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体验新版 GitCode,发现更多精彩内容 >>
提交
741046e8
编写于
6月 06, 2018
作者:
G
guosheng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix and enhance beam_search_op and beam_searc_decode_op to be comparable with python beam search
上级
01fdf17e
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
243 addition
and
62 deletion
+243
-62
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+2
-2
paddle/fluid/operators/beam_search_decode_op.cc
paddle/fluid/operators/beam_search_decode_op.cc
+31
-11
paddle/fluid/operators/beam_search_decode_op.h
paddle/fluid/operators/beam_search_decode_op.h
+110
-6
paddle/fluid/operators/beam_search_op.cc
paddle/fluid/operators/beam_search_op.cc
+62
-21
paddle/fluid/operators/beam_search_op.h
paddle/fluid/operators/beam_search_op.h
+30
-16
paddle/fluid/operators/tensor_array_read_write_op.cc
paddle/fluid/operators/tensor_array_read_write_op.cc
+2
-3
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+6
-3
未找到文件。
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
741046e8
...
...
@@ -288,8 +288,8 @@ set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
cc_test
(
gather_test SRCS gather_test.cc DEPS tensor
)
cc_test
(
scatter_test SRCS scatter_test.cc DEPS tensor
)
cc_test
(
beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor
)
cc_test
(
beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op
)
#
cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor)
#
cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op)
cc_test
(
strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory
)
cc_test
(
save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op
)
cc_test
(
save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op
)
...
...
paddle/fluid/operators/beam_search_decode_op.cc
浏览文件 @
741046e8
...
...
@@ -12,8 +12,10 @@ 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/beam_search_decode_op.h"
#include
<algorithm>
#include <string>
#include "paddle/fluid/operators/beam_search_decode_op.h"
#include "paddle/fluid/platform/device_context.h"
namespace
paddle
{
...
...
@@ -22,8 +24,11 @@ namespace operators {
struct
BeamSearchDecodeFunctor
{
BeamSearchDecodeFunctor
(
const
LoDTensorArray
&
step_ids
,
const
LoDTensorArray
&
step_scores
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
:
step_ids_origin_
(
step_ids
),
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
,
size_t
beam_size
,
int
end_id
)
:
beam_size_
(
beam_size
),
end_id_
(
end_id
),
step_ids_origin_
(
step_ids
),
step_scores_origin_
(
step_scores
),
id_tensor_
(
id_tensor
),
score_tensor_
(
score_tensor
)
{
...
...
@@ -67,6 +72,8 @@ struct BeamSearchDecodeFunctor {
void
operator
()()
const
;
bool
tensor_on_gpu_
;
size_t
beam_size_
;
int
end_id_
;
const
LoDTensorArray
&
step_ids_origin_
;
const
LoDTensorArray
&
step_scores_origin_
;
LoDTensorArray
step_ids_
=
LoDTensorArray
();
...
...
@@ -77,14 +84,18 @@ struct BeamSearchDecodeFunctor {
template
<
typename
T
>
void
BeamSearchDecodeFunctor
::
operator
()()
const
{
BeamSearchDecoder
<
T
>
beam_search_decoder
;
BeamSearchDecoder
<
T
>
beam_search_decoder
(
beam_size_
,
end_id_
)
;
// Check if the tensor is on GPU. If so, use the CPU copy instead
if
(
tensor_on_gpu_
)
{
beam_search_decoder
.
PackAllSteps
(
step_ids_
,
step_scores_
,
id_tensor_
,
score_tensor_
);
// beam_search_decoder.PackAllSteps(step_ids_, step_scores_, id_tensor_,
// score_tensor_);
beam_search_decoder
.
Backtrace
(
step_ids_
,
step_scores_
,
id_tensor_
,
score_tensor_
);
}
else
{
beam_search_decoder
.
PackAllSteps
(
step_ids_origin_
,
step_scores_origin_
,
id_tensor_
,
score_tensor_
);
// beam_search_decoder.PackAllSteps(step_ids_origin_, step_scores_origin_,
// id_tensor_, score_tensor_);
beam_search_decoder
.
