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

.pre-commit-config.yaml

@@ -17,10 +17,14 @@
     -   id: detect-private-key
         files: (?!.*third_party)^.*$ | (?!.*book)^.*$
     -   id: end-of-file-fixer
--   repo: https://github.com/PaddlePaddle/clang-format-pre-commit-hook.git
-    sha: 28c0ea8a67a3e2dbbf4822ef44e85b63a0080a29
+-   repo: local
     hooks:
-    -   id: clang-formater
+    -   id: clang-format
+        name: clang-format
+        description: Format files with ClangFormat.
+        entry: clang-format -i
+        language: system
+        files: \.(c|cc|cxx|cpp|h|hpp|hxx)$
 -   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

@@ -27,7 +27,7 @@ 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 g++ \
+    python-numpy 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 \

README.md

@@ -72,7 +72,7 @@ We provide [English](http://doc.paddlepaddle.org/develop/doc/) and
 
 - [Deep Learning 101](http://book.paddlepaddle.org/index.html)
 
-  You might want to start from the this online interactive book that can run in Jupyter Notebook.
+  You might want to start from this online interactive book that can run in Jupyter Notebook.
 
 - [Distributed Training](http://doc.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html)
 

cmake/external/mkldnn.cmake

@@ -20,34 +20,30 @@ INCLUDE(ExternalProject)
 
 SET(MKLDNN_PROJECT        "extern_mkldnn")
 SET(MKLDNN_SOURCES_DIR    ${THIRD_PARTY_PATH}/mkldnn)
-SET(MKLDNN_INSTALL_ROOT   ${CMAKE_INSTALL_PREFIX})
-IF(NOT "$ENV{HOME}" STREQUAL "/root")
-    SET(MKLDNN_INSTALL_ROOT  "$ENV{HOME}")
-ENDIF()
-
-SET(MKLDNN_INSTALL_DIR    "${MKLDNN_INSTALL_ROOT}/opt/paddle/third_party/mkldnn")
-SET(MKLDNN_INCLUDE_DIR    "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
+SET(MKLDNN_INSTALL_DIR    ${THIRD_PARTY_PATH}/install/mkldnn)
+SET(MKLDNN_INC_DIR        "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
 
-IF(WIN32)
-    MESSAGE(WARNING "It is not supported compiling with mkldnn in windows Paddle yet."
-      "Force WITH_MKLDNN=OFF")
-    SET(WITH_MKLDNN OFF)
+IF(WIN32 OR APPLE)
+    MESSAGE(WARNING 
+        "Windows or Mac is not supported with MKLDNN in Paddle yet."
+        "Force WITH_MKLDNN=OFF")
+    SET(WITH_MKLDNN OFF CACHE STRING "Disable MKLDNN in Windows and MacOS" FORCE)
     return()
-ELSE(WIN32)
-    SET(MKLDNN_LIBRARY "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
-    MESSAGE(STATUS "Set ${MKLDNN_INSTALL_DIR}/lib to runtime path")
-    SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
-    #SET(CMAKE_MACOSX_RPATH 1) # hold for MacOS
-    SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLDNN_INSTALL_DIR}/lib")
-ENDIF(WIN32)
+ENDIF()
+
+SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
+MESSAGE(STATUS "Set ${MKLDNN_INSTALL_DIR}/lib to runtime path")
+SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
+SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLDNN_INSTALL_DIR}/lib")
 
-INCLUDE_DIRECTORIES(${MKLDNN_INCLUDE_DIR})
+INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
 
 IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
     SET(MKLDNN_DEPENDS   ${MKLML_PROJECT})
     SET(MKLDNN_MKLROOT   ${MKLML_ROOT})
     SET(MKLDNN_IOMP_LIB  ${MKLML_IOMP_LIB})
     SET(MKLDNN_IOMP_DIR  ${MKLML_LIB_DIR})
+    MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
 ENDIF()
 
 ExternalProject_Add(
@@ -57,16 +53,15 @@ ExternalProject_Add(
     GIT_REPOSITORY      "https://github.com/01org/mkl-dnn.git"
     GIT_TAG             "v0.9"
     PREFIX              ${MKLDNN_SOURCES_DIR}
-    CONFIGURE_COMMAND   mkdir -p <SOURCE_DIR>/build
-    BUILD_COMMAND       cd <SOURCE_DIR>/build
-                        && cmake .. -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR} -DMKLROOT=${MKLDNN_MKLROOT}
-                        && $(MAKE)
-    INSTALL_COMMAND     cd <SOURCE_DIR>/build && $(MAKE) install
     UPDATE_COMMAND      ""
+    CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
+    CMAKE_ARGS          -DMKLROOT=${MKLDNN_MKLROOT}
+    CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
+                        -DMKLROOT:PATH=${MKLDNN_MKLROOT}
 )
 
 ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
-SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIBRARY})
+SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
 ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
-MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIBRARY}")
+MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
 LIST(APPEND external_project_dependencies mkldnn)

cmake/external/mklml.cmake

@@ -16,19 +16,23 @@ IF(NOT ${WITH_MKLML})
   return()
 ENDIF(NOT ${WITH_MKLML})
 
+IF(WIN32 OR APPLE)
+    MESSAGE(WARNING 
+        "Windows or Mac is not supported with MKLML in Paddle yet."
+        "Force WITH_MKLML=OFF")
+    SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in Windows and MacOS" FORCE)
+    return()
+ENDIF()
+
 INCLUDE(ExternalProject)
 
 SET(MKLML_PROJECT       "extern_mklml")
-SET(MKLML_VER           "mklml_lnx_2018.0.20170425")
+SET(MKLML_VER           "mklml_lnx_2018.0.20170720")
 SET(MKLML_URL           "https://github.com/01org/mkl-dnn/releases/download/v0.9/${MKLML_VER}.tgz")
 SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
 SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
-SET(MKLML_DST_DIR       "opt/paddle/third_party/mklml")
-SET(MKLML_INSTALL_ROOT  "${CMAKE_INSTALL_PREFIX}")
-IF(NOT "$ENV{HOME}" STREQUAL "/root")
-    SET(MKLML_INSTALL_ROOT  "$ENV{HOME}")
-ENDIF()
-
+SET(MKLML_DST_DIR       "mklml")
+SET(MKLML_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
 SET(MKLML_INSTALL_DIR   ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
 SET(MKLML_ROOT          ${MKLML_INSTALL_DIR}/${MKLML_VER})
 SET(MKLML_INC_DIR       ${MKLML_ROOT}/include)

cmake/flags.cmake

@@ -9,6 +9,11 @@ function(CheckCompilerCXX11Flag)
         if(${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 4.8)
             message(FATAL_ERROR "Unsupported GCC version. GCC >= 4.8 required.")
         endif()
+        # TODO(qijun) gcc 4.9 or later versions raise SEGV due to the optimization problem.
+        # Use Debug mode instead for now.
+        if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9 OR CMAKE_CXX_COMPILER_VERSION VERSION_EQUAL 4.9) 
+            set(CMAKE_BUILD_TYPE "Debug" CACHE STRING "" FORCE)
+        endif()
     elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
         # cmake >= 3.0 compiler id "AppleClang" on Mac OS X, otherwise "Clang"
         # Apple Clang is a different compiler than upstream Clang which havs different version numbers.

doc/api/v2/config/layer.rst

@@ -104,6 +104,11 @@ cross_channel_norm
 ------------------
 ..  autoclass:: paddle.v2.layer.cross_channel_norm
     :noindex:
+
+row_l2_norm
+-----------
+..  autoclass:: paddle.v2.layer.row_l2_norm
+    :noindex:
     
 Recurrent Layers
 ================
@@ -320,6 +325,11 @@ scaling
 ..  autoclass:: paddle.v2.layer.scaling
     :noindex:
 
+clip
+----
+..  autoclass:: paddle.v2.layer.clip
+    :noindex:
+
 slope_intercept
 ---------------
 ..  autoclass:: paddle.v2.layer.slope_intercept

paddle/CMakeLists.txt

@@ -15,7 +15,6 @@ if(Boost_FOUND)
   add_subdirectory(platform)
   add_subdirectory(framework)
   add_subdirectory(operators)
-  add_subdirectory(pybind)
 endif()
 
 if(WITH_C_API)

paddle/cuda/src/hl_cuda_cudnn.cc

@@ -1022,6 +1022,15 @@ void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
   real alpha = 1.0f;
   real beta = 1.0f;
   cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL;
+
+  int batch_size = ((cudnn_tensor_descriptor)inputDesc)->batch_size;
+  if (batch_size > 1024 && g_cudnn_lib_version < 6000) {
+    LOG(INFO) << " To process current batch data with size " << batch_size
+              << " (>1024), cudnnBatchNorm requires cuDNN version >= 6000."
+              << " If there is an error complaining CUDNN_STATUS_NOT_SUPPORTED,"
+              << " just recompile PaddlePaddle with cuDNN >= 6000, replacing"
+              << " current version " << g_cudnn_lib_version;
+  }
   CHECK_CUDNN(
       dynload::cudnnBatchNormalizationForwardInference(t_resource.cudnn_handle,
                                                        mode,

paddle/framework/CMakeLists.txt

@@ -31,8 +31,14 @@ py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.
 add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(framework_py_proto framework_py_proto_init)
 
-cc_library(net SRCS net.cc DEPS op_registry)
-cc_test(net_op_test SRCS net_op_test.cc DEPS net)
-
-cc_library(backward SRCS backward.cc DEPS net)
+cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward)
+cc_library(paddle_pybind SHARED
+    SRCS pybind.cc
+    DEPS pybind python backward
+	fc_op
+	sgd_op
+	add_op
+	mean_op
+	cross_entropy_op
+	recurrent_op)

paddle/framework/backward.cc

@@ -14,8 +14,8 @@
 
 #include "paddle/framework/backward.h"
 #include <list>
-#include "paddle/framework/net.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace framework {
@@ -32,7 +32,7 @@ static bool AllInSet(const std::vector<std::string>& names,
 }
 
 static std::shared_ptr<OperatorBase> NOP() {
-  auto net_op = std::make_shared<NetOp>();
+  auto net_op = std::make_shared<operators::NetOp>();
   net_op->type_ = "@NOP@";
   net_op->CompleteAddOp();
   return net_op;
@@ -77,11 +77,11 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
   }
 
   // Returned gradient network
-  auto net = std::make_shared<NetOp>();
+  auto net = std::make_shared<operators::NetOp>();
 
   if (forwardOp.IsNetOp()) {
     // Because forwardOp is a net op, it can static_cast.
-    auto& forwardNet = static_cast<const NetOp&>(forwardOp);
+    auto& forwardNet = static_cast<const operators::NetOp&>(forwardOp);
 
     // Map from output gradient variable name to operator's indices in backward
     // net. That operator generates that variable.
@@ -168,6 +168,9 @@ std::shared_ptr<OperatorBase> Backward(
   std::unordered_set<std::string> no_grad_names;
   no_grad_names.reserve(no_grad_vars.size());
 
+  no_grad_names.insert(OperatorBase::EMPTY_VAR_NAME() +
+                       OperatorBase::GRAD_VAR_SUFFIX());
+
   for (auto& name : no_grad_vars) {
     no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
   }

paddle/framework/backward_test.cc

@@ -15,8 +15,9 @@
 #include "paddle/framework/backward.h"
 
 #include <gtest/gtest.h>
-#include "paddle/framework/net.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/net_op.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace framework {
@@ -70,7 +71,7 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
   }
 };
 
-class FcOp : public NetOp {
+class FcOp : public ops::NetOp {
  public:
   void Init() override {
     AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
@@ -182,7 +183,8 @@ TEST(Backward, simple_op_not_need_grad) {
   auto no_input_gop = f::Backward(*fwd, {"X", "b"});
   ASSERT_NE(no_input_gop, nullptr);
   ASSERT_TRUE(no_input_gop->IsNetOp());
-  ASSERT_EQ(0UL, std::static_pointer_cast<f::NetOp>(no_input_gop)->ops_.size());
+  ASSERT_EQ(0UL,
+            std::static_pointer_cast<ops::NetOp>(no_input_gop)->ops_.size());
 }
 
 TEST(Backward, net_fc_backward_normal) {
@@ -191,7 +193,7 @@ TEST(Backward, net_fc_backward_normal) {
   ASSERT_NE(fwd, nullptr);
   std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
   ASSERT_TRUE(gop->IsNetOp());
-  auto net = static_cast<f::NetOp *>(gop.get());
+  auto net = static_cast<ops::NetOp *>(gop.get());
 
   ASSERT_NO_THROW(net->DebugString());
 
@@ -214,7 +216,7 @@ TEST(Backward, net_fc_backward_not_have_b) {
   ASSERT_NE(fwd, nullptr);
   std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
   ASSERT_TRUE(gop->IsNetOp());
-  auto net = static_cast<f::NetOp *>(gop.get());
+  auto net = static_cast<ops::NetOp *>(gop.get());
 
   ASSERT_NO_THROW(net->DebugString());
 
@@ -228,7 +230,7 @@ TEST(Backward, net_fc_backward_not_have_b) {
 }
 
 TEST(Backward, net_input_of_network_not_need_grad) {
-  f::NetOp net;
+  ops::NetOp net;
   net.AddOp(f::OpRegistry::CreateOp("fc", {"X", "W1", "b1"},
                                     {"mul_tmp_0", "add_tmp_0", "hidden0"}, {}));
   net.AddOp(f::OpRegistry::CreateOp("fc", {"hidden0", "W2", "b2"},
@@ -236,7 +238,7 @@ TEST(Backward, net_input_of_network_not_need_grad) {
   net.CompleteAddOp();
   auto bwd = Backward(net, {"X"});  // X@GRAD is not need.
   ASSERT_TRUE(bwd->IsNetOp());
-  auto bwd_net = static_cast<f::NetOp *>(bwd.get());
+  auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
 
   std::unordered_set<std::string> all_output = std::unordered_set<std::string>(
       bwd_net->outputs_.begin(), bwd_net->outputs_.end());
@@ -253,7 +255,7 @@ TEST(Backward, net_input_of_network_not_need_grad) {
 
