_branchSum"); + idntBlock(net, vector{ 128, 128, 512 }, 3, "res3c_branch", "res3d_branch2a res3d_branchSum"); + idntBlock(net, vector{ 128, 128, 512 }, 3, "res3d_branch", "res4a_branch1 res4a_branch2a"); + + convBlock(net, vector{ 256, 256, 1024 }, 3, 2, "res4a_branch", "res4b_branch2a res4b_branchSum"); + idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4b_branch", "res4c_branch2a res4c_branchSum"); + idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4c_branch", "res4d_branch2a res4d_branchSum"); + idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4d_branch", "res4e_branch2a res4e_branchSum"); + idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4e_branch", "res4f_branch2a res4f_branchSum"); + idntBlock(net, vector{ 256, 256, 1024 }, 3, "res4f_branch", "res5a_branch1 res5a_branch2a"); + + convBlock(net, vector{ 512, 512, 2048 }, 3, 2, "res5a_branch", "res5b_branch2a res5b_branchSum"); + idntBlock(net, vector{ 512, 512, 2048 }, 3, "res5b_branch", "res5c_branch2a res5c_branchSum"); + idntBlock(net, vector{ 512, 512, 2048 }, 3, "res5c_branch", "avg_pool"); + + net.addNode("avg_pool", sn::Pooling(7, 7, sn::poolType::avg), "fc1000") + .addNode("fc1000", sn::FullyConnected(1000, sn::active::none), "LS") .addNode("LS", sn::LossFunction(sn::lossType::softMaxToCrossEntropy), "Output"); return net; @@ -82,7 +84,7 @@ sn::Net createNet(sn::calcMode mode){ int main(int argc, char* argv[]){ - sn::Net snet = createNet(sn::calcMode::CPU); + sn::Net snet = createNet(); // using python for create file 'resNet50Weights.dat' as: // CMD: cd c:\cpp\other\skyNet\example\resnet50\ @@ -94,7 +96,7 @@ int main(int argc, char* argv[]){ return -1; } - string imgPath = "c:/cpp/other/skyNet/example/resnet50/images/airplane.jpg"; + string imgPath = "c:/cpp/other/skyNet/example/resnet50/images/elephant.jpg"; int classCnt = 1000, w = 224, h = 224; @@ -134,16 +136,21 @@ int main(int argc, char* argv[]){ refData[r * w + c] = pt[c]; } - snet.forward(false, inLayer, outLayer); - float* refOutput = outLayer.data(); + for (int i = 0; i < 100; ++i){ + double ctm = omp_get_wtime(); + snet.forward(false, inLayer, outLayer); + cout << std::to_string(omp_get_wtime() - ctm) << endl; + + float* refOutput = outLayer.data(); - int maxInx = distance(refOutput, max_element(refOutput, refOutput + classCnt)); + int maxInx = distance(refOutput, max_element(refOutput, refOutput + classCnt)); - // for check: c:\cpp\other\skyNet\example\resnet50\imagenet_class_index.json + // for check: c:\cpp\other\skyNet\example\resnet50\imagenet_class_index.json - cout << "inx " << maxInx << " accurate " << refOutput[maxInx] << snet.getLastErrorStr() << endl; - - system("pause"); + cout << "inx " << maxInx << " accurate " << refOutput[maxInx] << snet.getLastErrorStr() << endl; + } + + // system("pause"); return 0; } diff --git a/example/resnet50/createNet.py b/example/resnet50/createNet.py index bdc9744..40925cf 100644 --- a/example/resnet50/createNet.py +++ b/example/resnet50/createNet.py @@ -4,7 +4,7 @@ def main(): ### Create net - net = snResNet50.createNet(snType.calcMode.CPU) + net = snResNet50.createNet() ### Set weight weightTF = snWeight.getResNet50Weights() diff --git a/example/resnet50/python_example.py b/example/resnet50/python_example.py index 2b2b285..ae89836 100644 --- a/example/resnet50/python_example.py +++ b/example/resnet50/python_example.py @@ -6,7 +6,7 @@ ### Create net -net = snResNet50.createNet(snType.calcMode.CPU) +net = snResNet50.createNet() ### Set weight weightTF = snWeight.getResNet50Weights() @@ -27,45 +27,18 @@ outAr = np.zeros((1, 1, 1, 1000), ctypes.c_float) +import time -net.forward(False, x.copy(), outAr) +for i in range(100): + ct = time.time() + net.forward(False, x.copy(), outAr) + print(time.time() - ct) mx = np.argmax(outAr[0]) # for check: c:\cpp\other\skyNet\example\resnet50\imagenet_class_index.json - print('Predicted:', mx, 'val', outAr[0][0][0][mx]) -for j in range(1000): - print(j, ' val ', outAr[0][0][0][j]) - -################################# - -# Compare with TF -#from keras.applications.resnet50 import ResNet50 -#from keras.applications.resnet50 import preprocess_input, decode_predictions -# -#model = ResNet50(weights='imagenet') -# -# -# img = image.load_img(img_path, target_size=(224, 224)) -#x = image.img_to_array(img) -#x = np.expand_dims(x, axis=0) -#x = preprocess_input(x) -# -#preds = model.predict(x) -#decode the results into a list of tuples (class, description, probability) -#(one such list for each sample in the batch) - -#mx = np.argmax(preds[0]) - -#print('Predicted:', mx, 'val', preds[0][mx]) - -#print('Predicted:', decode_predictions(preds, top=3)[0]) -#Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)] - -#for j in range(1000): -# print(j, ' val ', preds[0][j]) diff --git a/example/unet/c#_example.cs b/example/unet/c#_example.cs index baec513..aa7373b 100644 --- a/example/unet/c#_example.cs +++ b/example/unet/c#_example.cs @@ -34,41 +34,41 @@ static void Main(string[] args) Console.WriteLine("Version snlib " + ver); snet.addNode("In", new sn.Input(), "C1") - .addNode("C1", new sn.Convolution(10, -1, sn::calcMode.type.CUDA), "C2") - .addNode("C2", new sn.Convolution(10, 0, sn::calcMode.type.CUDA), "P1 Crop1") + .addNode("C1", new sn.Convolution(10, -1), "C2") + .addNode("C2", new sn.Convolution(10, 0), "P1 Crop1") .addNode("Crop1", new sn.Crop(new sn.rect(0, 0, 487, 487)), "Rsz1") .addNode("Rsz1", new sn.Resize(new sn.diap(0, 10), new sn.diap(0, 10)), "Conc1") - .addNode("P1", new sn.Pooling(sn.calcMode.type.CUDA), "C3") + .addNode("P1", new sn.Pooling(), "C3") - .addNode("C3", new sn.Convolution(10, -1, sn::calcMode.type.CUDA), "C4") - .addNode("C4", new sn.Convolution(10, 0, sn::calcMode.type.CUDA), "P2 Crop2") + .addNode("C3", new sn.Convolution(10, -1), "C4") + .addNode("C4", new sn.Convolution(10, 0), "P2 Crop2") .addNode("Crop2", new sn.Crop(new sn.rect(0, 0, 247, 247)), "Rsz2") .addNode("Rsz2", new sn.Resize(new sn.diap(0, 10), new sn.diap(0, 10)), "Conc2") - .addNode("P2", new sn.Pooling(sn.calcMode.type.CUDA), "C5") + .addNode("P2", new sn.Pooling(), "C5") - .addNode("C5", new sn.Convolution(10, 0, sn.calcMode.type.CUDA), "C6") - .addNode("C6", new sn.Convolution(10, 0, sn.calcMode.type.CUDA), "DC1") - .addNode("DC1", new sn.Deconvolution(10, sn.calcMode.type.CUDA), "Rsz3") + .addNode("C5", new sn.Convolution(10, 0, ), "C6") + .addNode("C6", new sn.Convolution(10, 0, ), "DC1") + .addNode("DC1", new sn.Deconvolution(10, ), "Rsz3") .addNode("Rsz3", new sn.Resize(new sn.diap(0, 10), new sn.diap(10, 20)), "Conc2") .addNode("Conc2", new sn.Concat("Rsz2 Rsz3"), "C7") - .addNode("C7", new sn.Convolution(10, 0, sn.calcMode.type.CUDA), "C8") - .addNode("C8", new sn.Convolution(10, 0, sn.calcMode.type.CUDA), "DC2") - .addNode("DC2", new sn.Deconvolution(10, sn.calcMode.type.CUDA), "Rsz4") + .addNode("C7", new sn.Convolution(10, 0, ), "C8") + .addNode("C8", new sn.Convolution(10, 0, ), "DC2") + .addNode("DC2", new sn.Deconvolution(10, ), "Rsz4") .addNode("Rsz4", new sn.Resize(new sn.diap(0, 10), new sn.diap(10, 20)), "Conc1") .addNode("Conc1", new sn.Concat("Rsz1 Rsz4"), "C9") - .addNode("C9", new sn.Convolution(10, 0, sn.calcMode.type.CUDA), "C10"); + .addNode("C9", new sn.Convolution(10, 0, ), "C10"); - sn.Convolution convOut = new sn.Convolution(1, 0, sn.calcMode.type.CUDA); + sn.Convolution convOut = new sn.Convolution(1, 0, ); convOut.act = new sn.active(sn.active.type.sigmoid); snet.addNode("C10", convOut, "LS") .addNode("LS", new sn.LossFunction(sn.lossType.type.binaryCrossEntropy), "Output"); - string imgPath = "c://C++//skyNet//example//unet//images//"; - string targPath = "c://C++//skyNet//example//unet//labels//"; + string imgPath = "c://cpp//skyNet//example//unet//images//"; + string targPath = "c://cpp//skyNet//example//unet//labels//"; uint batchSz = 3, w = 512, h = 512, wo = 483, ho = 483; float lr = 0.001F; @@ -83,7 +83,7 @@ static void Main(string[] args) return; } - string wpath = "c:/C++/w.dat"; + string wpath = "c:/cpp/w.dat"; // if (snet.loadAllWeightFromFile(wpath)) // Console.WriteLine("Load weight ok path: " + wpath); // else diff --git a/example/unet/cpp_example.cpp b/example/unet/cpp_example.cpp index beda431..e457a71 100644 --- a/example/unet/cpp_example.cpp +++ b/example/unet/cpp_example.cpp @@ -48,41 +48,41 @@ int main(int argc, char* argv[]){ sn::Net snet; snet.addNode("In", sn::Input(), "C1") - .addNode("C1", sn::Convolution(10, -1, sn::calcMode::CUDA), "C2") - .addNode("C2", sn::Convolution(10, 0, sn::calcMode::CUDA), "P1 Crop1") + .addNode("C1", sn::Convolution(10, -1), "C2") + .addNode("C2", sn::Convolution(10, 0), "P1 Crop1") .addNode("Crop1", sn::Crop(sn::rect(0, 0, 487, 487)), "Rsz1") .addNode("Rsz1", sn::Resize(sn::diap(0, 10), sn::diap(0, 10)), "Conc1") - .addNode("P1", sn::Pooling(sn::calcMode::CUDA), "C3") + .addNode("P1", sn::Pooling(), "C3") - .addNode("C3", sn::Convolution(10, -1, sn::calcMode::CUDA), "C4") - .addNode("C4", sn::Convolution(10, 0, sn::calcMode::CUDA), "P2 Crop2") + .addNode("C3", sn::Convolution(10, -1), "C4") + .addNode("C4", sn::Convolution(10, 0), "P2 Crop2") .addNode("Crop2", sn::Crop(sn::rect(0, 0, 247, 