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884ce5d5
编写于
11月 13, 2017
作者:
D
dangqingqing
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into cmake_speed
上级
91d4fc69
39715861
变更
21
隐藏空白更改
内联
并排
Showing
21 changed file
with
915 addition
and
117 deletion
+915
-117
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
+0
-1
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
+0
-1
paddle/gserver/layers/MKLDNNConvLayer.cpp
paddle/gserver/layers/MKLDNNConvLayer.cpp
+0
-2
paddle/gserver/layers/MKLDNNConvLayer.h
paddle/gserver/layers/MKLDNNConvLayer.h
+1
-1
paddle/gserver/layers/MKLDNNFcLayer.cpp
paddle/gserver/layers/MKLDNNFcLayer.cpp
+0
-2
paddle/gserver/layers/MKLDNNPoolLayer.cpp
paddle/gserver/layers/MKLDNNPoolLayer.cpp
+0
-2
paddle/gserver/layers/ROIPoolLayer.cpp
paddle/gserver/layers/ROIPoolLayer.cpp
+1
-1
paddle/gserver/tests/test_MKLDNN.cpp
paddle/gserver/tests/test_MKLDNN.cpp
+1
-1
paddle/math/MKLDNNMatrix.cpp
paddle/math/MKLDNNMatrix.cpp
+1
-6
paddle/math/MKLDNNMatrix.h
paddle/math/MKLDNNMatrix.h
+32
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-0
paddle/operators/beam_search_decode_op.cc
paddle/operators/beam_search_decode_op.cc
+110
-0
paddle/operators/beam_search_decode_op.h
paddle/operators/beam_search_decode_op.h
+280
-0
paddle/operators/beam_search_decode_op_test.cc
paddle/operators/beam_search_decode_op_test.cc
+221
-0
paddle/operators/sequence_concat_op.cc
paddle/operators/sequence_concat_op.cc
+1
-1
paddle/platform/call_once.h
paddle/platform/call_once.h
+13
-11
python/paddle/v2/framework/layer_helper.py
python/paddle/v2/framework/layer_helper.py
+6
-5
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+105
-6
python/paddle/v2/framework/optimizer.py
python/paddle/v2/framework/optimizer.py
+21
-77
python/paddle/v2/framework/tests/test_create_op_doc_string.py
...on/paddle/v2/framework/tests/test_create_op_doc_string.py
+11
-0
python/paddle/v2/framework/tests/test_understand_sentiment_dynamic_lstm.py
...framework/tests/test_understand_sentiment_dynamic_lstm.py
+110
-0
未找到文件。
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
浏览文件 @
884ce5d5
...
...
@@ -54,7 +54,6 @@ void MKLDNNAddtoLayer::reshape(
ow
=
iw
;
reshapeOutput
(
oh
,
ow
);
resizeOutput
(
bs
,
oc
*
oh
*
ow
);
printSizeInfo
();
}
void
MKLDNNAddtoLayer
::
resetFwd
(
std
::
vector
<
primitive
>&
pipeline
,
...
...
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
浏览文件 @
884ce5d5
...
...
@@ -125,7 +125,6 @@ void MKLDNNBatchNormLayer::reshape(
<<
"Input channel can not be changed"
;
reshapeOutput
(
oh
,
ow
);
resizeOutput
(
bs
,
oc
*
oh
*
ow
);
printSizeInfo
();
}
void
MKLDNNBatchNormLayer
::
resetFwd
(
std
::
vector
<
primitive
>&
pipeline
,
...
...
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @
884ce5d5
...
...
@@ -102,8 +102,6 @@ void MKLDNNConvLayer::reshape(
reshapeOutput
(
oh
,
ow
);
resizeOutput
(
bs
,
oc
*
oh
*
ow
);
printSizeInfo
();
}
void
MKLDNNConvLayer
::
resetFwd
(
std
::
vector
<
primitive
>&
pipeline
,
...
...
paddle/gserver/layers/MKLDNNConvLayer.h
浏览文件 @
884ce5d5
...
...
@@ -92,7 +92,7 @@ public:
void
printSizeInfo
()
override
{
MKLDNNLayer
::
printSizeInfo
();
VLOG
(
MKLDNN_SIZES
)
<<
getName
()
<<
": fh: "
<<
fh_
<<
", fw: "
<<
fw_
<<
"
:
ph: "
<<
ph_
<<
", pw: "
<<
pw_
<<
", sh: "
<<
sh_
<<
"
,
ph: "
<<
ph_
<<
", pw: "
<<
pw_
<<
", sh: "
<<
sh_
<<
", sw: "
<<
sw_
<<
", dh: "
<<
dh_
<<
", dw: "
<<
dw_
;
}
...
...
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @
884ce5d5
...
...
@@ -84,8 +84,6 @@ void MKLDNNFcLayer::reshape(
reshapeOutput
(
oh
,
ow
);
resizeOutput
(
bs
,
oc
);
printSizeInfo
();
}
void
MKLDNNFcLayer
::
resetFwd
(
std
::
vector
<
primitive
>&
pipeline
,
...
...
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @
884ce5d5
...
...
@@ -71,8 +71,6 @@ void MKLDNNPoolLayer::reshape(
reshapeOutput
(
oh
,
ow
);
resizeOutput
(
bs
,
oc
*
oh
*
ow
);
printSizeInfo
();
}
void
MKLDNNPoolLayer
::
resetFwd
(
std
::
vector
<
primitive
>&
pipeline
,
...
...
paddle/gserver/layers/ROIPoolLayer.cpp
浏览文件 @
884ce5d5
...
...
@@ -98,7 +98,7 @@ void ROIPoolLayer::forward(PassType passType) {
size_t
roiStartH
=
round
(
bottomROIs
[
2
]
*
spatialScale_
);
size_t
roiEndW
=
round
(
bottomROIs
[
3
]
*
spatialScale_
);
size_t
roiEndH
=
round
(
bottomROIs
[
4
]
*
spatialScale_
);
CHECK_GE
(
roiBatchIdx
,
0
);
CHECK_GE
(
roiBatchIdx
,
0
UL
);
CHECK_LT
(
roiBatchIdx
,
batchSize
);
size_t
roiHeight
=
std
::
max
(
roiEndH
-
roiStartH
+
1
,
1UL
);
size_t
roiWidth
=
std
::
max
(
roiEndW
-
roiStartW
+
1
,
1UL
);
...
...
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @
884ce5d5
...
...
@@ -297,7 +297,7 @@ static void getAddtoConfig(TestConfig& cfg,
}
void
testAddtoLayer
(
const
testImageDesc
&
pm
,
const
size_t
nInputs
)
{
CHECK_GE
(
nInputs
,
1
);
CHECK_GE
(
nInputs
,
1
UL
);
TestConfig
dnnConfig
;
getAddtoConfig
(
dnnConfig
,
pm
,
nInputs
);
dnnConfig
.
layerConfig
.
set_type
(
"mkldnn_addto"
);
...
...
paddle/math/MKLDNNMatrix.cpp
浏览文件 @
884ce5d5
...
...
