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体验新版 GitCode,发现更多精彩内容 >>
提交
332b665f
编写于
3月 19, 2018
作者:
Y
yangyaming
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Enhanced cpp implementation and unit test.
上级
a431f984
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
101 addition
and
54 deletion
+101
-54
paddle/fluid/operators/lod_reset_op.cc
paddle/fluid/operators/lod_reset_op.cc
+47
-32
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/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 @
332b665f
...
...
@@ -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) is not provided, the target lod should b
e "
"specified by attribute `target_lod`
."
);
}
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
}
...
...
@@ -50,36 +49,42 @@ 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, 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, `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.
An example:
Given a float LoDTensor X with shape (6, 1), its transpose form represents
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
with LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like
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].
target_lod: [0, 4, 6]
If target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and
the sequences that the LoDTensor Output(Out) contains becomes:
[1.0, 2.0, 3.0, 4.0], [5.0, 6.0].
then we get an 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]
)DOC"
);
}
...
...
@@ -90,10 +95,16 @@ 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 +122,13 @@ 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 @
332b665f
...
...
@@ -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 @
332b665f
...
...
@@ -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/tests/unittests/test_lod_reset_op.py
浏览文件 @
332b665f
...
...
@@ -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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