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PaddleDetection
提交 ba68ce1a 编写于 作者: L Luo Tao
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Merge branch 'develop' into stride

上级 c1738e29 3133c09f
隐藏空白更改
内联 并排
Showing with 920 addition and 260 deletion
CMakeLists.txt
浏览文件 @ ba68ce1a
......@@ -12,19 +12,26 @@
# See the License for the specific language governing permissions and
# limitations under the License
cmake_minimum_required(VERSION 3.0)
project(paddle CXX C)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake")
set(PROJ_ROOT ${CMAKE_SOURCE_DIR})
include(system)
if(ANDROID)
cmake_minimum_required(VERSION 3.7)
else()
cmake_minimum_required(VERSION 3.0)
endif()
project(paddle CXX C)
find_package(Sphinx)
find_package(CUDA QUIET)
if(NOT CMAKE_CROSSCOMPILING)
find_package(CUDA QUIET)
endif(NOT CMAKE_CROSSCOMPILING)
find_package(Git REQUIRED)
find_package(Threads REQUIRED)
include(system)
include(simd)
################################ Configurations #######################################
......@@ -51,6 +58,21 @@ if(NOT CMAKE_BUILD_TYPE)
FORCE)
endif()
if(ANDROID)
if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 21")
endif()
set(WITH_GPU OFF CACHE STRING
"Disable GPU when cross-compiling for Android" FORCE)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when cross-compiling for Android" FORCE)
set(WITH_PYTHON OFF CACHE STRING
"Disable PYTHON when cross-compiling for Android" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android" FORCE)
endif(ANDROID)
set(THIRD_PARTY_PATH "${PROJ_ROOT}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
########################################################################################
......@@ -75,7 +97,6 @@ include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries
include(version) # set PADDLE_VERSION
include(coveralls) # set code coverage
include(configure) # add paddle env configuration
include_directories("${PROJ_ROOT}")
......
cmake/cblas.cmake
浏览文件 @ ba68ce1a
......@@ -19,9 +19,9 @@ set(CBLAS_FOUND OFF)
set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL")
find_path(MKL_INCLUDE_DIR mkl.h PATHS
find_path(MKL_INC_DIR mkl.h PATHS
${MKL_ROOT}/include)
find_path(MKL_INCLUDE_DIR mkl_lapacke.h PATHS
find_path(MKL_LAPACK_INC_DIR mkl_lapacke.h PATHS
${MKL_ROOT}/include)
find_library(MKL_CORE_LIB NAMES mkl_core PATHS
${MKL_ROOT}/lib
......@@ -34,15 +34,19 @@ find_library(MKL_INTEL_LP64 NAMES mkl_intel_lp64 PATHS
${MKL_ROOT}/lib/intel64)
if(MKL_INCLUDE_DIR AND MKL_CORE_LIB AND MKL_SEQUENTIAL_LIB AND MKL_INTEL_LP64)
if(MKL_INC_DIR AND MKL_CORE_LIB AND MKL_SEQUENTIAL_LIB AND MKL_INTEL_LP64)
set(CBLAS_PROVIDER MKL)
set(CBLAS_INC_DIR ${MKL_INCLUDE_DIR})
set(CBLAS_INC_DIR ${MKL_INC_DIR})
set(CBLAS_LIBRARIES ${MKL_INTEL_LP64}
${MKL_SEQUENTIAL_LIB}
${MKL_CORE_LIB})
add_definitions(-DPADDLE_USE_MKL)
message(STATUS "Found MKL (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
set(CBLAS_FOUND ON)
if(${MKL_LAPACK_INC_DIR})
add_definitions(-DPADDLE_USE_LAPACK)
message(STATUS "Found lapack in MKL (include: ${MKL_LAPACK_INC_DIR})")
endif()
return() # return file.
endif()
......@@ -68,13 +72,17 @@ find_library(ATLAS_CBLAS_LIB NAMES cblas libcblas.so.3
find_library(ATLAS_LIB NAMES lapack_atlas liblapack_atlas.so.3
PATHS ${ATLAS_LIB_SEARCH_PATHS})
if(ATLAS_INC_DIR AND ATLAS_CBLAS_LIB AND ATLAS_LIB)
if(ATLAS_INC_DIR AND ATLAS_CBLAS_LIB AND ATLAS_LIB AND NOT CBLAS_FOUND)
set(CBLAS_PROVIDER ATLAS)
set(CBLAS_INC_DIR ${ATLAS_INC_DIR} ${ATLAS_CLAPACK_INC_DIR})
set(CBLAS_INC_DIR ${ATLAS_INC_DIR})
set(CBLAS_LIBRARIES ${ATLAS_LIB} ${ATLAS_CBLAS_LIB})
add_definitions(-DPADDLE_USE_ATLAS)
message(STATUS "Found Atlas (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
message(STATUS "Found ATLAS (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
set(CBLAS_FOUND ON)
if(ATLAS_CLAPACK_INC_DIR)
add_definitions(-DPADDLE_USE_LAPACK)
message(STATUS "Found lapack in ATLAS (include: ${ATLAS_CLAPACK_INC_DIR})")
endif()
return()
endif()
......@@ -103,8 +111,12 @@ if(OPENBLAS_INC_DIR AND OPENBLAS_LIB)
set(CBLAS_PROVIDER OPENBLAS)
set(CBLAS_INC_DIR ${OPENBLAS_INC_DIR})
set(CBLAS_LIBRARIES ${OPENBLAS_LIB})
message(STATUS "Found OpenBlas (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
message(STATUS "Found OpenBLAS (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
set(CBLAS_FOUND ON)
if(OPENBLAS_LAPACKE_INC_DIR)
add_definitions(-DPADDLE_USE_LAPACK)
message(STATUS "Found lapack in OpenBLAS (include: ${OPENBLAS_LAPACKE_INC_DIR})")
endif()
return()
endif()
......
cmake/configure.cmake
浏览文件 @ ba68ce1a
......@@ -32,6 +32,14 @@ if(NOT WITH_PROFILER)
add_definitions(-DPADDLE_DISABLE_PROFILER)
endif(NOT WITH_PROFILER)
if(NOT CMAKE_CROSSCOMPILING)
if(WITH_AVX AND AVX_FOUND)
set(SIMD_FLAG ${AVX_FLAG})
elseif(SSE3_FOUND)
set(SIMD_FLAG ${SSE3_FLAG})
endif()
endif()
if(NOT WITH_GPU)
add_definitions(-DPADDLE_ONLY_CPU)
add_definitions(-DHPPL_STUB_FUNC)
......@@ -48,21 +56,12 @@ else()
message(FATAL_ERROR "Paddle need cudnn to compile")
endif()
if(WITH_AVX)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler ${AVX_FLAG}")
else(WITH_AVX)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler ${SSE3_FLAG}")
endif(WITH_AVX)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler ${SIMD_FLAG}")
# Include cuda and cudnn
include_directories(${CUDNN_INCLUDE_DIR})
include_directories(${CUDA_TOOLKIT_INCLUDE})
endif(NOT WITH_GPU)
if(WITH_AVX)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${AVX_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${AVX_FLAG}")
else(WITH_AVX)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SSE3_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SSE3_FLAG}")
endif(WITH_AVX)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
cmake/cudnn.cmake
浏览文件 @ ba68ce1a
if(NOT WITH_GPU)
return()
endif()
set(CUDNN_ROOT "" CACHE PATH "CUDNN ROOT")
find_path(CUDNN_INCLUDE_DIR cudnn.h
PATHS ${CUDNN_ROOT} ${CUDNN_ROOT}/include
......
