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0fa4b985
编写于
9月 26, 2017
作者:
Q
qiaolongfei
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差异文件
split elementwise_op.h into two header files
上级
bc30ba19
变更
10
显示空白变更内容
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Showing
10 changed file
with
215 addition
and
191 deletion
+215
-191
paddle/operators/elementwise_add_op.cc
paddle/operators/elementwise_add_op.cc
+1
-0
paddle/operators/elementwise_add_op.h
paddle/operators/elementwise_add_op.h
+1
-1
paddle/operators/elementwise_div_op.cc
paddle/operators/elementwise_div_op.cc
+1
-0
paddle/operators/elementwise_div_op.h
paddle/operators/elementwise_div_op.h
+1
-1
paddle/operators/elementwise_mul_op.cc
paddle/operators/elementwise_mul_op.cc
+1
-0
paddle/operators/elementwise_mul_op.h
paddle/operators/elementwise_mul_op.h
+1
-1
paddle/operators/elementwise_op.h
paddle/operators/elementwise_op.h
+9
-187
paddle/operators/elementwise_op_function.h
paddle/operators/elementwise_op_function.h
+198
-0
paddle/operators/elementwise_sub_op.cc
paddle/operators/elementwise_sub_op.cc
+1
-0
paddle/operators/elementwise_sub_op.h
paddle/operators/elementwise_sub_op.h
+1
-1
未找到文件。
paddle/operators/elementwise_add_op.cc
浏览文件 @
0fa4b985
...
...
@@ -13,6 +13,7 @@
limitations under the License. */
#include "paddle/operators/elementwise_add_op.h"
#include "paddle/operators/elementwise_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/operators/elementwise_add_op.h
浏览文件 @
0fa4b985
...
...
@@ -14,7 +14,7 @@
#pragma once
#include "paddle/operators/elementwise_op.h"
#include "paddle/operators/elementwise_op
_function
.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/operators/elementwise_div_op.cc
浏览文件 @
0fa4b985
...
...
@@ -13,6 +13,7 @@
limitations under the License. */
#include "paddle/operators/elementwise_div_op.h"
#include "paddle/operators/elementwise_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/operators/elementwise_div_op.h
浏览文件 @
0fa4b985
...
...
@@ -14,7 +14,7 @@
#pragma once
#include "paddle/operators/elementwise_op.h"
#include "paddle/operators/elementwise_op
_function
.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/operators/elementwise_mul_op.cc
浏览文件 @
0fa4b985
...
...
@@ -13,6 +13,7 @@
limitations under the License. */
#include "paddle/operators/elementwise_mul_op.h"
#include "paddle/operators/elementwise_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/operators/elementwise_mul_op.h
浏览文件 @
0fa4b985
...
...
@@ -13,7 +13,7 @@
limitations under the License. */
#pragma once
#include "paddle/operators/elementwise_op.h"
#include "paddle/operators/elementwise_op
_function
.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/operators/elementwise_op.h
浏览文件 @
0fa4b985
/* 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. */
#pragma once
#include <iostream>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
/*
* Out = X ⊙ Y
* If Y's shape does not match X' shape, they will be reshaped.
* For example:
* 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
* pre=2, n=3*4, post=5
* x.shape(2, 12, 5) * y.shape(1,12,1).broadcast(2,12,5)
* 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
* pre=2*3, n=4*5, post=1
* x.shape(2, 3, 20) * y.shape(1,1,20).broadcast(2,3,20)
*/
inline
void
get_mid_dims
(
const
framework
::
DDim
&
x_dims
,
const
framework
::
DDim
&
y_dims
,
const
int
axis
,
int
&
pre
,
int
&
n
,
int
&
post
)
{
pre
=
1
;
n
=
1
;
post
=
1
;
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
{
pre
*=
x_dims
[
i
];
}
for
(
int
i
=
0
;
i
<
y_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
x_dims
[
i
+
axis
],
y_dims
[
i
],
"Broadcast dimension mismatch."
);
n
*=
y_dims
[
i
];
}
for
(
int
i
=
axis
+
y_dims
.
size
();
i
<
x_dims
.
