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

doc/api/v2/config/layer.rst

@@ -434,9 +434,9 @@ lambda_cost
 ..  autoclass:: paddle.v2.layer.lambda_cost
     :noindex:
 
-mse_cost
+square_error_cost
 --------
-..  autoclass:: paddle.v2.layer.mse_cost
+..  autoclass:: paddle.v2.layer.square_error_cost
     :noindex:
 
 rank_cost

doc/design/graph.md

+# Design Doc: Computations as Graphs
+
+A primary goal of the refactorization of PaddlePaddle is a more flexible representation of deep learning computation, in particular, a graph of operators and variables, instead of sequences of layers as before.
+
+This document explains that the construction of a graph as three steps:
+
+- construct the forward part
+- construct the backward part
+- construct the optimization part
+
+Let us take the problem of image classification as a simple example.  The application program that trains the model looks like:
+
+```python
+x = layer.data("images")
+l = layer.data("label")
+y = layer.fc(x)
+cost = layer.mse(y, l)
+optimize(cost)
+train(cost, reader=mnist.train())
+```
+
+### Forward Part
+
+The first four lines of above program build the forward part of the graph.
+
+![](images/graph_construction_example_forward_only.png)
+
+In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x.  `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b.
+
+In this example, all operators are created as `OpDesc` protobuf messages, and all variables are `VarDesc`.  These protobuf messages are saved in a `BlockDesc` protobuf message.
+
+### Backward Part
+
+The fifth line `optimize(cost)` calls two functions, `ConstructBackwardGraph` and `ConstructOptimizationGraph`.
+
+`ConstructBackwardGraph` traverses the forward graph in the `BlockDesc` protobuf message and builds the backward part.
+
+![](images/graph_construction_example_forward_backward.png)
+
+According to the chain rule of gradient computation, `ConstructBackwardGraph` would
+
+1. create a gradient operator G for each operator F,
+1. make all inputs, outputs, and outputs' gradient of F as inputs of G,
+1. create gradients for all inputs of F, except for those who don't have gradients, like x and l, and
+1. make all these gradients as outputs of G.
+
+### Optimization Part
+
+For each parameter, like W and b created by `layer.fc`, marked as double circles in above graphs, `ConstructOptimizationGraph` creates an optimization operator to apply its gradient.  Here results in the complete graph:
+
+![](images/graph_construction_example_all.png)

doc/design/images/graph_construction_example.bash

+cat ./graph_construction_example.dot | \
+    sed 's/color=red/color=red, style=invis/g' | \
+    sed 's/color=green/color=green, style=invis/g' | \
+    dot -Tpng > graph_construction_example_forward_only.png
+
+cat ./graph_construction_example.dot | \
+    sed 's/color=green/color=green, style=invis/g' | \
+    dot -Tpng > graph_construction_example_forward_backward.png
+
+cat ./graph_construction_example.dot | \
+    dot -Tpng > graph_construction_example_all.png

doc/design/images/graph_construction_example.dot

+digraph ImageClassificationGraph {
+        ///////// The forward part /////////
+        FeedX [label="Feed", color=blue, shape=box];
+        FeedY [label="Feed", color=blue, shape=box];
+        FC [label="FC", color=blue, shape=box];
+        MSE [label="MSE", color=blue, shape=box];
+
+        x [label="x", color=blue, shape=oval];
+        l [label="l", color=blue, shape=oval];
+        y [label="y", color=blue, shape=oval];
+        W [label="W", color=blue, shape=doublecircle];
+        b [label="b", color=blue, shape=doublecircle];
+        cost [label="cost", color=blue, shape=oval];
+
+        FeedX -> x -> FC -> y -> MSE -> cost [color=blue];
+        FeedY -> l [color=blue];
+        W -> FC [color=blue];
+        b -> FC [color=blue];
+        l -> MSE [color=blue];
+
+        ////////// The backward part /////////
+        MSE_Grad [label="MSE_grad", color=red, shape=box];
+        FC_Grad [label="FC_grad", color=red, shape=box];
+
+        d_cost [label="d cost", color=red, shape=oval];
+        d_y [label="d y", color=red, shape=oval];
+        d_b [label="d b", color=red, shape=oval];
+        d_W [label="d W", color=red, shape=oval];
+
+        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];
+
+        x -> FC_Grad [color=red];
+        y -> FC_Grad [color=red];
+        d_y -> FC_Grad [color=red];
+        W -> FC_Grad -> d_W [color=red];
+        b -> FC_Grad -> d_b [color=red];
+
+        ////////// The optimizaiton part //////////
+
+        OPT_W [label="SGD", color=green, shape=box];
+        OPT_b [label="SGD", color=green, shape=box];
+
+        W -> OPT_W [color=green];
+        b -> OPT_b [color=green];
+        d_W -> OPT_W -> W [color=green];
+        d_b -> OPT_b -> b [color=green];
+
+        ////////// Groupings //////////
+
+        subgraph clusterMSE {
+                style=invis;
+                MSE;
+                MSE_Grad;
+        }
+
+        subgraph clusterFC {
+                style=invis;
+                FC;
+                FC_Grad;
+        }
+}

doc/getstarted/basic_usage/index_cn.rst

@@ -55,7 +55,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
     # 线性计算网络层: ȳ = wx + b
     ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
     # 计算误差函数，即  ȳ 和真实 y 之间的距离
-    cost = mse_cost(input= ȳ, label=y)
+    cost = square_error_cost(input= ȳ, label=y)
     outputs(cost)
 
 
@@ -69,7 +69,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
     
     - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。
     - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。
-    - **回归误差代价层**：回归误差代价层 `mse_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
+    - **回归误差代价层**：回归误差代价层 `square_error_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
 