cmake/external/gflags.cmake
浏览文件 @ ba68ce1a
......@@ -33,6 +33,8 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DBUILD_TESTING=OFF
......
cmake/external/glog.cmake
浏览文件 @ ba68ce1a
......@@ -35,6 +35,8 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DWITH_GFLAGS=ON
......
cmake/external/gtest.cmake
浏览文件 @ ba68ce1a
......@@ -43,6 +43,8 @@ IF(WITH_TESTING)
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DBUILD_GMOCK=ON
......
cmake/external/openblas.cmake
浏览文件 @ ba68ce1a
......@@ -54,6 +54,8 @@ IF(NOT ${CBLAS_FOUND})
"you need to set gfortran compiler: cmake .. -DCMAKE_Fortran_COMPILER=...")
ENDIF(NOT CMAKE_Fortran_COMPILER)
ADD_DEFINITIONS(-DPADDLE_USE_LAPACK)
ExternalProject_Add(
openblas
${EXTERNAL_PROJECT_LOG_ARGS}
......
cmake/external/python.cmake
浏览文件 @ ba68ce1a
......@@ -219,9 +219,9 @@ ELSE(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND)
ENDIF(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND)
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})
IF(NOT WITH_PYTHON)
IF(WITH_PYTHON)
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})
ELSE()
SET(PYTHON_LIBRARIES "")
ENDIF()
cmake/external/warpctc.cmake
浏览文件 @ ba68ce1a
......@@ -50,6 +50,8 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
CMAKE_ARGS -DWITH_GPU=${WITH_GPU}
CMAKE_ARGS -DWITH_OMP=${USE_OMP}
......
cmake/external/zlib.cmake
浏览文件 @ ba68ce1a
......@@ -22,7 +22,7 @@ SET(ZLIB_INCLUDE_DIR "${ZLIB_INSTALL_DIR}/include" CACHE PATH "zlib include dire
IF(WIN32)
SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/zlibstatic.lib" CACHE FILEPATH "zlib library." FORCE)
ELSE(WIN32)
set(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE)
SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE)
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR})
......@@ -36,6 +36,8 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${ZLIB_INSTALL_DIR}
CMAKE_ARGS -DBUILD_SHARED_LIBS=OFF
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
......
cmake/flags.cmake
浏览文件 @ ba68ce1a
......@@ -2,6 +2,7 @@
include(CheckCXXCompilerFlag)
include(CheckCCompilerFlag)
include(CheckCXXSymbolExists)
include(CheckTypeSize)
function(CheckCompilerCXX11Flag)
if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
......@@ -25,7 +26,7 @@ function(CheckCompilerCXX11Flag)
endfunction()
CheckCompilerCXX11Flag()
LIST(APPEND CMAKE_CXX_FLAGS -std=c++11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
# safe_set_flag
#
......@@ -83,6 +84,17 @@ if(NOT UINT64_MAX_EXISTS)
endif()
endif()
SET(CMAKE_EXTRA_INCLUDE_FILES "pthread.h")
CHECK_TYPE_SIZE(pthread_spinlock_t SPINLOCK_FOUND)
CHECK_TYPE_SIZE(pthread_barrier_t BARRIER_FOUND)
if(SPINLOCK_FOUND)
add_definitions(-DPADDLE_USE_PTHREAD_SPINLOCK)
endif(SPINLOCK_FOUND)
if(BARRIER_FOUND)
add_definitions(-DPADDLE_USE_PTHREAD_BARRIER)
endif(BARRIER_FOUND)
SET(CMAKE_EXTRA_INCLUDE_FILES "")
# Common flags. the compiler flag used for C/C++ sources whenever release or debug
# Do not care if this flag is support for gcc.
set(COMMON_FLAGS
......
cmake/simd.cmake
浏览文件 @ ba68ce1a
......@@ -2,6 +2,7 @@
# so that PaddlePaddle can unleash the vectorization power of muticore.
INCLUDE(CheckCXXSourceRuns)
INCLUDE(CheckCXXSourceCompiles)
IF(CMAKE_COMPILER_IS_GNUCC OR CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
set(MMX_FLAG "-mmmx")
......@@ -17,6 +18,8 @@ ELSEIF(MSVC)
SET(AVX2_FLAG "/arch:AVX2")
ENDIF()
set(CMAKE_REQUIRED_FLAGS_RETAINED ${CMAKE_REQUIRED_FLAGS})
# Check MMX
set(CMAKE_REQUIRED_FLAGS ${MMX_FLAG})
CHECK_CXX_SOURCE_RUNS("
......@@ -73,4 +76,5 @@ int main()
return 0;
}" AVX2_FOUND)
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_RETAINED})
mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND)
cmake/system.cmake
浏览文件 @ ba68ce1a
......@@ -67,6 +67,12 @@ MARK_AS_ADVANCED(HOST_SYSTEM CPU_CORES)
MESSAGE(STATUS "Found Paddle host system: ${HOST_SYSTEM}")
MESSAGE(STATUS "Found Paddle host system's CPU: ${CPU_CORES} cores")
IF(DEFINED CMAKE_SYSTEM_NAME)
IF(${CMAKE_SYSTEM_NAME} STREQUAL "Android")
SET(ANDROID TRUE)
ENDIF()
ENDIF()
# external dependencies log output
SET(EXTERNAL_PROJECT_LOG_ARGS
LOG_DOWNLOAD 0 # Wrap download in script to log output
......
cmake/util.cmake
浏览文件 @ ba68ce1a
......@@ -90,6 +90,10 @@ function(link_paddle_exe TARGET_NAME)
${RDMA_LD_FLAGS}
${RDMA_LIBS})
if(ANDROID)
target_link_libraries(${TARGET_NAME} log)
endif(ANDROID)
add_dependencies(${TARGET_NAME} ${external_project_dependencies})
endfunction()
......
paddle/cuda/include/hl_cpu_matrix_kernel.cuh
浏览文件 @ ba68ce1a
......@@ -17,7 +17,11 @@ limitations under the License. */
#include <stdio.h>
#include "hl_base.h"
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include "hl_neon_matrix_kernel.cuh"
#else
#include "hl_sse_matrix_kernel.cuh"
#endif
/**
* @brief cpu element wise unary operator.
