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03ea7320
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03ea7320
编写于
9月 13, 2017
作者:
X
Xinghai Sun
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Update cos_sim operator by following reviewer's comments.
上级
16fddf32
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
65 addition
and
59 deletion
+65
-59
paddle/operators/cos_sim_op.cc
paddle/operators/cos_sim_op.cc
+38
-28
paddle/operators/cos_sim_op.h
paddle/operators/cos_sim_op.h
+27
-31
未找到文件。
paddle/operators/cos_sim_op.cc
浏览文件 @
03ea7320
...
...
@@ -32,17 +32,18 @@ class CosSimOp : public framework::OperatorWithKernel {
// shape check
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dims
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
PADDLE_ENFORCE_EQ
(
framework
::
arity
(
x_dims
),
framework
::
arity
(
y_dims
),
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
y_dims
.
size
(),
"Ranks of Input(X) and Input(Y) must be equal."
);
PADDLE_ENFORCE_GE
(
framework
::
arity
(
x_dims
),
2
,
PADDLE_ENFORCE_GE
(
x_dims
.
size
(
),
2
,
"Rank of Input(X) must not be less than 2."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
1
,
framework
::
arity
(
x_dims
)),
framework
::
slice_ddim
(
y_dims
,
1
,
framework
::
arity
(
y_dims
)),
"All dimensions except 1st of Input(X) and Input(Y)
must be equal."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
1
,
x_dims
.
size
()),
framework
::
slice_ddim
(
y_dims
,
1
,
y_dims
.
size
(
)),
"All dimensions except the 1st of Input(X) and Input(Y) "
"
must be equal."
);
PADDLE_ENFORCE
(
x_dims
[
0
]
==
y_dims
[
0
]
||
y_dims
[
0
]
==
1
,
"
1st dimension of Input(Y) must be equal to Input(X) or
"
"just 1 (which will be broadcasted to match Input(X))."
);
"
The 1st dimension of Input(Y) must be equal to Input(X) or
"
"
just 1 (which will be broadcasted to match Input(X))."
);
// resize tensor
ctx
.
Output
<
Tensor
>
(
"Out"
)
->
Resize
({
x_dims
[
0
],
1
});
...
...
@@ -58,8 +59,14 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"X"
,
"The 1st input of cos_sim op."
);
AddInput
(
"Y"
,
"The 2nd input of cos_sim op."
);
AddOutput
(
"Out"
,
"The output of cos_sim op."
);
AddOutput
(
"XNorm"
,
"Row norm of the first input."
).
AsIntermediate
();
AddOutput
(
"YNorm"
,
"Row norm of the second input."
).
AsIntermediate
();
AddOutput
(
"XNorm"
,
"Norm of the first input, reduced along the 1st "
"dimension."
)
.
AsIntermediate
();
AddOutput
(
"YNorm"
,
"Norm of the second input, reduced along the 1st "
"dimension."
)
.
AsIntermediate
();
AddComment
(
R"DOC(
Cosine Similarity Operator.
...
...
@@ -95,29 +102,32 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
// shape check
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dims
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
PADDLE_ENFORCE_GE
(
framework
::
arity
(
x_dims
),
framework
::
arity
(
y_dims
),
"Ranks of Input(X) and Input(Y) must be equal."
);
PADDLE_ENFORCE_GE
(
framework
::
arity
(
x_dims
),
2
,
"Rank of Input(X) must not be less than 2."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
1
,
framework
::
arity
(
x_dims
)),
framework
::
slice_ddim
(
y_dims
,
1
,
framework
::
arity
(
y_dims
)),
"All dimensions except 1st of Input(X) and Input(Y) must be equal."
);
PADDLE_ENFORCE
(
x_dims
[
0
]
==
y_dims
[
0
]
||
y_dims
[
0
]
==
1
,
"1st dimension of Input(Y) must be equal to Input(X) or "
"just 1 (which will be broadcasted to match Input(X))."
);
auto
xnorm_dims
=
ctx
.
Input
<
Tensor
>
(
"XNorm"
)
->
dims
();
PADDLE_ENFORCE_EQ
(
xnorm_dims
,
framework
::
make_ddim
({
x_dims
[
0
],
1
}),
"Shape of Input(XNorm) must be [X.Dim(0), 1]."
);
auto
ynorm_dims
=
ctx
.
Input
<
Tensor
>
(
"YNorm"
)
->
dims
();
PADDLE_ENFORCE_EQ
(
ynorm_dims
,
framework
::
make_ddim
({
y_dims
[
0
],
1
}),
"Shape of Input(YNorm) must be [Y.Dim(0), 1]."
