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add activation macro

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update-doc-pybind
qijun 8 years ago
parent 3c49e7b1e4
commit d736fc0e00
11 changed files with 342 additions and 148 deletions
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6
paddle/framework/operator.h
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@ -139,9 +139,9 @@ class OperatorBase {
// Macro for define a clone method. // Macro for define a clone method.
// If you are writing an kernel operator, `Clone` will be defined when you // If you are writing an kernel operator, `Clone` will be defined when you
// register it. i.e. `Clone` method is not needed to define by yourself. // register it. i.e. `Clone` method is not needed to define by yourself.
#define DEFINE_OP_CLONE_METHOD(cls) \ #define DEFINE_OP_CLONE_METHOD(cls) \
std::unique_ptr<OperatorBase> Clone() const final { \ std::unique_ptr<::paddle::framework::OperatorBase> Clone() const final { \
return std::unique_ptr<OperatorBase>(new cls(*this)); \ return std::unique_ptr<::paddle::framework::OperatorBase>(new cls(*this)); \
} }
// Macro for define a default constructor for Operator. // Macro for define a default constructor for Operator.

115
paddle/operators/activation_op.cc
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@ -0,0 +1,115 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/activation_op.h"
#define FILL_ACTIVATION_OP \
public: \
using framework::OperatorWithKernel::OperatorWithKernel; \
\
protected: \
void InferShape(const framework::InferShapeContext &ctx) const override { \
ctx.Output<framework::Tensor>("Y")->Resize( \
ctx.Input<framework::Tensor>("X")->dims()); \
}
#define FILL_ACTIVATION_GRAD_OP \
public: \
using framework::OperatorWithKernel::OperatorWithKernel; \
\
protected: \
void InferShape(const framework::InferShapeContext &ctx) const override { \
ctx.Output<framework::Tensor>(framework::GradVarName("X")) \
->Resize(ctx.Input<framework::Tensor>("Y")->dims()); \
}
namespace paddle {
namespace operators {
class SigmoidOp : public framework::OperatorWithKernel {
FILL_ACTIVATION_OP
};
class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Sigmoid operator");
AddOutput("Y", "Output of Sigmoid operator");
AddComment("Sigmoid activation operator");
}
};
class SigmoidOpGrad : public framework::OperatorWithKernel {
FILL_ACTIVATION_GRAD_OP
};
class ExpOp : public framework::OperatorWithKernel {
FILL_ACTIVATION_OP
};
class ExpOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ExpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Exp operator");
AddOutput("Y", "Output of Exp operator");
AddComment("Exp activation operator");
}
};
class ExpOpGrad : public framework::OperatorWithKernel {
FILL_ACTIVATION_GRAD_OP
};
class ReluOp : public framework::OperatorWithKernel {
FILL_ACTIVATION_OP
};
class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu operator");
AddOutput("Y", "Output of Relu operator");
AddComment("Relu activation operator");
}
};
class ReluOpGrad : public framework::OperatorWithKernel {
FILL_ACTIVATION_GRAD_OP
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
ops::SigmoidOpGrad);
REGISTER_OP_CPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP(exp, ops::ExpOp, ops::ExpOpMaker, exp_grad, ops::ExpOpGrad);
REGISTER_OP_CPU_KERNEL(exp, ops::ExpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(exp_grad,
ops::ExpGradKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP(relu, ops::ReluOp, ops::ReluOpMaker, relu_grad, ops::ReluOpGrad);
REGISTER_OP_CPU_KERNEL(relu,
ops::ReluKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(relu_grad,
ops::ReluGradKernel<paddle::platform::CPUPlace, float>);

11
paddle/operators/sigmoid_op.cu → paddle/operators/activation_op.cu
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@ -13,7 +13,7 @@
limitations under the License. */ limitations under the License. */
#define EIGEN_USE_GPU #define EIGEN_USE_GPU
#include "paddle/operators/sigmoid_op.h" #include "paddle/operators/activation_op.h"
namespace ops = paddle::operators; namespace ops = paddle::operators;
@ -21,3 +21,12 @@ REGISTER_OP_GPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::GPUPlace, float>); ops::SigmoidKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL( REGISTER_OP_GPU_KERNEL(
sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::GPUPlace, float>); sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(exp, ops::ExpKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(exp_grad,
ops::ExpGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(relu,
ops::ReluKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(relu_grad,
ops::ReluGradKernel<paddle::platform::GPUPlace, float>);

