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e9495e76
编写于
10月 11, 2017
作者:
C
Cao Ying
提交者:
GitHub
10月 11, 2017
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差异文件
Merge pull request #4508 from Yancey1989/seqconcat_op
Add the sequence_concat operator.
上级
4b1f70d9
d68122ff
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
386 addition
and
0 deletion
+386
-0
paddle/operators/sequence_concat_op.cc
paddle/operators/sequence_concat_op.cc
+129
-0
paddle/operators/sequence_concat_op.cu
paddle/operators/sequence_concat_op.cu
+25
-0
paddle/operators/sequence_concat_op.h
paddle/operators/sequence_concat_op.h
+155
-0
python/paddle/v2/framework/tests/test_seq_concat_op.py
python/paddle/v2/framework/tests/test_seq_concat_op.py
+77
-0
未找到文件。
paddle/operators/sequence_concat_op.cc
0 → 100644
浏览文件 @
e9495e76
/* 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/sequence_concat_op.h"
namespace
paddle
{
namespace
operators
{
class
SequenceConcatOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
"X"
),
"Inputs(X) of SequenceConcatOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceConcatOp should not be null."
);
const
size_t
level
=
static_cast
<
size_t
>
(
ctx
->
Attrs
().
Get
<
int
>
(
"level"
));
const
size_t
axis
=
static_cast
<
size_t
>
(
ctx
->
Attrs
().
Get
<
int
>
(
"axis"
));
PADDLE_ENFORCE
(
level
==
0UL
||
level
==
1UL
,
"The sequence_concat operator only accepts sequence "
"or a nested sequence as its input."
);
auto
ins_dims
=
ctx
->
GetInputsDim
(
"X"
);
framework
::
DDim
out_dims
=
ins_dims
[
0
];
const
size_t
n
=
ins_dims
.
size
();
for
(
size_t
i
=
1
;
i
<
n
;
++
i
)
{
out_dims
[
axis
]
+=
ins_dims
[
i
][
axis
];
}
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
}
};
class
SequenceConcatOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SequenceConcatOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(A vector of LoDTensor), the input is a vector of LoDTensor, "
"each of which is a variable-length sequence or nested sequence."
)
.
AsDuplicable
();
AddOutput
(
"Out"
,
"(A LoDTensor), the variable-length output of "
"sequence_concat Op."
);
AddAttr
<
int
>
(
"axis"
,
"(int, default 0)"
"The axis which the inputs will be joined with. "
"If axis is 0, the inputs will be joined with LoD index."
)
.
SetDefault
(
0
);
AddAttr
<
int
>
(
"level"
,
"(int, default 0)"
"The level at which the inputs will be joined. "
"If the level is 0, the inputs will be joined at the nested "
"sequence level. "
"If the level is 1, the inputs will be joined at the "
"sequence level. "
"The level should be less than the level number of inputs."
)
.
SetDefault
(
0
);
AddComment
(
R"DOC(
The sequence_concat operator concatenates multiple LoDTensors.
It only supports sequence (LoD Tensor with level number is 1)
or a nested sequence (LoD tensor with level number is 2) as its input.
- Case1:
If the axis is other than 0(here, axis is 1 and level is 1),
each input should have the same LoD information and the LoD
information of the output keeps the same as the input.
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
- Case2:
If the axis is 0(here, leve is 0), the inputs are concatenated along
time steps, the LoD information of the output need to re-compute.
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,3,5}, {0,1,2,3,5}}; Dims(x1) = (5,3,4)
LoD(Out) = {{0,5,9}, {0,1,2,3,4,5,6,7,9}}; Dims(Out) = (9,3,4)
- Case3:
If the axis is 0(here, level is 1).
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,3,5}, {0,1,3,4,5}}; Dims(x1) = (5,3,4)
LoD(Out) = {{0,5,9}, {0,2,5,7,9}}; Dims(Out) = (9,3,4)
NOTE: The levels of all the inputs should be the same.
