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develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt
浏览文件 @ 93584eab
......@@ -23,15 +23,18 @@
- `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
-------------- | :----------------------
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](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。
......@@ -44,7 +47,7 @@ Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`
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)
......@@ -60,19 +63,19 @@ 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 <typename AttrType>
class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
......@@ -88,7 +91,7 @@ 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<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。
......@@ -97,7 +100,7 @@ The equation is: Out = scale*X
### 2. 定义Operator类
```c++
```cpp
class MulOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -122,13 +125,13 @@ 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)
......@@ -144,7 +147,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
### 3. 定义OpKernel类
```C++
```cpp
template <typename Place, typename T>
class MulKernel : public framework::OpKernel {
public:
......@@ -178,7 +181,7 @@ class MulKernel : public framework::OpKernel {
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<paddle::platform::CPUPlace, float>);
......@@ -192,7 +195,7 @@ REGISTER_OP_CPU_KERNEL(mul_grad,
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
......@@ -204,17 +207,18 @@ 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。
- 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)
```
```
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
```
```
make mul_op
```
## 绑定Python
......@@ -243,27 +247,17 @@ make mul_op
- 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
......@@ -287,11 +281,11 @@ class TestMulOp(unittest.TestCase):
- `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
```
develop/doc_cn/howto/dev/new_op_cn.html
浏览文件 @ 93584eab
......@@ -213,11 +213,14 @@
<li><code class="docutils literal"><span class="pre">framework::OperatorWithKernel</span></code>：继承自OperatorBase，Op有计算函数，称作有Kernel。</li>
<li><code class="docutils literal"><span class="pre">class</span> <span class="pre">OpProtoAndCheckerMaker</span></code>：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成</li>
</ul>
<p>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<code class="docutils literal"><span class="pre">OperatorBase</span></code>，后者继承自<code class="docutils literal"><span class="pre">OperatorWithKernel</span></code>。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：</p>
<p>内容 | 定义位置&#8212;&#8212;&#8212;&#8212;&#8211; | :&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;-OpProtoMake定义 | <code class="docutils literal"><span class="pre">.cc</span></code>文件，Backward Op不需要定义OpProtoMake
<p>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<code class="docutils literal"><span class="pre">OperatorBase</span></code>，后者继承自<code class="docutils literal"><span class="pre">OperatorWithKernel</span></code>。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：</p>
<p>内容 | 定义位置
&#8212;&#8212;&#8212;&#8212;&#8211; | :&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;-
OpProtoMake定义 | <code class="docutils literal"><span class="pre">.cc</span></code>文件，Backward Op不需要定义OpProtoMake
Op定义 | <code class="docutils literal"><span class="pre">.cc</span></code>文件
Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><span class="pre">.h</span></code>文件，否则，CPU可以在<code class="docutils literal"><span class="pre">.cc</span></code>文件，GPU可在<code class="docutils literal"><span class="pre">.cu</span></code>文件。
注册Op | Op注册在<code class="docutils literal"><span class="pre">.cc</span></code>文件；Kernel注册CPU在<code class="docutils literal"><span class="pre">.cc</span></code>文件，GPU在<code class="docutils literal"><span class="pre">.cu</span></code>文件</p>
Kernel实现 | CPU、GPU共享Kernel实现在<code class="docutils literal"><span class="pre">.h</span></code>文件中，否则，CPU 实现在<code class="docutils literal"><span class="pre">.cc</span></code>文件中，GPU 实现在<code class="docutils literal"><span class="pre">.cu</span></code>文件中。
注册Op | Op注册实现在<code class="docutils literal"><span class="pre">.cc</span></code>文件；Kernel注册CPU实现在<code class="docutils literal"><span class="pre">.cc</span></code>文件中，GPU实现在<code class="docutils literal"><span class="pre">.cu</span></code>文件中</p>
<p>实现新的op都添加至目录<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators">paddle/operators</a>下，文件命名以<code class="docutils literal"><span class="pre">*_op.h</span></code>（如有） 、 <code class="docutils literal"><span class="pre">*_op.cc</span></code> 、<code class="docutils literal"><span class="pre">*_op.cu</span></code>（如有）结尾。</p>
