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体验新版 GitCode,发现更多精彩内容 >>
提交
b8e75c1f
编写于
9月 12, 2017
作者:
Z
zchen0211
浏览文件
操作
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电子邮件补丁
差异文件
cond op
上级
aa90ef9c
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
438 addition
and
0 deletion
+438
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+2
-0
paddle/operators/cond_op.cc
paddle/operators/cond_op.cc
+45
-0
paddle/operators/cond_op.h
paddle/operators/cond_op.h
+232
-0
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+23
-0
python/paddle/v2/framework/op.py
python/paddle/v2/framework/op.py
+22
-0
python/paddle/v2/framework/tests/test_cond_op.py
python/paddle/v2/framework/tests/test_cond_op.py
+114
-0
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
b8e75c1f
...
...
@@ -55,12 +55,14 @@ set(DEPS_OPS
minus_op
mul_op
recurrent_op
cond_op
scale_op
)
op_library
(
identity_op DEPS scale_op
)
op_library
(
minus_op DEPS scale_op
)
op_library
(
mul_op DEPS math_function
)
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op
)
op_library
(
cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op
)
op_library
(
scale_op DEPS net_op
)
list
(
REMOVE_ITEM GENERAL_OPS
${
DEPS_OPS
}
)
...
...
paddle/operators/cond_op.cc
0 → 100644
浏览文件 @
b8e75c1f
/* 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/operators/cond_op.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace
paddle
{
namespace
operators
{
class
CondOpProtoAndCheckerMaker
:
public
OpProtoAndCheckerMaker
{
public:
CondOpProtoAndCheckerMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Cond"
,
"The condition, which is a bool vector"
);
AddInput
(
"Xs"
,
"Inputs of Subnets"
).
AsDuplicable
();
AddOutput
(
"Outs"
,
"Outputs of Cond_Op after merge"
).
AsDuplicable
();
AddOutput
(
"SubScopes"
,
"sub scopes for true and false branches"
);
AddOutput
(
"IndexTensors"
,
"Index Tensors contains indices for true/false"
);
AddComment
(
R"DOC(
Sample dependent Cond Operator:
The equation is: Out[i] = subnet_t[i], if Cond[i] == true
Out[i] = subnet_t[i], if Cond[i] == false
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OP_WITHOUT_GRADIENT
(
cond_op
,
paddle
::
operators
::
CondOp
,
paddle
::
operators
::
CondOpProtoAndCheckerMaker
);
paddle/operators/cond_op.h
0 → 100644
浏览文件 @
b8e75c1f
/* 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. */
#pragma once
#include <vector>
#include "glog/logging.h"
#include "paddle/framework/ddim.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/gather.h"
#include "paddle/operators/scatter.h"
namespace
paddle
{
namespace
operators
{
using
namespace
paddle
::
framework
;
class
CondOp
:
public
OperatorBase
{
public:
CondOp
(
const
std
::
string
&
type
,
const
VariableNameMap
&
inputs
,
const
VariableNameMap
&
outputs
,
const
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{
index_
.
resize
(
2
);
sub_net_op_
.
resize
(
2
);
LOG
(
INFO
)
<<
"Initialization Done."
;
}
CondOp
(
const
CondOp
&
o
)
:
framework
::
OperatorBase
(
static_cast
<
const
framework
::
OperatorBase
&>
(
o
))
{
// TODO(yuyang18): Implement copy ctor well.
PADDLE_THROW
(
"Not implemented"
);
}
void
CreateScope
(
const
Scope
&
scope
)
const
{
auto
sub_scopes_var
=
scope
.
FindVar
(
"SubScopes"
);
PADDLE_ENFORCE
(
sub_scopes_var
!=
nullptr
,
""
);
auto
sub_scopes
=
sub_scopes_var
->
GetMutable
<
std
::
vector
<
Scope
*>>
();
auto
&
sub_scope
=
scope
.
