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b23982a2
编写于
12月 21, 2017
作者:
Y
Yang Yu
浏览文件
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差异文件
Add ReorderLoDTensorByRank
It is useful to reorder RNN memory block.
上级
0f1c685c
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
306 addition
and
2 deletion
+306
-2
paddle/operators/reorder_lod_tensor_by_rank_op.cc
paddle/operators/reorder_lod_tensor_by_rank_op.cc
+225
-0
python/paddle/v2/fluid/framework.py
python/paddle/v2/fluid/framework.py
+4
-1
python/paddle/v2/fluid/layer_helper.py
python/paddle/v2/fluid/layer_helper.py
+6
-0
python/paddle/v2/fluid/layers/control_flow.py
python/paddle/v2/fluid/layers/control_flow.py
+24
-1
python/paddle/v2/fluid/tests/__init__.py
python/paddle/v2/fluid/tests/__init__.py
+0
-0
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
+47
-0
未找到文件。
paddle/operators/reorder_lod_tensor_by_rank_op.cc
0 → 100644
浏览文件 @
b23982a2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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/lod_rank_table.h>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/detail/safe_ref.h"
namespace
paddle
{
namespace
operators
{
class
ReorderLoDTensorProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ReorderLoDTensorProtoMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor) the input lod tensor need to be reordered."
);
AddInput
(
"RankTable"
,
"(LoDRankTable) the rank table that input need follow"
);
AddOutput
(
"Out"
,
"(LoDTensor) reordered lod tensor"
);
AddComment
(
R"DOC(ReorderLoDTensorLoDRankTable
Reorder the input X by the rank of `RankTable`. If `RankTable` is ordered by
index [3, 0, 2, 1]. Input X will reorder its sequence, the third sequence of
X will be the first sequence of Output.
NOTE: The RankTable does not need to be calculated by X.
)DOC"
);
}
};
class
ReorderLoDTensorByRankTableBase
:
public
framework
::
OperatorBase
{
public:
ReorderLoDTensorByRankTableBase
(
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
&
x
=
detail
::
Ref
(
scope
.
FindVar
(
Input
(
"X"
)),
"Cannot find input lod tensor variable %s"
,
Input
(
"X"
))
.
Get
<
framework
::
LoDTensor
>
();
auto
&
rank_table
=
detail
::
Ref
(
scope
.
FindVar
(
Input
(
"RankTable"
)),
"Cannot find input rank table variable %s"
,
Input
(
"RankTable"
))
.
Get
<
framework
::
LoDRankTable
>
();
auto
&
out
=
*
detail
::
Ref
(
scope
.
FindVar
(
Output
(
"Out"
)),
"Cannot find output lod tensor variable %s"
,
Output
(
"Out"
))
.
GetMutable
<
framework
::
LoDTensor
>
();
out
.
Resize
(
x
.
dims
());
out
.
mutable_data
(
x
.
place
(),
x
.
type
());
this
->
process
(
dev_ctx
,
x
,
rank_table
,
&
out
);
}
protected:
virtual
void
process
(
const
platform
::
DeviceContext
&
dev_ctx
,
const
framework
::
LoDTensor
&
x
,
const
framework
::
LoDRankTable
&
rank_table
,
framework
::
LoDTensor
*
out
)
const
=
0
;
struct
AbsoluteRankTableItem
{
size_t
offset
;
// the absolute/accumulated offset.
size_t
length
;
// the length
framework
::
LoD
lod
;
};
std
::
vector
<
AbsoluteRankTableItem
>
GetAbsoluteOffsetAndLengthByLoDRankTable
(
const
framework
::
LoDTensor
&
x
)
const
{
std
::
vector
<
AbsoluteRankTableItem
>
absolute_table
;
size_t
level
=
0
;
size_t
size
=
x
.
lod
()[
level
].
size
();
for
(
size_t
i
=
0
;
i
<
size
-
1
;
++
i
)
{
auto
lod_offset
=
framework
::
GetSubLoDAndAbsoluteOffset
(
x
.
lod
(),
i
,
i
+
1
,
level
);
auto
&
offset
=
lod_offset
.
second
;
absolute_table
.
emplace_back
();
absolute_table
.
back
().
length
=
offset
.
second
-
offset
.
first
;
absolute_table
.
back
().
offset
=
offset
.
first
;
absolute_table
.
back
().
lod
=
lod_offset
.
first
;
}
return
absolute_table
;
}
size_t
CopyTensorAndLod
(
const
platform
::
DeviceContext
&
dev_ctx
,
const
AbsoluteRankTableItem
&
item
,
const
framework
::
LoDTensor
&
x
,
framework
::
LoDTensor
*
out
,
size_t
out_offset
)
const
{
auto
&
out_lod
=
*
out
->
mutable_lod
();
auto
len
=
item
.
length
;
auto
x_offset
=
item
.
offset
;
if
(
out_lod
.
empty
())
{
for
(
size_t
i
=
0
;
i
<
item
.
lod
.
size
();
++
i
)
{
out_lod
.
push_back
(
std
::
vector
<
size_t
>
({
0
}));
}
}
for
(
size_t
i
=
0
;
i
<
out_lod
.
size
();
++
i
)
{
auto
&
out_v
=
out_lod
[
i
];
auto
&
new_lod_v
=
item
.
lod
[
i
];
for
(
auto
&
detail
:
new_lod_v
)
{
out_v
.
push_back
(
out_v
.
back
()
+
detail
);
}
}
auto
x_sliced
=
x
.
