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体验新版 GitCode,发现更多精彩内容 >>
提交
b9397b26
编写于
3月 13, 2018
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
remove concat_rows
上级
f1c3ecb2
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
88 addition
and
145 deletion
+88
-145
paddle/fluid/operators/lookup_table_op.cc
paddle/fluid/operators/lookup_table_op.cc
+26
-57
paddle/fluid/operators/lookup_table_op.cu
paddle/fluid/operators/lookup_table_op.cu
+6
-6
paddle/fluid/operators/lookup_table_op.h
paddle/fluid/operators/lookup_table_op.h
+7
-6
python/paddle/fluid/tests/unittests/test_concat_rows_op.py
python/paddle/fluid/tests/unittests/test_concat_rows_op.py
+0
-76
python/paddle/fluid/tests/unittests/test_lookup_table_op.py
python/paddle/fluid/tests/unittests/test_lookup_table_op.py
+49
-0
未找到文件。
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @
b9397b26
...
@@ -34,9 +34,12 @@ class LookupTableOp : public framework::OperatorWithKernel {
...
@@ -34,9 +34,12 @@ class LookupTableOp : public framework::OperatorWithKernel {
auto
ids_dims
=
ctx
->
GetInputDim
(
"Ids"
);
auto
ids_dims
=
ctx
->
GetInputDim
(
"Ids"
);
auto
ids_var_type
=
ctx
->
GetInputsVarType
(
"Ids"
).
front
();
auto
ids_var_type
=
ctx
->
GetInputsVarType
(
"Ids"
).
front
();
// lookup_table and concat_rows use the same InferShape, for lookup_table,
// ids_var_type should be LoDTensor, for concat_rows, it should be
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// SelectedRows.
// is LoDTensor, this tensor contains the ids to be looked up in W
// and it must be a column vector with rank = 2 while the 2nd dimension
// size must be 1, when Ids's type is SelectedRows, the rows of Ids
// contains the ids to be looked up in W;
if
(
ids_var_type
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
if
(
ids_var_type
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
PADDLE_ENFORCE_EQ
(
ids_dims
.
size
(),
2
);
PADDLE_ENFORCE_EQ
(
ids_dims
.
size
(),
2
);
PADDLE_ENFORCE_EQ
(
ids_dims
[
1
],
1
);
PADDLE_ENFORCE_EQ
(
ids_dims
[
1
],
1
);
...
@@ -60,70 +63,41 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -60,70 +63,41 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
LookupTableOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
LookupTableOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"W"
,
AddInput
(
"W"
,
"
An
input represents embedding tensors, "
"
(Tensor) The
input represents embedding tensors, "
"which is a learnable parameter."
);
"which is a learnable parameter."
);
AddInput
(
"Ids"
,
"An input with type int32 or int64 "
"contains the ids to be looked up in W. "
"Ids must be a column vector with rank = 2. "
"The 2nd dimension size must be 1."
);
AddOutput
(
"Out"
,
"The lookup results, which have the same type as W."
);
AddAttr
<
bool
>
(
"is_sparse"
,
"(boolean, default false) "
"Sparse update"
)
.
SetDefault
(
false
);
AddAttr
<
int64_t
>
(
"padding_idx"
,
"(int64, default -1) "
"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
);
AddComment
(
R"DOC(
Lookup Table Operator.
This operator is used to perform lookups on the parameter W,
then concatenated into a dense tensor.
The input Ids can carry the LoD (Level of Details) information,
or not. And the output only shares the LoD information with input Ids.
)DOC"
);
}
};
class
ConcatRowsOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ConcatRowsOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"W"
,
"(Tensor) The input tensor of concat_rows operator. "
"The rank of this tensor is 2."
);
AddInput
(
AddInput
(
"Ids"
,
"Ids"
,
"(SelectedRows) The rows of Ids contains the index to be looked up "
"(Tensor or SelectedRows) Ids's type can be Tensor or "
"SelectedRows, when Ids's type is Tensor, this tensor contains "
"the ids to be looked up in W and it must be a column vector with "
"rank = 2 while the 2nd dimension size must be 1; when Ids's type is "
"SelectedRows, the rows of Ids contains the ids to be looked up "
"in W."
);
"in W."
);
AddOutput
(
"Out"
,
AddOutput
(
"Out"
,
"(
SelectedRows or Tensor) The result of concatenating, which
"
"(
Tensor or SelectedRows) The lookup results, which have the
"
"
have the
same type as W."
);
"same type as W."
);
AddAttr
<
bool
>
(
"is_sparse"
,
AddAttr
<
bool
>
(
"is_sparse"
,
"(boolean, default
true) This attribution is invalid, it's
"
"(boolean, default
false)
"
"
only used by `Lookup Table Operator`
."
)
"
Sparse update
."
)
.
SetDefault
(
tru
e
);
.
SetDefault
(
fals
e
);
AddAttr
<
int64_t
>
(
"padding_idx"
,
AddAttr
<
int64_t
>
(
"padding_idx"
,
"(int64, default -1) "
"(int64, default -1) "
"If the value is -1, it makes no effect to lookup. "
"If the value is -1, it makes no effect to lookup. "
"Otherwise the given value indicates padding the output "
"Otherwise the given value indicates padding the output "
"with zeros whenever lookup encounters it in Ids."
