Merge remote-tracking branch 'origin/develop' into random_op

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)

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/cpplint.cmake

@@ -42,29 +42,21 @@ macro(add_style_check_target TARGET_NAME)
     if(WITH_STYLE_CHECK)
         set(SOURCES_LIST ${ARGN})
         list(REMOVE_DUPLICATES SOURCES_LIST)
-        list(SORT SOURCES_LIST)
-
         foreach(filename ${SOURCES_LIST})
-            set(LINT ON)
             foreach(pattern ${IGNORE_PATTERN})
                 if(filename MATCHES ${pattern})
-                    message(STATUS "DROP LINT ${filename}")
-                    set(LINT OFF)
+                    list(REMOVE_ITEM SOURCES_LIST ${filename})
                 endif()
             endforeach()
-            if(LINT MATCHES ON)
-                # cpplint code style
-                get_filename_component(base_filename ${filename} NAME)
-                set(CUR_GEN ${CMAKE_CURRENT_BINARY_DIR}/${base_filename}.cpplint)
-                add_custom_command(OUTPUT ${CUR_GEN} PRE_BUILD
+        endforeach()
+
+        if(SOURCES_LIST)
+            add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
                 COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py"
                         "--filter=${STYLE_FILTER}"
-                            "--write-success=${CUR_GEN}" ${filename}
-                    DEPENDS ${filename} ${PROJ_ROOT}/paddle/scripts/cpplint.py
+                        ${SOURCES_LIST}
+                COMMENT "cpplint: Checking source code style"
                 WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})        
-                add_custom_target(${base_filename}.cpplint DEPENDS ${CUR_GEN})
-                add_dependencies(${TARGET_NAME} ${base_filename}.cpplint)
         endif()
-        endforeach()
     endif()
 endmacro()

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/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()

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/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/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,
@@ -262,7 +266,7 @@ class ExecutionContext : public OperatorContext {
 
   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/function/nnpack/NNPACKConvOp.cpp

@@ -49,9 +49,7 @@ class NNPACKConvFunction : public ConvFunctionBase {
 public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
-    CHECK_EQ(groups_, (size_t)1);
     algorithm_ = get_nnp_convolution_algorithm(config.get<std::string>("algo"));
-    // algorithm_ = nnp_convolution_algorithm_auto;
     transform_strategy_ = nnp_convolution_transform_strategy_compute;
     nnp_status status = nnp_initialize();
     CHECK_EQ(status, nnp_status_success);
@@ -67,8 +65,7 @@ public:
     }
   }
 
-  virtual void check(const BufferArgs& inputs,
-                     const BufferArgs& outputs) override {
+  void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
     const TensorShape& input = inputs[0].shape();
     const TensorShape& filter = inputs[1].shape();
     const TensorShape& output = outputs[0].shape();
@@ -91,8 +88,8 @@ public:
     size_t filterHeight = getFilterHeight(filter);
     size_t filterWidth = getFilterWidth(filter);
     size_t outputChannels = output[1];
-    // size_t outputHeight = output[2];
-    // size_t outputWidth = output[3];
+    size_t outputHeight = output[2];
+    size_t outputWidth = output[3];
 
     nnp_size inputSize = {.width = inputWidth, .height = inputHeight};
     nnp_padding padding = {.top = (size_t)paddingH(),
@@ -171,20 +168,25 @@ public:
       }
     }
 