@@ -152,12 +152,7 @@ void MKLDNNMatrix::downSpatial() {
}
memory
::
desc
md
=
memory
::
desc
(
dstDims
,
getDtype
(),
dstFmt
);
memory
::
primitive_desc
pd
=
memory
::
primitive_desc
(
md
,
getEngine
());
mkldnn_primitive_t
result
;
mkldnn
::
error
::
wrap_c_api
(
mkldnn_primitive_create
(
&
result
,
pd
.
get
(),
nullptr
,
nullptr
),
"could not create a memory primitive"
);
reset
(
result
);
set_data_handle
(
data_
);
resetMKLDNNMemory
(
pd
,
data_
);
}
}
// namespace paddle
paddle/math/MKLDNNMatrix.h
浏览文件 @
884ce5d5
...
...
@@ -145,6 +145,27 @@ public:
m_
.
reset
();
}
/**
* override the CpuMatrix::resize
*/
void
resize
(
size_t
newHeight
,
size_t
newWidth
)
override
{
m_
->
resize
(
newHeight
,
newWidth
);
if
(
data_
==
m_
->
getData
()
&&
elementCnt_
==
newHeight
*
newWidth
)
{
return
;
}
CpuMatrix
::
setData
(
data_
);
height_
=
newHeight
;
width_
=
newWidth
;
elementCnt_
=
newHeight
*
newWidth
;
stride_
=
width_
;
auto
pd
=
mkldnn
::
memory
::
primitive_desc
(
mkldnn
::
memory
::
desc
({(
int
)
newHeight
,
(
int
)
newWidth
},
getDtype
(),
mkldnn
::
memory
::
format
::
nc
),
getEngine
());
resetMKLDNNMemory
(
pd
,
data_
);
}
/**
* override Matrix::getData
* check data before return
...
...
@@ -215,6 +236,17 @@ protected:
memory
::
format
srcFmt
,
memory
::
format
dstFmt
,
memory
::
dims
dm
);
/**
* reset this MKLDNN Memory from primitve desc
*/
void
resetMKLDNNMemory
(
memory
::
primitive_desc
pd
,
real
*
data
)
{
mkldnn_primitive_t
result
;
mkldnn
::
error
::
wrap_c_api
(
mkldnn_primitive_create
(
&
result
,
pd
.
get
(),
nullptr
,
nullptr
),
"could not create a memory primitive"
);
reset
(
result
);
set_data_handle
(
data
);
}
private:
// save the CpuMatrixPtr in case the buffer released outside
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
884ce5d5
...
...
@@ -225,6 +225,7 @@ set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
cc_test
(
gather_test SRCS gather_test.cc DEPS tensor
)
cc_test
(
net_op_test SRCS net_op_test.cc DEPS net_op
)
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
(
strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory
)
cc_test
(
dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc
rnn/recurrent_op_utils.cc
...
...
paddle/operators/beam_search_decode_op.cc
0 → 100644
浏览文件 @
884ce5d5
/* 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/beam_search_decode_op.h"
namespace
paddle
{
namespace
operators
{
class
BeamSearchDecodeOp
:
public
framework
::
OperatorBase
{
public:
BeamSearchDecodeOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
framework
::
ExecutionContext
ctx
(
*
this
,
scope
,
dev_ctx
);
const
LoDTensorArray
*
ids
=
ctx
.
Input
<
LoDTensorArray
>
(
"Ids"
);
const
LoDTensorArray
*
scores
=
ctx
.
Input
<
LoDTensorArray
>
(
"Scores"
);
const
size_t
step_num
=
ids
->
size
();
PADDLE_ENFORCE_GT
(
step_num
,
0UL
,
"beam search steps should be larger than 0"
);
const
size_t
source_num
=
ids
->
at
(
0
).
lod
().
at
(
0
).
size
()
-
1
;
PADDLE_ENFORCE_GT
(
source_num
,
0UL
,
"source num should be larger than 0"
);
for
(
size_t
i
=
0
;
i
<
step_num
;
++
i
)
{
PADDLE_ENFORCE_EQ
(
ids
->
at
(
i
).
lod
().
size
(),
2UL
,
"Level of LodTensor should be 2"
);
}
// prepare output
LoDTensor
*
sentenceIds
=
ctx
.
Output
<
LoDTensor
>
(
"SentenceIds"
);
LoDTensor
*
sentenceScores
=
ctx
.
Output
<
LoDTensor
>
(
"SentenceScores"
);
BeamSearchDecoder
<
float
>
beam_search_decoder
;
beam_search_decoder
.
PackAllSteps
(
*
ids
,
*
scores
,
sentenceIds
,
sentenceScores
);
}
};
class
BeamSearchDecodeOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
BeamSearchDecodeOpProtoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Ids"
,
"(LodTensorArray)"
"score of the candidate words in each step"
);
AddInput
(
"Scores"
,
"(LodTensorArray)"
"score of the candidate words in each step"
);
AddOutput
(
"SentenceIds"
,
"(LodTensor)"
"All possible result sentences of word ids"
);
AddOutput
(
"SentenceScores"
,
"(LodTensor)"
"All possible result sentences of word scores"
);
AddComment
(
R"DOC(
Pack the result of Beam search op into SentenceIds and SentenceScores.
)DOC"
);
}
};
class
BeamSearchDecodeInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"Ids"
),
"BeamSearchDecodeOp must has input Ids"
);
PADDLE_ENFORCE
(
context
->
HasInput
(
"Scores"
),
"BeamSearchDecodeOp must has input Scores"
);
PADDLE_ENFORCE
(
context
->
HasOutput
(
"SentenceIds"
),
"BeamSearchDecodeOp must has output SentenceIds"
);
PADDLE_ENFORCE
(
context
->
HasOutput
(
"SentenceScores"
),
"BeamSearchDecodeOp must has output SentenceScores"
);
}
};
class
BeamSearchDecodeInferVarType
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDescBind
&
op_desc
,
framework
::
BlockDescBind
*
block
)
const
override
{
for
(
auto
&
o
:
op_desc
.
Output
(
"SentenceIds"
))
{
block
->
Var
(
o
)
->
SetType
(
framework
::
VarDesc
::
LOD_TENSOR
);
}
for
(
auto
&
o
:
op_desc
.
Output
(
"SentenceScores"
))
{
block
->
Var
(
o
)
->
SetType
(
framework
::
VarDesc
::
LOD_TENSOR
);
}
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OPERATOR
(
beam_search_decode
,
paddle
::
operators
::
BeamSearchDecodeOp
,
paddle
::
operators
::
BeamSearchDecodeOpProtoMaker
,
paddle
::
operators
::
BeamSearchDecodeInferShape
,
paddle
::
operators
::
BeamSearchDecodeInferVarType
,
paddle
::
framework
::
EmptyGradOpMaker
);
paddle/operators/beam_search_decode_op.h
0 → 100644
浏览文件 @
884ce5d5
/* 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/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
using
LoDTensorArray
=
framework
::
LoDTensorArray
;
// all the lod have 2 levels.
// The First is source level, the second is sentence level.
// source level describe how many candidate words for this source.
