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体验新版 GitCode,发现更多精彩内容 >>
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37a94370
编写于
1月 18, 2018
作者:
F
fengjiayi
提交者:
GitHub
1月 18, 2018
浏览文件
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差异文件
Merge pull request #7538 from JiayiFeng/dev_elementwise_max_min
elementwise max min
上级
388aa51c
a37f6ad3
变更
13
显示空白变更内容
内联
并排
Showing
13 changed file
with
696 addition
and
41 deletion
+696
-41
paddle/gserver/tests/sequence_recurrent_group.py
paddle/gserver/tests/sequence_recurrent_group.py
+8
-8
paddle/operators/elementwise_add_op.h
paddle/operators/elementwise_add_op.h
+1
-33
paddle/operators/elementwise_max_op.cc
paddle/operators/elementwise_max_op.cc
+45
-0
paddle/operators/elementwise_max_op.cu
paddle/operators/elementwise_max_op.cu
+32
-0
paddle/operators/elementwise_max_op.h
paddle/operators/elementwise_max_op.h
+120
-0
paddle/operators/elementwise_min_op.cc
paddle/operators/elementwise_min_op.cc
+45
-0
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
+120
-0
paddle/operators/elementwise_op_function.h
paddle/operators/elementwise_op_function.h
+38
-0
python/paddle/v2/fluid/layers/ops.py
python/paddle/v2/fluid/layers/ops.py
+2
-0
python/paddle/v2/fluid/tests/test_edit_distance_op.py
python/paddle/v2/fluid/tests/test_edit_distance_op.py
+13
-0
python/paddle/v2/fluid/tests/test_elementwise_max_op.py
python/paddle/v2/fluid/tests/test_elementwise_max_op.py
+120
-0
python/paddle/v2/fluid/tests/test_elementwise_min_op.py
python/paddle/v2/fluid/tests/test_elementwise_min_op.py
+120
-0
未找到文件。
paddle/gserver/tests/sequence_recurrent_group.py
浏览文件 @
37a94370
# 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
#
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.
#
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.
from
paddle.trainer_config_helpers
import
*
######################## data source ################################
...
...
paddle/operators/elementwise_add_op.h
浏览文件 @
37a94370
...
...
@@ -28,39 +28,7 @@ template <typename DeviceContext, typename T>
class
ElementwiseAddKernel
:
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
<
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
();
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
;
}
ElementwiseComputeEx
<
AddFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
);
}
};
...
...
paddle/operators/elementwise_max_op.cc
0 → 100644
浏览文件 @
37a94370
/* 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/elementwise_max_op.h"
#include "paddle/operators/elementwise_op.h"
namespace
paddle
{
namespace
operators
{
class
ElementwiseMaxOpMaker
:
public
ElementwiseOpMaker
{
public:
ElementwiseMaxOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
ElementwiseOpMaker
(
proto
,
op_checker
)
{
SetComment
(
"Max"
,
"Out = max(X, Y)"
);
AddComment
(
comment_
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
elementwise_max
,
ops
::
ElementwiseOp
,
ops
::
ElementwiseMaxOpMaker
,
elementwise_max_grad
,
ops
::
ElementwiseOpGrad
);
REGISTER_OP_CPU_KERNEL
(
elementwise_max
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
elementwise_max_grad
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/operators/elementwise_max_op.cu
0 → 100644
浏览文件 @
37a94370
/* 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_max_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
elementwise_max
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseMaxKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
elementwise_max_grad
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseMaxGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/operators/elementwise_max_op.h
0 → 100644
浏览文件 @
37a94370
/* 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
MaxFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
>
b
?