+    size_t inputOffset = inputChannels / groups_ * inputHeight * inputWidth;
+    size_t outputOffset = outputChannels / groups_ * outputHeight * outputWidth;
+    size_t filterOffset = filter.getElements() / groups_;
+
     if (batchSize == 1) {
+      for (size_t g = 0; g < groups_; g++) {
         nnp_status status =
             nnp_convolution_inference(algorithm_,
                                       transform_strategy_,
-                                    inputChannels,
-                                    outputChannels,
+                                      inputChannels / groups_,
+                                      outputChannels / groups_,
                                       inputSize,
                                       padding,
                                       kernelSize,
                                       outputSubsampling,
-                                    inputData,
-                                    filterData,
+                                      inputData + inputOffset * g,
+                                      filterData + filterOffset * g,
                                       nullptr, /* bias */
-                                    outputData,
+                                      outputData + outputOffset * g,
                                       bufferPtr,
                                       sizePtr,
                                       nnp_activation_identity,
@@ -192,21 +194,24 @@ public:
                                       threadpool_, /* threadpool */
                                       nullptr);
         CHECK_EQ(status, nnp_status_success);
+      }
     } else {
+      for (size_t g = 0; g < groups_; g++) {
         // only supports stride = 1
         CHECK_EQ(strideH(), 1);
         CHECK_EQ(strideW(), 1);
-      nnp_status status = nnp_convolution_output(algorithm_,
+        nnp_status status =
+            nnp_convolution_output(algorithm_,
                                    batchSize,
-                                                 inputChannels,
-                                                 outputChannels,
+                                   inputChannels / groups_,
+                                   outputChannels / groups_,
                                    inputSize,
                                    padding,
                                    kernelSize,
-                                                 inputData,
-                                                 filterData,
+                                   inputData + inputOffset * g,
+                                   filterData + filterOffset * g,
                                    nullptr, /* bias */
-                                                 outputData,
+                                   outputData + outputOffset * g,
                                    bufferPtr,
                                    sizePtr,
                                    nnp_activation_identity,
@@ -216,6 +221,7 @@ public:
         CHECK_EQ(status, nnp_status_success);
       }
     }
+  }
 
   static void create_nnpack_threadpool() {
     if (FLAGS_nnpack_num_threads && threadpool_ == nullptr) {

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/tests/CMakeLists.txt

 # gserver pacakge unittests
 
-file(GLOB_RECURSE GSERVER_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.h")
-file(GLOB_RECURSE GSERVER_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cpp")
-add_style_check_target(paddle_gserver ${GSERVER_SOURCES})
-add_style_check_target(paddle_gserver ${GSERVER_HEADER})
-
 ################### test_ProtoDataProvider ############
 add_unittest_without_exec(test_ProtoDataProvider
     test_ProtoDataProvider.cpp)

paddle/operators/add_op.cc

@@ -20,8 +20,8 @@ namespace operators {
 class AddOp : public OperatorWithKernel {
  protected:
   void InferShape(const InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputSize() == 2, "Input size of AddOp must be two");
-    PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one");
+    PADDLE_ENFORCE_EQ(ctx.InputSize(), 2);
+    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1);
     PADDLE_ENFORCE(ctx.InputVar(0) != nullptr && ctx.InputVar(1) != nullptr,
                    "Inputs of AddOp must all be set");
     PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr,

paddle/operators/mul_op.cc

@@ -23,12 +23,16 @@ class MulOp : public OperatorWithKernel {
     PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
     auto dim0 = ctx.Input<Tensor>(0)->dims();
     auto dim1 = ctx.Input<Tensor>(1)->dims();
-    PADDLE_ENFORCE(dim0.size() == 2 && dim1.size() == 2,
-                   "The input of mul op must be matrix");
-    PADDLE_ENFORCE(
-        dim0[1] == dim1[0],
+    PADDLE_ENFORCE_EQ(dim0.size(), 2,
+                      "input X(%s) should be a tensor with 2 dims, a matrix",
+                      ctx.op_.Input("X"));
+    PADDLE_ENFORCE_EQ(dim1.size(), 2,
+                      "input Y(%s) should be a tensor with 2 dims, a matrix",
+                      ctx.op_.Input("Y"));
+    PADDLE_ENFORCE_EQ(
+        dim0[1], dim1[0],
         "First matrix's width must be equal with second matrix's height.");
-    PADDLE_ENFORCE(ctx.OutputSize() == 1, "The mul op must take one output");
+    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "The mul op takes only one output");
     ctx.Output<Tensor>(0)->Resize({dim0[0], dim1[1]});
   }
 };

