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体验新版 GitCode,发现更多精彩内容 >>
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861b84f5
编写于
1月 10, 2018
作者:
Y
Yibing Liu
提交者:
GitHub
1月 10, 2018
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差异文件
Merge pull request #5300 from kuke/ctc_edit_distance_dev
Add edit distance operator
上级
377424bf
fe0ef91a
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
437 addition
and
0 deletion
+437
-0
paddle/operators/edit_distance_op.cc
paddle/operators/edit_distance_op.cc
+98
-0
paddle/operators/edit_distance_op.cu
paddle/operators/edit_distance_op.cu
+149
-0
paddle/operators/edit_distance_op.h
paddle/operators/edit_distance_op.h
+96
-0
python/paddle/v2/fluid/tests/test_edit_distance_op.py
python/paddle/v2/fluid/tests/test_edit_distance_op.py
+94
-0
未找到文件。
paddle/operators/edit_distance_op.cc
0 → 100644
浏览文件 @
861b84f5
/* 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/edit_distance_op.h"
namespace
paddle
{
namespace
operators
{
class
EditDistanceOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Hyps"
),
"Input(Hyps) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Refs"
),
"Input(Refs) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) shouldn't be null."
);
auto
hyp_dims
=
ctx
->
GetInputDim
(
"Hyps"
);
auto
ref_dims
=
ctx
->
GetInputDim
(
"Refs"
);
PADDLE_ENFORCE
(
hyp_dims
.
size
()
==
2
&&
hyp_dims
[
1
]
==
1
,
"Input(Hyps) must be a 2-D LoDTensor with the 2nd dimension "
"equal to 1."
);
PADDLE_ENFORCE
(
ref_dims
.
size
()
==
2
&&
ref_dims
[
1
]
==
1
,
"Input(Refs) must be a 2-D LoDTensor with the 2nd dimension "
"equal to 1."
);
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"Refs"
));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
proto
::
DataType
::
FP32
,
ctx
.
device_context
());
}
};
class
EditDistanceOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
EditDistanceOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Hyps"
,
"(2-D LoDTensor<int>, 2nd dim. equal to 1) "
"The indices for hypothesis strings."
);
AddInput
(
"Refs"
,
"(2-D LoDTensor<int>, 2nd dim. equal to 1) "
"The indices for reference strings."
);
AddAttr
<
bool
>
(
"normalized"
,
"(bool, default false) Indicated whether to normalize "
"the edit distance by the length of reference string."
)
.
SetDefault
(
false
);
AddOutput
(
"Out"
,
"(2-D Tensor with shape [`batch_size` x 1]) "
"The output edit distances of EditDistance operator."
);
AddComment
(
R"DOC(
EditDistance operator computes the edit distances between a batch of hypothesis
strings and their references.
Edit distance, also called Levenshtein distance, measures how dissimilar two strings
are by counting the minimum number of operations to transform one string into anthor.
Here the operations include insertion, deletion, and substitution. For example,
given hypothesis string A = "kitten" and reference B = "sitting", the edit distance
is 3 for A will be transformed into B at least after two substitutions and one
insertion:
"kitten" -> "sitten" -> "sittin" -> "sitting"
Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with the total
number denoted by `batch_size`, and the separation is specified by the LoD information.
And the `batch_size` reference strings are arranged in order in the same way in the
LoDTensor Input(Refs).
Output(Out) contains the `batch_size` results and each stands for the edit stance
for a pair of strings respectively. If Attr(normalized) is true, the edit distance
will be divided by the length of reference string.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
edit_distance
,
ops
::
EditDistanceOp
,
ops
::
EditDistanceOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
edit_distance
,
ops
::
EditDistanceKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/edit_distance_op.cu
0 → 100644
浏览文件 @
861b84f5
/* 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 <algorithm>
#include "paddle/framework/op_registry.h"
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/gpu_info.h"
namespace
paddle
{
namespace
operators
{
using
platform
::
PADDLE_CUDA_NUM_THREADS
;
template
<
typename
T
>
__global__
void
FillFirstRow
(
T
*
dist
,
const
int
N
)
{
int
idx
=
blockDim
.
x
*
blockIdx
.
x
+
threadIdx
.
x
;
if
(
idx
<
N
+
1
)
{
dist
[
idx
]
=
idx
;
}
}
template
<
typename
T
>
__global__
void
FillFirstColumn
(
T
*
dist
,
const
int
M
,
const
int
N
)
{
int
idx
=
blockDim
.
x
*
blockIdx
.
x
+
threadIdx
.
