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提交 4d8f39b8 编写于 作者: S sweetsky0901
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into detection_output

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doc/howto/dev/new_op_cn.md
浏览文件 @ 4d8f39b8
......@@ -30,8 +30,8 @@
-------------- | :----------------------
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
Kernel实现 | CPU、CUDA共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，CUDA 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，CUDA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。**系统会根据文件名自动构建op和其对应的Python扩展。**
......@@ -153,7 +153,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
`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 DeviceContext`: 表示设备类型，不同设备(CPU、CUDA)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。
......@@ -165,7 +165,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
下面是 `MulKernel` `Compute`的实现：
```cpp
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -173,18 +173,16 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
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);
auto& device_context = context.template device_context<DeviceContext>();
math::matmul<DeviceContext, 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。**
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。**
`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)。
`MulOp`的CPU、CUDA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)。
为了使`OpKernel`的计算过程书写更加简单，并且CPU、CUDA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。
......@@ -197,9 +195,9 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
```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, ops::MulKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
ops::MulGradKernel<paddle::platform::CPUDeviceContext, float>);
```
在上面的代码中：
......@@ -209,17 +207,17 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
- `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::MulGradKernel`类。
- 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：
- 在 `.cu`文件中注册CUDA Kernel。
- 请注意，如果CUDA 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
#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>);
REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
```
### 5. 编译
......@@ -236,71 +234,55 @@ make mul_op
## 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`的单元测试](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、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单元测试
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：
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：
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。
4. 反向计算已经自动集成进测试框架，直接调用相应接口即可。
```python
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
from op_test import OpTest
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
class TestMulOp(OpTest):
def setUp(self):
self.type = "mul"
self.op_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
def test_check_output(self):
self.check_output()
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**。
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
```python
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_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
```
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：
下面解释代码中一些关键的地方:
- `self.op_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。
- 调用`create_op("mul")`创建反向Op对应的前向Op。
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。
- 第二个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`。
......@@ -328,5 +310,5 @@ ctest -R test_mul_op
- 为每个Op创建单独的`*_op.h`（如有）、`*_op.cc`和`*_op.cu`（如有）。不允许一个文件中包含多个Op，这将会导致编译出错。
- 注册Op时的类型名，需要和该Op的名字一样。即不允许在`A_op.cc`里面，注册`REGISTER_OP(B, ...)`等，这将会导致单元测试出错。
- 如果Op没有实现GPU Kernel，请不要创建空的`*_op.cu`，这将会导致单元测试出错。
- 如果Op没有实现CUDA Kernel，请不要创建空的`*_op.cu`，这将会导致单元测试出错。
- 如果多个Op依赖一些共用的函数，可以创建非`*_op.*`格式的文件来存放，如`gather.h`文件。
doc/howto/dev/new_op_en.md
浏览文件 @ 4d8f39b8
......@@ -28,8 +28,8 @@ An operator can be differentiated by whether in has kernel methods. An operator
-------------- | :----------------------
OpProtoMake definition | `.cc`files, Backward Op does not need an OpProtoMake interface.
Op definition | `.cc` files
Kernel implementation | The kernel methods shared between CPU and GPU are defined in `.h` files. CPU-specific kernels live in `.cc` files, while GPU-specific kernels are implemented in `.cu`files.
Registering the Op | Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the GPU implementation.
Kernel implementation | The kernel methods shared between CPU and CUDA are defined in `.h` files. CPU-specific kernels live in `.cc` files, while CUDA-specific kernels are implemented in `.cu`files.
Registering the Op | Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the CUDA implementation.
New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions. **
......@@ -151,7 +151,7 @@ Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, w
`MulKernel` inherits `framework::OpKernel`, which includes the following templates:
- `typename Place` denotes device type. When different devices, namely the CPU and the GPU, share the same kernel, this template needs to be added. If they don't share kernels, this must not be added. An example of a non-sharing kernel is [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
- `typename DeviceContext` denotes device context type. When different devices, namely the CPUDeviceContext and the CUDADeviceContext, share the same kernel, this template needs to be added. If they don't share kernels, this must not be added. An example of a non-sharing kernel is [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
- `typename T` denotes data type, such as `float` or `double`.
......@@ -163,7 +163,7 @@ Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, w
`MulKernel`'s implementation of `Compute` is as follows:
```cpp
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -171,16 +171,15 @@ Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, w
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);
auto& device_context = context.template device_context<DeviceContext>();
math::matmul<DeviceContext, T>(*X, false, *Y, false, 1, Z, 0, device_context);
}
};
```
Note that **different devices (CPU, GPU)share an Op definition; whether or not they share the same `OpKernel` depends on whether `Compute` calls functions that support both devices.**
Note that **different devices (CPU, CUDA)share an Op definition; whether or not they share the same `OpKernel` depends on whether `Compute` calls functions that support both devices.**
`MulOp`'s CPU and GPU share the same `Kernel`. A non-sharing `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
`MulOp`'s CPU and CUDA share the same `Kernel`. A non-sharing `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
To ease the writing of `OpKernel` compute, and for reusing code cross-device, [`Eigen-unsupported Tensor`](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md?fileviewer=file-view-default) module is used to implement `Compute` interface. To learn about how the Eigen library is used in PaddlePaddle, please see [usage document](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md).
......@@ -196,9 +195,9 @@ The definition of its corresponding backward operator, if applicable, is similar
```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, ops::MulKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
ops::MulGradKernel<paddle::platform::CPUDeviceContext, float>);
```
In that code block,
......@@ -208,17 +207,17 @@ The definition of its corresponding backward operator, if applicable, is similar
- `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulGradKernel`.
- Registering GPU Kernel in `.cu` files
- Note that if GPU Kernel is implemented using the `Eigen unsupported` module, then on top of `.cu`, a macro definition `#define EIGEN_USE_GPU` is needed, such as
- Registering CUDA Kernel in `.cu` files
- Note that if CUDA Kernel is implemented using the `Eigen unsupported` module, then on top of `.cu`, a macro definition `#define EIGEN_USE_GPU` is needed, such as
```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>);
REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
```
### 5. Compilation
......@@ -253,62 +252,50 @@ A forward operator unit test inherits `unittest.TestCase` and defines metaclass
2. Generating random input data.
3. Implementing the same computation logic in a Python script:
3. Implementing the same computation logic in a Python script.
4. Call check gradient function to check the backward operator.
```python
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
from op_test import OpTest
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
class TestMulOp(OpTest):
def setUp(self):
self.type = "mul"
self.op_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'])}
```
Get its output, and compare it with the forward operator's own output.
The code above first loads required packages. In addition, we have
- `self.type = "mul" ` defines the type that is identical to what the operator's registered type.
- `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
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
A backward operator unit test inherits `GradientChecker`, which inherits `unittest.TestCase`. As a result, **a backward operator unit test needs to be have the prefix `test_`**.
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
```python
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_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
```
Get its output, and compare it with the forward operator's own output.
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
The code above first loads required packages. In addition, we have
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
- `self.op_type = "mul" ` defines the type that is identical to what the operator's registered type.
- `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.
Some key points in the code above include:
Some key points in checking gradient above include:
- `create_op("mul")` creates the backward operator's corresponding forward operator.
- `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
- The first variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
- The second variable `"Out"` points to the network's final output target `Out`.
......@@ -338,5 +325,5 @@ ctest -R test_mul_op
- Every `*_op.h` (if applicable), `*_op.cc`, and `*_op.cu` (if applicable) must be created for a unique Op. Compiling will fail if multiple operators are included per file.
- The type with which an operator is registered needs to be identical to the Op's name. Registering `REGISTER_OP(B, ...)` in `A_op.cc` will cause unit testing failures.
- If the operator does not implement a GPU kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail.
- If the operator does not implement a CUDA kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail.
- If multiple operators rely on some shared methods, a file NOT named `*_op.*` can be created to store them, such as `gather.h`.
paddle/framework/op_registry.h
浏览文件 @ 4d8f39b8
......@@ -181,8 +181,8 @@ class OpKernelRegistrar : public Registrar {
return 0; \
}
#define REGISTER_OP_GPU_KERNEL(op_type, ...) \
REGISTER_OP_KERNEL(op_type, GPU, ::paddle::platform::GPUPlace, __VA_ARGS__)
#define REGISTER_OP_CUDA_KERNEL(op_type, ...) \
REGISTER_OP_KERNEL(op_type, CUDA, ::paddle::platform::GPUPlace, __VA_ARGS__)
#define REGISTER_OP_CPU_KERNEL(op_type, ...) \
REGISTER_OP_KERNEL(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
......@@ -217,7 +217,7 @@ class OpKernelRegistrar : public Registrar {
#else
#define USE_OP_KERNEL(op_type) \
USE_OP_DEVICE_KERNEL(op_type, CPU); \
USE_OP_DEVICE_KERNEL(op_type, GPU)
USE_OP_DEVICE_KERNEL(op_type, CUDA)
#endif
#define USE_NO_KERNEL_OP(op_type) USE_OP_ITSELF(op_type);
......@@ -226,9 +226,9 @@ class OpKernelRegistrar : public Registrar {
USE_OP_ITSELF(op_type); \
USE_OP_DEVICE_KERNEL(op_type, CPU);
#define USE_GPU_ONLY_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_DEVICE_KERNEL(op_type, GPU)
#define USE_CUDA_ONLY_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_DEVICE_KERNEL(op_type, CUDA)
#define USE_OP(op_type) \
USE_OP_ITSELF(op_type); \
......
paddle/framework/operator.cc
浏览文件 @ 4d8f39b8
......@@ -22,20 +22,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
template <>
Eigen::DefaultDevice& ExecutionContext::GetEigenDevice<
platform::CPUPlace, Eigen::DefaultDevice>() const {
return *device_context_.GetEigenDevice<platform::CPUPlace>();
}
#ifdef PADDLE_WITH_CUDA
template <>
Eigen::GpuDevice&
ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
return *device_context_.GetEigenDevice<platform::GPUPlace>();
}
#endif
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
......@@ -429,7 +415,7 @@ void OperatorWithKernel::Run(const Scope& scope,
}
OpKernelType OperatorWithKernel::GetKernelType(
const ExecutionContext& ctx) const {
return OpKernelType(IndicateDataType(ctx), ctx.device_context());
return OpKernelType(IndicateDataType(ctx), ctx.GetPlace());
}
DataType OperatorWithKernel::IndicateDataType(
const ExecutionContext& ctx) const {
......
paddle/framework/operator.h
浏览文件 @ 4d8f39b8
......@@ -276,17 +276,25 @@ class ExecutionContext {
out_tensor->set_lod(in_tensor.lod());
}
template <typename PlaceType,
typename DeviceType = typename platform::EigenDeviceConverter<
PlaceType>::EigenDeviceType>
DeviceType& GetEigenDevice() const;
platform::Place GetPlace() const { return device_context_.GetPlace(); }
template <typename DeviceContextType>
const DeviceContextType& device_context() const {
return *reinterpret_cast<const DeviceContextType*>(&device_context_);
}
const platform::DeviceContext& device_context() const {
return device_context_;
}
#ifdef PADDLE_WITH_CUDA
const inline platform::CUDADeviceContext& cuda_device_context() const {
PADDLE_ENFORCE(platform::is_gpu_place(device_context_.GetPlace()));
return *reinterpret_cast<const platform::CUDADeviceContext*>(
&device_context_);
}
#endif
//! Get actual name vector for this input.
const std::vector<std::string>& Inputs(const std::string& name) const {
return op_.Inputs(name);
......@@ -297,14 +305,6 @@ class ExecutionContext {
return op_.Outputs(name);
}
#ifdef PADDLE_WITH_CUDA
const inline platform::CUDADeviceContext& cuda_device_context() const {
PADDLE_ENFORCE(platform::is_gpu_place(device_context_.GetPlace()));
return *reinterpret_cast<const platform::CUDADeviceContext*>(
&device_context_);
}
#endif
private:
const OperatorBase& op_;
const Scope& scope_;
......
paddle/framework/operator_test.cc
浏览文件 @ 4d8f39b8
......@@ -115,7 +115,7 @@ class OpWithKernelTest : public OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {}
OpKernelType GetKernelType(const ExecutionContext& ctx) const override {
return OpKernelType(DataType::FP32, ctx.device_context());
return OpKernelType(DataType::FP32, ctx.GetPlace());
}
};
......
paddle/math/float16.h
浏览文件 @ 4d8f39b8
......@@ -101,7 +101,7 @@ public:
half tmp = __float2half(val);
x = *reinterpret_cast<uint16_t*>(&tmp);
#elif defined(PADDLE_NEON)
#elif defined(PADDLE_WITH_NATIVE_FP16)
float32x4_t tmp = vld1q_dup_f32(&val);
float16_t res = vget_lane_f16(vcvt_f16_f32(tmp), 0);
x = *reinterpret_cast<uint16_t*>(&res);
......@@ -252,7 +252,7 @@ public:
half tmp = *reinterpret_cast<const half*>(this);
return __half2float(tmp);
#elif defined(PADDLE_NEON)
#elif defined(PADDLE_WITH_NATIVE_FP16)
float16x4_t res = vld1_dup_f16(reinterpret_cast<const float16_t*>(this));
return vgetq_lane_f32(vcvt_f32_f16(res), 0);
......
paddle/operators/CMakeLists.txt
浏览文件 @ 4d8f39b8
......@@ -138,7 +138,7 @@ function(op_library TARGET)
if ("${TARGET}" STREQUAL "nccl_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_GPU_ONLY_OP(ncclAllReduce);\n")
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(ncclAllReduce);\n")
endif()
# reduce_op contains several operators
......
paddle/operators/accuracy_op.cc
浏览文件 @ 4d8f39b8
......@@ -57,7 +57,7 @@ class AccuracyOp : public framework::OperatorWithKernel {
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Out")->type()),
ctx.device_context());
ctx.GetPlace());
}
};
......
paddle/operators/accuracy_op.cu
浏览文件 @ 4d8f39b8
......@@ -104,5 +104,6 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
// FIXME(typhoonzero): types of T is for inference data.
