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894236a1
编写于
1月 05, 2018
作者:
Y
Yu Yang
提交者:
GitHub
1月 05, 2018
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差异文件
Merge pull request #6730 from tonyyang-svail/parallel_do
[WIP]: feature/parallel_do
上级
d06bbb12
01560660
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
568 addition
and
23 deletion
+568
-23
paddle/framework/backward.cc
paddle/framework/backward.cc
+2
-1
paddle/framework/lod_tensor.cc
paddle/framework/lod_tensor.cc
+80
-0
paddle/framework/lod_tensor.h
paddle/framework/lod_tensor.h
+7
-0
paddle/framework/operator.cc
paddle/framework/operator.cc
+8
-4
paddle/framework/tensor.h
paddle/framework/tensor.h
+11
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-0
paddle/operators/parallel_do_op.cc
paddle/operators/parallel_do_op.cc
+293
-0
python/paddle/v2/fluid/backward.py
python/paddle/v2/fluid/backward.py
+1
-0
python/paddle/v2/fluid/framework.py
python/paddle/v2/fluid/framework.py
+1
-1
python/paddle/v2/fluid/layers/control_flow.py
python/paddle/v2/fluid/layers/control_flow.py
+118
-17
python/paddle/v2/fluid/tests/test_parallel_op.py
python/paddle/v2/fluid/tests/test_parallel_op.py
+46
-0
未找到文件。
paddle/framework/backward.cc
浏览文件 @
894236a1
...
...
@@ -427,7 +427,8 @@ std::vector<std::unique_ptr<OpDesc>> MakeBlockBackward(
VLOG
(
5
)
<<
"Making backward "
<<
(
*
it
)
->
Type
()
<<
" op"
;
std
::
vector
<
std
::
unique_ptr
<
OpDesc
>>
op_grads
;
if
((
*
it
)
->
Type
()
==
"recurrent"
||
(
*
it
)
->
Type
()
==
"while"
)
{
if
((
*
it
)
->
Type
()
==
"recurrent"
||
(
*
it
)
->
Type
()
==
"while"
||
(
*
it
)
->
Type
()
==
"parallel_do"
)
{
int
step_block_idx
=
(
*
it
)
->
GetBlockAttr
(
"sub_block"
);
BlockDesc
*
backward_block
=
CreateStepBlock
(
program_desc
,
no_grad_vars
,
grad_to_var
,
step_block_idx
);
...
...
paddle/framework/lod_tensor.cc
浏览文件 @
894236a1
...
...
@@ -43,6 +43,22 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
return
os
;
}
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
LoDTensor
&
t
)
{
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
t
.
place
()));
PADDLE_ENFORCE
(
t
.
type
().
hash_code
()
==
typeid
(
float
).
hash_code
());
os
<<
"dim: "
<<
t
.
dims
()
<<
"
\n
"
;
os
<<
"lod: "
<<
t
.
lod
()
<<
"
\n
"
;
// only print first ten elements
int64_t
size
=
t
.
numel
()
<
10
?
t
.
numel
()
:
10
;
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
{
os
<<
t
.
data
<
float
>
()[
i
]
<<
" "
;
}
return
os
;
}
LoD
SliceLevels
(
const
LoD
&
in
,
size_t
level_begin
,
size_t
level_end
)
{
LoD
new_lod
;
new_lod
.
reserve
(
level_end
-
level_begin
);
...
...
@@ -244,5 +260,69 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor,
DeserializeFromStream
(
is
,
static_cast
<
Tensor
*>
(
tensor
),
dev_ctx
);
}
std
::
vector
<
LoDTensor
>
LoDTensor
::
SplitLoDTensor
(
const
std
::
vector
<
platform
::
Place
>
places
)
const
{
check_memory_size
();
// PADDLE_ENFORCE(lod().empty() || (lod().size() == 1 && lod()[0].empty())
// , "Disable parallel lod for now");
PADDLE_ENFORCE
(
lod
().
empty
(),
"Disable parallel lod for now"
);
PADDLE_ENFORCE
(
dims
()[
0
]
%
places
.
size
()
==
0
,
"Batch size should be divided by places size"
);
std
::
vector
<
LoDTensor
>
lods
;
for
(
size_t
place_idx
=
0
;
place_idx
<
places
.
size
();
++
place_idx
)
{
size_t
begin
=
place_idx
*
dims
()[
0
]
/
places
.
size
();
size_t
end
=
(
place_idx
+
1
)
*
dims
()[
0
]
/
places
.
size
();
auto
src
=
Slice
(
static_cast
<
int
>
(
begin
),
static_cast
<
int
>
(
end
));
LoDTensor
dst
;
dst
.