x
;
if
(
idx
<
M
+
1
)
{
dist
[
idx
*
(
N
+
1
)]
=
idx
;
}
}
template
<
typename
T
>
__global__
void
Levenshtein
(
T
*
dist
,
const
int
*
x1
,
const
int
*
x2
,
const
int
M
,
const
int
N
,
const
int
start
)
{
int
idx
=
blockDim
.
x
*
blockIdx
.
x
+
threadIdx
.
x
;
int
offset
=
N
;
int
index
=
start
+
idx
*
offset
;
int
row
=
index
/
(
N
+
1
);
int
col
=
index
%
(
N
+
1
);
if
(
row
>
0
&&
col
>
0
&&
row
<
M
+
1
&&
col
<
N
+
1
)
{
int
cost
=
x1
[
row
-
1
]
==
x2
[
col
-
1
]
?
0
:
1
;
int
dels
=
dist
[(
row
-
1
)
*
(
N
+
1
)
+
col
]
+
1
;
int
ins
=
dist
[
row
*
(
N
+
1
)
+
col
-
1
]
+
1
;
int
subs
=
dist
[(
row
-
1
)
*
(
N
+
1
)
+
(
col
-
1
)]
+
cost
;
dist
[
index
]
=
min
(
dels
,
min
(
ins
,
subs
));
}
}
template
<
typename
T
>
__global__
void
SetOutput
(
T
*
out
,
const
T
*
dist
,
const
int
M
,
const
int
N
,
bool
normalized
)
{
int
idx
=
blockDim
.
x
*
blockIdx
.
x
+
threadIdx
.
x
;
if
(
idx
==
0
)
{
out
[
0
]
=
normalized
?
dist
[
M
*
(
N
+
1
)
+
N
]
/
N
:
dist
[
M
*
(
N
+
1
)
+
N
];
}
}
template
<
typename
Place
,
typename
T
>
class
EditDistanceGPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
out_t
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
x1_t
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Hyps"
);
auto
*
x2_t
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Refs"
);
auto
normalized
=
ctx
.
Attr
<
bool
>
(
"normalized"
);
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
();
auto
hyp_lod
=
x1_t
->
lod
()[
0
];
auto
ref_lod
=
x2_t
->
lod
()[
0
];
PADDLE_ENFORCE
(
hyp_lod
.
size
()
==
ref_lod
.
size
(),
"Input(Hyps) and Input(Refs) must have the same batch size."
);
for
(
size_t
i
=
1
;
i
<
ref_lod
.
size
();
++
i
)
{
PADDLE_ENFORCE
(
ref_lod
[
i
]
>
ref_lod
[
i
-
1
],
"Reference string %d is empty."
,
i
);
}
auto
num_strs
=
hyp_lod
.
size
()
-
1
;
out_t
->
Resize
({
static_cast
<
int64_t
>
(
num_strs
),
1
});
out_t
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
out
=
out_t
->
data
<
T
>
();
T
distance
=
0.0
;
for
(
size_t
num
=
0
;
num
<
num_strs
;
num
++
)
{
auto
m
=
static_cast
<
int64_t
>
(
hyp_lod
[
num
+
1
]
-
hyp_lod
[
num
]);
auto
n
=
static_cast
<
int64_t
>
(
ref_lod
[
num
+
1
]
-
ref_lod
[
num
]);
if
(
m
==
0
||
n
==
0
)
{
distance
=
std
::
max
(
m
,
n
);
if
(
normalized
)
{
PADDLE_ENFORCE
(
n
>
0
,
"The reference string (#%d) cannot be empty "
"when Attr(normalized) is enabled."
,
n
);
distance
=
distance
/
n
;
}
memory
::
Copy
(
boost
::
get
<
Place
>
(
ctx
.
GetPlace
()),
out
+
num
,
platform
::
CPUPlace
(),
&
distance
,
sizeof
(
T
),
stream
);
}
else
{
framework
::
Tensor
dist_t
;
dist_t
.
Resize
({
m
+
1
,
n
+
1
});
dist_t
.
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
dist
=
dist_t
.
data
<
T
>
();
auto
x1
=
x1_t
->
data
<
int
>
()
+
hyp_lod
[
num
];
auto
x2
=
x2_t
->
data
<
int
>
()
+
ref_lod
[
num
];
FillFirstColumn
<
T
><<<
1
+
m
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
dist
,
m
,
n
);
FillFirstRow
<
T
><<<
1
+
n
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
dist
,
n
);
// Compute the elements of distance matrix in the anti-diagonal diretion
for
(
int64_t
slice
=
2
;
slice
<
m
+
n
+
1
;
++
slice
)
{
int
z_m
=
slice
<
m
+
1
?