);
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
"Out"
)
->
dims
();
PADDLE_ENFORCE_EQ
(
out_dims
,
framework
::
make_ddim
({
x_dims
[
0
],
1
}),
"Shape of Input(Out) must be [X.Dim(0), 1]."
);
auto
out_grad_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
PADDLE_ENFORCE_EQ
(
out_grad_dims
,
framework
::
make_ddim
({
x_dims
[
0
],
1
}),
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
y_dims
.
size
(),
"Ranks of Input(X) and Input(Y) must be equal."
);
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
"Rank of Input(X) must not be less than 2."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
1
,
x_dims
.
size
()),
framework
::
slice_ddim
(
y_dims
,
1
,
y_dims
.
size
()),
"All dimensions except the 1st of Input(X) and Input(Y) "
"must be equal."
);
PADDLE_ENFORCE
(
x_dims
[
0
]
==
y_dims
[
0
]
||
y_dims
[
0
]
==
1
,
"The 1st dimension of Input(Y) must be equal to Input(X) or"
" just 1 (which will be broadcasted to match Input(X))."
);
auto
target_xnorm_dims
=
framework
::
make_ddim
({
x_dims
[
0
],
1
}),
auto
target_ynorm_dims
=
framework
::
make_ddim
({
y_dims
[
0
],
1
}),
PADDLE_ENFORCE_EQ
(
xnorm_dims
,
target_xnorm_dims
,
"Shape of Input(XNorm) must be [X.Dim(0), 1]."
);
PADDLE_ENFORCE_EQ
(
ynorm_dims
,
target_ynorm_dims
,
"Shape of Input(YNorm) must be [Y.Dim(0), 1]."
);
PADDLE_ENFORCE_EQ
(
out_dims
,
target_xnorm_dims
,
"Shape of Input(Out) must be [X.Dim(0), 1]."
);
PADDLE_ENFORCE_EQ
(
out_grad_dims
,
target_xnorm_dims
,
"Shape of Input(Out@Grad) must be [X.Dim(0), 1]."
);
// resize tensor
...
...
paddle/operators/cos_sim_op.h
浏览文件 @
03ea7320
...
...
@@ -42,22 +42,23 @@ class CosSimKernel : public framework::OpKernel {
int
rows_x
=
in_x
->
dims
()[
0
];
int
rows_y
=
in_y
->
dims
()[
0
];
int
cols
=
framework
::
product
(
in_x
->
dims
())
/
rows_x
;
auto
x
=
EigenMatrix
<
T
>::
From
(
*
in_x
,
framework
::
make_ddim
({
rows_x
,
cols
})
);
auto
y
=
EigenMatrix
<
T
>::
From
(
*
in_y
,
framework
::
make_ddim
({
rows_y
,
cols
})
);
auto
x
=
EigenMatrix
<
T
>::
Reshape
(
*
in_x
,
1
);
auto
y
=
EigenMatrix
<
T
>::
Reshape
(
*
in_y
,
1
);
auto
z
=
EigenMatrix
<
T
>::
From
(
*
out_z
);
auto
x_norm
=
EigenMatrix
<
T
>::
From
(
*
out_x_norm
);
auto
y_norm
=
EigenMatrix
<
T
>::
From
(
*
out_y_norm
);
// compute
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
x_norm
.
device
(
place
)
=
x
.
square
().
sum
(
Eigen
::
array
<
int
,
1
>
({
1
})).
sqrt
();
y_norm
.
device
(
place
)
=
y
.
square
().
sum
(
Eigen
::
array
<
int
,
1
>
({
1
})).
sqrt
();
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
(
Eigen
::
array
<
int
,
1
>
({
1
})
);
auto
xy
=
(
x
*
y
.
broadcast
(
bcast
)).
sum
(
row_along
);
z
.
device
(
place
)
=
xy
/
x_norm
/
y_norm
.
broadcast
(
bcast
);
}
}
...
...