 定义了网络结构并保存为 `trainer_config.py` 之后，运行以下训练命令：
 

doc/getstarted/basic_usage/index_en.rst

@@ -49,7 +49,7 @@ To recover this relationship between ``X`` and ``Y``, we use a neural network wi
         x = data_layer(name='x', size=1)
         y = data_layer(name='y', size=1)
         y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
-        cost = mse_cost(input=y_predict, label=y)
+        cost = square_error_cost(input=y_predict, label=y)
         outputs(cost)
 
 Some of the most fundamental usages of PaddlePaddle are demonstrated:

doc/getstarted/concepts/src/train.py

@@ -8,7 +8,7 @@ paddle.init(use_gpu=False)
 x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(2))
 y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
 y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
-cost = paddle.layer.mse_cost(input=y_predict, label=y)
+cost = paddle.layer.square_error_cost(input=y_predict, label=y)
 
 # create parameters
 parameters = paddle.parameters.create(cost)

doc/getstarted/concepts/use_concepts_cn.rst

@@ -81,9 +81,9 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
 ..	code-block:: bash
 
     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
-    cost = paddle.layer.mse_cost(input=y_predict, label=y)
+    cost = paddle.layer.square_error_cost(input=y_predict, label=y)
 
-其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。
+其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上平方误差层。
 
 最后一层cost中记录了神经网络的所有拓扑结构，通过组合不同的layer，我们即可完成神经网络的搭建。
 
@@ -147,4 +147,4 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
 ..  literalinclude:: src/train.py
     :linenos:
 
-有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。
\ No newline at end of file
+有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。

doc/howto/dev/new_op_cn.md

@@ -178,13 +178,13 @@ class MulKernel : public framework::OpKernel {
 
 ```c++
 namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
 REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(mul_grad,
               ops::MulGradKernel<paddle::platform::CPUPlace, float>);
 ```
     
-  - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，并且注册`ops::MulOpGrad`为其反向Op。
+  - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`，
   - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
   - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
 
@@ -227,6 +227,12 @@ make mul_op
     USE_CPU_ONLY_OP(gather);
     ```
     
+    如果OP不带Kernel，则使用`USE_NO_KENREL_OP`:
+    
+    ```
+    USE_NO_KENREL_OP(recurrent);
+    ```
+    
     使用`USE_OP`告知编译器需要链接该Op的目标文件，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
     
     

doc/howto/usage/k8s/k8s_distributed_cn.md

@@ -213,7 +213,7 @@ I1116 09:10:17.123440    50 Util.cpp:130] Calling runInitFunctions
 I1116 09:10:17.123764    50 Util.cpp:143] Call runInitFunctions done.
 [WARNING 2016-11-16 09:10:17,227 default_decorators.py:40] please use keyword arguments in paddle config.
 [INFO 2016-11-16 09:10:17,239 networks.py:1282] The input order is [movie_id, title, genres, user_id, gender, age, occupation, rating]
-[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__mse_cost_0__]
+[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__square_error_cost_0__]
 I1116 09:10:17.392917    50 Trainer.cpp:170] trainer mode: Normal
 I1116 09:10:17.613910    50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
 I1116 09:10:17.680917    50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process

paddle/framework/backward.cc

@@ -182,7 +182,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
                    });
 
     // process recurrent gradient op as a special operator.
-    if (forwardOp.Type() == "recurrent_op") {
+    if (forwardOp.Type() == "recurrent") {
       // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or
       // this will result in infinite loop.
       const auto& rnnop =

paddle/framework/backward.md

@@ -18,7 +18,7 @@ A backward network is built up with several backward operators. Backward operato
 For example, we have got a `mul_op`, and we can register it's information and corresponding backward operator by the following macro:
 
 ```cpp
-REGISTER_OP(mul, MulOp, MulOpMaker, MulOpGrad);
+REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
 ```
 
 `mul` is the operator's type. `MulOp` and `MulOpMaker` are the operator class and the operator maker class respectively.

paddle/framework/backward_test.cc

@@ -148,14 +148,16 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
 namespace f = paddle::framework;
 namespace ops = paddle::operators;
 using EnforceNotMet = paddle::platform::EnforceNotMet;
-REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, f::NOP);
-REGISTER_OP(mul, f::NOP, f::MulOpMaker, f::NOP);
-REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, f::NOP);
+REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
+            f::NOP);
+REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
+REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
 REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
 REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
-REGISTER_OP(add, f::NOP, f::AddOpMaker, f::NOP);
+REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP);
 REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
-REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, f::NOP);
+REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
+            f::NOP);
 
 TEST(Backward, simple_op_grad) {
   auto fwd = f::OpRegistry::CreateOp(

paddle/framework/grad_op_builder_test.cc

@@ -54,8 +54,8 @@ TEST(GradOpBuilder, AddTwo) {
   EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
 }
 
-REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, f::NOP);
-REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, f::NOP);
+REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
+REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
 
 TEST(GradOpBuilder, MutiInOut) {
   std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(

