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b08ae0b1
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b08ae0b1
编写于
10月 30, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix code format and doc
上级
7942984f
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
77 addition
and
90 deletion
+77
-90
paddle/operators/math/context_project.h
paddle/operators/math/context_project.h
+53
-62
paddle/operators/sequence_conv_op.cc
paddle/operators/sequence_conv_op.cc
+17
-15
paddle/operators/sequence_conv_op.h
paddle/operators/sequence_conv_op.h
+7
-13
未找到文件。
paddle/operators/math/context_project.h
浏览文件 @
b08ae0b1
...
...
@@ -16,34 +16,36 @@ limitations under the License. */
#include "paddle/framework/eigen.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/im2col.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
/*
* \brief Context projection concatenate
features in adjacent time
steps in
* \brief Context projection concatenate
s features in adjacent time-
steps in
* a sequence. The i-th row of the output is the concatenation of
* context_length rows of the input. The context_length rows are the
* consecutive rows from the i+shift_start row.
* ContextProjectGradFunctor is the inverse process of ContextProjectFunctor.
*
* \param in Input data.
* \param Shape The shape of Input data
,
*
[mini
batch, input_hidden_size].
* \param Shape The shape of Input data
:
*
[mini-
batch, input_hidden_size].
*
* \param padding_data Padding data.
* \param Shape The shape of Padding data
,
* [up_pad + down_pad, input_hidden_size].
* \param Shape The shape of Padding data
:
*
[up_pad + down_pad, input_hidden_size].
*
* \param col Col data.
* \param Shape The shape of Col data
,
*
[mini
batch, context_length * input_hidden_size].
* \param Shape The shape of Col data
:
*
[mini-
batch, context_length * input_hidden_size].
*
* For a mini-batch of 2 variable lengths sentences, containing 3, and 1
* time-steps:
...
...
@@ -61,40 +63,37 @@ using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
* representation is 2.
*
* - Case1:
* If context_start is -1 and padding_trainable is false, we use zero to pad
* instead of learned weight to pad,
*
and the context_len
th is 3, the output (Out) is:
*
If context_start is -1 and padding_trainable is false, we use zero to pad
*
instead of learned weight to pad,
*
and the context_leng
th is 3, the output (Out) is:
*
* Out =[[0, 0, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, 0, 0 ]
*
[0, 0,
d1, d2, 0, 0 ]]
*
Out =[[0, 0, a1, a2, b1, b2;
*
a1, a2, b1, b2, c1, c2;
*
b1, b2, c1, c2, 0, 0 ]
*
[0, 0,
d1, d2, 0, 0 ]]
*
* - Case2:
* If context_start is -1 and padding_trainable is true, we use learned weight
* to pad,
*
and the context_len
th is 3, the output (Out) is:
*
If context_start is -1 and padding_trainable is true, we use learned weight
*
to pad,
*
and the context_leng
th is 3, the output (Out) is:
*
* Out = [[w1, w2, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, w3, w4]
* [w1, w2, d1, d2, w3, w4]]
*
Out = [[w1, w2, a1, a2, b1, b2;
*
a1, a2, b1, b2, c1, c2;
*
b1, b2, c1, c2, w3, w4]
*
[w1, w2, d1, d2, w3, w4]]
*
*/
template
<
typename
Place
,
typename
T
>
class
ContextProjectFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
LoDTensor
&
in
,
const
framework
::
Tensor
&
padding_data
,
framework
::
Tensor
&
col
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
LoDTensor
&
in
,
const
Tensor
&
padding_data
,
Tensor
&
col
,
bool
padding_trainable
,
int
context_start
,
int
context_length
,
int
context_stride
,
int
up_pad
,
int
down_pad
)
{
auto
lod_level_0
=
in
.
lod
()[
0
];
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
Place
,
float
>
im2col_ocf
;
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kOCF
,
Place
,
float
>
im2col_ocf
;
int
input_row_begin
,
input_row_end
;
int
sequence_height
,
sequence_width
;
...
...
@@ -106,19 +105,18 @@ class ContextProjectFunctor {
:
static_cast
<
int
>
(
lod_level_0
[
i
]);
input_row_end
=
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]);
framework
::
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
sequence_height
=
static_cast
<
int
>
(
out_t
.
dims
()[
0
]);
if
(
input_row_begin
<
input_row_end
)
{
framework
::
Tensor
in_t
=
in
.