// label data is always int64
REGISTER_OP_GPU_KERNEL(accuracy, paddle::operators::AccuracyOpCUDAKernel<float>,
paddle::operators::AccuracyOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(accuracy,
paddle::operators::AccuracyOpCUDAKernel<float>,
paddle::operators::AccuracyOpCUDAKernel<double>);
paddle/operators/accuracy_op.h
浏览文件 @ 4d8f39b8
......@@ -21,7 +21,7 @@ namespace operators {
using Tensor = framework::Tensor;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class AccuracyKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......
paddle/operators/activation_op.cc
浏览文件 @ 4d8f39b8
......@@ -611,16 +611,17 @@ REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker,
REGISTER_OP(swish, ops::ActivationOp, ops::SwishOpMaker, swish_grad,
ops::ActivationOpGrad);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_CPU_KERNEL( \
act_type, \
ops::ActivationKernel<paddle::platform::CPUPlace, ops::functor<float>>, \
ops::ActivationKernel<paddle::platform::CPUPlace, \
ops::functor<double>>); \
REGISTER_OP_CPU_KERNEL( \
act_type##_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, \
ops::grad_functor<float>>, \
ops::ActivationGradKernel<paddle::platform::CPUPlace, \
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_CPU_KERNEL( \
act_type, ops::ActivationKernel<paddle::platform::CPUDeviceContext, \
ops::functor<float>>, \
ops::ActivationKernel<paddle::platform::CPUDeviceContext, \
ops::functor<double>>); \
REGISTER_OP_CPU_KERNEL( \
act_type##_grad, \
ops::ActivationGradKernel<paddle::platform::CPUDeviceContext, \
ops::grad_functor<float>>, \
ops::ActivationGradKernel<paddle::platform::CPUDeviceContext, \
ops::grad_functor<double>>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL);
paddle/operators/activation_op.cu
浏览文件 @ 4d8f39b8
......@@ -17,16 +17,17 @@
namespace ops = paddle::operators;
#define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_GPU_KERNEL( \
act_type, \
ops::ActivationKernel<paddle::platform::GPUPlace, ops::functor<float>>, \
ops::ActivationKernel<paddle::platform::GPUPlace, \
ops::functor<double>>); \
REGISTER_OP_GPU_KERNEL( \
act_type##_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, \
ops::grad_functor<float>>, \
ops::ActivationGradKernel<paddle::platform::GPUPlace, \
#define REGISTER_ACTIVATION_CUDA_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_CUDA_KERNEL( \
act_type, ops::ActivationKernel<paddle::platform::CUDADeviceContext, \
ops::functor<float>>, \
ops::ActivationKernel<paddle::platform::CUDADeviceContext, \
ops::functor<double>>); \
REGISTER_OP_CUDA_KERNEL( \
act_type##_grad, \
ops::ActivationGradKernel<paddle::platform::CUDADeviceContext, \
ops::grad_functor<float>>, \
ops::ActivationGradKernel<paddle::platform::CUDADeviceContext, \
ops::grad_functor<double>>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_GPU_KERNEL);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CUDA_KERNEL);
paddle/operators/activation_op.h
浏览文件 @ 4d8f39b8
......@@ -19,7 +19,7 @@
namespace paddle {
namespace operators {
template <typename Place, typename Functor>
template <typename DeviceContext, typename Functor>
class ActivationKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
......@@ -32,18 +32,19 @@ class ActivationKernel
auto x = framework::EigenVector<T>::Flatten(*X);
auto y = framework::EigenVector<T>::Flatten(*Y);
auto place = context.GetEigenDevice<Place>();
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
auto attrs = functor.GetAttrs();
for (auto& attr : attrs) {
*attr.second = context.Attr<float>(attr.first);
}
functor(place, x, y);
functor(*place, x, y);
}
};
template <typename Place, typename Functor>
template <typename DeviceContext, typename Functor>
class ActivationGradKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
......@@ -59,13 +60,14 @@ class ActivationGradKernel
auto x = framework::EigenVector<T>::Flatten(*X);
auto y = framework::EigenVector<T>::Flatten(*Y);
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto place = context.GetEigenDevice<Place>();
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
auto attrs = functor.GetAttrs();
for (auto& attr : attrs) {
*attr.second = context.Attr<float>(attr.first);
}
functor(place, x, y, dy, dx);
functor(*place, x, y, dy, dx);
}
};
......
paddle/operators/adadelta_op.cc
浏览文件 @ 4d8f39b8
......@@ -92,12 +92,12 @@ for gradient descent.
Adadelta updates are as follows:
$$avgSquaredGradOut = \rho * avgSquaredGrad + (1 - \rho) * grad * grad \break
paramUpdate = - $\sqrt{((avgSquaredUpdate + \epsilon) /
(avgSquaredGrad_out + \epsilon))}$ * grad \break
avgSquaredUpdateOut = \rho * avgSquaredUpdate + (1 - \rho) *
{(paramUpdate)}^2 \break
paramOut = param + paramUpdate$$
$$
avg\_squared\_grad\_out = \rho * avg\_squared\_grad + (1 - \rho) * grad * grad \\
param\_update = - \sqrt{\frac{avg\_squared\_update + \epsilon}{avg\_squared\_grad\_out + \epsilon}} * grad \\
avg\_squared\_update\_out = \rho * avg\_squared\_update + (1 - \rho) * {param\_update}^2 \\
param\_out = param + param\_update
$$
)DOC");
}
......@@ -109,5 +109,5 @@ paramOut = param + paramUpdate$$
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker);
REGISTER_OP_CPU_KERNEL(
adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>,
ops::AdadeltaOpKernel<paddle::platform::CPUPlace, double>);
adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::AdadeltaOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/adadelta_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,6 +16,6 @@
#include "paddle/operators/adadelta_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
adadelta, ops::AdadeltaOpKernel<paddle::platform::GPUPlace, float>,
ops::AdadeltaOpKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_CUDA_KERNEL(
adadelta, ops::AdadeltaOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::AdadeltaOpKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/adadelta_op.h
浏览文件 @ 4d8f39b8
......@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class AdadeltaOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -51,7 +51,7 @@ class AdadeltaOpKernel : public framework::OpKernel<T> {
framework::EigenVector<T>::Flatten(*avg_squared_grad_out_tensor);
auto avg_squared_update_out =
framework::EigenVector<T>::Flatten(*avg_squared_update_out_tensor);
auto place = ctx.GetEigenDevice<Place>();
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
avg_squared_grad_out.device(place) =
rho * avg_squared_grad + (1 - rho) * grad.square();
......
paddle/operators/adagrad_op.cc
浏览文件 @ 4d8f39b8
......@@ -80,8 +80,8 @@ Adaptive Gradient Algorithm (Adagrad).
The update is done as follows:
$$momentOut = moment + grad * grad \break
paramOut = param - learningRate * grad / ($\sqrt{momentOut}$ + \epsilon) \break
$$moment\_out = moment + grad * grad \\
param\_out = param - \frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
$$
The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
......@@ -100,8 +100,8 @@ size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
} // namespace
template <typename T>
struct SparseAdagradFunctor<platform::CPUPlace, T> {
void operator()(const platform::DeviceContext& context,
struct SparseAdagradFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& context,
const framework::SelectedRows& grad,
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param) {
......@@ -120,7 +120,7 @@ struct SparseAdagradFunctor<platform::CPUPlace, T> {
{static_cast<int64_t>(merge_rows.size()), grad_width}),
context.GetPlace());
math::SetConstant<platform::CPUPlace, T> constant_functor;
math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
constant_functor(context, grad_merge->mutable_value(), 0.0);
auto* grad_merge_data = grad_merge->mutable_value()->data<T>();
......@@ -144,9 +144,9 @@ struct SparseAdagradFunctor<platform::CPUPlace, T> {
auto gs =
framework::EigenVector<T>::Flatten(*(grad_square->mutable_value()));
auto gm = framework::EigenVector<T>::Flatten(grad_merge->value());
gs.device(*context.GetEigenDevice<platform::CPUPlace>()) = gm * gm;
gs.device(*context.eigen_device()) = gm * gm;
math::SelectedRowsAddToTensor<platform::CPUPlace, T> functor;
math::SelectedRowsAddToTensor<platform::CPUDeviceContext, T> functor;
functor(context, *grad_square, moment);
// 3. update parameter
......@@ -164,13 +164,13 @@ struct SparseAdagradFunctor<platform::CPUPlace, T> {
}
};
template struct SparseAdagradFunctor<platform::CPUPlace, float>;
template struct SparseAdagradFunctor<platform::CPUPlace, double>;
template struct SparseAdagradFunctor<platform::CPUDeviceContext, float>;
template struct SparseAdagradFunctor<platform::CPUDeviceContext, double>;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adagrad, ops::AdagradOp, ops::AdagradOpMaker);
REGISTER_OP_CPU_KERNEL(
adagrad, ops::AdagradOpKernel<paddle::platform::CPUPlace, float>,
ops::AdagradOpKernel<paddle::platform::CPUPlace, double>);
adagrad, ops::AdagradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::AdagradOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/adagrad_op.cu
浏览文件 @ 4d8f39b8
......@@ -72,8 +72,8 @@ __global__ void SparseAdagradFunctorKernel(const T* grad, const int64_t* rows,
} // namespace
template <typename T>
struct SparseAdagradFunctor<platform::GPUPlace, T> {
void operator()(const platform::DeviceContext& context,
struct SparseAdagradFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& context,
const framework::SelectedRows& grad,
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param) {
......@@ -92,7 +92,7 @@ struct SparseAdagradFunctor<platform::GPUPlace, T> {
{static_cast<int64_t>(merge_rows.size()), grad_width}),
context.GetPlace());
math::SetConstant<platform::GPUPlace, T> constant_functor;
math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
constant_functor(context, grad_merge->mutable_value(), 0.0);
auto* grad_merge_data = grad_merge->mutable_value()->data<T>();
......@@ -119,9 +119,9 @@ struct SparseAdagradFunctor<platform::GPUPlace, T> {
auto gs =
framework::EigenVector<T>::Flatten(*(grad_square->mutable_value()));
auto gm = framework::EigenVector<T>::Flatten(grad_merge->value());
gs.device(*context.GetEigenDevice<platform::GPUPlace>()) = gm * gm;
gs.device(*context.eigen_device()) = gm * gm;
math::SelectedRowsAddToTensor<platform::GPUPlace, T> functor;
math::SelectedRowsAddToTensor<platform::CUDADeviceContext, T> functor;
functor(context, *grad_square, moment);
// 3. update parameter
......@@ -139,13 +139,13 @@ struct SparseAdagradFunctor<platform::GPUPlace, T> {
}
};
template struct SparseAdagradFunctor<platform::GPUPlace, float>;
template struct SparseAdagradFunctor<platform::GPUPlace, double>;
template struct SparseAdagradFunctor<platform::CUDADeviceContext, float>;
template struct SparseAdagradFunctor<platform::CUDADeviceContext, double>;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
adagrad, ops::AdagradOpKernel<paddle::platform::GPUPlace, float>,
ops::AdagradOpKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_CUDA_KERNEL(
adagrad, ops::AdagradOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::AdagradOpKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/adagrad_op.h
浏览文件 @ 4d8f39b8
......@@ -19,15 +19,15 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
struct SparseAdagradFunctor {
void operator()(const platform::DeviceContext& context,
void operator()(const DeviceContext& context,
const framework::SelectedRows& grad,
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param);
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class AdagradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -52,11 +52,11 @@ class AdagradOpKernel : public framework::OpKernel<T> {
auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
auto place = ctx.GetEigenDevice<Place>();
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
moment_out.device(place) = moment + grad * grad;
moment_out.device(*place) = moment + grad * grad;
Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
param_out.device(place) =
param_out.device(*place) =
param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
} else if (grad_var->IsType<framework::SelectedRows>()) {
auto* param_tensor = ctx.Input<framework::Tensor>("Param");
......@@ -65,8 +65,9 @@ class AdagradOpKernel : public framework::OpKernel<T> {
auto* moment_tensor = ctx.Input<framework::Tensor>("Moment");
PADDLE_ENFORCE_EQ(moment_tensor, moment_out_tensor);
SparseAdagradFunctor<Place, T> functor;
functor(ctx.device_context(), *ctx.Input<framework::SelectedRows>("Grad"),
SparseAdagradFunctor<DeviceContext, T> functor;
functor(ctx.template device_context<DeviceContext>(),
*ctx.Input<framework::SelectedRows>("Grad"),
*ctx.Input<framework::Tensor>("LearningRate"), epsilon,
moment_out_tensor, param_out_tensor);
} else {
......
paddle/operators/adam_op.cc
浏览文件 @ 4d8f39b8
......@@ -112,11 +112,13 @@ adaptive estimates of lower-order moments.