paddle/framework/op_registry.h

@@ -33,7 +33,8 @@ namespace framework {
 class OpRegistry {
  public:
   template <typename OpType, typename ProtoMakerType, typename GradOpType>
-  static void RegisterOp(const std::string& op_type) {
+  static void RegisterOp(const std::string& op_type,
+                         const std::string& grad_op_type) {
     PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
                    "'%s' is registered more than once.", op_type);
     OpInfo op_info;
@@ -42,9 +43,9 @@ class OpRegistry {
         const VariableNameMap& outputs, const AttributeMap& attrs) {
       return new OpType(type, inputs, outputs, attrs);
     };
+    op_info.grad_op_type_ = grad_op_type;
     if (std::type_index(typeid(ProtoMakerType)) !=
         std::type_index(typeid(NOPMaker))) {
-      op_info.grad_op_type_ = op_type + "_grad";
       op_info.proto_ = new OpProto;
       op_info.checker_ = new OpAttrChecker;
       auto maker = ProtoMakerType(op_info.proto_, op_info.checker_);
@@ -54,14 +55,15 @@ class OpRegistry {
           op_info.proto_->IsInitialized(),
           "Fail to initialize %s's OpProto, because %s is not initialized",
           op_type, op_info.proto_->InitializationErrorString());
-      // register gradient op
-      RegisterOp<GradOpType, NOPMaker, NOP>(op_info.grad_op_type_);
     } else {
-      op_info.grad_op_type_ = "";
       op_info.proto_ = nullptr;
       op_info.checker_ = nullptr;
     }
     OpInfoMap::Instance().Insert(op_type, op_info);
+    // register gradient op
+    if (!grad_op_type.empty()) {
+      RegisterOp<GradOpType, NOPMaker, NOP>(grad_op_type, "");
+    }
   }
 
   static std::unique_ptr<OperatorBase> CreateOp(const std::string& type,
@@ -90,8 +92,10 @@ class Registrar {
 template <typename OpType, typename ProtoMakerType, typename GradOpType>
 class OpRegistrar : public Registrar {
  public:
-  explicit OpRegistrar(const char* op_type) {
-    OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type);
+  explicit OpRegistrar(const char* op_type) { OpRegistrar(op_type, ""); }
+  OpRegistrar(const char* op_type, const char* grad_op_type) {
+    OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type,
+                                                               grad_op_type);
   }
 };
 
@@ -117,7 +121,8 @@ class OpKernelRegistrar : public Registrar {
 /**
  * Macro to register Operator.
  */
-#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_class)         \
+#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type,          \
+                    grad_op_class)                                            \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                             \
       __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
   class _OpClass_##op_type##_ : public op_class {                             \
@@ -132,14 +137,14 @@ class OpKernelRegistrar : public Registrar {
   };                                                                          \
   static ::paddle::framework::OpRegistrar<                                    \
       _OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_>       \
-      __op_registrar_##op_type##__(#op_type);                                 \
+      __op_registrar_##op_type##__(#op_type, #grad_op_type);                  \
   int TouchOpRegistrar_##op_type() {                                          \
     __op_registrar_##op_type##__.Touch();                                     \
     return 0;                                                                 \
   }
 
 #define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
-  REGISTER_OP(op_type, op_class, op_maker_class, ::paddle::framework::NOP)
+  REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP)
 
 /**
  * Macro to register OperatorKernel.
@@ -194,6 +199,8 @@ class OpKernelRegistrar : public Registrar {
   USE_OP_DEVICE_KERNEL(op_type, GPU)
 #endif
 
+#define USE_NO_KERNEL_OP(op_type) USE_OP_ITSELF(op_type);
+
 #define USE_CPU_ONLY_OP(op_type) \
   USE_OP_ITSELF(op_type);        \
   USE_OP_DEVICE_KERNEL(op_type, CPU);

paddle/gserver/evaluators/CTCErrorEvaluator.cpp

@@ -14,18 +14,20 @@ limitations under the License. */
 
 #include "Evaluator.h"
 #include "paddle/gserver/gradientmachines/NeuralNetwork.h"
+#include "paddle/utils/StringUtil.h"
 
 namespace paddle {
 
 /**
  * calculate sequence-to-sequence edit distance
  */
-class CTCErrorEvaluator : public NotGetableEvaluator {
+class CTCErrorEvaluator : public Evaluator {
 private:
   MatrixPtr outActivations_;
   int numTimes_, numClasses_, numSequences_, blank_;
   real deletions_, insertions_, substitutions_;
   int seqClassficationError_;
+  mutable std::unordered_map<std::string, real> evalResults_;
 
   std::vector<int> path2String(const std::vector<int>& path) {
     std::vector<int> str;
@@ -183,6 +185,18 @@ private:
     return stringAlignment(gtStr, recogStr);
   }
 
+  void storeLocalValues() const {
+    evalResults_["error"] = numSequences_ ? totalScore_ / numSequences_ : 0;
+    evalResults_["deletion_error"] =
+        numSequences_ ? deletions_ / numSequences_ : 0;
+    evalResults_["insertion_error"] =
+        numSequences_ ? insertions_ / numSequences_ : 0;
+    evalResults_["substitution_error"] =
+        numSequences_ ? substitutions_ / numSequences_ : 0;
+    evalResults_["sequence_error"] =
+        (real)seqClassficationError_ / numSequences_;
+  }
+
 public:
   CTCErrorEvaluator()
       : numTimes_(0),
@@ -245,16 +259,12 @@ public:
   }
 
   virtual void printStats(std::ostream& os) const {
-    os << config_.name() << "="
-       << (numSequences_ ? totalScore_ / numSequences_ : 0);
-    os << "  deletions error"
-       << "=" << (numSequences_ ? deletions_ / numSequences_ : 0);
-    os << "  insertions error"
-       << "=" << (numSequences_ ? insertions_ / numSequences_ : 0);
-    os << "  substitutions error"
-       << "=" << (numSequences_ ? substitutions_ / numSequences_ : 0);
-    os << "  sequences error"
-       << "=" << (real)seqClassficationError_ / numSequences_;
+    storeLocalValues();
+    os << config_.name() << " error = " << evalResults_["error"];
+    os << " deletions error = " << evalResults_["deletion_error"];
+    os << " insertions error = " << evalResults_["insertion_error"];
+    os << " substitution error = " << evalResults_["substitution_error"];
+    os << " sequence error = " << evalResults_["sequence_error"];
   }
 
