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

doc/design/graph.md

-# Design Doc: Computations as Graphs
+# Design Doc: Computations as a Graph
 
 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.
 
@@ -8,6 +8,8 @@ This document explains that the construction of a graph as three steps:
 - construct the backward part
 - construct the optimization part
 
+## The Construction of a Graph
+
 Let us take the problem of image classification as a simple example.  The application program that trains the model looks like:
 
 ```python
@@ -25,7 +27,9 @@ 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 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, and the initialization operators.
+
+Initialization operators are kind of "run-once" operators -- the `Run` method increments a class data member counter so to run at most once.  By doing so, a parameter wouldn't be initialized repeatedly, say, in every minibatch.
 
 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.
 
@@ -49,3 +53,18 @@ According to the chain rule of gradient computation, `ConstructBackwardGraph` wo
 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)
+
+## Block and Graph
+
+The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
+
+A Block keeps operators in an array `BlockDesc::ops`
+
+```protobuf
+message BlockDesc {
+  repeated OpDesc ops = 1;
+  repeated VarDesc vars = 2;
+}
+```
+
+in the order that there appear in user programs, like the Python program at the beginning of this article.  We can imagine that in `ops`,  we have some forward operators, followed by some gradient operators, and then some optimization operators.

doc/design/images/graph_construction_example.dot

@@ -2,6 +2,8 @@ digraph ImageClassificationGraph {
         ///////// The forward part /////////
         FeedX [label="Feed", color=blue, shape=box];
         FeedY [label="Feed", color=blue, shape=box];
+        InitW [label="Init", color=blue, shape=diamond];
+        Initb [label="Init", color=blue, shape=diamond];
         FC [label="FC", color=blue, shape=box];
         MSE [label="MSE", color=blue, shape=box];
 
@@ -14,6 +16,8 @@ digraph ImageClassificationGraph {
 
         FeedX -> x -> FC -> y -> MSE -> cost [color=blue];
         FeedY -> l [color=blue];
+        InitW -> W [color=blue];
+        Initb -> b [color=blue];
         W -> FC [color=blue];
         b -> FC [color=blue];
         l -> MSE [color=blue];

doc/howto/dev/new_op_cn.md

@@ -23,17 +23,20 @@
 - `framework::OperatorWithKernel`：继承自OperatorBase，Op有计算函数，称作有Kernel。
 - `class OpProtoAndCheckerMaker`：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成
 
-依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下：
+依据是否包含kernel，可以将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下：
 
-  
- 内容            | 定义位置         
---------------  | :----------------------  
+
+ 内容            | 定义位置
+--------------  | :----------------------
 OpProtoMake定义  | `.cc`文件，Backward Op不需要定义OpProtoMake
-Op定义           | `.cc`文件 
-Kernel实现       | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`.cc`文件，GPU可在`.cu`文件。 
-注册Op           | Op注册在`.cc`文件；Kernel注册CPU在`.cc`文件，GPU在`.cu`文件
-     
-     
+Op定义           | `.cc`文件
+Kernel实现       | CPU、GPU共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，GPU 实现在`.cu`文件中。
+注册Op           | Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，GPU实现在`.cu`文件中
+
+
+实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。
+
+
 下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。
 
 
@@ -42,9 +45,11 @@ Kernel实现       | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`
 
 ### 1. 定义ProtoMaker类
 
-矩阵乘的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释：
-    
-```
+矩阵乘法的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。
+
+首先定义`ProtoMaker`来描述该Op的输入、输出，并添加注释：
+
+```cpp
 class MulOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
@@ -59,20 +64,20 @@ The equation is: Out = X * Y
   }
 };
 ```
-   
-[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数包括2个：
+
+[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数含有2个参数：
 
    - `framework::OpProto` ： 前者存储Op的输入输出和参数属性，将用于Python API接口的生成。
    - `framework::OpAttrChecker` ：后者用于检查参数属性的合法性。
-   
-构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加该Op的注释，这些函数会将对应内容添加到`OpProto`中。
 
-在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，该命名尽可能的规范。
+构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加Op的注释。这些函数会将对应内容添加到`OpProto`中。
 
-   
-再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子：
-   
-```
+上面的代码在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，命名请遵守命名规范。
+
+
+再以[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)为例：
+
+```cpp
 template <typename AttrType>
 class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
@@ -87,17 +92,19 @@ The equation is: Out = scale*X
   }
 };
 ```
- 
- 在这个例子里，两处不同：
- 
-  - `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中。
-  - `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
-   
+
+这个例子有两处不同：
+
+- `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中，如果Op的某个输入不参与反向梯度的计算，请显示地调用`.NotInGradient()`进行设置。
+
+- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
+
 
 ### 2. 定义Operator类
 
+下面的点实现了MulOp的定义：
 
-```c++
+```cpp
 class MulOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -121,33 +128,46 @@ class MulOp : public framework::OperatorWithKernel {
 ```
 
 [`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22)继承自`OperatorWithKernel`。`public`成员：
-	 
-```c++
+
+```cpp
 using framework::OperatorWithKernel::OperatorWithKernel;
 ```
 
 这句表示使用基类`OperatorWithKernel`的构造函数，也可写成：
-   
-```c++
+
+```cpp
 MulOp(const std::string &type, const framework::VariableNameMap &inputs,
       const framework::VariableNameMap &outputs,
       const framework::AttributeMap &attrs)
   : OperatorWithKernel(type, inputs, outputs, attrs) {}
-```	
-	
+```
+
 还需要重写`InferShape`接口。`InferShape`为const函数，不能修改Op的成员变量，参数为`const framework::InferShapeContext &ctx`，通过该参数可获取到输入输出以及属性。它的功能是：
 
   - 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法。
   - 2). 设置输出Tensor的形状。
 
-通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和要讲到的注册函数一起放在`.cc`中
+通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和下面将要介绍的注册函数一起放在`.cc`中
 
 ### 3. 定义OpKernel类
 
-```C++
-template <typename Place, typename T>
-class MulKernel : public framework::OpKernel {
- public:
+`MulKernel`继承自`framework::OpKernel`，带有下面两个模板参数:
+
+- `typename  Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
+
+- `typename T` : 表示数据类型，如`float`, `double`等。
+
+需要为`MulKernel`类重写`Compute`接口。
+- `Compute`接受一个输入参数：`const framework::ExecutionContext& context`。
+- 与`InferShapeContext`相比，`ExecutionContext`增加了设备类型，同样可获取到输入输出和属性参数。
+- `Compute`函数里实现`OpKernel`的具体计算逻辑。
+
+下面是 `MulKernel` `Compute`的实现：
+
+  ```cpp
+  template <typename Place, typename T>
+  class MulKernel : public framework::OpKernel {
+  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* X = context.Input<Tensor>("X");
     auto* Y = context.Input<Tensor>("Y");
@@ -157,136 +177,136 @@ class MulKernel : public framework::OpKernel {
         const_cast<platform::DeviceContext*>(context.device_context_);
     math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
   }
-};
-```
+  };
+  ```
+
+需要注意：**不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。**
+
+`MulOp`的CPU、GPU实现共享同一个`Kernel`。`OpKernel`不共享的例子可以参考：[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
+
+为了使`OpKernel`的计算过程书写更加简单，并且CPU、GPU的代码可以复用，我们通常借助 Eigen unsupported Tensor模块来实现`Compute`接口。关于在PaddlePaddle中如何使用Eigen库，请参考[使用文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)。
+
+
+到此，前向Op实现完成。接下来，需要在`.cc`文件中注册该op和kernel。
+反向Op类的定义，反向OpKernel的定义与前向Op类似，这里不再赘述。**但需注意反向Op没有`ProtoMaker`**。
 
-`MulKernel`继承自`framework::OpKernel`，带有模板参数:
-
-  - `typename  Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
-  
- - `typename T` : 表示数据类型，如`float`, `double`等。
-   
-`MulKernel`需要重写`Compute`接口，该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型，同样可获取到输入输出和属性参数，`Compute`函数里写具体实现时。
-   
-注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 
-   
-到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。
-   
 ### 4. 注册Operator
 
-在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
+- 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
 
-```c++
-namespace ops = paddle::operators;
-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`，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。
-
-在 `.cu`文件中注册GPU Kernel。
-   
-```c++
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(mul_grad,
-                       ops::MulGradKernel<paddle::platform::GPUPlace, float>);
-```
+    ```cpp
+    namespace ops = paddle::operators;
+    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>);
+    ```
+
+   在上面的代码中：
+
+    - `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。
+
+
+- 在 `.cu`文件中注册GPU Kernel。
+    - 请注意，如果GPU Kernel的实现基于Eigen unsupported模块，那么在 `.cu`的开始请加上宏定义 `#define EIGEN_USE_GPU`，代码示例如下：
+
+    ```cpp
+    // if use Eigen unsupported module before include head files
+    #define EIGEN_USE_GPU
+
+    namespace ops = paddle::operators;
+    REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
+    REGISTER_OP_GPU_KERNEL(mul_grad,
+                           ops::MulGradKernel<paddle::platform::GPUPlace, float>);
+    ```
 
 ### 5. 编译
 
-在[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)文件中添加编译。
-   
-```
-op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
-```
-   
-下面命令可以编译：
-   
-```
-make mul_op
-```
+- 简单**无特殊依赖**的OP无需修改CMakeList.txt文件。[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt) 会自动将 `paddle/operators` 目录下新增的 `*_op.cc` 文件加入编译。
+- 较为复杂、**有额外依赖** 的operator仍需要修改[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)。如，`mul_op` 依赖 `math_function`，需要在`CMakeLists.txt`中添加如下内容：
+
+    ```
+    op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)		 +
+    ```
+
+- 运行下面命令可以进行编译：
+
+    ```
+    make mul_op
+    ```
 
 ## 绑定Python
 
-- 绑定Python 
- 
-    在 [`paddle/pybind/pybind.cc 
-`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc)文件中添加该类：
+- 绑定Python
+
+    在 [`paddle/pybind/pybind.cc
+`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) 使用`USE_OP`告知编译器需要链接的Op，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
 
     ```
     USE_OP(mul);
     ```
     如果只实现了CPU版本，则使用`USE_CPU_ONLY_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)。
-    
-    
+
+
  - 生成库
 
-   在 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件添加类到`DEPS`中，使得该Op可以链接到生成的lib库中。
-   
-   ```
-   if(WITH_PYTHON)
-     cc_library(paddle_pybind SHARED
-     SRCS pybind.cc
-     DEPS pybind python backward
-     mul_op
-     minus_op)
-   endif(WITH_PYTHON)
-   ```
+   无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动添加链接到生成的lib库中。
 
 ## 实现单元测试
 
-单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单测](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
+单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
 
-### 前向Operator单测
+### 前向Operator单元测试
 
-前向Op单测继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`，具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数，以及Python对比的输出值。
+前向Op单元测试继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`。各项更加具体的单元测试在`OpTestMeta`里完成。测试前向Operator，需要：
 
-```
-import unittest
-import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+1. 在`setUp`函数定义输入、输出，以及相关的属性参数。
+2. 生成随机的输入数据。
+3. 在Python脚本中实现与前向operator相同的计算逻辑，得到输出值，与operator前向计算的输出进行对比。
 
-class TestMulOp(unittest.TestCase):
-    __metaclass__ = OpTestMeta
 
-    def setUp(self):
-        self.type = "mul"
-        self.inputs = {
-            'X': np.random.random((32, 84)).astype("float32"),
-            'Y': np.random.random((84, 100)).astype("float32")
-        }
-        self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
-```
-   首先需要`import`必要的包,下面详细解释其他值：
-   
-   - `self.type = "mul" ` : 定义类型，和注册的类型一致。
-   - `self.inputs` : 定义输入，类型为Numpy.array，并初始化。
-   - `self.outputs` : 定义输出，并得到Python结算结果。
+  ```python
+  import unittest
+  import numpy as np
+  from gradient_checker import GradientChecker, create_op
+  from op_test_util import OpTestMeta
 
