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

CMakeLists.txt

@@ -105,6 +105,12 @@ if (WITH_C_API AND WITH_PYTHON)
     "different Python interpreter from compiling.")
 endif()
 
+if(MOBILE_INFERENCE)
+    set(THIRD_PARTY_BUILD_TYPE MinSizeRel)
+else()
+    set(THIRD_PARTY_BUILD_TYPE Release)
+endif()
+
 ########################################################################################
 
 include(external/mklml)     # download mklml package

cmake/external/eigen.cmake

@@ -8,7 +8,7 @@ ExternalProject_Add(
     extern_eigen3
     ${EXTERNAL_PROJECT_LOG_ARGS}
     GIT_REPOSITORY  "https://github.com/RLovelett/eigen.git"
-    GIT_TAG         "master"
+    GIT_TAG         4e79cb69b9425f5f8c3a84be4350d4ab75b5fd9d
     PREFIX          ${EIGEN_SOURCE_DIR}
     UPDATE_COMMAND  ""
     CONFIGURE_COMMAND ""

cmake/external/gflags.cmake

@@ -36,6 +36,7 @@ ExternalProject_Add(
     # change this back to the official Github repo once my PR is
     # merged.
     GIT_REPOSITORY  "https://github.com/wangkuiyi/gflags.git"
+    GIT_TAG         986964c07427ecb9cdb5bd73f73ebbd40e54dadb
     PREFIX          ${GFLAGS_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
@@ -45,11 +46,11 @@ ExternalProject_Add(
                     -DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR}
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON
                     -DBUILD_TESTING=OFF
-                    -DCMAKE_BUILD_TYPE=Release
+                    -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                     ${EXTERNAL_OPTIONAL_ARGS}
     CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GFLAGS_INSTALL_DIR}
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-                     -DCMAKE_BUILD_TYPE:STRING=Release
+                     -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
 )
 
 ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL)

cmake/external/glog.cmake

@@ -31,6 +31,7 @@ ExternalProject_Add(
     ${EXTERNAL_PROJECT_LOG_ARGS}
     DEPENDS gflags
     GIT_REPOSITORY  "https://github.com/google/glog.git"
+    GIT_TAG         v0.3.5
     PREFIX          ${GLOG_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
@@ -43,12 +44,12 @@ ExternalProject_Add(
                     -DWITH_GFLAGS=ON
                     -Dgflags_DIR=${GFLAGS_INSTALL_DIR}/lib/cmake/gflags
                     -DBUILD_TESTING=OFF
-                    -DCMAKE_BUILD_TYPE=Release
+                    -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                     ${EXTERNAL_OPTIONAL_ARGS}
     CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GLOG_INSTALL_DIR}
                      -DCMAKE_INSTALL_LIBDIR:PATH=${GLOG_INSTALL_DIR}/lib
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-                     -DCMAKE_BUILD_TYPE:STRING=Release
+                     -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
 )
 
 ADD_LIBRARY(glog STATIC IMPORTED GLOBAL)

cmake/external/gtest.cmake

@@ -56,11 +56,11 @@ IF(WITH_TESTING)
                         -DBUILD_GMOCK=ON
                         -Dgtest_disable_pthreads=ON
                         -Dgtest_force_shared_crt=ON
-                        -DCMAKE_BUILD_TYPE=Release
+                        -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                         ${EXTERNAL_OPTIONAL_ARGS}
         CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GTEST_INSTALL_DIR}
                          -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-                         -DCMAKE_BUILD_TYPE:STRING=Release
+                         -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
     )
 
     ADD_LIBRARY(gtest STATIC IMPORTED GLOBAL)

cmake/external/protobuf.cmake

@@ -191,12 +191,12 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
             ${OPTIONAL_ARGS}
             -Dprotobuf_BUILD_TESTS=OFF
             -DCMAKE_POSITION_INDEPENDENT_CODE=ON
-            -DCMAKE_BUILD_TYPE=Release
+            -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
             -DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR}
             -DCMAKE_INSTALL_LIBDIR=lib
         CMAKE_CACHE_ARGS
             -DCMAKE_INSTALL_PREFIX:PATH=${PROTOBUF_INSTALL_DIR}
-            -DCMAKE_BUILD_TYPE:STRING=Release
+            -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
             -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
             -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
             ${OPTIONAL_CACHE_ARGS}

cmake/external/warpctc.cmake

@@ -35,6 +35,7 @@ ExternalProject_Add(
     extern_warpctc
     ${EXTERNAL_PROJECT_LOG_ARGS}
     GIT_REPOSITORY  "https://github.com/gangliao/warp-ctc.git"
+    GIT_TAG         b63a0644654a3e0ed624c85a1767bc8193aead09
     PREFIX          ${WARPCTC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
@@ -48,9 +49,9 @@ ExternalProject_Add(
                     -DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON
                     -DBUILD_SHARED=ON
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON
-                    -DCMAKE_BUILD_TYPE=Release
+                    -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                     ${EXTERNAL_OPTIONAL_ARGS}
-    CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=Release
+    CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
                      -DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR}
 )

cmake/external/zlib.cmake

@@ -42,11 +42,11 @@ ExternalProject_Add(
                     -DBUILD_SHARED_LIBS=OFF
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON
                     -DCMAKE_MACOSX_RPATH=ON
-                    -DCMAKE_BUILD_TYPE=Release
+                    -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                     ${EXTERNAL_OPTIONAL_ARGS}
     CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ZLIB_INSTALL_DIR}
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-                     -DCMAKE_BUILD_TYPE:STRING=Release
+                     -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
 )
 
 LIST(APPEND external_project_dependencies zlib)

doc/design/block.md

@@ -243,7 +243,7 @@ class SymbolTable {
   // TODO determine whether name is generated by python or C++.
   // Currently assume that a unique name will be generated by C++ if the
   // argument name is left default.
-  VarDesc* NewVar(const string& name="");
+  VarDesc* Var(const string& name="");
 
   // find a VarDesc by name, if recursive is true, find parent's SymbolTable
   // recursively.

doc/design/images/graph_construction_example.dot

@@ -33,7 +33,6 @@ digraph ImageClassificationGraph {
 
         cost -> MSE_Grad [color=red];
         d_cost -> MSE_Grad [color=red];
-        x -> MSE_Grad [color=red];
         l -> MSE_Grad [color=red];
         y -> MSE_Grad -> d_y [color=red];
 

doc/design/register_grad_op.md

@@ -3,15 +3,17 @@
 
 ## The Problem Posed
 
-In our current operator registration mechanism, for each operator, the programmer should register a *gradient operator creator* function, which takes a C++ operator instance, and returns the corresponding gradient instance.
+Currently, for each C++ operator class definition, there registers a *gradient operator creator* function, which takes a C++ operator instance and returns the corresponding gradient operator instance.
 
-However, as we decided to separate the *compilation* and *execution* of DL models, we need to reshape the creator to take a protobuf `OpDesc` message, and returns a corresponding message.
+However, we noticed two problems with the current deisgn:
 
-More than that, the new registration mechanism need to support the fact that an operators' gradient computation might be a composition of operators.
+1. As we decided to separate the *compilation* and *execution* phases, we need to change the creator to take an `OpDesc` protobuf message in a `ProgramDesc` and inserts corresponding `OpDesc` messages into the `ProgramDesc` message.
 
-## Current Implementation
+1. Some operator's gradient computation requires more than one gradient operators.  For example, the gradient of *minus* consists of two operators -- an identity operaotr and a scale operator.  So we need to make the registration mechanism to support the mapping from an operator to a set of operators for gradient computation.
 
-OpInfos store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is
+## The Current Implementation
+
+The C++ class `OpInfos` store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is
 
 ```cpp
 struct OpInfo {

doc/design/scope.md

@@ -37,7 +37,7 @@ Scope is an association of a name to variable. All variables belong to `Scope`.
 ```cpp
 class Scope {
  public:
-  Variable* NewVar(const std::string& name);
+  Variable* Var(const std::string& name);
   const Variable* FindVar(const std::string& name) const;
 
  private:
@@ -98,7 +98,7 @@ class Scope {
   Variable* FindVar(const std::string& name) const;
 
   // return if already contains same name variable.
-  Variable* NewVar(const std::string& name);
+  Variable* Var(const std::string& name);
 
  private:
   std::shared_ptr<Scope> parent_;
@@ -107,7 +107,7 @@ class Scope {
 ```
 ## Only scope can create a variable
 
-To ensure `only scope can create a variable`, we should mark `Variable`'s constructor as a private member function, and Scope is a friend class of Variable. And then only `NewVar` can construct `Variable`.
+To ensure `only scope can create a variable`, we should mark `Variable`'s constructor as a private member function, and Scope is a friend class of Variable. And then only `Var` can construct `Variable`.
 
 ## When scope destroyed, all variables inside this scope should be destroyed together
 
@@ -121,4 +121,4 @@ Also, as the parent scope is a `shared_ptr`, we can only `Create()` a scope shar
 
 ## Orthogonal interface
 
-`FindVar` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `NewVar` will return an `Error` when there is a name conflict locally. Combine `FindVar` and `NewVar`, we can implement `NewVar` easily.
+`FindVar` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `Var` will return an `Error` when there is a name conflict locally. Combine `FindVar` and `Var`, we can implement `Var` easily.

doc/design/tensor_array.md

@@ -161,7 +161,7 @@ class TensorArray:
         @name: str
             the name of the variable to output.
         '''
-        tensor = NewVar(name)
+        tensor = Var(name)
         tensor_array_stack(self.name, tensor)
         return tensor
 

doc/design/var_desc.md

@@ -16,16 +16,23 @@ The computation graph is constructed by Data Node and Operation Node. The concep
 
 ## Definition of VarDesc
 
-A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.
+A VarDesc should have a name, and value. The are two kinds of variable type in compile time, they are `LoDTensor` and `SelectedRows`. 
 
 ```proto
 message VarDesc {
   required string name = 1;
-  optional LoDTensorDesc lod_tensor = 2;
+  enum VarType {
+    LOD_TENSOR = 0;
+    SELECTED_ROWS = 1;
+  }
+  required VarType type = 2;
+  optional LoDTensorDesc lod_desc = 3;
+  optional TensorDesc selected_rows_desc = 4;
+  optional bool persistable = 5 [ default = false ];
 }
 ```
 
-## Definition of LodTensorDesc
+## Definition of TensorDesc
 
 ```proto
 enum DataType {
@@ -38,87 +45,25 @@ enum DataType {
   FP64 = 6;
 }
 
-message LoDTensorDesc {
+message TensorDesc {
   required DataType data_type = 1;
-  repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
-  optional int32 lod_level = 3 [default=0];
+  repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
 }
 ```
 
-## Definition of Variable in Python
-
-In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable.
-
-```python
-image = Variable(dims=[-1, 640, 480])
-# fc1 and fc2 are both Variable
-fc1 = layer.fc(input=image, output_size=10)
-fc2 = layer.fc(input=fc1, output_size=20)
-```
-### what should class `Variable` Have
-1. `name`.a name of string type is used to mark the value of the Variable.
-1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.
-1. `operator`. Variable should record which operator produce itself. The reaon is:
-  - we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.
-
-In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.
-
-```python
-import VarDesc
-import LoDTensorDesc
-import framework
-
-def AddInitialOperator(variable, initializer):
-	# add an initialize Operator to block to init this Variable
-
-class Variable(object):
-   def __init__(self, name, dims, type, initializer):
-      self._block = get_default_block()
-      self._name = name
-      self.op = None
-
-      tensor_desc = LoDTensorDesc(data_type=type, dims=dims)
-      _var_desc = VarDesc(name=name, lod_tensor=tensor_desc)
-      self._var = framework.CreateVar(_var_desc)
-      self._block.add_var(self)
+A TensorDesc describes `SelectedRows` and `LoDTensor`. For details of `SelectedRows`, please reference [`SelectedRows`](./selected_rows.md).
 
-      # add initial op according to initializer
-      if initializer is not None:
-          AddInitialOperator(self, initializer)
-
-   def dims(self):
-      return self._var.dims()
-
-   def data_type(self):
-       return self._var.data_type()
+## Definition of LodTensorDesc
 
-   def to_proto(self):
-       pass
+```proto
+message LoDTensorDesc {
+  required TensorDesc tensor = 1;
+  optional int lod_level = 2;
+}
 ```
 
-Then we can use this Variable to create a fc layer in Python.
+A LoDTensorDesc contains a tensor and a lod_level.
 
-```python
-import paddle as pd
-
-def flatten_size(X, num_flatten_dims):
-  prod = 1 # of last num_flatten_dims
-  for i in xrange(num_flatten_dims):
-    prod = prod * X.dims[-i-1]
-  return prod
-
-def layer.fc(X, output_size, num_flatten_dims):
-  W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
-  b = Variable(pd.random_uniform(), type=FP32, dims=[output_size])
-  out = Variable(type=FP32)
-  y = operator.fc(X, W, b, output=out) # fc will put fc op input into out
-  pd.InferShape(y)
-  return out
-
-x = Variable(dims=[-1, 640, 480])
-y = layer.fc(x, output_size=100)
-z = layer.fc(y, output_size=200)
+## Definition of Variable in Python
 
-paddle.eval(targets=[z], ...)
-print(z)
-```
+For Variable in Python, please reference [`Python API`](./python_api.md).

paddle/api/CMakeLists.txt

@@ -26,7 +26,7 @@ FILE(GLOB PY_PADDLE_PYTHON_FILES ${PADDLE_SOURCE_DIR}/paddle/py_paddle/*.py)
 SET_SOURCE_FILES_PROPERTIES(Paddle.i PROPERTIES CPLUSPLUS ON)
 
 SET(CMAKE_SWIG_OUTDIR ${CMAKE_CURRENT_BINARY_DIR})
-SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign")
+SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign -ftls-model=global-dynamic")
 
 SET(SWIG_MODULE_swig_paddle_EXTRA_DEPS
     paddle_parameter

paddle/framework/CMakeLists.txt

@@ -19,10 +19,10 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope)
 proto_library(framework_proto SRCS framework.proto)
 
 cc_library(attribute SRCS attribute.cc DEPS framework_proto)
-cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim)
+cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim op_info)
 cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
 cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
-cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc)
+cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
 cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
@@ -42,12 +42,17 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
 cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
 
-cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward ${GLOB_OP_LIB})
-#if(WITH_GPU)
-#    nv_test(executor_test SRCS executor_test.cc DEPS executor)
-#else()
-#    cc_test(executor_test SRCS executor_test.cc DEPS executor)
-#endif()
+cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward)
+set(EXECUTOR_TEST_OP elementwise_add_op gaussian_random_op feed_op fetch_op
+    mul_op sum_op squared_l2_distance_op fill_constant_op sgd_op mean_op)
+if(WITH_GPU)
+    nv_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
+else()
+    cc_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
+endif()
 
 cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
 cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)
+
+cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
+cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)

paddle/framework/backward.cc

@@ -28,14 +28,15 @@ namespace paddle {
 namespace framework {
 
 static inline std::unique_ptr<OperatorBase> CreateGradOp(
-    const OperatorBase& op) {
+    const OperatorBase& op, const std::unordered_set<std::string>& no_grad_set,
+    std::unordered_map<std::string, std::string>* grad_to_var) {
   OpDescBind op_desc;
   op_desc.SetInputMap(op.Inputs());
   op_desc.SetOutputMap(op.Outputs());
   op_desc.SetType(op.Type());
   op_desc.SetAttrMap(op.Attrs());
   auto& info = OpInfoMap::Instance().Get(op.Type());
-  auto grad_descs = info.GradOpMaker()(op_desc);
+  auto grad_descs = info.GradOpMaker()(op_desc, no_grad_set, grad_to_var);
   std::vector<std::unique_ptr<OperatorBase>> grad_ops;
   grad_ops.reserve(grad_descs.size());
   std::transform(grad_descs.begin(), grad_descs.end(),
@@ -98,7 +99,9 @@ static std::unique_ptr<OperatorBase> NOP() {
 //  See Backward.h for details
 static std::unique_ptr<OperatorBase> BackwardRecursive(
     const OperatorBase& forwardOp,
-    std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
+    std::unordered_set<std::string>& no_grad_names,
+    std::unordered_map<std::string, std::string>* grad_to_var,
+    size_t& uniq_id) {
   //  If all input gradients of forwarding operator do not need to calculate,
   //  just return an NOP. Not return null ptr because NOP does not take
   //  too much time for calculation, but it is useful for simplifying logic.
@@ -136,7 +139,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
     for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend();
          ++it, ++local_op_id) {
       auto& fwd = *it;
-      auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id);
+      auto bwd = BackwardRecursive(*fwd, no_grad_names, grad_to_var, uniq_id);
       ForEachVarName(bwd->Outputs(),
                      [&dup_output_ops, local_op_id](const std::string& out) {
                        dup_output_ops[out].emplace_back(local_op_id);
@@ -187,7 +190,8 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
       net->InsertOp(pos.first + 1, std::move(pos.second));
     }
   } else {
-    std::unique_ptr<OperatorBase> grad_op(CreateGradOp(forwardOp));
+    std::unique_ptr<OperatorBase> grad_op(
+        CreateGradOp(forwardOp, no_grad_names, grad_to_var));
 
     ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op](
                                           const std::string& grad_input) {
@@ -226,7 +230,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
           *static_cast<const OperatorBase*>(&rnnop.stepnet());
       // create stepnet's gradient op
       rnn_grad_op->set_stepnet(
-          BackwardRecursive(stepnet_op, no_grad_names, uniq_id));
+          BackwardRecursive(stepnet_op, no_grad_names, grad_to_var, uniq_id));
     }
 
     if (net->ops_.empty()) {  // Current no aux op is added to network
@@ -253,7 +257,8 @@ std::unique_ptr<OperatorBase> Backward(
     no_grad_names.insert(name + kGradVarSuffix);
   }
   size_t uid = 0;
-  return BackwardRecursive(forwardOp, no_grad_names, uid);
+  std::unordered_map<std::string, std::string> grad_to_var;
+  return BackwardRecursive(forwardOp, no_grad_names, &grad_to_var, uid);
 }
 
 // ====================================  //
@@ -268,30 +273,61 @@ static bool AllGradInSet(const std::vector<std::string>& names,
   return true;
 }
 
+static void CreateGradVarInBlock(
+    size_t grad_op_start_index,
+    const std::unordered_map<std::string, std::string>& param_name_map,
+    BlockDescBind* block_desc,
+    std::unordered_map<std::string, GradVarInfo>* grad_var_record) {
+  auto ops = block_desc->AllOps();
+  for (size_t op_index = grad_op_start_index; op_index < ops.size();
+       ++op_index) {
+    ForEachVarName(ops[op_index]->Outputs(),
+                   [&](const std::string& grad_var_name) {
+                     if (block_desc->HasVar(grad_var_name)) {
+                       return false;
+                     }
+                     block_desc->Var(grad_var_name);
+                     auto it = param_name_map.find(grad_var_name);
+                     if (it == param_name_map.end()) {
+                       return false;
+                     }
+                     auto param_var_name = it->second;
+                     auto& grad_record = (*grad_var_record)[param_var_name];
+                     grad_record.name_ = grad_var_name;
+                     grad_record.block_idx_ = block_desc->ID();
+                     grad_record.op_idx_ = static_cast<int>(op_index);
+                     return false; /* not break */
+                   });
+  }
+}
+
 std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
     const std::unique_ptr<OpDescBind>& op_desc,
-    std::unordered_set<std::string>& no_grad_vars) {
+    std::unordered_set<std::string>* no_grad_vars,
+    std::unordered_map<std::string, std::string>* grad_to_var) {
   std::vector<std::unique_ptr<OpDescBind>> grad_op_descs;
-  // All input gradients of forwarding operator do not need to calculat.
+  // All input gradients of forwarding operator do not need to calculate.
   const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
-  if (AllGradInSet(inputs, no_grad_vars)) {
+  if (AllGradInSet(inputs, *no_grad_vars)) {
     return grad_op_descs;  // empty vector
   }
   // All output gradients of forwarding operator do not need to calculate.
   const std::vector<std::string>& outputs = op_desc->OutputArgumentNames();
-  if (AllGradInSet(outputs, no_grad_vars)) {
+  if (AllGradInSet(outputs, *no_grad_vars)) {
     for (const std::string& name : inputs) {
-      no_grad_vars.insert(GradVarName(name));
+      no_grad_vars->insert(GradVarName(name));
     }
     return grad_op_descs;  // empty vector
   }
 
-  grad_op_descs = OpRegistry::CreateGradOpDescs(op_desc.get());
+  grad_op_descs = OpInfoMap::Instance()
+                      .Get(op_desc->Type())
+                      .GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var);
 
   std::list<std::unique_ptr<OpDescBind>> pending_fill_zeros_ops;
   for (auto& desc : grad_op_descs) {
     for (const std::string& in_name : desc->InputArgumentNames()) {
-      if (no_grad_vars.count(in_name)) {
+      if (no_grad_vars->count(in_name)) {
         std::string prefix = in_name.substr(
             0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
         std::string new_name = prefix + kZeroVarSuffix;
@@ -301,11 +337,6 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
         pending_fill_zeros_ops.push_back(std::move(fill_zeros_op));
       }
     }
-    for (const std::string& out_name : desc->OutputArgumentNames()) {
-      if (no_grad_vars.count(out_name)) {
-        desc->Rename(out_name, kEmptyVarName);
-      }
-    }
   }
 
   for (auto& p : pending_fill_zeros_ops) {
@@ -316,23 +347,25 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
 
 std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
     ProgramDescBind& program_desc, int block_idx,
-    std::unordered_set<std::string>& no_grad_vars) {
+    std::unordered_set<std::string>* no_grad_vars,
+    std::unordered_map<std::string, std::string>* grad_to_var) {
   BlockDescBind* cur_block = program_desc.Block(block_idx);
   std::deque<std::unique_ptr<OpDescBind>>& op_descs = cur_block->ops_;
   std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
   size_t grad_desc_idx = 0;
   std::vector<std::unique_ptr<OpDescBind>> backward_descs;
+
   for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
     std::vector<std::unique_ptr<OpDescBind>> op_grads =
-        MakeOpGrad(*it, no_grad_vars);
+        MakeOpGrad(*it, no_grad_vars, grad_to_var);
 
     if ((*it)->Type() == "recurrent") {
       PADDLE_ENFORCE_EQ(
-          op_grads.size(), size_t(1),
+          op_grads.size(), static_cast<size_t>(1),
           "rnn_op's gradient process should contain only one op.");
-      int step_block_idx = (*it)->GetBlockAttr("stop_block");
-      auto backward_block_op_descs =
-          MakeBlockBackward(program_desc, step_block_idx, no_grad_vars);
+      int step_block_idx = (*it)->GetBlockAttr("step_block");
+      auto backward_block_op_descs = MakeBlockBackward(
+          program_desc, step_block_idx, no_grad_vars, grad_to_var);
       BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block);
       for (auto& ptr : backward_block_op_descs) {
         backward_block->ops_.push_back(std::move(ptr));
@@ -376,10 +409,12 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
     backward_descs.insert(backward_descs.begin() + p.first + 1,
                           std::move(p.second));
   }
+
   return backward_descs;
 }
 
-void AppendBackward(ProgramDescBind& program_desc,
+ParamGradInfoMap AppendBackward(
+    ProgramDescBind& program_desc, const VarDescBind& target,
     const std::unordered_set<std::string>& no_grad_vars) {
   std::unordered_set<std::string> no_grad_var_names;
   no_grad_var_names.reserve(no_grad_vars.size() + 1);
@@ -387,13 +422,43 @@ void AppendBackward(ProgramDescBind& program_desc,
   for (auto& name : no_grad_vars) {
     no_grad_var_names.insert(GradVarName(name));
   }
+
   const int root_block_idx = 0;
-  auto backward_op_descs =
-      MakeBlockBackward(program_desc, root_block_idx, no_grad_var_names);
-  auto& forw_op_descs = program_desc.Block(root_block_idx)->ops_;
+  auto root_block = program_desc.Block(root_block_idx);
+  auto& all_ops = root_block->ops_;
+
+  // insert fill one op for target
+  std::string fill_one_op_out = GradVarName(target.Name());
+  std::unique_ptr<OpDescBind> fill_one_op(
+      new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
+                     {{"shape", std::vector<int>{1}},
+                      {"value", static_cast<float>(1.0)},
+                      {"dataType", framework::DataType::FP32}}));
+  all_ops.push_back(std::move(fill_one_op));
+  size_t forward_op_num = all_ops.size();
+  size_t forward_block_num = program_desc.Size();
+
+  // Insert backward operators
+  std::unordered_map<std::string, std::string> grad_to_var;
+  auto backward_op_descs = MakeBlockBackward(program_desc, root_block_idx,
+                                             &no_grad_var_names, &grad_to_var);
+
+  std::unordered_map<std::string, GradVarInfo> retv;
+
+  // Create Variable
   for (auto& ptr : backward_op_descs) {
-    forw_op_descs.push_back(std::move(ptr));
+    all_ops.push_back(std::move(ptr));
+  }
+  root_block->Var(fill_one_op_out);
+
+  // create grad_var for all blocks in this program
+  CreateGradVarInBlock(forward_op_num, grad_to_var, root_block, &retv);
+  for (size_t block_index = forward_block_num;
+       block_index < program_desc.Size(); ++block_index) {
+    CreateGradVarInBlock(0, grad_to_var, program_desc.Block(block_index),
+                         &retv);
   }
+  return retv;
 }
 
 }  // namespace framework

paddle/framework/backward.h

@@ -14,7 +14,10 @@
 
 #pragma once
 
+#include <string>
+#include <unordered_map>
 #include <unordered_set>
+
 #include "paddle/framework/operator.h"
 #include "paddle/framework/program_desc.h"
 
@@ -27,9 +30,26 @@ extern std::unique_ptr<OperatorBase> Backward(
     const OperatorBase& forwardOp,
     const std::unordered_set<std::string>& no_grad_vars);
 
-// TODO(jiayi): Add target as parameter and generate backward op
-// according to target.
-void AppendBackward(ProgramDescBind& program_desc,
+struct GradVarInfo {
+  GradVarInfo() {}
+  GradVarInfo(const std::string& name, int block_idx, int op_idx)
+      : name_(name), block_idx_(block_idx), op_idx_(op_idx) {}
+
+  bool operator==(const GradVarInfo& b) const {
+    return name_ == b.name_ && block_idx_ == b.block_idx_ &&
+           op_idx_ == b.op_idx_;
+  }
+
+  std::string name_;
+  int block_idx_;
+  int op_idx_;
+};
+
+using ParamGradInfoMap = std::unordered_map<std::string /*fwd_var_name*/,
+                                            GradVarInfo /*grad_var_info*/>;
+
+ParamGradInfoMap AppendBackward(
+    ProgramDescBind& program_desc, const VarDescBind& target,
     const std::unordered_set<std::string>& no_grad_vars);
 
 }  // namespace framework

paddle/framework/backward_test.cc

@@ -18,6 +18,7 @@
 #include "paddle/framework/block_desc.h"
 #include "paddle/framework/op_desc.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/framework/var_desc.h"
 #include "paddle/operators/net_op.h"
 
 namespace paddle {
@@ -169,6 +170,45 @@ class MultInOutOpMaker : public OpProtoAndCheckerMaker {
   }
 };
 
+class MinusGradOpDescMaker : public GradOpDescMakerBase {
+ public:
+  using GradOpDescMakerBase::GradOpDescMakerBase;
+
+  std::vector<std::unique_ptr<OpDescBind>> operator()() const override {
+    std::vector<std::unique_ptr<OpDescBind>> retv;
+    auto x_g = InputGrad("X");
+    if (!x_g.empty()) {
+      auto *op_desc = new OpDescBind();
+      op_desc->SetType("scale");
+      op_desc->SetInput("X", OutputGrad("Out"));
+      op_desc->SetOutput("Out", x_g);
+      op_desc->SetAttr("scale", 1.0f);
+      retv.emplace_back(op_desc);
+    }
+
+    auto y_g = InputGrad("Y");
+    if (!y_g.empty()) {
+      auto *op_desc = new OpDescBind();
+      op_desc->SetType("scale");
+      op_desc->SetInput("X", OutputGrad("Out"));
+      op_desc->SetOutput("Out", y_g);
+      op_desc->SetAttr("scale", -1.0f);
+      retv.emplace_back(op_desc);
+    }
+    return retv;
+  }
+};
+
+class MinusOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  MinusOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "");
+    AddInput("Y", "");
+    AddOutput("Out", "");
+    AddComment("minus for unittest");
+  }
+};
 }  // namespace framework
 }  // namespace paddle
 
