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体验新版 GitCode,发现更多精彩内容 >>
提交
ae4b7fd5
编写于
9月 29, 2017
作者:
Y
Yu Yang
提交者:
GitHub
9月 29, 2017
浏览文件
操作
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差异文件
Merge pull request #4485 from reyoung/feature/BetterActivationKern
Unify Activation functions and simplify register code
上级
473af189
a8c6ce9b
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
198 addition
and
331 deletion
+198
-331
paddle/operators/activation_op.cc
paddle/operators/activation_op.cc
+13
-76
paddle/operators/activation_op.cu
paddle/operators/activation_op.cu
+11
-90
paddle/operators/activation_op.h
paddle/operators/activation_op.h
+174
-165
未找到文件。
paddle/operators/activation_op.cc
浏览文件 @
ae4b7fd5
...
...
@@ -206,120 +206,57 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sigmoid
,
ops
::
ActivationOp
,
ops
::
SigmoidOpMaker
,
sigmoid_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
sigmoid
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SigmoidFunctor
<
float
>>
);
REGISTER_OP_CPU_KERNEL
(
sigmoid_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SigmoidGradFunctor
<
float
>>
);
REGISTER_OP
(
exp
,
ops
::
ActivationOp
,
ops
::
ExpOpMaker
,
exp_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
exp
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
ExpFunctor
>
);
REGISTER_OP_CPU_KERNEL
(
exp_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
ExpGradFunctor
>
);
REGISTER_OP
(
relu
,
ops
::
ActivationOp
,
ops
::
ReluOpMaker
,
relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
relu
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
ReluFunctor
<
float
>>
);
REGISTER_OP_CPU_KERNEL
(
relu_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
ReluGradFunctor
<
float
>>
);
REGISTER_OP
(
tanh
,
ops
::
ActivationOp
,
ops
::
TanhOpMaker
,
tanh_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
tanh
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
TanhFunctor
>
);
REGISTER_OP_CPU_KERNEL
(
tanh_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
TanhGradFunctor
<
float
>>
);
REGISTER_OP
(
sqrt
,
ops
::
ActivationOp
,
ops
::
SqrtOpMaker
,
sqrt_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
sqrt
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SqrtFunctor
>
);
REGISTER_OP_CPU_KERNEL
(
sqrt_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SqrtGradFunctor
<
float
>>
);
REGISTER_OP
(
abs
,
ops
::
ActivationOp
,
ops
::
AbsOpMaker
,
abs_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
abs
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
AbsFunctor
>
);
REGISTER_OP_CPU_KERNEL
(
abs_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
AbsGradFunctor
>
);
REGISTER_OP
(
reciprocal
,
ops
::
ActivationOp
,
ops
::
ReciprocalOpMaker
,
reciprocal_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
reciprocal
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
ReciprocalFunctor
<
float
>>
);
REGISTER_OP_CPU_KERNEL
(
reciprocal_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
ReciprocalGradFunctor
<
float
>>
);
REGISTER_OP
(
log
,
ops
::
ActivationOp
,
ops
::
LogOpMaker
,
log_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
log
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
LogFunctor
>
);
REGISTER_OP_CPU_KERNEL
(
log_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
LogGradFunctor
<
float
>>
);
REGISTER_OP
(
square
,
ops
::
ActivationOp
,
ops
::
SquareOpMaker
,
square_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
square
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SquareFunctor
>
);
REGISTER_OP_CPU_KERNEL
(
square_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SquareGradFunctor
<
float
>>
);
REGISTER_OP
(
softsign
,
ops
::
ActivationOp
,
ops
::
SoftsignOpMaker
,
softsign_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
softsign
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SoftsignFunctor
<
float
>>
);
REGISTER_OP_CPU_KERNEL
(
softsign_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SoftsignGradFunctor
<
float
>>
);
REGISTER_OP
(
brelu
,
ops
::
ActivationOp
,
ops
::
BReluOpMaker
<
float
>
,
brelu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
brelu
,
ops
::
BReluKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
brelu_grad
,
ops
::
BReluGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP
(
soft_relu
,
ops
::
ActivationOp
,
ops
::
SoftReluOpMaker
<
float
>
,
soft_relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
soft_relu
,
ops
::
SoftReluKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
soft_relu_grad
,
ops
::
SoftReluGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP
(
pow
,
ops
::
ActivationOp
,
ops
::
PowOpMaker
<
float
>
,
pow_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
pow
,
ops
::
PowKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
pow_grad
,
ops
::
PowGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP
(
stanh
,
ops
::
ActivationOp
,
ops
::
STanhOpMaker
<
float
>
,
stanh_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
stanh
,
ops
::
STanhKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
stanh_grad
,
ops
::
STanhGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_CPU_KERNEL( \
act_type, \
paddle::operators::ActivationKernel<paddle::platform::CPUPlace, \
paddle::operators::functor<float>>); \
REGISTER_OP_CPU_KERNEL(act_type##_grad, \
paddle::operators::ActivationGradKernel< \
paddle::platform::CPUPlace, \
paddle::operators::grad_functor<float>>);
FOR_EACH_KERNEL_FUNCTOR
(
REGISTER_ACTIVATION_CPU_KERNEL
);
paddle/operators/activation_op.cu
浏览文件 @
ae4b7fd5
...