   virtual void distributeEval(ParameterClient2* client) {
@@ -272,6 +282,37 @@ public:
     seqClassficationError_ = (int)buf[4];
     numSequences_ = (int)buf[5];
   }
+
+  void getNames(std::vector<std::string>* names) {
+    storeLocalValues();
+    names->reserve(names->size() + evalResults_.size());
+    for (auto it = evalResults_.begin(); it != evalResults_.end(); ++it) {
+      names->push_back(config_.name() + "." + it->first);
+    }
+  }
+
+  real getValue(const std::string& name, Error* err) const {
+    storeLocalValues();
+
+    std::vector<std::string> buffers;
+    paddle::str::split(name, '.', &buffers);
+    auto it = evalResults_.find(buffers[buffers.size() - 1]);
+
+    if (it == evalResults_.end()) {
+      *err = Error("Evaluator does not have the key %s", name.c_str());
+      return 0.0f;
+    }
+
+    return it->second;
+  }
+
+  std::string getType(const std::string& name, Error* err) const {
+    this->getValue(name, err);
+    if (!err->isOK()) {
+      return "";
+    }
+    return "ctc_edit_distance";
+  }
 };
 
 REGISTER_EVALUATOR(ctc_edit_distance, CTCErrorEvaluator);

paddle/gserver/evaluators/ChunkEvaluator.cpp

@@ -268,7 +268,13 @@ public:
   }
 
   // get type of evaluator
-  std::string getTypeImpl() const { return "chunk"; }
+  std::string getType(const std::string& name, Error* err) const {
+    this->getValue(name, err);
+    if (!err->isOK()) {
+      return "";
+    }
+    return "chunk";
+  }
 
 private:
   void storeLocalValues() const {

paddle/gserver/evaluators/Evaluator.h

@@ -211,6 +211,7 @@ public:
     *err = Error("Not implemented");
     return .0f;
   }
+
   std::string getType(const std::string& name, Error* err) const {
     *err = Error("Not implemented");
     return "";
@@ -331,6 +332,7 @@ private:
 protected:
   std::string getTypeImpl() const;
 };
+
 /**
  * @brief precision, recall and f1 score Evaluator
  * \f[
@@ -358,6 +360,12 @@ public:
 
   virtual void distributeEval(ParameterClient2* client);
 
+  void getNames(std::vector<std::string>* names);
+
+  real getValue(const std::string& name, Error* err) const;
+
+  std::string getType(const std::string& name, Error* err) const;
+
   struct StatsInfo {
     /// numbers of true positives
     double TP;
@@ -428,11 +436,6 @@ private:
   mutable std::unordered_map<std::string, real> values_;
 
   void storeLocalValues() const;
-  // Evaluator interface
-public:
-  void getNames(std::vector<std::string>* names);
-  real getValue(const std::string& name, Error* err) const;
-  std::string getType(const std::string& name, Error* err) const;
 };
 
 /*

paddle/gserver/layers/Conv3DLayer.cpp

@@ -42,10 +42,10 @@ bool Conv3DLayer::init(const LayerMap &layerMap,
     if (sharedBiases_) {
       CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
       biases_ =
-          std::unique_ptr<Weight>(new Weight(1, numFilters_, biasParameter_));
+          std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
     } else {
       biases_ =
-          std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
+          std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
     }
   }
   return true;
@@ -224,20 +224,31 @@ void Conv3DLayer::bpropData(int i) {
 }
 
 void Conv3DLayer::bpropBiases() {
+  MatrixPtr biases = Matrix::create(biases_->getWGrad()->getData(),
+                                    1,
+                                    biases_->getWGrad()->getElementCnt(),
+                                    false,
+                                    useGpu_);
   MatrixPtr outGradMat = getOutputGrad();
+
   if (this->sharedBiases_) {
-    biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
+    biases->collectSharedBias(*outGradMat, 1.0f);
   } else {
-    biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
+    biases->collectBias(*outGradMat, 1.0f);
   }
 }
 
 void Conv3DLayer::addBias() {
   MatrixPtr outMat = getOutputValue();
+  MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
+                                  1,
+                                  biases_->getW()->getElementCnt(),
+                                  false,
+                                  useGpu_);
   if (this->sharedBiases_) {
-    outMat->addSharedBias(*(biases_->getW()), 1.0f);
+    outMat->addSharedBias(*(bias), 1.0f);
   } else {
-    outMat->addBias(*(biases_->getW()), 1.0f);
+    outMat->addBias(*(bias), 1.0f);
   }
 }
 

paddle/gserver/layers/DeConv3DLayer.cpp

@@ -42,10 +42,10 @@ bool DeConv3DLayer::init(const LayerMap &layerMap,
     if (sharedBiases_) {
       CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
       biases_ =
-          std::unique_ptr<Weight>(new Weight(1, numFilters_, biasParameter_));
+          std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
     } else {
       biases_ =
-          std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
+          std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
     }
   }
   return true;
@@ -191,21 +191,31 @@ void DeConv3DLayer::bpropWeights(int i) {}
 void DeConv3DLayer::bpropData(int i) {}
 
 void DeConv3DLayer::bpropBiases() {
+  MatrixPtr biases = Matrix::create(biases_->getWGrad()->getData(),
+                                    1,
+                                    biases_->getWGrad()->getElementCnt(),
+                                    false,
+                                    useGpu_);
   const MatrixPtr &outGradMat = getOutputGrad();
 
   if (this->sharedBiases_) {
-    biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
+    biases->collectSharedBias(*outGradMat, 1.0f);
   } else {
-    biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
+    biases->collectBias(*outGradMat, 1.0f);
   }
 }
 
 void DeConv3DLayer::addBias() {
   MatrixPtr outMat = getOutputValue();
+  MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
+                                  1,
+                                  biases_->getW()->getElementCnt(),
+                                  false,
+                                  useGpu_);
   if (this->sharedBiases_) {
-    outMat->addSharedBias(*(biases_->getW()), 1.0f);
+    outMat->addSharedBias(*(bias), 1.0f);
   } else {
-    outMat->addBias(*(biases_->getW()), 1.0f);
+    outMat->addBias(*(bias), 1.0f);
   }
 }
 

paddle/operators/add_op.cc

@@ -57,7 +57,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, ops::AddOpGrad);
+REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
 
