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3b3d210c
编写于
4月 02, 2018
作者:
Q
qiaolongfei
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
lookuptable support SelectedRows as table parameter
上级
997e9a1f
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
80 addition
and
42 deletion
+80
-42
paddle/fluid/framework/selected_rows.h
paddle/fluid/framework/selected_rows.h
+4
-1
paddle/fluid/operators/lookup_table_op.cc
paddle/fluid/operators/lookup_table_op.cc
+1
-1
paddle/fluid/operators/lookup_table_op.h
paddle/fluid/operators/lookup_table_op.h
+75
-40
未找到文件。
paddle/fluid/framework/selected_rows.h
浏览文件 @
3b3d210c
...
...
@@ -10,6 +10,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor.h"
...
...
@@ -52,7 +55,7 @@ class SelectedRows {
private:
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
// SelectedRows are simpl
el
y concated when adding together. Until a
// SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
Vector
<
int64_t
>
rows_
;
std
::
unique_ptr
<
Tensor
>
value_
{
nullptr
};
...
...
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @
3b3d210c
...
...
@@ -84,7 +84,7 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
"If the value is -1, it makes no effect to lookup. "
"Otherwise the given value indicates padding the output "
"with zeros whenever lookup encounters it in Ids."
)
.
SetDefault
(
-
1
);
.
SetDefault
(
kNoPadding
);
AddComment
(
R"DOC(
Lookup Table Operator.
...
...
paddle/fluid/operators/lookup_table_op.h
浏览文件 @
3b3d210c
...
...
@@ -14,6 +14,9 @@ limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
...
...
@@ -25,16 +28,37 @@ namespace operators {
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
using
SelectedRows
=
framework
::
SelectedRows
;
using
DDim
=
framework
::
DDim
;
static
const
int64_t
kNoPadding
=
-
1
;
inline
size_t
getIndex
(
const
std
::
vector
<
int64_t
>
&
rows
,
int64_t
value
)
{
auto
it
=
std
::
find
(
rows
.
begin
(),
rows
.
end
(),
value
);
PADDLE_ENFORCE
(
it
!=
rows
.
end
(),
"id should be in rows"
);
return
std
::
distance
(
rows
.
begin
(),
it
);
}
template
<
typename
T
>
class
LookupTableKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
table_t
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
ids_var
=
context
.
InputVar
(
"Ids"
);
Tensor
*
output_t
=
context
.
Output
<
Tensor
>
(
"Out"
);
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
table_var
=
context
.
InputVar
(
"W"
);
auto
*
ids_var
=
context
.
InputVar
(
"Ids"
);
Tensor
*
output_t
=
context
.
Output
<
Tensor
>
(
"Out"
);
int64_t
padding_idx
=
context
.
Attr
<
int64_t
>
(
"padding_idx"
);
int64_t
*
ids
;
DDim
table_dim
;
if
(
table_var
->
IsType
<
LoDTensor
>
())
{
table_dim
=
context
.
Input
<
LoDTensor
>
(
"W"
)
->
dims
();
}
else
if
(
table_var
->
IsType
<
SelectedRows
>
())
{
auto
*
table_t
=
context
.
Input
<
SelectedRows
>
(
"W"
);
table_dim
=
table_t
->
value
().
dims
();
}
else
{
PADDLE_THROW
(
"table only support LoDTensor and SelectedRows"
);
}
int64_t
*
ids
;
int64_t
ids_numel
;
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
...
...
@@ -42,39 +66,50 @@ class LookupTableKernel : public framework::OpKernel<T> {
// when Ids's type is SelectedRows, the rows of Ids contains the
// ids to be looked up in W.
if
(
ids_var
->
IsType
<
LoDTensor
>
())
{
auto
*
ids_t
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
data
<
int64_t
>
());
auto
*
ids_t
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
data
<
int64_t
>
());
ids_numel
=
ids_t
->
numel
();
}
else
if
(
ids_var
->
IsType
<
SelectedRows
>
())
{
auto
*
ids_t
=
context
.
Input
<
SelectedRows
>
(
"Ids"
);
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
rows
().
data
());
auto
*
ids_t
=
context
.
Input
<
SelectedRows
>
(
"Ids"
);
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
rows
().
data
());
ids_numel
=
ids_t
->
rows
().
size
();
output_t
->
Resize
({
ids_numel
,
table_
t
->
dims
()
[
1
]});
output_t
->
Resize
({
ids_numel
,
table_
dim
[
1
]});
}
else
{
PADDLE_THROW
(
"Unsupported Variable Type of Ids"
);
}
int64_t
padding_idx
=
context
.
Attr
<
int64_t
>
(
"padding_idx"
);
if
(
table_var
->
IsType
<
LoDTensor
>
())
{
auto
*
table_t
=
context
.
Input
<
LoDTensor
>
(
"W"
);
int64_t
row_number
=
table_t
->
dims
()[
0
];
int64_t
row_width
=
table_t
->
dims
()[
1
];
int
N
=
table_t
->
dims
()[
0
];
int
D
=
table_t
->
dims
()[
1
];
auto
*
table
=
table_t
->
data
<
T
>
();
auto
*
output
=
output_t
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
table
=
table_t
->
data
<
T
>
();
auto
*
output
=
output_t
->
mutable_data
<
T
>
(
context
.
