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c3c3c0b3
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c3c3c0b3
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
polish code, test=develop
上级
7389597c
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
176 addition
and
182 deletion
+176
-182
paddle/fluid/framework/mixed_vector.h
paddle/fluid/framework/mixed_vector.h
+0
-6
paddle/fluid/framework/selected_rows.cc
paddle/fluid/framework/selected_rows.cc
+52
-0
paddle/fluid/framework/selected_rows.h
paddle/fluid/framework/selected_rows.h
+9
-46
paddle/fluid/operators/hierarchical_sigmoid_op.cc
paddle/fluid/operators/hierarchical_sigmoid_op.cc
+1
-1
paddle/fluid/operators/hierarchical_sigmoid_op.h
paddle/fluid/operators/hierarchical_sigmoid_op.h
+38
-41
paddle/fluid/operators/math/matrix_bit_code.cc
paddle/fluid/operators/math/matrix_bit_code.cc
+30
-32
paddle/fluid/operators/math/matrix_bit_code.h
paddle/fluid/operators/math/matrix_bit_code.h
+42
-52
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+1
-1
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
+3
-3
未找到文件。
paddle/fluid/framework/mixed_vector.h
浏览文件 @
c3c3c0b3
...
@@ -488,12 +488,6 @@ class CPUVector : public std::vector<T, std::allocator<T>> {
...
@@ -488,12 +488,6 @@ class CPUVector : public std::vector<T, std::allocator<T>> {
return
os
;
return
os
;
}
}
size_t
size
()
const
noexcept
{
size_t
size
=
static_cast
<
size_t
>
(
std
::
vector
<
T
,
std
::
allocator
<
T
>>::
size
());
return
size
;
}
T
&
operator
[](
size_t
id
)
{
return
this
->
at
(
id
);
}
T
&
operator
[](
size_t
id
)
{
return
this
->
at
(
id
);
}
const
T
&
operator
[](
size_t
id
)
const
{
return
this
->
at
(
id
);
}
const
T
&
operator
[](
size_t
id
)
const
{
return
this
->
at
(
id
);
}
...
...
paddle/fluid/framework/selected_rows.cc
浏览文件 @
c3c3c0b3
...
@@ -140,6 +140,58 @@ bool SelectedRows::HasKey(int64_t key) const {
...
@@ -140,6 +140,58 @@ bool SelectedRows::HasKey(int64_t key) const {
:
true
;
:
true
;
}
}
int64_t
SelectedRows
::
AutoGrownIndex
(
int64_t
key
,
bool
auto_grown
,
bool
is_test
)
{
if
(
is_test
)
{
auto
iter
=
id_to_index_
.
find
(
key
);
if
(
iter
==
id_to_index_
.
end
())
{
return
-
1
;
}
else
{
return
iter
->
second
;
}
}
rwlock_
->
RDLock
();
auto
iter
=
id_to_index_
.
find
(
key
);
if
(
iter
==
id_to_index_
.
end
())
{
rwlock_
->
UNLock
();
if
(
!
auto_grown
)
{
PADDLE_THROW
(
"key %d not found"
,
key
);
}
rwlock_
->
WRLock
();
auto
map_size
=
id_to_index_
.
size
();
auto
vector_size
=
rows_
.
size
();
if
(
map_size
!=
vector_size
)
{
rwlock_
->
UNLock
();
PADDLE_THROW
(
"id_to_index_ size %d should have the same size with rows_ %d"
,
map_size
,
vector_size
);
}
auto
write_iter
=
id_to_index_
.
find
(
key
);
if
(
write_iter
==
id_to_index_
.
end
())
{
int
row_num
=
rows_
.
size
();
if
(
row_num
==
value_
->
dims
()[
0
])
{
rwlock_
->
UNLock
();
PADDLE_THROW
(
"selected rows is full, then length exceed %d"
,
row_num
);
}
// key logic to put a key into id_to_index_
rows_
.
push_back
(
key
);
auto
index
=
static_cast
<
int64_t
>
(
rows_
.
size
()
-
1
);
id_to_index_
[
key
]
=
index
;
rwlock_
->
UNLock
();
return
index
;
}
else
{
auto
index
=
write_iter
->
second
;
rwlock_
->
UNLock
();
return
index
;
}
}
else
{
auto
index
=
iter
->
second
;
rwlock_
->
UNLock
();
return
index
;
}
}
void
SelectedRows
::
SyncIndex
()
{
void
SelectedRows
::
SyncIndex
()
{
rwlock_
->
WRLock
();
rwlock_
->
WRLock
();
id_to_index_
.
clear
();
id_to_index_
.
clear
();
...
...
paddle/fluid/framework/selected_rows.h
浏览文件 @
c3c3c0b3
...
@@ -118,54 +118,17 @@ class SelectedRows {
...
@@ -118,54 +118,17 @@ class SelectedRows {
*
*
* @return index of the key.
* @return index of the key.
*/
*/
inline
int64_t
AutoGrownIndex
(
int64_t
key
,
bool
auto_grown
,
int64_t
AutoGrownIndex
(
int64_t
key
,
bool
auto_grown
,
bool
is_test
=
false
);
bool
is_test
=
false
)
{
if
(
is_test
)
{
/*
auto
iter
=
id_to_index_
.
find
(
key
);
* @brief Get the index of the key from id_to_index_ map.
if
(
iter
==
id_to_index_
.
end
())
{
*/
return
-
1
;
inline
int64_t
GetIndexFromId
(
int64_t
key
)
{
}
else
{
return
iter
->
second
;
}
}
rwlock_
->
RDLock
();
auto
iter
=
id_to_index_
.
find
(
key
);
auto
iter
=
id_to_index_
.
find
(
key
);
if
(
iter
==
id_to_index_
.
end
())
{
if
(
iter
==
id_to_index_
.
end
())
{
rwlock_
->
UNLock
();
return
-
1
;
if
(
!
auto_grown
)
{
PADDLE_THROW
(
"key %d not found"
,
key
);
}
rwlock_
->
WRLock
();
auto
map_size
=
id_to_index_
.