// sentence level describe these candidates belong to which prefix
const
size_t
kSourceLevel
=
0
;
const
size_t
kSentenceLevel
=
1
;
template
<
typename
T
>
struct
BeamNode
{
BeamNode
(
int64_t
word_id
,
T
score
)
:
word_id_
(
word_id
),
score_
(
score
)
{}
~
BeamNode
()
{
if
(
parent_
)
{
parent_
->
DropKid
(
this
);
if
(
parent_
->
kids_
.
size
()
==
0UL
)
{
delete
parent_
;
}
}
VLOG
(
3
)
<<
"Delete BeamNode root with word_id:"
<<
this
->
word_id_
;
}
void
AppendTo
(
BeamNode
*
parent
)
{
parent_
=
parent
;
parent
->
kids_
.
insert
(
this
);
}
void
DropKid
(
BeamNode
*
kid
)
{
kids_
.
erase
(
kid
);
}
BeamNode
*
parent_
=
nullptr
;
std
::
unordered_set
<
BeamNode
*>
kids_
;
int64_t
word_id_
;
T
score_
;
};
template
<
typename
T
>
using
BeamNodeVector
=
std
::
vector
<
std
::
unique_ptr
<
BeamNode
<
T
>>>
;
template
<
typename
T
>
struct
Sentence
{
std
::
vector
<
int64_t
>
word_ids
;
std
::
vector
<
T
>
scores
;
};
template
<
typename
T
>
using
SentenceVector
=
std
::
vector
<
Sentence
<
T
>>
;
template
<
typename
T
>
struct
BeamSearchDecoder
{
/**
* make a BeamNode and all it's related prefix BeanNode into a Sentence.
*/
Sentence
<
T
>
MakeSentence
(
const
BeamNode
<
T
>*
node
)
const
;
/**
* Param:
* cur_ids: LoDTensor of One step for word ID
* cur_scores: LoDTensor of One Step for word score
* prefixes_list: prefixes for each source sentence.
* sentence_vector_list: result sentence_vector for each source sentence.
* Return:
* a new prefixes list for each source of current step
*/
std
::
vector
<
BeamNodeVector
<
T
>>
PackTwoSteps
(
const
LoDTensor
&
cur_ids
,
const
LoDTensor
&
cur_scores
,
std
::
vector
<
BeamNodeVector
<
T
>>&
prefixes_list
,
std
::
vector
<
SentenceVector
<
T
>>*
sentence_vector_list
)
const
;
/**
* convert the result sentence_vector for each source sentence into two
* LodTensor.
* One is all candidate sentences with word id, one is all candidate sentences
* with word score.
* Param:
* sentence_vector_list: sentence_vector for each source sentence.
* id_tensor: result LoDTensor for sentences of id.
* score_tensor: result LoDTensor for sentences of score.
*/
void
ConvertSentenceVectorToLodTensor
(
std
::
vector
<
SentenceVector
<
T
>>
sentence_vector_list
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
;
/**
* Pack all steps of id/score LodTensor into sentence LoDTensor
* it's main logic is:
* ```python
* prefix
* result_sentence
* result_lod_tensor
*
* for (step in steps):
* prefix = PackTwoSteps(prefix, step, &result_sentence)
* ConvertSentenceVector<T>ToLodTensor(result_sentence, &result_lod_tensor)
* ```
*/
void
PackAllSteps
(
const
LoDTensorArray
&
step_ids
,
const
LoDTensorArray
&
step_scores
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
;
};
template
<
typename
T
>
Sentence
<
T
>
BeamSearchDecoder
<
T
>::
MakeSentence
(
const
BeamNode
<
T
>*
node
)
const
{
Sentence
<
T
>
sentence
;
while
(
node
!=
nullptr
)
{
sentence
.
word_ids
.
emplace_back
(
node
->
word_id_
);
sentence
.
scores
.
emplace_back
(
node
->
score_
);
node
=
node
->
parent_
;
}
std
::
reverse
(
std
::
begin
(
sentence
.
word_ids
),
std
::
end
(
sentence
.
word_ids
));
std
::
reverse
(
std
::
begin
(
sentence
.
scores
),
std
::
end
(
sentence
.
scores
));
return
sentence
;
}
template
<
typename
T
>
std
::
vector
<
BeamNodeVector
<
T
>>
BeamSearchDecoder
<
T
>::
PackTwoSteps
(
const
LoDTensor
&
cur_ids
,
const
LoDTensor
&
cur_scores
,
std
::
vector
<
BeamNodeVector
<
T
>>&
prefixes_list
,
std
::
vector
<
SentenceVector
<
T
>>*
sentence_vector_list
)
const
{
std
::
vector
<
BeamNodeVector
<
T
>>
result
;
for
(
size_t
src_idx
=
0
;
src_idx
<
cur_ids
.
lod
()[
kSourceLevel
].
size
()
-
1
;
++
src_idx
)
{
size_t
src_start
=
cur_ids
.
lod
().
at
(
kSourceLevel
)[
src_idx
];
size_t
src_end
=
cur_ids
.
lod
().
at
(
kSourceLevel
)[
src_idx
+
1
];
BeamNodeVector
<
T
>
beam_nodes
;
// if prefixes size is 0, it means this is the first step. In this step,
// all candidate id is the start of candidate sentences.
if
(
prefixes_list
.
empty
())
{
PADDLE_ENFORCE_EQ
(
cur_ids
.
lod
().
at
(
kSourceLevel
).
back
(),
cur_ids
.
lod
().
at
(
kSentenceLevel
).
back
(),
"in the first step"
);
for
(
size_t
id_idx
=
src_start
;
id_idx
<
src_end
;
++
id_idx
)
{
beam_nodes
.
push_back
(
std
::
unique_ptr
<
BeamNode
<
T
>>
(
new
BeamNode
<
T
>
(
cur_ids
.
data
<
int64_t
>
()[
id_idx
],
cur_scores
.
data
<
T
>
()[
id_idx
])));
}
}
else
{
BeamNodeVector
<
T
>&
prefixes
=
prefixes_list
[
src_idx
];
SentenceVector
<
T
>&
sentence_vector
=
(
*
sentence_vector_list
)[
src_idx
];
PADDLE_ENFORCE_EQ
(
src_end
-
src_start
,
prefixes
.
size
(),
"prefix and candidate set number should be the same"
);
auto
candidate_offset
=
cur_ids
.
lod
()[
kSentenceLevel
];
for
(
size_t
prefix_idx
=
0
;
prefix_idx
<
prefixes
.
size
();
++
prefix_idx
)
{
std
::
unique_ptr
<
BeamNode
<
T
>>&
prefix
=
prefixes
[
prefix_idx
];
size_t
candidate_start
=
candidate_offset
[
src_start
+
prefix_idx
];
size_t
candidate_end
=
candidate_offset
[
src_start
+
prefix_idx
+
1
];
if
(
candidate_start
==
candidate_end
)
{
VLOG
(
3
)
<<
"this sentence has no more candidate, "
"add to result sentence and rm it from beam tree"
;
sentence_vector
.
push_back
(
MakeSentence
(
prefix
.
get
()));
prefix
.
reset
();
}
else
{
for
(
size_t
candidate_idx
=
candidate_start
;
candidate_idx
<
candidate_end
;
++
candidate_idx
)
{
auto
*
candidate
=
new
BeamNode
<
T
>
(
cur_ids
.
data
<
int64_t
>
()[
candidate_idx
],
cur_scores
.
