From 93584eabc338d41dd2f0a663780de68353806731 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Wed, 6 Sep 2017 06:54:58 +0000 Subject: [PATCH] Deploy to GitHub Pages: c107512624a5fe88c15a6a8c0115b1aeb6cf7b31 --- .../_sources/howto/dev/new_op_cn.md.txt | 157 ++++++++-------- develop/doc_cn/howto/dev/new_op_cn.html | 168 +++++++++--------- develop/doc_cn/searchindex.js | 2 +- 3 files changed, 161 insertions(+), 166 deletions(-) diff --git a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt index e3bee32f8e..0d29865447 100644 --- a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt +++ b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt @@ -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。 @@ -43,8 +46,8 @@ 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： - -``` + +```cpp class MulOpMaker : public framework::OpProtoAndCheckerMaker { public: MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -59,20 +62,20 @@ 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`，构造函数包括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。 +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。 + - 再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子： - -``` + +```cpp template class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { public: @@ -87,17 +90,17 @@ The equation is: Out = scale*X } }; ``` - - 在这个例子里，两处不同： - + + 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。 - `AddAttr("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 -```c++ +```cpp class MulOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -121,20 +124,20 @@ 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。 @@ -144,7 +147,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs, ### 3. 定义OpKernel类 -```C++ +```cpp template class MulKernel : public framework::OpKernel { public: @@ -163,36 +166,36 @@ class MulKernel : public framework::OpKernel { `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)。 为了使得`OpKernel`的计算过程书写较为简单，CPU、GPU的代码可以复用，我们通常借助Eigen unsupported Tensor模块来实现。关于在paddle中如何使用Eigen库，请参考对应的使用[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，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。 -```c++ +```cpp namespace ops = paddle::operators; REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad); REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); REGISTER_OP_CPU_KERNEL(mul_grad, ops::MulGradKernel); ``` - + - `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的实现是基于Eigen unsupported模块，那么在 `.cu`的最前面请加上宏定义 `#define EIGEN_USE_GPU` - -```c++ + +```cpp // if use Eigen unsupported module before include head files #define EIGEN_USE_GPU @@ -204,66 +207,57 @@ REGISTER_OP_GPU_KERNEL(mul_grad, ### 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 +- 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： ``` 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` 文件会自动被添加链接到生成的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)。 -### 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。 -``` +```python import unittest import numpy as np from gradient_checker import GradientChecker, create_op @@ -281,17 +275,17 @@ class TestMulOp(unittest.TestCase): 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。 - -### 反向Operator单测 + +### 反向Operator单元测试 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。 -``` +```cpp class TestMulGradOp(GradientChecker): def setUp(self): self.op = create_op("mul") @@ -337,21 +331,22 @@ class TestMulGradOp(GradientChecker): - `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。 -### 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)里添加以下内容将单测加入工程中： ``` 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测： -``` +```bash make test ARGS="-R test_mul_op -V" ``` + 或者: -``` +```bash ctest -R test_mul_op ``` diff --git a/develop/doc_cn/howto/dev/new_op_cn.html b/develop/doc_cn/howto/dev/new_op_cn.html index 106b93fc6f..742d077efc 100644 --- a/develop/doc_cn/howto/dev/new_op_cn.html +++ b/develop/doc_cn/howto/dev/new_op_cn.html @@ -213,11 +213,14 @@
  • 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：

    -

    内容 | 定义位置————– | :———————-OpProtoMake定义 | .cc文件，Backward Op不需要定义OpProtoMake +

    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 +————– | :———————- +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

    +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为例来介绍如何写带Kernel的Operator。

    @@ -225,45 +228,45 @@ Kernel实现 | CPU、GPU共享Kernel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：

    -
    class MulOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The first input of mul op");
-    AddInput("Y", "The second input of mul op");
-    AddOutput("Out", "The output of mul op");
-    AddComment(R"DOC(
-Two Element Mul Operator.
-The equation is: Out = X * Y
-)DOC");
+
    class MulOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The first input of mul op");
+    AddInput("Y", "The second input of mul op");
+    AddOutput("Out", "The output of mul op");
+    AddComment(R"DOC(
+Two Element Mul Operator.
+The equation is: Out = X * Y
+)DOC");
   }
 };
 
    
 
    
-
    MulOpMaker继承自framework::OpProtoAndCheckerMaker，构造函数包括2个：
    
+
    MulOpMaker继承自framework::OpProtoAndCheckerMaker，构造函数包括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。
    
+
    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。
    
 
    再举个ScaleOp的例子：
    
-
    template <typename AttrType>
-class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The input tensor of scale operator.").NotInGradient();
-    AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
-    AddComment(R"DOC(Scale operator
-The equation is: Out = scale*X
-)DOC");
-    AddAttr<AttrType>("scale", "scale of scale operator.").SetDefault(1.0);
+
    template <typename AttrType>
+class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input tensor of scale operator.").NotInGradient();
+    AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
+    AddComment(R"DOC(Scale operator
+The equation is: Out = scale*X
+)DOC");
+    AddAttr<AttrType>("scale", "scale of scale operator.").SetDefault(1.0);
   }
 };
 
    
 
    
-
    在这个例子里，两处不同：
    
+
    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。
      • 
 
    • 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。
      • 
@@ -271,7 +274,7 @@ Kernel实现       | CPU、GPU共享Kernel在
 
      
 