<p>下面以矩阵乘操作，即<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc">MulOp</a>为例来介绍如何写带Kernel的Operator。</p>
</div>
<div class="section" id="c">
......@@ -225,45 +228,45 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
<div class="section" id="protomaker">
<span id="protomaker"></span><h3>1. 定义ProtoMaker类<a class="headerlink" href="#protomaker" title="永久链接至标题"></a></h3>
<p>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<code class="docutils literal"><span class="pre">ProtoMaker</span></code>来描述该Op的输入、输出及注释：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOpMaker</span> <span class="p">:</span> <span class="n">public</span> <span class="n">framework</span><span class="p">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
<span class="n">public</span><span class="p">:</span>
<span class="n">MulOpMaker</span><span class="p">(</span><span class="n">framework</span><span class="p">::</span><span class="n">OpProto</span> <span class="o">*</span><span class="n">proto</span><span class="p">,</span> <span class="n">framework</span><span class="p">::</span><span class="n">OpAttrChecker</span> <span class="o">*</span><span class="n">op_checker</span><span class="p">)</span>
<span class="p">:</span> <span class="n">OpProtoAndCheckerMaker</span><span class="p">(</span><span class="n">proto</span><span class="p">,</span> <span class="n">op_checker</span><span class="p">)</span> <span class="p">{</span>
<span class="n">AddInput</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;The first input of mul op&quot;</span><span class="p">);</span>
<span class="n">AddInput</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;The second input of mul op&quot;</span><span class="p">);</span>
<span class="n">AddOutput</span><span class="p">(</span><span class="s2">&quot;Out&quot;</span><span class="p">,</span> <span class="s2">&quot;The output of mul op&quot;</span><span class="p">);</span>
<span class="n">AddComment</span><span class="p">(</span><span class="sa">R</span><span class="s2">&quot;DOC(</span>
<span class="n">Two</span> <span class="n">Element</span> <span class="n">Mul</span> <span class="n">Operator</span><span class="o">.</span>
<span class="n">The</span> <span class="n">equation</span> <span class="ow">is</span><span class="p">:</span> <span class="n">Out</span> <span class="o">=</span> <span class="n">X</span> <span class="o">*</span> <span class="n">Y</span>
<span class="p">)</span><span class="n">DOC</span><span class="s2">&quot;);</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOpMaker</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
<span class="k">public</span><span class="o">:</span>
<span class="n">MulOpMaker</span><span class="p">(</span><span class="n">framework</span><span class="o">::</span><span class="n">OpProto</span> <span class="o">*</span><span class="n">proto</span><span class="p">,</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpAttrChecker</span> <span class="o">*</span><span class="n">op_checker</span><span class="p">)</span>
<span class="o">:</span> <span class="n">OpProtoAndCheckerMaker</span><span class="p">(</span><span class="n">proto</span><span class="p">,</span> <span class="n">op_checker</span><span class="p">)</span> <span class="p">{</span>
<span class="n">AddInput</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">,</span> <span class="s">&quot;The first input of mul op&quot;</span><span class="p">);</span>
<span class="n">AddInput</span><span class="p">(</span><span class="s">&quot;Y&quot;</span><span class="p">,</span> <span class="s">&quot;The second input of mul op&quot;</span><span class="p">);</span>
<span class="n">AddOutput</span><span class="p">(</span><span class="s">&quot;Out&quot;</span><span class="p">,</span> <span class="s">&quot;The output of mul op&quot;</span><span class="p">);</span>
<span class="n">AddComment</span><span class="p">(</span><span class="sa">R</span><span class="s">&quot;</span><span class="dl">DOC(</span><span class="s"></span>
<span class="s">Two Element Mul Operator.</span>
<span class="s">The equation is: Out = X * Y</span>
<span class="dl">)DOC</span><span class="s">&quot;</span><span class="p">);</span>
<span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p><a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43"><code class="docutils literal"><span class="pre">MulOpMaker</span></code></a>继承自<code class="docutils literal"><span class="pre">framework::OpProtoAndCheckerMaker</span></code>，构造函数包括2个：</p>