......
paddle/cuda/include/hl_matrix_base.cuh
浏览文件 @ ba68ce1a
......@@ -66,6 +66,8 @@ typedef BaseOp SSESquaredDiff;
typedef BaseOp SSEFirst;
typedef BaseOp SSESecond;
typedef BaseOp SSEClassificationError;
#elif defined(__ARM__NEON__) || defined(__ARM_NEON)
#include "hl_matrix_base_neon.cuh"
#else
#include "hl_matrix_base_sse.cuh"
#endif
......
paddle/cuda/include/hl_matrix_base_neon.cuh 0 → 100644
浏览文件 @ ba68ce1a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_MATRIX_BASE_NEON_CUH_
#define HL_MATRIX_BASE_NEON_CUH_
namespace aggregate {
class SSESum {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vaddq_f32(a, b);
}
};
class SSEMax {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vmaxq_f32(a, b);
}
};
class SSEMin {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vminq_f32(a, b);
}
};
} // namespace aggregate
namespace base {
namespace unary {
class SSEIdentity {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a) const {
return a;
}
};
} // namespace unary
namespace binary {
class SSEAdd {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vaddq_f32(a, b);
}
};
class SSEAdd2 {
public:
static const bool sse = true;
const real p1;
const real p2;
float32x4_t mp1;
float32x4_t mp2;
public:
SSEAdd2(const real s1, const real s2) : p1(s1), p2(s2) {
mp1 = vdupq_n_f32(p1);
mp2 = vdupq_n_f32(p2);
}
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
float32x4_t tmp1, tmp2;
tmp1 = vmulq_f32(mp1, a);
tmp2 = vmulq_f32(mp2, b);
return vaddq_f32(tmp1, tmp2);
}
};
class SSESub {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vsubq_f32(a, b);
}
};
class SSEMul {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vmulq_f32(a, b);
}
};
class SSEDiv {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
float32x4_t tmp;
tmp = vrecpeq_f32(b);
return vmulq_f32(a, tmp);
}
};
class SSESquaredDiff {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
float32x4_t tmp;
tmp = vsubq_f32(a, b);
return vmulq_f32(tmp, tmp);
}
};
class SSEFirst {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return a;
}
};
class SSESecond {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return b;
}
};
class SSEClassificationError {
public:
static const bool sse = true;
const real p;
float32x4_t mp;
uint32x4_t result;
public:
explicit SSEClassificationError(const real s) : p(s) {
mp = vdupq_n_f32(p);
result = vdupq_n_u32(1);
}
// TODO: to be check
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
uint32x4_t tmp1 = vcgtq_f32(a, mp);
uint32x4_t tmp2 = vcgtq_f32(b, mp);
uint32x4_t tmp3 = veorq_u32(tmp1, tmp2);
return vcvtq_f32_u32(vandq_u32(tmp3, result));
}
};
} // namespace binary
} // namespace base
#endif /* HL_MATRIX_BASE_NEON_CUH_ */
paddle/cuda/include/hl_matrix_type.cuh
浏览文件 @ ba68ce1a
......@@ -17,13 +17,20 @@ limitations under the License. */
#include "hl_base.h"
#ifdef __CUDA_ARCH__
#if defined(__CUDA_ARCH__)
#include <vector_types.h>
#ifndef PADDLE_TYPE_DOUBLE
typedef float4 vecType;
#else
typedef double2 vecType;
#endif
#elif (defined __ARM_NEON) || (defined __ARM_NEON__)
#include <arm_neon.h>
#ifndef PADDLE_TYPE_DOUBLE
typedef float32x4_t vecType;
#else
#error NEON instructions does not support double precision
#endif
#else
#include <mmintrin.h>
#include <xmmintrin.h>
......
paddle/cuda/include/hl_neon_matrix_kernel.cuh 0 → 100644
浏览文件 @ ba68ce1a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_NEON_MATRIX_KERNEL_CUH_
#define HL_NEON_MATRIX_KERNEL_CUH_
#include "hl_matrix_type.cuh"
#define VECTOR_SIZE 16
/* number of float in vector */
#define VECTOR_LEN 4
#define VECTOR_SET vdupq_n_f32
inline bool hl_check_align(size_t size) {
return !(size & (VECTOR_SIZE - 1));
}
inline bool hl_check_align(void *ptr) {
return hl_check_align(reinterpret_cast<size_t>(ptr));
}
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
float32x4_t rev = vrev64q_f32(mm);
float32x4_t tmp1 = agg.vecOp(rev, rev);
float32x2_t lo = vget_high_f32(rev);
float32x2_t hi = vget_low_f32(rev);
float32x4_t tmp2 = vcombine_f32(hi, lo);
float32x4_t ret = agg.vecOp(tmp1, tmp2);
return vgetq_lane_f32(ret, 0);
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda) {
for (int i = 0; i < dimM; i++, A += lda) {
vecType mm = VECTOR_SET(agg.init());
vecType *a = (vecType*)(A);
for (int j = 0; j < dimN / VECTOR_LEN; j++, a++) {
mm = agg.vecOp(mm, op.vecOp(*a));
}
int rem = dimN % VECTOR_LEN;
if (rem) {
real tmp = hl_agg_op(agg, mm);
real *a = A + (dimN / VECTOR_LEN) * VECTOR_LEN;
for (int j = 0; j < rem; j++) {
tmp = agg(tmp, op(a[j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
} else {
dst[i*ld] = sv(dst[i*ld], hl_agg_op(agg, mm));
}
}
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda,
real *B, int ldb) {
for (int i = 0; i < dimM; i++, A += lda, B += ldb) {
vecType mm = VECTOR_SET(agg.init());
vecType *a = (vecType*)(A);
vecType *b = (vecType*)(B);
for (int j = 0; j < dimN / VECTOR_LEN; j++, a++, b++) {
mm = agg.vecOp(mm, op.vecOp(*a, *b));
}
int rem = dimN % VECTOR_LEN;
if (rem) {
real tmp = hl_agg_op(agg, mm);
real *a = A + (dimN / VECTOR_LEN) * VECTOR_LEN;
real *b = B + (dimN / VECTOR_LEN) * VECTOR_LEN;
for (int j = 0; j < rem; j++) {
tmp = agg(tmp, op(a[j], b[j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
} else {
dst[i*ld] = sv(dst[i*ld], hl_agg_op(agg, mm));
}
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j], B[i * ldb + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
/*
* MaxRow greater than or equal dimN
* dimN is multiples of VECTOR_LEN
* so rem <= MaxRow / VECTOR_LEN
*/
template <int MaxRow, class Agg, class Op, class Saver>
void hl_sse_column_op_with_rem(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
vecType mm[MaxRow / VECTOR_LEN];
for (int n = 0; n < MaxRow / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
int rem = dimN % VECTOR_LEN;
if (rem) {
A += (dimN / VECTOR_LEN) * VECTOR_LEN;
dst += (dimN / VECTOR_LEN) * VECTOR_LEN;
hl_matrix_column_op(agg, op, sv, dimM, rem, dst, A, lda);
}
}
/*
* dimN is multiples of VECTOR_LEN
* dimN greater than Step
*/
template <int Step, class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