Adam updates:
$$moment_1_{out} = \beta_1 * moment_1 + (1 - \beta_1) * grad \break
moment_2_{out} = \beta_2 * moment_2 + (1 - \beta_2) * grad * grad \break
learningRate = learningRate *
$\sqrt{(1 - \beta_2_{pow})}$ / (1 - \beta_1_{pow}) \break
paramOut = param - learningRate * moment_1/ ($\sqrt{(moment_2)} + \epsilon)$$
$$
moment\_1\_out = \beta_1 * moment\_1 + (1 - \beta_1) * grad \\
moment\_2_\out = \beta_2 * moment\_2 + (1 - \beta_2) * grad * grad \\
learning\_rate = learning\_rate *
\frac{\sqrt{1 - \beta_{2\_pow}}}{1 - \beta_{1\_pow}} \\
param\_out = param - learning\_rate * \frac{moment\_1}{\sqrt{moment\_2} + \epsilon}
$$
)DOC");
}
......@@ -126,6 +128,6 @@ paramOut = param - learningRate * moment_1/ ($\sqrt{(moment_2)} + \epsilon)$$
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adam, ops::AdamOp, ops::AdamOpMaker);
REGISTER_OP_CPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::CPUPlace, float>,
ops::AdamOpKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
adam, ops::AdamOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::AdamOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/adam_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,6 +16,6 @@
#include "paddle/operators/adam_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::GPUPlace, float>,
ops::AdamOpKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_CUDA_KERNEL(
adam, ops::AdamOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::AdamOpKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/adam_op.h
浏览文件 @ 4d8f39b8
......@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class AdamOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -52,17 +52,17 @@ class AdamOpKernel : public framework::OpKernel<T> {
auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
auto moment1_out = framework::EigenVector<T>::Flatten(*moment1_out_tensor);
auto moment2_out = framework::EigenVector<T>::Flatten(*moment2_out_tensor);
auto place = ctx.GetEigenDevice<Place>();
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
moment1_out.device(place) = beta1 * moment1 + (1 - beta1) * grad;
moment2_out.device(place) = beta2 * moment2 + (1 - beta2) * grad.square();
moment1_out.device(*place) = beta1 * moment1 + (1 - beta1) * grad;
moment2_out.device(*place) = beta2 * moment2 + (1 - beta2) * grad.square();
// All of these are tensors of 1 element
auto lr_t = lr * (1 - beta2_pow).sqrt() / (1 - beta1_pow);
// Eigen does not support automatic broadcast
// Get dimensions of moment vector to broadcast lr_t
Eigen::DSizes<int, 1> m_dsize(moment1_out_tensor->numel());
param_out.device(place) =
param_out.device(*place) =
param -
lr_t.broadcast(m_dsize) *
(moment1_out / (moment2_out.sqrt() + epsilon));
......
paddle/operators/adamax_op.cc
浏览文件 @ 4d8f39b8
......@@ -108,10 +108,10 @@ Adam algorithm based on the infinity norm.
Adamax updates:
$$
momentOut = \beta_{1} * moment + (1 - \beta_{1}) * grad \\
infNormOut = max(\beta_{2} * infNorm + \epsilon, |grad|) \\
learningRate = \frac{learningRate}{1 - \beta_{1}^{Beta1Pow}} \\
paramOut = param - learningRate * \frac{momentOut}{infNormOut}
moment\_out = \beta_1 * moment + (1 - \beta_1) * grad \\
inf\_norm\_out = max(\beta_2 * inf\_norm + \epsilon, |grad|) \\
learning\_rate = \frac{learning\_rate}{1 - \beta_{1\_pow}} \\
param\_out = param - learning\_rate * \frac{moment\_out}{inf\_norm\_out}
$$
The original paper does not have an epsilon attribute.
......@@ -127,6 +127,6 @@ division by 0 error.
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adamax, ops::AdamaxOp, ops::AdamaxOpMaker);
REGISTER_OP_CPU_KERNEL(adamax,
ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>,
ops::AdamaxOpKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
adamax, ops::AdamaxOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::AdamaxOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/adamax_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,6 +16,6 @@
#include "paddle/operators/adamax_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adamax,
ops::AdamaxOpKernel<paddle::platform::GPUPlace, float>,
ops::AdamaxOpKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_CUDA_KERNEL(
adamax, ops::AdamaxOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::AdamaxOpKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/adamax_op.h
浏览文件 @ 4d8f39b8
......@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class AdamaxOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -51,14 +51,14 @@ class AdamaxOpKernel : public framework::OpKernel<T> {
auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
auto inf_norm_out =
framework::EigenVector<T>::Flatten(*inf_norm_out_tensor);
auto place = ctx.GetEigenDevice<Place>();
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
moment_out.device(place) = beta1 * moment + (1 - beta1) * grad;
inf_norm_out.device(place) =
moment_out.device(*place) = beta1 * moment + (1 - beta1) * grad;
inf_norm_out.device(*place) =
grad.abs().cwiseMax((beta2 * inf_norm) + epsilon);
auto lr_t = lr / (1 - beta1_pow);
Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
param_out.device(place) =
param_out.device(*place) =
param - lr_t.broadcast(m_dsize) * (moment_out / inf_norm_out);
}
};
......
paddle/operators/auc_op.h
浏览文件 @ 4d8f39b8
......@@ -25,7 +25,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class AucKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......
paddle/operators/batch_norm_op.cc
浏览文件 @ 4d8f39b8
......@@ -135,7 +135,8 @@ The required data format for this layer is one of the following:
};
template <typename T>
class BatchNormKernel<platform::CPUPlace, T> : public framework::OpKernel<T> {
class BatchNormKernel<platform::CPUDeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
......@@ -318,12 +319,12 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
PADDLE_THROW("can't find Y@GRAD");
}
return framework::OpKernelType(framework::ToDataType(t->type()),
ctx.device_context());
ctx.GetPlace());
}
};
template <typename T>
class BatchNormGradKernel<platform::CPUPlace, T>
class BatchNormGradKernel<platform::CPUDeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
......@@ -436,8 +437,9 @@ class BatchNormGradKernel<platform::CPUPlace, T>
namespace ops = paddle::operators;
REGISTER_OP(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
batch_norm_grad, ops::BatchNormGradOp);
REGISTER_OP_CPU_KERNEL(batch_norm,
ops::BatchNormKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
batch_norm,
ops::BatchNormKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
batch_norm_grad,
ops::BatchNormGradKernel<paddle::platform::CPUPlace, float>);
ops::BatchNormGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/batch_norm_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -47,7 +47,8 @@ void ExtractNCWHD(const framework::DDim &dims,
}
template <typename T>
class BatchNormKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
class BatchNormKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
......@@ -121,11 +122,12 @@ class BatchNormKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
saved_mean->mutable_data<T>(ctx.GetPlace());
saved_variance->mutable_data<T>(ctx.GetPlace());
math::SetConstant<platform::GPUPlace, T> functor;
functor(ctx.device_context(), saved_mean, 0);
functor(ctx.device_context(), saved_variance, 0);
auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
math::SetConstant<platform::CUDADeviceContext, T> functor;
functor(dev_ctx, saved_mean, 0);
functor(dev_ctx, saved_variance, 0);
auto handle = ctx.cuda_device_context().cudnn_handle();
auto handle = dev_ctx.cudnn_handle();
// Now, depending on whether we are running test or not, we have two paths.
if (is_test) {
......@@ -171,7 +173,7 @@ class BatchNormKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
};
template <typename T>
class BatchNormGradKernel<platform::GPUPlace, T>
class BatchNormGradKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
......@@ -244,11 +246,12 @@ class BatchNormGradKernel<platform::GPUPlace, T>
const void *saved_mean_data = saved_mean->template data<T>();
const void *saved_var_data = saved_var->template data<T>();
auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationBackward(
ctx.cuda_device_context().cudnn_handle(), mode_,
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(), data_desc_,
x->template data<T>(), data_desc_, d_y->template data<T>(), data_desc_,
dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_, x->template data<T>(),
data_desc_, d_y->template data<T>(), data_desc_,
d_x->template mutable_data<T>(ctx.GetPlace()), bn_param_desc_,
scale->template data<T>(),
d_scale->template mutable_data<T>(ctx.GetPlace()),
......@@ -266,8 +269,9 @@ class BatchNormGradKernel<platform::GPUPlace, T>
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(batch_norm,
ops::BatchNormKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
batch_norm,
ops::BatchNormKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
batch_norm_grad,
ops::BatchNormGradKernel<paddle::platform::GPUPlace, float>);
ops::BatchNormGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/batch_norm_op.h
浏览文件 @ 4d8f39b8
......@@ -34,13 +34,13 @@ inline TensorFormat StringToTensorFormat(const std::string& str) {
}
}
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class BatchNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override;
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class BatchNormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override;
......
paddle/operators/bilinear_tensor_product_op.cc
浏览文件 @ 4d8f39b8
......@@ -159,9 +159,12 @@ REGISTER_OP(bilinear_tensor_product, ops::BilinearTensorProductOp,
ops::BilinearTensorProductOpGrad);
REGISTER_OP_CPU_KERNEL(
bilinear_tensor_product,
ops::BilinearTensorProductKernel<paddle::platform::CPUPlace, float>,
ops::BilinearTensorProductKernel<paddle::platform::CPUPlace, double>);
ops::BilinearTensorProductKernel<paddle::platform::CPUDeviceContext, float>,
ops::BilinearTensorProductKernel<paddle::platform::CPUDeviceContext,
double>);
REGISTER_OP_CPU_KERNEL(
bilinear_tensor_product_grad,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUPlace, float>,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUPlace, double>);
ops::BilinearTensorProductGradKernel<paddle::platform::CPUDeviceContext,
float>,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUDeviceContext,
double>);
paddle/operators/bilinear_tensor_product_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,11 +16,15 @@ limitations under the License. */
#include "paddle/operators/bilinear_tensor_product_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
bilinear_tensor_product,
ops::BilinearTensorProductKernel<paddle::platform::GPUPlace, float>,
ops::BilinearTensorProductKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
ops::BilinearTensorProductKernel<paddle::platform::CUDADeviceContext,
float>,
ops::BilinearTensorProductKernel<paddle::platform::CUDADeviceContext,
double>);
REGISTER_OP_CUDA_KERNEL(
bilinear_tensor_product_grad,
ops::BilinearTensorProductGradKernel<paddle::platform::GPUPlace, float>,
ops::BilinearTensorProductGradKernel<paddle::platform::GPUPlace, double>);
ops::BilinearTensorProductGradKernel<paddle::platform::CUDADeviceContext,
float>,
ops::BilinearTensorProductGradKernel<paddle::platform::CUDADeviceContext,
double>);
paddle/operators/bilinear_tensor_product_op.h
浏览文件 @ 4d8f39b8
......@@ -27,7 +27,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class BilinearTensorProductKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -46,7 +46,8 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
int out_dim = weight_dims[0];
auto x_dim = weight_dims[1];
auto y_dim = weight_dims[2];
auto place = ctx.GetEigenDevice<Place>();
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// Create the intermediate variable to caculate the result of
// Input(X) multiplied by Input(Weight_i), the formula is:
......@@ -60,9 +61,9 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
auto output_col_vec = output_mat.chip(i, 1);
Tensor weight_mat =
weight->Slice(i, i + 1).Resize(framework::make_ddim({x_dim, y_dim}));
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
batch_size, y_dim, x_dim, 1, x->data<T>(),
weight_mat.data<T>(), 0, left_mul.data<T>());
math::gemm<DeviceContext, T>(dev_ctx, CblasNoTrans, CblasNoTrans,
batch_size, y_dim, x_dim, 1, x->data<T>(),
weight_mat.data<T>(), 0, left_mul.data<T>());
output_col_vec.device(place) =
(left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1));
}
......@@ -74,7 +75,7 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -96,8 +97,8 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
auto x_mat = EigenMatrix<T>::From(*x);
auto y_mat = EigenMatrix<T>::From(*y);
auto d_out_mat = EigenMatrix<T>::From(*d_out);
auto place = ctx.GetEigenDevice<Place>();
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// Create the intermediate variable to caculate the Output(Y@Grad).