)
"with zeros whenever lookup encounters it in Ids."
)
.
SetDefault
(
-
1
);
.
SetDefault
(
-
1
);
AddComment
(
R"DOC(
AddComment
(
R"DOC(
ConcatRows
Operator.
Lookup Table
Operator.
This operator is used to perform lookups on the W(dense tensor) according to
This operator is used to perform lookups on the parameter W,
rows contained by Idx(sparse tensor), then concatenates them into a sparse
then concatenated into a dense or sparse tensor.
tensor or dense tensor.
The type of Ids(Input) is SelectedRows.
The type of Ids(Input) is SelectedRows, Tensor or LoDTensor, when Ids's
type is SelectedRows, the rows of Ids contains the ids to be looked up in W;
when Ids's type is Tensor, this tensor contains the ids to be looked up in W
and it must be a column vector with rank = 2 while the 2nd dimension size must be 1,
at this time, Ids can carry the LoD (Level of Details) information, or not, and
the output only shares the LoD information with input Ids.
)DOC"
);
)DOC"
);
}
}
...
@@ -189,8 +163,3 @@ REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>,
...
@@ -189,8 +163,3 @@ REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>,
ops
::
LookupTableKernel
<
double
>
);
ops
::
LookupTableKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
lookup_table_grad
,
ops
::
LookupTableGradKernel
<
float
>
,
REGISTER_OP_CPU_KERNEL
(
lookup_table_grad
,
ops
::
LookupTableGradKernel
<
float
>
,
ops
::
LookupTableGradKernel
<
double
>
);
ops
::
LookupTableGradKernel
<
double
>
);
// concat_rows is used by regularization and it doesn't have gradient operation.
REGISTER_OPERATOR
(
concat_rows
,
ops
::
LookupTableOp
,
ops
::
ConcatRowsOpMaker
);
REGISTER_OP_CPU_KERNEL
(
concat_rows
,
ops
::
LookupTableKernel
<
float
>
,
ops
::
LookupTableKernel
<
double
>
);
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @
b9397b26
...
@@ -74,16 +74,16 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
...
@@ -74,16 +74,16 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
table_t
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
table_t
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
output_t
=
context
.
Output
<
Tensor
>
(
"Out"
);
int64_t
padding_idx
=
context
.
Attr
<
int64_t
>
(
"padding_idx"
);
int64_t
padding_idx
=
context
.
Attr
<
int64_t
>
(
"padding_idx"
);
auto
*
ids_var
=
context
.
InputVar
(
"Ids"
);
// int tensor
auto
*
ids_var
=
context
.
InputVar
(
"Ids"
);
int64_t
*
ids
;
int64_t
*
ids
;
int64_t
K
;
int64_t
K
;
auto
*
output_t
=
context
.
Output
<
Tensor
>
(
"Out"
);
// float tensor;
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// is LoDTensor, this tensor contains the ids to be looked up in W;
// lookup_table and concat_rows use the same kernel, for lookup_table,
// when Ids's type is SelectedRows, the rows of Ids contains the
// ids_var_type should be LoDTensor, for concat_rows, ids_var_type and
// ids to be looked up in W.
// out_var_type should be SelectedRows.
if
(
ids_var
->
IsType
<
LoDTensor
>
())
{
if
(
ids_var
->
IsType
<
LoDTensor
>
())
{
auto
*
ids_t
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
auto
*
ids_t
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
data
<
int64_t
>
());
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
data
<
int64_t
>
());
...
...
paddle/fluid/operators/lookup_table_op.h
浏览文件 @
b9397b26
...
@@ -30,15 +30,16 @@ template <typename T>
...
@@ -30,15 +30,16 @@ template <typename T>
class
LookupTableKernel
:
public
framework
::
OpKernel
<
T
>
{
class
LookupTableKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
table_t
=
context
.
Input
<
LoDTensor
>
(
"W"
);
// float tensor
auto
*
table_t
=
context
.
Input
<
LoDTensor
>
(
"W"
);
auto
*
ids_var
=
context
.
InputVar
(
"Ids"
);
// int tensor
auto
*
output_t
=
context
.
Output
<
Tensor
>
(
"Out"
);
auto
*
ids_var
=
context
.
InputVar
(
"Ids"
);
int64_t
*
ids
;
int64_t
*
ids
;
int64_t
ids_numel
;
int64_t
ids_numel
;
auto
*
output_t
=
context
.
Output
<
Tensor
>
(
"Out"
);
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
//
lookup_table and concat_rows use the same kernel, for lookup_table,
//
is LoDTensor, this tensor contains the ids to be looked up in W;
//
ids_var_type should be LoDTensor, for concat_rows, ids_var_type and
//
when Ids's type is SelectedRows, the rows of Ids contains the
//
out_var_type should be SelectedRows
.