Resize
(
src
.
dims
());
auto
&
dst_place
=
places
[
place_idx
];
auto
dst_ptr
=
dst
.
mutable_data
(
dst_place
,
src
.
type
());
// TODO(tonyyang-svail):
// change the following to framework::CopyFrom
auto
src_place
=
src
.
place
();
auto
src_ptr
=
src
.
data
<
void
>
();
auto
size
=
src
.
numel
()
*
SizeOfType
(
src
.
type
());
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_cpu_place
(
dst_place
))
{
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
),
dst_ptr
,
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
),
src_ptr
,
size
);
}
else
{
PADDLE_THROW
(
"Not Implemented"
);
}
lods
.
emplace_back
(
dst
);
}
return
lods
;
}
void
LoDTensor
::
MergeLoDTensor
(
const
std
::
vector
<
const
LoDTensor
*>
&
lod_tensors
,
platform
::
Place
place
)
{
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
place
));
PADDLE_ENFORCE
(
!
lod_tensors
.
empty
());
framework
::
DDim
new_dim
=
lod_tensors
[
0
]
->
dims
();
std
::
type_index
new_type
=
lod_tensors
[
0
]
->
type
();
for
(
auto
*
lod
:
lod_tensors
)
{
PADDLE_ENFORCE
(
new_dim
==
lod
->
dims
());
PADDLE_ENFORCE
(
new_type
==
lod
->
type
());
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
lod
->
place
()));
}
new_dim
[
0
]
*=
lod_tensors
.
size
();
Resize
(
new_dim
);
auto
*
dst_ptr
=
reinterpret_cast
<
uint8_t
*>
(
mutable_data
(
place
,
new_type
));
for
(
auto
*
src
:
lod_tensors
)
{
auto
size
=
src
->
numel
()
*
SizeOfType
(
src
->
type
());
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
place
),
dst_ptr
,
boost
::
get
<
platform
::
CPUPlace
>
(
src
->
place
()),
src
->
data
<
void
>
(),
size
);
dst_ptr
+=
size
;
}
}
}
// namespace framework
}
// namespace paddle
paddle/framework/lod_tensor.h
浏览文件 @
894236a1
...
...
@@ -58,6 +58,7 @@ using Vector = thrust::host_vector<
using
LoD
=
std
::
vector
<
Vector
<
size_t
>>
;
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
LoD
&
lod
);
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
LoDTensor
&
t
);
/*
* Slice levels from a LoD.
...
...
@@ -144,6 +145,12 @@ class LoDTensor : public Tensor {
*/
void
ShrinkInLevel
(
size_t
level
,
size_t
elem_begin
,
size_t
elem_end
);
std
::
vector
<
LoDTensor
>
SplitLoDTensor
(
const
std
::
vector
<
platform
::
Place
>
places
)
const
;
void
MergeLoDTensor
(
const
std
::
vector
<
const
LoDTensor
*>&
lod_tensors
,
platform
::
Place
place
);
private:
LoD
lod_
;
};
...
...
paddle/framework/operator.cc
浏览文件 @
894236a1
...
...
@@ -233,7 +233,8 @@ static const Tensor* GetTensorFromVar(const Variable* var) {
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
t
=
&
(
var
->
Get
<
SelectedRows
>
().
value
());
}
else
{
PADDLE_THROW
(
"Variable type must be LoDTensor/SelectedRows."
);
PADDLE_THROW
(
"Variable type_id %s, expect LoDTensor/SelectedRows."
,
var
->
Type
().
name
());
}
return
t
;
}
...
...