247)), "Rsz2") .addNode("Rsz2", sn::Resize(sn::diap(0, 10), sn::diap(0, 10)), "Conc2") - .addNode("P2", sn::Pooling(sn::calcMode::CUDA), "C5") + .addNode("P2", sn::Pooling(), "C5") - .addNode("C5", sn::Convolution(10, 0, sn::calcMode::CUDA), "C6") - .addNode("C6", sn::Convolution(10, 0, sn::calcMode::CUDA), "DC1") - .addNode("DC1", sn::Deconvolution(10, sn::calcMode::CUDA), "Rsz3") + .addNode("C5", sn::Convolution(10, 0), "C6") + .addNode("C6", sn::Convolution(10, 0), "DC1") + .addNode("DC1", sn::Deconvolution(10), "Rsz3") .addNode("Rsz3", sn::Resize(sn::diap(0, 10), sn::diap(10, 20)), "Conc2") .addNode("Conc2", sn::Concat("Rsz2 Rsz3"), "C7") - .addNode("C7", sn::Convolution(10, 0, sn::calcMode::CUDA), "C8") - .addNode("C8", sn::Convolution(10, 0, sn::calcMode::CUDA), "DC2") - .addNode("DC2", sn::Deconvolution(10, sn::calcMode::CUDA), "Rsz4") + .addNode("C7", sn::Convolution(10, 0), "C8") + .addNode("C8", sn::Convolution(10, 0), "DC2") + .addNode("DC2", sn::Deconvolution(10), "Rsz4") .addNode("Rsz4", sn::Resize(sn::diap(0, 10), sn::diap(10, 20)), "Conc1") .addNode("Conc1", sn::Concat("Rsz1 Rsz4"), "C9") - .addNode("C9", sn::Convolution(10, 0, sn::calcMode::CUDA), "C10"); + .addNode("C9", sn::Convolution(10, 0), "C10"); - sn::Convolution convOut(1, 0, sn::calcMode::CUDA); + sn::Convolution convOut(1, 0); convOut.act = sn::active::sigmoid; snet.addNode("C10", convOut, "LS") .addNode("LS", sn::LossFunction(sn::lossType::binaryCrossEntropy), "Output"); - string imgPath = "c://C++//skyNet//example//unet//images//"; - string labelPath = "c://C++//skyNet//example//unet//labels//"; + string imgPath = "c://cpp//skyNet//example//unet//images//"; + string labelPath = "c://cpp//skyNet//example//unet//labels//"; int batchSz = 10, w = 512, h = 512, wo = 483, ho = 483; float lr = 0.001F; vector imgName; diff --git a/example/unet/python_example.py b/example/unet/python_example.py index aefaf12..54d3361 100644 --- a/example/unet/python_example.py +++ b/example/unet/python_example.py @@ -1,9 +1,6 @@ import os -if os.name == 'nt': - os.chdir('C:\\Users\\work\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\libskynet') - from libskynet import* import numpy as np import imageio @@ -15,45 +12,45 @@ # create net net = snNet.Net() net.addNode("In", snOperator.Input(), "C1") \ - .addNode("C1", snOperator.Convolution(10, -1, snType.calcMode.CUDA), "C2") \ - .addNode("C2",snOperator.Convolution(10, 0, snType.calcMode.CUDA), "P1 Crop1") \ + .addNode("C1", snOperator.Convolution(10, -1), "C2") \ + .addNode("C2",snOperator.Convolution(10, 0), "P1 Crop1") \ .addNode("Crop1", snOperator.Crop(snType.rect(0, 0, 487, 487)), "Rsz1") \ .addNode("Rsz1", snOperator.Resize(snType.diap(0, 10), snType.diap(0, 10)), "Conc1") \ - .addNode("P1", snOperator.Pooling(snType.calcMode.CUDA), "C3") \ + .addNode("P1", snOperator.Pooling(), "C3") \ \ - .addNode("C3", snOperator.Convolution(10, -1, snType.calcMode.CUDA), "C4") \ - .addNode("C4", snOperator.Convolution(10, 0, snType.calcMode.CUDA), "P2 Crop2") \ + .addNode("C3", snOperator.Convolution(10, -1), "C4") \ + .addNode("C4", snOperator.Convolution(10, 0), "P2 Crop2") \ .addNode("Crop2", snOperator.Crop(snType.rect(0, 0, 247, 247)), "Rsz2") \ .addNode("Rsz2", snOperator.Resize(snType.diap(0, 10), snType.diap(0, 10)), "Conc2") \ - .addNode("P2", snOperator.Pooling(snType.calcMode.CUDA), "C5") \ + .addNode("P2", snOperator.Pooling(), "C5") \ \ - .addNode("C5", snOperator.Convolution(10, 0, snType.calcMode.CUDA), "C6") \ - .addNode("C6", snOperator.Convolution(10, 0, snType.calcMode.CUDA), "DC1") \ - .addNode("DC1", snOperator.Deconvolution(10, snType.calcMode.CUDA), "Rsz3") \ + .addNode("C5", snOperator.Convolution(10, 0), "C6") \ + .addNode("C6", snOperator.Convolution(10, 0), "DC1") \ + .addNode("DC1", snOperator.Deconvolution(10), "Rsz3") \ .addNode("Rsz3", snOperator.Resize(snType.diap(0, 10), snType.diap(10, 20)), "Conc2") \ \ .addNode("Conc2", snOperator.Concat("Rsz2 Rsz3"), "C7") \ \ - .addNode("C7", snOperator.Convolution(10, 0, snType.calcMode.CUDA), "C8") \ - .addNode("C8", snOperator.Convolution(10, 0, snType.calcMode.CUDA), "DC2") \ - .addNode("DC2", snOperator.Deconvolution(10, snType.calcMode.CUDA), "Rsz4") \ + .addNode("C7", snOperator.Convolution(10, 0), "C8") \ + .addNode("C8", snOperator.Convolution(10, 0), "DC2") \ + .addNode("DC2", snOperator.Deconvolution(10), "Rsz4") \ .addNode("Rsz4", snOperator.Resize(snType.diap(0, 10), snType.diap(10, 20)), "Conc1") \ \ .addNode("Conc1", snOperator.Concat("Rsz1 Rsz4"), "C9") \ \ - .addNode("C9", snOperator.Convolution(10, 0, snType.calcMode.CUDA), "C10") + .addNode("C9", snOperator.Convolution(10, 0), "C10") -convOut = snOperator.Convolution(1, 0, snType.calcMode.CUDA) +convOut = snOperator.Convolution(1, 0) convOut.act = snType.active.sigmoid; net.addNode("C10", convOut, "LS") \ .addNode('LS', snOperator.LossFunction(snType.lossType.binaryCrossEntropy), 'Output') # loadImg -pathImg = 'c:/C++/skyNet/example/unet/images/' +pathImg = 'c:/cpp/skyNet/example/unet/images/' imgList = os.listdir(pathImg) -pathLabel= 'c:/C++/skyNet/example/unet/labels/' +pathLabel= 'c:/cpp/skyNet/example/unet/labels/' labelsList = os.listdir(pathLabel) bsz = 5 diff --git a/mswin/VS12/snOperatorCUDA.vcxproj b/mswin/VS12/snOperatorCUDA.vcxproj index 0c954cb..660c08b 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj +++ b/mswin/VS12/snOperatorCUDA.vcxproj @@ -193,7 +193,6 @@ - @@ -243,6 +242,7 @@ + diff --git a/mswin/VS12/snOperatorCUDA.vcxproj.filters b/mswin/VS12/snOperatorCUDA.vcxproj.filters index dc1d4a2..fa7b9cf 100644 --- a/mswin/VS12/snOperatorCUDA.vcxproj.filters +++ b/mswin/VS12/snOperatorCUDA.vcxproj.filters @@ -102,9 +102,6 @@ Header Files - - Header Files - @@ -202,5 +199,8 @@ CUDA + + CUDA + \ No newline at end of file diff --git a/python/libskynet/snOperator.py b/python/libskynet/snOperator.py index 1238da6..010e907 100644 --- a/python/libskynet/snOperator.py +++ b/python/libskynet/snOperator.py @@ -46,9 +46,7 @@ class FullyConnected(): 'optimizer': optimizer.adam.value, # Optimizer of weights. Optional parameter 'dropOut': '0', # Random disconnection of neurons. Optional parameter [0..1.F] 'batchNorm': batchNormType.none.value, # Type of batch norm. Optional parameter - 'mode': calcMode.CPU.value, # Сalculation mode. Optional parameter 'gpuDeviceId': '0', # GPU Id. Optional parameter - 'gpuClearMem': '0', # Clear memory GPU. Optional parameter 'freeze': '0', # Do not change weights. Optional parameter 'useBias': '1', # +bias. Optional parameter 'weightInit': weightInit.he.value, # Type of initialization of weights. Optional parameter @@ -60,12 +58,9 @@ class FullyConnected(): def __init__(self, units, - act=active.relu, - mode=calcMode.CPU, - bnorm=batchNormType.none, - ): - self._params['units'] = str(units) - self._params['mode'] = mode.value + act=active.relu, + bnorm=batchNormType.none): + self._params['units'] = str(units) self._params['batchNorm'] = bnorm.value self._params['active'] = act.value @@ -90,9 +85,7 @@ class Convolution(): 'optimizer': optimizer.adam.value, # Optimizer of weights. Optional parameter 'dropOut': '0', # Random disconnection of neurons. Optional parameter [0..1.F] 'batchNorm': batchNormType.none.value, # Type of batch norm. Optional parameter - 'mode': calcMode.CPU.value, # Сalculation mode. Optional parameter 'gpuDeviceId': '0', # GPU Id. Optional parameter - 'gpuClearMem': '0', # Clear memory GPU. Optional parameter 'freeze': '0', # Do not change weights. Optional parameter 'useBias': '1', # +bias. Optional parameter 'weightInit': weightInit.he.value, # Type of initialization of weights. Optional parameter @@ -108,8 +101,7 @@ def __init__(self, padding=0, stride=1, bnorm=batchNormType.none, - act=active.relu, - mode=calcMode.CPU): + act=active.relu): self._params['filters'] = str(filters) self._params['fWidth'] = str(kernelSz[0]) self._params['fHeight'] = str(kernelSz[1]) @@ -117,8 +109,7 @@ def __init__(self, self._params['stride'] = str(stride) self._params['batchNorm'] = bnorm.value self._params['active'] = act.value - self._params['mode'] = mode.value - + def getParams(self): return self._params @@ -138,9 +129,7 @@ class Deconvolution(): 'optimizer': optimizer.adam.value, # Optimizer of weights. Optional parameter 'dropOut': '0', # Random disconnection of neurons. Optional parameter [0..1.F] 'batchNorm': batchNormType.none.value, # Type of batch norm. Optional parameter - 'mode': calcMode.CPU.value, # Сalculation mode. Optional parameter 'gpuDeviceId': '0', # GPU Id. Optional parameter - 'gpuClearMem': '0', # Clear memory GPU. Optional parameter 'freeze': '0', # Do not change weights. Optional parameter 'weightInit': weightInit.he.value, # Type of initialization of weights. Optional parameter 'decayMomentDW': '0.9', # Optimizer of weights moment change. Optional parameter [0..1.F] @@ -155,26 +144,20 @@ def __init__(self, opt=optimizer.adam, dropOut=0.0, bnorm=batchNormType.none, - mode=calcMode.CPU, - gpuDeviceId=0, - gpuClearMem=False, + gpuDeviceId=0, freeze=False): self._params['filters'] = str(filters) self._params['active'] = act.value self._params['optimizer'] = opt.value self._params['dropOut'] = str(dropOut) self._params['batchNorm'] = bnorm.value - self._params['mode'] = mode.value self._params['gpuDeviceId'] = str(gpuDeviceId) - self._params['gpuClearMem'] = '1' if gpuClearMem else '0' self._params['freeze'] = '1' if freeze else '0' def __init__(self, filters, - mode=calcMode.CPU, bnorm=batchNormType.none): self._params['filters'] = str(filters) - self._params['mode'] = mode.value self._params['batchNorm'] = bnorm.value def getParams(self): @@ -191,21 +174,17 @@ class Pooling(): 'kernel': '2', # Square Mask Size. Optional parameter (> 0) 'stride': '2', # Mask movement step. Optional parameter(> 0) 'pool': poolType.max.value, # Operator Type. Optional parameter - 'mode': calcMode.CPU.value, # Сalculation mode. Optional parameter - 'gpuDeviceId': '0', # GPU Id. Optional parameter - 'gpuClearMem': '0' # Clear memory GPU. Optional parameter - } + 'gpuDeviceId': '0' # GPU Id. Optional parameter + } def __init__(self, kernel=2, stride=2, - pool=poolType.max, - mode=calcMode.CPU): + pool=poolType.max): self._params['kernel'] = str(kernel) self._params['stride'] = str(stride) self._params['pool'] = pool.value - self._params['mode'] = mode.value - + def getParams(self): return self._params diff --git a/python/libskynet/snResNet50.py b/python/libskynet/snResNet50.py index bacc8fc..ad99f2e 100644 --- a/python/libskynet/snResNet50.py +++ b/python/libskynet/snResNet50.py @@ -8,8 +8,7 @@ def _idntBlock(net, kernelSize, filters, oprName, - nextOprName, - mode: snType.calcMode): + nextOprName): """The identity block is the block that has no conv layer at shortcut. # Arguments @@ -25,21 +24,21 @@ def _idntBlock(net, 0, # no padding 1, snType.batchNormType.beforeActive, - snType.active.relu, mode), + snType.active.relu), oprName + '2b') \ .addNode(oprName + '2b', snOperator.Convolution(filters[1], kernelSize, -1, # same padding 1, snType.batchNormType.beforeActive, - snType.active.relu, mode), + snType.active.relu), oprName + '2c') \ .addNode(oprName + '2c', snOperator.Convolution(filters[2], (1, 1), 0, # no padding 1, snType.batchNormType.beforeActive, - snType.active.none, mode), + snType.active.none), oprName + 'Sum') \ # summator net.addNode(oprName + 'Sum', snOperator.Summator(snType.summatorType.summ), oprName + 'Act') \ @@ -51,8 +50,7 @@ def _convBlock(net, filters, stride, oprName, - nextOprName, - mode: snType.calcMode): + nextOprName): """A block that has a conv layer at shortcut. # Arguments @@ -69,21 +67,21 @@ def _convBlock(net, 0, # no padding stride, snType.batchNormType.beforeActive, - snType.active.relu, mode), + snType.active.relu), oprName + '2b') \ .addNode(oprName + '2b', snOperator.Convolution(filters[1], kernelSize, -1, # same padding 1, snType.batchNormType.beforeActive, - snType.active.relu, mode), + snType.active.relu), oprName + '2c') \ .addNode(oprName + '2c', snOperator.Convolution(filters[2], (1, 1), 0, # no padding 1, snType.batchNormType.beforeActive, - snType.active.none, mode), + snType.active.none), oprName + 'Sum') \ # shortcut net.addNode(oprName + '1', snOperator.Convolution(filters[2], @@ -91,14 +89,14 @@ def _convBlock(net, 0, # no padding stride, snType.batchNormType.beforeActive, - snType.active.none, mode), + snType.active.none), oprName + 'Sum') \ # summator net.addNode(oprName + 'Sum', snOperator.Summator(snType.summatorType.summ), oprName + 'Act') \ .addNode(oprName + 'Act', snOperator.Activation(snType.active.relu), nextOprName) -def createNet(mode = snType.calcMode.CPU): +def createNet(): """ Create net :return: net @@ -108,48 +106,48 @@ def createNet(mode = snType.calcMode.CPU): net.addNode('In', snOperator.Input(), 'conv1') \ .addNode('conv1', - snOperator.Convolution(64, (7, 7), 3, 2, snType.batchNormType.beforeActive, snType.active.none, mode), + snOperator.Convolution(64, (7, 7), 3, 2, snType.batchNormType.beforeActive, snType.active.none), 'pool1_pad') \ - .addNode('pool1_pad', snOperator.Pooling(3, 2, snType.poolType.max, mode), 'res2a_branch1 res2a_branch2a') + .addNode('pool1_pad', snOperator.Pooling(3, 2, snType.poolType.max), 'res2a_branch1 res2a_branch2a') _convBlock(net, (3, 3), filters=[64, 64, 256], stride=1, oprName='res2a_branch', - nextOprName='res2b_branch2a res2b_branchSum', mode=mode) + nextOprName='res2b_branch2a res2b_branchSum') _idntBlock(net, (3, 3), filters=[64, 64, 256], oprName='res2b_branch', - nextOprName='res2c_branch2a res2c_branchSum', mode=mode) + nextOprName='res2c_branch2a res2c_branchSum') _idntBlock(net, (3, 3), filters=[64, 64, 256], oprName='res2c_branch', - nextOprName='res3a_branch1 res3a_branch2a', mode=mode) + nextOprName='res3a_branch1 res3a_branch2a') _convBlock(net, (3, 3), filters=[128, 128, 512], stride=2, oprName='res3a_branch', - nextOprName='res3b_branch2a res3b_branchSum', mode=mode) + nextOprName='res3b_branch2a res3b_branchSum') _idntBlock(net, (3, 3), filters=[128, 128, 512], oprName='res3b_branch', - nextOprName='res3c_branch2a res3c_branchSum', mode=mode) + nextOprName='res3c_branch2a res3c_branchSum') _idntBlock(net, (3, 3), filters=[128, 128, 512], oprName='res3c_branch', - nextOprName='res3d_branch2a res3d_branchSum', mode=mode) + nextOprName='res3d_branch2a res3d_branchSum') _idntBlock(net, (3, 3), filters=[128, 128, 512], oprName='res3d_branch', - nextOprName='res4a_branch1 res4a_branch2a', mode=mode) + nextOprName='res4a_branch1 res4a_branch2a') _convBlock(net, (3, 3), filters=[256, 256, 1024], stride=2, oprName='res4a_branch', - nextOprName='res4b_branch2a res4b_branchSum', mode=mode) + nextOprName='res4b_branch2a res4b_branchSum') _idntBlock(net, (3, 3), filters=[256, 256, 1024], oprName='res4b_branch', - nextOprName='res4c_branch2a res4c_branchSum', mode=mode) + nextOprName='res4c_branch2a res4c_branchSum') _idntBlock(net, (3, 3), filters=[256, 256, 1024], oprName='res4c_branch', - nextOprName='res4d_branch2a res4d_branchSum', mode=mode) + nextOprName='res4d_branch2a res4d_branchSum') _idntBlock(net, (3, 3), filters=[256, 256, 1024], oprName='res4d_branch', - nextOprName='res4e_branch2a res4e_branchSum', mode=mode) + nextOprName='res4e_branch2a res4e_branchSum') _idntBlock(net, (3, 3), filters=[256, 256, 1024], oprName='res4e_branch', - nextOprName='res4f_branch2a res4f_branchSum', mode=mode) + nextOprName='res4f_branch2a res4f_branchSum') _idntBlock(net, (3, 3), filters=[256, 256, 1024], oprName='res4f_branch', - nextOprName='res5a_branch1 res5a_branch2a', mode=mode) + nextOprName='res5a_branch1 res5a_branch2a') _convBlock(net, (3, 3), filters=[512, 512, 2048], stride=2, oprName='res5a_branch', - nextOprName='res5b_branch2a res5b_branchSum', mode=mode) + nextOprName='res5b_branch2a res5b_branchSum') _idntBlock(net, (3, 3), filters=[512, 512, 2048], oprName='res5b_branch', - nextOprName='res5c_branch2a res5c_branchSum', mode=mode) + nextOprName='res5c_branch2a res5c_branchSum') _idntBlock(net, (3, 3), filters=[512, 512, 2048], oprName='res5c_branch', - nextOprName='avg_pool', mode=mode) + nextOprName='avg_pool') - net.addNode('avg_pool', snOperator.Pooling(7, 7, snType.poolType.avg, mode), 'fc1000') \ - .addNode('fc1000', snOperator.FullyConnected(1000, snType.active.none, mode), 'LS') \ + net.addNode('avg_pool', snOperator.Pooling(7, 7, snType.poolType.avg), 'fc1000') \ + .addNode('fc1000', snOperator.FullyConnected(1000, snType.active.none), 'LS') \ .addNode('LS', snOperator.LossFunction(snType.lossType.softMaxToCrossEntropy), 'Output') return net diff --git a/python/libskynet/snType.py b/python/libskynet/snType.py index aff0b1c..c8608a9 100644 --- a/python/libskynet/snType.py +++ b/python/libskynet/snType.py @@ -60,12 +60,6 @@ class poolType(Enum): max = 'max' avg = 'avg' -class calcMode(Enum): - """Calc mode.""" - CPU = 'CPU' - CUDA = 'CUDA' - #OpenCL = 'OpenCL' - class lockType(Enum): """Lock type.""" lock = 'lock' diff --git a/src/snOperatorCPU/src/Operator/lossFunction.cpp b/src/snOperatorCPU/src/Operator/lossFunction.cpp index c2f56f6..1309a4b 100644 --- a/src/snOperatorCPU/src/Operator/lossFunction.cpp +++ b/src/snOperatorCPU/src/Operator/lossFunction.cpp @@ -149,8 +149,7 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ snSize tsz = inTns.size(); - snSize grsz = baseGrad_.size(); - if (grsz != tsz) + if (inTns != baseGrad_) baseGrad_.resize(tsz); auto smOut = baseOut_.getDataCPU(); diff --git a/src/snOperatorCUDA/src/CUDA/crop.cu b/src/snOperatorCUDA/src/CUDA/crop.cu new file mode 100644 index 0000000..24e3232 --- /dev/null +++ b/src/snOperatorCUDA/src/CUDA/crop.cu @@ -0,0 +1,77 @@ + +#include + +#include "../stdafx.h" +#include "../structurs.h" + +using namespace SN_Base; + + +__global__ void cropIn2Out(roi roi, snSize srcSz, snFloat* in, snFloat* out){ + + size_t srcStp = srcSz.w * srcSz.h, + dstStp = roi.w * roi.h; + + in += roi.x + roi.y * srcSz.w + srcStp * blockIdx.x * blockIdx.y; + out += dstStp * blockIdx.x * blockIdx.y; + + // gridDim.x - srcSz.d + // gridDim.y - srcSz.n + + unsigned int i = threadIdx.x; // blockDim.x <= roi.h + while (i < roi.h){ + + snFloat* pIn = in + i * srcSz.w, + * pOut = out + i * roi.w; + + unsigned int j = threadIdx.y; // blockDim.y <= roi.w + while (j < roi.w){ + + pOut[j] = pIn[j]; + + j += blockDim.y; + } + + i += blockDim.x; + } +} + +__global__ void cropOut2In(roi