提交 823bdd67 编写于 作者: F fengjiayi

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

......@@ -23,17 +23,20 @@
- `framework::OperatorWithKernel`:继承自OperatorBase,Op有计算函数,称作有Kernel。
- `class OpProtoAndCheckerMaker`:描述该Op的输入、输出、属性、注释,主要用于Python API接口生成
依据是否包含kernel,将Op分为两种:包含Kernel的Op和不包含kernel的Op,前者Op的定义继承自`OperatorBase`,后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写,简单总结Op需要包含的内容如下:
依据是否包含kernel,可以将Op分为两种:包含Kernel的Op和不包含kernel的Op,前者Op的定义继承自`OperatorBase`,后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写,简单总结Op需要包含的内容如下:
内容 | 定义位置
-------------- | :----------------------
内容 | 定义位置
-------------- | :----------------------
OpProtoMake定义 | `.cc`文件,Backward Op不需要定义OpProtoMake
Op定义 | `.cc`文件
Kernel实现 | CPU、GPU共享Kernel在`.h`文件,否则,CPU可以在`.cc`文件,GPU可在`.cu`文件。
注册Op | Op注册在`.cc`文件;Kernel注册CPU在`.cc`文件,GPU在`.cu`文件
Op定义 | `.cc`文件
Kernel实现 | CPU、GPU共享Kernel实现在`.h`文件中,否则,CPU 实现在`.cc`文件中,GPU 实现在`.cu`文件中。
注册Op | Op注册实现在`.cc`文件;Kernel注册CPU实现在`.cc`文件中,GPU实现在`.cu`文件中
实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下,文件命名以`*_op.h`(如有) 、 `*_op.cc``*_op.cu`(如有)结尾。
下面以矩阵乘操作,即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。
......@@ -42,9 +45,11 @@ Kernel实现 | CPU、GPU共享Kernel在`.h`文件,否则,CPU可以在`
### 1. 定义ProtoMaker类
矩阵乘的公式:$Out = X * Y$, 可见该计算由两个输入,一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释:
```
矩阵乘法的公式:$Out = X * Y$, 可见该计算由两个输入,一个输出组成。
首先定义`ProtoMaker`来描述该Op的输入、输出,并添加注释:
```cpp
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
......@@ -59,20 +64,20 @@ The equation is: Out = X * Y
}
};
```
[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`,构造函数包括2个
[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`,构造函数含有2个参数
- `framework::OpProto` : 前者存储Op的输入输出和参数属性,将用于Python API接口的生成。
- `framework::OpAttrChecker` :后者用于检查参数属性的合法性。
构造函数里通过`AddInput`添加输入参数,通过`AddOutput`添加输出参数,通过`AddComment`添加该Op的注释,这些函数会将对应内容添加到`OpProto`中。
`MulOp`中添加两个输入`X``Y`,添加了一个输出`Out`,并解释了各自含义,该命名尽可能的规范
构造函数里通过`AddInput`添加输入参数,通过`AddOutput`添加输出参数,通过`AddComment`添加Op的注释。这些函数会将对应内容添加到`OpProto`
再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子:
```
上面的代码在`MulOp`中添加两个输入`X``Y`,添加了一个输出`Out`,并解释了各自含义,命名请遵守命名规范。
再以[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)为例:
```cpp
template <typename AttrType>
class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
......@@ -87,17 +92,19 @@ The equation is: Out = scale*X
}
};
```
在这个例子里,两处不同:
- `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中。
- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数,作为参数属性,并且设置默认值为1.0。
这个例子有两处不同:
- `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中,如果Op的某个输入不参与反向梯度的计算,请显示地调用`.NotInGradient()`进行设置。
- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数,作为参数属性,并且设置默认值为1.0。
### 2. 定义Operator类
下面的点实现了MulOp的定义:
```c++
```cpp
class MulOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -121,33 +128,46 @@ class MulOp : public framework::OperatorWithKernel {
```
[`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22)继承自`OperatorWithKernel``public`成员:
```c++
```cpp
using framework::OperatorWithKernel::OperatorWithKernel;
```
这句表示使用基类`OperatorWithKernel`的构造函数,也可写成:
```c++
```cpp
MulOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
```
```
还需要重写`InferShape`接口。`InferShape`为const函数,不能修改Op的成员变量,参数为`const framework::InferShapeContext &ctx`,通过该参数可获取到输入输出以及属性。它的功能是:
- 1). 做检查, 尽早报错:检查输入数据维度、类型等是否合法。
- 2). 设置输出Tensor的形状。
通常`OpProtoMaker``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`
通常`OpProtoMaker``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`
### 3. 定义OpKernel类
```C++
template <typename Place, typename T>
class MulKernel : public framework::OpKernel {
public:
`MulKernel`继承自`framework::OpKernel`,带有下面两个模板参数:
- `typename Place`: 表示设备类型,不同设备(CPU、GPU)共享同一个Kernel时,需加该模板参数,不共享则不加,一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)
- `typename T` : 表示数据类型,如`float`, `double`等。
需要为`MulKernel`类重写`Compute`接口。
- `Compute`接受一个输入参数:`const framework::ExecutionContext& context`
-`InferShapeContext`相比,`ExecutionContext`增加了设备类型,同样可获取到输入输出和属性参数。
- `Compute`函数里实现`OpKernel`的具体计算逻辑。
下面是 `MulKernel` `Compute`的实现:
```cpp
template <typename Place, typename T>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* X = context.Input<Tensor>("X");
auto* Y = context.Input<Tensor>("Y");
......@@ -157,136 +177,136 @@ class MulKernel : public framework::OpKernel {
const_cast<platform::DeviceContext*>(context.device_context_);
math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
}
};
```
};
```
需要注意:**不同设备(CPU、GPU)共享一个Op定义,是否则共享同一个`OpKernel`,取决于`Compute`调用的函数是否支持不同设备。**
`MulOp`的CPU、GPU实现共享同一个`Kernel``OpKernel`不共享的例子可以参考:[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)
为了使`OpKernel`的计算过程书写更加简单,并且CPU、GPU的代码可以复用,我们通常借助 Eigen unsupported Tensor模块来实现`Compute`接口。关于在PaddlePaddle中如何使用Eigen库,请参考[使用文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)
到此,前向Op实现完成。接下来,需要在`.cc`文件中注册该op和kernel。
反向Op类的定义,反向OpKernel的定义与前向Op类似,这里不再赘述。**但需注意反向Op没有`ProtoMaker`**
`MulKernel`继承自`framework::OpKernel`,带有模板参数:
- `typename Place`: 表示设备类型,不同设备(CPU、GPU)共享同一个Kernel时,需加该模板参数,不共享则不加,一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)
- `typename T` : 表示数据类型,如`float`, `double`等。
`MulKernel`需要重写`Compute`接口,该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型,同样可获取到输入输出和属性参数,`Compute`函数里写具体实现时。
注意,不同设备(CPU、GPU)共享一个Op定义,是否则共享同一个`OpKernel`,取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel``OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)
到此前向Op实现完成,需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似,这里不再重复。但注意,反向Op没有`ProtoMaker`
### 4. 注册Operator
`.cc`文件中注册前向、反向Op类,注册CPU Kernel。
- `.cc`文件中注册前向、反向Op类,注册CPU Kernel。
```c++
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
```
- `REGISTER_OP` : 注册`ops::MulOp`类,类型名为`mul`,该类的`ProtoMaker``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``float`类型,同理,注册`ops::MulKernel`类。
`.cu`文件中注册GPU Kernel。
```c++
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::GPUPlace, float>);
```
```cpp
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
```
在上面的代码中:
- `REGISTER_OP` : 注册`ops::MulOp`类,类型名为`mul`,该类的`ProtoMaker`为`ops::MulOpMaker`,注册`ops::MulOpGrad`,类型名为`mul_grad`。
- `REGISTER_OP_WITHOUT_GRADIENT` : 用于注册没有反向的Op。
- `REGISTER_OP_CPU_KERNEL` :注册`ops::MulKernel`类,并特化模板参数为`paddle::platform::CPUPlace`和`float`类型,同理,注册`ops::MulKernel`类。
-`.cu`文件中注册GPU Kernel。
- 请注意,如果GPU Kernel的实现基于Eigen unsupported模块,那么在 `.cu`的开始请加上宏定义 `#define EIGEN_USE_GPU`,代码示例如下:
```cpp
// if use Eigen unsupported module before include head files
#define EIGEN_USE_GPU
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::GPUPlace, float>);
```
### 5. 编译
[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
[`paddle/pybind/pybind.cc
`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc)文件中添加该类:
- 绑定Python
在 [`paddle/pybind/pybind.cc