@@ -245,7 +246,8 @@ static Tensor* GetMutableTensorFromVar(Variable* var) {
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
t
=
var
->
GetMutable
<
SelectedRows
>
()
->
mutable_value
();
}
else
{
PADDLE_THROW
(
"Variable type must be LoDTensor/SelectedRows."
);
PADDLE_THROW
(
"Variable type_id %s, expect LoDTensor/SelectedRows."
,
var
->
Type
().
name
());
}
return
t
;
}
...
...
@@ -407,7 +409,8 @@ class RuntimeInferShapeContext : public InferShapeContext {
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
return
var
->
Get
<
SelectedRows
>
().
GetCompleteDims
();
}
else
{
PADDLE_THROW
(
"Variable type must be LoDTensor/SelectedRows."
);
PADDLE_THROW
(
"Variable %s type_id %s, expect LoDTensor/SelectedRows."
,
name
,
var
->
Type
().
name
());
}
}
...
...
@@ -418,7 +421,8 @@ class RuntimeInferShapeContext : public InferShapeContext {
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
var
->
GetMutable
<
SelectedRows
>
()
->
set_height
(
dim
[
0
]);
}
else
{
PADDLE_THROW
(
"Variable type must be LoDTensor/SelectedRows."
);
PADDLE_THROW
(
"Variable %s type_id %s, expect LoDTensor/SelectedRows."
,
name
,
var
->
Type
().
name
());
}
}
...
...
paddle/framework/tensor.h
浏览文件 @
894236a1
...
...
@@ -55,6 +55,8 @@ class Tensor {
template
<
typename
T
>
inline
const
T
*
data
()
const
;
inline
void
switch_place
(
platform
::
Place
new_place
);
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
...
...
@@ -200,6 +202,15 @@ class Tensor {
size_t
offset_
;
};
inline
void
Tensor
::
switch_place
(
platform
::
Place
new_place
)
{
if
(
holder_
->
place
()
==
new_place
)
{
return
;
}
// TODO(tonyyang-svail): do memcpy here.
PADDLE_THROW
(
"Not Implemented"
);
}
}
// namespace framework
}
// namespace paddle
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
894236a1
...
...
@@ -152,6 +152,7 @@ op_library(conv_transpose_op DEPS vol2col)
op_library
(
gru_op DEPS sequence2batch gru_compute
)
op_library
(
recurrent_op DEPS executor
)
op_library
(
cos_sim_op DEPS cos_sim_functor
)
op_library
(
parallel_do_op DEPS executor
)
# FIXME(typhoonzero): save/load depends lodtensor serialization functions
op_library
(
save_op DEPS lod_tensor
)
op_library
(
load_op DEPS lod_tensor
)
...
...
paddle/operators/parallel_do_op.cc
0 → 100644
浏览文件 @
894236a1
/* 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 <vector>
#include "paddle/framework/executor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/threadpool.h"
namespace
paddle
{
namespace
operators
{
static
constexpr
char
kInputs
[]
=
"inputs"
;
static
constexpr
char
kParameters
[]
=
"parameters"
;
static
constexpr
char
kPlaces
[]
=
"places"
;
static
constexpr
char
kOutputs
[]
=
"outputs"
;
static
constexpr
char
kParallelScopes
[]
=
"parallel_scopes"
;
static
constexpr
char
kParallelBlock
[]
=
"sub_block"
;
// using ParallelScopeVar = std::vector<framework::Scope *>;
using
LoDTensor
=
framework
::
LoDTensor
;
using
OperatorBase
=
framework
::
OperatorBase
;
void
SplitTensorAndMoveTensorToScopes
(
const
framework
::
Scope
&
scope
,
const
std
::
vector
<
framework
::
Scope
*>
&
sub_scopes
,
const
std
::
vector
<
platform
::
Place
>
&
places
,
const
std
::
vector
<
std
::
string
>
&
names
)
{
for
(
auto
&
argu
:
names
)
{
auto
*
var
=
scope
.
FindVar
(
argu
);
const
auto
&
tensor
=
var
->
Get
<
LoDTensor
>
();
auto
lod_tensors
=
tensor
.
