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5824d850
编写于
9月 14, 2017
作者:
Q
qijun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add activation operators and python unittests
上级
dadace31
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
626 addition
and
20 deletion
+626
-20
paddle/operators/activation_op.cc
paddle/operators/activation_op.cc
+209
-5
paddle/operators/activation_op.cu
paddle/operators/activation_op.cu
+82
-0
paddle/operators/activation_op.h
paddle/operators/activation_op.h
+170
-11
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+0
-2
python/paddle/v2/framework/tests/op_test.py
python/paddle/v2/framework/tests/op_test.py
+1
-1
python/paddle/v2/framework/tests/test_activation_op.py
python/paddle/v2/framework/tests/test_activation_op.py
+164
-1
未找到文件。
paddle/operators/activation_op.cc
浏览文件 @
5824d850
...
...
@@ -46,7 +46,7 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Sigmoid operator"
);
AddOutput
(
"Y"
,
"Output of Sigmoid operator"
);
AddComment
(
"Sigmoid activation operator"
);
AddComment
(
"Sigmoid activation operator
, sigmoid = 1 / (1 + exp(-x))
"
);
}
};
...
...
@@ -56,7 +56,7 @@ class ExpOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Exp operator"
);
AddOutput
(
"Y"
,
"Output of Exp operator"
);
AddComment
(
"Exp activation operator"
);
AddComment
(
"Exp activation operator
, exp(x) = e^x
"
);
}
};
...
...
@@ -66,7 +66,129 @@ class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Relu operator"
);
AddOutput
(
"Y"
,
"Output of Relu operator"
);
AddComment
(
"Relu activation operator"
);
AddComment
(
"Relu activation operator, relu(x) = max(x, 0)"
);
}
};
class
TanhOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
TanhOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Tanh operator"
);
AddOutput
(
"Y"
,
"Output of Tanh operator"
);
AddComment
(
"Tanh activation operator, tanh = (exp(x) - exp(-x)) / (exp(x) + "
"exp(-x))"
);
}
};
class
SqrtOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SqrtOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Sqrt operator"
);
AddOutput
(
"Y"
,
"Output of Sqrt operator"
);
AddComment
(
"Sqrt activation operator, sqrt(x) = x^(1/2)"
);
}
};
class
AbsOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
AbsOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Abs operator"
);
AddOutput
(
"Y"
,
"Output of Abs operator"
);
AddComment
(
"Abs activation operator, abs(x) = |x|"
);
}
};
class
ReciprocalOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ReciprocalOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Reciprocal operator"
);
AddOutput
(
"Y"
,
"Output of Reciprocal operator"
);
AddComment
(
"Reciprocal activation operator, reciprocal(x) = 1 / x"
);
}
};
class
LogOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
LogOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Log operator"
);
AddOutput
(
"Y"
,
"Output of Log operator"
);
AddComment
(
"Log activation operator, log(x) = natural logarithm of x"
);
}
};
class
SquareOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SquareOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Square operator"
);
AddOutput
(
"Y"
,
"Output of Square operator"
);
AddComment
(
"Square activation operator, square(x) = x^2"
);
}
};
template
<
typename
AttrType
>
class
BReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
BReluOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of BRelu operator"
);
AddOutput
(
"Y"
,
"Output of BRelu operator"
);
AddComment
(
"BRelu activation operator, brelu = max(min(x, t_min), t_max)"
);
AddAttr
<
AttrType
>
(
"t_min"
,
"The min marginal value of BRelu"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
0
));
AddAttr
<
AttrType
>
(
"t_max"
,
"The max marginal value of BRelu"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
24
));
}
};
template
<
typename
AttrType
>
class
SoftReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SoftReluOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of SoftRelu operator"
);
AddOutput
(
"Y"
,
"Output of SoftRelu operator"
);
AddComment
(
"SoftRelu activation operator, soft_relu = log(1 + exp(max(min(x, "
"threshold), threshold)))"
);
AddAttr
<
AttrType
>
(
"threshold"
,
"The threshold value of SoftRelu"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
40
));
}
};
template
<
typename
AttrType
>
class
PowOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
PowOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Pow operator"
);
AddOutput
(
"Y"
,
"Output of Pow operator"
);
AddComment
(
"Pow activation operator, pow(x, factor) = x^factor"
);
AddAttr
<
AttrType
>
(
"factor"
,
"The exponential factor of Pow"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
1
));
}
};
template
<
typename
AttrType
>
class
STanhOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
STanhOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of STanh operator"
);
AddOutput
(
"Y"
,
"Output of STanh operator"
);
AddComment
(
"STanh activation operator, stanh = b * tanh(a * x)"
);
AddAttr
<
AttrType
>
(
"scale_a"
,
"The scale parameter of a for the input"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
2
/
3
));
AddAttr
<
AttrType
>
(
"scale_b"
,
"The scale parameter of b for the input"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
1.7159
));
}
};
...
