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c31cbae5
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c31cbae5
编写于
12月 27, 2017
作者:
D
dzhwinter
提交者:
GitHub
12月 27, 2017
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电子邮件补丁
差异文件
Fix/transform (#7079)
* "fix data transform" * "split into next PR"
上级
f9a12296
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
51 addition
and
28 deletion
+51
-28
paddle/framework/data_transform.cc
paddle/framework/data_transform.cc
+1
-0
paddle/framework/data_transform_test.cc
paddle/framework/data_transform_test.cc
+49
-27
paddle/framework/tensor.h
paddle/framework/tensor.h
+1
-1
未找到文件。
paddle/framework/data_transform.cc
浏览文件 @
c31cbae5
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/data_transform.h"
#include "paddle/framework/lod_tensor.h"
namespace
paddle
{
namespace
framework
{
...
...
paddle/framework/data_transform_test.cc
浏览文件 @
c31cbae5
...
...
@@ -11,36 +11,63 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <array>
#include <vector>
#include "paddle/framework/data_transform.h"
#include <gtest/gtest.h>
#include "paddle/framework/data_transform.h"
namespace
paddle
{
namespace
framework
{
using
namespace
platform
;
/**
* @brief cross validation of different kernel type transform
* We use four bit map represent different combination.
* If the field has multiple possible value, only choose two of them.
* For DataType, only test the FP32(float), FP64(double).
* e.g. 0000 -> FP32, CPUPlace, kNHWC, kPlain
* 1111 -> FP64, GPUPlace, kNCHW, kMKLDNN
*/
std
::
array
<
proto
::
DataType
,
2
>
kDataType
=
{
proto
::
DataType
::
FP32
,
proto
::
DataType
::
FP64
};
std
::
array
<
Place
,
2
>
kPlace
=
{
CPUPlace
(),
CUDAPlace
(
0
)};
std
::
array
<
DataLayout
,
2
>
kDataLayout
=
{
DataLayout
::
kNHWC
,
DataLayout
::
kNCHW
,
};
std
::
array
<
LibraryType
,
2
>
kLibraryType
=
{
LibraryType
::
kPlain
,
LibraryType
::
kMKLDNN
,
};
OpKernelType
GenFromBit
(
const
std
::
vector
<
bool
>
bits
)
{
return
OpKernelType
(
kDataType
[
bits
[
0
]],
kPlace
[
bits
[
1
]],
kDataLayout
[
bits
[
2
]],
kLibraryType
[
bits
[
3
]]);
}
int
test_value
=
0
;
OpKernelType
kernel_type_1
(
proto
::
DataType
::
FP32
,
CPUPlace
(),
DataLayout
::
kNCHW
,
LibraryType
::
kCUDNN
);
OpKernelType
kernel_type_2
(
proto
::
DataType
::
FP32
,
CUDAPlace
(
0
),
DataLayout
::
kNCHW
,
LibraryType
::
kCUDNN
);
OpKernelType
kernel_type_3
(
proto
::
DataType
::
FP16
,
CUDAPlace
(
0
),
DataLayout
::
kNCHW
,
LibraryType
::
kCUDNN
);
auto
kernel0
=
GenFromBit
({
0
,
0
,
0
,
0
});
auto
kernel1
=
GenFromBit
({
0
,
0
,
0
,
1
});
auto
kernel2
=
GenFromBit
({
0
,
0
,
1
,
0
});
auto
kernel3
=
GenFromBit
({
0
,
0
,
1
,
1
});
void
type1_to_type2
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
void
TransDataType_t
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
test_value
++
;
}
void
type2_to_type3
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
void
TransDataLayout_t
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
test_value
--
;
}
void
type1_to_type3
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
void
TransLibraryType_t
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
test_value
+=
2
;
}
...
...
@@ -49,30 +76,25 @@ void type1_to_type3(std::vector<platform::DeviceContext*> ctx,
namespace
frw
=
paddle
::
framework
;
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel_type_1
,
frw
::
kernel_type_2
,
frw
::
type1_to_type2
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel_type_2
,
frw
::
kernel_type_3
,
frw
::
type2_to_type3
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel_type_1
,
frw
::
kernel_type_3
,
frw
::
type1_to_type3
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel0
,
frw
::
kernel1
,
frw
::
TransDataType_t
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel1
,
frw
::
kernel2
,
frw
::
TransDataLayout_t
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel0
,
frw
::
kernel2
,
frw
::
TransLibraryType_t
);
TEST
(
DataTransform
,
Register
)
{
using
namespace
paddle
::
framework
;
using
namespace
paddle
::
platform
;
auto
&
instance
=
DataTransformFnMap
::
Instance
();
ASSERT_EQ
(
instance
.
Map
().
size
(),
3UL
);
std
::
vector
<
DeviceContext
*>
ctx
;
paddle
::
framework
::
Variable
in
;
paddle
::
framework
::
Variable
out
;
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel_type_1
,
frw
::
kernel_type_2
))(
ctx
,
in
,
&
out
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel0
,
frw
::
kernel1
))(
ctx
,
in
,
&
out
);
ASSERT_EQ
(
test_value
,
1
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel_type_2
,
frw
::
kernel_type_3
))(
ctx
,
in
,
&
out
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel1
,
frw
::
kernel2
))(
ctx
,
in
,
&
out
);
ASSERT_EQ
(
test_value
,
0
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel_type_1
,
frw
::
kernel_type_3
))(
ctx
,
in
,
&
out
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel0
,
frw
::
kernel2
))(
ctx
,
in
,
&
out
);
ASSERT_EQ
(
test_value
,
2
);
}
paddle/framework/tensor.h
浏览文件 @
c31cbae5
...
...
@@ -178,7 +178,7 @@ class Tensor {
DDim
dims_
;
/**
* @brief the layout of memory block, default is N
CHW
.
* @brief the layout of memory block, default is N
HWC
.
*
* @note the memory allocation order, describe how weight/data is stored
* For example, in 4-D Tensor(rank=4), there are three commonly
...
...
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