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0ab2c436
编写于
10月 23, 2017
作者:
C
chengduoZH
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差异文件
Add sequence_project_functor
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ce960575
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4
隐藏空白更改
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4 changed file
with
234 addition
and
0 deletion
+234
-0
paddle/operators/math/CMakeLists.txt
paddle/operators/math/CMakeLists.txt
+2
-0
paddle/operators/math/sequence_project.cc
paddle/operators/math/sequence_project.cc
+26
-0
paddle/operators/math/sequence_project.cu
paddle/operators/math/sequence_project.cu
+28
-0
paddle/operators/math/sequence_project.h
paddle/operators/math/sequence_project.h
+178
-0
未找到文件。
paddle/operators/math/CMakeLists.txt
浏览文件 @
0ab2c436
...
...
@@ -7,6 +7,7 @@ if(WITH_GPU)
nv_library
(
cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator
)
nv_library
(
pooling SRCS pooling.cc pooling.cu DEPS device_context
)
nv_library
(
vol2col SRCS vol2col.cc vol2col.cu DEPS device_context
)
nv_library
(
sequence_project SRCS sequence_project.cc sequence_project.cu DEPS device_context
)
else
()
cc_library
(
math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator
)
cc_library
(
selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function
)
...
...
@@ -14,6 +15,7 @@ else()
cc_library
(
cross_entropy SRCS cross_entropy.cc DEPS operator
)
cc_library
(
pooling SRCS pooling.cc DEPS device_context
)
cc_library
(
vol2col SRCS vol2col.cc DEPS device_context
)
nv_library
(
sequence_project SRCS sequence_project.cc DEPS device_context
)
endif
()
cc_test
(
math_function_test SRCS math_function_test.cc DEPS math_function tensor
)
...
...
paddle/operators/math/sequence_project.cc
0 → 100644
浏览文件 @
0ab2c436
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/sequence_project.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
class
SequenceProjectFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
SequenceProjectFunctor
<
platform
::
CPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/sequence_project.cu
0 → 100644
浏览文件 @
0ab2c436
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#define EIGEN_USE_GPU
#include "paddle/operators/math/sequence_project.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
class
SequenceProjectFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
SequenceProjectFunctor
<
platform
::
GPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/sequence_project.h
0 → 100644
浏览文件 @
0ab2c436
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/eigen.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
// template <typename T, int MajorType = Eigen::RowMajor,
// typename IndexType = Eigen::DenseIndex>
// using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
/*
* \brief Converts the feature data of four dimensions(CDHW) into a colData of
* seven dimensions in the Vol2ColFunctor calculation,
* And in the Col2VolFunctor calculation, it is reversed.
*
* \param volData Vol data.
* \param volShape The shape of volData,
* [input_channels, input_depth, input_height, input_width].
* \param colData Column data.
* \param colShape The shape of colData.
*
* The shape of colData is:
* [input_channels, filter_depth, filter_height, filter_width, output_depth,
* output_height, output_width]
* So, it is easy to reshape into a convolution matrix for convolution
* calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the height is equal
* input_channels * filter_depth * filter_height * filter_width, and the width
* is equal output_depth * output_height * output_width.
*
* Reshape:
* shape of colData shape of convolution matrix
* [input_channels,
* filter_depth,
* filter_height,
* filter_width, ======> [height, width]
* output_depth,
* output_height,
* output_width]
*
* \note The caller needs to ensure that volShape.inputChannels is equal to
* colShape.inputChannels.
