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7c671466
编写于
5月 09, 2018
作者:
Y
yangyaming
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3 changed file
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paddle/fluid/operators/sequence_pad_op.cc
paddle/fluid/operators/sequence_pad_op.cc
+131
-0
paddle/fluid/operators/sequence_pad_op.cu
paddle/fluid/operators/sequence_pad_op.cu
+23
-0
paddle/fluid/operators/sequence_pad_op.h
paddle/fluid/operators/sequence_pad_op.h
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paddle/fluid/operators/sequence_pad_op.cc
0 → 100644
浏览文件 @
7c671466
/* 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_pad_op.h"
namespace
paddle
{
namespace
operators
{
class
SequencePadOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequencePadOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequencePadOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Only support 2-D tensor, rank of Input(X) should be 2."
);
auto
out_dims
=
x_dims
;
if
(
ctx
->
IsRuntime
())
{
framework
::
Variable
*
x_var
=
boost
::
get
<
framework
::
Variable
*>
(
ctx
->
GetInputVarPtrs
(
"X"
)[
0
]);
auto
&
x_lod
=
x_var
->
Get
<
LoDTensor
>
().
lod
();
PADDLE_ENFORCE_GE
(
x_lod
.
size
(),
1
,
"Input(X) should be sequences containing lod."
);
auto
last_level_lod
=
x_lod
[
x_lod
.
size
()
-
1
];
size_t
max_len
=
0
;
for
(
size_t
i
=
1
;
i
<
last_level_lod
.
size
();
++
i
)
{
auto
seq_len
=
last_level_lod
[
i
]
-
last_level_lod
[
i
-
1
];
max_len
=
max_len
<
seq_len
?
seq_len
:
max_len
;
}
out_dims
[
0
]
=
max_len
*
(
last_level_lod
.
size
()
-
1
);
}
else
{
framework
::
VarDesc
*
x_desc
=
boost
::
get
<
framework
::
VarDesc
*>
(
ctx
->
GetInputVarPtrs
(
"X"
)[
0
]);
PADDLE_ENFORCE_GE
(
x_desc
->
GetLoDLevel
(),
1
,
"Input(X) should be sequences containing lod."
);
out_dims
[
0
]
=
-
1
;
}
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
class
SequencePadOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SequencePadOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor, default LoDTensor<float>) Input variable which "
"should contain lod information. Length of each sequence would "
"be computed from the most bottom level lod."
);
AddOutput
(
"Out"
,
"(Tensor) Output variable which would be a common tensor "
"without lod. Each sequence would be padded to the maximum "
"length."
);
AddAttr
<
float
>
(
"pad_value"
,
"(float, default 0.0) Value to be padded "
"to the end of each sequence."
);
AddComment
(
R"DOC(
)DOC"
);
}
};
class
SequencePadGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequencePadGradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) of SequencePadGradOp should not be null."
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
framework
::
GradVarName
(
"X"
));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
sequence_pad
,
ops
::
SequencePadOp
,
ops
::
SequencePadOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
sequence_pad_grad
,
ops
::
SequencePadGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_pad
,
ops
::
SequencePadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequencePadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequencePadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequencePadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_pad_grad
,
ops
::
SequencePadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequencePadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequencePadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequencePadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/fluid/operators/sequence_pad_op.cu
0 → 100644
浏览文件 @
7c671466
/* 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_pad_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
sequence_pad
,
ops
::
SequencePadOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
sequence_pad_grad
,
ops
::
SequencePadGradOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
paddle/fluid/operators/sequence_pad_op.h
0 → 100644
浏览文件 @
7c671466
/* 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 "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
using
LoD
=
framework
::
LoD
;
// @TODO clean code
template
<
typename
DeviceContext
,
typename
T
>
class
SequencePadOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x_ptr
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out_ptr
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
out_ptr
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
pad_value
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"pad_value"
));
math
::
SetConstant
<
DeviceContext
,
T
>
set_func
;
set_func
(
ctx
.
template
device_context
<
DeviceContext
>(),
out_ptr
,
pad_value
);
auto
&
x_lod
=
x_ptr
->
lod
();
auto
&
x_last_level_lod
=
x_lod
[
x_lod
.
size
()
-
1
];
auto
seq_num
=
x_last_level_lod
.
size
()
-
1
;
auto
max_len
=
out_ptr
->
dims
()[
0
]
/
seq_num
;
PADDLE_ENFORCE_EQ
(
max_len
*
seq_num
,
out_ptr
->
dims
()[
0
],
"First dimension of `Out` should be equal to "
"maximum length mulplied by sequence number."
);
for
(
size_t
i
=
1
;
i
<
x_last_level_lod
.
size
();
++
i
)
{
auto
x_start
=
x_last_level_lod
[
i
-
1
];
auto
x_end
=
x_last_level_lod
[
i
];
auto
out_start
=
(
i
-
1
)
*
max_len
;
auto
out_end
=
out_start
+
(
x_end
-
x_start
);
auto
x_sub_tensor
=
x_ptr
->
Slice
(
x_start
,
x_end
);
auto
out_sub_tensor
=
out_ptr
->
Slice
(
out_start
,
out_end
);
framework
::
TensorCopy
(
x_sub_tensor
,
ctx
.
GetPlace
(),
&
out_sub_tensor
);
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SequencePadGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x_ptr
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
g_out_ptr
=
ctx
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
g_x_ptr
=
ctx
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
math
::
SetConstant
<
DeviceContext
,
T
>
set_func
;
set_func
(
ctx
.
template
device_context
<
DeviceContext
>(),
g_x_ptr
,
static_cast
<
T
>
(
0
));
auto
&
x_lod
=
x_ptr
->
lod
();
auto
&
x_last_level_lod
=
x_lod
[
x_lod
.
size
()
-
1
];
auto
seq_num
=
x_last_level_lod
.
size
()
-
1
;
int64_t
max_len
=
g_out_ptr
->
dims
()[
0
]
/
seq_num
;
PADDLE_ENFORCE_EQ
(
max_len
*
seq_num
,
g_out_ptr
->
dims
()[
0
],
"First dimension of `Out` should be equal to "
"maximum length mulplied by sequence number."
);
for
(
size_t
i
=
1
;
i
<
x_last_level_lod
.
size
();
++
i
)
{
auto
x_start
=
x_last_level_lod
[
i
-
1
];
auto
x_end
=
x_last_level_lod
[
i
];
auto
out_start
=
(
i
-
1
)
*
max_len
;
auto
out_end
=
out_start
+
(
x_end
-
x_start
);
auto
g_out_sub
=
g_out_ptr
->
Slice
(
out_start
,
out_end
);
auto
g_x_sub
=
g_x_ptr
->
Slice
(
x_start
,
x_end
);
framework
::
TensorCopy
(
g_x_sub
,
ctx
.
GetPlace
(),
&
g_out_sub
);
}
}
};
}
// namespace operators
}
// namespace paddle
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