Tensor x_scale;
x_scale.mutable_data<T>(framework::make_ddim({batch_size, x_dim}),
......@@ -110,18 +111,18 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
ctx.GetPlace());
auto y_scale_mat = EigenMatrix<T>::From(y_scale);
math::SetConstant<Place, T> set_zero;
math::SetConstant<DeviceContext, T> set_zero;
// Set Output(X@Grad) be zero.
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
set_zero(ctx.device_context(), d_x, static_cast<T>(0));
set_zero(dev_ctx, d_x, static_cast<T>(0));
}
// Set Output(Y@Grad) be zero.
if (d_y) {
d_y->mutable_data<T>(ctx.GetPlace());
set_zero(ctx.device_context(), d_y, static_cast<T>(0));
set_zero(dev_ctx, d_y, static_cast<T>(0));
}
// Caculate the Output(X@Grad) and Output(Y@Grad).
......@@ -137,18 +138,18 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
.broadcast(bcast_for_x) *
y_mat;
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasTrans,
batch_size, x_dim, y_dim, 1, y_scale.data<T>(),
weight_i.data<T>(), 1, d_x->data<T>());
math::gemm<DeviceContext, T>(
dev_ctx, CblasNoTrans, CblasTrans, batch_size, x_dim, y_dim, 1,
y_scale.data<T>(), weight_i.data<T>(), 1, d_x->data<T>());
}
if (d_y) {
x_scale_mat.device(place) =
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
.broadcast(bcast_for_y) *
x_mat;
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
batch_size, y_dim, x_dim, 1, x_scale.data<T>(),
weight_i.data<T>(), 1, d_y->data<T>());
math::gemm<DeviceContext, T>(
dev_ctx, CblasNoTrans, CblasNoTrans, batch_size, y_dim, x_dim, 1,
x_scale.data<T>(), weight_i.data<T>(), 1, d_y->data<T>());
}
}
}
......@@ -165,9 +166,9 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
.broadcast(bcast_for_weight) *
x_mat;
math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans,
x_dim, y_dim, batch_size, 1, x_scale.data<T>(),
y->data<T>(), 0, d_weight_i.data<T>());
math::gemm<DeviceContext, T>(dev_ctx, CblasTrans, CblasNoTrans, x_dim,
y_dim, batch_size, 1, x_scale.data<T>(),
y->data<T>(), 0, d_weight_i.data<T>());
}
}
......
paddle/operators/cast_op.cc
浏览文件 @ 4d8f39b8
......@@ -68,7 +68,7 @@ class CastOpGradMaker : public framework::SingleGradOpDescMaker {
} // namespace paddle
namespace ops = paddle::operators;
using CPU = paddle::platform::CPUPlace;
using CPU = paddle::platform::CPUDeviceContext;
REGISTER_OP_WITH_KERNEL(cast, ops::CastOpGradMaker, ops::CastOpInferShape,
ops::CastOpProtoMaker);
REGISTER_OP_CPU_KERNEL(cast, ops::CastOpKernel<CPU, float>,
......
paddle/operators/cast_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,7 +16,7 @@
template <typename T>
using CastOpKernel =
paddle::operators::CastOpKernel<paddle::platform::GPUPlace, T>;
paddle::operators::CastOpKernel<paddle::platform::CUDADeviceContext, T>;
REGISTER_OP_GPU_KERNEL(cast, CastOpKernel<float>, CastOpKernel<double>,
CastOpKernel<int>, CastOpKernel<int64_t>);
REGISTER_OP_CUDA_KERNEL(cast, CastOpKernel<float>, CastOpKernel<double>,
CastOpKernel<int>, CastOpKernel<int64_t>);
paddle/operators/cast_op.h
浏览文件 @ 4d8f39b8
......@@ -27,13 +27,13 @@ struct CastOpTransformFunctor {
HOSTDEVICE OutT operator()(InT in) const { return static_cast<OutT>(in); }
};
template <typename Place, typename InT>
template <typename DeviceContext, typename InT>
struct CastOpFunctor {
const framework::Tensor* in_;
framework::Tensor* out_;
const platform::DeviceContext& ctx_;
const DeviceContext& ctx_;
CastOpFunctor(const framework::Tensor* in, framework::Tensor* out,
const platform::DeviceContext& ctx)
const DeviceContext& ctx)
: in_(in), out_(out), ctx_(ctx) {}
template <typename OutT>
......@@ -42,13 +42,13 @@ struct CastOpFunctor {
auto numel = in_->numel();
auto* in_end = in_begin + numel;
auto* out_begin = out_->mutable_data<OutT>(ctx_.GetPlace());
platform::Transform<Place> trans;
platform::Transform<DeviceContext> trans;
trans(ctx_, in_begin, in_end, out_begin,
CastOpTransformFunctor<InT, OutT>());
}
};
template <typename Place, typename InT>
template <typename DeviceContext, typename InT>
class CastOpKernel : public framework::OpKernel<InT> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -56,7 +56,8 @@ class CastOpKernel : public framework::OpKernel<InT> {
auto* out = context.Output<framework::Tensor>("Out");
framework::VisitDataType(
static_cast<framework::DataType>(context.Attr<int>("out_dtype")),
CastOpFunctor<Place, InT>(in, out, context.device_context()));
CastOpFunctor<DeviceContext, InT>(
in, out, context.template device_context<DeviceContext>()));
}
};
......
paddle/operators/chunk_eval_op.h
浏览文件 @ 4d8f39b8
......@@ -23,7 +23,7 @@ namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ChunkEvalKernel : public framework::OpKernel<T> {
public:
struct Segment {
......
paddle/operators/clip_by_norm_op.cc
浏览文件 @ 4d8f39b8
......@@ -71,4 +71,5 @@ namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(clip_by_norm, ops::ClipByNormOp,
ops::ClipByNormOpMaker);
REGISTER_OP_CPU_KERNEL(
clip_by_norm, ops::ClipByNormKernel<paddle::platform::CPUPlace, float>);
clip_by_norm,
ops::ClipByNormKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/clip_by_norm_op.cu
浏览文件 @ 4d8f39b8
......@@ -15,5 +15,6 @@
#include "paddle/operators/clip_by_norm_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
clip_by_norm, ops::ClipByNormKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(
clip_by_norm,
ops::ClipByNormKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/clip_by_norm_op.h
浏览文件 @ 4d8f39b8
......@@ -26,7 +26,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ClipByNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -38,7 +38,8 @@ class ClipByNormKernel : public framework::OpKernel<T> {
auto x = EigenVector<T>::Flatten(*input);
auto out = EigenVector<T>::Flatten(*output);
auto x_norm = x.square().sum().sqrt();
auto place = context.GetEigenDevice<Place>();
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
auto temp = (x_norm <= max_norm).template cast<T>().eval();
auto scaling = temp + (static_cast<T>(1) - temp) * max_norm / x_norm;
......
paddle/operators/clip_op.cc
浏览文件 @ 4d8f39b8
......@@ -83,7 +83,7 @@ class ClipOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(clip, ops::ClipOp, ops::ClipOpMaker<float>, clip_grad,
ops::ClipOpGrad);
REGISTER_OP_CPU_KERNEL(clip,
ops::ClipKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(clip_grad,
ops::ClipGradKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
clip, ops::ClipKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
clip_grad, ops::ClipGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/clip_op.cu
浏览文件 @ 4d8f39b8
......@@ -15,7 +15,7 @@
#include "paddle/operators/clip_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(clip,
ops::ClipKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(clip_grad,
ops::ClipGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(
clip, ops::ClipKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
clip_grad, ops::ClipGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/clip_op.h
浏览文件 @ 4d8f39b8
......@@ -55,7 +55,7 @@ class ClipGradFunctor {
T max_;
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ClipKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -66,13 +66,13 @@ class ClipKernel : public framework::OpKernel<T> {
T* out_data = out->mutable_data<T>(context.GetPlace());
const T* x_data = x->data<T>();
int64_t numel = x->numel();
Transform<Place> trans;
trans(context.device_context(), x_data, x_data + numel, out_data,
ClipFunctor<T>(min, max));
Transform<DeviceContext> trans;
trans(context.template device_context<DeviceContext>(), x_data,
x_data + numel, out_data, ClipFunctor<T>(min, max));
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ClipGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -86,9 +86,9 @@ class ClipGradKernel : public framework::OpKernel<T> {
auto* d_x_data = d_x->mutable_data<T>(context.GetPlace());
const T* d_out_data = d_out->data<T>();
const T* x_data = x->data<T>();
Transform<Place> trans;
trans(context.device_context(), d_out_data, d_out_data + numel, x_data,
d_x_data, ClipGradFunctor<T>(min, max));
Transform<DeviceContext> trans;
trans(context.template device_context<DeviceContext>(), d_out_data,
d_out_data + numel, x_data, d_x_data, ClipGradFunctor<T>(min, max));
}
}
};
......
paddle/operators/compare_op.cu
浏览文件 @ 4d8f39b8
......@@ -14,10 +14,10 @@
#include "paddle/operators/compare_op.h"
REGISTER_LOGICAL_KERNEL(less_than, GPU, paddle::operators::LessThanFunctor);
REGISTER_LOGICAL_KERNEL(less_equal, GPU, paddle::operators::LessEqualFunctor);
REGISTER_LOGICAL_KERNEL(greater_than, GPU,
REGISTER_LOGICAL_KERNEL(less_than, CUDA, paddle::operators::LessThanFunctor);
REGISTER_LOGICAL_KERNEL(less_equal, CUDA, paddle::operators::LessEqualFunctor);
REGISTER_LOGICAL_KERNEL(greater_than, CUDA,
paddle::operators::GreaterThanFunctor);
REGISTER_LOGICAL_KERNEL(greater_equal, GPU,
REGISTER_LOGICAL_KERNEL(greater_equal, CUDA,
paddle::operators::GreaterEqualFunctor);
REGISTER_LOGICAL_KERNEL(equal, GPU, paddle::operators::EqualFunctor);
REGISTER_LOGICAL_KERNEL(equal, CUDA, paddle::operators::EqualFunctor);
paddle/operators/compare_op.h
浏览文件 @ 4d8f39b8
......@@ -59,7 +59,7 @@ struct EqualFunctor {
}
};
template <typename Place, typename Functor>
template <typename DeviceContext, typename Functor>
class CompareOpKernel
: public framework::OpKernel<typename Functor::ELEM_TYPE> {
public:
......@@ -69,24 +69,23 @@ class CompareOpKernel
auto* y = context.Input<framework::Tensor>("Y");
auto* out = context.Output<framework::Tensor>("Out");
Functor binary_func;
platform::Transform<Place> trans;
trans(context.device_context(), x->data<T>(), x->data<T>() + x->numel(),
y->data<T>(), out->mutable_data<bool>(context.GetPlace()),
binary_func);
platform::Transform<DeviceContext> trans;
trans(context.template device_context<DeviceContext>(), x->data<T>(),
x->data<T>() + x->numel(), y->data<T>(),
out->mutable_data<bool>(context.GetPlace()), binary_func);
}
};
} // namespace operators
} // namespace paddle
#define REGISTER_LOGICAL_KERNEL(op_type, dev, functor) \
REGISTER_OP_##dev##_KERNEL( \
op_type, \
::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
functor<int>>, \
::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
functor<int64_t>>, \
::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
functor<float>>, \
::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
functor<double>>);
#define REGISTER_LOGICAL_KERNEL(op_type, dev, functor) \
REGISTER_OP_##dev##_KERNEL( \
op_type, ::paddle::operators::CompareOpKernel< \
::paddle::platform::dev##DeviceContext, functor<int>>, \
::paddle::operators::CompareOpKernel< \
::paddle::platform::dev##DeviceContext, functor<int64_t>>, \
::paddle::operators::CompareOpKernel< \
::paddle::platform::dev##DeviceContext, functor<float>>, \
::paddle::operators::CompareOpKernel< \
::paddle::platform::dev##DeviceContext, functor<double>>);
paddle/operators/concat_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -14,7 +14,8 @@ limitations under the License. */
#include "paddle/operators/concat_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(concat,
ops::ConcatKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
concat_grad, ops::ConcatGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(
concat, ops::ConcatKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
concat_grad,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/concat_op.h
浏览文件 @ 4d8f39b8
......@@ -21,7 +21,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ConcatKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -43,7 +43,7 @@ class ConcatKernel : public framework::OpKernel<T> {
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ConcatGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
......