paddle/operators/recurrent_op.cc

@@ -36,6 +36,7 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
   InitMemories(step_scopes[0], true /*infer_shape_mode*/);
   Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
+
   for (size_t i = 0; i < seq_len_; i++) {
     if (i > 0) {
       rnn::LinkMemories(step_scopes, arg_->memories, i, -1,
@@ -56,6 +57,7 @@ void RecurrentAlgorithm::Run(const Scope& scope,
   Variable* net = scope.FindVar(arg_->step_net);
 
   for (size_t step_id = 0; step_id < seq_len_; step_id++) {
+    // create output alias variables
     if (step_id > 0) {
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1,
                         false /*infer_shape_mode*/);
@@ -67,22 +69,31 @@ void RecurrentAlgorithm::Run(const Scope& scope,
 }
 
 void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
-  // TODO(xxx) Only two scopes are needed for inference, this case will be
+  // TODO(superjom) Only two scopes are needed for inference, this case will be
   // supported later.
-  auto step_scopes =
-      scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
+  auto step_scopes_var = scope.FindVar(arg_->step_scopes);
+  PADDLE_ENFORCE(step_scopes_var != nullptr, "");
+  auto step_scopes = step_scopes_var->GetMutable<std::vector<Scope*>>();
+
+  // Now all variables in scope must be created outside of op.
+  auto net_var = scope.FindVar(arg_->step_net);
+  PADDLE_ENFORCE(net_var != nullptr, "no stepnet called %s in scope",
+                 arg_->step_net);
+  auto net_op = net_var->GetMutable<NetOp>();
+  PADDLE_ENFORCE(!net_op->outputs_.empty(), "net_op has no outputs");
 
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
       auto& step_scope = scope.NewScope();
 
-      // Now all variables in scope must be created outside of op.
-      auto net_op = scope.FindVar(arg_->step_net)->GetMutable<NetOp>();
+      // create step net's temp inputs
       for (auto& input : net_op->inputs_) {
         // the weight are located in parent scope
-        if (!step_scope.FindVar(input)) step_scope.NewVar(input);
+        if (!step_scope.FindVar(input))
+          step_scope.NewVar(input)->GetMutable<Tensor>();
       }
-      for (auto& output : net_op->outputs_) {
+      // create stepnet's outputs
+      for (const auto& output : net_op->outputs_) {
         step_scope.NewVar(output);
       }
       step_scopes->emplace_back(&step_scope);
@@ -100,6 +111,7 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
     Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
     if (infer_shape_mode) {
       pre_mem->Resize(boot_mem->dims());
+      PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2);
     } else {
       pre_mem->ShareDataWith<float>(*boot_mem);
     }

paddle/operators/rnn/recurrent_op_utils.cc

@@ -53,11 +53,13 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
     PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
                    outlinks[i].external);
     Tensor* output = output_var->GetMutable<Tensor>();
+
     if (infer_shape_mode) {
-      fmw::DDim step_dims = step_scopes[0]
-                                ->FindVar(outlinks[i].internal)
-                                ->GetMutable<Tensor>()
-                                ->dims();
+      auto step_scope_var = step_scopes[0]->FindVar(outlinks[i].internal);
+      PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
+                     outlinks[i].internal);
+      fmw::DDim step_dims =
+          step_scope_var->template GetMutable<Tensor>()->dims();
       std::vector<int> dims_vec = vectorize(step_dims);
       dims_vec.insert(dims_vec.begin(), seq_len);
       output->Resize(fmw::make_ddim(dims_vec));
@@ -79,14 +81,15 @@ void LinkMemories(const std::vector<Scope*>& scopes,
                   const std::vector<rnn::MemoryAttr>& memories,
                   const size_t step_id, const int offset,
                   bool infer_shape_mode) {
-  PADDLE_ENFORCE(step_id < scopes.size(),
-                 "step [%d] is out of range of step scopes' size [%d]", step_id,
-                 scopes.size());
-  PADDLE_ENFORCE(static_cast<int>(step_id) + offset >= 0,
+  PADDLE_ENFORCE_LT(step_id, scopes.size(),
+                    "step [%d] is out of range of step scopes' size [%d]",
+                    step_id, scopes.size());
+  PADDLE_ENFORCE_GE(static_cast<int>(step_id) + offset, 0,
                     "offset [%d] must be large than -[%d]", offset, step_id);
-  PADDLE_ENFORCE(step_id + offset < scopes.size(),
-                 "offset [%d] is out of range, it must be less than (%d - %d)",
-                 offset, scopes.size(), step_id);
+  PADDLE_ENFORCE_LT(
+      step_id + offset, scopes.size(),
+      "offset [%d] is out of range, it must be less than (%d - %d)", offset,
+      scopes.size(), step_id);
   auto scope = scopes[step_id];
   auto linked_scope = scopes[step_id + offset];
   for (auto& attr : memories) {