   ASSERT_EQ(2UL, bwd_net->ops_.size());
   ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
-  auto first_fc_grad = static_cast<f::NetOp *>(bwd_net->ops_[1].get());
+  auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
   ASSERT_EQ(3UL, first_fc_grad->ops_.size());
   ASSERT_EQ(
       f::OperatorBase::EMPTY_VAR_NAME(),
@@ -261,14 +263,14 @@ TEST(Backward, net_input_of_network_not_need_grad) {
 }
 
 TEST(Backward, net_shared_weight) {
-  f::NetOp net;
+  ops::NetOp net;
   net.AddOp(f::OpRegistry::CreateOp("mul", {"X", "W"}, {"Out"}, {}));
   net.AddOp(f::OpRegistry::CreateOp("mul", {"Out", "W"}, {"FinalOut"}, {}));
   net.CompleteAddOp();
 
   auto bwd = f::Backward(net, {});
   ASSERT_TRUE(bwd->IsNetOp());
-  auto bwd_net = static_cast<f::NetOp *>(bwd.get());
+  auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
   ASSERT_EQ(3UL, bwd_net->ops_.size());
   ASSERT_EQ("add", bwd_net->ops_[2]->type_);
 }
@@ -285,7 +287,7 @@ TEST(Backward, op_all_input_are_not_need) {
   auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
   auto backward = f::Backward(*fwd, {"X", "b"});
   ASSERT_TRUE(backward->IsNetOp());
-  auto net = static_cast<f::NetOp *>(backward.get());
+  auto net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_TRUE(net->ops_.empty());
 }
 
@@ -293,7 +295,7 @@ TEST(Backward, op_all_output_are_not_need) {
   auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
   auto backward = f::Backward(*fwd, {"Out"});
   ASSERT_TRUE(backward->IsNetOp());
-  auto net = static_cast<f::NetOp *>(backward.get());
+  auto net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_TRUE(net->ops_.empty());
 }
 
@@ -301,7 +303,7 @@ TEST(Backward, op_part_of_output_are_not_need) {
   auto fwd = f::OpRegistry::CreateOp("many_output_op", {"X"}, {"Y", "Z"}, {});
   auto backward = f::Backward(*fwd, {"Z"});
   ASSERT_TRUE(backward->IsNetOp());
-  auto net = static_cast<f::NetOp *>(backward.get());
+  auto net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_EQ(net->ops_.size(), 2UL);
 
   auto &fill_zero = *net->ops_[0];
@@ -341,7 +343,7 @@ TEST(Backward, op_part_of_input_are_not_need) {
 }
 
 TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
-  f::NetOp net;
+  ops::NetOp net;
   net.AddOp(f::OpRegistry::CreateOp("fc", {"x1", "w1", "b1"},
                                     {"mul_out1", "add_out1", "out1"}, {}));
   net.AddOp(f::OpRegistry::CreateOp("fc", {"out1", "w2", "b2"},
@@ -351,7 +353,7 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
   net.CompleteAddOp();
   auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
   ASSERT_TRUE(backward->IsNetOp());
-  auto bwd_net = static_cast<f::NetOp *>(backward.get());
+  auto bwd_net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_EQ(bwd_net->ops_.size(), 3UL);
   auto &grad_fc = *bwd_net->ops_[0];
   EXPECT_EQ(grad_fc.inputs_.size(),

paddle/framework/op_registry.h

@@ -400,6 +400,14 @@ class GradOpRegisterHelper {
     return 0;                                                                  \
   }
 
+/**
+ * Macro to Forbid user register Gradient Operator.
+ */
+#define NO_GRADIENT(__op_type)                          \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                       \
+      __reg_gradient_op__##__op_type##__op_type##_grad, \
+      "NO_GRADIENT must be in global namespace")
+
 /**
  * Macro to Register OperatorKernel.
  */

paddle/framework/operator.cc

@@ -20,16 +20,16 @@ namespace paddle {
 namespace framework {
 
 template <>
-Eigen::DefaultDevice* ExecutionContext::GetEigenDevice<
+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*
+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
 
@@ -52,7 +52,8 @@ std::vector<std::string> OperatorBase::Inputs(const std::string& name) const {
   PADDLE_ENFORCE(in_out_idxs_ != nullptr, "IO Idx could not be nullptr");
   auto input_format = GetAttr<std::vector<int>>("input_format");
   auto offset = in_out_idxs_->at(name);
-  PADDLE_ENFORCE(input_format.at((size_t)offset + 1) <= (int)inputs_.size(),
+  PADDLE_ENFORCE(input_format.at(static_cast<size_t>(offset) + 1) <=
+                     static_cast<int>(inputs_.size()),
                  "Input Out Of Range");
 
   return std::vector<std::string>{
@@ -78,7 +79,8 @@ std::vector<std::string> OperatorBase::Outputs(const std::string& name) const {
   PADDLE_ENFORCE(in_out_idxs_ != nullptr, "InOut Indice could not be nullptr");
   auto output_format = GetAttr<std::vector<int>>("output_format");
   auto offset = in_out_idxs_->at(name);
-  PADDLE_ENFORCE(output_format.at((size_t)offset + 1) <= (int)outputs_.size(),
+  PADDLE_ENFORCE(output_format.at(static_cast<size_t>(offset) + 1) <=
+                     static_cast<int>(outputs_.size()),
                  "Output Out of Range");
   return std::vector<std::string>{
       outputs_.begin() + output_format.at(offset),

paddle/framework/operator.h

@@ -55,6 +55,10 @@ class OperatorBase {
   /// e.g. Variable "x@GRAD" is the gradient of varibale "x".
   static std::string GRAD_VAR_SUFFIX() { return "@GRAD"; }
 
+  static std::string GRAD_VAR_NAME(const std::string& name) {
+    return name + GRAD_VAR_SUFFIX();
+  }
+
   /// Variables with this suffix are supposed to be filled up with zeros.
   static std::string ZERO_VAR_SUFFIX() { return "@ZERO"; }
 
@@ -161,22 +165,30 @@ class OperatorContext {
 
   template <typename T>
   const T* Input(const size_t index) const {
-    return &(InputVar(index)->Get<T>());
+    auto var = InputVar(index);
+    PADDLE_ENFORCE(var != nullptr, "Input(%d) should not be nullptr", index);
+    return &var->Get<T>();
   }
 
   template <typename T>
   T* Output(const size_t index) const {
-    return OutputVar(index)->GetMutable<T>();
+    auto var = OutputVar(index);
+    PADDLE_ENFORCE(var != nullptr, "Output(%d) should not be nullptr", index);
+    return var->GetMutable<T>();
   }
 
   template <typename T>
   const T* Input(const std::string& name) const {
-    return &(InputVar(name)->Get<T>());
+    auto var = InputVar(name);
+    PADDLE_ENFORCE(var != nullptr, "Input(%s) should not be nullptr", name);
+    return &var->Get<T>();
   }
 
   template <typename T>
   T* Output(const std::string& name) const {
-    return OutputVar(name)->GetMutable<T>();
+    auto var = OutputVar(name);
+    PADDLE_ENFORCE(var != nullptr, "Output(%s) should not be nullptr", name);
+    return var->GetMutable<T>();
   }
 
   template <typename T>
@@ -185,8 +197,12 @@ class OperatorContext {
     std::vector<const T*> res;
     res.reserve(names.size());
     std::transform(names.begin(), names.end(), std::back_inserter(res),
-                   [this](const std::string& name) {
-                     return &scope_.FindVar(name)->Get<T>();
+                   [&](const std::string& sub_name) {
+                     auto var = scope_.FindVar(sub_name);
+                     PADDLE_ENFORCE(var != nullptr,
+                                    "MultiInput(%s:%s) should not be nullptr",
+                                    name, sub_name);
+                     return &var->Get<T>();
                    });
     return res;
   }
@@ -197,8 +213,12 @@ class OperatorContext {
     std::vector<const T*> res;
     res.reserve(names.size());
     std::transform(names.begin(), names.end(), std::back_inserter(res),
-                   [this](const std::string& name) {
-                     return scope_.FindVar(name)->GetMutable<T>();
+                   [&](const std::string& sub_name) {
+                     auto var = scope_.FindVar(sub_name);
+                     PADDLE_ENFORCE(var != nullptr,
+                                    "MultiOutput(%s:%s) should not be nullptr",
+                                    name, sub_name);
+                     return var->GetMutable<T>();
                    });
     return res;
   }
@@ -237,7 +257,7 @@ class ExecutionContext : public OperatorContext {
   template <typename PlaceType,
             typename DeviceType =
                 typename EigenDeviceConverter<PlaceType>::EigenDeviceType>
-  DeviceType* GetEigenDevice() const;
+  DeviceType& GetEigenDevice() const;
 
   platform::Place GetPlace() const { return device_context_.GetPlace(); }
 

paddle/pybind/pybind.cc → paddle/framework/pybind.cc

@@ -4,7 +4,7 @@ Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-    http://www.apache.org/licenses/LICENSE-2.0
+http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
@@ -16,35 +16,43 @@ limitations under the License. */
 #include <fstream>
 #include <vector>
 
-#include "paddle/framework/net.h"
+#include "paddle/framework/backward.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
 #include "paddle/framework/scope.h"
-#include "paddle/pybind/tensor_bind.h"
+#include "paddle/framework/tensor_py.h"
+#include "paddle/operators/net_op.h"
+#include "paddle/operators/type_alias.h"
+#include "paddle/platform/enforce.h"
+#include "paddle/platform/place.h"
 #include "pybind11/numpy.h"
 #include "pybind11/pybind11.h"
 #include "pybind11/stl.h"
 
 namespace py = pybind11;
-namespace pd = paddle::framework;
 
 USE_OP(add_two);
 USE_OP(onehot_cross_entropy);
 USE_OP_WITHOUT_KERNEL(fc);
 USE_OP(sgd);
 USE_OP(mul);
+USE_OP(mean);
 USE_OP(sigmoid);
 USE_OP(softmax);
 USE_OP(rowwise_add);
 USE_OP_WITHOUT_KERNEL(recurrent_op);
-USE_OP(fill_zeros_like);
-
+namespace paddle {
+namespace framework {
 template <typename ClassType>
-void ExposeOperator(ClassType& m) {
+void ExposeOperator(ClassType &m) {
   m.def("infer_shape", &ClassType::type::InferShape)
       .def("run", &ClassType::type::Run)
+      .def("type",
+           [](const typename ClassType::type &op) -> std::string {
+             return op.type_;
+           })
       .def("outputs",
-           [](const typename ClassType::type& op) -> std::vector<std::string> {
+           [](const typename ClassType::type &op) -> std::vector<std::string> {
              return op.outputs_;
            })
       .def("__str__", &ClassType::type::DebugString);
@@ -55,71 +63,83 @@ static size_t UniqueIntegerGenerator() {
   return generator.fetch_add(1);
 }
 
+bool IsCompileGPU() {
+#ifdef PADDLE_ONLY_CPU
+  return false;
+#else
+  return true;
+#endif
+}
+
 PYBIND11_PLUGIN(core) {
   py::module m("core", "C++ core of PaddlePaddle");
 
-  py::class_<pd::Tensor>(m, "Tensor", py::buffer_protocol())
-      .def_buffer([](pd::Tensor& self) -> py::buffer_info {
-        return paddle::pybind::CastToPyBuffer(self);
-      })
+  py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
+      .def_buffer(
+          [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
       .def("get_dims",
-           [](const pd::Tensor& self) { return pd::vectorize(self.dims()); })
+           [](const Tensor &self) { return vectorize(self.dims()); })
       .def("set_dims",
-           [](pd::Tensor& self, const std::vector<int>& dim) {
-             self.Resize(pd::make_ddim(dim));
+           [](Tensor &self, const std::vector<int> &dim) {
+             self.Resize(make_ddim(dim));
            })
       .def("alloc_float",
-           [](pd::Tensor& self) {
-             self.mutable_data<float>(paddle::platform::CPUPlace());
+           [](Tensor &self, paddle::platform::GPUPlace &place) {
+             self.mutable_data<float>(place);
+           })
+      .def("alloc_float",
+           [](Tensor &self, paddle::platform::CPUPlace &place) {
+             self.mutable_data<float>(place);
+           })
+      .def("alloc_int",
+           [](Tensor &self, paddle::platform::CPUPlace &place) {
+             self.mutable_data<int>(place);
            })
       .def("alloc_int",
-           [](pd::Tensor& self) {
-             self.mutable_data<int>(paddle::platform::CPUPlace());
+           [](Tensor &self, paddle::platform::GPUPlace &place) {
+             self.mutable_data<int>(place);
            })
-      .def("set", paddle::pybind::PyTensorSetFromArray<float>)
-      .def("set", paddle::pybind::PyTensorSetFromArray<int>)
-      .def("shape",
-           [](pd::Tensor& self) { return pd::vectorize(self.dims()); });
+      .def("set", PyCPUTensorSetFromArray<float>)
+      .def("set", PyCPUTensorSetFromArray<int>)
+#ifndef PADDLE_ONLY_CPU
+      .def("set", PyCUDATensorSetFromArray<float>)
+      .def("set", PyCUDATensorSetFromArray<int>)
+#endif
+      .def("shape", [](Tensor &self) { return vectorize(self.dims()); });
 
-  py::class_<pd::Variable>(m, "Variable", R"DOC(Variable Class.
+  py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
 
 All parameter, weight, gradient are variables in Paddle.
 )DOC")
-      .def("is_int", [](const pd::Variable& var) { return var.IsType<int>(); })
+      .def("is_int", [](const Variable &var) { return var.IsType<int>(); })
       .def("set_int",
-           [](pd::Variable& var, int val) -> void {
-             *var.GetMutable<int>() = val;
-           })
-      .def("get_int",
-           [](const pd::Variable& var) -> int { return var.Get<int>(); })
+           [](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
+      .def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
       .def("get_tensor",
-           [](pd::Variable& self) -> pd::Tensor* {
-             return self.GetMutable<pd::Tensor>();
-           },
+           [](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
            py::return_value_policy::reference)
       .def("get_net",
-           [](pd::Variable& self) -> pd::NetOp* {
-             return self.GetMutable<pd::NetOp>();
+           [](Variable &self) -> ops::NetOp * {
+             return self.GetMutable<ops::NetOp>();
            },
            py::return_value_policy::reference);
 