roi, snSize srcSz, snFloat* in, snFloat* out){ + + size_t srcStp = srcSz.w * srcSz.h, + dstStp = roi.w * roi.h; + + in += roi.x + roi.y * srcSz.w + srcStp * blockIdx.x * blockIdx.y; + out += dstStp * blockIdx.x * blockIdx.y; + + // gridDim.x - srcSz.d + // gridDim.y - srcSz.n + + unsigned int i = threadIdx.x; // blockDim.x <= roi.h + while (i < roi.h){ + + snFloat* pIn = in + i * srcSz.w, + * pOut = out + i * roi.w; + + unsigned int j = threadIdx.y; // blockDim.y <= roi.w + while (j < roi.w){ + + pIn[j] = pOut[j]; + + j += blockDim.y; + } + + i += blockDim.x; + } +} + +void crop(bool inToOut, const roi& roi, const snSize& sz, snFloat* in, snFloat* out){ + + dim3 dimBlock(16, 16); + dim3 dimGrid(int(sz.d), int(sz.n)); + + if (inToOut) + cropIn2Out << < dimGrid, dimBlock >> >(roi, sz, in, out); + else + cropOut2In << < dimGrid, dimBlock >> >(roi, sz, in, out); +} diff --git a/src/snOperatorCUDA/src/CUDA/lossFunctions.cu b/src/snOperatorCUDA/src/CUDA/lossFunctions.cu index e933d5a..4f236f1 100644 --- a/src/snOperatorCUDA/src/CUDA/lossFunctions.cu +++ b/src/snOperatorCUDA/src/CUDA/lossFunctions.cu @@ -29,17 +29,16 @@ using namespace std; using namespace SN_Base; -__global__ void softMaxACrossEntropy(snSize iosz, snFloat* inout){ +__global__ void softMaxACrossEntropyFwd(snSize iosz, snFloat* inout){ size_t inStepByD = iosz.w * iosz.h, // step out by input inStepByN = inStepByD * iosz.d; // step out by batch // gridDim.x - number of out layers - // gridDim.y - batch size - + inout += blockIdx.x * inStepByN; - __shared__ int tmax; + __shared__ unsigned long long int tmax; __shared__ snFloat tsumm; tmax = 0; @@ -51,7 +50,7 @@ __global__ void softMaxACrossEntropy(snSize iosz, snFloat* inout){ while (i < inStepByN){ - atomicMax(&tmax, int(inout[i])); + atomicMax(&tmax, unsigned long long int(inout[i] * 100.F)); i += blockDim.x; } @@ -60,7 +59,7 @@ __global__ void softMaxACrossEntropy(snSize iosz, snFloat* inout){ while (i < inStepByN){ - inout[i] = (inout[i] - tmax > -20) ? exp(inout[i] - tmax) : 0.1E-8F; + inout[i] = ((inout[i] - tmax / 100.F) > -20) ? exp(inout[i] - tmax / 100.F) : 0.1E-8F; atomicAdd(&tsumm, inout[i]); @@ -77,20 +76,110 @@ __global__ void softMaxACrossEntropy(snSize iosz, snFloat* inout){ } } -void lossForward(const snSize& insz, snFloat* inout, lossType loss){ +__global__ void softMaxACrossEntropyBwd(snSize iosz, snFloat* out, snFloat* targ, snFloat* grad){ + + size_t inStepByD = iosz.w * iosz.h, // step out by input + inStepByN = inStepByD * iosz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + grad += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + out += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + targ += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + unsigned int i = threadIdx.x; + + while (i < inStepByD){ + + grad[i] = out[i] - targ[i]; + + i += blockDim.x; + } +} + +__global__ void binaryCrossEntropyBwd(snSize iosz, snFloat* out, snFloat* targ, snFloat* grad){ + + size_t inStepByD = iosz.w * iosz.h, // step out by input + inStepByN = inStepByD * iosz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + grad += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + out += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + targ += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + unsigned int i = threadIdx.x; + + while (i < inStepByD){ + + grad[i] = (out[i] - targ[i]) / (out[i] * (1.F - out[i])); + + i += blockDim.x; + } +} + +__global__ void regressionMSEBwd(snSize iosz, snFloat* out, snFloat* targ, snFloat* grad){ + + size_t inStepByD = iosz.w * iosz.h, // step out by input + inStepByN = inStepByD * iosz.d; // step out by batch + + // gridDim.x - number of out layers + // gridDim.y - batch size + + grad += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + out += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + targ += blockIdx.x * inStepByD + blockIdx.y * inStepByN; + + unsigned int i = threadIdx.x; + + while (i < inStepByD){ + + grad[i] = 2 * (out[i] - targ[i]) / inStepByN; + + i += blockDim.x; + } +} + + +void lossForward(const snSize& sz, snFloat* inout, lossType loss){ dim3 dimBlock(256); - dim3 dimGrid(int(insz.n)); + dim3 dimGrid(int(sz.n)); switch (loss){ case lossType::softMaxACrossEntropy: - softMaxACrossEntropy <<>>(insz, inout); + softMaxACrossEntropyFwd <<>>(sz, inout); + break; + + case lossType::binaryCrossEntropy: + break; + + case lossType::regressionMSE: break; } } -void lossBackward(const Tensor& inTns, snFloat* out, snFloat* targ, snFloat* grad, lossType loss){ +void lossBackward(const snSize& sz, snFloat* out, snFloat* targ, snFloat* grad, lossType loss){ - - + dim3 dimBlock(128); + dim3 dimGrid(int(sz.d), int(sz.n)); + + switch (loss){ + case lossType::softMaxACrossEntropy: + + softMaxACrossEntropyBwd << > >(sz, out, targ, grad); + break; + + case lossType::binaryCrossEntropy: + + binaryCrossEntropyBwd << > >(sz, out, targ, grad); + break; + + case lossType::regressionMSE: // Mean Square Error + + regressionMSEBwd << > >(sz, out, targ, grad); + break; + } } diff --git a/src/snOperatorCUDA/src/Operator/crop.cpp b/src/snOperatorCUDA/src/Operator/crop.cpp index a5d0d6d..ddc33aa 100644 --- a/src/snOperatorCUDA/src/Operator/crop.cpp +++ b/src/snOperatorCUDA/src/Operator/crop.cpp @@ -45,6 +45,9 @@ OperatorBase(net, name, node, prms){ ERROR_MESS("no set param 'roi'"); } +// CUDA/crop.cu +void crop(bool inToOut, const roi& roi, const snSize& sz, snFloat* in, snFloat* out); + std::vector Crop::Do(const operationParam& operPrm, const std::vector& neighbOpr){ if (operPrm.action == snAction::forward){ @@ -77,7 +80,7 @@ std::vector Crop::Do(const operationParam& operPrm, const std::vect snFloat* src = baseOut_.getDataGPU(), * dst = tns.getDataGPU(); - copyTo(true, roi_, baseSz_, src, dst); + crop(true, roi_, baseSz_, src, dst); baseOut_ = tns; } @@ -99,7 +102,7 @@ std::vector Crop::Do(const operationParam& operPrm, const std::vect snFloat* dst = tns.getDataGPU(), * src = baseGrad_.getDataGPU(); - copyTo(false, roi_, baseSz_, dst, src); + crop(false, roi_, baseSz_, dst, src); baseGrad_ = tns; } @@ -107,41 +110,4 @@ std::vector Crop::Do(const operationParam& operPrm, const std::vect return vector(); } -void Crop::copyTo(bool inToOut, const roi& roi, const snSize& srcSz, snFloat* in, snFloat* out){ - - size_t bsz = srcSz.d * srcSz.n, srcStp = srcSz.w * srcSz.h, dstStp = roi.w * roi.h; - - if (inToOut){ - for (size_t i = 0; i < bsz; ++i){ - - snFloat* pIn = in + roi.x + roi.y * srcSz.w + srcStp * i; - snFloat* pOut = out + dstStp * i; - - for (size_t j = 0; j < roi.h; ++j){ - - for (size_t k = 0; k < roi.w; ++k) - pOut[k] = pIn[k]; - - pIn += srcSz.w; - pOut += roi.w; - } - } - } - else{ - for (size_t i = 0; i < bsz; ++i){ - - snFloat* pIn = in + roi.x + roi.y * srcSz.w + srcStp * i; - snFloat* pOut = out + dstStp * i; - - for (size_t j = 0; j < roi.h; ++j){ - - for (size_t k = 0; k < roi.w; ++k) - pIn[k] = pOut[k]; - - pIn += srcSz.w; - pOut += roi.w; - } - } - } -} diff --git a/src/snOperatorCUDA/src/Operator/crop.h b/src/snOperatorCUDA/src/Operator/crop.h index aaa3553..231ae3a 100644 --- a/src/snOperatorCUDA/src/Operator/crop.h +++ b/src/snOperatorCUDA/src/Operator/crop.h @@ -25,6 +25,7 @@ #pragma once #include "snBase/snBase.h" +#include "../structurs.h" /// Trimming data @@ -39,16 +40,7 @@ class Crop final : SN_Base::OperatorBase{ std::vector Do(const SN_Base::operationParam&, const std::vector& neighbOpr) override; private: - - struct roi{ - size_t x, y, w, h; - - roi(size_t x_ = 0, size_t y_ = 0, size_t w_ = 0, size_t h_ = 0) : - x(x_), y(y_), w(w_), h(h_){} - }; - + roi roi_; SN_Base::snSize baseSz_; - - void copyTo(bool inToOut, const roi& roi, const SN_Base::snSize& srcSz, SN_Base::snFloat* in, SN_Base::snFloat* out); }; diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp index 2efee50..cd1a5c4 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -79,6 +79,12 @@ std::vector LossFunction::Do(const operationParam& operPrm, const s return std::vector(); } +// CUDA/lossFunctions.cu +void lossForward(const SN_Base::snSize& insz, SN_Base::snFloat* inout, lossType loss); + +// CUDA/lossFunctions.cu +void lossBackward(const SN_Base::snSize& outsz, SN_Base::snFloat* out, SN_Base::snFloat* targ, SN_Base::snFloat* grad, lossType loss); + void LossFunction::forward(const Tensor& inTns){ baseOut_ = inTns; @@ -105,63 +111,28 @@ void LossFunction::backward(const Tensor& inTns, const operationParam& operPrm){ snSize tsz = inTns.size(); - snSize grsz = baseGrad_.size(); - if (grsz != tsz) - baseGrad_.resize(tsz); - - auto smOut = baseOut_.getDataGPU(); - auto target = inTns.getDataGPU(); - auto grad = baseGrad_.getDataGPU(); - - switch (lossType_){ - case lossType::softMaxACrossEntropy:{ - - size_t nsz = tsz.size(); - for (size_t i = 0; i < nsz; ++i) - grad[i] = smOut[i] - target[i]; - - break; - } - - case lossType::binaryCrossEntropy:{ + if (inTns != baseGrad_) + baseGrad_.resize(tsz); + + if (lossType_ != lossType::userLoss){ - size_t nsz = tsz.size(); - for (size_t i = 0; i < nsz; ++i) - grad[i] = (smOut[i] - target[i]) / (smOut[i] * (1.F - smOut[i])); - - break; - } - // Mean Square Error - case lossType::regressionMSE:{ - - size_t