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体验新版 GitCode,发现更多精彩内容 >>
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72dd6b37
编写于
9月 18, 2018
作者:
C
chengduo
提交者:
GitHub
9月 18, 2018
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差异文件
Add sequence_expand_as_op (#13420)
* Add sequence_expand_as_op * follow comment
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d5455b22
变更
6
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Showing
6 changed file
with
594 addition
and
0 deletion
+594
-0
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/sequence_expand_as_op.cc
paddle/fluid/operators/sequence_expand_as_op.cc
+168
-0
paddle/fluid/operators/sequence_expand_as_op.cu
paddle/fluid/operators/sequence_expand_as_op.cu
+134
-0
paddle/fluid/operators/sequence_expand_as_op.h
paddle/fluid/operators/sequence_expand_as_op.h
+148
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+66
-0
python/paddle/fluid/tests/unittests/test_sequence_expand_as.py
...n/paddle/fluid/tests/unittests/test_sequence_expand_as.py
+77
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
72dd6b37
...
...
@@ -116,6 +116,7 @@ paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size
paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_expand ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.sequence_expand_as ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm_unit ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None))
paddle.fluid.layers.reduce_sum ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
...
...
paddle/fluid/operators/sequence_expand_as_op.cc
0 → 100644
浏览文件 @
72dd6b37
/* Copyright (c) 2018 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/fluid/operators/sequence_expand_as_op.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
LoDTensor
;
class
SequenceExpandAsOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceExpandAsOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"Input(Y) of SequenceExpandAsOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceExpandAsOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
out_dims
=
x_dims
;
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_dim
=
x_var
->
Get
<
LoDTensor
>
().
dims
();
auto
&
y_lod
=
y_var
->
Get
<
LoDTensor
>
().
lod
();
PADDLE_ENFORCE_EQ
(
y_lod
.
size
(),
1
,
"Level number of Input(Y)'s lod should be 1."
);
PADDLE_ENFORCE_EQ
(
static_cast
<
size_t
>
(
x_dim
[
0
]),
y_lod
[
0
].
size
()
-
1
,
"The first dimension of Input(X) should be equal "
"to the size of Input(Y)'s 0 level lod."
);
int64_t
out_first_dim
=
0
;
if
(
y_lod
[
0
].
size
()
<=
1
)
{
out_first_dim
=
x_dims
[
0
];
}
else
{
for
(
size_t
i
=
1
;
i
<
y_lod
[
0
].
size
();
++
i
)
{
out_first_dim
+=
(
y_lod
[
0
][
i
]
-
y_lod
[
0
][
i
-
1
]);
}
}
out_dims
[
0
]
=
out_first_dim
;
}
else
{
out_dims
[
0
]
=
-
1
;
}
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
ctx
->
ShareLoD
(
"Y"
,
/*->*/
"Out"
);
}
};
class
SequenceExpandAsOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor whose lod "
"level is at most 1."
);
AddInput
(
"Y"
,
"(LoDTensor, default LoDTensor<float>) Referred LoDTensor whose "
"lod (specified level) is referred by Input(X)."
);
AddOutput
(
"Out"
,
"(LodTensor, default LoDTensor<float>) Output LoDTensor which is "
"generated from Input(X) by referring lod of Input(Y)."
);
AddComment
(
R"DOC(
Sequence Expand As Operator.
This operator expands `X` according to the zeroth level lod of `Y`. Current
implementation requires the level number of Input(Y)'s lod should be 1, and
the first dimension of Input(X) should be equal to the size of Input(Y)'s zeroth
level lod, and lod of Input(X) is not considered.
Following are cases to better explain how this works:
Case 1:
Given a 1-level LoDTensor input(X)
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 3, 6, 7, 8]]
ref_level: 0
then we get 1-level LoDTensor
Out.lod = [[0, 3, 6, 7, 8]]
Out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
Out.dims = [8, 1]
Case 2:
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]]
ref_level: 0
then we get a common LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
Out.dims = [6, 2]
)DOC"
);
}
};
class
SequenceExpandAsOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
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"
)),
"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
);
ctx
->
ShareLoD
(
"X"
,
x_grad_name
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
sequence_expand_as
,
ops
::
SequenceExpandAsOp
,
ops
::
SequenceExpandAsOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
sequence_expand_as_grad
,
ops
::
SequenceExpandAsOpGrad
);
REGISTER_OP_CPU_KERNEL
(
sequence_expand_as
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_expand_as_grad
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/fluid/operators/sequence_expand_as_op.cu
0 → 100644
浏览文件 @
72dd6b37
/* Copyright (c) 2018 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 <algorithm>
#include "paddle/fluid/operators/sequence_expand_as_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
>
static
__global__
void
sequence_expand_as_kernel
(
const
T
*
in_data
,
const
size_t
*
expand_offset
,
const
size_t
src_hight
,
const
size_t
src_widht
,
T
*
out_data
)
{
for
(
int
h_id
=
blockIdx
.