//
ids to be looked up in W
.
if
(
ids_var
->
IsType
<
LoDTensor
>
())
{
if
(
ids_var
->
IsType
<
LoDTensor
>
())
{
auto
*
ids_t
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
auto
*
ids_t
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
data
<
int64_t
>
());
ids
=
const_cast
<
int64_t
*>
(
ids_t
->
data
<
int64_t
>
());
...
...
python/paddle/fluid/tests/unittests/test_concat_rows_op.py
已删除
100644 → 0
浏览文件 @
f1c3ecb2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
op_test
import
OpTest
class
TestConcatRowsOp
(
OpTest
):
def
check_with_place
(
self
,
place
):
scope
=
core
.
Scope
()
# create and initialize Grad Variable
height
=
10
rows
=
[
0
,
4
,
4
,
7
]
row_numel
=
12
ids_selected_rows
=
scope
.
var
(
'Ids'
).
get_selected_rows
()
ids_selected_rows
.
set_height
(
height
)
ids_selected_rows
.
set_rows
(
rows
)
np_array
=
np
.
ones
((
len
(
rows
),
row_numel
)).
astype
(
"float32"
)
ids_tensor
=
ids_selected_rows
.
get_tensor
()
ids_tensor
.
set
(
np_array
,
place
)
# create and initialize W Variable
W
=
scope
.
var
(
'W'
).
get_tensor
()
W_array
=
np
.
full
((
height
,
row_numel
),
1.0
).
astype
(
"float32"
)
for
i
in
range
(
height
):
W_array
[
i
]
*=
i
W
.
set
(
W_array
,
place
)
Out
=
scope
.
var
(
'Out'
).
get_selected_rows
()
Out_array
=
np
.
full
((
len
(
rows
),
row_numel
),
-
1.0
).
astype
(
"float32"
)
Out
.
set_height
(
height
)
Out
.
set_rows
(
rows
)
Out_tensor
=
Out
.
get_tensor
()
Out_tensor
.
set
(
Out_array
,
place
)
# create and run concat_rows_op operator
concat_rows_op
=
Operator
(
"concat_rows"
,
W
=
'W'
,
Ids
=
'Ids'
,
Out
=
'Out'
,
attrs
=
{
'is_sparse'
:
True
})
concat_rows_op
.
run
(
scope
,
place
)
# get and compare result
result_array
=
np
.
array
(
Out_tensor
)
for
idx
,
row
in
enumerate
(
rows
):
assert
(
row
==
result_array
[
idx
]).
all
()
def
test_concat_rows
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
core
.
CUDAPlace
(
0
))
for
place
in
places
:
self
.
check_with_place
(
place
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_lookup_table_op.py
浏览文件 @
b9397b26
...
@@ -14,6 +14,8 @@
...
@@ -14,6 +14,8 @@
import
unittest
import
unittest
import
numpy
as
np
import
numpy
as
np
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
op_test
import
OpTest
from
op_test
import
OpTest
...
@@ -47,5 +49,52 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
...
@@ -47,5 +49,52 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
pass
pass
# Testing look_up_table when Ids's type is SelectedRows.
class
TestLookupTableIdsIsSelectedRows
(
OpTest
):
def
check_with_place
(
self
,
place
):
scope
=
core
.
Scope
()
height
=
10
rows
=
[
0
,
4
,
4
,
7
]
row_numel
=
12
ids_selected_rows
=
scope
.
var
(
'Ids'
).
get_selected_rows
()
ids_selected_rows
.
set_height
(
height
)
ids_selected_rows
.
set_rows
(
rows
)
np_array
=
np
.
ones
((
len
(
rows
),
row_numel
)).
astype
(
"float32"
)
ids_tensor
=
ids_selected_rows
.
get_tensor
()
ids_tensor
.
set
(
np_array
,
place
)
W
=
scope
.
var
(
'W'
).
get_tensor
()
W_array
=
np
.
full
((
height
,
row_numel
),
1.0
).
astype
(
"float32"
)
for
i
in
range
(
height
):
W_array
[
i
]
*=
i
W
.
set
(
W_array
,
place
)
Out
=
scope
.
var
(
'Out'
).
get_selected_rows
()
Out_array
=
np
.
full
((
len
(
rows
),
row_numel
),
-
1.0
).
astype
(
"float32"
)
Out
.
set_height
(
height
)
Out
.
set_rows
(
rows
)
Out_tensor
=
Out
.
get_tensor
()
Out_tensor
.
set
(
Out_array
,
place
)
# create and run concat_rows_op operator
concat_rows_op
=
Operator
(
"lookup_table"
,
W
=
'W'
,
Ids
=
'Ids'
,
Out
=
'Out'
)
concat_rows_op
.
run
(
scope
,
place
)
# get and compare result
result_array
=
np
.
array
(
Out_tensor
)
for
idx
,
row
in
enumerate
(
rows
):
assert
(
row
==
result_array
[
idx
]).
all
()
def
test_concat_rows
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
core
.
CUDAPlace
(
0
))
for
place
in
places
:
self
.
check_with_place
(
place
)
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
unittest
.
main
()
unittest
.
main
()
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