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体验新版 GitCode,发现更多精彩内容 >>
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b0d9b68a
编写于
9月 14, 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
unify functions of mkldnn_fc and refine comments
上级
13d00053
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
251 addition
and
105 deletion
+251
-105
paddle/gserver/layers/MKLDNNConvLayer.cpp
paddle/gserver/layers/MKLDNNConvLayer.cpp
+5
-4
paddle/gserver/layers/MKLDNNFcLayer.cpp
paddle/gserver/layers/MKLDNNFcLayer.cpp
+181
-96
paddle/gserver/layers/MKLDNNFcLayer.h
paddle/gserver/layers/MKLDNNFcLayer.h
+59
-0
paddle/math/MKLDNNMatrix.h
paddle/math/MKLDNNMatrix.h
+6
-5
未找到文件。
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @
b0d9b68a
...
...
@@ -285,10 +285,9 @@ void MKLDNNConvLayer::resetWgtBiasValue(
wgt
=
MKLDNNMatrix
::
create
(
weight_
->
getW
(),
pd
->
weights_primitive_desc
());
VLOG
(
MKLDNN_FMTS
)
<<
"Weight value format: "
<<
wgt
->
getFormat
();
bias
=
nullptr
;
if
(
biases_
&&
biases_
->
getW
())
{
bias
=
MKLDNNMatrix
::
create
(
biases_
->
getW
(),
pd
->
bias_primitive_desc
());
}
bias
=
(
biases_
&&
biases_
->
getW
())
?
MKLDNNMatrix
::
create
(
biases_
->
getW
(),
pd
->
bias_primitive_desc
())
:
nullptr
;
}
void
MKLDNNConvLayer
::
resetOutValue
(
...
...
@@ -356,6 +355,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
void
MKLDNNConvLayer
::
resetBwdDataPD
(
std
::
shared_ptr
<
conv_bwdData
::
primitive_desc
>&
pd
)
{
pd
=
nullptr
;
if
(
inputLayers_
[
0
]
->
getOutput
().
grad
==
nullptr
)
{
return
;
}
...
...
@@ -476,6 +476,7 @@ void MKLDNNConvLayer::resetWgtBiasGrad(
<<
"primitive desc of weight grad and value should be equal"
;
VLOG
(
MKLDNN_FMTS
)
<<
"weight grad format: "
<<
wgt
->
getFormat
();
bias
=
nullptr
;
if
(
biasVal_
==
nullptr
)
{
return
;
}
...
...
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @
b0d9b68a
...
...
@@ -17,9 +17,6 @@ limitations under the License. */
using
namespace
mkldnn
;
// NOLINT
typedef
memory
::
format
format
;
typedef
inner_product_forward
fc_fwd
;
typedef
inner_product_backward_weights
fc_bwdWgt
;
typedef
inner_product_backward_data
fc_bwdData
;
namespace
paddle
{
...
...
@@ -93,35 +90,88 @@ void MKLDNNFcLayer::reshape(
printSizeInfo
();
}
void
MKLDNNFcLayer
::
resetFwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
void
MKLDNNFcLayer
::
resetFwd
(
std
::
vector
<
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
pipeline
.
clear
();
bool
hasBias
=
biases_
&&
biases_
->
getW
();
const
MatrixPtr
&
wgtVal
=
weight_
->
getW
();
const
MatrixPtr
&
biasVal
=
hasBias
?
biases_
->
getW
()
:
nullptr
;
const
MatrixPtr
&
outVal
=
output_
.
value
;
resetFwdBuffers
(
in
,
wgt
,
bias
,
out
);
resetFwdPD
(
fwdPD_
,
in
,
wgt
,
bias
,
out
);
resetFwdPipeline
(
pipeline
,
fwdPD_
,
in
,
wgt
,
bias
,
out
);
printValueFormatFlow
();
}
void
MKLDNNFcLayer
::
resetBwd
(
std
::
vector
<
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
std
::
shared_ptr
<
fc_bwdWgt
::
primitive_desc
>
bwdWgtPD
;
std
::
shared_ptr
<
fc_bwdData
::
primitive_desc
>
bwdDataPD
;
resetBwdBuffers
(
in
,
wgt
,
bias
,
out
);
resetBwdWgtPD
(
bwdWgtPD
,
wgt
,
bias
,
out
);
resetBwdDataPD
(
bwdDataPD
,
in
,
out
);
resetBwdPipeline
(
pipeline
,
bwdWgtPD
,
bwdDataPD
,
in
,
wgt
,
bias
,
out
);
printGradFormatFlow
();
}
void
MKLDNNFcLayer
::
updateInputData
()
{
inVal_
->
setData
(
getInputValue
(
0
,
CPU_DEVICE
)
->
getData
());
}
void
MKLDNNFcLayer
::
updateWeights
(
const
UpdateCallback
&
callback
)
{
weight_
->
getParameterPtr
()
->
incUpdate
(
callback
);
if
(
biases_
&&
biases_
->
getWGrad
())
{
biases_
->
getParameterPtr
()
->
incUpdate
(
callback
);
}
}
void
MKLDNNFcLayer
::
resetFwdBuffers
(
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
resetInValue
(
in
);
resetWgtBiasValue
(
wgt
,
bias
);
resetOutValue
(
out
);
}
void
MKLDNNFcLayer
::
resetInValue
(
MKLDNNMatrixPtr
&
in
)
{
if
(
inputIsOnlyMKLDNN
())
{
const
MatrixPtr
&
inVal
=
getInputValue
(
0
);
in
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
inVal
);
const
MatrixPtr
&
dnnIn
=
getInputValue
(
0
);
in
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
dnnIn
);
CHECK
(
in
)
<<
"Input should be MKLDNNMatrix"
;
}
else
{
CHECK_EQ
(
getPrev
(
0
)
->
getDeviceId
(),
CPU_DEVICE
)
<<
"Only support CPU yet"
;
const
MatrixPtr
&
inVal
=
getInputValue
(
0
,
CPU_DEVICE
);
const
MatrixPtr
&
cpuIn
=
getInputValue
(
0
,
CPU_DEVICE
);
in
=
MKLDNNMatrix
::
create
(
inVal
,
memory
::
dims
{
bs_
,
ic_
,
ih_
,
iw_
},
format
::
nchw
,
engine_
);
cpuIn
,
{
bs_
,
ic_
,
ih_
,
iw_
},
format
::
nchw
,
engine_
);
}
in
->
downSpatial
();
}
void
MKLDNNFcLayer
::
resetWgtBiasValue
(
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
)
{
wgt
=
MKLDNNMatrix
::
create
(
w
gtVal
,
memory
::
dims
{
oc_
,
ic_
,
ih_
,
iw_
},
format
::
oihw
,
engine_
);
w
eight_
->
getW
(),
{
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_
);
bias
=
(
biases_
&&
biases_
->
getW
())
?
MKLDNNMatrix
::
create
(
biases_
->
getW
(),
{
oc_
},
format
::
x
,
engine_
)
:
nullptr
;
}
void
MKLDNNFcLayer
::
resetOutValue
(
MKLDNNMatrixPtr
&
out
)
{
out
=
MKLDNNMatrix
::
create
(
output_
.
value
,
{
bs_
,
oc_
},
format
::
nc
,
engine_
);
// change original output value to mkldnn output value
output_
.
value
=
std
::
dynamic_pointer_cast
<
Matrix
>
(
out
);
if
(
!
outputIsOnlyMKLDNN
())
{
...
...
@@ -129,46 +179,59 @@ void MKLDNNFcLayer::resetFwd(std::vector<mkldnn::primitive>& pipeline,
// just share point
getOutput
(
CPU_DEVICE
).
value
->
setData
(
output_
.
value
->
getData
());
}
}
// create forward handle
void
MKLDNNFcLayer
::
resetFwdPD
(
std
::
shared_ptr
<
fc_fwd
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
in
,
MKLDNNMatrixPtr
wgt
,
MKLDNNMatrixPtr
bias
,
MKLDNNMatrixPtr
out
)
{
CHECK
(
in
);
CHECK
(
wgt
);
CHECK
(
out
);
prop_kind
pk
=
prop_kind
::
forward
;
fc_fwd
::
desc
fwdDesc
=
hasBias
?
fc_fwd
::
desc
(
pk
,
in
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
bias
->
getMemoryDesc
(),
out
->
getMemoryDesc
())
:
fc_fwd
::
desc
(
pk
,
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
,
*
wgt
,
*
bias
,
*
out
));
fc_fwd
::
desc
fwdDesc
=
bias
!=
nullptr
?
fc_fwd
::
desc
(
pk
,
in
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
bias
->
getMemoryDesc
(),
out
->
getMemoryDesc
())
:
fc_fwd
::
desc
(
pk
,
in
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
out
->
getMemoryDesc
());
pd
.
reset
(
new
fc_fwd
::
primitive_desc
(
fwdDesc
,
engine_
));
}
void
MKLDNNFcLayer
::
resetFwdPipeline
(
std
::
vector
<
primitive
>&
pipeline
,
std
::
shared_ptr
<
fc_fwd
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
pipeline
.
clear
();
if
(
bias
)
{
fwd_
.
reset
(
new
fc_fwd
(
*
pd
,
*
in
,
*
wgt
,
*
bias
,
*
out
));
}
else
{
fwd_
.
reset
(
new
fc_fwd
(
fwdPD
,
*
in
,
*
wgt
,
*
out
));
fwd_
.
reset
(
new
fc_fwd
(
*
pd
,
*
in
,
*
wgt
,
*
out
));
}
printValueFormatFlow
();
pipeline
.
push_back
(
*
fwd_
);
}
void
MKLDNNFcLayer
::
resetBwd
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
pipeline
.
clear
();
if
(
!
needResetBwd_
)
{
return
;
}
needResetBwd_
=
false
;
bool
hasBias
=
biases_
&&
biases_
->
getWGrad
();
void
MKLDNNFcLayer
::
resetBwdBuffers
(
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
resetOutGrad
(
out
);
resetWgtBiasGrad
(
wgt
,
bias
);
/// backward weight
CHECK
(
inVal_
)
<<
"Should have input value"
;
const
MatrixPtr
&
wgtGrad
=
weight_
->
getWGrad
();
const
MatrixPtr
&
biasGrad
=
hasBias
?
biases_
->
getWGrad
()
:
nullptr
;
resetInGrad
(
in
);
}
void
MKLDNNFcLayer
::
resetOutGrad
(
MKLDNNMatrixPtr
&
out
)
{
// TODO(TJ): merge outgrad
int
device
=
outputIsOnlyMKLDNN
()
?
MKLDNN_DEVICE
:
CPU_DEVICE
;
// for MKLDNN device:
...
...
@@ -178,66 +241,88 @@ void MKLDNNFcLayer::resetBwd(std::vector<mkldnn::primitive>& pipeline,
// 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
&
outGrad
=
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
->
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
->
getMemoryDesc
(),
bias
->
getMemoryDesc
(),
out
->
getMemoryDesc
())
:
fc_bwdWgt
::
desc
(
inVal_
->
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_
,
*
out
,
*
wgt
,
*
bias
));
}
else
{
bwdWgt_
.
reset
(
new
fc_bwdWgt
(
bwdWgtPD
,
*
inVal_
,
*
out
,
*
wgt
));
CHECK
(
outVal_
);
out
=
MKLDNNMatrix
::
create
(
getOutput
(
device
).
grad
,
outVal_
->
getPrimitiveDesc
());
}
void
MKLDNNFcLayer
::
resetWgtBiasGrad
(
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
)
{
CHECK
(
wgtVal_
);
wgt
=
MKLDNNMatrix
::
create
(
weight_
->
getWGrad
(),
wgtVal_
->
getPrimitiveDesc
());
bias
=
nullptr
;
if
(
biasVal_
==
nullptr
)
{
return
;
}
pipeline
.
push_back
(
*
bwdWgt_
);
bias
=
MKLDNNMatrix
::
create
(
biases_
->
getWGrad
(),
biasVal_
->
getPrimitiveDesc
());
}
/// backward data
void
MKLDNNFcLayer
::
resetInGrad
(
MKLDNNMatrixPtr
&
in
)
{
in
=
nullptr
;
const
MatrixPtr
&
inGrad
=
inputLayers_
[
0
]
->
getOutput
().
grad
;
if
(
inGrad
==
nullptr
)
{
return
;
}
if
(
getInput
(
0
,
MKLDNN_DEVICE
).
getAllCount
()
>
1
)
{
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
}
else
{
in
=
MKLDNNMatrix
::
create
(
inGrad
,
inVal_
->
getPrimitiveDesc
());
}
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
);
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
CHECK
(
inVal_
);
in
=
MKLDNNMatrix
::
create
(
inGrad
,
inVal_
->
getPrimitiveDesc
());
}
CHECK
(
wgtVal_
)
<<
"Should have weight memory"
;
bwdData_
.
reset
(
new
fc_bwdData
(
bwdDataPD
,
*
out
,
*
wgtVal_
,
*
in
));
printGradFormatFlow
();
pipeline
.
push_back
(
*
bwdData_
);
void
MKLDNNFcLayer
::
resetBwdWgtPD
(
std
::
shared_ptr
<
fc_bwdWgt
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
CHECK
(
inVal_
);
fc_bwdWgt
::
desc
bwdWgtDesc
=
bias
?
fc_bwdWgt
::
desc
(
inVal_
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
bias
->
getMemoryDesc
(),
out
->
getMemoryDesc
())
:
fc_bwdWgt
::
desc
(
inVal_
->
getMemoryDesc
(),
wgt
->
getMemoryDesc
(),
out
->
getMemoryDesc
());
pd
.
reset
(
new
fc_bwdWgt
::
primitive_desc
(
bwdWgtDesc
,
engine_
,
*
fwdPD_
));
}
void
MKLDNNFcLayer
::
updateInputData
()
{
inVal_
->
setData
(
getInputValue
(
0
,
CPU_DEVICE
)
->
getData
());
void
MKLDNNFcLayer
::
resetBwdDataPD
(
std
::
shared_ptr
<
fc_bwdData
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
out
)
{
pd
=
nullptr
;
if
(
in
==
nullptr
)
{
return
;
}
CHECK
(
wgtVal_
);
fc_bwdData
::
desc
bwdDataDesc
=
fc_bwdData
::
desc
(
in
->
getMemoryDesc
(),
wgtVal_
->
getMemoryDesc
(),
out
->
getMemoryDesc
());
pd
.
reset
(
new
fc_bwdData
::
primitive_desc
(
bwdDataDesc
,
engine_
,
*
fwdPD_
));
}
void
MKLDNNFcLayer
::
updateWeights
(
const
UpdateCallback
&
callback
)
{
weight_
->
getParameterPtr
()
->
incUpdate
(
callback
);
if
(
biases_
&&
biases_
->
getWGrad
())
{
biases_
->
getParameterPtr
()
->
incUpdate
(
callback
);
void
MKLDNNFcLayer
::
resetBwdPipeline
(
std
::
vector
<
primitive
>&
pipeline
,
std
::
shared_ptr
<
fc_bwdWgt
::
primitive_desc
>&
bwdWgtPD
,
std
::
shared_ptr
<
fc_bwdData
::
primitive_desc
>&
bwdDataPD
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
)
{
pipeline
.
clear
();
CHECK
(
inVal_
);
if
(
bias
)
{
bwdWgt_
.
reset
(
new
fc_bwdWgt
(
*
bwdWgtPD
,
*
inVal_
,
*
out
,
*
wgt
,
*
bias
));
}
else
{
bwdWgt_
.
reset
(
new
fc_bwdWgt
(
*
bwdWgtPD
,
*
inVal_
,
*
out
,
*
wgt
));
}
pipeline
.
push_back
(
*
bwdWgt_
);
if
(
bwdDataPD
==
nullptr
)
{
return
;
}
CHECK
(
wgtVal_
)
<<
"Should have weight memory"
;
bwdData_
.
reset
(
new
fc_bwdData
(
*
bwdDataPD
,
*
out
,
*
wgtVal_
,
*
in
));
pipeline
.
push_back
(
*
bwdData_
);
}
}
// namespace paddle
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @
b0d9b68a
...
...
@@ -18,6 +18,9 @@ limitations under the License. */
#include "mkldnn.hpp"
namespace
paddle
{
typedef
mkldnn
::
inner_product_forward
fc_fwd
;
typedef
mkldnn
::
inner_product_backward_weights
fc_bwdWgt
;
typedef
mkldnn
::
inner_product_backward_data
fc_bwdData
;
/**
* @brief A subclass of MKLDNNLayer fc layer.
...
...
@@ -32,6 +35,9 @@ protected:
// if has already init the weight
bool
hasInitedWgt_
;
// save forward primitive_desc, which can be used backward
std
::
shared_ptr
<
fc_fwd
::
primitive_desc
>
fwdPD_
;
// fc weight and bias
std
::
unique_ptr
<
Weight
>
weight_
;
std
::
unique_ptr
<
Weight
>
biases_
;
...
...
@@ -67,6 +73,59 @@ public:
void
convertWeightsFromPaddle
()
override
;
void
convertWeightsToPaddle
()
override
;
protected:
/**
* Forward functions: reset buffers(input, output, weight and bias),
* reset primitive descriptor,
* reset pipeline.
*/
void
resetFwdBuffers
(
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
);
void
resetInValue
(
MKLDNNMatrixPtr
&
in
);
void
resetWgtBiasValue
(
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
);
void
resetOutValue
(
MKLDNNMatrixPtr
&
out
);
void
resetFwdPD
(
std
::
shared_ptr
<
fc_fwd
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
in
,
MKLDNNMatrixPtr
wgt
,
MKLDNNMatrixPtr
bias
,
MKLDNNMatrixPtr
out
);
void
resetFwdPipeline
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
std
::
shared_ptr
<
fc_fwd
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
);
/**
* Backward functions: reset buffers(input, output, weight and bias),
* reset primitive descriptor for backward weight,
* reset primitive descriptor for backward data,
* reset pipeline.
*/
void
resetBwdBuffers
(
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
);
void
resetOutGrad
(
MKLDNNMatrixPtr
&
out
);
void
resetWgtBiasGrad
(
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
);
void
resetInGrad
(
MKLDNNMatrixPtr
&
in
);
void
resetBwdWgtPD
(
std
::
shared_ptr
<
fc_bwdWgt
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
);
void
resetBwdDataPD
(
std
::
shared_ptr
<
fc_bwdData
::
primitive_desc
>&
pd
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
out
);
void
resetBwdPipeline
(
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
std
::
shared_ptr
<
fc_bwdWgt
::
primitive_desc
>&
bwdWgtPD
,
std
::
shared_ptr
<
fc_bwdData
::
primitive_desc
>&
bwdDataPD
,
MKLDNNMatrixPtr
&
in
,
MKLDNNMatrixPtr
&
wgt
,
MKLDNNMatrixPtr
&
bias
,
MKLDNNMatrixPtr
&
out
);
};
}
// namespace paddle
paddle/math/MKLDNNMatrix.h
浏览文件 @
b0d9b68a
...
...
@@ -66,11 +66,12 @@ public:
/**
* Create reorder primitive.
* Create a mkldnn::reorder handle for converting src MKLDNNMatrix to dst.
* checkData: for whether to check the data handle of src and dst is the same.
* if true, means check it and do not want support inplace reorder;
* otherwise do not check data which means the created reorder
* maybe inplace buffer and do not guarantee the logical is correct
* since not all format or conversion support inplace.
* checkData: whether to check the data handle of src and dst.
* if true, it will check the data and do not allow them equal;
* otherwise, it will not check them, then the reorder created
* may have inplace buffer.
* Do not set false, if you can not guarantee the inplace logical
* would work with your reorder.
*/
static
std
::
shared_ptr
<
mkldnn
::
reorder
>
createReorder
(
const
MKLDNNMatrixPtr
&
src
,
...
...
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