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ed72af48
编写于
9月 01, 2017
作者:
X
Xinghai Sun
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差异文件
Add cos_sim op.
上级
2d31ab5f
变更
9
显示空白变更内容
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并排
Showing
9 changed file
with
267 addition
and
12 deletion
+267
-12
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-1
paddle/operators/cos_sim_op.cc
paddle/operators/cos_sim_op.cc
+91
-0
paddle/operators/cos_sim_op.cu
paddle/operators/cos_sim_op.cu
+22
-0
paddle/operators/cos_sim_op.h
paddle/operators/cos_sim_op.h
+93
-0
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+1
-0
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+1
-0
python/paddle/v2/framework/tests/gradient_checker.py
python/paddle/v2/framework/tests/gradient_checker.py
+14
-7
python/paddle/v2/framework/tests/op_test_util.py
python/paddle/v2/framework/tests/op_test_util.py
+4
-4
python/paddle/v2/framework/tests/test_cos_sim_op.py
python/paddle/v2/framework/tests/test_cos_sim_op.py
+40
-0
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
ed72af48
paddle/operators/cos_sim_op.cc
0 → 100644
浏览文件 @
ed72af48
/* 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/cos_sim_op.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
CosSimOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Input(Y) should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
(),
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
(),
"Dimensions of Input(X) and Input(Y) must be the same."
);
auto
dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
ctx
.
Output
<
Tensor
>
(
"Out"
)
->
Resize
({
dims
[
0
],
1
});
}
};
class
CosSimOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
CosSimOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The first input of cos_sim op."
);
AddInput
(
"Y"
,
"The second input of cos_sim op."
);
AddOutput
(
"Out"
,
"The output of cos_sim op."
);
AddComment
(
R"DOC(
Cosine Similarity Operator.
The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y))
)DOC"
);
}
};
class
CosSimOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Input(Y) should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null."
);
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dims
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
PADDLE_ENFORCE_EQ
(
x_dims
,
y_dims
,
"Dimensions of Input(X) and Input(Y) must be the same."
);
PADDLE_ENFORCE_EQ
(
out_dims
[
0
],
x_dims
[
0
],
"1st dimension of Out@GRAD must equal to Input(X)"
);
PADDLE_ENFORCE_EQ
(
out_dims
[
1
],
1
,
"1st dimension of Out@GRAD must equal to Input(X)"
);
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
x_grad
->
Resize
(
x_dims
);
y_grad
->
Resize
(
y_dims
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
cos_sim
,
ops
::
CosSimOp
,
ops
::
CosSimOpMaker
,
cos_sim_grad
,
ops
::
CosSimOpGrad
);
REGISTER_OP_CPU_KERNEL
(
cos_sim
,
ops
::
CosSimKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
cos_sim_grad
,
ops
::
CosSimGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/cos_sim_op.cu
0 → 100644
浏览文件 @
ed72af48
/* 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/cos_sim_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
cos_sim
,
ops
::
CosSimKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
cos_sim_grad
,
ops
::
CosSimGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/cos_sim_op.h
0 → 100644
浏览文件 @
ed72af48
/* 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/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
Place
,
typename
T
>
class
CosSimKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
z
=
context
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
x
->
dims
();
int
size
=
static_cast
<
int
>
(
framework
::
product
(
dims
));
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
X
=
EigenMatrix
<
T
>::
From
(
*
x
,
new_dims
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
y
,
new_dims
);
auto
Z
=
EigenMatrix
<
T
>::
From
(
*
z
,
new_dims
);
auto
XY
=
(
X
*
Y
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
}));
auto
XX
=
(
X
*
X
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
}));
auto
YY
=
(
Y
*
Y
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
}));
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
Z
.
device
(
place
)
=
XY
/
XX
.
sqrt
()
/
YY
.
sqrt
();
}
};
template
<
typename
Place
,
typename
T
>
class
CosSimGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
z
=
context
.
Input
<
Tensor
>
(
"Out"
);
auto
*
grad_x
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
grad_y
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
grad_z
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
grad_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
x
->
dims
();
int
size
=
static_cast
<
int
>
(
framework
::
product
(
dims
));
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
X
=
EigenMatrix
<
T
>::
From
(
*
x
,
new_dims
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
y
,
new_dims
);
auto
Z
=
EigenMatrix
<
T
>::
From
(
*
z
);
auto
dX
=
EigenMatrix
<
T
>::
From
(
*
grad_x
,
new_dims
);
auto
dY
=
EigenMatrix
<
T
>::
From
(
*
grad_y
,
new_dims
);
auto
dZ
=
EigenMatrix
<
T
>::
From
(
*
grad_z
);
auto
XX
=
(
X
*
X
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
}));
auto
YY
=
(
Y
*
Y
).
sum
(
Eigen
::
array
<
int
,
1
>
({
1
}));
Eigen
::
DSizes
<
int
,
2
>
bcast
(
1
,
dims
[
1
]);
auto
denominator_bcast
=
(
XX
.
sqrt
()
*
YY
.
sqrt
()).
broadcast
(
bcast
);
auto
Z_bcast
=
Z
.
broadcast
(
bcast
);
auto
dZ_bcast
=
dZ
.
broadcast
(
bcast
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
dX
.
device
(
place
)
=
dZ_bcast
*
(
Y
/
denominator_bcast
-
Z_bcast
*
X
/
XX
.
broadcast
(
bcast
));
dY
.
device
(
place
)
=
dZ_bcast
*
(
X
/
denominator_bcast
-
Z_bcast
*
Y
/
YY
.
broadcast
(
bcast
));
// dX.device(place) = X;
// Y.device(place) = Y;
}
};
}
// namespace operators
}
// namespace paddle
paddle/pybind/pybind.cc
浏览文件 @
ed72af48
...