paddle/operators/sigmoid_op.cc

@@ -37,10 +37,8 @@ class SigmoidOpMaker : public OpProtoAndCheckerMaker {
 
 class SigmoidOpGrad : public OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {}
-  std::string DebugString() const override {
-    LOG(INFO) << "SigmoidGrad";
-    return "";
+  void InferShape(const InferShapeContext &ctx) const override {
+    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
   }
 };
 
@@ -51,3 +49,5 @@ REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker);
 REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, ops::SigmoidOpGrad);
 
 REGISTER_OP_CPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(sigmoid_grad,
+                       ops::SigmoidGradKernel<ops::CPUPlace, float>);

paddle/operators/sigmoid_op.cu

@@ -16,3 +16,5 @@
 #include "paddle/operators/sigmoid_op.h"
 
 REGISTER_OP_GPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(sigmoid_grad,
+                       ops::SigmoidGradKernel<ops::GPUPlace, float>);

paddle/operators/sigmoid_op.h

@@ -27,6 +27,7 @@ class SigmoidKernel : public OpKernel {
     auto output = context.Output<Tensor>(0);
     output->mutable_data<T>(context.GetPlace());
 
+    // The clipping is used in Paddle's raw implenmention
     auto X = EigenVector<T>::Flatten(*input);
     auto Y = EigenVector<T>::Flatten(*output);
     auto place = context.GetEigenDevice<Place>();
@@ -34,5 +35,23 @@ class SigmoidKernel : public OpKernel {
     Y.device(place) = 1.0 / (1.0 + (-1.0 * X).exp());
   }
 };
+
+template <typename Place, typename T>
+class SigmoidGradKernel : public OpKernel {
+ public:
+  void Compute(const ExecutionContext& context) const override {
+    auto Y_t = context.Input<Tensor>("Y");
+    auto dY_t = context.Input<Tensor>(framework::GradVarName("Y"));
+    auto dX_t = context.Output<Tensor>(framework::GradVarName("X"));
+
+    dX_t->mutable_data<T>(context.GetPlace());
+
+    auto dX = EigenVector<T>::Flatten(*dX_t);
+    auto Y = EigenVector<T>::Flatten(*Y_t);
+    auto dY = EigenVector<T>::Flatten(*dY_t);
+    dX.device(context.GetEigenDevice<Place>()) = dY * Y * (1. - Y);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle

python/paddle/v2/framework/tests/CMakeLists.txt

-add_python_test(test_framework
-    test_protobuf.py
-    test_scope.py
-    test_default_scope_funcs.py
-    test_op_creation_methods.py
-    test_net.py
-    test_tensor.py
-    test_fc_op.py
-    test_add_two_op.py
-    test_sgd_op.py
-    test_mul_op.py
-    test_mean_op.py
-    test_sigmoid_op.py
-    test_softmax_op.py
-    test_rowwise_add_op.py
-    test_random_op.py
-    test_network.py
-    gradient_checker.py)
+py_test(test_net SRCS test_net.py)
+
+py_test(test_fc_op SRCS test_fc_op.py)
+py_test(test_scope SRCS test_scope.py)
+
+py_test(test_tensor SRCS test_tensor.py)
+py_test(test_mul_op SRCS test_mul_op.py)
+
+py_test(test_network SRCS test_network.py)
+py_test(test_mean_op SRCS test_mean_op.py)
+
+py_test(test_protobuf SRCS test_protobuf.py)
+
+py_test(test_add_two_op SRCS test_add_two_op.py)
+py_test(test_sigmoid_op SRCS test_sigmoid_op.py)
+py_test(test_softmax_op SRCS test_softmax_op.py)
+
+py_test(gradient_checker SRCS gradient_checker.py)
+
+py_test(test_rowwise_add_op SRCS test_rowwise_add_op.py)
+
+py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
+py_test(test_op_creation_methods SRCS test_op_creation_methods.py)