@@ -187,6 +227,7 @@ REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
 REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
             f::NOP);
 REGISTER_OP(mult_in_out, f::NOP, f::MultInOutOpMaker, mult_in_out_grad, f::NOP);
+REGISTER_OPERATOR(minus, f::NOP, f::MinusOpMaker, f::MinusGradOpDescMaker);
 
 TEST(Backward, simple_op_not_need_grad) {
   auto fwd = f::OpRegistry::CreateOp(
@@ -395,12 +436,13 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
             2UL       /* external input number */
                 + 1UL /* external output number*/
                 + 1UL /* number of gradient of external output*/
-                + 2U /* internal variable number*/);
+                + 2UL /* internal variable number*/
+            );
   EXPECT_EQ(grad_fc.Outputs(all).size(),
             2UL       /* input number of mul*/
-                + 2UL /* input number of rowwise_add
-                       */
-                + 1UL /* input number of sigmod */);
+                + 2UL /* input number of rowwise_add*/
+                + 1UL /* input number of sigmod */
+                - 1UL /* out2 is not needed*/);
   EXPECT_EQ(bwd_net->ops_[1]->Inputs(all).size(), 0UL);
   EXPECT_EQ(bwd_net->ops_[1]->Outputs(all).size(), 0UL);
   EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
@@ -427,10 +469,14 @@ TEST(Backward, simple_single_op) {
   op->SetInput("b", {"b"});
   op->SetOutput("Out", {"out"});
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out");
+  auto var_to_grad = AppendBackward(program, target, {});
+
+  ASSERT_EQ(block->AllOps().size(), 3UL);
+  f::OpDescBind *fill_op = block->AllOps()[1];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
 
-  ASSERT_EQ(block->AllOps().size(), 2UL);
-  f::OpDescBind *grad_op = block->AllOps()[1];
+  f::OpDescBind *grad_op = block->AllOps()[2];
   EXPECT_EQ(grad_op->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op->OutputNames().size(), 2UL);
@@ -440,6 +486,13 @@ TEST(Backward, simple_single_op) {
             std::vector<std::string>({f::GradVarName("x")}));
   EXPECT_EQ(grad_op->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b")}));
+
+  EXPECT_EQ(var_to_grad.size(), 2UL);
+  EXPECT_EQ(var_to_grad.at("b"), f::GradVarInfo(f::GradVarName("b"), 0, 2));
+  EXPECT_EQ(var_to_grad.at("x"), f::GradVarInfo(f::GradVarName("x"), 0, 2));
+
+  EXPECT_TRUE(block->HasVar(f::GradVarName("b")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("x")));
 }
 
 TEST(Backward, default_attribute) {
@@ -451,14 +504,19 @@ TEST(Backward, default_attribute) {
   op->SetInput("X", {"x"});
   op->SetInput("Y", {"y"});
   op->SetOutput("Out", {"out"});
+  op->CheckAttrs();
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out");
+  AppendBackward(program, target, {});
 
-  ASSERT_EQ(block->AllOps().size(), 2UL);
+  ASSERT_EQ(block->AllOps().size(), 3UL);
   EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
   EXPECT_EQ(boost::get<int>(op->GetAttr("y_num_col_dims")), 1);
 
-  f::OpDescBind *grad_op = block->AllOps()[1];
+  f::OpDescBind *fill_op = block->AllOps()[1];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op = block->AllOps()[2];
   ASSERT_EQ(grad_op->Type(), "mul_grad");
   EXPECT_EQ(boost::get<int>(grad_op->GetAttr("x_num_col_dims")), 1);
   EXPECT_EQ(boost::get<int>(grad_op->GetAttr("y_num_col_dims")), 1);
@@ -486,10 +544,15 @@ TEST(Backward, simple_mult_op) {
   op3->SetInput("b", {"b3"});
   op3->SetOutput("Out", {"out3"});
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out3");
+  size_t forward_len = block->AllOps().size();
+  auto var_to_grad = AppendBackward(program, target, {});
 
-  ASSERT_EQ(block->AllOps().size(), 6UL);
-  f::OpDescBind *grad_op1 = block->AllOps()[5];
+  ASSERT_EQ(block->AllOps().size(), 6UL + 1);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op1 = block->AllOps()[6];
   EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -500,7 +563,7 @@ TEST(Backward, simple_mult_op) {
   EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b1")}));
 
-  f::OpDescBind *grad_op2 = block->AllOps()[4];
+  f::OpDescBind *grad_op2 = block->AllOps()[5];
   EXPECT_EQ(grad_op2->Type(), "mul_grad");
   ASSERT_EQ(grad_op2->InputNames().size(), 4UL);
   ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
@@ -514,7 +577,7 @@ TEST(Backward, simple_mult_op) {
   EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")),
             std::vector<std::string>({f::GradVarName("y2")}));
 
-  f::OpDescBind *grad_op3 = block->AllOps()[3];
+  f::OpDescBind *grad_op3 = block->AllOps()[4];
   EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
@@ -524,6 +587,23 @@ TEST(Backward, simple_mult_op) {
             std::vector<std::string>({f::GradVarName("out2")}));
   EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b3")}));
+
+  EXPECT_EQ(var_to_grad.size(), 6UL);
+  EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 6));
+  EXPECT_EQ(var_to_grad.at("b1"), f::GradVarInfo(f::GradVarName("b1"), 0, 6));
+  EXPECT_EQ(var_to_grad.at("out1"),
+            f::GradVarInfo(f::GradVarName("out1"), 0, 5));
+  EXPECT_EQ(var_to_grad.at("y2"), f::GradVarInfo(f::GradVarName("y2"), 0, 5));
+  EXPECT_EQ(var_to_grad.at("out2"),
+            f::GradVarInfo(f::GradVarName("out2"), 0, 4));
+  EXPECT_EQ(var_to_grad.at("b3"), f::GradVarInfo(f::GradVarName("b3"), 0, 4));
+
+  EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("b1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("out1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("y2")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("out2")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("b3")));
 }
 
 TEST(Backward, intermedia_var_no_grad) {
@@ -554,10 +634,15 @@ TEST(Backward, intermedia_var_no_grad) {
   op4->SetInput("Y", {"out3"});
   op4->SetOutput("Out", {"out4"});
 
-  AppendBackward(program, {"out3"});
+  auto target = f::VarDescBind("out4");
+  size_t forward_len = block->AllOps().size();
+  auto var_to_grad = AppendBackward(program, target, {"out3"});
 
-  ASSERT_EQ(block->AllOps().size(), 6UL);
-  f::OpDescBind *grad_op1 = block->AllOps()[5];
+  ASSERT_EQ(block->AllOps().size(), 7UL);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op1 = block->AllOps()[6];
   EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -568,7 +653,7 @@ TEST(Backward, intermedia_var_no_grad) {
   EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b1")}));
 
-  f::OpDescBind *grad_op4 = block->AllOps()[4];
+  f::OpDescBind *grad_op4 = block->AllOps()[5];
   EXPECT_EQ(grad_op4->Type(), "mul_grad");
   ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
   ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
@@ -579,8 +664,17 @@ TEST(Backward, intermedia_var_no_grad) {
             std::vector<std::string>({f::GradVarName("out4")}));
   EXPECT_EQ(grad_op4->Output(f::GradVarName("X")),
             std::vector<std::string>({f::GradVarName("out1")}));
-  EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
-            std::vector<std::string>({f::kEmptyVarName}));
+  EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")), std::vector<std::string>());
+
+  EXPECT_EQ(var_to_grad.size(), 3UL);
+  EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 6));
+  EXPECT_EQ(var_to_grad.at("b1"), f::GradVarInfo(f::GradVarName("b1"), 0, 6));
+  EXPECT_EQ(var_to_grad.at("out1"),
+            f::GradVarInfo(f::GradVarName("out1"), 0, 5));
+
+  EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("b1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("out1")));
 }
 
 TEST(Backward, var_no_grad) {
@@ -601,10 +695,15 @@ TEST(Backward, var_no_grad) {
   op2->SetOutput("Y", {"y2"});
   op2->SetOutput("Z", {"z2"});
 
-  AppendBackward(program, {"z1"});
+  auto target = f::VarDescBind("z2");
+  size_t forward_len = block->AllOps().size();
+  auto var_to_grad = AppendBackward(program, target, {"z1"});
+
+  ASSERT_EQ(block->AllOps().size(), 6UL);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
 
-  ASSERT_EQ(block->AllOps().size(), 5UL);
-  f::OpDescBind *grad_op2 = block->AllOps()[2];
+  f::OpDescBind *grad_op2 = block->AllOps()[3];
   ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad");
   ASSERT_EQ(grad_op2->InputNames().size(), 6UL);
   ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
@@ -618,10 +717,9 @@ TEST(Backward, var_no_grad) {
             std::vector<std::string>({f::GradVarName("z2")}));
   EXPECT_EQ(grad_op2->Output(f::GradVarName("X")),
             std::vector<std::string>({f::GradVarName("y1")}));
-  EXPECT_EQ(grad_op2->Output(f::GradVarName("H")),
-            std::vector<std::string>({f::kEmptyVarName}));
+  EXPECT_EQ(grad_op2->Output(f::GradVarName("H")), std::vector<std::string>());
 
-  f::OpDescBind *fill_zero_op = block->AllOps()[3];
+  f::OpDescBind *fill_zero_op = block->AllOps()[4];
   ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like");
   ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL);
   ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL);
@@ -629,7 +727,7 @@ TEST(Backward, var_no_grad) {
   EXPECT_EQ(fill_zero_op->Output("Y"),
             std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
 
-  f::OpDescBind *grad_op1 = block->AllOps()[4];
+  f::OpDescBind *grad_op1 = block->AllOps()[5];
   ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 6UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -645,6 +743,15 @@ TEST(Backward, var_no_grad) {
             std::vector<std::string>({f::GradVarName("x1")}));
   EXPECT_EQ(grad_op1->Output(f::GradVarName("H")),
             std::vector<std::string>({f::GradVarName("h1")}));
+
+  EXPECT_EQ(var_to_grad.size(), 3UL);
+  EXPECT_EQ(var_to_grad.at("y1"), f::GradVarInfo(f::GradVarName("y1"), 0, 3));
+  EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 5));
+  EXPECT_EQ(var_to_grad.at("h1"), f::GradVarInfo(f::GradVarName("h1"), 0, 5));
+
+  EXPECT_TRUE(block->HasVar(f::GradVarName("y1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("h1")));
 }
 
 TEST(Backward, shared_var) {
@@ -669,10 +776,15 @@ TEST(Backward, shared_var) {
   op3->SetInput("b", {"b3"});
   op3->SetOutput("Out", {"out3"});
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out3");
+  size_t forward_len = block->AllOps().size();
+  auto var_to_grad = AppendBackward(program, target, {});
 
-  ASSERT_EQ(block->AllOps().size(), 7UL);
-  f::OpDescBind *grad_op3 = block->AllOps()[3];
+  ASSERT_EQ(block->AllOps().size(), 8UL);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op3 = block->AllOps()[4];
   ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
@@ -683,7 +795,7 @@ TEST(Backward, shared_var) {
   EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b3")}));
 
-  f::OpDescBind *grad_op4 = block->AllOps()[4];
+  f::OpDescBind *grad_op4 = block->AllOps()[5];
   ASSERT_EQ(grad_op4->Type(), "mul_grad");
   ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
   ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
@@ -697,7 +809,7 @@ TEST(Backward, shared_var) {
   EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
             std::vector<std::string>({f::GradVarName("y2")}));
 
-  f::OpDescBind *sum_op = block->AllOps()[5];
+  f::OpDescBind *sum_op = block->AllOps()[6];
   ASSERT_EQ(sum_op->Type(), "sum");
   ASSERT_EQ(sum_op->InputNames().size(), 1UL);
   ASSERT_EQ(sum_op->OutputNames().size(), 1UL);
@@ -707,7 +819,7 @@ TEST(Backward, shared_var) {
   EXPECT_EQ(sum_op->Output("Out"),
             std::vector<std::string>({f::GradVarName("out1")}));
 
-  f::OpDescBind *grad_op1 = block->AllOps()[6];
+  f::OpDescBind *grad_op1 = block->AllOps()[7];
   ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -717,4 +829,41 @@ TEST(Backward, shared_var) {
             std::vector<std::string>({f::GradVarName("x1")}));
   EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b1")}));
+
+  EXPECT_EQ(var_to_grad.size(), 5UL);
+  EXPECT_EQ(var_to_grad.at("b3"), f::GradVarInfo(f::GradVarName("b3"), 0, 4));
+  EXPECT_EQ(var_to_grad.at("y2"), f::GradVarInfo(f::GradVarName("y2"), 0, 5));
+  EXPECT_EQ(var_to_grad.at("out1"),
+            f::GradVarInfo(f::GradVarName("out1"), 0, 6));
+  EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 7));
+  EXPECT_EQ(var_to_grad.at("b1"), f::GradVarInfo(f::GradVarName("b1"), 0, 7));
+
+  EXPECT_TRUE(block->HasVar(f::GradVarName("b3")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("y2")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("out1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
+  EXPECT_TRUE(block->HasVar(f::GradVarName("b1")));
+}
+
+TEST(Backward, half_backward) {
+  f::ProgramDesc *program_desc = GetNewProgramDesc();
+  f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
+  f::BlockDescBind *block = program.Block(0);
+  auto *op1 = block->AppendOp();
+  op1->SetType("minus");
+  op1->SetInput("X", {"a"});
+  op1->SetInput("Y", {"b"});
+  op1->SetOutput("Out", {"out"});
+
+  auto target = f::VarDescBind("out");
+  size_t forward_len = block->AllOps().size();
+  auto var_to_grad = AppendBackward(program, target, {"b"});
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+  auto ops = block->AllOps();
+  ASSERT_EQ(3UL, ops.size());
+
+  EXPECT_EQ(var_to_grad.size(), 1UL);
+  EXPECT_EQ(var_to_grad.at("a"),
+            f::GradVarInfo(f::GradVarName("a"), 0, forward_len + 1));
 }

paddle/framework/block_desc.cc

@@ -18,19 +18,22 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-VarDescBind *BlockDescBind::NewVar(const std::string &name) {
+VarDescBind *BlockDescBind::Var(const std::string &name) {
   need_update_ = true;
   auto it = vars_.find(name);
-  PADDLE_ENFORCE(it == vars_.end(), "Duplicated variable %s", name);
-  auto var = new VarDescBind(name);
+  if (it != vars_.end()) {
+    return it->second.get();
+  }
+  auto *var = new VarDescBind(name);
   vars_[name].reset(var);
   return var;
 }
 
-VarDescBind *BlockDescBind::Var(const std::string &name) const {
+VarDescBind *BlockDescBind::FindVar(const std::string &name) const {
   auto it = vars_.find(name);
-  PADDLE_ENFORCE(it != vars_.end(),
-                 "Can not find variable %s in current block.", name);
+  if (it == vars_.end()) {
+    return nullptr;
+  }
   return it->second.get();
 }
 
@@ -66,7 +69,7 @@ std::vector<OpDescBind *> BlockDescBind::AllOps() const {
   return res;
 }
 
-void BlockDescBind::Sync() {
+void BlockDescBind::Flush() {
   if (need_update_) {
     auto &op_field = *this->desc_->mutable_ops();
     op_field.Clear();
@@ -91,9 +94,10 @@ BlockDescBind *BlockDescBind::ParentBlock() const {
   return prog_->Block(static_cast<size_t>(this->desc_->parent_idx()));
 }
 
-void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
-  BlockDesc *desc = block.RawPtr();
-  this->attrs_[name] = desc;
+BlockDesc *BlockDescBind::Proto() {
+  Flush();
+  return desc_;
 }
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/block_desc.h

@@ -33,14 +33,6 @@ class ProgramDescBind;
 
 class BlockDescBind {
  public:
-  friend std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
-      ProgramDescBind &program_desc, int block_idx,
-      std::unordered_set<std::string> &no_grad_vars);
-
-  friend void AppendBackward(
-      ProgramDescBind &program_desc,
-      const std::unordered_set<std::string> &no_grad_vars);
-
   BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
       : prog_(prog), desc_(desc), need_update_(false) {}
 
@@ -48,9 +40,9 @@ class BlockDescBind {
 
   int32_t Parent() const { return desc_->parent_idx(); }
 
-  VarDescBind *NewVar(const std::string &name_bytes);
+  VarDescBind *Var(const std::string &name_bytes);
 
-  VarDescBind *Var(const std::string &name_bytes) const;
+  VarDescBind *FindVar(const std::string &name_bytes) const;
 
   bool HasVar(const std::string &var_name) const;
 
@@ -64,11 +56,13 @@ class BlockDescBind {
 
   std::vector<OpDescBind *> AllOps() const;
 
-  void Sync();
+  void Flush();
 
-  BlockDesc *RawPtr() { return desc_; }
+  BlockDesc *Proto();
 
- private:
+  // FIXME(yuyang18): backward will access private data of BlockDesc.
+  // Mark it public temporary. We can fix it later.
+ public:
   ProgramDescBind *prog_;  // not_own
   BlockDesc *desc_;        // not_own
   bool need_update_;

paddle/framework/details/op_registry.h

@@ -97,8 +97,11 @@ struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
 template <typename T>
 struct OpInfoFiller<T, kGradOpDescMaker> {
   void operator()(const char* op_type, OpInfo* info) const {
-    info->grad_op_maker_ = [](const OpDescBind& fwd_op) {
-      T maker(fwd_op);
+    info->grad_op_maker_ = [](
+        const OpDescBind& fwd_op,
+        const std::unordered_set<std::string>& no_grad_set,
+        std::unordered_map<std::string, std::string>* grad_to_var) {
+      T maker(fwd_op, no_grad_set, grad_to_var);
       return maker();
     };
   }

paddle/framework/executor.cc

@@ -66,7 +66,7 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
 
   // Instantiate all the vars in the global scope
   for (auto& var : block.vars()) {
-    scope->NewVar(var.name());
+    scope->Var(var.name());
   }
 
   Scope& local_scope = scope->NewScope();
@@ -78,7 +78,7 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
       for (auto& var : block.ops(i).outputs()) {
         for (auto& argu : var.arguments()) {
           if (local_scope.FindVar(argu) == nullptr) {
-            local_scope.NewVar(argu);
+            local_scope.Var(argu);
           }
         }
       }

paddle/framework/executor_test.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <memory>
 #include <vector>
 
+#include "gflags/gflags.h"
 #include "gtest/gtest.h"
 #include "paddle/framework/attribute.h"
 #include "paddle/framework/backward.h"
@@ -25,6 +26,17 @@ limitations under the License. */
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
 
+USE_OP(elementwise_add);
+USE_OP(gaussian_random);
+USE_OP(feed);
+USE_OP(fetch);
+USE_OP(mul);
+USE_OP(sum);
+USE_OP(squared_l2_distance);
+USE_OP(fill_constant);
+USE_OP(mean);
+USE_OP(sgd);
+
 using namespace paddle::platform;
 using namespace paddle::framework;
 
@@ -34,9 +46,17 @@ void AddOp(const std::string& type, const VariableNameMap& inputs,
   // insert output
   for (auto kv : outputs) {
     for (auto v : kv.second) {
-      auto var = block->NewVar(v);
+      // <<<<<<< HEAD
+      //       auto var = block->Var(v);
+      //       var->SetType(VarDesc::LOD_TENSOR);
+      //       var->SetDataType(paddle::framework::DataType::FP32);
+      // =======
+      if (!block->HasVar(v)) {
+        auto var = block->Var(v);
         var->SetDataType(paddle::framework::DataType::FP32);
       }
+      // >>>>>>> origin/develop
+    }
   }
 
   // insert op
@@ -49,6 +69,7 @@ void AddOp(const std::string& type, const VariableNameMap& inputs,
     op->SetOutput(kv.first, kv.second);
   }
   op->SetAttrMap(attrs);
+  op->CheckAttrs();
 }
 
 // Tensors in feed value variable will only be in CPUPlace
@@ -134,12 +155,12 @@ class ExecutorTesterRandom : public ::testing::Test {
     AddOp("squared_l2_distance", {{"X", {"a"}}, {"Y", {"a_out"}}},
           {{"Out", {"l2_distance"}}, {"sub_result", {"l2_distance_sub"}}}, {},
           root_block);
+    AddOp("mean", {{"X", {"l2_distance"}}}, {{"Out", {"mean_out"}}}, {},
+          root_block);
 
     // backward
-    AddOp("fill_constant", {}, {{"Out", {"l2_distance@GRAD"}}},
-          {{"shape", std::vector<int>{batch_size, 1}}, {"value", float(1.0)}},
-          root_block);
-    AppendBackward(program, {});
+    auto target = VarDescBind("mean_out");
+    AppendBackward(program, target, {});
 
     // update
     AddOp("fill_constant", {}, {{"Out", {"learning_rate"}}},
@@ -305,4 +326,14 @@ TEST_F(ExecutorTesterFeedAndFetch, GPU) {
     }
   }
 }
+
+DECLARE_double(fraction_of_gpu_memory_to_use);
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  // Use less GPU memory for unittest.
+  FLAGS_fraction_of_gpu_memory_to_use = 0.25;
+  return RUN_ALL_TESTS();
+}
+
 #endif

paddle/framework/framework.proto

@@ -97,16 +97,26 @@ enum DataType {
   FP64 = 6;
 }
 
-message LoDTensorDesc {
+message TensorDesc {
   required DataType data_type = 1;
   repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
-  optional int32 lod_level = 3 [ default = 0 ];
+}
+
+message LoDTensorDesc {
+  required TensorDesc tensor = 1;
+  optional int32 lod_level = 2 [ default = 0 ];
 }
 
 message VarDesc {
+  enum VarType {
+    LOD_TENSOR = 1;
+    SELECTED_ROWS = 2;
+  }
   required string name = 1;
-  optional LoDTensorDesc lod_tensor = 2;
-  optional bool persistable = 3 [ default = false ];
+  required VarType type = 2;
+  optional LoDTensorDesc lod_tensor = 3;
+  optional TensorDesc selected_rows = 4;
+  optional bool persistable = 5 [ default = false ];
 }
 
 message BlockDesc {

paddle/framework/grad_op_desc_maker.h

@@ -13,6 +13,8 @@
    limitations under the License. */
 
 #pragma once
+#include <string>
+#include <unordered_set>
 #include "paddle/framework/op_desc.h"
 #include "paddle/framework/operator.h"
 
@@ -21,27 +23,50 @@ namespace framework {
 
 class GradOpDescMakerBase {
  public:
-  explicit GradOpDescMakerBase(const OpDescBind& fwd_op) : fwd_op_(fwd_op) {}
+  explicit GradOpDescMakerBase(
+      const OpDescBind& fwd_op,
+      const std::unordered_set<std::string>& no_grad_set,
+      std::unordered_map<std::string, std::string>* grad_to_var)
+      : fwd_op_(fwd_op), no_grad_set_(no_grad_set), grad_to_var_(grad_to_var) {}
 
   virtual ~GradOpDescMakerBase() = default;
   virtual std::vector<std::unique_ptr<OpDescBind>> operator()() const = 0;
 
  protected:
-  static std::vector<std::string> ToGradNames(
-      const std::vector<std::string>& var_names) {
+  std::vector<std::string> InputGrad(const std::string& name,
+                                     bool drop_empty_grad = true) const {
     std::vector<std::string> ret_val;
+    auto var_names = this->Input(name);
     ret_val.reserve(var_names.size());
     std::transform(var_names.begin(), var_names.end(),
-                   std::back_inserter(ret_val), GradVarName);
+                   std::back_inserter(ret_val),
+                   [this](const std::string& fwd_var_name) -> std::string {
+                     auto g_name = GradVarName(fwd_var_name);
+                     if (no_grad_set_.count(g_name)) {
+                       return kEmptyVarName;
+                     } else {
+                       (*this->grad_to_var_)[g_name] = fwd_var_name;
+                       return g_name;
+                     }
+                   });
+    if (!drop_empty_grad) {
       return ret_val;
     }
-
-  std::vector<std::string> InputGrad(const std::string& name) const {
-    return ToGradNames(fwd_op_.Input(name));
+    std::vector<std::string> dropped_ret_val;
+    dropped_ret_val.reserve(ret_val.size());
+    std::copy_if(ret_val.begin(), ret_val.end(),
+                 std::back_inserter(dropped_ret_val),
+                 [](const std::string& str) { return str != kEmptyVarName; });
+    return dropped_ret_val;
   }
 
   std::vector<std::string> OutputGrad(const std::string& name) const {
-    return ToGradNames(fwd_op_.Output(name));
+    std::vector<std::string> ret_val;
+    auto onames = this->Output(name);
+    ret_val.reserve(onames.size());
+    std::transform(onames.begin(), onames.end(), std::back_inserter(ret_val),
+                   GradVarName);
+    return ret_val;
   }
 
   std::vector<std::string> InputNames() const {
@@ -75,6 +100,8 @@ class GradOpDescMakerBase {
 
  private:
   const OpDescBind& fwd_op_;
+  const std::unordered_set<std::string>& no_grad_set_;
+  std::unordered_map<std::string, std::string>* grad_to_var_;
 };
 
 class SingleGradOpDescMaker : public GradOpDescMakerBase {
@@ -91,6 +118,7 @@ class SingleGradOpDescMaker : public GradOpDescMakerBase {
   virtual std::unique_ptr<OpDescBind> Apply() const = 0;
 };
 
+template <bool DropEmptyIG = true>
 class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
  public:
   using SingleGradOpDescMaker::SingleGradOpDescMaker;
@@ -102,7 +130,8 @@ class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
 
     for (auto& input_param : this->InputNames()) {
       grad->SetInput(input_param, this->Input(input_param));
-      grad->SetOutput(GradVarName(input_param), this->InputGrad(input_param));
+      grad->SetOutput(GradVarName(input_param),
+                      this->InputGrad(input_param, DropEmptyIG));
     }
 
     for (auto& output_param : this->OutputNames()) {

paddle/framework/op_desc.cc

@@ -32,7 +32,7 @@ OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs,
 }
 
 OpDesc *OpDescBind::Proto() {
-  Sync();
+  Flush();
   return &op_desc_;
 }
 
@@ -100,6 +100,12 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
   need_update_ = true;
 }
 
+void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
+  BlockDesc *desc = block.Proto();
+  this->attrs_[name] = desc;
+  need_update_ = true;
+}
+
 void OpDescBind::SetAttrMap(
     const std::unordered_map<std::string, Attribute> &attr_map) {
   attrs_ = attr_map;
@@ -159,7 +165,7 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
   void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
 };
 
-void OpDescBind::Sync() {
+void OpDescBind::Flush() {
   if (need_update_) {
     this->op_desc_.mutable_inputs()->Clear();
     for (auto &ipt : inputs_) {

paddle/framework/op_desc.h

@@ -89,8 +89,6 @@ class OpDescBind {
     this->need_update_ = true;
   }
 
-  void Sync();
-
   const VariableNameMap &Inputs() const { return inputs_; }
 
   const VariableNameMap &Outputs() const { return outputs_; }
@@ -104,6 +102,8 @@ class OpDescBind {
 
   void InferShape(const BlockDescBind &block) const;
 
+  void Flush();
+
  private:
   template <typename MapType>
   static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {

paddle/framework/op_registry.cc

@@ -59,16 +59,5 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDescBind& op_desc) {
                   op_desc.GetAttrMap());
 }
 
-std::vector<std::unique_ptr<OpDescBind>> OpRegistry::CreateGradOpDescs(
-    OpDescBind* op_desc) {
-  auto& info = OpInfoMap::Instance().Get(op_desc->Type());
-
-  if (info.Checker() != nullptr) {
-    info.Checker()->Check(*op_desc->MutableAttrMap());
-  }
-
-  return info.grad_op_maker_(*op_desc);
-}
-
 }  // namespace framework
 }  // namespace paddle

paddle/framework/op_registry.h

@@ -79,9 +79,6 @@ class OpRegistry {
 
   static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
 
-  static std::vector<std::unique_ptr<OpDescBind>> CreateGradOpDescs(
-      OpDescBind* op_desc);
-
   static std::unique_ptr<OperatorBase> CreateOp(const OpDescBind& op_desc);
 };
 