Slice
(
input_row_begin
,
input_row_end
);
Tensor
in_t
=
in
.
Slice
(
input_row_begin
,
input_row_end
);
std
::
vector
<
int64_t
>
output_shape
(
{
sequence_height
,
1
,
1
,
context_length
,
sequence_width
});
// output_height, output_width,
// input_channels, filter_height, filter_width
out_t
.
Resize
(
framework
::
make_ddim
(
output_shape
));
std
::
vector
<
int64_t
>
input_shape
(
...
...
@@ -134,9 +132,8 @@ class ContextProjectFunctor {
}
if
(
padding_trainable
)
{
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod_level_0
.
size
())
-
1
;
++
i
)
{
framework
::
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
sequence_height
=
static_cast
<
int
>
(
out_t
.
dims
()[
0
]);
...
...
@@ -150,10 +147,9 @@ class ContextProjectFunctor {
for
(
int
k
=
0
;
k
<
padding_rows
;
++
k
)
{
int
padding_size
=
k
+
context_length
<
up_pad
?
context_length
:
up_pad
-
k
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
k
*
context_length
,
k
*
context_length
+
padding_size
);
framework
::
Tensor
w_sub
=
padding_data
.
Slice
(
k
,
k
+
padding_size
);
// in this block, using EigenVector<T>::Flatten is ok too.
Tensor
out_t_sub
=
out_t
.
Slice
(
k
*
context_length
,
k
*
context_length
+
padding_size
);
Tensor
w_sub
=
padding_data
.
Slice
(
k
,
k
+
padding_size
);
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
out_t_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
w_sub_e
;
...
...
@@ -180,10 +176,11 @@ class ContextProjectFunctor {
}
if
(
padding_begin
>
0
||
sequence_height
==
context_start
)
padding_idx
=
padding_begin
+
t
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
Tensor
out_t_sub
=
out_t
.
Slice
(
(
down_pad_begin_row
+
t
)
*
context_length
-
padding_size
,
(
down_pad_begin_row
+
t
)
*
context_length
);
framework
::
Tensor
w_sub
=
padding_data
.
Slice
(
Tensor
w_sub
=
padding_data
.
Slice
(
up_pad
+
padding_idx
,
up_pad
+
padding_idx
+
padding_size
);
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
...
...
@@ -199,16 +196,13 @@ class ContextProjectFunctor {
template
<
typename
Place
,
typename
T
>
class
ContextProjectGradFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
LoDTensor
&
in
,
framework
::
Tensor
&
padding_data
,
framework
::
Tensor
&
col
,
bool
padding_trainable
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
LoDTensor
&
in
,
Tensor
&
padding_data
,
Tensor
&
col
,
bool
padding_trainable
,
int
context_start
,
int
context_length
,
int
context_stride
,
int
up_pad
,
int
down_pad
,
bool
input_grad
,
bool
pad_grad
)
{
auto
lod_level_0
=
in
.
lod
()[
0
];
paddle
::
operators
::
math
::
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
Place
,
float
>
col2im_ocf
;
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kOCF
,
Place
,
float
>
col2im_ocf
;
int
input_row_begin
,
input_row_end
;
int
sequence_height
,
sequence_width
;
...
...
@@ -221,20 +215,18 @@ class ContextProjectGradFunctor {
:
static_cast
<
int
>
(
lod_level_0
[
i
]);
input_row_end
=
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]);
framework
::
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
sequence_height
=
static_cast
<
int
>
(
out_t
.
dims
()[
0
]);
if
(
input_row_begin
<
input_row_end
)
{
framework
::
Tensor
in_t
=
in
.
Slice
(
input_row_begin
,
input_row_end
);
Tensor
in_t
=
in
.
Slice
(
input_row_begin
,
input_row_end
);
std
::
vector
<
int64_t
>
output_shape
(
{
sequence_height
,
1
,
1
,
context_length
,
sequence_width
});
// output_height, output_width,
// input_channels, filter_height, filter_width
out_t
.
Resize
(
framework
::
make_ddim
(
output_shape
));
std
::
vector
<
int64_t
>
input_shape
(
...
...
@@ -252,9 +244,8 @@ class ContextProjectGradFunctor {
if
(
pad_grad
)
{
if
(
padding_trainable
)
{
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod_level_0
.
size
())
-
1
;
++
i
)
{
framework
::
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
sequence_height
=
static_cast
<
int
>
(
out_t
.
dims
()[
0
]);
out_t
.
