Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into update_docker_docs

.pre-commit-config.yaml

@@ -24,7 +24,7 @@
         description: Format files with ClangFormat.
         entry: clang-format -i
         language: system
-        files: \.(c|cc|cxx|cpp|h|hpp|hxx)$
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
 -   repo: https://github.com/PaddlePaddle/pre-commit-golang
     sha: 8337620115c25ff8333f1b1a493bd031049bd7c0
     hooks:

CMakeLists.txt

@@ -36,8 +36,8 @@ include(simd)
 ################################ Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
-option(WITH_MKLDNN      "Compile PaddlePaddle with mkl-dnn support."    OFF)
-option(WITH_MKLML       "Compile PaddlePaddle with mklml package."      OFF)
+option(WITH_MKLDNN      "Compile PaddlePaddle with mkl-dnn support."    ${AVX_FOUND})
+option(WITH_MKLML       "Compile PaddlePaddle with mklml package."      ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
 option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        ON)
 option(WITH_SWIG_PY     "Compile PaddlePaddle with inference api"       ON)

Dockerfile

@@ -27,13 +27,16 @@ RUN apt-get update && \
     git python-pip python-dev openssh-server bison  \
     wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
     curl sed grep graphviz libjpeg-dev zlib1g-dev  \
-    python-numpy python-matplotlib gcc-4.8 g++-4.8 \
+    python-matplotlib gcc-4.8 g++-4.8 \
     automake locales clang-format-3.8 swig doxygen cmake  \
     liblapack-dev liblapacke-dev libboost-dev \
     clang-3.8 llvm-3.8 libclang-3.8-dev \
     net-tools && \
     apt-get clean -y
 
+# paddle is using numpy.flip, which is introduced since 1.12.0
+RUN pip --no-cache-dir install 'numpy>=1.12.0'
+
 # Install Go and glide
 RUN wget -O go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz && \
     tar -C /usr/local -xzf go.tgz && \

cmake/configure.cmake

@@ -74,8 +74,6 @@ if(WITH_MKLDNN)
         set(OPENMP_FLAGS "-fopenmp")
         set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
         set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
-        set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
-        set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
         set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
     else()

cmake/external/any.cmake

@@ -7,7 +7,7 @@ INCLUDE_DIRECTORIES(${ANY_SOURCE_DIR}/src/extern_lib_any)
 ExternalProject_Add(
     extern_lib_any
     ${EXTERNAL_PROJECT_LOG_ARGS}
-    GIT_REPOSITORY  "https://github.com/thelink2012/any.git"
+    GIT_REPOSITORY  "https://github.com/PaddlePaddle/any.git"
     GIT_TAG         "8fef1e93710a0edf8d7658999e284a1142c4c020"
     PREFIX          ${ANY_SOURCE_DIR}
     UPDATE_COMMAND  ""

cmake/external/gflags.cmake

@@ -28,7 +28,14 @@ INCLUDE_DIRECTORIES(${GFLAGS_INCLUDE_DIR})
 ExternalProject_Add(
     extern_gflags
     ${EXTERNAL_PROJECT_LOG_ARGS}
-    GIT_REPOSITORY  "https://github.com/gflags/gflags.git"
+    # TODO(yiwang): The annoying warnings mentioned in
+    # https://github.com/PaddlePaddle/Paddle/issues/3277 are caused by
+    # gflags.  I fired a PR https://github.com/gflags/gflags/pull/230
+    # to fix it.  Before it gets accepted by the gflags team, we use
+    # my personal fork, which contains above fix, temporarily.  Let's
+    # change this back to the official Github repo once my PR is
+    # merged.
+    GIT_REPOSITORY  "https://github.com/wangkuiyi/gflags.git"
     PREFIX          ${GFLAGS_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}

cmake/external/openblas.cmake

@@ -69,8 +69,13 @@ ENDIF(NOT ${CBLAS_FOUND})
 MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
 INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
 
-ADD_LIBRARY(cblas STATIC IMPORTED)
-SET_PROPERTY(TARGET cblas PROPERTY IMPORTED_LOCATION ${CBLAS_LIBRARIES})
+# FIXME(gangliao): generate cblas target to track all high performance
+# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
+SET(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cblas_dummy.c)
+FILE(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
+ADD_LIBRARY(cblas STATIC ${dummyfile})
+TARGET_LINK_LIBRARIES(cblas ${CBLAS_LIBRARIES})
+
 IF(NOT ${CBLAS_FOUND})
     ADD_DEPENDENCIES(cblas extern_openblas)
     LIST(APPEND external_project_dependencies cblas)

cmake/external/python.cmake

@@ -24,7 +24,6 @@ IF(WITH_PYTHON)
 ENDIF(WITH_PYTHON)
 
 SET(py_env "")
-SET(USE_VIRTUALENV_FOR_TEST 1)
 IF(PYTHONINTERP_FOUND)
     find_python_module(pip REQUIRED)
     find_python_module(numpy REQUIRED)

cmake/flags.cmake

@@ -115,7 +115,7 @@ set(COMMON_FLAGS
     -Wno-error=literal-suffix
     -Wno-error=sign-compare
     -Wno-error=unused-local-typedefs
-    -Wno-error=parentheses-equality # Warnings in Pybind11
+    -Wno-error=parentheses-equality # Warnings in pybind11
 )
 
 set(GPU_COMMON_FLAGS
@@ -195,6 +195,7 @@ endif()
 # Modern gpu architectures: Pascal
 if (CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0")
       list(APPEND __arch_flags " -gencode arch=compute_60,code=sm_60")
+      list(APPEND CUDA_NVCC_FLAGS --expt-relaxed-constexpr)
 endif()
 
 # Custom gpu architecture

cmake/generic.cmake

@@ -403,3 +403,16 @@ function(py_proto_compile TARGET_NAME)
   protobuf_generate_python(py_srcs ${py_proto_compile_SRCS})
   add_custom_target(${TARGET_NAME} ALL DEPENDS ${py_srcs})
 endfunction()
+
+function(py_test TARGET_NAME)
+  if(WITH_TESTING)
+    set(options STATIC static SHARED shared)
+    set(oneValueArgs "")
+    set(multiValueArgs SRCS DEPS)
+    cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})  
+    add_test(NAME ${TARGET_NAME}
+             COMMAND env PYTHONPATH=${PADDLE_PYTHON_PACKAGE_DIR}
+             python2 ${py_test_SRCS}
+             WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+  endif()
+endfunction()

cmake/util.cmake

@@ -149,9 +149,12 @@ endfunction()
 # Create a python unittest using run_python_tests.sh,
 # which takes care of making correct running environment
 function(add_python_test TEST_NAME)
-  add_test(NAME ${TEST_NAME}
-        COMMAND env PADDLE_PACKAGE_DIR=${PADDLE_PYTHON_PACKAGE_DIR}
-        bash ${PROJ_ROOT}/paddle/scripts/run_python_tests.sh
-        ${USE_VIRTUALENV_FOR_TEST} ${PYTHON_EXECUTABLE} ${ARGN}
+    foreach(arg ${ARGN})
+        get_filename_component(py_fn ${arg} NAME_WE)
+        set(TRG_NAME ${TEST_NAME}_${py_fn})
+        add_test(NAME ${TRG_NAME}
+                COMMAND env PYTHONPATH=${PADDLE_PYTHON_PACKAGE_DIR}
+                python2 ${arg}
                 WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+    endforeach()
 endfunction()

doc/design/mkldnn/README.MD

+# Intel® MKL-DNN on PaddlePaddle: Design Doc
+
+我们计划将Intel深度神经网络数学库(**MKL-DNN**\[[1](#references)\])集成到PaddlePaddle，充分展现英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。
+
+我们短期内的基本目标是：
+
+- 完成常用layer的MKL-DNN实现。
+- 完成常见深度神经网络VGG，GoogLeNet 和 ResNet的MKL-DNN实现。
+
+
+## Contents
+
+- [Overview](#overview)
+- [Actions](#actions)
+ 	- [CMake](#cmake)
+	- [Layers](#layers)
+	- [Activations](#activations)
+	- [Unit Tests](#unit-tests)
+	- [Protobuf Messages](#protobuf-messages)
+	- [Python API](#python-api)
+	- [Demos](#demos)
+	- [Benchmarking](#benchmarking)
+	- [Others](#others)
+- [Design Concerns](#design-concerns)
+
+## Overview
+
+我们会把MKL-DNN作为第三方库集成进PaddlePaddle，整体框架图
+<div align="center">
+<img src="image/overview.png" width=350><br/>
+Figure 1. PaddlePaddle on IA.
+</div>
+
+## Actions
+我们把集成方案大致分为了如下几个方面。
+
+### CMake
+我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。
+
+同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。
+
+所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
+
+**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。
+
+### Layers
+所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在
+`paddle/gserver/layers`中，并且文件名都会一以*Mkldnn*开头。
+
+所有MKL-DNN的layers都会继承于一个叫做`MkldnnLayer`的父类，该父类继承于PaddlePaddle的基类`Layer`。
+
+### Activations
+由于在PaddlePaddle中，激活函数是独立于layer概念的，所以会在`paddle/gserver/activations`目录下添加一个`MkldnnActivation.h`文件定义一些用于MKL-DNN的接口，实现方法还是会在`ActivationFunction.cpp`文件。
+
+### Unit Tests
+会在`paddle/gserver/test`目录下添加`test_Mkldnn.cpp`和`MkldnnTester.*`用于MKL-DNN的测试。
+
+Activation的测试，计划在PaddlePaddle原有的测试文件上直接添加新的测试type。
+
+### Protobuf Messages
+根据具体layer的需求可能会在`proto/ModelConfig.proto`里面添加必要的选项。
+
+### Python API
+目前只考虑**v1 API**。
+
+计划在`python/paddle/trainer/config_parser.py`里面添加`use_mkldnn`这个选择，方便用户选择使用MKL-DNN的layers。
+
+具体实现方式比如：
+
+```python
+use_mkldnn = bool(int(g_command_config_args.get("use_mkldnn", 0)))
+if use_mkldnn
+    self.layer_type = mkldnn_*
+```
+
+所有MKL-DNN的layer type会以*mkldnn_*开头，以示区分。 
+
+并且可能在`python/paddle/trainer_config_helper`目录下的`activations.py `和`layers.py`里面添加必要的MKL-DNN的接口。
+
+### Demos
+
+会在`v1_api_demo`目录下添加一个`mkldnn`的文件夹，里面放入一些用于MKL-DNN测试的demo脚本。
+
+### Benchmarking
+会考虑添加部分逻辑在`benchmark/paddle/image/run.sh`，添加使用MKL-DNN的测试。
+
+### Others
+1. 如果在使用MKL-DNN的情况下，会把CPU的Buffer对齐为64。
+2. 深入PaddlePaddle，寻找有没有其他可以优化的可能，进一步优化。比如可能会用OpenMP改进SGD的更新性能。
+
+## Design Concerns
+
+为了更好的符合PaddlePaddle的代码风格\[[2](#references)\]，同时又尽可能少的牺牲MKL-DNN的性能\[[3](#references)\]。
+
+我们总结出一些特别需要注意的点：
+
+1. 使用**deviceId_**。为了尽可能少的在父类Layer中添加变量或者函数，我们决定使用已有的`deviceId_`变量来区分layer的属性，定义`-2`为`MkldnnLayer`特有的设备ID。
+2. 重写父类Layer的**init**函数，修改`deviceId_`为`-2`，代表这个layer是用于跑在MKL-DNN的环境下。
+3. 创建`MkldnnMatrix`，用于管理MKL-DNN会用到的相关memory函数、接口以及会用的到格式信息。
+4. 创建`MkldnnBase`，定义一些除了layer和memory相关的类和函数。包括MKL-DNN会用到`MkldnnStream`和`CpuEngine`，和未来可能还会用到`FPGAEngine`等。
+5. 在**Argument**里添加两个`MkldnnMatrixPtr`，取名为`mkldnnValue`和`mkldnnGrad`，用于存放`MkldnnLayer`会用到的memory buffer。 并且添加函数cvt(会修改为一个更加合适的函数名)，用于处理"CPU device"和"MKL-DNN device"之间memory的相互转化。
+6. 在父类`Layer`中的`getOutput`函数中添加一段逻辑，用于判断`deviceId`，并针对device在MKL-DNN和CPU之间不统一的情况，做一个前期转换。 也就是调用`Argument`的cvt函数把output统一到需要的device上。
+7. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。
+
+## References
+
+1. [Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN)](https://github.com/01org/mkl-dnn "Intel MKL-DNN")
+2. [原来的方案](https://github.com/PaddlePaddle/Paddle/pull/3096)会引入**nextLayer**的信息。但是在PaddlePaddle中，无论是重构前的layer还是重构后的op，都不会想要知道next layer/op的信息。
+3. MKL-DNN的高性能格式与PaddlePaddle原有的`NCHW`不同(PaddlePaddle中的CUDNN部分使用的也是`NCHW`，所以不存在这个问题)，所以需要引入一个转换方法，并且只需要在必要的时候转换这种格式，才能更好的发挥MKL-DNN的性能。
+

paddle/.set_python_path.sh

@@ -21,22 +21,15 @@
 # 
 # It same as PYTHONPATH=${YOUR_PYTHON_PATH}:$PYTHONPATH {exec...}
 #
-
-if ! python -c "import paddle" >/dev/null 2>/dev/null; then
-  PYPATH=""
-  set -x
-  while getopts "d:" opt; do
+PYPATH=""
+set -x
+while getopts "d:" opt; do
   case $opt in
     d)
       PYPATH=$OPTARG
       ;;
   esac
-  done
-  shift $(($OPTIND - 1))
-  export PYTHONPATH=$PYPATH:$PYTHONPATH
-  $@
-else
-  echo "paddle package is already in your PYTHONPATH. But unittest need a clean environment."
-  echo "Please uninstall paddle package before start unittest. Try to 'pip uninstall paddle'"
-  exit 1
-fi
+done
+shift $(($OPTIND - 1))
+export PYTHONPATH=$PYPATH:$PYTHONPATH
+$@

paddle/api/test/CMakeLists.txt

-add_python_test(test_swig_api
-    testArguments.py testGradientMachine.py testMatrix.py testVector.py testTrain.py testTrainer.py)
+py_test(testTrain SRCS testTrain.py)
+py_test(testMatrix SRCS testMatrix.py)
+py_test(testVector SRCS testVector.py)
+py_test(testTrainer SRCS testTrainer.py)
+py_test(testArguments SRCS testArguments.py)
+py_test(testGradientMachine SRCS testGradientMachine.py)

paddle/cuda/src/hl_batch_transpose.cu

@@ -12,17 +12,15 @@ 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. */
 
-#include "hl_batch_transpose.h"
 #include "hl_base.h"
+#include "hl_batch_transpose.h"
 
 const int TILE_DIM = 64;
 const int BLOCK_ROWS = 16;
 
