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5413af8d
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5413af8d
编写于
6月 02, 2017
作者:
X
xzl
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
imporve pruning module
上级
da83d286
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
144 addition
and
11 deletion
+144
-11
paddle/parameter/ParameterUpdaterHook.cpp
paddle/parameter/ParameterUpdaterHook.cpp
+85
-5
proto/ParameterConfig.proto
proto/ParameterConfig.proto
+2
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+11
-4
python/paddle/trainer_config_helpers/attrs.py
python/paddle/trainer_config_helpers/attrs.py
+44
-2
python/paddle/v2/attr.py
python/paddle/v2/attr.py
+2
-0
未找到文件。
paddle/parameter/ParameterUpdaterHook.cpp
浏览文件 @
5413af8d
...
...
@@ -25,6 +25,9 @@ limitations under the License. */
#include "paddle/utils/Flags.h"
#include "paddle/utils/Util.h"
using
std
::
vector
;
using
std
::
pair
;
namespace
paddle
{
/**
...
...
@@ -131,6 +134,73 @@ private:
std
::
vector
<
bool
>
mask_
;
};
class
DynamicPruningHook
:
public
IParameterUpdaterHook
{
public:
explicit
DynamicPruningHook
(
const
ParameterUpdaterHookConfig
&
hookConfig
)
:
initCount_
(
0
)
{
sparsityRatio_
=
hookConfig
.
sparsity_ratio
();
}
static
bool
sortPairAscend
(
const
pair
<
real
,
size_t
>&
pair1
,
const
pair
<
real
,
size_t
>&
pair2
)
{
return
pair1
.
first
>
pair2
.
first
;
}
void
update
(
Parameter
*
para
)
{
updateThreadChecker_
.
check
();
auto
&
vec
=
para
->
getBuf
(
PARAMETER_GRADIENT
);
if
(
vec
)
{
vec
->
dotMul
(
*
maskVec_
);
}
}
void
generateMask
(
Parameter
*
para
)
{
VectorPtr
vec
=
para
->
getBuf
(
PARAMETER_VALUE
);
maskTemp_
=
Vector
::
create
(
para
->
getSize
(),
false
);
maskTemp_
->
zeroMem
();
real
*
dataPtr
=
maskTemp_
->
getData
();
VectorPtr
vecCpu
=
Vector
::
create
(
para
->
getSize
(),
false
);
vecCpu
->
copyFrom
(
*
vec
);
vector
<
pair
<
real
,
size_t
>>
param
;
for
(
size_t
i
=
0
;
i
<
para
->
getSize
();
i
++
)
param
.
push_back
(
std
::
make_pair
(
fabs
(
vecCpu
->
getData
()[
i
]),
i
));
std
::
sort
(
param
.
begin
(),
param
.
end
(),
sortPairAscend
);
for
(
size_t
i
=
0
;
i
<
para
->
getSize
()
*
sparsityRatio_
;
i
++
)
dataPtr
[
param
[
i
].
second
]
=
1.0
;
}
void
init
(
Parameter
*
para
)
{
generateMask
(
para
);
size_t
initCount
=
this
->
initCount_
.
fetch_add
(
1
);
CHECK_EQ
(
initCount
,
0UL
)
<<
"Currently the DynamicPruningHook must invoke "
"in same ParamterUpdater"
;
VLOG
(
3
)
<<
"Initialize Parameter "
<<
para
;
SetDevice
device
(
para
->
getDeviceId
());
// Currently just use a mask vector for hack.