paddle/operators/conv_cudnn_op.cc
浏览文件 @ 4d8f39b8
......@@ -57,18 +57,20 @@ REGISTER_OP(conv2d_cudnn, ops::ConvOp, ops::CudnnConv2DOpMaker,
REGISTER_OP(conv3d_cudnn, ops::ConvOp, ops::CudnnConv3DOpMaker,
conv3d_cudnn_grad, ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(conv2d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_cudnn_grad,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(conv3d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_cudnn_grad,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/conv_cudnn_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -118,7 +118,8 @@ class CudnnConvOpKernel : public framework::OpKernel<T> {
}
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionFwdAlgo_t algo;
auto handle = ctx.cuda_device_context().cudnn_handle();
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
......@@ -238,7 +239,8 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
workspace_size_limit = user_workspace_size * 1024 * 1024;
}
auto handle = ctx.cuda_device_context().cudnn_handle();
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
if (input_grad) {
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
......@@ -313,16 +315,16 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
} // namespace operators
} // namespace paddle
REGISTER_OP_GPU_KERNEL(conv2d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv2d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv2d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv2d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv3d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv3d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
paddle/operators/conv_op.cc
浏览文件 @ 4d8f39b8
......@@ -235,16 +235,18 @@ namespace ops = paddle::operators;
REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(conv2d,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
conv2d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_grad,
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(conv3d,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
conv3d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_grad,
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/conv_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -16,16 +16,18 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv2d,
ops::GemmConvKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
conv2d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_CUDA_KERNEL(
conv2d, ops::GemmConvKernel<paddle::platform::CUDADeviceContext, float>,
ops::GemmConvKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
conv2d_grad,
ops::GemmConvGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::GemmConvGradKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_GPU_KERNEL(conv3d,
ops::GemmConvKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
conv3d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_CUDA_KERNEL(
conv3d, ops::GemmConvKernel<paddle::platform::CUDADeviceContext, float>,
ops::GemmConvKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
conv3d_grad,
ops::GemmConvGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::GemmConvGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/conv_op.h
浏览文件 @ 4d8f39b8
......@@ -72,7 +72,7 @@ class ConvOpGrad : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override;
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class GemmConvKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -141,9 +141,10 @@ class GemmConvKernel : public framework::OpKernel<T> {
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<Place, T> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
math::Vol2ColFunctor<DeviceContext, T> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
auto& dev_ctx = context.template device_context<DeviceContext>();
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
......@@ -157,27 +158,26 @@ class GemmConvKernel : public framework::OpKernel<T> {
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(context.device_context(), in_slice, dilations, strides,
im2col(dev_ctx, in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(context.device_context(), in_slice, dilations, strides,
paddings, &col);
vol2col(dev_ctx, in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<Place, T>(context.device_context(), filter_slice, false,
col_matrix, false, T(1.0), &out_slice, T(0.0));
math::matmul<DeviceContext, T>(dev_ctx, filter_slice, false, col_matrix,
false, T(1.0), &out_slice, T(0.0));
}
}
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class GemmConvGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -256,14 +256,15 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
col_matrix.Resize(col_matrix_shape);
}
math::SetConstant<Place, T> set_zero;
math::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = context.template device_context<DeviceContext>();
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(context.device_context(), input_grad, static_cast<T>(0));
set_zero(dev_ctx, input_grad, static_cast<T>(0));
math::Col2VolFunctor<Place, T> col2vol;
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
math::Col2VolFunctor<DeviceContext, T> col2vol;
math::Col2ImFunctor<math::ColFormat::kCFO, DeviceContext, T> col2im;
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
......@@ -282,18 +283,17 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
col_matrix.ShareDataWith(in_grad_slice);
col_matrix.Resize(col_matrix_shape);
}
math::matmul<Place, T>(context.device_context(), filter_slice, true,
out_grad_slice, false, T(1.0), &col_matrix,
T(0.0));
math::matmul<DeviceContext, T>(dev_ctx, filter_slice, true,
out_grad_slice, false, T(1.0),
&col_matrix, T(0.0));
if (is_expand && data_dim == 2U) {
col2im(context.device_context(), col, dilations, strides,
col2im(dev_ctx, col, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&in_grad_slice);
} else if (is_expand && data_dim == 3U) {
col2vol(context.device_context(), col, dilations, strides, paddings,
&in_grad_slice);
col2vol(dev_ctx, col, dilations, strides, paddings, &in_grad_slice);
}
}
}
......@@ -303,9 +303,9 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
filter_grad->mutable_data<T>(context.GetPlace());
Tensor filter_grad_ = *filter_grad;
filter_grad_.Resize(filter_matrix_shape);
set_zero(context.device_context(), filter_grad, static_cast<T>(0));
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
math::Vol2ColFunctor<Place, T> vol2col;
set_zero(dev_ctx, filter_grad, static_cast<T>(0));
math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
math::Vol2ColFunctor<DeviceContext, T> vol2col;
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
......@@ -321,21 +321,20 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
im2col(context.device_context(), in_slice, dilations, strides,
im2col(dev_ctx, in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
vol2col(context.device_context(), in_slice, dilations, strides,
paddings, &col);
vol2col(dev_ctx, in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor filter_grad_slice =
filter_grad_.Slice(g * out_step, (g + 1) * out_step);
math::matmul<Place, T>(context.device_context(), out_grad_slice,
false, col_matrix, true, T(1.0),
&filter_grad_slice, T(1.0));
math::matmul<DeviceContext, T>(dev_ctx, out_grad_slice, false,
col_matrix, true, T(1.0),
&filter_grad_slice, T(1.0));
}
}
}
......
paddle/operators/conv_shift_op.cu
浏览文件 @ 4d8f39b8
......@@ -111,7 +111,8 @@ __global__ void ConvShiftDy(const T *x, const T *dout, int x_width, int y_width,
} // namespace
template <typename T>
class ConvShiftKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
class ConvShiftKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const Tensor *X = context.Input<Tensor>("X");
......@@ -132,7 +133,8 @@ class ConvShiftKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
dim3 grid_dim(num_x_blocks, batch_size);
auto stream = context.cuda_device_context().stream();
auto stream =
context.template device_context<platform::CUDADeviceContext>().stream();
ConvShiftForward<T><<<grid_dim, x_per_block, mem_per_block, stream>>>(
x_data, y_data, x_width, y_width, y_half_width, batch_size, out_data);
......@@ -140,7 +142,7 @@ class ConvShiftKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
};
template <typename T>
class ConvShiftGradKernel<platform::GPUPlace, T>
class ConvShiftGradKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
......@@ -159,8 +161,9 @@ class ConvShiftGradKernel<platform::GPUPlace, T>
int y_width = Y->dims()[1];
int y_half_width = (y_width - 1) / 2;
auto &device_ctx = context.cuda_device_context();
math::SetConstant<platform::GPUPlace, T> zero;
auto &device_ctx =
context.template device_context<platform::CUDADeviceContext>();
math::SetConstant<platform::CUDADeviceContext, T> zero;
const int x_per_block = 256;
int num_x_blocks = DivUp(x_width, x_per_block);
......@@ -186,8 +189,9 @@ class ConvShiftGradKernel<platform::GPUPlace, T>
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv_shift,
ops::ConvShiftKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
conv_shift,
ops::ConvShiftKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
conv_shift_grad,
ops::ConvShiftGradKernel<paddle::platform::GPUPlace, float>);
ops::ConvShiftGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/conv_shift_op.h
浏览文件 @ 4d8f39b8
......@@ -18,13 +18,13 @@
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ConvShiftKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override;
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ConvShiftGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override;
......
paddle/operators/conv_transpose_cudnn_op.cc
浏览文件 @ 4d8f39b8
......@@ -61,12 +61,13 @@ REGISTER_OP(conv2d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
double>);
REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
ops::CudnnConv3DTransposeOpMaker, conv3d_transpose_cudnn_grad,
......@@ -74,9 +75,10 @@ REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL(
conv3d_transpose_cudnn,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_transpose_cudnn_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
double>);
paddle/operators/conv_transpose_cudnn_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -83,7 +83,8 @@ class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
}
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionBwdDataAlgo_t algo;
auto handle = ctx.cuda_device_context().cudnn_handle();
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
// Get the algorithm
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
......@@ -165,7 +166,8 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
workspace_size_limit = user_workspace_size * 1024 * 1024;
}
auto handle = ctx.cuda_device_context().cudnn_handle();
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
if (input_grad) {
// choose backward algorithm for data
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
......@@ -234,16 +236,16 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv2d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv2d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv3d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_CUDA_KERNEL(conv3d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
paddle/operators/conv_transpose_op.cc
浏览文件 @ 4d8f39b8
......@@ -197,21 +197,23 @@ REGISTER_OP(conv2d_transpose, ops::ConvTransposeOp, ops::Conv2DTransposeOpMaker,
REGISTER_OP_CPU_KERNEL(
conv2d_transpose,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
double>);
REGISTER_OP(conv3d_transpose, ops::ConvTransposeOp, ops::Conv3DTransposeOpMaker,
conv3d_transpose_grad, ops::ConvTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
conv3d_transpose,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_transpose_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
double>);
paddle/operators/conv_transpose_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -16,20 +16,24 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
conv2d_transpose,
ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
ops::GemmConvTransposeKernel<paddle::platform::CUDADeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
conv2d_transpose_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, double>);
ops::GemmConvTransposeGradKernel<paddle::platform::CUDADeviceContext,
float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CUDADeviceContext,
double>);
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
conv3d_transpose,
ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
ops::GemmConvTransposeKernel<paddle::platform::CUDADeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
conv3d_transpose_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, double>);
ops::GemmConvTransposeGradKernel<paddle::platform::CUDADeviceContext,
float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CUDADeviceContext,
double>);
paddle/operators/conv_transpose_op.h
浏览文件 @ 4d8f39b8
......@@ -52,7 +52,7 @@ class ConvTransposeOpGrad : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override;
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class GemmConvTransposeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -109,11 +109,12 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
filter.Resize(filter_matrix_shape);
output->mutable_data<T>(context.GetPlace());
math::SetConstant<Place, T> set_zero;
set_zero(context.device_context(), output, static_cast<T>(0));
math::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = context.template device_context<DeviceContext>();
set_zero(dev_ctx, output, static_cast<T>(0));
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
math::Col2VolFunctor<Place, T> col2vol;
math::Col2ImFunctor<math::ColFormat::kCFO, DeviceContext, T> col2im;
math::Col2VolFunctor<DeviceContext, T> col2vol;
std::vector<int> dilations({1, 1, 1});
// convolution transpose: gemm + col2im or col2vol (similar to conv-backward
......@@ -127,29 +128,27 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// col_matrix = filter * input_batch
// of shape (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
math::matmul<Place, T>(context.device_context(), filter, true,
input_batch, false, static_cast<T>(1.0),
&col_matrix, static_cast<T>(0.0));
math::matmul<DeviceContext, T>(dev_ctx, filter, true, input_batch, false,
static_cast<T>(1.0), &col_matrix,
static_cast<T>(0.0));
if (data_dim == 2U) {
// col2im: col_matrix -> dy
// from (c * k_h * k_w, h * w) to (c, o_h, o_w)
col2im(context.device_context(), col,
std::vector<int>{dilations[0], dilations[1]}, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
col2im(dev_ctx, col, std::vector<int>{dilations[0], dilations[1]},
strides, std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&output_batch);
} else if (data_dim == 3U) {
// col2vol: col_matrix -> dy
// from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
col2vol(context.device_context(), col, dilations, strides, paddings,
&output_batch);
col2vol(dev_ctx, col, dilations, strides, paddings, &output_batch);
}
}
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -206,6 +205,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
auto& dev_ctx = context.template device_context<DeviceContext>();
if (input_grad || filter_grad) {
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
......@@ -217,19 +217,19 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
col_matrix.Resize(col_matrix_shape);
Tensor filter_grad_;
math::SetConstant<Place, T> set_zero;
math::SetConstant<DeviceContext, T> set_zero;
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
math::Vol2ColFunctor<Place, T> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
math::Vol2ColFunctor<DeviceContext, T> vol2col;
std::vector<int> dilations({1, 1, 1});
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(context.device_context(), input_grad, static_cast<T>(0));
set_zero(dev_ctx, input_grad, static_cast<T>(0));
}
if (filter_grad) { // filter size (m, c, k_h, k_w)
filter_grad->mutable_data<T>(context.GetPlace());
set_zero(context.device_context(), filter_grad, static_cast<T>(0));
set_zero(dev_ctx, filter_grad, static_cast<T>(0));
filter_grad_ = *filter_grad;
filter_grad_.Resize(filter_matrix_shape);
}
......@@ -242,7 +242,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
if (data_dim == 2U) {
// im2col: dy -> col matrix
// from (c, o_h, o_w) to (c * k_h * k_w, h * w)
im2col(context.device_context(), output_grad_batch,
im2col(dev_ctx, output_grad_batch,
std::vector<int>{dilations[0], dilations[1]}, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
......@@ -250,8 +250,8 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
} else if (data_dim == 3U) {
// vol2col: dy -> col_matrix
// from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
vol2col(context.device_context(), output_grad_batch, dilations,
strides, paddings, &col);
vol2col(dev_ctx, output_grad_batch, dilations, strides, paddings,
&col);
}
if (input_grad) {
......@@ -263,9 +263,9 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
// d, h, w)
math::matmul<Place, T>(context.device_context(), filter, false,
col_matrix, false, static_cast<T>(1.0),
&input_grad_batch, static_cast<T>(0.0));
math::matmul<DeviceContext, T>(
dev_ctx, filter, false, col_matrix, false, static_cast<T>(1.0),
&input_grad_batch, static_cast<T>(0.0));
}
if (filter_grad) {
// input batch
......@@ -275,9 +275,9 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
// k_h * k_w)
math::matmul<Place, T>(context.device_context(), in_batch, false,
col_matrix, true, static_cast<T>(1.0),
&filter_grad_, static_cast<T>(1.0));
math::matmul<DeviceContext, T>(dev_ctx, in_batch, false, col_matrix,
true, static_cast<T>(1.0),
&filter_grad_, static_cast<T>(1.0));
}
}
}
......