NewScope
();
sub_scopes
->
push_back
(
&
sub_scope
);
}
void
CreateIndexTensor
(
const
Scope
&
scope
)
const
{
auto
index_tensors_var
=
scope
.
FindVar
(
"IndexTensors"
);
PADDLE_ENFORCE
(
index_tensors_var
!=
nullptr
,
""
);
auto
&
index_tensors
=
*
index_tensors_var
->
GetMutable
<
std
::
vector
<
Tensor
*>>
();
Tensor
index_tensor
;
index_tensors
.
push_back
(
&
index_tensor
);
}
/**
* InferShape must be called before Run.
*/
void
InferShape
(
const
framework
::
Scope
&
scope
)
const
override
{
auto
sub_scopes_var
=
scope
.
FindVar
(
"SubScopes"
);
PADDLE_ENFORCE_NOT_NULL
(
sub_scopes_var
);
auto
&
sub_scopes
=
*
sub_scopes_var
->
GetMutable
<
std
::
vector
<
Scope
*>>
();
// auto& index_tensors =
// *scope.FindVar("IndexTensors")->GetMutable<std::vector<Tensor*>>();
for
(
int
i
=
0
;
i
<
2
;
++
i
)
{
// Create two sub scopes for true and false branches
// sub_scopes[0] for the true branch and sub_scopes[1] for the false
// branch
CreateScope
(
scope
);
// Create two tensors for true and false indices
// index_tensors[0] for the true branch and index_tensors[1] for the false
// branch
CreateIndexTensor
(
scope
);
for
(
auto
&
input
:
Inputs
(
"Xs"
))
{
// Create a new tensor in sub-scope for input-type tensor
Variable
*
v
=
sub_scopes
[
i
]
->
NewVar
(
input
);
Tensor
*
sub_input
=
v
->
GetMutable
<
Tensor
>
();
sub_input
->
Resize
(
scope
.
FindVar
(
input
)
->
GetMutable
<
Tensor
>
()
->
dims
());
}
// Inputs that do not require tailoring
/*for (auto& input : (*sub_net_op_[i]).Inputs()) {
// weights are located in the parent scope rather than sub scope
for (auto& var_name : input.second) {
if (!sub_scopes[i]->FindVar(var_name)) {
sub_scopes[i]->NewVar(var_name)->GetMutable<Tensor>();
}
}
}*/
// Outputs
for
(
auto
&
output
:
(
*
sub_net_op_
[
i
]).
Outputs
())
{
for
(
auto
&
var_name
:
output
.
second
)
{
sub_scopes
[
i
]
->
NewVar
(
var_name
);
}
}
// each net calls InferShape
LOG
(
INFO
)
<<
"OK 3"
;
sub_net_op_
[
i
]
->
InferShape
(
*
sub_scopes
[
i
]);
LOG
(
INFO
)
<<
"OK 4"
;
}
for
(
auto
&
output
:
Outputs
(
"Outs"
))
{
Tensor
*
tensor_t_out
=
sub_scopes
[
0
]
->
FindVar
(
output
)
->
GetMutable
<
Tensor
>
();
Tensor
*
tensor_f_out
=
sub_scopes
[
1
]
->
FindVar
(
output
)
->
GetMutable
<
Tensor
>
();
Tensor
*
tensor_out
=
scope
.
FindVar
(
output
)
->
GetMutable
<
Tensor
>
();
// check output size should be same
PADDLE_ENFORCE_EQ
(
tensor_t_out
->
dims
(),
tensor_f_out
->
dims
(),
"Outputs not of the same shape"
);
tensor_out
->
Resize
(
tensor_t_out
->
dims
());
}
LOG
(
INFO
)
<<
"OK 5"
;
}
// Set True Block
void
set_truenet
(
std
::
unique_ptr
<
OperatorBase
>
net
)
{
sub_net_op_
[
0
]
=
std
::
move
(
net
);
}
// Set False Block
void
set_falsenet
(
std
::
unique_ptr
<
OperatorBase
>
net
)
{
sub_net_op_
[
1
]
=
std
::
move
(
net
);
}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
sub_scopes
=
scope
.