python/paddle/v2/framework/tests/op_test_util.py

@@ -33,23 +33,28 @@ class OpTestMeta(type):
 
             for place in places:
                 for in_name in func.all_input_args:
-                    if hasattr(self, in_name):
+                    if hasattr(self, "inputs") and in_name in self.inputs:
                         kwargs[in_name] = in_name
                         var = scope.new_var(in_name).get_tensor()
-                        arr = getattr(self, in_name)
+                        arr = self.inputs[in_name]
                         var.set_dims(arr.shape)
                         var.set(arr, place)
                     else:
                         kwargs[in_name] = "@EMPTY@"
 
                 for out_name in func.all_output_args:
-                    if hasattr(self, out_name):
+                    if not hasattr(self, "outputs"):
+                        raise ValueError(
+                            "The test op must set self.outputs dict.")
+                    if out_name not in self.outputs:
+                        raise ValueError("The %s is not in self.outputs dict." %
+                                         (out_name))
                     kwargs[out_name] = out_name
                     scope.new_var(out_name).get_tensor()
 
                 for attr_name in func.all_attr_args:
-                    if hasattr(self, attr_name):
-                        kwargs[attr_name] = getattr(self, attr_name)
+                    if hasattr(self, "attrs") and attr_name in self.attrs:
+                        kwargs[attr_name] = self.attrs[attr_name]
 
                 op = func(**kwargs)
 
@@ -60,7 +65,7 @@ class OpTestMeta(type):
 
                 for out_name in func.all_output_args:
                     actual = numpy.array(scope.find_var(out_name).get_tensor())
-                    expect = getattr(self, out_name)
+                    expect = self.outputs[out_name]
                     numpy.isclose(actual, expect)
 
         obj.test_all = test_all

python/paddle/v2/framework/tests/test_add_two_op.py

@@ -12,9 +12,11 @@ class TestAddOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "add_two"
-        self.X = numpy.random.random((102, 105)).astype("float32")
-        self.Y = numpy.random.random((102, 105)).astype("float32")
-        self.Out = self.X + self.Y
+        self.inputs = {
+            'X': numpy.random.random((102, 105)).astype("float32"),
+            'Y': numpy.random.random((102, 105)).astype("float32")
+        }
+        self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
 
 
 class TestAddGradOp(unittest.TestCase):

python/paddle/v2/framework/tests/test_cross_entropy_op.py

@@ -7,15 +7,17 @@ class TestSGD(unittest.TestCase):
     __metaclass__ = OpTestMeta
 
     def setUp(self):
+        # TODO this unit test is not passed
         self.type = "onehot_cross_entropy"
         batch_size = 100
         class_num = 10
-        self.X = numpy.random.random((batch_size, class_num)).astype("float32")
-        self.label = 5 * numpy.ones(batch_size).astype("int32")
+        X = numpy.random.random((batch_size, class_num)).astype("float32")
+        label = 5 * numpy.ones(batch_size).astype("int32")
+        self.inputs = {'X': X, 'label': label}
         Y = []
         for i in range(0, batch_size):
-            Y.append(-numpy.log(self.X[i][self.label[i]]))
-        self.Y = numpy.array(Y).astype("float32")
+            Y.append(-numpy.log(X[i][label[i]]))
+        self.outputs = {'Y': numpy.array(Y).astype("float32")}
 