 REGISTER_OP_CPU_KERNEL(add_two,
                        ops::AddKernel<paddle::platform::CPUPlace, float>);

paddle/operators/cross_entropy_op.cc

@@ -67,7 +67,8 @@ OnehotCrossEntropy Operator.
 
 namespace ops = paddle::operators;
 REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
-            ops::OnehotCrossEntropyOpMaker, ops::OnehotCrossEntropyGradientOp);
+            ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
+            ops::OnehotCrossEntropyGradientOp);
 REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
                        ops::OnehotCrossEntropyOpKernel<float>);
 REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad,

paddle/operators/gather_op.cc

@@ -63,7 +63,8 @@ Out = X[Index]
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, ops::GatherGradOp);
+REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad,
+            ops::GatherGradOp);
 REGISTER_OP_CPU_KERNEL(gather,
                        ops::GatherOpKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/lookup_table_op.cc

@@ -66,7 +66,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 REGISTER_OP(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
-            ops::LookupTableOpGrad);
+            lookup_table_grad, ops::LookupTableOpGrad);
 
 REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>);
 REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>);

paddle/operators/mean_op.cc

@@ -54,7 +54,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradOp);
+REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp);
 REGISTER_OP_CPU_KERNEL(mean,
                        ops::MeanKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(mean_grad,

paddle/operators/minus_op.cc

@@ -79,8 +79,9 @@ class MinusGradOp : public NetOp {
 }  // namespace paddle
 
 USE_OP(scale);
-USE_OP_ITSELF(identity);
+USE_NO_KERNEL_OP(identity);
 namespace ops = paddle::operators;
-REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradOp<float>);
+REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
+            ops::MinusGradOp<float>);
 REGISTER_OP_CPU_KERNEL(minus,
                        ops::MinusKernel<paddle::platform::CPUPlace, float>);

paddle/operators/mul_op.cc

@@ -84,7 +84,7 @@ class MulOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
 REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(mul_grad,
                        ops::MulGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/recurrent_op.cc

@@ -235,5 +235,5 @@ RecurrentGradientOp::RecurrentGradientOp(
 }  // namespace paddle
 
 REGISTER_OP_WITHOUT_GRADIENT(
-    recurrent_op, paddle::operators::RecurrentOp,
+    recurrent, paddle::operators::RecurrentOp,
     paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);

paddle/operators/rowwise_add_op.cc

@@ -74,7 +74,7 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
-            ops::RowwiseAddGradOp);
+            rowwise_add_grad, ops::RowwiseAddGradOp);
 REGISTER_OP_CPU_KERNEL(
     rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/scale_op.cc

@@ -97,7 +97,7 @@ class IdentityOp : public NetOp {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>,
+REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
             ops::ScaleGradOp<float>);
 REGISTER_OP_CPU_KERNEL(scale,
                        ops::ScaleKernel<paddle::platform::CPUPlace, float>);

paddle/operators/scatter_op.cc

@@ -77,7 +77,8 @@ Out[Index] = Ref[Index] + Updates
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, ops::ScatterGradOp);
+REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, scatter_grad,
+            ops::ScatterGradOp);
 REGISTER_OP_CPU_KERNEL(scatter,
                        ops::ScatterOpKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/sigmoid_op.cc

@@ -53,7 +53,8 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, ops::SigmoidOpGrad);
+REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
+            ops::SigmoidOpGrad);
 REGISTER_OP_CPU_KERNEL(sigmoid,
                        ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/softmax_op.cc

@@ -62,7 +62,8 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, ops::SoftmaxOpGrad);
+REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, softmax_grad,
+            ops::SoftmaxOpGrad);
 REGISTER_OP_CPU_KERNEL(softmax,
                        ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/platform/CMakeLists.txt

@@ -22,3 +22,5 @@ ENDIF()
 cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
     system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS})
 nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)
+
+nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)