<p><a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43"><code class="docutils literal"><span class="pre">MulOpMaker</span></code></a>继承自<code class="docutils literal"><span class="pre">framework::OpProtoAndCheckerMaker</span></code>，构造函数包括2个参数：</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">framework::OpProto</span></code> ： 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。</li>
<li><code class="docutils literal"><span class="pre">framework::OpAttrChecker</span></code> ：后者用于检查参数属性的合法性。</li>
</ul>
<p>构造函数里通过<code class="docutils literal"><span class="pre">AddInput</span></code>添加输入参数，通过<code class="docutils literal"><span class="pre">AddOutput</span></code>添加输出参数，通过<code class="docutils literal"><span class="pre">AddComment</span></code>添加该Op的注释，这些函数会将对应内容添加到<code class="docutils literal"><span class="pre">OpProto</span></code>中。</p>
<p>在<code class="docutils literal"><span class="pre">MulOp</span></code>中添加两个输入<code class="docutils literal"><span class="pre">X</span></code>和<code class="docutils literal"><span class="pre">Y</span></code>，添加了一个输出<code class="docutils literal"><span class="pre">Out</span></code>，并解释了各自含义，该命名尽可能的规范。</p>
<p>在<code class="docutils literal"><span class="pre">MulOp</span></code>中添加两个输入<code class="docutils literal"><span class="pre">X</span></code>和<code class="docutils literal"><span class="pre">Y</span></code>，添加了一个输出<code class="docutils literal"><span class="pre">Out</span></code>，并解释了各自含义，命名请遵守命名规范。</p>
<p>再举个<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37"><code class="docutils literal"><span class="pre">ScaleOp</span></code></a>的例子：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">template</span> <span class="o">&lt;</span><span class="n">typename</span> <span class="n">AttrType</span><span class="o">&gt;</span>
<span class="k">class</span> <span class="nc">ScaleOpMaker</span> <span class="p">:</span> <span class="n">public</span> <span class="n">framework</span><span class="p">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
<span class="n">public</span><span class="p">:</span>
<span class="n">ScaleOpMaker</span><span class="p">(</span><span class="n">framework</span><span class="p">::</span><span class="n">OpProto</span> <span class="o">*</span><span class="n">proto</span><span class="p">,</span> <span class="n">framework</span><span class="p">::</span><span class="n">OpAttrChecker</span> <span class="o">*</span><span class="n">op_checker</span><span class="p">)</span>
<span class="p">:</span> <span class="n">OpProtoAndCheckerMaker</span><span class="p">(</span><span class="n">proto</span><span class="p">,</span> <span class="n">op_checker</span><span class="p">)</span> <span class="p">{</span>
<span class="n">AddInput</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;The input tensor of scale operator.&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">NotInGradient</span><span class="p">();</span>
<span class="n">AddOutput</span><span class="p">(</span><span class="s2">&quot;Out&quot;</span><span class="p">,</span> <span class="s2">&quot;The output tensor of scale operator.&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">NotInGradient</span><span class="p">();</span>
<span class="n">AddComment</span><span class="p">(</span><span class="sa">R</span><span class="s2">&quot;DOC(Scale operator</span>
<span class="n">The</span> <span class="n">equation</span> <span class="ow">is</span><span class="p">:</span> <span class="n">Out</span> <span class="o">=</span> <span class="n">scale</span><span class="o">*</span><span class="n">X</span>
<span class="p">)</span><span class="n">DOC</span><span class="s2">&quot;);</span>
<span class="n">AddAttr</span><span class="o">&lt;</span><span class="n">AttrType</span><span class="o">&gt;</span><span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span> <span class="s2">&quot;scale of scale operator.&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">SetDefault</span><span class="p">(</span><span class="mf">1.0</span><span class="p">);</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">AttrType</span><span class="o">&gt;</span>
<span class="k">class</span> <span class="nc">ScaleOpMaker</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
<span class="k">public</span><span class="o">:</span>
<span class="n">ScaleOpMaker</span><span class="p">(</span><span class="n">framework</span><span class="o">::</span><span class="n">OpProto</span> <span class="o">*</span><span class="n">proto</span><span class="p">,</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpAttrChecker</span> <span class="o">*</span><span class="n">op_checker</span><span class="p">)</span>