Slice
(
x_offset
,
x_offset
+
len
);
auto
out_sliced
=
out
->
Slice
(
out_offset
,
out_offset
+
len
);
framework
::
CopyFrom
(
x_sliced
,
out_sliced
.
place
(),
dev_ctx
,
&
out_sliced
);
out_offset
+=
len
;
return
out_offset
;
}
};
class
ReorderLoDTensorByRankTableOp
:
public
ReorderLoDTensorByRankTableBase
{
public:
ReorderLoDTensorByRankTableOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
ReorderLoDTensorByRankTableBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
protected:
void
process
(
const
platform
::
DeviceContext
&
dev_ctx
,
const
framework
::
LoDTensor
&
x
,
const
framework
::
LoDRankTable
&
rank_table
,
framework
::
LoDTensor
*
out
)
const
override
{
auto
absolute_table
=
GetAbsoluteOffsetAndLengthByLoDRankTable
(
x
);
size_t
out_offset
=
0
;
out
->
mutable_lod
()
->
clear
();
for
(
auto
&
item
:
rank_table
.
items
())
{
out_offset
=
this
->
CopyTensorAndLod
(
dev_ctx
,
absolute_table
[
item
.
index
],
x
,
out
,
out_offset
);
}
}
};
class
IdentityInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
context
->
SetOutputDim
(
"Out"
,
context
->
GetInputDim
(
"X"
));
}
};
class
ReorderLodTensorByRankGradOpMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
std
::
unique_ptr
<
framework
::
OpDescBind
>
Apply
()
const
override
{
auto
*
grad_op
=
new
framework
::
OpDescBind
();
grad_op
->
SetType
(
"reorder_lod_tensor_by_rank_grad"
);
grad_op
->
SetInput
(
"X"
,
OutputGrad
(
"Out"
));
grad_op
->
SetOutput
(
"Out"
,
InputGrad
(
"X"
));
grad_op
->
SetInput
(
"RankTable"
,
Input
(
"RankTable"
));
return
std
::
unique_ptr
<
framework
::
OpDescBind
>
(
grad_op
);
}
};
class
ReorderLoDTensorByRankGradOp
:
public
ReorderLoDTensorByRankTableBase
{
public:
ReorderLoDTensorByRankGradOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
ReorderLoDTensorByRankTableBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
protected:
void
process
(
const
platform
::
DeviceContext
&
dev_ctx
,
const
framework
::
LoDTensor
&
x
,
const
framework
::
LoDRankTable
&
rank_table
,
framework
::
LoDTensor
*
out
)
const
override
{
auto
absolute_table
=
GetAbsoluteOffsetAndLengthByLoDRankTable
(
x
);
// offsets = enumerate([item.index for item in rank_table.items()])
std
::
vector
<
std
::
pair
<
size_t
,
size_t
>>
offsets
;
offsets
.
reserve
(
rank_table
.
items
().
size
());
for
(
size_t
i
=
0
;
i
<
rank_table
.
items
().
size
();
++
i
)
{
offsets
.
push_back
({
i
,
rank_table
.
items
()[
i
].
index
});
}
// offsets.sort(key=lambda x: x[1])
std
::
sort
(
offsets
.
begin
(),
offsets
.
end
(),
[](
const
std
::
pair
<
size_t
,
size_t
>
&
a
,
const
std
::
pair
<
size_t
,
size_t
>
&
b
)
{
return
a
.
second
<
b
.
second
;
});
// Copy TensorAndLod
size_t
out_offset
=
0
;
for
(
auto
&
offset
:
offsets
)
{
out_offset
=
this
->
CopyTensorAndLod
(
dev_ctx
,
absolute_table
[
offset
.
first
],
x
,
out
,
out_offset
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
reorder_lod_tensor_by_rank
,
ops
::
ReorderLoDTensorByRankTableOp
,
ops
::
ReorderLodTensorByRankGradOpMaker
,
ops
::
ReorderLoDTensorProtoMaker
,
ops
::
IdentityInferShape
);
REGISTER_OPERATOR
(
reorder_lod_tensor_by_rank_grad
,
ops
::
ReorderLoDTensorByRankGradOp
,
ops
::
IdentityInferShape
);
python/paddle/v2/fluid/framework.py
浏览文件 @
b23982a2
...