FindVar
(
"SubScopes"
)
->
Get
<
std
::
vector
<
Scope
*>>
();
auto
index_tensors
=
scope
.
FindVar
(
"IndexTensors"
)
->
Get
<
std
::
vector
<
Tensor
*>>
();
std
::
string
cond_name
=
Input
(
"Cond"
);
Variable
*
cond_var
=
scope
.
FindVar
(
cond_name
);
PADDLE_ENFORCE_NOT_NULL
(
cond_var
)
const
Tensor
*
cond
=
cond_var
->
GetMutable
<
Tensor
>
();
// Step 1: get the true/false index at runtime
// index_[0]: vector<int>, contains all index for cond[i] == true
// index_[1]: vector<int>, contains all index for cond[i] == false
for
(
int
i
=
0
;
i
<
2
;
++
i
)
index_
[
i
].
clear
();
const
bool
*
cond_data
=
cond
->
data
<
bool
>
();
for
(
int
i
=
0
;
i
<
cond
->
dims
()[
0
];
++
i
)
{
if
(
cond_data
[
i
])
index_
[
0
].
push_back
(
i
);
else
index_
[
1
].
push_back
(
i
);
}
// put index_[0] and index_[1] into two tensors:
// index_tensor_[0] and index_tensor_[1]
framework
::
DDim
dim
=
paddle
::
framework
::
make_ddim
({
0
});
for
(
int
i
=
0
;
i
<
2
;
++
i
)
{
dim
[
0
]
=
index_
[
i
].
size
();
int
*
tmp_ptr
=
index_tensors
[
i
]
->
mutable_data
<
int
>
(
dim
,
platform
::
CPUPlace
());
index_tensors
[
i
]
->
Resize
(
dim
);
memcpy
(
tmp_ptr
,
index_
[
i
].
data
(),
dim
[
0
]
*
sizeof
(
int
));
}
// Step 2: collect data by calling gather
for
(
int
i
=
0
;
i
<
2
;
++
i
)
{
// i= 0/i for True and False branches respectively
for
(
auto
&
input
:
Inputs
(
"Xs"
))
{
// find Tensor
// Tensor* tensor_parent = scope.FindVar(input)->GetMutable<Tensor>();
Variable
*
v
=
scope
.
FindVar
(
input
);
Tensor
*
tensor_parent
=
v
->
GetMutable
<
Tensor
>
();
// Tensor* tensor_child =
// sub_scope_[i].FindVar(input)->GetMutable<Tensor>();
v
=
sub_scopes
[
i
]
->
FindVar
(
input
);
Tensor
*
tensor_child
=
v
->
GetMutable
<
Tensor
>
();
Gather
<
float
>
(
dev_ctx
.
GetPlace
(),
tensor_parent
,
index_tensors
[
i
],
tensor_child
);
}
}
// Step 3: run
for
(
int
i
=
0
;
i
<
2
;
++
i
)
sub_net_op_
[
i
]
->
Run
(
*
sub_scopes
[
i
],
dev_ctx
);
// Step 4: merge output results
for
(
int
i
=
0
;
i
<
2
;
++
i
)
{
// i= 0/i for True and False branches respectively
// for (auto& output : GetAttr<std::vector<std::string>>("sub_outputs")) {
for
(
auto
&
output
:
Outputs
(
"Outs"
))
{
// find Tensor
Variable
*
v
=
scope
.
FindVar
(
output
);
Tensor
*
tensor_parent
=
v
->
GetMutable
<
Tensor
>
();
v
=
sub_scopes
[
i
]
->
FindVar
(
output
);
Tensor
*
tensor_child
=
v
->
GetMutable
<
Tensor
>
();
ScatterUpdate
<
float
>
(
dev_ctx
.