data
<
T
>
()[
candidate_idx
]);
candidate
->
AppendTo
(
prefix
.
get
());
beam_nodes
.
push_back
(
std
::
unique_ptr
<
BeamNode
<
T
>>
(
candidate
));
}
prefix
.
release
();
}
}
}
result
.
push_back
(
std
::
move
(
beam_nodes
));
}
return
result
;
}
template
<
typename
T
>
void
BeamSearchDecoder
<
T
>::
ConvertSentenceVectorToLodTensor
(
std
::
vector
<
SentenceVector
<
T
>>
sentence_vector_list
,
LoDTensor
*
id_tensor
,
LoDTensor
*
score_tensor
)
const
{
size_t
src_num
=
sentence_vector_list
.
size
();
PADDLE_ENFORCE_NE
(
src_num
,
0
,
"src_num should not be 0"
);
std
::
vector
<
size_t
>
source_level_lod
=
{
0
};
std
::
vector
<
size_t
>
sentence_level_lod
=
{
0
};
std
::
vector
<
int64_t
>
id_data
;
std
::
vector
<
T
>
score_data
;
for
(
size_t
src_idx
=
0
;
src_idx
<
src_num
;
++
src_idx
)
{
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
());
sentence_level_lod
.
push_back
(
sentence_level_lod
.
back
()
+
sentence
.
word_ids
.
size
());
}
source_level_lod
.
push_back
(
source_level_lod
.
back
()
+
sentence_vector_list
[
src_idx
].
size
());
}
auto
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
paddle
::
platform
::
CPUDeviceContext
cpu_ctx
(
*
cpu_place
);
framework
::
LoD
lod
;
lod
.
push_back
(
source_level_lod
);
lod
.
push_back
(
sentence_level_lod
);
id_tensor
->
set_lod
(
lod
);
id_tensor
->
Resize
({
static_cast
<
int64_t
>
(
id_data
.
size
())});
id_tensor
->
mutable_data
<
int64_t
>
(
paddle
::
platform
::
CPUPlace
());
id_tensor
->
CopyFromVector
<
int64_t
>
(
id_data
,
cpu_ctx
);
score_tensor
->
set_lod
(
lod
);
score_tensor
->
Resize
({
static_cast
<
int64_t
>
(
score_data
.
size
())});
score_tensor
->
mutable_data
<
T
>
(
paddle
::
platform
::
CPUPlace
());
score_tensor
->
CopyFromVector
<
T
>
(
score_data
,
cpu_ctx
);
}
template
<
typename
T
>
void
BeamSearchDecoder
<
T
>::
PackAllSteps
(
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
;
PADDLE_ENFORCE_GT
(
src_num
,
0UL
,
"source num should be larger than 0"
);
// previous prefixes for each step,
// the init length is 0, means this is the first step.
std
::
vector
<
BeamNodeVector
<
T
>>
beamnode_vector_list
(
0
);
std
::
vector
<
SentenceVector
<
T
>>
sentence_vector_list
(
src_num
);
// pack all steps for one batch first, then another batch
for
(
size_t
step_id
=
0
;
step_id
<
step_num
;
++
step_id
)
{
beamnode_vector_list
=
PackTwoSteps
(
step_ids
.
at
(
step_id
),
step_scores
.
at
(
step_id
),
beamnode_vector_list
,
&
sentence_vector_list
);
}
// append last beam_node to result
for
(
size_t
src_idx
=
0
;
src_idx
<
src_num
;
++
src_idx
)
{
for
(
auto
&
beam_node
:
beamnode_vector_list
.
at
(
src_idx
))
{
sentence_vector_list
[
src_idx
].
push_back
(
MakeSentence
(
beam_node
.
get
()));
beam_node
.
reset
();
}
}
ConvertSentenceVectorToLodTensor
(
sentence_vector_list
,
id_tensor
,
score_tensor
);
}
}
// namespace operators
}
// namespace paddle
paddle/operators/beam_search_decode_op_test.cc
0 → 100644
浏览文件 @
884ce5d5
/* 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/beam_search_decode_op.h"
#include "gtest/gtest.h"
using
CPUPlace
=
paddle
::
platform
::
CPUPlace
;
using
LoD
=
paddle
::
framework
::
LoD
;
using
LoDTensor
=
paddle
::
framework
::
LoDTensor
;
using
LoDTensorArray
=
paddle
::
framework
::
LoDTensorArray
;
template
<
typename
T
>
using
BeamNode
=
paddle
::
operators
::
BeamNode
<
T
>
;
template
<
typename
T
>
using
BeamSearchDecoder
=
paddle
::
operators
::
BeamSearchDecoder
<
T
>
;
template
<
typename
T
>
using
Sentence
=
paddle
::
operators
::
Sentence
<
T
>
;
template
<
typename
T
>
using
BeamNodeVector
=
paddle
::
operators
::
BeamNodeVector
<
T
>
;
template
<
typename
T
>
using
SentenceVector
=
paddle
::
operators
::
SentenceVector
<
T
>
;
namespace
paddle
{
namespace
test
{
void
GenerateExample
(
const
std
::
vector
<
size_t
>&
level_0
,
const
std
::
vector
<
size_t
>&
level_1
,
const
std
::
vector
<
int
>&
data
,
LoDTensorArray
*
ids
,
LoDTensorArray
*
scores
)
{
PADDLE_ENFORCE_EQ
(
level_0
.
back
(),
level_1
.
size
()
-
1
,
"source level is used to describe candidate set"
);
PADDLE_ENFORCE_EQ
(
level_1
.
back
(),
data
.
size
(),
"the lowest level is used to describe data"
", so it's last element should be data length"
);
CPUPlace
place
;
LoD
lod
;
lod
.
push_back
(
level_0
);
lod
.
push_back
(
level_1
);
// Ids
LoDTensor
tensor_id
;
tensor_id
.
set_lod
(
lod
);
tensor_id
.
Resize
({
static_cast
<
int64_t
>
(
data
.
size
())});
// malloc memory
int64_t
*
id_ptr
=
tensor_id
.
mutable_data
<
int64_t
>
(
place
);
for
(
size_t
i
=
0
;
i
<
data
.
size
();
++
i
)
{
id_ptr
[
i
]
=
static_cast
<
int64_t
>
(
data
.
at
(
i
));
}
// Scores
LoDTensor
tensor_score
;
tensor_score
.
set_lod
(
lod
);
tensor_score
.
Resize
({
static_cast
<
int64_t
>
(
data
.
size
())});
// malloc memory
float
*
score_ptr
=
tensor_score
.
mutable_data
<
float
>
(
place
);
for
(
size_t
i
=
0
;
i
<
data
.
size
();
++
i
)
{
score_ptr
[
i
]
=
static_cast
<
float
>
(
data
.
at
(
i
));
}
ids
->
push_back
(
tensor_id
);
scores
->
push_back
(
tensor_score
);
}
}
// namespace test
}
// namespace paddle
TEST
(
BeamSearchDecodeOp
,
DeleteBeamNode
)
{
auto
*
root
=
new
BeamNode
<
float
>
(
0
,
0
);
auto
*
b1
=
new
BeamNode
<
float
>
(
1
,
1
);
auto
*
b2
=
new
BeamNode
<
float
>
(
2
,
2
);
auto
*
b3
=
new
BeamNode
<
float
>
(
3
,
3
);
b1
->
AppendTo
(
root
);
b2
->
AppendTo
(
root
);
b3
->
AppendTo
(
b1
);
delete
b3
;
delete
b2
;
}
TEST
(
BeamSearchDecodeOp
,
MakeSentence
)
{
auto
*
root
=
new
BeamNode
<
float
>
(
0
,
0
);
auto
*
b1
=
new
BeamNode
<
float
>
(
1
,
1
);
auto
*
end
=
new
BeamNode
<
float
>
(
2
,
2
);
b1
->
AppendTo
(
root
);
end
->
AppendTo
(
b1
);
BeamSearchDecoder
<
float
>
helper
;
Sentence
<
float
>
sentence
=
helper
.