- 
-### 反向Operator单测
+  class TestMulOp(unittest.TestCase):
+      __metaclass__ = OpTestMeta
 
-反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。
+      def setUp(self):
+          self.type = "mul"
+          self.inputs = {
+              'X': np.random.random((32, 84)).astype("float32"),
+              'Y': np.random.random((84, 100)).astype("float32")
+          }
+          self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
+  ```
 
-```
+上面的代码首先导入依赖的包，下面是对`setUp`函数中操作的重要变量的详细解释：
+
+- `self.type = "mul" ` : 定义类型，与operator注册时注册的类型一致。
+- `self.inputs` : 定义输入，类型为`numpy.array`，并初始化。
+- `self.outputs` : 定义输出，并在Python脚本中完成与operator同样的计算逻辑，返回Python端的计算结果。
+
+
+### 反向Operator单元测试
+
+反向Op单元测试继承自`GradientChecker`，而`GradientChecker`继承自`unittest.TestCase`，因此，**反向单元测试函数需要以`test_`开头**。
+
+```python
 class TestMulGradOp(GradientChecker):
     def setUp(self):
         self.op = create_op("mul")
@@ -320,33 +340,34 @@ class TestMulGradOp(GradientChecker):
             no_grad_set={"Y"})
 ```
 
-下面解释一些关键的地方:
+下面解释代码中一些关键的地方:
 
-   - 调用`create_op("mul")`创建反向Op对应的前向Op。
-   - 调用`compare_grad`函数对比CPU、GPU计算结果。
-   - `test_normal`中调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。
-      - 第一个参数`self.op` : 前向Op。
-      - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
-      - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
-      - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
-   - `test_ignore_x`和`test_ignore_y`分支测试只需要计算一个输入梯度的情况。
+- 调用`create_op("mul")`创建反向Op对应的前向Op。
+- 调用`compare_grad`函数对比CPU、GPU计算结果。
+- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
+  - 第一个参数`self.op` : 前向Op。
+  - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
+  - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
+  - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
+- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。
 
 
-### 编译和执行 
+### 编译和执行单元测试
 
-单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加编译：
+单元测试编写完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)中添加以下内容，将单元测试加入工程：
 
 ```
 py_test(test_mul_op SRCS test_mul_op.py)
 ```
 
-编译时需要打开`WITH_TESTING`, 即 `cmake paddle_dir -DWITH_TESTING=ON`，编译成功之后执行单测命令为：
+请注意，**不同于Op的编译测试，运行单元测试测时需要编译整个工程**，并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后，执行下面的命令来运行单元测试：
 
-```
+```bash
 make test ARGS="-R test_mul_op -V"
 ```
+
 或者:
 
-```
+```bash
 ctest -R test_mul_op
 ```

doc/howto/dev/use_eigen_cn.md

+## 在Paddle中如何使用Eigen
+
+神经网络本质上是一个计算图，计算需要的数据存放在`Tensor`中，而计算过程是由`Operartor`来描述的。在执行时，`Operator`调用对应`OpKernel`中的`Compute`接口，实现对`Tensor`的操作。
+
+
+### Eigen Tensor模块
+
+Eigen Tensor模块对element-wise计算提供了强大的支持，并且书写一份代码，可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块，因此可能测试不够完备，文档较少。
+
+关于Eigen Tensor模块的详细介绍请参考[文档1](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md) 和[文档2](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md)
+
+
+### paddle::framework::Tensor
+
+Paddle Tensor定义在framework目录下，其主要接口如下：
+
+```cpp
+class Tensor {
+ public:
+  /*! Return a pointer to mutable memory block. */
+  template <typename T>
+  inline T* data();
+  
+  /**
+   * @brief   Return a pointer to mutable memory block.
+   * @note    If not exist, then allocation.
+   */
+  template <typename T>
+  inline T* mutable_data(platform::Place place);
+  
+  /**
+   * @brief     Return a pointer to mutable memory block.
+   *
+   * @param[in] dims    The dimensions of the memory block.
+   * @param[in] place   The place of the memory block.
+   *
+   * @note      If not exist, then allocation.
+   */
+  template <typename T>
+  inline T* mutable_data(DDim dims, platform::Place place);
+  
+  /*! Resize the dimensions of the memory block. */
+  inline Tensor& Resize(const DDim& dims);
+  
+  /*! Return the dimensions of the memory block. */
+  inline const DDim& dims() const;
+
+ private:  
+  /*! holds the memory block if allocated. */
+  std::shared_ptr<Placeholder> holder_;
+  
+  /*! points to dimensions of memory block. */
+  DDim dim_;
+};
+```
+
+`Placeholder`的作用是延迟分配内存，即我们可以先定义一个Tensor，然后使用Resize接口设置Tensor的大小，最后再调用mutable_data接口分配实际的内存。
+
+```cpp
+paddle::framework::Tensor t;
+paddle::platform::CPUPlace place;
+// set size first
+t.Resize({2, 3});
+// allocate memory on CPU later
+t.mutable_data(place);
+```
+
+### paddle::framework::Tensor使用样例
+下面以AddOp为例说明Tensor的使用过程：
+
+- InferShape
+
+在运行神经网络计算图时，我们先调用每个`Operator`的`InferShape`接口，根据输入Tensor的大小来设置输出Tensor的大小，`Resize`接口会被调用。
+
+```cpp
+void InferShape(const framework::InferShapeContext &ctx) const override {
+  PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
+                    ctx.Input<Tensor>("Y")->dims(),
+                    "Two input of Add Op's dimension must be same.");
+  ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
+}
+```
+
+
+- Run
+
+`Operator`的`Run`接口最终会调用对应`OpKernel`的`Compute`接口，在这时真正的分配内存，`mutable_data`接口会被调用。
+
+```cpp
+void Compute(const framework::ExecutionContext& context) const override {
+  auto* input0 = context.Input<Tensor>("X");
+  auto* input1 = context.Input<Tensor>("Y");
+  auto* output = context.Output<Tensor>("Out");
+
+  output->mutable_data<T>(context.GetPlace());
+
+  auto x = EigenVector<T>::Flatten(*input0);
+  auto y = EigenVector<T>::Flatten(*input1);
+  auto z = EigenVector<T>::Flatten(*output);
+
+  auto place = context.GetEigenDevice<Place>();
+
+  z.device(place) = x + y;
+}
+```
+
+
+### paddle::framework::Tensor到EigenTensor的转换
+
+如上一小节所示，在具体的计算中，我们需要先把输入Tensor和输出Tensor转换为Eigen支持的格式。我们在[eigen.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen.h)中提供了一些全局函数用来实现paddle::framework::Tensor到EigenTensor/EigenMatrix/EigenVector/EigenScalar的转换。
+
+以EigenTensor为例，做一个介绍
+
+```cpp
+Tensor t;
+float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
+for (int i = 0; i < 1 * 2 * 3; i++) {
+  p[i] = static_cast<float>(i);
+}
+
+EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
+```
+
+From是EigenTensor模板提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。
+
+在Eigen中，不同rank的Tensor是不同类型，Vector是rank为1的Tensor。需要额外注意的是，EigenVector<T>::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor，在这里用EigenVector来表示；而EigenVector<T>::Flatten方法是把paddle中的一个Tensor进行reshape操作，压扁成为Eigen的一维Tensor，类型仍然为EigenVector。
+
+更多的转换方法请参考eigen_test.cc中的[单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen_test.cc)。
+
+
+
+### 实现计算
+
+当需要完成计算时，我们需要等式左边的EigenTensor调用device接口。在这里需要注意的是，这里的EigenTensor之间的运算只是改变了原有Tensor中的数据，而不会改变原有Tensor的shape信息。
+
+```cpp
+auto x = EigenVector<T>::Flatten(*input0);
+auto y = EigenVector<T>::Flatten(*input1);
+auto z = EigenVector<T>::Flatten(*output);
+auto place = context.GetEigenDevice<Place>();
+z.device(place) = x + y;
+```
+
+在这段代码中，input0/input1/output可以是任意维度的Tensor。我们调用了EigenVector的Flatten接口，把任意维度的Tensor转为了一维的EigenVector。而在计算结束之后，input0/input1/output的原有shape信息不变。如果想改变原有Tensor的shape信息，可以调用Resize接口进行改变。
+
+由于Eigen Tensor模块的文档较少，我们可以参考TensorFlow的[kernels](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/kernels)模块下的相关`OpKernel`的计算代码。

paddle/framework/ddim.cc

@@ -21,16 +21,16 @@ namespace framework {
 /// @cond HIDDEN
 
 template <int i>
-Dim<i> make_dim(const int* d) {
+Dim<i> make_dim(const int64_t* d) {
   return Dim<i>(*d, make_dim<i - 1>(d + 1));
 }
 
 template <>
-Dim<1> make_dim<1>(const int* d) {
+Dim<1> make_dim<1>(const int64_t* d) {
   return Dim<1>(*d);
 }
 