 // No bank-conflict transpose for a batch of data.
-__global__ void batchTransposeNoBankConflicts(real* odata,
-                                              const real* idata,
-                                              int numSamples, int width,
-                                              int height) {
+__global__ void batchTransposeNoBankConflicts(
+    real* odata, const real* idata, int numSamples, int width, int height) {
   __shared__ float tile[TILE_DIM][TILE_DIM + 1];
 
   const int x = blockIdx.x * TILE_DIM + threadIdx.x;
@@ -50,12 +48,12 @@ __global__ void batchTransposeNoBankConflicts(real* odata,
           newX] = tile[threadIdx.x][j];
 }
 
-void batchTranspose(const real* input, real* output, int width, int height,
-                    int batchSize) {
+void batchTranspose(
+    const real* input, real* output, int width, int height, int batchSize) {
   dim3 dimBlock(TILE_DIM, BLOCK_ROWS, 1);
   dim3 dimGrid(DIVUP(width, TILE_DIM), DIVUP(height, TILE_DIM), batchSize);
-  batchTransposeNoBankConflicts<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>
-      (output, input, batchSize, width, height);
+  batchTransposeNoBankConflicts<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>(
+      output, input, batchSize, width, height);
 
   CHECK_SYNC("batchTranspose failed!");
 }

paddle/cuda/src/hl_cuda_aggregate.cu

@@ -12,27 +12,23 @@ 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. */
 
-
+#include "hl_aggregate.h"
 #include "hl_base.h"
 #include "hl_cuda.h"
 #include "hl_cuda.ph"
-#include "hl_aggregate.h"
-#include "hl_thread.ph"
 #include "hl_matrix_base.cuh"
+#include "hl_thread.ph"
 #include "paddle/utils/Logging.h"
 
 /**
  * @brief   matrix row operator.
  */
-template<class Agg, int blockSize>
-__global__ void KeMatrixRowOp(Agg agg,
-                              real *E,
-                              real *Sum,
-                              int dimN) {
+template <class Agg, int blockSize>
+__global__ void KeMatrixRowOp(Agg agg, real *E, real *Sum, int dimN) {
   __shared__ real sum_s[blockSize];
-  int cnt = (dimN + blockSize -1) / blockSize;
-  int rowId = blockIdx.x + blockIdx.y*gridDim.x;
-  int index = rowId*dimN;
+  int cnt = (dimN + blockSize - 1) / blockSize;
+  int rowId = blockIdx.x + blockIdx.y * gridDim.x;
+  int index = rowId * dimN;
   int tid = threadIdx.x;
   int lmt = tid;
 
@@ -44,7 +40,7 @@ __global__ void KeMatrixRowOp(Agg agg,
   sum_s[tid] = tmp;
   __syncthreads();
 
-  for (int stride = blockSize/2; stride > 0; stride = stride/2) {
+  for (int stride = blockSize / 2; stride > 0; stride = stride / 2) {
     if (tid < stride) {
       sum_s[tid] = agg(sum_s[tid], sum_s[tid + stride]);
     }
@@ -58,29 +54,21 @@ __global__ void KeMatrixRowOp(Agg agg,
 }
 
 template <class Agg>
-void hl_matrix_row_op(Agg agg,
-                      real *A_d,
-                      real *C_d,
-                      int dimM,
-                      int dimN) {
+void hl_matrix_row_op(Agg agg, real *A_d, real *C_d, int dimM, int dimN) {
   int blocksX = dimM;
   int blocksY = 1;
   dim3 threads(128, 1);
   dim3 grid(blocksX, blocksY);
 
-  KeMatrixRowOp<Agg, 128><<< grid, threads, 0, STREAM_DEFAULT >>>
-           (agg, A_d, C_d, dimN);
+  KeMatrixRowOp<Agg, 128><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      agg, A_d, C_d, dimN);
 }
 
 void hl_matrix_row_sum(real *A_d, real *C_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
-  hl_matrix_row_op(aggregate::sum(),
-                   A_d,
-                   C_d,
-                   dimM,
-                   dimN);
+  hl_matrix_row_op(aggregate::sum(), A_d, C_d, dimM, dimN);
   CHECK_SYNC("hl_matrix_row_sum failed");
 }
 
@@ -88,11 +76,7 @@ void hl_matrix_row_max(real *A_d, real *C_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
-  hl_matrix_row_op(aggregate::max(),
-                   A_d,
-                   C_d,
-                   dimM,
-                   dimN);
+  hl_matrix_row_op(aggregate::max(), A_d, C_d, dimM, dimN);
   CHECK_SYNC("hl_matrix_row_max failed");
 }
 
@@ -100,23 +84,16 @@ void hl_matrix_row_min(real *A_d, real *C_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
-  hl_matrix_row_op(aggregate::min(),
-                   A_d,
-                   C_d,
-                   dimM,
-                   dimN);
+  hl_matrix_row_op(aggregate::min(), A_d, C_d, dimM, dimN);
   CHECK_SYNC("hl_matrix_row_min failed");
 }
 
 /**
  * @brief   matrix column operator.
  */
-template<class Agg>
-__global__ void KeMatrixColumnOp(Agg agg,
-                                 real *E,
-                                 real *Sum,
-                                 int dimM,
-                                 int dimN) {
+template <class Agg>
+__global__ void KeMatrixColumnOp(
+    Agg agg, real *E, real *Sum, int dimM, int dimN) {
   int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
   real tmp = agg.init();
   if (rowIdx < dimN) {
@@ -127,13 +104,10 @@ __global__ void KeMatrixColumnOp(Agg agg,
   }
 }
 
-template<class Agg, int blockDimX, int blockDimY>
-__global__ void KeMatrixColumnOp_S(Agg agg,
-                                   real *E,
-                                   real *Sum,
-                                   int dimM,
-                                   int dimN) {
-    __shared__ real _sum[blockDimX*blockDimY];
+template <class Agg, int blockDimX, int blockDimY>
+__global__ void KeMatrixColumnOp_S(
+    Agg agg, real *E, real *Sum, int dimM, int dimN) {
+  __shared__ real _sum[blockDimX * blockDimY];
   int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
   int index = threadIdx.y;
 
@@ -144,14 +118,14 @@ __global__ void KeMatrixColumnOp_S(Agg agg,
       index += blockDimY;
     }
   }
-  _sum[threadIdx.x + threadIdx.y*blockDimX] = tmp;
+  _sum[threadIdx.x + threadIdx.y * blockDimX] = tmp;
   __syncthreads();
 
   if (rowIdx < dimN) {
-    if (threadIdx.y ==0) {
+    if (threadIdx.y == 0) {
       real tmp = agg.init();
-      for (int i=0; i < blockDimY; i++) {
-        tmp = agg(tmp, _sum[threadIdx.x + i*blockDimX]);
+      for (int i = 0; i < blockDimY; i++) {
+        tmp = agg(tmp, _sum[threadIdx.x + i * blockDimX]);
       }
       Sum[rowIdx] = tmp;
     }
@@ -159,25 +133,21 @@ __global__ void KeMatrixColumnOp_S(Agg agg,
 }
 
 template <class Agg>
-void hl_matrix_column_op(Agg agg,
-                         real *A_d,
-                         real *C_d,
-                         int dimM,
-                         int dimN) {
+void hl_matrix_column_op(Agg agg, real *A_d, real *C_d, int dimM, int dimN) {
   if (dimN >= 8192) {
-    int blocksX = (dimN + 128 -1) / 128;
+    int blocksX = (dimN + 128 - 1) / 128;
     int blocksY = 1;
     dim3 threads(128, 1);
     dim3 grid(blocksX, blocksY);
-    KeMatrixColumnOp<Agg><<< grid, threads, 0, STREAM_DEFAULT >>>
-             (agg, A_d, C_d, dimM, dimN);
+    KeMatrixColumnOp<Agg><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        agg, A_d, C_d, dimM, dimN);
   } else {
-    int blocksX = (dimN + 32 -1) / 32;
+    int blocksX = (dimN + 32 - 1) / 32;
     int blocksY = 1;
     dim3 threads(32, 32);
     dim3 grid(blocksX, blocksY);
-    KeMatrixColumnOp_S<Agg, 32, 32><<< grid, threads, 0, STREAM_DEFAULT>>>
-             (agg, A_d, C_d, dimM, dimN);
+    KeMatrixColumnOp_S<Agg, 32, 32><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        agg, A_d, C_d, dimM, dimN);
   }
 
   return;
@@ -187,11 +157,7 @@ void hl_matrix_column_sum(real *A_d, real *C_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
-  hl_matrix_column_op(aggregate::sum(),
-                      A_d,
-                      C_d,
-                      dimM,
-                      dimN);
+  hl_matrix_column_op(aggregate::sum(), A_d, C_d, dimM, dimN);
 
   CHECK_SYNC("hl_matrix_column_sum failed");
 }
@@ -200,11 +166,7 @@ void hl_matrix_column_max(real *A_d, real *C_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
-  hl_matrix_column_op(aggregate::max(),
-                      A_d,
-                      C_d,
-                      dimM,
-                      dimN);
+  hl_matrix_column_op(aggregate::max(), A_d, C_d, dimM, dimN);
 
   CHECK_SYNC("hl_matrix_column_max failed");
 }
@@ -213,11 +175,7 @@ void hl_matrix_column_min(real *A_d, real *C_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
-  hl_matrix_column_op(aggregate::min(),
-                      A_d,
-                      C_d,
-                      dimM,
-                      dimN);
+  hl_matrix_column_op(aggregate::min(), A_d, C_d, dimM, dimN);
 
   CHECK_SYNC("hl_matrix_column_min failed");
 }
@@ -226,16 +184,16 @@ template <int blockSize>
 __global__ void KeVectorSum(real *E, real *Sum, int dimM) {
   __shared__ double sum_s[blockSize];
   int tid = threadIdx.x;
-  int index = blockIdx.y*blockDim.x+threadIdx.x;
+  int index = blockIdx.y * blockDim.x + threadIdx.x;
 
   sum_s[tid] = 0.0f;
   while (index < dimM) {
     sum_s[tid] += E[index];
-    index += blockDim.x*gridDim.y;
+    index += blockDim.x * gridDim.y;
   }
   __syncthreads();
 
-  for (int stride = blockSize/2; stride > 0; stride = stride/2) {
+  for (int stride = blockSize / 2; stride > 0; stride = stride / 2) {
     if (tid < stride) {
       sum_s[tid] += sum_s[tid + stride];
     }
@@ -261,36 +219,37 @@ void hl_vector_sum(real *A_d, real *C_h, int dimM) {
 
   struct _hl_event_st hl_event_st = {.cu_event = t_resource.event};
   hl_event_t hl_event = &hl_event_st;
-  while (!hl_cuda_event_is_ready(hl_event)) {}
+  while (!hl_cuda_event_is_ready(hl_event)) {
+  }
 
-  KeVectorSum<128><<< grid, threads, 0, STREAM_DEFAULT >>>
-           (A_d, t_resource.gpu_mem, dimM);
-  KeVectorSum<128><<< 1, threads, 0, STREAM_DEFAULT >>>
-           (t_resource.gpu_mem, t_resource.cpu_mem, 128);
+  KeVectorSum<128><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      A_d, t_resource.gpu_mem, dimM);
+  KeVectorSum<128><<<1, threads, 0, STREAM_DEFAULT>>>(
+      t_resource.gpu_mem, t_resource.cpu_mem, 128);
 
   hl_memcpy_async(C_h, t_resource.cpu_mem, sizeof(real), HPPL_STREAM_DEFAULT);
   hl_stream_record_event(HPPL_STREAM_DEFAULT, hl_event);
 
   hl_stream_synchronize(HPPL_STREAM_DEFAULT);
   cudaError_t err = (cudaError_t)hl_get_device_last_error();
-  CHECK_EQ(cudaSuccess, err)
-    << "CUDA error: " << hl_get_device_error_string((size_t)err);
+  CHECK_EQ(cudaSuccess, err) << "CUDA error: "
+                             << hl_get_device_error_string((size_t)err);
 }
 
 template <int blockSize>
 __global__ void KeVectorAbsSum(real *E, real *Sum, int dimM) {
   __shared__ double sum_s[blockSize];
   int tid = threadIdx.x;
-  int index = blockIdx.y*blockDim.x+threadIdx.x;
+  int index = blockIdx.y * blockDim.x + threadIdx.x;
 
   sum_s[tid] = 0.0f;
   while (index < dimM) {
     sum_s[tid] += abs(E[index]);
-    index += blockDim.x*gridDim.y;
+    index += blockDim.x * gridDim.y;
   }
   __syncthreads();
 
-  for (int stride = blockSize/2; stride > 0; stride = stride/2) {
+  for (int stride = blockSize / 2; stride > 0; stride = stride / 2) {
     if (tid < stride) {
       sum_s[tid] += sum_s[tid + stride];
     }
@@ -316,18 +275,19 @@ void hl_vector_abs_sum(real *A_d, real *C_h, int dimM) {
 
   struct _hl_event_st hl_event_st = {.cu_event = t_resource.event};
   hl_event_t hl_event = &hl_event_st;
-  while (!hl_cuda_event_is_ready(hl_event)) {}
+  while (!hl_cuda_event_is_ready(hl_event)) {
+  }
 
-  KeVectorAbsSum<128><<< grid, threads, 0, STREAM_DEFAULT >>>
-           (A_d, t_resource.gpu_mem, dimM);
-  KeVectorAbsSum<128><<< 1, threads, 0, STREAM_DEFAULT >>>
-           (t_resource.gpu_mem, t_resource.cpu_mem, 128);
+  KeVectorAbsSum<128><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      A_d, t_resource.gpu_mem, dimM);
+  KeVectorAbsSum<128><<<1, threads, 0, STREAM_DEFAULT>>>(
+      t_resource.gpu_mem, t_resource.cpu_mem, 128);
 
   hl_memcpy_async(C_h, t_resource.cpu_mem, sizeof(real), HPPL_STREAM_DEFAULT);
   hl_stream_record_event(HPPL_STREAM_DEFAULT, hl_event);
 
   hl_stream_synchronize(HPPL_STREAM_DEFAULT);
   cudaError_t err = (cudaError_t)hl_get_device_last_error();
-  CHECK_EQ(cudaSuccess, err)
-    << "CUDA error: " << hl_get_device_error_string((size_t)err);
+  CHECK_EQ(cudaSuccess, err) << "CUDA error: "
+                             << hl_get_device_error_string((size_t)err);
 }

paddle/cuda/src/hl_cuda_matrix.cu

@@ -12,22 +12,21 @@ 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. */
 
-
 #include "hl_base.h"
+#include "hl_device_functions.cuh"
+#include "hl_gpu_matrix_kernel.cuh"
 #include "hl_matrix.h"
-#include "hl_matrix_ops.cuh"
 #include "hl_matrix_apply.cuh"
+#include "hl_matrix_ops.cuh"
 #include "hl_sequence.h"
 #include "hl_sparse.ph"
 #include "paddle/utils/Logging.h"
-#include "hl_device_functions.cuh"
-#include "hl_gpu_matrix_kernel.cuh"
 
