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体验新版 GitCode,发现更多精彩内容 >>
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381c6a02
编写于
3月 20, 2018
作者:
Y
Yang yaming
提交者:
GitHub
3月 20, 2018
浏览文件
操作
浏览文件
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差异文件
Merge pull request #9100 from pkuyym/fix-9049
Enhance sequence_expand operator
上级
5271c32d
2c225525
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
305 addition
and
154 deletion
+305
-154
paddle/fluid/operators/math/math_function.cc
paddle/fluid/operators/math/math_function.cc
+2
-0
paddle/fluid/operators/math/math_function.cu
paddle/fluid/operators/math/math_function.cu
+2
-0
paddle/fluid/operators/sequence_expand_op.cc
paddle/fluid/operators/sequence_expand_op.cc
+124
-62
paddle/fluid/operators/sequence_expand_op.cu
paddle/fluid/operators/sequence_expand_op.cu
+9
-2
paddle/fluid/operators/sequence_expand_op.h
paddle/fluid/operators/sequence_expand_op.h
+101
-47
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+26
-23
python/paddle/fluid/tests/book/test_machine_translation.py
python/paddle/fluid/tests/book/test_machine_translation.py
+3
-3
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+2
-2
python/paddle/fluid/tests/unittests/test_sequence_expand.py
python/paddle/fluid/tests/unittests/test_sequence_expand.py
+36
-15
未找到文件。
paddle/fluid/operators/math/math_function.cc
浏览文件 @
381c6a02
...
...
@@ -371,6 +371,8 @@ template struct RowwiseAdd<platform::CPUDeviceContext, double>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
double
>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
int
>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
int64_t
>;
template
struct
RowwiseSum
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
RowwiseSum
<
platform
::
CPUDeviceContext
,
double
>;
...
...
paddle/fluid/operators/math/math_function.cu
浏览文件 @
381c6a02
...
...
@@ -422,6 +422,8 @@ struct RowwiseAdd<platform::CUDADeviceContext, T> {
template
struct
RowwiseAdd
<
platform
::
CUDADeviceContext
,
float
>;
template
struct
RowwiseAdd
<
platform
::
CUDADeviceContext
,
double
>;
template
struct
ColwiseSum
<
platform
::
CUDADeviceContext
,
float
>;
template
struct
ColwiseSum
<
platform
::
CUDADeviceContext
,
int
>;
template
struct
ColwiseSum
<
platform
::
CUDADeviceContext
,
int64_t
>;
// template struct ColwiseSum<platform::CUDADeviceContext, double>;
// The ColwiseSum<platform::CUDADeviceContext, double> failed in debug mode,
// and only failed for this case. So reimplemented it.
...
...
paddle/fluid/operators/sequence_expand_op.cc
浏览文件 @
381c6a02
...
...
@@ -17,7 +17,7 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
using
framework
::
LoD
Tensor
;
class
SequenceExpandOp
:
public
framework
::
OperatorWithKernel
{
public:
...
...
@@ -25,15 +25,71 @@ class SequenceExpandOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
));
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
));
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
));
framework
::
DDim
out_dim
;
auto
y_dim
=
ctx
->
GetInputDim
(
"Y"
);
out_dim
=
ctx
->
GetInputDim
(
"X"
);
out_dim
[
0
]
=
y_dim
[
0
];
ctx
->
ShareLoD
(
"Y"
,
"Out"
);
ctx
->
SetOutputDim
(
"Out"
,
out_dim
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceExpandOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"Input(Y) of SequenceExpandOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceExpandOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
out_dims
=
x_dims
;
int
ref_level
=
ctx
->
Attrs
().
Get
<
int
>
(
"ref_level"
);
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
"Dimension number of Input(X) should be at least 2."
);
if
(
ctx
->
IsRuntime
())
{
framework
::
Variable
*
x_var
=
boost
::
get
<
framework
::
Variable
*>
(
ctx
->
GetInputVarPtrs
(
"X"
)[
0
]);
framework
::
Variable
*
y_var
=
boost
::
get
<
framework
::
Variable
*>
(
ctx
->
GetInputVarPtrs
(
"Y"
)[
0
]);
auto
&
x_lod
=
x_var
->
Get
<
LoDTensor
>
().
lod
();
auto
&
y_lod
=
y_var
->
Get
<
LoDTensor
>
().
lod
();
PADDLE_ENFORCE_LE
(
x_lod
.
size
(),
1
,
"Level number of Input(X)'s lod should not be "
"greater than 1."