for (int j = 0; j < dimN / Step; j++, dst += Step, A += Step) {
vecType mm[Step / VECTOR_LEN];
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
}
int remRow = dimN % Step;
if (remRow) {
hl_sse_column_op_with_rem<Step>(agg, op, sv, dimM, remRow, dst, A, lda);
}
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
if (dimN <= 16) {
hl_sse_matrix_column_op<16>(agg, op, sv, dimM, dimN, dst, A, lda);
} else if (dimN <= 32) {
hl_sse_matrix_column_op<32>(agg, op, sv, dimM, dimN, dst, A, lda);
} else if (dimN <= 1024 || dimM <= 512) {
hl_sse_matrix_column_op<64>(agg, op, sv, dimM, dimN, dst, A, lda);
} else {
hl_sse_matrix_column_op<1024>(agg, op, sv, dimM, dimN, dst, A, lda);
}
}
template <int MaxRow, class Agg, class Op, class Saver>
void hl_sse_column_op_with_rem(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
vecType mm[MaxRow / VECTOR_LEN];
for (int n = 0; n < MaxRow / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
vecType *b = (vecType*)(B + i * ldb);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n], b[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
int rem = dimN % VECTOR_LEN;
if (rem) {
A += (dimN / VECTOR_LEN) * VECTOR_LEN;
B += (dimN / VECTOR_LEN) * VECTOR_LEN;
dst += (dimN / VECTOR_LEN) * VECTOR_LEN;
hl_matrix_column_op(agg, op, sv, dimM, rem, dst, A, lda, B, ldb);
}
}
template <int Step, class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
for (int j = 0; j < dimN / Step; j++, dst += Step, A += Step, B += Step) {
vecType mm[Step / VECTOR_LEN];
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
vecType *b = (vecType*)(B + i * ldb);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n], b[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
}
int remRow = dimN % Step;
if (remRow) {
hl_sse_column_op_with_rem<Step>(
agg, op, sv, dimM, remRow, dst, A, lda, B, ldb);
}
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
if (dimN <= 16) {
hl_sse_matrix_column_op<16>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
} else if (dimN <= 32) {
hl_sse_matrix_column_op<32>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
} else if (dimN <= 1024 || dimM <= 512) {
hl_sse_matrix_column_op<64>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
} else {
hl_sse_matrix_column_op<1024>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
}
}
#endif /* HL_NEON_MATRIX_KERNEL_CUH_ */
paddle/function/Function.h
浏览文件 @ ba68ce1a
......@@ -38,7 +38,7 @@ public:
if (err) {
*err = Error(e.what());
} else {
LOG(FATAL) << "Cannot get key " << key << "with error " << e.what();
LOG(FATAL) << "Cannot get key " << key << " with error " << e.what();
}
return T();
}
......
paddle/function/PadOpGpu.cu
浏览文件 @ ba68ce1a
......@@ -44,9 +44,9 @@ void Pad<DEVICE_TYPE_GPU>(real* outputs,
size_t nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
int cstart = pad.channelStart, cend = pad.channelEnd;
int hstart = pad.heightStart, hend = pad.heightEnd;
int wstart = pad.widthStart, wend = pad.widthEnd;
int cstart = pad.channel[0], cend = pad.channel[1];
int hstart = pad.height[0], hend = pad.height[1];
int wstart = pad.width[0], wend = pad.width[1];
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
......@@ -83,9 +83,9 @@ void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
int nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
int cstart = pad.channelStart, cend = pad.channelEnd;
int hstart = pad.heightStart, hend = pad.heightEnd;
int wstart = pad.widthStart, wend = pad.widthEnd;
int cstart = pad.channel[0], cend = pad.channel[1];
int hstart = pad.height[0], hend = pad.height[1];
int wstart = pad.width[0], wend = pad.width[1];
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
......
paddle/function/PadOpTest.cpp
浏览文件 @ ba68ce1a
......@@ -24,48 +24,22 @@ TEST(Pad, real) {
for (size_t imgSizeW : {5, 32, 96}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
FunctionCompare compare("Pad",
FuncConfig()
.set("cstart", 2)
.set("cend", 3)
.set("hstart", 1)
.set("hend", 2)
.set("wstart", 3)
.set("wend", 2));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{
numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, inDims));
compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT, outDims, ASSIGN_TO));
compare.run();
}
}
}
}
}
TEST(PadGrad, real) {
for (size_t numSamples : {5, 32}) {
for (size_t channels : {1, 5, 32}) {
for (size_t imgSizeH : {5, 33, 100}) {
for (size_t imgSizeW : {5, 32, 96}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
FunctionCompare compare("PadGrad",
FuncConfig()
.set("cstart", 2)
.set("cend", 3)
.set("hstart", 1)
.set("hend", 2)
.set("wstart", 3)
.set("wend", 2));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{
numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, outDims));
compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT, inDims, ASSIGN_TO));
compare.run();
for (bool test_grad : {false, true}) {
FunctionCompare compare(
test_grad ? "PadGrad" : "Pad",
FuncConfig()
.set<std::vector<uint32_t>>("channel", {2, 3})
.set<std::vector<uint32_t>>("height", {1, 2})
.set<std::vector<uint32_t>>("width", {3, 2}));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{
numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
compare.addInputs(
BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims));
compare.addOutputs(BufferArg(
VALUE_TYPE_FLOAT, test_grad ? inDims : outDims, ASSIGN_TO));
compare.run();
}
}
}
}
......
paddle/gserver/tests/LayerGradUtil.cpp
浏览文件 @ ba68ce1a
......@@ -778,8 +778,10 @@ void testProjectionGrad(ProjectionConfig conf,
config.biasSize = biasSize == 0 ? config.layerConfig.size() : biasSize;
config.layerConfig.set_bias_size(config.biasSize);
config.layerConfig.set_shared_biases(sharedBias);
config.inputDefs.push_back(
{inputType, "layer_0", conf.input_size(), parameterSize});
config.inputDefs.push_back({inputType,
"layer_0",
static_cast<size_t>(conf.input_size()),
parameterSize});
*config.layerConfig.add_inputs()->mutable_proj_conf() = conf;
config.testState = testState;
testLayerGrad(config, "mixed", batchSize, false, useGpu);
......
