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82091035
编写于
12月 22, 2017
作者:
T
tensor-tang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
follow comments and refine code
上级
0b080a42
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
125 addition
and
348 deletion
+125
-348
paddle/gserver/layers/MKLPackedGemm.h
paddle/gserver/layers/MKLPackedGemm.h
+0
-95
paddle/gserver/layers/MKLPackedRecurrentLayer.cpp
paddle/gserver/layers/MKLPackedRecurrentLayer.cpp
+12
-179
paddle/gserver/layers/MKLPackedRecurrentLayer.h
paddle/gserver/layers/MKLPackedRecurrentLayer.h
+13
-74
paddle/gserver/layers/MKLPackedWeight.h
paddle/gserver/layers/MKLPackedWeight.h
+100
-0
未找到文件。
paddle/gserver/layers/MKLPackedGemm.h
已删除
100644 → 0
浏览文件 @
0b080a42
/* Copyright (c) 2017 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/math/MathFunctions.h"
#include "paddle/math/Matrix.h"
namespace
paddle
{
class
MKLPackedGemm
{
protected:
real
*
weightPacked_
;
real
*
weightTPacked_
;
size_t
weightHeight_
;
size_t
weightWidth_
;
public:
explicit
MKLPackedGemm
(
MatrixPtr
weight
)
{
weightHeight_
=
weight
->
getHeight
();
weightWidth_
=
weight
->
getWidth
();
weightPacked_
=
cblas_sgemm_alloc
(
CblasBMatrix
,
1
,
weightWidth_
,
weightHeight_
);
weightTPacked_
=
cblas_sgemm_alloc
(
CblasBMatrix
,
1
,
weightWidth_
,
weightHeight_
);
cblas_sgemm_pack
(
CblasRowMajor
,
CblasBMatrix
,
CblasNoTrans
,
1
,
weightWidth_
,
weightHeight_
,
1.0
,
weight
->
getData
(),
weightWidth_
,
weightPacked_
);
cblas_sgemm_pack
(
CblasRowMajor
,
CblasBMatrix
,
CblasTrans
,
1
,
weightWidth_
,
weightHeight_
,
1.0
,
weight
->
getData
(),
weightWidth_
,
weightTPacked_
);
}
void
compute
(
MatrixPtr
batch2
,
MatrixPtr
batch1
,
bool
transW
=
false
)
{
if
(
transW
)
{
cblas_sgemm_compute
(
CblasRowMajor
,
CblasNoTrans
,
CblasPacked
,
batch2
->
getHeight
(),
weightWidth_
,
weightHeight_
,
batch1
->
getData
(),
weightHeight_
,
weightTPacked_
,
weightWidth_
,
1
,
batch2
->
getData
(),
weightWidth_
);
}
else
{
cblas_sgemm_compute
(
CblasRowMajor
,
CblasNoTrans
,
CblasPacked
,
batch2
->
getHeight
(),
weightWidth_
,
weightHeight_
,
batch1
->
getData
(),
weightHeight_
,
weightPacked_
,
weightWidth_
,
1
,
batch2
->
getData
(),
weightWidth_
);
}
}
~
MKLPackedGemm
()
{
cblas_sgemm_free
(
weightPacked_
);
cblas_sgemm_free
(
weightTPacked_
);
}
};
}
// namespace paddle
paddle/gserver/layers/MKLPackedRecurrentLayer.cpp
浏览文件 @
82091035
...
...
@@ -20,188 +20,21 @@ REGISTER_LAYER(mkl_packed_recurrent, MKLPackedRecurrentLayer);
bool
MKLPackedRecurrentLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
if
(
!
Layer
::
init
(
layerMap
,
parameterMap
))
return
false
;
CHECK_EQ
(
1U
,
inputLayers_
.
size
());
CHECK_EQ
(
1U
,
parameters_
.
size
());
CHECK_EQ
(
getSize
()
*
getSize
(),
parameters_
[
0
]
->
getSize
());
weight_
.
reset
(
new
Weight
(
getSize
(),
getSize
(),
parameters_
[
0
]));
if
(
biasParameter_
.
get
()
!=
NULL
)
{
bias_
.
reset
(
new
Weight
(
1
,
getSize
(),
biasParameter_
));
if
(
!