GetPlace
(),
tensor_child
,
index_tensors
[
i
],
tensor_parent
);
}
}
}
private:
// sub_net_op_[0]: subnet_t
// sub_net_op_[1]: subnet_f
std
::
vector
<
std
::
unique_ptr
<
framework
::
OperatorBase
>>
sub_net_op_
;
// index_[0]: True_index;
// index_[1]: False_index;
mutable
std
::
vector
<
std
::
vector
<
int
>>
index_
;
};
/*
class CondGradientOp final : public OperatorBase {
public:
void Init() override;
virtual void InferShape(const std::shared_ptr<Scope>& scope) const
override;
virtual void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const override;
};*/
}
// namespace operators
}
// namespace paddle
paddle/pybind/pybind.cc
浏览文件 @
b8e75c1f
...
...
@@ -41,6 +41,7 @@ USE_OP(softmax);
USE_OP
(
rowwise_add
);
USE_OP
(
fill_zeros_like
);
USE_NO_KERNEL_OP
(
recurrent
);
USE_NO_KERNEL_OP
(
cond
);
USE_OP
(
gaussian_random
);
USE_OP
(
uniform_random
);
USE_OP
(
lookup_table
);
...
...
@@ -324,6 +325,28 @@ All parameter, weight, gradient are variables in Paddle.
[](
operators
::
RecurrentOp
&
self
,
const
operators
::
NetOp
&
net
)
->
void
{
self
.
set_stepnet
(
net
.
Clone
());
});
// cond_op
py
::
class_
<
operators
::
CondOp
,
OperatorBase
>
(
m
,
"CondOp"
)
.
def_static
(
"create"
,
[](
py
::
bytes
protobin
)
->
operators
::
CondOp
*
{
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
cond_op
=
OpRegistry
::
CreateOp
(
desc
);
return
static_cast
<
operators
::
CondOp
*>
(
cond_op
.
release
());
})
.
def
(
"set_truenet"
,
[](
operators
::
CondOp
&
self
,
const
operators
::
NetOp
&
net
)
->
void
{
self
.
set_truenet
(
net
.
Clone
());
})
.
def
(
"set_falsenet"
,
[](
operators
::
CondOp
&
self
,
const
operators
::
NetOp
&
net
)
->
void
{
self
.
set_falsenet
(
net
.
Clone
());
});
m
.
def
(
"unique_integer"
,
UniqueIntegerGenerator
);
m
.
def
(
"is_compile_gpu"
,
IsCompileGPU
);
...
...
python/paddle/v2/framework/op.py
浏览文件 @
b8e75c1f
...
...
@@ -215,5 +215,27 @@ class __RecurrentOp__(object):
return
core
.
RecurrentOp
.
create
(
proto
.
SerializeToString
())
class
__CondOp__
(
object
):
__proto__
=
None
type
=
'cond_op'
def
__init__
(
self
):
# cache recurrent_op's proto
if
self
.
__proto__
is
None
:
for
op_proto
in
get_all_op_protos
():
if
op_proto
.
type
==
self
.
type
:
self
.
__proto__
=
op_proto
def
__call__
(
self
,
*
args
,
**
kwargs
):
if
self
.
type
not
in
args
and
'type'
not
in
kwargs
:
kwargs
[
'type'
]
=
self
.
type
# create proto
create_method
=
OpDescCreationMethod
(
self
.
__proto__
)
proto
=
create_method
(
*
args
,
**
kwargs
)
# create condop
return
core
.
CondOp
.
create
(
proto
.
