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体验新版 GitCode,发现更多精彩内容 >>
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f350be3e
编写于
11月 20, 2017
作者:
W
wanghaox
提交者:
GitHub
11月 20, 2017
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差异文件
Merge pull request #5546 from wanghaox/sub_sequence_op
Add sequence slice operator
上级
6da78d97
d68f861e
变更
4
隐藏空白更改
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并排
Showing
4 changed file
with
373 addition
and
0 deletion
+373
-0
paddle/operators/sequence_slice_op.cc
paddle/operators/sequence_slice_op.cc
+132
-0
paddle/operators/sequence_slice_op.cu
paddle/operators/sequence_slice_op.cu
+23
-0
paddle/operators/sequence_slice_op.h
paddle/operators/sequence_slice_op.h
+173
-0
python/paddle/v2/fluid/tests/test_sequence_slice_op.py
python/paddle/v2/fluid/tests/test_sequence_slice_op.py
+45
-0
未找到文件。
paddle/operators/sequence_slice_op.cc
0 → 100755
浏览文件 @
f350be3e
/* 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_slice_op.h"
namespace
paddle
{
namespace
operators
{
class
SequenceSliceOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceSliceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Offset"
),
"Input(Offset) of SequenceSliceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Length"
),
"Input(Length) of SequenceSliceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceSliceOp should not be null."
);
auto
input_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
offset_dim
=
ctx
->
GetInputDim
(
"Offset"
);
auto
length_dim
=
ctx
->
GetInputDim
(
"Length"
);
PADDLE_ENFORCE_EQ
(
offset_dim
.
size
(),
2UL
,
"Only support one level sequence now, The rank of offset must be 2."
);
PADDLE_ENFORCE_EQ
(
length_dim
.
size
(),
2UL
,
"Only support one level sequence now, The rank of Length must be 2."
);
// Initialize the output's dims to maximum,
// and re-set to real dims by the value of Offset and Length at kernel
ctx
->
SetOutputDim
(
"Out"
,
input_dims
);
}
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
class
SequenceSliceGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
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"
));
}
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
class
SequenceSliceOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SequenceSliceOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor), "
"the input of SequenceSliceOp."
);
AddInput
(
"Offset"
,
"(Tensor), "
"a vector<int> to describe the offset of every input sequence for "
"sub sequence item."
);
AddInput
(
"Length"
,
"(Tensor), "
"a vector<int> to describe the length of every input sequence for "
"sub sequence item."
);
AddOutput
(
"Out"
,
"(LoDTensor), the output of SequenceSliceOp."
);
AddComment
(
R"DOC(
Sequence slice operator
The operator crops a subsequence from given sequence with given start offset and subsequence length.
It only supports sequence (LoD Tensor with level number is 1).
- Case:
X = [[a1, a2;
b1, b2;
c1, c2]
[d1, d2;
e1, e2]]
LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)
Offset = [[0], [1]]; Length = [[2], [1]]
Out = [[a1, a2;
b1, b2]
[e1, e2]]
LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)
NOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sequence_slice
,
ops
::
SequenceSliceOp
,
ops
::
SequenceSliceOpMaker
,
sequence_slice_grad
,
ops
::
SequenceSliceGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_slice
,
ops
::
SequenceSliceOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_slice_grad
,
ops
::
SequenceSliceGradOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/sequence_slice_op.cu
0 → 100755
浏览文件 @
f350be3e
/* 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_slice_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sequence_slice
,
ops
::
SequenceSliceOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
sequence_slice_grad
,
ops
::
SequenceSliceGradOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/sequence_slice_op.h
0 → 100755
浏览文件 @
f350be3e
/* 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/math/math_function.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
>
inline
LoD
SequenceSliceLoD
(
const
T
&
in
,
const
int64_t
*
offset_data
,
const
int64_t
*
length_data
)
{
auto
out_lod
=
in
.
lod
();
size_t
lod_offset
=
0
;
auto
n
=
in
.
lod
()[
0
].
size
()
-
1
;
out_lod
[
0
][
0
]
=
0
;
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
lod_offset
+=
length_data
[
i
];
out_lod
[
0
][
i
+
1
]
=
lod_offset
;
}
return
out_lod
;
}
template
<
typename
Place
,
typename
T
>
class
SequenceSliceOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
offset
=
ctx
.
