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2629d43f
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2629d43f
编写于
1月 12, 2017
作者:
H
hedaoyuan
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差异文件
New FunctionTest
上级
441d00e6
变更
1
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Showing
1 changed file
with
126 addition
and
12 deletion
+126
-12
paddle/function/FunctionTest.h
paddle/function/FunctionTest.h
+126
-12
未找到文件。
paddle/function/FunctionTest.h
浏览文件 @
2629d43f
...
...
@@ -15,9 +15,33 @@ limitations under the License. */
#include "Function.h"
#include "paddle/math/Vector.h"
#include "paddle/math/tests/TensorCheck.h"
#include "paddle/testing/TestUtil.h"
namespace
paddle
{
/**
* \brief A class for comparing CPU and GPU implementations of Function.
*
*
* Use case:
* // Initializes a test object, the corresponding cpu and gpu Function
* // are constructed according to FunctionName and FuncConfig.
* FunctionCompare test(FunctionName, FuncConfig);
* // Prepare inputs and outputs arguments.
* // Here the input and output can not contain real data,
* // only contains the argument type and shape.
* test.addInputs(input1);
* test.addInputs(input2);
* test.addOutputs(output1);
* test.addOutputs(output2);
* // Run.
* // Will according to the type and shape of arguments(inputs_/outputs_),
* // automatic initialization cpu and gpu function required arguments
* // (cpuInputs_/cpuOutputs_/gpuInputs_/gpuOutputs_).
* // Call the CPU and GPU Function calculation results.
* // Compares CPU and GPU calculation results for consistency.
* test.run();
*/
class
FunctionCompare
{
public:
FunctionCompare
(
const
std
::
string
&
name
,
const
FuncConfig
&
config
)
...
...
@@ -27,6 +51,32 @@ public:
gpu
->
init
(
config
);
}
void
addInputs
(
const
BufferArg
&
input
)
{
inputs
.
push_back
(
input
);
}
void
addOutputs
(
const
BufferArg
&
output
)
{
outputs
.
push_back
(
output
);
}
void
run
()
{
// prepare cpu/gpu arguments
prepareArgs
();
// function calculate
cpu
->
calc
(
cpuInputs
,
cpuOutputs
);
gpu
->
calc
(
gpuInputs
,
gpuOutputs
);
// check outputs and inouts
auto
checkArgs
=
[
=
](
const
BufferArgs
&
cpuArgs
,
const
BufferArgs
&
gpuArgs
)
{
for
(
size_t
i
=
0
;
i
<
cpuArgs
.
size
();
i
++
)
{
auto
cpu
=
cpuArgs
[
i
];
auto
gpu
=
gpuArgs
[
i
];
CpuVector
cpuVector
(
cpu
.
shape
().
getElements
(),
(
real
*
)
cpu
.
getData
());
GpuVector
gpuVector
(
cpu
.
shape
().
getElements
(),
(
real
*
)
gpu
.
getData
());
autotest
::
TensorCheckErr
(
cpuVector
,
gpuVector
);
}
};
checkArgs
(
cpuOutputs
,
gpuOutputs
);
}
#if 0
void cmpWithArg(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) {
...
...
@@ -64,11 +114,10 @@ public:
};
initArgs(cpuInputs, gpuInputs, inputs);
initArgs(cpuOutputs, gpuOutputs, outputs);
initArgs
(
cpuInouts
,
gpuInouts
,
inouts
);
// function calculate
cpu
->
calc
(
cpuInputs
,
cpuOutputs
,
cpuInouts
);
gpu
->
calc
(
gpuInputs
,
gpuOutputs
,
gpuInouts
);
cpu->calc(cpuInputs, cpuOutputs);
gpu->calc(gpuInputs, gpuOutputs);
// check outputs and inouts
auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) {
...
...