size
();
auto
vector_size
=
rows_
.
size
();
if
(
map_size
!=
vector_size
)
{
rwlock_
->
UNLock
();
PADDLE_THROW
(
"id_to_index_ size %d should have the same size with rows_ %d"
,
map_size
,
vector_size
);
}
auto
write_iter
=
id_to_index_
.
find
(
key
);
if
(
write_iter
==
id_to_index_
.
end
())
{
int
row_num
=
rows_
.
size
();
if
(
row_num
==
value_
->
dims
()[
0
])
{
rwlock_
->
UNLock
();
PADDLE_THROW
(
"selected rows is full, then length exceed %d"
,
row_num
);
}
// key logic to put a key into id_to_index_
rows_
.
push_back
(
key
);
auto
index
=
static_cast
<
int64_t
>
(
rows_
.
size
()
-
1
);
id_to_index_
[
key
]
=
index
;
rwlock_
->
UNLock
();
return
index
;
}
else
{
auto
index
=
write_iter
->
second
;
rwlock_
->
UNLock
();
return
index
;
}
}
else
{
}
else
{
auto
index
=
iter
->
second
;
return
iter
->
second
;
rwlock_
->
UNLock
();
return
index
;
}
}
}
}
...
@@ -185,7 +148,7 @@ class SelectedRows {
...
@@ -185,7 +148,7 @@ class SelectedRows {
// SelectedRows add a Tensor, will the duplicate rows be handled.
// SelectedRows add a Tensor, will the duplicate rows be handled.
Vector
<
int64_t
>
rows_
;
Vector
<
int64_t
>
rows_
;
std
::
unordered_map
<
int64_t
,
int64_t
>
std
::
unordered_map
<
int64_t
,
int64_t
>
id_to_index_
;
// should not be used when
ids
has duplicate member
id_to_index_
;
// should not be used when
rows_
has duplicate member
std
::
unique_ptr
<
Tensor
>
value_
{
nullptr
};
std
::
unique_ptr
<
Tensor
>
value_
{
nullptr
};
int64_t
height_
;
// height indicates the underline tensor's height
int64_t
height_
;
// height indicates the underline tensor's height
std
::
unique_ptr
<
RWLock
>
rwlock_
{
nullptr
};
std
::
unique_ptr
<
RWLock
>
rwlock_
{
nullptr
};
...
...
paddle/fluid/operators/hierarchical_sigmoid_op.cc
浏览文件 @
c3c3c0b3
...
@@ -101,7 +101,7 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -101,7 +101,7 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
"it should have shape like [N, L], L is the length of the Path"
)
"it should have shape like [N, L], L is the length of the Path"
)
.
AsDispensable
();
.
AsDispensable
();
AddInput
(
AddInput
(
"PCode"
,
"P
ath
Code"
,
"(LoDTensor, optional), The Code on each Node of the Path from root "
"(LoDTensor, optional), The Code on each Node of the Path from root "
"to current word"
"to current word"
"it should have shape like [N, L], L is the length of the Path"
)
"it should have shape like [N, L], L is the length of the Path"
)
...
...
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @
c3c3c0b3
...
@@ -19,9 +19,11 @@ limitations under the License. */
...
@@ -19,9 +19,11 @@ limitations under the License. */
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/clip_op.h"
#include "paddle/fluid/operators/clip_op.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/matrix_bit_code.h"
#include "paddle/fluid/operators/math/matrix_bit_code.h"
#include "paddle/fluid/platform/transform.h"
#include "paddle/fluid/platform/transform.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
...
@@ -30,31 +32,26 @@ template <typename T, int MajorType = Eigen::RowMajor,
...
@@ -30,31 +32,26 @@ template <typename T, int MajorType = Eigen::RowMajor,
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
using
platform
::
Transform
;
using
platform
::
Transform
;
std
::
vector
<
int64_t
>
cal_rows
(
const
framework
::
LoDTensor
&
path
)
{
static
std
::
vector
<
int64_t
>
PathToRows
(
const
framework
::
LoDTensor
&
path
)
{
std
::
set
<
int64_t
>
tmp
;
std
::
set
<
int64_t
>
rows
;
std
::
vector
<
int64_t
>
rows
;
for
(
int64_t
i
=
0
;
i
<
path
.
numel
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
static_cast
<
size_t
>
(
path
.
dims
()[
0
]);
i
++
)
{
int64_t
row
=
path
.
data
<
int64_t
>
()[
i
];
for
(
size_t
j
=
0
;
j
<
static_cast
<
size_t
>
(
path
.
dims
()[
1
]);
j
++
)
{
if
(
row
<
0
)
{
int64_t
temp
=
continue
;
path
.
data
<
int64_t
>
()[
i
*
static_cast
<
size_t
>
(
path
.
dims
()[
1
])
+
j
];
if
(
temp
>=
0
)
{
tmp
.
insert
(
temp
);
}
}
}
rows
.
emplace
(
row
);
}
}
rows
.
assign
(
tmp
.
begin
(),
tmp
.
end
());
return
std
::
vector
<
int64_t
>
(
rows
.
begin
(),
rows
.
end
());
return
rows
;
}
}
template
<
typename
DeviceContext
,
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
HierarchicalSigmoidOpKernel
:
public
framework
::
OpKernel
<
T
>
{
class
HierarchicalSigmoidOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
in
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
);
auto
*
w
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"W"
);
auto
w
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"W"
)
);
auto
*
path
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PTable"
);
auto
*
path
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PTable"
);
auto
*
code
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PCode"
);
auto
*
code
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"P
ath
Code"
);
auto
*
label
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Label"
);
auto
label
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Label"
)
);
auto
*
bias
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Bias"
);
auto
*
bias
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Bias"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
auto
*
pre_out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"PreOut"
);
auto
*
pre_out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"PreOut"
);
...
@@ -65,7 +62,7 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
...
