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
077f936a
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
077f936a
编写于
1月 21, 2017
作者:
X
xutianbing
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Support SparseMatrixArg unit test using Daoyuan's new Function Test.
上级
316bf75a
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
203 addition
and
197 deletion
+203
-197
paddle/function/BufferArg.cpp
paddle/function/BufferArg.cpp
+0
-2
paddle/function/BufferArg.h
paddle/function/BufferArg.h
+43
-6
paddle/function/FunctionTest.h
paddle/function/FunctionTest.h
+81
-17
paddle/function/MulOp.h
paddle/function/MulOp.h
+2
-0
paddle/function/MulOpTest.cpp
paddle/function/MulOpTest.cpp
+77
-172
未找到文件。
paddle/function/BufferArg.cpp
浏览文件 @
077f936a
...
...
@@ -33,7 +33,6 @@ SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType)
:
BufferArg
(
sparse
,
argType
),
row_
(
reinterpret_cast
<
void
*>
(
sparse
.
getRows
()),
VALUE_TYPE_INT32
),
col_
(
reinterpret_cast
<
void
*>
(
sparse
.
getCols
()),
VALUE_TYPE_INT32
),
/// todo(tianbing), make sure how to get NNZ
nnz_
(
sparse
.
getElementCnt
()),
format_
(
sparse
.
getFormat
()),
type_
(
sparse
.
getValueType
())
{
...
...
@@ -44,7 +43,6 @@ SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType)
:
BufferArg
(
sparse
,
argType
),
row_
(
reinterpret_cast
<
void
*>
(
sparse
.
getRows
()),
VALUE_TYPE_INT32
),
col_
(
reinterpret_cast
<
void
*>
(
sparse
.
getCols
()),
VALUE_TYPE_INT32
),
/// todo(tianbing), make sure how to get NNZ
nnz_
(
sparse
.
getElementCnt
()),
format_
(
sparse
.
getFormat
()),
type_
(
sparse
.
getValueType
())
{
...
...
paddle/function/BufferArg.h
浏览文件 @
077f936a
...
...
@@ -71,17 +71,24 @@ public:
public:
BufferArg
(
ValueType
valueType
,
const
TensorShape
&
shape
,
ArgType
argType
=
UNSPECIFIED
)
ArgType
argType
=
UNSPECIFIED
,
bool
trans
=
false
)
:
buf_
(
nullptr
),
valueType_
(
valueType
),
shape_
(
shape
),
argType_
(
argType
)
{}
argType_
(
argType
),
trans_
(
trans
)
{}
BufferArg
(
void
*
buf
,
ValueType
valueType
,
const
TensorShape
&
shape
,
ArgType
argType
=
UNSPECIFIED
)
:
buf_
(
buf
),
valueType_
(
valueType
),
shape_
(
shape
),
argType_
(
argType
)
{}
ArgType
argType
=
UNSPECIFIED
,
bool
trans
=
false
)
:
buf_
(
buf
),
valueType_
(
valueType
),
shape_
(
shape
),
argType_
(
argType
),
trans_
(
trans
)
{}
BufferArg
(
void
*
buf
,
ValueType
valueType
)
:
buf_
(
buf
),
valueType_
(
valueType
)
{}
...
...
@@ -162,6 +169,7 @@ public:
ValueType
valueType
()
const
{
return
valueType_
;
}
BufferType
bufferType
()
const
{
return
bufferType_
;
}
const
TensorShape
&
shape
()
const
{
return
shape_
;
}
bool
isTransposed
()
const
{
return
trans_
;
}
bool
isSparseArg
()
const
{
return
TENSOR_SPARSE
==
bufferType_
;
}
bool
isSequenceArg
()
const
{
return
TENSOR_SEQUENCE_DATA
==
bufferType_
;
}
...
...
@@ -175,6 +183,7 @@ protected:
BufferType
bufferType_
{
TENSOR_UNKNOWN
};
ArgType
argType_
{
UNSPECIFIED
};
bool
trans_
{
false
};
// todo(tianbing), add deviceType_
// leading dimensions. The size is dims_.size()
// Dims lds_;
};
...
...
