Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop
wanghaox
7 years ago
commit
9216da3f09
@ -0,0 +1,188 @@
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if(NOT WITH_GPU)
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return()
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endif()
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set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
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set(paddle_known_gpu_archs7 "30 35 50 52")
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set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
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######################################################################################
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# A function for automatic detection of GPUs installed (if autodetection is enabled)
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# Usage:
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# detect_installed_gpus(out_variable)
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function(detect_installed_gpus out_variable)
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if(NOT CUDA_gpu_detect_output)
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set(cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
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file(WRITE ${cufile} ""
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"#include <cstdio>\n"
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"int main() {\n"
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" int count = 0;\n"
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" if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
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" if (count == 0) return -1;\n"
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" for (int device = 0; device < count; ++device) {\n"
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" cudaDeviceProp prop;\n"
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" if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
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" std::printf(\"%d.%d \", prop.major, prop.minor);\n"
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" }\n"
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" return 0;\n"
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"}\n")
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execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "-ccbin=${CUDA_HOST_COMPILER}"
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"--run" "${cufile}"
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WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
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RESULT_VARIABLE nvcc_res OUTPUT_VARIABLE nvcc_out
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ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
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if(nvcc_res EQUAL 0)
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# only keep the last line of nvcc_out
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STRING(REGEX REPLACE ";" "\\\\;" nvcc_out "${nvcc_out}")
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STRING(REGEX REPLACE "\n" ";" nvcc_out "${nvcc_out}")
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list(GET nvcc_out -1 nvcc_out)
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string(REPLACE "2.1" "2.1(2.0)" nvcc_out "${nvcc_out}")
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set(CUDA_gpu_detect_output ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from detect_installed_gpus tool" FORCE)
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endif()
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endif()
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if(NOT CUDA_gpu_detect_output)
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message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
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set(${out_variable} ${paddle_known_gpu_archs} PARENT_SCOPE)
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else()
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set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
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endif()
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endfunction()
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########################################################################
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# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
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# Usage:
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# select_nvcc_arch_flags(out_variable)
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function(select_nvcc_arch_flags out_variable)
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# List of arch names
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set(archs_names "Kepler" "Maxwell" "Pascal" "All" "Manual")
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set(archs_name_default "All")
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if(NOT CMAKE_CROSSCOMPILING)
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list(APPEND archs_names "Auto")
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endif()
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# set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
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set(CUDA_ARCH_NAME ${archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
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set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${archs_names} )
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mark_as_advanced(CUDA_ARCH_NAME)
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# verify CUDA_ARCH_NAME value
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if(NOT ";${archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
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string(REPLACE ";" ", " archs_names "${archs_names}")
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message(FATAL_ERROR "Only ${archs_names} architeture names are supported.")
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endif()
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if(${CUDA_ARCH_NAME} STREQUAL "Manual")
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set(CUDA_ARCH_BIN ${paddle_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
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set(CUDA_ARCH_PTX "50" CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
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mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
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else()
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unset(CUDA_ARCH_BIN CACHE)
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unset(CUDA_ARCH_PTX CACHE)
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endif()
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if(${CUDA_ARCH_NAME} STREQUAL "Kepler")
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set(cuda_arch_bin "30 35")
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elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
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set(cuda_arch_bin "50")
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elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
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set(cuda_arch_bin "60 61")
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elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
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set(cuda_arch_bin "70")
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elseif(${CUDA_ARCH_NAME} STREQUAL "All")
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set(cuda_arch_bin ${paddle_known_gpu_archs})
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elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
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detect_installed_gpus(cuda_arch_bin)
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else() # (${CUDA_ARCH_NAME} STREQUAL "Manual")
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set(cuda_arch_bin ${CUDA_ARCH_BIN})
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endif()
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# remove dots and convert to lists
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string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
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string(REGEX REPLACE "\\." "" cuda_arch_ptx "${CUDA_ARCH_PTX}")
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string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
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string(REGEX MATCHALL "[0-9]+" cuda_arch_ptx "${cuda_arch_ptx}")
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list(REMOVE_DUPLICATES cuda_arch_bin)
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list(REMOVE_DUPLICATES cuda_arch_ptx)
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set(nvcc_flags "")
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set(nvcc_archs_readable "")
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# Tell NVCC to add binaries for the specified GPUs
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foreach(arch ${cuda_arch_bin})
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if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
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# User explicitly specified PTX for the concrete BIN
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list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
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list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
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else()
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# User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
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list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
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list(APPEND nvcc_archs_readable sm_${arch})
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endif()
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endforeach()
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# Tell NVCC to add PTX intermediate code for the specified architectures
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foreach(arch ${cuda_arch_ptx})
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list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
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list(APPEND nvcc_archs_readable compute_${arch})
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endforeach()
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string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
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set(${out_variable} ${nvcc_flags} PARENT_SCOPE)
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set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
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endfunction()
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message(STATUS "CUDA detected: " ${CUDA_VERSION})
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if (${CUDA_VERSION} LESS 7.0)
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set(paddle_known_gpu_archs ${paddle_known_gpu_archs})
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elseif (${CUDA_VERSION} LESS 8.0) # CUDA 7.x
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set(paddle_known_gpu_archs ${paddle_known_gpu_archs7})
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list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
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list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
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elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
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set(paddle_known_gpu_archs ${paddle_known_gpu_archs8})
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list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
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list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
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# CUDA 8 may complain that sm_20 is no longer supported. Suppress the
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# warning for now.
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list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
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endif()
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include_directories(${CUDA_INCLUDE_DIRS})
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list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
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if(NOT WITH_DSO)
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list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
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endif(NOT WITH_DSO)
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# setting nvcc arch flags
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select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
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list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
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message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
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# Set C++11 support
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set(CUDA_PROPAGATE_HOST_FLAGS OFF)
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# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
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# So, don't set these flags here.
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list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
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list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
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list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
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# Set :expt-relaxed-constexpr to suppress Eigen warnings
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list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
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if(CMAKE_BUILD_TYPE STREQUAL "Debug")
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list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
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elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
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list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
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elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
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list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
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elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
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list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_MINSIZEREL})
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endif()
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mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
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mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
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@ -0,0 +1,58 @@
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## Evaluator Design
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### The Problem
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During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.
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### Evaluator Design
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Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.
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1. Initialize the metric state and add it into the block.
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2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.
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3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
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### Implementation
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This design is shown in python API.
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Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.
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```python
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class Evaluator(object):
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"""
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Evaluator Base class.
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"""
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def __init__(self, name, **kwargs):
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"""
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Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.
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Auc need four variables, `true_positives`,
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`true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append to main_program
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The initialization of Evaluator should be responsible for:
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create metric states and append to the main_program
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"""
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pass
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def _update_ops(self, input, label, **kwargs)
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"""
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Add mini-batch evaluator caculate operators to the main_program.
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Add increment operator to accumulate the metric states.
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"""
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def reset(self, executor, reset_program=None):
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"""
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Reset metric states at the begin of each pass/user specified batch number.
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Execute the reset_program to reset the states.
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"""
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def eval(self, executor, eval_program=None):
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"""
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Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
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Execute the eval_program and return the result.
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"""
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return eval_result
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```
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