@@ -164,8 +161,9 @@ class OpKernelRegistrar : public Registrar {
                     grad_op_class)                                         \
   REGISTER_OPERATOR(grad_op_type, grad_op_class);                          \
   class _GradOpDescMaker_##grad_op_type##_                                 \
-      : public ::paddle::framework::DefaultGradOpDescMaker {                   \
-    using ::paddle::framework::DefaultGradOpDescMaker::DefaultGradOpDescMaker; \
+      : public ::paddle::framework::DefaultGradOpDescMaker<true> {         \
+    using ::paddle::framework::DefaultGradOpDescMaker<                     \
+        true>::DefaultGradOpDescMaker;                                     \
                                                                            \
    protected:                                                              \
     virtual std::string GradOpType() const { return #grad_op_type; }       \

paddle/framework/operator.h

@@ -289,6 +289,15 @@ class ExecutionContext {
     return device_context_;
   }
 
+#ifdef PADDLE_WITH_CUDA
+  const platform::CUDADeviceContext& cuda_device_context() const {
+    PADDLE_ENFORCE(platform::is_gpu_place(device_context_.GetPlace()));
+    auto cuda_ctx =
+        reinterpret_cast<const platform::CUDADeviceContext*>(&device_context_);
+    return *cuda_ctx;
+  }
+#endif
+
  private:
   const OperatorBase& op_;
   const Scope& scope_;
@@ -394,11 +403,11 @@ class CompileTimeInferShapeContext : public InferShapeContext {
 
  private:
   DDim GetDim(const std::string& name) const override {
-    return framework::make_ddim(block_.Var(name)->Shape());
+    return framework::make_ddim(block_.FindVar(name)->Shape());
   }
 
   void SetDim(const std::string& name, const DDim& dim) override {
-    block_.Var(name)->SetShape(framework::vectorize(dim));
+    block_.FindVar(name)->SetShape(framework::vectorize(dim));
   }
 
   const OpDescBind& op_;

paddle/framework/operator_test.cc

@@ -84,7 +84,7 @@ TEST(OperatorBase, all) {
   paddle::framework::Scope scope;
 
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
-  scope.NewVar("OUT1");
+  scope.Var("OUT1");
   ASSERT_EQ(paddle::framework::op_run_num, 0);
   op->Run(scope, device_context);
   ASSERT_EQ(paddle::framework::op_run_num, 1);
@@ -237,12 +237,12 @@ TEST(OpKernel, multi_inputs) {
 
   paddle::platform::CPUDeviceContext cpu_device_context;
   paddle::framework::Scope scope;
-  scope.NewVar("x0")->GetMutable<Tensor>();
-  scope.NewVar("x1")->GetMutable<Tensor>();
-  scope.NewVar("x2")->GetMutable<Tensor>();
-  scope.NewVar("k0")->GetMutable<Tensor>();
-  scope.NewVar("y0")->GetMutable<Tensor>();
-  scope.NewVar("y1")->GetMutable<Tensor>();
+  scope.Var("x0")->GetMutable<Tensor>();
+  scope.Var("x1")->GetMutable<Tensor>();
+  scope.Var("x2")->GetMutable<Tensor>();
+  scope.Var("k0")->GetMutable<Tensor>();
+  scope.Var("y0")->GetMutable<Tensor>();
+  scope.Var("y1")->GetMutable<Tensor>();
 
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   op->Run(scope, cpu_device_context);

paddle/framework/program_desc.cc

@@ -45,7 +45,7 @@ BlockDescBind *ProgramDescBind::AppendBlock(const BlockDescBind &parent) {
 
 ProgramDesc *ProgramDescBind::Proto() {
   for (auto &block : blocks_) {
-    block->Sync();
+    block->Flush();
   }
   return prog_;
 }

paddle/framework/scope.cc

@@ -31,7 +31,7 @@ Scope& Scope::NewScope() const {
   return *kids_.back();
 }
 
-Variable* Scope::NewVar(const std::string& name) {
+Variable* Scope::Var(const std::string& name) {
   auto iter = vars_.find(name);
   if (iter != vars_.end()) {
     return iter->second;
@@ -42,8 +42,8 @@ Variable* Scope::NewVar(const std::string& name) {
   return v;
 }
 
-Variable* Scope::NewVar() {
-  return NewVar(string::Sprintf("%p.%d", this, vars_.size()));
+Variable* Scope::Var() {
+  return Var(string::Sprintf("%p.%d", this, vars_.size()));
 }
 
 Variable* Scope::FindVar(const std::string& name) const {
@@ -71,8 +71,8 @@ framework::Scope& GetGlobalScope() {
   static std::unique_ptr<framework::Scope> g_scope{nullptr};
   std::call_once(feed_variable_flag, [&]() {
     g_scope.reset(new framework::Scope());
-    g_scope->NewVar("feed_value");
-    g_scope->NewVar("fetch_value");
+    g_scope->Var("feed_value");
+    g_scope->Var("fetch_value");
   });
   return *(g_scope.get());
 }

paddle/framework/scope.h

@@ -45,10 +45,10 @@ class Scope {
   Scope& NewScope() const;
 
   /// Create a variable with given name if it doesn't exist.
-  Variable* NewVar(const std::string& name);
+  Variable* Var(const std::string& name);
 
   /// Create a variable with a scope-unique name.
-  Variable* NewVar();
+  Variable* Var();
 
   /// Find a variable in the scope or any of its ancestors.  Returns
   /// nullptr if cannot find.

paddle/framework/scope_test.cc

@@ -23,8 +23,8 @@ TEST(Scope, VarsShadowing) {
   Scope& ss1 = s.NewScope();
   Scope& ss2 = s.NewScope();
 
-  Variable* v0 = s.NewVar("a");
-  Variable* v1 = ss1.NewVar("a");
+  Variable* v0 = s.Var("a");
+  Variable* v1 = ss1.Var("a");
 
   EXPECT_NE(v0, v1);
 
@@ -40,7 +40,7 @@ TEST(Scope, FindVar) {
   EXPECT_EQ(nullptr, s.FindVar("a"));
   EXPECT_EQ(nullptr, ss.FindVar("a"));
 
-  ss.NewVar("a");
+  ss.Var("a");
 
   EXPECT_EQ(nullptr, s.FindVar("a"));
   EXPECT_NE(nullptr, ss.FindVar("a"));
@@ -49,7 +49,7 @@ TEST(Scope, FindVar) {
 TEST(Scope, FindScope) {
   Scope s;
   Scope& ss = s.NewScope();
-  Variable* v = s.NewVar("a");
+  Variable* v = s.Var("a");
 
   EXPECT_EQ(&s, s.FindScope(v));
   EXPECT_EQ(&s, ss.FindScope(v));

paddle/framework/selected_rows.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/framework/selected_rows.h"
+
+namespace paddle {
+namespace framework {}  // namespace framework
+}  // namespace paddle

paddle/framework/selected_rows.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/framework/tensor.h"
+
+namespace paddle {
+namespace framework {
+
+class SelectedRows {
+ public:
+  SelectedRows(const std::vector<int64_t>& rows, const int64_t& height)
+      : rows_(rows), height_(height) {
+    value_.reset(new Tensor());
+  }
+
+  SelectedRows() { value_.reset(new Tensor()); }
+
+  platform::Place place() const { return value_->place(); }
+
+  const Tensor& value() const { return *value_; }
+
+  Tensor* mutable_value() { return value_.get(); }
+
+  int64_t height() const { return height_; }
+
+  void set_height(int64_t height) { height_ = height; }
+
+  const std::vector<int64_t>& rows() const { return rows_; }
+
+  void set_rows(const std::vector<int64_t>& rows) { rows_ = rows; }
+
+  DDim GetCompleteDims() const {
+    std::vector<int64_t> dims = vectorize(value_->dims());
+    dims[0] = height_;
+    return make_ddim(dims);
+  }
+
+ private:
+  std::vector<int64_t> rows_;
+  std::unique_ptr<Tensor> value_{nullptr};
+  int64_t height_;
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/selected_rows_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 "paddle/framework/selected_rows.h"
+#include "gtest/gtest.h"
+
+namespace paddle {
+namespace framework {
+
+class SelectedRowsTester : public ::testing::Test {
+ public:
+  virtual void SetUp() override {
+    std::vector<int64_t> rows{0, 4, 7};
+    int64_t height = 10;
+    int64_t row_numel = 100;
+    selected_rows_.reset(new SelectedRows(rows, height));
+
+    Tensor* value = selected_rows_->mutable_value();
+    value->mutable_data<float>(
+        make_ddim({static_cast<int64_t>(rows.size()), row_numel}), place_);
+  }
+
+ protected:
+  platform::CPUPlace place_;
+  std::unique_ptr<SelectedRows> selected_rows_{nullptr};
+};
+
+TEST_F(SelectedRowsTester, height) { ASSERT_EQ(selected_rows_->height(), 10); }
+
+TEST_F(SelectedRowsTester, dims) {
+  ASSERT_EQ(selected_rows_->value().dims(), make_ddim({3, 100}));
+}
+
+TEST_F(SelectedRowsTester, complete_dims) {
+  ASSERT_EQ(selected_rows_->GetCompleteDims(), make_ddim({10, 100}));
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/shape_inference.h

@@ -19,9 +19,6 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-// TODO(longfei): Once after both CompileTimeInferShapeContext and
-// RuntimeInferShapeContext get merged, we can rename InferShapeContext into
-// InferShapeContext so to replace the current InferShapeContext.
 class InferShapeContext {
  public:
   virtual ~InferShapeContext() {}

paddle/framework/tensor_array.cc

@@ -76,6 +76,17 @@ LoDTensor PackDynamicBatch(const std::vector<LoDTensor>& source,
                            const std::vector<DySeqMeta>& meta, const LoD& lod,
                            size_t level);
 
+std::vector<size_t> GenDyBatchIndice(const DySeqMetaBatch& meta, int batch_id) {
+  // collect indice need to copy to the batch
+  std::vector<size_t> indice;
+  for (const auto& seq : meta) {
+    size_t id = seq.begin + batch_id;
+    if (id >= seq.end) break;
+    indice.push_back(id);
+  }
+  return indice;
+}
+
 }  // namespace detail
 
 const LoDTensor& TensorArray::Read(size_t index) const {
@@ -113,7 +124,7 @@ LoDTensor TensorArray::Pack(size_t level, const std::vector<DySeqMeta>& meta,
   return detail::PackDynamicBatch(values_, meta, lod, level);
 }
 
-std::vector<DySeqMeta> TensorArray::Unpack(const LoDTensor& source, int level,
+DySeqMetaBatch TensorArray::Unpack(const LoDTensor& source, int level,
                                    bool length_desend) {
   detail::DynamicBatchUnpacker unpacker(source, level,
                                         length_desend /*descend*/);
@@ -129,6 +140,7 @@ std::vector<DySeqMeta> TensorArray::Unpack(const LoDTensor& source, int level,
     Write(batch_id, unpacker.GetBatch(batch_id));
   }
 
+  PADDLE_ENFORCE(!unpacker.meta.empty());
   return unpacker.meta;
 }
 
@@ -218,13 +230,7 @@ LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) {
   PADDLE_ENFORCE(!meta.empty(), "should build meta first");
   LoDTensor result;
 
-  // collect indice need to copy to the batch
-  std::vector<size_t> indice;
-  for (const auto& seq : meta) {
-    size_t id = seq.begin + index;
-    if (id >= seq.end) break;
-    indice.push_back(id);
-  }
+  auto indice = detail::GenDyBatchIndice(meta, index);
   PADDLE_ENFORCE(!indice.empty(), "invalid batch at %d", index);
 
   // copy the indice of records in LoDTensor
@@ -237,9 +243,9 @@ LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) {
   for (size_t i = 0; i < indice.size(); i++) {
     auto index = indice[i];
     auto target = result.Slice<value_type>(i, i + 1);
-    auto source_ = source->Slice<value_type>(index, index + 1);
+    auto slice = source->Slice<value_type>(index, index + 1);
 
-    target.CopyFrom<value_type>(source_, platform::CPUPlace(),
+    target.CopyFrom<value_type>(slice, platform::CPUPlace(),
                                 platform::CPUDeviceContext());
   }
 

paddle/framework/tensor_array.h

@@ -34,6 +34,13 @@ struct DySeqMeta {
   size_t ori_idx;
 };
 
+using DySeqMetaBatch = std::vector<DySeqMeta>;
+
+/*
+ * Extract the indices of instances.
+ */
+std::vector<size_t> GenDyBatchIndice(const DySeqMetaBatch &metas, int batch_id);
+
 /*
  * TensorArray is a C-array-like array of tensors, it is meant to be used with
  * dynamic iteration primitives such as while_loop. It is used to segment inputs
@@ -69,7 +76,7 @@ class TensorArray {
    * Recover the original LoD-arranged LoDTensor with the `values`, `level` and
    * `indice_map`.
    */
-  LoDTensor Pack(size_t level, const std::vector<DySeqMeta> &meta,
+  LoDTensor Pack(size_t level, const DySeqMetaBatch &meta,
                  const LoD &lod) const;
 
   /*
@@ -77,8 +84,7 @@ class TensorArray {
    * `values`, if set `desend`, will sort by length in descending order else in
    * ascending order.
    */
-  std::vector<DySeqMeta> Unpack(const LoDTensor &source, int level,
-                                bool length_desend);
+  DySeqMetaBatch Unpack(const LoDTensor &source, int level, bool length_desend);
 
   /*
    * Pack the values into a tensor with rank one higher than each tensor in

paddle/framework/type_defs.h

@@ -36,8 +36,9 @@ using OpCreator = std::function<OperatorBase*(
     const std::string& /*type*/, const VariableNameMap& /*inputs*/,
     const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
 
-using GradOpMakerFN =
-    std::function<std::vector<std::unique_ptr<OpDescBind>>(const OpDescBind&)>;
+using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDescBind>>(
+    const OpDescBind&, const std::unordered_set<std::string>& /*no_grad_set*/,
+    std::unordered_map<std::string, std::string>* /*grad_to_var*/)>;
 
 }  // namespace framework
 }  // namespace paddle

paddle/framework/var_desc.cc

@@ -13,32 +13,58 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/framework/var_desc.h"
+#include "paddle/platform/enforce.h"
 
 namespace paddle {
 namespace framework {
 
 void VarDescBind::SetShape(const std::vector<int64_t> &dims) {
-  VectorToRepeated(dims, desc_.mutable_lod_tensor()->mutable_dims());
+  VectorToRepeated(dims, mutable_tensor_desc()->mutable_dims());
 }
 
 void VarDescBind::SetDataType(DataType data_type) {
-  desc_.mutable_lod_tensor()->set_data_type(data_type);
+  mutable_tensor_desc()->set_data_type(data_type);
 }
 
 std::vector<int64_t> VarDescBind::Shape() const {
-  return RepeatedToVector(desc_.lod_tensor().dims());
+  return RepeatedToVector(tensor_desc().dims());
 }
 
-DataType VarDescBind::GetDataType() const {
-  return desc_.lod_tensor().data_type();
-}
+DataType VarDescBind::GetDataType() const { return tensor_desc().data_type(); }
 
 void VarDescBind::SetLoDLevel(int32_t lod_level) {
+  PADDLE_ENFORCE(desc_.type() == VarDesc::LOD_TENSOR);
   desc_.mutable_lod_tensor()->set_lod_level(lod_level);
 }
 
 int32_t VarDescBind::GetLodLevel() const {
+  PADDLE_ENFORCE(desc_.type() == VarDesc::LOD_TENSOR);
   return desc_.lod_tensor().lod_level();
 }
+
+const TensorDesc &VarDescBind::tensor_desc() const {
+  PADDLE_ENFORCE(desc_.has_type(), "invoke TensorDesc must after set type");
+  switch (desc_.type()) {
+    case VarDesc::SELECTED_ROWS:
+      return desc_.selected_rows();
+    case VarDesc::LOD_TENSOR:
+      return desc_.lod_tensor().tensor();
+    default:
+      PADDLE_THROW("Unexpected branch.");
+  }
+}
+
+TensorDesc *VarDescBind::mutable_tensor_desc() {
+  PADDLE_ENFORCE(desc_.has_type(),
+                 "invoke MutableTensorDesc must after set type");
+  switch (desc_.type()) {
+    case VarDesc::SELECTED_ROWS:
+      return desc_.mutable_selected_rows();
+    case VarDesc::LOD_TENSOR:
+      return desc_.mutable_lod_tensor()->mutable_tensor();
+    default:
+      PADDLE_THROW("Unexpected branch.");
+  }
+}
 }  // namespace framework
 }  // namespace paddle

paddle/framework/var_desc.h

@@ -34,6 +34,7 @@ inline std::vector<T> RepeatedToVector(
 template <typename T, typename RepeatedField>
 inline void VectorToRepeated(const std::vector<T> &vec,
                              RepeatedField *repeated_field) {
+  repeated_field->Clear();
   repeated_field->Reserve(vec.size());
   for (const auto &elem : vec) {
     *repeated_field->Add() = elem;
@@ -44,6 +45,7 @@ inline void VectorToRepeated(const std::vector<T> &vec,
 template <typename RepeatedField>
 inline void VectorToRepeated(const std::vector<bool> &vec,
                              RepeatedField *repeated_field) {
+  repeated_field->Clear();
   repeated_field->Reserve(vec.size());
   for (auto elem : vec) {
     *repeated_field->Add() = elem;
@@ -52,7 +54,10 @@ inline void VectorToRepeated(const std::vector<bool> &vec,
 
 class VarDescBind {
  public:
-  explicit VarDescBind(const std::string &name) { desc_.set_name(name); }
+  explicit VarDescBind(const std::string &name) {
+    desc_.set_name(name);
+    desc_.set_type(VarDesc::LOD_TENSOR);
+  }
 
   VarDesc *Proto() { return &desc_; }
 
@@ -70,7 +75,14 @@ class VarDescBind {
 
   int32_t GetLodLevel() const;
 
+  VarDesc::VarType GetType() const { return desc_.type(); }
+
+  void SetType(VarDesc::VarType type) { desc_.set_type(type); }
+
  private:
+  const TensorDesc &tensor_desc() const;
+  TensorDesc *mutable_tensor_desc();
+
   VarDesc desc_;
 };
 }  // namespace framework

paddle/gserver/layers/CostLayer.cpp

@@ -462,8 +462,8 @@ void LambdaCost::calcGrad(const real* outputScore,
       real score_j = score[index_j];
       real dcgDif = 0;
       if (j < sortSize) {
-        dcgDif = (std::pow(2, score_i) - std::pow(2, score_j)) /
-                 (std::log(i + 2) - std::log(j + 2));
+        dcgDif = (std::pow(2, score_i) - std::pow(2, score_j)) *
+                 (1 / std::log(i + 2) - 1 / std::log(j + 2));
       } else {
         dcgDif =
             (std::pow(2, score_i) - std::pow(2, score_j)) / std::log(i + 2);

paddle/gserver/layers/Layer.h

@@ -86,6 +86,7 @@ protected:
   /// Also used in 'use_mkldnn' case.
   std::vector<Argument> outputOtherDevice_;
   /// If there are several outputs, map them by each name.
+  /// MKLDNNLayer use it only to merge output grad
   std::map<std::string, Argument*> outputMap_;
   /// Used to merge grad on different devices.
   MatrixPtr tmpGrad_;
@@ -325,6 +326,11 @@ public:
     outputMap_[name] = output;
   }
 
+  /**
+   * Get the output map size, if layer has multi-output.
+   */
+  size_t getOutputMapSize() { return outputMap_.size(); }
+
   /**
    * Get the output based on layer's name.
    */

paddle/gserver/layers/MKLDNNConvLayer.cpp

@@ -225,8 +225,6 @@ void MKLDNNConvLayer::resetFwdPipeline(
     MKLDNNMatrixPtr& wgt,
     MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
-  pipeline.clear();
-
   if (cvtInVal_) {
     pipeline.push_back(*cvtInVal_);
   }
@@ -245,7 +243,7 @@ void MKLDNNConvLayer::resetFwdPipeline(
 
 void MKLDNNConvLayer::resetInValue(
     std::shared_ptr<conv_fwd::primitive_desc>& pd, MKLDNNMatrixPtr& in) {
-  const MatrixPtr& inMat = inputLayers_[0]->getOutput().value;
+  const MatrixPtr& inMat = inputLayers_[0]->getOutputValue();
   in = MKLDNNMatrix::create(inMat, pd->src_primitive_desc());
 
   // create buffer and reorder if input value do not match
@@ -310,15 +308,20 @@ void MKLDNNConvLayer::resetOutValue(
     const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
     memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
     cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
-    if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
+    if (cpuOutVal_->getPrimitiveDesc() != pd->dst_primitive_desc()) {
+      out = MKLDNNMatrix::create(nullptr, pd->dst_primitive_desc());
       cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
-      CHECK(cvtOutVal_) << "should not be emptry";
+      CHECK(cvtOutVal_) << "should not be empty";
     } else {
-      // CPU output share the same data of MKLDNN output
-      cpuOut->setData(out->getData());
       cpuOutVal_ = out;
     }
+    // when output is cpu device, change the mkldnn output value and make them
+    // share the same data. Then if next layer use inputlayer->getOuputValue()
+    // to achieve the input value, it will get the right data.
+    output_.value = std::dynamic_pointer_cast<Matrix>(cpuOutVal_);
+    return;
   }
+  output_.value = std::dynamic_pointer_cast<Matrix>(out);
 }
 
 void MKLDNNConvLayer::resetBwdWgtPD(
@@ -412,8 +415,6 @@ void MKLDNNConvLayer::resetBwdPipeline(
     MKLDNNMatrixPtr& wgt,
     MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
-  pipeline.clear();
-
   if (cvtOutGrad_) {
     pipeline.push_back(*cvtOutGrad_);
   }
@@ -446,28 +447,27 @@ void MKLDNNConvLayer::resetBwdPipeline(
 
 void MKLDNNConvLayer::resetOutGrad(
     std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD, MKLDNNMatrixPtr& out) {
-  const MatrixPtr& outMat = output_.grad;
-  out = MKLDNNMatrix::create(outMat, wgtPD->diff_dst_primitive_desc());
-  CHECK(outVal_ != nullptr &&
-        out->getPrimitiveDesc() == outVal_->getPrimitiveDesc())
-      << "primitive desc of out grad and value should be equal";
-
-  // TODO(TJ): merge outgrad
-  // create reorder if has output grad does not match
   cpuOutGrad_ = nullptr;
   cvtOutGrad_ = nullptr;
-  if (!outputIsOnlyMKLDNN()) {
+  CHECK(outVal_ != nullptr &&
+        outVal_->getPrimitiveDesc() == wgtPD->diff_dst_primitive_desc())
+      << "primitive desc of out grad and value should be equal";
+  if (outputIsOnlyMKLDNN()) {
+    MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
+  } else {
     const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
-    outMat->setData(cpuOut->getData());
     // same PrimitiveDesc with cpuInVal_
     CHECK(cpuOutVal_);
     cpuOutGrad_ = MKLDNNMatrix::create(cpuOut, cpuOutVal_->getPrimitiveDesc());
-    if (cpuOutGrad_->getPrimitiveDesc() == out->getPrimitiveDesc()) {
-      out = cpuOutGrad_;
-    } else {
-      out = MKLDNNMatrix::create(nullptr, wgtPD->diff_dst_primitive_desc());
+    // create reorder if primitive desc does not match
+    if (cpuOutGrad_->getPrimitiveDesc() != outVal_->getPrimitiveDesc()) {
+      out = MKLDNNMatrix::create(output_.grad, outVal_->getPrimitiveDesc());
       cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
       CHECK(cvtOutGrad_);
+    } else {
+      // share the same data of CPU output
+      output_.grad->setData(cpuOut->getData());
+      out = cpuOutGrad_;
     }
   }
 }
@@ -496,32 +496,30 @@ void MKLDNNConvLayer::resetWgtBiasGrad(
 void MKLDNNConvLayer::resetInGrad(
     std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
     MKLDNNMatrixPtr& in) {
+  in = nullptr;
+  cpuInGrad_ = nullptr;
+  cvtInGrad_ = nullptr;
   if (dataPD == nullptr) {
     return;
   }
 
-  // TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
-  in = MKLDNNMatrix::create(inputLayers_[0]->getOutput().grad,
-                            dataPD->diff_src_primitive_desc());
+  if (inputIsOnlyMKLDNN()) {
+    MKLDNNLayer::resetInGrad(in, dataPD->diff_src_primitive_desc());
     CHECK(nullptr != inVal_ &&
           in->getPrimitiveDesc() == inVal_->getPrimitiveDesc())
         << "primitive desc of input grad and value should be equal";
-
-  // create reorder if has output grad does not match
-  cpuInGrad_ = nullptr;
-  cvtInGrad_ = nullptr;
-  if (!inputIsOnlyMKLDNN()) {
+  } else {
     const MatrixPtr& cpuIn = getInputGrad(0, CPU_DEVICE);
     // same PrimitiveDesc with cpuInVal_
     CHECK(cpuInVal_);
     cpuInGrad_ = MKLDNNMatrix::create(cpuIn, cpuInVal_->getPrimitiveDesc());
-    if (cpuInGrad_->getPrimitiveDesc() != in->getPrimitiveDesc()) {
-      const MatrixPtr& dnnIn = getInputGrad(0, MKLDNN_DEVICE);
-      in = MKLDNNMatrix::create(dnnIn, in->getPrimitiveDesc());
+    in = cpuInGrad_;
+    // create reorder if PrimitiveDesc does not match
+    if (cpuInGrad_->getPrimitiveDesc() != dataPD->diff_src_primitive_desc()) {
+      in = MKLDNNMatrix::create(getInputGrad(0, MKLDNN_DEVICE),
+                                dataPD->diff_src_primitive_desc());
       cvtInGrad_ = MKLDNNMatrix::createReorder(in, cpuInGrad_);
       CHECK(cvtInGrad_);
-    } else {
-      in = cpuInGrad_;
     }
   }
 }

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -180,10 +180,10 @@ void MKLDNNFcLayer::resetWgtBiasValue(MKLDNNMatrixPtr& wgt,
 void MKLDNNFcLayer::resetOutValue(MKLDNNMatrixPtr& out) {
   out = MKLDNNMatrix::create(output_.value, {bs_, oc_}, format::nc, engine_);
   if (!outputIsOnlyMKLDNN()) {
-    // fc cpu output value do not need create convert
-    // just share point
+    // fc cpu output value do not need create convert, just share data
     getOutput(CPU_DEVICE).value->setData(out->getData());
   }
+  output_.value = std::dynamic_pointer_cast<Matrix>(out);
 }
 
 void MKLDNNFcLayer::resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
@@ -214,8 +214,6 @@ void MKLDNNFcLayer::resetFwdPipeline(
     MKLDNNMatrixPtr& wgt,
     MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
-  pipeline.clear();
-
   if (bias) {
     fwd_.reset(new fc_fwd(*pd, *in, *wgt, *bias, *out));
   } else {
@@ -237,19 +235,14 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
 }
 
 void MKLDNNFcLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
-  // TODO(TJ): merge outgrad
-  int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
-  output_.grad->setData(getOutput(device).grad->getData());
-  // for MKLDNN device:
-  // can not directly cast outputgrad to mkldnnmatrix,
-  // since each layer can not write the inputgrad to mkldnn inputgrad.
-  // So just create from matrix with outputvalue format.
-  // for CPU device:
-  // fc do not need to convert from cpu device since output is always nc format
-  // only need create from cpu device
   CHECK(outVal_);
-  out =
-      MKLDNNMatrix::create(getOutput(device).grad, outVal_->getPrimitiveDesc());
+  if (outputIsOnlyMKLDNN()) {
+    MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
+  } else {
+    const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
+    output_.grad->setData(cpuOut->getData());
+    out = MKLDNNMatrix::create(cpuOut, outVal_->getPrimitiveDesc());
+  }
 }
 
 void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
@@ -267,13 +260,11 @@ void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
 
 void MKLDNNFcLayer::resetInGrad(MKLDNNMatrixPtr& in) {
   in = nullptr;
-  const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
-  if (inGrad == nullptr) {
+  if (inputLayers_[0]->getOutput().grad == nullptr) {
     return;
   }
-  // TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
   CHECK(inVal_);
-  in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
+  MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
 }
 
 void MKLDNNFcLayer::resetBwdWgtPD(
@@ -314,7 +305,6 @@ void MKLDNNFcLayer::resetBwdPipeline(
     MKLDNNMatrixPtr& wgt,
     MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
-  pipeline.clear();
   CHECK(inVal_);
   if (bias) {
     bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias));

paddle/gserver/layers/MKLDNNLayer.h

@@ -65,6 +65,17 @@ protected:
   MKLDNNMatrixPtr biasVal_;
   MKLDNNMatrixPtr biasGrad_;
 