@@ -65,7 +62,7 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
}
}
int64_t
code_length
=
int64_t
code_length
=
path
?
path
->
dims
()[
1
]
:
math
::
FindLastSet
(
num_classes
-
1
);
path
?
path
->
dims
()[
1
]
:
math
::
FindLastSet
(
num_classes
-
1
);
int64_t
batch_size
=
in
->
dims
()[
0
];
int64_t
batch_size
=
in
.
dims
()[
0
];
framework
::
LoDTensor
sum
;
framework
::
LoDTensor
sum
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
auto
*
pre_out_data
=
pre_out
->
mutable_data
<
T
>
(
auto
*
pre_out_data
=
pre_out
->
mutable_data
<
T
>
(
...
@@ -81,10 +78,10 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
...
@@ -81,10 +78,10 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
std
::
unique_ptr
<
math
::
MatrixBitCodeFunctor
<
T
>>
bit_code
;
std
::
unique_ptr
<
math
::
MatrixBitCodeFunctor
<
T
>>
bit_code
;
if
(
!
is_custom
)
{
if
(
!
is_custom
)
{
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
num_classes
,
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
num_classes
,
label
->
data
<
int64_t
>
()));
label
.
data
<
int64_t
>
()));
}
else
{
}
else
{
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
path
,
code
,
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
*
path
,
*
code
,
label
->
data
<
int64_t
>
()));
label
.
data
<
int64_t
>
()));
}
}
std
::
vector
<
int64_t
>
sum_dims
({
batch_size
,
1UL
});
std
::
vector
<
int64_t
>
sum_dims
({
batch_size
,
1UL
});
...
@@ -95,7 +92,7 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
...
@@ -95,7 +92,7 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
if
(
bias
)
{
if
(
bias
)
{
bit_code
->
Add
(
*
bias
,
pre_out
);
bit_code
->
Add
(
*
bias
,
pre_out
);
}
}
bit_code
->
Mul
(
pre_out
,
*
w
,
*
in
);
bit_code
->
Mul
(
pre_out
,
w
,
in
);
// clip to [-40, 40]
// clip to [-40, 40]
Transform
<
DeviceContext
>
trans
;
Transform
<
DeviceContext
>
trans
;
trans
(
ctx
.
template
device_context
<
DeviceContext
>(),
pre_out_data
,
trans
(
ctx
.
template
device_context
<
DeviceContext
>(),
pre_out_data
,
...
@@ -117,23 +114,23 @@ template <typename DeviceContext, typename T>
...
@@ -117,23 +114,23 @@ template <typename DeviceContext, typename T>
class
HierarchicalSigmoidGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
class
HierarchicalSigmoidGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
in
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
);
auto
*
w
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"W"
);
auto
w
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"W"
)
);
auto
*
path
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PTable"
);
auto
*
path
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PTable"
);
auto
*
code
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PCode"
);
auto
*
code
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"P
ath
Code"
);
auto
*
bias
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Bias"
);
auto
*
bias
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Bias"
);
auto
*
in_grad
=
auto
*
in_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
bool
is_sparse
=
ctx
.
Attr
<
bool
>
(
"is_sparse"
);
bool
is_sparse
=
ctx
.
Attr
<
bool
>
(
"is_sparse"
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
math
::
SetConstant
<
DeviceContext
,
T
>
zero
;
math
::
SetConstant
<
DeviceContext
,
T
>
zero
;
auto
*
label
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Label"
);
auto
label
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Label"
)
);
auto
*
pre_out
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PreOut"
);
auto
pre_out
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"PreOut"
)
);
auto
*
out_grad
=
auto
out_grad
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
ctx
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
))
)
;
framework
::
LoDTensor
pre_out_grad
;
framework
::
LoDTensor
pre_out_grad
;
pre_out_grad
.
mutable_data
<
T
>
(
pre_out
->
dims
(),
ctx
.
GetPlace
());
pre_out_grad
.
mutable_data
<
T
>
(
pre_out
.
dims
(),
ctx
.
GetPlace
());
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
zero
(
dev_ctx
,
in_grad
,
static_cast
<
T
>
(
0.0
));
zero
(
dev_ctx
,
in_grad
,
static_cast
<
T
>
(
0.0
));
...
@@ -147,16 +144,16 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
...
@@ -147,16 +144,16 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
std
::
unique_ptr
<
math
::
MatrixBitCodeFunctor
<
T
>>
bit_code
;
std
::
unique_ptr
<
math
::
MatrixBitCodeFunctor
<
T
>>
bit_code
;
if
(
!
is_custom
)
{
if
(
!
is_custom
)
{
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
num_classes
,
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
num_classes
,
label
->
data
<
int64_t
>
()));
label
.
data
<
int64_t
>
()));
}
else
{
}
else
{
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
path
,
code
,
bit_code
.
reset
(
new
math
::
MatrixBitCodeFunctor
<
T
>
(
*
path
,
*
code
,
label
->
data
<
int64_t
>
()));
label
.
data
<
int64_t
>
()));
}
}
auto
&
place
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
&
place
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
pre_out_mat
=
EigenMatrix
<
T
>::
From
(
*
pre_out
);
auto
pre_out_mat
=
EigenMatrix
<
T
>::
From
(
pre_out
);
auto
pre_out_grad_mat
=
EigenMatrix
<
T
>::
From
(
pre_out_grad
);
auto
pre_out_grad_mat
=
EigenMatrix
<
T
>::
From
(
pre_out_grad
);
auto
out_grad_mat
=
EigenMatrix
<
T
>::
From
(
*
out_grad
);
auto
out_grad_mat
=
EigenMatrix
<
T
>::
From
(
out_grad
);
Eigen
::
array
<
int
,
2
>
bcast
{
1
,
static_cast
<
int
>
(
pre_out_grad
.
dims
()[
1
])};
Eigen
::
array
<
int
,
2
>
bcast
{
1
,
static_cast
<
int
>
(
pre_out_grad
.
dims
()[
1
])};
...
@@ -181,17 +178,17 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
...