0
:
slice
-
m
;
int
z_n
=
slice
<
n
+
1
?
0
:
slice
-
n
;
int
size
=
slice
-
(
z_m
+
z_n
)
+
1
;
// number of elments in the same
// anti-diagonal line to update
// the start index at which computes from
int
start
=
slice
<
n
+
1
?
slice
:
(
z_n
+
1
)
*
(
n
+
1
)
-
1
;
Levenshtein
<
T
><<<
1
+
(
size
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
dist
,
x1
,
x2
,
m
,
n
,
start
);
}
SetOutput
<
T
><<<
1
,
1
,
0
,
stream
>>>
(
out
+
num
,
dist
,
m
,
n
,
normalized
);
}
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
edit_distance
,
ops
::
EditDistanceGPUKernel
<
paddle
::
platform
::
CUDAPlace
,
float
>
);
paddle/operators/edit_distance_op.h
0 → 100644
浏览文件 @
861b84f5
/* 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 <algorithm>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
Place
,
typename
T
>
class
EditDistanceKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
out_t
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
x1_t
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Hyps"
);
auto
*
x2_t
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Refs"
);
auto
normalized
=
ctx
.
Attr
<
bool
>
(
"normalized"
);
auto
hyp_lod
=
x1_t
->
lod
()[
0
];
auto
ref_lod
=
x2_t
->
lod
()[
0
];
PADDLE_ENFORCE
(
hyp_lod
.
size
()
==
ref_lod
.
size
(),
"Input(Hyps) and Input(Refs) must have the same batch size."
);
for
(
size_t
i
=
1
;
i
<
ref_lod
.
size
();
++
i
)
{
PADDLE_ENFORCE
(
ref_lod
[
i
]
>
ref_lod
[
i
-
1
],
"Reference string %d is empty."
,
i
);
}
auto
num_strs
=
hyp_lod
.
size
()
-
1
;
out_t
->
Resize
({
static_cast
<
int64_t
>
(
num_strs
),
1
});
out_t
->
mutable_data
<
float
>
(
ctx
.
GetPlace
());
auto
out
=
out_t
->
data
<
T
>
();
T
distance
=
0.0
;
for
(
size_t
num
=
0
;
num
<
num_strs
;
++
num
)
{
auto
m
=
static_cast
<
int64_t
>
(
hyp_lod
[
num
+
1
]
-
hyp_lod
[
num
]);
auto
n
=
static_cast
<
int64_t
>
(
ref_lod
[
num
+
1
]
-
ref_lod
[
num
]);
if
(
m
==
0
)
{
distance
=
n
;
}
else
if
(
n
==
0
)
{
distance
=
m
;
}
else
{
framework
::
Tensor
dist_t
;
dist_t
.
Resize
({
m
+
1
,
n
+
1
});
dist_t
.
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
dist
=
dist_t
.
data
<
T
>
();
auto
x1
=
x1_t
->
data
<
int
>
()
+
hyp_lod
[
num
];
auto
x2
=
x2_t
->
data
<
int
>
()
+
ref_lod
[
num
];
for
(
int64_t
i
=
0
;
i
<
m
+
1
;
++
i
)
{
dist
[
i
*
(
n
+
1
)]
=
i
;
}
for
(
int64_t
j
=
0
;
j
<
n
+
1
;
++
j
)
{
dist
[
j
]
=
j
;
}
for
(
int64_t
i
=
1
;
i
<
m
+
1
;
++
i
)
{
for
(
int64_t
j
=
1
;
j
<
n
+
1
;
++
j
)
{
int
cost
=
x1
[
i
-
1
]
==
x2
[
j
-
1
]
?
0
:
1
;
int
dels
=
dist
[(
i
-
1
)
*
(
n
+
1
)
+
j
]
+
1
;
int
ins
=
dist
[
i
*
(
n
+
1
)
+
(
j
-
1
)]
+
1
;
int
subs
=
dist
[(
i
-
1
)
*
(
n
+
1
)
+
(
j
-
1
)]
+
cost
;
dist
[
i
*
(
n
+
1
)
+
j
]
=
std
::
min
(
dels
,
std
::
min
(
ins
,
subs
));
}
}
distance
=
dist
[
m
*
(
n
+
1
)
+
n
];
}
if
(
normalized
)
{
PADDLE_ENFORCE
(
n
>
0
,
"The reference string (#%d) cannot be empty "
"when Attr(normalized) is enabled."