...
@@ -78,10 +200,10 @@ REGISTER_OP(sigmoid, ops::ActivationOp, ops::SigmoidOpMaker, sigmoid_grad,
ops
::
ActivationOpGrad
);
REGISTER_OP_CPU_KERNEL
(
sigmoid
,
ops
::
ActivationKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SigmoidFunctor
>
);
ops
::
SigmoidFunctor
<
float
>
>
);
REGISTER_OP_CPU_KERNEL
(
sigmoid_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
ops
::
SigmoidGradFunctor
>
);
ops
::
SigmoidGradFunctor
<
float
>
>
);
REGISTER_OP
(
exp
,
ops
::
ActivationOp
,
ops
::
ExpOpMaker
,
exp_grad
,
ops
::
ActivationOpGrad
);
...
...
@@ -100,3 +222,85 @@ REGISTER_OP_CPU_KERNEL(relu,
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
(
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
>
);
paddle/operators/activation_op.cu
浏览文件 @
5824d850
...
...
@@ -36,3 +36,85 @@ REGISTER_OP_GPU_KERNEL(relu,
REGISTER_OP_GPU_KERNEL
(
relu_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
ReluGradFunctor
<
float
>>
);
REGISTER_OP
(
tanh
,
ops
::
ActivationOp
,
ops
::
TanhOpMaker
,
tanh_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_GPU_KERNEL
(
tanh
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
TanhFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
tanh_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
TanhGradFunctor
<
float
>>
);
REGISTER_OP
(
sqrt
,
ops
::
ActivationOp
,
ops
::
SqrtOpMaker
,
sqrt_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_GPU_KERNEL
(
sqrt
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SqrtFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
sqrt_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SqrtGradFunctor
<
float
>>
);
REGISTER_OP
(
abs
,
ops
::
ActivationOp
,
ops
::
AbsOpMaker
,
abs_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_GPU_KERNEL
(
abs
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
AbsFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
abs_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
AbsGradFunctor
<
float
>>
);
REGISTER_OP
(
reciprocal
,
ops
::
ActivationOp
,
ops
::
ReciprocalOpMaker
,
reciprocal_grad
,
ops
::
ActivationOpGrad
);
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
(
log
,
ops
::
ActivationOp
,
ops
::
LogOpMaker
,
log_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_GPU_KERNEL
(
log
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
LogFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
log_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
LogGradFunctor
<
float
>>
);
REGISTER_OP
(
square
,
ops
::
ActivationOp
,
ops
::
SquareOpMaker
,
square_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_GPU_KERNEL
(
square
,
ops
::
ActivationKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
squareFunctor
<
float
>>
);
REGISTER_OP_GPU_KERNEL
(
square_grad
,
ops
::
ActivationGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
ops
::
SquareGradFunctor
<
float
>>
);
REGISTER_OP
(
brelu
,
ops
::
ActivationOp
,
ops
::
BReluOpMaker
<
float
>
,
brelu_grad
,
ops
::
ActivationOpGrad
);
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
(
soft_relu
,
ops
::
ActivationOp
,
ops
::
SoftReluOpMaker
<
float
>
,
soft_relu_grad
,
ops
::
ActivationOpGrad
);
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
(
pow
,
ops
::
ActivationOp
,
ops
::
PowOpMaker
<
float
>
,
pow_grad
,
ops
::
ActivationOpGrad
);
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
(
stanh
,
ops
::
ActivationOp
,
ops
::
STanhOpMaker
<
float
>
,
stanh_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP_GPU_KERNEL
(
stanh
,
ops
::
STanhKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
stanh_grad
,
ops
::
STanhGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
\ No newline at end of file
paddle/operators/activation_op.h
浏览文件 @
5824d850
...
...
@@ -55,19 +55,20 @@ class ActivationGradKernel : public framework::OpKernel {
}
};
// sigmoid
= 1 / (1 + exp(-x
)
// sigmoid
(x) = 1 / (1 + exp(-x)
)
template
<
typename
T
>
struct
SigmoidFunctor
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
y
.
device
(
d
)
=
1.