@@ -181,17 +178,17 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"W"
));
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"W"
));
w_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
w_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
zero
(
dev_ctx
,
w_grad
,
static_cast
<
T
>
(
0.0
));
zero
(
dev_ctx
,
w_grad
,
static_cast
<
T
>
(
0.0
));
bit_code
->
MulGradWeight
(
pre_out_grad
,
w_grad
,
*
in
);
bit_code
->
MulGradWeight
(
pre_out_grad
,
w_grad
,
in
);
}
else
{
}
else
{
framework
::
Vector
<
int64_t
>
real_rows
=
cal_r
ows
(
*
path
);
framework
::
Vector
<
int64_t
>
real_rows
=
PathToR
ows
(
*
path
);
auto
*
w_grad
=
auto
*
w_grad
=
ctx
.
Output
<
framework
::
SelectedRows
>
(
framework
::
GradVarName
(
"W"
));
ctx
.
Output
<
framework
::
SelectedRows
>
(
framework
::
GradVarName
(
"W"
));
w_grad
->
set_rows
(
real_rows
);
w_grad
->
set_rows
(
real_rows
);
// Build a map of id -> row_index to speed up finding the index of one id
// Build a map of id -> row_index to speed up finding the index of one id
w_grad
->
SyncIndex
();
w_grad
->
SyncIndex
();
w_grad
->
set_height
(
w
->
dims
()[
0
]);
w_grad
->
set_height
(
w
.
dims
()[
0
]);
auto
*
w_grad_value
=
w_grad
->
mutable_value
();
auto
*
w_grad_value
=
w_grad
->
mutable_value
();
framework
::
DDim
temp_dim
(
w
->
dims
());
framework
::
DDim
temp_dim
(
w
.
dims
());
set
(
temp_dim
,
0
,
real_rows
.
size
());
set
(
temp_dim
,
0
,
real_rows
.
size
());
w_grad_value
->
mutable_data
<
T
>
(
temp_dim
,
ctx
.
GetPlace
());
w_grad_value
->
mutable_data
<
T
>
(
temp_dim
,
ctx
.
GetPlace
());
...
@@ -211,9 +208,9 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
...
@@ -211,9 +208,9 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
zero
(
dev_ctx
,
bias_grad_value
,
static_cast
<
T
>
(
0.0
));
zero
(
dev_ctx
,
bias_grad_value
,
static_cast
<
T
>
(
0.0
));
bit_code
->
AddGrad
(
pre_out_grad
,
bias_grad
);
bit_code
->
AddGrad
(
pre_out_grad
,
bias_grad
);
}
}
bit_code
->
MulGradWeight
(
pre_out_grad
,
w_grad
,
*
in
);
bit_code
->
MulGradWeight
(
pre_out_grad
,
w_grad
,
in
);
}
}
bit_code
->
MulGradError
(
pre_out_grad
,
*
w
,
in_grad
);
bit_code
->
MulGradError
(
pre_out_grad
,
w
,
in_grad
);
}
}
};
};
...
...
paddle/fluid/operators/math/matrix_bit_code.cc
浏览文件 @
c3c3c0b3
...
@@ -19,12 +19,12 @@ namespace operators {
...
@@ -19,12 +19,12 @@ namespace operators {
namespace
math
{
namespace
math
{
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
Add
(
const
framework
::
LoD
Tensor
&
vec
,
void
MatrixBitCodeFunctor
<
T
>::
Add
(
const
framework
::
Tensor
&
vec
,
framework
::
LoD
Tensor
*
tmat
)
{
framework
::
Tensor
*
tmat
)
{
size_t
batch_size
=
tmat
->
dims
()[
0
];
size_t
batch_size
=
tmat
->
dims
()[
0
];
size_t
width
=
tmat
->
dims
()[
1
];
size_t
width
=
tmat
->
dims
()[
1
];
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
size_t
index
=
code
->
calc_index
(
j
);
size_t
index
=
code
->
calc_index
(
j
);
...
@@ -34,12 +34,12 @@ void MatrixBitCodeFunctor<T>::Add(const framework::LoDTensor& vec,
...
@@ -34,12 +34,12 @@ void MatrixBitCodeFunctor<T>::Add(const framework::LoDTensor& vec,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
AddGrad
(
const
framework
::
LoD
Tensor
&
tmat
,
void
MatrixBitCodeFunctor
<
T
>::
AddGrad
(
const
framework
::
Tensor
&
tmat
,
framework
::
LoD
Tensor
*
vec
)
{
framework
::
Tensor
*
vec
)
{
size_t
batch_size
=
tmat
.
dims
()[
0
];
size_t
batch_size
=
tmat
.
dims
()[
0
];
size_t
width
=
tmat
.
dims
()[
1
];
size_t
width
=
tmat
.
dims
()[
1
];
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
size_t
index
=
code
->
calc_index
(
j
);
size_t
index
=
code
->
calc_index
(
j
);
...
@@ -49,17 +49,16 @@ void MatrixBitCodeFunctor<T>::AddGrad(const framework::LoDTensor& tmat,
...
@@ -49,17 +49,16 @@ void MatrixBitCodeFunctor<T>::AddGrad(const framework::LoDTensor& tmat,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
AddGrad
(
const
framework
::
LoD
Tensor
&
tmat
,
void
MatrixBitCodeFunctor
<
T
>::
AddGrad
(
const
framework
::
Tensor
&
tmat
,
framework
::
SelectedRows
*
vec
)
{
framework
::
SelectedRows
*
vec
)
{
size_t
batch_size
=
tmat
.
dims
()[
0
];
size_t
batch_size
=
tmat
.
dims
()[
0
];
size_t
width
=
tmat
.
dims
()[
1
];
size_t
width
=
tmat
.
dims
()[
1
];
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
size_t
index
=
code
->
calc_index
(
j
);
size_t
index
=
code
->
calc_index
(
j
);
int64_t
row_index
=
int64_t
row_index
=
vec
->
GetIndexFromId
(
static_cast
<
int64_t
>
(
index
));
vec
->
AutoGrownIndex
(
static_cast
<
int64_t
>
(
index
),
false
,
true
);
vec
->
mutable_value
()
->
data
<
T
>
()[
row_index
]
+=
vec
->
mutable_value
()
->
data
<
T
>
()[
row_index
]
+=
tmat
.
data
<
T
>
()[
i
*
width
+
j
];
tmat
.
data
<
T
>
()[
i
*
width
+
j
];
}
}
...
