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 0d29865447f77601b66efe44383299f686a8bef9..58665e9f2b6299ec3959ed6858ab01d459f64dd8 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 @@ -45,7 +45,9 @@ Kernel实现 | CPU、GPU共享Kernel实现在`.h`文件中，否则，CPU ### 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 { @@ -63,17 +65,17 @@ 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个参数： +[`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。 +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。 -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。 +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。 -再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子： +再以[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)为例： ```cpp template @@ -91,14 +93,16 @@ The equation is: Out = scale*X }; ``` - 这个例子有两处不同： +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，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。 - - `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。 +- `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 +下面的点实现了MulOp的定义： ```cpp class MulOp : public framework::OperatorWithKernel { @@ -143,14 +147,27 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs, - 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 -```cpp -template -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 + class MulKernel : public framework::OpKernel { + public: void Compute(const framework::ExecutionContext& context) const override { auto* X = context.Input("X"); auto* Y = context.Input("Y"); @@ -160,50 +177,50 @@ class MulKernel : public framework::OpKernel { const_cast(context.device_context_); math::matmul(*X, false, *Y, false, 1, Z, 0, device_context); } -}; -``` + }; + ``` -`MulKernel`继承自`framework::OpKernel`，带有模板参数: +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。** - - `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)。 +`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)。 - - `typename T` : 表示数据类型，如`float`, `double`等。 +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)。 -`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。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`**。 -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) +### 4. 注册Operator -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`。 +- 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。 -### 4. 注册Operator + ```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); + ``` -在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。 + 在上面的代码中： -```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。 - - `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` +- 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 + ```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); -REGISTER_OP_GPU_KERNEL(mul_grad, - ops::MulGradKernel); -``` + namespace ops = paddle::operators; + REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); + REGISTER_OP_GPU_KERNEL(mul_grad, + ops::MulGradKernel); + ``` ### 5. 编译 @@ -225,7 +242,7 @@ REGISTER_OP_GPU_KERNEL(mul_grad, - 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： +`](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); @@ -242,50 +259,54 @@ REGISTER_OP_GPU_KERNEL(mul_grad, 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 - 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。 + 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： -```python -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： + ```python + 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 - - `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。 + 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。 +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**。 -```cpp +```python class TestMulGradOp(GradientChecker): def setUp(self): self.op = create_op("mul") @@ -319,27 +340,27 @@ 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)里添加以下内容将单测加入工程中： +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) ``` -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： +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" diff --git a/develop/doc_cn/howto/dev/new_op_cn.html b/develop/doc_cn/howto/dev/new_op_cn.html index 742d077efc2eb46819482968992da712726f33ac..659c0585f803fa5008a07d3d67d987ed1155da14 100644 --- a/develop/doc_cn/howto/dev/new_op_cn.html +++ b/develop/doc_cn/howto/dev/new_op_cn.html @@ -227,7 +227,8 @@ Kernel实现 | CPU、GPU共享Kernel实现在

实现C++类

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：

class MulOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
@@ -243,14 +244,14 @@ Kernel实现       | CPU、GPU共享Kernel实现在};
-

MulOpMaker继承自framework::OpProtoAndCheckerMaker，构造函数包括2个参数：

+

MulOpMaker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，命名请遵守命名规范。

-

再举个ScaleOp的例子：

+

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。

+

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为例：

template <typename AttrType>
 class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
@@ -268,12 +269,13 @@ Kernel实现       | CPU、GPU共享Kernel实现在
-
  • AddInput("X","...").NotInGradient() : 表示X这个输入不参与ScaleOp对应的梯度Op计算之中。
    • 
+
  • 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：

    class MulOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -312,38 +314,47 @@ Kernel实现       | CPU、GPU共享Kernel实现在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

    +

    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。
      • +
    • 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：

    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");
-    auto* Z = context.Output<Tensor>("Out");
-    Z->mutable_data<T>(context.GetPlace());
-    auto* device_context =
-        const_cast<platform::DeviceContext*>(context.device_context_);
-    math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
-  }
+public:
+void Compute(const framework::ExecutionContext& context) const override {
+  auto* X = context.Input<Tensor>("X");
+  auto* Y = context.Input<Tensor>("Y");
+  auto* Z = context.Output<Tensor>("Out");
+  Z->mutable_data<T>(context.GetPlace());
+  auto* device_context =
+      const_cast<platform::DeviceContext*>(context.device_context_);
+  math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
+}
 };
    -

    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。
      • -
    • 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。

    -

    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

    -

    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。

    +

    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。

    +

    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。

    +

    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。

    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。

      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>);
@@ -351,12 +362,19 @@ Kernel实现       | CPU、GPU共享Kernel实现在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 ： 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

      +
      • +
    +
      +

    • 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：
        • +
      // if use Eigen unsupported module before include head files
 #define EIGEN_USE_GPU
 
@@ -366,6 +384,8 @@ Kernel实现       | CPU、GPU共享Kernel实现在ops::MulGradKernel<paddle::platform::GPUPlace, float>);
      +
      • +

    5. 编译

    @@ -389,7 +409,7 @@ Kernel实现 | CPU、GPU共享Kernel实现在

    绑定Python

    -

    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。

    • 生成库

      -

      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。

      +

      无需修改 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。

    +

    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。

    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：

    +
      +
    1. 在setUp函数定义输入、输出，以及相关的属性参数。
      2. +
    2. 生成随机的输入数据。
      3. +
    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。
      4. +
    import unittest
 import numpy as np
 from gradient_checker import GradientChecker, create_op
@@ -433,70 +457,70 @@ Kernel实现       | CPU、GPU共享Kernel实现在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：

    +

    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，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。
      • +
    • 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")
+
    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。
    
+
    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:
    
+
    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。
        
+
      • 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分支测试只需要计算一个输入梯度的情况。
        • 
+
      • 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：

    +

    单元测试编写完成之后，在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)
    -

    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：

    +

    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：

    make test ARGS="-R test_mul_op -V"
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