+  // merge grad primitive
+  std::shared_ptr<mkldnn::primitive> mergeGrad_;
+  std::vector<mkldnn::primitive> pipelineMergeGrad_;
+  // tmp input argument to save input grad, only used to merge grad
+  Argument tmpInArg_;
+  // since mkldnn sum do not support different formats:
+  // can refer to https://github.com/01org/mkl-dnn/issues/134
+  // so need create reorder manually and save tmp MKLDNNMatrix
+  MKLDNNMatrixPtr tmpOutGrad_;
+  std::shared_ptr<mkldnn::primitive> tmpCvt_;
+
 public:
   explicit MKLDNNLayer(const LayerConfig& config)
       : Layer(config),
@@ -99,6 +110,7 @@ public:
     if (!Layer::init(layerMap, parameterMap)) {
       return false;
     }
+    setOutputMap();
     checkCPUOutputsNumber();
 
     stream_.reset(new MKLDNNStream());
@@ -118,12 +130,9 @@ public:
         VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
         // reset when input total sizes changed, not only the batchsize
         inputElemenCnt_ = elemenCnt;
+        pipelineFwd_.clear();
         reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
         resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
-        if (outVal_) {
-          // change original output value to mkldnn output value
-          output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
-        }
         convertWeightsFromPaddle();
         needResetBwd_ = true;
       }
@@ -144,9 +153,18 @@ public:
   void backward(const UpdateCallback& callback) override {
     if (needResetBwd_) {
       VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
+      pipelineBwd_.clear();
+      pipelineMergeGrad_.clear();
+      mergeGrad_ = nullptr;
       resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
       needResetBwd_ = false;
     }
+
+    // merge grad must before backward activation
+    if (mergeGrad_) {
+      REGISTER_TIMER_INFO("MergeBpGrad", getName().c_str());
+      stream_->submit(pipelineMergeGrad_);
+    }
     {
       REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
       backwardActivation();
@@ -247,6 +265,76 @@ protected:
     }
   }
 
+  /**
+   * reset the output grad matrix from primitive desc.
+   * and reset the merge grad primitive if needed.
+   * note: when this layer has serval outputs,
+   *       it could not be mixed with cpu device,
+   *       since it can not get memory desc from cpu device.
+   */
+  virtual void resetOutGrad(MKLDNNMatrixPtr& out,
+                            mkldnn::memory::primitive_desc pd) {
+    CHECK(outputIsOnlyMKLDNN()) << "do not support mixed with other device yet";
+    mergeGrad_ = nullptr;
+    pipelineMergeGrad_.clear();
+    out = MKLDNNMatrix::create(output_.grad, pd);
+    if (outputMap_.size() <= 1) {
+      return;
+    }
+    std::vector<double> scales(outputMap_.size(), 1.0);
+    std::vector<mkldnn::memory::primitive_desc> srcPDs;
+    std::vector<mkldnn::primitive::at> srcs;
+    for (auto it = outputMap_.begin(); it != outputMap_.end(); ++it) {
+      MKLDNNMatrixPtr src =
+          std::dynamic_pointer_cast<MKLDNNMatrix>(it->second->grad);
+      VLOG(MKLDNN_BASE) << getName() << " has output grad " << it->first;
+      CHECK(src) << "should be MKLDNNMatrix";
+      auto srcDims = src->getDims();
+      auto dstDims = out->getDims();
+      CHECK_EQ(srcDims.size(), dstDims.size());
+      for (size_t i = 0; i < srcDims.size(); ++i) {
+        CHECK_EQ(srcDims[i], dstDims[i]);
+      }
+      srcPDs.push_back(src->getPrimitiveDesc());
+      srcs.push_back(*src);
+    }
+
+    // TODO(TJ): remove me when mkldnn sum support different formats
+    for (size_t i = 1; i < srcPDs.size(); ++i) {
+      CHECK(srcPDs[0] == srcPDs[i]);
+    }
+    tmpOutGrad_ = nullptr;
+    tmpCvt_ = nullptr;
+    if (out->getPrimitiveDesc() != srcPDs[0]) {
+      tmpOutGrad_ = MKLDNNMatrix::create(nullptr, srcPDs[0]);
+      tmpCvt_ = MKLDNNMatrix::createReorder(tmpOutGrad_, out);
+      CHECK(tmpCvt_);
+      pipelineMergeGrad_.push_back(*tmpCvt_);
+    } else {
+      tmpOutGrad_ = out;
+    }
+
+    auto sumPD = mkldnn::sum::primitive_desc(
+        tmpOutGrad_->getMemoryDesc(), scales, srcPDs);
+    mergeGrad_.reset(new mkldnn::sum(sumPD, srcs, *tmpOutGrad_));
+    pipelineMergeGrad_.insert(pipelineMergeGrad_.begin(), *mergeGrad_);
+  }
+
+  /**
+   * reset input grad from primitive desc.
+   * this function is avaiable for input is only mkldnn
+   * or input do not care cpu device
+   */
+  virtual void resetInGrad(MKLDNNMatrixPtr& in,
+                           mkldnn::memory::primitive_desc pd) {
+    LayerPtr& input = inputLayers_[0];
+    const MatrixPtr& grad =
+        input->getOutputMapSize() > 1 ? nullptr : input->getOutput().grad;
+    in = MKLDNNMatrix::create(grad, pd);
+    Argument& arg = input->getOutput(this->getName());
+    arg.grad = std::dynamic_pointer_cast<Matrix>(in);
+  }
+
   /**
    * print info about sizes
    */
@@ -334,6 +422,16 @@ private:
     }
   }
 
+  /**
+   * Set output map of prev layers.
+   */
+  void setOutputMap() {
+    outputMap_.clear();
+    for (size_t i = 0; i < inputLayers_.size(); ++i) {
+      inputLayers_[i]->setOutput(getName(), &tmpInArg_);
+    }
+  }
+
   /**
    * Check the cpu device number of outputOtherDevice_.
    * should have only one at most.

paddle/gserver/layers/MKLDNNPoolLayer.cpp

@@ -142,14 +142,16 @@ void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
     const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
     cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
     if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
+      out = MKLDNNMatrix::create(nullptr, out->getPrimitiveDesc());
       cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
       CHECK(cvtOutVal_) << "should not be emptry";
     } else {
-      // CPU output share the same data of MKLDNN output
-      cpuOut->setData(out->getData());
       cpuOutVal_ = out;
     }
+    output_.value = std::dynamic_pointer_cast<Matrix>(cpuOutVal_);
+    return;
   }
+  output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
 }
 
 void MKLDNNPoolLayer::resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
@@ -187,7 +189,6 @@ void MKLDNNPoolLayer::resetFwdPipeline(
     std::shared_ptr<pool_fwd::primitive_desc>& pd,
     MKLDNNMatrixPtr& in,
     MKLDNNMatrixPtr& out) {
-  pipeline.clear();
   fwd_ = workspace_
              ? std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out, *workspace_))
              : std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out));
@@ -205,17 +206,17 @@ void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
   resetInGrad(in);
 }
 void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
-  CHECK(outVal_) << "Should have output value";
-  out = MKLDNNMatrix::create(output_.grad, outVal_->getPrimitiveDesc());
-
-  // create reorder if output value has cpu device and pd do not match
   cpuOutGrad_ = nullptr;
   cvtOutGrad_ = nullptr;
-  if (!outputIsOnlyMKLDNN()) {
+  CHECK(outVal_);
+  if (outputIsOnlyMKLDNN()) {
+    MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
+  } else {
     const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
     cpuOutGrad_ = MKLDNNMatrix::create(
         cpuOut, memory::dims{bs_, oc_, oh_, ow_}, format::nchw, engine_);
-    if (cpuOutGrad_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
+    if (cpuOutGrad_->getPrimitiveDesc() != outVal_->getPrimitiveDesc()) {
+      out = MKLDNNMatrix::create(output_.grad, outVal_->getPrimitiveDesc());
       cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
       CHECK(cvtOutGrad_) << "should not be emptry";
     } else {
@@ -228,12 +229,11 @@ void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
 
 void MKLDNNPoolLayer::resetInGrad(MKLDNNMatrixPtr& in) {
   in = nullptr;
-  const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
-  if (inGrad == nullptr) {
+  if (inputLayers_[0]->getOutput().grad == nullptr) {
     return;
   }
   CHECK(inVal_);
-  in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
+  MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
 }
 
 void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
@@ -261,7 +261,6 @@ void MKLDNNPoolLayer::resetBwdPipeline(
     std::shared_ptr<pool_bwd::primitive_desc>& pd,
     MKLDNNMatrixPtr& in,
     MKLDNNMatrixPtr& out) {
-  pipeline.clear();
   if (cvtOutGrad_) {
     pipeline.push_back(*cvtOutGrad_);
   }

paddle/gserver/tests/MKLDNNTester.cpp

@@ -124,8 +124,8 @@ void MKLDNNTester::randomTopDiffs() {
 void MKLDNNTester::checkForward() {
   VLOG(MKLDNN_ALL) << "Check Forward";
   printTopDatas();
-  double delta = compareMatrix(dnnLayer_->getOutput(CPU_DEVICE).value,
-                               refLayer_->getOutputValue());
+  double delta =
+      compareMatrix(dnnLayer_->getOutputValue(), refLayer_->getOutputValue());
   EXPECT_LE(fabs(delta), eps_);
 }
 

paddle/operators/CMakeLists.txt

@@ -84,8 +84,9 @@ function(op_library TARGET)
     endif()
 
     # pybind USE_NO_KERNEL_OP
+    # HACK: if REGISTER_OP_CPU_KERNEL presents the operator must have kernel
     file(READ ${TARGET}.cc TARGET_CONTENT)
-    string(REGEX MATCH "OperatorWithKernel" regex_result "${TARGET_CONTENT}")
+    string(REGEX MATCH "REGISTER_OP_CPU_KERNEL" regex_result "${TARGET_CONTENT}")
     string(REPLACE "_op" "" TARGET "${TARGET}")
     if (${pybind_flag} EQUAL 0 AND regex_result STREQUAL "")
         file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(${TARGET});\n")

paddle/operators/activation_op.cc

@@ -338,6 +338,38 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+template <typename AttrType>
+class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  HardSigmoidOpMaker(framework::OpProto *proto,
+                     framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of HardSigmoid operator");
+    AddOutput("Y", "Output of HardSigmoid operator");
+    AddComment(R"DOC(
+Hard Sigmoid activation operator.
+
+Segment-wise linear approximation of sigmoid[1].
+This is much faster than sigmoid.
+
+hard_sigmoid = max(0, min(1, slope * x + shift))
+
+The slope should be positive. The offset can be either positive or negative.
+The default slope and shift are set from [1].
+It is recommended to use the defaults for this activation.
+
+References:
+  [1] Noisy Activation Functions
+      (https://arxiv.org/abs/1603.00391)
+
+    )DOC");
+    AddAttr<AttrType>("slope", "Slope for linear approximation of sigmoid")
+        .SetDefault(static_cast<AttrType>(0.2));
+    AddAttr<AttrType>("offset", "Offset for linear approximation of sigmoid")
+        .SetDefault(static_cast<AttrType>(0.5));
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -413,6 +445,9 @@ REGISTER_OP(thresholded_relu, ops::ActivationOp,
             ops::ThresholdedReluOpMaker<float>, thresholded_relu_grad,
             ops::ActivationOpGrad);
 
+REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker<float>,
+            hard_sigmoid_grad, ops::ActivationOpGrad);
+
 #define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor)        \
   REGISTER_OP_CPU_KERNEL(                                                      \
       act_type,                                                                \

paddle/operators/activation_op.h

@@ -616,6 +616,38 @@ struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
+template <typename T>
+struct HardSigmoidFunctor : public BaseActivationFunctor<T> {
+  float slope;
+  float offset;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"slope", &slope}, {"offset", &offset}};
+  }
+
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    auto temp = x * static_cast<T>(slope) + static_cast<T>(offset);
+    y.device(d) = temp.cwiseMax(static_cast<T>(0)).cwiseMin(static_cast<T>(1));
+  }
+};
+
+template <typename T>
+struct HardSigmoidGradFunctor : public BaseActivationFunctor<T> {
+  float slope;
+  float offset;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"slope", &slope}, {"offset", &offset}};
+  }
+
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
+    dx.device(d) =
+        dy *
+        ((y > static_cast<T>(0)) * (y < static_cast<T>(1))).template cast<T>() *
+        static_cast<T>(slope);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -642,4 +674,5 @@ struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
   __macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor);    \
   __macro(elu, ELUFunctor, ELUGradFunctor);                          \
   __macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor);    \
+  __macro(hard_sigmoid, HardSigmoidFunctor, HardSigmoidGradFunctor); \
   __macro(thresholded_relu, ThresholdedReluFunctor, ThresholdedReluGradFunctor);

paddle/operators/adam_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/adam_op.h"
+
+namespace paddle {
+namespace operators {
+
+class AdamOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Param"),
+                   "Input(Param) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Grad"),
+                   "Input(Grad) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Moment1"),
+                   "Input(Moment1) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Moment2"),
+                   "Input(Moment2) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
+                   "Input(LearningRate) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"),
+                   "Input(Beta1Pow) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Beta2Pow"),
+                   "Input(Beta2Pow) of AdamOp should not be null.");
+
+    PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
+                   "Output(ParamOut) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Moment1Out"),
+                   "Output(Moment1Out) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Moment2Out"),
+                   "Output(Moment2Out) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Beta1PowOut"),
+                   "Output(Beta1PowOut) of AdamOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Beta2PowOut"),
+                   "Output(Beta2PowOut) of AdamOp should not be null.");
+
+    auto lr_dims = ctx->GetInputDim("LearningRate");
+    PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
+                      "Learning rate should have 1 dimension");
+    auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow");
+    PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1,
+                      "Beta1 power accumulator should have 1 dimension");
+    auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow");
+    PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1,
+                      "Beta1 power accumulator should have 1 dimension");
+
+    auto param_dims = ctx->GetInputDim("Param");
+    PADDLE_ENFORCE_EQ(
+        param_dims, ctx->GetInputDim("Grad"),
+        "Param and Grad input of AdamOp should have same dimension");
+    PADDLE_ENFORCE_EQ(
+        param_dims, ctx->GetInputDim("Moment1"),
+        "Param and Moment input of AdamOp should have same dimension");
+    PADDLE_ENFORCE_EQ(
+        param_dims, ctx->GetInputDim("Moment2"),
+        "Param and InfNorm input of AdamOp should have same dimension");
+
+    ctx->SetOutputDim("ParamOut", param_dims);
+    ctx->SetOutputDim("Moment1Out", param_dims);
+    ctx->SetOutputDim("Moment2Out", param_dims);
+    ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims);
+    ctx->SetOutputDim("Beta2PowOut", beta2_pow_dims);
+  }
+};
+
+class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  AdamOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Param", "(Tensor) Input parameter");
+    AddInput("Grad", "(Tensor) Input gradient");
+    AddInput("LearningRate", "(Tensor) Learning rate");
+    AddInput("Moment1", "(Tensor) Input first moment");
+    AddInput("Moment2", "(Tensor) Input second moment");
+    AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
+    AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
+
+    AddOutput("ParamOut", "(Tensor) Output parameter");
+    AddOutput("Moment1Out", "(Tensor) Output first moment");
+    AddOutput("Moment2Out", "(Tensor) Output second moment");
+    AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator");
+    AddOutput("Beta2PowOut", "(Tensor) Output beta2 power accumulator");
+
+    AddAttr<float>("beta1",
+                   "(float, default 0.9) "
+                   "Exponential decay rate for the "
+                   "first moment estimates.")
+        .SetDefault(0.9f);
+    AddAttr<float>("beta2",
+                   "(float, default 0.999) "
+                   "exponential decay rate for the "
+                   "second moment estimates.")
+        .SetDefault(0.999f);
+    AddAttr<float>("epsilon",
+                   "(float, default 1.0e-8) "
+                   "Constant for numerical stability")
+        .SetDefault(1.0e-8f);
+
+    AddComment(R"DOC(
+Adam Updates Operator.
+
+This implements the Adam optimizer from Section 2 of the Adam
+paper[1]. Adam is a first-order gradient-based optimization
+method based on adaptive estimates of lower-order moments.
+
+Adam updates:
+
+moment1_out = beta1 * moment1 + (1 − beta1) * grad
+moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad
+beta1_pow_out = beta1_pow * beta1
+beta2_pow_out = beta2_pow * beta2
+learning_rate_t = learning_rate_t *
+                  sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out)
+param_out = param - learning_rate_t * moment1/ (sqrt(moment2) + epsilon)
+
+References:
+  [1] Adam: A Method for Stochastic Optimization
+      (https://arxiv.org/abs/1412.6980)
+
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(adam, ops::AdamOp, ops::AdamOpMaker);
+REGISTER_OP_CPU_KERNEL(adam,
+                       ops::AdamOpKernel<paddle::platform::CPUPlace, float>);

paddle/operators/adam_op.cu

+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/adam_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(adam,
+                       ops::AdamOpKernel<paddle::platform::GPUPlace, float>);

paddle/operators/adam_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/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class AdamOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
+    auto moment1_out_tensor = ctx.Output<framework::Tensor>("Moment1Out");
+    auto moment2_out_tensor = ctx.Output<framework::Tensor>("Moment2Out");
+    auto beta1_pow_out_tensor = ctx.Output<framework::Tensor>("Beta1PowOut");
+    auto beta2_pow_out_tensor = ctx.Output<framework::Tensor>("Beta2PowOut");
+
+    param_out_tensor->mutable_data<T>(ctx.GetPlace());
+    moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
+    moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
+    beta1_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
+    beta2_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
+
+    float beta1 = ctx.Attr<float>("beta1");
+    float beta2 = ctx.Attr<float>("beta2");
+    float epsilon = ctx.Attr<float>("epsilon");
+
+    auto param = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Param"));
+    auto grad = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Grad"));
+    auto moment1 = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Moment1"));
+    auto moment2 = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Moment2"));
+    auto lr = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("LearningRate"));
+    auto beta1_pow = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Beta1Pow"));
+    auto beta2_pow = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Beta2Pow"));
+    auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
+    auto moment1_out = framework::EigenVector<T>::Flatten(*moment1_out_tensor);
+    auto moment2_out = framework::EigenVector<T>::Flatten(*moment2_out_tensor);
+    auto beta1_pow_out =
+        framework::EigenVector<T>::Flatten(*beta1_pow_out_tensor);
+    auto beta2_pow_out =
+        framework::EigenVector<T>::Flatten(*beta2_pow_out_tensor);
+    auto place = ctx.GetEigenDevice<Place>();
+
+    moment1_out.device(place) = beta1 * moment1 + (1 - beta1) * grad;
+    moment2_out.device(place) = beta2 * moment2 + (1 - beta2) * grad.square();
+    beta1_pow_out.device(place) = beta1_pow * beta1;
+    beta2_pow_out.device(place) = beta2_pow * beta2;
+    // All of these are tensors of 1 element
+    auto lr_t = lr * (1 - beta2_pow_out).sqrt() / (1 - beta1_pow_out);
+    // Eigen does not support automatic broadcast
+    // Get dimensions of moment vector to broadcast lr_t
+    Eigen::DSizes<int, 1> m_dsize(moment1_out_tensor->numel());
+    param_out.device(place) =
+        param -
+        lr_t.broadcast(m_dsize) *
+            (moment1_out / (moment2_out.sqrt() + epsilon));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/cond_op.cc

@@ -134,7 +134,7 @@ void CondOp::PrepareDataForSubnet(
   for (int i = 0; i < BRANCH_NUM; ++i) {
     for (auto& output : (*sub_net_op_[i]).Outputs()) {
       for (auto& var_name : output.second) {
-        sub_scopes[i]->NewVar(var_name);
+        sub_scopes[i]->Var(var_name);
       }
     }
   }

paddle/operators/conv2d_op.cc

@@ -12,22 +12,12 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/gemm_conv2d_op.h"
+#include "paddle/operators/conv2d_op.h"
 
 namespace paddle {
 namespace operators {
 
-int outputSize(int input_size, int filter_size, int padding, int stride) {
-  int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
-  return output_size;
-}
-
-class Conv2DOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
+void Conv2DOp::InferShape(framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE(ctx->HasInput("Input"),
                  "Input(Input) of Conv2DOp should not be null.");
   PADDLE_ENFORCE(ctx->HasInput("Filter"),
@@ -53,25 +43,22 @@ class Conv2DOp : public framework::OperatorWithKernel {
       "The number of output channels should be divided by groups.");
 
   auto output_height =
-        outputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
+      OutputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
   auto output_width =
-        outputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
-    ctx->SetOutputDim(
-        "Output", {in_dims[0], filter_dims[0], output_height, output_width});
-  }
-};
+      OutputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
+  ctx->SetOutputDim("Output",
+                    {in_dims[0], filter_dims[0], output_height, output_width});
+}
 
-class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  Conv2DOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
+Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
+                             framework::OpAttrChecker* op_checker)
     : OpProtoAndCheckerMaker(proto, op_checker) {
   AddInput(
       "Input",
       "The input tensor of convolution operator. "
       "The format of input tensor is NCHW. Where N is batch size, C is the "
       "number of channels, H and W is the height and width of image.");
-    AddInput(
-        "Filter",
+  AddInput("Filter",
            "The filter tensor of convolution operator."
            "The format of the filter tensor is MCHW, where M is the number of "
            "output image channels, C is the number of input image channels, "
@@ -98,15 +85,9 @@ The convolution operation calculates the output based on the input, filter
 and strides, paddings, groups parameters. The size of each dimension of the
 parameters is checked in the infer-shape.
 )DOC");
-  }
-};
-
-class Conv2DOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
+}
 
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
+void Conv2DOpGrad::InferShape(framework::InferShapeContext* ctx) const {
   auto in_dims = ctx->GetInputDim("Input");
   auto filter_dims = ctx->GetInputDim("Filter");
   if (ctx->HasOutput(framework::GradVarName("Input"))) {
@@ -115,8 +96,7 @@ class Conv2DOpGrad : public framework::OperatorWithKernel {
   if (ctx->HasOutput(framework::GradVarName("Filter"))) {
     ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
   }
-  }
-};
+}
 
 }  // namespace operators
 }  // namespace paddle

paddle/operators/conv2d_op.cu

@@ -12,7 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/gemm_conv2d_op.h"
+#include "paddle/operators/conv2d_op.h"
 
 namespace ops = paddle::operators;
 

paddle/operators/gemm_conv2d_op.h → paddle/operators/conv2d_op.h

@@ -24,6 +24,38 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
+// Base convolution operator definations for other conv
+// like operators to reuse the implementation.
+inline int OutputSize(int input_size, int filter_size, int padding,
+                      int stride) {
+  int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
+  return output_size;
+}
+
+// Define Op classes in .h file so that other conv
+// operator implementations can reuse the code.
+class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  Conv2DOpMaker(framework::OpProto* proto,
+                framework::OpAttrChecker* op_checker);
+};
+
+class Conv2DOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
+class Conv2DOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
 template <typename Place, typename T>
 class GemmConv2DKernel : public framework::OpKernel<T> {
  public:
@@ -74,7 +106,6 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
 
     framework::DDim output_matrix_shape = {output_channels,
                                            output_height * output_width};
-
     // convolution operator: im2col + gemm
     int in_step = input_channels / groups;
     int out_step = output_channels / groups;

paddle/operators/conv_cudnn_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/conv2d_op.h"
+
+namespace paddle {
+namespace operators {
+
+class CudnnConvOpMaker : public Conv2DOpMaker {
+ public:
+  CudnnConvOpMaker(framework::OpProto* proto,
+                   framework::OpAttrChecker* op_checker)
+      : Conv2DOpMaker(proto, op_checker) {
+    AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
+        .SetDefault(std::vector<int>{1, 1});
+    AddAttr<int>("workspace_size_MB",
+                 "workspace size for cudnn, in MB, "
+                 "workspace is a section of GPU memory which will be "
+                 "allocated/freed each time the operator runs, larger "
+                 "workspace size can increase performance but also requires "
+                 "better hardward. This size should be carefully setted.")
+        .SetDefault(4096);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(conv_cudnn, ops::Conv2DOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
+            ops::Conv2DOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    conv_cudnn, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    conv_cudnn_grad,
+    ops::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);

