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NiuTrans.Tensor
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杨迪
NiuTrans.Tensor
Commits
2bb8754f
Commit
2bb8754f
authored
Jul 26, 2018
by
xiaotong
Browse files
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Plain Diff
code for inference of fnnlm
parent
f31bc3fb
显示空白字符变更
内嵌
并排
正在显示
2 个修改的文件
包含
17 行增加
和
75 行删除
+17
-75
source/sample/fnnlm/FNNLM.cpp
+5
-0
source/tensor/core/arithmetic/MatrixMulBatched.cpp
+12
-75
没有找到文件。
source/sample/fnnlm/FNNLM.cpp
查看文件 @
2bb8754f
...
...
@@ -1106,6 +1106,7 @@ void Test(const char * test, const char * result, FNNModel &model)
/* the gold standard */
XTensor
gold
;
if
(
!
autoDiff
)
{
/* prepare an empty network for building the fnn */
FNNNet
net
;
...
...
@@ -1118,6 +1119,10 @@ void Test(const char * test, const char * result, FNNModel &model)
/* forward computation */
Forward
(
inputs
,
output
,
model
,
net
);
}
else
{
ForwardAutoDiff
(
inputs
,
output
,
model
);
}
/* prediction probabilities */
XTensor
probs
;
...
...
source/tensor/core/arithmetic/MatrixMulBatched.cpp
查看文件 @
2bb8754f
...
...
@@ -57,6 +57,11 @@ void _MatrixMulBatched(const XTensor * a, MATRIX_TRANS_TYPE transposedA,
CheckNTErrors
((
a
->
order
==
b
->
order
&&
a
->
order
==
c
->
order
),
"Input tensor and output tensor must have same order!"
);
if
(
a
->
devID
>=
0
||
b
->
devID
>=
0
||
c
->
devID
>=
0
)
{
_MatrixMulBatchedGPU
(
a
,
transposedA
,
b
,
transposedB
,
c
,
alpha
,
beta
);
return
;
}
int
an
=
transposedA
==
X_TRANS
?
a
->
dimSizeRDI
[
0
]
:
a
->
dimSizeRDI
[
1
];
int
am
=
transposedA
==
X_TRANS
?
a
->
dimSizeRDI
[
1
]
:
a
->
dimSizeRDI
[
0
];
int
bn
=
transposedB
==
X_TRANS
?
b
->
dimSizeRDI
[
0
]
:
b
->
dimSizeRDI
[
1
];
...
...
@@ -213,83 +218,15 @@ void _MatrixMulBatchedGPU(const XTensor * a, MATRIX_TRANS_TYPE transposedA,
blockNum
*=
a
->
dimSizeRDI
[
i
];
}
XList
*
aList
=
new
XList
(
10
);
XList
*
bList
=
new
XList
(
10
);
XList
*
cList
=
new
XList
(
10
);
int
aDimSize
[
2
]
=
{
-
a
->
dimSizeRDI
[
1
],
a
->
dimSizeRDI
[
0
]};
int
bDimSize
[
2
]
=
{
-
b
->
dimSizeRDI
[
1
],
b
->
dimSizeRDI
[
0
]};
int
cDimSize
[
2
]
=
{
-
c
->
dimSizeRDI
[
1
],
c
->
dimSizeRDI
[
0
]};
XTensor
*
tensorBuf
=
new
XTensor
[
blockNum
*
3
];
XTensor
*
aBuf
=
tensorBuf
;
XTensor
*
bBuf
=
tensorBuf
+
blockNum
;
XTensor
*
cBuf
=
tensorBuf
+
blockNum
*
2
;
for
(
int
p
=
0
;
p
<
blockNum
;
p
++
)
{
void
*
ap
=
(
char
*
)
a
->
data
+
aRealBlockSize
*
p
;
void
*
bp
=
(
char
*
)
b
->
data
+
bRealBlockSize
*
p
;
void
*
cp
=
(
char
*
)
c
->
data
+
cRealBlockSize
*
p
;
XTensor
*
ai
=
aBuf
+
p
;
XTensor
*
bi
=
bBuf
+
p
;
XTensor
*
ci
=
cBuf
+
p
;
InitTensor
(
ai
,
2
,
aDimSize
,
a
->
dataType
,
a
->
denseRatio
,
a
->
devID
,
a
->
mem
);
InitTensor
(
bi
,
2
,
bDimSize
,
b
->
dataType
,
b
->
denseRatio
,
b
->
devID
,
b
->
mem
);
InitTensor
(
ci
,
2
,
cDimSize
,
c
->
dataType
,
c
->
denseRatio
,
c
->
devID
,
c
->
mem
);
ai
->
data
=
ap
;
bi
->
data
=
bp
;
ci
->
data
=
cp
;
aList
->
Add
(
ai
);
bList
->
Add
(
bi
);
cList
->
Add
(
ci
);
}
if
(
a
->
devID
>=
0
&&
b
->
devID
>=
0
&&
c
->
devID
>=
0
)
{
#ifdef USE_CUDA
CheckNTErrors
((
a
->
devID
==
b
->
devID
&&
a
->
devID
==
c
->
devID
),
"The code must be run on the same GPU!"
);
int
devIDBackup
;
ProtectCudaDev
(
a
->
devID
,
devIDBackup
);
cublasHandle_t
*
handle
=
a
->
mem
!=
NULL
?
a
->
mem
->
GetCublasHandle
()
:
GDevs
.
GetCudaHandle
(
a
->
devID
);
_CudaBLASMatrixMULList
(
handle
,
aList
,
transposedA
,
bList
,
transposedB
,
cList
,
aList
->
count
,
alpha
,
beta
);
BacktoCudaDev
(
a
->
devID
,
devIDBackup
);
#else
ShowNTErrors
(
"Please specify USE_CUDA and recompile the code!"
);
#endif
}
else
{
CheckNTErrors
((
a
->
dataType
==
DEFAULT_DTYPE
),
"TODO!"
);
_MatrixMULBatchedCPU
(
aList
,
transposedA
,
bList
,
transposedB
,
cList
,
alpha
,
beta
);
}
for
(
int
i
=
0
;
i
<
aList
->
count
;
i
++
)
{
XTensor
*
ai
=
(
XTensor
*
)
aList
->
GetItem
(
i
);
ai
->
data
=
NULL
;;
}
for
(
int
i
=
0
;
i
<
bList
->
count
;
i
++
)
{
XTensor
*
bi
=
(
XTensor
*
)
bList
->
GetItem
(
i
);
bi
->
data
=
NULL
;
}
_CudaBLASMatrixMULBatchedStrided
(
handle
,
a
->
data
,
transposedA
,
a
->
dataType
,
aBlockSize
,
b
->
data
,
transposedB
,
b
->
dataType
,
bBlockSize
,
c
->
data
,
c
->
dataType
,
cBlockSize
,
blockNum
,
a
->
dimSizeRDI
[
1
],
a
->
dimSizeRDI
[
0
],
b
->
dimSizeRDI
[
1
],
b
->
dimSizeRDI
[
0
],
c
->
dimSizeRDI
[
1
],
c
->
dimSizeRDI
[
0
],
alpha
,
beta
);
for
(
int
i
=
0
;
i
<
cList
->
count
;
i
++
)
{
XTensor
*
ci
=
(
XTensor
*
)
cList
->
GetItem
(
i
);
ci
->
data
=
NULL
;
}
delete
[]
tensorBuf
;
delete
aList
;
delete
bList
;
delete
cList
;
}
/*
...
...
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