<span class="o">:</span> <span class="n">OpProtoAndCheckerMaker</span><span class="p">(</span><span class="n">proto</span><span class="p">,</span> <span class="n">op_checker</span><span class="p">)</span> <span class="p">{</span>
<span class="n">AddInput</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">,</span> <span class="s">&quot;The input tensor of scale operator.&quot;</span><span class="p">).</span><span class="n">NotInGradient</span><span class="p">();</span>
<span class="n">AddOutput</span><span class="p">(</span><span class="s">&quot;Out&quot;</span><span class="p">,</span> <span class="s">&quot;The output tensor of scale operator.&quot;</span><span class="p">).</span><span class="n">NotInGradient</span><span class="p">();</span>
<span class="n">AddComment</span><span class="p">(</span><span class="sa">R</span><span class="s">&quot;</span><span class="dl">DOC(</span><span class="s">Scale operator</span>
<span class="s">The equation is: Out = scale*X</span>
<span class="dl">)DOC</span><span class="s">&quot;</span><span class="p">);</span>
<span class="n">AddAttr</span><span class="o">&lt;</span><span class="n">AttrType</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;scale&quot;</span><span class="p">,</span> <span class="s">&quot;scale of scale operator.&quot;</span><span class="p">).</span><span class="n">SetDefault</span><span class="p">(</span><span class="mf">1.0</span><span class="p">);</span>
<span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p>在这个例子里，两处不同：</p>
<p>这个例子有两处不同：</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">AddInput(&quot;X&quot;,&quot;...&quot;).NotInGradient()</span></code> : 表示<code class="docutils literal"><span class="pre">X</span></code>这个输入不参与<code class="docutils literal"><span class="pre">ScaleOp</span></code>对应的梯度Op计算之中。</li>
<li><code class="docutils literal"><span class="pre">AddAttr&lt;AttrType&gt;(&quot;scale&quot;,</span> <span class="pre">&quot;...&quot;).SetDefault(1.0);</span></code> : 增加<code class="docutils literal"><span class="pre">scale</span></code>系数，作为参数属性，并且设置默认值为1.0。</li>
......@@ -271,7 +274,7 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
</div>
<div class="section" id="operator">
<span id="id2"></span><h3>2. 定义Operator类<a class="headerlink" href="#operator" title="永久链接至标题"></a></h3>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOp</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span> <span class="p">{</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOp</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span> <span class="p">{</span>
<span class="k">public</span><span class="o">:</span>
<span class="k">using</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="p">;</span>
......@@ -294,11 +297,11 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
</pre></div>
</div>
<p><a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22"><code class="docutils literal"><span class="pre">MulOp</span></code></a>继承自<code class="docutils literal"><span class="pre">OperatorWithKernel</span></code>。<code class="docutils literal"><span class="pre">public</span></code>成员：</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="k">using</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="p">;</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">using</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="p">;</span>
</pre></div>
</div>
<p>这句表示使用基类<code class="docutils literal"><span class="pre">OperatorWithKernel</span></code>的构造函数，也可写成：</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">MulOp</span><span class="p">(</span><span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">string</span> <span class="o">&amp;</span><span class="n">type</span><span class="p">,</span> <span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">VariableNameMap</span> <span class="o">&amp;</span><span class="n">inputs</span><span class="p">,</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="n">MulOp</span><span class="p">(</span><span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">string</span> <span class="o">&amp;</span><span class="n">type</span><span class="p">,</span> <span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">VariableNameMap</span> <span class="o">&amp;</span><span class="n">inputs</span><span class="p">,</span>
<span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">VariableNameMap</span> <span class="o">&amp;</span><span class="n">outputs</span><span class="p">,</span>