Backtrace
(
step_ids_origin_
,
step_scores_origin_
,
id_tensor_
,
score_tensor_
);
}
}
...
...
@@ -122,13 +133,17 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
"Level of LodTensor should be 2"
);
}
size_t
beam_size
=
ctx
.
Attr
<
int
>
(
"beam_size"
);
int
end_id
=
ctx
.
Attr
<
int
>
(
"end_id"
);
// prepare output
LoDTensor
*
sentenceIds
=
ctx
.
Output
<
LoDTensor
>
(
"SentenceIds"
);
LoDTensor
*
sentenceScores
=
ctx
.
Output
<
LoDTensor
>
(
"SentenceScores"
);
framework
::
VisitDataType
(
framework
::
ToDataType
(
scores
->
at
(
0
).
type
()),
BeamSearchDecodeFunctor
(
*
ids
,
*
scores
,
sentenceIds
,
sentenceScores
));
BeamSearchDecodeFunctor
(
*
ids
,
*
scores
,
sentenceIds
,
sentenceScores
,
beam_size
,
end_id
));
}
};
...
...
@@ -147,6 +162,9 @@ class BeamSearchDecodeOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddOutput
(
"SentenceScores"
,
"(LodTensor)"
"All possible result sentences of word scores"
);
AddAttr
<
int
>
(
"beam_size"
,
"beam size for beam search"
);
AddAttr
<
int
>
(
"end_id"
,
"the token id which indicates the end of a sequence"
);
AddComment
(
R"DOC(
Pack the result of Beam search op into SentenceIds and SentenceScores.
)DOC"
);
...
...
@@ -172,10 +190,12 @@ class BeamSearchDecodeInferVarType : public framework::VarTypeInference {
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
for
(
auto
&
o
:
op_desc
.
Output
(
"SentenceIds"
))
{
block
->
Var
(
o
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
auto
&
sentence_ids
=
block
->
FindRecursiveOrCreateVar
(
o
);
sentence_ids
.
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
}
for
(
auto
&
o
:
op_desc
.
Output
(
"SentenceScores"
))
{
block
->
Var
(
o
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
auto
&
sentence_scores
=
block
->
FindRecursiveOrCreateVar
(
o
);
sentence_scores
.
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
}
}
};
...
...
paddle/fluid/operators/beam_search_decode_op.h
浏览文件 @
741046e8
...
...
@@ -14,7 +14,9 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
...
...
@@ -72,6 +74,9 @@ using SentenceVector = std::vector<Sentence<T>>;
template
<
typename
T
>
struct
BeamSearchDecoder
{
BeamSearchDecoder
(
size_t
beam_size
,
int
end_id
)
:
beam_size_
(
beam_size
),
end_id_
(
end_id
)
{}
/**
* make a BeamNode and all it's related prefix BeanNode into a Sentence.
*/
...
...
@@ -103,7 +108,8 @@ struct BeamSearchDecoder {
*/
void
ConvertSentenceVectorToLodTensor
(
std
::
vector
<
SentenceVector
<
T
>>
sentence_vector_list
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
;
LoDTensor
*
score_tensor
,
bool
reverse
=
false
,
bool
sort_by_score
=
true
)
const
;
/**
* Pack all steps of id/score LodTensor into sentence LoDTensor
...
...
@@ -121,6 +127,13 @@ struct BeamSearchDecoder {
void
PackAllSteps
(
const
LoDTensorArray
&
step_ids
,
const
LoDTensorArray
&
step_scores
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
;
void
Backtrace
(
const
LoDTensorArray
&
step_ids
,
const
LoDTensorArray
&
step_scores
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
;
size_t
beam_size_
;
int
end_id_
;
};
template
<
typename
T
>
...
...