-  py::class_<pd::Scope>(m, "Scope", "")
+  py::class_<Scope>(m, "Scope", "")
       .def("new_var",
-           [](pd::Scope& self, const std::string& name) -> pd::Variable* {
+           [](Scope &self, const std::string &name) -> Variable * {
              return self.NewVar(name);
            },
            py::return_value_policy::reference)
-      .def("find_var", &pd::Scope::FindVar, py::return_value_policy::reference)
+      .def("find_var", &Scope::FindVar, py::return_value_policy::reference)
       .def(py::init<>())
-      .def("new_scope",
-           [](pd::Scope& self) -> pd::Scope* { return &self.NewScope(); },
+      .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
            py::return_value_policy::reference)
-      .def("drop_kids", &pd::Scope::DropKids);
+      .def("drop_kids", &Scope::DropKids);
 
   //! @note: Be careful! PyBind will return std::string as an unicode, not
   //! Python str. If you want a str object, you should cast them in Python.
   m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
-    auto& protos = pd::OpRegistry::protos();
+    auto &protos = OpRegistry::protos();
     std::vector<py::bytes> ret_values;
     for (auto it = protos.begin(); it != protos.end(); ++it) {
       PADDLE_ENFORCE(it->second.IsInitialized(),
@@ -134,47 +154,76 @@ All parameter, weight, gradient are variables in Paddle.
   m.def_submodule(
        "var_names",
        "The module will return special predefined variable name in Paddle")
-      .def("empty", pd::OperatorBase::EMPTY_VAR_NAME)
-      .def("temp", pd::OperatorBase::TMP_VAR_NAME);
-
+      .def("empty", OperatorBase::EMPTY_VAR_NAME)
+      .def("temp", OperatorBase::TMP_VAR_NAME);
+  // clang-format off
   py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
-      .def_static("cpu_context", []() -> paddle::platform::DeviceContext* {
-        return new paddle::platform::CPUDeviceContext();
-      });
-
-  py::class_<pd::OperatorBase, std::shared_ptr<pd::OperatorBase>> operator_base(
+      .def_static("create",
+                  [](paddle::platform::CPUPlace& place)
+                      -> paddle::platform::DeviceContext* {
+                    return new paddle::platform::CPUDeviceContext();
+                  })
+      .def_static("create",
+                  [](paddle::platform::GPUPlace& place)
+                      -> paddle::platform::DeviceContext* {
+#ifdef PADDLE_ONLY_CPU
+                    PADDLE_THROW("GPUPlace is not supported in CPU device.");
+#else
+                    return new paddle::platform::CUDADeviceContext(place);
+#endif
+                  });
+  // clang-format on
+
+  py::class_<paddle::platform::GPUPlace>(m, "GPUPlace").def(py::init<int>());
+
+  py::class_<paddle::platform::CPUPlace>(m, "CPUPlace").def(py::init<>());
+
+  py::class_<OperatorBase, std::shared_ptr<OperatorBase>> operator_base(
       m, "Operator");
 
   operator_base.def_static("create", [](py::bytes protobin) {
-    pd::OpDesc desc;
+    OpDesc desc;
     PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
                    "Cannot parse user input to OpDesc");
     PADDLE_ENFORCE(desc.IsInitialized(),
                    "User OpDesc is not initialized, reason %s",
                    desc.InitializationErrorString());
-    return pd::OpRegistry::CreateOp(desc);
+    return OpRegistry::CreateOp(desc);
   });
+
+  operator_base.def("backward",
+                    [](const OperatorBase &forwardOp,
+                       const std::unordered_set<std::string> &no_grad_vars) {
+                      return Backward(forwardOp, no_grad_vars);
+                    });
+
   ExposeOperator(operator_base);
 
-  py::class_<pd::NetOp, std::shared_ptr<pd::NetOp>> net(m, "Net");
+  py::class_<ops::NetOp, std::shared_ptr<ops::NetOp>> net(m, "Net");
 
   net.def_static("create",
-                 []() -> std::shared_ptr<pd::NetOp> {
-                   auto retv = std::make_shared<pd::NetOp>();
+                 []() -> std::shared_ptr<ops::NetOp> {
+                   auto retv = std::make_shared<ops::NetOp>();
                    retv->type_ = "plain_net";
                    return retv;
                  })
-      .def("add_op", &pd::NetOp::AddOp)
-      .def("add_op",
-           [](pd::NetOp& self, const std::shared_ptr<pd::NetOp>& net) -> void {
-             self.AddOp(std::static_pointer_cast<pd::OperatorBase>(net));
-           })
-      .def("complete_add_op", &pd::NetOp::CompleteAddOp)
+      .def("add_op", &ops::NetOp::AddOp)
+      .def(
+          "add_op",
+          [](ops::NetOp &self, const std::shared_ptr<ops::NetOp> &net) -> void {
+            self.AddOp(std::static_pointer_cast<OperatorBase>(net));
+          })
+      .def("complete_add_op", &ops::NetOp::CompleteAddOp)
       .def("complete_add_op",
-           [](std::shared_ptr<pd::NetOp>& self) { self->CompleteAddOp(); });
+           [](std::shared_ptr<ops::NetOp> &self) { self->CompleteAddOp(); });
+
   ExposeOperator(net);
 
   m.def("unique_integer", UniqueIntegerGenerator);
 
+  m.def("is_compile_gpu", IsCompileGPU);
+
   return m.ptr();
 }
+}  // namespace framework
+}  // namespace paddle

paddle/framework/tensor.h

@@ -26,19 +26,17 @@ limitations under the License. */
 #include "unsupported/Eigen/CXX11/Tensor"
 
 namespace paddle {
-namespace pybind {
-namespace details {  // forward declare
-template <bool less, size_t i, typename... args>
-struct CastToPyBufferImpl;
-}  // namespace details
-}  // namespace pybind
 
 namespace framework {
+namespace details {
+template <bool less, size_t i, typename... args>
+struct CastToPyBufferImpl;
+}
 
 class Tensor {
  public:
   template <bool less, size_t i, typename... args>
-  friend struct paddle::pybind::details::CastToPyBufferImpl;
+  friend struct details::CastToPyBufferImpl;
 
   template <typename T, size_t D, int MajorType, typename IndexType>
   friend struct EigenTensor;
@@ -167,4 +165,4 @@ class Tensor {
 }  // namespace framework
 }  // namespace paddle
 
-#include "paddle/framework/detail/tensor-inl.h"
+#include "paddle/framework/tensor_impl.h"

paddle/framework/detail/tensor-inl.h → paddle/framework/tensor_impl.h

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-
 #include "paddle/memory/memcpy.h"
 
 namespace paddle {
@@ -62,9 +61,11 @@ inline T* Tensor::mutable_data(platform::Place place) {
     if (platform::is_cpu_place(place)) {
       holder_.reset(new PlaceholderImpl<T, platform::CPUPlace>(
           boost::get<platform::CPUPlace>(place), size));
+    } else if (platform::is_gpu_place(place)) {
+#ifdef PADDLE_ONLY_CPU
+      PADDLE_THROW("'GPUPlace' is not supported in CPU only device.");
     }
-#ifndef PADDLE_ONLY_CPU
-    else if (platform::is_gpu_place(place)) {
+#else
       holder_.reset(new PlaceholderImpl<T, platform::GPUPlace>(
           boost::get<platform::GPUPlace>(place), size));
     }

paddle/pybind/tensor_bind.h → paddle/framework/tensor_py.h

@@ -13,15 +13,17 @@
    limitations under the License. */
 
 #pragma once
-#include <paddle/framework/tensor.h>
-#include <pybind11/numpy.h>
-#include <pybind11/pybind11.h>
+#include <string>
+#include "paddle/framework/tensor.h"
+#include "paddle/memory/memcpy.h"
+#include "pybind11/numpy.h"
+#include "pybind11/pybind11.h"
 
 namespace py = pybind11;
 
 namespace paddle {
 
-namespace pybind {
+namespace framework {
 
 namespace details {
 
@@ -40,9 +42,6 @@ template <size_t I, typename... ARGS>
 struct CastToPyBufferImpl<true, I, ARGS...> {
   using CUR_TYPE = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
   py::buffer_info operator()(framework::Tensor &tensor) {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(tensor.holder_->place()),
-                   "Only CPU tensor can cast to numpy array");
-
     if (std::type_index(typeid(CUR_TYPE)) == tensor.holder_->type()) {
       auto dim_vec = framework::vectorize(tensor.dims());
       std::vector<size_t> dims_outside;
@@ -56,14 +55,16 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
         strides[i - 1] = sizeof(CUR_TYPE) * prod;
         prod *= dims_outside[i - 1];
       }
-
+      framework::Tensor dst_tensor;
+      if (paddle::platform::is_gpu_place(tensor.holder_->place())) {
+        dst_tensor.CopyFrom<CUR_TYPE>(tensor, platform::CPUPlace());
+      } else if (paddle::platform::is_cpu_place(tensor.holder_->place())) {
+        dst_tensor = tensor;
+      }
       return py::buffer_info(
-          tensor.mutable_data<CUR_TYPE>(tensor.holder_->place()),
-          sizeof(CUR_TYPE),
-          py::format_descriptor<CUR_TYPE>::format(),
-          (size_t)framework::arity(tensor.dims()),
-          dims_outside,
-          strides);
+          dst_tensor.mutable_data<CUR_TYPE>(dst_tensor.holder_->place()),
+          sizeof(CUR_TYPE), py::format_descriptor<CUR_TYPE>::format(),
+          (size_t)framework::arity(dst_tensor.dims()), dims_outside, strides);
     } else {
       constexpr bool less = I + 1 < std::tuple_size<std::tuple<ARGS...>>::value;
       return CastToPyBufferImpl<less, I + 1, ARGS...>()(tensor);
@@ -77,9 +78,10 @@ inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
 }
 
 template <typename T>
-void PyTensorSetFromArray(
+void PyCPUTensorSetFromArray(
     framework::Tensor &self,
-    py::array_t<T, py::array::c_style | py::array::forcecast> array) {
+    py::array_t<T, py::array::c_style | py::array::forcecast> array,
+    paddle::platform::CPUPlace &place) {
   std::vector<int> dims;
   dims.reserve(array.ndim());
   for (size_t i = 0; i < array.ndim(); ++i) {
@@ -87,9 +89,28 @@ void PyTensorSetFromArray(
   }
 
   self.Resize(framework::make_ddim(dims));
-  auto *dst = self.mutable_data<T>(paddle::platform::CPUPlace());
+  auto *dst = self.mutable_data<T>(place);
   std::memcpy(dst, array.data(), sizeof(T) * array.size());
 }
 
+#ifndef PADDLE_ONLY_CPU
+template <typename T>
+void PyCUDATensorSetFromArray(
+    framework::Tensor &self,
+    py::array_t<T, py::array::c_style | py::array::forcecast> array,
+    paddle::platform::GPUPlace &place) {
+  std::vector<int> dims;
+  dims.reserve(array.ndim());
+  for (size_t i = 0; i < array.ndim(); ++i) {
+    dims.push_back((int)array.shape()[i]);
+  }
+
+  self.Resize(framework::make_ddim(dims));
+  auto *dst = self.mutable_data<T>(place);
+  paddle::platform::GpuMemcpySync(dst, array.data(), sizeof(T) * array.size(),
+                                  cudaMemcpyHostToDevice);
+}
+#endif
+
 }  // namespace pybind
 }  // namespace paddle

paddle/function/ConvOp.h

@@ -109,6 +109,13 @@ protected:
     return filter[filter.ndims() - 1];
   }
 
+  // determine whether im2col needs to be performed
+  inline bool isNeedIm2col(const TensorShape& filter) const {
+    return !(getFilterHeight(filter) == 1 && getFilterWidth(filter) == 1 &&
+             strideH() == 1 && strideW() == 1 && paddingH() == 0 &&
+             paddingW() == 0);
+  }
+
   std::vector<size_t> strides_;
   std::vector<size_t> paddings_;
 

paddle/function/GemmConvOp.cpp

@@ -66,16 +66,23 @@ public:
     real* inputData = inputs[0].data<real>();
     real* filterData = inputs[1].data<real>();
     real* outputData = outputs[0].data<real>();
+    bool needIm2col = isNeedIm2col(filter);
+
     TensorShape imShape =
         TensorShape({inputChannels / groups_, inputHeight, inputWidth});
-    TensorShape colShape = TensorShape({inputChannels / groups_,
-                                        filterHeight,
-                                        filterWidth,
-                                        outputHeight,
-                                        outputWidth});
 
-    resizeBuffer<Device>(colShape.getElements());
-    real* colData = reinterpret_cast<real*>(memory_->getBuf());
+    TensorShape colShape;
+    real* colData = NULL;
+
+    if (needIm2col) {
+      colShape = TensorShape({inputChannels / groups_,
+                              filterHeight,
+                              filterWidth,
+                              outputHeight,
+                              outputWidth});
+      resizeBuffer<Device>(colShape.getElements());
+      colData = reinterpret_cast<real*>(memory_->getBuf());
+    }
 
     Im2ColFunctor<kCFO, Device, real> im2col;
     GemmFunctor<Device, real> gemm;
@@ -86,15 +93,18 @@ public:
 
     for (size_t i = 0; i < batchSize; i++) {
       for (size_t g = 0; g < groups_; g++) {
-        im2col(inputData + g * inputOffset,
-               imShape,
-               colData,
-               colShape,
-               strideH(),
-               strideW(),
-               paddingH(),
-               paddingW());
-
+        if (needIm2col) {
+          im2col(inputData + g * inputOffset,
+                 imShape,
+                 colData,
+                 colShape,
+                 strideH(),
+                 strideW(),
+                 paddingH(),
+                 paddingW());
+        } else {
+          colData = inputData + g * inputOffset;
+        }
         int M = outputChannels / groups_;
         int N = outputHeight * outputWidth;
         int K = inputChannels / groups_ * filterHeight * filterWidth;
@@ -159,19 +169,27 @@ public:
     real* outputGrad = inputs[0].data<real>();
     real* filterData = inputs[1].data<real>();
     real* inputGrad = outputs[0].data<real>();
+    bool needIm2col = isNeedIm2col(filter);
+
     TensorShape imShape =
         TensorShape({inputChannels / groups_, inputHeight, inputWidth});
-    TensorShape colShape = TensorShape({inputChannels / groups_,
-                                        filterHeight,
-                                        filterWidth,
-                                        outputHeight,
-                                        outputWidth});
 
