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体验新版 GitCode,发现更多精彩内容 >>
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6a0c3428
编写于
9月 20, 2017
作者:
S
superjom
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
make RecurrentOp's backward work
上级
68399ab9
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
23 addition
and
28 deletion
+23
-28
paddle/framework/operator.cc
paddle/framework/operator.cc
+2
-2
paddle/operators/recurrent_op.cc
paddle/operators/recurrent_op.cc
+6
-6
paddle/operators/recurrent_op.h
paddle/operators/recurrent_op.h
+4
-1
paddle/operators/rnn/recurrent_op_utils.cc
paddle/operators/rnn/recurrent_op_utils.cc
+5
-3
paddle/operators/rnn/recurrent_op_utils.h
paddle/operators/rnn/recurrent_op_utils.h
+1
-1
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+0
-9
python/paddle/v2/framework/tests/test_recurrent_op.py
python/paddle/v2/framework/tests/test_recurrent_op.py
+5
-6
未找到文件。
paddle/framework/operator.cc
浏览文件 @
6a0c3428
...
...
@@ -60,8 +60,8 @@ std::string OperatorBase::Output(const std::string& name) const {
const
std
::
vector
<
std
::
string
>&
OperatorBase
::
Outputs
(
const
std
::
string
&
name
)
const
{
auto
it
=
outputs_
.
find
(
name
);
PADDLE_ENFORCE
(
it
!=
outputs_
.
end
(),
"Op %s does not have output
%s"
,
type_
,
name
);
PADDLE_ENFORCE
(
it
!=
outputs_
.
end
(),
"Op %s does not have output
called %s"
,
type_
,
name
);
return
it
->
second
;
}
...
...
paddle/operators/recurrent_op.cc
浏览文件 @
6a0c3428
...
...
@@ -128,8 +128,8 @@ const rnn::ArgumentName RecurrentOp::kArgName{
"memories"
,
"pre_memories"
,
"boot_memories"
};
const
rnn
::
ArgumentName
RecurrentGradientOp
::
kArgName
{
"step_net"
,
"step_scopes
"
,
"outlink@grad"
,
"inlink@grad
"
,
"memories"
,
"pre_memories"
,
"boot_memories@grad
"
};
"step_net"
,
"step_scopes
@GRAD"
,
"outlinks@GRAD"
,
"inlinks@GRAD
"
,
"memories"
,
"pre_memories"
,
"boot_memories@GRAD
"
};
RecurrentOp
::
RecurrentOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
...
...
@@ -225,13 +225,13 @@ RecurrentGradientOp::RecurrentGradientOp(
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{
rnn
::
InitArgument
(
kArgName
,
&
arg_
,
*
this
);
rnn
::
InitArgument
(
kArgName
,
&
arg_
,
*
this
,
true
/*is grad*/
);
alg_
.
Init
(
&
arg_
,
&
stepnet_
);
}
}
// namespace operators
}
// namespace paddle
REGISTER_OP
_WITHOUT_GRADIENT
(
recurrent
,
paddle
::
operators
::
RecurrentOp
,
paddle
::
operators
::
RecurrentAlgorithmProtoAndCheckerMaker
);
REGISTER_OP
(
recurrent
,
paddle
::
operators
::
RecurrentOp
,
paddle
::
operators
::
RecurrentAlgorithmProtoAndCheckerMaker
,
recurrent_grad
,
paddle
::
operators
::
RecurrentGradientOp
);
paddle/operators/recurrent_op.h
浏览文件 @
6a0c3428
...
...
@@ -22,7 +22,7 @@ namespace paddle {
namespace
operators
{
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO(
Yan Chunwei):
// TODO(
Superjom)
// 1. No-padding computing for sequences with indifinite length in one batch.
// 2. Hierarchical RNN for sequence with sub-sequence.
// 3. Internal Memory.
...
...
@@ -177,6 +177,9 @@ class RecurrentGradientOp : public framework::OperatorBase {
static
const
rnn
::
ArgumentName
kArgName
;
/*
* set a stepnet that is created according to a RecurrentOp's stepnet.
*/
void
set_stepnet
(
std
::
unique_ptr
<
OperatorBase
>
net
)
{
stepnet_
=
std
::
move
(
net
);
}
...
...
paddle/operators/rnn/recurrent_op_utils.cc
浏览文件 @
6a0c3428
...
...
@@ -109,14 +109,16 @@ void LinkMemories(const std::vector<Scope*>& scopes,
}
void
InitArgument
(
const
ArgumentName
&
name
,
Argument
*
arg
,
const
framework
::
OperatorBase
&
op
)
{
arg
->
step_scopes
=
op
.
Output
(
name
.
step_scopes
);
const
framework
::
OperatorBase
&
op
,
bool
is_grad
)
{
arg
->
step_scopes
=
is_grad
?
op
.
Input
(
name
.
step_scopes
)
:
op
.