<span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">AttributeMap</span> <span class="o">&amp;</span><span class="n">attrs</span><span class="p">)</span>
<span class="o">:</span> <span class="n">OperatorWithKernel</span><span class="p">(</span><span class="n">type</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">attrs</span><span class="p">)</span> <span class="p">{}</span>
......@@ -313,7 +316,7 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
</div>
<div class="section" id="opkernel">
<span id="opkernel"></span><h3>3. 定义OpKernel类<a class="headerlink" href="#opkernel" title="永久链接至标题"></a></h3>
<div class="highlight-C++"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">Place</span><span class="p">,</span> <span class="k">typename</span> <span class="n">T</span><span class="o">&gt;</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">Place</span><span class="p">,</span> <span class="k">typename</span> <span class="n">T</span><span class="o">&gt;</span>
<span class="k">class</span> <span class="nc">MulKernel</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpKernel</span> <span class="p">{</span>
<span class="k">public</span><span class="o">:</span>
<span class="kt">void</span> <span class="n">Compute</span><span class="p">(</span><span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">ExecutionContext</span><span class="o">&amp;</span> <span class="n">context</span><span class="p">)</span> <span class="k">const</span> <span class="k">override</span> <span class="p">{</span>
......@@ -341,7 +344,7 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
<div class="section" id="operator">
<span id="id3"></span><h3>4. 注册Operator<a class="headerlink" href="#operator" title="永久链接至标题"></a></h3>
<p>在<code class="docutils literal"><span class="pre">.cc</span></code>文件中注册前向、反向Op类，注册CPU Kernel。</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="k">namespace</span> <span class="n">ops</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">::</span><span class="n">operators</span><span class="p">;</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">namespace</span> <span class="n">ops</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">::</span><span class="n">operators</span><span class="p">;</span>
<span class="n">REGISTER_OP</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOp</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOpMaker</span><span class="p">,</span> <span class="n">mul_grad</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOpGrad</span><span class="p">);</span>
<span class="n">REGISTER_OP_CPU_KERNEL</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulKernel</span><span class="o">&lt;</span><span class="n">paddle</span><span class="o">::</span><span class="n">platform</span><span class="o">::</span><span class="n">CPUPlace</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
<span class="n">REGISTER_OP_CPU_KERNEL</span><span class="p">(</span><span class="n">mul_grad</span><span class="p">,</span>
......@@ -354,7 +357,7 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
<li><code class="docutils literal"><span class="pre">REGISTER_OP_CPU_KERNEL</span></code> ：注册<code class="docutils literal"><span class="pre">ops::MulKernel</span></code>类，并特化模板参数为<code class="docutils literal"><span class="pre">paddle::platform::CPUPlace</span></code>和<code class="docutils literal"><span class="pre">float</span></code>类型，同理，注册<code class="docutils literal"><span class="pre">ops::MulKernel</span></code>类。</li>
</ul>
<p>在 <code class="docutils literal"><span class="pre">.cu</span></code>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 <code class="docutils literal"><span class="pre">.cu</span></code>的最前面请加上宏定义 <code class="docutils literal"><span class="pre">#define</span> <span class="pre">EIGEN_USE_GPU</span></code></p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="c1">// if use Eigen unsupported module before include head files</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="c1">// if use Eigen unsupported module before include head files</span>
<span class="cp">#define EIGEN_USE_GPU</span>
<span class="k">namespace</span> <span class="n">ops</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">::</span><span class="n">operators</span><span class="p">;</span>
......@@ -366,14 +369,20 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
</div>
<div class="section" id="">
<span id="id4"></span><h3>5. 编译<a class="headerlink" href="#" title="永久链接至标题"></a></h3>
