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a5f1e6d6
编写于
7年前
作者:
X
Xinghai Sun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Update cos_sim operator by following reviewer's comments.
上级
91215bce
7 合并请求
!11636
[IMPORTANT] MKLDNN layout: Support for sum operator
,
!8482
Release/0.11.0
,
!8190
Release/0.11.0
,
!8189
Release/0.11.0
,
!6633
给线性回归的get-started代码加上了预测的示例~~
,
!4615
Feature/tensor array add python binding
,
!3815
Add cosine similarity operator.
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
76 addition
and
63 deletion
+76
-63
paddle/operators/cos_sim_op.cc
paddle/operators/cos_sim_op.cc
+2
-2
paddle/operators/cos_sim_op.h
paddle/operators/cos_sim_op.h
+50
-47
python/paddle/v2/framework/tests/test_cos_sim_op.py
python/paddle/v2/framework/tests/test_cos_sim_op.py
+24
-14
未找到文件。
paddle/operators/cos_sim_op.cc
浏览文件 @
a5f1e6d6
...
@@ -90,8 +90,8 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
...
@@ -90,8 +90,8 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
y_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
x_grad
->
Resize
(
x_dims
);
if
(
x_grad
)
x_grad
->
Resize
(
x_dims
);
y_grad
->
Resize
(
y_dims
);
if
(
y_grad
)
y_grad
->
Resize
(
y_dims
);
}
}
};
};
...
...
This diff is collapsed.
Click to expand it.
paddle/operators/cos_sim_op.h
浏览文件 @
a5f1e6d6
...
@@ -28,30 +28,30 @@ template <typename Place, typename T>
...
@@ -28,30 +28,30 @@ template <typename Place, typename T>
class
CosSimKernel
:
public
framework
::
OpKernel
{
class
CosSimKernel
:
public
framework
::
OpKernel
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
input_
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
input_
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
z
=
context
.
Output
<
Tensor
>
(
"Out"
);
auto
*
output_
z
=
context
.
Output
<
Tensor
>
(
"Out"
);
auto
*
x_norm
=
context
.
Output
<
Tensor
>
(
"XNorm"
);
auto
*
output_
x_norm
=
context
.
Output
<
Tensor
>
(
"XNorm"
);
auto
*
y_norm
=
context
.
Output
<
Tensor
>
(
"YNorm"
);
auto
*
output_
y_norm
=
context
.
Output
<
Tensor
>
(
"YNorm"
);
z
->
mutable_data
<
T
>
(
context
.
GetPlace
());
output_
z
->
mutable_data
<
T
>
(
context
.
GetPlace
());
x_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
output_
x_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
y_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
output_
y_norm
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
x
->
dims
();
auto
dims
=
input_
x
->
dims
();
int
size
=
static_cast
<
int
>
(
framework
::
product
(
dims
));
int
size
=
static_cast
<
int
>
(
framework
::
product
(
dims
));
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
X
=
EigenMatrix
<
T
>::
From
(
*
x
,
new_dims
);
auto
x
=
EigenMatrix
<
T
>::
From
(
*
input_
x
,
new_dims
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
y
,
new_dims
);
auto
y
=
EigenMatrix
<
T
>::
From
(
*
input_
y
,
new_dims
);
auto
Z
=
EigenMatrix
<
T
>::
From
(
*
z
);
auto
z
=
EigenMatrix
<
T
>::
From
(
*
output_
z
);
auto
XNorm
=
EigenMatrix
<
T
>::
From
(
*
x_norm
);
auto
x_norm
=
EigenMatrix
<
T
>::
From
(
*
output_
x_norm
);
auto
YNorm
=
EigenMatrix
<
T
>::
From
(
*
y_norm
);
auto
y_norm
=
EigenMatrix
<
T
>::
From
(
*
output_
y_norm
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
XY
=
(
X
*
Y
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
}));
auto
xy
=
(
x
*
y
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
}));
XNorm
.
device
(
place
)
=
(
X
*
X
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
})).
sqrt
();
x_norm
.
device
(
place
)
=
x
.
square
(
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
})).
sqrt
();
YNorm
.
device
(
place
)
=
(
Y
*
Y
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
})).
sqrt
();
y_norm
.
device
(
place
)
=
y
.
square
(
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
})).
sqrt
();
Z
.
device
(
place
)
=
XY
/
XNorm
/
YN
orm
;
z
.
device
(
place
)
=
xy
/
x_norm
/
y_n
orm
;
}
}
};
};
...
