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29065d43
编写于
9月 29, 2017
作者:
T
Tao Luo
提交者:
GitHub
9月 29, 2017
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差异文件
Merge pull request #4503 from luotao1/remove_rowwise_add_op
remove rowwise_add_op
上级
99130c6e
bb7f5558
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
1 addition
and
264 deletion
+1
-264
paddle/operators/fc_op.cc
paddle/operators/fc_op.cc
+1
-1
paddle/operators/rowwise_add_op.cc
paddle/operators/rowwise_add_op.cc
+0
-109
paddle/operators/rowwise_add_op.cu
paddle/operators/rowwise_add_op.cu
+0
-23
paddle/operators/rowwise_add_op.h
paddle/operators/rowwise_add_op.h
+0
-80
python/paddle/v2/framework/tests/test_rowwise_add_op.py
python/paddle/v2/framework/tests/test_rowwise_add_op.py
+0
-51
未找到文件。
paddle/operators/fc_op.cc
浏览文件 @
29065d43
...
...
@@ -100,7 +100,7 @@ class FCOp : public NetOp {
add_out
=
Output
(
"AddOut"
);
AppendOp
(
framework
::
OpRegistry
::
CreateOp
(
"
rowwise_add"
,
{{
"X"
,
{
sum_out
}},
{
"b
"
,
{
Input
(
"B"
)}}},
"
elementwise_add"
,
{{
"X"
,
{
sum_out
}},
{
"Y
"
,
{
Input
(
"B"
)}}},
{{
"Out"
,
{
add_out
}}},
{}));
}
else
{
if
(
Output
(
"AddOut"
)
!=
framework
::
kEmptyVarName
)
{
...
...
paddle/operators/rowwise_add_op.cc
已删除
100644 → 0
浏览文件 @
99130c6e
/* 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/rowwise_add_op.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
RowwiseAddOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of RowwiseAddOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"b"
),
"Input(b) of RowwiseAddOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of RowwiseAddOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
b_dims
=
ctx
->
GetInputDim
(
"b"
);
PADDLE_ENFORCE_GT
(
x_dims
.
size
(),
b_dims
.
size
(),
"The rank of input `X` must be larger than the one of input `b`."
);
int
num_col_dims
=
x_dims
.
size
()
-
b_dims
.
size
();
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
num_col_dims
,
x_dims
.
size
()),
b_dims
,
"The width of two operands must be same"
);
PADDLE_ENFORCE_EQ
(
ctx
->
Outputs
(
"Out"
).
size
(),
1
,
"The output size must be 1"
);
ctx
->
SetOutputDim
(
"Out"
,
x_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
class
RowwiseAddOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
RowwiseAddOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The left input of row-wise add op, must be matrix"
);
AddInput
(
"b"
,
"The right input of row-wise add op, must be vector"
);
AddOutput
(
"Out"
,
"The output of row-wise add op"
);
AddComment
(
R"DOC(Row-wise Add operator
for i in xrange(X.shape[0]):
Out = X[i] + b
)DOC"
);
}
};
class
RowwiseAddGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"X should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"b"
),
"b should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
b_dims
=
ctx
->
GetInputDim
(
"b"
);
PADDLE_ENFORCE_GT
(
x_dims
.
size
(),
b_dims
.
size
(),
"The rank of input `X` must be larger than the one of input `b`."
);
int64_t
num_col_dims
=
x_dims
.
size
()
-
b_dims
.
size
();
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
num_col_dims
,
x_dims
.