`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) 使用`USE_OP`告知编译器需要链接的Op,具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
```
USE_OP(mul);
```
如果只实现了CPU版本,则使用`USE_CPU_ONLY_OP`:
```
USE_CPU_ONLY_OP(gather);
```
如果OP不带Kernel,则使用`USE_NO_KENREL_OP`:
```
USE_NO_KENREL_OP(recurrent);
```
使用`USE_OP`告知编译器需要链接该Op的目标文件,具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
- 生成库
[`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件添加类到`DEPS`中,使得该Op可以链接到生成的lib库中。
```
if(WITH_PYTHON)
cc_library(paddle_pybind SHARED
SRCS pybind.cc
DEPS pybind python backward
mul_op
minus_op)
endif(WITH_PYTHON)
```
无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件,`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动添加链接到生成的lib库中。
## 实现单元测试
单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单](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
### 前向Operator单元测试
前向Op单测继承自`unittest.TestCase`,并定义元类`__metaclass__ = OpTestMeta`,具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数,以及Python对比的输出值。
前向Op单元测试继承自`unittest.TestCase`,并定义元类`__metaclass__ = OpTestMeta`。各项更加具体的单元测试在`OpTestMeta`里完成。测试前向Operator,需要:
```
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
1. 在`setUp`函数定义输入、输出,以及相关的属性参数。
2. 生成随机的输入数据。
3. 在Python脚本中实现与前向operator相同的计算逻辑,得到输出值,与operator前向计算的输出进行对比。
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "mul"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
```
首先需要`import`必要的包,下面详细解释其他值:
- `self.type = "mul" ` : 定义类型,和注册的类型一致。
- `self.inputs` : 定义输入,类型为Numpy.array,并初始化。
- `self.outputs` : 定义输出,并得到Python结算结果。
```python
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
### 反向Operator单测
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
反向Op单测继承自`GradientChecker`,而`GradientChecker`集成自`unittest.TestCase`,所以反向单测函数需要`test_`开头。
def setUp(self):
self.type = "mul"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
```
```
上面的代码首先导入依赖的包,下面是对`setUp`函数中操作的重要变量的详细解释:
- `self.type = "mul" ` : 定义类型,与operator注册时注册的类型一致。
- `self.inputs` : 定义输入,类型为`numpy.array`,并初始化。
- `self.outputs` : 定义输出,并在Python脚本中完成与operator同样的计算逻辑,返回Python端的计算结果。
### 反向Operator单元测试
反向Op单元测试继承自`GradientChecker`,而`GradientChecker`继承自`unittest.TestCase`,因此,**反向单元测试函数需要以`test_`开头**。
```python
class TestMulGradOp(GradientChecker):
def setUp(self):
self.op = create_op("mul")
......@@ -320,33 +340,34 @@ class TestMulGradOp(GradientChecker):
no_grad_set={"Y"})
```
下面解释一些关键的地方:
下面解释代码中一些关键的地方:
- 调用`create_op("mul")`创建反向Op对应的前向Op。
- 调用`compare_grad`函数对比CPU、GPU计算结果。
- `test_normal`中调用`check_grad`检查梯度稳定性,这里采用数值法检测梯度正确性。
- 第一个参数`self.op` : 前向Op。
- 第二个参数`self.inputs` : 输入词典,词典的Key和`ProtoMaker`定义保持一致。
- 第三个参数`["X", "Y"]` : 指定对输入变量`X``Y`做梯度检测。
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
- `test_ignore_x``test_ignore_y`分支测试只需要计算一个输入梯度的情况。
- 调用`create_op("mul")`创建反向Op对应的前向Op。
- 调用`compare_grad`函数对比CPU、GPU计算结果。
- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
- 第一个参数`self.op` : 前向Op。
- 第二个参数`self.inputs` : 输入词典,词典的Key和`ProtoMaker`定义保持一致。
- 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。
### 编译和执行
### 编译和执行单元测试
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
元测试编写完成之后,在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)中添加以下内容,将单元测试加入工程
```
py_test(test_mul_op SRCS test_mul_op.py)
```
编译时需要打开`WITH_TESTING`, 即 `cmake paddle_dir -DWITH_TESTING=ON`,编译成功之后执行单测命令为
请注意,**不同于Op的编译测试,运行单元测试测时需要编译整个工程**,并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后,执行下面的命令来运行单元测试
```
```bash
make test ARGS="-R test_mul_op -V"
```
或者:
```
```bash
ctest -R test_mul_op
```
## 在Paddle中如何使用Eigen
神经网络本质上是一个计算图,计算需要的数据存放在`Tensor`中,而计算过程是由`Operartor`来描述的。在执行时,`Operator`调用对应`OpKernel`中的`Compute`接口,实现对`Tensor`的操作。
### Eigen Tensor模块
Eigen Tensor模块对element-wise计算提供了强大的支持,并且书写一份代码,可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块,因此可能测试不够完备,文档较少。
关于Eigen Tensor模块的详细介绍请参考[文档1](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md)[文档2](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md)
### paddle::framework::Tensor
Paddle Tensor定义在framework目录下,其主要接口如下:
```cpp
class Tensor {
public:
/*! Return a pointer to mutable memory block. */
template <typename T>
inline T* data();
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(platform::Place place);
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
*
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(DDim dims, platform::Place place);
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
private:
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
/*! points to dimensions of memory block. */
DDim dim_;
};
```
`Placeholder`的作用是延迟分配内存,即我们可以先定义一个Tensor,然后使用Resize接口设置Tensor的大小,最后再调用mutable_data接口分配实际的内存。
```cpp
paddle::framework::Tensor t;
paddle::platform::CPUPlace place;
// set size first
t.Resize({2, 3});
// allocate memory on CPU later
t.mutable_data(place);
```
### paddle::framework::Tensor使用样例
下面以AddOp为例说明Tensor的使用过程:
- InferShape
在运行神经网络计算图时,我们先调用每个`Operator``InferShape`接口,根据输入Tensor的大小来设置输出Tensor的大小,`Resize`接口会被调用。
```cpp
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Two input of Add Op's dimension must be same.");
ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
}
```
- Run
`Operator``Run`接口最终会调用对应`OpKernel``Compute`接口,在这时真正的分配内存,`mutable_data`接口会被调用。
```cpp
void Compute(const framework::ExecutionContext& context) const override {
auto* input0 = context.Input<Tensor>("X");
auto* input1 = context.Input<Tensor>("Y");
auto* output = context.Output<Tensor>("Out");
output->mutable_data<T>(context.GetPlace());
auto x = EigenVector<T>::Flatten(*input0);
auto y = EigenVector<T>::Flatten(*input1);
auto z = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
z.device(place) = x + y;
}
```
### paddle::framework::Tensor到EigenTensor的转换
如上一小节所示,在具体的计算中,我们需要先把输入Tensor和输出Tensor转换为Eigen支持的格式。我们在[eigen.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen.h)中提供了一些全局函数用来实现paddle::framework::Tensor到EigenTensor/EigenMatrix/EigenVector/EigenScalar的转换。
以EigenTensor为例,做一个介绍
```cpp
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
```
From是EigenTensor模板提供的一个接口,可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数,因此在转换时需要显示的指定。
在Eigen中,不同rank的Tensor是不同类型,Vector是rank为1的Tensor。需要额外注意的是,EigenVector<T>::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor,在这里用EigenVector来表示;而EigenVector<T>::Flatten方法是把paddle中的一个Tensor进行reshape操作,压扁成为Eigen的一维Tensor,类型仍然为EigenVector。
更多的转换方法请参考eigen_test.cc中的[单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen_test.cc)
### 实现计算
当需要完成计算时,我们需要等式左边的EigenTensor调用device接口。在这里需要注意的是,这里的EigenTensor之间的运算只是改变了原有Tensor中的数据,而不会改变原有Tensor的shape信息。
```cpp
auto x = EigenVector<T>::Flatten(*input0);
auto y = EigenVector<T>::Flatten(*input1);
auto z = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
z.device(place) = x + y;
```
