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体验新版 GitCode,发现更多精彩内容 >>
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提交
484cff6e
编写于
3月 19, 2018
作者:
Y
Yang yaming
提交者:
GitHub
3月 19, 2018
浏览文件
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差异文件
Merge pull request #9204 from pkuyym/fix-9171
Enhance LoDResetOp and add python wrapper
上级
0821ee78
cd11b1bd
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
242 addition
and
55 deletion
+242
-55
paddle/fluid/operators/lod_reset_op.cc
paddle/fluid/operators/lod_reset_op.cc
+81
-31
paddle/fluid/operators/lod_reset_op.cu
paddle/fluid/operators/lod_reset_op.cu
+6
-2
paddle/fluid/operators/lod_reset_op.h
paddle/fluid/operators/lod_reset_op.h
+27
-16
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+98
-2
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+9
-0
python/paddle/fluid/tests/unittests/test_lod_reset_op.py
python/paddle/fluid/tests/unittests/test_lod_reset_op.py
+21
-4
未找到文件。
paddle/fluid/operators/lod_reset_op.cc
浏览文件 @
484cff6e
...
...
@@ -22,17 +22,16 @@ class LoDResetOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
// input check
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of LoDResetOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of LoDResetOp should not be null."
);
// If target LoD is not set form Input(), then it must be set from Attr().
if
(
!
ctx
->
HasInput
(
"
TargetLoD
"
))
{
if
(
!
ctx
->
HasInput
(
"
Y
"
))
{
auto
level0
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"target_lod"
);
PADDLE_ENFORCE
(
level0
.
size
()
>
1
,
"Target LoD is not found, should be set to be a valid on
e "
"through Input() or Attr()
."
);
PADDLE_ENFORCE
_GT
(
level0
.
size
(),
1
,
"If Input(Y) not provided, the target lod should b
e "
"specified by attribute `target_lod`
."
);
}
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
}
...
...
@@ -50,36 +49,77 @@ class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDResetOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor) The input tensor of lod_reset operator."
);
AddInput
(
"TargetLoD"
,
"(Tensor, optional) The target level 0 LoD from Input()."
)
AddInput
(
"X"
,
"(Tensor, LoDTensor) Input variable of LoDResetOp which "
"could be a Tensor or LoDTensor, where the data of output "
"variable inherits from."
);
AddInput
(
"Y"
,
"(Tensor, LoDTensor, optional) If provided and Y is LoDTensor, "
"lod of Input(Y) would be considered as the target lod first, "
"otherwise data of Input(Y) would be considered as the "
"target lod."
)
.
AsDispensable
();
AddOutput
(
"Out"
,
"(LoDTensor) The output tensor of lod_reset operator."
);
AddOutput
(
"Out"
,
"(LoDTensor) Output variable of LoDResetOp which should be a "
"LoDTensor."
);
AddAttr
<
std
::
vector
<
int
>>
(
"target_lod"
,
"The target level 0 LoD from Attr()."
)
.
SetDefault
(
std
::
vector
<
int
>
{});
AddComment
(
R"DOC(LoDReset operator
Reset LoD of Input(X) into a new one specified by Input(TargetLoD) or
Attr(target_lod), or set LoD for Input(X) if it doesn't have one.
Currently the lod_reset operator only supports the reset of level 0 LoD.
At least one of Input(TargetLoD) and Attr(target_lod) must be set,
and if both of them are set, Input(TargetLoD) will be chosen as the
target LoD.
Set LoD of `X` to a new one specified by `Y` or attribute `target_lod`. When `Y`
provided and `Y` is a LoDTensor, `Y.lod` would be considered as target LoD
first, otherwise `Y.data` would be considered as target LoD. If `Y` is not
provided, target LoD should be specified by attribute `target_lod`.
If target LoD is specified by `Y.data` or `target_lod`, only one level LoD
is supported.