@@ -78,61 +79,56 @@ class CosSimGradKernel : public framework::OpKernel {
auto
*
in_grad_z
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
// convert Tensor to Eigen Tensor
int
rows_x
=
in_x
->
dims
()[
0
];
int
rows_y
=
in_y
->
dims
()[
0
];
int
cols
=
framework
::
product
(
in_x
->
dims
())
/
rows_x
;
auto
x
=
EigenMatrix
<
T
>::
From
(
*
in_x
,
framework
::
make_ddim
({
rows_x
,
cols
}));
auto
y
=
EigenMatrix
<
T
>::
From
(
*
in_y
,
framework
::
make_ddim
({
rows_y
,
cols
}));
auto
x
=
EigenMatrix
<
T
>::
Reshape
(
*
in_x
,
1
);
auto
y
=
EigenMatrix
<
T
>::
Reshape
(
*
in_y
,
1
);
auto
z
=
EigenMatrix
<
T
>::
From
(
*
in_z
);
auto
x_norm
=
EigenMatrix
<
T
>::
From
(
*
in_x_norm
);
auto
y_norm
=
EigenMatrix
<
T
>::
From
(
*
in_y_norm
);
auto
dz
=
EigenMatrix
<
T
>::
From
(
*
in_grad_z
);
// compute gradident
Eigen
::
DSizes
<
int
,
2
>
bcast
(
1
,
cols
);
auto
z_bcast
=
z
.
broadcast
(
bcast
);
auto
dz_bcast
=
dz
.
broadcast
(
bcast
);
auto
x_snorm_bcast
=
x_norm
.
square
().
eval
().
broadcast
(
bcast
);
int
rows_x
=
in_x
->
dims
()[
0
];
int
rows_y
=
in_y
->
dims
()[
0
];
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
place
=
context
.
GetEigenDevice
<
Place
>
();
if
(
rows_x
==
rows_y
)
{
auto
y_snorm_bcast
=
y_norm
.
square
().
eval
().
broadcast
(
bcast
);
auto
norm_prod_bcast
=
(
x_norm
*
y_norm
).
eval
().
broadcast
(
bcast
);
auto
y_snorm_bcast
=
y_norm
.
square
().
eval
().
broadcast
(
bcast
_cols
);
auto
norm_prod_bcast
=
(
x_norm
*
y_norm
).
eval
().
broadcast
(
bcast
_cols
);
// compute dx
if
(
out_grad_x
)
{
out_grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dx
=
EigenMatrix
<
T
>::
From
(
*
out_grad_x
,
framework
::
make_ddim
({
rows_x
,
cols
}));
auto
dx
=
EigenMatrix
<
T
>::
Reshape
(
*
out_grad_x
,
1
);
auto
grad
=
y
/
norm_prod_bcast
-
z_bcast
*
x
/
x_snorm_bcast
;
dx
.
device
(
place
)
=
dz_bcast
*
grad
;
}
// compute dy
if
(
out_grad_y
)
{
out_grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dy
=
EigenMatrix
<
T
>::
From
(
*
out_grad_y
,
framework
::
make_ddim
({
rows_y
,
cols
}));
auto
grad
=
x
/
norm_prod_bcast
-
z_bcast
*
y
/
y_snorm_bcast
;
auto
dy
=
EigenMatrix
<
T
>::
Reshape
(
*
out_grad_y
,
1
)
auto
grad
=
x
/
norm_prod_bcast
-
z_bcast
*
y
/
y_snorm_bcast
;
dy
.
device
(
place
)
=
dz_bcast
*
grad
;
}
}
else
{
Eigen
::
DSizes
<
int
,
2
>
bcast_row
(
rows_x
,
1
);
auto
y_bcast
=
y
.
broadcast
(
bcast_row
);
auto
y_snorm_bcast
=
y_norm
.
square
().
eval
().
broadcast
(
bcast_row
).
eval
().
broadcast
(
bcast
);
auto
norm_prod_bcast
=
(
x_norm
*
y_norm
.
broadcast
(
bcast_row
)).
eval
().
broadcast
(
bcast
);
Eigen
::
DSizes
<
int
,
2
>
bcast_rows
(
rows_x
,
1
);
Eigen
::
DSizes
<
int
,
2
>
bcast_rows_cols
(
rows_x
,
1
);
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
.
broadcast
(
bcast_rows_cols
);
// compute dx
if
(
out_grad_x
)
{
out_grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dx
=
EigenMatrix
<
T
>::
From
(
*
out_grad_x
,
framework
::
make_ddim
({
rows_x
,
cols
}));
auto
dx
=
EigenMatrix
<
T
>::
Reshape
(
*
out_grad_x
,
1
);
auto
grad
=
y_bcast
/
norm_prod_bcast
-
z_bcast
*
x
/
x_snorm_bcast
;
dx
.
device
(
place
)
=
dz_bcast
*
grad
;
}
// compute dy
if
(
out_grad_y
)
{
out_grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dy
=
EigenMatrix
<
T
>::
From
(
*
out_grad_y
,
framework
::
make_ddim
({
rows_y
,
cols
}));
auto
dy
=
EigenMatrix
<
T
>::
Reshape
(
*
out_grad_y
,
1
);
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
}));
}
...
...
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