在这段代码中,input0/input1/output可以是任意维度的Tensor。我们调用了EigenVector的Flatten接口,把任意维度的Tensor转为了一维的EigenVector。而在计算结束之后,input0/input1/output的原有shape信息不变。如果想改变原有Tensor的shape信息,可以调用Resize接口进行改变。
由于Eigen Tensor模块的文档较少,我们可以参考TensorFlow的[kernels](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/kernels)模块下的相关`OpKernel`的计算代码。
......@@ -43,6 +43,10 @@ template <>
AttrType AttrTypeID<std::vector<std::string>>() {
return STRINGS;
}
template <>
AttrType AttrTypeID<std::vector<std::pair<int, int>>>() {
return INT_PAIRS;
}
Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
switch (attr_desc.type()) {
......@@ -76,6 +80,14 @@ Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
}
return val;
}
case paddle::framework::AttrType::INT_PAIRS: {
std::vector<std::pair<int, int>> val(attr_desc.int_pairs_size());
for (int i = 0; i < attr_desc.int_pairs_size(); ++i) {
val[i].first = attr_desc.int_pairs(i).first();
val[i].second = attr_desc.int_pairs(i).second();
}
return val;
}
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
......
......@@ -28,7 +28,8 @@ namespace paddle {
namespace framework {
typedef boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>>
std::vector<float>, std::vector<std::string>,
std::vector<std::pair<int, int>>>
Attribute;
typedef std::unordered_map<std::string, Attribute> AttributeMap;
......
......@@ -21,16 +21,16 @@ namespace framework {
/// @cond HIDDEN
template <int i>
Dim<i> make_dim(const int* d) {
Dim<i> make_dim(const int64_t* d) {
return Dim<i>(*d, make_dim<i - 1>(d + 1));
}
template <>
Dim<1> make_dim<1>(const int* d) {
Dim<1> make_dim<1>(const int64_t* d) {
return Dim<1>(*d);
}
void make_ddim(DDim& ddim, const int* dims, int n) {
void make_ddim(DDim& ddim, const int64_t* dims, int n) {
switch (n) {
case 1:
ddim = make_dim<1>(dims);
......@@ -67,13 +67,13 @@ void make_ddim(DDim& ddim, const int* dims, int n) {
/// @endcond
DDim make_ddim(std::initializer_list<int> dims) {
DDim make_ddim(std::initializer_list<int64_t> dims) {
DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size());
return result;
}
DDim make_ddim(const std::vector<int>& dims) {
DDim make_ddim(const std::vector<int64_t>& dims) {
DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size());
return result;
......@@ -81,12 +81,12 @@ DDim make_ddim(const std::vector<int>& dims) {
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes errors
class DynamicMutableIndexer : public boost::static_visitor<int&> {
class DynamicMutableIndexer : public boost::static_visitor<int64_t&> {
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D>
int& operator()(Dim<D>& dim) const {
int64_t& operator()(Dim<D>& dim) const {
return dim[idx_];
}
......@@ -94,12 +94,12 @@ class DynamicMutableIndexer : public boost::static_visitor<int&> {
int idx_;
};
class DynamicConstIndexer : public boost::static_visitor<int> {
class DynamicConstIndexer : public boost::static_visitor<int64_t> {
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D>
int operator()(const Dim<D>& dim) const {
int64_t operator()(const Dim<D>& dim) const {
return dim[idx_];
}
......@@ -109,22 +109,22 @@ class DynamicConstIndexer : public boost::static_visitor<int> {
/// @endcond
int& DDim::operator[](int idx) {
int64_t& DDim::operator[](int idx) {
return boost::apply_visitor(DynamicMutableIndexer(idx), var);
}
int DDim::operator[](int idx) const {
int64_t DDim::operator[](int idx) const {
return boost::apply_visitor(DynamicConstIndexer(idx), var);
}
ssize_t DDim::size() const { return arity(*this); }
int64_t DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const {
if (var.which() != d.getVar().which()) {
return false;
} else {
std::vector<int> v1 = vectorize(*this);
std::vector<int> v2 = vectorize(d);
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) {
......@@ -139,10 +139,10 @@ bool DDim::operator==(DDim d) const {
bool DDim::operator!=(DDim d) const { return !(*this == d); }
DDim DDim::operator+(DDim d) const {
std::vector<int> v1 = vectorize(*this);
std::vector<int> v2 = vectorize(d);
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int> v3;
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
......@@ -154,10 +154,10 @@ DDim DDim::operator+(DDim d) const {
}
DDim DDim::operator*(DDim d) const {
std::vector<int> v1 = vectorize(*this);
std::vector<int> v2 = vectorize(d);
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int> v3;
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
......@@ -168,15 +168,15 @@ DDim DDim::operator*(DDim d) const {
return make_ddim(v3);
}
int get(const DDim& ddim, int idx) { return ddim[idx]; }
int64_t get(const DDim& ddim, int idx) { return ddim[idx]; }
void set(DDim& ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : public boost::static_visitor<> {
std::vector<int>& vector;
std::vector<int64_t>& vector;
explicit VectorizeVisitor(std::vector<int>& v) : vector(v) {}
explicit VectorizeVisitor(std::vector<int64_t>& v) : vector(v) {}
template <typename T>
void operator()(const T& t) {
......@@ -188,19 +188,31 @@ struct VectorizeVisitor : public boost::static_visitor<> {
};
/// @endcond
std::vector<int> vectorize(const DDim& ddim) {
std::vector<int> result;
std::vector<int64_t> vectorize(const DDim& ddim) {
std::vector<int64_t> result;
VectorizeVisitor visitor(result);
boost::apply_visitor(visitor, ddim);
return result;
}
struct ProductVisitor : public boost::static_visitor<int64_t> {
template <int D>
int64_t operator()(const Dim<D>& dim) {
return product(dim);
}
};
int64_t product(const DDim& ddim) {
ProductVisitor visitor;
return boost::apply_visitor(visitor, ddim);
}
struct SliceVectorizeVisitor : public boost::static_visitor<> {
std::vector<int>& vector;
std::vector<int64_t>& vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int>& v, int b, int e)
SliceVectorizeVisitor(std::vector<int64_t>& v, int b, int e)
: vector(v), begin(b), end(e) {
PADDLE_ENFORCE(begin < end,
"Begin index must be less than end index in ddim slice.");
......@@ -228,25 +240,13 @@ struct SliceVectorizeVisitor : public boost::static_visitor<> {
};
DDim slice_ddim(const DDim& dim, int begin, int end) {
std::vector<int> vec;
std::vector<int64_t> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
boost::apply_visitor(visitor, dim);
return make_ddim(vec);
}
struct ProductVisitor : public boost::static_visitor<ssize_t> {
template <int D>
ssize_t operator()(const Dim<D>& dim) {
return product(dim);
}
};
ssize_t product(const DDim& ddim) {
ProductVisitor visitor;
return boost::apply_visitor(visitor, ddim);
}
/// \cond HIDDEN
struct ArityVisitor : boost::static_visitor<int> {
......@@ -280,7 +280,7 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
return os;
}
DDim::DDim(std::initializer_list<int> init_list) {
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
}
......
