提交 048254da 编写于 作者: G guosheng

Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into enhance-exclude-param-init

# 如何写新的Operator
- [概念简介](#概念简介)
- [实现C++类](#实现C++)
- [定义ProtoMaker类](#定义ProtoMaker类)
- [定义Operator类](#定义Operator类)
- [定义OpKernel类](#定义OpKernel类)
- [注册Operator](#注册Operator)
- [实现C++类](#实现c)
- [定义ProtoMaker类](#定义protomaker类)
- [定义Operator类](#定义operator类)
- [定义OpKernel类](#定义opkernel类)
- [注册Operator](#注册operator)
- [编译](#编译)
- [绑定Python](#绑定Python)
- [绑定Python](#绑定python)
- [实现单元测试](#实现单元测试)
- [前向Operator单测](#前向Operator单测)
- [反向Operator单测](#反向Operator单测)
- [前向Operator单测](#前向operator单测)
- [反向Operator单测](#反向operator单测)
- [编译和执行](#编译和执行)
- [注意事项](#注意事项)
## 概念简介
......@@ -43,7 +44,7 @@ Kernel实现 | CPU、CUDA共享Kernel实现在`.h`文件中,否则,CPU
## 实现C++类
### 1. 定义ProtoMaker类
### 定义ProtoMaker类
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。
......@@ -100,7 +101,7 @@ The equation is: Out = scale*X
- `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
### 定义Operator类
下面的点实现了MulOp的定义:
......@@ -149,7 +150,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
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
### 定义OpKernel类
`MulKernel`继承自`framework::OpKernel`,带有下面两个模板参数:
......@@ -177,6 +178,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
math::matmul<DeviceContext, T>(*X, false, *Y, false, 1, Z, 0, device_context);
}
};
```
需要注意:**不同设备(CPU、CUDA)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。**
......@@ -188,7 +190,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
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
### 注册Operator
-`.cc`文件中注册前向、反向Op类,注册CPU Kernel。
......@@ -220,7 +222,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
```
### 5. 编译
### 编译
运行下面命令可以进行编译:
......@@ -236,6 +238,7 @@ make mul_op
单测包括对比前向Op不同设备(CPU、CUDA)的实现、对比反向OP不同设备(CPU、CUDA)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)
### 前向Operator单测
Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp`里完成。测试Operator,需要:
......@@ -273,8 +276,7 @@ Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('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:
......@@ -282,6 +284,8 @@ Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp
- `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单测
而反向测试中:
- `test_check_grad_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`["X", "Y"]` : 指定对输入变量`X``Y`做梯度检测。
......@@ -290,7 +294,7 @@ Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp
- `test_check_grad_ingore_x``test_check_grad_ingore_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
### 编译和执行
`python/paddle/v2/framework/tests` 目录下新增的 `test_*.py` 单元测试会被自动加入工程进行编译。
......
# How to write a new operator
- [Background](#background)
- [Implementing C++ Types](#implementing-c++-types)
- [Defining ProtoMaker](#defining-protoMaker)
- [Implementing C++ Types](#implementing-c-types)
- [Defining ProtoMaker](#defining-protomaker)
- [Defining Operator](#defining-operator)
- [Registering Operator](#registering-operator)
- [Compilation](#compilation)
......@@ -41,7 +41,7 @@ Let's take matrix multiplication operator, [MulOp](https://github.com/PaddlePadd
## Implementing C++ Types
### 1. Defining Class ProtoMaker
### Defining ProtoMaker
Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and pne output.
......@@ -98,7 +98,7 @@ There are two changes in this example:
- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` adds `scale`constant as an attribute, and sets the default value to 1.0.
### 2. Defining Operator
### Defining Operator
The following code defines the interface for MulOp:
......@@ -147,7 +147,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, which also include the registration methods introduced later.
### 3. Defining OpKernel
### Defining OpKernel
`MulKernel` inherits `framework::OpKernel`, which includes the following templates:
......@@ -188,7 +188,7 @@ This concludes the forward implementation of an operator. Next its operation and
The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. **Note that a backward operator does not include a `ProtoMaker`**.
### 4. Registering Operator
### Registering Operator
- In `.cc` files, register forward and backward operator classes and the CPU kernel.
......@@ -220,7 +220,7 @@ The definition of its corresponding backward operator, if applicable, is similar
ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
```
### 5. Compilation
### Compilation
Run the following commands to compile.