@@ -267,8 +276,9 @@ public:
size_t
nnz
,
SparseFormat
format
,
SparseValueType
type
,
ArgType
argType
=
UNSPECIFIED
)
:
BufferArg
(
buf
,
valueType
,
shape
,
argType
),
ArgType
argType
=
UNSPECIFIED
,
bool
trans
=
false
)
:
BufferArg
(
buf
,
valueType
,
shape
,
argType
,
trans
),
row_
(
row
),
col_
(
col
),
nnz_
(
nnz
),
...
...
@@ -286,6 +296,33 @@ public:
}
}
SparseMatrixArg
(
ValueType
valueType
,
const
TensorShape
&
shape
,
size_t
nnz
,
SparseFormat
format
,
SparseValueType
type
,
ArgType
argType
=
UNSPECIFIED
,
bool
trans
=
false
)
:
BufferArg
(
valueType
,
shape
,
argType
,
trans
),
/// len of row_ : height + 1 (CSR), buf_ == nullptr
row_
(
format
==
SPARSE_CSR
?
BufferArg
(
VALUE_TYPE_INT32
,
TensorShape
{
shape
[
0
]
+
1
})
:
BufferArg
(
VALUE_TYPE_INT32
,
TensorShape
{
nnz
})),
/// len of col_ : width + 1 (CSC), buf_ == nullptr
col_
(
format
==
SPARSE_CSR
?
BufferArg
(
VALUE_TYPE_INT32
,
TensorShape
{
nnz
})
:
BufferArg
(
VALUE_TYPE_INT32
,
TensorShape
{
shape
[
1
]
+
1
})),
nnz_
(
nnz
),
format_
(
format
),
type_
(
type
)
{
bufferType_
=
TENSOR_SPARSE
;
/// todo(tianbing)
/// valueType and shape_.ndims() == 2 need to check before
/// this constructor to make sure row_ and col_ are right
CHECK
((
valueType
==
VALUE_TYPE_FLOAT
)
||
(
valueType
==
VALUE_TYPE_DOUBLE
));
CHECK_EQ
(
shape_
.
ndims
(),
(
size_t
)
2
);
}
SparseMatrixArg
(
const
CpuSparseMatrix
&
sparse
,
ArgType
argType
=
UNSPECIFIED
);
SparseMatrixArg
(
const
GpuSparseMatrix
&
sparse
,
ArgType
argType
=
UNSPECIFIED
);
...
...
paddle/function/FunctionTest.h
浏览文件 @
077f936a
...
...
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "Function.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/math/Vector.h"
#include "paddle/math/tests/TensorCheck.h"
#include "paddle/testing/TestUtil.h"
...
...
@@ -62,29 +64,41 @@ public:
cpuMemory_
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory_
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
cpuInputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
cpuMemory_
.
back
()
->
getBuf
(),
input
.
valueType
(),
input
.
shape
()));
gpuInputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
gpuMemory_
.
back
()
->
getBuf
(),
input
.
valueType
(),
input
.
shape
()));
cpuInputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
cpuMemory_
.
back
()
->
getBuf
(),
input
.
valueType
(),
input
.
shape
(),
UNSPECIFIED
,
input
.
isTransposed
()));
gpuInputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
gpuMemory_
.
back
()
->
getBuf
(),
input
.
valueType
(),
input
.
shape
(),
UNSPECIFIED
,
input
.
isTransposed
()));
}
// output need only contains shape, do not contains data.
void
addOutputs
(
const
BufferArg
&
output
)
{
void
addOutputs
(
const
BufferArg
&
output
,
ArgType
argType
=
ASSIGN_TO
)
{
size_t
size
=
output
.
shape
().
getElements
()
*
sizeOfValuType
(
output
.
valueType
());
cpuMemory_
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory_
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
cpuOutputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
cpuMemory_
.
back
()
->
getBuf
(),
output
.
valueType
(),
output
.
shape
(),
ASSIGN_TO
));
gpuOutputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
gpuMemory_
.
back
()
->
getBuf
(),
output
.
valueType
(),
output
.
shape
(),
ASSIGN_TO
));
cpuOutputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
cpuMemory_
.
back
()
->
getBuf
(),
output
.
valueType
(),
output
.
shape
(),
// todo(tianbing), argType = output.getArgType(), but default ASSIGN_TO
argType
,
output
.
isTransposed
()));
gpuOutputs_
.
emplace_back
(
std
::
make_shared
<
BufferArg
>
(
gpuMemory_
.
back
()
->
getBuf
(),
output
.
valueType
(),
output
.
shape
(),
// todo(tianbing), argType = output.getArgType(), but default ASSIGN_TO
argType
,
output
.
isTransposed
()));
}
void
addInputs
(
const
SequenceArg
&
input
)
{
...
