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体验新版 GitCode,发现更多精彩内容 >>
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b10df8bc
编写于
11月 27, 2018
作者:
J
JiabinYang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine code and add none bias ut, test=develop
上级
81e14576
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
94 addition
and
42 deletion
+94
-42
paddle/fluid/framework/selected_rows.h
paddle/fluid/framework/selected_rows.h
+2
-1
paddle/fluid/operators/hierarchical_sigmoid_op.cc
paddle/fluid/operators/hierarchical_sigmoid_op.cc
+32
-20
paddle/fluid/operators/hierarchical_sigmoid_op.h
paddle/fluid/operators/hierarchical_sigmoid_op.h
+2
-7
paddle/fluid/operators/math/matrix_bit_code.cc
paddle/fluid/operators/math/matrix_bit_code.cc
+2
-2
paddle/fluid/operators/math/matrix_bit_code.h
paddle/fluid/operators/math/matrix_bit_code.h
+1
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+5
-4
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
+50
-7
未找到文件。
paddle/fluid/framework/selected_rows.h
浏览文件 @
b10df8bc
...
...
@@ -118,7 +118,8 @@ class SelectedRows {
*
* @return index of the key.
*/
int64_t
AutoGrownIndex
(
int64_t
key
,
bool
auto_grown
,
bool
is_test
=
false
)
{
inline
int64_t
AutoGrownIndex
(
int64_t
key
,
bool
auto_grown
,
bool
is_test
=
false
)
{
if
(
is_test
)
{
auto
iter
=
id_to_index_
.
find
(
key
);
if
(
iter
==
id_to_index_
.
end
())
{
...
...
paddle/fluid/operators/hierarchical_sigmoid_op.cc
浏览文件 @
b10df8bc
...
...
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/hierarchical_sigmoid_op.h"
#include <string>
#include <vector>
namespace
paddle
{
namespace
operators
{
...
...
@@ -109,7 +109,8 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"Bias"
,
"(LoDTensor, optional), The bias is a tensor with shape or "
"[non_leaf_num, 1]"
"[num_classes - 1, 1]."
);
"[num_classes - 1, 1]."
)
.
AsDispensable
();
AddOutput
(
"Out"
,
"(LoDTensor, required) The output of hierarchical sigmoid operator."
...
...
@@ -173,31 +174,42 @@ class HierarchicalSigmoidGradOpGradVarTypeInference
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
auto
out_W_var_name
=
op_desc
.
Output
(
framework
::
GradVarName
(
"W"
)).
front
();
auto
out_Bias_var_name
=
op_desc
.
Output
(
framework
::
GradVarName
(
"Bias"
)).
front
();
auto
w_grad_var_name
=
op_desc
.
Output
(
framework
::
GradVarName
(
"W"
)).
front
();
auto
bias_grad_var_name_vec
=
op_desc
.
Output
(
framework
::
GradVarName
(
"Bias"
));
std
::
string
bias_grad_var_name
;
bool
hasBias
=
false
;
if
(
bias_grad_var_name_vec
.
size
())
{
hasBias
=
true
;
bias_grad_var_name
=
op_desc
.
Output
(
framework
::
GradVarName
(
"Bias"
)).
front
();
}
auto
attr
=
op_desc
.
GetAttr
(
"is_sparse"
);
bool
is_sparse
=
boost
::
get
<
bool
>
(
attr
);
if
(
is_sparse
)
{
VLOG
(
3
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"W"
)
<<
" is set to SelectedRows"
;
block
->
Var
(
out_W_var_name
)
->
SetType
(
framework
::
proto
::
VarType
::
SELECTED_ROWS
);
VLOG
(
3
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"Bias"
)
<<
" is set to SelectedRows"
;
block
->
Var
(
out_Bias_var_name
)
VLOG
(
30
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"W"
)
<<
" is set to SelectedRows"
;
block
->
Var
(
w_grad_var_name
)
->
SetType
(
framework
::
proto
::
VarType
::
SELECTED_ROWS
);
if
(
hasBias
)
{
VLOG
(
30
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"Bias"
)
<<
" is set to SelectedRows"
;
block
->
Var
(
bias_grad_var_name
)
->
SetType
(
framework
::
proto
::
VarType
::
SELECTED_ROWS
);
}
}
else
{
VLOG
(
3
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"W"
)
<<
" is set to LoDTensor"
;
block
->
Var
(
out_W_var_name
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
VLOG
(
3
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"Bias"
)
<<
" is set to LoDTensor"
;
block
->
Var
(
out_Bias_var_name
)
VLOG
(
30
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"W"
)
<<
" is set to LoDTensor"
;
block
->
Var
(
w_grad_var_name
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
if
(
hasBias
)
{
VLOG
(
30
)
<<
"hierarchical_sigmoid_grad op "
<<
framework
::
GradVarName
(
"Bias"
)
<<
" is set to LoDTensor"
;
block
->
Var
(
bias_grad_var_name
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
}
}
block
->
Var
(
out_W
_var_name
)
->
SetDataType
(
block
->
Var
(
"W"
)
->
GetDataType
());
block
->
Var
(
w_grad
_var_name
)
->
SetDataType
(
block
->
Var
(
"W"
)
->
GetDataType
());
}
};
...