...
@@ -389,7 +389,10 @@ class Operator(object):
%
(
in_proto
.
name
,
len
(
in_args
)))
in_arg_names
=
[]
for
arg
in
in_args
:
in_arg_names
.
append
(
arg
.
name
)
if
isinstance
(
arg
,
basestring
):
in_arg_names
.
append
(
arg
)
else
:
in_arg_names
.
append
(
arg
.
name
)
self
.
desc
.
set_input
(
in_proto
.
name
,
in_arg_names
)
else
:
self
.
desc
.
set_input
(
in_proto
.
name
,
[])
...
...
python/paddle/v2/fluid/layer_helper.py
浏览文件 @
b23982a2
...
...
@@ -194,3 +194,9 @@ class LayerHelper(object):
else
:
# For integer and boolean types, initialize with all zeros
return
Constant
()
def
is_instance
(
self
,
param_name
,
cls
):
param
=
self
.
kwargs
.
get
(
param_name
,
None
)
if
not
isinstance
(
param
,
cls
):
raise
TypeError
(
"The input {0} parameter of method {1} must be {2}"
,
param_name
,
self
.
layer_type
,
cls
.
__name__
)
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @
b23982a2
...
...
@@ -10,7 +10,7 @@ __all__ = [
'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'
'StaticRNN'
,
'reorder_lod_tensor_by_rank'
]
...
...
@@ -963,3 +963,26 @@ class DynamicRNN(object):
if
self
.
status
!=
DynamicRNN
.
IN_RNN
:
raise
ValueError
(
"{0} can only be invoked inside rnn block."
.
format
(
method
))
def
reorder_lod_tensor_by_rank
(
x
,
rank_table
):
"""
Args:
x(Variable):
rank_table(Variable):
Returns:
"""
helper
=
LayerHelper
(
'reorder_lod_tensor_by_rank'
,
**
locals
())
helper
.
is_instance
(
'x'
,
Variable
)
helper
.
is_instance
(
'rank_table'
,
Variable
)
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'reorder_lod_tensor_by_rank'
,
inputs
=
{
'X'
:
[
x
],
'RankTable'
:
[
rank_table
]},
outputs
=
{
'Out'
:
[
out
]})
return
out
python/paddle/v2/fluid/tests/__init__.py
0 → 100644
浏览文件 @
b23982a2
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
0 → 100644
浏览文件 @
b23982a2
import
unittest
import
paddle.v2.fluid
as
fluid
import
numpy
class
TestReorderLoDTensor
(
unittest
.
TestCase
):
def
test_reorder
(
self
):
dat
=
fluid
.
layers
.
data
(
name
=
'input'
,
shape
=
[
1
],
lod_level
=
2
)
dat
.
stop_gradient
=
False
rank_dat
=
fluid
.
layers
.
data
(
name
=
'ref'
,
shape
=
[
1
],
lod_level
=
1
)
table
=
fluid
.
layers
.
lod_rank_table
(
rank_dat
)
new_dat
=
fluid
.
layers
.
reorder_lod_tensor_by_rank
(
x
=
dat
,
rank_table
=
table
)
loss
=
fluid
.
layers
.
mean
(
x
=
new_dat
)
fluid
.
backward
.
append_backward_ops
(
loss
=
loss
)
cpu
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
cpu
)
exe
.
run
(
fluid
.
default_startup_program
())
ref
=
fluid
.
Tensor
()
ref_lod
=
[
0
,
3
,
4
,
7
,
8
,
14
]
ref
.
set_lod
([
ref_lod
])
ref
.
set
(
numpy
.
random
.
random
(
size
=
[
14
,
1
]).
astype
(
'float32'
),
cpu
)
input
=
fluid
.
Tensor
()
lod_level_0
=
numpy
.
random
.
randint
(
low
=
1
,
high
=
5
,
size
=
5
)
lod_level_0
=
[
0
]
+
numpy
.
cumsum
(
lod_level_0
).
tolist
()
lod_level_1
=
numpy
.
random
.
randint
(
low
=
1
,
high
=
5
,
size
=
lod_level_0
[
-
1
])
lod_level_1
=
[
0
]
+
numpy
.
cumsum
(
lod_level_1
).
tolist
()
input
.
set_lod
([
lod_level_0
,
lod_level_1
])
input
.
set
(
numpy
.
random
.
random
(
size
=
[
lod_level_1
[
-
1
],
1
]).
astype
(
'float32'
),
cpu
)
ig
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
'input'
:
input
,
'ref'
:
ref
},
fetch_list
=
[
'input@GRAD'
],
return_numpy
=
False
)[
0
]
self
.
assertAlmostEqual
(
numpy
.
array
(
ig
).
sum
(),
1.0
,
delta
=
0.001
)
self
.
assertEqual
(
input
.
lod
(),
ig
.
lod
())
if
__name__
==
'__main__'
:
unittest
.
main
()
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