@@ -200,7 +213,7 @@ std::vector<BeamNodeVector<T>> BeamSearchDecoder<T>::PackTwoSteps(
template
<
typename
T
>
void
BeamSearchDecoder
<
T
>::
ConvertSentenceVectorToLodTensor
(
std
::
vector
<
SentenceVector
<
T
>>
sentence_vector_list
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
{
LoDTensor
*
score_tensor
,
bool
reverse
,
bool
sort_by_score
)
const
{
size_t
src_num
=
sentence_vector_list
.
size
();
PADDLE_ENFORCE_NE
(
src_num
,
0
,
"src_num should not be 0"
);
...
...
@@ -211,11 +224,29 @@ void BeamSearchDecoder<T>::ConvertSentenceVectorToLodTensor(
std
::
vector
<
T
>
score_data
;
for
(
size_t
src_idx
=
0
;
src_idx
<
src_num
;
++
src_idx
)
{
if
(
sort_by_score
)
{
sort
(
sentence_vector_list
[
src_idx
].
begin
(),
sentence_vector_list
[
src_idx
].
end
(),
[
reverse
](
const
Sentence
<
T
>&
a
,
const
Sentence
<
T
>&
b
)
{
if
(
reverse
)
return
a
.
scores
.
front
()
>
b
.
scores
.
front
();
else
return
a
.
scores
.
back
()
>
b
.
scores
.
back
();
});
}
for
(
Sentence
<
T
>&
sentence
:
sentence_vector_list
[
src_idx
])
{
id_data
.
insert
(
id_data
.
end
(),
sentence
.
word_ids
.
begin
(),
sentence
.
word_ids
.
end
());
score_data
.
insert
(
score_data
.
end
(),
sentence
.
scores
.
begin
(),
sentence
.
scores
.
end
());
if
(
reverse
)
{
id_data
.
insert
(
id_data
.
end
(),
sentence
.
word_ids
.
rbegin
(),
sentence
.
word_ids
.
rend
());
score_data
.
insert
(
score_data
.
end
(),
sentence
.
scores
.
rbegin
(),
sentence
.
scores
.
rend
());
}
else
{
id_data
.
insert
(
id_data
.
end
(),
sentence
.
word_ids
.
begin
(),
sentence
.
word_ids
.
end
());
score_data
.
insert
(
score_data
.
end
(),
sentence
.
scores
.
begin
(),
sentence
.
scores
.
end
());
}
sentence_level_lod
.
push_back
(
sentence_level_lod
.
back
()
+
sentence
.
word_ids
.
size
());
}
...
...
@@ -278,5 +309,78 @@ void BeamSearchDecoder<T>::PackAllSteps(const LoDTensorArray& step_ids,
score_tensor
);
}
template
<
typename
T
>
void
BeamSearchDecoder
<
T
>::
Backtrace
(
const
LoDTensorArray
&
step_ids
,
const
LoDTensorArray
&
step_scores
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
{
PADDLE_ENFORCE
(
!
step_ids
.
empty
(),
"step num should be larger than 0"
);
PADDLE_ENFORCE_EQ
(
step_ids
.
size
(),
step_scores
.
size
(),
"step_ids and step_scores should be the same"
);
const
size_t
step_num
=
step_ids
.
size
();
const
size_t
src_num
=
step_ids
.
at
(
0
).
lod
().
at
(
kSourceLevel
).
size
()
-
1
;
std
::
vector
<
SentenceVector
<
T
>>
sentence_vector_list
(
src_num
,
SentenceVector
<
T
>
(
beam_size_
));
std
::
vector
<
std
::
vector
<
size_t
>>
prefix_idx_vector_list
(
src_num
,
std
::
vector
<
size_t
>
());
for
(
int
step_id
=
step_num
-
1
;
step_id
>=
0
;
--
step_id
)
{
auto
&
cur_ids
=
step_ids
.
at
(
step_id
);
auto
&
cur_scores
=
step_scores
.
at
(
step_id
);
for
(
size_t
src_idx
=
0
;
src_idx
<
src_num
;
++
src_idx
)
{
// for each source sentence
auto
&
sentence_vector
=
sentence_vector_list
.