 
 # TODO(superjom) add gradient check

python/paddle/v2/framework/tests/test_mean_op.py

@@ -8,8 +8,8 @@ class TestMeanOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "mean"
-        self.X = np.random.random((32, 784)).astype("float32")
-        self.Out = np.mean(self.X)
+        self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+        self.outputs = {'Out': np.mean(self.inputs['X'])}
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_mul_op.py

@@ -8,9 +8,11 @@ class TestMulOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "mul"
-        self.X = np.random.random((32, 84)).astype("float32")
-        self.Y = np.random.random((84, 100)).astype("float32")
-        self.Out = np.dot(self.X, self.Y)
+        self.inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'Y': np.random.random((84, 100)).astype("float32")
+        }
+        self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_recurrent_op.py

+import logging
 import paddle.v2.framework.core as core
 import unittest
 import numpy as np
@@ -7,10 +8,9 @@ ops = creation.op_creations
 
 
 def create_tensor(scope, name, shape):
-    tensor = scope.create_var(name).get_tensor()
+    tensor = scope.new_var(name).get_tensor()
     tensor.set_dims(shape)
-    tensor.alloc_float()
-    tensor.set(np.random.random(shape))
+    tensor.set(np.random.random(shape), core.CPUPlace())
     return tensor
 
 
@@ -31,40 +31,36 @@ class TestRNN(unittest.TestCase):
         - h
     '''
 
-    def init(self):
     input_dim = 30
     batch_size = 50
     weight_dim = 15
+    sent_len = 11
 
-        self.scope = core.Scope(None)
-
-        # create vars
-        create_tensor(self.scope, "x", [batch_size, input_dim])
-        create_tensor(self.scope, "W", [input_dim, weight_dim])
-        create_tensor(self.scope, "U", [weight_dim, weight_dim])
-        create_tensor(self.scope, "h_boot", [batch_size, weight_dim])
-
-        x_alias = "x@alias"
-        y_alias = "y@alias"
-        memory = "h@alias"
-        prememory = "h@pre"
-        output = "rnn_out"
-        output_alias = "rnn_out@alias"
-
-        # create step net
-        stepnet_var = self.scope.create_var("stepnet")
-        stepnet = stepnet_var.get_net()
-        # stepnet = core.Net.create()
-        x_fc_op = ops.fc(X=x_alias, W="W", Y="Wx")
-        h_fc_op = ops.fc(X=prememory, W="U", Y="Uh")
-        sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
-        sig_op = ops.sigmoid(X="sum", Y=memory)
-        stepnet.add_op(x_fc_op)
-        stepnet.add_op(h_fc_op)
-        stepnet.add_op(sum_op)
-        stepnet.add_op(sig_op)
-        stepnet.complete_add_op(True)
+    def init(self):
 
+        self.scope = core.Scope()
+
+        self.create_global_variables()
+        self.create_step_net()
+        rnn_op = self.create_rnn_op()
+        ctx = core.DeviceContext.create(core.CPUPlace())
+        print 'infer_shape'
+        rnn_op.infer_shape(self.scope)
+
+        rnn_op.run(self.scope, ctx)
+
+    def create_global_variables(self):
+        # create inlink
+        create_tensor(self.scope, "x",
+                      [self.sent_len, self.batch_size, self.input_dim])
+        create_tensor(self.scope, "W", [self.input_dim, self.input_dim])
+        create_tensor(self.scope, "U", [self.input_dim, self.input_dim])
+        create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim])
+        self.scope.new_var("step_scopes")
+        self.scope.new_var("h@alias")
+        self.scope.new_var("h")
+
+    def create_rnn_op(self):
         # create RNNOp
         rnnop = ops.recurrent_op(
             # inputs
@@ -72,17 +68,27 @@ class TestRNN(unittest.TestCase):
             boot_memories=["h_boot"],
             step_net="stepnet",
             # outputs
-            outlinks=[output],
+            outlinks=["h"],
             step_scopes="step_scopes",
             # attributes
             inlink_alias=["x@alias"],
-            outlink_alias=[output_alias],
-            pre_memories=[prememory],
-            memories=[memory])
+            outlink_alias=["h@alias"],
+            pre_memories=["h@pre"],
+            memories=["h@alias"])
+        return rnnop
 