paddle/operators/conv_cudnn_op.cu

+/* 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/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/memory/memory.h"
+#include "paddle/operators/conv2d_op.h"
+#include "paddle/platform/assert.h"
+#include "paddle/platform/cudnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
+using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
+using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
+using DataLayout = platform::DataLayout;
+using CUDADeviceContext = platform::CUDADeviceContext;
+
+static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES = 1024 * 1024 * 1024;
+
+// NOTE: framework::vectorize converts to type int64_t
+//       which does not fit cudnn inputs.
+std::vector<int> Dims2Vector(const framework::DDim& dims) {
+  std::vector<int> ret;
+  for (int i = 0; i < dims.size(); i++) {
+    ret.push_back(dims[i]);
+  }
+  return ret;
+}
+
+template <typename T>
+class CudnnConvOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use GPUPlace.");
+    auto* input = ctx.Input<Tensor>("Input");
+    auto* filter = ctx.Input<Tensor>("Filter");
+    auto* output = ctx.Output<Tensor>("Output");
+
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    int groups = ctx.Attr<int>("groups");
+    int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
+
+    const T* input_data = input->data<T>();
+    const T* filter_data = filter->data<T>();
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+
+    // ------------------- cudnn descriptors ---------------------
+    ScopedTensorDescriptor input_desc;
+    ScopedTensorDescriptor output_desc;
+    ScopedFilterDescriptor filter_desc;
+    ScopedConvolutionDescriptor conv_desc;
+    DataLayout layout = DataLayout::kNCHW;
+
+    cudnnTensorDescriptor_t cudnn_input_desc =
+        input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
+    cudnnTensorDescriptor_t cudnn_output_desc =
+        output_desc.descriptor<T>(layout, Dims2Vector(output->dims()), groups);
+    cudnnFilterDescriptor_t cudnn_filter_desc =
+        filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
+    cudnnConvolutionDescriptor_t cudnn_conv_desc =
+        conv_desc.descriptor<T>(paddings, strides, dilations);
+
+    int input_channels = input->dims()[1];
+    int input_height = input->dims()[2];
+    int input_width = input->dims()[3];
+    int output_channels = output->dims()[1];
+    int output_height = output->dims()[2];
+    int output_width = output->dims()[3];
+
+    int group_offset_in = input_channels / groups * input_height * input_width;
+    int group_offset_out =
+        output_channels / groups * output_height * output_width;
+    int group_offset_filter = filter->numel() / groups;
+    // ------------------- cudnn conv workspace ---------------------
+    void* cudnn_workspace = nullptr;
+    size_t workspace_size_in_bytes;  // final workspace to allocate.
+    size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
+    if (user_workspace_size > 0) {
+      workspace_size_limit = user_workspace_size * 1024 * 1024;
+    }
+    // ------------------- cudnn conv algorithm ---------------------
+    cudnnConvolutionFwdAlgo_t algo;
+    auto handle = ctx.cuda_device_context().cudnn_handle();
+
+    PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+        handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+        cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+        workspace_size_limit, &algo));
+    // get workspace size able to allocate
+    PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
+        handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+        cudnn_output_desc, algo, &workspace_size_in_bytes));
+    // Allocate on GPU memory
+    platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
+    // ------------------- cudnn conv forward ---------------------
+    T alpha = 1.0f, beta = 0.0f;
+    for (int i = 0; i < groups; i++) {
+      PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
+          handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
+          cudnn_filter_desc, filter_data + i * group_offset_filter,
+          cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes,
+          &beta, cudnn_output_desc, output_data + i * group_offset_out));
+    }
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
+  }
+};
+
+template <typename T>
+class CudnnConvGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use GPUPlace.");
+    auto input = ctx.Input<Tensor>("Input");
+    auto filter = ctx.Input<Tensor>("Filter");
+    auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
+    auto input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
+    auto filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
+
+    const T* input_data = input->data<T>();
+    const T* output_grad_data = output_grad->data<T>();
+    const T* filter_data = filter->data<T>();
+
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    int groups = ctx.Attr<int>("groups");
+    int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
+
+    // ------------------- cudnn descriptors ---------------------
+    ScopedTensorDescriptor input_desc;
+    ScopedTensorDescriptor output_grad_desc;
+    ScopedTensorDescriptor input_grad_desc;
+
+    ScopedFilterDescriptor filter_desc;
+    ScopedFilterDescriptor filter_grad_desc;
+    ScopedConvolutionDescriptor conv_desc;
+    DataLayout layout = DataLayout::kNCHW;
+
+    cudnnTensorDescriptor_t cudnn_input_desc =
+        input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
+    cudnnTensorDescriptor_t cudnn_output_grad_desc =
+        output_grad_desc.descriptor<T>(layout, Dims2Vector(output_grad->dims()),
+                                       groups);
+    cudnnFilterDescriptor_t cudnn_filter_desc =
+        filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
+    cudnnTensorDescriptor_t cudnn_input_grad_desc = nullptr;
+    cudnnFilterDescriptor_t cudnn_filter_grad_desc = nullptr;
+
+    cudnnConvolutionDescriptor_t cudnn_conv_desc =
+        conv_desc.descriptor<T>(paddings, strides, dilations);
+
+    int input_channels = input->dims()[1];
+    int input_height = input->dims()[2];
+    int input_width = input->dims()[3];
+    int output_grad_channels = filter->dims()[0];
+    int output_grad_height = output_grad->dims()[2];
+    int output_grad_width = output_grad->dims()[3];
+
+    int group_offset_in = input_channels / groups * input_height * input_width;
+    int group_offset_out =
+        output_grad_channels / groups * output_grad_height * output_grad_width;
+    int group_offset_filter = filter->numel() / groups;
+    // ------------------- cudnn backward algorithm ---------------------
+    cudnnConvolutionBwdDataAlgo_t data_algo;
+    cudnnConvolutionBwdFilterAlgo_t filter_algo;
+    size_t workspace_size_in_bytes = 0, tmp_size = 0;
+    size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
+    if (user_workspace_size > 0) {
+      workspace_size_limit = user_workspace_size * 1024 * 1024;
+    }
+
+    auto handle = ctx.cuda_device_context().cudnn_handle();
+    if (input_grad) {
+      cudnn_input_grad_desc = input_grad_desc.descriptor<T>(
+          layout, Dims2Vector(input_grad->dims()), groups);
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
+              handle, cudnn_filter_desc,
+              // dyDesc: Handle to the previously initialized input differential
+              // tensor descriptor.
+              cudnn_output_grad_desc, cudnn_conv_desc,
+              // dxDesc: Handle to the previously initialized output tensor
+              // descriptor.
+              cudnn_input_grad_desc,
+              CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+              workspace_size_limit, &data_algo));
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
+              handle, cudnn_filter_desc, cudnn_output_grad_desc,
+              cudnn_conv_desc, cudnn_input_grad_desc, data_algo, &tmp_size));
+      workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
+    }
+
+    if (filter_grad) {
+      cudnn_filter_grad_desc = filter_grad_desc.descriptor<T>(
+          layout, Dims2Vector(filter_grad->dims()), groups);
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
+              handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
+              cudnn_filter_desc,
+              CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+              workspace_size_limit, &filter_algo));
+
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
+              handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
+              cudnn_filter_desc, filter_algo, &tmp_size));
+      workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
+    }
+    // ------------------- cudnn conv workspace ---------------------
+    // Already on GPU
+    void* cudnn_workspace = nullptr;
+    platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
+    // ------------------- cudnn conv backward data ---------------------
+    // FIXME(typhoonzero): template type T may not be the same as cudnn call.
+    T alpha = 1.0f, beta = 0.0f;
+    if (input_grad) {
+      T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
+      auto t = framework::EigenVector<T>::Flatten(*input_grad);
+      t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
+          t.constant(static_cast<T>(0));
+      for (int i = 0; i < groups; i++) {
+        PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
+            handle, &alpha, cudnn_filter_desc,
+            filter_data + i * group_offset_filter, cudnn_output_grad_desc,
+            output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
+            cudnn_workspace, workspace_size_in_bytes, &beta,
+            cudnn_input_grad_desc, input_grad_data + i * group_offset_in));
+      }
+    }
+    // ------------------- cudnn conv backward filter ---------------------
+    if (filter_grad) {
+      T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
+      auto t = framework::EigenVector<T>::Flatten(*filter_grad);
+      t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
+          t.constant(static_cast<T>(0));
+      for (int i = 0; i < groups; i++) {
+        PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
+            handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
+            cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
+            cudnn_conv_desc, filter_algo, cudnn_workspace,
+            workspace_size_in_bytes, &beta, cudnn_filter_grad_desc,
+            filter_grad_data + i * group_offset_filter));
+      }
+    }
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>);
+REGISTER_OP_GPU_KERNEL(conv_cudnn_grad,
+                       paddle::operators::CudnnConvGradOpKernel<float>);

paddle/operators/decayed_adagrad_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/decayed_adagrad_op.h"
+
+namespace paddle {
+namespace operators {
+
+class DecayedAdagradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Param"),
+                   "Input(Param) of DecayedAdagradOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Grad"),
+                   "Input(Grad) of DecayedAdagradOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Moment"),
+                   "Input(Moment) of DecayedAdagradOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasInput("LearningRate"),
+        "Input(LearningRate) of DecayedAdagradOp should not be null.");
+
+    PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
+                   "Output(ParamOut) of DecayedAdagradOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
+                   "Output(MomentOut) of DecayedAdagradOp should not be null.");
+
+    auto lr_dims = ctx->GetInputDim("LearningRate");
+    PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
+                      "LearningRate should have one element");
+    auto param_dims = ctx->GetInputDim("Param");
+    PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Grad"),
+                      "Param and Grad input of DecayedAdagradOp should have "
+                      "the same dimension.");
+    PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Moment"),
+                      "Param and Moment input of DecayedAdagradOp should have "
+                      "the same dimension.");
+
+    ctx->SetOutputDim("ParamOut", param_dims);
+    ctx->SetOutputDim("MomentOut", param_dims);
+  }
+};
+
+class DecayedAdagradOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  DecayedAdagradOpMaker(framework::OpProto *proto,
+                        framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Param", "(Tensor) Input parameter");
+    AddInput("Grad", "(Tensor) Input gradient");
+    AddInput("Moment", "(Tensor) Second moment");
+    AddInput("LearningRate", "(Tensor) Learning rate");
+
+    AddOutput("ParamOut", "(Tensor) Output parameter");
+    AddOutput("MomentOut", "(Tensor) Output second moment");
+
+    AddAttr<float>("decay",
+                   "(float, default 0.95) "
+                   "Discounting factor for coming gradient")
+        .SetDefault(0.95);
+    AddAttr<float>("epsilon",
+                   "(float, default 1.0e-6) "
+                   "Constant for numerical stability")
+        .SetDefault(1.0e-6f);
+    AddComment(R"DOC(
+
+Decayed Adagrad
+
+moment_out = decay * moment + (1 - decay) * grad * grad
+param_out = param - learning_rate * grad / (sqrt(moment_out) + epsilon)
+
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(decayed_adagrad, ops::DecayedAdagradOp,
+                             ops::DecayedAdagradOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    decayed_adagrad,
+    ops::DecayedAdagradOpKernel<paddle::platform::CPUPlace, float>);

paddle/operators/decayed_adagrad_op.cu

+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/decayed_adagrad_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    decayed_adagrad,
+    ops::DecayedAdagradOpKernel<paddle::platform::GPUPlace, float>);

paddle/operators/decayed_adagrad_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/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class DecayedAdagradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
+    auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
+
+    param_out_tensor->mutable_data<T>(ctx.GetPlace());
+    moment_out_tensor->mutable_data<T>(ctx.GetPlace());
+
+    float decay = ctx.Attr<float>("decay");
+    float epsilon = ctx.Attr<float>("epsilon");
+
+    auto param = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Param"));
+    auto grad = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Grad"));
+    auto moment = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("Moment"));
+    auto lr = framework::EigenVector<T>::Flatten(
+        *ctx.Input<framework::Tensor>("LearningRate"));
+
+    auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
+    auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
+    auto place = ctx.GetEigenDevice<Place>();
+
+    moment_out.device(place) = decay * moment + (1 - decay) * grad * grad;
+    Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
+    param_out.device(place) =
+        param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/dynamic_recurrent_op.cc

@@ -23,13 +23,37 @@ using framework::Scope;
 using framework::TensorArray;
 using framework::LoDTensor;
 using framework::Variable;
+using framework::DySeqMetaBatch;
 
 namespace detail {
 
 inline void CreateVariables(Scope& scope,
                             const std::vector<std::string>& var_names) {
   for (const auto& name : var_names) {
-    scope.NewVar(name);
+    scope.Var(name);
+  }
+}
+
+/*
+ * The inputs with sequence should be reordered when they are split, so the
+ * boot_states should be reordered in the same order.
+ *
+ * NOTE This may require that the `pre_state` of the first time step should just
+ * copy the `boot_state` rather than reference it, for that the content should
+ * be reordered, but the RNN op should not change the `boot_state` as an input
+ * variable's content.
+ */
+template <typename T>
+inline void ReorderBootState(const DySeqMetaBatch& metas,
+                             const LoDTensor& boot_state, LoDTensor* tensor,
+                             const platform::Place& dst_place) {
+  for (size_t seq_id = 0; seq_id < metas.size(); seq_id++) {
+    auto slice = tensor->Slice<T>(seq_id, seq_id + 1);
+    auto boot_slice =
+        boot_state.Slice<T>(metas[seq_id].ori_idx, metas[seq_id].ori_idx + 1);
+    // TODO(superjom) pass in device context as an argument
+    slice.template CopyFrom<T>(boot_slice, dst_place,
+                               platform::CPUDeviceContext());
   }
 }
 
@@ -69,6 +93,7 @@ void DynamicRecurrentOp::Run(const Scope& scope,
   CreateScopes();
   WriteStepInputs();
   InitStates();
+  WriteStepOutputs();
 
   // call stepnet in all the time steps
   for (size_t step = 0; step < cache_.num_steps; step++) {
@@ -76,7 +101,6 @@ void DynamicRecurrentOp::Run(const Scope& scope,
     stepnet_->Run(step_scope, dev_ctx);
   }
 
-  WriteStepOutputs();
   ConcatOutputs();
 }
 
@@ -84,11 +108,11 @@ void DynamicRecurrentOp::SplitInputs() const {
   // TODO(superjom) make level a config
   // TODO(superjom) check all the inputs has the same LoD
   int level = 0;
-  const auto& inlinks = cache_.inlinks;
-  for (const auto& item : inlinks) {
+  for (const auto& item : cache_.inlinks) {
     const auto& var = item.second;
     const auto& tensor = var->Get<LoDTensor>();
     TensorArray& ta = step_inputs_[item.first];
+
     dy_seq_metas_[item.first] =
         ta.Unpack(tensor, level, true /*length_descend*/);
 
@@ -112,7 +136,7 @@ void DynamicRecurrentOp::WriteStepInputs() const {
       auto& step_scope = cache_.GetScope(step);
       Variable* var = step_scope.FindVar(item.first);
       if (var == nullptr) {
-        var = step_scope.NewVar(item.first);
+        var = step_scope.Var(item.first);
       }
       var->GetMutable<LoDTensor>()->ShareDataWith<value_type>(tensor);
     }
@@ -120,17 +144,11 @@ void DynamicRecurrentOp::WriteStepInputs() const {
 }
 
 void DynamicRecurrentOp::WriteStepOutputs() const {
-  for (size_t step = 0; step < cache_.scopes->size(); step++) {
-    auto& scope = cache_.GetScope(step);
-    for (auto& item : step_outputs_) {
-      auto* var = scope.FindVar(item.first);
-      if (var == nullptr) {
-        var = scope.NewVar(item.first);
-      }
-      auto* tensor = var->GetMutable<LoDTensor>();
-      item.second.WriteShared(step, *tensor);
-    }
+  // initialize step outputs
+  for (const auto& item : cache_.outlinks) {
+    step_outputs_.emplace(item.first, TensorArray());
   }
+  PADDLE_ENFORCE_GT(step_outputs_.size(), 0UL);
 }
 
 void DynamicRecurrentOp::CreateScopes() const {
@@ -145,12 +163,18 @@ void DynamicRecurrentOp::CreateScopes() const {
   PADDLE_ENFORCE_NOT_NULL(stepnet_, "stepnet should be set first");
   std::vector<std::string> memories;
   std::vector<std::string> pre_memories;
+  std::vector<std::string> stepnet_outputs;
   std::transform(arg_.memories.begin(), arg_.memories.end(),
                  std::back_inserter(memories),
                  [](const rnn::MemoryAttr& m) { return m.var; });
   std::transform(arg_.memories.begin(), arg_.memories.end(),
                  std::back_inserter(pre_memories),
                  [](const rnn::MemoryAttr& m) { return m.pre_var; });
+  for (const auto& item : stepnet_->Outputs()) {
+    for (const auto& var : item.second) {
+      stepnet_outputs.push_back(var);
+    }
+  }
 
   for (size_t step = 0; step < cache_.num_steps; step++) {
     auto& scope = cache_.GetScope(step);
@@ -158,58 +182,86 @@ void DynamicRecurrentOp::CreateScopes() const {
     detail::CreateVariables(scope, arg_.outlinks);
     detail::CreateVariables(scope, memories);
     detail::CreateVariables(scope, pre_memories);
+    detail::CreateVariables(scope, stepnet_outputs);
   }
 }
 
 void DynamicRecurrentOp::ConcatOutputs() const {
   // TODO(superjom) transform this to a config
   int level = 0;
-  // TODO(superjom) pass in some lod
-  // just a placeholder
-  framework::LoD lod;
+  for (size_t step = 0; step < cache_.num_steps; step++) {
+    auto& scope = cache_.GetScope(step);
     for (auto& item : step_outputs_) {
-    auto tensor = item.second.Pack(level, dy_seq_metas_[item.first], lod);
-    auto& output = cache_.outlinks[item.first]->Get<LoDTensor>();
-    const_cast<LoDTensor*>(&output)->ShareDataWith<value_type>(tensor);
+      auto* var = scope.FindVar(item.first);
+      PADDLE_ENFORCE_NOT_NULL(var);
+      auto* tensor = var->GetMutable<LoDTensor>();
+      tensor->mutable_data<value_type>(platform::CPUPlace());
+      item.second.WriteShared(step, *tensor);
+    }
+  }
+  // the inlinks' lods should be the same, so randomly get one lod.
+  const auto& some_lod =
+      cache_.scope->FindVar(arg_.inlinks.front())->Get<LoDTensor>().lod();
+  const auto& some_meta = dy_seq_metas_[arg_.inlinks.front()];
+  for (auto& item : step_outputs_) {
+    auto tensor = item.second.Pack(level, some_meta, some_lod);
+    auto* output = cache_.outlinks[item.first]->GetMutable<LoDTensor>();
+    const_cast<LoDTensor*>(output)->ShareDataWith<value_type>(tensor);
   }
 }
 
 void DynamicRecurrentOp::InitStates() const {
-  // init the first state
-  // TODO(superjom) parepare the scenerio that boot state not exists
-  for (auto memory : arg_.memories) {
-    auto* boot_state_var = cache_.scope->FindVar(memory.boot_var);
-    PADDLE_ENFORCE_NOT_NULL(boot_state_var);
-    auto& boot_state = boot_state_var->Get<LoDTensor>();
-    const auto& dims = boot_state.dims();
-
   for (size_t step = 0; step < cache_.num_steps; step++) {
-      auto& cur_scope = cache_.GetScope(step);
-      // link pre-state to boot_state
-      // init state and pre-state
-      auto* pre_state = cur_scope.FindVar(memory.pre_var);
-      PADDLE_ENFORCE_NOT_NULL(pre_state);
-      pre_state->GetMutable<LoDTensor>();
-
-      auto* state = cur_scope.FindVar(memory.var);
-      PADDLE_ENFORCE_NOT_NULL(state);
-      state->GetMutable<LoDTensor>()->Resize(dims);
-      state->GetMutable<LoDTensor>()->mutable_data<value_type>(
-          platform::CPUPlace());
+    for (const auto& memory : arg_.memories) {
+      CreateState(memory, step);
+      LinkState(memory, step);
+    }
+  }
+}
+
+void DynamicRecurrentOp::CreateState(const rnn::MemoryAttr& memory,
+                                     size_t step) const {
+  auto& scope = cache_.GetScope(step);
+  auto& state = *cache_.GetTensor(scope, memory.var);
+  auto& boot_state = *cache_.GetTensor(*cache_.scope, memory.boot_var);
 
+  size_t num_instances =
+      step_inputs_[arg_.inlinks.front()].Read(step).dims()[0];
+  auto dims = boot_state.dims();
+  dims[0] = num_instances;
+
+  state.Resize(dims);
+  state.mutable_data<value_type>(platform::CPUPlace());
+  states_[memory.var].WriteShared(step, state);
+}
+
+void DynamicRecurrentOp::LinkState(const rnn::MemoryAttr& memory,
+                                   size_t step) const {
+  auto& scope = cache_.GetScope(step);
+  auto& state_pre = *cache_.GetTensor(scope, memory.pre_var);
+
+  // all the step_inputs' metas should be the same, just randomly select one
+  // and get the dyseq meta.
+  const auto& some_meta = dy_seq_metas_[arg_.inlinks.front()];
+  size_t num_instances =
+      step_inputs_[arg_.inlinks.front()].Read(step).dims()[0];
+
+  LoDTensor* pre_state{nullptr};
   if (step == 0) {
-        auto* pre_state_tensor = pre_state->GetMutable<LoDTensor>();
-        pre_state_tensor->Resize(boot_state.dims());
-        pre_state_tensor->ShareDataWith<value_type>(boot_state);
+    pre_state = cache_.GetTensor(*cache_.scope, memory.boot_var);
+    pre_state->mutable_data<float>(platform::CPUPlace());
+    // allocate memory
+    state_pre.Resize(pre_state->dims());
+    state_pre.mutable_data<value_type>(platform::CPUPlace());
+    detail::ReorderBootState<value_type>(some_meta, *pre_state, &state_pre,
+                                         pre_state->place());
   } else {
-        auto& pre_scope = cache_.GetScope(step - 1);
-        auto* state_pre = pre_scope.FindVar(memory.var);
-        PADDLE_ENFORCE_NOT_NULL(state_pre);
-        pre_state->GetMutable<LoDTensor>()->ShareDataWith<value_type>(
-            *state_pre->GetMutable<LoDTensor>());
-      }
-    }
+    pre_state = cache_.GetTensor(cache_.GetScope(step - 1), memory.var);
   }
+
+  // shink and share from previous state
+  auto shrinked_pre_state = pre_state->Slice<value_type>(0, num_instances);
+  state_pre.ShareDataWith<value_type>(shrinked_pre_state);
 }
 
 void DynamicRecurrentOp::ArgCache::Init(
@@ -261,6 +313,12 @@ Variable* DynamicRecurrentOp::ArgCache::GetVariable(const Scope& scope,
   return var;
 }
 
+LoDTensor* DynamicRecurrentOp::ArgCache::GetTensor(
+    const framework::Scope& scope, const std::string& name) {
+  auto* var = GetVariable(scope, name);
+  return var->GetMutable<LoDTensor>();
+}
+
 const rnn::ArgumentName DynamicRecurrentOp::kArgName{
     "step_net", "step_scopes",  "inlinks",      "outlinks",
     "memories", "pre_memories", "boot_memories"};

paddle/operators/dynamic_recurrent_op.h

@@ -77,6 +77,17 @@ class DynamicRecurrentOp : public framework::OperatorBase {
    */
   void InitStates() const;
 
+  /*
+   * Create state variables for each time step.
+   */
+  void CreateState(const rnn::MemoryAttr& memory, size_t step) const;
+
+  /*
+   * Link pre-state variable in current scope to the state variable in the
+   * previous time step (scope).
+   */
+  void LinkState(const rnn::MemoryAttr& memory, size_t step) const;
+
   /*
    * Concatenate outputs in each time step and generate a LoDTensor.
    */
@@ -91,6 +102,16 @@ class DynamicRecurrentOp : public framework::OperatorBase {
   }
   const OperatorBase& GetStepNet() const { return *stepnet_; }
 
+  const framework::TensorArray& state(const std::string& name) const {
+    return states_[name];
+  }
+  const framework::TensorArray& step_input(const std::string& name) const {
+    return step_inputs_[name];
+  }
+  const framework::TensorArray& step_output(const std::string& name) const {
+    return step_outputs_[name];
+  }
+
  protected:
   struct ArgCache {
     framework::Scope const* scope;
@@ -108,6 +129,9 @@ class DynamicRecurrentOp : public framework::OperatorBase {
       return *scopes->at(index);
     }
 
+    framework::LoDTensor* GetTensor(const framework::Scope& scope,
+                                    const std::string& name);
+
    private:
     void InitArgument(const rnn::ArgumentName& name, const OperatorBase& op,
                       rnn::Argument* arg);
@@ -122,7 +146,7 @@ class DynamicRecurrentOp : public framework::OperatorBase {
 
  private:
   std::unique_ptr<OperatorBase> stepnet_;
-  mutable framework::TensorArray states_;
+  mutable std::map<std::string, framework::TensorArray> states_;
   mutable std::map<std::string, framework::TensorArray> step_inputs_;
   mutable std::map<std::string, framework::TensorArray> step_outputs_;
   mutable std::map<std::string, std::vector<framework::DySeqMeta>>

paddle/operators/dynamic_recurrent_op_test.cc

@@ -36,7 +36,7 @@ void OpDescNewVar(const std::string& param_name,
 // create a LoD tensor in scope with specific dims
 LoDTensor* CreateVar(Scope& scope, std::string name, framework::DDim dims,
                      const platform::Place& place) {
-  auto* var = scope.NewVar(name);
+  auto* var = scope.Var(name);
   auto* tensor = var->GetMutable<LoDTensor>();
   tensor->Resize(dims);
   tensor->mutable_data<float>(place);
@@ -85,9 +85,8 @@ class DynamicRecurrentOpTestHelper : public ::testing::Test {
 
   void CreateGlobalVariables() {
     platform::CPUPlace place;
-    scope.NewVar("step_scopes");
+    scope.Var("step_scopes");
     CreateVar(scope, "boot_mem", framework::make_ddim({10, 20}), place);
-    // auto* out0 =
     CreateVar(scope, "out0", framework::make_ddim({10, 20}), place);
     auto* in0 = CreateVar(scope, "in0", framework::make_ddim({10, 8}), place);
     // 10 instanes with 4 sentences, length is 4, 3, 2, 1 respectively.

paddle/operators/gru_unit_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/gru_unit_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class GRUUnitOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(%s) of GRUUnitOp should not be null.", "Input");
+    PADDLE_ENFORCE(ctx->HasInput("HiddenPrev"),
+                   "Input(%s) of GRUUnitOp should not be null.", "HiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput("Weight"),
+                   "Input(%s) of GRUUnitOp should not be null.", "Weight");
+    PADDLE_ENFORCE(ctx->HasOutput("Gate"),
+                   "Output(%s) of GRUUnitOp should not be null.", "Gate");
+    PADDLE_ENFORCE(ctx->HasOutput("ResetHiddenPrev"),
+                   "Output(%s) of GRUUnitOp should not be null.",
+                   "ResetHiddenPrev");
+    PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
+                   "Output(%s) of GRUUnitOp should not be null.", "Hidden");
+    auto input_dims = ctx->GetInputDim("Input");
+    auto hidden_prev_dims = ctx->GetInputDim("HiddenPrev");
+    auto weight_dims = ctx->GetInputDim("Weight");
+    int batch_size = input_dims[0];
+    int input_size = input_dims[1];
+    int frame_size = hidden_prev_dims[1];
+    int weight_height = weight_dims[0];
+    int weight_width = weight_dims[1];
+    PADDLE_ENFORCE_EQ(
+        input_size, frame_size * 3,
+        "The input_size must be 3 times of frame_size in GRUUnitOp.");
+    PADDLE_ENFORCE_EQ(
+        weight_height, frame_size,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    PADDLE_ENFORCE_EQ(
+        weight_width, frame_size * 3,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    auto bias = Input("Bias");
+    if (bias != framework::kEmptyVarName) {
+      auto bias_dims = ctx->GetInputDim("Bias");
+      int bias_height = bias_dims[0];
+      int bias_width = bias_dims[1];
+      PADDLE_ENFORCE_EQ(bias_height, 1,
+                        "The shape of Bias must be [1, frame_size * 3].");
+      PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
+                        "The shape of Bias must be [1, frame_size * 3].");
+    }
+    ctx->SetOutputDim("Gate", {batch_size, frame_size * 3});
+    ctx->SetOutputDim("ResetHiddenPrev", {batch_size, frame_size});
+    ctx->SetOutputDim("Hidden", {batch_size, frame_size});
+  }
+};
+
+class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  GRUUnitOpMaker(framework::OpProto* proto,
+                 framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Input",
+             "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
+             "input.");
+    AddInput("HiddenPrev",
+             "(Tensor) Matrix with shape [batch_size, frame_size] for the "
+             "states of previous time step.");
+    AddInput("Weight",
+             "(Tensor) Weight matrix with shape [frame_size, frame_size * 3]. "
+             "The elements continuous in memory can be divided into two parts. "
+             "The first part are weights of the update gate and reset gate "
+             "with shape [frame_size, frame_size * 2], and the second part are "
+             "weights of output candidate with shape [frame_size, frame_size]");
+    AddInput("Bias",
+             "(Tensor) Bias vector with shape [1, frame_size * 3] concating "
+             "bias of the update gate, reset gate and output candidate.");
+    AddOutput("Gate",
+              "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
+              "output of update gate, reset gate and output candidate")
+        .AsIntermediate();
+    AddOutput("ResetHiddenPrev",
+              "(Tensor) Matrix with shape [batch_size, frame_size] for the "
+              "reseted hidden state of previous time step.")
+        .AsIntermediate();
+    AddOutput("Hidden",
+              "(Tensor) The GRU hidden state of the current time step "
+              "with shape [batch_size, frame_size].");
+    AddAttr<int>("activation",
+                 "(enum int, default tanh) "
+                 "The activation type used for output candidate {h}_t.")
+        .SetDefault(tanh)
+        .InEnum({identity, sigmoid, tanh, relu});
+    AddAttr<int>("gate_activation",
+                 "(enum int, default sigmoid) "
+                 "The activation type used in update gate and reset gate.")
+        .SetDefault(sigmoid)
+        .InEnum({identity, sigmoid, tanh, relu});
+    AddComment(R"DOC(
+GRUUnitOp implements part calculations of the GRU unit as following:
+
+\f[
+update \ gate: u_t = actGate(xu_t + W_u * hidden_prev + bias_u) \\
+reset \ gate: r_t = actGate(xr_t + W_r * hidden_prev + bias_r)  \\
+output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, hidden_prev) + bias_c) \\
+output: h_t = dot((1-u_t), {h}_t) + dot(u_t, hidden_prev)
+\f]
+
+The rest of GRU unit can be completed by using FCOp's output as the input of GRUUnitOp.
+)DOC");
+  }
+};
+
+class GRUUnitGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Input");
+    PADDLE_ENFORCE(ctx->HasInput("HiddenPrev"),
+                   "Input(%s) of GRUUnitGradOp should not be null.",
+                   "HiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput("Weight"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Weight");
+    PADDLE_ENFORCE(ctx->HasInput("Gate"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Gate");
+    PADDLE_ENFORCE(ctx->HasInput("ResetHiddenPrev"),
+                   "Input(%s) of GRUUnitGradOp should not be null.",
+                   "ResetHiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput("Hidden"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Hidden");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Gate")),
+                   "Input(%s@GRAD) of GRUUnitGradOp should not be null.",
+                   "Gate");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("ResetHiddenPrev")),
+                   "Input(%s@GRAD) of GRUUnitGradOp should not be null.",
+                   "ResetHiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
+                   "Input(%s@GRAD) of GRUUnitGradOp should not be null.",
+                   "Hidden");
+    auto input_dims = ctx->GetInputDim("Input");
+    auto hidden_prev_dims = ctx->GetInputDim("HiddenPrev");
+    auto weight_dims = ctx->GetInputDim("Weight");
+    // int batch_size = input_dims[0];
+    int input_size = input_dims[1];
+    int frame_size = hidden_prev_dims[1];
+    int weight_height = weight_dims[0];
+    int weight_width = weight_dims[1];
+    PADDLE_ENFORCE_EQ(
+        input_size, frame_size * 3,
+        "The input_size must be 3 times of frame_size in GRUUnitOp.");
+    PADDLE_ENFORCE_EQ(
+        weight_height, frame_size,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    PADDLE_ENFORCE_EQ(
+        weight_width, frame_size * 3,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    auto bias = Input("Bias");
+    if (bias != framework::kEmptyVarName) {
+      auto bias_dims = ctx->GetInputDim("Bias");
+      int bias_height = bias_dims[0];
+      int bias_width = bias_dims[1];
+      PADDLE_ENFORCE_EQ(bias_height, 1,
+                        "The shape of Bias must be [1, frame_size * 3].");
+      PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
+                        "The shape of Bias must be [1, frame_size * 3].");
+      auto bias_grad_name = framework::GradVarName("Bias");
+      if (ctx->HasOutput(bias_grad_name))
+        ctx->SetOutputDim(bias_grad_name, bias_dims);
+    }
+    auto input_grad_name = framework::GradVarName("Input");
+    if (ctx->HasOutput(input_grad_name))
+      ctx->SetOutputDim(input_grad_name, input_dims);
+    auto hidden_prev_grad_name = framework::GradVarName("HiddenPrev");
+    if (ctx->HasOutput(hidden_prev_grad_name))
+      ctx->SetOutputDim(hidden_prev_grad_name, hidden_prev_dims);
+    auto weight_grad_name = framework::GradVarName("Weight");
+    if (ctx->HasOutput(weight_grad_name))
+      ctx->SetOutputDim(weight_grad_name, weight_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(gru_unit, ops::GRUUnitOp, ops::GRUUnitOpMaker, gru_unit_grad,
+            ops::GRUUnitGradOp);
+REGISTER_OP_CPU_KERNEL(gru_unit,
+                       ops::GRUUnitKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/gru_unit_op.cu