size
()),
b_dims
,
"The width of two operands must be same"
);
auto
x_grad_name
=
framework
::
GradVarName
(
"X"
);
auto
b_grad_name
=
framework
::
GradVarName
(
"b"
);
if
(
ctx
->
HasOutput
(
x_grad_name
))
{
ctx
->
SetOutputDim
(
x_grad_name
,
x_dims
);
}
if
(
ctx
->
HasOutput
(
b_grad_name
))
{
ctx
->
SetOutputDim
(
b_grad_name
,
b_dims
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
rowwise_add
,
ops
::
RowwiseAddOp
,
ops
::
RowwiseAddOpMaker
,
rowwise_add_grad
,
ops
::
RowwiseAddGradOp
);
REGISTER_OP_CPU_KERNEL
(
rowwise_add
,
ops
::
RowwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
rowwise_add_grad
,
ops
::
RowwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/rowwise_add_op.cu
已删除
100644 → 0
浏览文件 @
99130c6e
/* 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/rowwise_add_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
rowwise_add
,
ops
::
RowwiseAddKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
rowwise_add_grad
,
ops
::
RowwiseAddGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/rowwise_add_op.h
已删除
100644 → 0
浏览文件 @
99130c6e
/* 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
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
Place
,
typename
T
>
class
RowwiseAddKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
num_col_dims
=
context
.
Input
<
Tensor
>
(
"X"
)
->
dims
().
size
()
-
context
.
Input
<
Tensor
>
(
"b"
)
->
dims
().
size
();
auto
input
=
EigenMatrix
<
T
>::
Reshape
(
*
context
.
Input
<
Tensor
>
(
"X"
),
num_col_dims
);
auto
bias
=
EigenVector
<
T
>::
Flatten
(
*
context
.
Input
<
Tensor
>
(
"b"
));
auto
output
=
EigenMatrix
<
T
>::
Reshape
(
*
out
,
num_col_dims
);
const
int
bias_size
=
bias
.
dimension
(
0
);
const
int
rest_size
=
input
.
size
()
/
bias_size
;
Eigen
::
DSizes
<
int
,
1
>
one_d
(
input
.
size
());
Eigen
::
DSizes
<
int
,
1
>
bcast
(
rest_size
);
output
.
reshape
(
one_d
).
device
(
context
.
GetEigenDevice
<
Place
>
())
=
input
.
reshape
(
one_d
)
+
bias
.
broadcast
(
bcast
).
reshape
(
one_d
);
}
};
template
<
typename
Place
,
typename
T
>
class
RowwiseAddGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
dout
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dx
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
db
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"b"
));
int
num_col_dims
=
context
.
Input
<
Tensor
>
(
"X"
)
->
dims
().
size
()
-
context
.
Input
<
Tensor
>
(
"b"
)
->
dims
().
size
();
auto
out_grad
=
EigenMatrix
<
T
>::
Reshape
(
*
dout
,
num_col_dims
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
if
(
dx
)
{
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
());
EigenMatrix
<
T
>::
Reshape
(
*
dx
,
num_col_dims
).
device
(
place
)
=
out_grad
;
}
if
(
db
)
{
db
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
// colwise add
Eigen
::
array
<
int
,
1
>
dims
{{
0
}};
/* dimension to reduce */
EigenVector
<
T
>::
Flatten
(
*
db
).
device
(
place
)
=
out_grad
.
sum
(
dims
);
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_rowwise_add_op.py
已删除
100644 → 0
浏览文件 @
99130c6e
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestRowwiseAddOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"rowwise_add"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
5
,
10
]).
astype
(
"float32"
),
'b'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
10
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
add
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'b'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'b'
],
'Out'
)
def
test_check_grad_ingore_b
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
no_grad_set
=
set
(
'b'
))
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
([
'b'
],
'Out'
,
no_grad_set
=
set
(
'X'
))
class
TestRowwiseAddOp2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"rowwise_add"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
3
,
2
,
5
]).
astype
(
"float32"
),
'b'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
5
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
add
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'b'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'b'
],
'Out'
)
def
test_check_grad_ignore_b
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
no_grad_set
=
set
(
'b'
))
def
test_check_grad_ignore_x
(
self
):
self
.
check_grad
([
'b'
],
'Out'
,
no_grad_set
=
set
(
'X'
))
if
__name__
==
"__main__"
:
unittest
.
main
()
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