...
@@ -15,93 +15,14 @@
#define EIGEN_USE_GPU
#include "paddle/operators/activation_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sigmoid
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SigmoidFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
sigmoid_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SigmoidGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
exp
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
ExpFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
exp_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
ExpGradFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
relu
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
ReluFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
relu_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
ReluGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
tanh
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
TanhFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
tanh_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
TanhGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
sqrt
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SqrtFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
sqrt_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SqrtGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
abs
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
AbsFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
abs_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
AbsGradFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
reciprocal
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
ReciprocalFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
reciprocal_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
ReciprocalGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
log
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
LogFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
log_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
LogGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
square
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SquareFunctor
>
);
REGISTER_OP_GPU_KERNEL
(
square_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SquareGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
softsign
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SoftsignFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
softsign_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SoftsignGradFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
brelu
,
ops
::
BReluKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
brelu_grad
,
ops
::
BReluGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
soft_relu
,
ops
::
SoftReluKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
soft_relu_grad
,
ops
::
SoftReluGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
pow
,
ops
::
PowKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
pow_grad
,
ops
::
PowGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
stanh
,
ops
::
STanhKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
stanh_grad
,
ops
::
STanhGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
#define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_GPU_KERNEL( \
act_type, \
paddle::operators::ActivationKernel<paddle::platform::GPUPlace, \
paddle::operators::functor<float>>); \
REGISTER_OP_GPU_KERNEL(act_type##_grad, \
paddle::operators::ActivationGradKernel< \
paddle::platform::GPUPlace, \
paddle::operators::grad_functor<float>>);
FOR_EACH_KERNEL_FUNCTOR
(
REGISTER_ACTIVATION_GPU_KERNEL
);
paddle/operators/activation_op.h
浏览文件 @
ae4b7fd5
...
...
@@ -19,9 +19,12 @@
namespace
paddle
{
namespace
operators
{
template
<
typename
Place
,
typename
T
,
typename
Functor
>
class
ActivationKernel
:
public
framework
::
OpKernel
<
T
>
{
template
<
typename
Place
,
typename
Functor
>
class
ActivationKernel
:
public
framework
::
OpKernel
<
typename
Functor
::
ELEMENT_TYPE
>
{
public:
using
T
=
typename
Functor
::
ELEMENT_TYPE
;
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Output
<
framework
::
Tensor
>
(
"Y"
);
...
...
@@ -31,13 +34,20 @@ class ActivationKernel : public framework::OpKernel<T> {
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Y
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
Functor
functor
;
auto
attrs
=
functor
.
GetAttrs
();
for
(
auto
&
attr
:
attrs
)
{
*
attr
.
second
=
context
.
Attr
<
float
>
(
attr
.
first
);
}
functor
(
place
,
x
,
y
);
}
};
template
<
typename
Place
,
typename
T
,
typename
Functor
>
class
ActivationGradKernel
:
public
framework
::
OpKernel
<
T
>
{
template
<
typename
Place
,
typename
Functor
>
class
ActivationGradKernel
:
public
framework
::
OpKernel
<
typename
Functor
::
ELEMENT_TYPE
>
{
public:
using
T
=
typename
Functor
::
ELEMENT_TYPE
;
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Input
<
framework
::
Tensor
>
(
"Y"
);
...
...
@@ -51,159 +61,210 @@ class ActivationGradKernel : public framework::OpKernel<T> {
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
Functor
functor
;
auto
attrs
=
functor
.
GetAttrs
();
for
(
auto
&
attr
:
attrs
)
{
*
attr
.
second
=
context
.
