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体验新版 GitCode,发现更多精彩内容 >>
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f15e0830
编写于
8月 16, 2017
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Remove std::shared_ptr in Python & C++
* Also simplify pybind implementation by using OperatorBase as holder type.
上级
7f5338a7
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
71 addition
and
97 deletion
+71
-97
paddle/framework/backward.cc
paddle/framework/backward.cc
+2
-2
paddle/framework/backward.h
paddle/framework/backward.h
+1
-1
paddle/framework/backward_test.cc
paddle/framework/backward_test.cc
+1
-2
paddle/framework/pybind.cc
paddle/framework/pybind.cc
+50
-74
paddle/operators/net_op.h
paddle/operators/net_op.h
+1
-3
paddle/operators/recurrent_op.cc
paddle/operators/recurrent_op.cc
+10
-10
paddle/operators/recurrent_op.h
paddle/operators/recurrent_op.h
+6
-4
python/paddle/v2/framework/tests/gradient_checker.py
python/paddle/v2/framework/tests/gradient_checker.py
+0
-1
未找到文件。
paddle/framework/backward.cc
浏览文件 @
f15e0830
...
...
@@ -89,7 +89,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
}
// Returned gradient network
auto
net
=
std
::
unique_ptr
<
operators
::
NetOp
>
();
auto
net
=
std
::
unique_ptr
<
operators
::
NetOp
>
(
new
operators
::
NetOp
()
);
if
(
forwardOp
.
IsNetOp
())
{
// Because forwardOp is a net op, it can static_cast.
...
...
@@ -204,7 +204,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
}
// See header for comments
std
::
shared
_ptr
<
OperatorBase
>
Backward
(
std
::
unique
_ptr
<
OperatorBase
>
Backward
(
const
OperatorBase
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>&
no_grad_vars
)
{
std
::
unordered_set
<
std
::
string
>
no_grad_names
;
...
...
paddle/framework/backward.h
浏览文件 @
f15e0830
...
...
@@ -20,7 +20,7 @@ namespace framework {
// Create the backward operator from a forward operator.
// TODO(yuyang18): Add more API reference comment.
extern
std
::
shared
_ptr
<
OperatorBase
>
Backward
(
extern
std
::
unique
_ptr
<
OperatorBase
>
Backward
(
const
OperatorBase
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>&
no_grad_vars
);
}
// namespace framework
...
...
paddle/framework/backward_test.cc
浏览文件 @
f15e0830
...
...
@@ -180,8 +180,7 @@ TEST(Backward, simple_op_not_need_grad) {
auto
no_input_gop
=
f
::
Backward
(
*
fwd
,
{
"x"
,
"b"
});
ASSERT_NE
(
no_input_gop
,
nullptr
);
ASSERT_TRUE
(
no_input_gop
->
IsNetOp
());
ASSERT_EQ
(
0UL
,
std
::
static_pointer_cast
<
ops
::
NetOp
>
(
no_input_gop
)
->
ops_
.
size
());
ASSERT_EQ
(
0UL
,
static_cast
<
ops
::
NetOp
*>
(
no_input_gop
.
get
())
->
ops_
.
size
());
}
TEST
(
Backward
,
net_fc_backward_normal
)
{
...
...
paddle/framework/pybind.cc
浏览文件 @
f15e0830
...
...
@@ -48,29 +48,6 @@ namespace framework {
using
Tensor
=
framework
::
Tensor
;
template
<
typename
ClassType
>
void
ExposeOperator
(
ClassType
&
m
)
{
m
.
def
(
"infer_shape"
,
&
ClassType
::
type
::
InferShape
)
.
def
(
"run"
,
&
ClassType
::
type
::
Run
)
.
def
(
"type"
,
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
string
{
return
op
.
Type
();
})
.
def
(
"outputs"
,
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
map
<
std
::
string
,
std
::
vector
<
std
::
string
>>
{
return
op
.
Outputs
();
})
.
def
(
"inputs"
,
[](
const
typename
ClassType
::
type
&
op
)
{
return
op
.
Inputs
();
})
.
def
(
"__str__"
,
&
ClassType
::
type
::
DebugString
)
.
def
(
"no_intermediate_outputs"
,
[](
const
typename
ClassType
::
type
&
op
)
{
return
op
.
OutputVars
(
false
);
})
.
def
(
"support_gpu"
,
&
ClassType
::
type
::
SupportGPU
);
}
static
size_t
UniqueIntegerGenerator
()
{
static
std
::
atomic
<
size_t
>
generator
;
return
generator
.
fetch_add
(
1
);
...
...