);
PADDLE_ENFORCE_GT
(
y_lod
.
size
(),
0
,
"Level number of Input(Y)'s lod should be "
"greater than 0."
);
PADDLE_ENFORCE
(
ref_level
==
-
1
||
(
ref_level
>=
0
&&
ref_level
<
static_cast
<
int
>
(
y_lod
.
size
())),
"Invlid `ref_level`, which should be either equal to -1 "
"or in [0, %d)"
,
y_lod
.
size
());
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
if
(
x_lod
.
size
()
>
0
)
{
PADDLE_ENFORCE
(
x_lod
[
0
].
size
()
==
y_lod
[
ref_level
].
size
(),
"Level number of Input(X)'s lod could be 0. Otherwise "
"size of Input(X)'s first level lod should be equal to "
"size of Input(Y)'s referred level lod."
);
}
int64_t
out_first_dim
=
0
;
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
out_first_dim
=
x_dims
[
0
];
}
else
{
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
int
x_seq_len
=
1
;
if
(
x_lod
.
size
()
==
1
)
{
x_seq_len
=
x_lod
[
0
][
i
]
-
x_lod
[
0
][
i
-
1
];
}
out_first_dim
+=
(
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
])
*
x_seq_len
;
}
}
out_dims
[
0
]
=
out_first_dim
;
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
}
else
{
out_dims
[
0
]
=
-
1
;
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
}
};
...
...
@@ -42,83 +98,81 @@ class SequenceExpandOpMaker : public framework::OpProtoAndCheckerMaker {
SequenceExpandOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(
Tensor or LoDTensor) The input(X) of this operator can be a
"
"
LoDTensor or a base Tensor
."
);
"(
LoDTensor, default LoDTensor<float>) A 2-D LoDTensor whose lod
"
"
level is at most 1
."
);
AddInput
(
"Y"
,
"(LoDTensor)The reference input(Y) of sequence_expand op."
"It must be a LoDTensor with k-level(k>0)."
"The input(X) will be expanded according to LOD of input(Y)."
"The element numbers of last level in input(Y) "
"must be equal to dims[0] of input(X)."
);
"(LoDTensor, default LoDTensor<float>) Referred LoDTensor whose "
"lod (specified level) is referred by Input(X)."
);
AddOutput
(
"Out"
,
"(LodTensor)The output of sequence_expand op."
"The lod of output will be as same as input(Y)'s lod."
);
"(LodTensor, default LoDTensor<float>) Output LoDTensor which is "
"generated from Input(X) by referring lod of Input(Y)."
);
AddAttr
<
int
>
(
"ref_level"
,
"Specify lod level of Input(Y)."
).
SetDefault
(
-
1
);
AddComment
(
R"DOC(
Sequence Expand Operator.
This operator expands input(X) according to LOD of input(Y).
This operator expands `X` according to specified level lod of `Y`. Current
implementation constaints that lod level of `X` should be at most 1. Attribute
`ref_level` is used to specify which level lod of `Y` is referred to expand `X`.
If set `ref_level` to -1, then last level lod of `Y` would be referred.
Please note, rank of `X` should be at least 2, when the rank exceeds 2, `X`
would be viewed as a 2-D tensor.
Following are cases to better explain how this works:
Case 1:
Given a 2-level LoDTensor input(X)
X.lod = [[0, 2, 3],
[0, 1, 3, 4]]
X.data = [a, b, c, d]
Given a 1-level LoDTensor input(X)
X.lod = [[0, 2, 4]]
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 2-level LoDTensor
Out.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
Out.data = [a, a, a, b, b, b, c, d]
ref_level: 0
then we get 1-level LoDTensor
Out.lod = [[0, 2, 4, 6, 8]]
Out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
Out.dims = [8, 1]
Case 2:
Given 1-level LoDTensor input(X)
X.lod = [[0, 1, 4]]
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 2, 4],
[0, 3, 6, 6, 8]]
ref_level: 0
then we get 1-level LoDTensor
Out.lod = [[0, 1, 2, 5, 8]]
Out.data = [[a], [a], [b], [c], [d], [b], [c], [d]]
Out.dims = [8, 1]
Case 3:
Given a common Tensor input(X)
X.data = [
a, b, c
]
X.data = [
[a], [b], [c]
]
X.dims = [3, 1]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 1-level LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [a, a, b, c, c, c]
ref_level: -1
then we get a common Tensor
Out.data = [[a], [a], [b], [c], [c], [c]]
Out.dims = [6, 1]
Case
3
:
Case
4
:
Given a common Tensor input(X)
X.data = [[a, b], [c, d], [e, f]]
X.dims = [3, 2]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 1-level LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [[a,b], [a,b] [c,d], [e, f], [e, f], [e, f]]
ref_level: 0
then we get a common LoDTensor
Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
Out.dims = [6, 2]
Case 4:
Given 2-level a LoDTensor input(X)
X.lod = [[0, 2, 3],
[0, 1, 3, 4]]
X.data = [a, b, c, d]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 2, 4],
[0, 3, 6, 6, 8]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 2-level LoDTensor
Out.lod = [[0, 2, 4],
[0, 3, 6, 6, 8]]
Out.data = [a, a, a, b, b, b, d, d]
Out.dims = [8, 1]
)DOC"
);
}
};
...