@@ -86,24 +135,89 @@ public:
}
};
checkArgs(cpuOutputs, gpuOutputs);
checkArgs
(
cpuInouts
,
gpuInouts
);
}
#endif
std
::
shared_ptr
<
FunctionBase
>
getCpuFunction
()
const
{
return
cpu
;
}
std
::
shared_ptr
<
FunctionBase
>
getGpuFunction
()
const
{
return
gpu
;
}
protected:
void
prepareArgs
()
{
// TODO, if inputs has data
}
void
createArg
(
BufferArgs
&
cpuArgs
,
BufferArgs
&
gpuArgs
,
BufferArg
&
arg
)
{
size_t
size
=
arg
.
shape
().
getElements
()
*
sizeOfValuType
(
arg
.
valueType
());
cpuMemory_
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory_
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
cpuArgs
.
emplace_back
(
BufferArg
(
cpuMemory_
.
back
()
->
getBuf
()),
arg
.
valueType
(),
arg
.
shape
());
gpuArgs
.
emplace_back
(
BufferArg
(
gpuMemory_
.
back
()
->
getBuf
()),
arg
.
valueType
(),
arg
.
shape
());
}
void
createArg
(
BufferArgs
&
cpuArgs
,
BufferArgs
&
gpuArgs
,
SequenceArg
&
arg
)
{
size_t
batchSize
=
arg
.
shape
()[
0
];
size_t
numSeqs
=
batchSize
/
10
+
1
;
size_t
sizeId
=
(
numSeqs
+
1
)
*
sizeOfValuType
(
VALUE_TYPE_INT32
);
cpuMemory_
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory_
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
TensorShape
seqsId
({
numSeqs
+
1
});
void
*
cpuBuffer
=
cpuMemory_
.
back
()
->
getBuf
();
void
*
gpuBuffer
=
gpuMemory_
.
back
()
->
getBuf
();
size_t
size
=
arg
.
shape
().
getElements
()
*
sizeOfValuType
(
arg
.
valueType
());
cpuMemory_
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory_
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
cpuArgs
.
emplace_back
(
SequenceArg
(
cpuMemory_
.
back
()
->
getBuf
(),
arg
.
valueType
(),
arg
.
shape
(),
SequenceIdArg
(
cpuBuffer
,
seqsId
)));
gpuArgs
.
emplace_back
(
SequenceArg
(
gpuMemory_
.
back
()
->
getBuf
(),
arg
.
valueType
(),
arg
.
shape
(),
SequenceIdArg
(
gpuBuffer
,
seqsId
)));
}
// only init cpu argument, gpu argument copy from cpu argument.
void
initArg
(
BufferArg
&
arg
)
{
CpuVector
vector
(
arg
.
shape
().
getElements
(),
(
real
*
)
arg
.
data
());
vector
.
uniform
(
0.001
,
1
);
}
void
initArg
(
SequenceIdArg
&
arg
,
size_t
batchSize
)
{
size_t
numSeqs
=
arg
.
numSeqs
();
int
*
buf
=
arg
.
data
();
int
pos
=
0
;
size_t
maxLen
=
2
*
batchSize
/
numSeqs
;
for
(
int
i
=
0
;
i
<
numSeqs
;
++
i
)
{
int
len
=
uniformRandom
(
std
::
min
<
int64_t
>
(
maxLen
,
batchSize
-
pos
-
numSeqs
+
i
))
+
1
;
buf
[
i
]
=
pos
;
pos
+=
len
;
VLOG
(
1
)
<<
" len="
<<
len
;
}
buf
[
numSeqs
]
=
batchSize
;
}
protected:
std
::
shared_ptr
<
FunctionBase
>
cpu
;
std
::
shared_ptr
<
FunctionBase
>
gpu
;
std
::
vector
<
CpuMemHandlePtr
>
cpuMemory
;
std
::
vector
<
GpuMemHandlePtr
>
gpuMemory
;
Arguments
cpuInputs
;
Arguments
cpuOutputs
;
Arguments
cpuInouts
;
Arguments
gpuInputs
;
Arguments
gpuOutputs
;
Arguments
gpuInouts
;
std
::
vector
<
CpuMemHandlePtr
>
cpuMemory_
;
std
::
vector
<
GpuMemHandlePtr
>
gpuMemory_
;
// inputs and outputs
BufferArgs
inputs
;
BufferArgs
outputs
;
BufferArgs
cpuInputs_
;
BufferArgs
cpuOutputs_
;
BufferArgs
gpuInputs_
;
BufferArgs
gpuOutputs_
;
};
}
// namespace paddle
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