MakeSentence
(
end
);
delete
end
;
std
::
vector
<
int64_t
>
expect_ids
=
{
0
,
1
,
2
};
ASSERT_EQ
(
sentence
.
word_ids
,
expect_ids
);
std
::
vector
<
float
>
expect_scores
=
{
0
,
1
,
2
};
ASSERT_EQ
(
sentence
.
scores
,
expect_scores
);
}
TEST
(
BeamSearchDecodeOp
,
PackTwoStepsFistStep
)
{
CPUPlace
place
;
LoDTensorArray
ids
;
LoDTensorArray
scores
;
paddle
::
test
::
GenerateExample
(
std
::
vector
<
size_t
>
{
0
,
2
,
6
},
std
::
vector
<
size_t
>
{
0
,
1
,
2
,
3
,
4
,
5
,
6
},
std
::
vector
<
int
>
{
1
,
2
,
3
,
4
,
5
,
6
},
&
ids
,
&
scores
);
std
::
vector
<
BeamNodeVector
<
float
>>
beamnode_vector_list
;
std
::
vector
<
SentenceVector
<
float
>>
sentence_vector_list
(
2
,
SentenceVector
<
float
>
());
BeamSearchDecoder
<
float
>
helper
;
beamnode_vector_list
=
helper
.
PackTwoSteps
(
ids
[
0
],
scores
[
0
],
beamnode_vector_list
,
&
sentence_vector_list
);
ASSERT_EQ
(
beamnode_vector_list
.
size
(),
2UL
);
ASSERT_EQ
(
beamnode_vector_list
[
0
].
size
(),
2UL
);
ASSERT_EQ
(
beamnode_vector_list
[
1
].
size
(),
4UL
);
}
TEST
(
BeamSearchDecodeOp
,
PackTwoSteps
)
{
CPUPlace
place
;
// first source has three prefix
BeamNodeVector
<
float
>
source0_prefixes
;
source0_prefixes
.
push_back
(
std
::
unique_ptr
<
BeamNode
<
float
>>
(
new
BeamNode
<
float
>
(
1
,
1
)));
source0_prefixes
.
push_back
(
std
::
unique_ptr
<
BeamNode
<
float
>>
(
new
BeamNode
<
float
>
(
0
,
0
)));
source0_prefixes
.
push_back
(
std
::
unique_ptr
<
BeamNode
<
float
>>
(
new
BeamNode
<
float
>
(
3
,
3
)));
// second source has two prefix
BeamNodeVector
<
float
>
source1_prefixes
;
source1_prefixes
.
push_back
(
std
::
unique_ptr
<
BeamNode
<
float
>>
(
new
BeamNode
<
float
>
(
4
,
4
)));
source1_prefixes
.
push_back
(
std
::
unique_ptr
<
BeamNode
<
float
>>
(
new
BeamNode
<
float
>
(
5
,
5
)));
std
::
vector
<
BeamNodeVector
<
float
>>
beamnode_vector_list
;
std
::
vector
<
SentenceVector
<
float
>>
sentence_vector_list
(
2
,
SentenceVector
<
float
>
());
beamnode_vector_list
.
push_back
(
std
::
move
(
source0_prefixes
));
beamnode_vector_list
.
push_back
(
std
::
move
(
source1_prefixes
));
// generate data for one step
LoDTensorArray
ids
;
LoDTensorArray
scores
;
paddle
::
test
::
GenerateExample
(
std
::
vector
<
size_t
>
{
0
,
3
,
5
},
std
::
vector
<
size_t
>
{
0
,
1
,
1
,
3
,
4
,
5
},
std
::
vector
<
int
>
{
0
,
1
,
2
,
3
,
4
},
&
ids
,
&
scores
);
BeamSearchDecoder
<
float
>
helper1
;
beamnode_vector_list
=
helper1
.
PackTwoSteps
(
ids
[
0
],
scores
[
0
],
beamnode_vector_list
,
&
sentence_vector_list
);
ASSERT_EQ
(
sentence_vector_list
[
0
].
size
(),
1UL
);
ASSERT_EQ
(
sentence_vector_list
[
1
].
size
(),
0UL
);
ASSERT_EQ
(
beamnode_vector_list
[
0
].
size
(),
3UL
);
ASSERT_EQ
(
beamnode_vector_list
[
1
].
size
(),
2UL
);
}
TEST
(
BeamSearchDecodeOp
,
PackAllSteps
)
{
CPUPlace
place
;
// we will constuct a sample data with 3 steps and 2 source sentences
LoDTensorArray
ids
;
LoDTensorArray
scores
;
paddle
::
test
::
GenerateExample
(
std
::
vector
<
size_t
>
{
0
,
3
,
6
},
std
::
vector
<
size_t
>
{
0
,
1
,
2
,
3
,
4
,
5
,
6
},
std
::
vector
<
int
>
{
1
,
2
,
3
,
4
,
5
,
6
},
&
ids
,
&
scores
);
paddle
::
test
::
GenerateExample
(
std
::
vector
<
size_t
>
{
0
,
3
,
6
},
std
::
vector
<
size_t
>
{
0
,
1
,
1
,
3
,
5
,
5
,
6
},
std
::
vector
<
int
>
{
0
,
1
,
2
,
3
,
4
,
5
},
&
ids
,
&
scores
);
paddle
::
test
::
GenerateExample
(
std
::
vector
<
size_t
>
{
0
,
3
,
6
},
std
::
vector
<
size_t
>
{
0
,
0
,
1
,
2
,
3
,
4
,
5
},
std
::
vector
<
int
>
{
0
,
1
,
2
,
3
,
4
},
&
ids
,
&
scores
);
ASSERT_EQ
(
ids
.
size
(),
3UL
);
ASSERT_EQ
(
scores
.
size
(),
3UL
);
BeamSearchDecoder
<
float
>
helper
;
LoDTensor
id_tensor
;
LoDTensor
score_tensor
;
helper
.