@@ -67,13 +66,13 @@ void MatrixBitCodeFunctor<T>::AddGrad(const framework::LoDTensor& tmat,
...
@@ -67,13 +66,13 @@ void MatrixBitCodeFunctor<T>::AddGrad(const framework::LoDTensor& tmat,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
Sum
(
const
framework
::
LoD
Tensor
&
tmat
,
void
MatrixBitCodeFunctor
<
T
>::
Sum
(
const
framework
::
Tensor
&
tmat
,
framework
::
LoD
Tensor
*
sum
,
T
scale_sum
)
{
framework
::
Tensor
*
sum
,
T
scale_sum
)
{
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
o_width
=
tmat
.
dims
()[
1
];
size_t
o_width
=
tmat
.
dims
()[
1
];
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
T
sm
=
static_cast
<
T
>
(
0.0
);
T
sm
=
static_cast
<
T
>
(
0.0
);
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
if
(
code
->
calc_bit
(
j
))
{
if
(
code
->
calc_bit
(
j
))
{
...
@@ -87,9 +86,9 @@ void MatrixBitCodeFunctor<T>::Sum(const framework::LoDTensor& tmat,
...
@@ -87,9 +86,9 @@ void MatrixBitCodeFunctor<T>::Sum(const framework::LoDTensor& tmat,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
Mul
(
framework
::
LoD
Tensor
*
tmat
,
void
MatrixBitCodeFunctor
<
T
>::
Mul
(
framework
::
Tensor
*
tmat
,
const
framework
::
LoD
Tensor
&
weight
,
const
framework
::
Tensor
&
weight
,
const
framework
::
LoD
Tensor
&
input
)
{
const
framework
::
Tensor
&
input
)
{
size_t
num_samples
=
tmat
->
dims
()[
0
];
size_t
num_samples
=
tmat
->
dims
()[
0
];
size_t
tmat_width
=
tmat
->
dims
()[
1
];
size_t
tmat_width
=
tmat
->
dims
()[
1
];
size_t
input_width
=
input
.
dims
()[
1
];
size_t
input_width
=
input
.
dims
()[
1
];
...
@@ -98,7 +97,7 @@ void MatrixBitCodeFunctor<T>::Mul(framework::LoDTensor* tmat,
...
@@ -98,7 +97,7 @@ void MatrixBitCodeFunctor<T>::Mul(framework::LoDTensor* tmat,
auto
weight_value
=
weight
.
data
<
T
>
();
auto
weight_value
=
weight
.
data
<
T
>
();
auto
input_value
=
input
.
data
<
T
>
();
auto
input_value
=
input
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
size_t
index
=
code
->
calc_index
(
j
);
size_t
index
=
code
->
calc_index
(
j
);
...
@@ -113,9 +112,9 @@ void MatrixBitCodeFunctor<T>::Mul(framework::LoDTensor* tmat,
...
@@ -113,9 +112,9 @@ void MatrixBitCodeFunctor<T>::Mul(framework::LoDTensor* tmat,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
MulGradWeight
(
const
framework
::
LoD
Tensor
&
tmat
,
void
MatrixBitCodeFunctor
<
T
>::
MulGradWeight
(
const
framework
::
Tensor
&
tmat
,
framework
::
LoD
Tensor
*
weight
,
framework
::
Tensor
*
weight
,
const
framework
::
LoD
Tensor
&
input
)
{
const
framework
::
Tensor
&
input
)
{
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
input_width
=
input
.
dims
()[
1
];
size_t
input_width
=
input
.
dims
()[
1
];
size_t
tmat_width
=
tmat
.
dims
()[
1
];
size_t
tmat_width
=
tmat
.
dims
()[
1
];
...
@@ -124,7 +123,7 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
...
@@ -124,7 +123,7 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
auto
weight_value
=
weight
->
data
<
T
>
();
auto
weight_value
=
weight
->
data
<
T
>
();
auto
input_value
=
input
.
data
<
T
>
();
auto
input_value
=
input
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
size_t
index
=
code
->
calc_index
(
j
);
size_t
index
=
code
->
calc_index
(
j
);
...
@@ -138,9 +137,9 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
...
@@ -138,9 +137,9 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
MulGradWeight
(
const
framework
::
LoD
Tensor
&
tmat
,
void
MatrixBitCodeFunctor
<
T
>::
MulGradWeight
(
const
framework
::
Tensor
&
tmat
,
framework
::
SelectedRows
*
weight
,
framework
::
SelectedRows
*
weight
,
const
framework
::
LoD
Tensor
&
input
)
{
const
framework
::
Tensor
&
input
)
{
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
input_width
=
input
.
dims
()[
1
];
size_t
input_width
=
input
.
dims
()[
1
];
size_t
tmat_width
=
tmat
.
dims
()[
1
];
size_t
tmat_width
=
tmat
.
dims
()[
1
];
...
@@ -149,13 +148,12 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
...
@@ -149,13 +148,12 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
auto
weight_value
=
weight
->
mutable_value
()
->
data
<
T
>
();
auto
weight_value
=
weight
->
mutable_value
()
->
data
<
T
>
();
auto
input_value
=
input
.
data
<
T
>
();
auto
input_value
=
input
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
size_t
index
=
code
->
calc_index
(
j
);
size_t
index
=
code
->
calc_index
(
j
);
for
(
size_t
k
=
0
;
k
<
input_width
;
++
k
)
{
for
(
size_t
k
=
0
;
k
<
input_width
;
++
k
)
{
int64_t
row_index
=
int64_t
row_index
=
weight
->
GetIndexFromId
(
static_cast
<
int64_t
>
(
index
));
weight
->
AutoGrownIndex
(
static_cast
<
int64_t
>
(
index
),
false
,
true
);
weight_value
[
row_index
*
weight_width
+
k
]
+=
weight_value
[
row_index
*
weight_width
+
k
]
+=
tmat_value
[
i
*
tmat_width
+
j
]
*
input_value
[
input_width
*
i
+
k
];
tmat_value
[
i
*
tmat_width
+
j
]
*
input_value
[
input_width
*
i
+
k
];
}
}
...