-void make_ddim(DDim& ddim, const int* dims, int n) {
+void make_ddim(DDim& ddim, const int64_t* dims, int n) {
   switch (n) {
     case 1:
       ddim = make_dim<1>(dims);
@@ -67,13 +67,13 @@ void make_ddim(DDim& ddim, const int* dims, int n) {
 
 /// @endcond
 
-DDim make_ddim(std::initializer_list<int> dims) {
+DDim make_ddim(std::initializer_list<int64_t> dims) {
   DDim result(make_dim(0));
   make_ddim(result, dims.begin(), dims.size());
   return result;
 }
 
-DDim make_ddim(const std::vector<int>& dims) {
+DDim make_ddim(const std::vector<int64_t>& dims) {
   DDim result(make_dim(0));
   make_ddim(result, &dims[0], dims.size());
   return result;
@@ -81,12 +81,12 @@ DDim make_ddim(const std::vector<int>& dims) {
 
 /// @cond HIDDEN
 // XXX For some reason, putting this in an anonymous namespace causes errors
-class DynamicMutableIndexer : public boost::static_visitor<int&> {
+class DynamicMutableIndexer : public boost::static_visitor<int64_t&> {
  public:
   explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
 
   template <int D>
-  int& operator()(Dim<D>& dim) const {
+  int64_t& operator()(Dim<D>& dim) const {
     return dim[idx_];
   }
 
@@ -94,12 +94,12 @@ class DynamicMutableIndexer : public boost::static_visitor<int&> {
   int idx_;
 };
 
-class DynamicConstIndexer : public boost::static_visitor<int> {
+class DynamicConstIndexer : public boost::static_visitor<int64_t> {
  public:
   explicit DynamicConstIndexer(int idx) : idx_(idx) {}
 
   template <int D>
-  int operator()(const Dim<D>& dim) const {
+  int64_t operator()(const Dim<D>& dim) const {
     return dim[idx_];
   }
 
@@ -109,22 +109,22 @@ class DynamicConstIndexer : public boost::static_visitor<int> {
 
 /// @endcond
 
-int& DDim::operator[](int idx) {
+int64_t& DDim::operator[](int idx) {
   return boost::apply_visitor(DynamicMutableIndexer(idx), var);
 }
 
-int DDim::operator[](int idx) const {
+int64_t DDim::operator[](int idx) const {
   return boost::apply_visitor(DynamicConstIndexer(idx), var);
 }
 
-ssize_t DDim::size() const { return arity(*this); }
+int64_t DDim::size() const { return arity(*this); }
 
 bool DDim::operator==(DDim d) const {
   if (var.which() != d.getVar().which()) {
     return false;
   } else {
-    std::vector<int> v1 = vectorize(*this);
-    std::vector<int> v2 = vectorize(d);
+    std::vector<int64_t> v1 = vectorize(*this);
+    std::vector<int64_t> v2 = vectorize(d);
 
     for (unsigned int i = 0; i < v1.size(); i++) {
       if (v1[i] != v2[i]) {
@@ -139,10 +139,10 @@ bool DDim::operator==(DDim d) const {
 bool DDim::operator!=(DDim d) const { return !(*this == d); }
 
 DDim DDim::operator+(DDim d) const {
-  std::vector<int> v1 = vectorize(*this);
-  std::vector<int> v2 = vectorize(d);
+  std::vector<int64_t> v1 = vectorize(*this);
+  std::vector<int64_t> v2 = vectorize(d);
 
-  std::vector<int> v3;
+  std::vector<int64_t> v3;
 
   assert(v1.size() == v2.size());
 
@@ -154,10 +154,10 @@ DDim DDim::operator+(DDim d) const {
 }
 
 DDim DDim::operator*(DDim d) const {
-  std::vector<int> v1 = vectorize(*this);
-  std::vector<int> v2 = vectorize(d);
+  std::vector<int64_t> v1 = vectorize(*this);
+  std::vector<int64_t> v2 = vectorize(d);
 
-  std::vector<int> v3;
+  std::vector<int64_t> v3;
 
   assert(v1.size() == v2.size());
 
@@ -168,15 +168,15 @@ DDim DDim::operator*(DDim d) const {
   return make_ddim(v3);
 }
 
-int get(const DDim& ddim, int idx) { return ddim[idx]; }
+int64_t get(const DDim& ddim, int idx) { return ddim[idx]; }
 
 void set(DDim& ddim, int idx, int value) { ddim[idx] = value; }
 
 /// @cond HIDDEN
 struct VectorizeVisitor : public boost::static_visitor<> {
-  std::vector<int>& vector;
+  std::vector<int64_t>& vector;
 
-  explicit VectorizeVisitor(std::vector<int>& v) : vector(v) {}
+  explicit VectorizeVisitor(std::vector<int64_t>& v) : vector(v) {}
 
   template <typename T>
   void operator()(const T& t) {
@@ -188,31 +188,31 @@ struct VectorizeVisitor : public boost::static_visitor<> {
 };
 /// @endcond
 
-std::vector<int> vectorize(const DDim& ddim) {
-  std::vector<int> result;
+std::vector<int64_t> vectorize(const DDim& ddim) {
+  std::vector<int64_t> result;
   VectorizeVisitor visitor(result);
   boost::apply_visitor(visitor, ddim);
   return result;
 }
 
-struct ProductVisitor : public boost::static_visitor<ssize_t> {
+struct ProductVisitor : public boost::static_visitor<int64_t> {
   template <int D>
-  ssize_t operator()(const Dim<D>& dim) {
+  int64_t operator()(const Dim<D>& dim) {
     return product(dim);
   }
 };
 
-ssize_t product(const DDim& ddim) {
+int64_t product(const DDim& ddim) {
   ProductVisitor visitor;
   return boost::apply_visitor(visitor, ddim);
 }
 
 struct SliceVectorizeVisitor : public boost::static_visitor<> {
-  std::vector<int>& vector;
+  std::vector<int64_t>& vector;
   int begin;
   int end;
 
-  SliceVectorizeVisitor(std::vector<int>& v, int b, int e)
+  SliceVectorizeVisitor(std::vector<int64_t>& v, int b, int e)
       : vector(v), begin(b), end(e) {
     PADDLE_ENFORCE(begin < end,
                    "Begin index must be less than end index in ddim slice.");
@@ -240,7 +240,7 @@ struct SliceVectorizeVisitor : public boost::static_visitor<> {
 };
 
 DDim slice_ddim(const DDim& dim, int begin, int end) {
-  std::vector<int> vec;
+  std::vector<int64_t> vec;
   vec.reserve(end - begin);
   SliceVectorizeVisitor visitor(vec, begin, end);
   boost::apply_visitor(visitor, dim);
@@ -280,7 +280,7 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
   return os;
 }
 
-DDim::DDim(std::initializer_list<int> init_list) {
+DDim::DDim(std::initializer_list<int64_t> init_list) {
   *this = make_ddim(init_list);
 }
 }  // namespace framework

paddle/framework/ddim.h

@@ -40,7 +40,7 @@ struct DDim {
   template <int D>
   explicit DDim(const Dim<D>& in) : var(in) {}
 
-  /*implicit*/ DDim(std::initializer_list<int> init_list);
+  /*implicit*/ DDim(std::initializer_list<int64_t> init_list);
 
   template <int D>
   DDim& operator=(const Dim<D>& in) {
@@ -48,8 +48,8 @@ struct DDim {
     return *this;
   }
 
-  int& operator[](int idx);
-  int operator[](int idx) const;
+  int64_t& operator[](int idx);
+  int64_t operator[](int idx) const;
 
   template <typename Visitor>
   typename Visitor::result_type apply_visitor(Visitor& visitor) {
@@ -71,15 +71,15 @@ struct DDim {
 
   DDim operator*(DDim d) const;
 
-  ssize_t size() const;
+  int64_t size() const;
 };
 
 /**
- * \brief Make a DDim from std::vector<int>
+ * \brief Make a DDim from std::vector<int64_t>
  *
  * \param dims An vector of ints. Must be sized between [1, 9]
  */
-DDim make_ddim(const std::vector<int>& dims);
+DDim make_ddim(const std::vector<int64_t>& dims);
 
 /**
  * \brief Make a DDim from an initializer list
@@ -87,14 +87,14 @@ DDim make_ddim(const std::vector<int>& dims);
  * \param dims An initializer list of ints. Must be sized between [1, 9]
  *
  */
-DDim make_ddim(std::initializer_list<int> dims);
+DDim make_ddim(std::initializer_list<int64_t> dims);
 
-int get(const DDim& dim, int idx);
+int64_t get(const DDim& dim, int idx);
 void set(DDim& dim, int idx, int val);
 
-std::vector<int> vectorize(const DDim& ddim);
+std::vector<int64_t> vectorize(const DDim& ddim);
 
-ssize_t product(const DDim& ddim);
+int64_t product(const DDim& ddim);
 
 /**
  * \brief Slice a ddim

paddle/framework/ddim_test.cc

@@ -12,7 +12,7 @@ TEST(DDim, Equality) {
   EXPECT_EQ(ddim[2], 5);
 