RecurrentLayer
::
init
(
layerMap
,
parameterMap
))
return
false
;
packed_weight_
.
reset
(
new
MKLPackedWeight
(
weight_
->
getW
()));
packed_weight_
->
pack
();
if
(
needGradient_
)
{
packed_weightT_
.
reset
(
new
MKLPackedWeight
(
weight_
->
getW
(),
true
));
packed_weightT_
->
pack
();
}
reversed_
=
config_
.
reversed
();
sgemm_packed_
.
reset
(
new
MKLPackedGemm
(
weight_
->
getW
()));
return
true
;
}
void
MKLPackedRecurrentLayer
::
resetState
()
{
CHECK
(
!
reversed_
)
<<
"state is not allowed for reversed recurrent layer"
;
Matrix
::
resizeOrCreate
(
prevOutput_
,
1
,
getSize
(),
/* trans= */
false
,
useGpu_
);
prevOutput_
->
zeroMem
();
}
void
MKLPackedRecurrentLayer
::
setState
(
LayerStatePtr
state
)
{
CHECK
(
state
->
value
.
size
()
==
1
)
<<
"one matrix is expected for RNN state"
;
prevOutput_
->
copyFrom
(
*
(
state
->
value
[
0
]));
}
LayerStatePtr
MKLPackedRecurrentLayer
::
getState
()
{
LayerStatePtr
res
=
std
::
make_shared
<
LayerState
>
();
res
->
value
.
push_back
(
prevOutput_
->
clone
(
0
,
0
,
useGpu_
));
res
->
value
[
0
]
->
copyFrom
(
*
prevOutput_
);
return
res
;
}
void
MKLPackedRecurrentLayer
::
forward
(
PassType
passType
)
{
REGISTER_TIMER_INFO
(
"RecurrentFwTimer"
,
getName
().
c_str
());
Layer
::
forward
(
passType
);
const
Argument
&
input
=
getInput
(
0
);
CHECK
(
input
.
sequenceStartPositions
);
int
batchSize
=
input
.
getBatchSize
();
size_t
numSequences
=
input
.
getNumSequences
();
resetOutput
(
batchSize
,
getSize
());
CHECK_EQ
(
getSize
(),
input
.
value
->
getWidth
());
const
int
*
starts
=
input
.
sequenceStartPositions
->
getData
(
false
);
CHECK_EQ
(
starts
[
numSequences
],
batchSize
);
output_
.
value
->
assign
(
*
input
.
value
);
if
(
bias_
)
{
output_
.
value
->
addBias
(
*
bias_
->
getW
(),
1
);
}
if
(
!
FLAGS_rnn_use_batch
)
{
forwardSequence
(
batchSize
,
numSequences
,
starts
);
}
else
{
forwardBatch
(
batchSize
,
numSequences
,
starts
);
}
}
void
MKLPackedRecurrentLayer
::
forwardSequence
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
)
{
REGISTER_TIMER_INFO
(
"RecurrentFwSequence"
,
getName
().
c_str
());
frameOutput_
.
reserve
(
batchSize
);
for
(
int
i
=
frameOutput_
.
size
();
i
<
batchSize
;
++
i
)
{
Argument
arg
;
arg
.
value
=
Matrix
::
create
(
nullptr
,
/* height= */
1
,
getSize
(),
/* trans= */
false
,
useGpu_
);
arg
.
grad
=
Matrix
::
create
(
nullptr
,
/* height= */
1
,
getSize
(),
/* trans= */
false
,
useGpu_
);
frameOutput_
.
push_back
(
arg
);
}
for
(
int
i
=
0
;
i
<
batchSize
;
++
i
)
{
frameOutput_
[
i
].
value
->
setData
(
output_
.
value
->
getData
()
+
i
*
getSize
());
}
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
forwardOneSequence
(
starts
[
i
],
starts
[
i
+
1
]
-
starts
[
i
]);
}
}
void
MKLPackedRecurrentLayer
::
forwardOneSequence
(
int
start
,
int
length
)
{
if
(
!
