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90493691
编写于
12月 20, 2016
作者:
T
tianbingsz
提交者:
GitHub
12月 20, 2016
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #934 from tianbingsz/paddle_function_mat
Matrix API refactor
上级
dadd48a5
4fbf9499
变更
23
隐藏空白更改
内联
并排
Showing
23 changed file
with
247 addition
and
290 deletion
+247
-290
paddle/gserver/evaluators/Evaluator.cpp
paddle/gserver/evaluators/Evaluator.cpp
+1
-1
paddle/gserver/layers/ContextProjection.cpp
paddle/gserver/layers/ContextProjection.cpp
+6
-6
paddle/gserver/layers/ConvexCombinationLayer.cpp
paddle/gserver/layers/ConvexCombinationLayer.cpp
+3
-3
paddle/gserver/layers/ExpandConvBaseLayer.cpp
paddle/gserver/layers/ExpandConvBaseLayer.cpp
+3
-3
paddle/gserver/layers/FullMatrixProjection.cpp
paddle/gserver/layers/FullMatrixProjection.cpp
+4
-3
paddle/gserver/layers/FullyConnectedLayer.cpp
paddle/gserver/layers/FullyConnectedLayer.cpp
+4
-4
paddle/gserver/layers/LinearChainCRF.cpp
paddle/gserver/layers/LinearChainCRF.cpp
+1
-1
paddle/gserver/layers/LstmLayer.cpp
paddle/gserver/layers/LstmLayer.cpp
+13
-13
paddle/gserver/layers/MDLstmLayer.cpp
paddle/gserver/layers/MDLstmLayer.cpp
+4
-4
paddle/gserver/layers/OuterProdLayer.cpp
paddle/gserver/layers/OuterProdLayer.cpp
+3
-3
paddle/gserver/layers/RecurrentLayer.cpp
paddle/gserver/layers/RecurrentLayer.cpp
+16
-16
paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp
paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp
+5
-5
paddle/gserver/layers/TensorLayer.cpp
paddle/gserver/layers/TensorLayer.cpp
+4
-4
paddle/gserver/layers/TransposedFullMatrixProjection.cpp
paddle/gserver/layers/TransposedFullMatrixProjection.cpp
+4
-3
paddle/math/CpuSparseMatrix.cpp
paddle/math/CpuSparseMatrix.cpp
+8
-7
paddle/math/CpuSparseMatrix.h
paddle/math/CpuSparseMatrix.h
+1
-1
paddle/math/Matrix.cpp
paddle/math/Matrix.cpp
+89
-131
paddle/math/Matrix.h
paddle/math/Matrix.h
+24
-24
paddle/math/SparseMatrix.cpp
paddle/math/SparseMatrix.cpp
+27
-28
paddle/math/SparseMatrix.h
paddle/math/SparseMatrix.h
+2
-5
paddle/math/tests/test_SparseMatrix.cpp
paddle/math/tests/test_SparseMatrix.cpp
+7
-7
paddle/math/tests/test_matrixCompare.cpp
paddle/math/tests/test_matrixCompare.cpp
+16
-16
paddle/math/tests/test_sparseMatrixCompare.cpp
paddle/math/tests/test_sparseMatrixCompare.cpp
+2
-2
未找到文件。
paddle/gserver/evaluators/Evaluator.cpp
浏览文件 @
90493691
...
...
@@ -78,7 +78,7 @@ public:
useGpu
(
arguments
[
0
].
deviceId
));
errorMat
->
zeroMem
();
if
(
label
!=
nullptr
)
{
errorMat
->
classificationError
(
output
,
label
);
errorMat
->
classificationError
(
*
output
,
*
label
);
}
else
if
(
dynamic_cast
<
CpuSparseMatrix
*>
(
multiBinaryLabel
.
get
())
||
dynamic_cast
<
GpuSparseMatrix
*>
(
multiBinaryLabel
.
get
()))
{
errorMat
->
classificationErrorMulti
(
...
...
paddle/gserver/layers/ContextProjection.cpp
浏览文件 @
90493691
...
...
@@ -90,8 +90,8 @@ void ContextProjection::forward() {
REGISTER_TIMER_INFO
(
"ContextProjectionForward"
,
getName
().
c_str
());
bool
isPadding
=
config_
.
trainable_padding
();
out_
->
value
->
contextProjectionForward
(
in_
->
value
,
state_
?
state_
:
isPadding
?
weight_
->
getW
()
:
nullptr
,
*
(
in_
->
value
)
,
state_
?
state_
.
get
()
:
isPadding
?
weight_
->
getW
().
get
()
:
nullptr
,
*
startPositions
,
config_
.
context_length
(),
config_
.
context_start
(),
...
...
@@ -128,8 +128,8 @@ void ContextProjection::backward(const UpdateCallback& callback) {
bool
isPadding
=
config_
.
trainable_padding
();
if
(
!
out_
->
grad
->
useGpu
())
{
out_
->
grad
->
contextProjectionBackward
(
in_
->
grad
,
isPadding
?
weight_
->
getWGrad
()
:
nullptr
,
in_
->
grad
.
get
()
,
isPadding
?
weight_
->
getWGrad
()
.
get
()
:
nullptr
,
*
startPositions
,
config_
.
context_length
(),
config_
.
context_start
(),
...
...
@@ -137,7 +137,7 @@ void ContextProjection::backward(const UpdateCallback& callback) {
isPadding
);
}
else
{
if
(
in_
->
grad
)
{
out_
->
grad
->
contextProjectionBackwardData
(
in_
->
grad
,
out_
->
grad
->
contextProjectionBackwardData
(
*
(
in_
->
grad
)
,
*
startPositions
,
config_
.
context_length
(),
config_
.
context_start
());
...
...
@@ -145,7 +145,7 @@ void ContextProjection::backward(const UpdateCallback& callback) {
if
(
isPadding
&&
weight_
->
getWGrad
())
{
out_
->
grad
->
contextProjectionBackwardWeight
(
weight_
->
getWGrad
(
),
*
(
weight_
->
getWGrad
()
),
*
startPositions
,
config_
.
context_length
(),
config_
.
context_start
(),
...
...
paddle/gserver/layers/ConvexCombinationLayer.cpp
浏览文件 @
90493691
...
...
@@ -113,7 +113,7 @@ void ConvexCombinationLayer::forward(PassType passType) {
tmpRow0
->
setData
(
inV0
->
getData
()
+
i
*
weightDim
);
tmpRow1
->
setData
(
outV
->
getData
()
+
i
*
dataDim
);
tmpRow1
->
mul
(
tmpRow0
,
tmpMtx0
,
1
,
0
);
tmpRow1
->
mul
(
*
tmpRow0
,
*
tmpMtx0
,
1
,
0
);
}
}
...
...
@@ -136,7 +136,7 @@ void ConvexCombinationLayer::backward(const UpdateCallback& callback) {
tmpRow1
->
setData
(
outG
->
getData
()
+
i
*
dataDim
);
tmpMtx0
->
setData
(
inV1
->
getData
()
+
i
*
weightDim
*
dataDim
);
tmpRow0
->
mul
(
tmpRow1
,
tmpMtx0
->
getTranspose
(
),
1
,
1
);
tmpRow0
->
mul
(
*
tmpRow1
,
*
(
tmpMtx0
->
getTranspose
()
),
1
,
1
);
}
}
...
...
@@ -146,7 +146,7 @@ void ConvexCombinationLayer::backward(const UpdateCallback& callback) {
tmpRow1
->
setData
(
outG
->
getData
()
+
i
*
dataDim
);
tmpMtx0
->
setData
(
inG1
->
getData
()
+
i
*
weightDim
*
dataDim
);
tmpMtx0
->
mul
(
tmpRow0
->
getTranspose
(),
tmpRow1
,
1
,
1
);
tmpMtx0
->
mul
(
*
(
tmpRow0
->
getTranspose
()),
*
tmpRow1
,
1
,
1
);
}
}
}
...
...
paddle/gserver/layers/ExpandConvBaseLayer.cpp
浏览文件 @
90493691
...
...
@@ -150,7 +150,7 @@ void ExpandConvBaseLayer::expandFwdOnce(MatrixPtr image,
Matrix
::
create
(
wgtData
,
subM
,
subK
,
false
,
useGpu_
);
// mark transpose
MatrixPtr
B
=
Matrix
::
create
(
expInData
,
subK
,
subN
,
false
,
useGpu_
);
MatrixPtr
C
=
Matrix
::
create
(
outData
,
subM
,
subN
,
false
,
useGpu_
);
C
->
mul
(
A
,
B
,
1
,
1
);
C
->
mul
(
*
A
,
*
B
,
1
,
1
);
A
->
clear
();
B
->
clear
();
...
...
@@ -185,7 +185,7 @@ void ExpandConvBaseLayer::bpropActs(MatrixPtr out,
MatrixPtr
C
=
Matrix
::
create
(
expandInData
,
subK
,
subN
,
false
,
useGpu_
);
MatrixPtr
B
=
Matrix
::
create
(
localGradData
,
subM
,
subN
,
false
,
useGpu_
);
MatrixPtr
A
=
Matrix
::
create
(
wgtData
,
subM
,
subK
,
true
,
useGpu_
);
C
->
mul
(
A
,
B
);
// mul
C
->
mul
(
*
A
,
*
B
);
// mul
// clear the temporary matrix
A
->
clear
();
...
...