<p>在<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt">paddle/operators/CMakeLists.txt</a>文件中添加编译。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">op_library</span><span class="p">(</span><span class="n">mul_op</span> <span class="n">SRCS</span> <span class="n">mul_op</span><span class="o">.</span><span class="n">cc</span> <span class="n">mul_op</span><span class="o">.</span><span class="n">cu</span> <span class="n">DEPS</span> <span class="n">math_function</span><span class="p">)</span>
<ul>
<li><p class="first">简单<strong>无特殊依赖</strong>的OP无需修改CMakeList.txt文件。<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt">paddle/operators/CMakeLists.txt</a> 会自动将 <code class="docutils literal"><span class="pre">paddle/operators</span></code> 目录下新增的 <code class="docutils literal"><span class="pre">*_op.cc</span></code> 文件加入编译。</p>
</li>
<li><p class="first">较为复杂、<strong>有额外依赖</strong> 的operator仍需要修改<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt">paddle/operators/CMakeLists.txt</a>。如，<code class="docutils literal"><span class="pre">mul_op</span></code> 依赖 <code class="docutils literal"><span class="pre">math_function</span></code>，需要在<code class="docutils literal"><span class="pre">CMakeLists.txt</span></code>中添加如下内容：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">op_library</span><span class="p">(</span><span class="n">mul_op</span> <span class="n">SRCS</span> <span class="n">mul_op</span><span class="o">.</span><span class="n">cc</span> <span class="n">mul_op</span><span class="o">.</span><span class="n">cu</span> <span class="n">DEPS</span> <span class="n">math_function</span><span class="p">)</span> <span class="o">+</span>
</pre></div>
</div>
<p>下面命令可以编译：</p>
</li>
<li><p class="first">运行下面命令可以进行编译：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">mul_op</span>
</pre></div>
</div>
</li>
</ul>
</div>
</div>
<div class="section" id="python">
......@@ -397,16 +406,7 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
</ul>
<ul>
<li><p class="first">生成库</p>
<p>在 <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt"><code class="docutils literal"><span class="pre">paddle/pybind/CMakeLists.txt</span></code></a>文件添加类到<code class="docutils literal"><span class="pre">DEPS</span></code>中，使得该Op可以链接到生成的lib库中。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">if</span><span class="p">(</span><span class="n">WITH_PYTHON</span><span class="p">)</span>
<span class="n">cc_library</span><span class="p">(</span><span class="n">paddle_pybind</span> <span class="n">SHARED</span>
<span class="n">SRCS</span> <span class="n">pybind</span><span class="o">.</span><span class="n">cc</span>
<span class="n">DEPS</span> <span class="n">pybind</span> <span class="n">python</span> <span class="n">backward</span>
<span class="n">mul_op</span>
<span class="n">minus_op</span><span class="p">)</span>
<span class="n">endif</span><span class="p">(</span><span class="n">WITH_PYTHON</span><span class="p">)</span>
</pre></div>
</div>
<p>无需修改 <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt"><code class="docutils literal"><span class="pre">paddle/pybind/CMakeLists.txt</span></code></a>文件，<code class="docutils literal"><span class="pre">paddle/operators</span></code> 目录下新增的 <code class="docutils literal"><span class="pre">*_op.cc</span></code> 文件会自动被添加链接到生成的lib库中。</p>
</li>
</ul>
</div>
......@@ -414,15 +414,15 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
<span id="id5"></span><h2>实现单元测试<a class="headerlink" href="#" title="永久链接至标题"></a></h2>
<p>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<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py"><code class="docutils literal"><span class="pre">MulOp</span></code>的单测</a>。</p>
<div class="section" id="operator">
<span id="id6"></span><h3>前向Operator单测<a class="headerlink" href="#operator" title="永久链接至标题"></a></h3>
<span id="id6"></span><h3>前向Operator单元测试<a class="headerlink" href="#operator" title="永久链接至标题"></a></h3>
<p>前向Op单测继承自<code class="docutils literal"><span class="pre">unittest.TestCase</span></code>，并定义元类<code class="docutils literal"><span class="pre">__metaclass__</span> <span class="pre">=</span> <span class="pre">OpTestMeta</span></code>，具体单测流程在<code class="docutils literal"><span class="pre">OpTestMeta</span></code>里完成。需在<code class="docutils literal"><span class="pre">setUp</span></code>函数定义输入输出和属性参数，以及Python对比的输出值。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">unittest</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">gradient_checker</span> <span class="k">import</span> <span class="n">GradientChecker</span><span class="p">,</span> <span class="n">create_op</span>
<span class="kn">from</span> <span class="nn">op_test_util</span> <span class="k">import</span> <span class="n">OpTestMeta</span>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">unittest</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">gradient_checker</span> <span class="kn">import</span> <span class="n">GradientChecker</span><span class="p">,</span> <span class="n">create_op</span>
<span class="kn">from</span> <span class="nn">op_test_util</span> <span class="kn">import</span> <span class="n">OpTestMeta</span>
<span class="k">class</span> <span class="nc">TestMulOp</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
<span class="n">__metaclass__</span> <span class="o">=</span> <span class="n">OpTestMeta</span>
<span class="vm">__metaclass__</span> <span class="o">=</span> <span class="n">OpTestMeta</span>
<span class="k">def</span> <span class="nf">setUp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s2">&quot;mul&quot;</span>
......@@ -441,39 +441,39 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
</ul>
</div>
<div class="section" id="operator">
<span id="id7"></span><h3>反向Operator单测<a class="headerlink" href="#operator" title="永久链接至标题"></a></h3>
<span id="id7"></span><h3>反向Operator单元测试<a class="headerlink" href="#operator" title="永久链接至标题"></a></h3>
<p>反向Op单测继承自<code class="docutils literal"><span class="pre">GradientChecker</span></code>，而<code class="docutils literal"><span class="pre">GradientChecker</span></code>集成自<code class="docutils literal"><span class="pre">unittest.TestCase</span></code>，所以反向单测函数需要<code class="docutils literal"><span class="pre">test_</span></code>开头。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">TestMulGradOp</span><span class="p">(</span><span class="n">GradientChecker</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">setUp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="o">=</span> <span class="n">create_op</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;X&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">84</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">),</span>
<span class="s1">&#39;Y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">84</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nf">TestMulGradOp</span><span class="p">(</span><span class="n">GradientChecker</span><span class="p">)</span><span class="o">:</span>
<span class="n">def</span> <span class="n">setUp</span><span class="p">(</span><span class="n">self</span><span class="p">)</span><span class="o">:</span>
<span class="n">self</span><span class="p">.</span><span class="n">op</span> <span class="o">=</span> <span class="n">create_op</span><span class="p">(</span><span class="s">&quot;mul&quot;</span><span class="p">)</span>
<span class="n">self</span><span class="p">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
<span class="sc">&#39;X&#39;</span><span class="o">:</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">random</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">84</span><span class="p">)).</span><span class="n">astype</span><span class="p">(</span><span class="s">&quot;float32&quot;</span><span class="p">),</span>
<span class="sc">&#39;Y&#39;</span><span class="o">:</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">random</span><span class="p">((</span><span class="mi">84</span><span class="p">,</span> <span class="mi">100</span><span class="p">)).</span><span class="n">astype</span><span class="p">(</span><span class="s">&quot;float32&quot;</span><span class="p">)</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">test_cpu_gpu_compare</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">compare_grad</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">)</span>
<span class="n">def</span> <span class="nf">test_cpu_gpu_compare</span><span class="p">(</span><span class="n">self</span><span class="p">)</span><span class="o">:</span>
<span class="n">self</span><span class="p">.</span><span class="n">compare_grad</span><span class="p">(</span><span class="n">self</span><span class="p">.</span><span class="n">op</span><span class="p">,</span> <span class="n">self</span><span class="p">.</span><span class="n">inputs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">test_normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># mul op will enlarge the relative error</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">],</span> <span class="s2">&quot;Out&quot;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">def</span> <span class="n">test_normal</span><span class="p">(</span><span class="n">self</span><span class="p">)</span><span class="o">:</span>
