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体验新版 GitCode,发现更多精彩内容 >>
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2a76b42e
编写于
11月 08, 2017
作者:
Y
Yu Yang
提交者:
GitHub
11月 08, 2017
浏览文件
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差异文件
Merge pull request #5419 from reyoung/feature/shrink_memory_op
Feature/shrink memory op
上级
d4d8f742
272a272b
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
267 addition
and
38 deletion
+267
-38
paddle/operators/array_operator.h
paddle/operators/array_operator.h
+50
-0
paddle/operators/shrink_rnn_memory_op.cc
paddle/operators/shrink_rnn_memory_op.cc
+149
-0
paddle/operators/tensor_array_read_write_op.cc
paddle/operators/tensor_array_read_write_op.cc
+5
-35
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+16
-3
python/paddle/v2/framework/tests/test_shrink_rnn_memory.py
python/paddle/v2/framework/tests/test_shrink_rnn_memory.py
+47
-0
未找到文件。
paddle/operators/array_operator.h
0 → 100644
浏览文件 @
2a76b42e
/* 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. */
#pragma once
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
class
ArrayOp
:
public
framework
::
OperatorBase
{
public:
ArrayOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
protected:
size_t
GetOffset
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
{
auto
*
i
=
scope
.
FindVar
(
Input
(
"I"
));
PADDLE_ENFORCE
(
i
!=
nullptr
,
"I must be set"
);
auto
&
i_tensor
=
i
->
Get
<
framework
::
LoDTensor
>
();
PADDLE_ENFORCE_EQ
(
i_tensor
.
numel
(),
1
);
size_t
offset
;
if
(
platform
::
is_gpu_place
(
i_tensor
.
place
()))
{
// FIXME: Avoid copy from GPU to CPU
framework
::
Tensor
t
;
t
.
CopyFrom
(
i_tensor
,
platform
::
CPUPlace
(),
dev_ctx
);
dev_ctx
.
Wait
();
offset
=
static_cast
<
size_t
>
(
*
t
.
data
<
int64_t
>
());
}
else
{
offset
=
static_cast
<
size_t
>
(
*
i_tensor
.
data
<
int64_t
>
());
}
return
offset
;
}
};
}
// namespace operators
}
// namespace paddle
paddle/operators/shrink_rnn_memory_op.cc
0 → 100644
浏览文件 @
2a76b42e
/* 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/operators/array_operator.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
class
ShrinkRNNMemoryOp
:
public
ArrayOp
{
public:
ShrinkRNNMemoryOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
ArrayOp
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
*
x_var
=
scope
.
FindVar
(
Input
(
"X"
));
PADDLE_ENFORCE
(
x_var
!=
nullptr
,
"Input X must be set"
);
auto
&
x_tensor
=
x_var
->
Get
<
framework
::
LoDTensor
>
();
size_t
offset
=
this
->
GetOffset
(
scope
,
dev_ctx
);
auto
*
rank_table_var
=
scope
.
FindVar
(
Input
(
"RankTable"
));
PADDLE_ENFORCE
(
rank_table_var
!=
nullptr
,
"RankTable must be set"
);
auto
&
rank_table
=
rank_table_var
->
Get
<
framework
::
LoDRankTable
>
();
auto
&
rank_items
=
rank_table
.
items
();
int
dst_num_rows
=
std
::
lower_bound
(
rank_items
.
begin
(),
rank_items
.
end
(),
offset
,
[](
const
framework
::
LoDRankTable
::
TableItem
&
a
,
size_t
b
)
{
return
a
.
length
>
b
;
})
-
rank_items
.
begin
();
auto
*
out_var
=
scope
.
FindVar
(
Output
(
"Out"
));
PADDLE_ENFORCE
(
out_var
!=
nullptr
,
"Output Out must be set"
);
auto
&
out_tensor
=
*
out_var
->
GetMutable
<
framework
::
LoDTensor
>
();
if
(
dst_num_rows
!=
0
)
{
out_tensor
.
ShareDataWith
(
x_tensor
.
Slice
(
0
,
dst_num_rows
));
}
}
};
class
ShrinkRNNMemoryOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ShrinkRNNMemoryOpProtoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
""
);
AddInput
(
"RankTable"
,
""
);
AddInput
(
"I"
,
""
);
AddOutput
(
"Out"
,
""
);
AddComment
(
""
);
}
};
class
ShrinkRNNMemoryInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
));
PADDLE_ENFORCE
(
context
->
HasInput
(
"I"
));
PADDLE_ENFORCE
(
context
->
HasInput
(
"RankTable"
));
context
->
SetOutputDim
(
"Out"
,
context
->
GetInputDim
(
"X"
));
}
};
class
ShrinkRNNMemoryGradOp
:
public
ArrayOp
{
public:
ShrinkRNNMemoryGradOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
ArrayOp
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
*
dout_var
=
scope
.
