Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
暴走的小小菜鸟
Paddle
提交
ed72af48
P
Paddle
项目概览
暴走的小小菜鸟
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
ed72af48
编写于
9月 01, 2017
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
Add cos_sim op.
上级
2d31ab5f
变更
9
隐藏空白更改
内联
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
...
@@ -56,7 +56,7 @@ list(REMOVE_ITEM GENERAL_OPS
...
@@ -56,7 +56,7 @@ list(REMOVE_ITEM GENERAL_OPS
op_library
(
net_op SRCS net_op.cc
)
op_library
(
net_op SRCS net_op.cc
)
op_library
(
minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op
)
op_library
(
minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op
)
op_library
(
mul_op SRCS mul_op.cc mul_op.cu DEPS math_function
)
op_library
(
mul_op SRCS mul_op.cc mul_op.cu DEPS math_function
)
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op
)
DEPS framework_proto tensor operator net_op
)
op_library
(
scale_op SRCS scale_op.cc scale_op.cu DEPS net_op
)
op_library
(
scale_op SRCS scale_op.cc scale_op.cu DEPS net_op
)
...
...
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
...
@@ -36,13 +36,13 @@ def get_numeric_gradient(op,
...
@@ -36,13 +36,13 @@ def get_numeric_gradient(op,
in_place
=
False
):
in_place
=
False
):
"""
"""
Get Numeric Gradient for an operator's input.
Get Numeric Gradient for an operator's input.
:param op: C++ operator instance, could be an network
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
:param input_values: The input variables. Should be an dictionary, key is
variable name. Value is numpy array.
variable name. Value is numpy array.
:param output_name: The final output variable name.
:param output_name: The final output variable name.
:param input_to_check: The input variable need to get gradient.
:param input_to_check: The input variable need to get gradient.
:param delta: The perturbation value for numeric gradient method. The
:param delta: The perturbation value for numeric gradient method. The
smaller delta is, the more accurate result will get. But if that delta is
smaller delta is, the more accurate result will get. But if that delta is
too small, it could occur numerical stability problem.
too small, it could occur numerical stability problem.
:param local_scope: The local scope used for get_numeric_gradient.
:param local_scope: The local scope used for get_numeric_gradient.
...
@@ -229,9 +229,9 @@ class GradientChecker(unittest.TestCase):
...
@@ -229,9 +229,9 @@ class GradientChecker(unittest.TestCase):
"""Use relative error for the comparison.
"""Use relative error for the comparison.
:param numeric_grads: the numerical graidents.
:param numeric_grads: the numerical graidents.
:type numeric_grads: a list of numpy.array
:type numeric_grads: a list of numpy.array
:param analytic_grads: the analytical graidents.
:param analytic_grads: the analytical graidents.
:type analytic_grads: a list of numpy.array
:type analytic_grads: a list of numpy.array
:param name: the names of gradients, used to print for debug.
:param name: the names of gradients, used to print for debug.
:type names: a list of string
:type names: a list of string
:param msg_prefix: string info, used to print for debug.
:param msg_prefix: string info, used to print for debug.
...
@@ -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
...
@@ -6,13 +6,13 @@ from paddle.v2.framework.op import Operator
...
@@ -6,13 +6,13 @@ from paddle.v2.framework.op import Operator
class
OpTestMeta
(
type
):
class
OpTestMeta
(
type
):
"""
"""
Operator Test ClassMeta.
Operator Test ClassMeta.
It injects `test_all` method into user's OperatorTest class, to make Python
It injects `test_all` method into user's OperatorTest class, to make Python
unittest module run that method.
unittest module run that method.
The `test_all` read what value is stored in `self`. It use self's values to
The `test_all` read what value is stored in `self`. It use self's values to
create and run a operator, and check whether that op is OK or not.
create and run a operator, and check whether that op is OK or not.
See `test_add_two_op` for example usage.
See `test_add_two_op` for example usage.
"""
"""
...
...
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
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录