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784740d8
编写于
12月 11, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine cos-sim-op
上级
a91efdde
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
170 addition
and
74 deletion
+170
-74
paddle/operators/cos_sim_op.h
paddle/operators/cos_sim_op.h
+115
-74
paddle/operators/elementwise_op_function.h
paddle/operators/elementwise_op_function.h
+55
-0
未找到文件。
paddle/operators/cos_sim_op.h
浏览文件 @
784740d8
...
@@ -15,6 +15,7 @@
...
@@ -15,6 +15,7 @@
#pragma once
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/elementwise_add_op.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
...
@@ -27,6 +28,28 @@ template <typename T, int MajorType = Eigen::RowMajor,
...
@@ -27,6 +28,28 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
,
typename
DeviceContext
>
void
Function_forward
(
T
*
out
,
T
*
x_norm
,
T
*
y_norm
,
ElementIterator
<
T
,
DeviceContext
>&
x
,
ElementIterator
<
T
,
DeviceContext
>&
y
,
int
row
,
int
col
)
{
for
(
int
i
=
0
;
i
<
row
;
++
i
)
{
T
xx
=
0
;
T
yy
=
0
;
T
xy
=
0
;
for
(
int
j
=
0
;
j
<
col
;
++
j
)
{
xy
+=
(
*
x
)
*
(
*
y
);
xx
+=
(
*
x
)
*
(
*
x
);
yy
+=
(
*
y
)
*
(
*
y
);
++
y
;
++
x
;
}
x_norm
[
i
]
=
sqrt
(
xx
);
y_norm
[
i
]
=
sqrt
(
yy
);
out
[
i
]
=
xy
/
(
x_norm
[
i
]
*
y_norm
[
i
]);
}
}
template
<
typename
DeviceContext
,
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
CosSimKernel
:
public
framework
::
OpKernel
<
T
>
{
class
CosSimKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
...
@@ -41,32 +64,63 @@ class CosSimKernel : public framework::OpKernel<T> {
...
@@ -41,32 +64,63 @@ class CosSimKernel : public framework::OpKernel<T> {
out_x_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out_x_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out_y_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out_y_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// convert Tensor to Eigen Tensor
int
rows_x
=
in_x
->
dims
()[
0
];
int
rows_x
=
in_x
->
dims
()[
0
];
int
rows_y
=
in_y
->
dims
()[
0
];
int
rows_y
=
in_y
->
dims
()[
0
];
auto
x
=
EigenMatrix
<
T
>::
Reshape
(
*
in_x
,
1
);
auto
y
=
EigenMatrix
<
T
>::
Reshape
(
*
in_y
,
1
);
int
cols
=
framework
::
product
(
in_x
->
dims
())
/
rows_x
;
auto
z
=
EigenVector
<
T
>::
Flatten
(
*
out_z
);
auto
x_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
in_x
->
data
<
T
>
(),
rows_x
,
auto
x_norm
=
EigenVector
<
T
>::
Flatten
(
*
out_x_norm
);
cols
,
rows_x
,
cols
);
auto
y_norm
=
EigenVector
<
T
>::
Flatten
(
*
out_y_norm
);
auto
y_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
in_y
->
data
<
T
>
(),
rows_y
,
cols
,
rows_x
,
cols
);
// compute
auto
&
place
=
Function_forward
(
out_z
->
data
<
T
>
(),
out_x_norm
->
data
<
T
>
(),
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
out_y_norm
->
data
<
T
>
(),
x_iter
,
y_iter
,
rows_x
,
cols
);
auto
row_along
=
Eigen
::
array
<
int
,
1
>
({{
1
}});
//
x_norm
.
device
(
place
)
=
x
.
square
().
sum
(
row_along
).
sqrt
();
// // convert Tensor to Eigen Tensor
y_norm
.
device
(
place
)
=
y
.
square
().
sum
(
row_along
).
sqrt
();
//// int rows_x = in_x->dims()[0];
if
(
rows_x
==
rows_y
)
{
//// int rows_y = in_y->dims()[0];
auto
xy
=
(
x
*
y
).
sum
(
Eigen
::
array
<
int
,
1
>
({{
1
}}));
// auto x = EigenMatrix<T>::Reshape(*in_x, 1);
z
.
device
(
place
)
=
xy
/
x_norm
/
y_norm
;
// auto y = EigenMatrix<T>::Reshape(*in_y, 1);
}
else
{
// auto z = EigenVector<T>::Flatten(*out_z);
Eigen
::
DSizes
<
int
,
2
>
bcast
(
rows_x
,
1
);
// auto x_norm = EigenVector<T>::Flatten(*out_x_norm);
auto
xy
=
(
x
*
y
.
broadcast
(
bcast
)).
sum
(
row_along
);
// auto y_norm = EigenVector<T>::Flatten(*out_y_norm);
z
.
device
(
place
)
=
xy
/
x_norm
/
y_norm
.