SplitLoDTensor
(
places
);
for
(
auto
&
lod
:
lod_tensors
)
{
VLOG
(
3
)
<<
lod
.
dims
();
}
for
(
size_t
i
=
0
;
i
<
sub_scopes
.
size
();
++
i
)
{
*
sub_scopes
[
i
]
->
Var
(
argu
)
->
GetMutable
<
LoDTensor
>
()
=
lod_tensors
[
i
];
}
}
}
class
ParallelDoOp
:
public
framework
::
OperatorBase
{
public:
ParallelDoOp
(
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
::
Place
&
place
)
const
override
{
// get device context from pool
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
dev_ctx
=
*
pool
.
Get
(
place
);
auto
*
block
=
Attr
<
framework
::
BlockDesc
*>
(
kParallelBlock
);
auto
*
program
=
block
->
Program
();
// TODO(tonyyang-svail): get places from input
std
::
vector
<
platform
::
Place
>
places
;
places
.
emplace_back
(
platform
::
CPUPlace
());
places
.
emplace_back
(
platform
::
CPUPlace
());
auto
&
sub_scopes
=
*
scope
.
FindVar
(
Output
(
kParallelScopes
))
->
GetMutable
<
std
::
vector
<
framework
::
Scope
*>>
();
for
(
size_t
place_idx
=
0
;
place_idx
<
places
.
size
();
++
place_idx
)
{
sub_scopes
.
push_back
(
&
scope
.
NewScope
());
}
SplitTensorAndMoveTensorToScopes
(
scope
,
sub_scopes
,
places
,
Inputs
(
kInputs
));
std
::
vector
<
std
::
future
<
void
>>
workers
;
workers
.
reserve
(
places
.
size
());
for
(
size_t
place_idx
=
0
;
place_idx
<
places
.
size
();
++
place_idx
)
{
VLOG
(
3
)
<<
"Run "
<<
place_idx
;
auto
&
place
=
places
[
place_idx
];
auto
*
cur_scope
=
sub_scopes
[
place_idx
];
// copy parameter
// some version of boost lacks != for boost::variant
if
(
!
(
dev_ctx
.
GetPlace
()
==
place
))
{
PADDLE_THROW
(
"Not Implemented"
);
}
workers
.
emplace_back
(
framework
::
Async
([
program
,
cur_scope
,
place
,
block
]
{
framework
::
Executor
executor
(
place
);
executor
.
Run
(
*
program
,
cur_scope
,
block
->
ID
(),
false
/*create_local_scope*/
);
}));
}
for
(
auto
&
worker
:
workers
)
{
worker
.
wait
();
}
// merge output
for
(
auto
&
o_name
:
Outputs
(
kOutputs
))
{
std
::
vector
<
const
framework
::
LoDTensor
*>
lod_tensors
;
lod_tensors
.
reserve
(
sub_scopes
.
size
());
for
(
auto
*
sub_scope
:
sub_scopes
)
{
lod_tensors
.
emplace_back
(
&
sub_scope
->
FindVar
(
o_name
)
->
Get
<
LoDTensor
>
());
}
auto
*
lod_tensor_to_be_merged
=
scope
.
FindVar
(
o_name
)
->
GetMutable
<
LoDTensor
>
();
lod_tensor_to_be_merged
->
MergeLoDTensor
(
lod_tensors
,
dev_ctx
.
GetPlace
());
}
}
};
class
ParallelDoOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ParallelDoOpProtoMaker
(
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
kInputs
,
""
).
AsDuplicable
();
AddInput
(
kParameters
,
""
).
AsDuplicable
();
AddInput
(
kPlaces
,
""
);
AddOutput
(
kOutputs
,
""
).
AsDuplicable
();
AddOutput
(
kParallelScopes
,
""
);
AddAttr
<
framework
::
BlockDesc
*>
(
kParallelBlock
,
""
);
AddComment
(
R"DOC(
ParallelDo Operator.