 DEFINE_MATRIX_UNARY_OP(Zero, a = 0);
-DEFINE_MATRIX_TERNARY_PARAMETER_OP(_add, TWO_PARAMETER, c = p1*a + p2*b);
-void hl_matrix_add(real *A_d,
-                   real *B_d,
-                   real *C_d,
+DEFINE_MATRIX_TERNARY_PARAMETER_OP(_add, TWO_PARAMETER, c = p1 * a + p2 * b);
+void hl_matrix_add(real* A_d,
+                   real* B_d,
+                   real* C_d,
                    int dimM,
                    int dimN,
                    real alpha,
@@ -36,8 +35,8 @@ void hl_matrix_add(real *A_d,
   CHECK_NOTNULL(B_d);
   CHECK_NOTNULL(C_d);
 
-  hl_gpu_apply_ternary_op
-    <real, ternary::_add<real>, 0, 0>(ternary::_add<real>(alpha, beta),
+  hl_gpu_apply_ternary_op<real, ternary::_add<real>, 0, 0>(
+      ternary::_add<real>(alpha, beta),
       A_d,
       B_d,
       C_d,
@@ -50,12 +49,11 @@ void hl_matrix_add(real *A_d,
 }
 
 #ifdef PADDLE_TYPE_DOUBLE
-    #define THRESHOLD   128
+#define THRESHOLD 128
 #else
-    #define THRESHOLD   64
+#define THRESHOLD 64
 #endif
-__device__ __forceinline__
-void findMax(real* I,
+__device__ __forceinline__ void findMax(real* I,
                                         real* dfMax_s,
                                         int blockSize,
                                         int base,
@@ -89,8 +87,7 @@ void findMax(real* I,
   __syncthreads();
 }
 
-__device__ __forceinline__
-void subMaxAndExp(real* I,
+__device__ __forceinline__ void subMaxAndExp(real* I,
                                              real* O,
                                              int curIdx,
                                              int nextIdx,
@@ -115,8 +112,7 @@ void subMaxAndExp(real* I,
   __syncthreads();
 }
 
-__device__ __forceinline__
-void valueSum(real* O,
+__device__ __forceinline__ void valueSum(real* O,
                                          real* dfMax_s,
                                          int blockSize,
                                          int base,
@@ -141,13 +137,8 @@ void valueSum(real* O,
   __syncthreads();
 }
 
-__device__ __forceinline__
-void divSum(real* O,
-            real sum,
-            int curIdx,
-            int nextIdx,
-            int blockSize,
-            int dimN) {
+__device__ __forceinline__ void divSum(
+    real* O, real sum, int curIdx, int nextIdx, int blockSize, int dimN) {
   while (curIdx < dimN) {
     O[nextIdx] /= sum;
     nextIdx += blockSize;
@@ -155,8 +146,7 @@ void divSum(real* O,
   }
 }
 
-__device__ __forceinline__
-void softmax(real* I,
+__device__ __forceinline__ void softmax(real* I,
                                         real* O,
                                         real* dfMax_s,
                                         int blockSize,
@@ -167,8 +157,7 @@ void softmax(real* I,
   __shared__ real max;
 
   // find the max number
-  findMax(I, dfMax_s, blockSize, base, curIdx,
-          nextIdx, dimN, &max);
+  findMax(I, dfMax_s, blockSize, base, curIdx, nextIdx, dimN, &max);
 
   // sub max Value and do Exp operation
   subMaxAndExp(I, O, base, nextIdx, blockSize, dimN, max);
@@ -181,8 +170,8 @@ void softmax(real* I,
   divSum(O, dfMax_s[0], curIdx, nextIdx, blockSize, dimN);
 }
 
-template<int blockSize>
-__global__ void KeMatrixSoftMax(real *O, real *I, int dimN) {
+template <int blockSize>
+__global__ void KeMatrixSoftMax(real* O, real* I, int dimN) {
   int base = threadIdx.x;
   __shared__ real dfMax_s[blockSize];
   int nextIdx = blockIdx.x * dimN + base;
@@ -191,19 +180,18 @@ __global__ void KeMatrixSoftMax(real *O, real *I, int dimN) {
   softmax(I, O, dfMax_s, blockSize, base, curIdx, nextIdx, dimN);
 }
 
-void hl_matrix_softmax(real *A_d, real *C_d, int dimM, int dimN) {
+void hl_matrix_softmax(real* A_d, real* C_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
   dim3 block(512, 1);
   dim3 grid(dimM, 1);
-  KeMatrixSoftMax<512>
-           <<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, dimN);
+  KeMatrixSoftMax<512><<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, dimN);
   CHECK_SYNC("hl_matrix_softmax failed");
 }
 
-template<int blockSize>
-__global__ void KeSequenceSoftMax(real *O, real *I, const int* index) {
+template <int blockSize>
+__global__ void KeSequenceSoftMax(real* O, real* I, const int* index) {
   int base = threadIdx.x;
   int bid = blockIdx.x;
   __shared__ real dfMax_s[blockSize];
@@ -217,8 +205,8 @@ __global__ void KeSequenceSoftMax(real *O, real *I, const int* index) {
   softmax(I, O, dfMax_s, blockSize, base, curIdx, nextIdx, dimN);
 }
 
-void hl_sequence_softmax_forward(real *A_d,
-                                 real *C_d,
+void hl_sequence_softmax_forward(real* A_d,
+                                 real* C_d,
                                  const int* index,
                                  int numSequence) {
   CHECK_NOTNULL(A_d);
@@ -226,59 +214,48 @@ void hl_sequence_softmax_forward(real *A_d,
 
   dim3 block(512, 1);
   dim3 grid(numSequence, 1);
-  KeSequenceSoftMax<512>
-           <<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, index);
+  KeSequenceSoftMax<512><<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, index);
   CHECK_SYNC("hl_sequence_softmax_forward failed");
 }
 
-__global__ void KeMatrixDerivative(real *grad_d,
-                                   real *output_d,
-                                   real *sftmaxSum_d,
-                                   int dimM,
-                                   int dimN) {
-  int rowIdx = blockIdx.x*blockDim.x + threadIdx.x;
-  int colIdx = blockIdx.y*blockDim.y + threadIdx.y;
+__global__ void KeMatrixDerivative(
+    real* grad_d, real* output_d, real* sftmaxSum_d, int dimM, int dimN) {
+  int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
+  int colIdx = blockIdx.y * blockDim.y + threadIdx.y;
   int index;
 
   if (rowIdx < dimM && colIdx < dimN) {
-    index = rowIdx*dimN + colIdx;
+    index = rowIdx * dimN + colIdx;
     grad_d[index] = output_d[index] * (grad_d[index] - sftmaxSum_d[rowIdx]);
   }
 }
 
-void hl_matrix_softmax_derivative(real *grad_d,
-                                  real *output_d,
-                                  real *sftmaxSum_d,
-                                  int dimM,
-                                  int dimN) {
+void hl_matrix_softmax_derivative(
+    real* grad_d, real* output_d, real* sftmaxSum_d, int dimM, int dimN) {
   CHECK_NOTNULL(grad_d);
   CHECK_NOTNULL(output_d);
   CHECK_NOTNULL(sftmaxSum_d);
 
   int blocksX = (dimM + 0) / 1;
-  int blocksY = (dimN + 1024 -1) / 1024;
+  int blocksY = (dimN + 1024 - 1) / 1024;
   dim3 threads(1, 1024);
   dim3 grid(blocksX, blocksY);
 
-  KeMatrixDerivative<<< grid, threads, 0, STREAM_DEFAULT >>>
-           (grad_d, output_d, sftmaxSum_d, dimM, dimN);
+  KeMatrixDerivative<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      grad_d, output_d, sftmaxSum_d, dimM, dimN);
   CHECK_SYNC("hl_matrix_softmax_derivative failed");
 }
 
-__global__ void KeMatrixMultiBinaryCrossEntropy(real* output,
-                                                real* entropy,
-                                                int* row,
-                                                int* col,
-                                                int dimM,
-                                                int dimN) {
+__global__ void KeMatrixMultiBinaryCrossEntropy(
+    real* output, real* entropy, int* row, int* col, int dimM, int dimN) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   if (index < dimM) {
-    for (int i = 0; i < dimN; i ++) {
+    for (int i = 0; i < dimN; i++) {
       entropy[index] -= log(1 - output[index * dimN + i]);
     }
-    int *row_col = col + row[index];
+    int* row_col = col + row[index];
     int col_num = row[index + 1] - row[index];
-    for (int i = 0; i < col_num; i ++) {
+    for (int i = 0; i < col_num; i++) {
       real o = output[index * dimN + row_col[i]];
       entropy[index] -= log(o / (1 - o));
     }
@@ -299,37 +276,30 @@ void hl_matrix_multi_binary_cross_entropy(real* output,
   dim3 threads(n_threads);
   dim3 grid(blocks);
   hl_csr_matrix mat = (hl_csr_matrix)(csr_mat->matrix);
-  KeMatrixMultiBinaryCrossEntropy<<< grid, threads, 0, STREAM_DEFAULT >>>
-          (output, entropy, mat->csr_row, mat->csr_col, dimM, dimN);
+  KeMatrixMultiBinaryCrossEntropy<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      output, entropy, mat->csr_row, mat->csr_col, dimM, dimN);
   CHECK_SYNC("hl_matrix_multi_binary_cross_entropy failed");
 }
 
-__global__ void KeMatrixMultiBinaryCrossEntropyBp(real* output,
-                                                  real* grad,
-                                                  int* row,
-                                                  int* col,
-                                                  int dimM,
-                                                  int dimN) {
+__global__ void KeMatrixMultiBinaryCrossEntropyBp(
+    real* output, real* grad, int* row, int* col, int dimM, int dimN) {
   int row_idx = blockIdx.x * blockDim.x + threadIdx.x;
   if (row_idx < dimM) {
-    for (int i = 0; i < dimN; i ++) {
+    for (int i = 0; i < dimN; i++) {
       int index = row_idx * dimN + i;
       grad[index] += 1.0 / (1 - output[index]);
     }
     int col_num = row[row_idx + 1] - row[row_idx];
-    int *row_col = col + row[row_idx];
-    for (int i = 0; i < col_num; i ++) {
+    int* row_col = col + row[row_idx];
+    for (int i = 0; i < col_num; i++) {
       int index = row_idx * dimN + row_col[i];
       grad[index] -= 1.0 / (output[index] * (1 - output[index]));
     }
   }
 }
 
-void hl_matrix_multi_binary_cross_entropy_bp(real* output,
-                                             real* grad,
-                                             hl_sparse_matrix_s csr_mat,
-                                             int dimM,
-                                             int dimN) {
+void hl_matrix_multi_binary_cross_entropy_bp(
+    real* output, real* grad, hl_sparse_matrix_s csr_mat, int dimM, int dimN) {
   CHECK_NOTNULL(output);
   CHECK_NOTNULL(grad);
   CHECK_NOTNULL(csr_mat);
@@ -339,16 +309,13 @@ void hl_matrix_multi_binary_cross_entropy_bp(real* output,
   dim3 threads(n_threads);
   dim3 grid(blocks);
   hl_csr_matrix mat = (hl_csr_matrix)(csr_mat->matrix);
-  KeMatrixMultiBinaryCrossEntropyBp<<< grid, threads, 0, STREAM_DEFAULT >>>
-          (output, grad, mat->csr_row, mat->csr_col, dimM, dimN);
+  KeMatrixMultiBinaryCrossEntropyBp<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      output, grad, mat->csr_row, mat->csr_col, dimM, dimN);
   CHECK_SYNC("hl_matrix_multi_binary_cross_entropy_bp failed");
 }
 
-__global__ void KeMatrixCrossEntropy(real* O,
-                                     real* E,
-                                     int* label,
-                                     int dimM,
-                                     int dimN) {
+__global__ void KeMatrixCrossEntropy(
+    real* O, real* E, int* label, int dimM, int dimN) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int newBase;
   if (index < dimM) {
@@ -358,59 +325,49 @@ __global__ void KeMatrixCrossEntropy(real* O,
   }
 }
 
-void hl_matrix_cross_entropy(real* A_d,
-                             real* C_d,
-                             int* label_d,
-                             int dimM,
-                             int dimN) {
+void hl_matrix_cross_entropy(
+    real* A_d, real* C_d, int* label_d, int dimM, int dimN) {
   CHECK_NOTNULL(A_d);
   CHECK_NOTNULL(C_d);
 
   int blocks = (dimM + 1024 - 1) / 1024;
   dim3 threads(1024, 1);
   dim3 grid(blocks, 1);
-  KeMatrixCrossEntropy<<< grid, threads, 0, STREAM_DEFAULT >>>
-           (A_d, C_d, label_d, dimM, dimN);
+  KeMatrixCrossEntropy<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      A_d, C_d, label_d, dimM, dimN);
   CHECK_SYNC("hl_matrix_cross_entropy failed");
 }
 
-__global__ void KeMatrixCrossEntropyBp(real* grad_d,
-                                       real* output_d,
-                                       int* label_d,
-                                       int dimM,
-                                       int dimN) {
-  int rowIdx = blockIdx.x*blockDim.x + threadIdx.x;
-  int colIdx = blockIdx.y*blockDim.y + threadIdx.y;
+__global__ void KeMatrixCrossEntropyBp(
+    real* grad_d, real* output_d, int* label_d, int dimM, int dimN) {
+  int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
+  int colIdx = blockIdx.y * blockDim.y + threadIdx.y;
   int index;
   if (rowIdx < dimM && colIdx < dimN) {
-    index = rowIdx*dimN + colIdx;
+    index = rowIdx * dimN + colIdx;
     if (label_d[rowIdx] == colIdx) {
       grad_d[index] -= 1.0f / output_d[index];
     }
   }
 }
 