......@@ -40,7 +40,7 @@ struct DDim {
template <int D>
explicit DDim(const Dim<D>& in) : var(in) {}
/*implicit*/ DDim(std::initializer_list<int> init_list);
/*implicit*/ DDim(std::initializer_list<int64_t> init_list);
template <int D>
DDim& operator=(const Dim<D>& in) {
......@@ -48,8 +48,8 @@ struct DDim {
return *this;
}
int& operator[](int idx);
int operator[](int idx) const;
int64_t& operator[](int idx);
int64_t operator[](int idx) const;
template <typename Visitor>
typename Visitor::result_type apply_visitor(Visitor& visitor) {
......@@ -71,15 +71,15 @@ struct DDim {
DDim operator*(DDim d) const;
ssize_t size() const;
int64_t size() const;
};
/**
* \brief Make a DDim from std::vector<int>
* \brief Make a DDim from std::vector<int64_t>
*
* \param dims An vector of ints. Must be sized between [1, 9]
*/
DDim make_ddim(const std::vector<int>& dims);
DDim make_ddim(const std::vector<int64_t>& dims);
/**
* \brief Make a DDim from an initializer list
......@@ -87,14 +87,14 @@ DDim make_ddim(const std::vector<int>& dims);
* \param dims An initializer list of ints. Must be sized between [1, 9]
*
*/
DDim make_ddim(std::initializer_list<int> dims);
DDim make_ddim(std::initializer_list<int64_t> dims);
int get(const DDim& dim, int idx);
int64_t get(const DDim& dim, int idx);
void set(DDim& dim, int idx, int val);
std::vector<int> vectorize(const DDim& ddim);
std::vector<int64_t> vectorize(const DDim& ddim);
ssize_t product(const DDim& ddim);
int64_t product(const DDim& ddim);
/**
* \brief Slice a ddim
......
......@@ -12,7 +12,7 @@ TEST(DDim, Equality) {
EXPECT_EQ(ddim[2], 5);
// construct a DDim from a vector
std::vector<int> vec({9, 1, 5});
std::vector<int64_t> vec({9, 1, 5});
paddle::framework::DDim vddim = paddle::framework::make_ddim(vec);
EXPECT_EQ(ddim[0], 9);
EXPECT_EQ(ddim[1], 1);
......@@ -25,7 +25,7 @@ TEST(DDim, Equality) {
EXPECT_EQ(paddle::framework::get(ddim, 0), 6);
// vectorize a DDim
std::vector<int> res_vec = paddle::framework::vectorize(vddim);
std::vector<int64_t> res_vec = paddle::framework::vectorize(vddim);
EXPECT_EQ(res_vec[0], 9);
EXPECT_EQ(res_vec[1], 1);
EXPECT_EQ(res_vec[2], 5);
......
......@@ -17,13 +17,13 @@ struct Dim {
static constexpr int dimensions = i;
template <typename... Args>
HOSTDEVICE Dim(int _head, Args... _tail) : head(_head), tail(_tail...) {
HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
static_assert(sizeof...(_tail) == i - 1,
"Dim initialized with the wrong number of parameters");
}
HOSTDEVICE
Dim(int _head, const Dim<i - 1>& _tail) : head(_head), tail(_tail) {}
Dim(int64_t _head, const Dim<i - 1>& _tail) : head(_head), tail(_tail) {}
HOSTDEVICE
Dim() : head(0), tail() {}
......@@ -31,12 +31,12 @@ struct Dim {
/** Construct a Dim from a linear index and size. Uses Fortran order
* indexing. */
HOSTDEVICE
Dim(int idx, const Dim<i>& size)
Dim(int64_t idx, const Dim<i>& size)
: head(idx % size.head), tail(idx / size.head, size.tail) {}
/** Construct a Dim with each dimension set to the given index */
HOSTDEVICE
Dim(int idx) : head(idx), tail(idx) {}
Dim(int64_t idx) : head(idx), tail(idx) {}
HOSTDEVICE
bool operator==(const Dim<i>& o) const {
......@@ -47,13 +47,13 @@ struct Dim {
bool operator!=(const Dim<i>& o) const { return !(*this == o); }
HOSTDEVICE
int& operator[](int idx);
int64_t& operator[](int idx);
HOSTDEVICE
int operator[](int idx) const;
int64_t operator[](int idx) const;
HOST std::string to_string() const;
int head;
int64_t head;
Dim<i - 1> tail;
};
......@@ -63,7 +63,7 @@ struct Dim<1> {
static constexpr int dimensions = 1;
HOSTDEVICE
Dim(int _head) : head(_head) {}
Dim(int64_t _head) : head(_head) {}
HOSTDEVICE
Dim() : head(0) {}
......@@ -86,11 +86,11 @@ struct Dim<1> {
bool operator!=(const Dim<1>& o) const { return !(*this == o); }
HOSTDEVICE
int& operator[](int idx);
int64_t& operator[](int idx);
HOSTDEVICE
int operator[](int idx) const;
int64_t operator[](int idx) const;
int head;
int64_t head;
};
namespace {
......@@ -100,12 +100,12 @@ template <int i>
struct DimGetter {
// Return a copy if Dim is const
template <typename D>
HOSTDEVICE static int impl(const D& d) {
HOSTDEVICE static int64_t impl(const D& d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a reference if Dim is mutable
template <typename D>
HOSTDEVICE static int& impl(D& d) {
HOSTDEVICE static int64_t& impl(D& d) {
return DimGetter<i - 1>::impl(d.tail);
}
};
......@@ -115,18 +115,18 @@ template <>
struct DimGetter<0> {
// Return a copy if Dim is const
template <typename D>
HOSTDEVICE static int impl(const D& d) {
HOSTDEVICE static int64_t impl(const D& d) {
return d.head;
}
// Return a reference if Dim is mutable
template <typename D>
HOSTDEVICE static int& impl(D& d) {
HOSTDEVICE static int64_t& impl(D& d) {
return d.head;
}
};
template <int D>
HOSTDEVICE int& indexer(Dim<D>& dim, int idx) {
HOSTDEVICE int64_t& indexer(Dim<D>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
......@@ -141,7 +141,7 @@ HOSTDEVICE int& indexer(Dim<D>& dim, int idx) {
}
template <>
HOSTDEVICE int& indexer<1>(Dim<1>& dim, int idx) {
HOSTDEVICE int64_t& indexer<1>(Dim<1>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx != 0) {
throw std::invalid_argument("Invalid index");
......@@ -153,7 +153,7 @@ HOSTDEVICE int& indexer<1>(Dim<1>& dim, int idx) {
}
template <int D>
HOSTDEVICE int indexer(const Dim<D>& dim, int idx) {
HOSTDEVICE int64_t indexer(const Dim<D>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
......@@ -168,7 +168,7 @@ HOSTDEVICE int indexer(const Dim<D>& dim, int idx) {
}
template <>
HOSTDEVICE int indexer<1>(const Dim<1>& dim, int idx) {
HOSTDEVICE int64_t indexer<1>(const Dim<1>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx != 0) {
throw std::invalid_argument("Invalid index");
......@@ -182,73 +182,76 @@ HOSTDEVICE int indexer<1>(const Dim<1>& dim, int idx) {
} // namespace
// Static access to constant Dim
template <int i, int l>
HOSTDEVICE int get(const Dim<l>& d) {
HOSTDEVICE int64_t get(const Dim<l>& d) {
return DimGetter<i>::impl(d);
}
// Static access to mutable Dim
template <int i, int l>
HOSTDEVICE int& get(Dim<l>& d) {
HOSTDEVICE int64_t& get(Dim<l>& d) {
return DimGetter<i>::impl(d);
}
// Dynamic access to constant Dim