   // construct a DDim from a vector
-  std::vector<int> vec({9, 1, 5});
+  std::vector<int64_t> vec({9, 1, 5});
   paddle::framework::DDim vddim = paddle::framework::make_ddim(vec);
   EXPECT_EQ(ddim[0], 9);
   EXPECT_EQ(ddim[1], 1);
@@ -25,7 +25,7 @@ TEST(DDim, Equality) {
   EXPECT_EQ(paddle::framework::get(ddim, 0), 6);
 
   // vectorize a DDim
-  std::vector<int> res_vec = paddle::framework::vectorize(vddim);
+  std::vector<int64_t> res_vec = paddle::framework::vectorize(vddim);
   EXPECT_EQ(res_vec[0], 9);
   EXPECT_EQ(res_vec[1], 1);
   EXPECT_EQ(res_vec[2], 5);

paddle/framework/dim.h

@@ -17,13 +17,13 @@ struct Dim {
   static constexpr int dimensions = i;
 
   template <typename... Args>
-  HOSTDEVICE Dim(int _head, Args... _tail) : head(_head), tail(_tail...) {
+  HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
     static_assert(sizeof...(_tail) == i - 1,
                   "Dim initialized with the wrong number of parameters");
   }
 
   HOSTDEVICE
-  Dim(int _head, const Dim<i - 1>& _tail) : head(_head), tail(_tail) {}
+  Dim(int64_t _head, const Dim<i - 1>& _tail) : head(_head), tail(_tail) {}
 
   HOSTDEVICE
   Dim() : head(0), tail() {}
@@ -31,12 +31,12 @@ struct Dim {
   /** Construct a Dim from a linear index and size.  Uses Fortran order
    * indexing. */
   HOSTDEVICE
-  Dim(int idx, const Dim<i>& size)
+  Dim(int64_t idx, const Dim<i>& size)
       : head(idx % size.head), tail(idx / size.head, size.tail) {}
 
   /** Construct a Dim with each dimension set to the given index */
   HOSTDEVICE
-  Dim(int idx) : head(idx), tail(idx) {}
+  Dim(int64_t idx) : head(idx), tail(idx) {}
 
   HOSTDEVICE
   bool operator==(const Dim<i>& o) const {
@@ -47,13 +47,13 @@ struct Dim {
   bool operator!=(const Dim<i>& o) const { return !(*this == o); }
 
   HOSTDEVICE
-  int& operator[](int idx);
+  int64_t& operator[](int idx);
   HOSTDEVICE
-  int operator[](int idx) const;
+  int64_t operator[](int idx) const;
 
   HOST std::string to_string() const;
 
-  int head;
+  int64_t head;
   Dim<i - 1> tail;
 };
 
@@ -63,7 +63,7 @@ struct Dim<1> {
   static constexpr int dimensions = 1;
 
   HOSTDEVICE
-  Dim(int _head) : head(_head) {}
+  Dim(int64_t _head) : head(_head) {}
 
   HOSTDEVICE
   Dim() : head(0) {}
@@ -86,11 +86,11 @@ struct Dim<1> {
   bool operator!=(const Dim<1>& o) const { return !(*this == o); }
 
   HOSTDEVICE
-  int& operator[](int idx);
+  int64_t& operator[](int idx);
   HOSTDEVICE
-  int operator[](int idx) const;
+  int64_t operator[](int idx) const;
 
-  int head;
+  int64_t head;
 };
 
 namespace {
@@ -100,12 +100,12 @@ template <int i>
 struct DimGetter {
   // Return a copy if Dim is const
   template <typename D>
-  HOSTDEVICE static int impl(const D& d) {
+  HOSTDEVICE static int64_t impl(const D& d) {
     return DimGetter<i - 1>::impl(d.tail);
   }
   // Return a reference if Dim is mutable
   template <typename D>
-  HOSTDEVICE static int& impl(D& d) {
+  HOSTDEVICE static int64_t& impl(D& d) {
     return DimGetter<i - 1>::impl(d.tail);
   }
 };
@@ -115,18 +115,18 @@ template <>
 struct DimGetter<0> {
   // Return a copy if Dim is const
   template <typename D>
-  HOSTDEVICE static int impl(const D& d) {
+  HOSTDEVICE static int64_t impl(const D& d) {
     return d.head;
   }
   // Return a reference if Dim is mutable
   template <typename D>
-  HOSTDEVICE static int& impl(D& d) {
+  HOSTDEVICE static int64_t& impl(D& d) {
     return d.head;
   }
 };
 
 template <int D>
-HOSTDEVICE int& indexer(Dim<D>& dim, int idx) {
+HOSTDEVICE int64_t& indexer(Dim<D>& dim, int idx) {
 #ifndef __CUDA_ARCH__
   if (idx < 0) {
     throw std::invalid_argument("Tried to access a negative dimension");
@@ -141,7 +141,7 @@ HOSTDEVICE int& indexer(Dim<D>& dim, int idx) {
 }
 
 template <>
-HOSTDEVICE int& indexer<1>(Dim<1>& dim, int idx) {
+HOSTDEVICE int64_t& indexer<1>(Dim<1>& dim, int idx) {
 #ifndef __CUDA_ARCH__
   if (idx != 0) {
     throw std::invalid_argument("Invalid index");
@@ -153,7 +153,7 @@ HOSTDEVICE int& indexer<1>(Dim<1>& dim, int idx) {
 }
 
 template <int D>
-HOSTDEVICE int indexer(const Dim<D>& dim, int idx) {
+HOSTDEVICE int64_t indexer(const Dim<D>& dim, int idx) {
 #ifndef __CUDA_ARCH__
   if (idx < 0) {
     throw std::invalid_argument("Tried to access a negative dimension");
@@ -168,7 +168,7 @@ HOSTDEVICE int indexer(const Dim<D>& dim, int idx) {
 }
 
 template <>
-HOSTDEVICE int indexer<1>(const Dim<1>& dim, int idx) {
+HOSTDEVICE int64_t indexer<1>(const Dim<1>& dim, int idx) {
 #ifndef __CUDA_ARCH__
   if (idx != 0) {
     throw std::invalid_argument("Invalid index");
@@ -182,73 +182,76 @@ HOSTDEVICE int indexer<1>(const Dim<1>& dim, int idx) {
 }  // namespace
 // Static access to constant Dim
 template <int i, int l>
-HOSTDEVICE int get(const Dim<l>& d) {
+HOSTDEVICE int64_t get(const Dim<l>& d) {
   return DimGetter<i>::impl(d);
 }
 
 // Static access to mutable Dim
 template <int i, int l>
-HOSTDEVICE int& get(Dim<l>& d) {
+HOSTDEVICE int64_t& get(Dim<l>& d) {
   return DimGetter<i>::impl(d);
 }
 
 // Dynamic access to constant Dim
 template <int l>
-HOSTDEVICE int Dim<l>::operator[](int i) const {
+HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
   return indexer(*this, i);
 }
 
 // Dynamic access to mutable Dim
 template <int l>
-HOSTDEVICE int& Dim<l>::operator[](int i) {
+HOSTDEVICE int64_t& Dim<l>::operator[](int i) {
   return indexer(*this, i);
 }
 
 // Dynamic access to constant Dim
-inline HOSTDEVICE int Dim<1>::operator[](int i) const {
+inline HOSTDEVICE int64_t Dim<1>::operator[](int i) const {
   return indexer(*this, i);
 }
 
 // Dynamic access to mutable Dim
-inline HOSTDEVICE int& Dim<1>::operator[](int i) { return indexer(*this, i); }
+inline HOSTDEVICE int64_t& Dim<1>::operator[](int i) {
+  return indexer(*this, i);
+}
 
 // Dynamic access to constant Dim
 // without std::enable_if will try to instantiate this on get<0>(d)
 template <int l>
-HOSTDEVICE typename std::enable_if<(l > 0), int>::type get(const Dim<l>& d,
-                                                           int i) {
+HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l>& d,
+                                                               int i) {
   return d[i];
 }
 
 // Dynamic access to mutable Dim
 template <int l>
-HOSTDEVICE typename std::enable_if<(l > 0), int&>::type get(Dim<l>& d, int i) {
+HOSTDEVICE typename std::enable_if<(l > 0), int64_t&>::type get(Dim<l>& d,
+                                                                int i) {
   return d[i];
 }
 
 // Dot product of two dims
 template <int i>
-HOSTDEVICE int linearize(const Dim<i>& a, const Dim<i>& b) {
+HOSTDEVICE int64_t linearize(const Dim<i>& a, const Dim<i>& b) {
   return a.head * b.head + linearize(a.tail, b.tail);
 }
 
 // Base case dot product of two Dims
 // Notice it is inline because it is no longer a template
 template <>
-HOSTDEVICE inline int linearize(const Dim<1>& a, const Dim<1>& b) {
+HOSTDEVICE inline int64_t linearize(const Dim<1>& a, const Dim<1>& b) {
   return a.head * b.head;
 }
 
 // Product of a Dim
 template <int i>
-HOSTDEVICE int product(const Dim<i>& a, int prod = 1) {
+HOSTDEVICE int64_t product(const Dim<i>& a, int prod = 1) {
   return prod * a.head * product(a.tail);
 }
 
 // Base case product of a Dim
 // Notice it is inline because it is no longer a template
 template <>
-HOSTDEVICE inline int product(const Dim<1>& a, int prod) {
+HOSTDEVICE inline int64_t product(const Dim<1>& a, int prod) {
   return prod * a.head;
 }
 

paddle/framework/dim_test.cu

@@ -8,7 +8,7 @@ __global__ void test(paddle::framework::Dim<2>* o) {
   o[0] = paddle::framework::make_dim(5, 6);
 }
 
-__global__ void dyn_idx_gpu(int* o) {
+__global__ void dyn_idx_gpu(int64_t* o) {
   auto d = paddle::framework::make_dim(5, 6);
   o[0] = d[1];
 }
@@ -47,9 +47,9 @@ TEST(Dim, Equality) {
   EXPECT_EQ(b[1], 11);
 