-        ctx = core.DeviceContext.cpu_context()
-        rnnop.infer_shape(self.scope)
-        rnnop.run(self.scope, ctx)
+    def create_step_net(self):
+        var = self.scope.new_var("stepnet")
+        stepnet = var.get_net()
+
+        x_fc_op = ops.fc(X="x@alias", W="W", Y="Wx")
+        h_fc_op = ops.fc(X="h@pre", W="U", Y="Uh")
+        sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
+        sig_op = ops.sigmoid(X="sum", Y="h@alias")
+
+        for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
+            stepnet.add_op(op)
+        stepnet.complete_add_op(True)
 
     def test_recurrent(self):
         self.init()

python/paddle/v2/framework/tests/test_rowwise_add_op.py

@@ -8,9 +8,11 @@ class TestRowwiseAddOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "rowwise_add"
-        self.X = np.random.random((32, 84)).astype("float32")
-        self.b = np.random.random(84).astype("float32")
-        self.Out = np.add(self.X, self.b)
+        self.inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'b': np.random.random(84).astype("float32")
+        }
+        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_sgd_op.py

@@ -8,10 +8,13 @@ class TestSGD(unittest.TestCase):
 
     def setUp(self):
         self.type = "sgd"
-        self.param = numpy.random.random((102, 105)).astype("float32")
-        self.grad = numpy.random.random((102, 105)).astype("float32")
-        self.learning_rate = 0.1
-        self.param_out = self.param - self.learning_rate * self.grad
+        w = numpy.random.random((102, 105)).astype("float32")
+        g = numpy.random.random((102, 105)).astype("float32")
+        lr = 0.1
+
+        self.inputs = {'param': w, 'grad': g}
+        self.attrs = {'learning_rate': lr}
+        self.outputs = {'param_out': w - lr * g}
 
 
 if __name__ == "__main__":

python/paddle/v2/framework/tests/test_sigmoid_op.py

@@ -8,9 +8,12 @@ class TestSigmoidOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "sigmoid"
-        self.X = np.random.random((32, 100)).astype("float32")
-        self.Y = 1 / (1 + np.exp(-self.X))
+        self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
+        self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
 
 
+#class TestSigmoidGradOp(unittest.TestCase):
+#TODO(qingqing) add unit test
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_softmax_op.py

@@ -19,8 +19,10 @@ class TestSoftmaxOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "softmax"
-        self.X = np.random.random((32, 100)).astype("float32")
-        self.Y = np.apply_along_axis(stable_softmax, 1, self.X)
+        self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
+        self.outputs = {
+            'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
+        }
 
 
 class TestSoftmaxGradOp(unittest.TestCase):

python/paddle/v2/plot/tests/CMakeLists.txt

 if (NOT APPLE)
   # The Mac OS X backend will not be able to function correctly if Python is
   # not installed as a framework.
-  add_python_test(test_ploter test_ploter.py)
+  py_test(test_ploter SRCS test_ploter.py)
 endif()

python/paddle/v2/reader/tests/CMakeLists.txt

-add_python_test(reader_tests creator_test.py decorator_test.py)
+py_test(creator_test SRCS creator_test.py)
+py_test(decorator_test SRCS decorator_test.py)

python/paddle/v2/tests/CMakeLists.txt

-add_python_test(test_v2_api test_data_feeder.py test_op.py test_parameters.py
-test_layer.py test_rnn_layer.py test_topology.py test_image.py)
+py_test(test_op SRCS test_op.py)
+py_test(test_image SRCS test_image.py)
+py_test(test_layer SRCS test_layer.py)
+py_test(test_topology SRCS test_topology.py)
+py_test(test_rnn_layer SRCS test_rnn_layer.py)
+py_test(test_parameters SRCS test_parameters.py)
+py_test(test_data_feeder SRCS test_data_feeder.py)