FindVar
(
Input
(
framework
::
GradVarName
(
"Out"
)));
auto
*
dx_var
=
scope
.
FindVar
(
Output
(
framework
::
GradVarName
(
"X"
)));
PADDLE_ENFORCE
(
dx_var
!=
nullptr
,
"Input Gradient should not be nullptr"
);
auto
*
x_var
=
scope
.
FindVar
(
Input
(
"X"
));
PADDLE_ENFORCE
(
x_var
!=
nullptr
);
auto
&
x_tensor
=
x_var
->
Get
<
framework
::
LoDTensor
>
();
auto
&
dx_tensor
=
*
dx_var
->
GetMutable
<
framework
::
LoDTensor
>
();
dx_tensor
.
Resize
(
x_tensor
.
dims
());
dx_tensor
.
mutable_data
(
x_tensor
.
place
(),
x_tensor
.
type
());
if
(
dout_var
==
nullptr
)
{
// dx_tensor fill zero
math
::
set_constant
(
dev_ctx
,
&
dx_tensor
,
0.0
f
);
}
else
{
auto
&
dout_tensor
=
dout_var
->
Get
<
framework
::
LoDTensor
>
();
auto
height
=
dout_tensor
.
dims
()[
0
];
dx_tensor
.
Slice
(
0
,
static_cast
<
int
>
(
height
))
.
CopyFrom
(
dout_tensor
,
dout_tensor
.
place
(),
dev_ctx
);
if
(
dx_tensor
.
dims
()[
0
]
<
height
)
{
auto
rest_tensor
=
dx_tensor
.
Slice
(
static_cast
<
int
>
(
height
),
static_cast
<
int
>
(
dout_tensor
.
dims
()[
0
]));
math
::
set_constant
(
dev_ctx
,
&
rest_tensor
,
0.0
f
);
}
}
}
};
class
ShrinkRNNMemoryGradInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
));
PADDLE_ENFORCE
(
context
->
HasOutput
(
framework
::
GradVarName
(
"X"
)));
context
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
context
->
GetInputDim
(
"X"
));
}
};
class
ShrinkRNNGradOpMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
std
::
unique_ptr
<
framework
::
OpDescBind
>
Apply
()
const
override
{
auto
*
op
=
new
framework
::
OpDescBind
();
op
->
SetType
(
"shrink_rnn_memory_grad"
);
op
->
SetInput
(
"X"
,
Input
(
"X"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
OutputGrad
(
"Out"
));
op
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
InputGrad
(
"X"
));
op
->
SetAttrMap
(
Attrs
());
return
std
::
unique_ptr
<
framework
::
OpDescBind
>
(
op
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
shrink_rnn_memory
,
ops
::
ShrinkRNNMemoryOp
,
ops
::
ShrinkRNNMemoryInferShape
,
ops
::
ShrinkRNNMemoryOpProtoMaker
,
ops
::
ShrinkRNNGradOpMaker
);
REGISTER_OPERATOR
(
shrink_rnn_memory_grad
,
ops
::
ShrinkRNNMemoryGradOp
,
ops
::
ShrinkRNNMemoryGradInferShape
);
paddle/operators/tensor_array_read_write_op.cc
浏览文件 @
2a76b42e
...
...
@@ -11,48 +11,18 @@
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_tensor_array.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/array_operator.h"
namespace
paddle
{
namespace
operators
{
class
ArrayOpBase
:
public
framework
::
OperatorBase
{
public:
ArrayOpBase
(
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
{}
protected:
size_t
GetOffset
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
{
auto
*
i
=
scope
.
FindVar
(
Input
(
"I"
));
PADDLE_ENFORCE
(
i
!=
nullptr
,
"I must be set"
);
auto
&
i_tensor
=
i
->
Get
<
framework
::
LoDTensor
>
();
PADDLE_ENFORCE_EQ
(
i_tensor
.
numel
(),
1
);
size_t
offset
;
if
(
platform
::
is_gpu_place
(
i_tensor
.
place
()))
{
// FIXME: Avoid copy from GPU to CPU
framework
::
Tensor
t
;
t
.
CopyFrom
(
i_tensor
,
platform
::
CPUPlace
(),
dev_ctx
);
dev_ctx
.
Wait
();
offset
=
static_cast
<
size_t
>
(
*
t
.
data
<
int64_t
>
());
}
else
{
offset
=
static_cast
<
size_t
>
(
*
i_tensor
.
data
<
int64_t
>
());
}
return
offset
;
}
};
class
WriteToArrayOp
:
public
ArrayOp
Base
{
class
WriteToArrayOp
:
public
ArrayOp
{
public:
WriteToArrayOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
ArrayOp
Base
(
type
,
inputs
,
outputs
,
attrs
)
{}
:
ArrayOp
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
...