   // dynamic access on GPU
-  thrust::device_vector<int> r(1);
+  thrust::device_vector<int64_t> r(1);
   dyn_idx_gpu<<<1, 1>>>(thrust::raw_pointer_cast(r.data()));
-  int res = r[0];
+  int64_t res = r[0];
   EXPECT_EQ(res, 6);
 
   // ex_prefix_mul

paddle/framework/eigen.h

@@ -28,7 +28,7 @@ struct EigenDim {
   static Type From(const DDim& dims) {
     PADDLE_ENFORCE(arity(dims) == D, "D must match arity(DDim)");
     Type ret;
-    for (int d = 0; d < arity(dims); d++) {
+    for (int64_t d = 0; d < arity(dims); d++) {
       ret[d] = dims[d];
     }
     return ret;

paddle/framework/framework.proto

@@ -87,3 +87,24 @@ message OpProto {
   repeated Attr attrs = 4;
   required string comment = 5;
 }
+
+enum DataType {
+  BOOL = 0;
+  INT16 = 1;
+  INT32 = 2;
+  INT64 = 3;
+  FP16 = 4;
+  FP32 = 5;
+  FP64 = 6;
+}
+
+message LoDTensorDesc {
+  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 ];
+}
+
+message VarDesc {
+  required string name = 1;
+  optional LoDTensorDesc lod_tensor = 2;
+}

paddle/framework/grad_op_builder_test.cc

@@ -3,7 +3,7 @@
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
 
-USE_OP(add_two);
+USE_OP(add);
 
 namespace paddle {
 namespace framework {
@@ -41,7 +41,7 @@ namespace f = paddle::framework;
 
 TEST(GradOpBuilder, AddTwo) {
   std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
-      "add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
+      "add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
   std::shared_ptr<f::OperatorBase> grad_add_op =
       f::OpRegistry::CreateGradOp(*add_op);
   EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);

paddle/framework/op_registry_test.cc

@@ -80,7 +80,7 @@ TEST(OpRegistry, CreateOp) {
   paddle::framework::Scope scope;
   paddle::platform::CPUDeviceContext dev_ctx;
   op->Run(scope, dev_ctx);
-  float scale_get = op->GetAttr<float>("scale");
+  float scale_get = op->Attr<float>("scale");
   ASSERT_EQ(scale_get, scale);
 }
 
@@ -121,7 +121,7 @@ TEST(OpRegistry, DefaultValue) {
   paddle::framework::Scope scope;
   paddle::platform::CPUDeviceContext dev_ctx;
   op->Run(scope, dev_ctx);
-  ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
+  ASSERT_EQ(op->Attr<float>("scale"), 1.0);
 }
 
 TEST(OpRegistry, CustomChecker) {
@@ -172,38 +172,6 @@ TEST(OpRegistry, CustomChecker) {
   paddle::platform::CPUDeviceContext dev_ctx;
   paddle::framework::Scope scope;
   op->Run(scope, dev_ctx);
-  int test_attr = op->GetAttr<int>("test_attr");
+  int test_attr = op->Attr<int>("test_attr");
   ASSERT_EQ(test_attr, 4);
-}
-
-class TestAttrProtoMaker : public pd::OpProtoAndCheckerMaker {
- public:
-  TestAttrProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddAttr<float>("scale", "scale of test op");
-    AddAttr<float>("scale", "scale of test op");
-  }
-};
-
-TEST(ProtoMaker, DuplicatedAttr) {
-  pd::OpProto op_proto;
-  pd::OpAttrChecker op_checker;
-  auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
-  ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
-}
-
-class TestInOutProtoMaker : public pd::OpProtoAndCheckerMaker {
- public:
-  TestInOutProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("input", "input of test op");
-    AddInput("input", "input of test op");
-  }
-};
-
-TEST(ProtoMaker, DuplicatedInOut) {
-  pd::OpProto op_proto;
-  pd::OpAttrChecker op_checker;
-  auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
-  ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
-}
+}
\ No newline at end of file

paddle/framework/operator.h

@@ -69,7 +69,7 @@ class OperatorBase {
   virtual ~OperatorBase() {}
 
   template <typename T>
-  inline const T& GetAttr(const std::string& name) const {
+  inline const T& Attr(const std::string& name) const {
     PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",
                    name);
     return boost::get<T>(attrs_.at(name));
@@ -238,8 +238,8 @@ class InferShapeContext {
   const Scope& scope() const { return scope_; }
 
   template <typename T>
-  inline const T& GetAttr(const std::string& name) const {
-    return op_.GetAttr<T>(name);
+  inline const T& Attr(const std::string& name) const {
+    return op_.Attr<T>(name);
   }
 
   size_t InputSize(const std::string& name) const {

paddle/framework/operator_test.cc

@@ -263,4 +263,38 @@ TEST(Operator, Clone) {
   OperatorClone a("ABC", {}, {}, {});
   auto b = a.Clone();
   ASSERT_EQ(a.Type(), b->Type());
+}
+
+class TestAttrProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
+ public:
+  TestAttrProtoMaker(paddle::framework::OpProto* proto,
+                     paddle::framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddAttr<float>("scale", "scale of test op");
+    AddAttr<float>("scale", "scale of test op");
+  }
+};
+
+TEST(ProtoMaker, DuplicatedAttr) {
+  paddle::framework::OpProto op_proto;
+  paddle::framework::OpAttrChecker op_checker;
+  auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
+  ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
+}
+
+class TestInOutProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
+ public:
+  TestInOutProtoMaker(paddle::framework::OpProto* proto,
+                      paddle::framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("input", "input of test op");
+    AddInput("input", "input of test op");
+  }
+};
+
+TEST(ProtoMaker, DuplicatedInOut) {
+  paddle::framework::OpProto op_proto;
+  paddle::framework::OpAttrChecker op_checker;
+  auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
+  ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
 }
\ No newline at end of file

paddle/framework/tensor_impl.h

@@ -58,7 +58,7 @@ inline T* Tensor::mutable_data(platform::Place place) {
                     "Tensor's numel must be larger than zero to call "
                     "Tensor::mutable_data. Call Tensor::set_dim first.");
   /* some versions of boost::variant don't have operator!= */
-  size_t size = product(dims_) * sizeof(T);
+  int64_t size = product(dims_) * sizeof(T);
   if (holder_ == nullptr || !(holder_->place() == place) ||
       holder_->size() < size + offset_) {
     if (platform::is_cpu_place(place)) {
@@ -131,7 +131,7 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
   PADDLE_ENFORCE_LT(begin_idx, end_idx,
                     "Begin index must be less than end index.");
   PADDLE_ENFORCE_NE(dims_[0], 1, "Can not slice a tensor with dims_[0] = 1.");
-  int base = product(dims_) / dims_[0];
+  size_t base = product(dims_) / dims_[0];
   Tensor dst;
   dst.holder_ = holder_;
   DDim dst_dims = dims_;

paddle/gserver/layers/Conv3DLayer.cpp

@@ -83,8 +83,8 @@ void Conv3DLayer::forward(PassType passType) {
   int outWidth = getSize();
   resetOutput(batchSize, outWidth);
 
+  REGISTER_TIMER_INFO("FwdConv3D", getName().c_str());
   for (size_t i = 0; i != inputLayers_.size(); ++i) {
-    REGISTER_TIMER_INFO("FwdConv3D", getName().c_str());
     const MatrixPtr &inMat = getInputValue(i);
     const MatrixPtr &outMat = getOutputValue();
     int M = M_[i];
@@ -120,7 +120,6 @@ void Conv3DLayer::forward(PassType passType) {
     }
   }
   if (nullptr != this->biasParameter_) {
-    REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
     this->addBias();
   }
   forwardActivation();
@@ -134,15 +133,14 @@ void Conv3DLayer::backward(const UpdateCallback &callback) {
     biases_->getParameterPtr()->incUpdate(callback);
   }
 
+  REGISTER_TIMER_INFO("BwdConv3D", getName().c_str());
   for (size_t i = 0; i != inputLayers_.size(); ++i) {
-    REGISTER_TIMER_INFO("BwdConv3D", getName().c_str());
     if (weights_[i]->getWGrad()) {
       bpropWeights(i);
     }
     if (getInputGrad(i)) {
       bpropData(i);
     }
-    REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
     weights_[i]->getParameterPtr()->incUpdate(callback);
   }
 }

paddle/gserver/layers/DeConv3DLayer.cpp

@@ -84,8 +84,8 @@ void DeConv3DLayer::forward(PassType passType) {
   resetOutput(batchSize, outWidth);
   const MatrixPtr outMat = getOutputValue();
 
+  REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str());
   for (size_t i = 0; i != inputLayers_.size(); ++i) {
-    REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str());
     const MatrixPtr &inMat = getInputValue(i);
     int M = M_[i];
     int N = N_[i];
@@ -120,7 +120,6 @@ void DeConv3DLayer::forward(PassType passType) {
     }
   }
   if (nullptr != this->biasParameter_) {
-    REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
     this->addBias();
   }
   forwardActivation();
@@ -133,12 +132,12 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) {
     bpropBiases();
     biases_->getParameterPtr()->incUpdate(callback);
   }
+  REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str());
   for (size_t i = 0; i < inputLayers_.size(); ++i) {
     if (weights_[i]->getWGrad() || this->needGradient_) {
       int M = M_[i];
       int N = N_[i];
       int K = K_[i];
-      REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str());
       Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
       const MatrixPtr &inMat = getInputValue(i);
       for (int n = 0; n < batchSize; ++n) {
@@ -182,7 +181,6 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) {
           }
         }
       }
-      REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
       weights_[i]->getParameterPtr()->incUpdate(callback);
     }
   }

paddle/operators/CMakeLists.txt

@@ -14,27 +14,31 @@ function(op_library TARGET)
     cmake_parse_arguments(op_library "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN})
 