paddle/math/MathFunctions.cpp
浏览文件 @ ba68ce1a
......@@ -85,11 +85,16 @@ int getrf<float>(const CBLAS_ORDER order,
float* A,
const int lda,
int* ipiv) {
#ifdef PADDLE_USE_LAPACK
#ifdef PADDLE_USE_ATLAS
return clapack_sgetrf(order, M, N, A, lda, ipiv);
#else
return LAPACKE_sgetrf(order, M, N, A, lda, ipiv);
#endif
#else
LOG(FATAL) << "Not implemented";
#endif
return 0;
}
template <>
......@@ -99,11 +104,16 @@ int getrf<double>(const CBLAS_ORDER order,
double* A,
const int lda,
int* ipiv) {
#ifdef PADDLE_USE_LAPACK
#ifdef PADDLE_USE_ATLAS
return clapack_dgetrf(order, M, N, A, lda, ipiv);
#else
return LAPACKE_dgetrf(order, M, N, A, lda, ipiv);
#endif
#else
LOG(FATAL) << "Not implemented";
#endif
return 0;
}
template <>
......@@ -112,11 +122,16 @@ int getri<float>(const CBLAS_ORDER order,
float* A,
const int lda,
const int* ipiv) {
#ifdef PADDLE_USE_LAPACK
#ifdef PADDLE_USE_ATLAS
return clapack_sgetri(order, N, A, lda, ipiv);
#else
return LAPACKE_sgetri(order, N, A, lda, ipiv);
#endif
#else
LOG(FATAL) << "Not implemented";
#endif
return 0;
}
template <>
......@@ -125,11 +140,16 @@ int getri<double>(const CBLAS_ORDER order,
double* A,
const int lda,
const int* ipiv) {
#ifdef PADDLE_USE_LAPACK
#ifdef PADDLE_USE_ATLAS
return clapack_dgetri(order, N, A, lda, ipiv);
#else
return LAPACKE_dgetri(order, N, A, lda, ipiv);
#endif
#else
LOG(FATAL) << "Not implemented";
#endif
return 0;
}
template <>
......
paddle/math/MathFunctions.h
浏览文件 @ ba68ce1a
......@@ -17,11 +17,14 @@ limitations under the License. */
#ifdef PADDLE_USE_MKL
#include <mkl.h>
#ifdef PADDLE_USE_LAPACK
#include <mkl_lapacke.h>
#endif
#else
extern "C" {
#include <cblas.h>
}
#ifdef PADDLE_USE_LAPACK
#ifdef PADDLE_USE_ATLAS
extern "C" {
#include <clapack.h>
......@@ -30,6 +33,7 @@ extern "C" {
#include <lapacke.h>
#endif
#endif
#endif
#include <cmath>
......
paddle/math/Matrix.cpp
浏览文件 @ ba68ce1a
......@@ -2426,41 +2426,8 @@ void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) {
int lda = a->getStride();
int ldb = b->getStride();
int ldc = getStride();
#ifndef PADDLE_TYPE_DOUBLE
cblas_sgemm(CblasRowMajor,
a_trans,
b_trans,
M,
N,
K,
scaleAB,
A,
lda,
B,
ldb,
scaleT,
C,
ldc);
#else
cblas_dgemm(CblasRowMajor,
a_trans,
b_trans,
M,
N,
K,
scaleAB,
A,
lda,
B,
ldb,
scaleT,
C,
ldc);
// TODO(yuyang18): Is gemm defined other place?
#endif
VLOG(2) << " A[0]=" << A[0] << " A[1]=" << A[1] << " B[0]=" << B[0]
<< " B[1]=" << B[1] << " C[0]=" << C[0] << " C[1]=" << C[1];
gemm<real>(
a_trans, b_trans, M, N, K, scaleAB, A, lda, B, ldb, scaleT, C, ldc);
}
void CpuMatrix::mul(
......
paddle/math/SIMDFunctions.cpp
浏览文件 @ ba68ce1a
......@@ -13,119 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "SIMDFunctions.h"
#ifdef __SSE3__
#include <immintrin.h>
#endif
#include <algorithm>
#ifndef __AVX__
static void addto_sse(float* a, const float* b, size_t len) {
int offset = len % 16;
__m128 ma0, ma1, ma2, ma3;
__m128 mb0, mb1, mb2, mb3;
for (unsigned int k = 0; k < len / 16; k++, a += 16, b += 16) {
ma0 = _mm_load_ps(a);
ma1 = _mm_load_ps(a + 4);
ma2 = _mm_load_ps(a + 8);
ma3 = _mm_load_ps(a + 12);
mb0 = _mm_load_ps(b);
mb1 = _mm_load_ps(b + 4);
mb2 = _mm_load_ps(b + 8);
mb3 = _mm_load_ps(b + 12);
ma0 = _mm_add_ps(ma0, mb0);
ma1 = _mm_add_ps(ma1, mb1);
ma2 = _mm_add_ps(ma2, mb2);
ma3 = _mm_add_ps(ma3, mb3);
_mm_store_ps(a, ma0);
_mm_store_ps(a + 4, ma1);
_mm_store_ps(a + 8, ma2);
_mm_store_ps(a + 12, ma3);
}
for (int i = 0; i < offset; i++) a[i] += b[i];
}
static void batch_addto_sse(float* a, const float* b[], int batch, size_t len) {
int offset = len % 16;
__m128 ma0, ma1, ma2, ma3;
__m128 mb0, mb1, mb2, mb3;
for (unsigned int k = 0; k < len / 16; k++, a += 16) {
ma0 = _mm_load_ps(a);
ma1 = _mm_load_ps(a + 4);
ma2 = _mm_load_ps(a + 8);
ma3 = _mm_load_ps(a + 12);
for (int i = 0; i < batch; i++) {
mb0 = _mm_load_ps(b[i]);
mb1 = _mm_load_ps(b[i] + 4);
mb2 = _mm_load_ps(b[i] + 8);
mb3 = _mm_load_ps(b[i] + 12);
ma0 = _mm_add_ps(ma0, mb0);
ma1 = _mm_add_ps(ma1, mb1);
ma2 = _mm_add_ps(ma2, mb2);
ma3 = _mm_add_ps(ma3, mb3);
b[i] += 16;
}
_mm_store_ps(a, ma0);
_mm_store_ps(a + 4, ma1);
_mm_store_ps(a + 8, ma2);
_mm_store_ps(a + 12, ma3);
}
for (int i = 0; i < offset; i++) {
for (int k = 0; k < batch; k++) a[i] += b[k][i];
}
return;
}
static void col_max_sse(float* result,
const float* data,
int dim,
int numSamples) {
// first sample, direct copy
for (int d = 0; d < dim; ++d) {
result[d] = data[d];
}
int offset = dim % 16;
__m128 ma0, ma1, ma2, ma3;
__m128 mb0, mb1, mb2, mb3;
// first 16n dims
for (int k = 0; k < dim / 16; k++, result += 16, data += 16) {
ma0 = _mm_load_ps(result);
ma1 = _mm_load_ps(result + 4);
ma2 = _mm_load_ps(result + 8);
ma3 = _mm_load_ps(result + 12);
for (int i = 1; i < numSamples; i++) {
mb0 = _mm_load_ps(data + i * dim);
mb1 = _mm_load_ps(data + i * dim + 4);
mb2 = _mm_load_ps(data + i * dim + 8);
mb3 = _mm_load_ps(data + i * dim + 12);
ma0 = _mm_max_ps(ma0, mb0);
ma1 = _mm_max_ps(ma1, mb1);
ma2 = _mm_max_ps(ma2, mb2);
ma3 = _mm_max_ps(ma3, mb3);
}
_mm_store_ps(result, ma0);
_mm_store_ps(result + 4, ma1);
_mm_store_ps(result + 8, ma2);
_mm_store_ps(result + 12, ma3);
