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4f933312
编写于
1月 19, 2018
作者:
Y
Yang yaming
提交者:
GitHub
1月 19, 2018
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差异文件
Merge pull request #7662 from pkuyym/fix-6678
Add sequence reshape operator
上级
9fea1d43
b07ca1de
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4
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4 changed file
with
330 addition
and
0 deletion
+330
-0
paddle/operators/sequence_reshape_op.cc
paddle/operators/sequence_reshape_op.cc
+130
-0
paddle/operators/sequence_reshape_op.cu
paddle/operators/sequence_reshape_op.cu
+30
-0
paddle/operators/sequence_reshape_op.h
paddle/operators/sequence_reshape_op.h
+86
-0
python/paddle/v2/fluid/tests/test_sequence_reshape.py
python/paddle/v2/fluid/tests/test_sequence_reshape.py
+84
-0
未找到文件。
paddle/operators/sequence_reshape_op.cc
0 → 100644
浏览文件 @
4f933312
/* Copyright (c) 2018 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_reshape_op.h"
#include "paddle/framework/ddim.h"
namespace
paddle
{
namespace
operators
{
class
SequenceReshapeOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceReshapeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceReshapeOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
x_numel
=
product
(
x_dims
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2U
,
"Rank of Input(X) should be 2."
);
int
new_dim
=
ctx
->
Attrs
().
Get
<
int
>
(
"new_dim"
);
ctx
->
SetOutputDim
(
"Out"
,
{
x_numel
/
new_dim
,
static_cast
<
int64_t
>
(
new_dim
)});
}
};
class
SequenceReshapeOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SequenceReshapeOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor with shape "
"being [N, M]."
);
AddOutput
(
"Out"
,
"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor with "
"shape [T, new_dim] where T is calculated based on X.lod, M and "
"new_dim."
);
AddAttr
<
int
>
(
"new_dim"
,
"Sequence dimension of the output LoDTensor."
);
AddComment
(
R"DOC(
Sequence Reshape Operator.
This operator will rearrange the input sequences. The new dimension is set by
attribute and length of each sequence may change longer or shorter which is
decided by original length, original dimension and new dimension. The following
example will help to illustrate the function of this operator:
x is a LoDTensor:
x.lod = [[0, 2, 6]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8], [9, 10], [11, 12]]
x.dims = [6, 2]
set new_dim = 4
then out is a LoDTensor:
out.lod = [[0, 1, 3]]
out.data = [[1, 2, 3, 4],
[5, 6, 7, 8], [9, 10, 11, 12]]
out.dims = [3, 4]
Currently, only 1-level LoDTensor is supported and please make sure (original
length * original dimension) can be divided by new_dim with no remainder for
each sequence.
)DOC"
);
}
};
class
SequenceReshapeGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) of SequenceReshapeGradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceReshapeGradOp should not be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
framework
::
GradVarName
(
"X"
));
}
};
class
SequenceReshapeGradOpMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
*
op_desc_ptr
=
new
framework
::
OpDesc
();
op_desc_ptr
->
SetType
(
"sequence_reshape_grad"
);
op_desc_ptr
->
SetInput
(
"X"
,
Input
(
"X"
));
op_desc_ptr
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
OutputGrad
(
"Out"
));
op_desc_ptr
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
InputGrad
(
"X"
));
op_desc_ptr
->
SetAttrMap
(
Attrs
());
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
op_desc_ptr
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
sequence_reshape
,
ops
::
SequenceReshapeOp
,
ops
::
SequenceReshapeOpMaker
,
ops
::
SequenceReshapeGradOpMaker
);
REGISTER_OPERATOR
(
sequence_reshape_grad
,
ops
::
SequenceReshapeGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_reshape
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_reshape_grad
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
);
paddle/operators/sequence_reshape_op.cu
0 → 100644
浏览文件 @
4f933312
/* Copyright (c) 2018 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_reshape_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
sequence_reshape
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
SequenceReshapeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
sequence_reshape_grad
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
,
ops
::
SequenceReshapeGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
);
paddle/operators/sequence_reshape_op.h
0 → 100644
浏览文件 @
4f933312
/* Copyright (c) 2018 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"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceReshapeKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
int
out_width
=
context
.
Attr
<
int
>
(
"new_dim"
);
auto
in_dims
=
in
->
dims
();
int64_t
in_width
=
in_dims
[
1
];
auto
&
in_lod
=
in
->
lod
();
PADDLE_ENFORCE_EQ
(
in_lod
.
size
(),
1UL
,
"Only support one level sequence now."