...
@@ -107,10 +121,36 @@ public:
// TODO: need be implemented.
}
void
addInputs
(
const
SparseMatrixArg
&
input
)
{
cpuSparse_
=
std
::
make_shared
<
CpuSparseMatrix
>
(
input
.
shape
()[
0
],
input
.
shape
()[
1
],
input
.
nnz
(),
input
.
dataType
(),
input
.
dataFormat
(),
input
.
isTransposed
());
gpuSparse_
=
std
::
make_shared
<
GpuSparseMatrix
>
(
input
.
shape
()[
0
],
input
.
shape
()[
1
],
input
.
nnz
(),
input
.
dataType
(),
input
.
dataFormat
(),
input
.
isTransposed
());
/// init sparse matrix
hl_stream_t
stream
(
HPPL_STREAM_1
);
cpuSparse_
->
randomizeUniform
();
gpuSparse_
->
copyFrom
(
*
cpuSparse_
,
stream
);
hl_stream_synchronize
(
stream
);
cpuInputs_
.
emplace_back
(
std
::
make_shared
<
SparseMatrixArg
>
(
*
cpuSparse_
));
gpuInputs_
.
emplace_back
(
std
::
make_shared
<
SparseMatrixArg
>
(
*
gpuSparse_
));
}
void
run
()
{
// prepare cpu/gpu arguments
initInputs
();
initOutputs
();
// function calculate
auto
callFunction
=
[](
FunctionBase
*
function
,
std
::
vector
<
BufferArgPtr
>&
inputs
,
...
...
@@ -129,7 +169,7 @@ public:
callFunction
(
cpuFunc_
.
get
(),
cpuInputs_
,
cpuOutputs_
);
callFunction
(
gpuFunc_
.
get
(),
gpuInputs_
,
gpuOutputs_
);
// check outputs
and inouts
// check outputs
compareOutputs
();
}
...
...
@@ -140,6 +180,10 @@ public:
protected:
void
initInputs
()
{
for
(
size_t
i
=
0
;
i
<
cpuInputs_
.
size
();
i
++
)
{
if
(
cpuInputs_
[
i
]
->
isSparseArg
())
{
continue
;
/// sparse matrix already init
}
initArg
(
*
cpuInputs_
[
i
]);
// TODO: Need a BufferCopy used to copy from one BufferArg to another.
...
...
@@ -152,6 +196,25 @@ protected:
}
}
void
initOutputs
()
{
for
(
size_t
i
=
0
;
i
<
cpuOutputs_
.
size
();
i
++
)
{
if
(
cpuOutputs_
[
i
]
->
isSparseArg
())
{
LOG
(
INFO
)
<<
"output sparse matrix already init"
;
continue
;
}
initArg
(
*
cpuOutputs_
[
i
]);
// TODO: Need a BufferCopy used to copy from one BufferArg to another.
CpuVector
cpuVector
(
cpuOutputs_
[
i
]
->
shape
().
getElements
(),
(
real
*
)
cpuOutputs_
[
i
]
->
data
());
GpuVector
gpuVector
(
gpuOutputs_
[
i
]
->
shape
().
getElements
(),
(
real
*
)
gpuOutputs_
[
i
]
->
data
());
gpuVector
.
copyFrom
(
cpuVector
);
}
}
void
compareOutputs
()
{
for
(
size_t
i
=
0
;
i
<
cpuOutputs_
.
size
();
i
++
)
{
// TODO, Need a BufferCheck used to compare the two buffers.
...
...
@@ -159,7 +222,6 @@ protected:
auto
gpu
=
gpuOutputs_
[
i
];
CpuVector
cpuVector
(
cpu
->
shape
().
getElements
(),
(
real
*
)
cpu
->
data
());
GpuVector
gpuVector
(
cpu
->
shape
().
getElements
(),
(
real
*
)
gpu
->
data
());
autotest
::
TensorCheckErr
(
cpuVector
,
gpuVector
);
}
}
...
...
@@ -195,6 +257,8 @@ protected:
std
::
vector
<
BufferArgPtr
>
cpuOutputs_
;
std
::
vector
<
BufferArgPtr
>
gpuInputs_
;
std
::
vector
<
BufferArgPtr
>
gpuOutputs_
;
std
::
shared_ptr
<
CpuSparseMatrix
>
cpuSparse_
;
std
::
shared_ptr
<
GpuSparseMatrix
>
gpuSparse_
;
};
}
// namespace paddle
paddle/function/MulOp.h
浏览文件 @
077f936a
...