}
// last dims
for (int d = 0; d < offset; ++d) {
float sm = data[d];
for (int i = 1; i < numSamples; ++i) {
sm = std::max(sm, data[i * dim + d]);
}
result[d] = sm;
}
}
#else
#ifdef __AVX__
static void addto_avx(float* a, const float* b, size_t len) {
int offset = len % 32;
......@@ -355,17 +248,128 @@ static void decayL1_avx(
}
}
#elif defined(__SSE3__)
static void addto_sse(float* a, const float* b, size_t len) {
int offset = len % 16;
__m128 ma0, ma1, ma2, ma3;
__m128 mb0, mb1, mb2, mb3;
for (unsigned int k = 0; k < len / 16; k++, a += 16, b += 16) {
ma0 = _mm_load_ps(a);
ma1 = _mm_load_ps(a + 4);
ma2 = _mm_load_ps(a + 8);
ma3 = _mm_load_ps(a + 12);
mb0 = _mm_load_ps(b);
mb1 = _mm_load_ps(b + 4);
mb2 = _mm_load_ps(b + 8);
mb3 = _mm_load_ps(b + 12);
ma0 = _mm_add_ps(ma0, mb0);
ma1 = _mm_add_ps(ma1, mb1);
ma2 = _mm_add_ps(ma2, mb2);
ma3 = _mm_add_ps(ma3, mb3);
_mm_store_ps(a, ma0);
_mm_store_ps(a + 4, ma1);
_mm_store_ps(a + 8, ma2);
_mm_store_ps(a + 12, ma3);
}
for (int i = 0; i < offset; i++) a[i] += b[i];
}
static void batch_addto_sse(float* a, const float* b[], int batch, size_t len) {
int offset = len % 16;
__m128 ma0, ma1, ma2, ma3;
__m128 mb0, mb1, mb2, mb3;
for (unsigned int k = 0; k < len / 16; k++, a += 16) {
ma0 = _mm_load_ps(a);
ma1 = _mm_load_ps(a + 4);
ma2 = _mm_load_ps(a + 8);
ma3 = _mm_load_ps(a + 12);
for (int i = 0; i < batch; i++) {
mb0 = _mm_load_ps(b[i]);
mb1 = _mm_load_ps(b[i] + 4);
mb2 = _mm_load_ps(b[i] + 8);
mb3 = _mm_load_ps(b[i] + 12);
ma0 = _mm_add_ps(ma0, mb0);
ma1 = _mm_add_ps(ma1, mb1);
ma2 = _mm_add_ps(ma2, mb2);
ma3 = _mm_add_ps(ma3, mb3);
b[i] += 16;
}
_mm_store_ps(a, ma0);
_mm_store_ps(a + 4, ma1);
_mm_store_ps(a + 8, ma2);
_mm_store_ps(a + 12, ma3);
}
for (int i = 0; i < offset; i++) {
for (int k = 0; k < batch; k++) a[i] += b[k][i];
}
return;
}
static void col_max_sse(float* result,
const float* data,
int dim,
int numSamples) {
// first sample, direct copy
for (int d = 0; d < dim; ++d) {
result[d] = data[d];
}
int offset = dim % 16;
__m128 ma0, ma1, ma2, ma3;
__m128 mb0, mb1, mb2, mb3;
// first 16n dims
for (int k = 0; k < dim / 16; k++, result += 16, data += 16) {
ma0 = _mm_load_ps(result);
ma1 = _mm_load_ps(result + 4);
ma2 = _mm_load_ps(result + 8);
ma3 = _mm_load_ps(result + 12);
for (int i = 1; i < numSamples; i++) {
mb0 = _mm_load_ps(data + i * dim);
mb1 = _mm_load_ps(data + i * dim + 4);
mb2 = _mm_load_ps(data + i * dim + 8);
mb3 = _mm_load_ps(data + i * dim + 12);
ma0 = _mm_max_ps(ma0, mb0);
ma1 = _mm_max_ps(ma1, mb1);
ma2 = _mm_max_ps(ma2, mb2);
ma3 = _mm_max_ps(ma3, mb3);
}
_mm_store_ps(result, ma0);
_mm_store_ps(result + 4, ma1);
_mm_store_ps(result + 8, ma2);
_mm_store_ps(result + 12, ma3);
}
// last dims
for (int d = 0; d < offset; ++d) {
float sm = data[d];
for (int i = 1; i < numSamples; ++i) {
sm = std::max(sm, data[i * dim + d]);
}
result[d] = sm;
}
}
#endif
#ifndef __AVX__
#define SIMD_INVOKE(func, ...) func##_sse(__VA_ARGS__)
#else
#if defined(__AVX__)
#define SIMD_INVOKE(func, ...) func##_avx(__VA_ARGS__)
#elif defined(__SSE3__)
#define SIMD_INVOKE(func, ...) func##_sse(__VA_ARGS__)
#endif
namespace paddle {
namespace simd {
namespace internal {
#ifdef __SSE3__
void addToImpl(float* a, const float* b, size_t len) {
SIMD_INVOKE(addto, a, b, len);
}
......@@ -376,6 +380,7 @@ void batchAddToImpl(float* a, const float* b[], int batch, size_t len) {
void colMaxImpl(float* result, const float* data, int dim, int numSamples) {
SIMD_INVOKE(col_max, result, data, dim, numSamples);
}
#endif
#ifdef __AVX__
void decayL1AvxImpl(float* dst, float* src, float lambda, size_t len) {
......@@ -385,8 +390,8 @@ void decayL1AvxImpl(
float* dst, float* src, float* lr, float lambda, size_t len) {
decayL1_avx(dst, src, lr, lambda, len);
}
#endif
} // namespace internal
} // namespace simd
} // namespace paddle
paddle/math/SIMDFunctions.h
浏览文件 @ ba68ce1a
......@@ -128,17 +128,29 @@ void decayL1AvxImpl(
template <>
inline void addTo(float* a, const float* b, size_t len) {
#ifdef __SSE3__
internal::addToImpl(a, b, len);
#else
naive::addTo(a, b, len);
#endif
}
template <>
inline void batchAddTo(float* a, const float* b[], int batch, size_t len) {
#ifdef __SSE3__
internal::batchAddToImpl(a, b, batch, len);
#else
naive::batchAddTo(a, b, batch, len);
#endif
}
template <>
inline void colMax(float* result, const float* data, int dim, int numSamples) {
#ifdef __SSE3__
internal::colMaxImpl(result, data, dim, numSamples);
#else
naive::colMax(result, data, dim, numSamples);
#endif
}
template <>
......
paddle/math/Storage.cpp
浏览文件 @ ba68ce1a
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "Storage.h"
#include "Allocator.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
DEFINE_int32(pool_limit_size,
......@@ -62,7 +63,7 @@ PoolAllocator* StorageEngine::getGpuAllocator(int deviceId) {
}
if (gpuAllocator_[deviceId] == nullptr) {
std::string name =
"gpu" + std::to_string(deviceId) + std::string("_pool");
"gpu" + str::to_string(deviceId) + std::string("_pool");
gpuAllocator_[deviceId] =
new PoolAllocator(new GpuAllocator(), FLAGS_pool_limit_size, name);
}
......