PackAllSteps
(
ids
,
scores
,
&
id_tensor
,
&
score_tensor
);
LoD
lod
=
id_tensor
.
lod
();
std
::
vector
<
size_t
>
expect_source_lod
=
{
0
,
4
,
8
};
EXPECT_EQ
(
lod
[
0
],
expect_source_lod
);
std
::
vector
<
size_t
>
expect_sentence_lod
=
{
0
,
1
,
3
,
6
,
9
,
10
,
13
,
16
,
19
};
EXPECT_EQ
(
lod
[
1
],
expect_sentence_lod
);
// 2| 1, 0| 3, 1, 0| 3, 2, 1| 5| 4, 3, 2| 4, 4, 3| 6, 5, 4
std
::
vector
<
int
>
expect_data
=
{
2
,
1
,
0
,
3
,
1
,
0
,
3
,
2
,
1
,
5
,
4
,
3
,
2
,
4
,
4
,
3
,
6
,
5
,
4
};
ASSERT_EQ
(
id_tensor
.
dims
()[
0
],
static_cast
<
int64_t
>
(
expect_data
.
size
()));
for
(
size_t
i
=
0
;
i
<
expect_data
.
size
();
++
i
)
{
ASSERT_EQ
(
id_tensor
.
data
<
int64_t
>
()[
i
],
static_cast
<
int64_t
>
(
expect_data
[
i
]));
}
for
(
int64_t
i
=
0
;
i
<
id_tensor
.
dims
()[
0
];
++
i
)
{
ASSERT_EQ
(
score_tensor
.
data
<
float
>
()[
i
],
static_cast
<
float
>
(
id_tensor
.
data
<
int64_t
>
()[
i
]));
}
}
paddle/operators/sequence_concat_op.cc
浏览文件 @
884ce5d5
...
...
@@ -47,7 +47,7 @@ class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(
vector<LoDTensor>
) Input is a vector of LoDTensor, "
"(
LodTensorArray
) Input is a vector of LoDTensor, "
"each of which is a variable-length sequence or nested sequence."
)
.
AsDuplicable
();
AddOutput
(
"Out"
,
...
...
paddle/platform/call_once.h
浏览文件 @
884ce5d5
...
...
@@ -27,20 +27,22 @@ namespace platform {
This wrap is a hack to avoid this bug.
*/
template
<
class
Callable
,
class
...
Args
>
template
<
typename
Callable
,
typename
...
Args
>
inline
void
call_once
(
std
::
once_flag
&
flag
,
Callable
&&
f
,
Args
&&
...
args
)
{
bool
good
=
false
;
std
::
exception
ex
;
std
::
call_once
(
flag
,
[
&
]()
{
try
{
f
(
args
...);
good
=
true
;
}
catch
(
const
std
::
exception
&
e
)
{
ex
=
e
;
}
catch
(...)
{
ex
=
std
::
runtime_error
(
"excption caught in call_once"
);
}
});
std
::
call_once
(
flag
,
[
&
](
Args
&&
...
args
)
{
try
{
f
(
args
...);
good
=
true
;
}
catch
(
const
std
::
exception
&
e
)
{
ex
=
e
;
}
catch
(...)
{
ex
=
std
::
runtime_error
(
"excption caught in call_once"
);
}
},
args
...);
if
(
!
good
)
{
throw
std
::
exception
(
ex
);
}
...
...
python/paddle/v2/framework/layer_helper.py
浏览文件 @
884ce5d5
...
...
@@ -4,7 +4,7 @@ import itertools
from
paddle.v2.framework.framework
import
Variable
,
g_main_program
,
\
g_startup_program
,
unique_name
,
Program
from
paddle.v2.framework.initializer
import
ConstantInitializer
,
\
UniformInitializer
UniformInitializer
,
XavierInitializer
class
LayerHelper
(
object
):
...
...
@@ -61,7 +61,7 @@ class LayerHelper(object):
@
property
def
param_attr
(
self
):
default
=
{
'name'
:
None
,
'initializer'
:
Uniform
Initializer
()}
default
=
{
'name'
:
None
,
'initializer'
:
Xavier
Initializer
()}
actual
=
self
.
kwargs
.
get
(
'param_attr'
,
None
)
if
actual
is
None
:
actual
=
default
...
...
@@ -70,10 +70,11 @@ class LayerHelper(object):
actual
[
default_field
]
=
default
[
default_field
]
return
actual
@
property
def
bias_attr
(
self
):
default
=
{
'name'
:
None
,
'initializer'
:
Constant
Initializer
()}
default
=
{
'name'
:
None
,
'initializer'
:
Xavier
Initializer
()}
bias_attr
=
self
.
kwargs
.
get
(
'bias_attr'
,
None
)
if
bias_attr
is
Tru
e
:
if
bias_attr
is
Non
e
:
bias_attr
=
default
if
isinstance
(
bias_attr
,
dict
):
...
...
@@ -166,7 +167,7 @@ class LayerHelper(object):
num_flatten_dims
=
1
size
=
list
(
input_var
.
shape
[
num_flatten_dims
:])
bias_attr
=
self
.
bias_attr
()
bias_attr
=
self
.
bias_attr
if
not
bias_attr
:
return
input_var
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
884ce5d5
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.proto.framework_pb2
as
framework_pb2
from
paddle.v2.framework.framework
import
OpProtoHolder
,
Variable
,
Program
,
\
Operator
from
paddle.v2.framework.initializer
import
ConstantInitializer
,
\
NormalInitializer
from
paddle.v2.framework.layer_helper
import
LayerHelper
,
unique_name
import
re
import
cStringIO
__all__
=
[
'fc'
,
'data'
,
'cross_entropy'
,
'conv2d'
,
'pool2d'
,
'embedding'
,
'concat'
,
...
...
@@ -16,7 +18,7 @@ __all__ = [
def
fc
(
input
,
size
,
param_attr
=
None
,
bias_attr
=
Tru
e
,
bias_attr
=
Non
e
,
name
=
None
,
act
=
None
,
num_flatten_dims
=
1
,
...
...
@@ -125,6 +127,55 @@ def embedding(input,
return
tmp
# TODO(qijun): expose H0 and C0
def
dynamic_lstm
(
input
,
size
,
data_type
=
'float32'
,
param_attr
=
None
,
bias_attr
=
None
,
use_peepholes
=
True
,
is_reverse
=
False
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'tanh'
,
candidate_activation
=
'tanh'
,
main_program
=
None
,
startup_program
=
None
):
helper
=
LayerHelper
(
'lstm'
,
**
locals
())
size
=
size
/
4
weight
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
[
size
,
4
*
size
],
dtype
=
data_type
)
bias_size
=
[
1
,
7
*
size
]
if
not
use_peepholes
:
bias_size
[
1
]
=
4
*
size
bias
=
helper
.
create_parameter
(
attr
=
helper
.
bias_attr
,
shape
=
bias_size
,
dtype
=
data_type
,
suffix
=
'b'
)
hidden
=
helper
.
create_tmp_variable
(
data_type
)
cell
=
helper
.
create_tmp_variable
(
data_type
)
batch_gate
=
helper
.
create_tmp_variable
(
data_type
)
batch_cell_pre_act
=
helper
.
create_tmp_variable
(
data_type
)
helper
.
append_op
(
type
=
'lstm'
,
inputs
=
{
'Input'
:
input
,
'Weight'
:
weight
,
'Bias'
:
bias
},
outputs
=
{
'Hidden'
:
hidden
,
'Cell'
:
cell
,
'BatchGate'
:
batch_gate
,
'BatchCellPreAct'
:
batch_cell_pre_act
},
attrs
=
{
'use_peepholes'
:
use_peepholes
,
'is_reverse'
:
is_reverse
,
'gate_activation'
:
gate_activation
,
'cell_activation'
:
cell_activation
,
'candidate_activation'
:
candidate_activation
})
return
hidden
,
cell
def
data
(
name
,
shape
,
data_type
=
'float32'
,
...