nsz = tsz.size(); - for (size_t i = 0; i < nsz; ++i){ - - grad[i] = 2 * (smOut[i] - target[i]) / nsz; - } + auto smOut = baseOut_.getDataGPU(); + auto target = inTns.getDataGPU(); + auto grad = baseGrad_.getDataGPU(); - break; + lossBackward(tsz, smOut, target, grad, lossType_); } - - case lossType::userLoss:{ + else{ snSize outSz; snFloat* outData = nullptr; g_userCBack(this, basePrms_["cbackName"], node_, - false, inTns.size(), inTns.getDataCPU(), outSz, &outData); + false, tsz, inTns.getDataCPU(), outSz, &outData); if (outData){ baseGrad_.setDataCPU(outData, outSz); } else ERROR_MESS("not set 'outData' in userCBack"); - - break; - } - - - default: - ERROR_MESS("param 'lossType' indefined"); - break; } } diff --git a/src/snOperatorCUDA/src/lossFunctions.h b/src/snOperatorCUDA/src/lossFunctions.h index d4f171d..9188cc8 100644 --- a/src/snOperatorCUDA/src/lossFunctions.h +++ b/src/snOperatorCUDA/src/lossFunctions.h @@ -27,6 +27,3 @@ #include "snBase/snBase.h" #include "structurs.h" -void lossForward(const SN_Base::snSize& insz, SN_Base::snFloat* inout, lossType loss); - -void lossBackward(const SN_Base::Tensor& inTns, SN_Base::snFloat* out, SN_Base::snFloat* targ, SN_Base::snFloat* grad, lossType loss); diff --git a/src/snOperatorCUDA/src/structurs.h b/src/snOperatorCUDA/src/structurs.h index 4efc570..594dc47 100644 --- a/src/snOperatorCUDA/src/structurs.h +++ b/src/snOperatorCUDA/src/structurs.h @@ -71,4 +71,11 @@ enum class lossType{ binaryCrossEntropy = 1, regressionMSE = 2, userLoss = 3, +}; + +struct roi{ + size_t x, y, w, h; + + roi(size_t x_ = 0, size_t y_ = 0, size_t w_ = 0, size_t h_ = 0) : + x(x_), y(y_), w(w_), h(h_){} }; \ No newline at end of file From e3b295b0da4b25bcd140b03ba0bf277bc0340c83 Mon Sep 17 00:00:00 2001 From: Tyill Date: Thu, 25 Jul 2019 16:20:15 +0500 Subject: [PATCH 20/35] -develop --- src/snOperatorCUDA/src/lossFunctions.h | 29 -------------------------- 1 file changed, 29 deletions(-) delete mode 100644 src/snOperatorCUDA/src/lossFunctions.h diff --git a/src/snOperatorCUDA/src/lossFunctions.h b/src/snOperatorCUDA/src/lossFunctions.h deleted file mode 100644 index 9188cc8..0000000 --- a/src/snOperatorCUDA/src/lossFunctions.h +++ /dev/null @@ -1,29 +0,0 @@ -// -// SkyNet Project -// Copyright (C) 2018 by Contributors -// -// This code is licensed under the MIT License. -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files(the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and / or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions : -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -// THE SOFTWARE. -// -#pragma once - -#include "snBase/snBase.h" -#include "structurs.h" - From 2df0cb61b65339e22dfb9db38fe476ca9e75cd70 Mon Sep 17 00:00:00 2001 From: Tyill Date: Thu, 25 Jul 2019 18:06:40 +0500 Subject: [PATCH 21/35] -develop --- src/snOperatorCUDA/src/CUDA/common.cu | 21 ++++++++++--------- src/snOperatorCUDA/src/Operator/concat.cpp | 12 +++++------ .../src/Operator/convolution.cpp | 16 +++++++------- .../src/Operator/deconvolution.cpp | 16 +++++++------- .../src/Operator/fullyConnected.cpp | 16 +++++++------- src/snOperatorCUDA/src/Operator/resize.cpp | 13 ++++++------ src/snOperatorCUDA/src/cudaCommon.h | 6 ++++-- 7 files changed, 52 insertions(+), 48 deletions(-) diff --git a/src/snOperatorCUDA/src/CUDA/common.cu b/src/snOperatorCUDA/src/CUDA/common.cu index 23dbca8..1ef44f2 100644 --- a/src/snOperatorCUDA/src/CUDA/common.cu +++ b/src/snOperatorCUDA/src/CUDA/common.cu @@ -66,18 +66,19 @@ snFloat* memRealloc(size_t csz, size_t nsz, snFloat* data, int initVal){ return data; } -void memCpyCPU2GPU(size_t dstSz, SN_Base::snFloat* dstGPU, size_t srcSz, SN_Base::snFloat* srcCPU){ - - ASSERT_MESS(dstSz == srcSz, ""); - - cuAssert(cudaMemcpy(dstGPU, srcCPU, srcSz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyHostToDevice)); +void memCpyCPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcCPU){ + + cuAssert(cudaMemcpy(dstGPU, srcCPU, sz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyHostToDevice)); } + +void memCpyGPU2CPU(size_t sz, SN_Base::snFloat* dstCPU, SN_Base::snFloat* srcGPU){ -void memCpyGPU2CPU(size_t dstSz, SN_Base::snFloat* dstCPU, size_t srcSz, SN_Base::snFloat* srcGPU){ - - ASSERT_MESS(dstSz == srcSz, ""); - - cuAssert(cudaMemcpy(dstCPU, srcGPU, srcSz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToHost)); + cuAssert(cudaMemcpy(dstCPU, srcGPU, sz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToHost)); +} + +void memCpyGPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcGPU){ + + cuAssert(cudaMemcpy(dstGPU, srcGPU, sz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); } void memFree(snFloat* data){ diff --git a/src/snOperatorCUDA/src/Operator/concat.cpp b/src/snOperatorCUDA/src/Operator/concat.cpp index 77e6bfc..7f99dcc 100644 --- a/src/snOperatorCUDA/src/Operator/concat.cpp +++ b/src/snOperatorCUDA/src/Operator/concat.cpp @@ -23,6 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" +#include "../cudaCommon.h" #include "snOperatorCUDA/src/Operator/concat.h" #include "snAux/auxFunc.h" @@ -89,20 +90,19 @@ std::vector Concat::Do(const operationParam& operPrm, const std::ve baseOut_.resize(snSize(csz.w, csz.h, csz.d + nbsz.d, csz.n)); size_t sz = csz.w * csz.h * (csz.d + nbsz.d), - cstp = csz.w * csz.h * csz.d, - nstp = nbsz.w * nbsz.h * nbsz.d; + cstp = csz.w * csz.h * csz.d, + nstp = nbsz.w * nbsz.h * nbsz.d; for (size_t j = 0; j < csz.n; ++j){ snFloat* dst = baseOut_.getDataGPU() + sz * j, - *src = buff.getDataGPU() + cstp * j; - - memcpy(dst, src, cstp * sizeof(snFloat)); + * src = buff.getDataGPU() + cstp * j; + memCpyGPU2GPU(cstp, dst, src); dst += cstp; src = neighb->getOutput().getDataGPU() + nstp * j; - memcpy(dst, src, nstp * sizeof(snFloat)); + memCpyGPU2GPU(nstp, dst, src); } } } diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index a45bf95..4248e54 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -174,10 +174,10 @@ bool Convolution::setBatchNorm(const batchNorm& bn){ baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); - memCpyCPU2GPU(osz, baseBatchNorm_.mean, osz, bn.mean); - memCpyCPU2GPU(osz, baseBatchNorm_.varce, osz, bn.varce); - memCpyCPU2GPU(osz, baseBatchNorm_.scale, osz, bn.scale); - memCpyCPU2GPU(osz, baseBatchNorm_.schift, osz, bn.schift); + memCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); + memCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); + memCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); + memCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); baseBatchNorm_.sz = bn.sz; @@ -193,10 +193,10 @@ batchNorm Convolution::getBatchNorm()const{ auxCPUParams_["bn_scale"] = vector(csz); auxCPUParams_["bn_schift"] = vector(csz); - memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), csz, baseBatchNorm_.mean); - memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), csz, baseBatchNorm_.varce); - memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), csz, baseBatchNorm_.scale); - memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), csz, baseBatchNorm_.schift); + memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); + memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); + memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); + memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); batchNorm bn; bn.mean = auxCPUParams_["bn_mean"].data(); diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp index c6857c2..8237187 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -162,10 +162,10 @@ bool Deconvolution::setBatchNorm(const batchNorm& bn){ baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); - memCpyCPU2GPU(osz, baseBatchNorm_.mean, osz, bn.mean); - memCpyCPU2GPU(osz, baseBatchNorm_.varce, osz, bn.varce); - memCpyCPU2GPU(osz, baseBatchNorm_.scale, osz, bn.scale); - memCpyCPU2GPU(osz, baseBatchNorm_.schift, osz, bn.schift); + memCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); + memCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); + memCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); + memCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); baseBatchNorm_.sz = bn.sz; @@ -181,10 +181,10 @@ SN_Base::batchNorm Deconvolution::getBatchNorm()const{ auxCPUParams_["bn_scale"] = vector(csz); auxCPUParams_["bn_schift"] = vector(csz); - memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), csz, baseBatchNorm_.mean); - memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), csz, baseBatchNorm_.varce); - memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), csz, baseBatchNorm_.scale); - memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), csz, baseBatchNorm_.schift); + memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); + memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); + memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); + memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); batchNorm bn; bn.mean = auxCPUParams_["bn_mean"].data(); diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index 79d9f5d..f7ea990 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -168,10 +168,10 @@ bool FullyConnected::setBatchNorm(const batchNorm& bn){ baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); - memCpyCPU2GPU(osz, baseBatchNorm_.mean, osz, bn.mean); - memCpyCPU2GPU(osz, baseBatchNorm_.varce, osz, bn.varce); - memCpyCPU2GPU(osz, baseBatchNorm_.scale, osz, bn.scale); - memCpyCPU2GPU(osz, baseBatchNorm_.schift, osz, bn.schift); + memCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); + memCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); + memCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); + memCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); baseBatchNorm_.sz = bn.sz; @@ -187,10 +187,10 @@ batchNorm FullyConnected::getBatchNorm()const{ auxCPUParams_["bn_scale"] = vector(csz); auxCPUParams_["bn_schift"] = vector(csz); - memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), csz, baseBatchNorm_.mean); - memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), csz, baseBatchNorm_.varce); - memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), csz, baseBatchNorm_.scale); - memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), csz, baseBatchNorm_.schift); + memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); + memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); + memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); + memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); batchNorm bn; bn.mean = auxCPUParams_["bn_mean"].data(); diff --git a/src/snOperatorCUDA/src/Operator/resize.cpp b/src/snOperatorCUDA/src/Operator/resize.cpp index 1c23d0d..a691926 100644 --- a/src/snOperatorCUDA/src/Operator/resize.cpp +++ b/src/snOperatorCUDA/src/Operator/resize.cpp @@ -23,6 +23,7 @@ // THE SOFTWARE. // #include "../stdafx.h" +#include "../cudaCommon.h" #include "snOperatorCUDA/src/Operator/resize.h" #include "snAux/auxFunc.h" @@ -69,14 +70,14 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve baseOut_.resize(snSize(csz.w, csz.h, (end - begin), csz.n)); size_t sz = csz.w * csz.h * (end - begin), - offset = csz.w * csz.h * begin, - cstp = csz.w * csz.h * csz.d; + offset = csz.w * csz.h * begin, + cstp = csz.w * csz.h * csz.d; for (size_t j = 0; j < csz.n; ++j){ snFloat* dst = baseOut_.getDataGPU() + sz * j, - *src = buff.getDataGPU() + cstp * j + offset; + * src = buff.getDataGPU() + cstp * j + offset; - memcpy(dst, src, sz * sizeof(snFloat)); + memCpyGPU2GPU(sz, dst, src); } } } @@ -120,9 +121,9 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve for (size_t j = 0; j < csz.n; ++j){ snFloat* dst = baseGrad_.getDataGPU() + sz * j, - *src = buff.getDataGPU() + cstp * j + offset; + * src = buff.getDataGPU() + cstp * j + offset; - memcpy(dst, src, sz * sizeof(snFloat)); + memCpyGPU2GPU(sz, dst, src); } } } diff --git a/src/snOperatorCUDA/src/cudaCommon.h b/src/snOperatorCUDA/src/cudaCommon.h index 4ea416a..0f20214 100644 --- a/src/snOperatorCUDA/src/cudaCommon.h +++ b/src/snOperatorCUDA/src/cudaCommon.h @@ -30,8 +30,10 @@ SN_Base::snFloat* memAlloc(size_t sz, int initVal); SN_Base::snFloat* memRealloc(size_t csz, size_t nsz, SN_Base::snFloat*, int initVal); -void memCpyCPU2GPU(size_t dstSz, SN_Base::snFloat* dstGPU, size_t srcSz, SN_Base::snFloat* srcCPU); +void memCpyCPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcCPU); -void memCpyGPU2CPU(size_t dstSz, SN_Base::snFloat* dstCPU, size_t srcSz, SN_Base::snFloat* srcGPU); +void memCpyGPU2CPU(size_t sz, SN_Base::snFloat* dstCPU, SN_Base::snFloat* srcGPU); + +void memCpyGPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcGPU); void memFree(SN_Base::snFloat*); From fb342a9aab31753d4559e3a1c00a1fddebb48047 Mon Sep 17 00:00:00 2001 From: Tyill Date: Fri, 26 Jul 2019 09:00:43 +0500 Subject: [PATCH 22/35] -develop --- src/snOperatorCUDA/src/CUDA/common.cu | 19 +++-- src/snOperatorCUDA/src/Operator/concat.cpp | 4 +- .../src/Operator/convolution.cpp | 54 ++++++------ .../src/Operator/deconvolution.cpp | 83 ++++++++++--------- .../src/Operator/deconvolution.h | 3 +- .../src/Operator/fullyConnected.cpp | 54 ++++++------ .../src/Operator/lossFunction.cpp | 1 - src/snOperatorCUDA/src/Operator/pooling.cpp | 4 +- src/snOperatorCUDA/src/Operator/resize.cpp | 4 +- src/snOperatorCUDA/src/cudaCommon.h | 16 ++-- 10 files changed, 126 insertions(+), 116 deletions(-) diff --git a/src/snOperatorCUDA/src/CUDA/common.cu b/src/snOperatorCUDA/src/CUDA/common.cu index 1ef44f2..1606e62 100644 --- a/src/snOperatorCUDA/src/CUDA/common.cu +++ b/src/snOperatorCUDA/src/CUDA/common.cu @@ -29,12 +29,12 @@ using namespace SN_Base; -void setDeviceId(int id){ +void cuSetDeviceId(int id){ cuAssert(cudaSetDevice(id)); } -snFloat* memAlloc(size_t sz, int initVal){ +snFloat* cuMemAlloc(size_t sz, int initVal){ snFloat* mem = nullptr; cuAssert(cudaMalloc(&mem, sz * sizeof(snFloat))); @@ -44,7 +44,7 @@ snFloat* memAlloc(size_t sz, int initVal){ return mem; } -snFloat* memRealloc(size_t csz, size_t nsz, snFloat* data, int initVal){ +snFloat* cuMemRealloc(size_t csz, size_t nsz, snFloat* data, int initVal){ ASSERT_MESS(nsz > 0, ""); @@ -66,22 +66,27 @@ snFloat* memRealloc(size_t csz, size_t nsz, snFloat* data, int initVal){ return data; } -void memCpyCPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcCPU){ +void cuMemSet(size_t sz, snFloat* data, int val){ + + cuAssert(cudaMemset(data, val, sz * sizeof(snFloat))); +} + +void cuMemCpyCPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcCPU){ cuAssert(cudaMemcpy(dstGPU, srcCPU, sz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyHostToDevice)); } -void memCpyGPU2CPU(size_t sz, SN_Base::snFloat* dstCPU, SN_Base::snFloat* srcGPU){ +void cuMemCpyGPU2CPU(size_t sz, SN_Base::snFloat* dstCPU, SN_Base::snFloat* srcGPU){ cuAssert(cudaMemcpy(dstCPU, srcGPU, sz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToHost)); } -void memCpyGPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcGPU){ +void cuMemCpyGPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcGPU){ cuAssert(cudaMemcpy(dstGPU, srcGPU, sz * sizeof(snFloat), cudaMemcpyKind::cudaMemcpyDeviceToDevice)); } -void memFree(snFloat* data){ +void cuMemFree(snFloat* data){ cuAssert(cudaFree(data)); } \ No newline at end of file diff --git a/src/snOperatorCUDA/src/Operator/concat.cpp b/src/snOperatorCUDA/src/Operator/concat.cpp index 7f99dcc..bd443ab 100644 --- a/src/snOperatorCUDA/src/Operator/concat.cpp +++ b/src/snOperatorCUDA/src/Operator/concat.cpp @@ -97,12 +97,12 @@ std::vector Concat::Do(const operationParam& operPrm, const std::ve snFloat* dst = baseOut_.getDataGPU() + sz * j, * src = buff.getDataGPU() + cstp * j; - memCpyGPU2GPU(cstp, dst, src); + cuMemCpyGPU2GPU(cstp, dst, src); dst += cstp; src = neighb->getOutput().getDataGPU() + nstp * j; - memCpyGPU2GPU(nstp, dst, src); + cuMemCpyGPU2GPU(nstp, dst, src); } } } diff --git a/src/snOperatorCUDA/src/Operator/convolution.cpp b/src/snOperatorCUDA/src/Operator/convolution.cpp index 4248e54..5c85556 100644 --- a/src/snOperatorCUDA/src/Operator/convolution.cpp +++ b/src/snOperatorCUDA/src/Operator/convolution.cpp @@ -46,7 +46,7 @@ Convolution::Convolution(void* net, const string& name, const string& node, std: Convolution::~Convolution(){ - setDeviceId(gpuDeviceId_); + cuSetDeviceId(gpuDeviceId_); freeParamCUDA(convGPUParams_); } @@ -169,15 +169,15 @@ bool Convolution::setBatchNorm(const batchNorm& bn){ size_t csz = baseBatchNorm_.sz.size(), osz = bn.sz.size(); - baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); - baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); - baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); - baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); + baseBatchNorm_.mean = cuMemRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = cuMemRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = cuMemRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = cuMemRealloc(csz, osz, baseBatchNorm_.schift, 0); - memCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); - memCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); - memCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); - memCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); baseBatchNorm_.sz = bn.sz; @@ -193,10 +193,10 @@ batchNorm Convolution::getBatchNorm()const{ auxCPUParams_["bn_scale"] = vector(csz); auxCPUParams_["bn_schift"] = vector(csz); - memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); - memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); - memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); - memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); batchNorm bn; bn.mean = auxCPUParams_["bn_mean"].data(); @@ -211,7 +211,7 @@ batchNorm Convolution::getBatchNorm()const{ std::vector Convolution::Do(const operationParam& operPrm, const std::vector& neighbOpr){ - setDeviceId(gpuDeviceId_); + cuSetDeviceId(gpuDeviceId_); if (operPrm.action == snAction::forward){ @@ -350,9 +350,9 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz){ } // +bias? - /* if (!useBias_){ - memset(baseWeight_.getDataCPU() + stp * kernel, 0, kernel * sizeof(snFloat)); - }*/ + if (!convPrms_.useBias_){ + cuMemSet(kernel, baseWeight_.getDataGPU() + stp * kernel, 0); + } snSize outSz(0, 0, kernel, newsz.n); @@ -387,24 +387,24 @@ void Convolution::updateConfig(bool isLern, const snSize& newsz){ baseGrad_.resize(newsz); // aux array - auxGPUParams_["dWeight"] = memRealloc(0, ntp, auxGPUParams_["dWeight"], 0); - auxGPUParams_["dWPrev"] = memRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); - auxGPUParams_["dWGrad"] = memRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); + auxGPUParams_["dWeight"] = cuMemRealloc(0, ntp, auxGPUParams_["dWeight"], 0); + auxGPUParams_["dWPrev"] = cuMemRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); + auxGPUParams_["dWGrad"] = cuMemRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); } size_t csz = baseBatchNorm_.sz.w * baseBatchNorm_.sz.h * baseBatchNorm_.sz.d, osz = outSz.w * outSz.h * outSz.d; if (batchNormType_ != batchNormType::none){ - baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); - baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); - baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); - baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); + baseBatchNorm_.mean = cuMemRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = cuMemRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = cuMemRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = cuMemRealloc(csz, osz, baseBatchNorm_.schift, 0); if (isLern){ - baseBatchNorm_.norm = memRealloc(csz * outSz.n, osz * outSz.n, baseBatchNorm_.mean, 0); - baseBatchNorm_.dScale = memRealloc(csz, osz, baseBatchNorm_.dScale, 0); - baseBatchNorm_.dSchift = memRealloc(csz, osz, baseBatchNorm_.dSchift, 0); + baseBatchNorm_.norm = cuMemRealloc(csz * outSz.n, osz * outSz.n, baseBatchNorm_.norm, 0); + baseBatchNorm_.dScale = cuMemRealloc(csz, osz, baseBatchNorm_.dScale, 0); + baseBatchNorm_.dSchift = cuMemRealloc(csz, osz, baseBatchNorm_.dSchift, 0); } baseBatchNorm_.sz = outSz; diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.cpp b/src/snOperatorCUDA/src/Operator/deconvolution.cpp index 8237187..0d4f1d7 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.cpp +++ b/src/snOperatorCUDA/src/Operator/deconvolution.cpp @@ -45,7 +45,7 @@ Deconvolution::Deconvolution(void* net, const string& name, const string& node, Deconvolution::~Deconvolution(){ - setDeviceId(gpuDeviceId_); + cuSetDeviceId(gpuDeviceId_); freeParamCUDA(gpuParams_); } @@ -66,11 +66,14 @@ void Deconvolution::load(std::map& prms){ ERROR_MESS("not found (or not numder) param '" + name + "'"); }; - setIntParam("filters", false, true, deconvParams_.kernel); - setIntParam("fWidth", false, false, deconvParams_.fWidth); - setIntParam("fHeight", false, false, deconvParams_.fHeight); + setIntParam("filters", false, true, deconvPrms_.kernel); + setIntParam("fWidth", false, false, deconvPrms_.fWidth); + setIntParam("fHeight", false, false, deconvPrms_.fHeight); - setIntParam("stride", false, false, deconvParams_.stride); + if (prms.find("useBias") != prms.end()) + deconvPrms_.useBias_ = prms["useBias"] == "1"; + + setIntParam("stride", false, false, deconvPrms_.stride); if (prms.find("batchNorm") != prms.end()){ @@ -157,15 +160,15 @@ bool Deconvolution::setBatchNorm(const batchNorm& bn){ size_t csz = baseBatchNorm_.sz.size(), osz = bn.sz.size(); - baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); - baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); - baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); - baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); + baseBatchNorm_.mean = cuMemRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = cuMemRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = cuMemRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = cuMemRealloc(csz, osz, baseBatchNorm_.schift, 0); - memCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); - memCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); - memCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); - memCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); baseBatchNorm_.sz = bn.sz; @@ -181,10 +184,10 @@ SN_Base::batchNorm Deconvolution::getBatchNorm()const{ auxCPUParams_["bn_scale"] = vector(csz); auxCPUParams_["bn_schift"] = vector(csz); - memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); - memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); - memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); - memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); batchNorm bn; bn.mean = auxCPUParams_["bn_mean"].data(); @@ -199,7 +202,7 @@ SN_Base::batchNorm Deconvolution::getBatchNorm()const{ std::vector Deconvolution::Do(const operationParam& operPrm, const std::vector& neighbOpr){ - setDeviceId(gpuDeviceId_); + cuSetDeviceId(gpuDeviceId_); if (operPrm.action == snAction::forward){ @@ -247,7 +250,7 @@ void Deconvolution::forward(const SN_Base::Tensor& inTns, const operationParam& snSize outsz = baseOut_.size(); // calculation - forwardCUDA(deconvParams_, weight, insz, pInTns, outsz, out, gpuParams_); + forwardCUDA(deconvPrms_, weight, insz, pInTns, outsz, out, gpuParams_); /// dropOut if (dropOut_ > 0.F) @@ -296,7 +299,7 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& snFloat* dWeight = auxGPUParams_["dWeight"]; // calculation - backwardCUDA_GW(deconvParams_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); + backwardCUDA_GW(deconvPrms_, weight, insz, in, outsz, gradIn, grOut, dWeight, gpuParams_); snFloat* dWPrev = auxGPUParams_["dWPrev"]; snFloat* dWGrad = auxGPUParams_["dWGrad"]; @@ -314,14 +317,14 @@ void Deconvolution::backward(const SN_Base::Tensor& inTns, const operationParam& optimizerType_); } else{ // isFreeze - backwardCUDA_G(deconvParams_, weight, insz, outsz, gradIn, grOut, gpuParams_); + backwardCUDA_G(deconvPrms_, weight, insz, outsz, gradIn, grOut, gpuParams_); } } void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ - size_t stp = deconvParams_.fWidth * deconvParams_.fHeight * deconvParams_.kernel, + size_t stp = deconvPrms_.fWidth * deconvPrms_.fHeight * deconvPrms_.kernel, ntp = (stp + 1) * newsz.d; // leave the existing weights as they are, initialize the remainder @@ -331,19 +334,19 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ baseWeight_.resize(snSize(newsz.d, stp + 1)); if (wcsz == 0) - weightInit(baseWeight_, ntp - wcsz, stp + 1, deconvParams_.kernel, weightInitType_); + weightInit(baseWeight_, ntp - wcsz, stp + 1, deconvPrms_.kernel, weightInitType_); } // +bias? - /* if (!useBias_){ - memset(baseWeight_.getDataCPU() + stp * kernel, 0, kernel * sizeof(snFloat)); - }*/ + if (!deconvPrms_.useBias_){ + cuMemSet(newsz.d, baseWeight_.getDataGPU() + stp * newsz.d, 0); + } - snSize outSz(0, 0, deconvParams_.kernel, newsz.n); + snSize outSz(0, 0, deconvPrms_.kernel, newsz.n); - outSz.w = (newsz.w - 1) * deconvParams_.stride + deconvParams_.fWidth; - outSz.h = (newsz.h - 1) * deconvParams_.stride + deconvParams_.fHeight; + outSz.w = (newsz.w - 1) * deconvPrms_.stride + deconvPrms_.fWidth; + outSz.h = (newsz.h - 1) * deconvPrms_.stride + deconvPrms_.fHeight; baseOut_.resize(outSz); @@ -351,31 +354,31 @@ void Deconvolution::updateConfig(bool isLern, const snSize& newsz){ baseGrad_.resize(newsz); // aux array - auxGPUParams_["dWeight"] = memRealloc(0, ntp, auxGPUParams_["dWeight"], 0); - auxGPUParams_["dWPrev"] = memRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); - auxGPUParams_["dWGrad"] = memRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); + auxGPUParams_["dWeight"] = cuMemRealloc(0, ntp, auxGPUParams_["dWeight"], 0); + auxGPUParams_["dWPrev"] = cuMemRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); + auxGPUParams_["dWGrad"] = cuMemRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); } size_t csz = baseBatchNorm_.sz.w * baseBatchNorm_.sz.h * baseBatchNorm_.sz.d, osz = outSz.w * outSz.h * outSz.d; if (batchNormType_ != batchNormType::none){ - baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); - baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); - baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); - baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); + baseBatchNorm_.mean = cuMemRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = cuMemRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = cuMemRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = cuMemRealloc(csz, osz, baseBatchNorm_.schift, 0); if (isLern){ - baseBatchNorm_.norm = memRealloc(csz * outSz.n, osz * outSz.n, baseBatchNorm_.mean, 0); - baseBatchNorm_.dScale = memRealloc(csz, osz, baseBatchNorm_.dScale, 0); - baseBatchNorm_.dSchift = memRealloc(csz, osz, baseBatchNorm_.dSchift, 0); + baseBatchNorm_.norm = cuMemRealloc(csz * outSz.n, osz * outSz.n, baseBatchNorm_.norm, 0); + baseBatchNorm_.dScale = cuMemRealloc(csz, osz, baseBatchNorm_.dScale, 0); + baseBatchNorm_.dSchift = cuMemRealloc(csz, osz, baseBatchNorm_.dSchift, 0); } baseBatchNorm_.sz = outSz; baseBatchNorm_.sz.n = 1; } - iniParamCUDA(isLern, newsz, outSz, deconvParams_, &gpuParams_); + iniParamCUDA(isLern, newsz, outSz, deconvPrms_, &gpuParams_); } diff --git a/src/snOperatorCUDA/src/Operator/deconvolution.h b/src/snOperatorCUDA/src/Operator/deconvolution.h index 6f99d29..d38c2c2 100644 --- a/src/snOperatorCUDA/src/Operator/deconvolution.h +++ b/src/snOperatorCUDA/src/Operator/deconvolution.h @@ -51,8 +51,9 @@ class Deconvolution final : SN_Base::OperatorBase{ size_t fWidth = 3; ///< mask width size_t fHeight = 3; ///< mask height size_t stride = 1; ///< mask step + bool useBias_ = true; }; - deconvParams deconvParams_; + deconvParams deconvPrms_; activeType activeType_ = activeType::relu; ///< activation type optimizerType optimizerType_ = optimizerType::adam; ///< optimizer diff --git a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp index f7ea990..e93794e 100644 --- a/src/snOperatorCUDA/src/Operator/fullyConnected.cpp +++ b/src/snOperatorCUDA/src/Operator/fullyConnected.cpp @@ -45,7 +45,7 @@ FullyConnected::FullyConnected(void* net, const string& name, const string& node FullyConnected::~FullyConnected(){ - setDeviceId(gpuDeviceId_); + cuSetDeviceId(gpuDeviceId_); freeParamCUDA(gpuParams_); } @@ -86,10 +86,10 @@ void FullyConnected::load(std::map& prms){ if (batchNormType_ != batchNormType::none){ - baseBatchNorm_.mean = memRealloc(0, kernel_, baseBatchNorm_.mean, 0); - baseBatchNorm_.varce = memRealloc(0, kernel_, baseBatchNorm_.varce, 1); - baseBatchNorm_.scale = memRealloc(0, kernel_, baseBatchNorm_.scale, 1); - baseBatchNorm_.schift = memRealloc(0, kernel_, baseBatchNorm_.schift, 0); + baseBatchNorm_.mean = cuMemRealloc(0, kernel_, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = cuMemRealloc(0, kernel_, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = cuMemRealloc(0, kernel_, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = cuMemRealloc(0, kernel_, baseBatchNorm_.schift, 0); baseBatchNorm_.sz = snSize(kernel_); } @@ -163,15 +163,15 @@ bool FullyConnected::setBatchNorm(const batchNorm& bn){ size_t csz = baseBatchNorm_.sz.size(), osz = bn.sz.size(); - baseBatchNorm_.mean = memRealloc(csz, osz, baseBatchNorm_.mean, 0); - baseBatchNorm_.varce = memRealloc(csz, osz, baseBatchNorm_.varce, 1); - baseBatchNorm_.scale = memRealloc(csz, osz, baseBatchNorm_.scale, 1); - baseBatchNorm_.schift = memRealloc(csz, osz, baseBatchNorm_.schift, 0); + baseBatchNorm_.mean = cuMemRealloc(csz, osz, baseBatchNorm_.mean, 0); + baseBatchNorm_.varce = cuMemRealloc(csz, osz, baseBatchNorm_.varce, 1); + baseBatchNorm_.scale = cuMemRealloc(csz, osz, baseBatchNorm_.scale, 1); + baseBatchNorm_.schift = cuMemRealloc(csz, osz, baseBatchNorm_.schift, 0); - memCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); - memCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); - memCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); - memCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.mean, bn.mean); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.varce, bn.varce); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.scale, bn.scale); + cuMemCpyCPU2GPU(osz, baseBatchNorm_.schift, bn.schift); baseBatchNorm_.sz = bn.sz; @@ -187,10 +187,10 @@ batchNorm FullyConnected::getBatchNorm()const{ auxCPUParams_["bn_scale"] = vector(csz); auxCPUParams_["bn_schift"] = vector(csz); - memCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); - memCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); - memCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); - memCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_mean"].data(), baseBatchNorm_.mean); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_varce"].data(), baseBatchNorm_.varce); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_scale"].data(), baseBatchNorm_.scale); + cuMemCpyGPU2CPU(csz, auxCPUParams_["bn_schift"].data(), baseBatchNorm_.schift); batchNorm bn; bn.mean = auxCPUParams_["bn_mean"].data(); @@ -205,7 +205,7 @@ batchNorm FullyConnected::getBatchNorm()const{ std::vector FullyConnected::Do(const operationParam& operPrm, const std::vector& neighbOpr){ - setDeviceId(gpuDeviceId_); + cuSetDeviceId(gpuDeviceId_); if (operPrm.action == snAction::forward){ @@ -332,9 +332,9 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ } // +bias? - /* if (!useBias_){ - memset(baseWeight_.getDataCPU() + stp * kernel, 0, kernel * sizeof(snFloat)); - }*/ + if (!useBias_){ + cuMemSet(kernel_, baseWeight_.getDataGPU() + stp * kernel_, 0); + } baseOut_.resize(snSize(kernel_, 1, 1, newsz.n)); @@ -342,17 +342,17 @@ void FullyConnected::updateConfig(bool isLern, const snSize& newsz){ baseGrad_.resize(newsz); // aux array - auxGPUParams_["dWeight"] = memRealloc(0, ntp, auxGPUParams_["dWeight"], 0); - auxGPUParams_["dWPrev"] = memRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); - auxGPUParams_["dWGrad"] = memRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); + auxGPUParams_["dWeight"] = cuMemRealloc(0, ntp, auxGPUParams_["dWeight"], 0); + auxGPUParams_["dWPrev"] = cuMemRealloc(0, ntp, auxGPUParams_["dWPrev"], 0); + auxGPUParams_["dWGrad"] = cuMemRealloc(0, ntp, auxGPUParams_["dWGrad"], 0); if (batchNormType_ != batchNormType::none){ auto bcsz = baseBatchNorm_.sz.size(); - baseBatchNorm_.norm = memRealloc(bcsz, newsz.n * kernel_, baseBatchNorm_.norm, 0); - baseBatchNorm_.dScale = memRealloc(bcsz, kernel_, baseBatchNorm_.dScale, 0); - baseBatchNorm_.dSchift = memRealloc(bcsz, kernel_, baseBatchNorm_.dSchift, 0); + baseBatchNorm_.norm = cuMemRealloc(bcsz, newsz.n * kernel_, baseBatchNorm_.norm, 0); + baseBatchNorm_.dScale = cuMemRealloc(bcsz, kernel_, baseBatchNorm_.dScale, 0); + baseBatchNorm_.dSchift = cuMemRealloc(bcsz, kernel_, baseBatchNorm_.dSchift, 0); baseBatchNorm_.sz = snSize(kernel_); } diff --git a/src/snOperatorCUDA/src/Operator/lossFunction.cpp b/src/snOperatorCUDA/src/Operator/lossFunction.cpp index cd1a5c4..f53f110 100644 --- a/src/snOperatorCUDA/src/Operator/lossFunction.cpp +++ b/src/snOperatorCUDA/src/Operator/lossFunction.cpp @@ -23,7 +23,6 @@ // THE SOFTWARE. // #include "../stdafx.h" -#include "../lossFunctions.h" #include "snOperatorCUDA/src/Operator/lossFunction.h" using namespace std; diff --git a/src/snOperatorCUDA/src/Operator/pooling.cpp b/src/snOperatorCUDA/src/Operator/pooling.cpp index 29c262a..0988f47 100644 --- a/src/snOperatorCUDA/src/Operator/pooling.cpp +++ b/src/snOperatorCUDA/src/Operator/pooling.cpp @@ -40,7 +40,7 @@ Pooling::Pooling(void* net, const string& name, const string& node, std::map& prms){ std::vector Pooling::Do(const operationParam& operPrm, const std::vector& neighbOpr){ - setDeviceId(gpuDeviceId_); + cuSetDeviceId(gpuDeviceId_); if (operPrm.action == snAction::forward){ diff --git a/src/snOperatorCUDA/src/Operator/resize.cpp b/src/snOperatorCUDA/src/Operator/resize.cpp index a691926..abfb60e 100644 --- a/src/snOperatorCUDA/src/Operator/resize.cpp +++ b/src/snOperatorCUDA/src/Operator/resize.cpp @@ -77,7 +77,7 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve snFloat* dst = baseOut_.getDataGPU() + sz * j, * src = buff.getDataGPU() + cstp * j + offset; - memCpyGPU2GPU(sz, dst, src); + cuMemCpyGPU2GPU(sz, dst, src); } } } @@ -123,7 +123,7 @@ std::vector Resize::Do(const operationParam& operPrm, const std::ve snFloat* dst = baseGrad_.getDataGPU() + sz * j, * src = buff.getDataGPU() + cstp * j + offset; - memCpyGPU2GPU(sz, dst, src); + cuMemCpyGPU2GPU(sz, dst, src); } } } diff --git a/src/snOperatorCUDA/src/cudaCommon.h b/src/snOperatorCUDA/src/cudaCommon.h index 0f20214..a39d288 100644 --- a/src/snOperatorCUDA/src/cudaCommon.h +++ b/src/snOperatorCUDA/src/cudaCommon.h @@ -24,16 +24,18 @@ // #pragma once -void setDeviceId(int); +void cuSetDeviceId(int); -SN_Base::snFloat* memAlloc(size_t sz, int initVal); +SN_Base::snFloat* cuMemAlloc(size_t sz, int initVal); -SN_Base::snFloat* memRealloc(size_t csz, size_t nsz, SN_Base::snFloat*, int initVal); +SN_Base::snFloat* cuMemRealloc(size_t csz, size_t nsz, SN_Base::snFloat*, int initVal); -void memCpyCPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcCPU); +void cuMemSet(size_t sz, SN_Base::snFloat* data, int val); -void memCpyGPU2CPU(size_t sz, SN_Base::snFloat* dstCPU, SN_Base::snFloat* srcGPU); +void cuMemCpyCPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcCPU); + +void cuMemCpyGPU2CPU(size_t sz, SN_Base::snFloat* dstCPU, SN_Base::snFloat* srcGPU); -void memCpyGPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcGPU); +void cuMemCpyGPU2GPU(size_t sz, SN_Base::snFloat* dstGPU, SN_Base::snFloat* srcGPU); -void memFree(SN_Base::snFloat*); +void cuMemFree(SN_Base::snFloat*); From af6a89d9572b383756f7aa0eb29fc9aac35c665c Mon Sep 17 00:00:00 2001 From: Tyill Date: Fri, 26 Jul 2019 14:04:52 +0500 Subject: [PATCH 23/35] -develop --- mswin/Dependences/x86/libgcc_s_dw2-1.dll | Bin 121460 -> 0 bytes mswin/Dependences/x86/libgfortran-3.dll | Bin 1914705 -> 0 bytes mswin/Dependences/x86/libopenblas.dll | Bin 21327719 -> 0 bytes mswin/Dependences/x86/libopenblas.lib | Bin 4662132 -> 0 bytes mswin/Dependences/x86/libquadmath-0.dll | Bin 494180 -> 0 bytes mswin/Dependences/x86/libwinpthread-1.dll | Bin 65629 -> 0 bytes src/snOperatorCUDA/src/CUDA/batchNorm.cu | 282 ++++++++++++++-------- 7 files changed, 180 insertions(+), 102 deletions(-) delete mode 100644 mswin/Dependences/x86/libgcc_s_dw2-1.dll delete mode 100644 mswin/Dependences/x86/libgfortran-3.dll delete mode 100644 mswin/Dependences/x86/libopenblas.dll delete mode 100644 mswin/Dependences/x86/libopenblas.lib delete mode 100644 mswin/Dependences/x86/libquadmath-0.dll delete mode 100644 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