-    foreach(src ${op_library_SRCS})
-        if (${src} MATCHES ".*\\.cu$")
-            list(APPEND cu_srcs ${src})
-        elseif(${src} MATCHES ".*\\.cc$")
-            list(APPEND cc_srcs ${src})
-        else()
-            message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu")
+    list(LENGTH op_library_SRCS op_library_SRCS_len)
+    if (${op_library_SRCS_len} EQUAL 0)
+        if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cc)
+            list(APPEND cc_srcs ${TARGET}.cc)
         endif()
-    endforeach()
+        if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
+            list(APPEND cu_srcs ${TARGET}.cu)
+        endif()
+    else()
+        foreach(src ${op_library_SRCS})
+            if (${src} MATCHES ".*\\.cu$")
+                list(APPEND cu_srcs ${src})
+            elseif(${src} MATCHES ".*\\.cc$")
+                list(APPEND cc_srcs ${src})
+            else()
+                message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu")
+            endif()
+        endforeach()
+    endif()
 
     list(LENGTH cc_srcs cc_srcs_len)
     if (${cc_srcs_len} EQUAL 0)
         message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file")
     endif()
 
-    list(LENGTH cu_srcs cu_srcs_len)
-    list(LENGTH op_library_DEPS dep_len)
-    if (${cu_srcs_len} EQUAL 0 AND ${dep_len} EQUAL 0)
-        message(WARNING "The op library ${TARGET} not support GPU!")
-    endif()
-
     if (WITH_GPU)
         nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
                 ${op_common_deps})
@@ -46,22 +50,22 @@ endfunction()
 
 add_subdirectory(math)
 
-list(REMOVE_ITEM GENERAL_OPS
-     net_op
-     minus_op
-     mul_op
-     recurrent_op
-     scale_op)
-
-op_library(net_op SRCS net_op.cc)
-op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
-op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
+set(DEPS_OPS
+    identity_op
+    minus_op
+    mul_op
+    recurrent_op
+    scale_op)
+op_library(identity_op DEPS scale_op)
+op_library(minus_op DEPS scale_op)
+op_library(mul_op DEPS math_function)
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc 
   DEPS framework_proto tensor operator net_op)
-op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
+op_library(scale_op DEPS net_op)
 
+list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})
-    op_library(${src} SRCS ${src}.cc ${src}.cu)
+    op_library(${src})
 endforeach()
 
 set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")

paddle/operators/add_op.cc

@@ -57,7 +57,6 @@ class AddOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
+REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad);
 
-REGISTER_OP_CPU_KERNEL(add_two,
-                       ops::AddKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(add, ops::AddKernel<paddle::platform::CPUPlace, float>);

paddle/operators/add_op.cu

@@ -12,10 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#define EIGEN_USE_GPU
-#include "paddle/framework/op_registry.h"
 #include "paddle/operators/add_op.h"
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(add_two,
-                       ops::AddKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(add, ops::AddKernel<paddle::platform::GPUPlace, float>);

paddle/operators/cos_sim_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/cos_sim_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class CosSimOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
+    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
+                      ctx.Input<Tensor>("Y")->dims(),
+                      "Dimensions of Input(X) and Input(Y) must be the same.");
+
+    auto dims = ctx.Input<Tensor>("X")->dims();
+    ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
+    ctx.Output<Tensor>("XNorm")->Resize({dims[0], 1});
+    ctx.Output<Tensor>("YNorm")->Resize({dims[0], 1});
+  }
+};
+
+class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The first input of cos_sim op.");
+    AddInput("Y", "The second input of cos_sim op.");
+    AddOutput("Out", "The output of cos_sim op.");
+    AddOutput("XNorm", "Row norm of the first input.").AsIntermediate();
+    AddOutput("YNorm", "Row norm of the second input.").AsIntermediate();
+
+    AddComment(R"DOC(
+Cosine Similarity Operator.
+
+The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y))
+)DOC");
+  }
+};
+
+class CosSimOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
+                            "Input(XNorm) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
+                            "Input(YNorm) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) must not be null.");
+
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto y_dims = ctx.Input<Tensor>("Y")->dims();
+    auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
+    auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
+    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    PADDLE_ENFORCE_EQ(x_dims, y_dims,
+                      "Dimensions of Input(X) and Input(Y) must be the same.");
+    PADDLE_ENFORCE_EQ(xnorm_dims[0], x_dims[0],
+                      "1st dimension of XNorm must equal that of Input(X).");
+    PADDLE_ENFORCE_EQ(xnorm_dims[1], 1, "2st dimension of XNorm must be one.");
+    PADDLE_ENFORCE_EQ(ynorm_dims[0], y_dims[0],
+                      "1st dimension of YNorm must equal that of Input(Y).");
+    PADDLE_ENFORCE_EQ(ynorm_dims[1], 1, "2st dimension of YNorm must be one.");
+    PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
+                      "1st dimension of Out@GRAD must equal that of Input(X)");
+    PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
+
+    auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    if (x_grad) x_grad->Resize(x_dims);
+    if (y_grad) y_grad->Resize(y_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(cos_sim, ops::CosSimOp, ops::CosSimOpMaker, cos_sim_grad,
+            ops::CosSimOpGrad);
+REGISTER_OP_CPU_KERNEL(cos_sim,
+                       ops::CosSimKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    cos_sim_grad, ops::CosSimGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/gather_op.cu → paddle/operators/cos_sim_op.cu

@@ -13,8 +13,10 @@
    limitations under the License. */
 
 #define EIGEN_USE_GPU
-#include "paddle/operators/gather_op.h"
+#include "paddle/operators/cos_sim_op.h"
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(gather,
-                       ops::GatherOpKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(cos_sim,
+                       ops::CosSimKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    cos_sim_grad, ops::CosSimGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/cos_sim_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 {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class CosSimKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* input_x = context.Input<Tensor>("X");
+    auto* input_y = context.Input<Tensor>("Y");
+    auto* output_z = context.Output<Tensor>("Out");
+    auto* output_x_norm = context.Output<Tensor>("XNorm");
+    auto* output_y_norm = context.Output<Tensor>("YNorm");
+
+    output_z->mutable_data<T>(context.GetPlace());
+    output_x_norm->mutable_data<T>(context.GetPlace());
+    output_y_norm->mutable_data<T>(context.GetPlace());
+
+    auto dims = input_x->dims();
+    int size = static_cast<int>(framework::product(dims));
+    auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
+    auto x = EigenMatrix<T>::From(*input_x, new_dims);
+    auto y = EigenMatrix<T>::From(*input_y, new_dims);
+    auto z = EigenVector<T>::Flatten(*output_z);
+    auto x_norm = EigenVector<T>::Flatten(*output_x_norm);
+    auto y_norm = EigenVector<T>::Flatten(*output_y_norm);
+
+    auto place = context.GetEigenDevice<Place>();
+    auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
+    x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
+    y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
+    z.device(place) = xy / x_norm / y_norm;
+  }
+};
+
+template <typename Place, typename T>
+class CosSimGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* input_x = context.Input<Tensor>("X");
+    auto* input_y = context.Input<Tensor>("Y");
+    auto* input_z = context.Input<Tensor>("Out");
+    auto* input_x_norm = context.Input<Tensor>("XNorm");
+    auto* input_y_norm = context.Input<Tensor>("YNorm");
+    auto* output_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* output_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
+    auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto dims = input_x->dims();
+    int size = static_cast<int>(framework::product(dims));
+    auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
+    auto x = EigenMatrix<T>::From(*input_x, new_dims);
+    auto y = EigenMatrix<T>::From(*input_y, new_dims);
+    auto z = EigenMatrix<T>::From(*input_z);
+    auto x_norm = EigenMatrix<T>::From(*input_x_norm);
+    auto y_norm = EigenMatrix<T>::From(*input_y_norm);
+    auto dz = EigenMatrix<T>::From(*input_grad_z);
+
+    Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
+    auto z_bcast = z.broadcast(bcast);
+    auto dz_bcast = dz.broadcast(bcast);
+    auto place = context.GetEigenDevice<Place>();
+    auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast);
+    auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast);
+    auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast);
+    if (output_grad_x) {
+      output_grad_x->mutable_data<T>(context.GetPlace());
+      auto dx = EigenMatrix<T>::From(*output_grad_x, new_dims);
+      dx.device(place) =
+          dz_bcast * (y / norm_prod_bcast - z_bcast * x / x_snorm_bcast);
+    }
+    if (output_grad_y) {
+      output_grad_y->mutable_data<T>(context.GetPlace());
+      auto dy = EigenMatrix<T>::From(*output_grad_y, new_dims);
+      dy.device(place) =
+          dz_bcast * (x / norm_prod_bcast - z_bcast * y / y_snorm_bcast);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/gaussian_random_op.cc

@@ -19,20 +19,20 @@ template <typename T>
 class CPUGaussianRandomKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    float mean = context.GetAttr<float>("mean");
-    float std = context.GetAttr<float>("std");
+    float mean = context.Attr<float>("mean");
+    float std = context.Attr<float>("std");
     auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
 