Input
<
Tensor
>
(
"Offset"
);
auto
*
length
=
ctx
.
Input
<
Tensor
>
(
"Length"
);
auto
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
auto
lod
=
in
->
lod
();
auto
n
=
lod
[
0
].
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
lod
.
size
(),
1UL
,
"Only support one level sequence now."
);
PADDLE_ENFORCE_EQ
(
n
,
static_cast
<
size_t
>
(
length
->
dims
()[
0
]),
"The size of input-sequence and length-array should be the same"
)
PADDLE_ENFORCE_EQ
(
n
,
static_cast
<
size_t
>
(
offset
->
dims
()[
0
]),
"The size of input-sequence and offset-array should be the same"
)
const
int64_t
*
offset_data
=
offset
->
data
<
int64_t
>
();
const
int64_t
*
length_data
=
length
->
data
<
int64_t
>
();
framework
::
Tensor
offset_cpu
;
framework
::
Tensor
length_cpu
;
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
offset_cpu
.
mutable_data
<
T
>
(
offset
->
dims
(),
platform
::
CPUPlace
());
offset_cpu
.
CopyFrom
(
*
offset
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
offset_data
=
offset_cpu
.
data
<
int64_t
>
();
length_cpu
.
mutable_data
<
T
>
(
length
->
dims
(),
platform
::
CPUPlace
());
length_cpu
.
CopyFrom
(
*
length
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
length_data
=
length_cpu
.
data
<
int64_t
>
();
}
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
PADDLE_ENFORCE_LT
(
0
,
offset_data
[
i
],
"The offset[%d] must greater than zero."
,
i
)
PADDLE_ENFORCE_LT
(
0
,
length_data
[
i
],
"The length[%d] must greater than zero."
,
i
)
PADDLE_ENFORCE_LT
(
lod
[
0
][
i
]
+
offset_data
[
i
]
+
length_data
[
i
],
lod
[
0
][
i
+
1
],
"The target tensor's length overflow."
)
}
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
out_lod
=
SequenceSliceLoD
(
*
in
,
offset_data
,
length_data
);
auto
out_dims
=
in
->
dims
();
out_dims
[
0
]
=
out_lod
[
0
][
out_lod
[
0
].
size
()
-
1
];
out
->
Resize
(
out_dims
);
out
->
set_lod
(
out_lod
);
auto
in_stride
=
framework
::
stride
(
in
->
dims
());
auto
out_stride
=
framework
::
stride
(
out
->
dims
());
size_t
out_offset
=
0
;
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
Tensor
in_t
=
in
->
Slice
(
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]),
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]
+
length_data
[
i
]));
StridedMemcpy
<
T
>
(
ctx
.
device_context
(),
in_t
.
data
<
T
>
(),
in_stride
,
in_t
.
dims
(),
out_stride
,
out
->
data
<
T
>
()
+
out_offset
);
out_offset
+=
length_data
[
i
]
*
in_stride
[
0
];
}
}
};
template
<
typename
Place
,
typename
T
>
class
SequenceSliceGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
offset
=
ctx
.
Input
<
Tensor
>
(
"Offset"
);
auto
*
length
=
ctx
.
Input
<
Tensor
>
(
"Length"
);
auto
*
out_grad
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
const
int64_t
*
offset_data
=
offset
->
data
<
int64_t
>
();
const
int64_t
*
length_data
=
length
->
data
<
int64_t
>
();
framework
::
Tensor
offset_cpu
;
framework
::
Tensor
length_cpu
;
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
offset_cpu
.
mutable_data
<
T
>
(
offset
->
dims
(),
platform
::
CPUPlace
());
offset_cpu
.