template <int l>
HOSTDEVICE int Dim<l>::operator[](int i) const {
HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
return indexer(*this, i);
}
// Dynamic access to mutable Dim
template <int l>
HOSTDEVICE int& Dim<l>::operator[](int i) {
HOSTDEVICE int64_t& Dim<l>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
inline HOSTDEVICE int Dim<1>::operator[](int i) const {
inline HOSTDEVICE int64_t Dim<1>::operator[](int i) const {
return indexer(*this, i);
}
// Dynamic access to mutable Dim
inline HOSTDEVICE int& Dim<1>::operator[](int i) { return indexer(*this, i); }
inline HOSTDEVICE int64_t& Dim<1>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
// without std::enable_if will try to instantiate this on get<0>(d)
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int>::type get(const Dim<l>& d,
int i) {
HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l>& d,
int i) {
return d[i];
}
// Dynamic access to mutable Dim
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int&>::type get(Dim<l>& d, int i) {
HOSTDEVICE typename std::enable_if<(l > 0), int64_t&>::type get(Dim<l>& d,
int i) {
return d[i];
}
// Dot product of two dims
template <int i>
HOSTDEVICE int linearize(const Dim<i>& a, const Dim<i>& b) {
HOSTDEVICE int64_t linearize(const Dim<i>& a, const Dim<i>& b) {
return a.head * b.head + linearize(a.tail, b.tail);
}
// Base case dot product of two Dims
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int linearize(const Dim<1>& a, const Dim<1>& b) {
HOSTDEVICE inline int64_t linearize(const Dim<1>& a, const Dim<1>& b) {
return a.head * b.head;
}
// Product of a Dim
template <int i>
HOSTDEVICE int product(const Dim<i>& a, int prod = 1) {
HOSTDEVICE int64_t product(const Dim<i>& a, int prod = 1) {
return prod * a.head * product(a.tail);
}
// Base case product of a Dim
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int product(const Dim<1>& a, int prod) {
HOSTDEVICE inline int64_t product(const Dim<1>& a, int prod) {
return prod * a.head;
}
......
......@@ -8,7 +8,7 @@ __global__ void test(paddle::framework::Dim<2>* o) {
o[0] = paddle::framework::make_dim(5, 6);
}
__global__ void dyn_idx_gpu(int* o) {
__global__ void dyn_idx_gpu(int64_t* o) {
auto d = paddle::framework::make_dim(5, 6);
o[0] = d[1];
}
......@@ -47,9 +47,9 @@ TEST(Dim, Equality) {
EXPECT_EQ(b[1], 11);
// dynamic access on GPU
thrust::device_vector<int> r(1);
thrust::device_vector<int64_t> r(1);
dyn_idx_gpu<<<1, 1>>>(thrust::raw_pointer_cast(r.data()));
int res = r[0];
int64_t res = r[0];
EXPECT_EQ(res, 6);
// ex_prefix_mul
......
......@@ -28,7 +28,7 @@ struct EigenDim {
static Type From(const DDim& dims) {
PADDLE_ENFORCE(arity(dims) == D, "D must match arity(DDim)");
Type ret;
for (int d = 0; d < arity(dims); d++) {
for (int64_t d = 0; d < arity(dims); d++) {
ret[d] = dims[d];
}
return ret;
......
......@@ -22,8 +22,14 @@ enum AttrType {
INTS = 3;
FLOATS = 4;
STRINGS = 5;
INT_PAIRS = 6;
}
message IntPair {
required int32 first = 1;
required int32 second = 2;
};
// OpDesc describes an instance of a C++ framework::OperatorBase
// derived class type.
message OpDesc {
......@@ -37,6 +43,7 @@ message OpDesc {
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
repeated IntPair int_pairs = 9;
};
message Var {
......
......@@ -3,7 +3,7 @@
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
USE_OP(add_two);
USE_OP(add);
namespace paddle {
namespace framework {
......@@ -41,7 +41,7 @@ namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
"add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
"add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
......
......@@ -174,36 +174,4 @@ TEST(OpRegistry, CustomChecker) {
op->Run(scope, dev_ctx);
int test_attr = op->GetAttr<int>("test_attr");
ASSERT_EQ(test_attr, 4);
}
class TestAttrProtoMaker : public pd::OpProtoAndCheckerMaker {
public:
TestAttrProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<float>("scale", "scale of test op");
AddAttr<float>("scale", "scale of test op");
}
};
TEST(ProtoMaker, DuplicatedAttr) {
pd::OpProto op_proto;
pd::OpAttrChecker op_checker;
auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
}
class TestInOutProtoMaker : public pd::OpProtoAndCheckerMaker {
public:
TestInOutProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of test op");
AddInput("input", "input of test op");
}
};
TEST(ProtoMaker, DuplicatedInOut) {
pd::OpProto op_proto;
pd::OpAttrChecker op_checker;
auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
}
}
\ No newline at end of file
......@@ -263,4 +263,38 @@ TEST(Operator, Clone) {
OperatorClone a("ABC", {}, {}, {});
auto b = a.Clone();
ASSERT_EQ(a.Type(), b->Type());
}
class TestAttrProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
TestAttrProtoMaker(paddle::framework::OpProto* proto,
paddle::framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<float>("scale", "scale of test op");
AddAttr<float>("scale", "scale of test op");
}
};
TEST(ProtoMaker, DuplicatedAttr) {
paddle::framework::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
}
class TestInOutProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
TestInOutProtoMaker(paddle::framework::OpProto* proto,
paddle::framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of test op");
AddInput("input", "input of test op");
}
};
TEST(ProtoMaker, DuplicatedInOut) {
paddle::framework::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
}
\ No newline at end of file
......@@ -58,7 +58,7 @@ inline T* Tensor::mutable_data(platform::Place place) {
"Tensor's numel must be larger than zero to call "
"Tensor::mutable_data. Call Tensor::set_dim first.");
/* some versions of boost::variant don't have operator!= */
size_t size = product(dims_) * sizeof(T);
int64_t size = product(dims_) * sizeof(T);
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
......@@ -131,7 +131,7 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
PADDLE_ENFORCE_LT(begin_idx, end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE_NE(dims_[0], 1, "Can not slice a tensor with dims_[0] = 1.");
int base = product(dims_) / dims_[0];
size_t base = product(dims_) / dims_[0];
Tensor dst;
dst.holder_ = holder_;
DDim dst_dims = dims_;
......