...
@@ -15,6 +15,8 @@ limitations under the License. */
#pragma once
#include "Function.h"
/// todo(tianbing), delete it
#include <iostream>
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
...
...
paddle/function/MulOpTest.cpp
浏览文件 @
077f936a
...
...
@@ -24,58 +24,39 @@ limitations under the License. */
using
namespace
paddle
;
// NOLINT
/**
* C
= alpha * C + beta * (A * B)
, A, B, C dense matrix
* C
+= A * B
, A, B, C dense matrix
* dense = dense * dense
*/
void
testDDDMatrix
(
bool
transa
,
bool
transb
,
int
dimM
,
int
dimN
,
int
dimK
)
{
real
alpha
=
1.5
;
real
beta
=
2.0
;
const
auto
cpuFunc
=
FunctionBase
::
funcRegistrar_
.
createByType
(
"MulOp-CPU"
);
cpuFunc
->
init
(
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
const
auto
gpuFunc
=
FunctionBase
::
funcRegistrar_
.
createByType
(
"MulOp-GPU"
);
gpuFunc
->
init
(
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
int
heightA
=
(
transa
==
false
)
?
dimM
:
dimK
;
int
widthA
=
(
transa
==
false
)
?
dimK
:
dimM
;
int
heightB
=
(
transb
==
false
)
?
dimK
:
dimN
;
int
widthB
=
(
transb
==
false
)
?
dimN
:
dimK
;
int
heightC
=
dimM
;
int
widthC
=
dimN
;
auto
cpuA
=
std
::
make_shared
<
CpuMatrix
>
(
heightA
,
widthA
,
transa
);
auto
cpuB
=
std
::
make_shared
<
CpuMatrix
>
(
heightB
,
widthB
,
transb
);
auto
cpuC
=
std
::
make_shared
<
CpuMatrix
>
(
heightC
,
widthC
);
auto
gpuA
=
std
::
make_shared
<
GpuMatrix
>
(
heightA
,
widthA
,
transa
);
auto
gpuB
=
std
::
make_shared
<
GpuMatrix
>
(
heightB
,
widthB
,
transb
);
auto
gpuC
=
std
::
make_shared
<
GpuMatrix
>
(
heightC
,
widthC
);
cpuA
->
randomizeUniform
();
cpuB
->
randomizeUniform
();
cpuC
->
randomizeUniform
();
gpuA
->
copyFrom
(
*
cpuA
);
gpuB
->
copyFrom
(
*
cpuB
);
gpuC
->
copyFrom
(
*
cpuC
);
BufferArgs
cpuInputs
;
BufferArgs
cpuOutputs
;
cpuInputs
.
addArg
(
*
cpuA
);
cpuInputs
.
addArg
(
*
cpuB
);
cpuOutputs
.
addArg
(
*
cpuC
,
ADD_TO
);
cpuFunc
->
calc
(
cpuInputs
,
cpuOutputs
);
BufferArgs
gpuInputs
;
BufferArgs
gpuOutputs
;
gpuInputs
.
addArg
(
*
gpuA
);
gpuInputs
.
addArg
(
*
gpuB
);
gpuOutputs
.
addArg
(
*
gpuC
,
ADD_TO
);
gpuFunc
->
calc
(
gpuInputs
,
gpuOutputs
);
autotest
::
TensorCheckErr
(
*
cpuC
,
*
gpuC
);
void
testFuncDDDMatrix
(
bool
transa
,
bool
transb
,
size_t
dimM
,
size_t
dimN
,
size_t
dimK
)
{
real
alpha
=
1.0
;
real
beta
=
1.0
;
size_t
heightA
=
(
transa
==
false
)
?
dimM
:
dimK
;
size_t
widthA
=
(
transa
==
false
)
?
dimK
:
dimM
;
size_t
heightB
=
(
transb
==
false
)
?
dimK
:
dimN
;
size_t
widthB
=
(
transb
==
false
)
?