Example 1:
Given a 1-level LoDTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
attr(target_lod): [0, 4, 6]
then we get a 1-level LoDTensor:
Out.lod = [[ 0, 4, 6 ]]
Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
Out.dims = [6, 1]
Example 2:
An example:
Given a float LoDTensor X with shape (6, 1), its transpose form represents
Given a 1-level LoDTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
input(Y) is a Tensor:
Y.data = [[0, 2, 6]]
Y.dims = [1, 3]
with LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like
then we get a 1-level LoDTensor:
Out.lod = [[ 0, 2, 6 ]]
Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
Out.dims = [6, 1]
[1.0, 2.0], [3.0, 4.0, 5.0], [6.0].
Example 3:
If target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and
the sequences that the LoDTensor Output(Out) contains becomes:
Given a 1-level LoDTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
[1.0, 2.0, 3.0, 4.0], [5.0, 6.0].
input(Y) is a 2-level LoDTensor:
Y.lod = [[0, 2, 4], [0, 2, 5, 6]]
Y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
Y.dims = [6, 1]
then we get a 2-level LoDTensor:
Out.lod = [[0, 2, 4], [0, 2, 5, 6]]
Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
Out.dims = [6, 1]
)DOC"
);
}
...
...
@@ -90,10 +130,16 @@ class LoDResetGradOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of LoDResetGradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) shouldn't be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
"Input(Out@Grad) of LoDResetGradOp should not be null."
);
auto
x_grad_name
=
framework
::
GradVarName
(
"X"
);
if
(
ctx
->
HasOutput
(
x_grad_name
))
{
ctx
->
SetOutputDim
(
x_grad_name
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
x_grad_name
);
}
}
protected:
...
...
@@ -111,9 +157,13 @@ class LoDResetGradOp : public framework::OperatorWithKernel {
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
lod_reset
,
ops
::
LoDResetOp
,
ops
::
LoDResetOpMaker
,
lod_reset_grad
,
ops
::
LoDResetGradOp
);
REGISTER_OP_CPU_KERNEL
(
lod_reset
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
lod_reset
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
lod_reset_grad
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
);
paddle/fluid/operators/lod_reset_op.cu
浏览文件 @
484cff6e
...
...
@@ -18,8 +18,12 @@ namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL
(
lod_reset
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
ops
::
LoDResetKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
LoDResetKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
lod_reset_grad
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
LoDResetGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/fluid/operators/lod_reset_op.h
浏览文件 @
484cff6e
...
...
@@ -26,35 +26,46 @@ class LoDResetKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
auto
*
in
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
*
lod_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"TargetLoD"
);
auto
*
lod_t
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Y"
);
out
->
ShareDataWith
(
*
in
);
std
::
vector
<
int
>
level0
;
if
(
lod_t
)
{
auto
*
lod
=
lod_t
->
data
<
int
>
();
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
framework
::
Tensor
lod_cpu
;
framework
::
TensorCopy
(
*
lod_t
,
platform
::
CPUPlace
(),
ctx
.
device_context
(),
&
lod_cpu
);
lod
=
lod_cpu
.
data
<
int
>
();
if
(
lod_t
->
lod
().
size
()
>
0
)
{
auto
y_lod
=
lod_t
->
lod
();
auto
last_level
=
y_lod
[
y_lod
.
size
()
-
1
];
PADDLE_ENFORCE_EQ
(
last_level
.
back
(),
in
->
dims
()[
0
],
"Last value of `Y`'s last level LoD should be equal "
"to the first dimension of `X`"
);
out
->
set_lod
(
y_lod
);
return
;
// early return, since lod already set
}
else
{
auto
*
lod
=
lod_t
->
data
<
int
>
();
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
framework
::
Tensor
lod_cpu
;
framework
::
TensorCopy
(
*
lod_t
,
platform
::
CPUPlace
(),
ctx
.
device_context
(),
&
lod_cpu
);
lod
=
lod_cpu
.
data
<
int
>
();
}
level0
=
std
::
vector
<
int
>
(
lod
,
lod
+
lod_t
->
numel
());
}
level0
=
std
::
vector
<
int
>
(
lod
,
lod
+
lod_t
->
numel
());
}
else
{
level0
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"target_lod"
);
}
PADDLE_ENFORCE
(
level0
.
size
()
>
1UL
,
"The s
ize of target LoD should be greater than 1."