......@@ -83,8 +83,8 @@ void Conv3DLayer::forward(PassType passType) {
int outWidth = getSize();
resetOutput(batchSize, outWidth);
REGISTER_TIMER_INFO("FwdConv3D", getName().c_str());
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("FwdConv3D", getName().c_str());
const MatrixPtr &inMat = getInputValue(i);
const MatrixPtr &outMat = getOutputValue();
int M = M_[i];
......@@ -120,7 +120,6 @@ void Conv3DLayer::forward(PassType passType) {
}
}
if (nullptr != this->biasParameter_) {
REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
this->addBias();
}
forwardActivation();
......@@ -134,15 +133,14 @@ void Conv3DLayer::backward(const UpdateCallback &callback) {
biases_->getParameterPtr()->incUpdate(callback);
}
REGISTER_TIMER_INFO("BwdConv3D", getName().c_str());
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("BwdConv3D", getName().c_str());
if (weights_[i]->getWGrad()) {
bpropWeights(i);
}
if (getInputGrad(i)) {
bpropData(i);
}
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
weights_[i]->getParameterPtr()->incUpdate(callback);
}
}
......
......@@ -84,8 +84,8 @@ void DeConv3DLayer::forward(PassType passType) {
resetOutput(batchSize, outWidth);
const MatrixPtr outMat = getOutputValue();
REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str());
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str());
const MatrixPtr &inMat = getInputValue(i);
int M = M_[i];
int N = N_[i];
......@@ -120,7 +120,6 @@ void DeConv3DLayer::forward(PassType passType) {
}
}
if (nullptr != this->biasParameter_) {
REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
this->addBias();
}
forwardActivation();
......@@ -133,12 +132,12 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) {
bpropBiases();
biases_->getParameterPtr()->incUpdate(callback);
}
REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str());
for (size_t i = 0; i < inputLayers_.size(); ++i) {
if (weights_[i]->getWGrad() || this->needGradient_) {
int M = M_[i];
int N = N_[i];
int K = K_[i];
REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str());
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
const MatrixPtr &inMat = getInputValue(i);
for (int n = 0; n < batchSize; ++n) {
......@@ -182,7 +181,6 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) {
}
}
}
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
weights_[i]->getParameterPtr()->incUpdate(callback);
}
}
......
......@@ -14,27 +14,31 @@ function(op_library TARGET)
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}"
"${multiValueArgs}" ${ARGN})
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*\\.cc$")
list(APPEND cc_srcs ${src})
else()
message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu")
list(LENGTH op_library_SRCS op_library_SRCS_len)
if (${op_library_SRCS_len} EQUAL 0)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cc)
list(APPEND cc_srcs ${TARGET}.cc)
endif()
endforeach()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
list(APPEND cu_srcs ${TARGET}.cu)
endif()
else()
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*\\.cc$")
list(APPEND cc_srcs ${src})
else()
message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu")
endif()
endforeach()
endif()
list(LENGTH cc_srcs cc_srcs_len)
if (${cc_srcs_len} EQUAL 0)
message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file")
endif()
list(LENGTH cu_srcs cu_srcs_len)
list(LENGTH op_library_DEPS dep_len)
if (${cu_srcs_len} EQUAL 0 AND ${dep_len} EQUAL 0)
message(WARNING "The op library ${TARGET} not support GPU!")
endif()
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
......@@ -46,22 +50,22 @@ endfunction()
add_subdirectory(math)
list(REMOVE_ITEM GENERAL_OPS
net_op
minus_op
mul_op
recurrent_op
scale_op)
op_library(net_op SRCS net_op.cc)
op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
set(DEPS_OPS
identity_op
minus_op
mul_op
recurrent_op
scale_op)
op_library(identity_op DEPS scale_op)
op_library(minus_op DEPS scale_op)
op_library(mul_op DEPS math_function)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op)
op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
op_library(scale_op DEPS net_op)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
op_library(${src} SRCS ${src}.cc ${src}.cu)
op_library(${src})
endforeach()
set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
......
......@@ -57,7 +57,6 @@ class AddOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad);
REGISTER_OP_CPU_KERNEL(add_two,
ops::AddKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(add, ops::AddKernel<paddle::platform::CPUPlace, float>);
......@@ -12,10 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/add_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(add_two,
ops::AddKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(add, ops::AddKernel<paddle::platform::GPUPlace, float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/cos_sim_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class CosSimOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Dimensions of Input(X) and Input(Y) must be the same.");
auto dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
ctx.Output<Tensor>("XNorm")->Resize({dims[0], 1});
ctx.Output<Tensor>("YNorm")->Resize({dims[0], 1});
}
};
class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of cos_sim op.");
AddInput("Y", "The second input of cos_sim op.");
AddOutput("Out", "The output of cos_sim op.");
AddOutput("XNorm", "Row norm of the first input.").AsIntermediate();
AddOutput("YNorm", "Row norm of the second input.").AsIntermediate();
AddComment(R"DOC(
Cosine Similarity Operator.
The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y))
)DOC");
}
};
class CosSimOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
"Input(XNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
"Input(YNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
PADDLE_ENFORCE_EQ(x_dims, y_dims,
"Dimensions of Input(X) and Input(Y) must be the same.");
PADDLE_ENFORCE_EQ(xnorm_dims[0], x_dims[0],
"1st dimension of XNorm must equal that of Input(X).");
PADDLE_ENFORCE_EQ(xnorm_dims[1], 1, "2st dimension of XNorm must be one.");
PADDLE_ENFORCE_EQ(ynorm_dims[0], y_dims[0],
"1st dimension of YNorm must equal that of Input(Y).");
PADDLE_ENFORCE_EQ(ynorm_dims[1], 1, "2st dimension of YNorm must be one.");
PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
"1st dimension of Out@GRAD must equal that of Input(X)");
PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(cos_sim, ops::CosSimOp, ops::CosSimOpMaker, cos_sim_grad,
ops::CosSimOpGrad);
REGISTER_OP_CPU_KERNEL(cos_sim,
ops::CosSimKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
cos_sim_grad, ops::CosSimGradKernel<paddle::platform::CPUPlace, float>);
......@@ -13,8 +13,10 @@
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/gather_op.h"
#include "paddle/operators/cos_sim_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(gather,
ops::GatherOpKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(cos_sim,
ops::CosSimKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
cos_sim_grad, ops::CosSimGradKernel<paddle::platform::GPUPlace, float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class CosSimKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input_x = context.Input<Tensor>("X");
auto* input_y = context.Input<Tensor>("Y");
auto* output_z = context.Output<Tensor>("Out");
auto* output_x_norm = context.Output<Tensor>("XNorm");
auto* output_y_norm = context.Output<Tensor>("YNorm");
output_z->mutable_data<T>(context.GetPlace());
output_x_norm->mutable_data<T>(context.GetPlace());
output_y_norm->mutable_data<T>(context.GetPlace());
auto dims = input_x->dims();
int size = static_cast<int>(framework::product(dims));
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix<T>::From(*input_x, new_dims);
auto y = EigenMatrix<T>::From(*input_y, new_dims);
auto z = EigenVector<T>::Flatten(*output_z);
auto x_norm = EigenVector<T>::Flatten(*output_x_norm);
auto y_norm = EigenVector<T>::Flatten(*output_y_norm);
auto place = context.GetEigenDevice<Place>();
auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
z.device(place) = xy / x_norm / y_norm;
}
};
template <typename Place, typename T>
class CosSimGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input_x = context.Input<Tensor>("X");
auto* input_y = context.Input<Tensor>("Y");
auto* input_z = context.Input<Tensor>("Out");
auto* input_x_norm = context.Input<Tensor>("XNorm");
auto* input_y_norm = context.Input<Tensor>("YNorm");
auto* output_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* output_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
auto dims = input_x->dims();
int size = static_cast<int>(framework::product(dims));
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix<T>::From(*input_x, new_dims);
auto y = EigenMatrix<T>::From(*input_y, new_dims);
auto z = EigenMatrix<T>::From(*input_z);
auto x_norm = EigenMatrix<T>::From(*input_x_norm);
auto y_norm = EigenMatrix<T>::From(*input_y_norm);
auto dz = EigenMatrix<T>::From(*input_grad_z);
Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
auto z_bcast = z.broadcast(bcast);
auto dz_bcast = dz.broadcast(bcast);
auto place = context.GetEigenDevice<Place>();
auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast);
auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast);
auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast);
if (output_grad_x) {
output_grad_x->mutable_data<T>(context.GetPlace());
auto dx = EigenMatrix<T>::From(*output_grad_x, new_dims);
dx.device(place) =
dz_bcast * (y / norm_prod_bcast - z_bcast * x / x_snorm_bcast);
}
if (output_grad_y) {
output_grad_y->mutable_data<T>(context.GetPlace());
auto dy = EigenMatrix<T>::From(*output_grad_y, new_dims);
dy.device(place) =
dz_bcast * (x / norm_prod_bcast - z_bcast * y / y_snorm_bcast);
}
}
};
} // namespace operators
} // namespace paddle
......@@ -31,8 +31,8 @@ class CPUGaussianRandomKernel : public framework::OpKernel {
}
engine.seed(seed);
std::normal_distribution<T> dist(mean, std);
ssize_t size = framework::product(tensor->dims());
for (ssize_t i = 0; i < size; ++i) {
int64_t size = framework::product(tensor->dims());
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
}
......@@ -46,9 +46,14 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
auto dims = GetAttr<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
temp.push_back(static_cast<int64_t>(dim));
}
PADDLE_ENFORCE(dims.size() > 0UL,
"dims can be one int or array. dims must be set.");
tensor->Resize(framework::make_ddim(dims));
tensor->Resize(framework::make_ddim(temp));
}
};
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/net_op.h"
#include "paddle/operators/scale_op.h"
namespace paddle {
namespace operators {
// identity is a alias of scale op. This is also a example for creating a alias
// operator.