a
:
b
;
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
ElementwiseMaxKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
ElementwiseComputeEx
<
MaxFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
);
}
};
template
<
typename
T
>
struct
ElementwiseMaxGradFunctor
{
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
ElementwiseMaxBroadCastGradFunctor
{
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
ElementwiseMaxBroadCast2GradFunctor
{
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
ElementwiseMaxGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
ElementwiseGradCompute
<
DeviceContext
,
T
,
ElementwiseMaxGradFunctor
<
T
>
,
ElementwiseMaxBroadCastGradFunctor
<
T
>
,
ElementwiseMaxBroadCast2GradFunctor
<
T
>>
(
ctx
);
}
};
}
// namespace operators
}
// namespace paddle
paddle/operators/elementwise_min_op.cc
0 → 100644
浏览文件 @
37a94370
/* 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/elementwise_min_op.h"
#include "paddle/operators/elementwise_op.h"
namespace
paddle
{
namespace
operators
{
class
ElementwiseMinOpMaker
:
public
ElementwiseOpMaker
{
public:
ElementwiseMinOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
ElementwiseOpMaker
(
proto
,
op_checker
)
{
SetComment
(
"Max"
,
"Out = min(X, Y)"
);
AddComment
(
comment_
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
elementwise_min
,
ops
::
ElementwiseOp
,
ops
::
ElementwiseMinOpMaker
,
elementwise_min_grad
,
ops
::
ElementwiseOpGrad
);
REGISTER_OP_CPU_KERNEL
(
elementwise_min
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
ElementwiseMinKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
elementwise_min_grad
,
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
>
);
paddle/operators/elementwise_min_op.cu
0 → 100644
浏览文件 @
37a94370
/* 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 @
37a94370
/* 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
{
ElementwiseComputeEx
<
MinFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
);
}
};
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
paddle/operators/elementwise_op_function.h
浏览文件 @
37a94370
...
...
@@ -356,5 +356,43 @@ void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
return
;
}
}
template
<
typename
Functor
,
typename
DeviceContext
,
typename
T
>
void
ElementwiseComputeEx
(
const
framework
::
ExecutionContext
&
ctx
)
{
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
<
Functor
,
T
,
DeviceContext
>
functor
(
x
,
y
,
z
,
ctx
.
template
device_context
<
DeviceContext
>(),
Functor
());
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
;
}
}
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/layers/ops.py
浏览文件 @
37a94370
...
...
@@ -55,6 +55,8 @@ __all__ = [
'elementwise_div'
,
'elementwise_sub'
,
'elementwise_mul'
,
'elementwise_max'
,
'elementwise_min'
,
'clip'
,
'sequence_softmax'
,
]
+
__activations__
...
...
python/paddle/v2/fluid/tests/test_edit_distance_op.py
浏览文件 @
37a94370
# Copyright (c) 2018 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
...
...
python/paddle/v2/fluid/tests/test_elementwise_max_op.py
0 → 100644
浏览文件 @
37a94370
# Copyright (c) 2018 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestElementwiseOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
# If x and y have the same value, the max() is not differentiable.
# So we generate test data by the following method
# to avoid them being too close to each other.
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
[
13
,
17
]).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.005
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMaxOp_Vector
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
random
((
32
,
)).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
32
,
)).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
(
32
,
)).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
class
TestElementwiseMaxOp_broadcast_0
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
2
,
)).
astype
(
np
.
float32
)
y
=
x
[:,
0
,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
2
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
0
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
2
,
1
,
1
))
}
class
TestElementwiseMaxOp_broadcast_1
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
1
))
}
class
TestElementwiseMaxOp_broadcast_2
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
4
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
0
,
:]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
4
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
1
,
4
))
}
class
TestElementwiseMaxOp_broadcast_3
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
,
5
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
4
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
4
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
4
,
1
))
}
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/fluid/tests/test_elementwise_min_op.py
0 → 100644
浏览文件 @
37a94370
# Copyright (c) 2018 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestElementwiseOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
# If x and y have the same value, the min() is not differentiable.
# So we generate test data by the following method
# to avoid them being too close to each other.
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
[
13
,
17
]).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.005
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMaxOp_Vector
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
random
((
32
,
)).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
32
,
)).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
(
32
,
)).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
class
TestElementwiseMaxOp_broadcast_0
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
2
,
)).
astype
(
np
.
float32
)
y
=
x
[:,
0
,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
2
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
0
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
2
,
1
,
1
))
}
class
TestElementwiseMaxOp_broadcast_1
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
1
))
}
class
TestElementwiseMaxOp_broadcast_2
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
4
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
0
,
:]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
4
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
1
,
4
))
}
class
TestElementwiseMaxOp_broadcast_3
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
,
5
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
4
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
4
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
4
,
1
))
}
if
__name__
==
'__main__'
:
unittest
.
main
()
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