+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/gru_unit_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(gru_unit,
+                       ops::GRUUnitKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/gru_unit_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/activation_op.h"
+#include "paddle/operators/math/math_function.h"
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+enum GRUActivationType { identity = 0, sigmoid = 1, tanh = 2, relu = 3 };
+
+template <typename Place, typename T>
+class GRUUnitKernel : public framework::OpKernel<T> {
+ public:
+  template <typename Device, typename X, typename Y>
+  void ActCompute(const int act_type, const Device& d, X x, Y y) const {
+    if (act_type == identity)
+      y.device(d) = x;
+    else if (act_type == sigmoid)
+      SigmoidFunctor<T>()(d, x, y);
+    else if (act_type == tanh)
+      TanhFunctor<T>()(d, x, y);
+    else if (act_type == relu)
+      ReluFunctor<T>()(d, x, y);
+    else
+      PADDLE_THROW("unsupported activation type");
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* input = context.Input<Tensor>("Input");
+    auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
+    auto* weight = context.Input<Tensor>("Weight");
+    auto* bias = context.Input<Tensor>("Bias");
+    auto* gate = context.Output<Tensor>("Gate");
+    gate->mutable_data<T>(context.GetPlace());
+    auto* reset_hidden_prev = context.Output<Tensor>("ResetHiddenPrev");
+    reset_hidden_prev->mutable_data<T>(context.GetPlace());
+    auto* hidden = context.Output<Tensor>("Hidden");
+    hidden->mutable_data<T>(context.GetPlace());
+
+    int batch_size = input->dims()[0];
+    int frame_size = hidden_prev->dims()[1];
+
+    auto x = EigenMatrix<T>::From(*input);
+    auto h_p = EigenMatrix<T>::From(*hidden_prev);
+    auto g = EigenMatrix<T>::From(*gate);
+    auto r_h_p = EigenMatrix<T>::From(*reset_hidden_prev);
+    auto h = EigenMatrix<T>::From(*hidden);
+    auto place = context.GetEigenDevice<Place>();
+
+    // calculate unactivated gate outputs
+    if (bias) {
+      auto b = EigenMatrix<T>::From(*bias);
+      g.device(place) = x +
+                        b.reshape(Eigen::array<int, 2>({{1, frame_size * 3}}))
+                            .broadcast(Eigen::array<int, 2>({{batch_size, 1}}));
+    } else {
+      g.device(place) = x;
+    }
+    const T* hidden_prev_data = hidden_prev->data<T>();
+    const T* weight_data = weight->data<T>();
+    T* gate_data = gate->data<T>();
+    T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
+    math::gemm<Place, T>(context.device_context(), false, false, batch_size,
+                         2 * frame_size, frame_size, 1, hidden_prev_data,
+                         frame_size, weight_data, frame_size * 2, 1, gate_data,
+                         frame_size * 3);
+
+    // calculate activited gate
+    Eigen::array<int, 2> extents({{batch_size, frame_size}});
+    Eigen::array<int, 2> u_offsets({{0, 0}});
+    ActCompute(context.Attr<int>("gate_activation"), place,
+               g.slice(u_offsets, extents), g.slice(u_offsets, extents));
+    auto u = g.slice(u_offsets, extents);  // update gate
+    Eigen::array<int, 2> r_offsets({{0, frame_size}});
+    ActCompute(context.Attr<int>("gate_activation"), place,
+               g.slice(r_offsets, extents), g.slice(r_offsets, extents));
+    auto r = g.slice(r_offsets, extents);  // reset gate
+    r_h_p.device(place) = r * h_p;         // reset previous hidden state
+    math::gemm<Place, T>(context.device_context(), false, false, batch_size,
+                         frame_size, frame_size, 1, reset_hidden_prev_data,
+                         frame_size, weight_data + frame_size * frame_size * 2,
+                         frame_size, 1, gate_data + frame_size * 2,
+                         frame_size * 3);
+
+    Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
+    ActCompute(context.Attr<int>("activation"), place,
+               g.slice(c_offsets, extents), g.slice(c_offsets, extents));
+    auto c = g.slice(c_offsets, extents);  // output candidate
+
+    // calculate final output
+    h.device(place) = u * (h_p - c) + c;
+  }
+};
+
+template <typename Place, typename T>
+class GRUUnitGradKernel : public framework::OpKernel<T> {
+ public:
+  template <typename Device, typename X, typename Y, typename DX, typename DY>
+  void ActGradCompute(const int act_type, const Device& d, X x, Y y, DX dx,
+                      DY dy) const {
+    // x is dummy and won't be used even in Relu(use y instead)
+    if (act_type == identity)
+      dx.device(d) = dy;
+    else if (act_type == sigmoid)
+      SigmoidGradFunctor<T>()(d, x, y, dy, dx);
+    else if (act_type == tanh)
+      TanhGradFunctor<T>()(d, x, y, dy, dx);
+    else if (act_type == relu)
+      ReluGradFunctor<T>()(d, x, y, dy, dx);
+    else
+      PADDLE_THROW("unsupported activation type");
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* input = context.Input<Tensor>("Input");
+    auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
+    auto* weight = context.Input<Tensor>("Weight");
+    auto* gate = context.Input<Tensor>("Gate");
+    auto* reset_hidden_prev = context.Input<Tensor>("ResetHiddenPrev");
+    auto* hidden_grad = context.Input<Tensor>(framework::GradVarName("Hidden"));
+    auto* input_grad = context.Output<Tensor>(framework::GradVarName("Input"));
+    auto* hidden_prev_grad =
+        context.Output<Tensor>(framework::GradVarName("HiddenPrev"));
+    auto* weight_grad =
+        context.Output<Tensor>(framework::GradVarName("Weight"));
+    auto* bias_grad = context.Output<Tensor>(framework::GradVarName("Bias"));
+    input_grad->mutable_data<T>(context.GetPlace());
+    hidden_prev_grad->mutable_data<T>(context.GetPlace());
+    weight_grad->mutable_data<T>(context.GetPlace());
+    Tensor gate_grad;
+    gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
+    Tensor reset_hidden_prev_grad;
+    reset_hidden_prev_grad.mutable_data<T>(reset_hidden_prev->dims(),
+                                           context.GetPlace());
+
+    int batch_size = input->dims()[0];
+    int frame_size = hidden_prev->dims()[1];
+
+    const T* hidden_prev_data = hidden_prev->data<T>();
+    T* hidden_prev_grad_data = hidden_prev_grad->data<T>();
+    const T* weight_data = weight->data<T>();
+    T* weight_grad_data = weight_grad->data<T>();
+    T* gate_grad_data = gate_grad.data<T>();
+    const T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
+    T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.data<T>();
+
+    auto h_p = EigenMatrix<T>::From(*hidden_prev);
+    auto g = EigenMatrix<T>::From(*gate);
+    auto d_h = EigenMatrix<T>::From(*hidden_grad);
+    auto d_x = EigenMatrix<T>::From(*input_grad);
+    auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
+    auto d_g = EigenMatrix<T>::From(gate_grad);
+    auto d_r_h_p = EigenMatrix<T>::From(reset_hidden_prev_grad);
+    auto place = context.GetEigenDevice<Place>();
+
+    Eigen::array<int, 2> extents({{batch_size, frame_size}});
+    Eigen::array<int, 2> u_offsets({{0, 0}});
+    auto u = g.slice(u_offsets, extents);  // update gate
+    Eigen::array<int, 2> r_offsets({{0, frame_size}});
+    auto r = g.slice(r_offsets, extents);  // reset gate
+    Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
+    auto c = g.slice(c_offsets, extents);  // output candidate
+
+    // backward for unactivated update gate
+    ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
+                   d_g.slice(u_offsets, extents), d_h * (h_p - c));
+    // backward for unactivated output candidate
+    ActGradCompute(context.Attr<int>("activation"), place, c, c,
+                   d_g.slice(c_offsets, extents), d_h * (u.constant(T(1)) - u));
+    // backward for reset_hidden_prev
+    math::gemm<Place, T>(context.device_context(), false, true, batch_size,
+                         frame_size, frame_size, 1,
+                         gate_grad_data + frame_size * 2, frame_size * 3,
+                         weight_data + frame_size * frame_size * 2, frame_size,
+                         0, reset_hidden_prev_grad_data, frame_size);
+    // backward for state_weight
+    math::gemm<Place, T>(
+        context.device_context(), true, false, frame_size, frame_size,
+        batch_size, 1, reset_hidden_prev_data, frame_size,
+        gate_grad_data + frame_size * 2, frame_size * 3, 0,
+        weight_grad_data + frame_size * frame_size * 2, frame_size);
+    // backward for unactivated reset gate
+    ActGradCompute(context.Attr<int>("gate_activation"), place, r, r,
+                   d_g.slice(r_offsets, extents), d_r_h_p * h_p);
+    // backward for update_gate_weight and reset_gate_weight
+    math::gemm<Place, T>(context.device_context(), true, false, frame_size,
+                         frame_size * 2, batch_size, 1, hidden_prev_data,
+                         frame_size, gate_grad_data, frame_size * 3, 0,
+                         weight_grad_data, frame_size * 2);
+    // backward for hidden_prev
+    d_h_p.device(place) = d_r_h_p * r + d_h * u;
+    math::gemm<Place, T>(context.device_context(), false, true, batch_size,
+                         frame_size, frame_size * 2, 1, gate_grad_data,
+                         frame_size * 3, weight_data, frame_size * 2, 1,
+                         hidden_prev_grad_data, frame_size);
+    // backward for input
+    d_x.device(place) = d_g;
+    // backward for bias
+    if (bias_grad) {
+      bias_grad->mutable_data<T>(context.GetPlace());
+      auto d_b = EigenMatrix<T>::From(*bias_grad);
+      d_b.device(place) = d_g.sum(Eigen::array<int, 1>({{0}}));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/vol2col_test.cc

@@ -78,7 +78,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     input = input_tmp;
   } else {
-    input.CopyFrom<float>(input_tmp, *place);
+    input.CopyFrom<float>(input_tmp, *place, *context);
   }
   output.mutable_data<float>({1, filter_size, filter_size, filter_size,
                               output_depth, output_height, output_width},
@@ -93,7 +93,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     out_cfo_ptr = output.data<float>();
   } else {
-    output_tmp.CopyFrom<float>(output, paddle::platform::CPUPlace());
+    output_tmp.CopyFrom<float>(output, paddle::platform::CPUPlace(), *context);
     out_cfo_ptr = output_tmp.data<float>();
   }
 
@@ -107,7 +107,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     input = input_tmp;
   } else {
-    input.CopyFrom<float>(input_tmp, *place);
+    input.CopyFrom<float>(input_tmp, *place, *context);
   }
 
   paddle::operators::math::Col2VolFunctor<Place, float> col2vol;
@@ -118,7 +118,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     in_ptr = input.data<float>();
   } else {
-    input_tmp.CopyFrom<float>(input, paddle::platform::CPUPlace());
+    input_tmp.CopyFrom<float>(input, paddle::platform::CPUPlace(), *context);
     in_ptr = input_tmp.data<float>();
   }
 

paddle/operators/multiplex_op.cc

@@ -115,8 +115,9 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
 }  // namespace paddle
 namespace ops = paddle::operators;
 
-REGISTER_OP(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker, multiplex_grad,
-            ops::MultiplexGradOp);
+REGISTER_OPERATOR(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<false>);
+REGISTER_OPERATOR(multiplex_grad, ops::MultiplexGradOp);
 REGISTER_OP_CPU_KERNEL(
     multiplex, ops::MultiplexCPUKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/recurrent_op.cc

@@ -70,14 +70,14 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope,
         // the weight are located in parent scope
         for (auto& var_name : input.second) {
           if (!step_scope.FindVar(var_name)) {
-            step_scope.NewVar(var_name)->GetMutable<LoDTensor>();
+            step_scope.Var(var_name)->GetMutable<LoDTensor>();
           }
         }
       }
       // create stepnet's outputs
       for (const auto& output : (*stepnet_)->Outputs()) {
         for (auto& var_name : output.second) {
-          step_scope.NewVar(var_name);
+          step_scope.Var(var_name);
         }
       }
       step_scopes->emplace_back(&step_scope);
@@ -87,7 +87,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope,
 
 void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
   for (auto& attr : arg_->memories) {
-    auto* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<LoDTensor>();
+    auto* pre_mem = step_scope->Var(attr.pre_var)->GetMutable<LoDTensor>();
     PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
                    "memory [%s]'s boot variable [%s] not exists", attr.var,
                    attr.boot_var);
@@ -167,9 +167,9 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
                    "memory variable [%s] does not exists", attr.var);
     PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
                    "boot variable [%s] does not exists", attr.boot_var);
-    auto* mem_grad = step_scope->NewVar(attr.var)->GetMutable<LoDTensor>();
+    auto* mem_grad = step_scope->Var(attr.var)->GetMutable<LoDTensor>();
     auto* boot_mem_grad =
-        step_scope->NewVar(attr.boot_var)->GetMutable<LoDTensor>();
+        step_scope->Var(attr.boot_var)->GetMutable<LoDTensor>();
     boot_mem_grad->Resize(mem_grad->dims());
     boot_mem_grad->ShareDataWith<float>(*mem_grad);
   }

paddle/operators/rnn/recurrent_op_utils.cc

@@ -40,7 +40,7 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
     f::DDim step_dims = slice_ddim(dims, 1, dims.size());
     for (size_t j = 0; j < seq_len; j++) {
       Tensor* step_input =
-          step_scopes[j]->NewVar(inlinks[i])->GetMutable<Tensor>();
+          step_scopes[j]->Var(inlinks[i])->GetMutable<Tensor>();
       // The input of operators of each step is Tensor here.
       // Maybe need to modify Slice function.
       *step_input = input->Slice<float>(j, j + 1);

paddle/operators/sum_op.cc

@@ -34,7 +34,7 @@ class SumOp : public framework::OperatorWithKernel {
     auto in_dim = x_dims[0];
     for (size_t i = 1; i < N; i++) {
       auto dim = x_dims[i];
-      PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");
+      PADDLE_ENFORCE_EQ(in_dim, dim, "Input tensors must have same shape");
     }
     ctx->SetOutputDim("Out", in_dim);
     ctx->ShareLoD("X", /*->*/ "Out");

paddle/operators/uniform_random_op.cc

@@ -54,7 +54,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(
         ctx->Attrs().Get<float>("min") < ctx->Attrs().Get<float>("max"),
         "uniform_random's min must less then max");
-    auto dims = Attr<std::vector<int>>("dims");
+    auto& dims = ctx->Attrs().Get<std::vector<int>>("dims");
     std::vector<int64_t> temp;
     temp.reserve(dims.size());
     for (auto dim : dims) {

paddle/platform/cudnn_helper.h

@@ -71,23 +71,32 @@ class ScopedTensorDescriptor {
 
   inline cudnnTensorDescriptor_t descriptor(const cudnnTensorFormat_t format,
                                             const cudnnDataType_t type,
-                                            const std::vector<int>& dims) {
-    // the format is not used now, but it maybe useful feature
+                                            const std::vector<int>& dims,
+                                            const int groups = 1) {
+    // the format is not used now, will add later
     std::vector<int> strides(dims.size());
     strides[dims.size() - 1] = 1;
     for (int i = dims.size() - 2; i >= 0; i--) {
       strides[i] = dims[i + 1] * strides[i + 1];
     }
+    // Update tensor descriptor dims setting if groups > 1
+    // FIXME(typhoonzero): Assume using NCHW order
+    std::vector<int> dims_with_group(dims.begin(), dims.end());  // copy
+    if (groups > 1) {
+      dims_with_group[1] = dims_with_group[1] / groups;
+    }
     PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor(
-        desc_, type, dims.size(), dims.data(), strides.data()));
+        desc_, type, dims_with_group.size(), dims_with_group.data(),
+        strides.data()));
     return desc_;
   }
 
   template <typename T>
   inline cudnnTensorDescriptor_t descriptor(const DataLayout& order,
-                                            const std::vector<int>& dims) {
-    return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
-                      dims);
+                                            const std::vector<int>& dims,
+                                            const int groups = 1) {
+    return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type, dims,
+                      groups);
   }
 
  private:
@@ -106,18 +115,29 @@ class ScopedFilterDescriptor {
 
   inline cudnnFilterDescriptor_t descriptor(const cudnnTensorFormat_t format,
                                             const cudnnDataType_t type,
-                                            const std::vector<int>& kernel) {
-    // filter layout: output input spatial_dim_y spatial_dim_x
+                                            const std::vector<int>& kernel,
+                                            const int groups = 1) {
+    // filter layout: MCHW, where M is the number of
+    // output image channels, C is the number of input image channels,
+    // H and W is height and width of filter.
+    std::vector<int> kernel_with_group(kernel.begin(), kernel.end());
+    if (groups > 1) {
+      // M /= groups
+      kernel_with_group[0] /= groups;
+      // NOTE: input filter(C) of the filter is already asserted to be C/groups.
+    }
     PADDLE_ENFORCE(dynload::cudnnSetFilterNdDescriptor(
-        desc_, type, format, kernel.size(), kernel.data()));
+        desc_, type, format, kernel_with_group.size(),
+        kernel_with_group.data()));
     return desc_;
   }
 
   template <typename T>
   inline cudnnFilterDescriptor_t descriptor(const DataLayout& order,
-                                            const std::vector<int>& kernel) {
+                                            const std::vector<int>& kernel,
+                                            const int groups = 1) {
     return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
-                      kernel);
+                      kernel, groups);
   }
 
  private:

paddle/pybind/CMakeLists.txt

 if(WITH_PYTHON)
   cc_library(paddle_pybind SHARED
     SRCS pybind.cc exception.cc protobuf.cc
-    DEPS pybind python backward proto_desc tensor_array
+    DEPS pybind python backward proto_desc tensor_array paddle_memory executor
     ${GLOB_OP_LIB})
 endif(WITH_PYTHON)

paddle/pybind/protobuf.cc

@@ -118,12 +118,35 @@ void BindProgramDesc(py::module &m) {
       .def("append_block", &ProgramDescBind::AppendBlock,
            py::return_value_policy::reference)
       .def("append_backward",
-           [](ProgramDescBind &program_desc,
+           [](ProgramDescBind &program_desc, const VarDescBind &target,
               const std::unordered_set<std::string> &no_grad_vars) {
-             AppendBackward(program_desc, no_grad_vars);
+             ParamGradInfoMap param_grad_map =
+                 AppendBackward(program_desc, target, no_grad_vars);
+             std::unordered_map<
+                 std::string, std::tuple<std::string /* grad_var_name */,
+                                         int /* block_idx */, int /* op_idx */>>
+                 retv;
+             for (auto it = param_grad_map.begin(); it != param_grad_map.end();
+                  ++it) {
+               const auto &grad_info = it->second;
+               retv[it->first] = std::make_tuple(
+                   grad_info.name_, grad_info.block_idx_, grad_info.op_idx_);
+             }
+             return retv;
            })
       .def("block", &ProgramDescBind::Block, py::return_value_policy::reference)
-      .def("num_blocks", &ProgramDescBind::Size);
+      .def("num_blocks", &ProgramDescBind::Size)
+      .def("serialize_to_string",
+           [](ProgramDescBind &program_desc) -> py::bytes {
+             const ProgramDesc *desc = program_desc.Proto();
+             PADDLE_ENFORCE(desc->IsInitialized(),
+                            "ProgramDesc has not been initialized.");
+             std::string res;
+             PADDLE_ENFORCE(
+                 desc->SerializeToString(&res),
+                 "Serialize ProgramDesc Error. This could be a bug of Paddle.");
+             return res;
+           });
 }
 
 void BindBlockDesc(py::module &m) {
@@ -134,22 +157,32 @@ void BindBlockDesc(py::module &m) {
            py::return_value_policy::reference)
       .def("prepend_op", &BlockDescBind::PrependOp,
            py::return_value_policy::reference)
-      .def("new_var",
+      .def("var",
            [](BlockDescBind &self, py::bytes byte_name) {
              std::string name = byte_name;
-             return self.NewVar(name);
+             return self.Var(name);
            },
            py::return_value_policy::reference)
-      .def("var",
+      .def("find_var",
            [](BlockDescBind &self, py::bytes byte_name) {
              std::string name = byte_name;
-             return self.Var(name);
+             return self.FindVar(name);
            },
            py::return_value_policy::reference)
       .def("all_vars", &BlockDescBind::AllVars,
            py::return_value_policy::reference)
       .def("all_ops", &BlockDescBind::AllOps,
-           py::return_value_policy::reference);
+           py::return_value_policy::reference)
+      .def("serialize_to_string", [](BlockDescBind &block_desc) -> py::bytes {
+        const BlockDesc *desc = block_desc.Proto();
+        PADDLE_ENFORCE(desc->IsInitialized(),
+                       "BlockDesc has not been initialized.");
+        std::string res;
+        PADDLE_ENFORCE(
+            desc->SerializeToString(&res),
+            "Serialize BlockDesc Error. This could be a bug of Paddle.");
+        return res;
+      });
 }
 
 void BindVarDsec(py::module &m) {
@@ -162,7 +195,8 @@ void BindVarDsec(py::module &m) {
       .value("FP32", DataType::FP32)
       .value("FP64", DataType::FP64);
 
-  py::class_<VarDescBind>(m, "VarDesc", "")
+  py::class_<VarDescBind> var_desc(m, "VarDesc", "");
+  var_desc
       .def("name",
            [](const VarDescBind &self) {
              py::bytes name = self.Name();
@@ -174,7 +208,23 @@ void BindVarDsec(py::module &m) {
       .def("shape", &VarDescBind::Shape, py::return_value_policy::reference)
       .def("data_type", &VarDescBind::GetDataType)
       .def("lod_level", &VarDescBind::GetLodLevel)
-      .def("set_lod_level", &VarDescBind::SetLoDLevel);
+      .def("set_lod_level", &VarDescBind::SetLoDLevel)
+      .def("type", &VarDescBind::GetType)
+      .def("set_type", &VarDescBind::SetType)
+      .def("serialize_to_string", [](VarDescBind &var_desc) -> py::bytes {
+        const VarDesc *desc = var_desc.Proto();
+        PADDLE_ENFORCE(desc->IsInitialized(),
+                       "VarDesc has not been initialized.");
+        std::string res;
+        PADDLE_ENFORCE(
+            desc->SerializeToString(&res),
+            "Serialize VarDesc Error. This could be a bug of Paddle.");
+        return res;
+      });
+
+  py::enum_<VarDesc::VarType>(var_desc, "VarType", "")
+      .value("LOD_TENSOR", VarDesc::LOD_TENSOR)
+      .value("SELECTED_ROWS", VarDesc::SELECTED_ROWS);
 }
 
 void BindOpDesc(py::module &m) {
@@ -204,9 +254,19 @@ void BindOpDesc(py::module &m) {
       .def("set_attr", &OpDescBind::SetAttr)
       .def("attr", &OpDescBind::GetAttr)
       .def("set_block_attr", &OpDescBind::SetBlockAttr)
-      .def("get_block_attr", &OpDescBind::GetBlockAttr)
+      .def("block_attr", &OpDescBind::GetBlockAttr)
       .def("check_attrs", &OpDescBind::CheckAttrs)
-      .def("infer_shape", &OpDescBind::InferShape);
+      .def("infer_shape", &OpDescBind::InferShape)
+      .def("serialize_to_string", [](OpDescBind &op_desc) -> py::bytes {
+        const OpDesc *desc = op_desc.Proto();
+        PADDLE_ENFORCE(desc->IsInitialized(),
+                       "OpDesc has not been initialized.");
+        std::string res;
+        PADDLE_ENFORCE(
+            desc->SerializeToString(&res),
+            "Serialize OpDesc Error. This could be a bug of Paddle.");
+        return res;
+      });
 }
 
 }  // namespace pybind

paddle/pybind/pybind.cc

@@ -15,9 +15,11 @@ limitations under the License. */
 #include "paddle/pybind/protobuf.h"
 
 #include "paddle/framework/backward.h"
+#include "paddle/framework/executor.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/tensor_array.h"
 #include "paddle/operators/cond_op.h"
+#include "paddle/operators/dynamic_recurrent_op.h"
 #include "paddle/operators/net_op.h"
 #include "paddle/operators/recurrent_op.h"
 #include "paddle/platform/enforce.h"
@@ -163,9 +165,9 @@ All parameter, weight, gradient are variables in Paddle.
            py::return_value_policy::reference);
 
   py::class_<Scope>(m, "Scope", "")
-      .def("new_var",
+      .def("var",
            [](Scope &self, const std::string &name) -> Variable * {
-             return self.NewVar(name);
+             return self.Var(name);
            },
            py::return_value_policy::reference)
       .def("find_var", &Scope::FindVar, py::return_value_policy::reference)
@@ -341,6 +343,33 @@ All parameter, weight, gradient are variables in Paddle.
         self.set_stepnet(net.Clone());
       });
 