71
paddle/operators/activation_op.h
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@ -0,0 +1,71 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/activation_functor.h"
#define ACTIVATION_KERNEL_NAME(ACTIVATION_NAME) ACTIVATION_NAME##Kernel
#define DEFINE_ACTIVATION_KERNEL(ACTIVATION_NAME) \
template <typename Place, typename T> \
class ACTIVATION_KERNEL_NAME(ACTIVATION_NAME) : public framework::OpKernel { \
public: \
void Compute(const framework::ExecutionContext& context) const override { \
auto* X = context.Input<framework::Tensor>("X"); \
auto* Y = context.Output<framework::Tensor>("Y"); \
Y->mutable_data<T>(context.GetPlace()); \
math::ACTIVATION_NAME<Place, T> functor; \
auto* device_context = context.device_context(); \
functor(*device_context, *X, Y); \
} \
};
#define DEFINE_ACTIVATION_GRAD_KERNEL(ACTIVATION_GRAD_NAME) \
template <typename Place, typename T> \
class ACTIVATION_KERNEL_NAME(ACTIVATION_GRAD_NAME) \
: public framework::OpKernel { \
public: \
void Compute(const framework::ExecutionContext& context) const override { \
auto* X = context.Input<framework::Tensor>("X"); \
auto* Y = context.Input<framework::Tensor>("Y"); \
auto* dY = \
context.Input<framework::Tensor>(framework::GradVarName("Y")); \
auto* dX = \
context.Output<framework::Tensor>(framework::GradVarName("X")); \
dX->mutable_data<T>(context.GetPlace()); \
math::ACTIVATION_GRAD_NAME<Place, T> functor; \
auto* device_context = context.device_context(); \
functor(*device_context, *X, *Y, *dY, dX); \
} \
};
namespace paddle {
namespace operators {
DEFINE_ACTIVATION_KERNEL(Sigmoid);
DEFINE_ACTIVATION_GRAD_KERNEL(SigmoidGrad);
DEFINE_ACTIVATION_KERNEL(Exp);
DEFINE_ACTIVATION_GRAD_KERNEL(ExpGrad);
DEFINE_ACTIVATION_KERNEL(Relu);
DEFINE_ACTIVATION_GRAD_KERNEL(ReluGrad);
} // namespace operators
} // namespace paddle

20
paddle/operators/math/activation.h
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@ -1,20 +0,0 @@
#include "paddle/framework/eigen.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
template <typename Place, typename T>
struct sigmoid {
void operator()(const platform::DeviceContext& deice_context,
const framework::Tensor& input, framework::Tensor* output) {
auto x = framework::EigenVector<T>::Flatten(*output);
auto y = framework::EigenVector<T>::Flatten(input);
auto* place = device_context.get_eigen_device<Place>();
y.device(*place) = 1. / (1. + (-x).exp());
}
};
}
}
}

96
paddle/operators/math/activation_functor.h
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@ -0,0 +1,96 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
template <typename Place, typename T>
struct Sigmoid {
void operator()(const platform::DeviceContext& device_context,
const framework::Tensor& X, framework::Tensor* Y) {
auto x = framework::EigenVector<T>::Flatten(X);
auto y = framework::EigenVector<T>::Flatten(*Y);
auto* place = device_context.template get_eigen_device<Place>();
y.device(*place) = 1. / (1. + (-x).exp());
}
};
template <typename Place, typename T>
struct SigmoidGrad {
void operator()(const platform::DeviceContext& device_context,
const framework::Tensor& X, const framework::Tensor& Y,
const framework::Tensor& dY, framework::Tensor* dX) {
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto y = framework::EigenVector<T>::Flatten(Y);
auto dy = framework::EigenVector<T>::Flatten(dY);
auto* place = device_context.template get_eigen_device<Place>();
dx.device(*place) = dy * y * (1. - y);
}
};
template <typename Place, typename T>
struct Exp {
void operator()(const platform::DeviceContext& device_context,
const framework::Tensor& input, framework::Tensor* output) {
auto x = framework::EigenVector<T>::Flatten(input);
auto y = framework::EigenVector<T>::Flatten(*output);
auto* place = device_context.template get_eigen_device<Place>();
y.device(*place) = x.exp();
}
};
template <typename Place, typename T>
struct ExpGrad {
void operator()(const platform::DeviceContext& device_context,
const framework::Tensor& X, const framework::Tensor& Y,
const framework::Tensor& dY, framework::Tensor* dX) {
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto dy = framework::EigenVector<T>::Flatten(dY);
auto* place = device_context.template get_eigen_device<Place>();
dx.device(*place) = dy.exp();
}
};
template <typename Place, typename T>
struct Relu {
void operator()(const platform::DeviceContext& device_context,
const framework::Tensor& input, framework::Tensor* output) {
auto x = framework::EigenVector<T>::Flatten(input);
auto y = framework::EigenVector<T>::Flatten(*output);
auto* place = device_context.template get_eigen_device<Place>();
y.device(*place) = x.cwiseMax(static_cast<T>(0));
}
};
template <typename Place, typename T>
struct ReluGrad {
void operator()(const platform::DeviceContext& device_context,
const framework::Tensor& X, const framework::Tensor& Y,
const framework::Tensor& dY, framework::Tensor* dX) {
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto dy = framework::EigenVector<T>::Flatten(dY);
auto x = framework::EigenVector<T>::Flatten(X);
auto* place = device_context.template get_eigen_device<Place>();
dx.device(*place) = dy * (x > static_cast<T>(0)).template cast<T>();
}
};
} // namespace math
} // namespace operators
} // namespace paddle