Attr
<
float
>
(
attr
.
first
);
}
functor
(
place
,
x
,
y
,
dy
,
dx
);
}
};
template
<
typename
T
>
struct
BaseActivationFunctor
{
using
ELEMENT_TYPE
=
T
;
using
AttrPair
=
std
::
vector
<
std
::
pair
<
const
char
*
,
float
*>>
;
AttrPair
GetAttrs
()
{
return
AttrPair
();
}
};
// sigmoid(x) = 1 / (1 + exp(-x))
template
<
typename
T
>
struct
SigmoidFunctor
{
struct
SigmoidFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
(
-
x
).
exp
());
}
};
template
<
typename
T
>
struct
SigmoidGradFunctor
{
struct
SigmoidGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
y
*
(
static_cast
<
T
>
(
1
)
-
y
);
}
};
// exp(x) = e^x
struct
ExpFunctor
{
template
<
typename
T
>
struct
ExpFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
exp
();
}
};
struct
ExpGradFunctor
{
template
<
typename
T
>
struct
ExpGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
y
;
}
};
// relu(x) = max(x, 0)
template
<
typename
T
>
struct
ReluFunctor
{
struct
ReluFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
cwiseMax
(
static_cast
<
T
>
(
0
));
}
};
template
<
typename
T
>
struct
ReluGradFunctor
{
struct
ReluGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
(
x
>
static_cast
<
T
>
(
0
)).
template
cast
<
T
>();
}
};
// tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
struct
TanhFunctor
{
template
<
typename
T
>
struct
TanhFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
tanh
();
}
};
template
<
typename
T
>
struct
TanhGradFunctor
{
struct
TanhGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
(
static_cast
<
T
>
(
1
)
-
y
*
y
);
}
};
// sqrt(x) = x^(1/2)
struct
SqrtFunctor
{
template
<
typename
T
>
struct
SqrtFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
sqrt
();
}
};
template
<
typename
T
>
struct
SqrtGradFunctor
{
struct
SqrtGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
const
Y
y_conj
=
Eigen
::
numext
::
conj
(
y
);
dx
.
device
(
d
)
=
static_cast
<
T
>
(
0.5
)
*
dy
/
y_conj
;
}
};
// abs(x) = |x|
struct
AbsFunctor
{
template
<
typename
T
>
struct
AbsFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
abs
();
}
};
struct
AbsGradFunctor
{
template
<
typename
T
>
struct
AbsGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
x
.
sign
();
}
};
// reciprocal(x) = 1 / x
template
<
typename
T
>
struct
ReciprocalFunctor
{
struct
ReciprocalFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
static_cast
<
T
>
(
1
)
/
x
;
}
};
template
<
typename
T
>
struct
ReciprocalGradFunctor
{
struct
ReciprocalGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
static_cast
<
T
>
(
-
1
)
*
y
*
y
;
}
};
// log(x) = natural logarithm of x
struct
LogFunctor
{
template
<
typename
T
>
struct
LogFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
log
();
}
};
template
<
typename
T
>
struct
LogGradFunctor
{
struct
LogGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
(
static_cast
<
T
>
(
1
)
/
x
);
}
};
// square(x) = x^2
struct
SquareFunctor
{
template
<
typename
T
>
struct
SquareFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
square
();
}
};
template
<
typename
T
>
struct
SquareGradFunctor
{
struct
SquareGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
static_cast
<
T
>
(
2
)
*
x
;
}
};
template
<
typename
T
>
struct
BReluFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
t_min
;
float
t_max
;
// NOTE: Explicit hides the `BaseActivationFunctor<T>::GetAttrs`
// not polymorphism for speed.
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"t_min"
,
&
t_min
},
{
"t_max"
,
&
t_max
}};
}
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
cwiseMax
(
t_min
).
cwiseMin
(
t_max
);
}
};
template
<
typename
T
>
struct
BReluGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
t_min
;
float
t_max
;
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"t_min"
,
&
t_min
},
{
"t_max"
,
&
t_max
}};
}
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
((
x
>
t_min
)
*
(
x
<
t_max
)).
template
cast
<
T
>();
}
};
// softsign(x) = x / (1 + |x|)
template
<
typename
T
>
struct
SoftsignFunctor
{
struct
SoftsignFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
y
.
device
(
d
)
=
x
/
(
static_cast
<
T
>
(
1
)
+
x
.
abs
());
...
...
@@ -213,7 +274,7 @@ struct SoftsignFunctor {
// d(softsign(x))/dx = 1 / (1 + |x|)^2
// Taken from https://en.wikipedia.org/wiki/Activation_function
template
<
typename
T
>
struct
SoftsignGradFunctor
{
struct
SoftsignGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
dx
.
device
(
d
)
=
...
...