Output
(
name
.
step_scopes
);
arg
->
inlinks
=
op
.
Inputs
(
name
.
inlinks
);
arg
->
outlinks
=
op
.
Outputs
(
name
.
outlinks
);
auto
boot_memories
=
op
.
Inputs
(
name
.
boot_memories
);
auto
boot_memories
=
is_grad
?
op
.
Outputs
(
name
.
boot_memories
)
:
op
.
Inputs
(
name
.
boot_memories
);
// attributes
auto
memories
=
op
.
Attr
<
std
::
vector
<
std
::
string
>>
(
name
.
memories
);
...
...
paddle/operators/rnn/recurrent_op_utils.h
浏览文件 @
6a0c3428
...
...
@@ -78,7 +78,7 @@ void LinkMemories(const std::vector<Scope*>& step_scopes,
const
int
offset
,
bool
infer_shape_mode
);
void
InitArgument
(
const
ArgumentName
&
name
,
Argument
*
arg
,
const
framework
::
OperatorBase
&
op
);
const
framework
::
OperatorBase
&
op
,
bool
is_grad
=
false
);
}
// namespace rnn
}
// namespace operators
...
...
paddle/pybind/pybind.cc
浏览文件 @
6a0c3428
...
...
@@ -311,15 +311,6 @@ All parameter, weight, gradient are variables in Paddle.
self
.
set_falsenet
(
net
.
Clone
());
});
rnn
.
def
(
"backward"
,
[](
const
operators
::
RecurrentOp
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>
&
no_grad_vars
)
{
const
auto
&
op
=
*
static_cast
<
const
OperatorBase
*>
(
&
forwardOp
);
return
Backward
(
op
,
no_grad_vars
);
});
ExposeOperator
(
rnn
);
m
.
def
(
"unique_integer"
,
UniqueIntegerGenerator
);
m
.
def
(
"is_compile_gpu"
,
IsCompileGPU
);
...
...
python/paddle/v2/framework/tests/test_recurrent_op.py
浏览文件 @
6a0c3428
...
...
@@ -3,7 +3,7 @@ import paddle.v2.framework.core as core
import
unittest
import
numpy
as
np
from
paddle.v2.framework.op
import
Operator
,
RecurrentOp
from
gradient_checker
import
GradientChecker
from
op_test
import
get_numeric_gradient
def
py_sigmoid
(
x
):
...
...
@@ -48,7 +48,7 @@ class PySimpleRNN(object):
else
:
pre_mem
=
self
.
h_boot
xW
=
np
.
matmul
(
x
,
self
.
W
)
hU
=
np
.
matmul
(
mem
,
self
.
U
)
hU
=
np
.
matmul
(
pre_
mem
,
self
.
U
)
sum
=
xW
+
hU
self
.
mems
[
step_id
]
=
py_sigmoid
(
sum
)
...
...
@@ -159,6 +159,7 @@ class RecurrentOpTest(unittest.TestCase):
print
print
'py_output'
,
py_output
self
.
assertEqual
(
pd_output
.
shape
,
py_output
.
shape
)
self
.
assertTrue
(
np
.
isclose
(
pd_output
,
py_output
,
rtol
=
0.1
).
all
())
class
RecurrentGradientOpTest
(
unittest
.
TestCase
):
...
...
@@ -172,8 +173,6 @@ class RecurrentGradientOpTest(unittest.TestCase):
outlinks
=
[
"h"
],
step_scopes
=
"step_scopes"
,
# attributes
inlink_alias
=
[
"x@alias"
],
outlink_alias
=
[
"h@alias"
],
pre_memories
=
[
"h@pre"
],
memories
=
[
"h@alias"
])
...
...
@@ -181,11 +180,11 @@ class RecurrentGradientOpTest(unittest.TestCase):
stepnet
=
core
.
Net
.
create
()
x_fc_op
=
Operator
(
"mul"
,
X
=
"x@alias"
,
Y
=
"W"
,
Out
=
"Wx"
)
h_fc_op
=
Operator
(
"mul"
,
X
=
"h@pre"
,
Y
=
"U"
,
Out
=
"Uh"
)
sum_op
=
Operator
(
"add
_two
"
,
X
=
"Wx"
,
Y
=
"Uh"
,
Out
=
"sum"
)
sum_op
=
Operator
(
"add"
,
X
=
"Wx"
,
Y
=
"Uh"
,
Out
=
"sum"
)
sig_op
=
Operator
(
"sigmoid"
,
X
=
"sum"
,
Y
=
"h@alias"
)
for
op
in
[
x_fc_op
,
h_fc_op
,
sum_op
,
sig_op
]:
stepnet
.
a
d
d_op
(
op
)
stepnet
.
a
ppen
d_op
(
op
)
stepnet
.
complete_add_op
(
True
)
self
.
forward_op
.
set_stepnet
(
stepnet
)
...
...
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