@@ -207,70 +184,69 @@ All parameter, weight, gradient are variables in Paddle.
.
def
(
py
::
init
<>
())
.
def
(
"__str__"
,
string
::
to_string
<
const
platform
::
CPUPlace
&>
);
py
::
class_
<
OperatorBase
>
operator_base
(
m
,
"Operator"
);
operator_base
.
def_static
(
"create"
,
[](
py
::
bytes
protobin
)
{
OpDesc
desc
;
PADDLE_ENFORCE
(
desc
.
ParsePartialFromString
(
protobin
),
"Cannot parse user input to OpDesc"
);
PADDLE_ENFORCE
(
desc
.
IsInitialized
(),
"User OpDesc is not initialized, reason %s"
,
desc
.
InitializationErrorString
());
return
OpRegistry
::
CreateOp
(
desc
);
});
operator_base
.
def
(
"backward"
,
[](
const
OperatorBase
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>
&
no_grad_vars
)
{
return
Backward
(
forwardOp
,
no_grad_vars
);
});
ExposeOperator
(
operator_base
);
py
::
class_
<
operators
::
NetOp
>
net
(
m
,
"Net"
);
py
::
class_
<
OperatorBase
>
(
m
,
"Operator"
)
.
def_static
(
"create"
,
[](
py
::
bytes
protobin
)
{
OpDesc
desc
;
PADDLE_ENFORCE
(
desc
.
ParsePartialFromString
(
protobin
),
"Cannot parse user input to OpDesc"
);
PADDLE_ENFORCE
(
desc
.
IsInitialized
(),
"User OpDesc is not initialized, reason %s"
,
desc
.
InitializationErrorString
());
return
OpRegistry
::
CreateOp
(
desc
);
})
.
def
(
"backward"
,
[](
const
OperatorBase
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>
&
no_grad_vars
)
{
return
Backward
(
forwardOp
,
no_grad_vars
).
release
();
})
.
def
(
"infer_shape"
,
&
OperatorBase
::
InferShape
)
.
def
(
"run"
,
&
OperatorBase
::
Run
)
.
def
(
"type"
,
[](
const
OperatorBase
&
op
)
->
std
::
string
{
return
op
.
Type
();
})
.
def
(
"outputs"
,
[](
const
OperatorBase
&
op
)
->
std
::
map
<
std
::
string
,
std
::
vector
<
std
::
string
>>
{
return
op
.
Outputs
();
})
.
def
(
"inputs"
,
[](
const
OperatorBase
&
op
)
{
return
op
.
Inputs
();
})
.
def
(
"__str__"
,
&
OperatorBase
::
DebugString
)
.
def
(
"no_intermediate_outputs"
,
[](
const
OperatorBase
&
op
)
{
return
op
.
OutputVars
(
false
);
})
.
def
(
"support_gpu"
,
&
OperatorBase
::
SupportGPU
);
net
.
def_static
(
"create"
,
[]()
->
operators
::
NetOp
*
{
auto
*
retv
=
new
operators
::
NetOp
;
retv
->
SetType
(
"plain_net"
);
return
retv
;
})
py
::
class_
<
operators
::
NetOp
,
OperatorBase
>
(
m
,
"Net"
)
.
def_static
(
"create"
,
[]()
->
operators
::
NetOp
*
{
auto
*
retv
=
new
operators
::
NetOp
;
retv
->
SetType
(
"plain_net"
);
return
retv
;
})
.
def
(
"add_op"
,
[](
operators
::
NetOp
&
self
,
const
OperatorBase
&
op
)
{
self
.
AddOp
(
op
);
})
.
def
(
"add_op"
,
[](
operators
::
NetOp
&
self
,
const
operators
::
NetOp
&
net
)
->
void
{
self
.
AddOp
(
net
);
})
.
def
(
"add_op"
,
[](
operators
::
NetOp
&
self
,
const
operators
::
RecurrentOp
&
rnn
)
->
void
{
self
.
AddOp
(
rnn
);
})
.
def
(
"complete_add_op"
,
&
operators
::
NetOp
::
CompleteAddOp
)
.
def
(
"complete_add_op"
,
[](
std
::
shared_ptr
<
operators
::
NetOp
>
&
self
)
{
self
->
CompleteAddOp
();
});
ExposeOperator
(
net
);
// recurrent_op
py
::
class_
<
operators
::
RecurrentOp
>
rnn
(
m
,
"RecurrentOp"
);
rnn
.
def_static
(
"create"
,
[](
py
::
bytes
protobin
)
->
operators
::
RecurrentOp
*
{
OpDesc
desc
;
PADDLE_ENFORCE
(
desc
.