reversed_
)
{
if
(
prevOutput_
)
{
frameOutput_
[
start
].
value
->
mul
(
*
prevOutput_
,
*
weight_
->
getW
(),
1
,
1
);
}
activation_
->
forward
(
frameOutput_
[
start
]).
check
();
for
(
int
i
=
1
;
i
<
length
;
++
i
)
{
frameOutput_
[
start
+
i
].
value
->
mul
(
*
frameOutput_
[
start
+
i
-
1
].
value
,
*
weight_
->
getW
(),
1
,
1
);
activation_
->
forward
(
frameOutput_
[
start
+
i
]).
check
();
}
if
(
prevOutput_
)
{
prevOutput_
->
assign
(
*
frameOutput_
[
start
+
length
-
1
].
value
);
}
}
else
{
activation_
->
forward
(
frameOutput_
[
start
+
length
-
1
]).
check
();
for
(
int
i
=
length
-
2
;
i
>=
0
;
--
i
)
{
frameOutput_
[
start
+
i
].
value
->
mul
(
*
frameOutput_
[
start
+
i
+
1
].
value
,
*
weight_
->
getW
(),
1
,
1
);
activation_
->
forward
(
frameOutput_
[
start
+
i
]).
check
();
}
}
}
void
MKLPackedRecurrentLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
REGISTER_TIMER_INFO
(
"RecurrentBwTimer"
,
getName
().
c_str
());
const
Argument
&
input
=
getInput
(
0
);
CHECK
(
input
.
sequenceStartPositions
);
int
batchSize
=
input
.
getBatchSize
();
const
int
*
starts
=
input
.
sequenceStartPositions
->
getData
(
false
);
size_t
numSequences
=
input
.
getNumSequences
();
if
(
!
FLAGS_rnn_use_batch
)
{
backwardSequence
(
batchSize
,
numSequences
,
starts
);
}
else
{
backwardBatch
(
batchSize
,
numSequences
,
starts
);
}
if
(
input
.
grad
)
{
input
.
grad
->
add
(
*
output_
.
grad
);
}
if
(
bias_
&&
bias_
->
getWGrad
())
{
bias_
->
getWGrad
()
->
collectBias
(
*
output_
.
grad
,
1
);
bias_
->
getParameterPtr
()
->
incUpdate
(
callback
);
}
weight_
->
getParameterPtr
()
->
incUpdate
(
callback
);
sgemm_packed_
.
reset
(
new
MKLPackedGemm
(
weight_
->
getW
()));
}
void
MKLPackedRecurrentLayer
::
backwardSequence
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
)
{
REGISTER_TIMER_INFO
(
"RecurrentBwSequence"
,
getName
().
c_str
());
for
(
int
i
=
0
;
i
<
batchSize
;
++
i
)
{
frameOutput_
[
i
].
grad
->
setData
(
output_
.
grad
->
getData
()
+
i
*
getSize
());
}
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
backwardOneSequence
(
starts
[
i
],
starts
[
i
+
1
]
-
starts
[
i
]);
}
}
void
MKLPackedRecurrentLayer
::
backwardOneSequence
(
int
start
,
int
length
)
{
MatrixPtr
weightT
=
weight_
->
getW
()
->
getTranspose
();
if
(
!
reversed_
)
{
for
(
int
i
=
length
-
1
;
i
>
0
;
--
i
)
{
activation_
->
backward
(
frameOutput_
[
start
+
i
]).
check
();
frameOutput_
[
start
+
i
-
1
].
grad
->
mul
(
*
frameOutput_
[
start
+
i
].
grad
,
*
weightT
,
1
,
1
);
}
activation_
->
backward
(
frameOutput_
[
start
]).
check
();
if
(
weight_
->
getWGrad
())
{
weight_
->
getWGrad
()
->
mul
(
*
output_
.
value
->
subMatrix
(
start
,
length
-
1
)
->
getTranspose
(),
*
output_
.
grad
->
subMatrix
(
start
+
1
,
length
-
1
),
1
,
1
);
}
}
else
{
for
(
int
i
=
0
;
i
<
length
-
1
;
++
i
)
{
activation_
->
backward
(
frameOutput_
[
start
+
i
]).