x
;
h_id
<
src_hight
;
h_id
+=
gridDim
.
x
)
{
int
span
=
expand_offset
[
h_id
+
1
]
-
expand_offset
[
h_id
];
if
(
span
==
0
)
continue
;
const
T
*
src
=
in_data
+
h_id
*
src_widht
;
for
(
int
w_id
=
threadIdx
.
x
;
w_id
<
src_widht
;
w_id
+=
blockDim
.
x
)
{
T
ele
=
src
[
w_id
];
int
offset
=
expand_offset
[
h_id
]
*
src_widht
;
for
(
int
k
=
0
;
k
<
span
;
++
k
)
{
out_data
[
offset
+
k
*
src_widht
+
w_id
]
=
ele
;
}
}
}
}
template
<
typename
T
>
static
__global__
void
sequence_expand_as_grad_kernel
(
const
T
*
dout_data
,
const
size_t
*
expand_offset
,
const
size_t
dst_hight
,
const
size_t
dst_width
,
T
*
dx_data
)
{
for
(
int
h_id
=
blockIdx
.
x
;
h_id
<
dst_hight
;
h_id
+=
gridDim
.
x
)
{
T
*
dst
=
dx_data
+
h_id
*
dst_width
;
int
span
=
expand_offset
[
h_id
+
1
]
-
expand_offset
[
h_id
];
for
(
int
w_id
=
threadIdx
.
x
;
w_id
<
dst_width
;
w_id
+=
blockDim
.
x
)
{
T
result
=
0
;
for
(
int
k
=
0
;
k
<
span
;
++
k
)
{
int
offset
=
(
expand_offset
[
h_id
]
+
k
)
*
dst_width
;
const
T
*
src
=
dout_data
+
offset
;
result
+=
src
[
w_id
];
}
dst
[
w_id
]
=
result
;
}
}
}
template
<
typename
T
>
struct
SequenceExpandFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
const
LoDTensor
&
x
,
const
framework
::
Vector
<
size_t
>
&
ref_lod
,
/*expand referenced lod*/
LoDTensor
*
out
)
{
int
hight
=
x
.
dims
()[
0
];
int
width
=
framework
::
product
(
x
.
dims
())
/
hight
;
const
int
kThreadsPerBlock
=
1024
;
int
thread_x
=
kThreadsPerBlock
;
if
(
width
<
kThreadsPerBlock
)
{
// block_cols is aligned by 32.
thread_x
=
((
width
+
31
)
>>
5
)
<<
5
;
}
int
max_threads
=
context
.
GetMaxPhysicalThreadCount
();
int
block_x
=
std
::
max
(
max_threads
/
thread_x
,
1
);
dim3
block_size
(
thread_x
);
dim3
grid_size
(
block_x
);
sequence_expand_as_kernel
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
x
.
data
<
T
>
(),
ref_lod
.
CUDAData
(
context
.
GetPlace
()),
hight
,
width
,
out
->
mutable_data
<
T
>
(
context
.
GetPlace
()));
}
};
template
<
typename
T
>
struct
SequenceExpandAsGradFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
const
LoDTensor
&
dout
,
const
framework
::
Vector
<
size_t
>
&
ref_lod
,
/*expand based lod*/
LoDTensor
*
dx
)
{
int
hight
=
dx
->
dims
()[
0
];
int
width
=
framework
::
product
(
dx
->
dims
())
/
hight
;
const
int
kThreadsPerBlock
=
1024
;
int
thread_x
=
kThreadsPerBlock
;
if
(
width
<
kThreadsPerBlock
)
{
// block_cols is aligned by 32.
thread_x
=
((
width
+
31
)
>>
5
)
<<
5
;
}
int
max_threads
=
context
.