// @TODO(yuyang18): Implemented the mask operation in vector.
if
(
para
->
useGpu
())
{
maskVec_
=
Vector
::
create
(
para
->
getSize
(),
para
->
useGpu
());
maskVec_
->
copyFrom
(
*
maskTemp_
);
}
else
{
maskVec_
=
maskTemp_
;
}
auto
&
vec
=
para
->
getBuf
(
PARAMETER_VALUE
);
vec
->
dotMul
(
*
maskVec_
);
}
private:
SameThreadChecker
updateThreadChecker_
;
std
::
atomic
<
size_t
>
initCount_
;
VectorPtr
maskVec_
;
VectorPtr
maskTemp_
;
real
sparsityRatio_
;
};
IParameterUpdaterHook
::
IParameterUpdaterHook
()
{}
IParameterUpdaterHook
::~
IParameterUpdaterHook
()
{}
...
...
@@ -156,8 +226,7 @@ private:
static
WeakKVCache
<
std
::
pair
<
std
::
string
,
int
>
,
IParameterUpdaterHook
,
StringIntPairHasher
>
g_hookCache_
;
StringIntPairHasher
>
g_hookCache_
;
/**
* ParameterUpdaterHook actually factory method.
...
...
@@ -165,11 +234,22 @@ static WeakKVCache<std::pair<std::string, int>,
static
IParameterUpdaterHook
*
createImpl
(
const
ParameterUpdaterHookConfig
&
config
)
{
auto
&
type
=
config
.
type
();
if
(
type
==
"pruning"
)
{
if
(
config
.
has_purning_mask_filename
())
{
if
(
type
==
"pruning
_static
"
)
{
if
(
config
.
has_purning_mask_filename
())
return
new
StaticPruningHook
(
config
.
purning_mask_filename
());
}
else
LOG
(
FATAL
)
<<
"There must be mask_filename parameter for "
<<
type
<<
" Hook"
;
}
else
if
(
type
==
"pruning"
)
{
if
(
config
.
has_sparsity_ratio
())
return
new
DynamicPruningHook
(
config
);
else
LOG
(
FATAL
)
<<
"There must be sparsity_ratio parameter for "
<<
type
<<
" Hook"
;
}
LOG
(
FATAL
)
<<
"Unknown Hook type: "
<<
type
;
return
nullptr
;
}
...
...
proto/ParameterConfig.proto
浏览文件 @
5413af8d
...
...
@@ -26,7 +26,9 @@ enum ParameterInitStrategy {
message
ParameterUpdaterHookConfig
{
required
string
type
=
1
;
//hook type such as 'pruning', 'pruning_static'
optional
string
purning_mask_filename
=
2
;
optional
double
sparsity_ratio
=
3
;
}
message
ParameterConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
5413af8d
...
...
@@ -3171,12 +3171,19 @@ def Layer(name, type, **xargs):
@
config_func
def
ParameterHook
(
type
,
**
kwargs
):
if
type
==
'pruning'
:
if
type
==
'pruning_static'
:
hook
=
ParameterUpdaterHookConfig
()
hook
.
type
=
type
mask_filename
=
kwargs
.
get
(
'mask_filename'
,
None
)
assert
mask_filename
is
not
None
hook
.
pruning_mask_filename
=
mask_filename
return
hook
elif
type
==
'pruning'
:
hook
=
ParameterUpdaterHookConfig
()
hook
.
type
=
type
hook
.
purning_mask_filename
=
mask_filename
sparsity_ratio
=
kwargs
.
get
(
'sparsity_ratio'
,
None
)
assert
sparsity_ratio
is
not
None
hook
.
sparsity_ratio
=
sparsity_ratio
return
hook
else
:
return
None
...
...
@@ -3283,13 +3290,13 @@ def Parameter(name,
if
update_hooks
is
not
None
:
if
hasattr
(
update_hooks
,
'__call__'
):
update_hooks
=
update_hooks
(
para
.
name
)
update_hooks
=
update_hooks
()
if
isinstance
(
update_hooks
,
list
):
for
hook
in
update_hooks
:
para
.
update_hooks
.
extend
([
hook
])
else
:
para
.
update_hooks
.
extend
(
update_hooks
)
para
.
update_hooks
.
extend
(
[
update_hooks
]
)
g_parameter_map
[
name
]
=
para
...