paddle/platform/cudnn_helper.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/platform/dynload/cudnn.h"
+#include "paddle/platform/enforce.h"
+#include "paddle/platform/macros.h"
+
+namespace paddle {
+namespace platform {
+
+enum class DataLayout {
+  kNHWC,
+  kNCHW,
+  kNCHW_VECT_C,
+};
+
+enum class PoolingMode {
+  kMaximum,
+  kAverage,
+};
+
+template <typename T>
+class CudnnDataType;
+
+template <>
+class CudnnDataType<float> {
+ public:
+  static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
+};
+
+template <>
+class CudnnDataType<double> {
+ public:
+  static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
+};
+
+inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) {
+  switch (order) {
+    case DataLayout::kNHWC:
+      return CUDNN_TENSOR_NHWC;
+    case DataLayout::kNCHW:
+      return CUDNN_TENSOR_NCHW;
+    default:
+      PADDLE_THROW("Unknown cudnn equivalent for order");
+  }
+  return CUDNN_TENSOR_NCHW;
+}
+
+class ScopedTensorDescriptor {
+ public:
+  ScopedTensorDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnCreateTensorDescriptor(&desc_));
+  }
+  ~ScopedTensorDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnDestroyTensorDescriptor(desc_));
+  }
+
+  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
+    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];
+    }
+    PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor(
+        desc_, type, dims.size(), dims.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);
+  }
+
+ private:
+  cudnnTensorDescriptor_t desc_;
+  DISABLE_COPY_AND_ASSIGN(ScopedTensorDescriptor);
+};
+
+class ScopedFilterDescriptor {
+ public:
+  ScopedFilterDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnCreateFilterDescriptor(&desc_));
+  }
+  ~ScopedFilterDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnDestroyFilterDescriptor(desc_));
+  }
+
+  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
+    PADDLE_ENFORCE(dynload::cudnnSetFilterNdDescriptor(
+        desc_, type, format, kernel.size(), kernel.data()));
+    return desc_;
+  }
+
+  template <typename T>
+  inline cudnnFilterDescriptor_t descriptor(const DataLayout& order,
+                                            const std::vector<int>& kernel) {
+    return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
+                      kernel);
+  }
+
+ private:
+  cudnnFilterDescriptor_t desc_;
+  DISABLE_COPY_AND_ASSIGN(ScopedFilterDescriptor);
+};
+
+class ScopedConvolutionDescriptor {
+ public:
+  ScopedConvolutionDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnCreateConvolutionDescriptor(&desc_));
+  }
+  ~ScopedConvolutionDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnDestroyConvolutionDescriptor(desc_));
+  }
+
+  inline cudnnConvolutionDescriptor_t descriptor(
+      cudnnDataType_t type, const std::vector<int>& pads,
+      const std::vector<int>& strides, const std::vector<int>& dilations) {
+    PADDLE_ENFORCE_EQ(pads.size(), strides.size());
+    PADDLE_ENFORCE_EQ(pads.size(), dilations.size());
+
+#if CUDNN_VERSION < 6000
+    // cudnn v5 does not support dilation conv, the argument is called upscale
+    // instead of dilations and it is must be one.
+    for (size_t i = 0; i < dilations.size(); ++i) {
+      PADDLE_ENFORCE_EQ(
+          dilations[i], 1,
+          "Dilations conv is not supported in this cuDNN version");
+    }
+#endif
+
+    PADDLE_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor(
+        desc_, pads.size(), pads.data(), strides.data(), dilations.data(),
+        CUDNN_CROSS_CORRELATION, type));
+    return desc_;
+  }
+
+  template <typename T>
+  inline cudnnConvolutionDescriptor_t descriptor(
+      const std::vector<int>& pads, const std::vector<int>& strides,
+      const std::vector<int>& dilations) {
+    return descriptor(CudnnDataType<T>::type, pads, strides, dilations);
+  }
+
+ private:
+  cudnnConvolutionDescriptor_t desc_;
+  DISABLE_COPY_AND_ASSIGN(ScopedConvolutionDescriptor);
+};
+
+class ScopedPoolingDescriptor {
+ public:
+  ScopedPoolingDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnCreatePoolingDescriptor(&desc_));
+  }
+  ~ScopedPoolingDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnDestroyPoolingDescriptor(desc_));
+  }
+
+  inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode,
+                                             const std::vector<int>& kernel,
+                                             const std::vector<int>& pads,
+                                             const std::vector<int>& strides) {
+    PADDLE_ENFORCE_EQ(kernel.size(), pads.size());
+    PADDLE_ENFORCE_EQ(kernel.size(), strides.size());
+    PADDLE_ENFORCE(dynload::cudnnSetPoolingNdDescriptor(
+        desc_, (mode == PoolingMode::kMaximum
+                    ? CUDNN_POOLING_MAX
+                    : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING),
+        CUDNN_PROPAGATE_NAN,  // Always propagate nans.
+        kernel.size(), kernel.data(), pads.data(), strides.data()));
+    return desc_;
+  }
+
+ private:
+  cudnnPoolingDescriptor_t desc_;
+  DISABLE_COPY_AND_ASSIGN(ScopedPoolingDescriptor);
+};
+
+}  // namespace platform
+}  // namespace paddle