-    resizeBuffer<Device>(colShape.getElements());
-    real* colData = reinterpret_cast<real*>(memory_->getBuf());
+    TensorShape colShape;
+    real* colData = NULL;
+
+    if (needIm2col) {
+      colShape = TensorShape({inputChannels / groups_,
+                              filterHeight,
+                              filterWidth,
+                              outputHeight,
+                              outputWidth});
+      resizeBuffer<Device>(colShape.getElements());
+      colData = reinterpret_cast<real*>(memory_->getBuf());
+    }
 
     Col2ImFunctor<kCFO, Device, real> col2im;
     GemmFunctor<Device, real> gemm;
+
     size_t inputOffset = imShape.getElements();
     size_t outputOffset =
         (outputChannels / groups_) * outputHeight * outputWidth;
@@ -182,6 +200,11 @@ public:
         int K = outputChannels / groups_;
         int N = outputHeight * outputWidth;
         int M = inputChannels / groups_ * filterHeight * filterWidth;
+        real scale = 0.0f;
+        if (!needIm2col) {
+          colData = inputGrad + g * inputOffset;
+          scale = 1.0f;
+        }
         gemm(CblasTrans,
              CblasNoTrans,
              M,
@@ -192,17 +215,19 @@ public:
              M,
              outputGrad + g * outputOffset,
              N,
-             0.0f,
+             scale,
              colData,
              N);
-        col2im(inputGrad + g * inputOffset,
-               imShape,
-               colData,
-               colShape,
-               strideH(),
-               strideW(),
-               paddingH(),
-               paddingW());
+        if (needIm2col) {
+          col2im(inputGrad + g * inputOffset,
+                 imShape,
+                 colData,
+                 colShape,
+                 strideH(),
+                 strideW(),
+                 paddingH(),
+                 paddingW());
+        }
       }
       inputGrad += inputChannels * inputHeight * inputWidth;
       outputGrad += outputChannels * outputHeight * outputWidth;
@@ -255,16 +280,23 @@ public:
     real* outputGrad = inputs[0].data<real>();
     real* inputData = inputs[1].data<real>();
     real* filterGrad = outputs[0].data<real>();
+    bool needIm2col = isNeedIm2col(filter);
+
     TensorShape imShape =
         TensorShape({inputChannels / groups_, inputHeight, inputWidth});
-    TensorShape colShape = TensorShape({inputChannels / groups_,
-                                        filterHeight,
-                                        filterWidth,
-                                        outputHeight,
-                                        outputWidth});
 
-    resizeBuffer<Device>(colShape.getElements());
-    real* colData = reinterpret_cast<real*>(memory_->getBuf());
+    TensorShape colShape;
+    real* colData = NULL;
+
+    if (needIm2col) {
+      colShape = TensorShape({inputChannels / groups_,
+                              filterHeight,
+                              filterWidth,
+                              outputHeight,
+                              outputWidth});
+      resizeBuffer<Device>(colShape.getElements());
+      colData = reinterpret_cast<real*>(memory_->getBuf());
+    }
 
     Im2ColFunctor<kCFO, Device, real> im2col;
     GemmFunctor<Device, real> gemm;
@@ -274,15 +306,18 @@ public:
     size_t filterOffset = filter.getElements() / groups_;
     for (size_t i = 0; i < batchSize; i++) {
       for (size_t g = 0; g < groups_; g++) {
-        im2col(inputData + g * inputOffset,
-               imShape,
-               colData,
-               colShape,
-               strideH(),
-               strideW(),
-               paddingH(),
-               paddingW());
-
+        if (needIm2col) {
+          im2col(inputData + g * inputOffset,
+                 imShape,
+                 colData,
+                 colShape,
+                 strideH(),
+                 strideW(),
+                 paddingH(),
+                 paddingW());
+        } else {
+          colData = inputData + g * inputOffset;
+        }
         int M = outputChannels / groups_;
         int K = outputHeight * outputWidth;
         int N = inputChannels / groups_ * filterHeight * filterWidth;

paddle/gserver/layers/ClipLayer.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "Layer.h"
+
+namespace paddle {
+
+/**
+ * A layer for clipping the input value by the threshold.
+ * \f[
+ *   out[i] = \min\left(\max\left(in[i],p_{1}\right),p_{2}\right)
+ * \f]
+ */
+
+class ClipLayer : public Layer {
+protected:
+  double min_;
+  double max_;
+
+public:
+  explicit ClipLayer(const LayerConfig& config) : Layer(config) {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+};
+
+REGISTER_LAYER(clip, ClipLayer);
+
+bool ClipLayer::init(const LayerMap& layerMap,
+                     const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+
+  CHECK_EQ(inputLayers_.size(), 1U);
+  auto layerConf = config_.inputs(0).clip_conf();
+  min_ = layerConf.min();
+  max_ = layerConf.max();
+  CHECK_LT(min_, max_);
+  return true;
+}
+
+void ClipLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  MatrixPtr inV = getInputValue(0);
+  resetOutput(inV->getHeight(), inV->getWidth());
+  MatrixPtr outV = getOutputValue();
+  outV->copyFrom(*inV);
+  outV->clip(min_, max_);
+}
+
+void ClipLayer::backward(const UpdateCallback& callback) {
+  MatrixPtr inV = getInputValue(0);
+  MatrixPtr inG = getInputGrad(0);
+  if (inG) {
+    MatrixPtr outV = getOutputValue();
+    MatrixPtr outG = getOutputGrad();
+    MatrixPtr tmpMtx;
+    Matrix::resizeOrCreate(
+        tmpMtx, outG->getHeight(), outG->getWidth(), false, useGpu_);
+    tmpMtx->clipDerivative(*inV, min_, max_);
+    inG->addDotMul(*outG, *tmpMtx, 1, 1);
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/RowL2NormLayer.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "Layer.h"
+
+namespace paddle {
+
+/**
+ * A layer for L2 normalization in each row,
+ * \f[
+ *   out[i] = \frac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
+ * \f]
+ * where the size of \f$in\f$ is (batchSize x dataDim),
+ * and the size of \f$out\f$ is (batchSize x dataDim).
+ */
+
+class RowL2NormLayer : public Layer {
+protected:
+  MatrixPtr inSquare_;
+  MatrixPtr l2NormReciprocal_;
+  MatrixPtr dotSum_;
+
+public:
+  explicit RowL2NormLayer(const LayerConfig& config) : Layer(config) {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+};
+
+REGISTER_LAYER(row_l2_norm, RowL2NormLayer);
+
+bool RowL2NormLayer::init(const LayerMap& layerMap,
+                          const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+
+  CHECK_EQ(inputLayers_.size(), 1U);
+
+  return true;
+}
+
+void RowL2NormLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  MatrixPtr inV = getInputValue(0);
+
+  /* malloc memory for the output_ if necessary */
+  size_t batchSize = inV->getHeight();
+  size_t dataDim = getSize();
+  CHECK_EQ(dataDim, inV->getWidth());
+  resetOutput(batchSize, dataDim);
+  MatrixPtr outV = getOutputValue();
+
+  Matrix::resizeOrCreate(inSquare_, batchSize, dataDim, false, useGpu_);
+  inV->square2(*inSquare_);
+  Matrix::resizeOrCreate(l2NormReciprocal_, batchSize, 1, false, useGpu_);
+  inSquare_->rowSum(*l2NormReciprocal_);
+  l2NormReciprocal_->sqrt2(*l2NormReciprocal_);
+  l2NormReciprocal_->scalarDiv(*l2NormReciprocal_, 1.0);
+  outV->rowScale(0, *inV, *l2NormReciprocal_);
+}
+
+void RowL2NormLayer::backward(const UpdateCallback& callback) {
+  MatrixPtr inV = getInputValue(0);
+  MatrixPtr inG = getInputGrad(0);
+  MatrixPtr outV = getOutputValue();
+  MatrixPtr outG = getOutputGrad();
+  size_t batchSize = inV->getHeight();
+
+  // inG[ij] += outG[ij] / l2NormReciprocal
+  // inG[ij] += -inV[ij] * l2NormReciprocal * l2NormReciprocal * DotMul(outG[i],
+  // inV[i])
+  if (inG) {
+    Matrix::resizeOrCreate(dotSum_, batchSize, 1, false, useGpu_);
+    dotSum_->zeroMem();
+    dotSum_->rowDotMul(0, *outG, *outV);
+    dotSum_->dotMul(*dotSum_, *l2NormReciprocal_);
+    dotSum_->dotMul(*dotSum_, *l2NormReciprocal_);
+    inSquare_->rowScale(0, *inV, *dotSum_);
+    inG->sub(*inSquare_);
+    inG->addRowScale(0, *outG, *l2NormReciprocal_);
+  }
+}
+
+}  // namespace paddle

paddle/gserver/tests/test_LayerGrad.cpp

@@ -1899,6 +1899,36 @@ TEST(Layer, CropLayer) {
   }
 }
 
+TEST(Layer, ClipLayer) {
+  const size_t batchSize = 128;
+  const size_t size = 512;
+  TestConfig config;
+  config.layerConfig.set_type("clip");
+  config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  ClipConfig* layerConf = input->mutable_clip_conf();
+  double p1 = std::rand() / (double)RAND_MAX;
+  double p2 = std::rand() / (double)RAND_MAX;
+  layerConf->set_min(std::min(p1, p2));
+  layerConf->set_max(std::max(p1, p2));
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "clip", batchSize, false, useGpu, false);
+  }
+}
+
+TEST(Layer, RowL2NormLayer) {
+  const size_t batchSize = 128;
+  const size_t size = 512;
+  TestConfig config;
+  config.layerConfig.set_type("row_l2_norm");
+  config.layerConfig.set_size(size);
+  config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
+  config.layerConfig.add_inputs();
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "row_l2_norm", batchSize, false, useGpu, false);
+  }
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);

paddle/math/BaseMatrix.cu

@@ -442,6 +442,13 @@ DEFINE_MATRIX_UNARY_PARAMETER_OP(Clip, TWO_PARAMETER,
 template<class T>
 void BaseMatrixT<T>::clip(T p1, T p2) { applyUnary(unary::Clip<T>(p1, p2)); }
 
+DEFINE_MATRIX_BINARY_PARAMETER_OP(ClipDerivative, TWO_PARAMETER,
+    a = b < p1 ? 0 : (b > p2 ? 0 : 1));
+template<class T>
+void BaseMatrixT<T>::clipDerivative(BaseMatrixT& b, T p1, T p2) {
+  applyBinary(binary::ClipDerivative<T>(p1, p2), b);
+}
+
 DEFINE_MATRIX_UNARY_PARAMETER_OP(BiggerThanScalar, ONE_PARAMETER,
                                  a = a > p ? 1.0f : 0.0f);
 template<class T>

paddle/math/BaseMatrix.h

@@ -488,6 +488,13 @@ public:
    */
   void clip(T p1, T p2);
 
+  /**
+   * this = b < low ? 0 : 1
+   *
+   * this = b > high ? 0 : 1
+   */
+  void clipDerivative(BaseMatrixT& b, T p1, T p2);
+
   /**
    * @code
    * a = a > p ? 1.0f : 0.0f

paddle/operators/CMakeLists.txt

@@ -41,22 +41,27 @@ function(op_library TARGET)
     endif()
 endfunction()
 
+cc_library(net_op SRCS net_op.cc DEPS op_registry)
+cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
+
 op_library(add_op SRCS add_op.cc add_op.cu)
 cc_test(add_op_test SRCS add_op_test.cc DEPS add_op)
 
+op_library(mean_op SRCS mean_op.cc mean_op.cu)
+cc_test(mean_op_test SRCS mean_op_test.cc DEPS mean_op)
+
 op_library(mul_op SRCS mul_op.cc mul_op.cu)
 op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
-op_library(sigmoid_op SRCS sigmoid_op.cu sigmoid_op.cc)
+
+op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu)
 op_library(softmax_op SRCS softmax_op.cc softmax_op.cu)
 op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu)
 op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
 
-op_library(fc_op SRCS fc_op.cc DEPS mul_op rowwise_add_op sigmoid_op
-        softmax_op net)
-
 op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
 
-op_library(recurrent_network_op SRCS recurrent_network_op.cc DEPS op_desc
-tensor op_registry operator net)
-cc_test(recurrent_network_op_test SRCS recurrent_network_op_test.cc DEPS
-recurrent_network_op gtest mul_op add_op)
+op_library(fc_op
+    SRCS fc_op.cc
+    DEPS mul_op rowwise_add_op sigmoid_op softmax_op net_op)
+op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net_op)
+cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)

paddle/operators/add_op.cc

@@ -50,10 +50,6 @@ The equation is: Out = X + Y
 class AddOpGrad : public OperatorWithKernel {
 protected:
   void InferShape(const InferShapeContext &ctx) const override {}
-  std::string DebugString() const override {
-    LOG(INFO) << "AddOpGrad";
-    return "";
-  }
 };
 
 }  // namespace operators

paddle/operators/add_op.cu

+#define EIGEN_USE_GPU
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/add_op.h"
 

paddle/operators/add_op.h

@@ -28,10 +28,13 @@ public:
 
     output->mutable_data<T>(context.GetPlace());
 
-    EigenVector<T>::Flatten(*output).device(
-        *(context.GetEigenDevice<Place>())) =
-        framework::EigenVector<T>::Flatten(*input0) +
-        framework::EigenVector<T>::Flatten(*input1);
+    auto X = EigenVector<T>::Flatten(*input0);
+    auto Y = EigenVector<T>::Flatten(*input1);
+    auto Z = EigenVector<T>::Flatten(*output);
+
+    auto place = context.GetEigenDevice<Place>();
+
+    Z.device(place) = X + Y;
   }
 };
 

paddle/operators/cross_entropy_op.cu

+#define EIGEN_USE_GPU
 #include "paddle/operators/cross_entropy_op.h"
 
 REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,

paddle/operators/mean_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/mean_op.h"
+
+namespace paddle {
+namespace operators {
+
+class MeanOp : public OperatorWithKernel {
+protected:
+  void InferShape(const InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE(ctx.InputSize() == 1, "Input size of AddOp must be one");
+    PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one");
+    PADDLE_ENFORCE(ctx.InputVar(0) != nullptr && ctx.OutputVar(0) != nullptr,
+                   "Input/Output of MeanOp must be initialized.");
+    ctx.Output<Tensor>(0)->Resize(framework::make_ddim({1}));
+  }
+};
+
+class MeanOpMaker : public OpProtoAndCheckerMaker {
+public:
+  MeanOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input of mean op");
+    AddOutput("Out", "The output of mean op").IgnoreGradient();
+    AddComment("Mean Operator");
+  }
+};
+
+class MeanGradOp : public OperatorWithKernel {
+protected:
+  void InferShape(const InferShapeContext &ctx) const override {
+    ctx.Output<Tensor>("X" + GRAD_VAR_SUFFIX())
+        ->Resize(ctx.Input<Tensor>("X")->dims());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker);
+REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel<ops::CPUPlace, float>);
+REGISTER_GRADIENT_OP(mean, mean_grad, ops::MeanGradOp);
+REGISTER_OP_CPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::CPUPlace, float>);

paddle/operators/mean_op.cu

+#define EIGEN_USE_GPU
+
+#include "paddle/operators/mean_op.h"
+
+REGISTER_OP_GPU_KERNEL(mean, ops::MeanKernel<ops::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::GPUPlace, float>);
\ No newline at end of file

paddle/operators/mean_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/operators/type_alias.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class MeanKernel : public OpKernel {
+public:
+  void Compute(const ExecutionContext& context) const override {
+    auto input = context.Input<Tensor>(0);
+    auto output = context.Output<Tensor>(0);
+
+    output->mutable_data<T>(context.GetPlace());
+
+    auto X = EigenVector<T>::Flatten(*input);
+    auto y = EigenScalar<T>::From(*output);
+    auto place = context.GetEigenDevice<Place>();
+
+    y.device(place) = X.mean();
+  }
+};
+
+template <typename Place, typename T>
+class MeanGradKernel : public OpKernel {
+public:
+  void Compute(const ExecutionContext& context) const override {
+    auto OG = context.Input<Tensor>("Out" + OperatorBase::GRAD_VAR_SUFFIX());
+    PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
+                   "Mean Gradient should be scalar");
+    auto IG = context.Output<Tensor>("X" + OperatorBase::GRAD_VAR_SUFFIX());
+    IG->mutable_data<T>(context.GetPlace());
+
+    T ig_size = (T)framework::product(IG->dims());
+
+    EigenVector<T>::Flatten(*IG).device(context.GetEigenDevice<Place>()) =
+        EigenScalar<T>::From(*OG) / ig_size;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/mean_op_test.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gtest/gtest.h>
+
+#include <paddle/framework/op_registry.h>
+
+USE_OP(mean);
+
+TEST(MeanOp, GetOpProto) {
+  auto& protos = paddle::framework::OpRegistry::protos();
+  auto it = protos.find("mean");
+  ASSERT_NE(it, protos.end());
+}

paddle/operators/mul_op.cu

@@ -12,6 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
+#define EIGEN_USE_GPU
 #include "paddle/operators/mul_op.h"
 
 REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<ops::GPUPlace, float>);
\ No newline at end of file

paddle/operators/mul_op.h

@@ -26,13 +26,18 @@ public:
     Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
         {Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
 
+    auto input0 = context.Input<Tensor>("X");
+    auto input1 = context.Input<Tensor>("Y");
     auto output = context.Output<Tensor>(0);
+
     output->mutable_data<T>(context.GetPlace());
 
-    EigenMatrix<T>::From(*output).device(*(context.GetEigenDevice<Place>())) =
-        EigenMatrix<T>::From(*context.Input<Tensor>("X"))
-            .contract(EigenMatrix<T>::From(*context.Input<Tensor>("Y")),
-                      dim_pair);
+    auto X = EigenMatrix<T>::From(*input0);
+    auto Y = EigenMatrix<T>::From(*input1);
+    auto Z = EigenMatrix<T>::From(*output);
+    auto place = context.GetEigenDevice<Place>();
+
+    Z.device(place) = X.contract(Y, dim_pair);
   }
 };
 }  // namespace operators

paddle/framework/net.cc → paddle/operators/net_op.cc

@@ -14,11 +14,11 @@
   limitations under the License.
 */
 
-#include "paddle/framework/net.h"
+#include "paddle/operators/net_op.h"
 #include "paddle/framework/op_registry.h"
 
 namespace paddle {
-namespace framework {
+namespace operators {
 
 void NetOp::CompleteAddOp(bool calc) {
   add_op_done_ = true;
@@ -74,5 +74,5 @@ std::string NetOp::DebugString() const {
 
 bool NetOp::IsNetOp() const { return true; }
 
-}  // namespace framework
+}  // namespace operators
 }  // namespace paddle

paddle/framework/net.h → paddle/operators/net_op.h

@@ -14,15 +14,17 @@ limitations under the License. */
 
 #pragma once
 
-#include <paddle/framework/op_desc.pb.h>
-#include <paddle/framework/operator.h>
+#include "paddle/framework/op_desc.pb.h"
 #include "paddle/framework/op_proto.pb.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
 #include "paddle/framework/scope.h"
+#include "paddle/operators/type_alias.h"
 #include "paddle/platform/device_context.h"
 
 namespace paddle {
-namespace framework {
+namespace operators {
+
 /**
  * @brief Network is also a type of Operator
  *
@@ -37,13 +39,13 @@ namespace framework {
  * This is the base class of network, all the networks should implement the APIs
  * it defines.
  */
-class NetOp : public OperatorBase {
- public:
+class NetOp : public framework::OperatorBase {
+public:
   /**
    * Infer all the operators' input and output variables' shapes, will be called
    * before every mini-batch
    */
-  void InferShape(const Scope& scope) const override {
+  void InferShape(const framework::Scope& scope) const override {
     for (auto& op : ops_) {
       op->InferShape(scope);
     }
@@ -56,7 +58,7 @@ class NetOp : public OperatorBase {
    * scope will be used instead. If no OpContext is provicded, default context
    * will be used.
    */
-  void Run(const Scope& scope,
+  void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
     for (auto& op : ops_) {
       op->Run(scope, dev_ctx);
@@ -88,7 +90,7 @@ class NetOp : public OperatorBase {
 
   std::vector<std::shared_ptr<OperatorBase>> ops_;
 
- private:
+private:
   bool add_op_done_{false};
 
   template <typename T, typename KeyType>
@@ -97,5 +99,5 @@ class NetOp : public OperatorBase {
   }
 };
 
-}  // namespace framework
+}  // namespace operators
 }  // namespace paddle

paddle/framework/net_op_test.cc → paddle/operators/net_op_test.cc

+#include "paddle/operators/net_op.h"
+
 #include <gtest/gtest.h>
-#include <paddle/framework/net.h>
-#include <paddle/framework/op_registry.h>
-#include <paddle/framework/operator.h>
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
 
 namespace paddle {
-namespace framework {
+namespace operators {
 
 static int infer_shape_cnt = 0;
 static int run_cnt = 0;
 
 class TestOp : public OperatorBase {
- public:
+public:
   void InferShape(const framework::Scope& scope) const override {
     ++infer_shape_cnt;
   }
@@ -21,7 +23,7 @@ class TestOp : public OperatorBase {
 };
 
 class EmptyOp : public OperatorBase {
- public:
+public:
   void InferShape(const Scope& scope) const override {}
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {}
@@ -73,7 +75,7 @@ TEST(OpKernel, all) {
   ASSERT_THROW(net->AddOp(op2), paddle::platform::EnforceNotMet);
 }
 
-TEST(Net, insert_op) {
+TEST(NetOp, insert_op) {
   NetOp net;
   auto op1 = std::make_shared<EmptyOp>();
   op1->inputs_ = {"x", "w1", "b1"};
@@ -85,5 +87,5 @@ TEST(Net, insert_op) {
   ASSERT_EQ(3UL, net.ops_.size());
 }
 
-}  // namespace framework
+}  // namespace operators
 }  // namespace paddle

paddle/operators/recurrent_network_op.cc → paddle/operators/recurrent_op.cc

@@ -12,14 +12,14 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/recurrent_network_op.h"
+#include "paddle/operators/recurrent_op.h"
 
 #include <glog/logging.h>
 #include <cstring>
 #include <sstream>
 
-#include "paddle/framework/net.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/net_op.h"
 #include "paddle/platform/enforce.h"
 
 namespace paddle {
@@ -29,11 +29,15 @@ namespace rnn {
 
 void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& inlinks,
-                   const size_t seq_len) {
+                   const size_t seq_len,
+                   bool infer_shape_mode) {
   PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
   for (size_t i = 0; i < inlinks.size(); ++i) {
-    Tensor* input =
-        step_scopes[0]->FindVar(inlinks[i].external)->GetMutable<Tensor>();
+    auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
+    PADDLE_ENFORCE(input_var != nullptr,
+                   "input link [%s] is not in scope.",
+                   inlinks[i].external);
+    Tensor* input = input_var->GetMutable<Tensor>();
     DDim dims = input->dims();
     PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
                    "all the inlinks must have same length");
@@ -41,7 +45,9 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
     for (size_t j = 0; j < seq_len; j++) {
       Tensor* step_input =
           step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
-      *step_input = input->Slice<float>(j, j + 1);
+      if (!infer_shape_mode) {
+        *step_input = input->Slice<float>(j, j + 1);
+      }
       step_input->Resize(step_dims);
     }
   }
@@ -49,36 +55,41 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
 
 void ConcatOutputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& outlinks,
-                   const size_t seq_len) {
+                   const size_t seq_len,
+                   bool infer_shape_mode) {
   for (size_t i = 0; i < outlinks.size(); i++) {
-    Tensor* output =
-        step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
-
-    // TODO(qingiqng) remove following code after adding
-    // InferShape in RecurrentGradientOp
-    DDim step_dims = step_scopes[0]
-                         ->FindVar(outlinks[i].internal)
-                         ->GetMutable<Tensor>()
-                         ->dims();
-    std::vector<int> dims_vec = vectorize(step_dims);
-    dims_vec.insert(dims_vec.begin(), seq_len);
-    output->mutable_data<float>(make_ddim(dims_vec), platform::CPUPlace());
-
-    for (size_t j = 0; j < seq_len; j++) {
-      Tensor* step_output =
-          step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
-      // TODO(luotao02) data type and platform::DeviceContext() should set
-      // correctly
-      (output->Slice<float>(j, j + 1))
-          .CopyFrom<float>(*step_output, platform::CPUPlace());
+    auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
+    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) {
+      DDim step_dims = step_scopes[0]
+                           ->FindVar(outlinks[i].internal)
+                           ->GetMutable<Tensor>()
+                           ->dims();
+      std::vector<int> dims_vec = vectorize(step_dims);
+      dims_vec.insert(dims_vec.begin(), seq_len);
+      output->Resize(make_ddim(dims_vec));
+    } else {
+      output->mutable_data<float>(platform::CPUPlace());
+      for (size_t j = 0; j < seq_len; j++) {
+        Tensor* step_output =
+            step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
+        // TODO(luotao02) data type and platform::DeviceContext() should set
+        // correctly
+        (output->Slice<float>(j, j + 1))
+            .CopyFrom<float>(*step_output, platform::CPUPlace());
+      }
     }
   }
 }
 
 void LinkMemories(const std::vector<Scope*>& scopes,
                   const std::vector<rnn::MemoryAttr>& memories,
-                  size_t step_id,
-                  int offset) {
+                  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,
@@ -95,18 +106,13 @@ void LinkMemories(const std::vector<Scope*>& scopes,
   auto scope = scopes[step_id];
   auto linked_scope = scopes[step_id + offset];
   for (auto& attr : memories) {
-    auto mem = scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
-    // maybe share variable is better?
+    auto mem = scope->FindVar(attr.pre_var)->GetMutable<Tensor>();
     auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
-    mem->ShareDataWith<float>(*linked_mem);
-
-    // TODO(qingqing) remove following code
-    // the memory of current step should be allocated in step net
-    auto m = scope->NewVar(attr.var)->GetMutable<Tensor>();
-    // for unit test, as addOp and mulOp are null currently, if not
-    // mutable_data, mem.data() in output will be error. We will
-    // remove this line after merge the correct addOp and mulOp.
-    m->mutable_data<float>(mem->dims(), platform::CPUPlace());
+    if (infer_shape_mode) {
+      mem->Resize(linked_mem->dims());
+    } else {
+      mem->ShareDataWith<float>(*linked_mem);
+    }
   }
 }
 
@@ -175,60 +181,39 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
                  ->dims()[0];
   CreateScopes(scope);
   auto step_scopes = GetStepScopes(scope);
-
-  // SegmentInputs is called in InferShape. The input must hold memory in
-  // SegmentInputs. But the other op only set dimension for the output in
-  // InferShape. That's a problem. Wether the RNN op needs InferShape or not?
-  // Wether the following functions (SegmentInputs, InitMemories, ...) need
-  // to rewrite for RNN op?
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
-
-  InitMemories(step_scopes[0]);
-
-  PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
-                 "stepnet [%s] is not in scope.",
-                 arg_->step_net);
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
+  InitMemories(step_scopes[0], true /*infer_shape_mode*/);
   Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
-  // If the InferShape is called in OperatorBase's run function,
-  // the rnn op only needs to do InferShape for the first time step
   for (size_t i = 0; i < seq_len_; i++) {
     if (i > 0) {
-      rnn::LinkMemories(step_scopes, arg_->memories, i, -1);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, i, -1, true /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->InferShape(*step_scopes[i]);
   }
-
-  auto outlinks = arg_->outlinks;
-  for (size_t i = 0; i < outlinks.size(); i++) {
-    DDim step_dims = step_scopes[0]
-                         ->FindVar(outlinks[i].internal)
-                         ->GetMutable<Tensor>()
-                         ->dims();
-    std::vector<int> dims_vec = vectorize(step_dims);
-    // now only support fixed length
-    dims_vec.insert(dims_vec.begin(), seq_len_);
-    Tensor* output =
-        step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
-    output->Resize(make_ddim(dims_vec));
-  }
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
 }
 
 void RecurrentAlgorithm::Run(const Scope& scope,
                              const platform::DeviceContext& dev_ctx) const {
   auto step_scopes = GetStepScopes(scope);
-
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
+  InitMemories(step_scopes[0], false /*infer_shape_mode*/);
   Variable* net = scope.FindVar(arg_->step_net);
+
   for (size_t step_id = 0; step_id < seq_len_; step_id++) {
-    // the link memory is done in InferShape
-    // maybe remove following code after testing
     if (step_id > 0) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, step_id, -1, false /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
   }
-
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
 }
 
 void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
@@ -244,18 +229,19 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
       // Now all variables in scope must be created outside of op.
       auto net_op = scope.FindVar(arg_->step_net)->GetMutable<NetOp>();
       for (auto& input : net_op->inputs_) {
+        // the weight are located in parent scope
         if (!step_scope.FindVar(input)) step_scope.NewVar(input);
       }
       for (auto& output : net_op->outputs_) {
         step_scope.NewVar(output);
       }
-
       step_scopes->emplace_back(&step_scope);
     }
   }
 }
 