...
@@ -191,6 +242,58 @@ def _convert_(name):
return
re
.
sub
(
'([a-z0-9])([A-Z])'
,
r
'\1_\2'
,
s1
).
lower
()
def
_generate_doc_string_
(
op_proto
):
"""
Generate docstring by OpProto
Args:
op_proto (framework_pb2.OpProto): a protobuf message typed OpProto
Returns:
str: the document string
"""
def
_type_to_str_
(
tp
):
return
framework_pb2
.
AttrType
.
Name
(
tp
)
if
not
isinstance
(
op_proto
,
framework_pb2
.
OpProto
):
raise
TypeError
(
"OpProto should be `framework_pb2.OpProto`"
)
buf
=
cStringIO
.
StringIO
()
buf
.
write
(
op_proto
.
comment
)
buf
.
write
(
'
\n
Args:
\n
'
)
for
each_input
in
op_proto
.
inputs
:
line_begin
=
' {0}: '
.
format
(
_convert_
(
each_input
.
name
))
buf
.
write
(
line_begin
)
buf
.
write
(
each_input
.
comment
)
buf
.
write
(
'
\n
'
)
buf
.
write
(
' '
*
len
(
line_begin
))
buf
.
write
(
'Duplicable: '
)
buf
.
write
(
str
(
each_input
.
duplicable
))
buf
.
write
(
' Optional: '
)
buf
.
write
(
str
(
each_input
.
dispensable
))
buf
.
write
(
'
\n
'
)
for
each_attr
in
op_proto
.
attrs
:
buf
.
write
(
' '
)
buf
.
write
(
each_attr
.
name
)
buf
.
write
(
' ('
)
buf
.
write
(
_type_to_str_
(
each_attr
.
type
))
buf
.
write
(
'): '
)
buf
.
write
(
each_attr
.
comment
)
buf
.
write
(
'
\n
'
)
if
len
(
op_proto
.
outputs
)
!=
0
:
buf
.
write
(
'
\n
Returns:
\n
'
)
buf
.
write
(
' '
)
for
each_opt
in
op_proto
.
outputs
:
if
not
each_opt
.
intermediate
:
break
buf
.
write
(
each_opt
.
comment
)
return
buf
.
getvalue
()
def
_create_op_func_
(
op_type
):
"""
Create an Operator for a Function.
...
...
@@ -249,11 +352,6 @@ def _create_op_func_(op_type):
return
dtype
def
func
(
**
kwargs
):
"""
This function implements the function for the operator. This process
involves doing the sanity check (using the function above), reading
inputs from protobuf and applying the activations on top.
"""
helper
=
LayerHelper
(
op_type
,
**
kwargs
)
dtype
=
infer_and_check_data_type
(
op_proto
,
**
kwargs
)
...
...
@@ -277,6 +375,7 @@ def _create_op_func_(op_type):
func
.
__name__
=
op_type
globals
()[
op_type
]
=
func
func
.
__doc__
=
_generate_doc_string_
(
op_proto
)
global
__all__
__all__
.
append
(
op_type
)
...
...
python/paddle/v2/framework/optimizer.py
浏览文件 @
884ce5d5
...
...
@@ -35,15 +35,21 @@ class Optimizer(object):
"""
raise
NotImplementedError
()
def
_initialize_tensors
(
self
,
block
):
"""Create all necessary tensors, that will be shared for all parameter updates.
Tensors like learning rate should be initialized here.
Args:
block: the block in which the loss variable is present
"""
pass
def
_create_param_lr
(
self
,
param_and_grad
):
# create learning rate variable for every parameter
param
=
param_and_grad
[
0
]
param_lr
=
param
.
optimize_attr
[
'learning_rate'
]
param_lr_shape
=
[
1
]
param_lr_var
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
"learning_rate"
),
dtype
=
'float32'
,
shape
=
param_lr_shape
,
lod_level
=
1
,
persistable
=
True
)
param_lr
=
param_lr
*
self
.
_learning_rate
self
.
helper
.
set_variable_initializer
(
var
=
param_lr_var
,
initializer
=
ConstantInitializer
(
param_lr
))
return
param_lr_var
def
_create_accumulators
(
self
,
block
,
parameters
):
"""Create all accumulators needed by the parameters
...
...
@@ -161,8 +167,6 @@ class Optimizer(object):
startup_program
=
startup_program
)
self
.
_create_accumulators
(
loss
.
block
,
[
p
[
0
]
for
p
in
parameters_and_grads
])
# Create any necessary tensors
self
.
_initialize_tensors
(
loss
.
block
)
optimize_ops
=
[]
for
param_and_grad
in
parameters_and_grads
:
...
...
@@ -214,27 +218,16 @@ class SGDOptimizer(Optimizer):
self
.
type
=
"sgd"
self
.
_learning_rate
=
learning_rate
def
_initialize_tensors
(
self
,
block
):
lr_shape
=
[
1
]
# create a variable for learning_rate
self
.
_lr
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
"learning_rate"
),
dtype
=
'float32'
,
shape
=
lr_shape
,
lod_level
=
1
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
var
=
self
.
_lr
,
initializer
=
ConstantInitializer
(
self
.
_learning_rate
))
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
assert
isinstance
(
block
,
framework
.
Block
)
# create the optimize op
sgd_op
=
block
.
append_op
(
type
=
self
.
type
,
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"LearningRate"
:
self
.
_
lr
"LearningRate"
:
self
.
_
create_param_lr
(
param_and_grad
)
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
]})
...
...
@@ -259,19 +252,6 @@ class MomentumOptimizer(Optimizer):
self
.
_momentum
=
momentum
self
.
_use_nesterov
=
bool
(
use_nesterov
)
def
_initialize_tensors
(
self
,
block
):
assert
isinstance
(
block
,
framework
.
Block
)
lr_shape
=
[
1
]
# create a variable for learning_rate
self
.
_lr
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
"learning_rate"
),
dtype
=
'float32'
,
shape
=
lr_shape
,
lod_level
=
1
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
var
=
self
.
_lr
,
initializer
=
ConstantInitializer
(
self
.
_learning_rate
))
def
_create_accumulators
(
self
,
block
,
parameters
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
@@ -290,7 +270,7 @@ class MomentumOptimizer(Optimizer):
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"Velocity"
:
velocity_acc
,
"LearningRate"
:
self
.
_
lr
"LearningRate"
:
self
.
_
create_param_lr
(
param_and_grad
)
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
...
...
@@ -315,18 +295,6 @@ class AdagradOptimizer(Optimizer):
self
.
_learning_rate
=
learning_rate
self
.
_epsilon
=
epsilon
def
_initialize_tensors
(
self
,
block
):
lr_shape
=
[
1
]
# create a variable for learning_rate
self
.
_lr
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
"learning_rate"
),
dtype
=
'float32'
,
shape
=
lr_shape
,
lod_level
=
1
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
var
=
self
.
_lr
,
initializer
=
ConstantInitializer
(
self
.