,
n
);
distance
=
distance
/
n
;
}
out
[
num
]
=
distance
;
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/tests/test_edit_distance_op.py
0 → 100644
浏览文件 @
861b84f5
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
Levenshtein
(
hyp
,
ref
):
""" Compute the Levenshtein distance between two strings.
:param hyp: hypothesis string in index
:type hyp: list
:param ref: reference string in index
:type ref: list
"""
m
=
len
(
hyp
)
n
=
len
(
ref
)
if
m
==
0
:
return
n
if
n
==
0
:
return
m
dist
=
np
.
zeros
((
m
+
1
,
n
+
1
)).
astype
(
"float32"
)
for
i
in
range
(
0
,
m
+
1
):
dist
[
i
][
0
]
=
i
for
j
in
range
(
0
,
n
+
1
):
dist
[
0
][
j
]
=
j
for
i
in
range
(
1
,
m
+
1
):
for
j
in
range
(
1
,
n
+
1
):
cost
=
0
if
hyp
[
i
-
1
]
==
ref
[
j
-
1
]
else
1
deletion
=
dist
[
i
-
1
][
j
]
+
1
insertion
=
dist
[
i
][
j
-
1
]
+
1
substitution
=
dist
[
i
-
1
][
j
-
1
]
+
cost
dist
[
i
][
j
]
=
min
(
deletion
,
insertion
,
substitution
)
return
dist
[
m
][
n
]
class
TestEditDistanceOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"edit_distance"
normalized
=
False
x1
=
np
.
array
([[
0
,
12
,
3
,
5
,
8
,
2
]]).
astype
(
"int32"
)
x2
=
np
.
array
([[
0
,
12
,
4
,
7
,
8
]]).
astype
(
"int32"
)
x1
=
np
.
transpose
(
x1
)
x2
=
np
.
transpose
(
x2
)
x1_lod
=
[
0
,
1
,
5
]
x2_lod
=
[
0
,
3
,
4
]
num_strs
=
len
(
x1_lod
)
-
1
distance
=
np
.
zeros
((
num_strs
,
1
)).
astype
(
"float32"
)
for
i
in
range
(
0
,
num_strs
):
distance
[
i
]
=
Levenshtein
(
hyp
=
x1
[
x1_lod
[
i
]:
x1_lod
[
i
+
1
]],
ref
=
x2
[
x2_lod
[
i
]:
x2_lod
[
i
+
1
]])
if
normalized
is
True
:
len_ref
=
x2_lod
[
i
+
1
]
-
x2_lod
[
i
]
distance
[
i
]
=
distance
[
i
]
/
len_ref
self
.
attrs
=
{
'normalized'
:
normalized
}
self
.
inputs
=
{
'Hyps'
:
(
x1
,
[
x1_lod
]),
'Refs'
:
(
x2
,
[
x2_lod
])}
self
.
outputs
=
{
'Out'
:
distance
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestEditDistanceOpNormalized
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"edit_distance"
normalized
=
True
x1
=
np
.
array
([[
0
,
10
,
3
,
6
,
5
,
8
,
2
]]).
astype
(
"int32"
)
x2
=
np
.
array
([[
0
,
10
,
4
,
6
,
7
,
8
]]).
astype
(
"int32"
)
x1
=
np
.
transpose
(
x1
)
x2
=
np
.
transpose
(
x2
)
x1_lod
=
[
0
,
1
,
3
,
6
]
x2_lod
=
[
0
,
2
,
3
,
5
]
num_strs
=
len
(
x1_lod
)
-
1
distance
=
np
.
zeros
((
num_strs
,
1
)).
astype
(
"float32"
)
for
i
in
range
(
0
,
num_strs
):
distance
[
i
]
=
Levenshtein
(
hyp
=
x1
[
x1_lod
[
i
]:
x1_lod
[
i
+
1
]],
ref
=
x2
[
x2_lod
[
i
]:
x2_lod
[
i
+
1
]])
if
normalized
is
True
:
len_ref
=
x2_lod
[
i
+
1
]
-
x2_lod
[
i
]
distance
[
i
]
=
distance
[
i
]
/
len_ref
self
.
attrs
=
{
'normalized'
:
normalized
}
self
.
inputs
=
{
'Hyps'
:
(
x1
,
[
x1_lod
]),
'Refs'
:
(
x2
,
[
x2_lod
])}
self
.
outputs
=
{
'Out'
:
distance
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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