broadcast
(
bcast
);
//
}
// // compute
// auto& place =
// *context.template device_context<DeviceContext>().eigen_device();
// auto row_along = Eigen::array<int, 1>({{1}});
// x_norm.device(place) = x.square().sum(row_along).sqrt();
// y_norm.device(place) = y.square().sum(row_along).sqrt();
// if (rows_x == rows_y) {
// auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
// z.device(place) = xy / x_norm / y_norm;
// } else {
// Eigen::DSizes<int, 2> bcast(rows_x, 1);
// auto xy = (x * y.broadcast(bcast)).sum(row_along);
// z.device(place) = xy / x_norm / y_norm.broadcast(bcast);
// }
}
}
};
};
template
<
typename
T
,
typename
DeviceContext
>
void
Function_element
(
T
*
result
,
ElementIterator
<
T
,
DeviceContext
>
dz
,
ElementIterator
<
T
,
DeviceContext
>
y
,
ElementIterator
<
T
,
DeviceContext
>
x_norm
,
ElementIterator
<
T
,
DeviceContext
>
y_norm
,
ElementIterator
<
T
,
DeviceContext
>
z
,
ElementIterator
<
T
,
DeviceContext
>
x
,
int
num
,
int
block
)
{
for
(
int
i
=
0
;
i
<
num
;
++
i
)
{
result
[
i
%
block
]
+=
(
*
dz
)
*
((
*
y
)
/
((
*
x_norm
)
*
(
*
y_norm
))
-
(
*
z
)
*
(
*
x
)
/
((
*
x_norm
)
*
(
*
x_norm
)));
++
dz
;
++
y
;
++
x_norm
;
++
y_norm
;
++
z
;
++
x
;
}
}
template
<
typename
DeviceContext
,
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
CosSimGradKernel
:
public
framework
::
OpKernel
<
T
>
{
class
CosSimGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
...
@@ -81,63 +135,50 @@ class CosSimGradKernel : public framework::OpKernel<T> {
...
@@ -81,63 +135,50 @@ class CosSimGradKernel : public framework::OpKernel<T> {
auto
*
out_grad_y
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
out_grad_y
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
in_grad_z
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
in_grad_z
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
// convert Tensor to Eigen Tensor
auto
x
=
EigenMatrix
<
T
>::
Reshape
(
*
in_x
,
1
);
auto
y
=
EigenMatrix
<
T
>::
Reshape
(
*
in_y
,
1
);
auto
z
=
EigenMatrix
<
T
>::
Reshape
(
*
in_z
,
1
);
auto
x_norm
=
EigenMatrix
<
T
>::
Reshape
(
*
in_x_norm
,
1
);
auto
y_norm
=
EigenMatrix
<
T
>::
Reshape
(
*
in_y_norm
,
1
);
auto
dz
=
EigenMatrix
<
T
>::
Reshape
(
*
in_grad_z
,
1
);
// compute gradident
// compute gradident
int
rows_x
=
in_x
->
dims
()[
0
];
int
rows_x
=
in_x
->
dims
()[
0
];
int
rows_y
=
in_y
->
dims
()[
0
];
int
rows_y
=
in_y
->
dims
()[
0
];
int
cols
=
framework
::
product
(
in_x
->
dims
())
/
rows_x
;
int
cols
=
framework
::
product
(
in_x
->
dims
())
/
rows_x
;
Eigen
::
DSizes
<
int
,
2
>
bcast_cols
(
1
,
cols
);
auto
z_bcast
=
z
.
broadcast
(
bcast_cols
);
//////////////////////////////
auto
dz_bcast
=
dz
.
broadcast
(
bcast_cols
);
// ##
auto
x_snorm_bcast
=
x_norm
.
square
().
eval
().
broadcast
(
bcast_cols
);
auto
x_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
in_x
->
data
<
T
>
(),
rows_x
,
auto
&
place
=
cols
,
rows_x
,
cols
);
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
y_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
in_y
->
data
<
T
>
(),
rows_y
,
if
(
rows_x
==
rows_y
)
{
cols
,
rows_x
,
cols
);
auto
y_snorm_bcast
=
y_norm
.
square
().
eval
().
broadcast
(
bcast_cols
);
auto
z_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
in_z
->
data
<
T
>
(),
rows_x
,
1
,
auto
norm_prod_bcast
=
(
x_norm
*
y_norm
).
eval
().
broadcast
(
bcast_cols
);
rows_x
,
cols
);
// compute dx
auto
dz_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
in_grad_z
->
data
<
T
>
(),
if
(
out_grad_x
)
{
rows_x
,
1
,
rows_x
,
cols
);
out_grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_norm_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
auto
dx
=
EigenMatrix
<
T
>::
Reshape
(
*
out_grad_x
,
1
);
in_x_norm
->
data
<
T
>
(),
rows_x
,
1
,
rows_x
,
cols
);
auto
grad
=
y
/
norm_prod_bcast
-
z_bcast
*
x
/
x_snorm_bcast
;
auto
y_norm_iter
=
ElementIterator
<
T
,
DeviceContext
>
(
dx
.