Resize
({
sequence_height
*
context_length
,
sequence_width
});
...
...
@@ -266,10 +257,9 @@ class ContextProjectGradFunctor {
for
(
int
k
=
0
;
k
<
padding_rows
;
++
k
)
{
int
padding_size
=
k
+
context_length
<
up_pad
?
context_length
:
up_pad
-
k
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
k
*
context_length
,
k
*
context_length
+
padding_size
);
framework
::
Tensor
w_sub
=
padding_data
.
Slice
(
k
,
k
+
padding_size
);
// in this block, using EigenVector<T>::Flatten is ok too.
Tensor
out_t_sub
=
out_t
.
Slice
(
k
*
context_length
,
k
*
context_length
+
padding_size
);
Tensor
w_sub
=
padding_data
.
Slice
(
k
,
k
+
padding_size
);
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
w_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
...
...
@@ -298,10 +288,11 @@ class ContextProjectGradFunctor {
}
if
(
padding_begin
>
0
||
sequence_height
==
context_start
)
padding_idx
=
padding_begin
+
t
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
Tensor
out_t_sub
=
out_t
.
Slice
(
(
down_pad_begin_row
+
t
)
*
context_length
-
padding_size
,
(
down_pad_begin_row
+
t
)
*
context_length
);
framework
::
Tensor
w_sub
=
padding_data
.
Slice
(
Tensor
w_sub
=
padding_data
.
Slice
(
up_pad
+
padding_idx
,
up_pad
+
padding_idx
+
padding_size
);
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
...
...
paddle/operators/sequence_conv_op.cc
浏览文件 @
b08ae0b1
...
...
@@ -31,18 +31,19 @@ class SequenceConvOp : public framework::OperatorWithKernel {
"Output(Out) of SequenceConvOp should not be null."
);
int
context_length
=
ctx
->
Attrs
().
Get
<
int
>
(
"contextLength"
);
bool
padding_trainable
=
ctx
->
Attrs
().
Get
<
bool
>
(
"paddingTrainable"
);
int
context_start
=
ctx
->
Attrs
().
Get
<
int
>
(
"contextStart"
);
auto
in_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
PADDLE_ENFORCE
(
ctx
->
Attrs
().
Get
<
int
>
(
"contextStride"
)
==
1
,
"Currently, SequenceConvOp only supports contextStride=1."
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
2
&&
filter_dims
.
size
()
==
2
,
"Input(X, Filter) should be 2-D tensor."
);
PADDLE_ENFORCE
(
filter_dims
[
0
]
==
context_length
*
in_dims
[
1
],
"Filter's height should be context_length * "
"
number_of_input_features
."
);
"
input_hidden_size
."
);
if
(
padding_trainable
)
{
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"paddingTrainable"
)
)
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"PaddingData"
),
"Input(PaddingData) of SequenceConvOp should not be null."
);
...
...
@@ -88,6 +89,7 @@ class SequenceConvGradOp : public framework::OperatorWithKernel {
}
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
framework
::
GradVarName
(
"X"
),
"X"
);
}
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Filter"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Filter"
),
...
...
@@ -105,13 +107,13 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
"X"
,
"(LoDTensor) the input(X) is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T,
D
), where, T is the "
"total time steps in this mini-batch,
D is the input feature
size."
);
"this LoDTensor is a matrix with shape (T,
N
), where, T is the "
"total time steps in this mini-batch,
N is the input_hidden_
size."
);
AddInput
(
"PaddingData"
,
"(Tensor, optional) the input(PaddingData) is an optional "
"parameter, and it is learnable. "
"This is a tensor with shape (
N, D), where N
is the "
"top_pad + bottom_pad,
D is the input feature
size. In order to "
"This is a tensor with shape (
P, N), where P
is the "
"top_pad + bottom_pad,
N is the input_hidden_
size. In order to "
"ensure the equal length of sequence before and after "
"convolution, it is necessary to fill the top and bottom of each "
"sequence according to context_length, context_stride and "
...
...
@@ -120,17 +122,17 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"Filter"
,
"(Tensor) the input(Filter) is an learnable parameter."
"This is a tensor with shape (
N, D), where N
is the "
"context_length * input_hidden_size,
D
is the output feature size."
);
"This is a tensor with shape (
K, M), where K
is the "
"context_length * input_hidden_size,
M
is the output feature size."