-void hl_matrix_cross_entropy_bp(real* grad_d,
-                                real* output_d,
-                                int* label_d,
-                                int dimM,
-                                int dimN) {
+void hl_matrix_cross_entropy_bp(
+    real* grad_d, real* output_d, int* label_d, int dimM, int dimN) {
   CHECK_NOTNULL(grad_d);
   CHECK_NOTNULL(output_d);
   CHECK_NOTNULL(label_d);
 
-  int blocksX = (dimM + 0)/1;
-  int blocksY = (dimN + 1024 -1) / 1024;
+  int blocksX = (dimM + 0) / 1;
+  int blocksY = (dimN + 1024 - 1) / 1024;
   dim3 threads(1, 1024);
   dim3 grid(blocksX, blocksY);
-  KeMatrixCrossEntropyBp<<< grid, threads, 0, STREAM_DEFAULT >>>
-           (grad_d, output_d, label_d, dimM, dimN);
+  KeMatrixCrossEntropyBp<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      grad_d, output_d, label_d, dimM, dimN);
   CHECK_SYNC("hl_matrix_cross_entropy_bp failed");
 }
 
 void hl_matrix_zero_mem(real* data, int num) {
-  hl_gpu_apply_unary_op(
-        unary::Zero<real>(), data, 1, num, num);
+  hl_gpu_apply_unary_op(unary::Zero<real>(), data, 1, num, num);
 }
 
 __global__ void KeParamReluForward(real* output,
@@ -423,8 +380,8 @@ __global__ void KeParamReluForward(real* output,
   int ty = blockIdx.y * blockDim.y + threadIdx.y;
   if (tx < width && ty < height) {
     int index = ty * width + tx;
-    output[index] = input[index] > 0 ? input[index] :
-        input[index] * w[tx / partial_sum];
+    output[index] =
+        input[index] > 0 ? input[index] : input[index] * w[tx / partial_sum];
   }
 }
 
@@ -439,14 +396,14 @@ void hl_param_relu_forward(real* output,
   CHECK_NOTNULL(w);
   dim3 threads(16, 16);
   int blockX = (width + 16 - 1) / 16;
-  int blockY = (height + 16 -1) / 16;
+  int blockY = (height + 16 - 1) / 16;
   dim3 grid(blockX, blockY);
-  KeParamReluForward<<<grid, threads, 0, STREAM_DEFAULT>>>
-    (output, input, w, width, height, partial_sum);
+  KeParamReluForward<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      output, input, w, width, height, partial_sum);
   CHECK_SYNC("hl_param_relu_forward failed");
 }
 
-template<int blockSize>
+template <int blockSize>
 __global__ void KeParamReluBackWardW(real* grad_w,
                                      real* grad_o,
                                      real* input,
@@ -491,8 +448,8 @@ void hl_param_relu_backward_w(real* grad_w,
   int grid_num = width / partial_sum;
   dim3 threads(blockSize, 1);
   dim3 grid(grid_num, 1);
-  KeParamReluBackWardW<blockSize><<<grid, threads, 0, STREAM_DEFAULT>>>
-    (grad_w, grad_o, input, width, height, partial_sum);
+  KeParamReluBackWardW<blockSize><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      grad_w, grad_o, input, width, height, partial_sum);
   CHECK_SYNC("hl_param_relu_backward_w failed");
 }
 
@@ -524,19 +481,15 @@ void hl_param_relu_backward_diff(real* grad_o,
   CHECK_NOTNULL(diff);
   dim3 threads(16, 16);
   int blockX = (width + 16 - 1) / 16;
-  int blockY = (height + 16 -1) / 16;
+  int blockY = (height + 16 - 1) / 16;
   dim3 grid(blockX, blockY);
-  KeParamReluBackwardDiff<<<grid, threads, 0, STREAM_DEFAULT>>>
-      (grad_o, data, w, diff, width, height, partial_sum);
+  KeParamReluBackwardDiff<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      grad_o, data, w, diff, width, height, partial_sum);
   CHECK_SYNC("hl_param_relu_backward_diff failed");
 }
 
-__global__ void KeMatrixAddSharedBias(real* A,
-                                      real* B,
-                                      const int channel,
-                                      const int M,
-                                      const int N,
-                                      real scale) {
+__global__ void KeMatrixAddSharedBias(
+    real* A, real* B, const int channel, const int M, const int N, real scale) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int dim = N / channel;
   if (index < M * N) {
@@ -554,15 +507,14 @@ void hl_matrix_add_shared_bias(real* A_d,
                                real scale) {
   const int blocks = 512;
   const int grids = DIVUP(dimM * dimN, blocks);
-  KeMatrixAddSharedBias<<<grids, blocks, 0, STREAM_DEFAULT>>>
-    (A_d, B_d, channel, dimM, dimN, scale);
+  KeMatrixAddSharedBias<<<grids, blocks, 0, STREAM_DEFAULT>>>(
+      A_d, B_d, channel, dimM, dimN, scale);
   CHECK_SYNC("hl_matrix_add_shared_bias failed");
 }
 
-
 template <int blockSize>
-__global__ void KeMatrixCollectSharedBias(real *B,
-                                          real *A,
+__global__ void KeMatrixCollectSharedBias(real* B,
+                                          real* A,
                                           const int channel,
                                           const int M,
                                           const int N,
@@ -611,14 +563,13 @@ void hl_matrix_collect_shared_bias(real* B_d,
   const int limit = 64;
   int grids = (dimM * dim) < limit ? DIVUP(channel, blocks) : channel;
 
-  KeMatrixCollectSharedBias<blocks>
-      <<< grids, blocks, 0, STREAM_DEFAULT>>>
-      (B_d, A_d, channel, dimM, dimN, dim, limit, scale);
+  KeMatrixCollectSharedBias<blocks><<<grids, blocks, 0, STREAM_DEFAULT>>>(
+      B_d, A_d, channel, dimM, dimN, dim, limit, scale);
   CHECK_SYNC("hl_matrix_collect_shared_bias failed");
 }
 
-__global__ void keMatrixRotate(real* mat, real* matRot,
-                               int dimM, int dimN, bool clockWise) {
+__global__ void keMatrixRotate(
+    real* mat, real* matRot, int dimM, int dimN, bool clockWise) {
   int idx = blockIdx.x * blockDim.x + threadIdx.x;
   if (idx < dimM * dimN) {
     int i = idx / dimN;
@@ -631,13 +582,13 @@ __global__ void keMatrixRotate(real* mat, real* matRot,
   }
 }
 
-void hl_matrix_rotate(real *mat, real* matRot,
-                      int dimM, int dimN, bool clockWise) {
+void hl_matrix_rotate(
+    real* mat, real* matRot, int dimM, int dimN, bool clockWise) {
   CHECK_NOTNULL(mat);
   CHECK_NOTNULL(matRot);
   const int threads = 512;
   const int blocks = DIVUP(dimM * dimN, threads);
-    keMatrixRotate<<< blocks, threads, 0, STREAM_DEFAULT >>>
-            (mat, matRot, dimM, dimN, clockWise);
+  keMatrixRotate<<<blocks, threads, 0, STREAM_DEFAULT>>>(
+      mat, matRot, dimM, dimN, clockWise);
   CHECK_SYNC("hl_matrix_rotate failed");
 }

paddle/cuda/src/hl_cuda_sequence.cu

@@ -16,36 +16,36 @@ limitations under the License. */
 #include "hl_device_functions.cuh"
 #include "paddle/utils/Logging.h"
 
-__global__ void KeMaxSequenceForward(real *input,
-                                     const int *sequence,
+__global__ void KeMaxSequenceForward(real* input,
+                                     const int* sequence,
                                      real* output,
-                                     int *index,
+                                     int* index,
                                      int numSequences,
                                      int dim) {
   int dimIdx = threadIdx.x;
   int sequenceId = blockIdx.x;
   if (sequenceId >= numSequences) return;
   int start = sequence[sequenceId];
-  int end = sequence[sequenceId+1];
+  int end = sequence[sequenceId + 1];
 
   for (int i = dimIdx; i < dim; i += blockDim.x) {
     real tmp = -HL_FLOAT_MAX;
     int tmpId = -1;
     for (int insId = start; insId < end; insId++) {
-      if (tmp < input[insId*dim + i]) {
-        tmp = input[insId*dim + i];
+      if (tmp < input[insId * dim + i]) {
+        tmp = input[insId * dim + i];
         tmpId = insId;
       }
     }
-    output[sequenceId*dim + i] = tmp;
-    index[sequenceId*dim + i] = tmpId;
+    output[sequenceId * dim + i] = tmp;
+    index[sequenceId * dim + i] = tmpId;
   }
 }
 
 void hl_max_sequence_forward(real* input,
                              const int* sequence,
                              real* output,
-                             int *index,
+                             int* index,
                              int numSequences,
                              int dim) {
   CHECK_NOTNULL(input);
@@ -55,29 +55,23 @@ void hl_max_sequence_forward(real* input,
 
   dim3 threads(256, 1);
   dim3 grid(numSequences, 1);
-  KeMaxSequenceForward<<< grid, threads, 0, STREAM_DEFAULT >>>
-      (input, sequence, output, index, numSequences, dim);
+  KeMaxSequenceForward<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      input, sequence, output, index, numSequences, dim);
   CHECK_SYNC("hl_max_sequence_forward failed");
 }
 
-__global__ void KeMaxSequenceBackward(real *outputGrad,
-                                      int *index,
-                                      real* inputGrad,
-                                      int numSequences,
-                                      int dim) {
+__global__ void KeMaxSequenceBackward(
+    real* outputGrad, int* index, real* inputGrad, int numSequences, int dim) {
   int idx = threadIdx.x + blockIdx.x * blockDim.x;
   int colIdx = idx % dim;
-  if (idx < numSequences*dim) {
+  if (idx < numSequences * dim) {
     int insId = index[idx];
     inputGrad[insId * dim + colIdx] += outputGrad[idx];
   }
 }
 
-void hl_max_sequence_backward(real* outputGrad,
-                              int *index,
-                              real* inputGrad,
-                              int numSequences,
-                              int dim) {
+void hl_max_sequence_backward(
+    real* outputGrad, int* index, real* inputGrad, int numSequences, int dim) {
   CHECK_NOTNULL(outputGrad);
   CHECK_NOTNULL(index);
   CHECK_NOTNULL(inputGrad);
@@ -85,12 +79,12 @@ void hl_max_sequence_backward(real* outputGrad,
   unsigned int blocks = (numSequences * dim + 128 - 1) / 128;
   dim3 threads(128, 1);
   dim3 grid(blocks, 1);
-  KeMaxSequenceBackward<<< grid, threads, 0, STREAM_DEFAULT >>>
-      (outputGrad, index, inputGrad, numSequences, dim);
+  KeMaxSequenceBackward<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      outputGrad, index, inputGrad, numSequences, dim);
   CHECK_SYNC("hl_max_sequence_backward failed");
 }
 
-template<int blockDimX, int blockDimY, int gridDimX, bool AddRow>
+template <int blockDimX, int blockDimY, int gridDimX, bool AddRow>
 __global__ void KeMatrixAddRows(real* output,
                                 real* table,
                                 int* ids,
@@ -104,8 +98,8 @@ __global__ void KeMatrixAddRows(real* output,
   while (sampleId < numSamples) {
     int tableId = ids[sampleId];
     if ((0 <= tableId) && (tableId < tableSize)) {
-      real *outputData = output + sampleId * dim;
-      real *tableData = table + tableId * dim;
+      real* outputData = output + sampleId * dim;
+      real* tableData = table + tableId * dim;
       for (int i = idx; i < dim; i += blockDimX) {
         if (AddRow == 0) {
           outputData[i] += tableData[i];
@@ -114,15 +108,18 @@ __global__ void KeMatrixAddRows(real* output,
         }
       }
     }
-    sampleId += blockDimY*gridDimX;
+    sampleId += blockDimY * gridDimX;
   }
 }
 
-template<int blockDimX, int blockDimY, int gridDimX, bool seq2batch, bool isAdd>
-__global__
-void KeSequence2Batch(real *batch,
-                      real *sequence,
-                      const int *batchIndex,
+template <int blockDimX,
+          int blockDimY,
+          int gridDimX,
+          bool seq2batch,
+          bool isAdd>
+__global__ void KeSequence2Batch(real* batch,
+                                 real* sequence,
+                                 const int* batchIndex,
                                  int seqWidth,
                                  int batchCount) {
   int idx = threadIdx.x;
@@ -130,8 +127,8 @@ void KeSequence2Batch(real *batch,
   int id = blockIdx.x + idy * gridDimX;
   while (id < batchCount) {
     int seqId = batchIndex[id];
-    real* batchData = batch + id*seqWidth;
-    real* seqData = sequence + seqId*seqWidth;
+    real* batchData = batch + id * seqWidth;
+    real* seqData = sequence + seqId * seqWidth;
     for (int i = idx; i < seqWidth; i += blockDimX) {
       if (seq2batch) {
         if (isAdd) {
@@ -147,13 +144,13 @@ void KeSequence2Batch(real *batch,
         }
       }
     }
-    id += blockDimY*gridDimX;
+    id += blockDimY * gridDimX;
   }
 }
 
-void hl_sequence2batch_copy(real *batch,
-                            real *sequence,
-                            const int *batchIndex,
+void hl_sequence2batch_copy(real* batch,
+                            real* sequence,
+                            const int* batchIndex,
                             int seqWidth,
                             int batchCount,
                             bool seq2batch) {
@@ -164,18 +161,18 @@ void hl_sequence2batch_copy(real *batch,
   dim3 threads(128, 8);
   dim3 grid(8, 1);
   if (seq2batch) {
-    KeSequence2Batch<128, 8, 8, 1, 0><<< grid, threads, 0, STREAM_DEFAULT >>>
-      (batch, sequence, batchIndex, seqWidth, batchCount);
+    KeSequence2Batch<128, 8, 8, 1, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        batch, sequence, batchIndex, seqWidth, batchCount);
   } else {
-    KeSequence2Batch<128, 8, 8, 0, 0><<< grid, threads, 0, STREAM_DEFAULT >>>
-      (batch, sequence, batchIndex, seqWidth, batchCount);
+    KeSequence2Batch<128, 8, 8, 0, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        batch, sequence, batchIndex, seqWidth, batchCount);
   }
   CHECK_SYNC("hl_sequence2batch_copy failed");
 }
 