@@ -252,7 +252,7 @@ void ExpandConvBaseLayer::bpropWeights(MatrixPtr image,
MatrixPtr
A
=
Matrix
::
create
(
expandInData
,
subK
,
subN
,
true
,
useGpu_
);
MatrixPtr
B
=
Matrix
::
create
(
gradData
,
subM
,
subN
,
false
,
useGpu_
);
MatrixPtr
C
=
Matrix
::
create
(
wGradData
,
subM
,
subK
,
false
,
useGpu_
);
C
->
mul
(
B
,
A
,
1
,
1
);
C
->
mul
(
*
B
,
*
A
,
1
,
1
);
A
->
clear
();
B
->
clear
();
...
...
paddle/gserver/layers/FullMatrixProjection.cpp
浏览文件 @
90493691
...
...
@@ -28,7 +28,7 @@ FullMatrixProjection::FullMatrixProjection(const ProjectionConfig& config,
void
FullMatrixProjection
::
forward
()
{
REGISTER_TIMER_INFO
(
"FwMulTimer"
,
getName
().
c_str
());
out_
->
value
->
mul
(
in_
->
value
,
weight_
->
getW
(
),
1
,
1
);
out_
->
value
->
mul
(
*
(
in_
->
value
),
*
(
weight_
->
getW
()
),
1
,
1
);
}
void
FullMatrixProjection
::
backward
(
const
UpdateCallback
&
callback
)
{
...
...
@@ -37,7 +37,8 @@ void FullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the W-gradient for the current layer */
if
(
weight_
->
getWGrad
())
{
REGISTER_TIMER_INFO
(
"GradMulTimer"
,
getName
().
c_str
());
weight_
->
getWGrad
()
->
mul
(
in_
->
value
->
getTranspose
(),
out_
->
grad
,
1
,
1
);
weight_
->
getWGrad
()
->
mul
(
*
(
in_
->
value
->
getTranspose
()),
*
(
out_
->
grad
),
1
,
1
);
}
// If callback does not change value, backward propagation error
...
...
@@ -47,7 +48,7 @@ void FullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the input layers error */
if
(
in_
->
grad
)
{
REGISTER_TIMER_INFO
(
"BpMulTimer"
,
getName
().
c_str
());
in_
->
grad
->
mul
(
out_
->
grad
,
weight_
->
getW
()
->
getTranspose
(
),
1
,
1
);
in_
->
grad
->
mul
(
*
(
out_
->
grad
),
*
(
weight_
->
getW
()
->
getTranspose
()
),
1
,
1
);
}
hl_set_sync_flag
(
syncFlag
);
...
...
paddle/gserver/layers/FullyConnectedLayer.cpp
浏览文件 @
90493691
...
...
@@ -84,8 +84,8 @@ void FullyConnectedLayer::forward(PassType passType) {
auto
input
=
getInput
(
i
);
CHECK
(
input
.
value
)
<<
"The input of 'fc' layer must be matrix"
;
REGISTER_TIMER_INFO
(
"FwMulTimer"
,
getName
().
c_str
());
i
==
0
?
outV
->
mul
(
input
.
value
,
weights_
[
i
]
->
getW
(),
1
,
0
)
:
outV
->
mul
(
input
.
value
,
weights_
[
i
]
->
getW
(),
1
,
1
);
i
==
0
?
outV
->
mul
(
*
input
.
value
,
*
weights_
[
i
]
->
getW
(),
1
,
0
)
:
outV
->
mul
(
*
input
.
value
,
*
weights_
[
i
]
->
getW
(),
1
,
1
);
}
/* add the bias-vector */
...
...
@@ -123,7 +123,7 @@ void FullyConnectedLayer::backward(const UpdateCallback& callback) {
MatrixPtr
oGrad
=
getOutputGrad
();
{
REGISTER_TIMER_INFO
(
"GradMulTimer"
,
getName
().
c_str
());
weights_
[
i
]
->
getWGrad
()
->
mul
(
input_T
,
oGrad
,
1
,
1
);
weights_
[
i
]
->
getWGrad
()
->
mul
(
*
input_T
,
*
oGrad
,
1
,
1
);
}
}
...
...
@@ -136,7 +136,7 @@ void FullyConnectedLayer::backward(const UpdateCallback& callback) {
if
(
NULL
!=
preGrad
)
{
MatrixPtr
weights_T
=
weights_
[
i
]
->
getW
()
->
getTranspose
();
REGISTER_TIMER_INFO
(
"BpMulTimer"
,
getName
().
c_str
());
preGrad
->
mul
(
getOutputGrad
(),
weights_T
,
1
,
1
);
preGrad
->
mul
(
*
getOutputGrad
(),
*
weights_T
,
1
,
1
);
}
hl_set_sync_flag
(
syncFlag
);
...
...
paddle/gserver/layers/LinearChainCRF.cpp
浏览文件 @
90493691
...
...
@@ -59,7 +59,7 @@ real LinearChainCRF::forward(real* x, int* s, int length) {
matX
->
rowMax
(
*
maxX_
);
expX_
->
assign
(
*
matX
);
// subtract max to avoid overflow or underflow
expX_
->
mul
(
maxX_
,
ones_
,
(
real
)
-
1
,
(
real
)
1
);
expX_
->
mul
(
*
maxX_
,
*
ones_
,
(
real
)
-
1
,
(
real
)
1
);
expX_
->
exp2
();
real
*
a
=
a_
->
getData
();
...
...
paddle/gserver/layers/LstmLayer.cpp
浏览文件 @
90493691
...
...
@@ -316,7 +316,7 @@ void LstmLayer::forwardSequence(int batchSize,
}
if
(
prevOutput_
)
{
frameGate
->
setData
(
lstmValue
.
gateValue
);
frameGate
->
mul
(
prevOutput_
,
weight_
->
getW
(),
1
,
1
);
frameGate
->
mul
(
*
prevOutput_
,
*
weight_
->
getW
(),
1
,
1
);
}
}
AsyncGpuBlock
asyncGpuBlock
;
...
...
@@ -338,7 +338,7 @@ void LstmLayer::forwardSequence(int batchSize,
frameOutput
->
setData
(
lstmValue
.
outputValue
);
nextFrame
(
reversed_
,
getSize
());
frameGate
->
setData
(
lstmValue
.
gateValue
);
frameGate
->
mul
(
frameOutput
,
weight_
->
getW
(),
1
,
1
);
frameGate
->
mul
(
*
frameOutput
,
*
weight_
->
getW
(),
1
,
1
);
}
}
if
(
n
!=
numSequences
-
1
)
{
...
...
@@ -348,7 +348,7 @@ void LstmLayer::forwardSequence(int batchSize,
if
(
!
reversed_
)
{
if
(
!
prevState_
)
lstmValue
.
prevStateValue
=
nullptr
;
if
(
prevOutput_
)
{
frameGate
->
mul
(
frameOutput
,
weight_
->
getW
(),
1
,
1
);
frameGate
->
mul
(
*
frameOutput
,
*
weight_
->
getW
(),
1
,
1
);
}
}
else
{
lstmValue
.
prevStateValue
=
nullptr
;
...
...
@@ -470,7 +470,7 @@ void LstmLayer::backwardSequence(int batchSize,
frameGate
->
setData
(
lstmGrad
.
gateGrad
);
nextFrame
(
reversed_
,
getSize
());
frameOutput
->
setData
(
lstmGrad
.
outputGrad
);
frameOutput
->
mul
(
frameGate
,
weightT
,
1
,
1
);
frameOutput
->
mul
(
*
frameGate
,
*
weightT
,
1
,
1
);
}
else
{
nextFrame
(
reversed_
,
getSize
());
}
...
...
@@ -479,14 +479,14 @@ void LstmLayer::backwardSequence(int batchSize,
if
(
weight_
->
getWGrad
())
{
if
(
!
reversed_
)
{
weight_
->
getWGrad
()
->
mul
(
output_
.
value
->
subMatrix
(
start
,
length
-
1
)
->
getTranspose
(),
gate_
.
grad
->
subMatrix
(
start
+
1
,
length
-
1
),
*
output_
.
value
->
subMatrix
(
start
,
length
-
1
)
->
getTranspose
(),
*
gate_
.
grad
->
subMatrix
(
start
+
1
,
length
-
1
),
1
,
1
);
}
else
{
weight_
->
getWGrad
()
->
mul
(
output_
.
value
->
subMatrix
(
start
+
1
,
length
-
1
)
->
getTranspose
(),
gate_
.
grad
->
subMatrix
(
start
,
length
-
1
),
*
output_
.
value
->
subMatrix
(
start
+
1
,
length
-
1
)
->
getTranspose
(),
*
gate_
.
grad
->
subMatrix
(
start
,
length
-
1
),
1
,
1
);
}
...
...
@@ -541,7 +541,7 @@ void LstmLayer::forwardBatch(int batchSize,
if
(
n
!=
0
)
{
MatrixPtr
batch1
=
batchValue_
->
getBatchValue
(
n
-
1
,
batchSize
);
gateValue
->
mul
(
batch1
,
weight_
->
getW
(),
1
,
1
);
gateValue
->
mul
(
*
batch1
,
*
weight_
->
getW
(),
1
,
1
);
}
else
if
(
prevOutput_
)
{
Matrix
::
resizeOrCreate
(
prevBatchOutput2_
,
gateValue
->
getHeight
(),
...
...
@@ -549,7 +549,7 @@ void LstmLayer::forwardBatch(int batchSize,
false
,
useGpu_
);
batchValue_
->
prevOutput2Batch
(
*
prevOutput_
,
*
prevBatchOutput2_
);
gateValue
->
mul
(
prevBatchOutput2_
,
weight_
->
getW
(),
1
,
1
);
gateValue
->
mul
(
*
prevBatchOutput2_
,
*
weight_
->
getW
(),
1
,
1
);
batchValue_
->
prevOutput2Batch
(
*
prevState_
,
*
totalState_
->
subMatrix
(
0
,
numSequences
));
...
...
@@ -672,16 +672,16 @@ void LstmLayer::backwardBatch(int batchSize,
if
(
n
!=
0
)
{
MatrixPtr
tmp
=
batchGrad_
->
getBatchValue
(
n
-
1
,
batchSize
);
tmp
->
mul
(
gateGrad
,
weightT
,
1
,
1
);
tmp
->
mul
(
*
gateGrad
,
*
weightT
,
1
,
1
);
}
if
(
n
!=
0
&&
weight_
->
getWGrad
())
{
/* backward weight */
MatrixPtr
outputValue
=
batchValue_
->
getBatchValue
(
n
-
1
,
batchSize
);
weight_
->
getWGrad
()
->
mul
(
outputValue
->
getTranspose
(),
gateGrad
,
1
,
1
);
weight_
->
getWGrad
()
->
mul
(
*
outputValue
->
getTranspose
(),
*
gateGrad
,
1
,
1
);
}
else
if
(
prevOutput_
&&
weight_
->
getWGrad
())
{
weight_
->
getWGrad
()
->
mul
(
prevBatchOutput2_
->
getTranspose
(),
gateGrad
,
1
,
1
);
*
prevBatchOutput2_
->
getTranspose
(),
*
gateGrad
,
1
,
1
);
}
}
}
...
...
paddle/gserver/layers/MDLstmLayer.cpp
浏览文件 @
90493691
...
...
@@ -547,7 +547,7 @@ void MDLstmLayer::forwardOneSequence(int start, CoordIterator& coordIter) {
if
(
coordIter
.
getPrePos
(
delays_
,
i
,
prePos
))
{
int
preOffset
=
coordIter
.
offset
(
prePos
);
frameGate_
[
start
+
offset
].
value
->
mul
(
frameOutput_
[
start
+
preOffset
].
value
,
weight_
->
getW
(),
1.0
,
1.0
);
*
frameOutput_
[
start
+
preOffset
].
value
,
*
weight_
->
getW
(),
1.0
,
1.0
);
}
}
forwardGate2OutputSequence
(
start
,
coordIter
);
...
...
@@ -747,11 +747,11 @@ void MDLstmLayer::backwardOneSequence(int start, CoordIterator& coordIter) {
if
(
coordIter
.
getPrePos
(
delays_
,
i
,
prePos
))
{
int
preOffset
=
coordIter
.
offset
(
prePos
);
frameOutput_
[
start
+
preOffset
].
grad
->
mul
(
frameGate_
[
start
+
offset
].
grad
,
weightT
,
1.0
,
1.0
);
*
frameGate_
[
start
+
offset
].
grad
,
*
weightT
,
1.0
,
1.0
);
if
(
weight_
->
getWGrad
())
{
weight_
->
getWGrad
()
->
mul
(
frameOutput_
[
start
+
preOffset
].
value
->
getTranspose
(),
frameGate_
[
start
+
offset
].
grad
,
*
frameOutput_
[
start
+
preOffset
].
value
->
getTranspose
(),
*
frameGate_
[
start
+
offset
].
grad
,
1.0
,
1.0
);
}
...
...
paddle/gserver/layers/OuterProdLayer.cpp
浏览文件 @
90493691
...
...
@@ -96,7 +96,7 @@ void OuterProdLayer::forward(PassType passType) {
tmpRow0
->
setData
(
inV0
->
getData
()
+
i
*
dim0
);
tmpRow1
->
setData
(
inV1
->
getData
()
+
i
*
dim1
);
tmpMtx0
->
mul
(
tmpRow0
->
getTranspose
(),
tmpRow1
);
tmpMtx0
->
mul
(
*
tmpRow0
->
getTranspose
(),
*
tmpRow1
);
}
}
}
...
...
@@ -121,7 +121,7 @@ void OuterProdLayer::backward(const UpdateCallback& callback) {
tmpRow0
->
setData
(
inG0
->
getData
()
+
i
*
dim0
);
tmpRow1
->
setData
(
inV1
->
getData
()
+
i
*
dim1
);
tmpRow0
->
mul
(
tmpRow1
,
tmpMtx0
->
getTranspose
(),
1
,
1
);
tmpRow0
->
mul
(
*
tmpRow1
,
*
tmpMtx0
->
getTranspose
(),
1
,
1
);
}
}
...
...
@@ -131,7 +131,7 @@ void OuterProdLayer::backward(const UpdateCallback& callback) {
tmpRow0
->
setData
(
inV0
->
getData
()
+
i
*
dim0
);
tmpRow1
->
setData
(
inG1
->
getData
()
+
i
*
dim1
);
tmpRow1
->
mul
(
tmpRow0
,
tmpMtx0
,
1
,
1
);
tmpRow1
->
mul
(
*
tmpRow0
,
*
tmpMtx0
,
1
,
1
);
}
}
}
...
...
paddle/gserver/layers/RecurrentLayer.cpp
浏览文件 @
90493691
...
...
@@ -215,12 +215,12 @@ void RecurrentLayer::forwardSequence(int batchSize,
void
RecurrentLayer
::
forwardOneSequence
(
int
start
,
int
length
)
{
if
(
!
reversed_
)
{
if
(
prevOutput_
)
{
frameOutput_
[
start
].
value
->
mul
(
prevOutput_
,
weight_
->
getW
(),
1
,
1
);
frameOutput_
[
start
].
value
->
mul
(
*
prevOutput_
,
*
weight_
->
getW
(),
1
,
1
);
}
activation_
->
forward
(
frameOutput_
[
start
]);
for
(
int
i
=
1
;
i
<
length
;
++
i
)
{
frameOutput_
[
start
+
i
].
value
->
mul
(
frameOutput_
[
start
+
i
-
1
].
value
,
weight_
->
getW
(),
1
,
1
);
*
frameOutput_
[
start
+
i
-
1
].
value
,
*
weight_
->
getW
(),
1
,
1
);
activation_
->
forward
(
frameOutput_
[
start
+
i
]);
}
if
(
prevOutput_
)
{
...
...
@@ -230,7 +230,7 @@ void RecurrentLayer::forwardOneSequence(int start, int length) {
activation_
->
forward
(
frameOutput_
[
start
+
length
-
1
]);
for
(
int
i
=
length
-
2
;
i
>=
0
;
--
i
)
{
frameOutput_
[
start
+
i
].
value
->
mul
(
frameOutput_
[
start
+
i
+
1
].
value
,
weight_
->
getW
(),
1
,
1
);
*
frameOutput_
[
start
+
i
+
1
].
value
,
*
weight_
->
getW
(),
1
,
1
);
activation_
->
forward
(
frameOutput_
[
start
+
i
]);
}
}
...
...
@@ -282,13 +282,13 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
for
(
int
i
=
length
-
1
;
i
>
0
;
--
i
)
{
activation_
->
backward
(
frameOutput_
[
start
+
i
]);
frameOutput_
[
start
+
i
-
1
].
grad
->
mul
(
frameOutput_
[
start
+
i
].
grad
,
weightT
,
1
,
1
);
*
frameOutput_
[
start
+
i
].
grad
,
*
weightT
,
1
,
1
);
}
activation_
->
backward
(
frameOutput_
[
start
]);
if
(
weight_
->
getWGrad
())
{
weight_
->
getWGrad
()
->
mul
(
output_
.
value
->
subMatrix
(
start
,
length
-
1
)
->
getTranspose
(),
output_
.
grad
->
subMatrix
(
start
+
1
,
length
-
1
),
*
output_
.
value
->
subMatrix
(
start
,
length
-
1
)
->
getTranspose
(),
*
output_
.
grad
->
subMatrix
(
start
+
1
,
length
-
1
),
1
,
1
);
}
...
...
@@ -296,13 +296,13 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
for
(
int
i
=
0
;
i
<
length
-
1
;
++
i
)
{
activation_
->
backward
(
frameOutput_
[
start
+
i
]);
frameOutput_
[
start
+
i
+
1
].
grad
->
mul
(
frameOutput_
[
start
+
i
].
grad
,
weightT
,
1
,
1
);
*
frameOutput_
[
start
+
i
].
grad
,
*
weightT
,
1
,
1
);
}
activation_
->
backward
(
frameOutput_
[
start
+
length
-
1
]);
if
(
weight_
->
getWGrad
())
{
weight_
->
getWGrad
()
->
mul
(
output_
.
value
->
subMatrix
(
start
+
1
,
length
-
1
)
->
getTranspose
(),
output_
.
grad
->
subMatrix
(
start
,
length
-
1
),
*
output_
.
value
->
subMatrix
(
start
+
1
,
length
-
1
)
->
getTranspose
(),
*
output_
.
grad
->
subMatrix
(
start
,
length
-
1
),
1
,
1
);
}
...
...
@@ -329,7 +329,7 @@ void RecurrentLayer::forwardBatch(int batchSize,
if
(
n
!=
0
)
{
MatrixPtr
batch1
=
batchValue_
->
getBatchValue
(
n
-
1
,
batch2
->
getHeight
());
batch2
->
mul
(
batch1
,
weight_
->
getW
(),
1
,
1
);
batch2
->
mul
(
*
batch1
,
*
weight_
->
getW
(),
1
,
1
);
}
Argument
arg
;
arg
.
value
=
batch2
;
...
...
@@ -367,14 +367,14 @@ void RecurrentLayer::backwardBatch(int batchSize,
if
(
n
!=
0
)
{
batch1
=
batchGrad_
->
getBatchValue
(
n
-
1
,
batch2
->
getHeight
());
batch1
->
mul
(
batch2
,
weightT
,
1
,
1
);
batch1
->
mul
(
*
batch2
,
*
weightT
,
1
,
1
);
}
if
(
backwardByBatch
&&
weight_
->
getWGrad
())
{
if
(
n
!=
0
)
{
/* backward weight */
batch1
=
batchValue_
->
getBatchValue
(
n
-
1
,
batch2
->
getHeight
());
weight_
->
getWGrad
()
->
mul
(
batch1
->
getTranspose
(),
batch2
,
1
,
1
);
weight_
->
getWGrad
()
->
mul
(
*
batch1
->
getTranspose
(),
*
batch2
,
1
,
1
);
}
}
}
...
...
@@ -389,14 +389,14 @@ void RecurrentLayer::backwardBatch(int batchSize,
int
len
=
starts
[
seq
+
1
]
-
starts
[
seq
];
if
(
!
reversed_
)
{
weight_
->
getWGrad
()
->
mul
(
output_
.
value
->
subMatrix
(
starts
[
seq
],
len
-
1
)
->
getTranspose
(),
output_
.
grad
->
subMatrix
(
starts
[
seq
]
+
1
,
len
-
1
),
*
output_
.
value
->
subMatrix
(
starts
[
seq
],
len
-
1
)
->
getTranspose
(),
*
output_
.
grad
->
subMatrix
(
starts
[
seq
]
+
1
,
len
-
1
),
1
,
1
);
}
else
{
weight_
->
getWGrad
()
->
mul
(
output_
.
value
->
subMatrix
(
starts
[
seq
]
+
1
,
len
-
1
)
->
getTranspose
(),
output_
.
grad
->
subMatrix
(
starts
[
seq
],
len
-
1
),
*
output_
.
value
->
subMatrix
(
starts
[
seq
]
+
1
,
len
-
1
)
->
getTranspose
(),
*
output_
.
grad
->
subMatrix
(
starts
[
seq
],
len
-
1
),
1
,
1
);
}
...
...
paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp
浏览文件 @
90493691
...
...
@@ -155,20 +155,20 @@ void SelectiveFullyConnectedLayer::forward(PassType passType) {
// manully compute the multiplication of
// the input vector and the selected rows.
REGISTER_TIMER
(
"selective.plain"
);
interOutput_
->
mul
(
input
,
weight
->
getTranspose
(),
1
,
scaleT
);
interOutput_
->
mul
(
*
input
,
*
weight
->
getTranspose
(),
1
,
scaleT
);
}
else
{
// if the indecies is not sparse enough,
// use full mul instead
REGISTER_TIMER
(
"selective.mul"
);
if
(
fullOutput_
)
{
interOutput_
->
mul
(
input
,
weight
->
getTranspose
(),
1
,
scaleT
);
interOutput_
->
mul
(
*
input
,
*
weight
->
getTranspose
(),
1
,
scaleT
);
}
else
{
Matrix
::
resizeOrCreate
(
mmat_
,
hsize
,
wsize
,
/*trans=*/
false
,
/*useGpu=*/
useGpu_
);
mmat_
->
mul
(
input
,
weight
->
getTranspose
());
mmat_
->
mul
(
*
input
,
*
weight
->
getTranspose
());
interOutput_
->
add3
(
mmat_
);
}
}
...
...
@@ -242,14 +242,14 @@ void SelectiveFullyConnectedLayer::backward(const UpdateCallback& callback) {
MatrixPtr
preGrad
=
getInputGrad
(
i
);
if
(
preGrad
)
{
REGISTER_TIMER_INFO
(
"BpMulTimer"
,
getName
().
c_str
());
preGrad
->
mul
(
interOutGrad_
,
weights_
[
i
]
->
getW
(),
1
,
1
);
preGrad
->
mul
(
*
interOutGrad_
,
*
weights_
[
i
]
->
getW
(),
1
,
1
);
}
MatrixPtr
wGrad
=
weights_
[
i
]
->
getWGrad
();
if
(
wGrad
)
{
REGISTER_TIMER_INFO
(
"GradMulTimer"
,
getName
().
c_str
());
MatrixPtr
input
=
getInputValue
(
i
);
wGrad
->
mul
(
interOutGrad_
->
getTranspose
(),
input
,
1
,
1
);
wGrad
->
mul
(
*
interOutGrad_
->
getTranspose
(),
*
input
,
1
,
1
);
}
{
...
...
paddle/gserver/layers/TensorLayer.cpp
浏览文件 @
90493691
...
...
@@ -77,7 +77,7 @@ void TensorLayer::forward(PassType passType) {
REGISTER_TIMER_INFO
(
"TensorFwMulTimer"
,
getName
().
c_str
());
for
(
size_t
i
=
0
;
i
<
getSize
();
++
i
)
{
MatrixPtr
weights
=
weights_
[
i
]
->
getW
();
tmpMat
->
mul
(
input1
,
weights
,
1
,
0
);
tmpMat
->
mul
(
*
input1
,
*
weights
,
1
,
0
);
outV
->
rowDotMul
(
i
,
*
tmpMat
,
*
input2
);
}
}
...
...
@@ -112,7 +112,7 @@ void TensorLayer::backward(const UpdateCallback& callback) {
if
(
weights_
[
i
]
->
getWGrad
())
{
tmpMat
->
rowScale
(
i
,
*
input1
,
*
oGrad
);
MatrixPtr
input1_T
=
tmpMat
->
getTranspose
();
weights_
[
i
]
->
getWGrad
()
->
mul
(
input1_T
,
input2
,
1
,
1
);
weights_
[
i
]
->
getWGrad
()
->
mul
(
*
input1_T
,
*
input2
,
1
,
1
);
}
}
}
...
...
@@ -130,11 +130,11 @@ void TensorLayer::backward(const UpdateCallback& callback) {
if
(
NULL
!=
preGrad1
)
{
/* (grad * e2) * trans(W) */
tmpMat
->
rowScale
(
i
,
*
input2
,
*
oGrad
);
MatrixPtr
weights_T
=
weights
->
getTranspose
();
preGrad1
->
mul
(
tmpMat
,
weights_T
,
1
,
1
);
preGrad1
->
mul
(
*
tmpMat
,
*
weights_T
,
1
,
1
);
}
if
(
NULL
!=
preGrad2
)
{
/* (grad * e1) * W */
tmpMat
->
rowScale
(
i
,
*
input1
,
*
oGrad
);
preGrad2
->
mul
(
tmpMat
,
weights
,
1
,
1
);
preGrad2
->
mul
(
*
tmpMat
,
*
weights
,
1
,
1
);
}
}
}
...
...
paddle/gserver/layers/TransposedFullMatrixProjection.cpp
浏览文件 @
90493691
...
...
@@ -46,7 +46,7 @@ TransposedFullMatrixProjection::TransposedFullMatrixProjection(
void
TransposedFullMatrixProjection
::
forward
()
{
REGISTER_TIMER_INFO
(
"FwMulTimer"
,
getName
().
c_str
());
out_
->
value
->
mul
(
in_
->
value
,
weight_
->
getW
()
->
getTranspose
(
),
1
,
1
);
out_
->
value
->
mul
(
*
(
in_
->
value
),
*
(
weight_
->
getW
()
->
getTranspose
()
),
1
,
1
);
}
void
TransposedFullMatrixProjection
::
backward
(
const
UpdateCallback
&
callback
)
{
...
...
@@ -55,7 +55,8 @@ void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the W-gradient for the current layer */
if
(
weight_
->
getWGrad
())
{
REGISTER_TIMER_INFO
(
"GradMulTimer"
,
getName
().
c_str
());
weight_
->
getWGrad
()
->
mul
(
out_
->
grad
->
getTranspose
(),
in_
->
value
,
1
,
1
);
weight_
->
getWGrad
()
->
mul
(
*
(
out_
->
grad
->
getTranspose
()),
*
(
in_
->
value
),
1
,
1
);
}
// If callback does not change value, backprop error asynchronously so that
...
...
@@ -69,7 +70,7 @@ void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the input layers error */
if
(
in_
->
grad
)
{
REGISTER_TIMER_INFO
(
"BpMulTimer"
,
getName
().
c_str
());
in_
->
grad
->
mul
(
out_
->
grad
,
weight_
->
getW
(
),
1
,
1
);
in_
->
grad
->
mul
(
*
(
out_
->
grad
),
*
(
weight_
->
getW
()
),
1
,
1
);
}
hl_set_sync_flag
(
syncFlag
);
...
...
paddle/math/CpuSparseMatrix.cpp
浏览文件 @
90493691
...
...
@@ -163,15 +163,16 @@ MatrixPtr CpuSparseMatrix::getTranspose() {
SparseValueType
CpuSparseMatrix
::
getValueType
()
{
return
valueType_
;
}
void
CpuSparseMatrix
::
mul
(
MatrixPtr
a
,
MatrixPtr
b
,
real
scaleAB
,
real
scaleT
)
{
void
CpuSparseMatrix
::
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
)
{
CHECK
(
!
isTransposed
())
<<
"Not supported"
;
const
auto
a_ptr
=
dynamic_cast
<
const
CpuMatrix
*>
(
&
a
);
const
auto
b_ptr
=
dynamic_cast
<
const
CpuMatrix
*>
(
&
b
);
if
(
dynamic_cast
<
CpuMatrix
*>
(
a
.
get
())
&&
dynamic_cast
<
CpuMatrix
*>
(
b
.
get
()))
{
CpuMatrix
::
mul
(
dynamic_cast
<
CpuMatrix
*>
(
a
.
get
()),
dynamic_cast
<
CpuMatrix
*>
(
b
.
get
()),
this
,
scaleAB
,
scaleT
);
if
(
a_ptr
&&
b_ptr
)
{
CpuMatrix
::
mul
((
CpuMatrix
*
)
a_ptr
,
(
CpuMatrix
*
)
b_ptr
,
this
,
scaleAB
,
scaleT
);
}
else
{
LOG
(
FATAL
)
<<
"not supported"
;
}
...
...
paddle/math/CpuSparseMatrix.h
浏览文件 @
90493691
...
...
@@ -203,7 +203,7 @@ public:
/// mem MUST be alloced outside (memAlloc=false)
void
transpose
(
MatrixPtr
matTrans
,
bool
memAlloc
);
void
mul
(
MatrixPtr
A
,
MatrixPtr
B
,
real
alpha
,
real
beta
);
void
mul
(
const
Matrix
&
A
,
const
Matrix
&
B
,
real
alpha
,
real
beta
);
/**
* @brief sparseMatrix += denseMatrix
...
...
paddle/math/Matrix.cpp
浏览文件 @
90493691
...
...
@@ -582,18 +582,16 @@ void GpuMatrix::mul(const GpuMatrix& a,
}
/* this = a*b */
void
GpuMatrix
::
mul
(
const
MatrixPtr
a
,
const
MatrixPtr
b
)
{
mul
(
a
,
b
,
1.0
,
0.0
);
}
void
GpuMatrix
::
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
)
{
mul
(
a
,
b
,
1.0
,
0.0
);
}
void
GpuMatrix
::
mul
(
const
Matrix
Ptr
a
,
const
Matrix
Ptr
b
,
void
GpuMatrix
::
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
)
{
GpuMatrixPtr
a_ptr
=
std
::
dynamic_pointer_cast
<
GpuMatrix
>
(
a
);
GpuMatrixPtr
b_ptr
=
std
::
dynamic_pointer_cast
<
GpuMatrix
>
(
b
);
GpuSparseMatrixPtr
a_ptr_s
=
std
::
dynamic_pointer_cast
<
GpuSparseMatrix
>
(
a
);
GpuSparseMatrixPtr
b_ptr_s
=
std
::
dynamic_pointer_cast
<
GpuSparseMatrix
>
(
b
);
const
auto
a_ptr
=
dynamic_cast
<
const
GpuMatrix
*>
(
&
a
);
const
auto
b_ptr
=
dynamic_cast
<
const
GpuMatrix
*>
(
&
b
);
const
auto
a_ptr_s
=
dynamic_cast
<
const
GpuSparseMatrix
*>
(
&
a
);
const
auto
b_ptr_s
=
dynamic_cast
<
const
GpuSparseMatrix
*>
(
&
b
);
if
(
a_ptr
&&
b_ptr
)
{
mul
(
*
a_ptr
,
*
b_ptr
,
scaleAB
,
scaleT
);
...
...
@@ -766,20 +764,19 @@ void GpuMatrix::maxoutBackward(Matrix& a,
}
/*calulate the error of classification */
void
GpuMatrix
::
classificationError
(
MatrixPtr
output
,
IVectorPtr
label
)
{
GpuMatrixPtr
output_ptr
=
std
::
dynamic_pointer_cast
<
GpuMatrix
>
(
output
);
GpuIVectorPtr
label_ptr
=
std
::
dynamic_pointer_cast
<
GpuIVector
>
(
label
);
void
GpuMatrix
::
classificationError
(
Matrix
&
output
,
IVector
&
label
)
{
auto
output_ptr
=
dynamic_cast
<
const
GpuMatrix
*>
(
&
output
);
auto
label_ptr
=
dynamic_cast
<
const
GpuIVector
*>
(
&
label
);
CHECK
(
output_ptr
&&
label_ptr
)
<<
"Invalid argument pointer"
;
CHECK
(
height_
==
output_ptr
->
height_
&&
width_
==
1
)
<<
"Matrix dimensions are not equal"
;
real
*
output_d
=
output_ptr
->
data_
;
real
*
recResult_d
=
data_
;
int
*
label_d
=
label_ptr
->
getData
();
hl_matrix_classification_error
(
output_d
,
label_d
,
recResult_d
,
height_
,
output_ptr
->
width_
);
hl_matrix_classification_error
((
real
*
)
output_ptr
->
data_
,
(
int
*
)
label_ptr
->
getData
(),
data_
,
height_
,
output_ptr
->
width_
);
}
/* copy -log(output[i * width + label]) to this->data[i] */
...
...
@@ -1370,86 +1367,62 @@ void GpuMatrix::maxSequenceBackward(Matrix& outputGrad,
hl_max_sequence_backward
(
outGrad
,
maxIndex
,
inputGrad
,
numSequences
,
dim
);
}
void
GpuMatrix
::
contextProjectionForward
(
Matrix
Ptr
input
,
Matrix
Ptr
weight
,
void
GpuMatrix
::
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
input
.
get
()
));
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
&
input
));
CHECK
(
dynamic_cast
<
const
GpuIVector
*>
(
&
sequence
));
if
(
weight
)
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
weight
.
get
()));
size_t
numSequences
=
sequence
.
getSize
()
-
1
;
int64_t
inputDim
=
input
->
getWidth
();
int64_t
dim
=
getWidth
();
CHECK_EQ
(
dim
,
inputDim
*
contextLength
);
real
*
outData
=
getData
();
real
*
inputData
=
input
->
getData
();
const
int
*
starts
=
sequence
.
getData
();
if
(
weight
)
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
weight
));
CHECK_EQ
(
getWidth
(),
input
.
getWidth
()
*
contextLength
);
hl_context_projection_forward
(
input
Data
,
s
tarts
,
hl_context_projection_forward
(
input
.
getData
()
,
s
equence
.
getData
()
,
isPadding
?
weight
->
getData
()
:
NULL
,
outData
,
numSequences
,
input
Dim
,
getData
()
,
sequence
.
getSize
()
-
1
,
input
.
getWidth
()
,
contextLength
,
contextStart
,
beginPad
,
isPadding
);
}
void
GpuMatrix
::
contextProjectionBackwardData
(
Matrix
Ptr
inputGrad
,
void
GpuMatrix
::
contextProjectionBackwardData
(
Matrix
&
inputGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
)
{
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
inputGrad
.
get
()
));
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
&
inputGrad
));
CHECK
(
dynamic_cast
<
const
GpuIVector
*>
(
&
sequence
));
CHECK_EQ
(
getWidth
(),
inputGrad
.
getWidth
()
*
contextLength
);
size_t
numSequences
=
sequence
.
getSize
()
-
1
;
int64_t
inputDim
=
inputGrad
->
getWidth
();
int64_t
dim
=
getWidth
();
CHECK_EQ
(
dim
,
inputDim
*
contextLength
);
real
*
outGrad
=
getData
();
real
*
inGrad
=
inputGrad
->
getData
();
const
int
*
starts
=
sequence
.
getData
();
hl_context_projection_backward_data
(
outGrad
,
starts
,
inGrad
,
numSequences
,
inputDim
,
hl_context_projection_backward_data
(
getData
(),
sequence
.
getData
(),
inputGrad
.
getData
(),
sequence
.
getSize
()
-
1
,
inputGrad
.
getWidth
(),
contextLength
,
contextStart
);
}
void
GpuMatrix
::
contextProjectionBackwardWeight
(
Matrix
Ptr
weightGrad
,
void
GpuMatrix
::
contextProjectionBackwardWeight
(
Matrix
&
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
int
totalPad
,
size_t
beginPad
)
{
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
weightGrad
.
get
()
));
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
&
weightGrad
));
CHECK
(
dynamic_cast
<
const
GpuIVector
*>
(
&
sequence
));
CHECK_EQ
(
getWidth
(),
weightGrad
.
getWidth
()
*
contextLength
);
size_t
numSequences
=
sequence
.
getSize
()
-
1
;
int64_t
weightDim
=
weightGrad
->
getWidth
();
int64_t
dim
=
getWidth
();
CHECK_EQ
(
dim
,
weightDim
*
contextLength
);
real
*
outGrad
=
getData
();
real
*
wtGrad
=
weightGrad
->
getData
();
const
int
*
starts
=
sequence
.
getData
();
hl_context_projection_backward_weight
(
outGrad
,
starts
,
wtGrad
,
numSequences
,
weightDim
,
hl_context_projection_backward_weight
(
getData
(),
sequence
.
getData
(),
weightGrad
.
getData
(),
sequence
.
getSize
()
-
1
,
weightGrad
.
getWidth
(),
totalPad
,
contextLength
,
contextStart
,
...
...
@@ -2371,23 +2344,21 @@ void CpuMatrix::maxSequenceBackward(Matrix& outputGrad,
}
}
void
CpuMatrix
::
contextProjectionForward
(
Matrix
Ptr
input
,
Matrix
Ptr
weight
,
void
CpuMatrix
::
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
input
.
get
()));
CHECK
(
dynamic_cast
<
const
CpuIVector
*>
(
&
sequence
));
if
(
weight
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
weight
.
get
()));
size_t
numSequences
=
sequence
.
getSize
()
-
1
;
int64_t
inputDim
=
input
->
getWidth
();
int64_t
dim
=
getWidth
();
CHECK_EQ
(
dim
,
inputDim
*
contextLength
);
const
int
*
starts
=
sequence
.
getData
();
auto
input_ptr
=
dynamic_cast
<
CpuMatrix
*>
(
&
input
);
auto
seq_ptr
=
dynamic_cast
<
const
CpuIVector
*>
(
&
sequence
);
CHECK
(
input_ptr
&&
seq_ptr
);
if
(
weight
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
weight
));
CHECK_EQ
(
getWidth
(),
input_ptr
->
getWidth
()
*
contextLength
);
const
int
*
starts
=
seq_ptr
->
getData
();
size_t
numSequences
=
seq_ptr
->
getSize
()
-
1
;
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
for
(
int
j
=
0
;
j
<
contextLength
;
++
j
)
{
int
begin
=
starts
[
i
]
+
contextStart
+
j
;
...
...
@@ -2400,7 +2371,7 @@ void CpuMatrix::contextProjectionForward(MatrixPtr input,
MatrixPtr
mat
=
this
->
subMatrix
(
starts
[
i
],
padSize
);
if
(
isPadding
)
{
MatrixPtr
sub
=
weight
->
subMatrix
(
j
,
padSize
);
mat
->
addAtOffset
(
*
sub
,
j
*
input
Dim
);
mat
->
addAtOffset
(
*
sub
,
j
*
input
_ptr
->
getWidth
()
);
}
dstBegin
=
starts
[
i
]
+
padSize
;
begin
=
starts
[
i
];
...
...
@@ -2412,41 +2383,36 @@ void CpuMatrix::contextProjectionForward(MatrixPtr input,
if
(
isPadding
)
{
MatrixPtr
sub
=
weight
->
subMatrix
(
beginPad
+
contextStart
+
j
-
padSize
,
padSize
);
mat
->
addAtOffset
(
*
sub
,
j
*
input
Dim
);
mat
->
addAtOffset
(
*
sub
,
j
*
input
_ptr
->
getWidth
()
);
}
dstEnd
=
starts
[
i
+
1
]
-
padSize
;
end
=
starts
[
i
+
1
];
}
if
(
end
<=
begin
)
continue
;
MatrixPtr
src
=
input
->
subMatrix
(
begin
,
end
-
begin
);
MatrixPtr
src
=
input
_ptr
->
subMatrix
(
begin
,
end
-
begin
);
MatrixPtr
dst
=
this
->
subMatrix
(
dstBegin
,
dstEnd
-
dstBegin
);
dst
->
addAtOffset
(
*
src
,
j
*
input
Dim
);
dst
->
addAtOffset
(
*
src
,
j
*
input
_ptr
->
getWidth
()
);
}
}
}
void
CpuMatrix
::
contextProjectionBackward
(
Matrix
Ptr
inputGrad
,
Matrix
Ptr
weightGrad
,
void
CpuMatrix
::
contextProjectionBackward
(
Matrix
*
inputGrad
,
Matrix
*
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
if
(
inputGrad
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
inputGrad
.
get
()
));
if
(
weightGrad
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
weightGrad
.
get
()
));
if
(
inputGrad
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
inputGrad
));
if
(
weightGrad
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
weightGrad
));
CHECK
(
dynamic_cast
<
const
CpuIVector
*>
(
&
sequence
));
int64_t
inputDim
=
0
;
int64_t
dim
=
getWidth
();
size_t
numSequences
=
sequence
.
getSize
()
-
1
;
const
int
*
starts
=
sequence
.
getData
();
if
(
inputGrad
)
{
inputDim
=
inputGrad
->
getWidth
();
}
else
{
inputDim
=
weightGrad
->
getWidth
();
}
CHECK_EQ
(
dim
,
inputDim
*
contextLength
);
int64_t
inputDim
=
inputGrad
?
inputGrad
->
getWidth
()
:
weightGrad
?
weightGrad
->
getWidth
()
:
0
;
CHECK_EQ
(
getWidth
(),
inputDim
*
contextLength
);
const
int
*
starts
=
sequence
.
getData
();
size_t
numSequences
=
sequence
.
getSize
()
-
1
;
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
for
(
int
j
=
0
;
j
<
contextLength
;
++
j
)
{
int
begin
=
starts
[
i
]
+
contextStart
+
j
;
...
...
@@ -2630,29 +2596,22 @@ void CpuMatrix::sequenceAvgForward(Matrix& a,
}
/* this = scaleAB*(a*b) + scaleT*this*/
void
CpuMatrix
::
mul
(
const
Matrix
Ptr
a
,
const
Matrix
Ptr
b
,
void
CpuMatrix
::
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
)
{
CHECK
(
!
isTransposed
())
<<
"Not supported"
;
const
auto
a_ptr
=
dynamic_cast
<
const
CpuMatrix
*>
(
&
a
);
const
auto
b_ptr
=
dynamic_cast
<
const
CpuMatrix
*>
(
&
b
);
const
auto
a_ptr_s
=
dynamic_cast
<
const
CpuSparseMatrix
*>
(
&
a
);
const
auto
b_ptr_s
=
dynamic_cast
<
const
CpuSparseMatrix
*>
(
&
b
);
if
(
dynamic_cast
<
CpuMatrix
*>
(
a
.
get
())
&&
dynamic_cast
<
CpuMatrix
*>
(
b
.
get
()))
{
mul
(
dynamic_cast
<
CpuMatrix
*>
(
a
.
get
()),
dynamic_cast
<
CpuMatrix
*>
(
b
.
get
()),
scaleAB
,
scaleT
);
}
else
if
(
dynamic_cast
<
CpuSparseMatrix
*>
(
a
.
get
())
&&
dynamic_cast
<
CpuMatrix
*>
(
b
.
get
()))
{
mul
(
dynamic_cast
<
CpuSparseMatrix
*>
(
a
.
get
()),
dynamic_cast
<
CpuMatrix
*>
(
b
.
get
()),
scaleAB
,
scaleT
);
}
else
if
(
dynamic_cast
<
CpuMatrix
*>
(
a
.
get
())
&&
dynamic_cast
<
CpuSparseMatrix
*>
(
b
.
get
()))
{
mul
(
dynamic_cast
<
CpuMatrix
*>
(
a
.
get
()),
dynamic_cast
<
CpuSparseMatrix
*>
(
b
.
get
()),
scaleAB
,
scaleT
);
if
(
a_ptr
&&
b_ptr
)
{
mul
((
CpuMatrix
*
)
a_ptr
,
(
CpuMatrix
*
)
b_ptr
,
scaleAB
,
scaleT
);
}
else
if
(
a_ptr_s
&&
b_ptr
)
{
mul
((
CpuSparseMatrix
*
)
a_ptr_s
,
(
CpuMatrix
*
)
b_ptr
,
scaleAB
,
scaleT
);
}
else
if
(
a_ptr
&&
b_ptr_s
)
{
mul
((
CpuMatrix
*
)
a_ptr
,
(
CpuSparseMatrix
*
)
b_ptr_s
,
scaleAB
,
scaleT
);
}
else
{
LOG
(
FATAL
)
<<
"Not supported"
;
}
...
...
@@ -3321,7 +3280,7 @@ void CpuMatrix::addColumnVector(const Matrix& b) {
}
/* this = a*b */
void
CpuMatrix
::
mul
(
const
Matrix
Ptr
a
,
const
MatrixPtr
b
)
{
void
CpuMatrix
::
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
)
{
return
mul
(
a
,
b
,
1.0
,
0.0
);
}
...
...
@@ -3544,21 +3503,20 @@ void CpuMatrix::rowNormalizeL1(Matrix& out) {
}
/* calulate classification error */
void
CpuMatrix
::
classificationError
(
Matrix
Ptr
output
,
IVectorPtr
label
)
{
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
output
.
get
()
));
CHECK
(
dynamic_cast
<
CpuIVector
*>
(
label
.
get
()
));
void
CpuMatrix
::
classificationError
(
Matrix
&
output
,
IVector
&
label
)
{
CHECK
(
dynamic_cast
<
const
CpuMatrix
*>
(
&
output
));
CHECK
(
dynamic_cast
<
const
CpuIVector
*>
(
&
label
));
size_t
numSamples
=
getHeight
();
size_t
dim
=
output
->
getWidth
();
CHECK_EQ
(
label
->
getSize
(),
numSamples
);
CHECK_EQ
(
output
->
getHeight
(),
numSamples
);
CHECK_EQ
(
getWidth
(),
(
size_t
)
1
);
size_t
numSamples
=
getHeight
();
CHECK_EQ
(
label
.
getSize
(),
numSamples
);
CHECK_EQ
(
output
.
getHeight
(),
numSamples
);
real
*
out
=
output
->
getData
();
real
*
result
=
getData
();
int
*
lbl
=
label
->
getData
();
real
maxData
;
int
maxIndex
;
size_t
dim
=
output
.
getWidth
();
real
*
out
=
output
.
getData
();
int
*
lbl
=
label
.
getData
();
real
maxData
=
0.0
;
int
maxIndex
=
-
1
;
for
(
size_t
i
=
0
;
i
<
numSamples
;
++
i
)
{
CHECK_GE
(
lbl
[
i
],
0
);
CHECK_LT
((
size_t
)
lbl
[
i
],
dim
);
...
...
@@ -3570,7 +3528,7 @@ void CpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) {
maxData
=
out
[
i
*
dim
+
j
];
}
}
result
[
i
]
=
(
maxIndex
!=
lbl
[
i
]);
getData
()
[
i
]
=
(
maxIndex
!=
lbl
[
i
]);
}
}
...
...
paddle/math/Matrix.h
浏览文件 @
90493691
...
...
@@ -444,8 +444,8 @@ public:
* this = scaleAB*(a*b) + scaleT*this
* @endcode
*/
virtual
void
mul
(
const
Matrix
Ptr
a
,
const
Matrix
Ptr
b
,
virtual
void
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
)
{
LOG
(
FATAL
)
<<
"Not implemented"
;
...
...
@@ -643,7 +643,7 @@ public:
* this = a*b
* @endcode
*/
virtual
void
mul
(
const
Matrix
Ptr
a
,
const
MatrixPtr
b
)
{
virtual
void
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
)
{
LOG
(
FATAL
)
<<
"Not implemented"
;
}
...
...
@@ -835,7 +835,7 @@ public:
*
* output[i] = 0 if row i is correct.
*/
virtual
void
classificationError
(
Matrix
Ptr
output
,
IVectorPtr
label
)
{
virtual
void
classificationError
(
Matrix
&
output
,
IVector
&
label
)
{
LOG
(
FATAL
)
<<
"Not implemented"
;
}
...
...
@@ -997,8 +997,8 @@ public:
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
contextProjectionForward
(
Matrix
Ptr
input
,
Matrix
Ptr
weight
,
virtual
void
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
...
...
@@ -1007,8 +1007,8 @@ public:
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
contextProjectionBackward
(
Matrix
Ptr
inputGrad
,
Matrix
Ptr
weightGrad
,
virtual
void
contextProjectionBackward
(
Matrix
*
inputGrad
,
Matrix
*
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
...
...
@@ -1017,14 +1017,14 @@ public:
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
contextProjectionBackwardData
(
Matrix
Ptr
inputGrad
,
virtual
void
contextProjectionBackwardData
(
Matrix
&
inputGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
)
{
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
contextProjectionBackwardWeight
(
Matrix
Ptr
weightGrad
,
virtual
void
contextProjectionBackwardWeight
(
Matrix
&
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
...
...
@@ -1272,14 +1272,14 @@ public:
* this = scaleAB*(a*b) + scaleT*this
* @endcode
*/
void
mul
(
const
Matrix
Ptr
a
,
const
MatrixPtr
b
,
real
scaleAB
,
real
scaleT
);
void
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
);
/**
* @code
* this = a*b
* @endcode
*/
void
mul
(
const
Matrix
Ptr
a
,
const
MatrixPtr
b
);
void
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
);
void
mul
(
const
GpuMatrix
&
a
,
const
GpuMatrix
&
b
,
real
scaleAB
,
real
scaleT
);
...
...
@@ -1373,7 +1373,7 @@ public:
void
check
(
std
::
ostream
&
os
,
Matrix
&
refMat
,
bool
printDiff
=
true
);
void
randomizeUniform
();
void
classificationError
(
Matrix
Ptr
output
,
IVectorPtr
label
);
void
classificationError
(
Matrix
&
output
,
IVector
&
label
);
void
convExpand
(
Matrix
&
feature
,
int
feaImgHeight
,
...
...
@@ -1487,20 +1487,20 @@ public:
const
IVector
&
sequence
,
IVector
&
index
);
void
contextProjectionForward
(
Matrix
Ptr
input
,
Matrix
Ptr
weight
,
void
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
);
void
contextProjectionBackwardData
(
Matrix
Ptr
inputGrad
,
void
contextProjectionBackwardData
(
Matrix
&
inputGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
);
void
contextProjectionBackwardWeight
(
Matrix
Ptr
weightGrad
,
void
contextProjectionBackwardWeight
(
Matrix
&
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
...
...
@@ -1713,16 +1713,16 @@ public:
const
IVector
&
sequence
,
IVector
&
index
);
void
contextProjectionForward
(
Matrix
Ptr
input
,
Matrix
Ptr
weight
,
void
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
);
void
contextProjectionBackward
(
Matrix
Ptr
inputGrad
,
Matrix
Ptr
weightGrad
,
void
contextProjectionBackward
(
Matrix
*
inputGrad
,
Matrix
*
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
...
...
@@ -1784,7 +1784,7 @@ public:
void
addColumnVector
(
const
Matrix
&
b
);
void
mul
(
const
Matrix
Ptr
a
,
const
MatrixPtr
b
,
real
scaleAB
,
real
scaleT
);
void
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
);
void
mul
(
CpuMatrix
*
a
,
CpuMatrix
*
b
,
real
scaleAB
,
real
scaleT
);
void
mul
(
CpuMatrix
*
a
,
CpuSparseMatrix
*
b
,
real
scaleAB
,
real
scaleT
);
...
...
@@ -1807,7 +1807,7 @@ public:
virtual
void
mul
(
CpuSparseMatrix
*
a
,
CpuMatrix
*
b
,
real
scaleAB
,
real
scaleT
);
void
mul
(
const
Matrix
Ptr
a
,
const
MatrixPtr
b
);
void
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
);
void
rightMul
(
Matrix
&
b
,
real
scaleAB
,
real
scaleT
);
void
rightMul
(
Matrix
&
b
);
...
...
@@ -1881,7 +1881,7 @@ public:
void
randomizeUniform
();
void
classificationError
(
Matrix
Ptr
output
,
IVectorPtr
label
);
void
classificationError
(
Matrix
&
output
,
IVector
&
label
);
void
addByBitCode
(
size_t
numClasses
,
const
IVector
&
codes
,
const
Matrix
&
vec
);
...
...
paddle/math/SparseMatrix.cpp
浏览文件 @
90493691
...
...
@@ -571,49 +571,48 @@ void GpuSparseMatrix::transpose(MatrixPtr matTrans, bool memAlloc) {
hl_stream_synchronize
(
stream
);
}
void
GpuSparseMatrix
::
mul
(
const
GpuMatrix
Ptr
a
,
const
GpuMatrix
Ptr
b
,
void
GpuSparseMatrix
::
mul
(
const
GpuMatrix
&
a
,
const
GpuMatrix
&
b
,
real
scaleAB
,
real
scaleT
)
{
CHECK
(
a
->
useGpu_
&&
b
->
useGpu_
)
<<
"type not match"
;
CHECK
(
a
.
useGpu_
&&
b
.
useGpu_
)
<<
"type not match"
;
CHECK
(
!
trans_
)
<<
"trans not supported"
;
real
*
A_d
=
a
->
getData
();
real
*
B_d
=
b
->
getData
();
real
*
A_d
=
(
real
*
)
a
.
getData
();
real
*
B_d
=
(
real
*
)
b
.
getData
();
hl_sparse_matrix_s
C_d
=
sMatrix_
.
get
();
hl_trans_op_t
a_trans
=
a
->
trans_
?
HPPL_OP_T
:
HPPL_OP_N
;
hl_trans_op_t
b_trans
=
b
->
trans_
?
HPPL_OP_T
:
HPPL_OP_N
;
if
(
!
a
->
trans_
&&
!
b
->
trans_
)
{
CHECK
(
height_
==
a
->
getHeight
());
CHECK
(
width_
==
b
->
getWidth
());
CHECK
(
a
->
getWidth
()
==
b
->
getHeight
());
}
else
if
(
a
->
trans_
&&
!
b
->
trans_
)
{
CHECK
(
height_
==
a
->
getWidth
());
CHECK
(
width_
==
b
->
getWidth
());
CHECK
(
a
->
getHeight
()
==
b
->
getHeight
());
}
else
if
(
!
a
->
trans_
&&
b
->
trans_
)
{
CHECK
(
height_
==
a
->
getHeight
());
CHECK
(
width_
==
b
->
getHeight
());
CHECK
(
a
->
getWidth
()
==
b
->
getWidth
());
hl_trans_op_t
a_trans
=
a
.
trans_
?
HPPL_OP_T
:
HPPL_OP_N
;
hl_trans_op_t
b_trans
=
b
.
trans_
?
HPPL_OP_T
:
HPPL_OP_N
;
if
(
!
a
.
trans_
&&
!
b
.
trans_
)
{
CHECK
(
height_
==
a
.
getHeight
());
CHECK
(
width_
==
b
.
getWidth
());
CHECK
(
a
.
getWidth
()
==
b
.
getHeight
());
}
else
if
(
a
.
trans_
&&
!
b
.
trans_
)
{
CHECK
(
height_
==
a
.
getWidth
());
CHECK
(
width_
==
b
.
getWidth
());
CHECK
(
a
.
getHeight
()
==
b
.
getHeight
());
}
else
if
(
!
a
.
trans_
&&
b
.
trans_
)
{
CHECK
(
height_
==
a
.
getHeight
());
CHECK
(
width_
==
b
.
getHeight
());
CHECK
(
a
.
getWidth
()
==
b
.
getWidth
());
}
else
{
LOG
(
INFO
)
<<
"Not support"
;
}
int
dimM
=
height_
;
int
dimN
=
width_
;
int
dimK
=
!
b
->
trans_
?
b
->
getHeight
()
:
b
->
getWidth
();
int
dimK
=
!
b
.
trans_
?
b
.
getHeight
()
:
b
.
getWidth
();
hl_sparse_matrix_mul
(
A_d
,
a_trans
,
B_d
,
b_trans
,
C_d
,
dimM
,
dimN
,
dimK
,
scaleAB
,
scaleT
);
}
void
GpuSparseMatrix
::
mul
(
const
Matrix
Ptr
a
,
const
Matrix
Ptr
b
,
void
GpuSparseMatrix
::
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
)
{
if
(
std
::
dynamic_pointer_cast
<
GpuMatrix
>
(
a
)
&&
std
::
dynamic_pointer_cast
<
GpuMatrix
>
(
b
))
{
GpuMatrixPtr
a_ptr
=
std
::
dynamic_pointer_cast
<
GpuMatrix
>
(
a
);
GpuMatrixPtr
b_ptr
=
std
::
dynamic_pointer_cast
<
GpuMatrix
>
(
b
);
mul
(
a_ptr
,
b_ptr
,
scaleAB
,
scaleT
);
const
auto
a_ptr
=
dynamic_cast
<
const
GpuMatrix
*>
(
&
a
);
const
auto
b_ptr
=
dynamic_cast
<
const
GpuMatrix
*>
(
&
b
);
if
(
a_ptr
&&
b_ptr
)
{
mul
(
*
a_ptr
,
*
b_ptr
,
scaleAB
,
scaleT
);
}
else
{
LOG
(
FATAL
)
<<
"not supported"
;
}
...
...
paddle/math/SparseMatrix.h
浏览文件 @
90493691
...
...
@@ -104,10 +104,7 @@ public:
size_t
newNnz
,
SparseValueType
valueType
);
void
mul
(
const
GpuMatrixPtr
a
,
const
GpuMatrixPtr
b
,
real
scaleAB
,
real
scaleT
);
void
mul
(
const
GpuMatrix
&
a
,
const
GpuMatrix
&
b
,
real
scaleAB
,
real
scaleT
);
/// B = A , B.trans = !A.trans
MatrixPtr
getTranspose
();
...
...
@@ -218,7 +215,7 @@ protected:
void
copyRow
(
int
offsets
,
size_t
colNum
,
const
sparse_float_value_t
*
row
);
public:
void
mul
(
const
Matrix
Ptr
a
,
const
MatrixPtr
b
,
real
scaleAB
,
real
scaleT
);
void
mul
(
const
Matrix
&
a
,
const
Matrix
&
b
,
real
scaleAB
,
real
scaleT
);
void
copyFrom
(
CpuSparseMatrix
&
src
,
hl_stream_t
stream
);
void
copyFrom
(
GpuSparseMatrix
&
src
,
hl_stream_t
stream
);
...
...
paddle/math/tests/test_SparseMatrix.cpp
浏览文件 @
90493691
...
...
@@ -33,8 +33,8 @@ TEST(Matrix, CopyCpuMatrixToSparseMatrix) {
ret2
(
new
CpuMatrix
(
HEIGHT
,
WIDTH_TEST
));
ret1
->
zeroMem
();
ret2
->
zeroMem
();
ret1
->
mul
(
testMatrix
,
mulCpuMatrix
,
1.0
,
1.0
);
ret2
->
mul
(
testCpuMatrix
,
mulCpuMatrix
,
1.0
,
1.0
);
ret1
->
mul
(
*
testMatrix
,
*
mulCpuMatrix
,
1.0
,
1.0
);
ret2
->
mul
(
*
testCpuMatrix
,
*
mulCpuMatrix
,
1.0
,
1.0
);
checkMatrixEqual
(
ret1
,
ret2
);
}
...
...
@@ -147,9 +147,9 @@ void test_sparse_matrix_mul(MatrixPara paraA,
hl_stream_synchronize
(
stream
);
/*matrix mul*/
cpuMatrixC
->
mul
(
cpuMatrixA
,
cpuMatrixB
,
1.0
,
1.0
);
gpuMatrixC
->
mul
(
gpuMatrixA
,
gpuMatrixB
,
1.0
,
1.0
);
cpuDenseC
->
mul
(
cpuDenseA
,
cpuDenseB
,
1.0
,
1.0
);
cpuMatrixC
->
mul
(
*
cpuMatrixA
,
*
cpuMatrixB
,
1.0
,
1.0
);
gpuMatrixC
->
mul
(
*
gpuMatrixA
,
*
gpuMatrixB
,
1.0
,
1.0
);
cpuDenseC
->
mul
(
*
cpuDenseA
,
*
cpuDenseB
,
1.0
,
1.0
);
gpuMatrixC_d2h
->
copyFrom
(
*
gpuMatrixC
,
stream
);
hl_stream_synchronize
(
stream
);
...
...
@@ -224,8 +224,8 @@ TEST(Matrix, CopySparseMatrixToGpuSparseMatrix) {
MatrixPtr
ret2
(
new
GpuMatrix
(
HEIGHT
,
WIDTH_TEST
));
ret1
->
zeroMem
();
ret2
->
zeroMem
();
ret1
->
mul
(
testMatrix
,
mulCpuMatrix
,
1.0
,
1.0
);
ret2
->
mul
(
testGpuMatrix
,
mulGpuMatrix
,
1.0
,
1.0
);
ret1
->
mul
(
*
testMatrix
,
*
mulCpuMatrix
,
1.0
,
1.0
);
ret2
->
mul
(
*
testGpuMatrix
,
*
mulGpuMatrix
,
1.0
,
1.0
);
checkMatrixEqual
(
ret1
,
ret2
);
}
...
...
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @
90493691
...
...
@@ -65,16 +65,16 @@ void testMatrixProjectionForward(int contextStart,
// calculate
int
beginPad
=
std
::
max
(
0
,
-
contextStart
);
cpuOutput
->
contextProjectionForward
(
cpuInput
,
cpuWeight
,
cpuOutput
->
contextProjectionForward
(
*
cpuInput
,
cpuWeight
.
get
()
,
*
cpuSequence
,
contextLength
,
contextStart
,
beginPad
,
padding
);
gpuOutput
->
contextProjectionForward
(
gpuInput
,
gpuWeight
,
gpuOutput
->
contextProjectionForward
(
*
gpuInput
,
gpuWeight
.
get
()
,
*
gpuSequence
,
contextLength
,
contextStart
,
...
...
@@ -120,17 +120,17 @@ void testMatrixProjectionBackward(int contextStart,
// calculate
int
beginPad
=
std
::
max
(
0
,
-
contextStart
);
cpuOutputGrad
->
contextProjectionBackward
(
cpuInputGrad
,
cpuWeightGrad
,
cpuOutputGrad
->
contextProjectionBackward
(
cpuInputGrad
.
get
()
,
cpuWeightGrad
.
get
()
,
*
cpuSequence
,
contextLength
,
contextStart
,
beginPad
,
padding
);
gpuOutputGrad
->
contextProjectionBackwardData
(
gpuInputGrad
,
*
gpuSequence
,
contextLength
,
contextStart
);
*
gpuInputGrad
,
*
gpuSequence
,
contextLength
,
contextStart
);
if
(
padding
)
{
gpuOutputGrad
->
contextProjectionBackwardWeight
(
gpuWeightGrad
,
gpuOutputGrad
->
contextProjectionBackwardWeight
(
*
gpuWeightGrad
,
*
gpuSequence
,
contextLength
,
contextStart
,
...
...
@@ -318,7 +318,7 @@ void testMatrixInverse(int height) {
cpu
->
randomizeUniform
();
MatrixPtr
cpuT
=
cpu
->
getTranspose
();
MatrixPtr
outputCheck
=
std
::
make_shared
<
CpuMatrix
>
(
height
,
height
);
outputCheck
->
mul
(
cpu
,
cpuT
);
outputCheck
->
mul
(
*
cpu
,
*
cpuT
);
cpu
->
setDiag
(
1.0
);
cpu
->
add
(
*
outputCheck
);
...
...
@@ -328,7 +328,7 @@ void testMatrixInverse(int height) {
TensorCheckErr
(
*
cpuI
,
*
gpuI
);
outputCheck
->
mul
(
cpu
,
cpuI
);
outputCheck
->
mul
(
*
cpu
,
*
cpuI
);
cpu
->
setDiag
(
1.0
);
TensorCheckErr
(
*
cpu
,
*
outputCheck
);
}
...
...
@@ -509,8 +509,8 @@ void testMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) {
gpuB
->
copyFrom
(
*
cpuB
);
gpuC
->
copyFrom
(
*
cpuC
);
cpuC
->
mul
(
cpuA
,
cpuB
,
alpha
,
beta
);
gpuC
->
mul
(
gpuA
,
gpuB
,
alpha
,
beta
);
cpuC
->
mul
(
*
cpuA
,
*
cpuB
,
alpha
,
beta
);
gpuC
->
mul
(
*
gpuA
,
*
gpuB
,
alpha
,
beta
);
TensorCheckErr
(
*
cpuC
,
*
gpuC
);
}
...
...
@@ -581,8 +581,8 @@ void testSubMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) {
MatrixPtr
subCpuC
=
cpuC
->
subMatrix
(
startM
,
endM
,
startN
,
endN
);
MatrixPtr
subGpuC
=
gpuC
->
subMatrix
(
startM
,
endM
,
startN
,
endN
);
subCpuC
->
mul
(
subCpuA
,
subCpuB
,
alpha
,
beta
);
subGpuC
->
mul
(
subGpuA
,
subGpuB
,
alpha
,
beta
);
subCpuC
->
mul
(
*
subCpuA
,
*
subCpuB
,
alpha
,
beta
);
subGpuC
->
mul
(
*
subGpuA
,
*
subGpuB
,
alpha
,
beta
);
TensorCheckErr
(
*
cpuC
,
*
gpuC
);
}
...
...
@@ -939,8 +939,8 @@ void testClassificationError(int numSamples, int dim) {
gpuOutput
->
copyFrom
(
*
cpuOutput
);
gpuLabel
->
copyFrom
(
*
cpuLabel
);
cpuError
->
classificationError
(
cpuOutput
,
cpuLabel
);
gpuError
->
classificationError
(
gpuOutput
,
gpuLabel
);
cpuError
->
classificationError
(
*
cpuOutput
,
*
cpuLabel
);
gpuError
->
classificationError
(
*
gpuOutput
,
*
gpuLabel
);
TensorCheckEqual
(
*
cpuError
,
*
gpuError
);
}
...
...
paddle/math/tests/test_sparseMatrixCompare.cpp
浏览文件 @
90493691
...
...
@@ -102,8 +102,8 @@ void testSpMatrixMul(int M, int N, int K, real rate) {
gpuC
->
copyFrom
(
*
cpuC
,
stream
);
hl_stream_synchronize
(
stream
);
cpuC
->
mul
(
cpuA
,
cpuB
->
getTranspose
(),
1
,
1
);
gpuC
->
mul
(
gpuA
,
gpuB
->
getTranspose
(),
1
,
1
);
cpuC
->
mul
(
*
cpuA
,
*
cpuB
->
getTranspose
(),
1
,
1
);
gpuC
->
mul
(
*
gpuA
,
*
gpuB
->
getTranspose
(),
1
,
1
);
MatrixPtr
outputCheck
(
new
CpuSparseMatrix
(
M
,
N
,
nnz
));
outputCheck
->
copyFrom
(
*
gpuC
,
stream
);
...
...
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