...
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @
b10df8bc
...
...
@@ -33,7 +33,6 @@ using platform::Transform;
std
::
vector
<
int64_t
>
cal_rows
(
const
framework
::
LoDTensor
&
path
)
{
std
::
set
<
int64_t
>
tmp
;
std
::
vector
<
int64_t
>
rows
;
rows
.
clear
();
for
(
size_t
i
=
0
;
i
<
static_cast
<
size_t
>
(
path
.
dims
()[
0
]);
i
++
)
{
for
(
size_t
j
=
0
;
j
<
static_cast
<
size_t
>
(
path
.
dims
()[
1
]);
j
++
)
{
int64_t
temp
=
...
...
@@ -63,8 +62,6 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
bool
is_custom
=
false
;
if
(
path
)
{
is_custom
=
true
;
}
else
{
is_custom
=
false
;
}
int64_t
code_length
=
path
?
path
->
dims
()[
1
]
:
math
::
FindLastSet
(
num_classes
-
1
);
...
...
@@ -96,7 +93,7 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
out_mat
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
out
);
if
(
bias
)
{
bit_code
->
Add
(
pre_out
,
*
bias
);
bit_code
->
Add
(
*
bias
,
pre_out
);
}
bit_code
->
Mul
(
pre_out
,
*
w
,
*
in
);
// clip to [-40, 40]
...
...
@@ -145,8 +142,6 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
bool
is_custom
=
false
;
if
(
path
)
{
is_custom
=
true
;
}
else
{
is_custom
=
false
;
}
std
::
unique_ptr
<
math
::
MatrixBitCodeFunctor
<
T
>>
bit_code
;
...
...
@@ -192,7 +187,7 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
auto
*
w_grad
=
ctx
.
Output
<
framework
::
SelectedRows
>
(
framework
::
GradVarName
(
"W"
));
w_grad
->
set_rows
(
real_rows
);
//
build ids -> rows index map
//
Build a map of id -> row_index to speed up finding the index of one id
w_grad
->
SyncIndex
();
w_grad
->
set_height
(
w
->
dims
()[
0
]);
auto
*
w_grad_value
=
w_grad
->
mutable_value
();
...
...
paddle/fluid/operators/math/matrix_bit_code.cc
浏览文件 @
b10df8bc
...
...
@@ -19,8 +19,8 @@ namespace operators {
namespace
math
{
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
Add
(
framework
::
LoDTensor
*
tmat
,
const
framework
::
LoDTensor
&
vec
)
{
void
MatrixBitCodeFunctor
<
T
>::
Add
(
const
framework
::
LoDTensor
&
vec
,
framework
::
LoDTensor
*
tmat
)
{
size_t
batch_size
=
tmat
->
dims
()[
0
];
size_t
width
=
tmat
->
dims
()[
1
];
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
...
...
paddle/fluid/operators/math/matrix_bit_code.h
浏览文件 @
b10df8bc
...
...
@@ -234,7 +234,7 @@ class MatrixBitCodeFunctor {
/* For j < code_length
tmat(i, j) += vec(0, index(i, j))
*/
void
Add
(
framework
::
LoDTensor
*
tmat
,
const
framework
::
LoDTensor
&
vec
);
void
Add
(
const
framework
::
LoDTensor
&
vec
,
framework
::
LoDTensor
*
tmat
);
/* For j < code_length
vec(0, index(i, j)) += tmat(i, j)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
b10df8bc
...
...
@@ -4535,12 +4535,12 @@ def nce(input,
def
hsigmoid
(
input
,
label
,
num_classes
=
None
,
non_leaf_num
=
None
,
ptable
=
None
,
pcode
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
name
=
None
,
non_leaf_num
=
None
,
ptable
=
None
,
pcode
=
None
,
is_costum
=
False
,
is_sparse
=
False
):
"""
...