paddle/platform/cudnn_helper_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/platform/cudnn_helper.h"
+#include <gtest/gtest.h>
+
+TEST(CudnnHelper, ScopedTensorDescriptor) {
+  using paddle::platform::ScopedTensorDescriptor;
+  using paddle::platform::DataLayout;
+
+  ScopedTensorDescriptor tensor_desc;
+  std::vector<int> shape = {2, 4, 6, 6};
+  auto desc = tensor_desc.descriptor<float>(DataLayout::kNCHW, shape);
+
+  cudnnDataType_t type;
+  int nd;
+  std::vector<int> dims(4);
+  std::vector<int> strides(4);
+  paddle::platform::dynload::cudnnGetTensorNdDescriptor(
+      desc, 4, &type, &nd, dims.data(), strides.data());
+
+  EXPECT_EQ(nd, 4);
+  for (size_t i = 0; i < dims.size(); ++i) {
+    EXPECT_EQ(dims[i], shape[i]);
+  }
+  EXPECT_EQ(strides[3], 1);
+  EXPECT_EQ(strides[2], 6);
+  EXPECT_EQ(strides[1], 36);
+  EXPECT_EQ(strides[0], 144);
+}
+
+TEST(CudnnHelper, ScopedFilterDescriptor) {
+  using paddle::platform::ScopedFilterDescriptor;
+  using paddle::platform::DataLayout;
+
+  ScopedFilterDescriptor filter_desc;
+  std::vector<int> shape = {2, 3, 3};
+  auto desc = filter_desc.descriptor<float>(DataLayout::kNCHW, shape);
+
+  cudnnDataType_t type;
+  int nd;
+  cudnnTensorFormat_t format;
+  std::vector<int> kernel(3);
+  paddle::platform::dynload::cudnnGetFilterNdDescriptor(desc, 3, &type, &format,
+                                                        &nd, kernel.data());
+
+  EXPECT_EQ(GetCudnnTensorFormat(DataLayout::kNCHW), format);
+  EXPECT_EQ(nd, 3);
+  for (size_t i = 0; i < shape.size(); ++i) {
+    EXPECT_EQ(kernel[i], shape[i]);
+  }
+}
+
+TEST(CudnnHelper, ScopedConvolutionDescriptor) {
+  using paddle::platform::ScopedConvolutionDescriptor;
+
+  ScopedConvolutionDescriptor conv_desc;
+  std::vector<int> src_pads = {2, 2, 2};
+  std::vector<int> src_strides = {1, 1, 1};
+  std::vector<int> src_dilations = {1, 1, 1};
+  auto desc = conv_desc.descriptor<float>(src_pads, src_strides, src_dilations);
+
+  cudnnDataType_t type;
+  cudnnConvolutionMode_t mode;
+  int nd;
+  std::vector<int> pads(3);
+  std::vector<int> strides(3);
+  std::vector<int> dilations(3);
+  paddle::platform::dynload::cudnnGetConvolutionNdDescriptor(
+      desc, 3, &nd, pads.data(), strides.data(), dilations.data(), &mode,
+      &type);
+
+  EXPECT_EQ(nd, 3);
+  for (size_t i = 0; i < src_pads.size(); ++i) {
+    EXPECT_EQ(pads[i], src_pads[i]);
+    EXPECT_EQ(strides[i], src_strides[i]);
+    EXPECT_EQ(dilations[i], src_dilations[i]);
+  }
+  EXPECT_EQ(mode, CUDNN_CROSS_CORRELATION);
+}
+
+TEST(CudnnHelper, ScopedPoolingDescriptor) {
+  using paddle::platform::ScopedPoolingDescriptor;
+  using paddle::platform::PoolingMode;
+
+  ScopedPoolingDescriptor pool_desc;
+  std::vector<int> src_kernel = {2, 2, 5};
+  std::vector<int> src_pads = {1, 1, 2};
+  std::vector<int> src_strides = {2, 2, 3};
+  auto desc = pool_desc.descriptor(PoolingMode::kMaximum, src_kernel, src_pads,
+                                   src_strides);
+
+  cudnnPoolingMode_t mode;
+  cudnnNanPropagation_t nan_t = CUDNN_PROPAGATE_NAN;
+  int nd;
+  std::vector<int> kernel(3);
+  std::vector<int> pads(3);
+  std::vector<int> strides(3);
+  paddle::platform::dynload::cudnnGetPoolingNdDescriptor(
+      desc, 3, &mode, &nan_t, &nd, kernel.data(), pads.data(), strides.data());
+
+  EXPECT_EQ(nd, 3);
+  for (size_t i = 0; i < src_pads.size(); ++i) {
+    EXPECT_EQ(kernel[i], src_kernel[i]);
+    EXPECT_EQ(pads[i], src_pads[i]);
+    EXPECT_EQ(strides[i], src_strides[i]);
+  }
+  EXPECT_EQ(mode, CUDNN_POOLING_MAX);
+}

paddle/platform/dynload/CMakeLists.txt

 cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags)
-nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc)
+nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc DEPS dynamic_loader)

paddle/platform/dynload/cudnn.h

@@ -62,19 +62,27 @@ extern void* cudnn_dso_handle;
 #define CUDNN_DNN_ROUTINE_EACH(__macro)             \
   __macro(cudnnSetTensor4dDescriptor);              \
   __macro(cudnnSetTensor4dDescriptorEx);            \
+  __macro(cudnnSetTensorNdDescriptor);              \
+  __macro(cudnnGetTensorNdDescriptor);              \
   __macro(cudnnGetConvolutionNdForwardOutputDim);   \
   __macro(cudnnGetConvolutionForwardAlgorithm);     \
   __macro(cudnnCreateTensorDescriptor);             \
   __macro(cudnnDestroyTensorDescriptor);            \
   __macro(cudnnCreateFilterDescriptor);             \
   __macro(cudnnSetFilter4dDescriptor);              \
+  __macro(cudnnSetFilterNdDescriptor);              \
+  __macro(cudnnGetFilterNdDescriptor);              \
   __macro(cudnnSetPooling2dDescriptor);             \
+  __macro(cudnnSetPoolingNdDescriptor);             \
+  __macro(cudnnGetPoolingNdDescriptor);             \
   __macro(cudnnDestroyFilterDescriptor);            \
   __macro(cudnnCreateConvolutionDescriptor);        \
   __macro(cudnnCreatePoolingDescriptor);            \
   __macro(cudnnDestroyPoolingDescriptor);           \
   __macro(cudnnSetConvolution2dDescriptor);         \
   __macro(cudnnDestroyConvolutionDescriptor);       \
+  __macro(cudnnSetConvolutionNdDescriptor);         \
+  __macro(cudnnGetConvolutionNdDescriptor);         \
   __macro(cudnnCreate);                             \
   __macro(cudnnDestroy);                            \
   __macro(cudnnSetStream);                          \

paddle/platform/macros.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
+
+// Disable the copy and assignment operator for a class.
+#ifndef DISABLE_COPY_AND_ASSIGN
+#define DISABLE_COPY_AND_ASSIGN(classname) \
+ private:                                  \
+  classname(const classname&) = delete;    \
+  classname& operator=(const classname&) = delete
+#endif

paddle/pybind/pybind.cc

@@ -39,12 +39,12 @@ USE_OP(sigmoid);
 USE_OP(softmax);
 USE_OP(rowwise_add);
 USE_OP(fill_zeros_like);
-USE_OP_ITSELF(recurrent_op);
+USE_NO_KERNEL_OP(recurrent);
 USE_OP(gaussian_random);
 USE_OP(uniform_random);
 USE_OP(lookup_table);
 USE_OP(scale);
-USE_OP_ITSELF(identity);
+USE_NO_KERNEL_OP(identity);
 USE_OP(minus);
 USE_CPU_ONLY_OP(gather);
 USE_CPU_ONLY_OP(scatter);

python/paddle/trainer_config_helpers/layers.py

@@ -53,7 +53,7 @@ __all__ = [
     'cos_sim',
     'hsigmoid',
     'conv_projection',
-    'mse_cost',
+    'square_error_cost',
     'regression_cost',
     'classification_cost',
     'LayerOutput',
@@ -4240,13 +4240,18 @@ def __cost_input__(input, label, weight=None):
 