...
@@ -129,12 +183,14 @@ class SequenceExpandOpGrad : public framework::OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
));
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Out"
));
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
)
,
"Input(X) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Out"
)
,
"Input(Out) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"The input(Out@GRAD) should not be null"
);
"Input(Out@GRAD) should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
x_grad_name
=
framework
::
GradVarName
(
"X"
);
if
(
ctx
->
HasOutput
(
x_grad_name
))
{
ctx
->
SetOutputDim
(
x_grad_name
,
x_dims
);
}
...
...
@@ -149,7 +205,13 @@ REGISTER_OP(sequence_expand, ops::SequenceExpandOp, ops::SequenceExpandOpMaker,
sequence_expand_grad
,
ops
::
SequenceExpandOpGrad
);
REGISTER_OP_CPU_KERNEL
(
sequence_expand
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_expand_grad
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/fluid/operators/sequence_expand_op.cu
浏览文件 @
381c6a02
...
...
@@ -18,7 +18,14 @@ limitations under the License. */
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
sequence_expand
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
SequenceExpandKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
sequence_expand_grad
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
SequenceExpandGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/fluid/operators/sequence_expand_op.h
浏览文件 @
381c6a02
...
...
@@ -16,45 +16,75 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "
unsupported/Eigen/CXX11/Tensor
"
#include "
paddle/fluid/operators/math/math_function.h
"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceExpandKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
auto
x_dims
=
x
->
dims
();
auto
*
y
=
context
.
Input
<
LoDTensor
>
(
"Y"
);
PADDLE_ENFORCE
(
!
y
->
lod
().
empty
(),
"y should have lod"
);
PADDLE_ENFORCE_EQ
(
static_cast
<
size_t
>
(
x_dims
[
0
]),
y
->
lod
().
back
().
size
()
-
1
,
"The size of last lod level in Input(Y)"
"must be equal to dims[0] of Input(X)."
);
out
->
set_lod
(
y
->
lod
());
auto
*
place
=
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
size_t
element_len
=
framework
::
product
(
x_dims
)
/
x_dims
[
0
];
T
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
out_starts
=
out
->
lod
().
back
();
for
(
size_t
i
=
0
;
i
<
out_starts
.
size
()
-
1
;
i
++
)
{
int
scale
=
out_starts
[
i
+
1
]
-
out_starts
[
i
];
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
x_t
(
x_data
,
1
,
element_len
);
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
out_t
(
out_data
,
scale
,
element_len
);
Eigen
::
array
<
int
,
2
>
cast
({{
scale
,
1
}});
out_t
.
device
(
*
place
)
=
x_t
.
broadcast
(
cast
);
x_data
+=
element_len
;
out_data
+=
element_len
*
scale
;
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
int
ref_level
=
context
.
Attr
<
int
>
(
"ref_level"
);
auto
&
x_lod
=
x
->
lod
();
auto
&
y_lod
=
y
->
lod
();
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
framework
::
TensorCopy
(
*
x
,
context
.
GetPlace
(),
out
);
return
;
}
auto
&
out_lod
=
*
out
->
mutable_lod
();
if
(
x_lod
.
size
()
==
1
)
{
out_lod
.
resize
(
1
);
out_lod
[
0
]
=
{
0
};
}
int
out_offset
=
0
;
auto
&
eigen_place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
int
x_start
=
i
-
1
;
int
x_end
=
i
;
if
(
x_lod
.
size
()
==
1
)
{
x_start
=
x_lod
[
0
][
i
-
1
];
x_end
=
x_lod
[
0
][
i
];
}
int
x_seq_len
=
x_end
-
x_start
;
if
(
repeat_num
>
0
)
{
auto
x_sub_tensor
=
x
->
Slice
(
x_start
,
x_end
);
x_sub_tensor
.