check
();
frameOutput_
[
start
+
i
+
1
].
grad
->
mul
(
*
frameOutput_
[
start
+
i
].
grad
,
*
weightT
,
1
,
1
);
}
activation_
->
backward
(
frameOutput_
[
start
+
length
-
1
]).
check
();
if
(
weight_
->
getWGrad
())
{
weight_
->
getWGrad
()
->
mul
(
*
output_
.
value
->
subMatrix
(
start
+
1
,
length
-
1
)
->
getTranspose
(),
*
output_
.
grad
->
subMatrix
(
start
,
length
-
1
),
1
,
1
);
}
RecurrentLayer
::
backward
(
callback
);
packed_weight_
->
pack
();
if
(
needGradient_
)
{
packed_weightT_
->
pack
();
}
}
...
...
@@ -227,7 +60,7 @@ void MKLPackedRecurrentLayer::forwardBatch(int batchSize,
batchValue_
->
getBatchValue
(
n
-
1
,
batch2
->
getHeight
());
// batch2->mul(*batch1, *weight_->getW(), 1, 1);
sgemm_packed
_
->
compute
(
batch2
,
batch1
);
packed_weight
_
->
compute
(
batch2
,
batch1
);
}
#pragma omp parallel for collapse(2)
...
...
@@ -272,7 +105,7 @@ void MKLPackedRecurrentLayer::backwardBatch(int batchSize,
if
(
n
!=
0
)
{
batch1
=
batchGrad_
->
getBatchValue
(
n
-
1
,
batch2
->
getHeight
());
// batch1->mul(*batch2, *weightT, 1, 1);
sgemm_packed_
->
compute
(
batch1
,
batch2
,
true
);
packed_weightT_
->
compute
(
batch1
,
batch2
);
}
if
(
backwardByBatch
&&
weight_
->
getWGrad
())
{
...
...
paddle/gserver/layers/MKLPackedRecurrentLayer.h
浏览文件 @
82091035
...
...
@@ -16,7 +16,8 @@ limitations under the License. */
#include <gflags/gflags.h>
#include "Layer.h"
#include "MKLPackedGemm.h"
#include "MKLPackedWeight.h"
#include "RecurrentLayer.h"
#include "SequenceToBatch.h"
#include "paddle/utils/Stat.h"
...
...
@@ -45,90 +46,28 @@ namespace paddle {
* them by rnn_use_batch flag.
*/
class
MKLPackedRecurrentLayer
:
public
Layer
{
class
MKLPackedRecurrentLayer
:
public
Recurrent
Layer
{
public:
explicit
MKLPackedRecurrentLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
explicit
MKLPackedRecurrentLayer
(
const
LayerConfig
&
config
)
:
RecurrentLayer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forward
(
PassType
passType
)
override
;
void
backward
(
const
UpdateCallback
&
callback
)
override
;
void
resetState
()
override
;
void
setState
(
LayerStatePtr
state
)
override
;
LayerStatePtr
getState
()
override
;
protected:
/**
* @brief If user do not set --rnn_use_batch=true, it will
* compute rnn forward one sequence by one sequence in default.
* @param batchSize Total words number of all samples in this batch.
* @param numSequences The sample number.
* @param starts Each start position of each samples.
*/
void
forwardSequence
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
);
/**
* @brief Compute rnn forward by one sequence.
* @param start The start position of this sequence (or sample).
* @param length The length of this sequence (or sample), namely the words
* number of this sequence.
*/
void
forwardOneSequence
(
int
start
,
int
length
);
/**
* @brief Compute rnn backward one sequence by onesequence.
* @param batchSize Total words number of all samples in this batch.
* @param numSequences The sample number.
* @param starts Each start position of each samples.
*/
void
backwardSequence
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
);
/**
* @brief Compute rnn backward by one sequence.
* @param start The start position of this sequence (or sample).
* @param length The length of this sequence (or sample), namely the words
* number of this sequence.