template <typename AttrType>
class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
public:
IdentityOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "input tensor of identity op");
AddOutput("Out", "output tensor of identity op");
AddComment("identity operator. Just a alias of scale op which scale = 1.0");
}
};
template <typename AttrType>
class IdentityOp : public NetOp {
public:
IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
AppendOp(framework::OpRegistry::CreateOp(
"scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
{{"scale", static_cast<AttrType>(1)}}));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
ops::IdentityOpMaker<float>);
......@@ -30,12 +30,12 @@ class LookupTableKernel : public framework::OpKernel {
auto ids_t = context.Input<Tensor>("Ids"); // int tensor
auto output_t = context.Output<Tensor>("Out"); // float tensor
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
int N = table_t->dims()[0];
int D = table_t->dims()[1];
auto ids = ids_t->data<int32_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
for (size_t i = 0; i < product(ids_t->dims()); ++i) {
for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
......@@ -51,8 +51,8 @@ class LookupTableGradKernel : public framework::OpKernel {
auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
size_t N = d_table_t->dims()[0];
size_t D = d_table_t->dims()[1];
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
auto ids = ids_t->data<int32_t>();
const T* d_output = d_output_t->data<T>();
T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
......@@ -61,10 +61,10 @@ class LookupTableGradKernel : public framework::OpKernel {
t.device(context.GetEigenDevice<platform::CPUPlace>()) =
t.constant(static_cast<T>(0));
for (size_t i = 0; i < product(ids_t->dims()); ++i) {
for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
for (size_t j = 0; j < D; ++j) {
for (int j = 0; j < D; ++j) {
d_table[ids[i] * D + j] += d_output[i * D + j];
}
}
......
......@@ -61,7 +61,7 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
outlinks[i].internal);
f::DDim step_dims = step_scope_var->template GetMutable<Tensor>()->dims();
std::vector<int> dims_vec = vectorize(step_dims);
std::vector<int64_t> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(f::make_ddim(dims_vec));
} else {
......
......@@ -48,7 +48,7 @@ The equation is: Out = scale*X
}
};
// Identity Op's gradient is identity op, too.
// Scale Op's gradient is scale op, too.
// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
template <typename AttrType>
class ScaleGradOp : public NetOp {
......@@ -65,33 +65,6 @@ class ScaleGradOp : public NetOp {
}
};
// identity is a alias of scale op. This is also a example for creating a alias
// operator.
template <typename AttrType>
class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
public:
IdentityOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "input tensor of identity op");
AddOutput("Out", "output tensor of identity op");
AddComment("identity operator. Just a alias of scale op which scale = 1.0");
}
};
template <typename AttrType>
class IdentityOp : public NetOp {
public:
IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
AppendOp(framework::OpRegistry::CreateOp(
"scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
{{"scale", static_cast<AttrType>(1)}}));
}
};
} // namespace operators
} // namespace paddle
......@@ -101,5 +74,3 @@ REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
ops::ScaleGradOp<float>);
REGISTER_OP_CPU_KERNEL(scale,
ops::ScaleKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
ops::IdentityOpMaker<float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/scatter_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(scatter,
ops::ScatterOpKernel<paddle::platform::GPUPlace, float>);
......@@ -24,7 +24,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
"The input of softmax op must be matrix");
"The input of softmax op must be a matrix.");
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......@@ -34,9 +34,27 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
SoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "input of softmax");
AddOutput("Y", "output of softmax");
AddComment("Softmax Op");
AddInput("X",
"The input tensor of softmax. "
"2-D with shape [batch_size, input_feature_dimensions].");
AddOutput("Y", "The normalized values with the same shape as X.");
AddComment(R"DOC(
The input of softmax operator is a 2-D tensor with shape N x K (N is the
batch_size, K is the dimension of input feature). The output tensor has the
same shape as the input tensor.
For each row of the input tensor, the softmax operator squashes the
K-dimensional vector of arbitrary real values to a K-dimensional vector of real
values in the range [0, 1] that add up to 1. Specifically, it computes the
exponential of the given dimension and the sum of exponential values of all
the other dimensions in the K-dimensional vector input. Then the ratio of the
exponential of the given dimension and the sum of exponential values of all
the other dimensions is the output of the softmax operator.
For each row `i` and each column `j` in X, we have:
Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j]))
)DOC");
}
};
......
......@@ -35,8 +35,8 @@ class CPUUniformRandomKernel : public framework::OpKernel {
std::uniform_real_distribution<T> dist(
static_cast<T>(context.GetAttr<float>("min")),
static_cast<T>(context.GetAttr<float>("max")));
ssize_t size = framework::product(tensor->dims());
for (ssize_t i = 0; i < size; ++i) {
int64_t size = framework::product(tensor->dims());
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
}
......@@ -52,7 +52,12 @@ class UniformRandomOp : public framework::OperatorWithKernel {
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto dims = GetAttr<std::vector<int>>("dims");
tensor->Resize(framework::make_ddim(dims));
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
temp.push_back(static_cast<int64_t>(dim));
}
tensor->Resize(framework::make_ddim(temp));
}
};
......
......@@ -30,7 +30,7 @@ limitations under the License. */
namespace py = pybind11;
USE_OP(add_two);
USE_OP(add);
USE_OP(onehot_cross_entropy);
USE_OP(sgd);
USE_OP(mul);
......@@ -46,6 +46,7 @@ USE_OP(lookup_table);
USE_OP(scale);
USE_NO_KERNEL_OP(identity);
USE_OP(minus);
USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
......@@ -76,7 +77,7 @@ PYBIND11_PLUGIN(core) {
.def("get_dims",
[](const Tensor &self) { return vectorize(self.dims()); })
.def("set_dims",
[](Tensor &self, const std::vector<int> &dim) {
[](Tensor &self, const std::vector<int64_t> &dim) {
self.Resize(make_ddim(dim));
})
.def("alloc_float",
......