-    unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
     std::minstd_rand engine;
     if (seed == 0) {
       seed = std::random_device()();
     }
     engine.seed(seed);
     std::normal_distribution<T> dist(mean, std);
-    ssize_t size = framework::product(tensor->dims());
-    for (ssize_t i = 0; i < size; ++i) {
+    int64_t size = framework::product(tensor->dims());
+    for (int64_t i = 0; i < size; ++i) {
       data[i] = dist(engine);
     }
   }
@@ -45,10 +45,15 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext& context) const override {
     auto* tensor = context.Output<framework::Tensor>("Out");
-    auto dims = GetAttr<std::vector<int>>("dims");
+    auto dims = Attr<std::vector<int>>("dims");
+    std::vector<int64_t> temp;
+    temp.reserve(dims.size());
+    for (auto dim : dims) {
+      temp.push_back(static_cast<int64_t>(dim));
+    }
     PADDLE_ENFORCE(dims.size() > 0UL,
                    "dims can be one int or array. dims must be set.");
-    tensor->Resize(framework::make_ddim(dims));
+    tensor->Resize(framework::make_ddim(temp));
   }
 };
 

paddle/operators/gaussian_random_op.cu

@@ -42,13 +42,13 @@ class GPUGaussianRandomKernel : public framework::OpKernel {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
-    unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
     if (seed == 0) {
       std::random_device rd;
       seed = rd();
     }
-    T mean = static_cast<T>(context.GetAttr<float>("mean"));
-    T std = static_cast<T>(context.GetAttr<float>("std"));
+    T mean = static_cast<T>(context.Attr<float>("mean"));
+    T std = static_cast<T>(context.Attr<float>("std"));
     thrust::counting_iterator<unsigned int> index_sequence_begin(0);
     ssize_t N = framework::product(tensor->dims());
     thrust::transform(index_sequence_begin, index_sequence_begin + N,

paddle/operators/identity_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/net_op.h"
+#include "paddle/operators/scale_op.h"
+
+namespace paddle {
+namespace operators {
+
+// identity is a alias of scale op. This is also a example for creating a alias
+// operator.
+template <typename AttrType>
+class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  IdentityOpMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "input tensor of identity op");
+    AddOutput("Out", "output tensor of identity op");
+    AddComment("identity operator. Just a alias of scale op which scale = 1.0");
+  }
+};
+
+template <typename AttrType>
+class IdentityOp : public NetOp {
+ public:
+  IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
+             const framework::VariableNameMap &outputs,
+             const framework::AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    AppendOp(framework::OpRegistry::CreateOp(
+        "scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
+        {{"scale", static_cast<AttrType>(1)}}));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
+                             ops::IdentityOpMaker<float>);

paddle/operators/rnn/recurrent_op_utils.cc

@@ -61,7 +61,7 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
       PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
                      outlinks[i].internal);
       f::DDim step_dims = step_scope_var->template GetMutable<Tensor>()->dims();
-      std::vector<int> dims_vec = vectorize(step_dims);
+      std::vector<int64_t> dims_vec = vectorize(step_dims);
       dims_vec.insert(dims_vec.begin(), seq_len);
       output->Resize(f::make_ddim(dims_vec));
     } else {
@@ -109,7 +109,7 @@ void InitArgument(const ArgumentName& name, Argument* arg,
   arg->step_scopes = op.Output(name.step_scopes);
 
   auto inlinks = op.Inputs(name.inlinks);
-  auto inlink_alias = op.GetAttr<std::vector<std::string>>(name.inlink_alias);
+  auto inlink_alias = op.Attr<std::vector<std::string>>(name.inlink_alias);
   PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(),
                  "the size of inlinks and inlink_alias don't match:%d,%d",
                  inlinks.size(), inlink_alias.size());
@@ -121,7 +121,7 @@ void InitArgument(const ArgumentName& name, Argument* arg,
   }
 
   auto outlinks = op.Outputs(name.outlinks);
-  auto outlink_alias = op.GetAttr<std::vector<std::string>>(name.outlink_alias);
+  auto outlink_alias = op.Attr<std::vector<std::string>>(name.outlink_alias);
   PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(),
                  "the size of outlinks and outlink_alias don't match:%d,%d",
                  outlinks.size(), outlink_alias.size());
@@ -135,8 +135,8 @@ void InitArgument(const ArgumentName& name, Argument* arg,
   auto boot_memories = op.Inputs(name.boot_memories);
 
   // attributes
-  auto memories = op.GetAttr<std::vector<std::string>>(name.memories);
-  auto pre_memories = op.GetAttr<std::vector<std::string>>(name.pre_memories);
+  auto memories = op.Attr<std::vector<std::string>>(name.memories);
+  auto pre_memories = op.Attr<std::vector<std::string>>(name.pre_memories);
 
   PADDLE_ENFORCE(memories.size() == boot_memories.size(),
                  "the size of memories, boot_memories don't match:%d,%d",

paddle/operators/scale_op.cc

@@ -48,7 +48,7 @@ The equation is: Out = scale*X
   }
 };
 
-// Identity Op's gradient is identity op, too.
+// Scale Op's gradient is scale op, too.
 // Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
 template <typename AttrType>
 class ScaleGradOp : public NetOp {
@@ -60,38 +60,11 @@ class ScaleGradOp : public NetOp {
     AppendOp(framework::OpRegistry::CreateOp(
         "scale", {{"X", {Input(framework::GradVarName("Out"))}}},
         {{"Out", {Output(framework::GradVarName("X"))}}},
-        {{"scale", GetAttr<AttrType>("scale")}}));
+        {{"scale", Attr<AttrType>("scale")}}));
     CompleteAddOp(false);
   }
 };
 
-// identity is a alias of scale op. This is also a example for creating a alias
-// operator.
-template <typename AttrType>
-class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  IdentityOpMaker(framework::OpProto *proto,
-                  framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "input tensor of identity op");
-    AddOutput("Out", "output tensor of identity op");
-    AddComment("identity operator. Just a alias of scale op which scale = 1.0");
-  }
-};
-
-template <typename AttrType>
-class IdentityOp : public NetOp {
- public:
-  IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
-             const framework::VariableNameMap &outputs,
-             const framework::AttributeMap &attrs)
-      : NetOp(type, inputs, outputs, attrs) {
-    AppendOp(framework::OpRegistry::CreateOp(
-        "scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
-        {{"scale", static_cast<AttrType>(1)}}));
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle
 
@@ -101,5 +74,3 @@ REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
             ops::ScaleGradOp<float>);
 REGISTER_OP_CPU_KERNEL(scale,
                        ops::ScaleKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
-                             ops::IdentityOpMaker<float>);

paddle/operators/scale_op.h

@@ -27,7 +27,7 @@ class ScaleKernel : public framework::OpKernel {
     auto* in = context.Input<framework::Tensor>("X");
     tensor->mutable_data<T>(in->place());
 
-    auto scale = static_cast<T>(context.GetAttr<AttrType>("scale"));
+    auto scale = static_cast<T>(context.Attr<AttrType>("scale"));
 
     auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
     auto eigen_in = framework::EigenVector<T>::Flatten(*in);

paddle/operators/scatter_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/scatter_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(scatter,
-                       ops::ScatterOpKernel<paddle::platform::GPUPlace, float>);

paddle/operators/sgd_op.h

@@ -31,7 +31,7 @@ class SGDOpKernel : public framework::OpKernel {
     auto param = ctx.Input<Tensor>("param");
     auto grad = ctx.Input<Tensor>("grad");
     auto param_out = ctx.Output<Tensor>("param_out");
-    float lr = ctx.GetAttr<float>("learning_rate");
+    float lr = ctx.Attr<float>("learning_rate");
 
     param_out->mutable_data<T>(ctx.GetPlace());
 

paddle/operators/softmax_op.cc

@@ -24,7 +24,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
-                   "The input of softmax op must be matrix");
+                   "The input of softmax op must be a matrix.");
     ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
@@ -34,9 +34,27 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
   SoftmaxOpMaker(framework::OpProto *proto,
                  framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "input of softmax");
-    AddOutput("Y", "output of softmax");
-    AddComment("Softmax Op");
+    AddInput("X",
+             "The input tensor of softmax. "
+             "2-D with shape [batch_size, input_feature_dimensions].");
+    AddOutput("Y", "The normalized values with the same shape as X.");
+    AddComment(R"DOC(
+The input of softmax operator is a 2-D tensor with shape N x K (N is the
+batch_size, K is the dimension of input feature). The output tensor has the
+same shape as the input tensor.
+
+For each row of the input tensor, the softmax operator squashes the
+K-dimensional vector of arbitrary real values to a K-dimensional vector of real
+values in the range [0, 1] that add up to 1. Specifically, it computes the
+exponential of the given dimension and the sum of exponential values of all
+the other dimensions in the K-dimensional vector input. Then the ratio of the
+exponential of the given dimension and the sum of exponential values of all
+the other dimensions is the output of the softmax operator.
+
+For each row `i` and each column `j` in X, we have:
+    Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j]))
+
+)DOC");
   }
 };
 

paddle/operators/uniform_random_op.cc

@@ -26,17 +26,17 @@ class CPUUniformRandomKernel : public framework::OpKernel {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
-    unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
     std::minstd_rand engine;
     if (seed == 0) {
       seed = std::random_device()();
     }
     engine.seed(seed);
     std::uniform_real_distribution<T> dist(
-        static_cast<T>(context.GetAttr<float>("min")),
-        static_cast<T>(context.GetAttr<float>("max")));
-    ssize_t size = framework::product(tensor->dims());
-    for (ssize_t i = 0; i < size; ++i) {
+        static_cast<T>(context.Attr<float>("min")),
+        static_cast<T>(context.Attr<float>("max")));
+    int64_t size = framework::product(tensor->dims());
+    for (int64_t i = 0; i < size; ++i) {
       data[i] = dist(engine);
     }
   }
@@ -48,11 +48,16 @@ class UniformRandomOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext& ctx) const override {
-    PADDLE_ENFORCE(GetAttr<float>("min") < GetAttr<float>("max"),
+    PADDLE_ENFORCE(Attr<float>("min") < Attr<float>("max"),
                    "uniform_random's min must less then max");
     auto* tensor = ctx.Output<framework::Tensor>("Out");
-    auto dims = GetAttr<std::vector<int>>("dims");
-    tensor->Resize(framework::make_ddim(dims));
+    auto dims = Attr<std::vector<int>>("dims");
+    std::vector<int64_t> temp;
+    temp.reserve(dims.size());
+    for (auto dim : dims) {
+      temp.push_back(static_cast<int64_t>(dim));
+    }
+    tensor->Resize(framework::make_ddim(temp));
   }
 };
 

paddle/operators/uniform_random_op.cu

@@ -45,13 +45,13 @@ class GPUUniformRandomKernel : public framework::OpKernel {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
-    unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
     if (seed == 0) {
       std::random_device rd;
       seed = rd();
     }
-    T min = static_cast<T>(context.GetAttr<float>("min"));
-    T max = static_cast<T>(context.GetAttr<float>("max"));
+    T min = static_cast<T>(context.Attr<float>("min"));
+    T max = static_cast<T>(context.Attr<float>("max"));
     thrust::counting_iterator<unsigned int> index_sequence_begin(0);
     ssize_t N = framework::product(tensor->dims());
     thrust::transform(index_sequence_begin, index_sequence_begin + N,

paddle/platform/cudnn_helper.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <vector>
 #include "paddle/platform/dynload/cudnn.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/macros.h"

paddle/pybind/pybind.cc

@@ -30,7 +30,7 @@ limitations under the License. */
 
 namespace py = pybind11;
 