-void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
+void RecurrentAlgorithm::InitMemories(Scope* step_scope,
+                                      bool infer_shape_mode) const {
   for (auto& attr : arg_->memories) {
     Tensor* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
     PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
@@ -263,13 +249,11 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
                    attr.var,
                    attr.boot_var);
     Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
-    pre_mem->ShareDataWith<float>(*boot_mem);
-
-    // TODO(qingqing) remove following code
-    // the memory of current step should be allocated in step net
-    // here for unit test
-    auto cur_step_mem = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
-    cur_step_mem->mutable_data<float>(boot_mem->dims(), platform::CPUPlace());
+    if (infer_shape_mode) {
+      pre_mem->Resize(boot_mem->dims());
+    } else {
+      pre_mem->ShareDataWith<float>(*boot_mem);
+    }
   }
 }
 
@@ -307,13 +291,14 @@ public:
       : OpProtoAndCheckerMaker(proto, op_checker) {
     const auto& name = RecurrentOp::kArgName;
     // inputs and outputs stored in proto
-    AddInput(name.inlinks, "the input that need to be segmented for each step.")
+    AddInput(name.inlinks,
+             "the inputs that need to be segmented for each step.")
         .SetMultiple();
     AddInput(name.boot_memories, "variables to initialize memories.")
         .SetMultiple();
     AddInput(name.step_net, "network shared by all steps.");
 
-    AddOutput(name.outlinks, "the output that need to concated for all steps.")
+    AddOutput(name.outlinks, "the outputs that need to concated for all steps.")
         .SetMultiple();
     AddOutput(name.step_scopes, "step scopes");
 
@@ -331,34 +316,39 @@ public:
 void RecurrentGradientAlgorithm::Run(
     const Scope& scope, const platform::DeviceContext& dev_ctx) const {
   auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
-  PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
-                 "step net is not in scope.");
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
   Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, step_id, 1, false /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
   }
-  LinkBootMemoryGradients(step_scopes[0]);
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
+  LinkBootMemoryGradients(step_scopes[0], false);
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
 }
 
 void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
-    Scope* step_scope) const {
+    Scope* step_scope, bool infer_shape_mode) const {
   for (auto& attr : arg_->memories) {
-    Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
-    PADDLE_ENFORCE(mem_grad != nullptr,
-                   "boot_tensor should be retrieved before");
+    PADDLE_ENFORCE(step_scope->FindVar(attr.var) != nullptr,
+                   "memory variable [%s] does not exists",
+                   attr.var);
     PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
-                   "memory [%s]'s boot variable [%s] not exists",
-                   attr.var,
+                   "boot variable [%s] does not exists",
                    attr.boot_var);
+    Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
     Tensor* boot_mem_grad =
         step_scope->NewVar(attr.boot_var)->GetMutable<Tensor>();
-    boot_mem_grad->ShareDataWith<float>(*mem_grad);
+    if (infer_shape_mode) {
+      boot_mem_grad->Resize(mem_grad->dims());
+    } else {
+      boot_mem_grad->ShareDataWith<float>(*mem_grad);
+    }
   }
 }
 
@@ -367,34 +357,20 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
                  ->GetMutable<Tensor>()
                  ->dims()[0];
   auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
-
-  PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
-                 "step net is not in scope.");
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
   Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
-
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, step_id, 1, true /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->InferShape(*step_scopes[step_id]);
   }
-
-  auto outlinks = arg_->outlinks;
-  for (size_t i = 0; i < outlinks.size(); i++) {
-    DDim step_dims = step_scopes[0]
-                         ->FindVar(outlinks[i].internal)
-                         ->GetMutable<Tensor>()
-                         ->dims();
-    std::vector<int> dims_vec = vectorize(step_dims);
-    // now only support fixed length
-    dims_vec.insert(dims_vec.begin(), seq_len_);
-    Tensor* output =
-        step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
-    output->Resize(make_ddim(dims_vec));
-  }
-  LinkBootMemoryGradients(step_scopes[0]);
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
+  LinkBootMemoryGradients(step_scopes[0], true /*infer_shape_mode*/);
 }
 
 void RecurrentGradientOp::Init() {

paddle/operators/recurrent_network_op.h → paddle/operators/recurrent_op.h

@@ -72,19 +72,22 @@ struct ArgumentName {
  */
 void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& inlinks,
-                   const size_t seq_len);
+                   const size_t seq_len,
+                   bool infer_shape_mode);
 
 /**
  * Process outputs of step nets and merge to variables.
  */
 void ConcatOutputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& outlinks,
-                   const size_t seq_len);
+                   const size_t seq_len,
+                   bool infer_shape_mode);
 
 void LinkMemories(const std::vector<Scope*>& step_scopes,
                   const std::vector<MemoryAttr>& memories,
-                  size_t step_id,
-                  int offset);
+                  const size_t step_id,
+                  const int offset,
+                  bool infer_shape_mode);
 
 void InitArgument(const ArgumentName& name, Argument* arg);
 
@@ -122,7 +125,7 @@ protected:
     return *scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
   }
 
-  void InitMemories(Scope* step_scopes) const;
+  void InitMemories(Scope* step_scopes, bool infer_shape_mode) const;
 
 private:
   std::unique_ptr<rnn::Argument> arg_;
@@ -145,7 +148,7 @@ public:
 
   void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
 
-  void LinkBootMemoryGradients(Scope* step_scopes) const;
+  void LinkBootMemoryGradients(Scope* step_scopes, bool infer_shape_mode) const;
 
   /**
    * InferShape must be called before Run.

paddle/operators/recurrent_network_op_test.cc → paddle/operators/recurrent_op_test.cc

@@ -11,14 +11,15 @@
   limitations under the License.
 */
 
+#include "paddle/operators/recurrent_op.h"
+
 #include <glog/logging.h>
 #include <gtest/gtest.h>
 
-#include "paddle/framework/net.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
 #include "paddle/framework/tensor.h"
-#include "paddle/operators/recurrent_network_op.h"
+#include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace operators {
@@ -55,7 +56,7 @@ protected:
     w->GetMutable<Tensor>()->mutable_data<float>(
         make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
 
-    for (auto boot : std::vector<std::string>{"x_boot", "h_boot"}) {
+    for (auto boot : std::vector<std::string>{"h_boot"}) {
       LOG(INFO) << "create global variable " << boot;
       Variable* h_boot = scope_.NewVar(boot);
       h_boot->GetMutable<Tensor>()->mutable_data<float>(
@@ -79,7 +80,6 @@ protected:
     op_desc.add_inputs("x0");
     op_desc.add_inputs("x1");
     // boot_memories 3
-    op_desc.add_inputs("x_boot");
     op_desc.add_inputs("h_boot");
     // step net 5
     op_desc.add_inputs("step_net");
@@ -91,7 +91,7 @@ protected:
     auto _input_format = std::vector<int>{
         0,  // in_link
         3,  // memories
-        5   // step_net
+        4   // step_net
     };
     auto input_format = op_desc.add_attrs();
     input_format->set_name("input_format");
@@ -129,12 +129,11 @@ protected:
       inlink_alias->add_strings(item);
     }
     // pre memories
-    for (const auto& item :
-         std::vector<std::string>{"rnn/x@pre", "rnn/h@pre"}) {
+    for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
       pre_memories->add_strings(item);
     }
     // memories
-    for (const auto& item : std::vector<std::string>{"rnn/x", "rnn/h"}) {
+    for (const auto& item : std::vector<std::string>{"rnn/h"}) {
       memories->add_strings(item);
     }
     // output alias
@@ -151,14 +150,11 @@ protected:
     LOG(INFO) << "create variable step_net";
     Variable* var = scope_.NewVar("step_net");
     auto net = var->GetMutable<NetOp>();
-    // rnn/s is net's input or output?
-    net->inputs_ = {"rnn/h@pre", "rnn/w", "rnn/x"};
-    net->inputs_ = {"rnn/s", "rnn/h"};
     net->AddOp(
         OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
 
     net->AddOp(
-        OpRegistry::CreateOp("add_two", {"rnn/x", "rnn/s"}, {"rnn/h"}, {}));
+        OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
     net->CompleteAddOp();
   }
 
@@ -297,7 +293,10 @@ protected:
     inlink.internal = "rnn/x";
     auto step_scopes =
         scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
-    rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10);
+    rnn::SegmentInputs(*step_scopes,
+                       std::vector<rnn::Link>{inlink},
+                       10,
+                       true /*infer_shape_mode*/);
   }
 
   void LinkeMemories() {
@@ -311,7 +310,8 @@ protected:
     auto step_scopes =
         scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
     for (int i = 1; i < 10; ++i) {
-      rnn::LinkMemories(*step_scopes, memories, i, -1);
+      rnn::LinkMemories(
+          *step_scopes, memories, i, -1, true /*infer_shape_mode*/);
     }
   }
 
@@ -333,14 +333,14 @@ TEST(RecurrentOp, LinkMemories) {
   using namespace paddle::operators;
 
   // create and init step scopes
-  int len = 10;
+  size_t len = 10;
   std::vector<Scope*> step_scopes;
-  for (int i = 0; i < len; ++i) {
+  for (size_t i = 0; i < len; ++i) {
     auto scope = new Scope();
     scope->NewVar("pre_h");
     auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
     float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
-    for (int j = 0; j < 15 * 20; ++j) {
+    for (size_t j = 0; j < 15 * 20; ++j) {
       data[j] = rand() * (1. / (double)RAND_MAX);
     }
     step_scopes.push_back(scope);
@@ -354,24 +354,24 @@ TEST(RecurrentOp, LinkMemories) {
   std::vector<rnn::MemoryAttr> memories;
   memories.push_back(mem_attr);
 
-  for (int i = 1; i < len; ++i) {
-    rnn::LinkMemories(step_scopes, memories, i, -1);
+  for (size_t i = 1; i < len; ++i) {
+    rnn::LinkMemories(step_scopes, memories, i, -1, false /*infer_shape_mode*/);
   }
   // check
-  for (int i = 0; i < len - 1; ++i) {
+  for (size_t i = 0; i < len - 1; ++i) {
     const float* a =
         step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
     const float* b = step_scopes[i + 1]
                          ->FindVar("pre_h")
                          ->GetMutable<Tensor>()
                          ->data<float>();
-    for (size_t i = 0; i < 15 * 20; ++i) {
-      ASSERT_FLOAT_EQ(a[i], b[i]);
+    for (size_t j = 0; j < 15 * 20; ++j) {
+      ASSERT_FLOAT_EQ(a[j], b[j]);
     }
   }
 
   for (int i = len - 2; i >= 0; --i) {
-    rnn::LinkMemories(step_scopes, memories, i, 1);
+    rnn::LinkMemories(step_scopes, memories, i, 1, false /*infer_shape_mode*/);
   }
   // check
   for (int i = len - 2; i >= 0; --i) {
@@ -379,8 +379,8 @@ TEST(RecurrentOp, LinkMemories) {
         step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
     const float* b =
         step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
-    for (size_t i = 0; i < 15 * 20; ++i) {
-      ASSERT_FLOAT_EQ(a[i], b[i]);
+    for (size_t j = 0; j < 15 * 20; ++j) {
+      ASSERT_FLOAT_EQ(a[j], b[j]);
     }
   }
 
@@ -391,9 +391,3 @@ TEST(RecurrentOp, LinkMemories) {
 
 USE_OP(add_two);
 USE_OP(mul);
-
-// int main() {
-//  //! TODO(yuyang18): Temporary disable this unit-test because implementation
-//  //! error.
-//  return 0;
-//}
\ No newline at end of file

paddle/operators/rowwise_add_op.cu

+#define EIGEN_USE_GPU
 #include "paddle/operators/rowwise_add_op.h"
 
 REGISTER_OP_GPU_KERNEL(rowwise_add,

paddle/operators/rowwise_add_op.h

@@ -33,7 +33,7 @@ public:
     const int rest_size = input.size() / bias_size;
     Eigen::DSizes<int, 1> one_d(input.size());
     Eigen::DSizes<int, 1> bcast(rest_size);
-    output.reshape(one_d).device(*(context.GetEigenDevice<Place>())) =
+    output.reshape(one_d).device(context.GetEigenDevice<Place>()) =
         input.reshape(one_d) + bias.broadcast(bcast).reshape(one_d);
   }
 };

paddle/operators/sgd_op.cu

+#define EIGEN_USE_GPU
 #include "paddle/operators/sgd_op.h"
 
 REGISTER_OP_GPU_KERNEL(sgd, ops::SGDOpKernel<ops::GPUPlace, float>);
\ No newline at end of file

paddle/operators/sgd_op.h

@@ -29,8 +29,12 @@ public:
 
     param_out->mutable_data<T>(ctx.GetPlace());
 
-    EigenVector<T>::Flatten(*param_out).device(*(ctx.GetEigenDevice<Place>())) =
-        EigenVector<T>::Flatten(*param) - lr * EigenVector<T>::Flatten(*grad);
+    auto p = EigenVector<T>::Flatten(*param);
+    auto g = EigenVector<T>::Flatten(*grad);
+    auto o = EigenVector<T>::Flatten(*param_out);
+    auto place = ctx.GetEigenDevice<Place>();
+
+    o.device(place) = p - lr * g;
   }
 };
 

paddle/operators/sigmoid_op.cu

+#define EIGEN_USE_GPU
 #include "paddle/operators/sigmoid_op.h"
 