@@ -221,153 +282,101 @@ struct SoftsignGradFunctor {
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
BReluKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Output
<
framework
::
Tensor
>
(
"Y"
);
auto
t_min
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"t_min"
));
auto
t_max
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"t_max"
));
Y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Y
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
y
.
device
(
place
)
=
x
.
cwiseMax
(
t_min
).
cwiseMin
(
t_max
);
template
<
typename
T
>
struct
SoftReluFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
threshold
;
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"threshold"
,
&
threshold
}};
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
BReluGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
dY
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
dX
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
t_min
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"t_min"
));
auto
t_max
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"t_max"
));
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dy
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dY
);
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
dx
.
device
(
place
)
=
dy
*
((
x
>
t_min
)
*
(
x
<
t_max
)).
template
cast
<
T
>();
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
auto
temp
=
x
.
cwiseMax
(
-
threshold
).
cwiseMin
(
threshold
);
y
.
device
(
d
)
=
(
static_cast
<
T
>
(
1
)
+
temp
.
exp
()).
log
();
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
SoftReluKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Output
<
framework
::
Tensor
>
(
"Y"
);
auto
threshold
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"threshold"
));
Y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Y
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
temp
=
x
.
cwiseMax
(
-
threshold
).
cwiseMin
(
threshold
).
eval
();
y
.
device
(
place
)
=
(
static_cast
<
T
>
(
1
)
+
temp
.
exp
()).
log
();
template
<
typename
T
>
struct
SoftReluGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
threshold
;
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"threshold"
,
&
threshold
}};
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
SoftReluGradKernel
:
public
framework
::
OpKernel
<
T
>
{
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"
));
auto
threshold
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"threshold"
));
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Y
);
auto
dy
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dY
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
auto
temp
=
((
x
>
-
threshold
)
*
(
x
<
threshold
)).
template
cast
<
T
>().
eval
();
dx
.
device
(
place
)
=
dy
*
(
static_cast
<
T
>
(
1
)
-
(
-
y
).
exp
())
*
temp
;
dx
.
device
(
d
)
=
dy
*
(
static_cast
<
T
>
(
1
)
-
(
-
y
).
exp
())
*
temp
;
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
PowKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Output
<
framework
::
Tensor
>
(
"Y"
);
auto
factor
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"factor"
));
Y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Y
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
y
.
device
(
place
)
=
x
.
pow
(
factor
);
template
<
typename
T
>
struct
PowFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
factor
;
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"factor"
,
&
factor
}};
}
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
x
.
pow
(
factor
);
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
PowGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
dY
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
dX
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
factor
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"factor"
));
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dy
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dY
);
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
dx
.
device
(
place
)
=
dy
*
factor
*
x
.
pow
(
factor
-
static_cast
<
T
>
(
1
));
template
<
typename
T
>
struct
PowGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
factor
;
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"factor"
,
&
factor
}};
}
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dy
*
factor
*
x
.
pow
(
factor
-
static_cast
<
T
>
(
1
));
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
STanhKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Output
<
framework
::
Tensor
>
(
"Y"
);
auto
scale_a
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"scale_a"
));
auto
scale_b
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"scale_b"
));
Y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
template
<
typename
T
>
struct
STanhFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
scale_a
;
float
scale_b
;
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"scale_a"
,
&
scale_a
},
{
"scale_b"
,
&
scale_b
}};
}
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Y
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
y
.
device
(
place
)
=
scale_b
*
(
scale_a
*
x
).
tanh
();
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
const
{
y
.
device
(
d
)
=
scale_b
*
(
scale_a
*
x
).
tanh
();
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
STanhGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
dY
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
dX
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
scale_a
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"scale_a"
));
auto
scale_b
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"scale_b"
));
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dy
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dY
);
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
template
<
typename
T
>
struct
STanhGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
float
scale_a
;
float
scale_b
;
typename
BaseActivationFunctor
<
T
>::
AttrPair
GetAttrs
()
{
return
{{
"scale_a"
,
&
scale_a
},
{
"scale_b"
,
&
scale_b
}};
}
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
const
{
auto
temp
=
(
scale_a
*
x
).
tanh
()
*
(
scale_a
*
x
).
tanh
();
dx
.
device
(
place
)
=
dy
*
scale_a
*
scale_b
*
(
static_cast
<
T
>
(
1
)
-
temp
);
dx
.
device
(
d
)
=
dy
*
scale_a
*
scale_b
*
(
static_cast
<
T
>
(
1
)
-
temp
);
}
};
}
// namespace operators
}
// namespace paddle
#define FOR_EACH_KERNEL_FUNCTOR(__macro) \
__macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor); \
__macro(exp, ExpFunctor, ExpGradFunctor); \
__macro(relu, ReluFunctor, ReluGradFunctor); \
__macro(tanh, TanhFunctor, TanhGradFunctor); \
__macro(sqrt, SqrtFunctor, SqrtGradFunctor); \
__macro(abs, AbsFunctor, AbsGradFunctor); \
__macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
__macro(log, LogFunctor, LogGradFunctor); \
__macro(square, SquareFunctor, SquareGradFunctor); \
__macro(brelu, BReluFunctor, BReluGradFunctor); \
__macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor); \
__macro(pow, PowFunctor, PowGradFunctor); \
__macro(stanh, STanhFunctor, STanhGradFunctor); \
__macro(softsign, SoftsignFunctor, SoftsignGradFunctor)
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