_learning_rate
))
def
_create_accumulators
(
self
,
block
,
parameters
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
@@ -346,7 +314,7 @@ class AdagradOptimizer(Optimizer):
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"Moment"
:
moment_acc
,
"LearningRate"
:
self
.
_
lr
"LearningRate"
:
self
.
_
create_param_lr
(
param_and_grad
)
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"MomentOut"
:
moment_acc
},
...
...
@@ -378,18 +346,6 @@ class AdamOptimizer(Optimizer):
self
.
_beta2
=
beta2
self
.
_epsilon
=
epsilon
def
_initialize_tensors
(
self
,
block
):
lr_shape
=
[
1
]
# create a variable for learning_rate
self
.
_lr
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
"learning_rate"
),
dtype
=
'float32'
,
shape
=
lr_shape
,
lod_level
=
1
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
var
=
self
.
_lr
,
initializer
=
ConstantInitializer
(
self
.
_learning_rate
))
def
_create_accumulators
(
self
,
block
,
parameters
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
@@ -433,7 +389,7 @@ class AdamOptimizer(Optimizer):
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"LearningRate"
:
self
.
_
lr
,
"LearningRate"
:
self
.
_
create_param_lr
(
param_and_grad
)
,
"Moment1"
:
moment1
,
"Moment2"
:
moment2
,
"Beta1Pow"
:
self
.
_beta1_pow_acc
,
...
...
@@ -495,18 +451,6 @@ class AdamaxOptimizer(Optimizer):
self
.
_beta2
=
beta2
self
.
_epsilon
=
epsilon
def
_initialize_tensors
(
self
,
block
):
lr_shape
=
[
1
]
# create a variable for learning_rate
self
.
_lr
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
"learning_rate"
),
dtype
=
'float32'
,
shape
=
lr_shape
,
lod_level
=
1
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
var
=
self
.
_lr
,
initializer
=
ConstantInitializer
(
self
.
_learning_rate
))
def
_create_accumulators
(
self
,
block
,
parameters
):
# Create beta1 power accumulator tensor
beta_shape
=
[
1
]
...
...
@@ -536,7 +480,7 @@ class AdamaxOptimizer(Optimizer):
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"LearningRate"
:
self
.
_
lr
,
"LearningRate"
:
self
.
_
create_param_lr
(
param_and_grad
)
,
"Moment"
:
moment
,
"InfNorm"
:
inf_norm
,
"Beta1Pow"
:
self
.
_beta1_pow_acc
...
...
python/paddle/v2/framework/tests/test_create_op_doc_string.py
0 → 100644
浏览文件 @
884ce5d5
import
unittest
import
paddle.v2.framework.layers
as
layers
class
TestDocString
(
unittest
.
TestCase
):
def
test_layer_doc_string
(
self
):
print
layers
.
dropout
.
__doc__
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/framework/tests/test_understand_sentiment_dynamic_lstm.py
0 → 100644
浏览文件 @
884ce5d5
import
paddle.v2
as
paddle
import
paddle.v2.framework.layers
as
layers
import
paddle.v2.framework.nets
as
nets
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.optimizer
as
optimizer
from
paddle.v2.framework.framework
import
Program
,
g_main_program
,
g_startup_program
from
paddle.v2.framework.executor
import
Executor
import
numpy
as
np
def
stacked_lstm_net
(
input_dim
,
class_dim
=
2
,
emb_dim
=
128
,
hid_dim
=
512
,
stacked_num
=
3
):
assert
stacked_num
%
2
==
1
data
=
layers
.
data
(
name
=
"words"
,
shape
=
[
1
],
data_type
=
"int64"
)
label
=
layers
.
data
(
name
=
"label"
,
shape
=
[
1
],
data_type
=
"int64"
)
emb
=
layers
.
embedding
(
input
=
data
,
size
=
[
input_dim
,
emb_dim
])
# add bias attr
# TODO(qijun) linear act
fc1
=
layers
.
fc
(
input
=
emb
,
size
=
hid_dim
)
lstm1
,
cell1
=
layers
.
dynamic_lstm
(
input
=
fc1
,
size
=
hid_dim
)
inputs
=
[
fc1
,
lstm1
]
for
i
in
range
(
2
,
stacked_num
+
1
):
fc
=
layers
.
fc
(
input
=
inputs
,
size
=
hid_dim
)
lstm
,
cell
=
layers
.
dynamic_lstm
(
input
=
fc
,
size
=
hid_dim
,
is_reverse
=
(
i
%
2
)
==
0
)
inputs
=
[
fc
,
lstm
]
fc_last
=
layers
.
sequence_pool
(
input
=
inputs
[
0
],
pool_type
=
'max'
)
lstm_last
=
layers
.
sequence_pool
(
input
=
inputs
[
1
],
pool_type
=
'max'
)
prediction
=
layers
.
fc
(
input
=
[
fc_last
,
lstm_last
],
size
=
class_dim
,
act
=
'softmax'
)
cost
=
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
layers
.
mean
(
x
=
cost
)
adam_optimizer
=
optimizer
.
AdamOptimizer
(
learning_rate
=
0.002
)
opts
=
adam_optimizer
.
minimize
(
avg_cost
)
acc
=
layers
.
accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
acc
def
to_lodtensor
(
data
,
place
):
seq_lens
=
[
len
(
seq
)
for
seq
in
data
]
cur_len
=
0
lod
=
[
cur_len
]
for
l
in
seq_lens
:
cur_len
+=
l
lod
.
append
(
cur_len
)
flattened_data
=
np
.
concatenate
(
data
,
axis
=
0
).
astype
(
"int64"
)
flattened_data
=
flattened_data
.
reshape
([
len
(
flattened_data
),
1
])
res
=
core
.
LoDTensor
()
res
.
set
(
flattened_data
,
place
)
res
.
set_lod
([
lod
])
return
res
def
main
():
BATCH_SIZE
=
100
PASS_NUM
=
5
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
print
"load word dict successfully"
dict_dim
=
len
(
word_dict
)
class_dim
=
2
cost
,
acc
=
stacked_lstm_net
(
input_dim
=
dict_dim
,
class_dim
=
class_dim
)
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
imdb
.
train
(
word_dict
),
buf_size
=
1000
),
batch_size
=
BATCH_SIZE
)
place
=
core
.
CPUPlace
()
exe
=
Executor
(
place
)
exe
.
run
(
g_startup_program
)
for
pass_id
in
xrange
(
PASS_NUM
):
for
data
in
train_data
():
tensor_words
=
to_lodtensor
(
map
(
lambda
x
:
x
[
0
],
data
),
place
)
label
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int64"
)
label
=
label
.
reshape
([
BATCH_SIZE
,
1
])
tensor_label
=
core
.
LoDTensor
()
tensor_label
.
set
(
label
,
place
)
outs
=
exe
.
run
(
g_main_program
,
feed
=
{
"words"
:
tensor_words
,
"label"
:
tensor_label
},
fetch_list
=
[
cost
,
acc
])
cost_val
=
np
.
array
(
outs
[
0
])
acc_val
=
np
.
array
(
outs
[
1
])
print
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
))
if
cost_val
<
1.0
and
acc_val
>
0.7
:
exit
(
0
)
exit
(
1
)
if
__name__
==
'__main__'
:
main
()
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