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0885de47
编写于
11月 01, 2017
作者:
Y
Yang Yang(Tony)
提交者:
Yu Yang
11月 01, 2017
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电子邮件补丁
差异文件
first commit (#5286)
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a3435044
变更
3
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Showing
3 changed file
with
287 addition
and
1 deletion
+287
-1
paddle/operators/rnn_memory_helper_op.cc
paddle/operators/rnn_memory_helper_op.cc
+154
-0
python/paddle/v2/framework/framework.py
python/paddle/v2/framework/framework.py
+3
-1
python/paddle/v2/framework/tests/test_rnn_memory_helper_op.py
...on/paddle/v2/framework/tests/test_rnn_memory_helper_op.py
+130
-0
未找到文件。
paddle/operators/rnn_memory_helper_op.cc
0 → 100644
浏览文件 @
0885de47
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace
paddle
{
namespace
operators
{
class
RNNMemoryHelperOp
:
public
framework
::
OperatorBase
{
public:
RNNMemoryHelperOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
mem_var_name
=
Input
(
"X"
);
auto
*
mem_var
=
scope
.
FindVar
(
mem_var_name
);
PADDLE_ENFORCE
(
mem_var
!=
nullptr
,
"Cannot find mem_var in scope, mem_var_name is %s"
,
mem_var_name
);
auto
out_name
=
this
->
Output
(
"Out"
);
auto
*
out_var
=
scope
.
FindVar
(
out_name
);
PADDLE_ENFORCE
(
out_var
!=
nullptr
,
"Cannot find out_var in scope, out_var_name is %s"
,
out_name
);
auto
*
out_tensor
=
out_var
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
&
mem_tensor
=
mem_var
->
Get
<
framework
::
LoDTensor
>
();
out_tensor
->
ShareDataWith
(
mem_tensor
);
out_tensor
->
set_lod
(
mem_tensor
.
lod
());
}
};
class
RNNMemoryHelperOpShapeInference
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
""
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
""
);
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
class
RNNMemoryHelperOpInfoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
RNNMemoryHelperOpInfoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
""
);
AddOutput
(
"Out"
,
""
);
AddAttr
<
int
>
(
"data_type"
,
"(int, default 5 (FP32)) "
"Output data type"
)
.
SetDefault
(
framework
::
DataType
::
FP32
);
AddComment
(
""
);
}
};
class
RNNMemoryHelperGradOp
:
public
framework
::
OperatorBase
{
public:
RNNMemoryHelperGradOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
out_grad_var_name
=
Input
(
framework
::
GradVarName
(
"Out"
));
auto
*
out_grad_var
=
scope
.
FindVar
(
out_grad_var_name
);
auto
in_grad_var_name
=
Output
(
framework
::
GradVarName
(
"X"
));
auto
*
in_grad_var
=
scope
.
FindVar
(
in_grad_var_name
);
PADDLE_ENFORCE
(
in_grad_var
!=
nullptr
,
"Cannot find in_grad_var in scope, name is %s"
,
in_grad_var_name
);
if
(
out_grad_var
==
nullptr
)
{
VLOG
(
5
)
<<
"Using fill constant 0 as starting gradient"
;
auto
in_var_name
=
Input
(
"X"
);
auto
*
in_var
=
scope
.
FindVar
(
in_var_name
);
auto
&
in_var_tensor
=
in_var
->
Get
<
framework
::
LoDTensor
>
();
framework
::
AttributeMap
attrs
;
attrs
[
"data_type"
]
=
framework
::
ToDataType
(
in_var_tensor
.
type
());
attrs
[
"shape"
]
=
framework
::
vectorize2int
(
in_var_tensor
.