-void hl_sequence2batch_add(real *batch,
-                           real *sequence,
-                           int *batchIndex,
+void hl_sequence2batch_add(real* batch,
+                           real* sequence,
+                           int* batchIndex,
                            int seqWidth,
                            int batchCount,
                            bool seq2batch) {
@@ -186,18 +183,17 @@ void hl_sequence2batch_add(real *batch,
   dim3 threads(128, 8);
   dim3 grid(8, 1);
   if (seq2batch) {
-    KeSequence2Batch<128, 8, 8, 1, 1><<< grid, threads, 0, STREAM_DEFAULT >>>
-      (batch, sequence, batchIndex, seqWidth, batchCount);
+    KeSequence2Batch<128, 8, 8, 1, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        batch, sequence, batchIndex, seqWidth, batchCount);
   } else {
-    KeSequence2Batch<128, 8, 8, 0, 1><<< grid, threads, 0, STREAM_DEFAULT >>>
-      (batch, sequence, batchIndex, seqWidth, batchCount);
+    KeSequence2Batch<128, 8, 8, 0, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        batch, sequence, batchIndex, seqWidth, batchCount);
   }
   CHECK_SYNC("hl_sequence2batch_add failed");
 }
 
-template<bool normByTimes, bool seq2batch>
-__global__
-void KeSequence2BatchPadding(real* batch,
+template <bool normByTimes, bool seq2batch>
+__global__ void KeSequence2BatchPadding(real* batch,
                                         real* sequence,
                                         const int* sequenceStartPositions,
                                         const size_t sequenceWidth,
@@ -276,37 +272,49 @@ void hl_sequence2batch_copy_padding(real* batch,
   if (seq2batch) {
     /* sequence -> batch */
     if (normByTimes) {
-      KeSequence2BatchPadding<1, 1><<< grid, threads, 0, STREAM_DEFAULT >>>(
-              batch, sequence, sequenceStartPositions,
-              sequenceWidth, maxSequenceLength, numSequences);
+      KeSequence2BatchPadding<1, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
+          batch,
+          sequence,
+          sequenceStartPositions,
+          sequenceWidth,
+          maxSequenceLength,
+          numSequences);
     } else {
-      KeSequence2BatchPadding<0, 1><<< grid, threads, 0, STREAM_DEFAULT >>>(
-              batch, sequence, sequenceStartPositions,
-              sequenceWidth, maxSequenceLength, numSequences);
+      KeSequence2BatchPadding<0, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
+          batch,
+          sequence,
+          sequenceStartPositions,
+          sequenceWidth,
+          maxSequenceLength,
+          numSequences);
     }
   } else {
     /* batch -> sequence */
     if (normByTimes) {
-      KeSequence2BatchPadding<1, 0><<< grid, threads, 0, STREAM_DEFAULT >>>(
-              batch, sequence, sequenceStartPositions,
-              sequenceWidth, maxSequenceLength, numSequences);
+      KeSequence2BatchPadding<1, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
+          batch,
+          sequence,
+          sequenceStartPositions,
+          sequenceWidth,
+          maxSequenceLength,
+          numSequences);
     } else {
-      KeSequence2BatchPadding<0, 0><<< grid, threads, 0, STREAM_DEFAULT >>>(
-              batch, sequence, sequenceStartPositions,
-              sequenceWidth, maxSequenceLength, numSequences);
+      KeSequence2BatchPadding<0, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
+          batch,
+          sequence,
+          sequenceStartPositions,
+          sequenceWidth,
+          maxSequenceLength,
+          numSequences);
     }
   }
 
   CHECK_SYNC("hl_sequence2batch_copy_padding failed");
 }
 
-__device__ inline float my_rsqrt(float x) {
-  return rsqrtf(x);
-}
+__device__ inline float my_rsqrt(float x) { return rsqrtf(x); }
 
-__device__ inline double my_rsqrt(double x) {
-  return rsqrt(x);
-}
+__device__ inline double my_rsqrt(double x) { return rsqrt(x); }
 
 __global__ void KeSequenceAvgForward(real* dst,
                                      real* src,
@@ -327,8 +335,8 @@ __global__ void KeSequenceAvgForward(real* dst,
     for (int i = start; i < end; i++) {
       sum += src[i * width + col];
     }
-    sum = mode == 1 ? sum :
-        (mode == 0 ? sum / seqLength : sum * my_rsqrt((real)seqLength));
+    sum = mode == 1 ? sum : (mode == 0 ? sum / seqLength
+                                       : sum * my_rsqrt((real)seqLength));
     dst[gid] += sum;
   }
 }
@@ -349,8 +357,8 @@ void hl_sequence_avg_forward(real* dst,
   CHECK(mode == 0 || mode == 1 || mode == 2)
       << "mode error in hl_sequence_avg_forward!";
 
-  KeSequenceAvgForward<<< grid, block, 0, STREAM_DEFAULT >>>
-           (dst, src, starts, height, width, mode);
+  KeSequenceAvgForward<<<grid, block, 0, STREAM_DEFAULT>>>(
+      dst, src, starts, height, width, mode);
   CHECK_SYNC("hl_sequence_avg_forward failed");
 }
 
@@ -370,8 +378,8 @@ __global__ void KeSequenceAvgBackward(real* dst,
     int seqLength = end - start;
     if (seqLength == 0) return;
     real grad = src[gid];
-    grad = mode == 1 ? grad :
-        (mode == 0 ? grad / seqLength : grad * my_rsqrt((real)seqLength));
+    grad = mode == 1 ? grad : (mode == 0 ? grad / seqLength
+                                         : grad * my_rsqrt((real)seqLength));
     for (int i = start; i < end; i++) {
       dst[i * width + col] += grad;
     }
@@ -394,7 +402,7 @@ void hl_sequence_avg_backward(real* dst,
   CHECK(mode == 0 || mode == 1 || mode == 2)
       << "mode error in hl_sequence_avg_backward!";
 
-  KeSequenceAvgBackward<<< grid, block, 0, STREAM_DEFAULT >>>
-           (dst, src, starts, height, width, mode);
+  KeSequenceAvgBackward<<<grid, block, 0, STREAM_DEFAULT>>>(
+      dst, src, starts, height, width, mode);
   CHECK_SYNC("hl_sequence_avg_backward failed");
 }

paddle/cuda/src/hl_perturbation_util.cu

@@ -12,13 +12,12 @@ 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. */
 
-
-#include <cmath>
 #include <stdlib.h>
-#include "hl_cuda.h"
-#include "hl_time.h"
+#include <cmath>
 #include "hl_base.h"
+#include "hl_cuda.h"
 #include "hl_perturbation_util.cuh"
+#include "hl_time.h"
 
 #define _USE_MATH_DEFINES
 
@@ -30,10 +29,16 @@ limitations under the License. */
  * centerX, centerY: translation.
  * sourceX, sourceY: output coordinates in the original image.
  */
-__device__ void getTranformCoord(int x, int y, real theta, real scale,
-                                 real tgtCenter, real imgCenter,
-                                 real centerR, real centerC,
-                                 int* sourceX, int* sourceY) {
+__device__ void getTranformCoord(int x,
+                                 int y,
+                                 real theta,
+                                 real scale,
+                                 real tgtCenter,
+                                 real imgCenter,
+                                 real centerR,
+                                 real centerC,
+                                 int* sourceX,
+                                 int* sourceY) {
   real H[4] = {cosf(-theta), -sinf(-theta), sinf(-theta), cosf(-theta)};
 
   // compute coornidates in the rotated and scaled image
@@ -57,11 +62,17 @@ __device__ void getTranformCoord(int x, int y, real theta, real scale,
  * created by Wei Xu (genome), converted by Jiang Wang
  */
 
-__global__ void kSamplingPatches(const real* imgs, real* targets,
-                                 int imgSize, int tgtSize, const int channels,
-                                 int samplingRate, const real* thetas,
-                                 const real* scales, const int* centerRs,
-                                 const int* centerCs, const real padValue,
+__global__ void kSamplingPatches(const real* imgs,
+                                 real* targets,
+                                 int imgSize,
+                                 int tgtSize,
+                                 const int channels,
+                                 int samplingRate,
+                                 const real* thetas,
+                                 const real* scales,
+                                 const int* centerRs,
+                                 const int* centerCs,
+                                 const real padValue,
                                  const int numImages) {
   const int caseIdx = blockIdx.x * 4 + threadIdx.x;
   const int pxIdx = blockIdx.y * 128 + threadIdx.y;
@@ -80,8 +91,15 @@ __global__ void kSamplingPatches(const real* imgs, real* targets,
     const int pxY = pxIdx / tgtSize;
 
     int srcPxX, srcPxY;
-    getTranformCoord(pxX, pxY, thetas[imgIdx], scales[imgIdx], tgtCenter,
-                     imgCenter, centerCs[caseIdx], centerRs[caseIdx], &srcPxX,
+    getTranformCoord(pxX,
+                     pxY,
+                     thetas[imgIdx],
+                     scales[imgIdx],
+                     tgtCenter,
+                     imgCenter,
+                     centerCs[caseIdx],
+                     centerRs[caseIdx],
+                     &srcPxX,
                      &srcPxY);
 
     imgs += (imgIdx * imgPixels + srcPxY * imgSize + srcPxX) * channels;
@@ -100,10 +118,15 @@ __global__ void kSamplingPatches(const real* imgs, real* targets,
  *
  * created by Wei Xu
  */
-void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
-                                int*& gpuCenterR, int*& gpuCenterC,
-                                int numImages, int imgSize, real rotateAngle,
-                                real scaleRatio, int samplingRate,
+void hl_generate_disturb_params(real*& gpuAngle,
+                                real*& gpuScaleRatio,
+                                int*& gpuCenterR,
+                                int*& gpuCenterC,
+                                int numImages,
+                                int imgSize,
+                                real rotateAngle,
+                                real scaleRatio,
+                                int samplingRate,
                                 bool isTrain) {
   // The number of output samples.
   int numPatches = numImages * samplingRate;
@@ -123,7 +146,8 @@ void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
     for (int i = 0; i < numImages; i++) {
       r_angle[i] =
           (rotateAngle * M_PI / 180.0) * (rand() / (RAND_MAX + 1.0)  // NOLINT
-                                          - 0.5);
+                                          -
+                                          0.5);
       s_ratio[i] =
           1 + (rand() / (RAND_MAX + 1.0) - 0.5) * scaleRatio;  // NOLINT
     }
@@ -140,8 +164,10 @@ void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
         int pxY =
             (int)(real(imgSize - 1) * rand() / (RAND_MAX + 1.0));  // NOLINT
 
-        const real H[4] = {cos(-r_angle[i]), -sin(-r_angle[i]),
-                           sin(-r_angle[i]), cos(-r_angle[i])};
+        const real H[4] = {cos(-r_angle[i]),
+                           -sin(-r_angle[i]),
+                           sin(-r_angle[i]),
+                           cos(-r_angle[i])};
         real x = pxX - imgCenter;
         real y = pxY - imgCenter;
         real xx = H[0] * x + H[1] * y;
@@ -185,9 +211,12 @@ void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
   delete[] center_c;
 }
 
-void hl_conv_random_disturb_with_params(const real* images, int imgSize,
-                                        int tgtSize, int channels,
-                                        int numImages, int samplingRate,
+void hl_conv_random_disturb_with_params(const real* images,
+                                        int imgSize,
+                                        int tgtSize,
+                                        int channels,
+                                        int numImages,
+                                        int samplingRate,
                                         const real* gpuRotationAngle,
                                         const real* gpuScaleRatio,
                                         const int* gpuCenterR,
@@ -202,29 +231,59 @@ void hl_conv_random_disturb_with_params(const real* images, int imgSize,
   dim3 threadsPerBlock(4, 128);
   dim3 numBlocks(DIVUP(numPatches, 4), DIVUP(targetSize, 128));
 
-  kSamplingPatches <<<numBlocks, threadsPerBlock>>>
-      (images, target, imgSize, tgtSize, channels, samplingRate,
-      gpuRotationAngle, gpuScaleRatio, gpuCenterR, gpuCenterC,
-      paddingValue, numImages);
+  kSamplingPatches<<<numBlocks, threadsPerBlock>>>(images,
+                                                   target,
+                                                   imgSize,
+                                                   tgtSize,
+                                                   channels,
+                                                   samplingRate,
+                                                   gpuRotationAngle,
+                                                   gpuScaleRatio,
+                                                   gpuCenterR,
+                                                   gpuCenterC,
+                                                   paddingValue,
+                                                   numImages);
 
   hl_device_synchronize();
 }
 
-void hl_conv_random_disturb(const real* images, int imgSize,
-                            int tgtSize, int channels, int numImages,
-                            real scaleRatio, real rotateAngle,
-                            int samplingRate, real* gpu_r_angle,
-                            real* gpu_s_ratio, int* gpu_center_r,
-                            int* gpu_center_c, int paddingValue,
-                            bool isTrain, real* targets) {
+void hl_conv_random_disturb(const real* images,
+                            int imgSize,
+                            int tgtSize,
+                            int channels,
+                            int numImages,
+                            real scaleRatio,
+                            real rotateAngle,
+                            int samplingRate,
+                            real* gpu_r_angle,
+                            real* gpu_s_ratio,
+                            int* gpu_center_r,
+                            int* gpu_center_c,
+                            int paddingValue,
+                            bool isTrain,
+                            real* targets) {
   // generate the random disturbance sequence and the sampling locations
-  hl_generate_disturb_params(gpu_r_angle, gpu_s_ratio, gpu_center_r,
-                  gpu_center_c, numImages, imgSize, rotateAngle,
-                  scaleRatio, samplingRate, isTrain);
-
-  hl_conv_random_disturb_with_params(
-                  images, imgSize, tgtSize, channels, numImages,
-                  samplingRate, gpu_r_angle, gpu_s_ratio,
-                  gpu_center_r, gpu_center_r, paddingValue,
+  hl_generate_disturb_params(gpu_r_angle,
+                             gpu_s_ratio,
+                             gpu_center_r,
+                             gpu_center_c,
+                             numImages,
+                             imgSize,
+                             rotateAngle,
+                             scaleRatio,
+                             samplingRate,
+                             isTrain);
+
+  hl_conv_random_disturb_with_params(images,
+                                     imgSize,
+                                     tgtSize,
+                                     channels,
+                                     numImages,
+                                     samplingRate,
+                                     gpu_r_angle,
+                                     gpu_s_ratio,
+                                     gpu_center_r,
+                                     gpu_center_r,
+                                     paddingValue,
                                      targets);
 }

paddle/cuda/src/hl_table_apply.cu

@@ -12,15 +12,16 @@ 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. */
 
-
 #include "hl_base.h"
-#include "hl_device_functions.cuh"
 #include "hl_cuda.h"
+#include "hl_device_functions.cuh"
 #include "paddle/utils/Logging.h"
 