...
@@ -122,13 +92,13 @@ class WriteToArrayInferVarType : public framework::VarTypeInference {
}
};
class
ReadFromArrayOp
:
public
ArrayOp
Base
{
class
ReadFromArrayOp
:
public
ArrayOp
{
public:
ReadFromArrayOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
ArrayOp
Base
(
type
,
inputs
,
outputs
,
attrs
)
{}
:
ArrayOp
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
*
x
=
scope
.
FindVar
(
Input
(
"X"
));
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
2a76b42e
...
...
@@ -891,13 +891,13 @@ def zeros(shape, dtype, main_program=None):
def
increment
(
x
,
value
=
1.0
,
main_program
=
None
):
helper
=
LayerHelper
(
"increment"
,
**
locals
())
tmp
=
helper
.
create_tmp_variable
(
dtype
=
x
.
data_type
)
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
data_type
)
helper
.
append_op
(
type
=
'increment'
,
inputs
=
{
'X'
:
[
x
]},
outputs
=
{
'Out'
:
[
tmp
]},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'step'
:
value
})
return
tmp
return
out
def
array_write
(
x
,
i
,
array
=
None
,
main_program
=
None
):
...
...
@@ -928,3 +928,16 @@ def array_read(array, i, main_program=None):
'I'
:
[
i
]},
outputs
=
{
'Out'
:
[
out
]})
return
out
def
shrink_memory
(
x
,
i
,
table
,
main_program
=
None
):
helper
=
LayerHelper
(
'shrink_memory'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
data_type
)
helper
.
append_op
(
type
=
'shrink_rnn_memory'
,
inputs
=
{
'X'
:
[
x
],
'I'
:
[
i
],
'RankTable'
:
[
table
]},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{})
return
out
python/paddle/v2/framework/tests/test_shrink_rnn_memory.py
0 → 100644
浏览文件 @
2a76b42e
import
unittest
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.executor
import
Executor
import
paddle.v2.framework.layers
as
layers
from
paddle.v2.framework.backward
import
append_backward_ops
from
paddle.v2.framework.framework
import
g_main_program
import
numpy
class
TestShrinkRNNMemory
(
unittest
.
TestCase
):
def
test_shrink_rnn_memory
(
self
):
x
=
layers
.
data
(
'x'
,
shape
=
[
100
],
data_type
=
'float32'
)
x
.
stop_gradient
=
False
table
=
layers
.
lod_rank_table
(
x
=
x
)
i
=
layers
.
zeros
(
dtype
=
'int64'
,
shape
=
[
1
])
mem1
=
layers
.
shrink_memory
(
x
=
x
,
i
=
i
,
table
=
table
)
i
=
layers
.
increment
(
x
=
i
)
i
.
stop_gradient
=
True
mem2
=
layers
.
shrink_memory
(
x
=
mem1
,
i
=
i
,
table
=
table
)
i
=
layers
.
increment
(
x
=
i
)
i
.
stop_gradient
=
True
mem3
=
layers
.
shrink_memory
(
x
=
mem2
,
i
=
i
,
table
=
table
)
cpu
=
core
.
CPUPlace
()
tensor
=
core
.
LoDTensor
()
tensor
.
set_lod
([[
0
,
2
,
5
,
6
]])
tensor_np
=
numpy
.
random
.
random
(
size
=
(
3
,
100
)).
astype
(
'float32'
)
tensor
.
set
(
tensor_np
,
cpu
)
exe
=
Executor
(
cpu
)
outs
=
map
(
numpy
.
array
,
exe
.
run
(
feed
=
{
'x'
:
tensor
},
fetch_list
=
[
mem1
,
mem2
,
mem3
]))
self
.
assertTrue
(
numpy
.
allclose
(
tensor_np
[
0
:
3
],
outs
[
0
]))
self
.
assertTrue
(
numpy
.
allclose
(
tensor_np
[
0
:
2
],
outs
[
1
]))
self
.
assertTrue
(
numpy
.
allclose
(
tensor_np
[
0
:
1
],
outs
[
2
]))
mem3_mean
=
layers
.
mean
(
x
=
mem3
)
append_backward_ops
(
loss
=
mem3_mean
)
x_grad
=
map
(
numpy
.
array
,
exe
.
run
(
feed
=
{
'x'
:
tensor
},
fetch_list
=
[
g_main_program
.
global_block
().
var
(
'x@GRAD'
)
]))[
0
]
self
.
assertAlmostEqual
(
1.0
,
x_grad
.
sum
(),
delta
=
0.1
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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