Resize
({
1
,
x_sub_tensor
.
numel
()});
int
out_start
=
out_offset
;
if
(
x_lod
.
size
()
==
1
)
{
out_start
=
out_lod
[
0
][
out_offset
];
}
auto
out_sub_tensor
=
out
->
Slice
(
out_start
,
out_start
+
x_seq_len
*
repeat_num
);
out_sub_tensor
.
Resize
({
repeat_num
,
x_sub_tensor
.
dims
()[
1
]});
EigenMatrix
<
T
>::
From
(
out_sub_tensor
).
device
(
eigen_place
)
=
EigenMatrix
<
T
>::
From
(
x_sub_tensor
)
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
repeat_num
,
1
}}));
}
for
(
int
j
=
0
;
j
<
repeat_num
;
++
j
)
{
if
(
x_lod
.
size
()
==
1
)
{
out_lod
[
0
].
push_back
(
out_lod
[
0
].
back
()
+
x_seq_len
);
}
out_offset
++
;
}
}
}
};
...
...
@@ -75,27 +105,51 @@ template <typename DeviceContext, typename T>
class
SequenceExpandGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
d
_out
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
g
_out
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Input
<
LoDTensor
>
(
"Out"
);
auto
*
d_x
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
out_last_level
=
out
->
lod
().
back
();
d_x
->
set_lod
(
x
->
lod
());
const
T
*
d_out_data
=
d_out
->
data
<
T
>
();
T
*
d_x_data
=
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
size_t
element_len
=
d_out
->
numel
()
/
d_out
->
dims
()[
0
];
for
(
size_t
i
=
0
;
i
<
out_last_level
.
size
()
-
1
;
++
i
)
{
size_t
repeat
=
out_last_level
[
i
+
1
]
-
out_last_level
[
i
];
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
d_out_t
(
d_out_data
,
static_cast
<
int
>
(
repeat
),
element_len
);
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
d_x_t
(
d_x_data
,
static_cast
<
int
>
(
element_len
));
auto
place
=
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
d_x_t
.
device
(
*
place
)
=
d_out_t
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
d_out_data
+=
(
repeat
*
element_len
);
d_x_data
+=
element_len
;
auto
*
y
=
context
.
Input
<
LoDTensor
>
(
"Y"
);
auto
*
g_x
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
int
ref_level
=
context
.
Attr
<
int
>
(
"ref_level"
);
g_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
g_x
->
set_lod
(
x
->
lod
());
auto
&
x_lod
=
x
->
lod
();
auto
&
y_lod
=
y
->
lod
();
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
// just copy the gradient
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
framework
::
TensorCopy
(
*
g_out
,
context
.
GetPlace
(),
g_x
);
return
;
}
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
math
::
SetConstant
<
DeviceContext
,
T
>
set_zero
;
set_zero
(
dev_ctx
,
g_x
,
static_cast
<
T
>
(
0
));
int
g_out_offset
=
0
;
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
if
(
repeat_num
>
0
)
{
int
x_start
=
i
-
1
;
int
x_end
=
i
;
if
(
x_lod
.
size
()
==
1
)
{
x_start
=
x_lod
[
0
][
i
-
1
];
x_end
=
x_lod
[
0
][
i
];
}
int
x_seq_len
=
x_end
-
x_start
;
auto
g_x_sub
=
g_x
->
Slice
(
x_start
,
x_end
);
g_x_sub
.
Resize
(
flatten_to_1d
(
g_x_sub
.
dims
()));
int
g_out_end
=
g_out_offset
+
repeat_num
*
x_seq_len
;
auto
g_out_sub
=
g_out
->
Slice
(
g_out_offset
,
g_out_end
);
g_out_sub
.
Resize
({
repeat_num
,
g_x_sub
.
dims
()[
0
]});
math
::
ColwiseSum
<
DeviceContext
,
T
>
col_sum
;
col_sum
(
dev_ctx
,
g_out_sub
,
&
g_x_sub
);
g_out_offset
+=
repeat_num
*
x_seq_len
;
}
}
}
};
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
381c6a02
...
...