@@ -164,9 +162,9 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
...
@@ -164,9 +162,9 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::LoDTensor& tmat,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
MulGradError
(
const
framework
::
LoD
Tensor
&
tmat
,
void
MatrixBitCodeFunctor
<
T
>::
MulGradError
(
const
framework
::
Tensor
&
tmat
,
const
framework
::
LoD
Tensor
&
weight
,
const
framework
::
Tensor
&
weight
,
framework
::
LoD
Tensor
*
input
)
{
framework
::
Tensor
*
input
)
{
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
num_samples
=
tmat
.
dims
()[
0
];
size_t
tmat_width
=
tmat
.
dims
()[
1
];
size_t
tmat_width
=
tmat
.
dims
()[
1
];
size_t
input_width
=
input
->
dims
()[
1
];
size_t
input_width
=
input
->
dims
()[
1
];
...
@@ -176,7 +174,7 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::LoDTensor& tmat,
...
@@ -176,7 +174,7 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::LoDTensor& tmat,
auto
input_value
=
input
->
data
<
T
>
();
auto
input_value
=
input
->
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
size_t
index
=
code
->
calc_index
(
j
);
size_t
index
=
code
->
calc_index
(
j
);
...
@@ -191,11 +189,11 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::LoDTensor& tmat,
...
@@ -191,11 +189,11 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::LoDTensor& tmat,
}
}
template
<
typename
T
>
template
<
typename
T
>
void
MatrixBitCodeFunctor
<
T
>::
Sub
(
framework
::
LoD
Tensor
*
tmat
)
{
void
MatrixBitCodeFunctor
<
T
>::
Sub
(
framework
::
Tensor
*
tmat
)
{
size_t
num_samples
=
tmat
->
dims
()[
0
];
size_t
num_samples
=
tmat
->
dims
()[
0
];
size_t
o_width
=
tmat
->
dims
()[
1
];
size_t
o_width
=
tmat
->
dims
()[
1
];
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
)
{
auto
code
=
code_table
->
get_code
(
i
);
auto
code
=
code_table
_
->
get_code
(
i
);
int
code_length
=
code
->
get_length
();
int
code_length
=
code
->
get_length
();
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
for
(
int
j
=
0
;
j
<
code_length
;
++
j
)
{
if
(
code
->
calc_bit
(
j
))
{
if
(
code
->
calc_bit
(
j
))
{
...
...
paddle/fluid/operators/math/matrix_bit_code.h
浏览文件 @
c3c3c0b3
...
@@ -132,13 +132,15 @@ class SimpleCode : public Code {
...
@@ -132,13 +132,15 @@ class SimpleCode : public Code {
size_t
c_
;
size_t
c_
;
};
};
template
<
typename
R
>
template
<
typename
T
>
class
CustomCode
:
public
Code
{
class
CustomCode
:
public
Code
{
public:
public:
CustomCode
(
const
framework
::
LoDTensor
*
ptable
,
CustomCode
(
const
framework
::
Tensor
&
ptable
,
const
framework
::
Tensor
&
pcode
,
const
framework
::
LoDTensor
*
pcode
,
const
int64_t
*
ids
,
const
int64_t
*
ids
,
int
index
)
const
int
index
)
:
ids_
(
ids
),
index_
(
index
)
{
:
ptable_
(
ptable
),
pcode_
(
pcode
),
ids_
(
ids
),
index_
(
index
)
{}
ptable_
=
ptable
.
Slice
(
index
,
index
+
1
);
pcode_
=
pcode
.
Slice
(
index
,
index
+
1
);
}
/**
/**
* Here the id of root shoud be 1 rather than 0, thus the encoding of class c
* Here the id of root shoud be 1 rather than 0, thus the encoding of class c
* is `c + num_classes` and all siblings can get the same weight indice using
* is `c + num_classes` and all siblings can get the same weight indice using
...
@@ -148,20 +150,13 @@ class CustomCode : public Code {
...
@@ -148,20 +150,13 @@ class CustomCode : public Code {
* Binary classification path is the suffixes of encoding, thus leave out the
* Binary classification path is the suffixes of encoding, thus leave out the
* left most bit in calc_bit.
* left most bit in calc_bit.
*/
*/
size_t
calc_index
(
int
bit
)
const
{
size_t
calc_index
(
int
bit
)
const
{
return
ptable_
.
data
<
T
>
()[
bit
];
}
return
ptable_
bool
calc_bit
(
int
bit
)
const
{
return
pcode_
.
data
<
T
>
()[
bit
];
}
->
data
<
R
>
()[
index_
*
static_cast
<
int
>
(
ptable_
->
dims
()[
1
])
+
bit
];
}
bool
calc_bit
(
int
bit
)
const
{
return
pcode_
->
data
<
R
>
()[
index_
*
static_cast
<
int
>
(
ptable_
->
dims
()[
1
])
+
bit
];
}
int
get_length
()
const
{
int
get_length
()
const
{
int
length
=
0
;
int
length
=
0
;
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
ptable_
->
dims
()[
1
]);
i
++
)
{
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
ptable_
.
dims
()[
1
]);
i
++
)
{
if
(
ptable_
->
data
<
R
>
()[
index_
*
static_cast
<
int
>
(
ptable_
->
dims
()[
1
])
+
if
(
ptable_
.
data
<
T
>
()[
i
]
>=
0
)
{
i
]
>=
0
)
{
length
++
;
length
++
;
}
else
{
}
else
{
return
length
;
return
length
;
...
@@ -171,15 +166,15 @@ class CustomCode : public Code {
...