-template<int blockDimX, int blockDimY, int gridDimX, bool AddRow>
-__global__ void KeMatrixAddRows(real* output, int ldo,
-                                real* table, int ldt,
+template <int blockDimX, int blockDimY, int gridDimX, bool AddRow>
+__global__ void KeMatrixAddRows(real* output,
+                                int ldo,
+                                real* table,
+                                int ldt,
                                 int* ids,
                                 int numSamples,
                                 int tableSize,
@@ -31,8 +32,8 @@ __global__ void KeMatrixAddRows(real* output, int ldo,
   while (idy < numSamples) {
     int tableId = ids[idy];
     if ((0 <= tableId) && (tableId < tableSize)) {
-      real *out = output + idy * ldo;
-      real *tab = table + tableId * ldt;
+      real* out = output + idy * ldo;
+      real* tab = table + tableId * ldt;
       for (int i = idx; i < dim; i += blockDimX) {
         if (AddRow) {
           paddle::paddleAtomicAdd(&tab[i], out[i]);
@@ -45,8 +46,10 @@ __global__ void KeMatrixAddRows(real* output, int ldo,
   }
 }
 
-void hl_matrix_select_rows(real* output, int ldo,
-                           real* table, int ldt,
+void hl_matrix_select_rows(real* output,
+                           int ldo,
+                           real* table,
+                           int ldt,
                            int* ids,
                            int numSamples,
                            int tableSize,
@@ -57,14 +60,16 @@ void hl_matrix_select_rows(real* output, int ldo,
 
   dim3 threads(128, 8);
   dim3 grid(8, 1);
-  KeMatrixAddRows<128, 8, 8, 0><<< grid, threads, 0, STREAM_DEFAULT >>>
-    (output, ldo, table, ldt, ids, numSamples, tableSize, dim);
+  KeMatrixAddRows<128, 8, 8, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      output, ldo, table, ldt, ids, numSamples, tableSize, dim);
 
   CHECK_SYNC("hl_matrix_select_rows failed");
 }
 
-void hl_matrix_add_to_rows(real* table, int ldt,
-                           real* input, int ldi,
+void hl_matrix_add_to_rows(real* table,
+                           int ldt,
+                           real* input,
+                           int ldi,
                            int* ids,
                            int numSamples,
                            int tableSize,
@@ -75,16 +80,15 @@ void hl_matrix_add_to_rows(real* table, int ldt,
 
   dim3 threads(128, 8);
   dim3 grid(8, 1);
-  KeMatrixAddRows<128, 8, 8, 1><<< grid, threads, 0, STREAM_DEFAULT >>>
-    (input, ldi, table, ldt, ids, numSamples, tableSize, dim);
+  KeMatrixAddRows<128, 8, 8, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      input, ldi, table, ldt, ids, numSamples, tableSize, dim);
 
   CHECK_SYNC("hl_matrix_add_to_rows failed");
 }
 
-template<class T, int blockDimX, int gridDimX>
-__global__ void KeVectorSelect(T* dst, int sized,
-                               const T* src, int sizes,
-                               const int* ids, int sizei) {
+template <class T, int blockDimX, int gridDimX>
+__global__ void KeVectorSelect(
+    T* dst, int sized, const T* src, int sizes, const int* ids, int sizei) {
   int idx = threadIdx.x + blockDimX * blockIdx.x;
   while (idx < sizei) {
     int index = ids[idx];
@@ -95,9 +99,8 @@ __global__ void KeVectorSelect(T* dst, int sized,
 }
 
 template <class T>
-void hl_vector_select_from(T* dst, int sized,
-                           const T* src, int sizes,
-                           const int* ids, int sizei) {
+void hl_vector_select_from(
+    T* dst, int sized, const T* src, int sizes, const int* ids, int sizei) {
   CHECK_NOTNULL(dst);
   CHECK_NOTNULL(src);
   CHECK_NOTNULL(ids);
@@ -105,18 +108,17 @@ void hl_vector_select_from(T* dst, int sized,
 
   dim3 threads(512, 1);
   dim3 grid(8, 1);
-  KeVectorSelect<T, 512, 8><<< grid, threads, 0, STREAM_DEFAULT >>>
-    (dst, sized, src, sizes, ids, sizei);
+  KeVectorSelect<T, 512, 8><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      dst, sized, src, sizes, ids, sizei);
 
   CHECK_SYNC("hl_vector_select_from failed");
 }
 
-template
-void hl_vector_select_from(real* dst, int sized,
-                           const real* src, int sizes,
-                           const int* ids, int sizei);
-template
-void hl_vector_select_from(int* dst, int sized,
-                           const int* src, int sizes,
-                           const int* ids, int sizei);
-
+template void hl_vector_select_from(real* dst,
+                                    int sized,
+                                    const real* src,
+                                    int sizes,
+                                    const int* ids,
+                                    int sizei);
+template void hl_vector_select_from(
+    int* dst, int sized, const int* src, int sizes, const int* ids, int sizei);

paddle/cuda/src/hl_top_k.cu

@@ -12,45 +12,37 @@ 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. */
 
-
 #include "hl_base.h"
-#include "hl_top_k.h"
 #include "hl_sparse.ph"
+#include "hl_top_k.h"
 #include "paddle/utils/Logging.h"
 
 // using namespace hppl;
 
 struct Pair {
-  __device__ __forceinline__
-  Pair() {}
+  __device__ __forceinline__ Pair() {}
 
-  __device__ __forceinline__
-  Pair(real value, int id) : v_(value), id_(id) {}
+  __device__ __forceinline__ Pair(real value, int id) : v_(value), id_(id) {}
 
-  __device__ __forceinline__
-  void set(real value, int id) {
+  __device__ __forceinline__ void set(real value, int id) {
     v_ = value;
     id_ = id;
   }
 
-  __device__ __forceinline__
-  void operator=(const Pair& in) {
+  __device__ __forceinline__ void operator=(const Pair& in) {
     v_ = in.v_;
     id_ = in.id_;
   }
 
-  __device__ __forceinline__
-  bool operator<(const real value) const {
+  __device__ __forceinline__ bool operator<(const real value) const {
     return (v_ < value);
   }
 
-  __device__ __forceinline__
-  bool operator<(const Pair& in) const {
+  __device__ __forceinline__ bool operator<(const Pair& in) const {
     return (v_ < in.v_) || ((v_ == in.v_) && (id_ > in.id_));
   }
 
-  __device__ __forceinline__
-  bool operator>(const Pair& in) const {
+  __device__ __forceinline__ bool operator>(const Pair& in) const {
     return (v_ > in.v_) || ((v_ == in.v_) && (id_ < in.id_));
   }
 
@@ -58,8 +50,9 @@ struct Pair {
   int id_;
 };
 
-__device__ __forceinline__
-void addTo(Pair topK[], const Pair &p, int beamSize) {
+__device__ __forceinline__ void addTo(Pair topK[],
+                                      const Pair& p,
+                                      int beamSize) {
   for (int k = beamSize - 2; k >= 0; k--) {
     if (topK[k] < p) {
       topK[k + 1] = topK[k];
@@ -71,9 +64,8 @@ void addTo(Pair topK[], const Pair &p, int beamSize) {
   topK[0] = p;
 }
 
-template<int beamSize>
-__device__ __forceinline__
-void addTo(Pair topK[], const Pair &p) {
+template <int beamSize>
+__device__ __forceinline__ void addTo(Pair topK[], const Pair& p) {
   for (int k = beamSize - 2; k >= 0; k--) {
     if (topK[k] < p) {
       topK[k + 1] = topK[k];
@@ -85,9 +77,9 @@ void addTo(Pair topK[], const Pair &p) {
   topK[0] = p;
 }
 
-template<int blockSize>
-__device__ __forceinline__
-void getTopK(Pair topK[], real *src, int idx, int dim, int beamSize) {
+template <int blockSize>
+__device__ __forceinline__ void getTopK(
+    Pair topK[], real* src, int idx, int dim, int beamSize) {
   while (idx < dim) {
     if (topK[beamSize - 1] < src[idx]) {
       Pair tmp(src[idx], idx);
@@ -97,10 +89,9 @@ void getTopK(Pair topK[], real *src, int idx, int dim, int beamSize) {
   }
 }
 
-template<int blockSize>
-__device__ __forceinline__
-void getTopK(Pair topK[], real *src, int idx, int dim,
-             const Pair& max, int beamSize) {
+template <int blockSize>
+__device__ __forceinline__ void getTopK(
+    Pair topK[], real* src, int idx, int dim, const Pair& max, int beamSize) {
   while (idx < dim) {
     if (topK[beamSize - 1] < src[idx]) {
       Pair tmp(src[idx], idx);
@@ -112,10 +103,9 @@ void getTopK(Pair topK[], real *src, int idx, int dim,
   }
 }
 
-template<int blockSize>
-__device__ __forceinline__
-void getTopK(Pair topK[], real *val, int *col,
-             int idx, int dim, int beamSize) {
+template <int blockSize>
+__device__ __forceinline__ void getTopK(
+    Pair topK[], real* val, int* col, int idx, int dim, int beamSize) {
   while (idx < dim) {
     if (topK[beamSize - 1] < val[idx]) {
       Pair tmp(val[idx], col[idx]);
@@ -125,10 +115,14 @@ void getTopK(Pair topK[], real *val, int *col,
   }
 }
 
-template<int blockSize>
-__device__ __forceinline__
-void getTopK(Pair topK[], real *val, int *col, int idx, int dim,
-             const Pair& max, int beamSize) {
+template <int blockSize>
+__device__ __forceinline__ void getTopK(Pair topK[],
+                                        real* val,
+                                        int* col,
+                                        int idx,
+                                        int dim,
+                                        const Pair& max,
+                                        int beamSize) {
   while (idx < dim) {
     if (topK[beamSize - 1] < val[idx]) {
       Pair tmp(val[idx], col[idx]);
@@ -140,12 +134,16 @@ void getTopK(Pair topK[], real *val, int *col, int idx, int dim,
   }
 }
 
-template<int maxLength, int blockSize>
-__device__ __forceinline__
-void threadGetTopK(Pair topK[], int& beam, int beamSize,
+template <int maxLength, int blockSize>
+__device__ __forceinline__ void threadGetTopK(Pair topK[],
+                                              int& beam,
+                                              int beamSize,
                                               real* src,
-                   bool& firstStep, bool& isEmpty, Pair& max,
-                   int dim, const int tid) {
+                                              bool& firstStep,
+                                              bool& isEmpty,
+                                              Pair& max,
+                                              int dim,
+                                              const int tid) {
   if (beam > 0) {
     int length = beam < beamSize ? beam : beamSize;
     if (firstStep) {
@@ -160,8 +158,7 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
         }
       }
       if (!isEmpty) {
-        getTopK<blockSize>(topK + maxLength - beam, src, tid, dim,
-                           max, length);
+        getTopK<blockSize>(topK + maxLength - beam, src, tid, dim, max, length);
       }
     }
 
@@ -171,12 +168,17 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
   }
 }
 
-template<int maxLength, int blockSize>
-__device__ __forceinline__
-void threadGetTopK(Pair topK[], int& beam, int beamSize,
-                   real* val, int* col,
-                   bool& firstStep, bool& isEmpty, Pair& max,
-                   int dim, const int tid) {
+template <int maxLength, int blockSize>
+__device__ __forceinline__ void threadGetTopK(Pair topK[],
+                                              int& beam,
+                                              int beamSize,
+                                              real* val,
+                                              int* col,
+                                              bool& firstStep,
+                                              bool& isEmpty,
+                                              Pair& max,
+                                              int dim,
+                                              const int tid) {
   if (beam > 0) {
     int length = beam < beamSize ? beam : beamSize;
     if (firstStep) {
@@ -191,8 +193,8 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
         }
       }
       if (!isEmpty) {
-        getTopK<blockSize>(topK + maxLength - beam, val, col, tid, dim,
-                           max, length);
+        getTopK<blockSize>(
+            topK + maxLength - beam, val, col, tid, dim, max, length);
       }
     }
 
@@ -202,12 +204,16 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
   }
 }
 
-template<int maxLength, int blockSize>
-__device__ __forceinline__
-void blockReduce(Pair* shTopK, int* maxId, Pair topK[],
-                 real** topVal, int** topIds,
-                 int& beam, int& beamSize,
-                 const int tid, const int warp) {
+template <int maxLength, int blockSize>
+__device__ __forceinline__ void blockReduce(Pair* shTopK,
+                                            int* maxId,
+                                            Pair topK[],
+                                            real** topVal,
+                                            int** topIds,
+                                            int& beam,
+                                            int& beamSize,
+                                            const int tid,
+                                            const int warp) {
   while (true) {
     __syncthreads();
     if (tid < blockSize / 2) {
@@ -218,7 +224,7 @@ void blockReduce(Pair* shTopK, int* maxId, Pair topK[],
       }
     }
     __syncthreads();
-    for (int stride = blockSize / 4; stride > 0; stride = stride/2) {
+    for (int stride = blockSize / 4; stride > 0; stride = stride / 2) {
       if (tid < stride) {
         if (shTopK[maxId[tid]] < shTopK[maxId[tid + stride]]) {
           maxId[tid] = maxId[tid + stride];
@@ -257,10 +263,12 @@ void blockReduce(Pair* shTopK, int* maxId, Pair topK[],
  * 3. go to the second setp, until one thread's topK value is null;
  * 4. go to the first setp, until get the topK value.
  */
-template<int maxLength, int blockSize>
-__global__ void KeMatrixTopK(real* topVal, int ldv,
-                             int * topIds,
-                             real* src, int lds,
+template <int maxLength, int blockSize>
+__global__ void KeMatrixTopK(real* topVal,
+                             int ldv,
+                             int* topIds,
+                             real* src,
+                             int lds,
                              int dim,
                              int beamSize) {
   __shared__ Pair shTopK[blockSize];
@@ -281,18 +289,19 @@ __global__ void KeMatrixTopK(real* topVal, int ldv,
     topK[k].set(-HL_FLOAT_MAX, -1);
   }
   while (beamSize) {
-    threadGetTopK<maxLength, blockSize>
-      (topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
+    threadGetTopK<maxLength, blockSize>(
+        topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
 
     shTopK[tid] = topK[0];
-    blockReduce<maxLength, blockSize>
-      (shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
+    blockReduce<maxLength, blockSize>(
+        shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
   }
 }
 