-USE_OP(add_two);
+USE_OP(add);
 USE_OP(onehot_cross_entropy);
 USE_OP(sgd);
 USE_OP(mul);
@@ -46,6 +46,7 @@ USE_OP(lookup_table);
 USE_OP(scale);
 USE_NO_KERNEL_OP(identity);
 USE_OP(minus);
+USE_OP(cos_sim);
 USE_CPU_ONLY_OP(gather);
 USE_CPU_ONLY_OP(scatter);
 
@@ -76,7 +77,7 @@ PYBIND11_PLUGIN(core) {
       .def("get_dims",
            [](const Tensor &self) { return vectorize(self.dims()); })
       .def("set_dims",
-           [](Tensor &self, const std::vector<int> &dim) {
+           [](Tensor &self, const std::vector<int64_t> &dim) {
              self.Resize(make_ddim(dim));
            })
       .def("alloc_float",

paddle/pybind/tensor_py.h

@@ -85,7 +85,7 @@ void PyCPUTensorSetFromArray(
     framework::Tensor &self,
     py::array_t<T, py::array::c_style | py::array::forcecast> array,
     paddle::platform::CPUPlace &place) {
-  std::vector<int> dims;
+  std::vector<int64_t> dims;
   dims.reserve(array.ndim());
   for (size_t i = 0; i < array.ndim(); ++i) {
     dims.push_back((int)array.shape()[i]);
@@ -102,7 +102,7 @@ void PyCUDATensorSetFromArray(
     framework::Tensor &self,
     py::array_t<T, py::array::c_style | py::array::forcecast> array,
     paddle::platform::GPUPlace &place) {
-  std::vector<int> dims;
+  std::vector<int64_t> dims;
   dims.reserve(array.ndim());
   for (size_t i = 0; i < array.ndim(); ++i) {
     dims.push_back((int)array.shape()[i]);

python/paddle/trainer/PyDataProvider2.py

@@ -27,6 +27,14 @@ class SequenceType(object):
     SEQUENCE = 1
     SUB_SEQUENCE = 2
 
+    @classmethod
+    def tostring(cls, value):
+        for k in cls.__dict__:
+            if not k.startswith('__'):
+                if getattr(cls, k) == value:
+                    return cls.__name__ + '.' + k
+        return 'INVALID(' + str(value) + ')'
+
 
 # TODO(yuyang18): Add string data type here.
 class DataType(object):
@@ -35,6 +43,14 @@ class DataType(object):
     SparseValue = 2
     Index = 3
 
+    @classmethod
+    def tostring(cls, value):
+        for k in cls.__dict__:
+            if not k.startswith('__'):
+                if getattr(cls, k) == value:
+                    return cls.__name__ + '.' + k
+        return 'INVALID(' + str(value) + ')'
+
 
 class CacheType(object):
     NO_CACHE = 0  # No cache at all
@@ -69,6 +85,26 @@ class InputType(object):
         self.seq_type = seq_type
         self.type = tp
 
+    def __repr__(self):
+        """
+        Return a human readable representation like 'InputType(dim=25921, 
+            seq_type=SequenceType.NO_SEQUENCE, type=DataType.Dense)'
+        """
+        repr_str = type(self).__name__
+        repr_str += '('
+        serialize_func_map = {
+            'dim': repr,
+            'seq_type': SequenceType.tostring,
+            'type': DataType.tostring
+        }
+        for idx, k in enumerate(self.__slots__):
+            if idx != 0:
+                repr_str += ', '
+            repr_str += (
+                k + '=' + serialize_func_map.get(k, repr)(getattr(self, k)))
+        repr_str += ')'
+        return repr_str
+
 
 def dense_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
     """

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

@@ -4,6 +4,7 @@ py_test(test_scope SRCS test_scope.py)
 
 py_test(test_tensor SRCS test_tensor.py)
 py_test(test_mul_op SRCS test_mul_op.py)
+py_test(test_cos_sim_op SRCS test_cos_sim_op.py)
 
 py_test(test_mean_op SRCS test_mean_op.py)
 

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

@@ -36,13 +36,13 @@ def get_numeric_gradient(op,
                          in_place=False):
     """
     Get Numeric Gradient for an operator's input.
-    
-    :param op: C++ operator instance, could be an network 
-    :param input_values: The input variables. Should be an dictionary, key is 
+
+    :param op: C++ operator instance, could be an network
+    :param input_values: The input variables. Should be an dictionary, key is
     variable name. Value is numpy array.
-    :param output_name: The final output variable name. 
+    :param output_name: The final output variable name.
     :param input_to_check: The input variable need to get gradient.
-    :param delta: The perturbation value for numeric gradient method. The 
+    :param delta: The perturbation value for numeric gradient method. The
     smaller delta is, the more accurate result will get. But if that delta is
      too small, it could occur numerical stability problem.
     :param local_scope: The local scope used for get_numeric_gradient.
@@ -229,9 +229,9 @@ class GradientChecker(unittest.TestCase):
         """Use relative error for the comparison.
 
         :param numeric_grads: the numerical graidents.
-        :type numeric_grads: a list of numpy.array 
+        :type numeric_grads: a list of numpy.array
         :param analytic_grads: the analytical graidents.
-        :type analytic_grads: a list of numpy.array 
+        :type analytic_grads: a list of numpy.array
         :param name: the names of gradients, used to print for debug.
         :type names: a list of string
         :param msg_prefix: string info, used to print for debug.

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

@@ -6,13 +6,13 @@ from paddle.v2.framework.op import Operator
 class OpTestMeta(type):
     """
     Operator Test ClassMeta.
-    
-    It injects `test_all` method into user's OperatorTest class, to make Python 
+
+    It injects `test_all` method into user's OperatorTest class, to make Python
     unittest module run that method.
-    
+
     The `test_all` read what value is stored in `self`. It use self's values to
     create and run a operator, and check whether that op is OK or not.
-    
+
     See `test_add_two_op` for example usage.
     """
 

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

@@ -11,7 +11,7 @@ class TestAddOp(unittest.TestCase):
     __metaclass__ = OpTestMeta
 
     def setUp(self):
-        self.type = "add_two"
+        self.type = "add"
         self.inputs = {
             'X': numpy.random.random((102, 105)).astype("float32"),
             'Y': numpy.random.random((102, 105)).astype("float32")

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

+import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+
+class TestCosSimOp(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "cos_sim"
+        self.inputs = {
+            'X': np.random.random((32, 64)).astype("float32"),
+            'Y': np.random.random((32, 64)).astype("float32")
+        }
+        expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
+        expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
+        expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
+            expect_x_norm / expect_y_norm
+        self.outputs = {
+            'XNorm': np.expand_dims(expect_x_norm, 1),
+            'YNorm': np.expand_dims(expect_y_norm, 1),
+            'Out': np.expand_dims(expect_out, 1)
+        }
+
+
+class TestCosSimGradOp(GradientChecker):
+    def setUp(self):
+        self.op = create_op("cos_sim")
+        self.inputs = {
+            'X': np.random.random((10, 5)).astype("float32"),
+            'Y': np.random.random((10, 5)).astype("float32")
+        }
+
+    def test_cpu_gpu_compare(self):
+        self.compare_grad(self.op, self.inputs)
+
+    def test_normal(self):
+        self.check_grad(
+            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.05)
+
+    def test_ignore_x(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["Y"],
+            "Out",
+            max_relative_error=0.05,
+            no_grad_set={"X"})
+
+    def test_ignore_y(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["X"],
+            "Out",
+            max_relative_error=0.05,
+            no_grad_set={"Y"})
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -7,7 +7,7 @@ from gradient_checker import get_numeric_gradient
 
 class GetNumericGradientTest(unittest.TestCase):
     def test_add_op(self):
-        add_op = Operator('add_two', X="X", Y="Y", Out="Z")
+        add_op = Operator('add', X="X", Y="Y", Out="Z")
         x = numpy.random.random((10, 1)).astype("float32")
         y = numpy.random.random((10, 1)).astype("float32")
 

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

@@ -15,7 +15,7 @@ def fc(X, W, Y):
 class TestNet(unittest.TestCase):
     def test_net_all(self):
         net = core.Net.create()
-        op1 = Operator("add_two", X="X", Y="Y", Out="Out")
+        op1 = Operator("add", X="X", Y="Y", Out="Out")
         net.append_op(op1)
 
         net2 = core.Net.create()
@@ -26,7 +26,7 @@ class TestNet(unittest.TestCase):
 
         expected = '''
 Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
-    Op(add_two), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
+    Op(add), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
     Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
         Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
             Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.

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

@@ -193,10 +193,10 @@ class TestOpDescCreationMethod(unittest.TestCase):
 
 class TestOpCreations(unittest.TestCase):
     def test_all(self):
-        add_op = op.Operator("add_two", X="a", Y="b", Out="z")
+        add_op = op.Operator("add", X="a", Y="b", Out="z")
         self.assertIsNotNone(add_op)
         # Invoke C++ DebugString()
-        self.assertEqual('Op(add_two), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
+        self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
                          str(add_op))
 
 

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

@@ -146,7 +146,7 @@ class TestRecurrentOp(unittest.TestCase):
         stepnet = core.Net.create()
         x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
         h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
-        sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum")
+        sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
         sig_op = Operator("sigmoid", X="sum", Y="h@alias")
 
         for op in [x_fc_op, h_fc_op, sum_op, sig_op]: