Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
171fee2c
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
大约 1 年 前同步成功
通知
695
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
171fee2c
编写于
9月 12, 2017
作者:
T
Tao Luo
提交者:
GitHub
9月 12, 2017
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #4011 from tensor-tang/refine
Refine MKLDNNLayer and MKLDNNTester
上级
6d0d29f6
7f7fa325
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
325 addition
and
230 deletion
+325
-230
paddle/gserver/layers/Layer.h
paddle/gserver/layers/Layer.h
+6
-5
paddle/gserver/layers/MKLDNNFcLayer.cpp
paddle/gserver/layers/MKLDNNFcLayer.cpp
+81
-131
paddle/gserver/layers/MKLDNNFcLayer.h
paddle/gserver/layers/MKLDNNFcLayer.h
+17
-24
paddle/gserver/layers/MKLDNNLayer.h
paddle/gserver/layers/MKLDNNLayer.h
+169
-32
paddle/gserver/tests/MKLDNNTester.cpp
paddle/gserver/tests/MKLDNNTester.cpp
+45
-33
paddle/gserver/tests/MKLDNNTester.h
paddle/gserver/tests/MKLDNNTester.h
+7
-5
未找到文件。
paddle/gserver/layers/Layer.h
浏览文件 @
171fee2c
...
...
@@ -49,6 +49,12 @@ struct LayerState {
};
typedef
std
::
shared_ptr
<
LayerState
>
LayerStatePtr
;
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum
PADDLE_DEVICE_ID
{
MKLDNN_DEVICE
=
-
2
,
CPU_DEVICE
=
-
1
,
};
/**
* @brief Base class for layer.
* Define necessary variables and functions for every layer.
...
...
@@ -59,11 +65,6 @@ protected:
LayerConfig
config_
;
/// whether to use GPU
bool
useGpu_
;
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum
PADDLE_DEVICE_ID
{
MKLDNN_DEVICE
=
-
2
,
CPU_DEVICE
=
-
1
,
};
/// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ...
int
deviceId_
;
/// Input layers
...
...
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @
171fee2c
...
...
@@ -14,7 +14,6 @@ limitations under the License. */
#include "MKLDNNFcLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
using
namespace
mkldnn
;
// NOLINT
typedef
memory
::
format
format
;
...
...
@@ -40,6 +39,8 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap,
oc_
=
getSize
();
oh_
=
1
;
ow_
=
1
;
ih_
=
1
;
iw_
=
1
;
// input size can not change in FC
iLayerSize_
=
inputLayers_
[
0
]
->
getSize
();
...
...
@@ -77,67 +78,53 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
wgtVal_
->
reorderDataTo
(
wgtVal_
,
dstFmt
,
targetDim
);
}
void
MKLDNNFcLayer
::
reshape
()
{
const
Argument
&
input
=
getInput
(
0
,
getPrev
(
0
)
->
getDeviceId
());
int
batchSize
=
input
.
getBatchSize
();
if
(
bs_
==
batchSize
)
{
return
;
}
bs_
=
batchSize
;
ih_
=
input
.
getFrameHeight
();
iw_
=
input
.
getFrameWidth
();
if
(
ih_
==
0
)
{
ih_
=
1
;
}
if
(
iw_
==
0
)
{
iw_
=
1
;
}
CHECK_EQ
(
iLayerSize_
,
inputLayers_
[
0
]
->
getSize
());
ic_
=
iLayerSize_
/
(
ih_
*
iw_
);
CHECK_EQ
(
size_t
(
ic_
*
ih_
*
iw_
),
iLayerSize_
)
<<
"not divisible"
;
CHECK_EQ
(
size_t
(
oc_
),
getSize
());
printSizeInfo
();
void
MKLDNNFcLayer
::
reshape
(
int
&
bs
,
int
&
ic
,
int
&
ih
,
int
&
iw
,
int
oc
,
int
&
oh
,
int
&
ow
)
{
reshapeInput
(
bs
,
ih
,
iw
);
// reset output
output_
.
setFrameHeight
(
oh_
);
output_
.
setFrameWidth
(
ow_
)
;
resetOutput
(
bs_
,
oc_
);
CHECK_EQ
(
iLayerSize_
,
inputLayers_
[
0
]
->
getSize
());
ic
=
iLayerSize_
/
(
ih
*
iw
);
CHECK_EQ
(
size_t
(
ic
*
ih
*
iw
),
iLayerSize_
)
<<
"not divisible"
;
CHECK_EQ
(
size_t
(
oc
),
getSize
()
);
// reset mkldnn forward
resetFwd
();
needResetBwd_
=
true
;
reshapeOutput
(
oh
,
ow
);
resizeOutput
(
bs
,
oc
);
convertWeightsFromPaddle
();
printSizeInfo
();
}
void
MKLDNNFcLayer
::
resetFwd
()
{
void
MKLDNNFcLayer
::
resetFwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
pipeline
.
clear
();
bool
hasBias
=
biases_
&&
biases_
->
getW
();
const
MatrixPtr
&
wgt
=
weight_
->
getW
();
const
MatrixPtr
&
bias
=
hasBias
?
biases_
->
getW
()
:
nullptr
;
const
MatrixPtr
&
out
=
output_
.
value
;
const
MatrixPtr
&
wgt
Val
=
weight_
->
getW
();
const
MatrixPtr
&
bias
Val
=
hasBias
?
biases_
->
getW
()
:
nullptr
;
const
MatrixPtr
&
out
Val
=
output_
.
value
;
if
(
inputIsOnlyMKLDNN
())
{
const
MatrixPtr
&
in
=
getInputValue
(
0
);
in
Val_
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
in
);
CHECK
(
in
Val_
)
<<
"Input should be MKLDNNMatrix"
;
const
MatrixPtr
&
in
Val
=
getInputValue
(
0
);
in
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
inVal
);
CHECK
(
in
)
<<
"Input should be MKLDNNMatrix"
;
}
else
{
CHECK_EQ
(
getPrev
(
0
)
->
getDeviceId
(),
CPU_DEVICE
)
<<
"Only support CPU yet"
;
const
MatrixPtr
&
in
=
getInputValue
(
0
,
CPU_DEVICE
);
in
Val_
=
MKLDNNMatrix
::
create
(
in
,
memory
::
dims
{
bs_
,
ic_
,
ih_
,
iw_
},
format
::
nchw
,
engine_
);
}
in
Val_
->
downSpatial
();
wgt
Val_
=
MKLDNNMatrix
::
create
(
wgt
,
memory
::
dims
{
oc_
,
ic_
,
ih_
,
iw_
},
format
::
oihw
,
engine_
);
wgt
Val_
->
downSpatial
();
bias
Val_
=
hasBias
?
MKLDNNMatrix
::
create
(
bias
,
{
oc_
},
format
::
x
,
engine_
)
:
nullptr
;
out
Val_
=
MKLDNNMatrix
::
create
(
out
,
{
bs_
,
oc_
},
format
::
nc
,
engine_
);
const
MatrixPtr
&
in
Val
=
getInputValue
(
0
,
CPU_DEVICE
);
in
=
MKLDNNMatrix
::
create
(
in
Val
,
memory
::
dims
{
bs_
,
ic_
,
ih_
,
iw_
},
format
::
nchw
,
engine_
);
}
in
->
downSpatial
();
wgt
=
MKLDNNMatrix
::
create
(
wgt
Val
,
memory
::
dims
{
oc_
,
ic_
,
ih_
,
iw_
},
format
::
oihw
,
engine_
);
wgt
->
downSpatial
();
bias
=
hasBias
?
MKLDNNMatrix
::
create
(
biasVal
,
{
oc_
},
format
::
x
,
engine_
)
:
nullptr
;
out
=
MKLDNNMatrix
::
create
(
outVal
,
{
bs_
,
oc_
},
format
::
nc
,
engine_
);
// change original output value to mkldnn output value
output_
.
value
=
std
::
dynamic_pointer_cast
<
Matrix
>
(
out
Val_
);
output_
.
value
=
std
::
dynamic_pointer_cast
<
Matrix
>
(
out
);
if
(
!
outputIsOnlyMKLDNN
())
{
copyOutputInfoToOtherDevice
();
// fc cpu output value do not need create convert
// just share point
getOutput
(
CPU_DEVICE
).
value
->
setData
(
output_
.
value
->
getData
());
...
...
@@ -146,27 +133,31 @@ void MKLDNNFcLayer::resetFwd() {
// create forward handle
prop_kind
pk
=
prop_kind
::
forward
;
fc_fwd
::
desc
fwdDesc
=
hasBias
?
fc_fwd
::
desc
(
pk
,
in
Val_
->
getMemoryDesc
(),
wgt
Val_
->
getMemoryDesc
(),
bias
Val_
->
getMemoryDesc
(),
out
Val_
->
getMemoryDesc
())
in
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
bias
->
getMemoryDesc
(),
out
->
getMemoryDesc
())
:
fc_fwd
::
desc
(
pk
,
in
Val_
->
getMemoryDesc
(),
wgt
Val_
->
getMemoryDesc
(),
out
Val_
->
getMemoryDesc
());
in
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
out
->
getMemoryDesc
());
fc_fwd
::
primitive_desc
fwdPD
=
fc_fwd
::
primitive_desc
(
fwdDesc
,
engine_
);
if
(
hasBias
)
{
fwd_
.
reset
(
new
fc_fwd
(
fwdPD
,
*
in
Val_
,
*
wgtVal_
,
*
biasVal_
,
*
outVal_
));
fwd_
.
reset
(
new
fc_fwd
(
fwdPD
,
*
in
,
*
wgt
,
*
bias
,
*
out
));
}
else
{
fwd_
.
reset
(
new
fc_fwd
(
fwdPD
,
*
in
Val_
,
*
wgtVal_
,
*
outVal_
));
fwd_
.
reset
(
new
fc_fwd
(
fwdPD
,
*
in
,
*
wgt
,
*
out
));
}
printValueFormatFlow
();
pipelineFwd_
.
clear
();
pipelineFwd_
.
push_back
(
*
fwd_
);
pipeline
.
push_back
(
*
fwd_
);
}
void
MKLDNNFcLayer
::
resetBwd
()
{
void
MKLDNNFcLayer
::
resetBwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
pipeline
.
clear
();
if
(
!
needResetBwd_
)
{
return
;
}
...
...
@@ -175,8 +166,8 @@ void MKLDNNFcLayer::resetBwd() {
/// backward weight
CHECK
(
inVal_
)
<<
"Should have input value"
;
const
MatrixPtr
&
wgt
=
weight_
->
getWGrad
();
const
MatrixPtr
&
bias
=
hasBias
?
biases_
->
getWGrad
()
:
nullptr
;
const
MatrixPtr
&
wgt
Grad
=
weight_
->
getWGrad
();
const
MatrixPtr
&
bias
Grad
=
hasBias
?
biases_
->
getWGrad
()
:
nullptr
;
// TODO(TJ): merge outgrad
int
device
=
outputIsOnlyMKLDNN
()
?
MKLDNN_DEVICE
:
CPU_DEVICE
;
...
...
@@ -187,107 +178,66 @@ void MKLDNNFcLayer::resetBwd() {
// for CPU device:
// fc do not need to convert from cpu device since output is always nc format
// only need create from cpu device
const
MatrixPtr
&
out
=
getOutput
(
device
).
grad
;
out
Grad_
=
MKLDNNMatrix
::
create
(
out
,
outVal_
->
getPrimitiveDesc
());
wgt
Grad_
=
MKLDNNMatrix
::
create
(
wgt
,
wgtVal_
->
getPrimitiveDesc
());
bias
Grad_
=
hasBias
?
MKLDNNMatrix
::
create
(
bias
,
biasVal_
->
getPrimitiveDesc
())
:
nullptr
;
const
MatrixPtr
&
out
Grad
=
getOutput
(
device
).
grad
;
out
=
MKLDNNMatrix
::
create
(
outGrad
,
outVal_
->
getPrimitiveDesc
());
wgt
=
MKLDNNMatrix
::
create
(
wgtGrad
,
wgtVal_
->
getPrimitiveDesc
());
bias
=
hasBias
?
MKLDNNMatrix
::
create
(
biasGrad
,
biasVal_
->
getPrimitiveDesc
())
:
nullptr
;
// create memory primitive desc
fc_fwd
::
desc
fwdDesc
=
fc_fwd
::
desc
(
prop_kind
::
forward
,
inVal_
->
getMemoryDesc
(),
wgt
Grad_
->
getMemoryDesc
(),
out
Grad_
->
getMemoryDesc
());
wgt
->
getMemoryDesc
(),
out
->
getMemoryDesc
());
fc_fwd
::
primitive_desc
fwdPD
=
fc_fwd
::
primitive_desc
(
fwdDesc
,
engine_
);
fc_bwdWgt
::
desc
bwdWgtDesc
=
hasBias
?
fc_bwdWgt
::
desc
(
inVal_
->
getMemoryDesc
(),
wgt
Grad_
->
getMemoryDesc
(),
bias
Grad_
->
getMemoryDesc
(),
out
Grad_
->
getMemoryDesc
())
wgt
->
getMemoryDesc
(),
bias
->
getMemoryDesc
(),
out
->
getMemoryDesc
())
:
fc_bwdWgt
::
desc
(
inVal_
->
getMemoryDesc
(),
wgt
Grad_
->
getMemoryDesc
(),
out
Grad_
->
getMemoryDesc
());
wgt
->
getMemoryDesc
(),
out
->
getMemoryDesc
());
fc_bwdWgt
::
primitive_desc
bwdWgtPD
=
fc_bwdWgt
::
primitive_desc
(
bwdWgtDesc
,
engine_
,
fwdPD
);
if
(
hasBias
)
{
bwdWgt_
.
reset
(
new
fc_bwdWgt
(
bwdWgtPD
,
*
inVal_
,
*
outGrad_
,
*
wgtGrad_
,
*
biasGrad_
));
bwdWgt_
.
reset
(
new
fc_bwdWgt
(
bwdWgtPD
,
*
inVal_
,
*
out
,
*
wgt
,
*
bias
));
}
else
{
bwdWgt_
.
reset
(
new
fc_bwdWgt
(
bwdWgtPD
,
*
inVal_
,
*
out
Grad_
,
*
wgtGrad_
));
bwdWgt_
.
reset
(
new
fc_bwdWgt
(
bwdWgtPD
,
*
inVal_
,
*
out
,
*
wgt
));
}
pipelineBwd_
.
clear
();
pipelineBwd_
.
push_back
(
*
bwdWgt_
);
pipeline
.
push_back
(
*
bwdWgt_
);
/// backward data
const
MatrixPtr
&
in
=
inputLayers_
[
0
]
->
getOutput
().
grad
;
if
(
in
==
nullptr
)
{
const
MatrixPtr
&
in
Grad
=
inputLayers_
[
0
]
->
getOutput
().
grad
;
if
(
in
Grad
==
nullptr
)
{
return
;
}
if
(
getInput
(
0
,
MKLDNN_DEVICE
).
getAllCount
()
>
1
)
{
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
}
else
{
in
Grad_
=
MKLDNNMatrix
::
create
(
in
,
inVal_
->
getPrimitiveDesc
());
in
=
MKLDNNMatrix
::
create
(
inGrad
,
inVal_
->
getPrimitiveDesc
());
}
fc_bwdData
::
desc
bwdDataDesc
=
fc_bwdData
::
desc
(
inVal_
->
getMemoryDesc
(),
wgtGrad_
->
getMemoryDesc
(),
outGrad_
->
getMemoryDesc
());
fc_bwdData
::
desc
bwdDataDesc
=
fc_bwdData
::
desc
(
inVal_
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
out
->
getMemoryDesc
());
fc_bwdData
::
primitive_desc
bwdDataPD
=
fc_bwdData
::
primitive_desc
(
bwdDataDesc
,
engine_
,
fwdPD
);
CHECK
(
wgtVal_
)
<<
"Should have weight memory"
;
bwdData_
.
reset
(
new
fc_bwdData
(
bwdDataPD
,
*
out
Grad_
,
*
wgtVal_
,
*
inGrad_
));
bwdData_
.
reset
(
new
fc_bwdData
(
bwdDataPD
,
*
out
,
*
wgtVal_
,
*
in
));
printGradFormatFlow
();
pipeline
Bwd_
.
push_back
(
*
bwdData_
);
pipeline
.
push_back
(
*
bwdData_
);
}
void
MKLDNNFcLayer
::
updateInputData
()
{
if
(
inputLayers_
[
0
]
->
getType
()
!=
"data"
)
{
return
;
}
real
*
iData
=
getInputValue
(
0
,
CPU_DEVICE
)
->
getData
();
inVal_
->
setData
(
iData
);
inVal_
->
setData
(
getInputValue
(
0
,
CPU_DEVICE
)
->
getData
());
}
void
MKLDNNFcLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
reshape
();
{
REGISTER_TIMER_INFO
(
"mkldnn_FwdTimer"
,
getName
().
c_str
());
updateInputData
();
// just submit forward pipeline
stream_
->
submit
(
pipelineFwd_
);
}
/* activation */
{
REGISTER_TIMER_INFO
(
"FwActTimer"
,
getName
().
c_str
());
forwardActivation
();
}
}
void
MKLDNNFcLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
/* Do derivation */
{
REGISTER_TIMER_INFO
(
"BpActTimer"
,
getName
().
c_str
());
backwardActivation
();
}
{
REGISTER_TIMER_INFO
(
"mkldnn_bwdTimer"
,
getName
().
c_str
());
resetBwd
();
// just sumbmit backward pipeline
stream_
->
submit
(
pipelineBwd_
);
}
{
REGISTER_TIMER_INFO
(
"WeightUpdate"
,
getName
().
c_str
());
weight_
->
getParameterPtr
()
->
incUpdate
(
callback
);
if
(
biases_
&&
biases_
->
getWGrad
())
{
biases_
->
getParameterPtr
()
->
incUpdate
(
callback
);
}
void
MKLDNNFcLayer
::
updateWeights
(
const
UpdateCallback
&
callback
)
{
weight_
->
getParameterPtr
()
->
incUpdate
(
callback
);
if
(
biases_
&&
biases_
->
getWGrad
())
{
biases_
->
getParameterPtr
()
->
incUpdate
(
callback
);
}
}
}
// namespace paddle
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @
171fee2c
...
...
@@ -45,35 +45,28 @@ public:
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
convertWeightsFromPaddle
()
override
;
void
convertWeightsToPaddle
()
override
;
void
reshape
(
int
&
bs
,
int
&
ic
,
int
&
ih
,
int
&
iw
,
int
oc
,
int
&
oh
,
int
&
ow
)
override
;
void
forward
(
PassType
passType
)
override
;
void
resetFwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
override
;
void
backward
(
const
UpdateCallback
&
callback
)
override
;
void
resetBwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
override
;
void
updateInputData
()
override
;
protected:
/**
* reshape the input image sizes
* and reset output buffer size
* and reset mkldnn forward
*/
void
reshape
();
/**
* reset the forward primitve and memory
* only would be called when input size changes
*/
void
resetFwd
();
/**
* reset the backward primitve and memory for mkldnn fc
* only would be called when needed
*/
void
resetBwd
();
void
updateWeights
(
const
UpdateCallback
&
callback
)
override
;
void
convertWeightsFromPaddle
()
override
;
void
convertWeightsToPaddle
()
override
;
};
}
// namespace paddle
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @
171fee2c
...
...
@@ -19,6 +19,7 @@ limitations under the License. */
#include "MKLDNNBase.h"
#include "mkldnn.hpp"
#include "paddle/math/MKLDNNMatrix.h"
#include "paddle/utils/Stat.h"
DECLARE_bool
(
use_mkldnn
);
...
...
@@ -33,6 +34,8 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
*/
class
MKLDNNLayer
:
public
Layer
{
protected:
// input value element count
size_t
inputElemenCnt_
;
// batch size
int
bs_
;
// input image channel, height and width
...
...
@@ -52,7 +55,7 @@ protected:
std
::
vector
<
mkldnn
::
primitive
>
pipelineFwd_
;
std
::
vector
<
mkldnn
::
primitive
>
pipelineBwd_
;
// MKLDNNMatrixPtr
// MKLDNNMatrixPtr
with internal format
MKLDNNMatrixPtr
inVal_
;
MKLDNNMatrixPtr
inGrad_
;
MKLDNNMatrixPtr
outVal_
;
...
...
@@ -65,6 +68,7 @@ protected:
public:
explicit
MKLDNNLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
),
inputElemenCnt_
(
0
),
bs_
(
0
),
ic_
(
0
),
ih_
(
0
),
...
...
@@ -95,12 +99,104 @@ public:
if
(
!
Layer
::
init
(
layerMap
,
parameterMap
))
{
return
false
;
}
checkCPUOutputsNumber
();
stream_
.
reset
(
new
MKLDNNStream
());
engine_
=
CPUEngine
::
Instance
().
getEngine
();
return
true
;
}
void
forward
(
PassType
passType
)
override
{
passType_
=
passType
;
{
REGISTER_TIMER_INFO
(
"mkldnn_FwdTimer"
,
getName
().
c_str
());
CHECK
(
!
inputLayers_
.
empty
());
copySeqInfoToOutputs
();
size_t
elemenCnt
=
inputLayers_
[
0
]
->
getOutput
().
value
->
getElementCnt
();
if
(
inputElemenCnt_
!=
elemenCnt
)
{
// reset when input total sizes changed, not only the batchsize
inputElemenCnt_
=
elemenCnt
;
reshape
(
bs_
,
ic_
,
ih_
,
iw_
,
oc_
,
oh_
,
ow_
);
resetFwd
(
pipelineFwd_
,
inVal_
,
wgtVal_
,
biasVal_
,
outVal_
);
convertWeightsFromPaddle
();
needResetBwd_
=
true
;
}
if
(
inputLayers_
[
0
]
->
getType
()
==
"data"
)
{
updateInputData
();
}
stream_
->
submit
(
pipelineFwd_
);
}
/* activation */
{
REGISTER_TIMER_INFO
(
"FwActTimer"
,
getName
().
c_str
());
forwardActivation
();
}
}
void
backward
(
const
UpdateCallback
&
callback
)
override
{
/* Do derivation */
{
REGISTER_TIMER_INFO
(
"BpActTimer"
,
getName
().
c_str
());
backwardActivation
();
}
{
REGISTER_TIMER_INFO
(
"mkldnn_bwdTimer"
,
getName
().
c_str
());
if
(
needResetBwd_
)
{
resetBwd
(
pipelineBwd_
,
inGrad_
,
wgtGrad_
,
biasGrad_
,
outGrad_
);
needResetBwd_
=
false
;
}
stream_
->
submit
(
pipelineBwd_
);
}
{
REGISTER_TIMER_INFO
(
"WeightUpdate"
,
getName
().
c_str
());
updateWeights
(
callback
);
}
}
/**
* reshape the input image sizes
* and reset output image and buffer size
* output channel can not be changed
*/
virtual
void
reshape
(
int
&
bs
,
int
&
ic
,
int
&
ih
,
int
&
iw
,
int
oc
,
int
&
oh
,
int
&
ow
)
=
0
;
/**
* reset the mkldnn forward primitve and memory
* only would be called when input size changes
*/
virtual
void
resetFwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
=
0
;
/**
* reset the mkldnn backward primitve and memory for mkldnn fc
* only would be called when needed
*/
virtual
void
resetBwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
=
0
;
/**
* Update input value data when input layer is "data" type.
* Since the input value data address might be changed.
*/
virtual
void
updateInputData
()
{}
/**
* Update weights and biases if necessary.
*/
virtual
void
updateWeights
(
const
UpdateCallback
&
callback
)
{}
/**
* convert weight from paddle format to mkldnn format
* weight_ will be override
...
...
@@ -114,10 +210,38 @@ public:
virtual
void
convertWeightsToPaddle
()
{}
/**
* Update input value data when input layer is "data" type.
* Since the input value data address might be changed.
* add this interface as public for unit test
*/
virtual
void
updateInputData
()
{}
void
addOutputArgument
(
int
deviceId
)
{
Layer
::
addOutputArgument
(
deviceId
);
}
protected:
/**
* reshape the input image sizes and input batchsize
*/
virtual
void
reshapeInput
(
int
&
batchsize
,
int
&
height
,
int
&
width
)
{
const
Argument
&
input
=
inputLayers_
[
0
]
->
getOutput
();
batchsize
=
input
.
getBatchSize
();
int
h
=
input
.
getFrameHeight
();
int
w
=
input
.
getFrameWidth
();
if
(
h
!=
0
)
{
height
=
h
;
}
if
(
w
!=
0
)
{
width
=
w
;
}
}
/**
* reshape output image sizes
*/
virtual
void
reshapeOutput
(
size_t
height
,
size_t
width
)
{
output_
.
setFrameHeight
(
height
);
output_
.
setFrameWidth
(
width
);
for
(
size_t
i
=
0
;
i
<
outputOtherDevice_
.
size
();
i
++
)
{
outputOtherDevice_
[
i
].
setFrameHeight
(
height
);
outputOtherDevice_
[
i
].
setFrameWidth
(
width
);
}
}
/**
* print info about sizes
...
...
@@ -133,8 +257,8 @@ public:
*/
virtual
void
printValueFormatFlow
()
{
if
(
inVal_
&&
outVal_
)
{
VLOG
(
MKLDNN_FMTS
)
<<
"value format flow --- "
<<
inVal_
->
getFormat
()
<<
" >>> "
<<
outVal_
->
getFormat
();
VLOG
(
MKLDNN_FMTS
)
<<
inVal_
->
getFormat
()
<<
" >>> "
<<
outVal_
->
getFormat
();
}
}
...
...
@@ -143,36 +267,12 @@ public:
*/
virtual
void
printGradFormatFlow
()
{
if
(
inGrad_
&&
outGrad_
)
{
VLOG
(
MKLDNN_FMTS
)
<<
"grad format flow --- "
<<
inGrad_
->
getFormat
()
<<
" <<< "
<<
outGrad_
->
getFormat
();
VLOG
(
MKLDNN_FMTS
)
<<
inGrad_
->
getFormat
()
<<
" <<< "
<<
outGrad_
->
getFormat
();
}
}
protected:
/**
* copy image size and sequence info to other device
* @note: can not directly use Layer::copyOutputToOtherDevice since here only
* copy base info and do not copy data value
*/
void
copyOutputInfoToOtherDevice
()
{
int
cnt
=
0
;
for
(
size_t
i
=
0
;
i
<
outputOtherDevice_
.
size
();
i
++
)
{
outputOtherDevice_
[
i
].
setFrameHeight
(
output_
.
getFrameHeight
());
outputOtherDevice_
[
i
].
setFrameWidth
(
output_
.
getFrameWidth
());
outputOtherDevice_
[
i
].
sequenceStartPositions
=
output_
.
sequenceStartPositions
;
outputOtherDevice_
[
i
].
subSequenceStartPositions
=
output_
.
subSequenceStartPositions
;
outputOtherDevice_
[
i
].
cpuSequenceDims
=
output_
.
cpuSequenceDims
;
if
(
outputOtherDevice_
[
i
].
deviceId
==
CPU_DEVICE
)
{
++
cnt
;
}
}
if
(
cnt
>
1
)
{
LOG
(
WARNING
)
<<
"should not have more than one CPU devie"
;
}
}
/**
* If input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
...
...
@@ -205,6 +305,7 @@ protected:
*/
void
setDevice
(
int
id
)
{
deviceId_
=
id
;
}
private:
/**
* Set deviceId of the params used in this layer.
*/
...
...
@@ -228,6 +329,42 @@ protected:
parameter
->
setDevice
(
id
);
}
}
/**
* Check the cpu device number of outputOtherDevice_.
* should have only one at most.
*/
void
checkCPUOutputsNumber
(
int
max
=
1
)
{
int
cnt
=
0
;
for
(
size_t
i
=
0
;
i
<
outputOtherDevice_
.
size
();
i
++
)
{
if
(
outputOtherDevice_
[
i
].
deviceId
==
CPU_DEVICE
)
{
++
cnt
;
}
}
CHECK_LE
(
cnt
,
max
)
<<
"too much CPU devies"
;
}
/**
* copy SeqInfo from input layer to this output and other output devices.
* @note: do not use getInput(0) since it used this deviceId_,
* use "inputLayers_[0]->getOutput()" instead.
*/
void
copySeqInfoToOutputs
()
{
if
(
inputLayers_
.
empty
()
||
!
needSequenceInfo_
)
{
return
;
}
const
Argument
&
input
=
inputLayers_
[
0
]
->
getOutput
();
output_
.
sequenceStartPositions
=
input
.
sequenceStartPositions
;
output_
.
subSequenceStartPositions
=
input
.
subSequenceStartPositions
;
output_
.
cpuSequenceDims
=
input
.
cpuSequenceDims
;
for
(
size_t
i
=
0
;
i
<
outputOtherDevice_
.
size
();
i
++
)
{
outputOtherDevice_
[
i
].
sequenceStartPositions
=
output_
.
sequenceStartPositions
;
outputOtherDevice_
[
i
].
subSequenceStartPositions
=
output_
.
subSequenceStartPositions
;
outputOtherDevice_
[
i
].
cpuSequenceDims
=
output_
.
cpuSequenceDims
;
}
}
};
}
// namespace paddle
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @
171fee2c
...
...
@@ -63,8 +63,12 @@ void MKLDNNTester::reset(const TestConfig& dnn,
initTestLayer
(
configs_
[
i
],
&
(
layerMaps_
[
i
]),
&
(
parameters_
[
i
]),
&
(
testLayers_
[
i
]));
}
dnnLayer_
=
testLayers_
[
DNN
];
refLayer_
=
testLayers_
[
REF
];
dnnLayer_
=
std
::
dynamic_pointer_cast
<
MKLDNNLayer
>
(
testLayers_
[
DNN
]);
CHECK
(
dnnLayer_
);
// for comparison with Paddle reference results,
// need manually add cpu device output for test
dnnLayer_
->
addOutputArgument
(
CPU_DEVICE
);
EXPECT_EQ
(
dataLayers_
[
DNN
].
size
(),
dataLayers_
[
REF
].
size
());
EXPECT_EQ
(
parameters_
[
DNN
].
size
(),
parameters_
[
REF
].
size
());
...
...
@@ -109,20 +113,22 @@ void MKLDNNTester::randomBotDatas() {
void
MKLDNNTester
::
randomTopDiffs
()
{
refLayer_
->
getOutputGrad
()
->
randomizeUniform
();
dnnLayer_
->
getOutputGrad
()
->
copyFrom
(
*
(
refLayer_
->
getOutputGrad
()));
VLOG
(
lvl_
)
<<
"Random dom Backward Input, TopDiff: "
;
dnnLayer_
->
getOutput
(
CPU_DEVICE
)
.
grad
->
copyFrom
(
*
(
refLayer_
->
getOutputGrad
()));
VLOG
(
lvl_
)
<<
"Random Backward Input, TopDiff: "
;
printMatrix
(
refLayer_
->
getOutputGrad
());
}
void
MKLDNNTester
::
checkForward
()
{
printTopDatas
();
double
delta
=
compareMatrix
(
testLayers_
[
DNN
]
->
getOutputValue
(),
testLayers_
[
REF
]
->
getOutputValue
());
VLOG
(
MKLDNN_ALL
)
<<
"Check Forward"
;
printTopDatas
();
double
delta
=
compareMatrix
(
dnnLayer_
->
getOutput
(
-
1
).
value
,
refLayer_
->
getOutputValue
());
EXPECT_LE
(
fabs
(
delta
),
eps_
);
}
void
MKLDNNTester
::
checkBackwardData
()
{
VLOG
(
MKLDNN_ALL
)
<<
"Check Backward Data"
;
// TODO(TJ): uncomment me when batch norm ready
// const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
for
(
size_t
i
=
0
;
i
<
dataLayers_
[
DNN
].
size
();
++
i
)
{
...
...
@@ -144,14 +150,12 @@ void MKLDNNTester::checkBackwardData() {
}
void
MKLDNNTester
::
checkBackwardWgts
()
{
VLOG
(
MKLDNN_ALL
)
<<
"Check Backward Weight"
;
CHECK_EQ
(
parameters_
[
DNN
].
size
(),
parameters_
[
REF
].
size
());
vector
<
VectorPtr
>
dnnWgts
;
// used to temply save mkldnn weights
saveWgt
(
parameters_
[
DNN
],
dnnWgts
);
const
MKLDNNLayerPtr
dnnlayer
=
std
::
dynamic_pointer_cast
<
MKLDNNLayer
>
(
dnnLayer_
);
CHECK
(
dnnlayer
);
dnnlayer
->
convertWeightsToPaddle
();
dnnLayer_
->
convertWeightsToPaddle
();
for
(
size_t
i
=
0
;
i
<
parameters_
[
DNN
].
size
();
++
i
)
{
const
VectorPtr
&
dnn
=
parameters_
[
DNN
][
i
]
->
getBuf
(
PARAMETER_VALUE
);
const
VectorPtr
&
ref
=
parameters_
[
REF
][
i
]
->
getBuf
(
PARAMETER_VALUE
);
...
...
@@ -189,38 +193,38 @@ void MKLDNNTester::restoreWgt(const vector<VectorPtr>& from,
}
// clear parameters grad
void
MKLDNNTester
::
clearWgtDiffs
()
{
void
MKLDNNTester
::
clearWgtDiffs
(
size_t
id
)
{
CHECK_LE
(
id
,
parameters_
.
size
());
for
(
size_t
n
=
0
;
n
<
parameters_
.
size
();
++
n
)
{
for
(
size_t
i
=
0
;
i
<
parameters_
[
n
].
size
();
++
i
)
{
const
VectorPtr
&
grad
=
parameters_
[
n
][
i
]
->
getBuf
(
PARAMETER_GRADIENT
);
if
(
grad
)
{
grad
->
zeroMem
();
if
(
id
==
n
||
id
==
parameters_
.
size
())
{
for
(
size_t
i
=
0
;
i
<
parameters_
[
n
].
size
();
++
i
)
{
const
VectorPtr
&
grad
=
parameters_
[
n
][
i
]
->
getBuf
(
PARAMETER_GRADIENT
);
if
(
grad
)
{
grad
->
zeroMem
();
}
}
}
}
}
void
MKLDNNTester
::
clearBotDiffs
()
{
// dnn and ref
void
MKLDNNTester
::
clearBotDiffs
(
size_t
id
)
{
CHECK_LE
(
id
,
dataLayers_
.
size
());
for
(
size_t
n
=
0
;
n
<
dataLayers_
.
size
();
++
n
)
{
// all inputs layers
for
(
size_t
i
=
0
;
i
<
dataLayers_
[
n
].
size
();
++
i
)
{
dataLayers_
[
n
][
i
]
->
getOutputGrad
()
->
zeroMem
();
if
(
id
==
n
||
id
==
dataLayers_
.
size
())
{
// clear inputs layers of this specific layer
for
(
size_t
i
=
0
;
i
<
dataLayers_
[
n
].
size
();
++
i
)
{
dataLayers_
[
n
][
i
]
->
getOutputGrad
()
->
zeroMem
();
}
}
}
}
void
MKLDNNTester
::
clearBotDiffs
(
int
n
)
{
CHECK_LT
(
n
,
NUM
);
// all inputs layers
for
(
size_t
i
=
0
;
i
<
dataLayers_
[
n
].
size
();
++
i
)
{
dataLayers_
[
n
][
i
]
->
getOutputGrad
()
->
zeroMem
();
}
}
void
MKLDNNTester
::
clearTopDatas
()
{
void
MKLDNNTester
::
clearTopDatas
(
size_t
id
)
{
CHECK_LE
(
id
,
testLayers_
.
size
());
for
(
size_t
i
=
0
;
i
<
testLayers_
.
size
();
++
i
)
{
testLayers_
[
i
]
->
getOutputValue
()
->
zeroMem
();
if
(
id
==
i
||
id
==
testLayers_
.
size
())
{
testLayers_
[
i
]
->
getOutputValue
()
->
zeroMem
();
}
}
}
...
...
@@ -300,16 +304,24 @@ void MKLDNNTester::runOnce() {
checkForward
();
// test backward
// simple updater
UpdateCallback
updateCallback
=
[](
Parameter
*
para
)
{
auto
&
grad
=
para
->
getBuf
(
PARAMETER_GRADIENT
);
auto
&
value
=
para
->
getBuf
(
PARAMETER_VALUE
);
real
lr
=
1e-3
;
value
->
add
(
*
grad
,
lr
);
};
randomTopDiffs
();
dnnLayer_
->
backward
(
nullptr
);
refLayer_
->
backward
(
nullptr
);
dnnLayer_
->
backward
(
updateCallback
);
refLayer_
->
backward
(
updateCallback
);
checkBackwardData
();
checkBackwardWgts
();
// clear buffers
// ref code will addto the diff, dnn code will writeto it
// and clearTopDatas(
) and clearWgtDiffs() should be coverd by test
layers
// and clearTopDatas(
REF) should be coverd by ref
layers
clearBotDiffs
(
REF
);
clearWgtDiffs
(
REF
);
}
void
MKLDNNTester
::
run
(
const
TestConfig
&
dnn
,
...
...
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @
171fee2c
...
...
@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#include "LayerGradUtil.h"
#include "paddle/gserver/layers/MKLDNNBase.h"
#include "paddle/gserver/layers/MKLDNNLayer.h"
namespace
paddle
{
...
...
@@ -40,7 +41,8 @@ protected:
vector
<
LayerMap
>
layerMaps_
;
vector
<
vector
<
ParameterPtr
>>
parameters_
;
vector
<
LayerPtr
>
testLayers_
;
LayerPtr
dnnLayer_
,
refLayer_
;
LayerPtr
refLayer_
;
MKLDNNLayerPtr
dnnLayer_
;
/// run some iterations, all the result should pass
size_t
iter_
;
...
...
@@ -88,10 +90,10 @@ private:
void
checkBackwardData
();
void
checkBackwardWgts
();
void
clearWgtDiffs
();
void
clear
BotDiffs
(
);
void
clearBotDiffs
(
int
n
);
// clear specific layer
void
clearTopDatas
();
// clear specific layer, clear all when id equals NUM
void
clear
WgtDiffs
(
size_t
id
=
NUM
);
void
clearBotDiffs
(
size_t
id
=
NUM
);
void
clearTopDatas
(
size_t
id
=
NUM
);
void
printTopDatas
();
void
printMatrix
(
const
MatrixPtr
&
m
);
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录