);
PADDLE_ENFORCE_EQ
(
in_dims
[
0
],
in_lod
[
0
].
back
(),
"Inconsistent size between X.shape[0] and X.lod()[0].back()."
);
auto
in_lod_l0
=
in_lod
[
0
];
int
seq_num
=
in_lod_l0
.
size
()
-
1
;
if
(
in_width
==
out_width
)
{
out
->
set_lod
(
in
->
lod
());
}
else
{
auto
&
out_lod
=
*
out
->
mutable_lod
();
out_lod
.
resize
(
1
);
out_lod
[
0
].
resize
(
seq_num
+
1
);
out_lod
[
0
][
0
]
=
0
;
for
(
int
i
=
0
;
i
<
seq_num
;
++
i
)
{
size_t
seq_len
=
in_lod_l0
[
i
+
1
]
-
in_lod_l0
[
i
];
size_t
offset
=
0
;
offset
=
(
seq_len
*
in_width
)
/
out_width
;
PADDLE_ENFORCE_EQ
(
offset
*
out_width
,
seq_len
*
in_width
,
"Please make sure (sequence_length * dimension) can "
"be divided by new_dim with no remainder for each "
"sequence. The %dth sequence is invalid."
,
i
+
1
);
out_lod
[
0
][
i
+
1
]
=
out_lod
[
0
][
i
]
+
offset
;
}
}
framework
::
Copy
(
*
in
,
context
.
GetPlace
(),
out
);
out
->
Resize
({
static_cast
<
int64_t
>
(
out
->
lod
()[
0
].
back
()),
out_width
});
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceReshapeGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x_tensor_ptr
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
outg_tensor_ptr
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
xg_tensor_ptr
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
xg_tensor_ptr
->
mutable_data
<
T
>
(
context
.
GetPlace
());
framework
::
Copy
(
*
outg_tensor_ptr
,
context
.
GetPlace
(),
xg_tensor_ptr
);
xg_tensor_ptr
->
Resize
(
x_tensor_ptr
->
dims
());
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/tests/test_sequence_reshape.py
0 → 100644
浏览文件 @
4f933312
# Copyright (c) 2018 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.
import
unittest
import
numpy
as
np
import
math
from
op_test
import
OpTest
class
TestSequenceReshape
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'sequence_reshape'
dimension
=
12
x_lod
=
[[
0
,
4
,
5
,
8
,
11
]]
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
24
]).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'new_dim'
:
dimension
}
out
,
out_lod
=
self
.
compute_output
(
x
,
x_lod
,
dimension
)
self
.
outputs
=
{
'Out'
:
(
out
,
out_lod
)}
def
compute_output
(
self
,
x
,
x_lod
,
dimension
):
x_width
=
x
.
shape
[
1
]
out_lod
=
[[
0
]]
for
i
in
xrange
(
len
(
x_lod
[
0
])
-
1
):
seq_len
=
x_lod
[
0
][
i
+
1
]
-
x_lod
[
0
][
i
]
offset
=
(
seq_len
*
x_width
)
/
dimension
assert
int
(
offset
)
*
dimension
==
seq_len
*
x_width
out_lod
[
0
].
append
(
out_lod
[
0
][
-
1
]
+
int
(
offset
))
out
=
np
.
zeros
(
shape
=
(
out_lod
[
0
][
-
1
],
dimension
)).
astype
(
'float32'
)
out
.
ravel
()[:]
=
x
.
ravel
()[:]
return
out
,
out_lod
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
)
class
TestSequenceReshape_reduce
(
TestSequenceReshape
):
def
setUp
(
self
):
self
.
op_type
=
'sequence_reshape'
dimension
=
24
x_lod
=
[[
0
,
4
,
6
,
8
,
12
]]
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
12
,
12
]).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'new_dim'
:
dimension
}
out
,
out_lod
=
self
.
compute_output
(
x
,
x_lod
,
dimension
)
self
.
outputs
=
{
'Out'
:
(
out
,
out_lod
)}
class
TestSequenceReshape_same
(
TestSequenceReshape
):
def
setUp
(
self
):
self
.
op_type
=
'sequence_reshape'
dimension
=
12
x_lod
=
[[
0
,
4
,
6
,
8
,
12
]]
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
12
,
12
]).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'new_dim'
:
dimension
}
out
,
out_lod
=
self
.
compute_output
(
x
,
x_lod
,
dimension
)
self
.
outputs
=
{
'Out'
:
(
out
,
out_lod
)}
if
__name__
==
'__main__'
:
unittest
.
main
()
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