GetPlace
());
if
(
padding_idx
==
-
1
)
{
for
(
int64_t
i
=
0
;
i
<
ids_numel
;
++
i
)
{
PADDLE_ENFORCE_LT
(
ids
[
i
],
N
);
if
(
padding_idx
!=
kNoPadding
&&
ids
[
i
]
==
padding_idx
)
{
memset
(
output
+
i
*
row_width
,
0
,
row_width
*
sizeof
(
T
));
}
else
{
PADDLE_ENFORCE_LT
(
ids
[
i
],
row_number
);
PADDLE_ENFORCE_GE
(
ids
[
i
],
0
);
memcpy
(
output
+
i
*
D
,
table
+
ids
[
i
]
*
D
,
D
*
sizeof
(
T
));
memcpy
(
output
+
i
*
row_width
,
table
+
ids
[
i
]
*
row_width
,
row_width
*
sizeof
(
T
));
}
}
else
{
}
}
else
if
(
table_var
->
IsType
<
SelectedRows
>
())
{
const
auto
&
table_t
=
table_var
->
Get
<
SelectedRows
>
();
int64_t
row_width
=
table_t
.
value
().
dims
()[
1
];
const
auto
*
table
=
table_t
.
value
().
data
<
T
>
();
auto
*
output
=
output_t
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int64_t
i
=
0
;
i
<
ids_numel
;
++
i
)
{
if
(
ids
[
i
]
==
padding_idx
)
{
memset
(
output
+
i
*
D
,
0
,
D
*
sizeof
(
T
));
if
(
padding_idx
!=
kNoPadding
&&
ids
[
i
]
==
padding_idx
)
{
memset
(
output
+
i
*
row_width
,
0
,
row_width
*
sizeof
(
T
));
}
else
{
PADDLE_ENFORCE_LT
(
ids
[
i
],
N
);
PADDLE_ENFORCE_GE
(
ids
[
i
],
0
);
memcpy
(
output
+
i
*
D
,
table
+
ids
[
i
]
*
D
,
D
*
sizeof
(
T
));
auto
id_index
=
getIndex
(
table_t
.
rows
(),
ids
[
i
]);
memcpy
(
output
+
i
*
row_width
,
table
+
id_index
*
row_width
,
row_width
*
sizeof
(
T
));
}
}
}
...
...
@@ -84,17 +119,17 @@ class LookupTableKernel : public framework::OpKernel<T> {
template
<
typename
T
>
class
LookupTableGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
bool
is_sparse
=
context
.
Attr
<
bool
>
(
"is_sparse"
);
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if
(
is_sparse
)
{
auto
*
ids
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
auto
*
table
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
d_output
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_table
=
context
.
Output
<
SelectedRows
>
(
framework
::
GradVarName
(
"W"
));
auto
*
ids
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
auto
*
table
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
d_output
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_table
=
context
.
Output
<
SelectedRows
>
(
framework
::
GradVarName
(
"W"
));
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
auto
ids_dim
=
ids
->
dims
();
framework
::
Vector
<
int64_t
>
new_rows
;
...
...
@@ -104,31 +139,31 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
}
d_table
->
set_rows
(
new_rows
);
auto
*
d_table_value
=
d_table
->
mutable_value
();
auto
*
d_table_value
=
d_table
->
mutable_value
();
d_table_value
->
Resize
({
ids_dim
[
0
],
table
->
dims
()[
1
]});
d_table_value
->
mutable_data
<
T
>
(
context
.
GetPlace
());
d_table
->
set_height
(
table
->
dims
()[
0
]);
auto
*
d_output_data
=
d_output
->
data
<
T
>
();
auto
*
d_table_data
=
d_table_value
->
data
<
T
>
();
auto
*
d_output_data
=
d_output
->
data
<
T
>
();
auto
*
d_table_data
=
d_table_value
->
data
<
T
>
();
PADDLE_ENFORCE_EQ
(
d_table_value
->
dims
(),
d_output
->
dims
());
memcpy
(
d_table_data
,
d_output_data
,
sizeof
(
T
)
*
d_output
->
numel
());
}
else
{
auto
*
ids
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
auto
*
d_output
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_table
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"W"
));
auto
*
table
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
ids
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
auto
*
d_output
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_table
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"W"
));
auto
*
table
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
auto
ids_dim
=
ids
->
dims
();
int
N
=
table
->
dims
()[
0
];
int
D
=
d_output
->
dims
()[
1
];
auto
*
d_output_data
=
d_output
->
data
<
T
>
();
auto
*
d_table_data
=
d_table
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
d_output_data
=
d_output
->
data
<
T
>
();
auto
*
d_table_data
=
d_table
->
mutable_data
<
T
>
(
context
.
GetPlace
());
memset
(
d_table_data
,
0
,
d_table
->
numel
()
*
sizeof
(
T
));
...
...
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