<span class="cp"># mul op will enlarge the relative error</span>
<span class="n">self</span><span class="p">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="n">self</span><span class="p">.</span><span class="n">op</span><span class="p">,</span> <span class="n">self</span><span class="p">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s">&quot;X&quot;</span><span class="p">,</span> <span class="s">&quot;Y&quot;</span><span class="p">],</span> <span class="s">&quot;Out&quot;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">test_ignore_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Y&quot;</span><span class="p">],</span>
<span class="s2">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">def</span> <span class="n">test_ignore_x</span><span class="p">(</span><span class="n">self</span><span class="p">)</span><span class="o">:</span>
<span class="n">self</span><span class="p">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="n">self</span><span class="p">.</span><span class="n">op</span><span class="p">,</span>
<span class="n">self</span><span class="p">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s">&quot;Y&quot;</span><span class="p">],</span>
<span class="s">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;X&quot;</span><span class="p">})</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s">&quot;X&quot;</span><span class="p">})</span>
<span class="k">def</span> <span class="nf">test_ignore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;X&quot;</span><span class="p">],</span>
<span class="s2">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">def</span> <span class="n">test_ignore_y</span><span class="p">(</span><span class="n">self</span><span class="p">)</span><span class="o">:</span>
<span class="n">self</span><span class="p">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="n">self</span><span class="p">.</span><span class="n">op</span><span class="p">,</span>
<span class="n">self</span><span class="p">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s">&quot;X&quot;</span><span class="p">],</span>
<span class="s">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;Y&quot;</span><span class="p">})</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s">&quot;Y&quot;</span><span class="p">})</span>
</pre></div>
</div>
<p>下面解释一些关键的地方:</p>
......@@ -491,17 +491,17 @@ Kernel实现 | CPU、GPU共享Kernel在<code class="docutils literal"><spa
</ul>
</div>
<div class="section" id="">
<span id="id8"></span><h3>编译和执行<a class="headerlink" href="#" title="永久链接至标题"></a></h3>
<p>单测完成之后，在<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt"><code class="docutils literal"><span class="pre">python/paddle/v2/framework/tests/CMakeLists.txt</span></code></a>里添加编译：</p>
<span id="id8"></span><h3>编译和执行单元测试<a class="headerlink" href="#" title="永久链接至标题"></a></h3>
<p>单测完成之后，在<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt"><code class="docutils literal"><span class="pre">python/paddle/v2/framework/tests/CMakeLists.txt</span></code></a>里添加以下内容将单测加入工程中：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">py_test</span><span class="p">(</span><span class="n">test_mul_op</span> <span class="n">SRCS</span> <span class="n">test_mul_op</span><span class="o">.</span><span class="n">py</span><span class="p">)</span>
</pre></div>
</div>
<p>编译时需要打开<code class="docutils literal"><span class="pre">WITH_TESTING</span></code>, 即 <code class="docutils literal"><span class="pre">cmake</span> <span class="pre">paddle_dir</span> <span class="pre">-DWITH_TESTING=ON</span></code>，编译成功之后执行单测命令为：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">test</span> <span class="n">ARGS</span><span class="o">=</span><span class="s2">&quot;-R test_mul_op -V&quot;</span>
<p>请注意，<strong>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</strong>，并且编译时需要打开<code class="docutils literal"><span class="pre">WITH_TESTING</span></code>, 即<code class="docutils literal"><span class="pre">cmake</span> <span class="pre">paddle_dir</span> <span class="pre">-DWITH_TESTING=ON</span></code>。编译成功后，执行下面的命令来运行单测：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>make <span class="nb">test</span> <span class="nv">ARGS</span><span class="o">=</span><span class="s2">&quot;-R test_mul_op -V&quot;</span>
</pre></div>
</div>
<p>或者:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">ctest</span> <span class="o">-</span><span class="n">R</span> <span class="n">test_mul_op</span>
<div class="highlight-bash"><div class="highlight"><pre><span></span>ctest -R test_mul_op
</pre></div>
</div>
</div>
......
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