at
(
src_idx
);
auto
&
prefix_idx_vector
=
prefix_idx_vector_list
.
at
(
src_idx
);
size_t
src_prefix_start
=
cur_ids
.
lod
().
at
(
kSourceLevel
)[
src_idx
];
size_t
src_prefix_end
=
cur_ids
.
lod
().
at
(
kSourceLevel
)[
src_idx
+
1
];
if
(
prefix_idx_vector
.
empty
())
{
// be finished and pruned at this step
// or the last time step
for
(
size_t
prefix_idx
=
src_prefix_start
;
prefix_idx
<
src_prefix_end
;
++
prefix_idx
)
{
size_t
candidate_start
=
cur_ids
.
lod
().
at
(
kSentenceLevel
)[
prefix_idx
];
size_t
candidate_end
=
cur_ids
.
lod
().
at
(
kSentenceLevel
)[
prefix_idx
+
1
];
for
(
size_t
candidate_idx
=
candidate_start
;
candidate_idx
<
candidate_end
;
++
candidate_idx
)
{
prefix_idx_vector
.
push_back
(
prefix_idx
);
size_t
idx
=
prefix_idx_vector
.
size
()
-
1
;
auto
cur_id
=
cur_ids
.
data
<
int64_t
>
()[
candidate_idx
];
auto
cur_score
=
cur_scores
.
data
<
T
>
()[
candidate_idx
];
sentence_vector
.
at
(
idx
).
word_ids
.
push_back
(
cur_id
);
sentence_vector
.
at
(
idx
).
scores
.
push_back
(
cur_score
);
}
}
}
else
{
// use prefix_idx_vector to backtrace
size_t
src_candidate_start
=
cur_ids
.
lod
().
at
(
kSentenceLevel
)[
src_prefix_start
];
size_t
prefix_idx
=
src_prefix_start
;
size_t
candidate_num
=
cur_ids
.
lod
().
at
(
kSentenceLevel
)[
prefix_idx
+
1
]
-
cur_ids
.
lod
().
at
(
kSentenceLevel
)[
prefix_idx
];
for
(
size_t
idx
=
0
;
idx
<
prefix_idx_vector
.
size
();
++
idx
)
{
auto
candidate_idx
=
prefix_idx_vector
.
at
(
idx
);
auto
cur_id
=
cur_ids
.
data
<
int64_t
>
()[
candidate_idx
];
auto
cur_score
=
cur_scores
.
data
<
T
>
()[
candidate_idx
];
if
(
cur_id
!=
end_id_
||
sentence_vector
.
at
(
idx
).
word_ids
.
empty
())
{
// to skip redundant end tokens
sentence_vector
.
at
(
idx
).
word_ids
.
push_back
(
cur_id
);
sentence_vector
.
at
(
idx
).
scores
.
push_back
(
cur_score
);
}
while
(
src_candidate_start
+
candidate_num
<=
candidate_idx
)
{
// search the corresponding prefix
prefix_idx
++
;
candidate_num
+=
cur_ids
.
lod
().
at
(
kSentenceLevel
)[
prefix_idx
+
1
]
-
cur_ids
.
lod
().
at
(
kSentenceLevel
)[
prefix_idx
];
}
prefix_idx_vector
.
at
(
idx
)
=
prefix_idx
;
}
}
}
}
ConvertSentenceVectorToLodTensor
(
sentence_vector_list
,
id_tensor
,
score_tensor
,
true
,
true
);
}
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/beam_search_op.cc
浏览文件 @
741046e8
...
...