@@ -1809,52 +1809,52 @@ def conv2d_transpose(input,
return
out
def
sequence_expand
(
x
,
y
,
name
=
None
):
def
sequence_expand
(
x
,
y
,
ref_level
=-
1
,
name
=
None
):
"""Sequence Expand Layer. This layer will expand the input variable **x**
according to LoD information of **y**. And the following examples will
explain how sequence_expand works:
according to specified level lod of **y**. Please note that lod level of
**x** is at most 1 and rank of **x** is at least 2. When rank of **x**
is greater than 2, then it would be viewed as a 2-D tensor.
Following examples will explain how sequence_expand works:
.. code-block:: text
* Case 1
x is a LoDTensor:
x.lod = [[0, 2, 3],
[0, 1, 3, 4]]
x.data = [a, b, c, d]
x.lod = [[0, 2, 4]]
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
y is a LoDTensor:
y.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
with condition len(y.lod[-1]) - 1 == x.dims[0]
ref_level: 0
then output is a 2-level LoDTensor:
out.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
out.data = [a, a, a, b, b, b, c, d]
then output is a 1-level LoDTensor:
out.lod = [[0, 2, 4, 6, 8]]
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]
* Case 2
x is a Tensor:
x.data = [
a, b, c
]
x.data = [
[a], [b], [c]
]
x.dims = [3, 1]
y is a LoDTensor:
y.lod = [[0, 2,
3, 6
]]
y.lod = [[0, 2,
2, 5
]]
with condition len(y.lod[-1]) - 1 == x.dims[0]
then output is a 1-level LoDTensor:
out.lod = [[0, 2, 3, 6]]
out.data = [a, a, b, c, c, c]
out.dims = [6, 1]
ref_level: -1
then output is a Tensor:
out.data = [[a], [a], [c], [c], [c]]
out.dims = [5, 1]
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): The input variable which is a LoDTensor.
ref_level (int): Lod level of `y` to be referred by `x`. If set to -1,
refer the last level of lod.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
will be named automatically.
Returns:
Variable: The expanded variable which is a LoDTensor.
...
...
@@ -1865,14 +1865,17 @@ def sequence_expand(x, y, name=None):
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
y = fluid.layers.data(name='y', shape=[10, 20],
dtype='float32', lod_level=1)
out = layers.sequence_expand(x=x, y=y)
out = layers.sequence_expand(x=x, y=y
, ref_level=0
)
"""
helper
=
LayerHelper
(
'sequence_expand'
,
input
=
x
,
**
locals
())
dtype
=
helper
.
input_dtype
()
tmp
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
'sequence_expand'
,
inputs
=
{
'X'
:
x
,
'Y'
:
y
},
outputs
=
{
'Out'
:
tmp
})
type
=
'sequence_expand'
,
inputs
=
{
'X'
:
x
,
'Y'
:
y
},
outputs
=
{
'Out'
:
tmp
},
attrs
=
{
'ref_level'
:
ref_level
})
return
tmp
...
...
python/paddle/fluid/tests/book/test_machine_translation.py
浏览文件 @
381c6a02
...
...
@@ -118,12 +118,12 @@ def decoder_decode(context, is_sparse):
is_sparse
=
is_sparse
)
# use rnn unit to update rnn
current_state
=
pd
.
fc
(
input
=
[
pre_
ids_emb
,
pre_state_expanded
],
current_state
=
pd
.
fc
(
input
=
[
pre_
state_expanded
,
pre_ids_emb
],
size
=
decoder_size
,
act
=
'tanh'
)
current_state_with_lod
=
pd
.
lod_reset
(
x
=
current_state
,
y
=
pre_score
)
# use score to do beam search
current_score
=
pd
.
fc
(
input
=
current_state
,
current_score
=
pd
.
fc
(
input
=
current_state
_with_lod
,
size
=
target_dict_dim
,
act
=
'softmax'
)
topk_scores
,
topk_indices
=
pd
.
topk
(
current_score
,
k
=
50
)
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
381c6a02
...
...
@@ -181,8 +181,8 @@ class TestBook(unittest.TestCase):
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
=
1
)
self
.
assertIsNotNone
(
layers
.
sequence_expand
(
x
=
x
,
y
=
y
))
name
=
'y'
,
shape
=
[
10
,
20
],
dtype
=
'float32'
,
lod_level
=
2
)
self
.
assertIsNotNone
(
layers
.
sequence_expand
(
x
=
x
,
y
=
y
,
ref_level
=
1
))
print
(
str
(
program
))
def
test_lstm_unit
(
self
):
...