...
python/paddle/trainer_config_helpers/attrs.py
浏览文件 @
5413af8d
...
...
@@ -14,7 +14,8 @@
from
paddle.trainer.config_parser
import
*
__all__
=
[
'ParamAttr'
,
'ExtraAttr'
,
'ParameterAttribute'
,
'ExtraLayerAttribute'
'HookAttr'
,
'ParamAttr'
,
'ExtraAttr'
,
'ParameterAttribute'
,
'ExtraLayerAttribute'
]
...
...
@@ -55,6 +56,42 @@ def is_compatible_with(x, Type):
return
False
class
HookAttribute
(
object
):
"""
Hook Attribute object. The hook is an auxiliary operation that occurs
during network propagation. Such as pruning operation, It will cut off
redundant parameters in the network before training. More detail can see
here paddle/parameter/ParameterUpdaterHook.cpp
NOTE: IT IS A HIGH LEVEL USER INTERFACE.
:param type: Hook type, eg: 'pruning', 'pruning_static'
:type type: string
:param mask_file: Must be specified if hook type is 'pruning_static',
the network reads the mask from the file to determine which parameters should be cut off
:type mask_file: string
:param sparsity_ratio: Must be specified if hook type is 'pruning',
the network will hold the sparsity_ratio maximum parameters, and cut off the rest.
:type sparsity_ratio: float number between 0 and 1
"""
def
__init__
(
self
,
type
,
mask_filename
=
None
,
sparsity_ratio
=
None
):
self
.
type
=
type
self
.
mask_filename
=
mask_filename
self
.
sparsity_ratio
=
sparsity_ratio
assert
is_compatible_with
(
self
.
sparsity_ratio
,
float
),
'sparisity_ratio must be float type'
assert
self
.
sparsity_ratio
<=
1
and
self
.
sparsity_ratio
>=
0
,
'sparisity must be a flaot between [0, 1] '
def
__call__
(
self
):
return
ParameterHook
(
self
.
type
,
mask_filename
=
self
.
mask_filename
,
sparsity_ratio
=
self
.
sparsity_ratio
)
class
ParameterAttribute
(
object
):
"""
Parameter Attributes object. To fine-tuning network training process, user
...
...
@@ -109,7 +146,8 @@ class ParameterAttribute(object):
learning_rate
=
None
,
momentum
=
None
,
gradient_clipping_threshold
=
None
,
sparse_update
=
False
):
sparse_update
=
False
,
update_hooks
=
None
):
self
.
attr
=
{}
if
is_static
:
...
...
@@ -162,6 +200,9 @@ class ParameterAttribute(object):
self
.
attr
[
'gradient_clipping_threshold'
]
=
\
gradient_clipping_threshold
if
update_hooks
:
self
.
attr
[
'update_hooks'
]
=
update_hooks
def
set_default_parameter_name
(
self
,
name
):
"""
Set default parameter name. If parameter not set, then will use default
...
...
@@ -237,5 +278,6 @@ class ExtraLayerAttribute(object):
return
attr
.
attr
HookAttr
=
HookAttribute
ParamAttr
=
ParameterAttribute
ExtraAttr
=
ExtraLayerAttribute
python/paddle/v2/attr.py
浏览文件 @
5413af8d
...
...
@@ -17,10 +17,12 @@ import paddle.trainer_config_helpers.attrs
__all__
=
[
"Param"
,
"Extra"
,
"Hook"
,
]
Param
=
paddle
.
trainer_config_helpers
.
attrs
.
ParameterAttribute
Extra
=
paddle
.
trainer_config_helpers
.
attrs
.
ExtraLayerAttribute
Hook
=
paddle
.
trainer_config_helpers
.
attrs
.
HookAttribute
for
each
in
paddle
.
trainer_config_helpers
.
attrs
.
__all__
:
globals
()[
each
]
=
getattr
(
paddle
.
trainer_config_helpers
.
attrs
,
each
)
...
...
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