);
PADDLE_ENFORCE
(
level0
[
0
]
==
0
,
"Target LoD should be a vector starting from 0."
);
PADDLE_ENFORCE
(
level0
.
back
()
==
in
->
dims
()[
0
],
"Target LoD should be a vector end with the "
"first dimension of Input(X)."
);
PADDLE_ENFORCE
_GT
(
level0
.
size
(),
1UL
,
"S
ize of target LoD should be greater than 1."
);
PADDLE_ENFORCE
_EQ
(
level0
[
0
],
0
,
"Target LoD should be a vector starting from 0."
);
PADDLE_ENFORCE
_EQ
(
level0
.
back
(),
in
->
dims
()[
0
],
"Target LoD should be a vector end with the "
"first dimension of Input(X)."
);
for
(
size_t
i
=
0
;
i
<
level0
.
size
()
-
1
;
++
i
)
{
PADDLE_ENFORCE
(
level0
[
i
+
1
]
>
level0
[
i
],
"Target LoD should be an ascending vector."
);
}
out
->
ShareDataWith
(
*
in
);
// cast level0 to size_t
std
::
vector
<
size_t
>
ulevel0
(
level0
.
size
(),
0
);
std
::
transform
(
level0
.
begin
(),
level0
.
end
(),
ulevel0
.
begin
(),
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
484cff6e
...
...
@@ -73,6 +73,7 @@ __all__ = [
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'lod_reset'
,
]
...
...
@@ -2225,7 +2226,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
keep_dim (bool|False): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
name(str|None): A name for this layer(optional). If set None, the
name(str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
...
...
@@ -2241,7 +2242,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_prod(x) # [0.0002268]
fluid.layers.reduce_prod(x, dim=0) # [0.02, 0.06, 0.3, 0.63]
fluid.layers.reduce_prod(x, dim=-1) # [0.027, 0.0084]
fluid.layers.reduce_prod(x, dim=1,
fluid.layers.reduce_prod(x, dim=1,
keep_dim=True) # [[0.027], [0.0084]]
"""
helper
=
LayerHelper
(
'reduce_prod'
,
**
locals
())
...
...
@@ -3292,3 +3293,98 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
counter
.
stop_gradient
=
True
return
counter
def
lod_reset
(
x
,
y
=
None
,
target_lod
=
None
):
"""
LoD Reset Operator. Set LoD of **x** to a new one specified by **y** or
**target_lod**. When **y** provided, **y.lod** would be considered as target
LoD first, otherwise **y.data** would be considered as target LoD. If **y**
is not provided, target LoD should be specified by **target_lod**.
If target LoD is specified by **Y.data** or **target_lod**, only one level
LoD is supported.
.. code-block:: text
* Example 1:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
target_lod: [0, 4, 6]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 4, 6 ]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 2:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a Tensor:
y.data = [[0, 2, 6]]
y.dims = [1, 3]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 2, 6 ]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 3:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a 2-level LoDTensor:
y.lod = [[0, 2, 4], [0, 2, 5, 6]]
y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
y.dims = [6, 1]
then we get a 2-level LoDTensor:
out.lod = [[0, 2, 4], [0, 2, 5, 6]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
Args:
x (Variable): Input variable which could be a Tensor or LodTensor.
y (Variable|None): If provided, output's LoD would be derived from y.
target_lod (list|tuple|None): One level LoD which should be considered
as target LoD when y not provided.
Returns:
Variable: Output variable with LoD specified by this operator.