)DOC"
);
}
};
class
ParallelDoGradOp
:
public
OperatorBase
{
public:
ParallelDoGradOp
(
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
::
Place
&
place
)
const
override
{
// // get device context from pool
// platform::DeviceContextPool &pool =
// platform::DeviceContextPool::Instance();
// auto &dev_ctx = *pool.Get(place);
auto
*
block
=
Attr
<
framework
::
BlockDesc
*>
(
kParallelBlock
);
auto
*
program
=
block
->
Program
();
auto
&
sub_scopes
=
scope
.
FindVar
(
Input
(
kParallelScopes
))
->
Get
<
std
::
vector
<
framework
::
Scope
*>>
();
// TODO(tonyyang-svail): get places from input
std
::
vector
<
platform
::
Place
>
places
;
places
.
emplace_back
(
platform
::
CPUPlace
());
places
.
emplace_back
(
platform
::
CPUPlace
());
// feed output@grad
SplitTensorAndMoveTensorToScopes
(
scope
,
sub_scopes
,
places
,
Inputs
(
framework
::
GradVarName
(
kOutputs
)));
for
(
auto
&
s
:
Inputs
(
framework
::
GradVarName
(
kOutputs
)))
{
VLOG
(
3
)
<<
s
;
VLOG
(
3
)
<<
scope
.
FindVar
(
s
)
->
Get
<
LoDTensor
>
();
for
(
auto
*
sub_scope
:
sub_scopes
)
{
VLOG
(
3
)
<<
sub_scope
->
FindVar
(
s
)
->
Get
<
LoDTensor
>
();
}
}
// exe run
std
::
vector
<
std
::
future
<
void
>>
workers
;
for
(
size_t
place_idx
=
0
;
place_idx
<
places
.
size
();
++
place_idx
)
{
VLOG
(
3
)
<<
"Run "
<<
place_idx
;
auto
&
place
=
places
[
place_idx
];
auto
*
cur_scope
=
sub_scopes
[
place_idx
];
// execute
workers
.
emplace_back
(
framework
::
Async
([
program
,
cur_scope
,
place
,
block
]
{
framework
::
Executor
executor
(
place
);
executor
.
Run
(
*
program
,
cur_scope
,
block
->
ID
(),
false
/*create_local_scope*/
);
}));
}
for
(
auto
&
worker
:
workers
)
{
worker
.
wait
();
}
// merge grad
for
(
auto
&
s
:
Outputs
(
framework
::
GradVarName
(
kParameters
)))
{
VLOG
(
3
)
<<
s
;
auto
&
t
=
sub_scopes
[
0
]
->
FindVar
(
s
)
->
Get
<
LoDTensor
>
();
VLOG
(
3
)
<<
t
;
std
::
string
s_buf
=
s
+
"@BUF"
;
auto
*
t_buf
=
sub_scopes
[
0
]
->
Var
(
s_buf
)
->
GetMutable
<
LoDTensor
>
();
for
(
size_t
place_idx
=
1
;
place_idx
<
places
.
size
();
++
place_idx
)
{
auto
&
tt
=
sub_scopes
[
place_idx
]
->
FindVar
(
s
)
->
Get
<
LoDTensor
>
();
VLOG
(
3
)
<<
place_idx
;
VLOG
(
3
)
<<
tt
;
framework
::
CopyFrom
(
tt
,
places
[
0
],
t_buf
);
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
s
,
s_buf
}}},
{{
"Out"
,
{
s
}}},
framework
::
AttributeMap
{});
sum_op
->
Run
(
*
sub_scopes
[
0
],
place
);
}
VLOG
(
3
)
<<
t
;
framework
::
CopyFrom
(
t
,
place
,
scope
.