dimN
:
dimK
;
size_t
heightC
=
dimM
;
size_t
widthC
=
dimN
;
// init Test object
FunctionCompare
test
(
"MulOp"
,
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
// prepare input arguments
/// matrix A : HA * WA
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
heightA
,
widthA
},
UNSPECIFIED
,
transa
));
/// matrix B: HB * WB
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
heightB
,
widthB
},
UNSPECIFIED
,
transb
));
/// output matrix C: HC * WC
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
heightC
,
widthC
}),
ADD_TO
);
// run Function
test
.
run
();
}
TEST
(
M
atrix
,
DDD
Mul
)
{
LOG
(
INFO
)
<<
"test for dense = dense * dense matrix"
;
TEST
(
M
ulOp
,
DDDMatrix
Mul
)
{
LOG
(
INFO
)
<<
"
function
test for dense = dense * dense matrix"
;
for
(
const
auto
transa
:
{
false
,
true
})
{
for
(
const
auto
transb
:
{
false
,
true
})
{
for
(
const
auto
dimM
:
{
1
,
10
,
100
})
{
...
...
@@ -89,7 +70,7 @@ TEST(Matrix, DDDMul) {
<<
" dimM="
<<
std
::
setw
(
5
)
<<
dimM
<<
" dimN="
<<
std
::
setw
(
5
)
<<
dimN
<<
" dimK="
<<
std
::
setw
(
5
)
<<
dimK
;
testDDDMatrix
(
transa
,
transb
,
dimM
,
dimN
,
dimK
);
test
Func
DDDMatrix
(
transa
,
transb
,
dimM
,
dimN
,
dimK
);
}
}
}
...
...
@@ -101,71 +82,33 @@ TEST(Matrix, DDDMul) {
* C += A * B, B, C dense, A sparse
* dense = sparse * dense
*/
void
testDSparseDMatrix
(
void
test
Func
DSparseDMatrix
(
size_t
dimM
,
size_t
dimN
,
size_t
dimK
,
size_t
nnz
,
SparseFormat
FORMAT
)
{
real
alpha
=
1.0
;
real
beta
=
1.0
;
const
auto
cpuFunc
=
FunctionBase
::
funcRegistrar_
.
createByType
(
"MulOp-CPU"
);
cpuFunc
->
init
(
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
const
auto
gpuFunc
=
FunctionBase
::
funcRegistrar_
.
createByType
(
"MulOp-GPU"
);
gpuFunc
->
init
(
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
CpuSparseMatrix
cpuMatrixA
(
dimM
,
dimK
,
nnz
,
FLOAT_VALUE
,
FORMAT
,
false
);
GpuSparseMatrix
gpuMatrixA
(
dimM
,
dimK
,
nnz
,
FLOAT_VALUE
,
FORMAT
,
false
);
CpuMatrix
cpuDenseA
(
dimM
,
dimK
,
false
);
auto
cpuMatrixB
=
Matrix
::
create
(
dimK
,
dimN
,
false
,
false
);
auto
gpuMatrixB
=
Matrix
::
create
(
dimK
,
dimN
,
false
,
true
);
auto
cpuDenseB
=
Matrix
::
create
(
dimK
,
dimN
,
false
,
false
);
auto
cpuMatrixC
=
Matrix
::
create
(
dimM
,
dimN
,
false
,
false
);
auto
gpuMatrixC
=
Matrix
::
create
(
dimM
,
dimN
,
false
,
true
);
auto
cpuDenseC
=
Matrix
::
create
(
dimM
,
dimN
,
false
,
false
);
/*matrix init*/
hl_stream_t
stream
(
HPPL_STREAM_1
);
cpuMatrixA
.
randomizeUniform
();
cpuMatrixB
->
randomizeUniform
();
cpuMatrixC
->
randomizeUniform
();
gpuMatrixA
.
copyFrom
(
cpuMatrixA
,
stream
);
gpuMatrixB
->
copyFrom
(
*
cpuMatrixB
,
stream
);
gpuMatrixC
->
copyFrom
(
*
cpuMatrixC
,
stream
);
cpuDenseA
.
copyFrom
(
cpuMatrixA
);
cpuDenseB
->
copyFrom
(
*
cpuMatrixB
);
cpuDenseC
->
copyFrom
(
*
cpuMatrixC
);
hl_stream_synchronize
(
stream
);
/*matrix mul*/
BufferArgs
cpuInputs
;
BufferArgs
cpuOutputs
;
cpuInputs
.
addArg
(
cpuMatrixA
);
cpuInputs
.
addArg
(
*
cpuMatrixB
);
cpuOutputs
.
addArg
(
*
cpuMatrixC
,
ADD_TO
);
cpuFunc
->
calc
(
cpuInputs
,
cpuOutputs
);
BufferArgs
gpuInputs
;
BufferArgs
gpuOutputs
;
gpuInputs
.
addArg
(
gpuMatrixA
);
gpuInputs
.
addArg
(
*
gpuMatrixB
);
gpuOutputs
.
addArg
(
*
gpuMatrixC
,
ADD_TO
);
gpuFunc
->
calc
(
gpuInputs
,
gpuOutputs
);
BufferArgs
denseInputs
;
BufferArgs
denseOutputs
;
denseInputs
.
addArg
(
cpuDenseA
);
denseInputs
.
addArg
(
*
cpuDenseB
);
denseOutputs
.
addArg
(
*
cpuDenseC
,
ADD_TO
);
cpuFunc
->
calc
(
denseInputs
,
denseOutputs
);
/*check result*/
autotest
::
TensorCheckErr
(
*
cpuMatrixC
,
*
cpuDenseC
);
autotest
::
TensorCheckErr
(
*
cpuMatrixC
,
*
gpuMatrixC
);
// init Test object
FunctionCompare
test
(
"MulOp"
,
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
// prepare input arguments
/// sparse matrix A : M * K
test
.
addInputs
(
SparseMatrixArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
dimM
,
dimK
},
nnz
,
FORMAT
,
FLOAT_VALUE
,
UNSPECIFIED
,
false
));
/// matrix B: K * N
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
dimK
,
dimN
}));
/// output matrix C: M * N
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
dimM
,
dimN
}),
ADD_TO
);
// run Function
test
.
run
();
}
TEST
(
M
atrix
,
DSparseDMul
)
{
LOG
(
INFO
)
<<
"test for dense = sparse * dense matrix"
;
TEST
(
M
uLOp
,
DSparseDMul
)
{
LOG
(
INFO
)
<<
"
function
test for dense = sparse * dense matrix"
;
for
(
const
auto
dimM
:
{
10
,
100
,
1000
})
{
for
(
const
auto
dimN
:
{
10
,
100
})
{
for
(
const
auto
dimK
:
{
3
,
10
})
{
...
...
@@ -177,7 +120,7 @@ TEST(Matrix, DSparseDMul) {
<<
" dimK="
<<
std
::
setw
(
5
)
<<
dimK
<<
" nnz="
<<
std
::
setw
(
5
)
<<
nnz
<<
" format="
<<
std
::
setw
(
5
)
<<
FORMAT
;
testDSparseDMatrix
(
dimM
,
dimN
,
dimK
,
nnz
,
FORMAT
);
test
Func
DSparseDMatrix
(
dimM
,
dimN
,
dimK
,
nnz
,
FORMAT
);
}
}
}
...
...
@@ -189,72 +132,34 @@ TEST(Matrix, DSparseDMul) {
* C += A * B, A, C dense, B sparse
* dense = dense * sparse
*/
void
testDDSparseMatrix
(
void
test
Func
DDSparseMatrix
(
size_t
dimM
,
size_t
dimN
,
size_t
dimK
,
size_t
nnz
,
SparseFormat
FORMAT
)
{
real
alpha
=
1.0
;
real
beta
=
1.0
;
const
auto
cpuFunc
=
FunctionBase
::
funcRegistrar_
.
createByType
(
"MulOp-CPU"
);
cpuFunc
->
init
(
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
const
auto
gpuFunc
=
FunctionBase
::
funcRegistrar_
.
createByType
(
"MulOp-GPU"
);
gpuFunc
->
init
(
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
auto
cpuMatrixA
=
Matrix
::
create
(
dimM
,
dimK
,
false
,
false
);
auto
gpuMatrixA
=
Matrix
::
create
(
dimM
,
dimK
,
false
,
true
);
auto
cpuDenseA
=
Matrix
::
create
(
dimM
,
dimK
,
false
,
false
);
CpuSparseMatrix
cpuMatrixB
(
dimK
,
dimN
,
nnz
,
FLOAT_VALUE
,
FORMAT
,
false
);
GpuSparseMatrix
gpuMatrixB
(
dimK
,
dimN
,
nnz
,
FLOAT_VALUE
,
FORMAT
,
false
);
auto
cpuDenseB
=
Matrix
::
create
(
dimK
,
dimN
,
false
,
false
);
auto
cpuMatrixC
=
Matrix
::
create
(
dimM
,
dimN
,
false
,
false
);
auto
gpuMatrixC
=
Matrix
::
create
(
dimM
,
dimN
,
false
,
true
);
auto
cpuDenseC
=
Matrix
::
create
(
dimM
,
dimN
,
false
,
false
);
/*matrix init*/
hl_stream_t
stream
(
HPPL_STREAM_1
);
cpuMatrixA
->
randomizeUniform
();
cpuMatrixB
.