ParsePartialFromString
(
protobin
),
"Cannot parse user input to OpDesc"
);
PADDLE_ENFORCE
(
desc
.
IsInitialized
(),
"User OpDesc is not initialized, reason %s"
,
desc
.
InitializationErrorString
());
auto
rnn_op
=
OpRegistry
::
CreateOp
(
desc
);
return
static_cast
<
operators
::
RecurrentOp
*>
(
rnn_op
.
release
());
})
py
::
class_
<
operators
::
RecurrentOp
,
OperatorBase
>
(
m
,
"RecurrentOp"
)
.
def_static
(
"create"
,
[](
py
::
bytes
protobin
)
->
operators
::
RecurrentOp
*
{
OpDesc
desc
;
PADDLE_ENFORCE
(
desc
.
ParsePartialFromString
(
protobin
),
"Cannot parse user input to OpDesc"
);
PADDLE_ENFORCE
(
desc
.
IsInitialized
(),
"User OpDesc is not initialized, reason %s"
,
desc
.
InitializationErrorString
());
auto
rnn_op
=
OpRegistry
::
CreateOp
(
desc
);
return
static_cast
<
operators
::
RecurrentOp
*>
(
rnn_op
.
release
());
})
.
def
(
"set_stepnet"
,
[](
operators
::
RecurrentOp
&
self
,
const
operators
::
NetOp
&
net
)
->
void
{
self
.
set_stepnet
(
net
.
Clone
());
});
ExposeOperator
(
rnn
);
m
.
def
(
"unique_integer"
,
UniqueIntegerGenerator
);
...
...
paddle/operators/net_op.h
浏览文件 @
f15e0830
...
...
@@ -41,9 +41,7 @@ class NetOp : public framework::OperatorBase {
NetOp
(
const
std
::
string
&
type
,
const
VarNameMap
&
inputs
,
const
VarNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
);
NetOp
(
const
NetOp
&
o
)
:
framework
::
OperatorBase
(
static_cast
<
const
framework
::
OperatorBase
&>
(
o
))
{
NetOp
(
const
NetOp
&
o
)
:
framework
::
OperatorBase
(
o
.
type_
,
{},
{},
o
.
attrs_
)
{
this
->
ops_
.
reserve
(
o
.
ops_
.
size
());
std
::
transform
(
o
.
ops_
.
begin
(),
o
.
ops_
.
end
(),
std
::
back_inserter
(
this
->
ops_
),
...
...
paddle/operators/recurrent_op.cc
浏览文件 @
f15e0830
...
...
@@ -42,7 +42,7 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
i
,
-
1
,
true
/*infer_shape_mode*/
);
}
stepnet_
->
InferShape
(
*
step_scopes
[
i
]);
(
*
stepnet_
)
->
InferShape
(
*
step_scopes
[
i
]);
}
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
true
/*infer_shape_mode*/
);
...
...
@@ -61,7 +61,7 @@ void RecurrentAlgorithm::Run(const Scope& scope,
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
-
1
,
false
/*infer_shape_mode*/
);
}
stepnet_
->
Run
(
*
step_scopes
[
step_id
],
dev_ctx
);
(
*
stepnet_
)
->
Run
(
*
step_scopes
[
step_id
],
dev_ctx
);
}
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
false
/*infer_shape_mode*/
);
...
...
@@ -76,15 +76,15 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
// Now all variables in scope must be created outside of op.
PADDLE_ENFORCE_NOT_NULL
(
stepnet_
);
PADDLE_ENFORCE
(
!
stepnet_
->
Outputs
().
empty
(),
"stepnet_ op has no outputs"
);
PADDLE_ENFORCE
(
!
stepnet_
->
Outputs
().
empty
(),
"net_op has no outputs"
);
PADDLE_ENFORCE
(
!
(
*
stepnet_
)
->
Outputs
().
empty
(),
"stepnet_ op has no outputs"
);
PADDLE_ENFORCE
(
!
(
*
stepnet_
)
->
Outputs
().
empty
(),
"net_op has no outputs"
);
if
(
seq_len_
>
step_scopes
->
size
())
{
for
(
size_t
i
=
step_scopes
->
size
();
i
<
seq_len_
;
++
i
)
{
auto
&
step_scope
=
scope
.
NewScope
();
// create step net's temp inputs
for
(
auto
&
input
:
stepnet_
->
Inputs
())
{
for
(
auto
&
input
:
(
*
stepnet_
)
->
Inputs
())
{
// the weight are located in parent scope
for
(
auto
&
var_name
:
input
.
second
)
{
if
(
!
step_scope
.