FindVar
(
s
)
->
GetMutable
<
LoDTensor
>
());
}
}
};
class
ParallelDoGradOpDescMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
virtual
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
{
auto
*
grad
=
new
framework
::
OpDesc
();
grad
->
SetType
(
"parallel_do_grad"
);
for
(
auto
&
input_param
:
this
->
InputNames
())
{
VLOG
(
3
)
<<
input_param
;
grad
->
SetInput
(
input_param
,
this
->
Input
(
input_param
));
grad
->
SetOutput
(
framework
::
GradVarName
(
input_param
),
this
->
InputGrad
(
input_param
,
false
));
}
for
(
auto
&
output_param
:
this
->
OutputNames
())
{
if
(
output_param
==
kParallelScopes
)
{
grad
->
SetInput
(
output_param
,
this
->
Output
(
output_param
));
grad
->
SetInput
(
framework
::
GradVarName
(
output_param
),
this
->
Output
(
output_param
));
}
else
{
grad
->
SetInput
(
output_param
,
this
->
Output
(
output_param
));
grad
->
SetInput
(
framework
::
GradVarName
(
output_param
),
this
->
OutputGrad
(
output_param
));
}
}
grad
->
SetAttrMap
(
this
->
Attrs
());
grad
->
SetBlockAttr
(
kParallelBlock
,
*
grad_block_
[
0
]);
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
grad
);
}
};
class
ParallelDoGradOpShapeInference
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
std
::
vector
<
std
::
string
>
input
{
kParameters
,
kInputs
};
std
::
vector
<
std
::
string
>
output
{
kOutputs
};
for
(
auto
&
s
:
input
)
{
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
s
));
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
s
)),
"Cannot find the gradient variable %s"
,
framework
::
GradVarName
(
s
));
}
for
(
auto
&
s
:
output
)
{
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
s
));
}
for
(
auto
&
s
:
input
)
{
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
s
),
ctx
->
GetInputsDim
(
s
));
}
if
(
ctx
->
HasInputs
(
kParameters
))
{
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
kParameters
)));
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
kParameters
),
ctx
->
GetInputsDim
(
kParameters
));
}
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OPERATOR
(
parallel_do
,
paddle
::
operators
::
ParallelDoOp
,
paddle
::
operators
::
ParallelDoOpProtoMaker
,
paddle
::
operators
::
ParallelDoGradOpDescMaker
);
REGISTER_OPERATOR
(
parallel_do_grad
,
paddle
::
operators
::
ParallelDoGradOp
,
paddle
::
operators
::
ParallelDoGradOpShapeInference
);
python/paddle/v2/fluid/backward.py
浏览文件 @
894236a1
...
...
@@ -205,6 +205,7 @@ def _append_backward_ops_(target,
# Getting op's corresponding grad_op
grad_op_desc
,
op_grad_to_var
=
core
.
get_grad_op_desc
(
op
.
desc
,
no_grad_dict
[
block
.
idx
],
grad_sub_block_list
)
grad_op_descs
.
extend
(
grad_op_desc
)
grad_to_var
.
update
(
op_grad_to_var
)
...
...
python/paddle/v2/fluid/framework.py
浏览文件 @
894236a1
...
...
@@ -448,7 +448,7 @@ class Operator(object):
no_kernel_op_set
=
{
'feed'
,
'fetch'
,
'save'
,
'load'
,
'recurrent'
,
'rnn_memory_helper_grad'
,
'conditional_block'
,
'while'
,
'send'
,
'recv'
'recv'
,
'parallel_do'
}
if
type
not
in
no_kernel_op_set
:
self
.
desc
.
infer_var_type
(
self
.
block
.
desc
)
...
...
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @
894236a1
...
...
@@ -6,12 +6,13 @@ import contextlib
from
..registry
import
autodoc
__all__
=
[
'split_lod_tensor'
,
'merge_lod_tensor'
,
'BlockGuard'
,
'StaticRNNGuard'
,
'StaticRNNMemoryLink'
,
'WhileGuard'
,
'While'
,
'lod_rank_table'
,
'max_sequence_len'
,
'topk'
,
'lod_tensor_to_array'
,
'array_to_lod_tensor'
,
'increment'
,
'array_write'
,
'create_array'
,
'less_than'
,
'array_read'
,
'shrink_memory'
,
'array_length'
,
'IfElse'
,
'DynamicRNN'
,
'ConditionalBlock'
,
'StaticRNN'
,
'reorder_lod_tensor_by_rank'
'split_lod_tensor'
,
'merge_lod_tensor'
,
'BlockGuard'
,
'BlockGuardWithCompletion'
,
'StaticRNNMemoryLink'
,
'WhileGuard'
,
'While'
,
'lod_rank_table'
,
'max_sequence_len'
,
'topk'
,
'lod_tensor_to_array'
,
'array_to_lod_tensor'
,
'increment'
,
'array_write'
,
'create_array'
,
'less_than'
,
'array_read'
,
'shrink_memory'
,
'array_length'
,
'IfElse'
,
'DynamicRNN'
,
'ConditionalBlock'
,
'StaticRNN'
,
'reorder_lod_tensor_by_rank'
,
'ParallelDo'
]
...
