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db1b128f
编写于
3月 26, 2018
作者:
D
dzhwinter
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1 changed file
with
114 addition
and
47 deletion
+114
-47
paddle/fluid/operators/sequence_expand_op.h
paddle/fluid/operators/sequence_expand_op.h
+114
-47
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paddle/fluid/operators/sequence_expand_op.h
浏览文件 @
db1b128f
...
...
@@ -13,15 +13,19 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <numeric> // std::itoa
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/
platform/device_context
.h"
#include "paddle/fluid/
operators/math/math_function
.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
DeviceContext
,
typename
T
>
struct
SequenceExpandFunctor
{
...
...
@@ -38,23 +42,35 @@ template <typename T>
struct
SequenceExpandFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
LoDTensor
&
x
,
LoDTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
size_t
element_len
=
framework
::
product
(
x_dims
)
/
x_dims
[
0
];
const
T
*
x_data
=
x
.
data
<
T
>
();
T
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
out_starts
=
out
->
lod
().
back
();
for
(
size_t
i
=
0
;
i
<
out_starts
.
size
()
-
1
;
i
++
)
{
int
scale
=
out_starts
[
i
+
1
]
-
out_starts
[
i
];
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
x_t
(
x_data
,
1
,
element_len
);
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
out_t
(
out_data
,
scale
,
element_len
);
Eigen
::
array
<
int
,
2
>
cast
({{
scale
,
1
}});
out_t
.
device
(
*
context
.
eigen_device
())
=
x_t
.
broadcast
(
cast
);
x_data
+=
element_len
;
out_data
+=
element_len
*
scale
;
auto
&
out_lod
=
out
->
lod
()[
0
];
framework
::
Vector
<
size_t
>
x_lod
;
if
(
x
.
lod
()
==
1
)
{
x_lod
=
x
.
lod
()[
0
];
}
else
{
x_lod
.
reserve
(
out_lod
.
size
());
std
::
itoa
(
x_lod
.
begin
(),
x_lod
.
end
(),
0
);
// fill 0 ~ out_lod.size()-1
}
int
out_offset
=
0
;
auto
&
eigen_place
=
*
context
.
eigen_device
();
for
(
size_t
i
=
1
;
i
<
out_lod
.
size
();
++
i
)
{
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
int
x_start
=
x_lod
[
i
-
1
];
int
x_end
=
x_lod
[
i
];
int
x_seq_len
=
x_end
-
x_start
;
if
(
repeat_num
>
0
)
{
auto
x_sub_tensor
=
x
->
Slice
(
x_start
,
x_end
);
x_sub_tensor
.
Resize
({
1
,
x_sub_tensor
.
numel
()});
int
out_start
=
out_offset
;
if
(
x_lod
.
size
()
==
1
)
{
out_start
=
out_lod
[
0
][
out_offset
];
}
auto
out_sub_tensor
=
out
->
Slice
(
out_start
,
out_start
+
x_seq_len
*
repeat_num
);
out_sub_tensor
.
Resize
({
repeat_num
,
x_sub_tensor
.
dims
()[
1
]});
EigenMatrix
<
T
>::
From
(
out_sub_tensor
).
device
(
eigen_place
)
=
EigenMatrix
<
T
>::
From
(
x_sub_tensor
)
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
repeat_num
,
1
}}));
}
}
}
};
...
...
@@ -64,15 +80,42 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
auto
x_dims
=
x
->
dims
();
auto
*
y
=
context
.
Input
<
LoDTensor
>
(
"Y"
);
PADDLE_ENFORCE
(
!
y
->
lod
().
empty
(),
"y should have lod"
);
PADDLE_ENFORCE_EQ
(
static_cast
<
size_t
>
(
x_dims
[
0
]),
y
->
lod
().
back
().
size
()
-
1
,
"The size of last lod level in Input(Y)"
"must be equal to dims[0] of Input(X)."
);
out
->
set_lod
(
y
->
lod
());
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
int
ref_level
=
context
.
Attr
<
int
>
(
"ref_level"
);
auto
&
x_lod
=
x
->
lod
();
auto
&
y_lod
=
y
->
lod
();
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
framework
::
TensorCopy
(
*
x
,
context
.