@@ -59,41 +59,44 @@ template <typename Place, typename T>
...
@@ -59,41 +59,44 @@ template <typename Place, typename T>
class
CosSimGradKernel
:
public
framework
::
OpKernel
{
class
CosSimGradKernel
:
public
framework
::
OpKernel
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
input_
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
input_
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
z
=
context
.
Input
<
Tensor
>
(
"Out"
);
auto
*
input_
z
=
context
.
Input
<
Tensor
>
(
"Out"
);
auto
*
x_norm
=
context
.
Input
<
Tensor
>
(
"XNorm"
);
auto
*
input_
x_norm
=
context
.
Input
<
Tensor
>
(
"XNorm"
);
auto
*
y_norm
=
context
.
Input
<
Tensor
>
(
"YNorm"
);
auto
*
input_
y_norm
=
context
.
Input
<
Tensor
>
(
"YNorm"
);
auto
*
grad_x
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
output_
grad_x
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
grad_y
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
output_
grad_y
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
grad_z
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
input_
grad_z
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
input_x
->
dims
();
grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
x
->
dims
();
int
size
=
static_cast
<
int
>
(
framework
::
product
(
dims
));
int
size
=
static_cast
<
int
>
(
framework
::
product
(
dims
));
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
X
=
EigenMatrix
<
T
>::
From
(
*
x
,
new_dims
);
auto
x
=
EigenMatrix
<
T
>::
From
(
*
input_x
,
new_dims
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
y
,
new_dims
);
auto
y
=
EigenMatrix
<
T
>::
From
(
*
input_y
,
new_dims
);
auto
Z
=
EigenMatrix
<
T
>::
From
(
*
z
);
auto
z
=
EigenMatrix
<
T
>::
From
(
*
input_z
);
auto
X_norm
=
EigenMatrix
<
T
>::
From
(
*
x_norm
);
auto
x_norm
=
EigenMatrix
<
T
>::
From
(
*
input_x_norm
);
auto
Y_norm
=
EigenMatrix
<
T
>::
From
(
*
y_norm
);
auto
y_norm
=
EigenMatrix
<
T
>::
From
(
*
input_y_norm
);
auto
dX
=
EigenMatrix
<
T
>::
From
(
*
grad_x
,
new_dims
);
auto
dz
=
EigenMatrix
<
T
>::
From
(
*
input_grad_z
);
auto
dY
=
EigenMatrix
<
T
>::
From
(
*
grad_y
,
new_dims
);
auto
dZ
=
EigenMatrix
<
T
>::
From
(
*
grad_z
);
Eigen
::
DSizes
<
int
,
2
>
bcast
(
1
,
new_dims
[
1
]);
Eigen
::
DSizes
<
int
,
2
>
bcast
(
1
,
new_dims
[
1
]);
auto
Z_bcast
=
Z
.
broadcast
(
bcast
);
auto
z_bcast
=
z
.
broadcast
(
bcast
);
auto
d
Z_bcast
=
dZ
.
broadcast
(
bcast
);
auto
d
z_bcast
=
dz
.
broadcast
(
bcast
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
X_snorm_bcast
=
X_norm
.
square
().
eval
().
broadcast
(
bcast
);
auto
x_snorm_bcast
=
x_norm
.
square
().
eval
().
broadcast
(
bcast
);
auto
Y_snorm_bcast
=
Y_norm
.
square
().
eval
().