*/
template
<
typename
Place
,
typename
T
>
class
SequenceProjectFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
LoDTensor
*&
in
,
const
framework
::
LoDTensor
*
padding_data
,
framework
::
LoDTensor
*
col
,
bool
padding_trainable
,
int
context_start
,
int
context_length
,
int
context_stride
,
int
up_pad
,
int
down_pad
)
{
auto
lod_level_0
=
in
->
lod
()[
0
];
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
Place
,
float
>
im2col_ocf
;
int
input_row_begin
,
input_row_end
;
int
sequence_height
,
sequence_width
;
sequence_width
=
in
->
dims
()[
1
];
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod_level_0
.
size
())
-
1
;
++
i
)
{
input_row_begin
=
(
context_start
>
0
)
?
static_cast
<
int
>
(
lod_level_0
[
i
])
+
context_start
:
static_cast
<
int
>
(
lod_level_0
[
i
]);
input_row_end
=
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]);
framework
::
Tensor
out_t
=
col
->
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
sequence_height
=
static_cast
<
int
>
(
out_t
.
dims
()[
0
]);
std
::
vector
<
int64_t
>
output_shape
(
{
sequence_height
,
1
,
1
,
context_length
,
sequence_width
});
// output_height, output_width,
// input_channels, filter_height, filter_width
out_t
.
Resize
(
framework
::
make_ddim
(
output_shape
));
if
(
input_row_begin
<
input_row_end
)
{
framework
::
Tensor
in_t
=
in
->
Slice
(
input_row_begin
,
input_row_end
);
std
::
vector
<
int64_t
>
input_shape
(
{
1
,
input_row_end
-
input_row_begin
,
sequence_width
});
// input_channels, input_height, input_width
in_t
.
Resize
(
framework
::
make_ddim
(
input_shape
));
im2col_ocf
(
context
,
in_t
,
out_t
,
/*stride_height*/
context_stride
,
/*stride_width*/
0
,
up_pad
,
down_pad
);
}
if
(
padding_trainable
)
{
// add up trainable data
out_t
.
Resize
(
framework
::
make_ddim
(
{
sequence_height
*
context_length
,
sequence_width
}));
if
(
up_pad
>
0
)
{
// add up pad
int
padding_rows
=
std
::
min
(
up_pad
,
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]
-
lod_level_0
[
i
]));
for
(
int
k
=
0
;
k
<
padding_rows
;
++
k
)
{
int
padding_size
=
k
+
context_length
<
up_pad
?
context_length
:
up_pad
-
k
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
k
*
context_length
,
k
*
context_length
+
padding_size
);
framework
::
Tensor
w_sub
=
padding_data
->
Slice
(
k
,
k
+
padding_size
);
// in this block, using EigenVector<T>::Flatten is ok too.
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
out_t_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
w_sub_e
;
}
}
if
(
down_pad
>
0
)
{
// add down pad
int
down_pad_begin_row
=
std
::
max
(
0
,
(
sequence_height
-
context_start
-
context_length
)
+
1
)
+
1
;
int
padding_begin
=
std
::
max
(
0
,
context_start
-
sequence_height
);
int
padding_size
=
sequence_height
-
context_start
>=
context_length
?
1
:
context_length
-
(
sequence_height
-
context_start
);
if
(
context_start
>=
sequence_height
)
padding_size
=
context_length
;
int
padding_idx
=
padding_begin
;
for
(
int
t
=
0
;
t
+
down_pad_begin_row
<=
sequence_height
;
++
t
,
++
padding_size
)
{
if
(
context_start
>=
sequence_height
)
padding_size
=
context_length
;
if
(
padding_size
>
context_length
)
{
padding_size
=
context_length
;
padding_idx
++
;
}
if
(
padding_begin
>
0
||
sequence_height
==
context_start
)
padding_idx
=
padding_begin
+
t
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
(
down_pad_begin_row
+
t
)
*
context_length
-
padding_size
,
(
down_pad_begin_row
+
t
)
*
context_length
);
framework
::
Tensor
w_sub
=
padding_data
->
Slice
(
up_pad
+
padding_idx
,
up_pad
+
padding_idx
+
padding_size
);
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
out_t_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
w_sub_e
;
}
}
}
out_t
.
Resize
(
framework
::
make_ddim
(
{
sequence_height
,
context_length
*
sequence_width
}));
}
}
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
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