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f5cd9619
编写于
1月 15, 2018
作者:
F
fengjiayi
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
complete elementwise_min_op
上级
acf37ad6
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
188 addition
and
4 deletion
+188
-4
paddle/operators/elementwise_max_op.h
paddle/operators/elementwise_max_op.h
+3
-3
paddle/operators/elementwise_min_op.cc
paddle/operators/elementwise_min_op.cc
+1
-1
paddle/operators/elementwise_min_op.cu
paddle/operators/elementwise_min_op.cu
+32
-0
paddle/operators/elementwise_min_op.h
paddle/operators/elementwise_min_op.h
+152
-0
未找到文件。
paddle/operators/elementwise_max_op.h
浏览文件 @
f5cd9619
...
...
@@ -79,7 +79,7 @@ struct ElementwiseMaxGradFunctor {
}
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
d
)
=
(
y_e
>=
x
_e
).
template
cast
<
T
>()
*
dz_e
;
dy_e
.
device
(
d
)
=
(
x_e
<=
y
_e
).
template
cast
<
T
>()
*
dz_e
;
}
}
};
...
...
@@ -104,7 +104,7 @@ struct ElementwiseMaxBroadCastGradFunctor {
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
d
)
=
((
y_e_bcast
>=
x_e
).
template
cast
<
T
>()
*
dz_e
)
dy_e
.
device
(
d
)
=
((
x_e
<=
y_e_bcast
).
template
cast
<
T
>()
*
dz_e
)
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
pre
,
n
))
.
sum
(
Eigen
::
array
<
int
,
1
>
{{
0
}});
}
...
...
@@ -131,7 +131,7 @@ struct ElementwiseMaxBroadCast2GradFunctor {
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
d
)
=
((
y_e_bcast
>=
x_e
).
template
cast
<
T
>()
*
dz_e
)
dy_e
.
device
(
d
)
=
((
x_e
<=
y_e_bcast
).
template
cast
<
T
>()
*
dz_e
)
.
reshape
(
Eigen
::
DSizes
<
int
,
3
>
(
pre
,
n
,
post
))
.
sum
(
Eigen
::
array
<
int
,
2
>
{{
0
,
2
}});
}
...
...
paddle/operators/elementwise_min_op.cc
浏览文件 @
f5cd9619
...
...
@@ -42,4 +42,4 @@ REGISTER_OP_CPU_KERNEL(
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
\ No newline at end of file
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/operators/elementwise_min_op.cu
0 → 100644
浏览文件 @
f5cd9619
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/elementwise_min_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
elementwise_min
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
elementwise_min_grad
,
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseMinGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/operators/elementwise_min_op.h
0 → 100644
浏览文件 @
f5cd9619
/* 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/operators/elementwise_op_function.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
struct
MinFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
<
b
?
a
:
b
;
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
ElementwiseMinKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
using
Tensor
=
framework
::
Tensor
;
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
TransformFunctor
<
MinFunctor
<
T
>
,
T
,
DeviceContext
>
functor
(
x
,
y
,
z
,
ctx
.
template
device_context
<
DeviceContext
>(),
MinFunctor
<
T
>
());
auto
x_dims
=
x
->
dims
();
auto
y_dims
=
y
->
dims
();
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
y_dims
.
size
(),
"Rank of first input must >= rank of second input."
);
if
(
x_dims
==
y_dims
)
{
functor
.
Run
();
return
;
}
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
axis
=
(
axis
==
-
1
?
x_dims
.
size
()
-
y_dims
.
size
()
:
axis
);
PADDLE_ENFORCE
(
axis
>=
0
&&
axis
<
x_dims
.
size
(),
"Axis should be in range [0, x_dims)"
);
int
pre
,
n
,
post
;
get_mid_dims
(
x_dims
,
y_dims
,
axis
,
pre
,
n
,
post
);
if
(
post
==
1
)
{
functor
.
RunRowWise
(
n
,
pre
);
return
;
}
else
{
functor
.
RunMidWise
(
n
,
pre
,
post
);
return
;
}
}
};
template
<
typename
T
>
struct
ElementwiseMinGradFunctor
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
Z
,
typename
dX
,
typename
dY
,
typename
dZ
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
Z
z
,
dX
dx
,
dY
dy
,
dZ
dz
)
{
auto
x_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x
);
auto
y_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
y
);
auto
dz_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dz
);
if
(
dx
)
{
auto
dx_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dx
);
dx_e
.
device
(
d
)
=
(
x_e
<
y_e
).
template
cast
<
T
>()
*
dz_e
;
}
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
d
)
=
(
x_e
>=
y_e
).
template
cast
<
T
>()
*
dz_e
;
}
}
};
template
<
typename
T
>
struct
ElementwiseMinBroadCastGradFunctor
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
Z
,
typename
dX
,
typename
dY
,
typename
dZ
,
typename
Pre
,
typename
N
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
Z
z
,
dX
dx
,
dY
dy
,
dZ
dz
,
Pre
pre
,
N
n
)
{
auto
x_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x
);
auto
y_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
y
);
auto
dz_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dz
);
auto
y_e_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
()));
if
(
dx
)
{
auto
dx_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dx
);
dx_e
.
device
(
d
)
=
(
x_e
<
y_e_bcast
).
template
cast
<
T
>()
*
dz_e
;
}
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
d
)
=
((
x_e
>=
y_e_bcast
).
template
cast
<
T
>()
*
dz_e
)
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
pre
,
n
))
.
sum
(
Eigen
::
array
<
int
,
1
>
{{
0
}});
}
}
};
template
<
typename
T
>
struct
ElementwiseMinBroadCast2GradFunctor
{
template
<
typename
Device
,
typename
X
,
typename
Y
,
typename
Z
,
typename
dX
,
typename
dY
,
typename
dZ
,
typename
Pre
,
typename
N
,
typename
Post
>
void
operator
()(
Device
d
,
X
x
,
Y
y
,
Z
z
,
dX
dx
,
dY
dy
,
dZ
dz
,
Pre
pre
,
N
n
,
Post
post
)
{
auto
x_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x
);
auto
y_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
y
);
auto
dz_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dz
);
auto
y_e_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
()));
if
(
dx
)
{
auto
dx_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dx
);
dx_e
.
device
(
d
)
=
(
x_e
<
y_e_bcast
).
template
cast
<
T
>()
*
dz_e
;
}
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
d
)
=
((
x_e
>=
y_e_bcast
).
template
cast
<
T
>()
*
dz_e
)
.
reshape
(
Eigen
::
DSizes
<
int
,
3
>
(
pre
,
n
,
post
))
.
sum
(
Eigen
::
array
<
int
,
2
>
{{
0
,
2
}});
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
ElementwiseMinGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
ElementwiseGradCompute
<
DeviceContext
,
T
,
ElementwiseMinGradFunctor
<
T
>
,
ElementwiseMinBroadCastGradFunctor
<
T
>
,
ElementwiseMinBroadCast2GradFunctor
<
T
>>
(
ctx
);
}
};
}
// namespace operators
}
// namespace paddle
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