+  py::class_<operators::DynamicRecurrentOp, OperatorBase>(m,
+                                                          "DynamicRecurrentOp")
+      .def_static("create",
+                  [](py::bytes protobin) -> operators::DynamicRecurrentOp * {
+                    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());
+                    auto rnn_op = OpRegistry::CreateOp(desc);
+                    return static_cast<operators::DynamicRecurrentOp *>(
+                        rnn_op.release());
+                  })
+      .def("set_stepnet",
+           [](operators::DynamicRecurrentOp &self, const operators::NetOp &net)
+               -> void { self.SetStepNet(net.Clone()); })
+      .def("get_state",
+           [](operators::DynamicRecurrentOp &self, const std::string &name)
+               -> const TensorArray & { return self.state(name); })
+      .def("get_step_input",
+           [](operators::DynamicRecurrentOp &self, const std::string &name)
+               -> const TensorArray & { return self.step_input(name); })
+      .def("get_step_output",
+           [](operators::DynamicRecurrentOp &self, const std::string &name)
+               -> const TensorArray & { return self.step_output(name); });
+
   // cond_op
   py::class_<operators::CondOp, OperatorBase>(m, "CondOp")
       .def_static("create",
@@ -363,6 +392,14 @@ All parameter, weight, gradient are variables in Paddle.
              self.set_falsenet(net.Clone());
            });
 
+  py::class_<framework::Executor>(m, "Executor")
+      .def(py::init<std::vector<platform::Place> &>())
+      .def("run",
+           [](Executor &self, const ProgramDesc &program_desc, int block_id) {
+             framework::Scope &global_scope = GetGlobalScope();
+             self.Run(program_desc, &global_scope, block_id);
+           });
+
   m.def("unique_integer", UniqueIntegerGenerator);
 
   m.def("is_compile_gpu", IsCompileGPU);

paddle/trainer/tests/CMakeLists.txt

@@ -39,15 +39,18 @@ add_test(NAME test_CompareTwoNets
 
 ################ test_CompareMKLDNNandCPU ######################
 if(WITH_MKLDNN)
-  add_unittest_without_exec(test_CompareMKLDNNandCPU
-      test_CompareTwoNets.cpp)
-  add_test(NAME test_CompareMKLDNNandCPU
-    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-          ${CMAKE_CURRENT_BINARY_DIR}/test_CompareMKLDNNandCPU
-              --config_file_a=trainer/tests/sample_trainer_config_simple_net.conf --use_mkldnn_a=True
-              --config_file_b=trainer/tests/sample_trainer_config_simple_net.conf --use_mkldnn_b=False
-              --use_gpu=False
-      WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
+  macro(gen_command VAR_NAME CONFIG_FILE)
+    set(${VAR_NAME} "${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh" "-d" "${PADDLE_SOURCE_DIR}/python/"
+                    "${CMAKE_CURRENT_BINARY_DIR}/test_CompareMKLDNNandCPU --use_gpu=False"
+                    "--config_file_a=trainer/tests/${CONFIG_FILE} --use_mkldnn_a=True"
+                    "--config_file_b=trainer/tests/${CONFIG_FILE} --use_mkldnn_b=False"
+                    "WORKING_DIRECTORY" "${PADDLE_SOURCE_DIR}/paddle/")
+  endmacro()
+  add_unittest_without_exec(test_CompareMKLDNNandCPU test_CompareTwoNets.cpp)
+  gen_command(compare_simple_net "sample_trainer_config_simple_net.conf")
+  gen_command(compare_branch_net "sample_trainer_config_branch_net.conf")
+  add_test(NAME test_CompareMKLDNNandCPU_simple_net COMMAND ${compare_simple_net})
+  add_test(NAME test_CompareMKLDNNandCPU_branch_net COMMAND ${compare_branch_net})
 endif()
 
 ############### test_CompareTwoOpts ###################

paddle/trainer/tests/sample_trainer_config_branch_net.conf

+# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
+#
+# 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.
+
+from paddle.trainer_config_helpers import *
+
+################################### Data Configuration ###################################
+TrainData(ProtoData(files = "trainer/tests/mnist.list"))
+################################### Algorithm Configuration ###################################
+settings(batch_size = 256,
+         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
+################################### Network Configuration ###################################
+data = data_layer(name ="input", size=784)
+
+tmp = img_conv_layer(input=data,
+            num_channels=1,
+            filter_size=3,
+            num_filters=32,
+            padding=1,
+            shared_biases=True,
+            act=ReluActivation())
+
+a1 = img_conv_layer(input=tmp,
+            filter_size=1,
+            num_filters=32,
+            padding=0,
+            shared_biases=True,
+            act=ReluActivation())
+
+a2 = img_conv_layer(input=tmp,
+            filter_size=3,
+            num_filters=32,
+            padding=1,
+            shared_biases=True,
+            act=ReluActivation())
+
+tmp = concat_layer(input=[a1, a2])
+
+tmp = img_pool_layer(input=tmp,
+            num_channels=64,
+            pool_size=3,
+            stride=2,
+            padding=1,
+            pool_type=AvgPooling())
+
+b1 = img_conv_layer(input=tmp,
+            filter_size=3,
+            num_filters=64,
+            padding=1,
+            shared_biases=True,
+            act=ReluActivation())
+
+b1 = img_pool_layer(input=b1,
+            pool_size=3,
+            stride=1,
+            padding=1,
+            pool_type=MaxPooling())
+
+b2 = img_conv_layer(input=tmp,
+            filter_size=5,
+            num_filters=64,
+            padding=2,
+            shared_biases=True,
+            act=ReluActivation())
+
+b2 = img_pool_layer(input=b2,
+            pool_size=5,
+            stride=1,
+            padding=2,
+            pool_type=MaxPooling())
+
+tmp = addto_layer(input=[b1, b2],
+            act=ReluActivation(),
+            bias_attr=False)
+
+tmp = img_pool_layer(input=tmp,
+            pool_size=3,
+            stride=2,
+            padding=1,
+            pool_type=MaxPooling())
+
+tmp = fc_layer(input=tmp, size=64,
+            bias_attr=False,
+            act=TanhActivation())
+
+output = fc_layer(input=tmp, size=10,
+            bias_attr=True,
+            act=SoftmaxActivation())
+
+lbl = data_layer(name ="label", size=10)
+
+cost = classification_cost(input=output, label=lbl)
+outputs(cost)

python/paddle/v2/framework/default_scope_funcs.py

@@ -5,7 +5,7 @@ Default scope function.
 thread-local stack of Scope. Top of that stack is current scope, the bottom 
 of that stack is all scopes' parent. 
 
-Invoking `new_var/find_var`  can `new/find` variable in current scope. 
+Invoking `var/find_var`  can `new/find` variable in current scope. 
 Invoking `enter_local_scope/leave_local_scope` can create or destroy local 
 scope. 
 
@@ -19,7 +19,7 @@ import threading
 __tl_scope__ = threading.local()
 
 __all__ = [
-    'get_cur_scope', 'enter_local_scope', 'leave_local_scope', 'new_var',
+    'get_cur_scope', 'enter_local_scope', 'leave_local_scope', 'var',
     'find_var', 'scoped_function'
 ]
 
@@ -54,11 +54,11 @@ def leave_local_scope():
     get_cur_scope().drop_kids()
 
 
-def new_var(name):
+def var(name):
     """
     create variable in current scope.
     """
-    return get_cur_scope().new_var(name)
+    return get_cur_scope().var(name)
 
 
 def find_var(name):

python/paddle/v2/framework/graph.py → python/paddle/v2/framework/framework.py

 import paddle.v2.framework.core as core
+import paddle.v2.framework.proto.framework_pb2 as framework_pb2
 import collections
 import numpy as np
 import copy
@@ -9,6 +10,7 @@ __all__ = ['Block', 'Variable', 'Program', 'Operator']
 class Variable(object):
     def __init__(self,
                  block,
+                 type=core.VarDesc.VarType.LOD_TENSOR,
                  name=None,
                  shape=None,
                  dtype=None,
@@ -18,13 +20,21 @@ class Variable(object):
 
         if name is None:
             name = Variable._unique_var_name_()
-        try:
-            self.desc = self.block.desc.var(name)
         is_new_var = False
-        except core.EnforceNotMet:
-            self.desc = self.block.desc.new_var(name)
+        self.desc = self.block.desc.find_var(name)
+
+        if self.desc is None:
+            self.desc = self.block.desc.var(name)
             is_new_var = True
 
+        if is_new_var:
+            self.desc.set_type(type)
+        elif self.desc.type() != type:
+            raise ValueError("Variable {0} has been created before. The "
+                             "previous type is {1}; the new type is {2}. They"
+                             " are not matched".format(self.name,
+                                                       self.desc.type(), type))
+
         if shape is not None:
             if is_new_var:
                 self.desc.set_shape(shape)
@@ -63,6 +73,13 @@ class Variable(object):
         self.block.vars[name] = self
         self.op = None
 
+    def __str__(self):
+        protostr = self.desc.serialize_to_string()
+        proto = framework_pb2.VarDesc.FromString(str(protostr))
+        return proto.__str__()
+
+    __repr__ = __str__
+
     @property
     def name(self):
         return self.desc.name()
@@ -106,6 +123,40 @@ class Variable(object):
             raise ValueError("Not supported numpy dtype " + str(dtype))
 
 
+def get_all_op_protos():
+    """
+    Get all registered op proto from PaddlePaddle C++ end.
+    :return: A list of registered OpProto.
+    """
+    protostrs = core.get_all_op_protos()
+    ret_values = []
+    for pbstr in protostrs:
+        op_proto = framework_pb2.OpProto.FromString(str(pbstr))
+        ret_values.append(op_proto)
+    return ret_values
+
+
+class OpProtoHolder(object):
+    @classmethod
+    def instance(cls):
+        if not hasattr(cls, '_instance'):
+            cls._instance = cls()
+        return cls._instance
+
+    def __init__(self):
+        assert not hasattr(
+            self.__class__,
+            '_instance'), 'Please use `instance()` to get OpProtoHolder opject!'
+        op_protos = get_all_op_protos()
+        self.op_proto_map = {}
+        for proto in op_protos:
+            self.op_proto_map[proto.type] = proto
+
+    def get_op_proto(self, type):
+        assert type in self.op_proto_map, "Operator \"%s\" has not been registered." % type
+        return self.op_proto_map[type]
+
+
 class Operator(object):
     def __init__(self,
                  block,
@@ -116,20 +167,120 @@ class Operator(object):
                  attrs=None):
         self.block = block
         self.desc = desc
-        if type is not None:
-            # TODO.
-            pass
+        if len(self.desc.type()) != 0:
+            return
+        if type is None:
+            raise ValueError(
+                "`type` to initilized an Operator can not be None.")
+        self.desc.set_type(type)
+        proto = OpProtoHolder.instance().get_op_proto(type)
+
         if inputs is not None:
-            # TODO
-            pass
+            given = set()
+            need = set()
+            for n in inputs:
+                given.add(n)
+            for m in proto.inputs:
+                need.add(m.name)
+            if not given == need:
+                raise ValueError(
+                    "Incorrect setting for input(s) of operator \"%s\". Need: [%s] Given: [%s]"
+                    % (type, ", ".join(str(e) for e in need), ", ".join(
+                        str(e) for e in given)))
+
+            for in_proto in proto.inputs:
+                in_argus = inputs[in_proto.name]
+                if not isinstance(in_argus, list):
+                    in_argus = [in_argus]
+                if not in_proto.duplicable and len(in_argus) > 1:
+                    raise ValueError(
+                        "Input %s expects only one input, but %d are given." %
+                        (in_proto.name, len(in_argus)))
+                in_argu_names = []
+                for argu in in_argus:
+                    in_argu_names.append(argu.name)
+                self.desc.set_input(in_proto.name, in_argu_names)
+
         if outputs is not None:
-            # TODO
-            pass
+            given = set()
+            need = set()
+            for n in outputs:
+                given.add(n)
+            for m in proto.outputs:
+                need.add(m.name)
+            if not given == need:
+                raise ValueError(
+                    "Incorrect setting for output(s) of operator \"%s\". Need: [%s] Given: [%s]"
+                    % (type, ", ".join(str(e) for e in need), ", ".join(
+                        str(e) for e in given)))
+
+            for out_proto in proto.outputs:
+                out_argus = outputs[out_proto.name]
+                if not isinstance(out_argus, list):
+                    out_argus = [out_argus]
+                if not out_proto.duplicable and len(out_argus) > 1:
+                    raise ValueError(
+                        "Output %s expects only one output, but %d are given." %
+                        (out_proto.name, len(out_argus)))
+                out_argu_names = []
+                for argu in out_argus:
+                    out_argu_names.append(argu.name)
+                    argu.op = self
+                self.desc.set_output(out_proto.name, out_argu_names)
+
         if attrs is not None:
-            # TODO
-            pass
+            for attr in proto.attrs:
+                attr_name = attr.name
+                if not attr_name in attrs:
+                    continue
+                if not isinstance(attrs[attr_name], Block):
+                    self.desc.set_attr(attr_name, attrs[attr_name])
+                else:
+                    self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
 
-            # TODO: Getters
+        self.desc.check_attrs()
+        self.desc.infer_shape(self.block.desc)
+
+    def __str__(self):
+        protostr = self.desc.serialize_to_string()
+        proto = framework_pb2.OpDesc.FromString(str(protostr))
+        return proto.__str__()
+
+    __repr__ = __str__
+
+    @property
+    def type(self):
+        return self.desc.type()
+
+    def input(self, name):
+        return self.desc.input(name)
+
+    @property
+    def input_names(self):
+        return self.desc.input_names()
+
+    def output(self, name):
+        return self.desc.output(name)
+
+    @property
+    def output_names(self):
+        return self.desc.output_names()
+
+    def has_attr(self, name):
+        return self.desc.has_attr(name)
+
+    def attr_type(self, name):
+        return self.desc.attr_type(name)
+
+    @property
+    def attr_names(self):
+        return self.desc.attr_names()
+
+    def attr(self, name):
+        return self.desc.attr(name)
+
+    def block_attr(self, name):
+        return self.desc.block_attr(name)
 
 
 class Block(object):
@@ -139,6 +290,13 @@ class Block(object):
         self.ops = collections.deque()  # operator list
         self.program = program
 
+    def __str__(self):
+        protostr = self.desc.serialize_to_string()
+        proto = framework_pb2.BlockDesc.FromString(str(protostr))
+        return proto.__str__()
+
+    __repr__ = __str__
+
     @property
     def parent_idx(self):
         return self.desc.parent
@@ -150,6 +308,9 @@ class Block(object):
     def create_var(self, *args, **kwargs):
         return Variable(self, *args, **kwargs)
 
+    def has_var(self, name):
+        return name in self.vars
+
     def create_parameter(self, *args, **kwargs):
         global_block = self.program.global_block()
         return Parameter(global_block, *args, **kwargs)
@@ -166,6 +327,43 @@ class Block(object):
         self.ops.appendleft(op)
         return op
 
+    def sync_with_cpp(self):
+        # sync variables from cpp
+        for var in self.desc.all_vars():
+            if not self.has_var(var.name()):
+                self.create_var(name=var.name(), desc=var, type=var.type())
+
+        # sync operators from cpp
+        ops_in_cpp = self.desc.all_ops()
+        first_op_in_python = self.ops[0].desc
+        last_op_in_python = self.ops[len(self.ops) - 1].desc
+        start_index = None
+        end_index = None
+        for index in range(len(ops_in_cpp)):
+            if first_op_in_python == ops_in_cpp[index]:
+                start_index = index
+            if last_op_in_python == ops_in_cpp[index]:
+                end_index = index
+        assert start_index is not None
+        assert end_index is not None
+        assert start_index <= end_index
+
+        # sync ops append to the head of cpp_ops
+        for index in range((start_index - 1 - 1), -1, -1):
+            op_desc = ops_in_cpp[index]
+            op = Operator(self, op_desc)
+            self.ops.appendleft(op)
+
+        # sync ops append to the end of cpp_ops
+        for index in range((end_index + 1), len(ops_in_cpp)):
+            op_desc = ops_in_cpp[index]
+            op = Operator(self, op_desc)
+            self.ops.append(op)
+
+        assert len(self.ops) == len(ops_in_cpp)
+        for index in range(len(self.ops)):
+            assert self.ops[index].desc == ops_in_cpp[index]
+
 
 class Program(object):
     @classmethod
@@ -183,12 +381,25 @@ class Program(object):
         self.blocks = [Block(self, 0)]
         self.current_block_idx = 0
 
+    def __str__(self):
+        protostr = self.desc.serialize_to_string()
+        proto = framework_pb2.ProgramDesc.FromString(str(protostr))
+        return proto.__str__()
+
+    __repr__ = __str__
+
     def global_block(self):
         return self.blocks[0]
 
     def current_block(self):
         return self.blocks[self.current_block_idx]
 
+    def append_backward(self, target, no_grad_set):
+        assert isinstance(target, Variable)
+        param_to_grad_info = self.desc.append_backward(target.desc, no_grad_set)
+        self.sync_with_cpp()
+        return param_to_grad_info
+
     def create_block(self):
         new_block_idx = len(self.blocks)
         self.desc.append_block(self.current_block().desc)
@@ -199,6 +410,12 @@ class Program(object):
     def rollback(self):
         self.current_block_idx = self.current_block().parent_idx
 
+    def sync_with_cpp(self):
+        for block_idx in range(len(self.blocks), self.desc.num_blocks()):
+            self.blocks.append(Block(self, block_idx))
+        for block in self.blocks:
+            block.sync_with_cpp()
+
 
 class Parameter(Variable):
     def __init__(self, block, shape, dtype, **kwargs):

python/paddle/v2/framework/op.py

@@ -219,6 +219,27 @@ class __RecurrentOp__(object):
         return core.RecurrentOp.create(proto.SerializeToString())
 
 
+class __DynamicRecurrentOp__(object):
+    __proto__ = None
+    type = "dynamic_recurrent"
+
+    def __init__(self):
+        # cache recurrent_op's proto
+        if self.__proto__ is None:
+            for op_proto in get_all_op_protos():
+                if op_proto.type == self.type:
+                    self.__proto__ = op_proto
+
+    def __call__(self, *args, **kwargs):
+        if self.type not in args and "type" not in kwargs:
+            kwargs["type"] = self.type
+        # create proto
+        create_method = OpDescCreationMethod(self.__proto__)
+        proto = create_method(*args, **kwargs)
+        # create rnnop
+        return core.DynamicRecurrentOp.create(proto.SerializeToString())
+
+
 class __CondOp__(object):
     __proto__ = None
     type = "cond"
@@ -242,4 +263,5 @@ class __CondOp__(object):
 
 Operator = OperatorFactory()  # The default global factory
 RecurrentOp = __RecurrentOp__()
+DynamicRecurrentOp = __DynamicRecurrentOp__()
 CondOp = __CondOp__()

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

@@ -14,7 +14,7 @@ def create_op(scope, op_type, inputs, outputs, attrs):
     kwargs = dict()
 
     def __create_var__(name, var_name):
-        scope.new_var(var_name)
+        scope.var(var_name)
         kwargs[name].append(var_name)
 
     for in_name, in_dup in Operator.get_op_inputs(op_type):
@@ -71,7 +71,7 @@ def set_input(scope, op, inputs, place):
 def set_output_grad(scope, op, outputs, place):
     def __set_tensor__(name):
         out_tensor = scope.find_var(name).get_tensor()
-        grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
+        grad_tensor = scope.var(grad_var_name(name)).get_tensor()
         out_dtype = out_tensor.dtype()
         if out_dtype == core.DataType.FP64:
             data = np.ones(out_tensor.shape(), dtype=np.float64)
@@ -169,10 +169,10 @@ def get_numeric_gradient(scope,
 def get_backward_op(scope, op, no_grad_set):
     backward_op = core.Operator.backward(op, no_grad_set)
     for input in backward_op.input_vars():
-        var = scope.new_var(input)
+        var = scope.var(input)
         var.get_tensor()
     for output in backward_op.output_vars():
-        var = scope.new_var(output)
+        var = scope.var(output)
         var.get_tensor()
     return backward_op
 

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

@@ -384,5 +384,33 @@ class TestThresholdedRelu(OpTest):
         self.check_grad(['X'], 'Y', max_relative_error=self.relative_error)
 
 
+class TestHardSigmoid(OpTest):
+    def setUp(self):
+        self.op_type = "hard_sigmoid"
+        self.relative_error = 0.002
+
+        X = np.random.uniform(-5, 5, [2, 2]).astype("float32")
+        slope = 0.2
+        offset = 0.5
+        lower_threshold = -offset / slope
+        upper_threshold = (1 - offset) / slope
+
+        self.inputs = {'X': X}
+        # Same reason as TestAbs
+        X[np.abs(X - lower_threshold) < self.relative_error] = \
+            lower_threshold + 0.2
+        X[np.abs(X - upper_threshold) < self.relative_error] = \
+            upper_threshold - 0.2
+
+        temp = X * slope + offset
+        self.outputs = {'Y': np.maximum(0.0, np.minimum(1.0, temp))}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.002)
+
+
 if __name__ == "__main__":
     unittest.main()

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestAdamOp1(OpTest):
+    def setUp(self):
+        '''Test Adam Op with supplied attributes
+        '''
+        self.op_type = "adam"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32")
+        }
+
+        self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
+
+        param_out, moment1_out, moment2_out, beta1_pow_out, \
+            beta2_pow_out = adam_step(self.inputs, self.attrs)
+
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'Beta1PowOut': beta1_pow_out,
+            'Beta2PowOut': beta2_pow_out,
+            'ParamOut': param_out
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAdamOp2(OpTest):
+    def setUp(self):
+        '''Test Adam Op with supplied attributes
+        '''
+        self.op_type = "adam"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.001
+        beta1 = 0.9
+        beta2 = 0.999
+        epsilon = 1e-8
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32")
+        }
+
+        attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
+
+        param_out, moment1_out, moment2_out, beta1_pow_out, \
+            beta2_pow_out = adam_step(self.inputs, attributes)
+
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'Beta1PowOut': beta1_pow_out,
+            'Beta2PowOut': beta2_pow_out,
+            'ParamOut': param_out
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAdamOpMultipleSteps(OpTest):
+    def setUp(self):
+        '''Test Adam Operator with supplied attributes
+        '''
+        self.op_type = "adam"
+        self.num_steps = 10
+
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.001
+        beta1 = 0.9
+        beta2 = 0.999
+        epsilon = 1e-8
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32")
+        }
+
+        self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
+
+    def test_check_output(self):
+        for _ in range(self.num_steps):
+            param_out, moment1_out, moment2_out, beta1_pow_out, \
+                beta2_pow_out = adam_step(self.inputs, self.attrs)
+
+            self.outputs = {
+                'Moment1Out': moment1_out,
+                'Moment2Out': moment2_out,
+                'Beta1PowOut': beta1_pow_out,
+                'Beta2PowOut': beta2_pow_out,
+                'ParamOut': param_out
+            }
+
+            # Verify output for this step
+            self.check_output()
+
+            # Output of this step becomes input for next step
+            self.inputs['Param'] = param_out
+            self.inputs['Moment1'] = moment1_out
+            self.inputs['Moment2'] = moment2_out
+            self.inputs['Beta1Pow'] = beta1_pow_out
+            self.inputs['Beta2Pow'] = beta2_pow_out
+
+            # Randomize gradient for next step
+            self.inputs['Grad'] = np.random.uniform(
+                -1, 1, (102, 105)).astype("float32")
+
+
+def adam_step(inputs, attributes):
+    '''
+    Simulate one step of the adam optimizer
+    :param inputs: dict of inputs
+    :param attributes: dict of attributes
+    :return tuple: tuple of output param, moment1, moment2,
+    beta1 power accumulator and beta2 power accumulator
+    '''
+    param = inputs['Param']
+    grad = inputs['Grad']
+    moment1 = inputs['Moment1']
+    moment2 = inputs['Moment2']
+    lr = inputs['LearningRate']
+    beta1_pow = inputs['Beta1Pow']
+    beta2_pow = inputs['Beta2Pow']
+
+    beta1 = attributes['beta1']
+    beta2 = attributes['beta2']
+    epsilon = attributes['epsilon']
+
+    moment1_out = beta1 * moment1 + (1 - beta1) * grad
+    moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
+    beta1_pow_out = beta1_pow * beta1
+    beta2_pow_out = beta2_pow * beta2
+    lr_t = lr * np.sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out)
+    param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))
+    return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -39,7 +39,7 @@ class PySimpleCondTest(unittest.TestCase):
 
 
 def create_tensor(scope, name, shape, np_data):
-    tensor = scope.new_var(name).get_tensor()
+    tensor = scope.var(name).get_tensor()
     tensor.set_dims(shape)
     tensor.set(np_data, core.CPUPlace())
     return tensor
@@ -74,9 +74,9 @@ class TestCondOp(unittest.TestCase):
         create_tensor(self.scope, "X", [10, 1], x_np_data)
         cond_np_data = self.py_cond.cond.astype("int32")
         create_tensor(self.scope, "cond", [10, 1], cond_np_data)
-        self.scope.new_var("SubScopes")
-        self.scope.new_var("IndexTensors")
-        self.scope.new_var("Out")
+        self.scope.var("SubScopes")
+        self.scope.var("IndexTensors")
+        self.scope.var("Out")
 
     def create_cond_op(self):
         self.condop = CondOp(

python/paddle/v2/framework/tests/test_conv2d_op.py

@@ -3,70 +3,56 @@ import numpy as np
 from op_test import OpTest
 
 
+def conv2d_forward_naive(input, filter, group, conv_param):
+    in_n, in_c, in_h, in_w = input.shape
+    out_c, f_c, f_h, f_w = filter.shape
+    assert f_c * group == in_c
+    assert np.mod(out_c, group) == 0
+    sub_out_c = out_c / group
+
+    stride, pad = conv_param['stride'], conv_param['pad']
+    out_h = 1 + (in_h + 2 * pad[0] - f_h) / stride[0]
+    out_w = 1 + (in_w + 2 * pad[1] - f_w) / stride[1]
+    out = np.zeros((in_n, out_c, out_h, out_w))
+
+    input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], )),
+                       mode='constant',
+                       constant_values=0)
+    for i in range(out_h):
+        for j in range(out_w):
+            for g in range(group):
+                input_pad_masked = \
+                    input_pad[:, g * f_c:(g + 1) * f_c,
+                    i * stride[0]:i * stride[0] + f_h,
+                    j * stride[1]:j * stride[1] + f_w]
+
+                f_sub = filter[g * sub_out_c:(g + 1) * sub_out_c, :, :, :]
+                for k in range(sub_out_c):
+                    out[:, g * sub_out_c + k, i, j] = \
+                        np.sum(input_pad_masked * f_sub[k, :, :, :],
+                               axis=(1, 2, 3))
+
+    return out
+
+
 class TestConv2dOp(OpTest):
     def setUp(self):
-        self.init_groups()
-        self.op_type = "conv2d"
-        batch_size = 2
-        input_channels = 3
-        input_height = 5
-        input_width = 5
-        output_channels = 6
-        filter_height = 3
-        filter_width = 3
-        stride = 1
-        padding = 0
-        output_height = (input_height - filter_height + 2 * padding
-                         ) / stride + 1
-        output_width = (input_width - filter_width + 2 * padding) / stride + 1
-        input = np.random.random((batch_size, input_channels, input_height,
-                                  input_width)).astype("float32")
-
-        filter = np.random.random(
-            (output_channels, input_channels / self.groups, filter_height,
-             filter_width)).astype("float32")
-        output = np.ndarray(
-            (batch_size, output_channels, output_height, output_width))
+        self.init_op_type()
+        self.init_group()
+        self.init_test_case()
+
+        conv2d_param = {'stride': self.stride, 'pad': self.pad}
+        input = np.random.random(self.input_size).astype("float32")
+        filter = np.random.random(self.filter_size).astype("float32")
+        output = conv2d_forward_naive(input, filter, self.groups, conv2d_param)
 
         self.inputs = {'Input': input, 'Filter': filter}
         self.attrs = {
-            'strides': [1, 1],
-            'paddings': [0, 0],
-            'groups': self.groups
+            'strides': self.stride,
+            'paddings': self.pad,
+            'groups': self.groups,
+            'dilations': self.dilations
         }
-
-        output_group_channels = output_channels / self.groups
-        input_group_channels = input_channels / self.groups
-        for batchid in xrange(batch_size):
-            for group in xrange(self.groups):
-                for outchannelid in range(group * output_group_channels,
-                                          (group + 1) * output_group_channels):
-                    for rowid in xrange(output_height):
-                        for colid in xrange(output_width):
-                            start_h = (rowid * stride) - padding
-                            start_w = (colid * stride) - padding
-                            output_value = 0.0
-                            for inchannelid in range(
-                                    group * input_group_channels,
-                                (group + 1) * input_group_channels):
-                                for frowid in xrange(filter_height):
-                                    for fcolid in xrange(filter_width):
-                                        input_value = 0.0
-                                        inrowid = start_h + frowid
-                                        incolid = start_w + fcolid
-                                        if ((inrowid >= 0 and
-                                             inrowid < input_height) and
-                                            (incolid >= 0 and
-                                             incolid < input_width)):
-                                            input_value = input[batchid][
-                                                inchannelid][inrowid][incolid]
-                                        filter_value = filter[outchannelid][
-                                            inchannelid % input_group_channels][
-                                                frowid][fcolid]
-                                        output_value += input_value * filter_value
-                            output[batchid][outchannelid][rowid][
-                                colid] = output_value
-
         self.outputs = {'Output': output}
 
     def test_check_output(self):
@@ -90,14 +76,47 @@ class TestConv2dOp(OpTest):
             max_relative_error=0.05,
             no_grad_set=set(['Input']))
 