/
(
1.
+
(
-
x
).
exp
());
y
.
device
(
d
)
=
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
(
-
x
).
exp
());
}
};
template
<
typename
T
>
struct
SigmoidGradFunctor
{
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
)
=
dy
*
y
*
(
1.
-
y
);
dx
.
device
(
d
)
=
dy
*
y
*
(
static_cast
<
T
>
(
1
)
-
y
);
}
};
...
...
@@ -103,7 +104,7 @@ struct ReluGradFunctor {
}
};
// tanh = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
// tanh
(x)
= (exp(x) - exp(-x)) / (exp(x) + exp(-x))
struct
TanhFunctor
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
...
...
@@ -115,7 +116,7 @@ template <typename T>
struct
TanhGradFunctor
{
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
)
=
dy
*
(
T
(
1
)
-
y
*
y
);
dx
.
device
(
d
)
=
dy
*
(
static_cast
<
T
>
(
1
)
-
y
*
y
);
}
};
...
...
@@ -131,7 +132,7 @@ template <typename T>
struct
SqrtGradFunctor
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
dY
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
dY
dy
,
dX
dx
)
{
const
T
y_conj
=
Eigen
::
numext
::
conj
(
y
);
const
Y
y_conj
=
Eigen
::
numext
::
conj
(
y
);
dx
.
device
(
d
)
=
static_cast
<
T
>
(
0.5
)
*
dy
/
y_conj
;
}
};
...
...
@@ -144,19 +145,27 @@ struct AbsFunctor {
}
};
struct
AbsGradFunctor
{
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
)
=
dy
*
x
.
sign
();
}
};
// reciprocal(x) = 1 / x
template
<
typename
T
>
struct
ReciprocalFunctor
{
template
<
typename
Device
,
typename
X
,
typename
Y
>
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
y
.
device
(
d
)
=
1.
/
x
;
y
.
device
(
d
)
=
static_cast
<
T
>
(
1
)
/
x
;
}
};
template
<
typename
T
>
struct
ReciprocalGradFunctor
{
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
)
=
dy
*
(
-
1.0
)
*
y
*
y
;
dx
.
device
(
d
)
=
dy
*
static_cast
<
T
>
(
-
1
)
*
y
*
y
;
}
};
...
...
@@ -168,10 +177,11 @@ struct LogFunctor {
}
};
template
<
typename
T
>
struct
LogGradFunctor
{
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
)
=
dy
*
(
1.
/
x
);
dx
.
device
(
d
)
=
dy
*
(
static_cast
<
T
>
(
1
)
/
x
);
}
};
...
...
@@ -181,12 +191,161 @@ struct SquareFunctor {
void
operator
()(
Device
d
,
X
x
,
Y
y
)
{
y
.
device
(
d
)
=
x
.
square
();
}
}
}
;
template
<
typename
T
>
struct
SquareGradFunctor
{
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
)
=
dy
*
2
*
x
;
dx
.
device
(
d
)
=
dy
*
static_cast
<
T
>
(
2
)
*
x
;
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
BReluKernel
:
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"
);
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
Place
,
typename
T
,
typename
AttrType
=
T
>
class
BReluGradKernel
:
public
framework
::
OpKernel
{
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
Place
,
typename
T
,
typename
AttrType
=
T
>
class
SoftReluKernel
:
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"
);
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
Place
,
typename
T
,
typename
AttrType
=
T
>
class
SoftReluGradKernel
:
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"
));
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
>
();
auto
temp
=
((
x
>
-
threshold
)
*
(
x
<
threshold
)).
template
cast
<
T
>().
eval
();
dx
.
device
(
place
)
=
dy
*
(
static_cast
<
T
>
(
1
)
-
(
-
y
).
exp
())
*
temp
;
}
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
PowKernel
:
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"
);
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
Place
,
typename
T
,
typename
AttrType
=
T
>
class
PowGradKernel
:
public
framework
::
OpKernel
{
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
Place
,
typename
T
,
typename
AttrType
=
T
>
class
STanhKernel
:
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"
);
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
());
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
Place
,
typename
T
,
typename
AttrType
=
T
>
class
STanhGradKernel
:
public
framework
::
OpKernel
{
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
>
();
auto
temp
=
(
scale_a
*
x
).
tanh
()
*
(
scale_a
*
x
).
tanh
();
dx
.
device
(
place
)
=
dy
*
scale_a
*
scale_b
*
(
static_cast
<
T
>
(
1
)
-
temp
);
}
};
...