-template<int maxLength, int blockSize>
-__global__ void KeSMatrixTopK(real* topVal, int ldv,
-                              int * topIds,
+template <int maxLength, int blockSize>
+__global__ void KeSMatrixTopK(real* topVal,
+                              int ldv,
+                              int* topIds,
                               real* val,
                               int* row,
                               int* col,
@@ -330,18 +339,20 @@ __global__ void KeSMatrixTopK(real* topVal, int ldv,
     topK[k].set(-HL_FLOAT_MAX, -1);
   }
   while (beamSize) {
-    threadGetTopK<maxLength, blockSize>
-      (topK, beam, beamSize, val, col, firstStep, isEmpty, max, dim, tid);
+    threadGetTopK<maxLength, blockSize>(
+        topK, beam, beamSize, val, col, firstStep, isEmpty, max, dim, tid);
 
     shTopK[tid] = topK[0];
-    blockReduce<maxLength, blockSize>
-      (shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
+    blockReduce<maxLength, blockSize>(
+        shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
   }
 }
 
-void hl_matrix_top_k(real* topVal, int ldv,
-                     int * topIds,
-                     real* src, int lds,
+void hl_matrix_top_k(real* topVal,
+                     int ldv,
+                     int* topIds,
+                     real* src,
+                     int lds,
                      int dim,
                      int beamSize,
                      int numSamples) {
@@ -353,33 +364,32 @@ void hl_matrix_top_k(real* topVal, int ldv,
 
   dim3 threads(256, 1);
   dim3 grid(numSamples, 1);
-  KeMatrixTopK<5, 256><<< grid, threads, 0, STREAM_DEFAULT >>>
-    (topVal, ldv, topIds, src, lds, dim, beamSize);
+  KeMatrixTopK<5, 256><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      topVal, ldv, topIds, src, lds, dim, beamSize);
 
   CHECK_SYNC("hl_matrix_top_k failed");
 }
 
-void hl_sparse_matrix_top_k(real* topVal, int ldv,
-                            int * topIds,
+void hl_sparse_matrix_top_k(real* topVal,
+                            int ldv,
+                            int* topIds,
                             hl_sparse_matrix_s src,
                             int beamSize,
                             int numSamples) {
   CHECK_NOTNULL(topVal);
   CHECK_NOTNULL(topIds);
   CHECK_NOTNULL(src);
-  CHECK_EQ(src->format, HL_SPARSE_CSR)
-    <<"sparse matrix format error!";
+  CHECK_EQ(src->format, HL_SPARSE_CSR) << "sparse matrix format error!";
 
   hl_csr_matrix csr = (hl_csr_matrix)src->matrix;
-  if (csr->csr_val == NULL || csr->csr_row == NULL ||
-      csr->csr_col == NULL) {
+  if (csr->csr_val == NULL || csr->csr_row == NULL || csr->csr_col == NULL) {
     LOG(FATAL) << "parameter src is null!";
   }
 
   dim3 threads(256, 1);
   dim3 grid(numSamples, 1);
-  KeSMatrixTopK<5, 256><<< grid, threads, 0, STREAM_DEFAULT >>>
-    (topVal, ldv, topIds, csr->csr_val, csr->csr_row, csr->csr_col, beamSize);
+  KeSMatrixTopK<5, 256><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      topVal, ldv, topIds, csr->csr_val, csr->csr_row, csr->csr_col, beamSize);
 
   CHECK_SYNC("hl_sparse_matrix_top_k failed");
 }
@@ -392,10 +402,12 @@ void hl_sparse_matrix_top_k(real* topVal, int ldv,
  * 3. go to the second setp, until one thread's topK value is null;
  * 4. go to the first setp, until get the topK value.
  */
-template<int maxLength, int blockSize>
-__global__ void KeMatrixTopKClassificationError(real* topVal, int ldv,
-                                                int * topIds,
-                                                real* src, int lds,
+template <int maxLength, int blockSize>
+__global__ void KeMatrixTopKClassificationError(real* topVal,
+                                                int ldv,
+                                                int* topIds,
+                                                real* src,
+                                                int lds,
                                                 int dim,
                                                 int beamSize,
                                                 int* label,
@@ -420,12 +432,12 @@ __global__ void KeMatrixTopKClassificationError(real* topVal, int ldv,
   }
 
   while (beamSize) {
-    threadGetTopK<maxLength, blockSize>
-      (topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
+    threadGetTopK<maxLength, blockSize>(
+        topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
 
     shTopK[tid] = topK[0];
-    blockReduce<maxLength, blockSize>
-      (shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
+    blockReduce<maxLength, blockSize>(
+        shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
   }
 
   __syncthreads();
@@ -440,9 +452,11 @@ __global__ void KeMatrixTopKClassificationError(real* topVal, int ldv,
   }
 }
 
-void hl_matrix_classification_error(real* topVal, int ldv,
+void hl_matrix_classification_error(real* topVal,
+                                    int ldv,
                                     int* topIds,
-                                   real* src, int lds,
+                                    real* src,
+                                    int lds,
                                     int dim,
                                     int topkSize,
                                     int numSamples,
@@ -456,9 +470,8 @@ void hl_matrix_classification_error(real* topVal, int ldv,
 
   dim3 threads(256, 1);
   dim3 grid(numSamples, 1);
-  KeMatrixTopKClassificationError<5, 256>
-  <<< grid, threads, 0, STREAM_DEFAULT >>>
-  (topVal, ldv, topIds, src, lds, dim, topkSize, label, recResult);
+  KeMatrixTopKClassificationError<5, 256><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      topVal, ldv, topIds, src, lds, dim, topkSize, label, recResult);
 
   CHECK_SYNC("hl_matrix_top_k classification error failed");
 }

paddle/framework/attribute.proto

@@ -12,7 +12,7 @@ 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. */
 
-syntax="proto2";
+syntax = "proto2";
 package paddle.framework;
 
 // Attribute Type for paddle's Op.

paddle/framework/op_desc.proto

@@ -12,7 +12,7 @@ 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. */
 
-syntax="proto2";
+syntax = "proto2";
 package paddle.framework;
 
 import "attribute.proto";

paddle/framework/op_proto.proto

@@ -15,10 +15,11 @@ limitations under the License. */
 // Protocol Message for 3rd-party language binding.
 //
 // Paddle Python package will use `OpProto` to generate op creation methods.
-// The op creation methods take user's input and generate `OpDesc` proto message,
+// The op creation methods take user's input and generate `OpDesc` proto
+// message,
 // then pass `OpDesc` to C++ side and create Op pointer.
 //
-syntax="proto2";
+syntax = "proto2";
 package paddle.framework;
 
 import "attribute.proto";
@@ -32,13 +33,14 @@ message AttrProto {
   // Supported attribute type.
   required AttrType type = 2;
 
-    // Supported attribute comments. It helps 3rd-party language generate doc-string.
+  // Supported attribute comments. It helps 3rd-party language generate
+  // doc-string.
   required string comment = 3;
 
   // If that attribute is generated, it means the Paddle third language
   // binding has responsibility to fill that attribute. End-User should
   // not set that attribute.
-    optional bool generated = 4 [default=false];
+  optional bool generated = 4 [ default = false ];
 }
 
 // Input or output message for 3rd-party language binding.
@@ -48,7 +50,8 @@ message VarProto {
   // e.g. `cos(a, b, output, ...)`, "a", "b", "output" are names.
   required string name = 1;
 
-    // The comment for that input. It helps 3rd-party language generate doc-string.
+  // The comment for that input. It helps 3rd-party language generate
+  // doc-string.
   required string comment = 2;
 
   // Is that input/output could be a list or not.
@@ -70,7 +73,7 @@ message VarProto {
   //      }
   //   }
   //
-    optional bool multiple = 3 [default=false];
+  optional bool multiple = 3 [ default = false ];
 
   // It marks that output is a temporary output. That output is not used by
   // user, but used by other op internally as input. If other op is not use
@@ -83,7 +86,7 @@ message VarProto {
   // attrs = {
   //   "temporary_index": [1]
   // }
-    optional bool temporary = 4 [default=false];
+  optional bool temporary = 4 [ default = false ];
 
   // The gradient of operator can be ignored immediately
   // e.g. operator AddOp, y = x1 + x2, the gradient of dy/dx1, dy/dx2
@@ -110,5 +113,4 @@ message OpProto {
 
   // The type of that Op.
   required string type = 5;
-
 }

paddle/framework/operator.cc

@@ -22,14 +22,14 @@ namespace framework {
 template <>
 Eigen::DefaultDevice& ExecutionContext::GetEigenDevice<
     platform::CPUPlace, Eigen::DefaultDevice>() const {
-  return *device_context_.get_eigen_device<Eigen::DefaultDevice>();
+  return *device_context_->get_eigen_device<Eigen::DefaultDevice>();
 }
 
 #ifndef PADDLE_ONLY_CPU
 template <>
 Eigen::GpuDevice&
 ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
-  return *device_context_.get_eigen_device<Eigen::GpuDevice>();
+  return *device_context_->get_eigen_device<Eigen::GpuDevice>();
 }
 #endif
 

paddle/framework/operator.h

@@ -174,7 +174,11 @@ class OperatorContext {
   template <typename T>
   T* Output(const size_t index) const {
     auto var = OutputVar(index);
-    PADDLE_ENFORCE(var != nullptr, "Output(%d) should not be nullptr", index);
+    PADDLE_ENFORCE(
+        var != nullptr,
+        "Output(%d) not be nullptr, which means variable [%s] does not "
+        "exist in scope",
+        index, op_.outputs_[index]);
     return var->GetMutable<T>();
   }
 
@@ -252,7 +256,7 @@ struct EigenDeviceConverter<platform::GPUPlace> {
 class ExecutionContext : public OperatorContext {
  public:
   ExecutionContext(const OperatorBase* op, const Scope& scope,
-                   const platform::DeviceContext& device_context)
+                   const platform::DeviceContext* device_context)
       : OperatorContext(op, scope), device_context_(device_context) {}
 
   template <typename PlaceType,
@@ -260,9 +264,9 @@ class ExecutionContext : public OperatorContext {
                 typename EigenDeviceConverter<PlaceType>::EigenDeviceType>
   DeviceType& GetEigenDevice() const;
 
-  platform::Place GetPlace() const { return device_context_.GetPlace(); }
+  platform::Place GetPlace() const { return device_context_->GetPlace(); }
 
-  const platform::DeviceContext& device_context_;
+  const platform::DeviceContext* device_context_;
 };
 
 class OpKernel {
@@ -311,7 +315,7 @@ class OperatorWithKernel : public OperatorBase {
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const final {
     auto& opKernel = AllOpKernels().at(type_).at(OpKernelKey(dev_ctx));
-    opKernel->Compute(ExecutionContext(this, scope, dev_ctx));
+    opKernel->Compute(ExecutionContext(this, scope, &dev_ctx));
   }
 
   static std::unordered_map<std::string /* op_type */, OpKernelMap>&

paddle/framework/operator_test.cc

@@ -157,22 +157,22 @@ class CPUKernalMultiInputsTest : public OpKernel {
     ASSERT_EQ(xs[2], "x2");
 
     auto inVar0 = ctx.MultiInputVar("xs");
-    ASSERT_EQ(inVar0.size(), 3);
+    ASSERT_EQ(inVar0.size(), 3U);
 
     auto intVar1 = ctx.InputVar("k");
     ASSERT_NE(intVar1, nullptr);
 
     auto outVar0 = ctx.MultiOutputVar("ys");
-    ASSERT_EQ(outVar0.size(), 2);
+    ASSERT_EQ(outVar0.size(), 2U);
 
     auto inTensor0 = ctx.MultiInput<Tensor>("xs");
-    ASSERT_EQ(inTensor0.size(), 3);
+    ASSERT_EQ(inTensor0.size(), 3U);
 
     auto intTensor1 = ctx.Input<Tensor>("k");
     ASSERT_NE(intTensor1, nullptr);
 
     auto outTensor0 = ctx.MultiOutput<Tensor>("ys");
-    ASSERT_EQ(outTensor0.size(), 2);
+    ASSERT_EQ(outTensor0.size(), 2U);
 
     auto k = ctx.op_.Input("k");
     ASSERT_EQ(k, "k0");

paddle/function/BlockExpandOpTest.cpp

@@ -18,10 +18,10 @@ limitations under the License. */
 namespace paddle {
 
 TEST(BlockExpandForward, real) {
-  for (size_t batchSize : {5, 32}) {
-    for (size_t channels : {1, 5, 32}) {
-      for (size_t inputHeight : {5, 33, 100}) {
-        for (size_t inputWidth : {5, 32, 96}) {
+  for (size_t batchSize : {5}) {
+    for (size_t channels : {1, 5}) {
+      for (size_t inputHeight : {5, 33}) {
+        for (size_t inputWidth : {5, 32}) {
           for (size_t block : {1, 3, 5}) {
             for (size_t stride : {1, 2}) {
               for (size_t padding : {0, 1}) {
@@ -61,10 +61,10 @@ TEST(BlockExpandForward, real) {
 }
 
 TEST(BlockExpandBackward, real) {
-  for (size_t batchSize : {5, 32}) {
-    for (size_t channels : {1, 5, 32}) {
-      for (size_t inputHeight : {5, 33, 100}) {
-        for (size_t inputWidth : {5, 32, 96}) {
+  for (size_t batchSize : {5}) {
+    for (size_t channels : {1, 5}) {
+      for (size_t inputHeight : {5, 33}) {
+        for (size_t inputWidth : {5, 32}) {
           for (size_t block : {1, 3, 5}) {
             for (size_t stride : {1, 2}) {
               for (size_t padding : {0, 1}) {

paddle/function/BufferArgTest.cpp

@@ -32,7 +32,7 @@ TEST(BufferTest, SequenceIdArg) {
                          sizeOfValuType(VALUE_TYPE_INT32));
   SequenceIdArg buffer(memory.getBuf(), shape);
   EXPECT_EQ(buffer.data(), memory.getBuf());
-  EXPECT_EQ(buffer.numSeqs(), 9);
+  EXPECT_EQ(buffer.numSeqs(), 9U);
 }
 