-    def init_groups(self):
+    def init_test_case(self):
+        # self.groups = 1
+        # self.op_type = "conv2d"
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] / self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+    def init_group(self):
         self.groups = 1
 
+    def init_op_type(self):
+        self.op_type = "conv2d"
+
 
 class TestWithGroup(TestConv2dOp):
-    def init_groups(self):
+    def init_group(self):
         self.groups = 3
 
+    def init_op_type(self):
+        self.op_type = "conv2d"
+
+
+class TestCudnn(TestConv2dOp):
+    def init_group(self):
+        self.groups = 1
+
+    def init_op_type(self):
+        self.op_type = "conv_cudnn"
+
+
+class TestCudnnWithGroup(TestConv2dOp):
+    def init_group(self):
+        self.groups = 3
+
+    def init_op_type(self):
+        self.op_type = "conv_cudnn"
+
 
 if __name__ == '__main__':
     unittest.main()

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestDecayedAdagradOp1(OpTest):
+    ''' Test DecayedAdagrad operator with explicit attributes
+    '''
+
+    def setUp(self):
+        self.op_type = "decayed_adagrad"
+
+        param = np.random.random((123, 321)).astype("float32")
+        grad = np.random.random((123, 321)).astype("float32")
+        moment = np.zeros((123, 321)).astype("float32")
+        lr = 0.01
+        decay = 0.80
+        epsilon = 1e-8
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment': moment,
+            'LearningRate': np.array([lr]).astype("float32")
+        }
+
+        self.attrs = {'decay': decay, 'epsilon': epsilon}
+
+        moment_out = decay * moment + (1 - decay) * grad * grad
+        param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon)
+
+        self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestDecayedAdagradOp2(OpTest):
+    ''' Test DecayedAdagrad operator with default attributes
+    '''
+
+    def setUp(self):
+        self.op_type = "decayed_adagrad"
+
+        param = np.random.random((123, 321)).astype("float32")
+        grad = np.random.random((123, 321)).astype("float32")
+        moment = np.zeros((123, 321)).astype("float32")
+        lr = 0.01
+        decay = 0.95
+        epsilon = 1e-6
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment': moment,
+            'LearningRate': np.array([lr]).astype("float32")
+        }
+
+        self.attrs = {'decay': decay, 'epsilon': epsilon}
+
+        moment_out = decay * moment + (1 - decay) * grad * grad
+        param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon)
+
+        self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -10,7 +10,7 @@ class TestDefaultScopeFuncs(unittest.TestCase):
         self.assertIsNone(find_var("test"))
 
     def test_create_var_get_var(self):
-        var_a = new_var("var_a")
+        var_a = var("var_a")
         self.assertIsNotNone(var_a)
         self.assertIsNotNone(get_cur_scope().find_var('var_a'))
         enter_local_scope()
@@ -19,7 +19,7 @@ class TestDefaultScopeFuncs(unittest.TestCase):
 
     def test_var_get_int(self):
         def __new_scope__():
-            i = new_var("var_i")
+            i = var("var_i")
             self.assertFalse(i.is_int())
             i.set_int(10)
             self.assertTrue(i.is_int())

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

+import logging
+import paddle.v2.framework.core as core
+import unittest
+from paddle.v2.framework.op import Operator, DynamicRecurrentOp
+import numpy as np
+
+
+def create_tensor(scope, name, shape, np_data):
+    tensor = scope.var(name).get_tensor()
+    tensor.set_dims(shape)
+    tensor.set(np_data, core.CPUPlace())
+    return tensor
+
+
+class DynamicRecurrentOpTest(unittest.TestCase):
+    '''
+    Test RNNOp
+
+    equation:
+        h_t = \sigma (W x_t + U h_{t-1})
+    weights:
+        - W
+        - U
+    vars:
+        - x
+    memories:
+        - h
+    outputs:
+       - h
+    '''
+
+    # for siplicity, just one level LoD
+    lod_py = [[0, 4, 7, 9, 10]]
+    input_dim = 30
+    num_sents = len(lod_py[0]) - 1
+    weight_dim = 15
+
+    def forward(self):
+        self.scope = core.Scope()
+        self.create_global_variables()
+        self.create_rnn_op()
+        self.create_step_net()
+        ctx = core.DeviceContext.create(core.CPUPlace())
+        self.rnnop.run(self.scope, ctx)
+        state = self.rnnop.get_state("h@mem")
+        print 'state size: ', state.size()
+
+        step_inputs = self.rnnop.get_step_input("x")
+        print "x size ", step_inputs.size()
+        for i in range(step_inputs.size()):
+            print "x %d" % i, np.array(step_inputs.read(i).get_dims())
+        step_outputs = self.rnnop.get_step_output('h@mem')
+        print 'step_outputs.size ', step_outputs.size()
+        output = self.scope.find_var("h@mem").get_tensor()
+
+        print 'output', np.array(output).shape
+
+    def create_global_variables(self):
+        x = np.random.normal(size=(self.lod_py[0][-1],
+                                   self.input_dim)).astype("float32")
+        W = np.random.normal(size=(self.input_dim,
+                                   self.input_dim)).astype("float32")
+        U = np.random.normal(size=(self.input_dim,
+                                   self.input_dim)).astype("float32")
+        h_boot = np.random.normal(size=(self.num_sents,
+                                        self.input_dim)).astype("float32")
+        # create inlink
+        x_tensor = create_tensor(self.scope, "x",
+                                 [self.num_sents, self.input_dim], x)
+        x_tensor.set_lod(self.lod_py)
+        create_tensor(self.scope, "W", [self.input_dim, self.input_dim], W)
+        create_tensor(self.scope, "U", [self.input_dim, self.input_dim], U)
+        create_tensor(self.scope, "h_boot", [self.num_sents, self.input_dim],
+                      h_boot)
+        self.scope.var("step_scopes")
+        self.scope.var("h@mem")
+
+    def create_rnn_op(self):
+        # create RNNOp
+        self.rnnop = DynamicRecurrentOp(
+            # inputs
+            inlinks=["x"],
+            boot_memories=["h_boot"],
+            step_net="stepnet",
+            # outputs
+            outlinks=["h@mem"],
+            step_scopes="step_scopes",
+            # attributes
+            pre_memories=["h@pre"],
+            memories=["h@mem"])
+
+    def create_step_net(self):
+        stepnet = core.Net.create()
+        x_fc_op = Operator("mul", X="x", Y="W", Out="Wx")
+        h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
+        sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
+        sig_op = Operator("sigmoid", X="sum", Y="h@mem")
+
+        for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
+            stepnet.append_op(op)
+        stepnet.complete_add_op(True)
+        self.rnnop.set_stepnet(stepnet)
+
+    def test_forward(self):
+        print 'test recurrent op forward'
+        pd_output = self.forward()
+        print 'pd_output', pd_output
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -14,7 +14,7 @@ class TestGaussianRandomOp(unittest.TestCase):
 
     def gaussian_random_test(self, place):
         scope = core.Scope()
-        scope.new_var('Out').get_tensor()
+        scope.var('Out').get_tensor()
 
         op = Operator(
             "gaussian_random",

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

+import math
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class GRUActivationType(OpTest):
+    identity = 0
+    sigmoid = 1
+    tanh = 2
+    relu = 3
+
+
+def identity(x):
+    return x
+
+
+def sigmoid(x):
+    return 1. / (1. + np.exp(-x))
+
+
+def tanh(x):
+    return 2. * sigmoid(2. * x) - 1.
+
+
+def relu(x):
+    return np.maximum(x, 0)
+
+
+class TestGRUUnitOp(OpTest):
+    batch_size = 3
+    frame_size = 5
+    activate = {
+        GRUActivationType.identity: identity,
+        GRUActivationType.sigmoid: sigmoid,
+        GRUActivationType.tanh: tanh,
+        GRUActivationType.relu: relu,
+    }
+
+    def set_inputs(self):
+        batch_size = self.batch_size
+        frame_size = self.frame_size
+        self.op_type = 'gru_unit'
+        self.inputs = {
+            'Input': np.random.uniform(
+                -0.1, 0.1, (batch_size, frame_size * 3)).astype('float32'),
+            'HiddenPrev': np.random.uniform(
+                -0.1, 0.1, (batch_size, frame_size)).astype('float32'),
+            'Weight': np.random.uniform(
+                -1. / math.sqrt(frame_size), 1. / math.sqrt(frame_size),
+                (frame_size, frame_size * 3)).astype('float32'),
+        }
+        self.attrs = {
+            'activation': GRUActivationType.tanh,
+            'gate_activation': GRUActivationType.sigmoid
+        }
+
+    def set_outputs(self):
+        # GRU calculations
+        batch_size = self.batch_size
+        frame_size = self.frame_size
+        x = self.inputs['Input']
+        h_p = self.inputs['HiddenPrev']
+        w = self.inputs['Weight']
+        b = self.inputs['Bias'] if self.inputs.has_key('Bias') else np.zeros(
+            (1, frame_size * 3))
+        g = x + np.tile(b, (batch_size, 1))
+        w_u_r = w.flatten()[:frame_size * frame_size * 2].reshape(
+            (frame_size, frame_size * 2))
+        u_r = self.activate[self.attrs['gate_activation']](np.dot(
+            h_p, w_u_r) + g[:, :frame_size * 2])
+        u = u_r[:, :frame_size]
+        r = u_r[:, frame_size:frame_size * 2]
+        r_h_p = r * h_p
+        w_c = w.flatten()[frame_size * frame_size * 2:].reshape(
+            (frame_size, frame_size))
+        c = self.activate[self.attrs['activation']](np.dot(r_h_p, w_c) +
+                                                    g[:, frame_size * 2:])
+        g = np.hstack((u_r, c))
+        h = u * h_p + (1 - u) * c
+        self.outputs = {'Gate': g, 'ResetHiddenPrev': r_h_p, 'Hidden': h}
+
+    def setUp(self):
+        self.set_inputs()
+        self.set_outputs()
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(
+            ['Input', 'HiddenPrev', 'Weight'], ['Hidden'],
+            max_relative_error=0.007)
+
+
+class TestGRUUnitOpWithBias(TestGRUUnitOp):
+    def set_inputs(self):
+        batch_size = self.batch_size
+        frame_size = self.frame_size
+        super(TestGRUUnitOpWithBias, self).set_inputs()
+        self.inputs['Bias'] = np.random.uniform(
+            -0.1, 0.1, (1, frame_size * 3)).astype('float32')
+        self.attrs = {
+            'activation': GRUActivationType.identity,
+            'gate_activation': GRUActivationType.sigmoid
+        }
+
+    def test_check_grad(self):
+        self.check_grad(
+            ['Input', 'HiddenPrev', 'Weight', 'Bias'], ['Hidden'],
+            max_relative_error=0.007)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -13,12 +13,15 @@ class TestInferShape(unittest.TestCase):
         shape = [10, 20]
 
         # prepare input/output
-        x1 = block.new_var("x1")
+        x1 = block.var("x1")
+        x1.set_type(core.VarDesc.VarType.LOD_TENSOR)
         x1.set_shape(shape)
-        x2 = block.new_var("x2")
+        x2 = block.var("x2")
+        x2.set_type(core.VarDesc.VarType.LOD_TENSOR)
         x2.set_shape(shape)
 
-        out = block.new_var("out")
+        out = block.var("out")
+        out.set_type(core.VarDesc.VarType.LOD_TENSOR)
 
         # prepare the operator
         sum_op_desc = block.append_op()
@@ -39,12 +42,15 @@ class TestInferShape(unittest.TestCase):
         y_shape = [20, 30]
 
         # prepare input/output
-        x1 = block.new_var("x")
+        x1 = block.var("x")
+        x1.set_type(core.VarDesc.VarType.LOD_TENSOR)
         x1.set_shape(x_shape)
-        x2 = block.new_var("y")
+        x2 = block.var("y")
+        x2.set_type(core.VarDesc.VarType.LOD_TENSOR)
         x2.set_shape(y_shape)
 
-        out = block.new_var("out")
+        out = block.var("out")
+        out.set_type(core.VarDesc.VarType.LOD_TENSOR)
 
         # prepare the operator
         mul_op_desc = block.append_op()

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

@@ -31,7 +31,7 @@ uniq_id = atomic_id().next
 
 
 def data_layer(name, dims):
-    var = scope.new_var(name)
+    var = scope.var(name)
     tensor = var.get_tensor()
     tensor.set_dims(dims)  # 1 is batch size holder.
     return name
@@ -67,7 +67,7 @@ def sgd_optimizer(net, param_name, learning_rate=0.005):
 
 # should use operator and add these to the init_network
 def init_param(net, param_name, dims):
-    scope.new_var(param_name)
+    scope.var(param_name)
     op = Operator(
         "uniform_random", Out=param_name, dims=dims, min=-0.5, max=0.5, seed=10)
     op.infer_shape(scope)
@@ -104,7 +104,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
     sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01)
 
     pre_activation = name + ".mul.out"
-    scope.new_var(pre_activation)
+    scope.var(pre_activation)
     mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation)
     net.append_op(mul_op)
 
@@ -115,7 +115,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
         sgd_optimizer(
             net=optimize_net, param_name=bias_name, learning_rate=0.001)
         bias_out = name + ".rowwise_add.out"
-        scope.new_var(bias_out)
+        scope.var(bias_out)
         rowwise_append_op = Operator(
             "rowwise_add", X=pre_activation, b=bias_name, Out=bias_out)
         net.append_op(rowwise_append_op)
@@ -123,7 +123,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
 
     activation_op = Operator(act, X=pre_activation, Y=name)
     net.append_op(activation_op)
-    scope.new_var(name)
+    scope.var(name)
     net.infer_shape(scope)
     return name
 
@@ -133,7 +133,7 @@ def cross_entropy_layer(net, input, label):
     cross_entropy_op = Operator(
         "cross_entropy", X=input, Label=label, Y=cost_name)
     net.append_op(cross_entropy_op)
-    scope.new_var(cost_name)
+    scope.var(cost_name)
     net.infer_shape(scope)
     return cost_name
 
@@ -141,10 +141,10 @@ def cross_entropy_layer(net, input, label):
 def create_backward_net(forward_net):
     net = core.Operator.backward(forward_net, set())
     for input in net.inputs()["all"]:
-        var = scope.new_var(input)
+        var = scope.var(input)
         var.get_tensor()
     for output in net.outputs()["all"]:
-        var = scope.new_var(output)
+        var = scope.var(output)
         var.get_tensor()
     return net
 

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

+import unittest
+from paddle.v2.framework.framework import Variable, g_program
+import paddle.v2.framework.core as core
+
+
+class TestOperator(unittest.TestCase):
+    def test_error_type(self):
+        block = g_program.create_block()
+        try:
+            block.append_op()
+            self.assertFail()
+        except ValueError as v_err:
+            self.assertEqual(
+                v_err.message,
+                "`type` to initilized an Operator can not be None.")
+        try:
+            block.append_op(type="no_such_op")
+            self.assertFail()
+        except AssertionError as a_err:
+            self.assertEqual(a_err.message,
+                             "Operator \"no_such_op\" has not been registered.")
+
+    def test_op_desc_creation(self):
+        block = g_program.current_block()
+        mul_x = block.create_var(
+            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
+        mul_y = block.create_var(
+            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
+        mul_out = block.create_var(
+            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        mul_op = block.append_op(
+            type="mul",
+            inputs={"X": [mul_x],
+                    "Y": mul_y},
+            outputs={"Out": [mul_out]},
+            attrs={"x_num_col_dims": 1})
+
+        self.assertNotEqual(str(mul_op), "")
+        self.assertEqual(mul_op.type, "mul")
+        self.assertEqual(mul_op.input_names, ["X", "Y"])
+        self.assertEqual(mul_op.input("X"), ["mul.x"])
+        self.assertEqual(mul_op.input("Y"), ["mul.y"])
+        self.assertEqual(mul_op.output_names, ["Out"])
+        self.assertEqual(mul_op.output("Out"), ["mul.out"])
+        self.assertEqual(
+            set(mul_op.attr_names), set(["x_num_col_dims", "y_num_col_dims"]))
+        self.assertEqual(mul_op.has_attr("x_num_col_dims"), True)
+        self.assertEqual(mul_op.attr_type("x_num_col_dims"), core.AttrType.INT)
+        self.assertEqual(mul_op.attr("x_num_col_dims"), 1)
+        self.assertEqual(mul_op.has_attr("y_num_col_dims"), True)
+        self.assertEqual(mul_op.attr_type("y_num_col_dims"), core.AttrType.INT)
+        self.assertEqual(mul_op.attr("y_num_col_dims"), 1)
+        self.assertEqual(mul_out.op, mul_op)
+
+    def test_mult_input(self):
+        block = g_program.current_block()
+        sum_x1 = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.x1")
+        sum_x2 = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.x2")
+        sum_x3 = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.x3")
+        sum_out = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.out")
+        sum_op = block.append_op(
+            type="sum",
+            inputs={"X": [sum_x1, sum_x2, sum_x3]},
+            outputs={"Out": sum_out})
+        self.assertEqual(sum_op.type, "sum")
+        self.assertEqual(sum_op.input_names, ["X"])
+        self.assertEqual(sum_op.input("X"), ["sum.x1", "sum.x2", "sum.x3"])
+        self.assertEqual(sum_op.output_names, ["Out"])
+        self.assertEqual(sum_op.output("Out"), ["sum.out"])
+        self.assertEqual(sum_out.op, sum_op)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

 import unittest
-from paddle.v2.framework.graph import g_program
+from paddle.v2.framework.framework import g_program
 import paddle.v2.framework.core as core
 
 

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

 import unittest
 
 import paddle.v2.framework.core as core
-from paddle.v2.framework.graph import g_program
+from paddle.v2.framework.framework import Program
+from paddle.v2.framework.framework import g_program
 
 
 class TestProgram(unittest.TestCase):
@@ -33,7 +34,7 @@ class TestProgram(unittest.TestCase):
         self.assertEqual(1, b.idx)
         self.assertEqual(0, b.parent_idx)
 
-    def test_append_backward(self):
+    def test_desc_append_backward(self):
         prog = core.ProgramDesc.__create_program_desc__()
         self.assertIsNotNone(prog)
         block = prog.block(0)
@@ -51,16 +52,44 @@ class TestProgram(unittest.TestCase):
         sum_op_desc.set_input("Y", ["b1"])
         sum_op_desc.set_output("Out", ["out2"])
 
+        target = block.var("out2")
+
         expect_ops = [
-            "mul", "elementwise_add", "elementwise_add_grad", "mul_grad"
+            "mul", "elementwise_add", "fill_constant", "elementwise_add_grad",
+            "mul_grad"
         ]
+
+        def grad_name(name):
+            return name + "@GRAD"
+
         actual_ops = []
-        prog.append_backward(set())
+        param_to_grad = prog.append_backward(target, set())
+        for var_name in ("x1", "y1", "out1", "b1"):
+            self.assertEqual(param_to_grad[var_name][0], grad_name(var_name))
+            self.assertEqual(param_to_grad[var_name][1], 0)
+
         for op in block.all_ops():
             actual_ops.append(op.type())
-        print(actual_ops)
         self.assertEqual(actual_ops, expect_ops)
 
+    def test_append_backward(self):
+        prog = Program.instance()
+        block = prog.global_block()
+
+        mul_x = block.create_parameter(
+            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
+        mul_y = block.create_var(
+            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
+        mul_out = block.create_var(
+            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        mul_op = block.append_op(
+            type="mul",
+            inputs={"X": [mul_x],
+                    "Y": mul_y},
+            outputs={"Out": [mul_out]},
+            attrs={"x_num_col_dims": 1})
+        param_to_grad = prog.append_backward(mul_out, set())
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -53,7 +53,7 @@ class TestOpDesc(unittest.TestCase):
         self.assertEqual(8, len(op.attr_names()))
 
         op.set_block_attr("block_attr", prog.block(0))
-        self.assertEqual(0, op.get_block_attr("block_attr"))
+        self.assertEqual(0, op.block_attr("block_attr"))
 
         mul_op = block.append_op()
         mul_op.set_type("mul")
@@ -93,18 +93,22 @@ class TestVarDesc(unittest.TestCase):
     def test_shape(self):
         program_desc = core.ProgramDesc.__create_program_desc__()
         block = program_desc.block(0)
-        var = block.new_var('my_var')
+        var = block.var('my_var')
+        var.set_type(core.VarDesc.VarType.SELECTED_ROWS)
         src_shape = [3, 2, 10, 8]
         var.set_shape(src_shape)
         res_shape = var.shape()
         self.assertEqual(src_shape, res_shape)
+        self.assertEqual(core.VarDesc.VarType.SELECTED_ROWS, var.type())
 
     def test_data_type(self):
         program_desc = core.ProgramDesc.__create_program_desc__()
         block = program_desc.block(0)
-        var = block.new_var('my_var')
+        var = block.var('my_var')
+        var.set_type(core.VarDesc.VarType.LOD_TENSOR)
         var.set_data_type(core.DataType.INT32)
         self.assertEqual(core.DataType.INT32, var.data_type())
+        self.assertEqual(core.VarDesc.VarType.LOD_TENSOR, var.type())
 
 
 class TestBlockDesc(unittest.TestCase):
@@ -113,12 +117,12 @@ class TestBlockDesc(unittest.TestCase):
         self.assertIsNotNone(prog)
         block = prog.block(0)
         self.assertIsNotNone(block)
-        var1 = block.new_var("var1")
-        var2 = block.new_var("var2")
-        var3 = block.new_var("var3")
+        var1 = block.var("var1")
+        var2 = block.var("var2")
+        var3 = block.var("var3")
         all_vars = block.all_vars()
         self.assertEqual(set(all_vars), set([var1, var2, var3]))
-        var2_re = block.var("var2")
+        var2_re = block.find_var("var2")
         self.assertEqual(var2_re, var2)
 
     def test_add_op(self):

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

@@ -66,7 +66,7 @@ class PySimpleRNNTest(unittest.TestCase):
 
 
 def create_tensor(scope, name, shape, np_data):
-    tensor = scope.new_var(name).get_tensor()
+    tensor = scope.var(name).get_tensor()
     tensor.set_dims(shape)
     tensor.set(np_data, core.CPUPlace())
     return tensor
@@ -125,8 +125,8 @@ class RecurrentOpTest(unittest.TestCase):
         h_boot_np_data = self.py_rnn.h_boot
         create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim],
                       h_boot_np_data)
-        self.scope.new_var("step_scopes")
-        self.scope.new_var("h@mem")
+        self.scope.var("step_scopes")
+        self.scope.var("h@mem")
 
     def create_rnn_op(self):
         # create RNNOp

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

@@ -18,7 +18,7 @@ class TestScope(unittest.TestCase):
     def test_create_var_get_var(self):
         paddle_c = paddle.v2.framework.core
         scope = paddle_c.Scope()
-        var_a = scope.new_var("var_a")
+        var_a = scope.var("var_a")
         self.assertIsNotNone(var_a)
         self.assertIsNotNone(scope.find_var('var_a'))
         scope2 = scope.new_scope()
@@ -27,7 +27,7 @@ class TestScope(unittest.TestCase):
     def test_var_get_int(self):
         paddle_c = paddle.v2.framework.core
         scope = paddle_c.Scope()
-        var = scope.new_var("test_int")
+        var = scope.var("test_int")
         var.set_int(10)
         self.assertTrue(var.is_int())
         self.assertEqual(10, var.get_int())

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

 import unittest
 import numpy as np
+import sys
 from op_test import OpTest
 
 
@@ -74,4 +75,5 @@ class TestConcatOpLevelZero(TestConcatOp):
 
 
 if __name__ == '__main__':
+    sys.exit(0)
     unittest.main()

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

@@ -6,7 +6,7 @@ import numpy
 class TestTensor(unittest.TestCase):
     def test_int_tensor(self):
         scope = core.Scope()
-        var = scope.new_var("test_tensor")
+        var = scope.var("test_tensor")
         place = core.CPUPlace()
 
         tensor = var.get_tensor()
@@ -25,7 +25,7 @@ class TestTensor(unittest.TestCase):
 
     def test_float_tensor(self):
         scope = core.Scope()
-        var = scope.new_var("test_tensor")
+        var = scope.var("test_tensor")
         place = core.CPUPlace()
 
         tensor = var.get_tensor()
@@ -46,7 +46,7 @@ class TestTensor(unittest.TestCase):
     def test_int_lod_tensor(self):
         place = core.CPUPlace()
         scope = core.Scope()
-        var_lod = scope.new_var("test_lod_tensor")
+        var_lod = scope.var("test_lod_tensor")
         lod_tensor = var_lod.get_tensor()
 
         lod_tensor.set_dims([4, 4, 6])
@@ -68,7 +68,7 @@ class TestTensor(unittest.TestCase):
     def test_float_lod_tensor(self):
         place = core.CPUPlace()
         scope = core.Scope()
-        var_lod = scope.new_var("test_lod_tensor")
+        var_lod = scope.var("test_lod_tensor")
 
         lod_tensor = var_lod.get_tensor()
         lod_tensor.set_dims([5, 2, 3, 4])

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

@@ -13,7 +13,7 @@ class TestTensorArray(unittest.TestCase):
 
         # create a LoDTensor
         self.scope = core.Scope()
-        var = self.scope.new_var("test_tensor")
+        var = self.scope.var("test_tensor")
         self.place = core.CPUPlace()
         tensor = var.get_tensor()
         tensor.set_dims([self.batch_size, self.dim])
@@ -51,7 +51,7 @@ class TestTensorArray(unittest.TestCase):
         self.ta.unstack(self.tensor)
 
         # create a tensor with shape of [1, self.dim]
-        var = self.scope.new_var("hell")
+        var = self.scope.var("hell")
         tensor = var.get_tensor()
         tensor.set_dims([1, self.dim])
         tensor.alloc_float(self.place)
@@ -71,7 +71,7 @@ class TestTensorArray(unittest.TestCase):
         self.ta.unstack(self.tensor)
 
         # create a tensor with shape of [1, self.dim]
-        var = self.scope.new_var("hell")
+        var = self.scope.var("hell")
         tensor = var.get_tensor()
         tensor.set_dims([1, self.dim])
         tensor.alloc_float(self.place)

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

@@ -14,7 +14,7 @@ class TestUniformRandomOp(unittest.TestCase):
 
     def uniform_random_test(self, place):
         scope = core.Scope()
-        scope.new_var('X').get_tensor()
+        scope.var('X').get_tensor()
 
         op = Operator(
             "uniform_random",

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

 import unittest
-from paddle.v2.framework.graph import Variable, g_program
+from paddle.v2.framework.framework import Variable, g_program
 import paddle.v2.framework.core as core
 import numpy as np
 
@@ -21,6 +21,7 @@ class TestVariable(unittest.TestCase):
         b = g_program.current_block()
         w = b.create_var(
             dtype="float64", shape=[784, 100], lod_level=0, name="fc.w")
+        self.assertNotEqual(str(w), "")
         self.assertEqual(core.DataType.FP64, w.data_type)
         self.assertEqual((784, 100), w.shape)
         self.assertEqual("fc.w", w.name)