)DOC"
);
}
};
class
SequenceConcatGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"The gradient of Out should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
"X"
)),
"The gradient of X should not be null."
);
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputsDim
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sequence_concat
,
ops
::
SequenceConcatOp
,
ops
::
SequenceConcatOpMaker
,
sequence_concat_grad
,
ops
::
SequenceConcatGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_concat
,
ops
::
SequenceConcatOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_concat_grad
,
ops
::
SequenceConcatGradOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/sequence_concat_op.cu
0 → 100644
浏览文件 @
e9495e76
/* 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/sequence_concat_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sequence_concat
,
ops
::
SequenceConcatOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
sequence_concat_grad
,
ops
::
SequenceConcatGradOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/sequence_concat_op.h
0 → 100644
浏览文件 @
e9495e76
/* 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/op_registry.h"
#include "paddle/operators/strided_memcpy.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
using
LoD
=
framework
::
LoD
;
template
<
typename
T
>
LoD
concatLoD
(
const
std
::
vector
<
const
T
*>
ins
,
const
size_t
axis
,
const
size_t
level
)
{
auto
out_lod
=
ins
[
0
]
->
lod
();
const
size_t
n
=
ins
.
size
();
if
(
axis
==
0UL
)
{
for
(
size_t
i
=
1
;
i
<
n
;
++
i
)
{
for
(
size_t
j
=
0
;
j
<
ins
[
i
]
->
lod
()[
0
].
size
();
++
j
)
{
out_lod
[
0
][
j
]
+=
ins
[
i
]
->
lod
()[
0
][
j
];
}
if
(
ins
[
0
]
->
NumLevels
()
==
2
)
{
for
(
size_t
j
=
1
;
j
<
ins
[
i
]
->
lod
()[
1
].
size
();
++
j
)
{
if
(
level
==
0UL
)
{
out_lod
[
1
].
push_back
(
out_lod
[
1
].
back
()
+
ins
[
i
]
->
lod
()[
1
][
j
]
-
ins
[
i
]
->
lod
()[
1
][
j
-
1
]);
}
else
if
(
level
==
1UL
)
{
out_lod
[
1
][
j
]
+=
ins
[
1
]
->
lod
()[
1
][
j
];
}
}
}
}
}
return
out_lod
;
}
template
<
typename
Place
,
typename
T
>
class
SequenceConcatOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
ins
=
ctx
.
MultiInput
<
LoDTensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
const
size_t
axis
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
const
size_t
level
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"level"
));
const
size_t
n
=
ins
.
size
();
for
(
size_t
i
=
1
;
i
<
n
;
++
i
)
{
PADDLE_ENFORCE_EQ
(
ins
[
0
]
->
NumLevels
(),
ins
[
i
]
->
NumLevels
(),
"The levels of all the input LoDTensors "
"should be the same."
);
PADDLE_ENFORCE_EQ
(
ins
[
0
]
->
dims
().
size
(),
ins
[
i
]
->
dims
().
size
(),
"The dimension size of all the input LoDTensors "
"should be the same."
);
const
size_t
dims_size
=
ins
[
i
]
->
dims
().
size
();
for
(
size_t
j
=
0
;
j
<
dims_size
;
++
j
)
{
if
(
j
==
axis
)
continue
;
PADDLE_ENFORCE_EQ
(
ins
[
0
]
->
dims
()[
j
],
ins
[
i
]
->
dims
()[
j
],
"Except for the dimension of the specified "
"axis along which all the inputs are concatenated, "
"dimensions of all the other axises of the input "
"LoDTensors should be the same."
);
}
}
PADDLE_ENFORCE_GT
(
ins
[
0
]
->
NumLevels
(),
level
,
"The levels of all the input LoDTensors "
"should be greater than the specify level"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
out_lod
=
concatLoD
<
LoDTensor
>
(
ins
,
axis
,
level
);
out
->
set_lod
(
out_lod
);
auto
out_lod_level
=
out_lod
[
level
];
for
(
size_t
i
=
0
;
i
<
out_lod_level
.
size
()
-
1
;
++
i
)
{
Tensor
out_t
=
out
->
Slice
<
T
>
(
static_cast
<
int
>
(
out_lod_level
[
i
]),
static_cast
<
int
>
(
out_lod_level
[
i
+
1
]));
auto
out_stride
=
framework
::
stride
(
out_t
.
dims
());
size_t
offset
=
0
;
for
(
size_t
j
=
0
;
j
<
n
;
++
j
)
{
auto
in_lod_level
=
ins
[
j
]
->
lod
()[
level
];
auto
in_stride
=
framework
::
stride
(
ins
[
j
]
->
dims
());
Tensor
in_t
=
ins
[
j
]
->
Slice
<
T
>
(
static_cast
<
int
>
(
in_lod_level
[
i
]),
static_cast
<
int
>
(
in_lod_level
[
i
+
1
]));
size_t
axis_dim
=
in_t
.
dims
()[
axis
];
StridedMemcpy
<
T
>
(
ctx
.
device_context
(),
in_t
.
data
<
T
>
(),
in_stride
,
in_t
.
dims
(),
out_stride
,
out_t
.
data
<
T
>
()
+
offset
);
offset
+=
axis_dim
*
in_stride
[
axis
];
}
}
}
};
template
<
typename
Place
,
typename
T
>
class
SequenceConcatGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
ins
=
ctx
.
MultiInput
<
framework
::
LoDTensor
>
(
"X"
);
auto
*
out_grad
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
x_grads
=
ctx
.
MultiOutput
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
size_t
axis
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
size_t
level
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"level"
));
const
size_t
n
=
x_grads
.
size
();
// Set Grad(X) LoD as X
for
(
size_t
i
=
0
;
i
<
n
;
i
++
)
{
x_grads
[
i
]
->
set_lod
(
ins
[
i
]
->
lod
());
x_grads
[
i
]
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
auto
out_lod
=
concatLoD
<
LoDTensor
>
(
ins
,
axis
,
level
);
auto
out_lod_level
=
out_lod
[
level
];
for
(
size_t
i
=
0
;
i
<
out_lod_level
.
size
()
-
1
;
++
i
)
{
Tensor
out_grad_t
=
out_grad
->
Slice
<
T
>
(
static_cast
<
int
>
(
out_lod_level
[
i
]),
static_cast
<
int
>
(
out_lod_level
[
i
+
1
]));
auto
out_grad_stride
=
framework
::
stride
(
out_grad_t
.
dims
());
size_t
offset
=
0
;
for
(
size_t
j
=
0
;
j
<
n
;
++
j
)
{
auto
x_grad_lod_level
=
x_grads
[
j
]
->
lod
()[
level
];
auto
x_grad_stride
=
framework
::
stride
(
x_grads
[
j
]
->
dims
());
Tensor
x_grad_t
=
x_grads
[
j
]
->
Slice
<
T
>
(
static_cast
<
int
>
(
x_grad_lod_level
[
i
]),
static_cast
<
int
>
(
x_grad_lod_level
[
i
+
1
]));
size_t
axis_dim
=
x_grad_t
.
dims
()[
axis
];
StridedMemcpy
<
T
>
(
ctx
.
device_context
(),
out_grad_t
.
data
<
T
>
()
+
offset
,
out_grad_stride
,
out_grad_t
.
dims
(),
x_grad_stride
,
x_grad_t
.
data
<
T
>
());
offset
+=
axis_dim
*
out_grad_stride
[
axis
];
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_seq_concat_op.py
0 → 100644
浏览文件 @
e9495e76
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestConcatOp
(
OpTest
):
def
set_data
(
self
):
# two level, batch size is 3
x0
=
np
.
random
.
random
((
4
,
6
,
3
)).
