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be3fa792
编写于
9月 29, 2017
作者:
Y
Yancey1989
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差异文件
add sequence concat op
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4 changed file
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336 addition
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+336
-0
paddle/operators/Sequence_concat_op.cu
paddle/operators/Sequence_concat_op.cu
+25
-0
paddle/operators/sequence_concat_op.cc
paddle/operators/sequence_concat_op.cc
+106
-0
paddle/operators/sequence_concat_op.h
paddle/operators/sequence_concat_op.h
+148
-0
python/paddle/v2/framework/tests/test_seq_concat_op.py
python/paddle/v2/framework/tests/test_seq_concat_op.py
+57
-0
未找到文件。
paddle/operators/Sequence_concat_op.cu
0 → 100644
浏览文件 @
be3fa792
/* 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.cc
0 → 100644
浏览文件 @
be3fa792
/* 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
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE_GT
(
ctx
->
Inputs
(
"X"
).
size
(),
0UL
,
"Inputs(X) of SequenceConcatOp should not be empty."
);
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
,
"Sequence Concat Op only support one or two sequence now."
);
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"
,
"Multip LodTensors, the variable-length inputs of "
"SequenceConcatOp"
)
.
AsDuplicable
();
AddOutput
(
"Out"
,
"A float LodTensor, the variable-length output of "
"SequenceConcatOp."
);
AddAttr
<
int
>
(
"axis"
,
"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"
,
"The level which the inputs will be joined with."
"If level is 0, the inputs will be joined with word."
"If level is 1, the inputs will be joined with sentence."
)
.
SetDefault
(
0
);
AddComment
(
R"DOC(
SequenceConcatOp concat multip LodTensors and only supports one or two levels.
- Case1:
axis is 1, level is 1, the Lod of Inputs are the same,
LoD(x0) = {{0,2,4},{0,1,2,3,4}}; Dims(x0) = (2,3,4)
LoD(x1) = {{0,2,4},{0,1,2,3,4}}; Dims(x1) = (2,4,4)
LoD(Out) = {{0,2,4},{01,2,3,4}}; Dims(Out) = (2,7,4)
- Case2:
If axis is 0, level is 1, the Lod of inputs are different,
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (2,3,4)
LoD(x1) = {{0,3,5}, {0,1,3,4,5}}; Dims(x1) = (3,3,4)
LoD(Out) = {{0,5,9}, {0,1,2,4,5,6,7,8,9}}; Dims(Out) = (5,3,4)
)DOC"
);
}
};
class
SequenceConcatGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Gradient of Out should not be null."
);
PADDLE_ENFORCE_GT
(
ctx
->
Outputs
(
framework
::
GradVarName
(
"X"
)).
size
(),
0UL
,
"Gradient of X should not be empty."
)
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.h
0 → 100644
浏览文件 @
be3fa792
/* 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
;
// Concat Lod, the initialized Lod of Output is lod(x0),
// if axis is not 0, the LoD(Out) will be the same as Inputs, if axis is 0:
// Case1:
// There is one level, the Output LoD will be modified:
// LoD(x0) = {{0,2,4}}
// LoD(x1) = {{0,1,5}}
// LoD(Out) = {{0,3,9}}
// Case2:
// There is two level, and concat level is 1,
// the Output LoD will be modified as followed:
// LoD(x0) = {{0,2,4}, {0,1,2,3,4}}
// LoD(x1) = {{0,3,5}, {0,1,3,4,5}}
// LoD(Out) = {{0,5,9}, {0,1,2,4,5,6,7,8,9}}
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
)
{
if
(
level
==
0
)
{
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
];
}
}
}
else
if
(
level
==
1
)
{
for
(
size_t
i
=
1
;
i
<
n
;
i
++
)
{
PADDLE_ENFORCE_EQ
(
ins
[
i
]
->
NumLevels
(),
2UL
,
"All the LoDTensors of Inputs(X) should "
"have two level."
);
for
(
size_t
j
=
0
;
j
<
ins
[
i
]
->
lod
()[
0
].
size
();
j
++
)
{
out_lod
[
0
].
push_back
(
ins
[
i
]
->
lod
()[
0
][
j
]);
}
for
(
size_t
j
=
0
;
j
<
ins
[
i
]
->
lod
()[
1
].
size
();
j
++
)
{
out_lod
[
1
][
j
]
+=
ins
[
i
]
->
lod
()[
1
][
j
];
}
}
}
}
return
out_lod
;
}
template
<
typename
Place
,
typename
T
>
class
SequenceConcatOpKernel
:
public
framework
::
OpKernel
{
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
();
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
{
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 @
be3fa792
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
((
11
,
6
,
3
)).
astype
(
'float32'
)
lod0
=
[[
0
,
2
,
5
,
11
],
[
0
,
1
,
2
,
5
,
7
,
11
]]
x1
=
np
.
random
.
random
((
11
,
8
,
3
)).
astype
(
'float32'
)
lod1
=
[[
0
,
2
,
5
,
11
],
[
0
,
1
,
2
,
5
,
7
,
11
]]
axis
=
1
level
=
1
self
.
inputs
=
{
'X'
:
[(
'x0'
,
(
x0
,
lod0
)),
(
'x1'
,
(
x1
,
lod1
))]}
self
.
attrs
=
{
'axis'
:
axis
,
'level'
:
level
}
outs
=
[]
for
i
in
range
(
5
):
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
((
12
,
6
,
3
)).
astype
(
'float32'
)
lod0
=
[[
0
,
3
,
9
,
12
],
[
0
,
2
,
3
,
5
,
9
,
12
]]
x1
=
np
.
random
.
random
((
11
,
6
,
3
)).
astype
(
'float32'
)
lod1
=
[[
0
,
2
,
5
,
11
],
[
0
,
1
,
2
,
5
,
7
,
11
]]
axis
=
0
level
=
1
self
.
inputs
=
{
'X'
:
[(
'x0'
,
(
x0
,
lod0
)),
(
'x1'
,
(
x1
,
lod1
))]}
self
.
attrs
=
{
'axis'
:
axis
,
'level'
:
level
}
outs
=
[]
for
i
in
range
(
5
):
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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