...
python/paddle/fluid/tests/unittests/test_sequence_expand.py
浏览文件 @
381c6a02
...
...
@@ -27,12 +27,36 @@ class TestSequenceExpand(OpTest):
def
compute
(
self
):
x
=
self
.
inputs
[
'X'
]
x_data
,
x_lod
=
x
if
type
(
x
)
==
tuple
else
(
x
,
None
)
n
=
1
+
x_data
.
shape
[
0
]
if
not
x_lod
else
len
(
x_lod
[
0
])
y_data
,
y_lod
=
self
.
inputs
[
'Y'
]
repeats
=
[((
y_lod
[
-
1
][
i
+
1
]
-
y_lod
[
-
1
][
i
]))
for
i
in
range
(
len
(
y_lod
[
-
1
])
-
1
)]
out
=
x_data
.
repeat
(
repeats
,
axis
=
0
)
self
.
outputs
=
{
'Out'
:
out
}
if
hasattr
(
self
,
'attrs'
):
ref_level
=
self
.
attrs
[
'ref_level'
]
else
:
ref_level
=
len
(
y_lod
)
-
1
out
=
np
.
zeros
(
shape
=
((
0
,
)
+
x_data
.
shape
[
1
:]),
dtype
=
x_data
.
dtype
)
if
x_lod
is
None
:
x_idx
=
[
i
for
i
in
xrange
(
x_data
.
shape
[
0
]
+
1
)]
else
:
x_idx
=
x_lod
[
0
]
out_lod
=
[[
0
]]
for
i
in
xrange
(
1
,
len
(
y_lod
[
ref_level
])):
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
]
x_len
=
x_idx
[
i
]
-
x_idx
[
i
-
1
]
if
repeat_num
>
0
:
x_sub
=
x_data
[
x_idx
[
i
-
1
]:
x_idx
[
i
],
:]
x_sub
=
np
.
repeat
(
x_sub
,
repeat_num
,
axis
=
0
)
out
=
np
.
vstack
((
out
,
x_sub
))
if
x_lod
is
not
None
:
for
j
in
xrange
(
repeat_num
):
out_lod
[
0
].
append
(
out_lod
[
0
][
-
1
]
+
x_len
)
if
x_lod
is
None
:
self
.
outputs
=
{
'Out'
:
out
}
else
:
self
.
outputs
=
{
'Out'
:
(
out
,
out_lod
)}
def
setUp
(
self
):
self
.
op_type
=
'sequence_expand'
...
...
@@ -52,7 +76,8 @@ class TestSequenceExpandCase1(TestSequenceExpand):
x_lod
=
[[
0
,
2
,
5
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
2
,
5
],
[
0
,
2
,
4
,
7
,
10
,
13
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
self
.
inputs
=
{
'X'
:
x_data
,
'Y'
:
(
y_data
,
y_lod
)}
self
.
attrs
=
{
'ref_level'
:
0
}
class
TestSequenceExpandCase2
(
TestSequenceExpand
):
...
...
@@ -60,8 +85,9 @@ class TestSequenceExpandCase2(TestSequenceExpand):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
1
,
2
,
2
]).
astype
(
'float32'
)
x_lod
=
[[
0
,
1
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
2
,
2
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
2
]]
y_lod
=
[[
0
,
2
]
,
[
0
,
2
]
]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
self
.
attrs
=
{
'ref_level'
:
0
}
class
TestSequenceExpandCase3
(
TestSequenceExpand
):
...
...
@@ -75,14 +101,9 @@ class TestSequenceExpandCase3(TestSequenceExpand):
class
TestSequenceExpandCase4
(
TestSequenceExpand
):
def
set_data
(
self
):
x_data
=
np
.
array
(
[
0.1
,
0.3
,
0.2
,
0.15
,
0.25
,
0.2
,
0.15
,
0.25
,
0.1
,
0.3
]).
reshape
(
[
2
,
5
]).
astype
(
'float32'
)
x_lod
=
[[
0
,
1
,
2
,
]]
data
=
[
0.1
,
0.3
,
0.2
,
0.15
,
0.25
,
0.2
,
0.15
,
0.25
,
0.1
,
0.3
]
x_data
=
np
.
array
(
data
).
reshape
([
5
,
2
]).
astype
(
'float32'
)
x_lod
=
[[
0
,
2
,
5
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
1
,
2
],
[
0
,
1
,
2
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
...
...
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