@@ -46,6 +46,7 @@ USE_OP(lookup_table);
...
@@ -46,6 +46,7 @@ USE_OP(lookup_table);
USE_OP
(
scale
);
USE_OP
(
scale
);
USE_OP_ITSELF
(
identity
);
USE_OP_ITSELF
(
identity
);
USE_OP
(
minus
);
USE_OP
(
minus
);
USE_OP
(
cos_sim
);
USE_CPU_ONLY_OP
(
gather
);
USE_CPU_ONLY_OP
(
gather
);
USE_CPU_ONLY_OP
(
scatter
);
USE_CPU_ONLY_OP
(
scatter
);
...
...
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
ed72af48
...
@@ -4,6 +4,7 @@ py_test(test_scope SRCS test_scope.py)
...
@@ -4,6 +4,7 @@ py_test(test_scope SRCS test_scope.py)
py_test
(
test_tensor SRCS test_tensor.py
)
py_test
(
test_tensor SRCS test_tensor.py
)
py_test
(
test_mul_op SRCS test_mul_op.py
)
py_test
(
test_mul_op SRCS test_mul_op.py
)
py_test
(
test_cos_sim_op SRCS test_cos_sim_op.py
)
py_test
(
test_mean_op SRCS test_mean_op.py
)
py_test
(
test_mean_op SRCS test_mean_op.py
)
...
...
python/paddle/v2/framework/tests/gradient_checker.py
浏览文件 @
ed72af48
...
@@ -304,6 +304,13 @@ class GradientChecker(unittest.TestCase):
...
@@ -304,6 +304,13 @@ class GradientChecker(unittest.TestCase):
# get analytical gradients according to different device
# get analytical gradients according to different device
analytic_grads
=
self
.
__get_gradient
(
forward_op
,
backward_op
,
analytic_grads
=
self
.
__get_gradient
(
forward_op
,
backward_op
,
input_vars
,
check_names
,
place
)
input_vars
,
check_names
,
place
)
#print(numeric_grads[0], numeric_grads[0].shape)
print
(
"dim0: "
,
numeric_grads
[
0
],
numeric_grads
[
0
].
shape
)
print
(
"dim0: "
,
analytic_grads
[
0
],
analytic_grads
[
0
].
shape
)
print
(
"---------------------"
)
print
(
"dim1: "
,
numeric_grads
[
1
],
numeric_grads
[
1
].
shape
)
print
(
"dim1: "
,
analytic_grads
[
1
],
analytic_grads
[
1
].
shape
)
assert
False
self
.
__assert_is_close
(
numeric_grads
,
analytic_grads
,
check_names
,
self
.
__assert_is_close
(
numeric_grads
,
analytic_grads
,
check_names
,
max_relative_error
,
max_relative_error
,
"Gradient Check On %s"
%
str
(
place
))
"Gradient Check On %s"
%
str
(
place
))
python/paddle/v2/framework/tests/op_test_util.py
浏览文件 @
ed72af48
python/paddle/v2/framework/tests/test_cos_sim_op.py
0 → 100644
浏览文件 @
ed72af48
import
unittest
import
numpy
as
np
from
gradient_checker
import
GradientChecker
,
create_op
from
op_test_util
import
OpTestMeta
class
TestCosSimOp
(
unittest
.
TestCase
):
__metaclass__
=
OpTestMeta
def
setUp
(
self
):
self
.
type
=
"cos_sim"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
84
)).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
((
32
,
84
)).
astype
(
"float32"
)
}
expect
=
(
self
.
inputs
[
'X'
]
*
self
.
inputs
[
'Y'
]).
sum
(
axis
=
1
)
/
\
np
.
linalg
.
norm
(
self
.
inputs
[
'X'
],
axis
=
1
)
/
\
np
.
linalg
.
norm
(
self
.
inputs
[
'Y'
],
axis
=
1
)
expect
=
np
.
expand_dims
(
expect
,
1
)
self
.
outputs
=
{
'Out'
:
expect
}
class
CosSimGradOpTest
(
GradientChecker
):
def
test_cos_sim
(
self
):
op
=
create_op
(
"cos_sim"
)
#inputs = {
#'X': np.random.random((2, 2)).astype("float32"),
#'Y': np.random.random((2, 2)).astype("float32")
#}
inputs
=
{
'X'
:
np
.
array
([[
0.9
,
0.6
],
[
1.9
,
1.6
]]).
astype
(
"float32"
),
'Y'
:
np
.
array
([[
0.7
,
0.8
],
[
1.7
,
1.8
]]).
astype
(
"float32"
)
}
print
(
inputs
)
self
.
check_grad
(
op
,
inputs
,
set
([
"X"
,
"Y"
]),
"Out"
,
max_relative_error
=
0.5
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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