61
paddle/operators/sigmoid_op.cc
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@ -1,61 +0,0 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/sigmoid_op.h"
namespace paddle {
namespace operators {
class SigmoidOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "sigmoid input");
AddOutput("Y", "sigmoid output");
AddComment("Sigmoid function");
}
};
class SigmoidOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("Y")->dims());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
ops::SigmoidOpGrad);
REGISTER_OP_CPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::CPUPlace, float>);

62
paddle/operators/sigmoid_op.h
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@ -1,62 +0,0 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class SigmoidKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto input = context.Input<Tensor>("X");
auto output = context.Output<Tensor>("Y");
output->mutable_data<T>(context.GetPlace());
// The clipping is used in Paddle's raw implenmention
auto X = EigenVector<T>::Flatten(*input);
auto Y = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
Y.device(place) = 1. / (1. + (-X).exp());
}
};
template <typename Place, typename T>
class SigmoidGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto Y_t = context.Input<Tensor>("Y");
auto dY_t = context.Input<Tensor>(framework::GradVarName("Y"));
auto dX_t = context.Output<Tensor>(framework::GradVarName("X"));
dX_t->mutable_data<T>(context.GetPlace());
auto dX = EigenVector<T>::Flatten(*dX_t);
auto Y = EigenVector<T>::Flatten(*Y_t);
auto dY = EigenVector<T>::Flatten(*dY_t);
dX.device(context.GetEigenDevice<Place>()) = dY * Y * (1. - Y);
}
};
} // namespace operators
} // namespace paddle

4
paddle/pybind/pybind.cc
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@ -36,7 +36,6 @@ USE_OP(onehot_cross_entropy);
USE_OP(sgd); USE_OP(sgd);
USE_OP(mul); USE_OP(mul);
USE_OP(mean); USE_OP(mean);
USE_OP(sigmoid);
USE_OP(softmax); USE_OP(softmax);
USE_OP(rowwise_add); USE_OP(rowwise_add);
USE_OP(fill_zeros_like); USE_OP(fill_zeros_like);
@ -55,6 +54,9 @@ USE_OP(top_k);
USE_OP(squared_l2_distance); USE_OP(squared_l2_distance);
USE_OP(sum); USE_OP(sum);
USE_OP(reshape); USE_OP(reshape);
USE_OP(sigmoid);
USE_OP(exp);
USE_OP(relu);
namespace paddle { namespace paddle {
namespace framework { namespace framework {

22
python/paddle/v2/framework/tests/test_exp_op.py
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@ -0,0 +1,22 @@
import unittest
import numpy as np
from op_test import OpTest
class TestExp(OpTest):
def setUp(self):
self.op_type = "exp"
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.exp(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y", max_relative_error=0.007)
if __name__ == '__main__':
unittest.main()

22
python/paddle/v2/framework/tests/test_relu_op.py
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@ -0,0 +1,22 @@
import unittest
import numpy as np
from op_test import OpTest
class TestExp(OpTest):
def setUp(self):
self.op_type = "exp"
self.inputs = {
'X': np.random.uniform(-1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.maximum(self.inputs['X'], 0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y", max_relative_error=0.007)
if __name__ == '__main__':
unittest.main()
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