...
@@ -132,29 +133,129 @@ class BlockGuard(object):
return
True
class
StaticRNNGuard
(
BlockGuard
):
class
ParallelDo
(
object
):
"""
StaticRNNGuard
class.
ParallelDo
class.
StaticRNNGuard class is used to create a StaticRNN block in a program.
ParallelDo class is used to create a ParallelDo.
"""
def
__init__
(
self
,
places
,
name
=
None
):
self
.
helper
=
LayerHelper
(
"parallel_do"
,
name
=
name
)
self
.
inputs
=
[]
self
.
places
=
places
self
.
outputs
=
[]
self
.
status
=
StaticRNN
.
BEFORE_RNN_BLOCK
def
do
(
self
):
return
BlockGuardWithCompletion
(
self
)
def
parent_block
(
self
):
prog
=
self
.
helper
.
main_program
parent_idx
=
prog
.
current_block
().
parent_idx
assert
parent_idx
>=
0
parent_block
=
prog
.
block
(
parent_idx
)
return
parent_block
def
__call__
(
self
,
*
args
,
**
kwargs
):
if
self
.
status
!=
StaticRNN
.
AFTER_RNN_BLOCK
:
raise
ValueError
(
"RNN output can only be retrieved after rnn block"
)
if
len
(
self
.
outputs
)
==
0
:
raise
ValueError
(
"RNN has no output"
)
elif
len
(
self
.
outputs
)
==
1
:
return
self
.
outputs
[
0
]
else
:
return
self
.
outputs
def
read_input
(
self
,
var
):
self
.
inputs
.
append
(
var
)
return
var
def
write_output
(
self
,
var
):
self
.
outputs
.
append
(
var
)
def
get_parameters
(
self
):
main_program
=
self
.
helper
.
main_program
current_block
=
main_program
.
current_block
()
parent_block
=
self
.
parent_block
()
local_inputs
=
set
()
for
op
in
current_block
.
ops
:
for
oname
in
op
.
output_names
:
for
out_var_name
in
op
.
output
(
oname
):
local_inputs
.
add
(
out_var_name
)
for
var
in
self
.
inputs
:
local_inputs
.
add
(
var
.
name
)
params
=
list
()
for
op
in
current_block
.
ops
:
for
iname
in
op
.
input_names
:
for
in_var_name
in
op
.
input
(
iname
):
if
in_var_name
not
in
local_inputs
:
params
.
append
(
in_var_name
)
return
[
parent_block
.
var
(
name
)
for
name
in
params
]
def
complete_op
(
self
):
main_program
=
self
.
helper
.
main_program
current_block
=
main_program
.
current_block
()
parent_block
=
self
.
parent_block
()
step_scope
=
parent_block
.
create_var
(
type
=
core
.
VarDesc
.
VarType
.
STEP_SCOPES
)
self
.
outputs
=
[
parent_block
.
create_var
(
name
=
o
.
name
,
shape
=
o
.
shape
,
dtype
=
o
.
dtype
,
lod_level
=
o
.
lod_level
,
persistable
=
o
.
persistable
,
stop_gradient
=
o
.
stop_gradient
)
for
o
in
self
.
outputs
]
inputs
=
[
parent_block
.
var
(
i
.
name
)
for
i
in
self
.
inputs
]
outputs
=
[
parent_block
.
var
(
o
.
name
)
for
o
in
self
.
outputs
]
parent_block
.
append_op
(
type
=
'parallel_do'
,
inputs
=
{
'inputs'
:
inputs
,
'parameters'
:
self
.
get_parameters
(),
'places'
:
self
.
places
},
outputs
=
{
'outputs'
:
outputs
,
'parallel_scopes'
:
[
step_scope
]},
attrs
=
{
'sub_block'
:
current_block
})
class
BlockGuardWithCompletion
(
BlockGuard
):
"""
BlockGuardWithCompletion class.