broadcast
(
bcast
);
auto
y_snorm_bcast
=
y_norm
.
square
().
eval
().
broadcast
(
bcast
);
auto
norm_prod_bcast
=
(
X_norm
*
Y_norm
).
eval
().
broadcast
(
bcast
);
auto
norm_prod_bcast
=
(
x_norm
*
y_norm
).
eval
().
broadcast
(
bcast
);
dX
.
device
(
place
)
=
if
(
output_grad_x
)
{
dZ_bcast
*
(
Y
/
norm_prod_bcast
-
Z_bcast
*
X
/
X_snorm_bcast
);
output_grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
dY
.
device
(
place
)
=
auto
dx
=
EigenMatrix
<
T
>::
From
(
*
output_grad_x
,
new_dims
);
dZ_bcast
*
(
X
/
norm_prod_bcast
-
Z_bcast
*
Y
/
Y_snorm_bcast
);
dx
.
device
(
place
)
=
dz_bcast
*
(
y
/
norm_prod_bcast
-
z_bcast
*
x
/
x_snorm_bcast
);
}
if
(
output_grad_y
)
{
output_grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dy
=
EigenMatrix
<
T
>::
From
(
*
output_grad_y
,
new_dims
);
dy
.
device
(
place
)
=
dz_bcast
*
(
x
/
norm_prod_bcast
-
z_bcast
*
y
/
y_snorm_bcast
);
}
}
}
};
};
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/framework/tests/test_cos_sim_op.py
浏览文件 @
a5f1e6d6
...
@@ -24,26 +24,36 @@ class TestCosSimOp(unittest.TestCase):
...
@@ -24,26 +24,36 @@ class TestCosSimOp(unittest.TestCase):
}
}
class
CosSimGradOpTest
(
GradientChecker
):
class
TestCosSimGradOp
(
GradientChecker
):
def
test_cos_sim_2d
(
self
):
def
setUp
(
self
):
op
=
create_op
(
"cos_sim"
)
self
.
op
=
create_op
(
"cos_sim"
)
inputs
=
{
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
10
,
5
)).
astype
(
"float32"
),
'X'
:
np
.
random
.
random
((
10
,
5
)).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
((
10
,
5
)).
astype
(
"float32"
)
'Y'
:
np
.
random
.
random
((
10
,
5
)).
astype
(
"float32"
)
}
}
self
.
compare_grad
(
op
,
inputs
)
def
test_cpu_gpu_compare
(
self
):
self
.
compare_grad
(
self
.
op
,
self
.
inputs
)
def
test_normal
(
self
):
self
.
check_grad
(
self
.
check_grad
(
op
,
inputs
,
set
([
"X"
,
"Y"
])
,
"Out"
,
max_relative_error
=
0.05
)
self
.
op
,
self
.
inputs
,
[
"X"
,
"Y"
]
,
"Out"
,
max_relative_error
=
0.05
)
def
test_cos_sim_3d
(
self
):
def
test_ignore_x
(
self
):
op
=
create_op
(
"cos_sim"
)
self
.
check_grad
(
inputs
=
{
self
.
op
,
'X'
:
np
.
random
.
random
((
10
,
5
,
2
)).
astype
(
"float32"
),
self
.
inputs
,
[
"Y"
],
'Y'
:
np
.
random
.
random
((
10
,
5
,
2
)).
astype
(
"float32"
)
"Out"
,
}
max_relative_error
=
0.05
,
self
.
compare_grad
(
op
,
inputs
)
no_grad_set
=
{
"X"
})
def
test_ignore_y
(
self
):
self
.
check_grad
(
self
.
check_grad
(
op
,
inputs
,
set
([
"X"
,
"Y"
]),
"Out"
,
max_relative_error
=
0.05
)
self
.
op
,
self
.
inputs
,
[
"X"
],
"Out"
,
max_relative_error
=
0.05
,
no_grad_set
=
{
"Y"
})
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
...
...
This diff is collapsed.
Click to expand it.
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