);
AddOutput
(
"Out"
,
"(LoDTensor) the output(Out) is a LodTensor, which support "
"variable-time length output sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T,
D
), where, T is the "
"total time steps in this mini-batch,
D
is the output feature size."
);
"this LoDTensor is a matrix with shape (T,
M
), where, T is the "
"total time steps in this mini-batch,
M
is the output feature size."
);
AddAttr
<
bool
>
(
"paddingTrainable"
,
"(bool, default
false) the padding data of SequenceConvOp "
"(bool, default
:
false) the padding data of SequenceConvOp "
"is trainable or not."
)
.
SetDefault
(
false
);
AddAttr
<
int
>
(
"contextLength"
,
...
...
@@ -138,7 +140,7 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
"height of the convolution kernel."
)
.
GreaterThan
(
0
);
AddAttr
<
int
>
(
"contextStart"
,
"(int, default
0) the contextStart of SequenceConvOp "
"(int, default
:
0) the contextStart of SequenceConvOp "
"represents the beginning of the convolution of the number of "
"rows of sequence, which can be negative. The negative number "
"means to pad contextStart time-steps of zeros or learnable "
...
...
@@ -147,7 +149,7 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
"instance."
)
.
SetDefault
(
0
);
AddAttr
<
int
>
(
"contextStride"
,
"(int, default
1) the contextStride of SequenceConvOp "
"(int, default
:
1) the contextStride of SequenceConvOp "
"represents the stride length of convolution kernel. "
"Currently, SequenceConvOp only supports"
"contextStride=1."
)
...
...
@@ -156,7 +158,7 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
SequenceConvOp performs convolution operation on features of
context
_l
ength time-steps of each instance.
context
L
ength time-steps of each instance.
The convolution operation calculates the output based on the input, filter
and strides, paddings parameters. The size of each dimension of the
parameters is checked in the infer-shape. In order to ensure the equal
...
...
paddle/operators/sequence_conv_op.h
浏览文件 @
b08ae0b1
...
...
@@ -40,7 +40,6 @@ class SequenceConvKernel : public framework::OpKernel<T> {
int
context_stride
=
context
.
Attr
<
int
>
(
"contextStride"
);
bool
padding_trainable
=
context
.
Attr
<
bool
>
(
"paddingTrainable"
);
// InferShape by in_lod
PADDLE_ENFORCE_EQ
(
in
->
lod
().
size
(),
1UL
,
"Only support one level sequence now."
);
...
...
@@ -51,20 +50,17 @@ class SequenceConvKernel : public framework::OpKernel<T> {
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
int
sequence_width
;
sequence_width
=
static_cast
<
int
>
(
in
->
dims
()[
1
]);
int
sequence_width
=
static_cast
<
int
>
(
in
->
dims
()[
1
]);
// Use col_shape in the im2col calculation.
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
sequence_width
*
context_leng
th
};
context_length
*
sequence_wid
th
};
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
math
::
SetConstant
<
Place
,
T
>
set_zero
;
// Because if padding_trainable is false, padding data should be zeros.
math
::
SetConstant
<
Place
,
T
>
set_zero
;
set_zero
(
context
.
device_context
(),
&
col
,
static_cast
<
T
>
(
0
));
paddle
::
operators
::
math
::
ContextProjectFunctor
<
Place
,
T
>
seq_project_functor
;
math
::
ContextProjectFunctor
<
Place
,
T
>
seq_project_functor
;
seq_project_functor
(
context
.
device_context
(),
*
in
,
*
padding_data
,
col
,
padding_trainable
,
context_start
,
context_length
,
...
...
@@ -79,8 +75,8 @@ template <typename Place, typename T>
class
SequenceConvGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
filter_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
auto
*
padding_data_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"PaddingData"
));
...
...
@@ -113,10 +109,8 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
*
out_g
,
false
,
*
filter
,
true
,
T
(
1.0
),
&
col
,
T
(
1.0
));
}
paddle
::
operators
::
math
::
ContextProjectFunctor
<
Place
,
T
>
seq_project_functor
;
paddle
::
operators
::
math
::
ContextProjectGradFunctor
<
Place
,
T
>
seq_project_grad_functor
;
math
::
ContextProjectFunctor
<
Place
,
T
>
seq_project_functor
;
math
::
ContextProjectGradFunctor
<
Place
,
T
>
seq_project_grad_functor
;
if
(
in_g
)
{
in_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
...
...
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