size
();
++
i
)
{
post
*=
x_dims
[
i
];
}
}
#define EIGEN_FUNCTOR(name, eigen_op) \
struct Eigen##name##Functor { \
template <typename Place, typename T> \
inline void Run(const framework::Tensor* x, const framework::Tensor* y, \
framework::Tensor* z, \
const framework::ExecutionContext& ctx) { \
auto x_e = framework::EigenVector<T>::Flatten(*x); \
auto y_e = framework::EigenVector<T>::Flatten(*y); \
auto z_e = framework::EigenVector<T>::Flatten(*z); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_e); \
} \
template <typename Place, typename T> \
inline void RunBroadCast(const framework::Tensor* x, \
const framework::Tensor* y, framework::Tensor* z, \
const framework::ExecutionContext& ctx, int pre, \
int n) { \
auto x_e = framework::EigenVector<T>::Flatten(*x); \
auto y_e = framework::EigenVector<T>::Flatten(*y); \
auto z_e = framework::EigenVector<T>::Flatten(*z); \
auto y_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())); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast); \
} \
template <typename Place, typename T> \
inline void RunBroadCast2(const framework::Tensor* x, \
const framework::Tensor* y, \
framework::Tensor* z, \
const framework::ExecutionContext& ctx, int pre, \
int n, int post) { \
auto x_e = framework::EigenVector<T>::Flatten(*x); \
auto y_e = framework::EigenVector<T>::Flatten(*y); \
auto z_e = framework::EigenVector<T>::Flatten(*z); \
auto y_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())); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast); \
} \
}
template
<
class
functor
,
typename
Place
,
typename
T
>
void
ElementwiseCompute
(
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
());
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
||
product
(
y_dims
)
==
1
)
{
functor
f
;
f
.
template
Run
<
Place
,
T
>(
x
,
y
,
z
,
ctx
);
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
f
;
f
.
template
RunBroadCast
<
Place
,
T
>(
x
,
y
,
z
,
ctx
,
pre
,
n
);
return
;
}
else
{
functor
f
;
f
.
template
RunBroadCast2
<
Place
,
T
>(
x
,
y
,
z
,
ctx
,
pre
,
n
,
post
);
return
;
}
}
#define EIGEN_ADD(x, y) ((x) + (y))
EIGEN_FUNCTOR
(
Add
,
EIGEN_ADD
);
#define EIGEN_SUB(x, y) ((x) - (y))
EIGEN_FUNCTOR
(
Sub
,
EIGEN_SUB
);
#define EIGEN_MUL(x, y) ((x) * (y))
EIGEN_FUNCTOR
(
Mul
,
EIGEN_MUL
);
#define EIGEN_DIV(x, y) ((x) / (y))
EIGEN_FUNCTOR
(
Div
,
EIGEN_DIV
);
template
<
typename
Place
,
typename
T
,
typename
functor
,
typename
functor1
,
typename
broadcastfunctor
,
typename
broadcast2functor
>
void
ElementwiseGradCompute
(
const
framework
::
ExecutionContext
&
ctx
)
{
using
Tensor
=
framework
::
Tensor
;
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
out
=
ctx
.
Input
<
Tensor
>
(
"Out"
);
auto
*
dout
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
place
=
ctx
.
GetEigenDevice
<
Place
>
();
auto
x_dims
=
x
->
dims
();
auto
y_dims
=
y
->
dims
();
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
if
(
dx
)
{
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
if
(
dy
)
{
dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
if
(
x_dims
==
y_dims
)
{
functor
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
);
return
;
}
if
(
product
(
y_dims
)
==
1
)
{
functor1
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
);
return
;
}
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
axis
=
(
axis
==
-
1
?
x_dims
.
size
()
-
y_dims
.
size
()
:
axis
);
int
pre
,
n
,
post
;
get_mid_dims
(
x_dims
,
y_dims
,
axis
,
pre
,
n
,
post
);
if
(
post
==
1
)
{
broadcastfunctor
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
,
pre
,
n
);
return
;
}
else
{
broadcast2functor
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
,
pre
,
n
,
post
);
return
;
}
}
class
ElementwiseOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
paddle/operators/elementwise_op_function.h
0 → 100644
浏览文件 @
0fa4b985
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
/*
* Out = X ⊙ Y
* If Y's shape does not match X' shape, they will be reshaped.
* For example:
* 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
* pre=2, n=3*4, post=5
* x.shape(2, 12, 5) * y.shape(1,12,1).broadcast(2,12,5)
* 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
* pre=2*3, n=4*5, post=1
* x.shape(2, 3, 20) * y.shape(1,1,20).broadcast(2,3,20)
*/
inline
void
get_mid_dims
(
const
framework
::
DDim
&
x_dims
,
const
framework
::
DDim
&
y_dims
,
const
int
axis
,
int
&
pre
,
int
&
n
,
int
&
post
)
{
pre
=
1
;
n
=
1
;
post
=
1
;
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
{
pre
*=
x_dims
[
i
];
}
for
(
int
i
=
0
;
i
<
y_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
x_dims
[
i
+
axis
],
y_dims
[
i
],
"Broadcast dimension mismatch."