paddle/pserver/ParameterServer2.cpp
浏览文件 @ ba68ce1a
......@@ -29,6 +29,7 @@ limitations under the License. */
#include "paddle/utils/Flags.h"
#include "paddle/utils/GlobalConstants.h"
#include "paddle/utils/Stat.h"
#include "paddle/utils/StringUtil.h"
DEFINE_int32(pserver_num_threads, 1, "number of threads for sync op exec");
DEFINE_double(async_lagged_ratio_min,
......@@ -218,7 +219,8 @@ void ParameterServer2::setConfig(const SetConfigRequest& request,
callback(response);
/// always defined, barrier slowest node function need it.
statSet_.reset(new StatSet("ParameterServer" + std::to_string(serverId_)));
statSet_.reset(new StatSet("ParameterServer" +
str::to_string(static_cast<int>(serverId_))));
}
real bufferSum(const std::vector<ParameterServer2::Buffer>& buffers) {
......
paddle/utils/CpuId.cpp
浏览文件 @ ba68ce1a
......@@ -19,7 +19,7 @@ limitations under the License. */
/// for MSVC
#define CPUID(info, x) __cpuidex(info, x, 0)
#else
#elif !defined(__ANDROID__)
#include <cpuid.h>
......@@ -31,6 +31,7 @@ limitations under the License. */
namespace paddle {
SIMDFlags::SIMDFlags() {
#if !defined(__ANDROID__)
unsigned int cpuInfo[4];
// CPUID: https://en.wikipedia.org/wiki/CPUID
// clang-format off
......@@ -51,6 +52,9 @@ SIMDFlags::SIMDFlags() {
CPUID(cpuInfo, 0x80000001);
simd_flags_ |= cpuInfo[2] & (1 << 16) ? SIMD_FMA4 : SIMD_NONE;
// clang-fotmat on
#else
simd_flags_ = SIMD_NEON;
#endif
}
SIMDFlags const* SIMDFlags::instance() {
......
paddle/utils/CpuId.h
浏览文件 @ ba68ce1a
......@@ -30,6 +30,7 @@ enum simd_t {
SIMD_AVX = 1 << 8, ///< AVX
SIMD_AVX2 = 1 << 9, ///< AVX 2
SIMD_AVX512 = 1 << 10, ///< AVX 512
SIMD_NEON = 1 << 11, /// NEON
};
// clang-format on
......@@ -96,6 +97,7 @@ private:
#define HAS_AVX HAS_SIMD(SIMD_AVX)
#define HAS_AVX2 HAS_SIMD(SIMD_AVX2)
#define HAS_AVX512 HAS_SIMD(SIMD_AVX512)
#define HAS_NEON HAS_SIMD(SIMD_NEON)
// clang-format on
/**
......
paddle/utils/Logging.cpp
浏览文件 @ ba68ce1a
......@@ -18,6 +18,7 @@ limitations under the License. */
*/
#include "Logging.h"
#include <cstdlib>
namespace paddle {
......
paddle/utils/StringUtil.h
浏览文件 @ ba68ce1a
......@@ -54,6 +54,25 @@ inline T toWithStatus(const std::string& s, bool* ok = nullptr) {
return v;
}
/**
* Cast type T to string with status.
*
* @param [in] v input value of type T.
* @param [out] ok status, return true if there is no error in casting. Set
* nullptr if user don't care error at all.
* @return result of casting. If error occurred, a empty string will be
* returned.
*/
template <class T>
inline std::string toWithStatus(const T v, bool* ok = nullptr) {
std::ostringstream sout;
sout << v;
if (ok) {
*ok = !sout.fail();
}
return sout.str();
}
/// Convert string to type T. It makes sure all the characters in s are used.
/// Otherwise it will abort.
///
......@@ -67,6 +86,18 @@ inline T to(const std::string& s) {
return v;
}
/// Convert type T to string.
///
/// @tparam T type of input value
/// @param v input value of type T
template <class T>
std::string to_string(T v) {
bool ok;
std::string s = toWithStatus<T>(v, &ok);
CHECK(ok) << "Cannot convert v(" << v << ") to type std::string";
return s;
}
} // namespace str
#undef DEFINE_STRING_CONVERSION
......
paddle/utils/Util.cpp
浏览文件 @ ba68ce1a
......@@ -15,11 +15,16 @@ limitations under the License. */
#include "Util.h"
#include <dirent.h>
#include <pmmintrin.h>
#include <signal.h>
#include <sys/stat.h>
#include <sys/types.h>
#ifdef __SSE__
#include <xmmintrin.h>
#endif
#ifdef __SSE3__
#include <pmmintrin.h>
#endif
#include <fstream>
#include <mutex>
......@@ -163,8 +168,12 @@ void initMain(int argc, char** argv) {
installProfilerSwitch();
#ifdef __SSE__
_MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON);
#endif
#ifdef __SSE3__
_MM_SET_DENORMALS_ZERO_MODE(_MM_DENORMALS_ZERO_ON);
#endif
if (FLAGS_seed == 0) {
unsigned int t = time(NULL);
......