CopyFrom
(
*
offset
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
offset_data
=
offset_cpu
.
data
<
int64_t
>
();
length_cpu
.
mutable_data
<
T
>
(
length
->
dims
(),
platform
::
CPUPlace
());
length_cpu
.
CopyFrom
(
*
length
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
length_data
=
length_cpu
.
data
<
int64_t
>
();
}
auto
lod
=
in
->
lod
();
auto
out_lod
=
out_grad
->
lod
();
if
(
x_grad
)
{
x_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
x_grad
->
set_lod
(
in
->
lod
());
math
::
SetConstant
<
Place
,
T
>
set_zero
;
set_zero
(
ctx
.
device_context
(),
x_grad
,
static_cast
<
T
>
(
0
));
auto
out_grad_stride
=
framework
::
stride
(
out_grad
->
dims
());
for
(
size_t
i
=
0
;
i
<
out_lod
[
0
].
size
()
-
1
;
++
i
)
{
Tensor
out_grad_t
=
out_grad
->
Slice
(
static_cast
<
int
>
(
out_lod
[
0
][
i
]),
static_cast
<
int
>
(
out_lod
[
0
][
i
+
1
]));
auto
out_grad_stride
=
framework
::
stride
(
out_grad_t
.
dims
());
auto
x_grad_stride
=
framework
::
stride
(
x_grad
->
dims
());
Tensor
x_grad_t
=
x_grad
->
Slice
(
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]),
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]
+
length_data
[
i
]));
StridedMemcpy
<
T
>
(
ctx
.
device_context
(),
out_grad_t
.
data
<
T
>
(),
out_grad_stride
,
out_grad_t
.
dims
(),
x_grad_stride
,
x_grad_t
.
data
<
T
>
());
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/tests/test_sequence_slice_op.py
0 → 100755
浏览文件 @
f350be3e
import
unittest
import
numpy
as
np
import
sys
from
op_test
import
OpTest
class
TestSequenceSliceOp
(
OpTest
):
def
set_data
(
self
):
self
.
init_test_case
()
# only supprot one level LoD
x
=
np
.
random
.
random
(
self
.
x_dim
).
astype
(
'float32'
)
lod
=
self
.
x_lod
offset
=
np
.
array
(
self
.
offset
).
astype
(
"int64"
)
length
=
np
.
array
(
self
.
length
).
astype
(
"int64"
)
self
.
inputs
=
{
'X'
:
(
x
,
lod
),
'Offset'
:
offset
,
'Length'
:
length
}
outs
=
[]
#np.zeros((100, 3, 2)).astype('float32')
out_lod
=
[[
0
]]
out_lod_offset
=
0
for
i
in
range
(
len
(
offset
)):
sub_x
=
x
[
lod
[
0
][
i
]
+
offset
[
i
,
0
]:
lod
[
0
]
[
i
]
+
offset
[
i
,
0
]
+
length
[
i
,
0
],
:]
out_lod_offset
=
out_lod_offset
+
len
(
sub_x
)
outs
.
append
(
sub_x
)
out_lod
[
0
].
append
(
out_lod_offset
)
outs
=
np
.
concatenate
(
outs
,
axis
=
0
)
self
.
outputs
=
{
'Out'
:
(
outs
,
out_lod
)}
def
init_test_case
(
self
):
self
.
x_dim
=
(
100
,
3
,
2
)
self
.
x_lod
=
[[
0
,
20
,
40
,
60
,
80
,
100
]]
self
.
offset
=
[[
1
],
[
2
],
[
3
],
[
4
],
[
5
]]
self
.
length
=
[[
10
],
[
8
],
[
6
],
[
4
],
[
2
]]
def
setUp
(
self
):
self
.
op_type
=
"sequence_slice"
self
.
set_data
()
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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