...
@@ -4583,7 +4583,8 @@ def hsigmoid(input,
will be named automatically. Default: None.
is_costum: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is
set you need to set ptable/pcode/non_leaf_num, otherwise num_classes should be set
is_sparse: (bool|False)using sparse update instead of dense update
is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient
of W and input will be sparse.
Returns:
Out: (LodTensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
...
...
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
浏览文件 @
b10df8bc
...
...
@@ -110,12 +110,13 @@ def hsigmoidWithCustomTree(x, w, ptable, pcode, label, bias, num_classes):
pre_output
=
np
.
zeros
((
batch_size
,
code_length
))
pre_sum
=
np
.
zeros
((
batch_size
,
1
))
out
=
np
.
zeros
((
batch_size
,
1
)).
astype
(
"float32"
)
for
i
in
range
(
batch_size
):
code_table
=
CodeTableWithCustomTree
(
ptable
,
pcode
,
i
)
length
=
code_table
.
get_length
()
for
j
in
range
(
length
):
idx
=
code_table
.
cal_index
(
j
)
pre_output
[
i
][
j
]
+=
bias
[
idx
][
0
]
if
isinstance
(
bias
,
np
.
ndarray
):
for
i
in
range
(
batch_size
):
code_table
=
CodeTableWithCustomTree
(
ptable
,
pcode
,
i
)
length
=
code_table
.
get_length
()
for
j
in
range
(
length
):
idx
=
code_table
.
cal_index
(
j
)
pre_output
[
i
][
j
]
+=
bias
[
idx
][
0
]
for
i
in
range
(
batch_size
):
code_table
=
CodeTableWithCustomTree
(
ptable
,
pcode
,
i
)
length
=
code_table
.
get_length
()
...
...
@@ -215,11 +216,11 @@ class TestHSigmoidOpWithSparseGrad(unittest.TestCase):
cost
=
fluid
.
layers
.
hsigmoid
(
input
=
emb
,
label
=
label
,
bias_attr
=
True
,
non_leaf_num
=
3
,
ptable
=
ptable
,
pcode
=
pcode
,
is_costum
=
True
,
bias_attr
=
True
,
is_sparse
=
is_sparse
)
avg_cost
=
fluid
.
layers
.
reduce_mean
(
cost
)
...
...
@@ -299,5 +300,47 @@ class TestHSigmoidOpWithCostumTree(OpTest):
self
.
check_grad
([
'Bias'
,
'X'
,
'W'
],
[
'Out'
],
no_grad_set
=
set
(
'Label'
))
class
TestHSigmoidOpWithCostumTreeWithoutBias
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"hierarchical_sigmoid"
num_classes
=
6
#using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
feature_size
=
8
batch_size
=
4
x
=
np
.
random
.
random
((
batch_size
,
feature_size
)).
astype
(
"float32"
)
*
2
w
=
np
.
random
.
random
(
(
num_classes
-
1
,
feature_size
)).
astype
(
"float32"
)
*
2
label
=
np
.
array
([
0
,
1
,
4
,
5
])
ptable
=
np
.
array
(
[(
0
,
2
,
-
1
,
-
1
,
-
1
),
(
0
,
1
,
3
,
-
1
,
-
1
),
(
0
,
1
,
4
,
-
1
,
-
1
),
(
0
,
2
,
-
1
,
-
1
,
-
1
)])
#np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
pcode
=
np
.
array
([(
0
,
0
,
-
1
,
-
1
,
-
1
),
(
1
,
1
,
1
,
-
1
,
-
1
),
(
1
,
0
,
0
,
-
1
,
-
1
),
(
0
,
1
,
-
1
,
-
1
,
-
1
)])
#np.array to store
# bias = np.random.random((num_classes - 1, 1)).astype("float32")
self
.
attrs
=
{
'num_classes'
:
num_classes
,
'is_sparse'
:
False
}
self
.
inputs
=
{
'X'
:
x
,
'W'
:
w
,
'PTable'
:
ptable
,
'PCode'
:
pcode
,
'Label'
:
label
,
}
pre_output
,
out
=
hsigmoidWithCustomTree
(
x
=
x
,
w
=
w
,
ptable
=
ptable
,
pcode
=
pcode
,
label
=
label
,
bias
=
None
,
num_classes
=
num_classes
)
self
.
outputs
=
{
'PreOut'
:
pre_output
,
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
,
'W'
],
[
'Out'
],
no_grad_set
=
set
(
'Label'
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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