GetPlace
(),
out
);
return
;
}
auto
&
out_lod
=
*
out
->
mutable_lod
();
// x lod level is at most 1.
if
(
x_lod
.
size
()
==
0
)
{
out_lod
=
y_lod
[
ref_level
];
}
else
if
(
x_lod
.
size
()
==
1
)
{
out_lod
.
resize
(
1
);
out_lod
[
0
]
=
{
0
};
int
out_offset
=
0
;
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
int
x_start
=
x_lod
[
0
][
i
-
1
];
int
x_end
=
x_lod
[
0
][
i
];
int
x_seq_len
=
x_end
-
x_start
;
for
(
int
j
=
0
;
j
<
repeat_num
;
++
j
)
{
out_lod
[
0
].
push_back
(
out_lod
[
0
].
back
()
+
x_seq_len
);
out_offset
++
;
}
}
}
SequenceExpandFunctor
<
DeviceContext
,
T
>
functor
;
functor
(
context
.
template
device_context
<
DeviceContext
>(),
*
x
,
out
);
}
...
...
@@ -94,21 +137,31 @@ template <typename T>
struct
SequenceExpandGradFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
LoDTensor
&
x
,
const
LoDTensor
&
out
,
const
LoDTensor
&
dout
,
LoDTensor
*
dx
)
{
auto
out_last_level
=
out
.
lod
().
back
();
const
T
*
d_out_data
=
dout
.
data
<
T
>
();
T
*
d_x_data
=
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
());
size_t
element_len
=
dout
.
numel
()
/
dout
.
dims
()[
0
];
for
(
size_t
i
=
0
;
i
<
out_last_level
.
size
()
-
1
;
++
i
)
{
size_t
repeat
=
out_last_level
[
i
+
1
]
-
out_last_level
[
i
];
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
d_out_t
(
d_out_data
,
static_cast
<
int
>
(
repeat
),
element_len
);
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
d_x_t
(
d_x_data
,
static_cast
<
int
>
(
element_len
));
d_x_t
.
device
(
*
context
.
eigen_device
())
=
d_out_t
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
d_out_data
+=
(
repeat
*
element_len
);
d_x_data
+=
element_len
;
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
math
::
SetConstant
<
DeviceContext
,
T
>
set_zero
;
set_zero
(
dev_ctx
,
g_x
,
static_cast
<
T
>
(
0
));
int
g_out_offset
=
0
;
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
if
(
repeat_num
>
0
)
{
int
x_start
=
i
-
1
;
int
x_end
=
i
;
if
(
x_lod
.
size
()
==
1
)
{
x_start
=
x_lod
[
0
][
i
-
1
];
x_end
=
x_lod
[
0
][
i
];
}
int
x_seq_len
=
x_end
-
x_start
;
auto
g_x_sub
=
g_x
->
Slice
(
x_start
,
x_end
);
g_x_sub
.
Resize
(
flatten_to_1d
(
g_x_sub
.
dims
()));
int
g_out_end
=
g_out_offset
+
repeat_num
*
x_seq_len
;
auto
g_out_sub
=
g_out
->
Slice
(
g_out_offset
,
g_out_end
);
g_out_sub
.
Resize
({
repeat_num
,
g_x_sub
.
dims
()[
0
]});
math
::
ColwiseSum
<
DeviceContext
,
T
>
col_sum
;
col_sum
(
dev_ctx
,
g_out_sub
,
&
g_x_sub
);
g_out_offset
+=
repeat_num
*
x_seq_len
;
}
}
}
};
...
...
@@ -117,15 +170,29 @@ template <typename DeviceContext, typename T>
class
SequenceExpandGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
g_out
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Input
<
LoDTensor
>
(
"Out"
);
auto
*
d_out
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
y
=
context
.
Input
<
LoDTensor
>
(
"Y"
);
auto
*
g_x
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
int
ref_level
=
context
.
Attr
<
int
>
(
"ref_level"
);
g_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
g_x
->
set_lod
(
x
->
lod
());
auto
&
x_lod
=
x
->
lod
();
auto
&
y_lod
=
y
->
lod
();
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
// just copy the gradient
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
framework
::
TensorCopy
(
*
g_out
,
context
.
GetPlace
(),
g_x
);
return
;
}
auto
*
d_x
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
d_x
->
set_lod
(
x
->
lod
());
SequenceExpandGradFunctor
<
DeviceContext
,
T
>
functor
;
functor
(
context
.
template
device_context
<
DeviceContext
>(),
*
x
,
*
out
,
*
d
_out
,
d
_x
);
functor
(
context
.
template
device_context
<
DeviceContext
>(),
*
x
,
*
y
,
*
g
_out
,
g
_x
);
}
};
...
...
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