paddle/operators/cos_sim_op.cc
浏览文件 @ 4d8f39b8
......@@ -155,7 +155,8 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
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>);
cos_sim, ops::CosSimKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
cos_sim_grad,
ops::CosSimGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/cos_sim_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,7 +16,8 @@
#include "paddle/operators/cos_sim_op.h"
namespace ops = paddle::operators;
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>);
REGISTER_OP_CUDA_KERNEL(
cos_sim, ops::CosSimKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
cos_sim_grad,
ops::CosSimGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/cos_sim_op.h
浏览文件 @ 4d8f39b8
......@@ -27,7 +27,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class CosSimKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -51,7 +51,8 @@ class CosSimKernel : public framework::OpKernel<T> {
auto y_norm = EigenVector<T>::Flatten(*out_y_norm);
// compute
auto place = context.GetEigenDevice<Place>();
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
auto row_along = Eigen::array<int, 1>({{1}});
x_norm.device(place) = x.square().sum(row_along).sqrt();
y_norm.device(place) = y.square().sum(row_along).sqrt();
......@@ -66,7 +67,7 @@ class CosSimKernel : public framework::OpKernel<T> {
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class CosSimGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -96,7 +97,8 @@ class CosSimGradKernel : public framework::OpKernel<T> {
auto z_bcast = z.broadcast(bcast_cols);
auto dz_bcast = dz.broadcast(bcast_cols);
auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast_cols);
auto place = context.GetEigenDevice<Place>();
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
if (rows_x == rows_y) {
auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast_cols);
auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast_cols);
......
paddle/operators/crf_decoding_op.cc
浏览文件 @ 4d8f39b8
......@@ -135,5 +135,6 @@ namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(crf_decoding, ops::CRFDecodingOp,
ops::CRFDecodingOpMaker);
REGISTER_OP_CPU_KERNEL(
crf_decoding, ops::CRFDecodingOpKernel<paddle::platform::CPUPlace, float>,
ops::CRFDecodingOpKernel<paddle::platform::CPUPlace, double>);
crf_decoding,
ops::CRFDecodingOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::CRFDecodingOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/crf_decoding_op.h
浏览文件 @ 4d8f39b8
......@@ -24,7 +24,7 @@ using framework::LoDTensor;
using framework::LoD;
using framework::Tensor;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class CRFDecodingOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -44,8 +44,8 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
const size_t seq_num = lod[level].size() - 1;
int64_t* path = decoded_path->mutable_data<int64_t>(platform::CPUPlace());
math::SetConstant<platform::CPUPlace, int64_t>()(ctx.device_context(),
decoded_path, 0);
math::SetConstant<DeviceContext, int64_t>()(
ctx.template device_context<DeviceContext>(), decoded_path, 0);
for (size_t i = 0; i < seq_num; ++i) {
int start_pos = static_cast<int>(lod[level][i]);
int end_pos = static_cast<int>(lod[level][i + 1]);
......
paddle/operators/crop_op.cc
浏览文件 @ 4d8f39b8
......@@ -133,5 +133,5 @@ class CropOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(crop, ops::CropOp, ops::CropOpMaker, crop_grad, ops::CropOpGrad);
REGISTER_OP_CPU_KERNEL(crop, ops::CropKernel<float>);
REGISTER_OP_CPU_KERNEL(crop_grad,
ops::CropGradKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
crop_grad, ops::CropGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/crop_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,6 +16,6 @@
#include "paddle/operators/crop_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(crop, ops::CropKernel<float>);
REGISTER_OP_GPU_KERNEL(crop_grad,
ops::CropGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(crop, ops::CropKernel<float>);
REGISTER_OP_CUDA_KERNEL(
crop_grad, ops::CropGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/crop_op.h
浏览文件 @ 4d8f39b8
......@@ -49,7 +49,7 @@ class CropKernel : public framework::OpKernel<T> {
}
};
template <typename Place, typename T, size_t D>
template <typename DeviceContext, typename T, size_t D>
void CropGradFunction(const framework::ExecutionContext& context) {
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x != nullptr) {
......@@ -63,12 +63,13 @@ void CropGradFunction(const framework::ExecutionContext& context) {
}
auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
d_x_tensor.device(context.GetEigenDevice<Place>()) =
d_x_tensor.device(
*context.template device_context<DeviceContext>().eigen_device()) =
d_out_tensor.pad(paddings, 0);
}
}
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class CropGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -76,22 +77,22 @@ class CropGradKernel : public framework::OpKernel<T> {
context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
switch (rank) {
case 1:
CropGradFunction<Place, T, 1>(context);
CropGradFunction<DeviceContext, T, 1>(context);
break;
case 2:
CropGradFunction<Place, T, 2>(context);
CropGradFunction<DeviceContext, T, 2>(context);
break;
case 3:
CropGradFunction<Place, T, 3>(context);
CropGradFunction<DeviceContext, T, 3>(context);
break;
case 4:
CropGradFunction<Place, T, 4>(context);
CropGradFunction<DeviceContext, T, 4>(context);
break;
case 5:
CropGradFunction<Place, T, 5>(context);
CropGradFunction<DeviceContext, T, 5>(context);
break;
case 6:
CropGradFunction<Place, T, 6>(context);
CropGradFunction<DeviceContext, T, 6>(context);
break;
default:
PADDLE_THROW(
......
paddle/operators/cross_entropy_op.cu
浏览文件 @ 4d8f39b8
......@@ -53,8 +53,9 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel<T> {
Tensor* y = ctx.Output<Tensor>("Y");
y->mutable_data<T>(ctx.GetPlace());
math::CrossEntropyFunctor<platform::GPUPlace, T>()(
ctx.device_context(), y, x, label, ctx.Attr<bool>("soft_label"));
math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
ctx.template device_context<platform::CUDADeviceContext>(), y, x, label,
ctx.Attr<bool>("soft_label"));
}
};
......@@ -80,15 +81,17 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel<T> {
int block = 512;
int grid = (batch_size * class_num + block - 1) / block;
auto stream = ctx.cuda_device_context().stream();
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto stream = dev_ctx.stream();
if (ctx.Attr<bool>("soft_label")) {
auto* label_data = label->data<T>();
SoftCrossEntropyGradientKernel<T><<<grid, block, 0, stream>>>(
dx_data, dy_data, x_data, label_data, batch_size, class_num);
} else {
math::SetConstant<platform::GPUPlace, T> functor;
functor(ctx.device_context(), dx, 0);
math::SetConstant<platform::CUDADeviceContext, T> functor;
functor(dev_ctx, dx, 0);
auto* label_data = label->data<int64_t>();
grid = (batch_size + block - 1) / block;
CrossEntropyGradientKernel<T><<<grid, block, 0, stream>>>(
......@@ -101,8 +104,8 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(cross_entropy, ops::CrossEntropyOpCUDAKernel<float>,
ops::CrossEntropyOpCUDAKernel<double>);
REGISTER_OP_GPU_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpCUDAKernel<float>,
ops::CrossEntropyGradientOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(cross_entropy, ops::CrossEntropyOpCUDAKernel<float>,
ops::CrossEntropyOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpCUDAKernel<float>,
ops::CrossEntropyGradientOpCUDAKernel<double>);
paddle/operators/cross_entropy_op.h
浏览文件 @ 4d8f39b8
......@@ -37,8 +37,9 @@ class CrossEntropyOpKernel : public framework::OpKernel<T> {
Tensor* y = ctx.Output<Tensor>("Y");
y->mutable_data<T>(ctx.GetPlace());
math::CrossEntropyFunctor<platform::CPUPlace, T>()(
ctx.device_context(), y, x, labels, ctx.Attr<bool>("soft_label"));
math::CrossEntropyFunctor<platform::CPUDeviceContext, T>()(
ctx.template device_context<platform::CPUDeviceContext>(), y, x, labels,
ctx.Attr<bool>("soft_label"));
}
};
......@@ -61,7 +62,8 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
auto lbl_mat = EigenMatrix<T>::From(*label);
auto dx_mat = EigenMatrix<T>::From(*dx);
dx_mat.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
dx_mat.device(*ctx.template device_context<platform::CPUDeviceContext>()
.eigen_device()) =
-(lbl_mat *
dy_mat.broadcast(Eigen::DSizes<int64_t, 2>(1, class_num)) / x_mat);
} else {
......@@ -70,8 +72,8 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
const T* x_data = x->data<T>();
const int64_t* label_data = label->data<int64_t>();
math::SetConstant<platform::CPUPlace, T> functor;
functor(ctx.device_context(), dx, 0);
math::SetConstant<platform::CPUDeviceContext, T> functor;
functor(ctx.template device_context<platform::CPUDeviceContext>(), dx, 0);
for (int64_t i = 0; i < batch_size; ++i) {
PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);
......
paddle/operators/decayed_adagrad_op.cc
浏览文件 @ 4d8f39b8
......@@ -99,4 +99,4 @@ REGISTER_OP_WITHOUT_GRADIENT(decayed_adagrad, ops::DecayedAdagradOp,
ops::DecayedAdagradOpMaker);
REGISTER_OP_CPU_KERNEL(
decayed_adagrad,
ops::DecayedAdagradOpKernel<paddle::platform::CPUPlace, float>);
ops::DecayedAdagradOpKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/decayed_adagrad_op.cu
浏览文件 @ 4d8f39b8
......@@ -16,6 +16,6 @@
#include "paddle/operators/decayed_adagrad_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
decayed_adagrad,
ops::DecayedAdagradOpKernel<paddle::platform::GPUPlace, float>);
ops::DecayedAdagradOpKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/decayed_adagrad_op.h
浏览文件 @ 4d8f39b8
......@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class DecayedAdagradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -43,7 +43,7 @@ class DecayedAdagradOpKernel : public framework::OpKernel<T> {
auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
auto place = ctx.GetEigenDevice<Place>();
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
moment_out.device(place) = decay * moment + (1 - decay) * grad * grad;
Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
......