 }  // namespace paddle

paddle/function/ContextProjectionOpGpu.cu

@@ -12,8 +12,8 @@ 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. */
 
-#include "hl_base.h"
 #include "ContextProjectionOp.h"
+#include "hl_base.h"
 
 namespace paddle {
 
@@ -30,7 +30,7 @@ __global__ void KeContextProjectionForward(const real* input,
   int block_size = blockDim.x;
   int sequenceId = blockIdx.x;
   int seq_start = sequence[sequenceId];
-  int seq_end = sequence[sequenceId+1];
+  int seq_end = sequence[sequenceId + 1];
   real value = 0;
 
   int instances = seq_end - seq_start + context_length - 1;
@@ -50,7 +50,8 @@ __global__ void KeContextProjectionForward(const real* input,
           if (padding) {
             value =
                 weight[(begin_pad + i + context_start - (seq_end - seq_start)) *
-                         input_dim + idx];
+                           input_dim +
+                       idx];
           } else {
             continue;
           }
@@ -108,13 +109,25 @@ void hl_context_projection_forward(const real* input,
   dim3 grid(blocks_x, blocks_y);
 
   if (weight) {
-    KeContextProjectionForward<true><<< grid, threads, 0, STREAM_DEFAULT >>>
-      (input, sequence, weight, output, input_dim,
-       context_length, context_start, begin_pad);
+    KeContextProjectionForward<true><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        input,
+        sequence,
+        weight,
+        output,
+        input_dim,
+        context_length,
+        context_start,
+        begin_pad);
   } else {
-    KeContextProjectionForward<false><<< grid, threads, 0, STREAM_DEFAULT >>>
-      (input, sequence, weight, output, input_dim,
-       context_length, context_start, begin_pad);
+    KeContextProjectionForward<false><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        input,
+        sequence,
+        weight,
+        output,
+        input_dim,
+        context_length,
+        context_start,
+        begin_pad);
   }
   CHECK_SYNC("hl_context_projection_forward failed");
 }
@@ -148,7 +161,7 @@ __global__ void KeContextProjectionBackwardData(const real* out_grad,
   int block_size = blockDim.x;
   int sequenceId = blockIdx.x;
   int seq_start = sequence[sequenceId];
-  int seq_end = sequence[sequenceId+1];
+  int seq_end = sequence[sequenceId + 1];
   real value = 0;
 
   int instances = seq_end - seq_start + context_length - 1;
@@ -211,8 +224,8 @@ void hl_context_projection_backward_data(const real* out_grad,
   int blocks_y = 1;
   dim3 threads(block_size, 1);
   dim3 grid(blocks_x, blocks_y);
-  KeContextProjectionBackwardData<<< grid, threads, 0, STREAM_DEFAULT >>>
-    (out_grad, sequence, input_grad, input_dim, context_length, context_start);
+  KeContextProjectionBackwardData<<<grid, threads, 0, STREAM_DEFAULT>>>(
+      out_grad, sequence, input_grad, input_dim, context_length, context_start);
   CHECK_SYNC("hl_context_projection_backward_data failed");
 }
 
@@ -231,7 +244,7 @@ void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
                                       context_start);
 }
 
-template<int THREADS_X, int THREADS_Y>
+template <int THREADS_X, int THREADS_Y>
 __global__ void KeContextProjectionBackwardWeight(const real* out_grad,
                                                   const int* sequence,
                                                   real* w_grad,
@@ -254,17 +267,17 @@ __global__ void KeContextProjectionBackwardWeight(const real* out_grad,
   if (weight_idx < w_dim) {
     for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) {
       int seq_start = sequence[seqId];
-      int seq_end = sequence[seqId+1];
-      output_r = const_cast<real*>(out_grad)
-                    + seq_start * w_dim * context_length;
+      int seq_end = sequence[seqId + 1];
+      output_r =
+          const_cast<real*>(out_grad) + seq_start * w_dim * context_length;
 
       if (context_start < 0) {
         if (padId + context_start < 0) {
           instanceId = padId;
         } else {
           // begin_pad > 0;
-          instanceId = (padId - begin_pad) +
-            (seq_end - seq_start) - context_start;
+          instanceId =
+              (padId - begin_pad) + (seq_end - seq_start) - context_start;
         }
       } else {
         if (padId + (seq_end - seq_start) < context_start) {
@@ -275,10 +288,11 @@ __global__ void KeContextProjectionBackwardWeight(const real* out_grad,
         }
       }
 
-      int outx = (instanceId - context_length) < 0 ?
-                 instanceId : (context_length - 1);
-      int outy = (instanceId - context_length) < 0 ?
-                 0 : (instanceId - (context_length - 1));
+      int outx =
+          (instanceId - context_length) < 0 ? instanceId : (context_length - 1);
+      int outy = (instanceId - context_length) < 0
+                     ? 0
+                     : (instanceId - (context_length - 1));
       output_r += outy * w_dim * context_length + outx * w_dim;
       for (int j = outy; j < seq_end - seq_start; j++) {
         value += output_r[weight_idx];
@@ -290,7 +304,7 @@ __global__ void KeContextProjectionBackwardWeight(const real* out_grad,
   }
   __syncthreads();
 
-  for (int stride = THREADS_Y/2; stride > 0; stride = stride/2) {
+  for (int stride = THREADS_Y / 2; stride > 0; stride = stride / 2) {
     if (idy < stride) {
       sum_s[idy][idx] += sum_s[idy + stride][idx];
     }
@@ -339,16 +353,21 @@ void hl_context_projection_backward_weight(const real* out_grad,
   dim3 threads(threads_x, threads_y);
   dim3 grid(blocks_x, 1);
 
-  KeContextProjectionBackwardWeight<32, 32>
-    <<< grid, threads, 0, STREAM_DEFAULT >>>
-    (out_grad, sequence, w_grad, num_sequences, w_dim,
-     context_length, context_start, begin_pad);
+  KeContextProjectionBackwardWeight<32,
+                                    32><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      out_grad,
+      sequence,
+      w_grad,
+      num_sequences,
+      w_dim,
+      context_length,
+      context_start,
+      begin_pad);
   CHECK_SYNC("hl_context_projection_backward_weight failed");
 }
 
 template <>
-void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
-        const GpuMatrix& out_grad,
+void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
                                                       GpuMatrix& w_grad,
                                                       const GpuIVector& seq_vec,
                                                       size_t context_length,
@@ -378,15 +397,10 @@ void ContextProjectionBackward<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
                                                 size_t total_pad) {
   if (in_grad) {
     ContextProjectionBackwardData<DEVICE_TYPE_GPU>(
-                out_grad,
-                in_grad,
-                sequence,
-                context_length,
-                context_start);
+        out_grad, in_grad, sequence, context_length, context_start);
   }
   if (is_padding && w_grad) {
-        ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
-                out_grad,
+    ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(out_grad,
                                                      w_grad,
                                                      sequence,
                                                      context_length,

paddle/function/CosSimOpGpu.cu

@@ -12,13 +12,13 @@ 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. */
 
+#include "CosSimOp.h"
 #include "hl_base.h"
 #include "hl_device_functions.cuh"
-#include "CosSimOp.h"
 
 namespace paddle {
 
-template<int block_size>
+template <int block_size>
 __global__ void KeCosSim(real* output,
                          const real* input1,
                          const real* input2,
@@ -78,8 +78,8 @@ void hlCossim(real* output,
   dim3 threads(block_size, 1);
   dim3 grid(1, input1_height);
 
-  KeCosSim<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>
-    (output, input1, input2, width, input1_height, input2_height, scale);
+  KeCosSim<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      output, input1, input2, width, input1_height, input2_height, scale);
   CHECK_SYNC("hlCossim failed");
 }
 
@@ -99,7 +99,7 @@ void CosSimForward<DEVICE_TYPE_GPU>(GpuMatrix& out_mat,
   hlCossim(out, x, y, dim, in1_mat.getHeight(), in2_mat.getHeight(), scale);
 }
 
-template<int block_size>
+template <int block_size>
 __global__ void KeCosSimDerivative(const real* grad,
                                    const real* output,
                                    const real* prev_out_x,
@@ -148,13 +148,12 @@ __global__ void KeCosSimDerivative(const real* grad,
   if (xy[0] == 0) {
     real reciprocal = 1.0 / (sqrt(xx[0]) * sqrt(yy[0]));
     for (int index = tid; index < width; index += block_size) {
-      prev_grad_x[index] +=
-        scale * grad[ty] * prev_out_y[index] * reciprocal;
+      prev_grad_x[index] += scale * grad[ty] * prev_out_y[index] * reciprocal;
       if (input2_height > 1) {
-        prev_grad_y[index] +=
-          scale * grad[ty] * prev_out_x[index] * reciprocal;
+        prev_grad_y[index] += scale * grad[ty] * prev_out_x[index] * reciprocal;
       } else {
-        paddle::paddleAtomicAdd(prev_grad_y + index,
+        paddle::paddleAtomicAdd(
+            prev_grad_y + index,
             scale * grad[ty] * prev_out_x[index] * reciprocal);
       }
     }
@@ -163,16 +162,17 @@ __global__ void KeCosSimDerivative(const real* grad,
     real reciprocalSquareSumX = 1.0 / xx[0];
     real reciprocalSquareSumY = 1.0 / yy[0];
     for (int index = tid; index < width; index += block_size) {
-      prev_grad_x[index] += output[ty] * grad[ty] *
-        (prev_out_y[index] * reciprocalXY -
+      prev_grad_x[index] +=
+          output[ty] * grad[ty] * (prev_out_y[index] * reciprocalXY -
                                    prev_out_x[index] * reciprocalSquareSumX);
       if (input2_height > 1) {
-        prev_grad_y[index] += output[ty] * grad[ty] *
-          (prev_out_x[index] * reciprocalXY -
+        prev_grad_y[index] +=
+            output[ty] * grad[ty] * (prev_out_x[index] * reciprocalXY -
                                      prev_out_y[index] * reciprocalSquareSumY);
       } else {
-        paddle::paddleAtomicAdd(prev_grad_y + index, output[ty] * grad[ty] *
-          (prev_out_x[index] * reciprocalXY -
+        paddle::paddleAtomicAdd(
+            prev_grad_y + index,
+            output[ty] * grad[ty] * (prev_out_x[index] * reciprocalXY -
                                      prev_out_y[index] * reciprocalSquareSumY));
       }
     }
@@ -198,9 +198,17 @@ void hlCossimDerivative(const real* grad,
   const int block_size = 256;
   dim3 threads(block_size, 1);
   dim3 grid(1, input1_height);
-  KeCosSimDerivative<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>
-    (grad, output, prev_out_x, prev_out_y, prev_grad_x, prev_grad_y, width,
-        input1_height, input2_height, scale);
+  KeCosSimDerivative<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>(
+      grad,
+      output,
+      prev_out_x,
+      prev_out_y,
+      prev_grad_x,
+      prev_grad_y,
+      width,
+      input1_height,
+      input2_height,
+      scale);
   CHECK_SYNC("hlCossimDerivate failed");
 }
 
@@ -214,8 +222,8 @@ void CosSimBackward<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
                                      real scale) {
   CHECK(out_grad.getData() && out_val.getData() && in1_val.getData() &&
         in2_val.getData() && in1_grad.getData() && in2_grad.getData());
-  CHECK(out_grad.useGpu_ && out_val.useGpu_ && in1_val.useGpu_
-        && in2_val.useGpu_ && in1_grad.useGpu_ && in2_grad.useGpu_)
+  CHECK(out_grad.useGpu_ && out_val.useGpu_ && in1_val.useGpu_ &&
+        in2_val.useGpu_ && in1_grad.useGpu_ && in2_grad.useGpu_)
       << "Matrix types are not equally GPU";
 
   size_t dim = in1_val.getWidth();

paddle/function/CropOpGpu.cu

@@ -12,15 +12,23 @@ 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. */
 
-#include "hl_base.h"
 #include "CropOp.h"
+#include "hl_base.h"
 
 namespace paddle {
 
-__global__ void KeCrop(real* outputs, const real* inputs,
-                      int inC, int inH, int inW,
-                      int cropC, int cropH, int cropW,
-                      int outC, int outH, int outW, int nthreads) {
+__global__ void KeCrop(real* outputs,
+                       const real* inputs,
+                       int inC,
+                       int inH,
+                       int inW,
+                       int cropC,
+                       int cropH,
+                       int cropW,
+                       int outC,
+                       int outH,
+                       int outW,
+                       int nthreads) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < nthreads) {
     const int w = idx % outW;
@@ -58,16 +66,33 @@ void Crop<DEVICE_TYPE_GPU>(real* outputs,
   int blockSize = 1024;
   int gridSize = (nth + blockSize - 1) / blockSize;
 
-  KeCrop<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
-    (outputs, inputs, inC, inH, inW, cropC, cropH, cropW,
-     outC, outH, outW, nth);
+  KeCrop<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(outputs,
+                                                     inputs,
+                                                     inC,
+                                                     inH,
+                                                     inW,
+                                                     cropC,
+                                                     cropH,
+                                                     cropW,
+                                                     outC,
+                                                     outH,
+                                                     outW,
+                                                     nth);
   CHECK_SYNC("Crop");
 }
 
-__global__ void KeCropDiff(const real* inGrad, real* outGrad,
-                          int inC, int inH, int inW,
-                          int cropC, int cropH, int cropW,
-                          int outC, int outH, int outW, int nthreads) {
+__global__ void KeCropDiff(const real* inGrad,
+                           real* outGrad,
+                           int inC,
+                           int inH,
+                           int inW,
+                           int cropC,
+                           int cropH,
+                           int cropW,
+                           int outC,
+                           int outH,
+                           int outW,
+                           int nthreads) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < nthreads) {
     const int w = idx % inW;
@@ -107,9 +132,18 @@ void CropGrad<DEVICE_TYPE_GPU>(const real* inGrad,
   int blockSize = 1024;
   int gridSize = (nth + blockSize - 1) / blockSize;
 
-  KeCropDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
-    (inGrad, outGrad, inC, inH, inW, cropC, cropH, cropW,
-     outC, outH, outW, nth);
+  KeCropDiff<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(inGrad,
+                                                         outGrad,
+                                                         inC,
+                                                         inH,
+                                                         inW,
+                                                         cropC,
+                                                         cropH,
+                                                         cropW,
+                                                         outC,
+                                                         outH,
+                                                         outW,
+                                                         nth);
   CHECK_SYNC("CropGrad");
 }
 

paddle/function/MulOpGpu.cu

@@ -12,8 +12,8 @@ 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. */
 
-#include "hl_base.h"
 #include "MulOp.h"
+#include "hl_base.h"
 #include "paddle/math/Matrix.h"
 #include "paddle/math/SparseMatrix.h"
 

paddle/gserver/layers/ExpandConvLayer.cpp

@@ -57,8 +57,7 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
       convGradFilterType = "GemmConvGradFilter";
     }
 
-    if (FLAGS_use_nnpack) {
-      CHECK_EQ(isDeconv_, false);
+    if (FLAGS_use_nnpack && !isDeconv_) {
       createFunction(forward_,
                      "NNPACKConv",
                      FuncConfig()

paddle/gserver/layers/PrintLayer.cpp

@@ -29,7 +29,7 @@ public:
       vals.push_back(s.str());
     }
     size_t pos = 0;
-    int i = 0;
+    size_t i = 0;
     std::ostringstream s;
     const std::string& format = config_.user_arg();
     while (true) {