GetMaxPhysicalThreadCount
();
int
block_x
=
std
::
max
(
max_threads
/
thread_x
,
1
);
dim3
block_size
(
thread_x
);
dim3
grid_size
(
block_x
);
sequence_expand_as_grad_kernel
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
dout
.
data
<
T
>
(),
ref_lod
.
CUDAData
(
context
.
GetPlace
()),
hight
,
width
,
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
()));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
sequence_expand_as
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
SequenceExpandAsKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
sequence_expand_as_grad
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
SequenceExpandAsGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/fluid/operators/sequence_expand_as_op.h
0 → 100644
浏览文件 @
72dd6b37
/* Copyright (c) 2018 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 <numeric> // std::iota
#include <sstream>
#include <vector>
#include "glog/logging.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
struct
SequenceExpandFunctor
{
void
operator
()(
const
DeviceContext
&
ctx
,
const
framework
::
LoDTensor
&
x
,
const
framework
::
Vector
<
size_t
>
&
ref_lod
,
/*expand referenced lod*/
framework
::
LoDTensor
*
out
);
};
template
<
typename
DeviceContext
,
typename
T
>
struct
SequenceExpandAsGradFunctor
{
void
operator
()(
const
DeviceContext
&
ctx
,
const
framework
::
LoDTensor
&
dout
,
const
framework
::
Vector
<
size_t
>
&
ref_lod
,
/*expand referenced lod*/
framework
::
LoDTensor
*
dx
);
};
template
<
typename
T
>
struct
SequenceExpandFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
LoDTensor
&
x
,
const
framework
::
Vector
<
size_t
>
&
ref_lod
,
/*expand referenced lod*/
framework
::
LoDTensor
*
out
)
{
int64_t
hight
=
x
.
dims
()[
0
];
int64_t
width
=
framework
::
product
(
x
.
dims
())
/
hight
;
const
T
*
in_data
=
x
.
data
<
T
>
();
T
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
h_id
=
0
;
h_id
<
hight
;
++
h_id
)
{
size_t
span
=
ref_lod
[
h_id
+
1
]
-
ref_lod
[
h_id
];
if
(
span
==
0
)
continue
;
const
T
*
src
=
in_data
+
h_id
*
width
;
for
(
int64_t
w_id
=
0
;
w_id
<
width
;
++
w_id
)
{
T
ele
=
src
[
w_id
];
size_t
offset
=
ref_lod
[
h_id
]
*
width
;
for
(
size_t
k
=
0
;
k
<
span
;
++
k
)
{
out_data
[
offset
+
k
*
width
+
w_id
]
=
ele
;
}
}
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceExpandAsKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
framework
::
LoDTensor
>
(
"Y"
);
auto
*
out
=
context
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
auto
&
y_lod
=
y
->
lod
();
PADDLE_ENFORCE_EQ
(
y_lod
.
size
(),
1
,
"LoD of Y should be 1."
);
PADDLE_ENFORCE_GT
(
y_lod
[
0
].
size
(),
1
,
"."
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
SequenceExpandFunctor
<
DeviceContext
,
T
>
seq_espand_functor
;
seq_espand_functor
(
dev_ctx
,
*
x
,
y_lod
[
0
],
out
);
}
};
/*
*Given Grad(Out)
*
* Grad(Out).lod = [[0, 3, 6]]
* Grad(Out).data = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
* Then
* Grad(X).data = [(0.1 + 0.2 + 0.3), (0.4 + 0.5 + 0.6)]
* = [0.6, 1.5]
* Grad(X).lod = Input(X).lod
*
* */
template
<
typename
T
>
struct
SequenceExpandAsGradFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
LoDTensor
&
dout
,
const
framework
::
Vector
<
size_t
>
&
ref_lod
,
/*expand referenced lod*/
framework
::
LoDTensor
*
dx
)
{
int64_t
hight
=
dx
->
dims
()[
0
];
int64_t
width
=
framework
::
product
(
dx
->
dims
())
/
hight
;
const
T
*
dout_data
=
dout
.
data
<
T
>
();
T
*
dx_data
=
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int64_t
h_id
=
0
;
h_id
<
hight
;
++
h_id
)
{
T
*
dst
=
dx_data
+
h_id
*
width
;
size_t
span
=
ref_lod
[
h_id
+
1
]
-
ref_lod
[
h_id
];
for
(
int64_t
w_id
=
0
;
w_id
<
width
;
++
w_id
)
{
T
result
=
0
;
for
(
size_t
k
=
0
;
k
<
span
;
++
k
)
{
size_t
offset
=
(
ref_lod
[
h_id
]
+
k
)
*
width
;
result
+=
dout_data
[
offset
+
w_id
];
}
dst
[
w_id
]
=
result
;
}
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceExpandAsGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
g_out
=
context
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
y
=
context
.