@@ -12,25 +12,27 @@ 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/beam_search_op.h"
#include <algorithm>
#include <limits>
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/beam_search_op.h"
namespace
paddle
{
namespace
operators
{
void
BeamSearch
::
operator
()(
const
framework
::
LoDTensor
&
pre_ids
,
const
framework
::
LoDTensor
&
pre_scores
,
framework
::
LoDTensor
*
selected_ids
,
framework
::
LoDTensor
*
selected_scores
)
{
auto
abs_lod
=
framework
::
ToAbsOffset
(
ids_
->
lod
());
auto
&
high_level
=
abs_lod
[
lod_level_
];
auto
items
=
SelectTopBeamSizeItems
();
auto
items
=
SelectTopBeamSizeItems
(
pre_ids
,
pre_scores
);
auto
selected_items
=
ToMap
(
items
,
high_level
.
back
());
VLOG
(
3
)
<<
"selected_items:"
;
for
(
size_t
i
=
0
;
i
<
selected_items
.
size
();
++
i
)
{
...
...
@@ -39,7 +41,8 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
VLOG
(
3
)
<<
ItemToString
(
item
);
}
}
PruneEndidCandidates
(
pre_ids
,
&
selected_items
);
PruneEndBeams
(
pre_ids
,
&
selected_items
);
// calculate the output tensor's height
size_t
num_instances
=
std
::
accumulate
(
std
::
begin
(
selected_items
),
std
::
end
(
selected_items
),
0
,
...
...
@@ -61,12 +64,6 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
size_t
low_offset
=
0
;
for
(
auto
&
items
:
selected_items
)
{
low_level
.
push_back
(
low_offset
);
sort
(
items
.
begin
(),
items
.
end
(),
[](
const
Item
&
a
,
const
Item
&
b
)
{
if
(
a
.
offset
<
b
.
offset
)
{
return
true
;
}
return
a
.
id
<
b
.
id
;
});
for
(
auto
&
item
:
items
)
{
ids_data
[
low_offset
]
=
item
.
id
;
scores_data
[
low_offset
]
=
item
.
score
;
...
...
@@ -86,6 +83,33 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
selected_scores
->
set_lod
(
lod
);
}
void
BeamSearch
::
PruneEndBeams
(
const
framework
::
LoDTensor
&
pre_ids
,
std
::
vector
<
std
::
vector
<
Item
>>
*
items
)
{
auto
*
pre_ids_data
=
pre_ids
.
data
<
int64_t
>
();
auto
abs_lod
=
framework
::
ToAbsOffset
(
ids_
->
lod
());
auto
&
high_level
=
abs_lod
[
lod_level_
];
for
(
size_t
src_idx
=
0
;
src_idx
<
high_level
.
size
();
++
src_idx
)
{
size_t
src_prefix_start
=
high_level
[
src_idx
];
size_t
src_prefix_end
=
high_level
[
src_idx
+
1
];
bool
finish_flag
=
true
;
for
(
size_t
offset
=
src_prefix_start
;
offset
<
src_prefix_end
;
offset
++
)
{
for
(
auto
&
item
:
items
->
at
(
offset
))
{
if
(
item
.
id
!=
static_cast
<
size_t
>
(
end_id_
)
||
pre_ids_data
[
offset
]
!=
end_id_
)
{
finish_flag
=
false
;
break
;
}
}
if
(
!
finish_flag
)
break
;
}
if
(
finish_flag
)
{
// all branchs of the beam (source sentence) end and
// prune this beam
for
(
size_t
offset
=
src_prefix_start
;
offset
<
src_prefix_end
;
offset
++
)
items
->
at
(
offset
).
clear
();
}
}
}
int
BeamSearch
::
PruneEndidCandidates
(
const
framework
::
LoDTensor
&
pre_ids
,
std
::
vector
<
std
::
vector
<
Item
>>
*
items
)
{
auto
*
pre_ids_data
=
pre_ids
.
data
<
int64_t
>
();
...
...