astype
(
'float32'
)
lod0
=
[[
0
,
2
,
4
],
[
0
,
1
,
2
,
3
,
4
]]
x1
=
np
.
random
.
random
((
4
,
8
,
3
)).
astype
(
'float32'
)
lod1
=
[[
0
,
2
,
4
],
[
0
,
1
,
2
,
3
,
4
]]
axis
=
1
level
=
1
self
.
inputs
=
{
'X'
:
[(
'x0'
,
(
x0
,
lod0
)),
(
'x1'
,
(
x1
,
lod1
))]}
self
.
attrs
=
{
'axis'
:
axis
,
'level'
:
level
}
outs
=
[]
for
i
in
range
(
4
):
sub_x0
=
x0
[
lod0
[
level
][
i
]:
lod0
[
level
][
i
+
1
],
:]
sub_x1
=
x1
[
lod1
[
level
][
i
]:
lod1
[
level
][
i
+
1
],
:]
outs
.
append
(
np
.
concatenate
((
sub_x0
,
sub_x1
),
axis
=
axis
))
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
outs
,
axis
=
0
)}
def
setUp
(
self
):
self
.
op_type
=
"sequence_concat"
self
.
set_data
()
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
class
TestConcatOpDiffLod
(
TestConcatOp
):
def
set_data
(
self
):
# two level, batch size is 3
x0
=
np
.
random
.
random
((
4
,
6
,
3
)).
astype
(
'float32'
)
lod0
=
[[
0
,
2
,
4
],
[
0
,
1
,
2
,
3
,
4
]]
x1
=
np
.
random
.
random
((
5
,
6
,
3
)).
astype
(
'float32'
)
lod1
=
[[
0
,
3
,
5
],
[
0
,
1
,
2
,
3
,
5
]]
axis
=
0
level
=
1
self
.
inputs
=
{
'X'
:
[(
'x0'
,
(
x0
,
lod0
)),
(
'x1'
,
(
x1
,
lod1
))]}
self
.
attrs
=
{
'axis'
:
axis
,
'level'
:
level
}
outs
=
[]
for
i
in
range
(
4
):
sub_x0
=
x0
[
lod0
[
level
][
i
]:
lod0
[
level
][
i
+
1
],
:]
sub_x1
=
x1
[
lod1
[
level
][
i
]:
lod1
[
level
][
i
+
1
],
:]
outs
.
append
(
np
.
concatenate
((
sub_x0
,
sub_x1
),
axis
=
axis
))
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
outs
,
axis
=
0
)}
class
TestConcatOpLevelZero
(
TestConcatOp
):
def
set_data
(
self
):
# two level, batch size is 3
x0
=
np
.
random
.
random
((
4
,
3
,
4
)).
astype
(
'float32'
)
lod0
=
[[
0
,
2
,
4
],
[
0
,
1
,
2
,
3
,
4
]]
x1
=
np
.
random
.
random
((
5
,
3
,
4
)).
astype
(
'float32'
)
lod1
=
[[
0
,
3
,
5
],
[
0
,
1
,
3
,
4
,
5
]]
axis
=
0
level
=
0
self
.
inputs
=
{
'X'
:
[(
'x0'
,
(
x0
,
lod0
)),
(
'x1'
,
(
x1
,
lod1
))]}
self
.
attrs
=
{
'axis'
:
axis
,
'level'
:
level
}
outs
=
[]
for
i
in
range
(
2
):
sub_x0
=
x0
[
lod0
[
level
][
i
]:
lod0
[
level
][
i
+
1
],
:]
sub_x1
=
x1
[
lod1
[
level
][
i
]:
lod1
[
level
][
i
+
1
],
:]
outs
.
append
(
np
.
concatenate
((
sub_x0
,
sub_x1
),
axis
=
axis
))
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
outs
,
axis
=
0
)}
if
__name__
==
'__main__'
:
unittest
.
main
()
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