Raises:
ValueError: If y and target_lod are both None.
Examples:
.. code-block:: python
x = layers.data(name='x', shape=[10])
y = layers.data(name='y', shape=[10, 20], lod_level=2)
out = layers.lod_reset(x=x, y=y)
"""
helper
=
LayerHelper
(
"lod_reset"
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
if
y
is
not
None
:
helper
.
append_op
(
type
=
"lod_reset"
,
inputs
=
{
'X'
:
x
,
'Y'
:
y
},
outputs
=
{
'Out'
:
out
})
elif
target_lod
is
not
None
:
helper
.
append_op
(
type
=
"lod_reset"
,
inputs
=
{
'X'
:
x
},
attrs
=
{
'target_lod'
:
target_lod
},
outputs
=
{
'Out'
:
out
})
else
:
raise
ValueError
(
"y and target_lod should not be both None."
)
return
out
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
484cff6e
...
...
@@ -327,6 +327,15 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
loss
)
print
(
str
(
program
))
def
test_lod_reset
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
10
],
dtype
=
'float32'
)
y
=
layers
.
data
(
name
=
'y'
,
shape
=
[
10
,
20
],
dtype
=
'float32'
,
lod_level
=
2
)
print
(
layers
.
lod_reset
(
x
=
x
,
y
=
y
))
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_lod_reset_op.py
浏览文件 @
484cff6e
...
...
@@ -42,7 +42,7 @@ class TestLodResetOpByInput(OpTest):
target_lod_0
=
[
0
,
4
,
7
,
10
]
self
.
inputs
=
{
'X'
:
(
x
,
lod
),
'
TargetLoD
'
:
np
.
array
([
target_lod_0
]).
astype
(
'int32'
)
'
Y
'
:
np
.
array
([
target_lod_0
]).
astype
(
'int32'
)
}
self
.
outputs
=
{
'Out'
:
(
x
,
[
target_lod_0
])}
...
...
@@ -50,7 +50,7 @@ class TestLodResetOpByInput(OpTest):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
,
no_grad_set
=
set
(
"
TargetLoD
"
))
self
.
check_grad
([
"X"
],
"Out"
,
no_grad_set
=
set
(
"
Y
"
))
class
TestLodResetOpBoth
(
OpTest
):
...
...
@@ -62,7 +62,7 @@ class TestLodResetOpBoth(OpTest):
target_lod_0_in
=
[
0
,
4
,
7
,
10
]
self
.
inputs
=
{
'X'
:
(
x
,
lod
),
'
TargetLoD
'
:
np
.
array
(
target_lod_0_in
).
astype
(
'int32'
)
'
Y
'
:
np
.
array
(
target_lod_0_in
).
astype
(
'int32'
)
}
self
.
attrs
=
{
'target_lod'
:
target_lod_0_attr
}
self
.
outputs
=
{
'Out'
:
(
x
,
[
target_lod_0_in
])}
...
...
@@ -71,7 +71,24 @@ class TestLodResetOpBoth(OpTest):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
,
no_grad_set
=
set
(
"TargetLoD"
))
self
.
check_grad
([
"X"
],
"Out"
,
no_grad_set
=
set
(
"Y"
))
class
TestLodResetOpYIsLoDTensor
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"lod_reset"
x
=
np
.
random
.
random
((
10
,
20
)).
astype
(
"float32"
)
lod
=
[[
0
,
3
,
5
,
10
]]
y
=
np
.
random
.
random
((
10
,
10
)).
astype
(
"float32"
)
target_lod_0
=
[[
0
,
4
,
7
,
10
]]
self
.
inputs
=
{
'X'
:
(
x
,
lod
),
'Y'
:
(
y
,
target_lod_0
)}
self
.
outputs
=
{
'Out'
:
(
x
,
target_lod_0
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
,
no_grad_set
=
set
(
"Y"
))
if
__name__
==
'__main__'
:
...
...
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