 REGISTER_OP_GPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::GPUPlace, float>);

paddle/operators/sigmoid_op.h

@@ -27,9 +27,11 @@ public:
     auto output = context.Output<Tensor>(0);
     output->mutable_data<T>(context.GetPlace());
 
-    EigenVector<T>::Flatten(*output).device(
-        *(context.GetEigenDevice<Place>())) =
-        1.0 / (1.0 + (-1.0 * EigenVector<T>::Flatten(*input)).exp());
+    auto X = EigenVector<T>::Flatten(*input);
+    auto Y = EigenVector<T>::Flatten(*output);
+    auto place = context.GetEigenDevice<Place>();
+
+    Y.device(place) = 1.0 / (1.0 + (-1.0 * X).exp());
   }
 };
 }  // namespace operators

paddle/operators/softmax_op.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
 
-   http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License. */
 #include "paddle/operators/softmax_op.h"
 
 namespace paddle {
@@ -19,12 +20,13 @@ namespace operators {
 class SoftmaxOp : public OperatorWithKernel {
 protected:
   void InferShape(const InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputSize() == 1, "Only one input is need for softmax");
-    PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims().size() == 2,
+    PADDLE_ENFORCE(ctx.InputSize() == 1UL,
+                   "Only one input is need for softmax");
+    PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
                    "The input of softmax op must be matrix");
-    PADDLE_ENFORCE(ctx.OutputSize() == 1,
+    PADDLE_ENFORCE(ctx.OutputSize() == 1UL,
                    "Only one output is need for softmax");
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
+    ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
 
@@ -40,10 +42,19 @@ public:
 
 class SoftmaxOpGrad : public OperatorWithKernel {
 protected:
-  void InferShape(const InferShapeContext &ctx) const override {}
-  std::string DebugString() const override {
-    LOG(INFO) << "SoftmaxOpGrad";
-    return "";
+  void InferShape(const InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE(ctx.InputSize() == 3UL,
+                   "Input of SoftmaxOpGrad should be 3, X, Y, YG");
+    PADDLE_ENFORCE(ctx.OutputSize() == 1UL,
+                   "Output of SoftmaxOpGrad should be 1");
+    PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
+    PADDLE_ENFORCE(ctx.InputVar(GRAD_VAR_NAME("Y")) != nullptr,
+                   "Input(Y@GRAD) should not be null");
+    PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
+                       ctx.Input<Tensor>(GRAD_VAR_NAME("Y"))->dims(),
+                   "the shape of Input(0) and Input(1) should be the same");
+    ctx.Output<Tensor>(GRAD_VAR_NAME("X"))
+        ->Resize(ctx.Input<Tensor>("Y")->dims());
   }
 };
 
@@ -51,5 +62,7 @@ protected:
 }  // namespace paddle
 
 REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
-REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
 REGISTER_OP_CPU_KERNEL(softmax, ops::SoftmaxKernel<ops::CPUPlace, float>);
+REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
+REGISTER_OP_CPU_KERNEL(softmax_grad,
+                       ops::SoftmaxGradKernel<ops::CPUPlace, float>);

paddle/operators/softmax_op.cu

+#define EIGEN_USE_GPU
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/softmax_op.h"
 
 REGISTER_OP_GPU_KERNEL(softmax, ops::SoftmaxKernel<ops::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(softmax_grad, ops::SoftmaxGradKernel<ops::GPUPlace, float>);

paddle/operators/softmax_op.h

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
 
-   http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License. */
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #pragma once
 
+#include "paddle/framework/ddim.h"
+#include "paddle/framework/operator.h"
+#include "paddle/framework/tensor.h"
 #include "paddle/operators/type_alias.h"
 
 namespace paddle {
@@ -23,8 +26,8 @@ template <typename Place, typename T>
 class SoftmaxKernel : public OpKernel {
 public:
   void Compute(const ExecutionContext& context) const override {
-    auto input = context.Input<Tensor>(0);
-    auto output = context.Output<Tensor>(0);
+    auto input = context.Input<Tensor>("X");
+    auto output = context.Output<Tensor>("Y");
     output->mutable_data<T>(context.GetPlace());
 
     auto logits = EigenMatrix<T>::From(*input);
@@ -46,9 +49,9 @@ public:
                                .reshape(batch_by_one)
                                .broadcast(one_by_class));
 
-    softmax.device(*(context.GetEigenDevice<Place>())) = shifted_logits.exp();
+    softmax.device(context.GetEigenDevice<Place>()) = shifted_logits.exp();
 
-    softmax.device(*(context.GetEigenDevice<Place>())) =
+    softmax.device(context.GetEigenDevice<Place>()) =
         (softmax *
          softmax.sum(along_class)
              .inverse()
@@ -57,5 +60,38 @@ public:
              .broadcast(one_by_class));
   }
 };
+
+template <typename Place, typename T>
+class SoftmaxGradKernel : public OpKernel {
+public:
+  void Compute(const ExecutionContext& context) const override {
+    std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
+
+    auto Y = context.Input<Tensor>("Y");
+    auto dY = context.Input<Tensor>(OperatorBase::GRAD_VAR_NAME("Y"));
+    auto dX = context.Output<Tensor>(OperatorBase::GRAD_VAR_NAME("X"));
+    dX->mutable_data<T>(context.GetPlace());
+
+    const int batch_size = Y->dims()[0];
+    const int class_num = Y->dims()[1];
+
+    Eigen::DSizes<int, 1> along_class(1);
+    Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
+    Eigen::DSizes<int, 2> one_by_class(1, class_num);
+
+    auto Y_eigen = EigenMatrix<T>::From(*Y);
+    auto dY_eigen = EigenMatrix<T>::From(*dY);
+    auto dX_eigen = EigenMatrix<T>::From(*dX);
+    auto place = context.GetEigenDevice<Place>();
+
+    auto dot = (Y_eigen * dY_eigen)
+                   .sum(along_class)
+                   .eval()
+                   .reshape(batch_by_one)
+                   .broadcast(one_by_class);
+    dX_eigen.device(place) = (dY_eigen - dot) * Y_eigen;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle

paddle/operators/type_alias.h

@@ -15,13 +15,14 @@
 #pragma once
 
 #include "paddle/framework/eigen.h"
-#include "paddle/framework/net.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace operators {
 
 using OpKernel = framework::OpKernel;
+using OperatorBase = framework::OperatorBase;
 using InferShapeContext = framework::InferShapeContext;
 using ExecutionContext = framework::ExecutionContext;
 using Variable = framework::Variable;
@@ -43,14 +44,16 @@ template <typename T,
           typename IndexType = Eigen::DenseIndex>
 using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
 using Tensor = framework::Tensor;
+using Scope = framework::Scope;
 using OperatorWithKernel = framework::OperatorWithKernel;
+using OperatorBase = framework::OperatorBase;
 using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
 using OpProto = framework::OpProto;
 using OpAttrChecker = framework::OpAttrChecker;
 using CPUPlace = platform::CPUPlace;
 using GPUPlace = platform::GPUPlace;
-using NetOp = framework::NetOp;
 using OpRegistry = framework::OpRegistry;
+
 }  // namespace operators
 }  // namespace paddle
 

paddle/platform/enforce.h

@@ -144,12 +144,12 @@ inline void throw_on_error(T e) {
   throw_on_error(e, "");
 }
 
-#define PADDLE_THROW(...)                                      \
-  do {                                                         \
-    throw ::paddle::platform::EnforceNotMet(                   \
-        std::make_exception_ptr(                               \
-            std::runtime_error(string::Sprintf(__VA_ARGS__))), \
-        __FILE__, __LINE__);                                   \
+#define PADDLE_THROW(...)                                              \
+  do {                                                                 \
+    throw ::paddle::platform::EnforceNotMet(                           \
+        std::make_exception_ptr(                                       \
+            std::runtime_error(paddle::string::Sprintf(__VA_ARGS__))), \
+        __FILE__, __LINE__);                                           \
   } while (0)
 
 #define PADDLE_ENFORCE(...)                                             \

paddle/pybind/CMakeLists.txt

-cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python
-        add_op fc_op sgd_op cross_entropy_op recurrent_network_op fill_zeros_like_op)
+cc_library(paddle_pybind SHARED
+    SRCS pybind.cc
+    DEPS pybind python backward
+	fc_op
+	sgd_op
+	add_op
+	mean_op
+	cross_entropy_op
+	recurrent_op
+	fill_zeros_like_op)

paddle/scripts/docker/build.sh

@@ -69,7 +69,7 @@ cat <<EOF
 Installing ...
 ========================================
 EOF
-make install
+make install -j `nproc`
 pip install /usr/local/opt/paddle/share/wheels/*.whl
 paddle version
 
@@ -122,7 +122,7 @@ cat <<EOF
 Generating .deb package ...
 ========================================
 EOF
-cpack -D CPACK_GENERATOR='DEB' ..
+cpack -D CPACK_GENERATOR='DEB' -j `nproc` ..
 
 
 cat <<EOF
@@ -148,7 +148,7 @@ cat >> /paddle/build/Dockerfile <<EOF
 ADD *.deb /
 # run paddle version to install python packages first
 RUN apt-get update &&\
-    apt-get install -y python-pip && pip install -U pip && \
+    apt-get install -y wget python-pip && pip install -U pip && \
     dpkg -i /*.deb ; apt-get install -f -y && \
     apt-get clean -y && \
     rm -f /*.deb && \

paddle/scripts/travis/build_doc.sh

@@ -6,14 +6,14 @@ mkdir -p $TRAVIS_BUILD_DIR/build
 cd $TRAVIS_BUILD_DIR/build
 
 # Compile paddle binaries first
-cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_GOLANG=ON -DWITH_STYLE_CHECK=OFF
+cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_GOLANG=ON -DWITH_STYLE_CHECK=OFF
 
 mkdir output
 make -j `nproc`
 find .. -name '*whl' | xargs pip install  # install all wheels.
 rm -rf *
 # Compile Documentation only.
-cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=ON
+cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
 make -j `nproc` paddle_docs paddle_docs_cn
 
 # check websites for broken links

proto/ModelConfig.proto

@@ -298,6 +298,11 @@ message DetectionOutputConfig {
   optional uint32 width = 9 [default = 1];
 }
 
+message ClipConfig {
+  required double min = 1;
+  required double max = 2;
+}
+
 message LayerInputConfig {
   required string input_layer_name = 1;
   optional string input_parameter_name = 2;
@@ -318,6 +323,7 @@ message LayerInputConfig {
   optional RowConvConfig row_conv_conf = 15;
   optional MultiBoxLossConfig multibox_loss_conf = 16;
   optional DetectionOutputConfig detection_output_conf = 17;
+  optional ClipConfig clip_conf = 18;
 }
 
 message LayerConfig {

python/paddle/trainer/config_parser.py

@@ -2198,6 +2198,20 @@ class RowConvLayer(LayerBase):
         self.create_input_parameter(0, psize, dims)
 
 
+@config_layer('clip')
+class ClipLayer(LayerBase):
+    def __init__(self, name, inputs, min, max, **xargs):
+        super(ClipLayer, self).__init__(name, 'clip', 0, inputs=inputs, **xargs)
+        config_assert(
+            len(self.inputs) == 1,
+            'ClipLayer must have one and only one input.')
+        config_assert(min < max, 'min must be less than max.')
+        input_layer = self.get_input_layer(0)
+        self.set_layer_size(input_layer.size)
+        self.config.inputs[0].clip_conf.min = min
+        self.config.inputs[0].clip_conf.max = max
+
+
 # key: cost type
 # value: cost class
 g_cost_map = {}
@@ -2754,6 +2768,16 @@ class SumToOneNormLayer(LayerBase):
         self.set_layer_size(input_layer0.size)
 
 
+@config_layer('row_l2_norm')
+class RowL2NormLayer(LayerBase):
+    def __init__(self, name, inputs, **xargs):
+        super(RowL2NormLayer, self).__init__(
+            name, 'row_l2_norm', 0, inputs=inputs, **xargs)
+        config_assert(len(self.inputs) == 1, 'RowL2NormLayer must have 1 input')
+        input_layer = self.get_input_layer(0)
+        self.set_layer_size(input_layer.size)
+
+
 @config_layer('cos_vm')
 class CosSimVecMatLayer(LayerBase):
     def __init__(self, name, size, inputs, cos_scale=1.0, device=None):

python/paddle/trainer_config_helpers/layers.py

@@ -76,6 +76,7 @@ __all__ = [
     'trans_layer',
     'rotate_layer',
     'sum_to_one_norm_layer',
+    'row_l2_norm_layer',
     'get_output_layer',
     'LayerType',
     'context_projection',
@@ -128,6 +129,7 @@ __all__ = [
     'prelu_layer',
     'gated_unit_layer',
     'crop_layer',
+    'clip_layer',
     'slice_projection',
 ]
 
@@ -160,6 +162,7 @@ class LayerType(object):
     BATCH_NORM_LAYER = 'batch_norm'
     NORM_LAYER = 'norm'
     SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm'
+    ROW_L2_NORM_LAYER = 'row_l2_norm'
     ADDTO_LAYER = 'addto'
 
     CONCAT_LAYER = 'concat'
@@ -221,6 +224,7 @@ class LayerType(object):
 
     PRELU = 'prelu'
     CROP_LAYER = 'crop'
+    CLIP_LAYER = 'clip'
 
     @staticmethod
     def is_layer_type(type_name):
@@ -2889,6 +2893,42 @@ def sum_to_one_norm_layer(input, name=None, layer_attr=None):
         name, LayerType.SUM_TO_ONE_NORM_LAYER, parents=[input], size=input.size)
 