dims
());
attrs
[
"value"
]
=
0.0
f
;
auto
zero_op
=
framework
::
OpRegistry
::
CreateOp
(
"fill_constant"
,
{},
{{
"Out"
,
{
in_grad_var_name
}}},
attrs
);
zero_op
->
Run
(
scope
,
dev_ctx
);
}
else
{
auto
&
out_grad_tensor
=
out_grad_var
->
Get
<
framework
::
LoDTensor
>
();
auto
*
in_grad_tensor
=
in_grad_var
->
GetMutable
<
framework
::
LoDTensor
>
();
in_grad_tensor
->
ShareDataWith
(
out_grad_tensor
);
in_grad_tensor
->
set_lod
(
out_grad_tensor
.
lod
());
}
}
};
class
RNNMemoryHelperGradOpInfoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
RNNMemoryHelperGradOpInfoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
framework
::
GradVarName
(
"Out"
),
""
);
AddInput
(
"X"
,
""
);
AddInput
(
"Out"
,
""
);
AddOutput
(
framework
::
GradVarName
(
"X"
),
""
);
AddAttr
<
int
>
(
"data_type"
,
"(int, default 5 (FP32)) "
"Output data type"
)
.
SetDefault
(
framework
::
DataType
::
FP32
);
AddComment
(
""
);
}
};
class
RNNMemoryHelperGradOpShapeInference
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
auto
x_grad_name
=
framework
::
GradVarName
(
"X"
);
auto
out_grad_name
=
framework
::
GradVarName
(
"Out"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
out_grad_name
),
""
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
x_grad_name
),
""
);
ctx
->
SetOutputDim
(
x_grad_name
,
ctx
->
GetInputDim
(
out_grad_name
));
ctx
->
ShareLoD
(
out_grad_name
,
/*->*/
x_grad_name
);
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OPERATOR
(
rnn_memory_helper
,
paddle
::
operators
::
RNNMemoryHelperOp
,
paddle
::
operators
::
RNNMemoryHelperOpInfoMaker
,
paddle
::
operators
::
RNNMemoryHelperOpShapeInference
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
rnn_memory_helper_grad
,
paddle
::
operators
::
RNNMemoryHelperGradOp
,
paddle
::
operators
::
RNNMemoryHelperGradOpInfoMaker
,
paddle
::
operators
::
RNNMemoryHelperGradOpShapeInference
);
python/paddle/v2/framework/framework.py
浏览文件 @
0885de47
...
...
@@ -264,7 +264,9 @@ class Operator(object):
self
.
desc
.
set_attr
(
attr_name
,
attrs
[
attr_name
])
self
.
desc
.
check_attrs
()
no_kernel_op_set
=
{
'feed'
,
'fetch'
,
'save'
,
'load'
}
no_kernel_op_set
=
{
'feed'
,
'fetch'
,
'save'
,
'load'
,
'rnn_memory_helper_grad'
}
if
type
not
in
no_kernel_op_set
:
self
.
desc
.
infer_var_type
(
self
.
block
.
desc
)
self
.
desc
.
infer_shape
(
self
.
block
.
desc
)
...
...
python/paddle/v2/framework/tests/test_rnn_memory_helper_op.py
0 → 100644
浏览文件 @
0885de47
import
unittest
from
paddle.v2.framework.framework
import
Program
from
paddle.v2.framework.executor
import
Executor
from
paddle.v2.framework.backward
import
append_backward_ops
import
numpy
as
np
import
paddle.v2.framework.core
as
core
def
create_tensor
(
np_data
,
place
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
np_data
,
place
)
return
tensor
class
RNNMemoryHelperOpTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
program
=
Program
()
self
.
place
=
core
.
CPUPlace
()
self
.
X
=
self
.
program
.
global_block
().
create_var
(
name
=
'X'
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
self
.
Out
=
self
.
program
.
global_block
().
create_var
(
name
=
'Out'
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
self
.
program
.
global_block
().
append_op
(
type
=
'rnn_memory_helper'
,
inputs
=
{
"X"
:
self
.
X
},
outputs
=
{
"Out"
:
self
.