paddle/operators/dropout_op.cc
浏览文件 @ 4d8f39b8
......@@ -100,6 +100,8 @@ namespace ops = paddle::operators;
REGISTER_OP(dropout, ops::DropoutOp, ops::DropoutOpMaker<float>, dropout_grad,
ops::DropoutOpGrad<float>);
REGISTER_OP_CPU_KERNEL(
dropout, ops::CPUDropoutKernel<paddle::platform::CPUPlace, float, float>);
dropout,
ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float, float>);
REGISTER_OP_CPU_KERNEL(
dropout_grad, ops::DropoutGradKernel<paddle::platform::CPUPlace, float>);
dropout_grad,
ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/dropout_op.cu
浏览文件 @ 4d8f39b8
......@@ -58,7 +58,7 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
auto X = EigenMatrix<T>::Reshape(*x, 1);
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto place = context.GetEigenDevice<Place>();
auto& place = *context.template device_context<Place>().eigen_device();
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
......@@ -80,7 +80,9 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
dropout, ops::GPUDropoutKernel<paddle::platform::GPUPlace, float, float>);
REGISTER_OP_GPU_KERNEL(
dropout_grad, ops::DropoutGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(
dropout,
ops::GPUDropoutKernel<paddle::platform::CUDADeviceContext, float, float>);
REGISTER_OP_CUDA_KERNEL(
dropout_grad,
ops::DropoutGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/dropout_op.h
浏览文件 @ 4d8f39b8
......@@ -25,7 +25,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T, typename AttrType>
template <typename DeviceContext, typename T, typename AttrType>
class CPUDropoutKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -55,13 +55,14 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
} else {
auto X = EigenMatrix<T>::Reshape(*x, 1);
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto place = context.GetEigenDevice<Place>();
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
Y.device(place) = X * dropout_prob;
}
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class DropoutGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -77,7 +78,8 @@ class DropoutGradKernel : public framework::OpKernel<T> {
auto dX = EigenMatrix<T>::Reshape(*grad_x, 1);
auto dY = EigenMatrix<T>::Reshape(*grad_y, 1);
auto place = context.GetEigenDevice<Place>();
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
dX.device(place) = dY * M;
}
};
......
paddle/operators/elementwise_add_op.cc
浏览文件 @ 4d8f39b8
......@@ -34,13 +34,13 @@ REGISTER_OP(elementwise_add, ops::ElementwiseOp, ops::ElementwiseAddOpMaker,
elementwise_add_grad, ops::ElementwiseOpGrad);
REGISTER_OP_CPU_KERNEL(
elementwise_add,
ops::ElementwiseAddKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseAddKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseAddKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseAddKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
elementwise_add_grad,
ops::ElementwiseAddGradKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseAddGradKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseAddGradKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseAddGradKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseAddGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseAddGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseAddGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseAddGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/operators/elementwise_add_op.cu
浏览文件 @ 4d8f39b8
......@@ -17,15 +17,16 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
elementwise_add,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_add_grad,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, int64_t>);
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/operators/elementwise_add_op.h
浏览文件 @ 4d8f39b8
......@@ -24,7 +24,7 @@ struct AddFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseAddKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -34,8 +34,8 @@ class ElementwiseAddKernel : public framework::OpKernel<T> {
auto* y = ctx.Input<Tensor>("Y");
auto* z = ctx.Output<Tensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
TransformFunctor<AddFunctor<T>, T, Place> functor(
x, y, z, ctx.device_context(), AddFunctor<T>());
TransformFunctor<AddFunctor<T>, T, DeviceContext> functor(
x, y, z, ctx.template device_context<DeviceContext>(), AddFunctor<T>());
auto x_dims = x->dims();
auto y_dims = y->dims();
......@@ -137,11 +137,11 @@ struct ElementwiseAddBroadCast2GradFunctor {
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseAddGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElementwiseGradCompute<Place, T, ElementwiseAddGradFunctor<T>,
ElementwiseGradCompute<DeviceContext, T, ElementwiseAddGradFunctor<T>,
ElementwiseAddOneGradFunctor<T>,
ElementwiseAddBroadCastGradFunctor<T>,
ElementwiseAddBroadCast2GradFunctor<T>>(ctx);
......
paddle/operators/elementwise_div_op.cc
浏览文件 @ 4d8f39b8
......@@ -35,13 +35,13 @@ REGISTER_OP(elementwise_div, ops::ElementwiseOp, ops::ElementwiseDivOpMaker,
elementwise_div_grad, ops::ElementwiseOpGrad);
REGISTER_OP_CPU_KERNEL(
elementwise_div,
ops::ElementwiseDivKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseDivKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseDivKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseDivKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseDivKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseDivKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseDivKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseDivKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
elementwise_div_grad,
ops::ElementwiseDivGradKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseDivGradKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseDivGradKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseDivGradKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseDivGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseDivGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseDivGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseDivGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/operators/elementwise_div_op.cu
浏览文件 @ 4d8f39b8
......@@ -17,15 +17,16 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
elementwise_div,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_div_grad,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, int64_t>);
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/operators/elementwise_div_op.h
浏览文件 @ 4d8f39b8
......@@ -19,11 +19,11 @@
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseDivKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElementwiseCompute<EigenDivFunctor, Place, T>(ctx);
ElementwiseCompute<EigenDivFunctor, DeviceContext, T>(ctx);
}
};
......@@ -102,11 +102,11 @@ struct ElementwiseDivBroadCast2GradFunctor {
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseDivGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElementwiseGradCompute<Place, T, ElementwiseDivGradFunctor<T>,
ElementwiseGradCompute<DeviceContext, T, ElementwiseDivGradFunctor<T>,
ElementwiseDivGradFunctor<T>,
ElementwiseDivBroadCastGradFunctor<T>,
ElementwiseDivBroadCast2GradFunctor<T>>(ctx);
......
paddle/operators/elementwise_mul_op.cc
浏览文件 @ 4d8f39b8
......@@ -36,13 +36,13 @@ REGISTER_OP(elementwise_mul, ops::ElementwiseOp, ops::ElementwiseMulOpMaker,
elementwise_mul_grad, ops::ElementwiseOpGrad);
REGISTER_OP_CPU_KERNEL(
elementwise_mul,
ops::ElementwiseMulKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseMulKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseMulKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseMulKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseMulKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseMulKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseMulKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseMulKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
elementwise_mul_grad,
ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseMulGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseMulGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseMulGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseMulGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/operators/elementwise_mul_op.cu
浏览文件 @ 4d8f39b8
......@@ -17,15 +17,16 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
elementwise_mul,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_mul_grad,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, int64_t>);
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/operators/elementwise_mul_op.h
浏览文件 @ 4d8f39b8
......@@ -18,11 +18,11 @@
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseMulKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElementwiseCompute<EigenMulFunctor, Place, T>(ctx);
ElementwiseCompute<EigenMulFunctor, DeviceContext, T>(ctx);
}
};
......@@ -101,11 +101,11 @@ struct ElementwiseMulBroadCast2GradFunctor {
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseMulGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElementwiseGradCompute<Place, T, ElementwiseMulGradFunctor<T>,
ElementwiseGradCompute<DeviceContext, T, ElementwiseMulGradFunctor<T>,
ElementwiseMulGradFunctor<T>,
ElementwiseMulBroadCastGradFunctor<T>,
ElementwiseMulBroadCast2GradFunctor<T>>(ctx);
......
paddle/operators/elementwise_op_function.h
浏览文件 @ 4d8f39b8
......@@ -59,17 +59,17 @@ inline void get_mid_dims(const framework::DDim& x_dims,
}
}
template <typename T, typename Place>
template <typename T, typename DeviceContext>
class RowwiseTransformIterator;
template <typename T, typename Place>
template <typename T, typename DeviceContext>
class MidWiseTransformIterator;
template <typename T>
class RowwiseTransformIterator<T, platform::CPUPlace> {
class RowwiseTransformIterator<T, platform::CPUDeviceContext> {
public:
RowwiseTransformIterator(const T* ptr, int n) : ptr_(ptr), i_(0), n_(n) {}
RowwiseTransformIterator<T, platform::CPUPlace>& operator++() {
RowwiseTransformIterator<T, platform::CPUDeviceContext>& operator++() {
++i_;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
......@@ -77,13 +77,13 @@ class RowwiseTransformIterator<T, platform::CPUPlace> {
return *this;
}
bool operator==(
const RowwiseTransformIterator<T, platform::CPUPlace>& rhs) const {
bool operator==(const RowwiseTransformIterator<T, platform::CPUDeviceContext>&
rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator!=(
const RowwiseTransformIterator<T, platform::CPUPlace>& rhs) const {
bool operator!=(const RowwiseTransformIterator<T, platform::CPUDeviceContext>&
rhs) const {
return (ptr_ + i_) != &(*rhs);
}
......@@ -96,12 +96,12 @@ class RowwiseTransformIterator<T, platform::CPUPlace> {
};
template <typename T>
class MidWiseTransformIterator<T, platform::CPUPlace> {
class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
public:
MidWiseTransformIterator(const T* ptr, int n, int post)
: ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}
MidWiseTransformIterator<T, platform::CPUPlace>& operator++() {
MidWiseTransformIterator<T, platform::CPUDeviceContext>& operator++() {
++j_;
i_ = j_ / post_;
if (UNLIKELY(i_ == n_)) {
......@@ -111,13 +111,13 @@ class MidWiseTransformIterator<T, platform::CPUPlace> {
return *this;
}
bool operator==(
const MidWiseTransformIterator<T, platform::CPUPlace>& rhs) const {
bool operator==(const MidWiseTransformIterator<T, platform::CPUDeviceContext>&
rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator!=(
const MidWiseTransformIterator<T, platform::CPUPlace>& rhs) const {
bool operator!=(const MidWiseTransformIterator<T, platform::CPUDeviceContext>&
rhs) const {
return (ptr_ + i_) != &(*rhs);
}
......@@ -133,12 +133,12 @@ class MidWiseTransformIterator<T, platform::CPUPlace> {
#ifdef __NVCC__
template <typename T>
class RowwiseTransformIterator<T, platform::GPUPlace>
class RowwiseTransformIterator<T, platform::CUDADeviceContext>
: public thrust::iterator_adaptor<
RowwiseTransformIterator<T, platform::GPUPlace>, const T*> {
RowwiseTransformIterator<T, platform::CUDADeviceContext>, const T*> {
public:
typedef thrust::iterator_adaptor<
RowwiseTransformIterator<T, platform::GPUPlace>, const T*>
RowwiseTransformIterator<T, platform::CUDADeviceContext>, const T*>
super_t;
HOSTDEVICE RowwiseTransformIterator(const T* x, int n)
: super_t(x), begin_(x), n_(n){};
......@@ -153,12 +153,12 @@ class RowwiseTransformIterator<T, platform::GPUPlace>
};
template <typename T>
class MidWiseTransformIterator<T, platform::GPUPlace>
class MidWiseTransformIterator<T, platform::CUDADeviceContext>
: public thrust::iterator_adaptor<
MidWiseTransformIterator<T, platform::GPUPlace>, const T*> {
MidWiseTransformIterator<T, platform::CUDADeviceContext>, const T*> {
public:
typedef thrust::iterator_adaptor<
MidWiseTransformIterator<T, platform::GPUPlace>, const T*>
MidWiseTransformIterator<T, platform::CUDADeviceContext>, const T*>
super_t;
HOSTDEVICE MidWiseTransformIterator(const T* x, int n, int post)
: super_t(x), begin_(x), n_(n), post_(post){};
......@@ -174,12 +174,11 @@ class MidWiseTransformIterator<T, platform::GPUPlace>
};
#endif
template <typename Functor, typename T, typename Place>
template <typename Functor, typename T, typename DeviceContext>
class TransformFunctor {
public:
TransformFunctor(const framework::Tensor* x, const framework::Tensor* y,
framework::Tensor* z, const platform::DeviceContext& ctx,
Functor func)
framework::Tensor* z, const DeviceContext& ctx, Functor func)
: x_(x->data<T>()),
y_(y->data<T>()),
z_(z->mutable_data<T>(ctx.GetPlace())),
......@@ -188,20 +187,20 @@ class TransformFunctor {
func_(func) {}
inline void Run() const {
platform::Transform<Place> trans;
platform::Transform<DeviceContext> trans;
trans(ctx_, x_, x_ + nx_, y_, z_, func_);
}
inline void RunRowWise(int n, int pre) const {
platform::Transform<Place> trans;
trans(ctx_, x_, x_ + nx_, RowwiseTransformIterator<T, Place>(y_, n), z_,
func_);
platform::Transform<DeviceContext> trans;
trans(ctx_, x_, x_ + nx_, RowwiseTransformIterator<T, DeviceContext>(y_, n),
z_, func_);
}
inline void RunMidWise(int n, int pre, int post) const {
platform::Transform<Place> trans;
trans(ctx_, x_, x_ + nx_, MidWiseTransformIterator<T, Place>(y_, n, post),
z_, func_);
platform::Transform<DeviceContext> trans;
trans(ctx_, x_, x_ + nx_,
MidWiseTransformIterator<T, DeviceContext>(y_, n, post), z_, func_);
}
private:
......@@ -209,22 +208,24 @@ class TransformFunctor {
const T* y_;
T* z_;
int64_t nx_;
const platform::DeviceContext& ctx_;
const DeviceContext& ctx_;
Functor func_;
};
#define EIGEN_FUNCTOR(name, eigen_op) \
struct Eigen##name##Functor { \
template <typename Place, typename T> \
template <typename DeviceContext, typename T> \
inline void Run(const framework::Tensor* x, const framework::Tensor* y, \
framework::Tensor* z, \
const framework::ExecutionContext& ctx) { \
auto x_e = framework::EigenVector<T>::Flatten(*x); \
auto y_e = framework::EigenVector<T>::Flatten(*y); \
auto z_e = framework::EigenVector<T>::Flatten(*z); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_e); \
z_e.device( \
*ctx.template device_context<DeviceContext>().eigen_device()) = \
eigen_op(x_e, y_e); \
} \
template <typename Place, typename T> \
template <typename DeviceContext, typename T> \
inline void RunBroadCast(const framework::Tensor* x, \
const framework::Tensor* y, framework::Tensor* z, \
const framework::ExecutionContext& ctx, int pre, \
......@@ -235,9 +236,11 @@ class TransformFunctor {
auto y_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n)) \
.broadcast(Eigen::DSizes<int, 2>(pre, 1)) \
.reshape(Eigen::DSizes<int, 1>(x_e.size())); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast); \
z_e.device( \
*ctx.template device_context<DeviceContext>().eigen_device()) = \
eigen_op(x_e, y_bcast); \
} \
template <typename Place, typename T> \
template <typename DeviceContext, typename T> \
inline void RunBroadCast2(const framework::Tensor* x, \
const framework::Tensor* y, \
framework::Tensor* z, \
......@@ -249,11 +252,13 @@ class TransformFunctor {
auto y_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1)) \
.broadcast(Eigen::DSizes<int, 3>(pre, 1, post)) \
.reshape(Eigen::DSizes<int, 1>(x_e.size())); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast); \
z_e.device( \
*ctx.template device_context<DeviceContext>().eigen_device()) = \
eigen_op(x_e, y_bcast); \
} \
}
template <class functor, typename Place, typename T>
template <class functor, typename DeviceContext, typename T>
void ElementwiseCompute(const framework::ExecutionContext& ctx) {
using Tensor = framework::Tensor;
......@@ -269,7 +274,7 @@ void ElementwiseCompute(const framework::ExecutionContext& ctx) {
if (x_dims == y_dims) {
functor f;
f.template Run<Place, T>(x, y, z, ctx);
f.template Run<DeviceContext, T>(x, y, z, ctx);
return;
}
......@@ -282,11 +287,11 @@ void ElementwiseCompute(const framework::ExecutionContext& ctx) {
get_mid_dims(x_dims, y_dims, axis, pre, n, post);
if (post == 1) {
functor f;
f.template RunBroadCast<Place, T>(x, y, z, ctx, pre, n);
f.template RunBroadCast<DeviceContext, T>(x, y, z, ctx, pre, n);
return;
} else {
functor f;
f.template RunBroadCast2<Place, T>(x, y, z, ctx, pre, n, post);
f.template RunBroadCast2<DeviceContext, T>(x, y, z, ctx, pre, n, post);
return;
}
}
......@@ -303,8 +308,9 @@ EIGEN_FUNCTOR(Mul, EIGEN_MUL);
#define EIGEN_DIV(x, y) ((x) / (y))
EIGEN_FUNCTOR(Div, EIGEN_DIV);
template <typename Place, typename T, typename functor, typename functor1,
typename broadcastfunctor, typename broadcast2functor>
template <typename DeviceContext, typename T, typename functor,
typename functor1, typename broadcastfunctor,
typename broadcast2functor>
void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
using Tensor = framework::Tensor;
......@@ -313,7 +319,7 @@ void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
auto* out = ctx.Input<Tensor>("Out");
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto place = ctx.GetEigenDevice<Place>();
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto x_dims = x->dims();
auto y_dims = y->dims();
......