...
paddle/pybind/pybind.cc
浏览文件 @
5824d850
...
...
@@ -55,8 +55,6 @@ USE_OP(squared_l2_distance);
USE_OP
(
sum
);
USE_OP
(
reshape
);
USE_OP
(
sigmoid
);
USE_OP
(
exp
);
USE_OP
(
relu
);
namespace
paddle
{
namespace
framework
{
...
...
python/paddle/v2/framework/tests/op_test.py
浏览文件 @
5824d850
...
...
@@ -196,7 +196,7 @@ class OpTest(unittest.TestCase):
self
.
assertTrue
(
np
.
allclose
(
actual
,
expect
,
atol
=
1e-05
),
"output name: "
+
out_name
+
"has diff"
)
"output name: "
+
out_name
+
"
has diff"
)
def
check_output
(
self
):
places
=
[
core
.
CPUPlace
()]
...
...
python/paddle/v2/framework/tests/test_activation_op.py
浏览文件 @
5824d850
...
...
@@ -21,7 +21,9 @@ class TestExp(OpTest):
class
TestRelu
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"relu"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
-
1
,
1
,
[
4
,
4
]).
astype
(
"float32"
)}
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
x
=
np
.
sign
(
x
)
*
np
.
exp
(
np
.
abs
(
x
))
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'Y'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
0
)}
def
test_check_output
(
self
):
...
...
@@ -42,6 +44,167 @@ class TestSigmoid(OpTest):
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Y'
,
max_relative_error
=
0.008
)
class
TestTanh
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"tanh"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Y'
:
np
.
tanh
(
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
)
class
TestSqrt
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sqrt"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Y'
:
np
.
sqrt
(
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
)
class
TestAbs
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"abs"
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
x
=
np
.
sign
(
x
)
*
np
.
exp
(
np
.
abs
(
x
))
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'Y'
:
np
.
abs
(
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
)
class
TestReciprocal
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"reciprocal"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
1
,
2
,
[
11
,
17
]).
astype
(
"float32"
)}
self
.
outputs
=
{
'Y'
:
np
.
reciprocal
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Y'
,
max_relative_error
=
0.01
)
class
TestLog
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"log"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Y'
:
np
.
log
(
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
)
class
TestSquare
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"square"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Y'
:
np
.
square
(
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
)
class
TestBRelu
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"brelu"
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
4
,
4
]).
astype
(
"float32"
)
x
=
2
*
np
.
sign
(
x
)
*
np
.
exp
(
np
.
abs
(
x
))
self
.
inputs
=
{
'X'
:
x
}
t_min
=
0
t_max
=
4
self
.
attrs
=
{
't_min'
:
t_min
,
't_max'
:
t_max
}
t
=
np
.
copy
(
x
)
t
[
t
<
t_min
]
=
t_min
t
[
t
>
t_max
]
=
t_max
self
.
outputs
=
{
'Y'
:
t
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Y'
,
max_relative_error
=
0.02
)
class
TestSoftRelu
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"soft_relu"
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
4
,
4
]).
astype
(
"float32"
)
x
=
2
*
np
.
sign
(
x
)
*
np
.
exp
(
np
.
abs
(
x
))
self
.
inputs
=
{
'X'
:
x
}
threshold
=
4
self
.
attrs
=
{
'threshold'
:
threshold
}
t
=
np
.
copy
(
x
)
t
[
t
<
-
threshold
]
=
-
threshold
t
[
t
>
threshold
]
=
threshold
self
.
outputs
=
{
'Y'
:
np
.
log
((
np
.
exp
(
t
)
+
1
))}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Y'
,
max_relative_error
=
0.02
)
class
TestPow
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"pow"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
1
,
2
,
[
11
,
17
]).
astype
(
"float32"
)}
self
.
attrs
=
{
'factor'
:
3
}
self
.
outputs
=
{
'Y'
:
np
.
power
(
self
.
inputs
[
'X'
],
3
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Y'
,
max_relative_error
=
0.02
)
class
TestSTanh
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"stanh"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
scale_a
=
2.0
/
3.0
scale_b
=
1.7159
self
.
attrs
=
{
'scale_a'
:
scale_a
,
'scale_b'
:
scale_b
}
self
.
outputs
=
{
'Y'
:
scale_b
*
np
.
tanh
(
self
.
inputs
[
'X'
]
*
scale_a
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Y'
,
max_relative_error
=
0.007
)
...
...
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