@@ -171,15 +166,15 @@ class CustomCode : public Code {
}
}
private:
private:
const
framework
::
LoDTensor
*
ptable_
;
framework
::
Tensor
ptable_
;
const
framework
::
LoDTensor
*
pcode_
;
framework
::
Tensor
pcode_
;
const
int64_t
*
ids_
;
const
int64_t
*
ids_
;
const
int
index_
;
const
int
index_
;
};
};
class
SimpleCodeTable
:
public
CodeTable
{
class
SimpleCodeTable
:
public
CodeTable
{
public:
public:
explicit
SimpleCodeTable
(
size_t
num_classes
,
const
int64_t
*
ids
)
SimpleCodeTable
(
size_t
num_classes
,
const
int64_t
*
ids
)
:
num_classes_
(
num_classes
),
ids_
(
ids
)
{}
:
num_classes_
(
num_classes
),
ids_
(
ids
)
{}
std
::
unique_ptr
<
Code
>
get_code
(
int64_t
code
)
const
{
std
::
unique_ptr
<
Code
>
get_code
(
int64_t
code
)
const
{
std
::
unique_ptr
<
Code
>
coder
(
new
SimpleCode
(
code
,
num_classes_
,
ids_
));
std
::
unique_ptr
<
Code
>
coder
(
new
SimpleCode
(
code
,
num_classes_
,
ids_
));
...
@@ -193,97 +188,92 @@ class SimpleCodeTable : public CodeTable {
...
@@ -193,97 +188,92 @@ class SimpleCodeTable : public CodeTable {
const
int64_t
*
ids_
;
const
int64_t
*
ids_
;
};
};
template
<
typename
R
>
template
<
typename
T
>
class
CustomCodeTable
:
public
CodeTable
{
class
CustomCodeTable
:
public
CodeTable
{
public:
public:
explicit
CustomCodeTable
(
const
framework
::
LoDTensor
*
ptable
,
CustomCodeTable
(
const
framework
::
Tensor
&
ptable
,
const
framework
::
LoDTensor
*
pcode
,
const
framework
::
Tensor
&
pcode
,
const
int64_t
*
ids
)
const
int64_t
*
ids
)
:
ptable_
(
ptable
),
pcode_
(
pcode
),
ids_
(
ids
)
{}
:
ptable_
(
ptable
),
pcode_
(
pcode
),
ids_
(
ids
)
{}
std
::
unique_ptr
<
Code
>
get_code
(
int64_t
code
)
const
{
std
::
unique_ptr
<
Code
>
get_code
(
int64_t
code
)
const
{
std
::
unique_ptr
<
Code
>
coder
(
new
CustomCode
<
R
>
(
ptable_
,
pcode_
,
ids_
,
code
));
std
::
unique_ptr
<
Code
>
coder
(
new
CustomCode
<
T
>
(
ptable_
,
pcode_
,
ids_
,
code
));
return
coder
;
return
coder
;
}
}
size_t
size
()
const
{
return
static_cast
<
size_t
>
(
ptable_
->
dims
()[
1
]);
}
size_t
size
()
const
{
return
static_cast
<
size_t
>
(
ptable_
.
dims
()[
1
]);
}
int
get_max_code_length
()
const
{
int
get_max_code_length
()
const
{
return
static_cast
<
size_t
>
(
ptable_
->
dims
()[
1
]);
return
static_cast
<
size_t
>
(
ptable_
.
dims
()[
1
]);
}
}
private:
private:
const
framework
::
LoDTensor
*
ptable_
;
const
framework
::
Tensor
&
ptable_
;
const
framework
::
LoDTensor
*
pcode_
;
const
framework
::
Tensor
&
pcode_
;
const
int64_t
*
ids_
;
const
int64_t
*
ids_
;
};
};
template
<
typename
T
>
template
<
typename
T
>
class
MatrixBitCodeFunctor
{
class
MatrixBitCodeFunctor
{
public:
public:
explicit
MatrixBitCodeFunctor
(
size_t
num_classes
,
const
int64_t
*
ids
)
MatrixBitCodeFunctor
(
size_t
num_classes
,
const
int64_t
*
ids
)
:
num_classes_
(
num_classes
),
:
num_classes_
(
num_classes
),
ids_
(
ids
),
ids_
(
ids
),
code_table
(
new
SimpleCodeTable
(
num_classes
,
ids
))
{}
code_table
_
(
new
SimpleCodeTable
(
num_classes
,
ids
))
{}
explicit
MatrixBitCodeFunctor
(
const
framework
::
LoDTensor
*
ptable
,
MatrixBitCodeFunctor
(
const
framework
::
Tensor
&
ptable
,
const
framework
::
LoDTensor
*
pcode
,
const
framework
::
Tensor
&
pcode
,
const
int64_t
*
ids
)
const
int64_t
*
ids
)
:
num_classes_
(
static_cast
<
size_t
>
(
ptable
.