device
(
place
)
=
dz_bcast
*
grad
;
in_y_norm
->
data
<
T
>
(),
rows_y
,
1
,
rows_x
,
cols
);
}
// ##
// compute dy
//////////////////////////////
if
(
out_grad_y
)
{
// compute dx
out_grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
if
(
out_grad_x
)
{
auto
dy
=
EigenMatrix
<
T
>::
Reshape
(
*
out_grad_y
,
1
);
out_grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
grad
=
x
/
norm_prod_bcast
-
z_bcast
*
y
/
y_snorm_bcast
;
dy
.
device
(
place
)
=
dz_bcast
*
grad
;
//////////////////////////////
}
// ##
}
else
{
Function_element
(
out_grad_x
->
data
<
T
>
(),
dz_iter
,
y_iter
,
x_norm_iter
,
Eigen
::
DSizes
<
int
,
2
>
bcast_rows
(
rows_x
,
1
);
y_norm_iter
,
z_iter
,
x_iter
,
rows_x
*
cols
,
Eigen
::
DSizes
<
int
,
2
>
bcast_rows_cols
(
rows_x
,
cols
);
rows_x
*
cols
);
auto
y_bcast
=
y
.
broadcast
(
bcast_rows
);
// ##
auto
y_snorm_bcast
=
y_norm
.
square
().
eval
().
broadcast
(
bcast_rows_cols
);
//////////////////////////////
auto
norm_prod_bcast
=
(
x_norm
*
y_norm
.
eval
().
broadcast
(
bcast_rows
))
}
.
eval
()
// compute dy
.
broadcast
(
bcast_cols
);
if
(
out_grad_y
)
{
// compute dx
out_grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
if
(
out_grad_x
)
{
out_grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
//////////////////////////////
auto
dx
=
EigenMatrix
<
T
>::
Reshape
(
*
out_grad_x
,
1
);
// ##
auto
grad
=
y_bcast
/
norm_prod_bcast
-
z_bcast
*
x
/
x_snorm_bcast
;
Function_element
(
out_grad_y
->
data
<
T
>
(),
dz_iter
,
x_iter
,
y_norm_iter
,
dx
.
device
(
place
)
=
dz_bcast
*
grad
;
x_norm_iter
,
z_iter
,
y_iter
,
rows_x
*
cols
,
}
rows_y
*
cols
);
// compute dy
// ##
if
(
out_grad_y
)
{
//////////////////////////////
out_grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dy
=
EigenVector
<
T
>::
Flatten
(
*
out_grad_y
);
auto
grad
=
x
/
norm_prod_bcast
-
z_bcast
*
y_bcast
/
y_snorm_bcast
;
dy
.
device
(
place
)
=
(
dz_bcast
*
grad
).
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
}
}
}
}
}
};
};
...
...
paddle/operators/elementwise_op_function.h
浏览文件 @
784740d8
...
@@ -131,6 +131,61 @@ class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
...
@@ -131,6 +131,61 @@ class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
int
post_
;
int
post_
;
};
};
template
<
typename
T
,
typename
Place
>
class
ElementIterator
;
// Fixed(zcd) : Only support 2D
template
<
typename
T
>
class
ElementIterator
<
T
,
platform
::
CPUDeviceContext
>
{
public:
ElementIterator
(
const
T
*
ptr
,
int
t_m
,
int
t_n
,
int
m
,
int
n
)
:
ptr_
(
ptr
),
index_
(
0
),
i_
(
0
),
j_
(
0
),
t_m_
(
t_m
),
t_n_
(
t_n
),
m_
(
m
),
n_
(
n
)
{}
ElementIterator
<
T
,
platform
::
CPUDeviceContext
>&
operator
++
()
{
++
j_
;
if
((
j_
==
n_
))
{
j_
=
0
;
++
i_
;
}
int
t_i
=
(
t_m_
==
1
)
?
0
:
i_
;
int
t_j
=
(
t_n_
==
1
)
?
0
:
j_
;
index_
=
t_i
*
t_n_
+
t_j
;
return
*
this
;
}
bool
operator
==
(
const
ElementIterator
<
T
,
platform
::
CPUDeviceContext
>&
rhs
)
const
{
return
(
ptr_
+
index_
)
==
&
(
*
rhs
);
}
bool
operator
!=
(
const
ElementIterator
<
T
,
platform
::
CPUDeviceContext
>&
rhs
)
const
{
return
(
ptr_
+
index_
)
!=
&
(
*
rhs
);
}
const
T
&
operator
*
()
{
return
ptr_
[
index_
];
}
private:
// t_m_ == m_ || t_n_ == n_ || (t_m_ == 1 && t_m_ == 1)
const
T
*
ptr_
;
int
index_
;
int
i_
;
int
j_
;
int64_t
t_m_
;
int64_t
t_n_
;
int64_t
m_
;
int64_t
n_
;
};
#ifdef __NVCC__
#ifdef __NVCC__
template
<
typename
T
>
template
<
typename
T
>
class
RowwiseTransformIterator
<
T
,
platform
::
CUDADeviceContext
>
class
RowwiseTransformIterator
<
T
,
platform
::
CUDADeviceContext
>
...
...
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