*/
void
backwardOneSequence
(
int
start
,
int
length
);
void
forwardBatch
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
)
override
;
/**
* @brief Reorganize input into batches and compute rnn forward batch
* by batch. It will convert batch shape to sequence after finishing forward.
* The batch info can refer to SequenceToBatch class.
* @param batchSize Total words number of all samples in this batch.
* @param numSequences The sample number.
* @param starts Each start position of each samples.
*/
void
forwardBatch
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
);
/**
* @brief Reorganize input into batches and compute rnn forward batch
* by batch.
* @param batchSize Total words number of all samples in this batch.
* @param numSequences The sample number.
* @param starts Each start position of each samples.
*/
void
backwardBatch
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
);
void
backwardBatch
(
int
batchSize
,
size_t
numSequences
,
const
int
*
starts
)
override
;
protected:
std
::
unique_ptr
<
Weight
>
weight_
;
std
::
unique_ptr
<
Weight
>
bias_
;
/// frameOutput_[i] is used to hold the i-th sample of output_
std
::
vector
<
Argument
>
frameOutput_
;
MatrixPtr
prevOutput_
;
/// Whether compute rnn by reverse.
bool
reversed_
;
/// If compute batch by batch, batchValue_ will be used to save the
/// reorganized input value.
std
::
unique_ptr
<
SequenceToBatch
>
batchValue_
;
/// If compute batch by batch, batchGrad_ will be used to save the
/// gradient with respect to reorganized input value.
std
::
unique_ptr
<
SequenceToBatch
>
batchGrad_
;
std
::
unique_ptr
<
MKLPackedGemm
>
sgemm_packed_
;
std
::
unique_ptr
<
MKLPackedWeight
>
packed_weight_
;
std
::
unique_ptr
<
MKLPackedWeight
>
packed_weightT_
;
};
}
// namespace paddle
paddle/gserver/layers/MKLPackedWeight.h
0 → 100644
浏览文件 @
82091035
/* 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/math/MathFunctions.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/parameter/Weight.h"
namespace
paddle
{
class
MKLPackedWeight
{
protected:
real
*
weight_
;
real
*
packedWeight_
;
size_t
height_
;
size_t
width_
;
bool
transW_
;
public:
MKLPackedWeight
(
MatrixPtr
weight
,
bool
transW
=
false
)
{
packedWeight_
=
nullptr
;
weight_
=
weight
->
getData
();
height_
=
weight
->
getHeight
();
width_
=
weight
->
getWidth
();
transW_
=
transW
;
}
~
MKLPackedWeight
()
{
free_
();
}
void
pack
()
{
pack_
(
weight_
);
}
void
compute
(
MatrixPtr
dst
,
MatrixPtr
src
)
{
cblas_sgemm_compute
(
CblasRowMajor
,
CblasNoTrans
,
CblasPacked
,
src
->
getHeight
(),
transW_
?
height_
:
width_
,
transW_
?
width_
:
height_
,
src
->
getData
(),
src
->
getWidth
(),
packedWeight_
,
width_
,
1.0
,
dst
->
getData
(),
dst
->
getWidth
());
}
void
compute
(
size_t
M
,
real
*
A
,
size_t
lda
,
real
*
C
,
size_t
ldc
)
{
cblas_sgemm_compute
(
CblasRowMajor
,
CblasNoTrans
,
CblasPacked
,
M
,
width_
,
height_
,
A
,
lda
,
packedWeight_
,
width_
,
1.0
,
C
,
ldc
);
}
protected:
void
pack_
(
real
*
src
)
{
if
(
!
packedWeight_
)
{
packedWeight_
=
cblas_sgemm_alloc
(
CblasBMatrix
,
1
,
width_
,
height_
);
}
cblas_sgemm_pack
(
CblasRowMajor
,
CblasBMatrix
,
transW_
?
CblasTrans
:
CblasNoTrans
,
1
,
transW_
?
height_
:
width_
,
transW_
?
width_
:
height_
,
1.0
,
src
,
width_
,
packedWeight_
);
}
void
free_
()
{
if
(
packedWeight_
)
{
cblas_sgemm_free
(
packedWeight_
);
}
}
};
}
// namespace paddle
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