@@ -115,13 +139,14 @@ std::vector<std::vector<BeamSearch::Item>> BeamSearch::ToMap(
return
result
;
}
std
::
vector
<
std
::
vector
<
BeamSearch
::
Item
>>
BeamSearch
::
SelectTopBeamSizeItems
()
{
std
::
vector
<
std
::
vector
<
BeamSearch
::
Item
>>
BeamSearch
::
SelectTopBeamSizeItems
(
const
framework
::
LoDTensor
&
pre_ids
,
const
framework
::
LoDTensor
&
pre_scores
)
{
std
::
vector
<
std
::
vector
<
Item
>>
result
;
std
::
vector
<
Item
>
items
;
// for each source sentence, select the top beam_size items across all
// candidate sets.
while
(
NextItemSet
(
&
items
))
{
while
(
NextItemSet
(
pre_ids
,
pre_scores
,
&
items
))
{
std
::
nth_element
(
std
::
begin
(
items
),
std
::
begin
(
items
)
+
beam_size_
,
std
::
end
(
items
),
[](
const
Item
&
a
,
const
Item
&
b
)
{
// TODO(superjom) make score's comparation customizable.
...
...
@@ -146,7 +171,9 @@ BeamSearch::SelectTopBeamSizeItems() {
}
// the candidates of a source
bool
BeamSearch
::
NextItemSet
(
std
::
vector
<
BeamSearch
::
Item
>
*
items
)
{
bool
BeamSearch
::
NextItemSet
(
const
framework
::
LoDTensor
&
pre_ids
,
const
framework
::
LoDTensor
&
pre_scores
,
std
::
vector
<
BeamSearch
::
Item
>
*
items
)
{
if
(
sent_offset_
>=
ids_
->
NumElements
(
lod_level_
))
{
return
false
;
}
...
...
@@ -164,14 +191,25 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
instance_dim
*=
ids
.
dims
()[
i
];
}
auto
*
pre_ids_data
=
pre_ids
.
data
<
int64_t
>
();
auto
*
pre_scores_data
=
pre_scores
.
data
<
float
>
();
items
->
clear
();
items
->
reserve
(
framework
::
product
(
ids
.
dims
()));
for
(
size_t
offset
=
abs_lod
[
lod_level_
][
sent_offset_
];
offset
<
abs_lod
[
lod_level_
][
sent_offset_
+
1
];
offset
++
)
{
for
(
size_t
d
=
0
;
d
<
instance_dim
;
d
++
)
{
const
size_t
dim_offset
=
offset
*
instance_dim
+
d
;
items
->
emplace_back
(
offset
,
ids_data
[
dim_offset
],
scores_data
[
dim_offset
]);
auto
pre_id
=
pre_ids_data
[
offset
];
auto
pre_score
=
pre_scores_data
[
offset
];
if
(
pre_id
==
end_id_
)
{
// Allocate all probability mass to eos_id for finished branchs and the
// other
// candidate ids can be ignored.
items
->
emplace_back
(
offset
,
end_id_
,
pre_score
);
}
else
{
for
(
size_t
d
=
0
;
d
<
instance_dim
;
d
++
)
{
const
size_t
dim_offset
=
offset
*
instance_dim
+
d
;
items
->
emplace_back
(
offset
,
ids_data
[
dim_offset
],
scores_data
[
dim_offset
]);
}
}
}
...
...
@@ -199,7 +237,8 @@ class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void
Make
()
override
{
// inputs and outputs stored in proto
AddInput
(
"pre_ids"
,
"ids in previous step"
);
AddInput
(
"pre_ids"
,
"ids in the previous step"
);
AddInput
(
"pre_scores"
,
"accumulated scores in the previous step"
);
AddInput
(
"ids"
,
"a LoDTensor of shape of [None,k]"
);
AddInput
(
"scores"
,
"a LoDTensor that has the same shape and LoD with `ids`"
);
...
...
@@ -253,10 +292,12 @@ class BeamSearchInferVarType : public framework::VarTypeInference {
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
for
(
auto
&
o
:
op_desc
.
Output
(
"selected_ids"
))
{
block
->
Var
(
o
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
auto
&
selected_ids
=
block
->
FindRecursiveOrCreateVar
(
o
);
selected_ids
.
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
}
for
(
auto
&
o
:
op_desc
.