dims
()[
1
])),
:
num_classes_
(
static_cast
<
size_t
>
(
ptable
->
dims
()[
1
])),
ids_
(
ids
),
ids_
(
ids
),
code_table
(
new
CustomCodeTable
<
int64_t
>
(
ptable
,
pcode
,
ids
))
{}
code_table
_
(
new
CustomCodeTable
<
int64_t
>
(
ptable
,
pcode
,
ids
))
{}
/* For j < code_length
/* For j < code_length
tmat(i, j) += vec(0, index(i, j))
tmat(i, j) += vec(0, index(i, j))
*/
*/
void
Add
(
const
framework
::
LoDTensor
&
vec
,
framework
::
LoD
Tensor
*
tmat
);
void
Add
(
const
framework
::
Tensor
&
vec
,
framework
::
Tensor
*
tmat
);
/* For j < code_length
/* For j < code_length
vec(0, index(i, j)) += tmat(i, j)
vec(0, index(i, j)) += tmat(i, j)
*/
*/
void
AddGrad
(
const
framework
::
LoDTensor
&
tmat
,
framework
::
LoD
Tensor
*
vec
);
void
AddGrad
(
const
framework
::
Tensor
&
tmat
,
framework
::
Tensor
*
vec
);
/* For selected rows For j < code_length
/* For selected rows For j < code_length
vec(0, index(i, j)) += tmat(i, j)
vec(0, index(i, j)) += tmat(i, j)
*/
*/
void
AddGrad
(
const
framework
::
LoD
Tensor
&
tmat
,
framework
::
SelectedRows
*
vec
);
void
AddGrad
(
const
framework
::
Tensor
&
tmat
,
framework
::
SelectedRows
*
vec
);
/* For j < code_length
/* For j < code_length
sum(i, 0) = \sum_j bit(i, j) * tmat(i, j)
sum(i, 0) = \sum_j bit(i, j) * tmat(i, j)
*/
*/
void
Sum
(
const
framework
::
LoDTensor
&
tmat
,
framework
::
LoDTensor
*
sum
,
void
Sum
(
const
framework
::
Tensor
&
tmat
,
framework
::
Tensor
*
sum
,
T
scale_sum
);
T
scale_sum
);
/* For j < code_length
/* For j < code_length
tmat(i, j) -= bit(i, j)
tmat(i, j) -= bit(i, j)
*/
*/
void
Sub
(
framework
::
LoD
Tensor
*
tmat
);
void
Sub
(
framework
::
Tensor
*
tmat
);
/* For j < code_length
/* For j < code_length
input.row(i) += tmat(i, j) * weight.row(index(i, j))
input.row(i) += tmat(i, j) * weight.row(index(i, j))
*/
*/
void
Mul
(
framework
::
LoDTensor
*
tmat
,
const
framework
::
LoD
Tensor
&
weight
,
void
Mul
(
framework
::
Tensor
*
tmat
,
const
framework
::
Tensor
&
weight
,
const
framework
::
LoD
Tensor
&
input
);
const
framework
::
Tensor
&
input
);
/* For index(i, j) >= 0:
/* For index(i, j) >= 0:
weight.row(index(i, j)) += tmat(i, j) * input.row(i)
weight.row(index(i, j)) += tmat(i, j) * input.row(i)
*/
*/
void
MulGradWeight
(
const
framework
::
LoDTensor
&
tmat
,
void
MulGradWeight
(
const
framework
::
Tensor
&
tmat
,
framework
::
Tensor
*
weight
,
framework
::
LoDTensor
*
weight
,
const
framework
::
Tensor
&
input
);
const
framework
::
LoDTensor
&
input
);
/* For SelectedRows Weight, For index(i, j) >= 0:
/* For SelectedRows Weight, For index(i, j) >= 0:
weight.row(index(i, j)) += tmat(i, j) * input.row(i)
weight.row(index(i, j)) += tmat(i, j) * input.row(i)
*/
*/
void
MulGradWeight
(
const
framework
::
LoD
Tensor
&
tmat
,
void
MulGradWeight
(
const
framework
::
Tensor
&
tmat
,
framework
::
SelectedRows
*
weight
,
framework
::
SelectedRows
*
weight
,
const
framework
::
LoD
Tensor
&
input
);
const
framework
::
Tensor
&
input
);
/* For j < code_length
/* For j < code_length
input.row(i) += tmat(i, j) * weight.row(index(i, j))
input.row(i) += tmat(i, j) * weight.row(index(i, j))
*/
*/
void
MulGradError
(
const
framework
::
LoDTensor
&
tmat
,
void
MulGradError
(
const
framework
::
Tensor
&
tmat
,
const
framework
::
LoDTensor
&
weight
,
const
framework
::
Tensor
&
weight
,
framework
::
Tensor
*
input
);
framework
::
LoDTensor
*
input
);
size_t
num_classes_
;
size_t
num_classes_
;
const
int64_t
*
ids_
;
const
int64_t
*
ids_
;
std
::
unique_ptr
<
CodeTable
>
code_table
;
std
::
unique_ptr
<
CodeTable
>
code_table
_
;
};
};
}
// namespace math
}
// namespace math
}
// namespace operators
}
// namespace operators
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
c3c3c0b3
...
@@ -4639,7 +4639,7 @@ def hsigmoid(input,
...
@@ -4639,7 +4639,7 @@ def hsigmoid(input,
"X"
:
input
,
"X"
:
input
,
"W"
:
weights
,
"W"
:
weights
,
"PTable"
:
ptable
,
"PTable"
:
ptable
,
"PCode"
:
pcode
,
"P
ath
Code"
:
pcode
,
"Label"
:
label
"Label"
:
label
}
}
if
helper
.
bias_attr
:
if
helper
.
bias_attr
:
...
...
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
浏览文件 @
c3c3c0b3
...
@@ -185,7 +185,7 @@ class TestHSigmoidOpSparse(OpTest):
...
@@ -185,7 +185,7 @@ class TestHSigmoidOpSparse(OpTest):
'X'
:
x
,
'X'
:
x
,
'W'
:
w
,
'W'
:
w
,
'PTable'
:
ptable
,
'PTable'
:
ptable
,
'PCode'
:
pcode
,
'P
ath
Code'
:
pcode
,
'Label'
:
label
,
'Label'
:
label
,
'Bias'
:
bias
'Bias'
:
bias
}
}
...
@@ -285,7 +285,7 @@ class TestHSigmoidOpWithCostumTree(OpTest):
...
@@ -285,7 +285,7 @@ class TestHSigmoidOpWithCostumTree(OpTest):
'X'
:
x
,
'X'
:
x
,
'W'
:
w
,
'W'
:
w
,
'PTable'
:
ptable
,
'PTable'
:
ptable
,
'PCode'
:
pcode
,
'P
ath
Code'
:
pcode
,
'Label'
:
label
,
'Label'
:
label
,
'Bias'
:
bias
'Bias'
:
bias
}
}
...
@@ -322,7 +322,7 @@ class TestHSigmoidOpWithCostumTreeWithoutBias(OpTest):
...
@@ -322,7 +322,7 @@ class TestHSigmoidOpWithCostumTreeWithoutBias(OpTest):
'X'
:
x
,
'X'
:
x
,
'W'
:
w
,
'W'
:
w
,
'PTable'
:
ptable
,
'PTable'
:
ptable
,
'PCode'
:
pcode
,
'P
ath
Code'
:
pcode
,
'Label'
:
label
,
'Label'
:
label
,
}
}
pre_output
,
out
=
hsigmoidWithCustomTree
(
pre_output
,
out
=
hsigmoidWithCustomTree
(
...
...
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