......@@ -284,8 +284,7 @@ A forward operator unit test inherits `unittest.TestCase` and defines metaclass
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
```
Get its output, and compare it with the forward operator's own output.
The code above first loads required packages. In addition, we have
......@@ -294,6 +293,8 @@ The code above first loads required packages. In addition, we have
- `self.inputs` defines input, with type `numpy.array` and initializes it.
- `self.outputs` defines output and completes the same operator computation in the Python script, and returns its result from the Python script.
### Testing Backward Operators
Some key points in checking gradient above include:
- `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
......
......@@ -28,6 +28,7 @@ limitations under the License. */
#include "hl_top_k.h"
#include "paddle/utils/Logging.h"
#include "NEONFunctions.h"
#include "paddle/function/GemmFunctor.h"
#include "paddle/utils/ThreadLocal.h"
......@@ -4165,16 +4166,36 @@ void CpuMatrix::print(std::ostream& os) const {
void CpuMatrix::paramReluForward(Matrix& data, Matrix& W) {
real* input = data.getData();
real* w = W.getData();
real* output = data_;
size_t numElements = data.getWidth();
size_t numSamples = data.getHeight();
size_t paraSize = W.getHeight() * W.getWidth();
CHECK(!(numElements % paraSize)); // this check from ParameterReluLayer::init
size_t partial_sum = numElements / paraSize;
if (paraSize == numElements) {
for (size_t n = 0; n < numSamples * numElements; ++n) {
output[n] = input[n] > 0 ? input[n] : input[n] * w[n % numElements];
}
return;
}
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
for (size_t n = 0; n < numSamples; ++n) {
for (size_t i = 0; i < paraSize; i++) {
neon::prelu(
input + i * partial_sum, w[i], output + i * partial_sum, partial_sum);
}
input = input + numElements;
output = output + numElements;
}
#else
for (size_t n = 0, k = 0; n < numSamples; ++n) {
for (size_t i = 0; i < numElements; ++i, ++k) {
data_[k] = input[k] > 0 ? input[k] : input[k] * w[i / partial_sum];
output[k] = input[k] > 0 ? input[k] : input[k] * w[i / partial_sum];
}
}
#endif
}
void CpuMatrix::paramReluBackwardW(Matrix& oGrad, Matrix& data) {
......
......@@ -49,6 +49,46 @@ void relu(const float* a, float* b, int len) {
}
}
// b[i] = a[i] > 0.0f ? a[i] : a[i] * w
void prelu(const float* a, float w, float* b, int len) {
int offset = len % 16;
float32x4_t ma0, ma1, ma2, ma3;
float32x4_t zero = vdupq_n_f32(0.f);
float32x4_t vw = vdupq_n_f32(w);
for (int k = 0; k < len / 16; k++, a += 16, b += 16) {
ma0 = vld1q_f32(a);
ma1 = vld1q_f32(a + 4);
ma2 = vld1q_f32(a + 8);
ma3 = vld1q_f32(a + 12);
uint32x4_t flag0 = vcgtq_f32(ma0, zero);
uint32x4_t flag1 = vcgtq_f32(ma1, zero);
uint32x4_t flag2 = vcgtq_f32(ma2, zero);
uint32x4_t flag3 = vcgtq_f32(ma3, zero);
float32x4_t mul0 = vmulq_f32(ma0, vw);
float32x4_t mul1 = vmulq_f32(ma1, vw);
float32x4_t mul2 = vmulq_f32(ma2, vw);
float32x4_t mul3 = vmulq_f32(ma3, vw);
ma0 = vbslq_f32(flag0, ma0, mul0);
ma1 = vbslq_f32(flag1, ma1, mul1);
ma2 = vbslq_f32(flag2, ma2, mul2);
ma3 = vbslq_f32(flag3, ma3, mul3);
vst1q_f32(b, ma0);
vst1q_f32(b + 4, ma1);
vst1q_f32(b + 8, ma2);
vst1q_f32(b + 12, ma3);
}
for (int i = 0; i < offset; i++) {
b[i] = a[i] > 0.0f ? a[i] : a[i] * w;
}
}
} // namespace neon
} // namespace paddle
......
......@@ -18,6 +18,7 @@ namespace paddle {
namespace neon {
void relu(const float* a, float* b, int len);
void prelu(const float* a, float w, float* b, int len);
} // namespace neon
} // namespace paddle
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