      2. 定义Operator类
      
-
      class MulOp : public framework::OperatorWithKernel {
+
      class MulOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
@@ -294,11 +297,11 @@ Kernel实现       | CPU、GPU共享Kernel在
      
 
      
 
      MulOp继承自OperatorWithKernel。public成员：
      
-
      using framework::OperatorWithKernel::OperatorWithKernel;
+
      using framework::OperatorWithKernel::OperatorWithKernel;
 
      
 
      
 
      这句表示使用基类OperatorWithKernel的构造函数，也可写成：
      
-
      MulOp(const std::string &type, const framework::VariableNameMap &inputs,
+
      MulOp(const std::string &type, const framework::VariableNameMap &inputs,
       const framework::VariableNameMap &outputs,
       const framework::AttributeMap &attrs)
   : OperatorWithKernel(type, inputs, outputs, attrs) {}
@@ -313,7 +316,7 @@ Kernel实现       | CPU、GPU共享Kernel在
 
      
 
      3. 定义OpKernel类
      
-
      template <typename Place, typename T>
+
      template <typename Place, typename T>
 class MulKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
@@ -341,7 +344,7 @@ Kernel实现       | CPU、GPU共享Kernel在
 
      4. 注册Operator
      
 
      在.cc文件中注册前向、反向Op类，注册CPU Kernel。
      
-
      namespace ops = paddle::operators;
+
      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,
@@ -354,7 +357,7 @@ Kernel实现       | CPU、GPU共享Kernel在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的实现是基于Eigen unsupported模块，那么在 .cu的最前面请加上宏定义 #define EIGEN_USE_GPU
    
-
    // if use Eigen unsupported module before include head files
+
    // if use Eigen unsupported module before include head files
 #define EIGEN_USE_GPU
 
 namespace ops = paddle::operators;
@@ -366,14 +369,20 @@ Kernel实现       | CPU、GPU共享Kernel在
 
    
 
    5. 编译
    
-
    在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)
+
      
+
    • 简单无特殊依赖的OP无需修改CMakeList.txt文件。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。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)       +
 
      
 
      
-
      下面命令可以编译：
      
+
      • 
+
    • 运行下面命令可以进行编译：
      
 
      make mul_op
 
      
 
      
+
      • 
+
    
 
    
 
    
 
    
@@ -397,16 +406,7 @@ Kernel实现       | CPU、GPU共享Kernel在
 
      
 
    • 生成库
      
-
      在 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文件，paddle/operators 目录下新增的 *_op.cc 文件会自动被添加链接到生成的lib库中。
      
 
      • 
 
    
 
    
@@ -414,15 +414,15 @@ Kernel实现       | CPU、GPU共享Kernel在
    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。
    
 
    
-
    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。
    
-
    import unittest
-import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+
    import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
 
 class TestMulOp(unittest.TestCase):
-    __metaclass__ = OpTestMeta
+    __metaclass__ = OpTestMeta
 
     def setUp(self):
         self.type = "mul"
@@ -441,39 +441,39 @@ Kernel实现       | CPU、GPU共享Kernel在
 
    
 
    
-
    反向Operator单测
    
+
    反向Operator单元测试
    
 
    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。
    
-
    class TestMulGradOp(GradientChecker):
-    def setUp(self):
-        self.op = create_op("mul")
-        self.inputs = {
-            'X': np.random.random((32, 84)).astype("float32"),
-            'Y': np.random.random((84, 100)).astype("float32")
+
    class TestMulGradOp(GradientChecker):
+    def setUp(self):
+        self.op = create_op("mul")
+        self.inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'Y': np.random.random((84, 100)).astype("float32")
         }
 
-    def test_cpu_gpu_compare(self):
-        self.compare_grad(self.op, self.inputs)
+    def test_cpu_gpu_compare(self):
+        self.compare_grad(self.op, self.inputs)
 
-    def test_normal(self):
-        # mul op will enlarge the relative error
-        self.check_grad(
-            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+    def test_normal(self):
+        # mul op will enlarge the relative error
+        self.check_grad(
+            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
 
-    def test_ignore_x(self):
-        self.check_grad(
-            self.op,
-            self.inputs, ["Y"],
-            "Out",
+    def test_ignore_x(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["Y"],
+            "Out",
             max_relative_error=0.5,
-            no_grad_set={"X"})
+            no_grad_set={"X"})
 
-    def test_ignore_y(self):
-        self.check_grad(
-            self.op,
-            self.inputs, ["X"],
-            "Out",
+    def test_ignore_y(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["X"],
+            "Out",
             max_relative_error=0.5,
-            no_grad_set={"Y"})
+            no_grad_set={"Y"})
 
    
 
    
 
    下面解释一些关键的地方:
    
@@ -491,17 +491,17 @@ Kernel实现       | CPU、GPU共享Kernel在
 
    
 
    
-
    编译和执行
    
-
    单测完成之后，在python/paddle/v2/framework/tests/CMakeLists.txt里添加编译：
    
+
    编译和执行单元测试
    
+
    单测完成之后，在python/paddle/v2/framework/tests/CMakeLists.txt里添加以下内容将单测加入工程中：
    
 
    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：
    
-
    make test ARGS="-R test_mul_op -V"
+
    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测：
    
+
    make test ARGS="-R test_mul_op -V"
 
    
 
    
 
    或者:
    
-
    ctest -R test_mul_op
+
    ctest -R test_mul_op
 
    
 
    
 
    
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
index 88e3ada8b2..e0482a197c 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
@@ -1 +1 @@
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