SerializeToString
())
Operator
=
OperatorFactory
()
# The default global factory
RecurrentOp
=
__RecurrentOp__
()
CondOp
=
__CondOp__
()
python/paddle/v2/framework/tests/test_cond_op.py
0 → 100644
浏览文件 @
b8e75c1f
import
logging
import
paddle.v2.framework.core
as
core
import
unittest
import
numpy
as
np
from
paddle.v2.framework.op
import
Operator
,
CondOp
class
PySimpleCond
(
object
):
'''
A simple implementation of dynamic if-else based on numpy
'''
def
__init__
(
self
):
array
=
[
True
]
*
10
for
i
in
range
(
1
,
10
,
2
):
array
[
i
]
=
False
self
.
cond
=
np
.
array
(
array
)
self
.
x
=
np
.
ones
(
shape
=
(
10
,
1
))
def
forward
(
self
):
self
.
index_t
=
np
.
where
(
self
.
cond
)
self
.
index_f
=
np
.
where
(
self
.
cond
==
False
)
y_t
=
self
.
x
[
self
.
index_t
]
y_f
=
self
.
x
[
self
.
index_f
]
y_t
=
y_t
*
2.
y_f
=
y_f
*
(
-
2.
)
output
=
np
.
zeros
(
shape
=
(
10
,
1
))
output
[
self
.
index_t
]
=
y_t
output
[
self
.
index_f
]
=
y_f
return
output
class
PySimpleCondTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
condnn
=
PySimpleCond
()
def
test_forward
(
self
):
output
=
self
.
condnn
.
forward
()
print
'output'
,
output
def
create_tensor
(
scope
,
name
,
shape
,
np_data
):
tensor
=
scope
.
new_var
(
name
).
get_tensor
()
tensor
.
set_dims
(
shape
)
tensor
.
set
(
np_data
,
core
.
CPUPlace
())
return
tensor
class
TestCondOp
(
unittest
.
TestCase
):
'''
Test CondOp
equation:
cond = [True, False, True, False, ...]
y[index_t] = x[index_t] * 2.
y[index_f] = x[index_f] * -2.
outputs:
y
'''
def
setUp
(
self
):
self
.
py_cond
=
PySimpleCond
()
def
forward
(
self
):
self
.
scope
=
core
.
Scope
()
self
.
create_global_variables
()
self
.
create_cond_op
()
self
.
create_sub_net
()
ctx
=
core
.
DeviceContext
.
create
(
core
.
CPUPlace
())
print
'running infer shape'
print
self
.
scope
.
find_var
(
"SubScopes"
)
self
.
condop
.
infer_shape
(
self
.
scope
)
print
'ok 2'
self
.
condop
.
run
(
self
.
scope
,
ctx
)
print
'ok 3'
return
np
.
array
(
self
.
scope
.
find_var
(
"Outs"
).
get_tensor
())
def
create_global_variables
(
self
):
x_np_data
=
self
.
py_cond
.
x
create_tensor
(
self
.
scope
,
"x"
,
[
10
,
1
],
x_np_data
)
cond_np_data
=
self
.
py_cond
.
cond
create_tensor
(
self
.
scope
,
"cond"
,
[
10
,
1
],
x_np_data
)
self
.
scope
.
new_var
(
"SubScopes"
)
self
.
scope
.
new_var
(
"IndexTensors"
)
self
.
scope
.
new_var
(
"Outs"
)
def
create_cond_op
(
self
):
self
.
condop
=
CondOp
(
Cond
=
"cond"
,
Xs
=
[
"x"
],
Outs
=
[
'Out_final'
],
SubScopes
=
"SubScopes"
,
IndexTensors
=
"IndexTensors"
)
def
create_sub_net
(
self
):
truenet
=
core
.
Net
.
create
()
scale_op_t
=
Operator
(
"scale"
,
X
=
'X'
,
Y
=
'Out'
,
scale
=
2.
)
truenet
.
append_op
(
scale_op_t
)
truenet
.
complete_add_op
(
True
)
self
.
condop
.
set_truenet
(
truenet
)
falsenet
=
core
.
Net
.
create
()
scale_op_t
=
Operator
(
"scale"
,
X
=
'X'
,
Y
=
'Out'
,
scale
=-
2.
)
falsenet
.
append_op
(
scale_op_t
)
falsenet
.
complete_add_op
(
True
)
self
.
condop
.
set_falsenet
(
falsenet
)
def
test_forward
(
self
):
print
'test cond op forward'
py_output
=
self
.
forward
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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