paddle/operators/elementwise_sub_op.cc
浏览文件 @ 4d8f39b8
......@@ -34,13 +34,13 @@ REGISTER_OP(elementwise_sub, ops::ElementwiseOp, ops::ElementwiseSubOpMaker,
elementwise_sub_grad, ops::ElementwiseOpGrad);
REGISTER_OP_CPU_KERNEL(
elementwise_sub,
ops::ElementwiseSubKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseSubKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseSubKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseSubKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
elementwise_sub_grad,
ops::ElementwiseSubGradKernel<paddle::platform::CPUPlace, float>,
ops::ElementwiseSubGradKernel<paddle::platform::CPUPlace, double>,
ops::ElementwiseSubGradKernel<paddle::platform::CPUPlace, int>,
ops::ElementwiseSubGradKernel<paddle::platform::CPUPlace, int64_t>);
ops::ElementwiseSubGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseSubGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseSubGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseSubGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/operators/elementwise_sub_op.cu
浏览文件 @ 4d8f39b8
......@@ -17,15 +17,16 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
elementwise_sub,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_sub_grad,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, int64_t>);
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/operators/elementwise_sub_op.h
浏览文件 @ 4d8f39b8
......@@ -18,11 +18,11 @@
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseSubKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElementwiseCompute<EigenSubFunctor, Place, T>(ctx);
ElementwiseCompute<EigenSubFunctor, DeviceContext, T>(ctx);
}
};
......@@ -101,11 +101,11 @@ struct ElementwiseSubBroadCast2GradFunctor {
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ElementwiseSubGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElementwiseGradCompute<Place, T, ElementwiseSubGradFunctor<T>,
ElementwiseGradCompute<DeviceContext, T, ElementwiseSubGradFunctor<T>,
ElementwiseSubOneGradFunctor<T>,
ElementwiseSubBroadCastGradFunctor<T>,
ElementwiseSubBroadCast2GradFunctor<T>>(ctx);
......
paddle/operators/expand_op.cc
浏览文件 @ 4d8f39b8
......@@ -130,7 +130,8 @@ class ExpandGradOp : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(expand, ops::ExpandOp, ops::ExpandOpMaker, expand_grad,
ops::ExpandGradOp);
REGISTER_OP_CPU_KERNEL(expand,
ops::ExpandKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
expand_grad, ops::ExpandGradKernel<paddle::platform::CPUPlace, float>);
expand, ops::ExpandKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
expand_grad,
ops::ExpandGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/expand_op.cu
浏览文件 @ 4d8f39b8
......@@ -17,7 +17,8 @@
#include "paddle/operators/expand_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(expand,
ops::ExpandKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
expand_grad, ops::ExpandGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(
expand, ops::ExpandKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
expand_grad,
ops::ExpandGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/expand_op.h
浏览文件 @ 4d8f39b8
......@@ -56,7 +56,7 @@ template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ExpandKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -83,12 +83,13 @@ class ExpandKernel : public framework::OpKernel<T> {
auto x = EigenTensor<T, Rank>::From(*in0);
out0->mutable_data<T>(context.GetPlace());
auto y = EigenTensor<T, Rank>::From(*out0);
auto place = context.GetEigenDevice<Place>();
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
y.device(place) = x.broadcast(bcast_dims);
}
};
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class ExpandGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -164,7 +165,8 @@ class ExpandGradKernel : public framework::OpKernel<T> {
reduce_dims[i] = reduce_dims_vec[i];
}
auto out_grad = EigenVector<T>::Flatten(*in0);
x_grad.device(context.GetEigenDevice<Place>()) =
x_grad.device(
*context.template device_context<DeviceContext>().eigen_device()) =
out_grad.reshape(reshape_dims).sum(reduce_dims).reshape(x.dimensions());
}
};
......
paddle/operators/fill_constant_batch_size_like_op.cc
浏览文件 @ 4d8f39b8
......@@ -100,8 +100,11 @@ REGISTER_OPERATOR(fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
double>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
int64_t>);
paddle/operators/fill_constant_batch_size_like_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -16,10 +16,13 @@
#include "paddle/framework/op_registry.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
REGISTER_OP_CUDA_KERNEL(
fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
double>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/operators/fill_constant_batch_size_like_op.h
浏览文件 @ 4d8f39b8
......@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -27,8 +27,9 @@ class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
out->mutable_data<T>(ctx.GetPlace());
auto value = ctx.Attr<float>("value");
math::SetConstant<Place, T> setter;
setter(ctx.device_context(), out, static_cast<T>(value));
math::SetConstant<DeviceContext, T> setter;
setter(ctx.template device_context<DeviceContext>(), out,
static_cast<T>(value));
}
};
......
paddle/operators/fill_op.cc 0 → 100644
浏览文件 @ 4d8f39b8
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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/framework/data_type.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
struct FillOpVisitor {
FillOpVisitor(framework::LoDTensor *tensor, const std::vector<float> &value)
: tensor_(tensor), value_(value) {}
template <typename T>
void operator()() const {
platform::CPUPlace cpu;
auto *data = tensor_->mutable_data<T>(cpu);
std::transform(value_.data(), value_.data() + tensor_->numel(), data,
[](float dat) { return static_cast<T>(dat); });
}
framework::LoDTensor *tensor_;
const std::vector<float> &value_;
};
class FillOp : public framework::OperatorBase {
public:
FillOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto &out =
detail::Ref(detail::Ref(scope.FindVar(Output("Out")),
"Cannot find variable %s", Output("Out"))
.GetMutable<framework::LoDTensor>());
out.Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
auto dtype = static_cast<framework::DataType>(Attr<int>("dtype"));
platform::CPUPlace cpu;
auto force_cpu = Attr<bool>("force_cpu");
out.mutable_data(force_cpu ? cpu : dev_ctx.GetPlace(),
framework::ToTypeIndex(dtype));
framework::LoDTensor tensor;
if (force_cpu || platform::is_cpu_place(dev_ctx.GetPlace())) {
tensor.ShareDataWith(out);
} else {
// Always make tensor in CPU memory.
tensor.Resize(out.dims());
tensor.mutable_data(cpu, framework::ToTypeIndex(dtype));
}
framework::VisitDataType(
dtype, FillOpVisitor(&tensor, Attr<std::vector<float>>("value")));
if (!force_cpu && platform::is_gpu_place(dev_ctx.GetPlace())) {
// Copy tensor to out
framework::CopyFrom(tensor, dev_ctx.GetPlace(), dev_ctx, &out);
}
}
};
class FillOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FillOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddComment(R"DOC(Fill operator
Fill an tensor with `value` and `shape`. The type of the tensor is specify by
`dtype`.
)DOC");
AddOutput("Out", "(LoDTensor) The output tensor.");
AddAttr<std::vector<float>>(
"value", "The float values of tensor, which are flatten in row major");
AddAttr<std::vector<int>>("shape", "The shape of output tensor");
AddAttr<int>("dtype", "The data type of output tensor, Default is float")
.SetDefault(framework::DataType::FP32);
AddAttr<bool>("force_cpu",
"Whether the output tensor must be at CPU memory or not. "
"Default is false.")
.SetDefault(false);
}
};
class FillOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
context->SetOutputDim(
"Out",
framework::make_ddim(context->Attrs().Get<std::vector<int>>("shape")));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fill, ops::FillOp, ops::FillOpInferShape, ops::FillOpMaker);
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 4d8f39b8
......@@ -54,8 +54,9 @@ namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, ops::FillZerosLikeOp,
ops::FillZerosLikeOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, double>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, bool>);
fill_zeros_like,
ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, int>,
ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, float>,
ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, double>,
ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, bool>);
paddle/operators/fill_zeros_like_op.cu.cc
浏览文件 @ 4d8f39b8
......@@ -16,9 +16,10 @@
#include "paddle/framework/op_registry.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, double>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, bool>);
REGISTER_OP_CUDA_KERNEL(
fill_zeros_like,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, int>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, float>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, double>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, bool>);
paddle/operators/fill_zeros_like_op.h
浏览文件 @ 4d8f39b8
......@@ -19,15 +19,16 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class FillZerosLikeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* out = context.Output<framework::Tensor>("Y");
out->mutable_data<T>(context.GetPlace());
math::SetConstant<Place, T> setter;
setter(context.device_context(), out, static_cast<T>(0));
math::SetConstant<DeviceContext, T> setter;
setter(context.template device_context<DeviceContext>(), out,
static_cast<T>(0));
}
};
......
paddle/operators/ftrl_op.cc
浏览文件 @ 4d8f39b8
......@@ -135,5 +135,5 @@ The paper that proposed Follow The Regularized Leader (FTRL):
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(ftrl, ops::FTRLOp, ops::FTRLOpMaker);
REGISTER_OP_CPU_KERNEL(ftrl,
ops::FTRLOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
ftrl, ops::FTRLOpKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/ftrl_op.cu
浏览文件 @ 4d8f39b8
......@@ -15,5 +15,5 @@ specific language governing permissions and limitations under the License. */
#include "paddle/operators/ftrl_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(ftrl,
ops::FTRLOpKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(
ftrl, ops::FTRLOpKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/ftrl_op.h
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paddle/operators/gather.cu.h
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paddle/operators/gather_op.cu
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paddle/operators/gather_op.h
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