BlockGuardWithCompletion class is used to create an op with a block in a program.
"""
def
__init__
(
self
,
rnn
):
if
not
isinstance
(
rnn
,
StaticRNN
):
raise
TypeError
(
"StaticRNNGuard takes a StaticRNN"
)
super
(
StaticRNNGuard
,
self
).
__init__
(
rnn
.
helper
.
main_program
)
if
not
(
isinstance
(
rnn
,
StaticRNN
)
or
isinstance
(
rnn
,
ParallelDo
)):
raise
TypeError
(
"BlockGuardWithCompletion takes a StaticRNN or ParallelDo"
)
super
(
BlockGuardWithCompletion
,
self
).
__init__
(
rnn
.
helper
.
main_program
)
self
.
rnn
=
rnn
def
__enter__
(
self
):
self
.
rnn
.
status
=
StaticRNN
.
IN_RNN_BLOCK
return
super
(
StaticRNNGuard
,
self
).
__enter__
()
return
super
(
BlockGuardWithCompletion
,
self
).
__enter__
()
def
__exit__
(
self
,
exc_type
,
exc_val
,
exc_tb
):
if
exc_type
is
not
None
:
return
False
self
.
rnn
.
status
=
StaticRNN
.
AFTER_RNN_BLOCK
self
.
rnn
.
complete_rnn_op
()
return
super
(
StaticRNNGuard
,
self
).
__exit__
(
exc_type
,
exc_val
,
exc_tb
)
self
.
rnn
.
complete_op
()
return
super
(
BlockGuardWithCompletion
,
self
).
__exit__
(
exc_type
,
exc_val
,
exc_tb
)
class
StaticRNNMemoryLink
(
object
):
...
...
@@ -200,7 +301,7 @@ class StaticRNN(object):
self
.
seq_len
=
None
def
step
(
self
):
return
StaticRNNGuard
(
self
)
return
BlockGuardWithCompletion
(
self
)
def
_assert_in_rnn_block_
(
self
,
method
):
if
self
.
status
!=
StaticRNN
.
IN_RNN_BLOCK
:
...
...
@@ -316,7 +417,7 @@ class StaticRNN(object):
else
:
return
self
.
outputs
def
complete_
rnn_
op
(
self
):
def
complete_op
(
self
):
main_program
=
self
.
helper
.
main_program
rnn_block
=
main_program
.
current_block
()
parent_block
=
self
.
parent_block
()
...
...
python/paddle/v2/fluid/tests/test_parallel_op.py
0 → 100644
浏览文件 @
894236a1
import
unittest
import
paddle.v2.fluid.layers
as
layers
import
paddle.v2.fluid
as
fluid
from
paddle.v2.fluid.framework
import
Program
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
import
numpy
as
np
import
paddle.v2.fluid.core
as
core
class
ParallelOpTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
x
=
layers
.
data
(
shape
=
[
-
1
,
30
,
40
],
dtype
=
'float32'
,
name
=
'x'
,
append_batch_size
=
False
,
stop_gradient
=
False
)
places
=
fluid
.
default_main_program
().
global_block
().
create_var
()
pd
=
layers
.
ParallelDo
(
places
=
places
)
with
pd
.
do
():
data
=
pd
.
read_input
(
x
)
hidden
=
layers
.
fc
(
input
=
data
,
size
=
7
)
pd
.
write_output
(
hidden
)
data
=
pd
()
loss
=
layers
.
mean
(
x
=
data
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
sgd_optimizer
.
minimize
(
loss
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
exe
.
run
(
fluid
.
default_startup_program
())
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
x
.
name
:
np
.
random
.
uniform
(
0.1
,
0.6
,
(
20
,
30
,
40
)).
astype
(
"float32"
)
})
def
test_forward
(
self
):
pass
if
__name__
==
'__main__'
:
unittest
.
main
()
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