Input
<
framework
::
LoDTensor
>
(
"Y"
);
auto
*
g_x
=
context
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
g_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
SequenceExpandAsGradFunctor
<
DeviceContext
,
T
>
functor
;
functor
(
context
.
template
device_context
<
DeviceContext
>(),
*
g_out
,
y
->
lod
()[
0
],
g_x
);
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
72dd6b37
...
...
@@ -54,6 +54,7 @@ __all__ = [
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'lstm_unit'
,
'reduce_sum'
,
...
...
@@ -2666,6 +2667,71 @@ def sequence_expand(x, y, ref_level=-1, name=None):
return
tmp
def
sequence_expand_as
(
x
,
y
,
name
=
None
):
"""Sequence Expand As Layer. This layer will expand the input variable **x**
according to the zeroth level lod of **y**. Current implementation requires
the level number of Input(Y)'s lod must be 1, and the first dimension of
Input(X) should be equal to the size of Input(Y)'s zeroth level lod, and
lod of Input(X) is not considered.
Following examples will explain how sequence_expand_as works:
.. code-block:: text
* Case 1:
Given a 1-level LoDTensor input(X)
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 3, 6, 7, 8]]
ref_level: 0
then we get 1-level LoDTensor
Out.lod = [[0, 3, 6, 7, 8]]
Out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
Out.dims = [8, 1]
* Case 2:
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]]
ref_level: 0
then we get a common LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
Out.dims = [6, 2]
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): The input variable which is a LoDTensor.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The expanded variable which is a LoDTensor.
Examples:
.. code-block:: python
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_as(x=x, y=y)
"""
helper
=
LayerHelper
(
'sequence_expand_as'
,
input
=
x
,
**
locals
())
dtype
=
helper
.
input_dtype
()
tmp
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
'sequence_expand_as'
,
inputs
=
{
'X'
:
x
,
'Y'
:
y
},
outputs
=
{
'Out'
:
tmp
})
return
tmp
@
templatedoc
()
def
sequence_pad
(
x
,
pad_value
,
maxlen
=
None
):
"""
...
...
python/paddle/fluid/tests/unittests/test_sequence_expand_as.py
0 → 100644
浏览文件 @
72dd6b37
# Copyright (c) 2018 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestSequenceExpandAs
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'sequence_expand_as'
self
.
set_data
()
self
.
compute
()
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
3
,
1
]).
astype
(
'float32'
)
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
8
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
1
,
3
,
4
]]
self
.
inputs
=
{
'X'
:
x_data
,
'Y'
:
(
y_data
,
y_lod
)}
def
compute
(
self
):
x
=
self
.
inputs
[
'X'
]
x_data
,
x_lod
=
x
if
type
(
x
)
==
tuple
else
(
x
,
None
)
y_data
,
y_lod
=
self
.
inputs
[
'Y'
]
assert
len
(
y_lod
)
==
1
and
len
(
y_lod
[
0
])
==
x_data
.
shape
[
0
]
repeats
=
[]
for
i
in
range
(
len
(
y_lod
[
0
])):
repeat_num
=
y_lod
[
0
][
i
]
if
repeat_num
==
0
:
continue
repeats
.
extend
([
i
for
_
in
range
(
repeat_num
)])
out_data
=
x_data
[
repeats
]
self
.
outputs
=
{
'Out'
:
(
out_data
,
y_lod
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
)
class
TestSequenceExpandAsCase1
(
TestSequenceExpandAs
):
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
5
,
1
]).
astype
(
'float32'
)
x_lod
=
[[
2
,
3
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
10
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
2
,
2
,
0
,
3
,
3
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
class
TestSequenceExpandAsCase2
(
TestSequenceExpandAs
):
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
1
,
2
,
2
]).
astype
(
'float32'
)
x_lod
=
[[
1
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
2
,
2
]).
astype
(
'float32'
)
y_lod
=
[[
2
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
if
__name__
==
'__main__'
:
unittest
.
main
()
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