Out
},
attrs
=
{})
def
test_forward
(
self
):
x_np
=
np
.
random
.
normal
(
size
=
(
2
,
3
)).
astype
(
"float32"
)
self
.
feed_map
=
{
'X'
:
create_tensor
(
x_np
,
self
.
place
)}
self
.
fetch_list
=
[
self
.
Out
]
exe
=
Executor
(
self
.
place
)
out
=
exe
.
run
(
self
.
program
,
feed
=
self
.
feed_map
,
fetch_list
=
self
.
fetch_list
)
np
.
isclose
(
np
.
array
(
out
[
0
]),
x_np
,
rtol
=
1e-5
)
class
RNNMemoryHelperGradOpTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
program
=
Program
()
self
.
place
=
core
.
CPUPlace
()
self
.
input_names
=
[
'X'
,
'Out'
,
'Out@GRAD'
]
self
.
input_vars
=
{
name
:
self
.
program
.
global_block
().
create_var
(
name
=
name
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
for
name
in
self
.
input_names
}
self
.
output_names
=
[
'X@GRAD'
]
self
.
output_vars
=
{
name
:
self
.
program
.
global_block
().
create_var
(
name
=
name
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
for
name
in
self
.
output_names
}
self
.
program
.
global_block
().
append_op
(
type
=
'rnn_memory_helper_grad'
,
inputs
=
self
.
input_vars
,
outputs
=
self
.
output_vars
,
attrs
=
{})
def
test_backward
(
self
):
self
.
feed_map
=
{
name
:
create_tensor
(
np
.
random
.
normal
(
size
=
(
2
,
3
)).
astype
(
"float32"
),
self
.
place
)
for
name
in
self
.
input_names
}
self
.
fetch_list
=
[
self
.
output_vars
[
'X@GRAD'
]]
exe
=
Executor
(
self
.
place
)
out
=
exe
.
run
(
self
.
program
,
feed
=
self
.
feed_map
,
fetch_list
=
self
.
fetch_list
)
np
.
isclose
(
np
.
array
(
out
[
0
]),
self
.
feed_map
[
'Out@GRAD'
],
rtol
=
1e-5
)
class
RNNMemoryHelperGradOpWithoutInputTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
program
=
Program
()
self
.
fake_program
=
Program
()
self
.
place
=
core
.
CPUPlace
()
self
.
input_names
=
[
'X'
,
'Out'
]
self
.
input_vars
=
{
name
:
self
.
program
.
global_block
().
create_var
(
name
=
name
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
for
name
in
self
.
input_names
}
self
.
input_vars
[
"Out@GRAD"
]
=
\
self
.
fake_program
.
global_block
().
create_var
(
name
=
"Out@GRAD"
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
self
.
output_names
=
[
'X@GRAD'
]
self
.
output_vars
=
{
name
:
self
.
program
.
global_block
().
create_var
(
name
=
name
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
for
name
in
self
.
output_names
}
self
.
program
.
global_block
().
append_op
(
type
=
'rnn_memory_helper_grad'
,
inputs
=
self
.
input_vars
,
outputs
=
self
.
output_vars
,
attrs
=
{})
def
test_backward
(
self
):
self
.
feed_map
=
{
name
:
create_tensor
(
np
.
random
.
normal
(
size
=
(
2
,
3
)).
astype
(
"float32"
),
self
.
place
)
for
name
in
[
'X'
,
'Out'
]
}
self
.
fetch_list
=
[
self
.
output_vars
[
'X@GRAD'
]]
exe
=
Executor
(
self
.
place
)
out
=
exe
.
run
(
self
.
program
,
feed
=
self
.
feed_map
,
fetch_list
=
self
.
fetch_list
)
np
.
isclose
(
np
.
array
(
out
[
0
]),
np
.
zeros
(
shape
=
(
2
,
3
)).
astype
(
"float32"
),
rtol
=
1e-5
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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