 @wrap_name_default()
 @layer_support()
-def mse_cost(input, label, weight=None, name=None, coeff=1.0, layer_attr=None):
+def square_error_cost(input,
+                      label,
+                      weight=None,
+                      name=None,
+                      coeff=1.0,
+                      layer_attr=None):
     """
-    mean squared error cost:
+    sum of square error cost:
 
     ..  math::
 
-        \\frac{1}{N}\sum_{i=1}^N(t_i-y_i)^2
+        cost = \\sum_{i=1}^N(t_i-y_i)^2
 
     :param name: layer name.
     :type name: basestring
@@ -4275,7 +4280,7 @@ def mse_cost(input, label, weight=None, name=None, coeff=1.0, layer_attr=None):
     return LayerOutput(name, LayerType.COST, parents=parents, size=1)
 
 
-regression_cost = mse_cost
+regression_cost = square_error_cost
 
 
 @wrap_name_default("cost")
@@ -5800,9 +5805,9 @@ def huber_regression_cost(input,
                           coeff=1.0,
                           layer_attr=None):
     """
-    In statistics, the Huber loss is a loss function used in robust regression, 
-    that is less sensitive to outliers in data than the squared error loss. 
-    Given a prediction f(x), a label y and :math:`\delta`, the loss function 
+    In statistics, the Huber loss is a loss function used in robust regression,
+    that is less sensitive to outliers in data than the squared error loss.
+    Given a prediction f(x), a label y and :math:`\delta`, the loss function
     is defined as:
 
     .. math:
@@ -5850,13 +5855,13 @@ def huber_classification_cost(input,
                               coeff=1.0,
                               layer_attr=None):
     """
-    For classification purposes, a variant of the Huber loss called modified Huber 
-    is sometimes used. Given a prediction f(x) (a real-valued classifier score) and 
-    a true binary class label :math:`y\in \left \{-1, 1 \right \}`, the modified Huber 
+    For classification purposes, a variant of the Huber loss called modified Huber
+    is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
+    a true binary class label :math:`y\in \left \{-1, 1 \right \}`, the modified Huber
     loss is defined as:
 
     .. math:
-       loss = \max \left ( 0, 1-yf(x) \right )^2, yf(x)\geq 1 
+       loss = \max \left ( 0, 1-yf(x) \right )^2, yf(x)\geq 1
        loss = -4yf(x), \text{otherwise}
 
     The example usage is:

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

@@ -45,7 +45,7 @@ layers {
   coeff: 1.0
 }
 layers {
-  name: "__mse_cost_0__"
+  name: "__square_error_cost_0__"
   type: "square_error"
   size: 1
   active_type: ""
@@ -130,7 +130,7 @@ input_layer_names: "label"
 input_layer_names: "weight"
 input_layer_names: "multi_class_label"
 output_layer_names: "__cost_0__"
-output_layer_names: "__mse_cost_0__"
+output_layer_names: "__square_error_cost_0__"
 output_layer_names: "__nce_layer_0__"
 evaluators {
   name: "classification_error_evaluator"
@@ -146,7 +146,7 @@ sub_models {
   layer_names: "weight"
   layer_names: "__fc_layer_0__"
   layer_names: "__cost_0__"
-  layer_names: "__mse_cost_0__"
+  layer_names: "__square_error_cost_0__"
   layer_names: "multi_class_label"
   layer_names: "__nce_layer_0__"
   input_layer_names: "input"
@@ -154,7 +154,7 @@ sub_models {
   input_layer_names: "weight"
   input_layer_names: "multi_class_label"
   output_layer_names: "__cost_0__"
-  output_layer_names: "__mse_cost_0__"
+  output_layer_names: "__square_error_cost_0__"
   output_layer_names: "__nce_layer_0__"
   evaluator_names: "classification_error_evaluator"
   is_recurrent_layer_group: false

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

@@ -10,7 +10,7 @@ fc = fc_layer(input=data, size=10, act=SoftmaxActivation())
 outputs(
     classification_cost(
         input=fc, label=lbl, weight=wt),
-    mse_cost(
+    square_error_cost(
         input=fc, label=lbl, weight=wt),
     nce_layer(
         input=fc,

python/paddle/v2/framework/op.py

@@ -179,7 +179,7 @@ class OperatorFactory(object):
 
 class __RecurrentOp__(object):
     __proto__ = None
-    type = 'recurrent_op'
+    type = 'recurrent'
 
     def __init__(self):
         # cache recurrent_op's proto

python/paddle/v2/tests/test_layer.py

@@ -134,8 +134,9 @@ class CostLayerTest(unittest.TestCase):
         cost3 = layer.cross_entropy_cost(input=inference, label=label)
         cost4 = layer.cross_entropy_with_selfnorm_cost(
             input=inference, label=label)
-        cost5 = layer.mse_cost(input=inference, label=label)
-        cost6 = layer.mse_cost(input=inference, label=label, weight=weight)
+        cost5 = layer.square_error_cost(input=inference, label=label)
+        cost6 = layer.square_error_cost(
+            input=inference, label=label, weight=weight)
         cost7 = layer.multi_binary_label_cross_entropy_cost(
             input=inference, label=label)
         cost8 = layer.rank_cost(left=score, right=score, label=score)