......@@ -85,7 +85,7 @@ void PyCPUTensorSetFromArray(
framework::Tensor &self,
py::array_t<T, py::array::c_style | py::array::forcecast> array,
paddle::platform::CPUPlace &place) {
std::vector<int> dims;
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
......@@ -102,7 +102,7 @@ void PyCUDATensorSetFromArray(
framework::Tensor &self,
py::array_t<T, py::array::c_style | py::array::forcecast> array,
paddle::platform::GPUPlace &place) {
std::vector<int> dims;
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
......
......@@ -27,6 +27,14 @@ class SequenceType(object):
SEQUENCE = 1
SUB_SEQUENCE = 2
@classmethod
def tostring(cls, value):
for k in cls.__dict__:
if not k.startswith('__'):
if getattr(cls, k) == value:
return cls.__name__ + '.' + k
return 'INVALID(' + str(value) + ')'
# TODO(yuyang18): Add string data type here.
class DataType(object):
......@@ -35,6 +43,14 @@ class DataType(object):
SparseValue = 2
Index = 3
@classmethod
def tostring(cls, value):
for k in cls.__dict__:
if not k.startswith('__'):
if getattr(cls, k) == value:
return cls.__name__ + '.' + k
return 'INVALID(' + str(value) + ')'
class CacheType(object):
NO_CACHE = 0 # No cache at all
......@@ -69,6 +85,26 @@ class InputType(object):
self.seq_type = seq_type
self.type = tp
def __repr__(self):
"""
Return a human readable representation like 'InputType(dim=25921,
seq_type=SequenceType.NO_SEQUENCE, type=DataType.Dense)'
"""
repr_str = type(self).__name__
repr_str += '('
serialize_func_map = {
'dim': repr,
'seq_type': SequenceType.tostring,
'type': DataType.tostring
}
for idx, k in enumerate(self.__slots__):
if idx != 0:
repr_str += ', '
repr_str += (
k + '=' + serialize_func_map.get(k, repr)(getattr(self, k)))
repr_str += ')'
return repr_str
def dense_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
"""
......
......@@ -94,9 +94,14 @@ class OpDescCreationMethod(object):
new_attr.floats.extend(user_defined_attr)
elif attr.type == framework_pb2.STRINGS:
new_attr.strings.extend(user_defined_attr)
elif attr.type == framework_pb2.INT_PAIRS:
for p in user_defined_attr:
pair = new_attr.pairs.add()
pair.first = p[0]
pair.second = p[1]
else:
raise NotImplementedError("Not support attribute type " +
attr.type)
str(attr.type))
return op_desc
......
......@@ -4,6 +4,7 @@ py_test(test_scope SRCS test_scope.py)
py_test(test_tensor SRCS test_tensor.py)
py_test(test_mul_op SRCS test_mul_op.py)
py_test(test_cos_sim_op SRCS test_cos_sim_op.py)
py_test(test_mean_op SRCS test_mean_op.py)
......
......@@ -36,13 +36,13 @@ def get_numeric_gradient(op,
in_place=False):
"""
Get Numeric Gradient for an operator's input.
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
variable name. Value is numpy array.
:param output_name: The final output variable name.
:param output_name: The final output variable name.
:param input_to_check: The input variable need to get gradient.
:param delta: The perturbation value for numeric gradient method. The
:param delta: The perturbation value for numeric gradient method. The
smaller delta is, the more accurate result will get. But if that delta is
too small, it could occur numerical stability problem.
:param local_scope: The local scope used for get_numeric_gradient.
......@@ -229,9 +229,9 @@ class GradientChecker(unittest.TestCase):
"""Use relative error for the comparison.
:param numeric_grads: the numerical graidents.
:type numeric_grads: a list of numpy.array
:type numeric_grads: a list of numpy.array
:param analytic_grads: the analytical graidents.
:type analytic_grads: a list of numpy.array
:type analytic_grads: a list of numpy.array
:param name: the names of gradients, used to print for debug.
:type names: a list of string
:param msg_prefix: string info, used to print for debug.
......
......@@ -6,13 +6,13 @@ from paddle.v2.framework.op import Operator
class OpTestMeta(type):
"""
Operator Test ClassMeta.
It injects `test_all` method into user's OperatorTest class, to make Python
It injects `test_all` method into user's OperatorTest class, to make Python
unittest module run that method.
The `test_all` read what value is stored in `self`. It use self's values to
create and run a operator, and check whether that op is OK or not.
See `test_add_two_op` for example usage.
"""
......
......@@ -11,7 +11,7 @@ class TestAddOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "add_two"
self.type = "add"
self.inputs = {
'X': numpy.random.random((102, 105)).astype("float32"),
'Y': numpy.random.random((102, 105)).astype("float32")
......
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
class TestCosSimOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "cos_sim"
self.inputs = {
'X': np.random.random((32, 64)).astype("float32"),
'Y': np.random.random((32, 64)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
expect_x_norm / expect_y_norm
self.outputs = {
'XNorm': np.expand_dims(expect_x_norm, 1),
'YNorm': np.expand_dims(expect_y_norm, 1),
'Out': np.expand_dims(expect_out, 1)
}
class TestCosSimGradOp(GradientChecker):
def setUp(self):
self.op = create_op("cos_sim")
self.inputs = {
'X': np.random.random((10, 5)).astype("float32"),
'Y': np.random.random((10, 5)).astype("float32")
}
def test_cpu_gpu_compare(self):
self.compare_grad(self.op, self.inputs)
def test_normal(self):
self.check_grad(
self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.05)
def test_ignore_x(self):
self.check_grad(
self.op,
self.inputs, ["Y"],
"Out",
max_relative_error=0.05,
no_grad_set={"X"})
def test_ignore_y(self):
self.check_grad(
self.op,
self.inputs, ["X"],
"Out",
max_relative_error=0.05,
no_grad_set={"Y"})
if __name__ == '__main__':
unittest.main()
......@@ -7,7 +7,7 @@ from gradient_checker import get_numeric_gradient
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
add_op = Operator('add_two', X="X", Y="Y", Out="Z")
add_op = Operator('add', X="X", Y="Y", Out="Z")
x = numpy.random.random((10, 1)).astype("float32")
y = numpy.random.random((10, 1)).astype("float32")
......
......@@ -15,7 +15,7 @@ def fc(X, W, Y):
class TestNet(unittest.TestCase):
def test_net_all(self):
net = core.Net.create()
op1 = Operator("add_two", X="X", Y="Y", Out="Out")
op1 = Operator("add", X="X", Y="Y", Out="Out")
net.append_op(op1)
net2 = core.Net.create()
......@@ -26,7 +26,7 @@ class TestNet(unittest.TestCase):
expected = '''
Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
Op(add_two), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
Op(add), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
......
......@@ -193,10 +193,10 @@ class TestOpDescCreationMethod(unittest.TestCase):
class TestOpCreations(unittest.TestCase):
def test_all(self):
add_op = op.Operator("add_two", X="a", Y="b", Out="z")
add_op = op.Operator("add", X="a", Y="b", Out="z")
self.assertIsNotNone(add_op)
# Invoke C++ DebugString()
self.assertEqual('Op(add_two), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
str(add_op))
......
......@@ -146,7 +146,7 @@ class TestRecurrentOp(unittest.TestCase):
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum")
sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
sig_op = Operator("sigmoid", X="sum", Y="h@alias")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
......
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