FindVar
(
var_name
))
{
...
...
@@ -93,7 +93,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
}
}
// create stepnet's outputs
for
(
const
auto
&
output
:
stepnet_
->
Outputs
())
{
for
(
const
auto
&
output
:
(
*
stepnet_
)
->
Outputs
())
{
for
(
auto
&
var_name
:
output
.
second
)
{
step_scope
.
NewVar
(
var_name
);
}
...
...
@@ -136,7 +136,7 @@ RecurrentOp::RecurrentOp(const std::string& type,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{
rnn
::
InitArgument
(
kArgName
,
&
arg_
,
*
this
);
alg_
.
Init
(
&
arg_
,
stepnet_
.
get
()
);
alg_
.
Init
(
&
arg_
,
&
stepnet_
);
}
class
RecurrentAlgorithmProtoAndCheckerMaker
...
...
@@ -178,7 +178,7 @@ void RecurrentGradientAlgorithm::Run(
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
1
,
false
/*infer_shape_mode*/
);
}
stepnet_
->
Run
(
*
step_scopes
[
step_id
],
dev_ctx
);
(
*
stepnet_
)
->
Run
(
*
step_scopes
[
step_id
],
dev_ctx
);
}
LinkBootMemoryGradients
(
step_scopes
[
0
],
false
);
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
...
...
@@ -215,7 +215,7 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
1
,
true
/*infer_shape_mode*/
);
}
stepnet_
->
InferShape
(
*
step_scopes
[
step_id
]);
(
*
stepnet_
)
->
InferShape
(
*
step_scopes
[
step_id
]);
}
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
true
/*infer_shape_mode*/
);
...
...
@@ -228,7 +228,7 @@ RecurrentGradientOp::RecurrentGradientOp(
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{
rnn
::
InitArgument
(
kArgName
,
&
arg_
,
*
this
);
alg_
.
Init
(
&
arg_
,
stepnet_
.
get
()
);
alg_
.
Init
(
&
arg_
,
&
stepnet_
);
}
}
// namespace operators
...
...
paddle/operators/recurrent_op.h
浏览文件 @
f15e0830
...
...
@@ -34,7 +34,8 @@ class RecurrentAlgorithm {
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
;
void
Init
(
rnn
::
Argument
*
arg
,
framework
::
OperatorBase
*
stepnet
)
{
void
Init
(
rnn
::
Argument
*
arg
,
std
::
unique_ptr
<
framework
::
OperatorBase
>*
stepnet
)
{
PADDLE_ENFORCE_NOT_NULL
(
stepnet
,
"stepnet should be set before."
);
arg_
=
arg
;
stepnet_
=
stepnet
;
...
...
@@ -63,7 +64,7 @@ class RecurrentAlgorithm {
void
InitMemories
(
framework
::
Scope
*
step_scopes
,
bool
infer_shape_mode
)
const
;
private:
framework
::
OperatorBase
*
stepnet_
;
std
::
unique_ptr
<
framework
::
OperatorBase
>
*
stepnet_
;
rnn
::
Argument
*
arg_
;
mutable
size_t
seq_len_
;
};
...
...
@@ -80,7 +81,8 @@ class RecurrentGradientAlgorithm {
* operator.
*/
public:
void
Init
(
rnn
::
Argument
*
arg
,
framework
::
OperatorBase
*
stepnet
)
{
void
Init
(
rnn
::
Argument
*
arg
,
std
::
unique_ptr
<
framework
::
OperatorBase
>*
stepnet
)
{
PADDLE_ENFORCE_NOT_NULL
(
stepnet
,
"stepnet should be set before."
);
arg_
=
std
::
move
(
arg
);
stepnet_
=
stepnet
;
...
...
@@ -107,7 +109,7 @@ class RecurrentGradientAlgorithm {
private:
rnn
::
Argument
*
arg_
;
mutable
size_t
seq_len_
;
framework
::
OperatorBase
*
stepnet_
;
std
::
unique_ptr
<
framework
::
OperatorBase
>
*
stepnet_
;
};
class
RecurrentOp
:
public
framework
::
OperatorBase
{
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...
python/paddle/v2/framework/tests/gradient_checker.py
浏览文件 @
f15e0830
...
...
@@ -165,7 +165,6 @@ class GradientChecker(unittest.TestCase):
for
no_grad
in
no_grad_set
:
if
no_grad
not
in
in_names
:
raise
ValueError
(
"no_grad should be in in_names"
)
backward_op
=
core
.
Operator
.
backward
(
forward_op
,
no_grad_set
)
bwd_outputs
=
backward_op
.
outputs
()
...
...
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