);
n
*=
y_dims
[
i
];
}
for
(
int
i
=
axis
+
y_dims
.
size
();
i
<
x_dims
.
size
();
++
i
)
{
post
*=
x_dims
[
i
];
}
}
#define EIGEN_FUNCTOR(name, eigen_op) \
struct Eigen##name##Functor { \
template <typename Place, typename T> \
inline void Run(const framework::Tensor* x, const framework::Tensor* y, \
framework::Tensor* z, \
const framework::ExecutionContext& ctx) { \
auto x_e = framework::EigenVector<T>::Flatten(*x); \
auto y_e = framework::EigenVector<T>::Flatten(*y); \
auto z_e = framework::EigenVector<T>::Flatten(*z); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_e); \
} \
template <typename Place, typename T> \
inline void RunBroadCast(const framework::Tensor* x, \
const framework::Tensor* y, framework::Tensor* z, \
const framework::ExecutionContext& ctx, int pre, \
int n) { \
auto x_e = framework::EigenVector<T>::Flatten(*x); \
auto y_e = framework::EigenVector<T>::Flatten(*y); \
auto z_e = framework::EigenVector<T>::Flatten(*z); \
auto y_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())); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast); \
} \
template <typename Place, typename T> \
inline void RunBroadCast2(const framework::Tensor* x, \
const framework::Tensor* y, \
framework::Tensor* z, \
const framework::ExecutionContext& ctx, int pre, \
int n, int post) { \
auto x_e = framework::EigenVector<T>::Flatten(*x); \
auto y_e = framework::EigenVector<T>::Flatten(*y); \
auto z_e = framework::EigenVector<T>::Flatten(*z); \
auto y_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())); \
z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast); \
} \
}
template
<
class
functor
,
typename
Place
,
typename
T
>
void
ElementwiseCompute
(
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
());
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
||
product
(
y_dims
)
==
1
)
{
functor
f
;
f
.
template
Run
<
Place
,
T
>(
x
,
y
,
z
,
ctx
);
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
f
;
f
.
template
RunBroadCast
<
Place
,
T
>(
x
,
y
,
z
,
ctx
,
pre
,
n
);
return
;
}
else
{
functor
f
;
f
.
template
RunBroadCast2
<
Place
,
T
>(
x
,
y
,
z
,
ctx
,
pre
,
n
,
post
);
return
;
}
}
#define EIGEN_ADD(x, y) ((x) + (y))
EIGEN_FUNCTOR
(
Add
,
EIGEN_ADD
);
#define EIGEN_SUB(x, y) ((x) - (y))
EIGEN_FUNCTOR
(
Sub
,
EIGEN_SUB
);
#define EIGEN_MUL(x, y) ((x) * (y))
EIGEN_FUNCTOR
(
Mul
,
EIGEN_MUL
);
#define EIGEN_DIV(x, y) ((x) / (y))
EIGEN_FUNCTOR
(
Div
,
EIGEN_DIV
);
template
<
typename
Place
,
typename
T
,
typename
functor
,
typename
functor1
,
typename
broadcastfunctor
,
typename
broadcast2functor
>
void
ElementwiseGradCompute
(
const
framework
::
ExecutionContext
&
ctx
)
{
using
Tensor
=
framework
::
Tensor
;
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
out
=
ctx
.
Input
<
Tensor
>
(
"Out"
);
auto
*
dout
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
place
=
ctx
.
GetEigenDevice
<
Place
>
();
auto
x_dims
=
x
->
dims
();
auto
y_dims
=
y
->
dims
();
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
if
(
dx
)
{
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
if
(
dy
)
{
dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
if
(
x_dims
==
y_dims
)
{
functor
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
);
return
;
}
if
(
product
(
y_dims
)
==
1
)
{
functor1
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
);
return
;
}
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
axis
=
(
axis
==
-
1
?
x_dims
.
size
()
-
y_dims
.
size
()
:
axis
);
int
pre
,
n
,
post
;
get_mid_dims
(
x_dims
,
y_dims
,
axis
,
pre
,
n
,
post
);
if
(
post
==
1
)
{
broadcastfunctor
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
,
pre
,
n
);
return
;
}
else
{
broadcast2functor
f
;
f
(
place
,
x
,
y
,
out
,
dx
,
dy
,
dout
,
pre
,
n
,
post
);
return
;
}
}
}
// namespace operators
}
// namespace paddle
paddle/operators/elementwise_sub_op.cc
浏览文件 @
0fa4b985
...
...
@@ -13,6 +13,7 @@
limitations under the License. */
#include "paddle/operators/elementwise_sub_op.h"
#include "paddle/operators/elementwise_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/operators/elementwise_sub_op.h
浏览文件 @
0fa4b985
...
...
@@ -13,7 +13,7 @@
limitations under the License. */
#pragma once
#include "paddle/operators/elementwise_op.h"
#include "paddle/operators/elementwise_op
_function
.h"
namespace
paddle
{
namespace
operators
{
...
...
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