randomizeUniform
();
cpuMatrixC
->
randomizeUniform
();
gpuMatrixA
->
copyFrom
(
*
cpuMatrixA
,
stream
);
gpuMatrixB
.
copyFrom
(
cpuMatrixB
,
stream
);
gpuMatrixC
->
copyFrom
(
*
cpuMatrixC
,
stream
);
cpuDenseA
->
copyFrom
(
*
cpuMatrixA
);
cpuDenseB
->
copyFrom
(
cpuMatrixB
);
cpuDenseC
->
copyFrom
(
*
cpuMatrixC
);
hl_stream_synchronize
(
stream
);
/*matrix mul*/
BufferArgs
cpuInputs
;
BufferArgs
cpuOutputs
;
cpuInputs
.
addArg
(
*
cpuMatrixA
);
cpuInputs
.
addArg
(
cpuMatrixB
);
cpuOutputs
.
addArg
(
*
cpuMatrixC
,
ADD_TO
);
cpuFunc
->
calc
(
cpuInputs
,
cpuOutputs
);
BufferArgs
gpuInputs
;
BufferArgs
gpuOutputs
;
gpuInputs
.
addArg
(
*
gpuMatrixA
);
gpuInputs
.
addArg
(
gpuMatrixB
);
gpuOutputs
.
addArg
(
*
gpuMatrixC
,
ADD_TO
);
gpuFunc
->
calc
(
gpuInputs
,
gpuOutputs
);
BufferArgs
denseInputs
;
BufferArgs
denseOutputs
;
denseInputs
.
addArg
(
*
cpuDenseA
);
denseInputs
.
addArg
(
*
cpuDenseB
);
denseOutputs
.
addArg
(
*
cpuDenseC
,
ADD_TO
);
cpuFunc
->
calc
(
denseInputs
,
denseOutputs
);
/*check result*/
autotest
::
TensorCheckErr
(
*
cpuMatrixC
,
*
cpuDenseC
);
autotest
::
TensorCheckErr
(
*
cpuMatrixC
,
*
gpuMatrixC
);
// init Test object
FunctionCompare
test
(
"MulOp"
,
FuncConfig
().
set
(
"scaleAB"
,
alpha
).
set
(
"scaleT"
,
beta
));
// prepare input arguments
/// matrix A : M * K
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
dimM
,
dimK
}));
/// matrix B: K * N
test
.
addInputs
(
SparseMatrixArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
dimK
,
dimN
},
nnz
,
FORMAT
,
FLOAT_VALUE
,
UNSPECIFIED
,
false
));
/// output matrix C: M * N
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
dimM
,
dimN
}),
ADD_TO
);
// run Function
test
.
run
();
}
TEST
(
M
atrix
,
DDSparseMul
)
{
LOG
(
INFO
)
<<
"test for dense = dense * sparse matrix"
;
TEST
(
M
ulOp
,
DDSparseMul
)
{
LOG
(
INFO
)
<<
"
function
test for dense = dense * sparse matrix"
;
for
(
const
auto
dimM
:
{
10
,
100
,
1000
})
{
for
(
const
auto
dimN
:
{
10
,
100
})
{
for
(
const
auto
dimK
:
{
3
,
10
})
{
...
...
@@ -266,7 +171,7 @@ TEST(Matrix, DDSparseMul) {
<<
" dimK="
<<
std
::
setw
(
5
)
<<
dimK
<<
" nnz="
<<
std
::
setw
(
5
)
<<
nnz
<<
" format="
<<
std
::
setw
(
5
)
<<
FORMAT
;
testDDSparseMatrix
(
dimM
,
dimN
,
dimK
,
nnz
,
FORMAT
);
test
Func
DDSparseMatrix
(
dimM
,
dimN
,
dimK
,
nnz
,
FORMAT
);
}
}
}
...
...
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