 
+@wrap_name_default()
+@layer_support()
+def row_l2_norm_layer(input, name=None, layer_attr=None):
+    """
+    A layer for L2-normalization in each row.
+
+    .. math::
+       out[i] = \frac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
+
+    where the size of :math:`in` is (batchSize x dataDim) ,
+    and the size of :math:`out` is a (batchSize x dataDim) .
+
+    The example usage is:
+
+    .. code-block:: python
+
+       row_l2_norm_layer = row_l2_norm_layer(input=layer)
+
+    :param input: Input layer.
+    :type input: LayerOutput
+    :param name: Layer name.
+    :type name: basestring
+    :param layer_attr: extra layer attributes.
+    :type layer_attr: ExtraLayerAttribute.
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    Layer(
+        name=name,
+        type=LayerType.ROW_L2_NORM_LAYER,
+        inputs=[input.name],
+        **ExtraAttr.to_kwargs(layer_attr))
+    return LayerOutput(
+        name, LayerType.ROW_L2_NORM_LAYER, parents=[input], size=input.size)
+
+
 @wrap_name_default("addto")
 @wrap_act_default(act=LinearActivation())
 @wrap_bias_attr_default(has_bias=False)
@@ -6046,3 +6086,36 @@ def crop_layer(input, offset, axis=2, shape=None, name=None, layer_attr=None):
         layer_type=LayerType.CROP_LAYER,
         parents=input,
         size=l.config.size)
+
+
+@wrap_name_default("clip")
+def clip_layer(input, min, max, name=None):
+    """
+    A layer for clipping the input value by the threshold.
+
+    .. math::
+
+        out[i] = \min\left(\max\left(in[i],p_{1}\right),p_{2}\right)
+
+    .. code-block:: python
+
+        clip = clip_layer(input=input_layer, min=-10, max=10)
+
+    :param name: The Layer Name.
+    :type name: basestring
+    :param input: The input layer.
+    :type input: LayerOutput.
+    :param min: The lower threshold for clipping.
+    :type min: double
+    :param max: The upper threshold for clipping.
+    :type max: double
+    :return: LayerOutput
+    """
+    Layer(
+        name=name,
+        type=LayerType.CLIP_LAYER,
+        inputs=[input.name],
+        min=min,
+        max=max)
+    return LayerOutput(
+        name, LayerType.CLIP_LAYER, parents=[input], size=input.size)

python/paddle/trainer_config_helpers/tests/configs/file_list.sh

@@ -7,6 +7,6 @@ test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
 test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
 test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
 test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
-test_recursive_topology test_gated_unit_layer)
+test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer)
 
 export whole_configs=(test_split_datasource)

python/paddle/trainer_config_helpers/tests/configs/protostr/test_clip_layer.protostr

+type: "nn"
+layers {
+  name: "input"
+  type: "data"
+  size: 300
+  active_type: ""
+}
+layers {
+  name: "__clip_0__"
+  type: "clip"
+  size: 300
+  active_type: ""
+  inputs {
+    input_layer_name: "input"
+    clip_conf {
+      min: -10
+      max: 10
+    }
+  }
+}
+input_layer_names: "input"
+output_layer_names: "__clip_0__"
+sub_models {
+  name: "root"
+  layer_names: "input"
+  layer_names: "__clip_0__"
+  input_layer_names: "input"
+  output_layer_names: "__clip_0__"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_l2_norm_layer.protostr

+type: "nn"
+layers {
+  name: "input"
+  type: "data"
+  size: 300
+  active_type: ""
+}
+layers {
+  name: "__row_l2_norm_layer_0__"
+  type: "row_l2_norm"
+  size: 300
+  active_type: ""
+  inputs {
+    input_layer_name: "input"
+  }
+}
+input_layer_names: "input"
+output_layer_names: "__row_l2_norm_layer_0__"
+sub_models {
+  name: "root"
+  layer_names: "input"
+  layer_names: "__row_l2_norm_layer_0__"
+  input_layer_names: "input"
+  output_layer_names: "__row_l2_norm_layer_0__"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/test_clip_layer.py

+from paddle.trainer_config_helpers import *
+
+data = data_layer(name='input', size=300)
+clip = clip_layer(input=data, min=-10, max=10)
+
+outputs(clip)

python/paddle/trainer_config_helpers/tests/configs/test_row_l2_norm_layer.py

+from paddle.trainer_config_helpers import *
+
+data = data_layer(name='input', size=300)
+row_l2_norm = row_l2_norm_layer(input=data)
+
+outputs(row_l2_norm)

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

@@ -8,8 +8,8 @@ add_python_test(test_framework
     test_fc_op.py
     test_add_two_op.py
     test_sgd_op.py
-    test_cross_entropy_op.py
     test_mul_op.py
+    test_mean_op.py
     test_sigmoid_op.py
     test_softmax_op.py
     test_rowwise_add_op.py

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

@@ -26,40 +26,45 @@ class OpTestMeta(type):
 
             scope = core.Scope()
             kwargs = dict()
+            places = []
+            places.append(core.CPUPlace())
+            if core.is_compile_gpu():
+                places.append(core.GPUPlace(0))
 
-            for in_name in func.all_input_args:
-                if hasattr(self, in_name):
-                    kwargs[in_name] = in_name
-                    var = scope.new_var(in_name).get_tensor()
-                    arr = getattr(self, in_name)
-                    var.set_dims(arr.shape)
-                    var.set(arr)
-                else:
-                    kwargs[in_name] = "@EMPTY@"
+            for place in places:
+                for in_name in func.all_input_args:
+                    if hasattr(self, in_name):
+                        kwargs[in_name] = in_name
+                        var = scope.new_var(in_name).get_tensor()
+                        arr = getattr(self, 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):
-                    kwargs[out_name] = out_name
-                    scope.new_var(out_name).get_tensor()
+                for out_name in func.all_output_args:
+                    if hasattr(self, 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)
+                for attr_name in func.all_attr_args:
+                    if hasattr(self, attr_name):
+                        kwargs[attr_name] = getattr(self, attr_name)
 
-            op = func(**kwargs)
+                op = func(**kwargs)
 
-            op.infer_shape(scope)
+                op.infer_shape(scope)
 
-            ctx = core.DeviceContext.cpu_context()
-            op.run(scope, ctx)
+                ctx = core.DeviceContext.create(place)
+                op.run(scope, ctx)
 
-            for out_name in func.all_output_args:
-                actual = numpy.array(scope.find_var(out_name).get_tensor())
-                expect = getattr(self, out_name)
-                # TODO(qijun) The default decimal is 7, but numpy.dot and eigen.mul
-                # has some diff, and could not pass unittest. So I set decimal 3 here.
-                # And I will check this in future.
-                numpy.testing.assert_almost_equal(actual, expect, decimal=3)
+                for out_name in func.all_output_args:
+                    actual = numpy.array(scope.find_var(out_name).get_tensor())
+                    expect = getattr(self, out_name)
+                    # TODO(qijun) The default decimal is 7, but numpy.dot and eigen.mul
+                    # has some diff, and could not pass unittest. So I set decimal 3 here.
+                    # And I will check this in future.
+                    numpy.testing.assert_almost_equal(actual, expect, decimal=3)
 
         obj.test_all = test_all
         return obj

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

 import unittest
-from op_test_util import OpTestMeta
+
 import numpy
+import paddle.v2.framework.core as core
+import paddle.v2.framework.create_op_creation_methods as creation
+
+from op_test_util import OpTestMeta
 
 
 class TestAddOp(unittest.TestCase):
@@ -8,10 +12,19 @@ class TestAddOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "add_two"
-        self.X = numpy.random.random((342, 345)).astype("float32")
-        self.Y = numpy.random.random((342, 345)).astype("float32")
+        self.X = numpy.random.random((102, 105)).astype("float32")
+        self.Y = numpy.random.random((102, 105)).astype("float32")
         self.Out = self.X + self.Y
 
 
+class TestAddGradOp(unittest.TestCase):
+    def test_add_grad(self):
+        op = creation.op_creations.add_two(X="X", Y="Y", Out="Out")
+        backward_op = core.Operator.backward(op, set())
+        self.assertEqual(backward_op.type(), "add_two_grad")
+        expected = '''Op(add_two_grad), inputs:(X, Y, Out, Out@GRAD), outputs:(X@GRAD, Y@GRAD).'''
+        self.assertEqual(expected, str(backward_op))
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -7,17 +7,19 @@ import paddle.v2.framework.create_op_creation_methods as creation
 class TestFc(unittest.TestCase):
     def test_fc(self):
         scope = core.Scope()
+        place = core.CPUPlace()
         x = scope.new_var("X")
+
         x_tensor = x.get_tensor()
         x_tensor.set_dims([1000, 784])
-        x_tensor.alloc_float()
+        x_tensor.alloc_float(place)
 
         w = scope.new_var("W")
         w_tensor = w.get_tensor()
         w_tensor.set_dims([784, 100])
-        w_tensor.alloc_float()
+        w_tensor.alloc_float(place)
 
-        w_tensor.set(numpy.random.random((784, 100)).astype("float32"))
+        w_tensor.set(numpy.random.random((784, 100)).astype("float32"), place)
 
         # Set a real numpy array here.
         # x_tensor.set(numpy.array([]))
@@ -32,7 +34,7 @@ class TestFc(unittest.TestCase):
         op.infer_shape(scope)
         self.assertEqual([1000, 100], tensor.shape())
 
-        ctx = core.DeviceContext.cpu_context()
+        ctx = core.DeviceContext.create(place)
 
         op.run(scope, ctx)
 

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

+import unittest
+from op_test_util import OpTestMeta
+import numpy as np
+
+
+class TestMeanOp(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "mean"
+        self.X = np.random.random((32, 784)).astype("float32")
+        self.Out = np.mean(self.X)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -8,8 +8,8 @@ class TestMulOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "mul"
-        self.X = np.random.random((32, 784)).astype("float32")
-        self.Y = np.random.random((784, 100)).astype("float32")
+        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)
 
 

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

@@ -8,8 +8,8 @@ class TestRowwiseAddOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "rowwise_add"
-        self.X = np.random.random((32, 784)).astype("float32")
-        self.b = np.random.random(784).astype("float32")
+        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)
 
 

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

@@ -8,8 +8,8 @@ class TestSGD(unittest.TestCase):
 
     def setUp(self):
         self.type = "sgd"
-        self.param = numpy.random.random((342, 345)).astype("float32")
-        self.grad = numpy.random.random((342, 345)).astype("float32")
+        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
 

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

 import unittest
-from op_test_util import OpTestMeta
+
 import numpy as np
+import paddle.v2.framework.core as core
+import paddle.v2.framework.create_op_creation_methods as creation
+
+from op_test_util import OpTestMeta
 
 
 def stable_softmax(x):
@@ -19,5 +23,63 @@ class TestSoftmaxOp(unittest.TestCase):
         self.Y = np.apply_along_axis(stable_softmax, 1, self.X)
 
 
+class TestSoftmaxGradOp(unittest.TestCase):
+    def test_softmax_grad(self):
+        op = creation.op_creations.softmax(X="X", Y="Y")
+        backward_op = core.Operator.backward(op, set())
+        self.assertEqual(backward_op.type(), "softmax_grad")
+        expected = '''Op(softmax_grad), inputs:(X, Y, Y@GRAD), outputs:(X@GRAD).'''
+        self.assertEqual(expected, str(backward_op))
+
+        batch_size = 3
+        class_num = 5
+        # Initialize X and add 1e-2 for numerical stability
+        Y = np.random.rand(batch_size, class_num).astype(np.float32)
+        Y = Y + 1e-2
+        dY = np.random.rand(batch_size, class_num).astype(np.float32)
+
+        # Reference implementation of cross entropy with soft labels
+        def label_softmax_grad(Y, dY):
+            dX = Y * 0.0
+            for i in range(batch_size):
+                d = np.dot(Y[i, :], dY[i, :])
+                dX[i, :] = Y[i, :] * (dY[i, :] - d)
+            return dX
+
+        expected = label_softmax_grad(Y, dY)
+
+        scope = core.Scope()
+        places = []
+        places.append(core.CPUPlace())
+        if core.is_compile_gpu():
+            places.append(core.GPUPlace(0))
+
+        for place in places:
+            y = scope.new_var("Y")
+            y_tensor = y.get_tensor()
+            y_tensor.set_dims([batch_size, class_num])
+            y_tensor.alloc_float(place)
+            y_tensor.set(Y, place)
+
+            dy = scope.new_var("Y@GRAD")
+            dy_tensor = dy.get_tensor()
+            dy_tensor.set_dims([batch_size, class_num])
+            dy_tensor.alloc_float(place)
+            dy_tensor.set(dY, place)
+
+            x = scope.new_var("X")
+            dx = scope.new_var("X@GRAD")
+
+            tensor = scope.find_var("X@GRAD").get_tensor()
+            backward_op.infer_shape(scope)
+            self.assertEqual([batch_size, class_num], tensor.shape())
+
+            ctx = core.DeviceContext.create(place)
+            backward_op.run(scope, ctx)
+            actual = np.array(tensor)
+
+            np.testing.assert_almost_equal(actual, expected, decimal=3)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -7,16 +7,17 @@ class TestScope(unittest.TestCase):
     def test_int_tensor(self):
         scope = core.Scope()
         var = scope.new_var("test_tensor")
+        place = core.CPUPlace()
+
         tensor = var.get_tensor()
 
         tensor.set_dims([1000, 784])
-        tensor.alloc_int()
-
+        tensor.alloc_int(place)
         tensor_array = numpy.array(tensor)
         self.assertEqual((1000, 784), tensor_array.shape)
         tensor_array[3, 9] = 1
         tensor_array[19, 11] = 2
-        tensor.set(tensor_array)
+        tensor.set(tensor_array, place)
 
         tensor_array_2 = numpy.array(tensor)
         self.assertEqual(1.0, tensor_array_2[3, 9])
@@ -25,16 +26,18 @@ class TestScope(unittest.TestCase):
     def test_float_tensor(self):
         scope = core.Scope()
         var = scope.new_var("test_tensor")
+        place = core.CPUPlace()
+
         tensor = var.get_tensor()
 
         tensor.set_dims([1000, 784])
-        tensor.alloc_float()
+        tensor.alloc_float(place)
 
         tensor_array = numpy.array(tensor)
         self.assertEqual((1000, 784), tensor_array.shape)
         tensor_array[3, 9] = 1.0
         tensor_array[19, 11] = 2.0
-        tensor.set(tensor_array)
+        tensor.set(tensor_array, place)
 
         tensor_array_2 = numpy.array(tensor)
         self.assertAlmostEqual(1.0, tensor_array_2[3, 9])