Output
(
"selected_scores"
))
{
block
->
Var
(
o
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
auto
&
selected_scores
=
block
->
FindRecursiveOrCreateVar
(
o
);
selected_scores
.
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR
);
}
}
};
...
...
paddle/fluid/operators/beam_search_op.h
浏览文件 @
741046e8
...
...
@@ -132,6 +132,7 @@ class BeamSearch {
* that means no candidates is provided, and the task will stop running.
*/
void
operator
()(
const
framework
::
LoDTensor
&
pre_ids
,
const
framework
::
LoDTensor
&
pre_scores
,
framework
::
LoDTensor
*
selected_ids
,
framework
::
LoDTensor
*
selected_scores
);
/*
...
...
@@ -152,6 +153,14 @@ class BeamSearch {
};
protected:
/*
* Prune the source sentences all branchs finished, and it is optional.
* Pruning must one step later than finishing, since the end tokens
* must be writed out. Also the finished branchs with top 1 score can
* be pruned.
*/
void
PruneEndBeams
(
const
framework
::
LoDTensor
&
pre_ids
,
std
::
vector
<
std
::
vector
<
Item
>>*
items
);
/*
* Delete all the records that follows the end token.
*/
...
...
@@ -160,7 +169,7 @@ class BeamSearch {
/*
* Transform the items into a map whose key is offset, value is the items.
* NOTE low performance
* NOTE low performance
.
*/
std
::
vector
<
std
::
vector
<
Item
>>
ToMap
(
const
std
::
vector
<
std
::
vector
<
Item
>>&
inputs
,
size_t
element_num
);
...
...
@@ -168,12 +177,16 @@ class BeamSearch {
/*
* For each source, select top beam_size records.
*/
std
::
vector
<
std
::
vector
<
Item
>>
SelectTopBeamSizeItems
();
std
::
vector
<
std
::
vector
<
Item
>>
SelectTopBeamSizeItems
(
const
framework
::
LoDTensor
&
pre_ids
,
const
framework
::
LoDTensor
&
pre_scores
);
/*
* Get the items of next source sequence, return false if no remaining items.
*/
bool
NextItemSet
(
std
::
vector
<
Item
>*
items
);
bool
NextItemSet
(
const
framework
::
LoDTensor
&
pre_ids
,
const
framework
::
LoDTensor
&
pre_scores
,
std
::
vector
<
Item
>*
items
);
private:
size_t
beam_size_
;
...
...
@@ -192,24 +205,25 @@ template <typename DeviceContext, typename T>
class
BeamSearchOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
ids_var
=
context
.
Input
<
framework
::
LoDTensor
>
(
"ids"
);
auto
*
scores_var
=
context
.
Input
<
framework
::
LoDTensor
>
(
"scores"
);
auto
*
pre_ids_var
=
context
.
Input
<
framework
::
LoDTensor
>
(
"pre_ids"
);
PADDLE_ENFORCE_NOT_NULL
(
ids_var
);
PADDLE_ENFORCE_NOT_NULL
(
scores_var
);
PADDLE_ENFORCE_NOT_NULL
(
pre_ids_var
);
auto
*
ids
=
context
.
Input
<
framework
::
LoDTensor
>
(
"ids"
);
auto
*
scores
=
context
.
Input
<
framework
::
LoDTensor
>
(
"scores"
);
auto
*
pre_ids
=
context
.
Input
<
framework
::
LoDTensor
>
(
"pre_ids"
);
auto
*
pre_scores
=
context
.
Input
<
framework
::
LoDTensor
>
(
"pre_scores"
);
PADDLE_ENFORCE_NOT_NULL
(
ids
);
PADDLE_ENFORCE_NOT_NULL
(
scores
);
PADDLE_ENFORCE_NOT_NULL
(
pre_ids
);
PADDLE_ENFORCE_NOT_NULL
(
pre_scores
);
size_t
level
=
context
.
Attr
<
int
>
(
"level"
);
size_t
beam_size
=
context
.