paddle/utils/arch/linux/Locks.cpp
浏览文件 @ ba68ce1a
......@@ -15,6 +15,7 @@ limitations under the License. */
#include "paddle/utils/Locks.h"
#include <semaphore.h>
#include <unistd.h>
#include "paddle/utils/Logging.h"
namespace paddle {
class SemaphorePrivate {
......@@ -26,7 +27,10 @@ Semaphore::Semaphore(int initValue) : m(new SemaphorePrivate()) {
sem_init(&m->sem, 0, initValue);
}
Semaphore::~Semaphore() { sem_destroy(&m->sem); }
Semaphore::~Semaphore() {
sem_destroy(&m->sem);
delete m;
}
bool Semaphore::timeWait(struct timespec* ts) {
return (0 == sem_timedwait(&m->sem, ts));
......@@ -36,36 +40,101 @@ void Semaphore::wait() { sem_wait(&m->sem); }
void Semaphore::post() { sem_post(&m->sem); }
#ifdef PADDLE_USE_PTHREAD_SPINLOCK
class SpinLockPrivate {
public:
inline SpinLockPrivate() { pthread_spin_init(&lock_, 0); }
inline ~SpinLockPrivate() { pthread_spin_destroy(&lock_); }
inline void lock() { pthread_spin_lock(&lock_); }
inline void unlock() { pthread_spin_unlock(&lock_); }
pthread_spinlock_t lock_;
char padding_[64 - sizeof(pthread_spinlock_t)];
};
SpinLock::SpinLock() : m(new SpinLockPrivate()) {}
#else
SpinLock::~SpinLock() { delete m; }
#include <atomic>
class SpinLockPrivate {
public:
inline void lock() {
while (lock_.test_and_set(std::memory_order_acquire)) {
}
}
inline void unlock() { lock_.clear(std::memory_order_release); }
std::atomic_flag lock_ = ATOMIC_FLAG_INIT;
char padding_[64 - sizeof(lock_)]; // Padding to cache line size
};
void SpinLock::lock() { pthread_spin_lock(&m->lock_); }
#endif
void SpinLock::unlock() { pthread_spin_unlock(&m->lock_); }
SpinLock::SpinLock() : m(new SpinLockPrivate()) {}
SpinLock::~SpinLock() { delete m; }
void SpinLock::lock() { m->lock(); }
void SpinLock::unlock() { m->unlock(); }
#ifdef PADDLE_USE_PTHREAD_BARRIER
class ThreadBarrierPrivate {
public:
pthread_barrier_t barrier_;
inline explicit ThreadBarrierPrivate(int count) {
pthread_barrier_init(&barrier_, nullptr, count);
}
inline ~ThreadBarrierPrivate() { pthread_barrier_destroy(&barrier_); }
inline void wait() { pthread_barrier_wait(&barrier_); }
};
ThreadBarrier::ThreadBarrier(int count) : m(new ThreadBarrierPrivate()) {
pthread_barrier_init(&m->barrier_, nullptr, count);
}
#else
ThreadBarrier::~ThreadBarrier() {
pthread_barrier_destroy(&m->barrier_);
delete m;
}
class ThreadBarrierPrivate {
public:
pthread_mutex_t mutex_;
pthread_cond_t cond_;
int count_;
int tripCount_;
inline explicit ThreadBarrierPrivate(int cnt) : count_(0), tripCount_(cnt) {
CHECK_NE(cnt, 0);
CHECK_GE(pthread_mutex_init(&mutex_, 0), 0);
CHECK_GE(pthread_cond_init(&cond_, 0), 0);
}
inline ~ThreadBarrierPrivate() {
pthread_cond_destroy(&cond_);
pthread_mutex_destroy(&mutex_);
}
/**
* @brief wait
* @return true if the last wait
*/
inline bool wait() {
pthread_mutex_lock(&mutex_);
++count_;
if (count_ >= tripCount_) {
count_ = 0;
pthread_cond_broadcast(&cond_);
pthread_mutex_unlock(&mutex_);
return true;
} else {
pthread_cond_wait(&cond_, &mutex_);
pthread_mutex_unlock(&mutex_);
return false;
}
}
};
#endif
void ThreadBarrier::wait() { pthread_barrier_wait(&m->barrier_); }
ThreadBarrier::ThreadBarrier(int count) : m(new ThreadBarrierPrivate(count)) {}
ThreadBarrier::~ThreadBarrier() { delete m; }
void ThreadBarrier::wait() { m->wait(); }
} // namespace paddle
paddle/utils/tests/test_CustomStackTrace.cpp
浏览文件 @ ba68ce1a
......@@ -19,6 +19,7 @@ limitations under the License. */
#include "paddle/utils/CustomStackTrace.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
DEFINE_int32(test_thread_num, 10, "testing thread number");
......@@ -69,11 +70,11 @@ TEST(CustomStackTrace, normalTrain) {
while (countDown-- > 0) {
start.wait();
for (size_t i = 0; i < layerSize; ++i) {
tracer.push("layer_" + std::to_string(i));
tracer.push("layer_" + paddle::str::to_string(i));
}
tracer.pop("");
for (size_t i = 0; i < layerSize; ++i) {
tracer.pop("layer_" + std::to_string(layerSize - 1 - i));
tracer.pop("layer_" + paddle::str::to_string(layerSize - 1 - i));
}
finish.wait();
}
......@@ -89,7 +90,7 @@ TEST(CustomStackTrace, normalTest) {
while (countDown-- > 0) {
start.wait();
for (size_t i = 0; i < layerSize; ++i) {
tracer.push("layer_" + std::to_string(i));
tracer.push("layer_" + paddle::str::to_string(i));
}
tracer.clear(); // in forward test, tracer will clear after forward.
finish.wait();
......
paddle/utils/tests/test_CustomStackTracePrint.cpp
浏览文件 @ ba68ce1a
......@@ -13,13 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/utils/CustomStackTrace.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
int main(int argc, char** argv) {
paddle::initMain(argc, argv);
for (size_t i = 0; i < 1000; ++i) {
paddle::gLayerStackTrace.push("layer_" + std::to_string(i));
paddle::gLayerStackTrace.push("layer_" + paddle::str::to_string(i));
if (i == 998) {
throw "Unhandle exception";
}
......
paddle/utils/tests/test_SIMDFlags.cpp
浏览文件 @ ba68ce1a
......@@ -18,7 +18,8 @@ limitations under the License. */
using namespace paddle; // NOLINT
TEST(SIMDFlags, gccTest) {
#if (defined(__GNUC__) || defined(__GNUG__)) && !(defined(__clang__))
#if (defined(__GNUC__) || defined(__GNUG__)) && !(defined(__clang__)) && \
!defined(__arm__)
// clang-format off
CHECK(!__builtin_cpu_supports("sse") != HAS_SSE);
CHECK(!__builtin_cpu_supports("sse2") != HAS_SSE2);
......@@ -43,4 +44,5 @@ TEST(SIMDFlags, normalPrint) {
LOG(INFO) << "Has AVX: " << std::boolalpha << HAS_AVX;
LOG(INFO) << "Has AVX2: " << std::boolalpha << HAS_AVX2;
LOG(INFO) << "Has AVX512: " << std::boolalpha << HAS_AVX512;
LOG(INFO) << "Has NEON: " << std::boolalpha << HAS_NEON;
}
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ ba68ce1a
......@@ -1940,7 +1940,7 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
@layer_support()
def hsigmoid(input,
label,
num_classes,
num_classes=None,
name=None,
bias_attr=None,
param_attr=None,
......@@ -1956,8 +1956,7 @@ def hsigmoid(input,
.. code-block:: python
cost = hsigmoid(input=[layer1, layer2],
label=data_layer,
num_classes=3)
label=data_layer)
:param input: Input layers. It could be a LayerOutput or list/tuple of
LayerOutput.
......@@ -1965,12 +1964,14 @@ def hsigmoid(input,
:param label: Label layer.
:type label: LayerOutput
:param num_classes: number of classes.
:type num_classes: int
:type num_classes: int|None
:param name: layer name
:type name: basestring
:param bias_attr: Bias attribute. None means default bias.
False means no bias.
:type bias_attr: ParameterAttribute|False
:param param_attr: Parameter Attribute. None means default parameter.
:type param_attr: ParameterAttribute|None
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
......@@ -1990,6 +1991,11 @@ def hsigmoid(input,
assert isinstance(label, LayerOutput)
assert label.layer_type == LayerType.DATA
if num_classes is None:
num_classes = label.size
if num_classes is None or num_classes <= 2:
raise ValueError("hsigmoid label size must larger than 2.")
ipts_for_layer = []
parents = []
for each_input, each_param_attr in zip(input, param_attr):
......
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