Attr
<
int
>
(
"beam_size"
);
int
end_id
=
context
.
Attr
<
int
>
(
"end_id"
);
BeamSearch
alg
(
*
ids_var
,
*
scores_var
,
level
,
beam_size
,
end_id
);
auto
selected_ids_var
=
context
.
Output
<
framework
::
LoDTensor
>
(
"selected_ids"
);
auto
selected_scores_var
=
BeamSearch
alg
(
*
ids
,
*
scores
,
level
,
beam_size
,
end_id
);
auto
selected_ids
=
context
.
Output
<
framework
::
LoDTensor
>
(
"selected_ids"
);
auto
selected_scores
=
context
.
Output
<
framework
::
LoDTensor
>
(
"selected_scores"
);
PADDLE_ENFORCE_NOT_NULL
(
selected_ids
_var
);
PADDLE_ENFORCE_NOT_NULL
(
selected_scores
_var
);
alg
(
*
pre_ids
_var
,
selected_ids_var
,
selected_scores_var
);
PADDLE_ENFORCE_NOT_NULL
(
selected_ids
);
PADDLE_ENFORCE_NOT_NULL
(
selected_scores
);
alg
(
*
pre_ids
,
*
pre_scores
,
selected_ids
,
selected_scores
);
}
};
}
// namespace operators
...
...
paddle/fluid/operators/tensor_array_read_write_op.cc
浏览文件 @
741046e8
...
...
@@ -38,15 +38,14 @@ class WriteToArrayOp : public ArrayOp {
<<
" to "
<<
offset
+
1
;
out
->
resize
(
offset
+
1
);
}
auto
*
out_tensor
=
&
out
->
at
(
offset
);
out_tensor
->
set_lod
(
x_tensor
.
lod
());
if
(
x_tensor
.
memory_size
()
>
0
)
{
auto
*
out_tensor
=
&
out
->
at
(
offset
);
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
dev_ctx
=
*
pool
.
Get
(
place
);
TensorCopy
(
x_tensor
,
place
,
dev_ctx
,
out_tensor
);
out_tensor
->
set_lod
(
x_tensor
.
lod
());
}
else
{
VLOG
(
10
)
<<
"WARNING: The input tensor 'x_tensor' holds no memory, so "
"nothing has been written to output array["
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
741046e8
...
...
@@ -1686,7 +1686,7 @@ def layer_norm(input,
return
helper
.
append_activation
(
layer_norm_out
)
def
beam_search_decode
(
ids
,
scores
,
name
=
None
):
def
beam_search_decode
(
ids
,
scores
,
beam_size
,
end_id
,
name
=
None
):
helper
=
LayerHelper
(
'beam_search_decode'
,
**
locals
())
sentence_ids
=
helper
.
create_tmp_variable
(
dtype
=
ids
.
dtype
)
sentence_scores
=
helper
.
create_tmp_variable
(
dtype
=
ids
.
dtype
)
...
...
@@ -1698,7 +1698,9 @@ def beam_search_decode(ids, scores, name=None):
outputs
=
{
"SentenceIds"
:
sentence_ids
,
"SentenceScores"
:
sentence_scores
})
},
attrs
=
{
"beam_size"
:
beam_size
,
"end_id"
:
end_id
})
return
sentence_ids
,
sentence_scores
...
...
@@ -1926,7 +1928,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
return
tmp
def
beam_search
(
pre_ids
,
ids
,
scores
,
beam_size
,
end_id
,
level
=
0
):
def
beam_search
(
pre_ids
,
pre_scores
,
ids
,
scores
,
beam_size
,
end_id
,
level
=
0
):
'''
This function implements the beam search algorithm.
'''
...
...
@@ -1941,6 +1943,7 @@ def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
type
=
'beam_search'
,
inputs
=
{
'pre_ids'
:
pre_ids
,
'pre_scores'
:
pre_scores
,
'ids'
:
ids
,
'scores'
:
scores
,
},
...
...
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