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8580dce3
编写于
9月 18, 2017
作者:
武
武毅
提交者:
GitHub
9月 18, 2017
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差异文件
Refine accuracy_op CUDA kernel (#4097)
* refind accuracy_op * follow comments * follow comments
上级
59c48f98
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
35 addition
and
17 deletion
+35
-17
paddle/operators/accuracy_op.cu
paddle/operators/accuracy_op.cu
+25
-13
paddle/platform/cuda_helper.h
paddle/platform/cuda_helper.h
+5
-0
python/paddle/v2/framework/tests/test_accuracy_op.py
python/paddle/v2/framework/tests/test_accuracy_op.py
+5
-4
未找到文件。
paddle/operators/accuracy_op.cu
浏览文件 @
8580dce3
...
@@ -12,26 +12,38 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
...
@@ -12,26 +12,38 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#include <thrust/execution_policy.h>
#include <thrust/reduce.h>
#include "paddle/operators/accuracy_op.h"
#include "paddle/operators/accuracy_op.h"
#include "paddle/platform/cuda_helper.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
using
platform
::
PADDLE_CUDA_NUM_THREADS
;
__global__
void
AccuracySingleKernel
(
const
int
N
,
const
int
D
,
const
int
top_k
,
template
<
int
BlockSize
>
const
int
*
Xdata
,
const
int
*
labelData
,
__global__
void
AccuracyCudaKernel
(
const
int
N
,
const
int
D
,
const
int
*
Xdata
,
float
*
accuracy
)
{
const
int
*
labeldata
,
float
*
accuracy
)
{
int
correct
=
0
;
int
count
=
0
;
for
(
int
row
=
0
;
row
<
N
;
row
++
)
{
__shared__
int
total
[
BlockSize
];
const
int
label
=
labelData
[
row
];
for
(
int
col
=
0
;
col
<
D
;
col
++
)
{
// support only 1 block
const
int
pred
=
Xdata
[
row
*
D
+
col
];
for
(
int
i
=
threadIdx
.
x
;
i
<
(
N
);
i
+=
BlockSize
)
{
if
(
pred
==
label
)
{
for
(
int
j
=
0
;
j
<
D
;
++
j
)
{
++
correct
;
if
(
Xdata
[
i
*
D
+
j
]
==
labeldata
[
i
])
{
++
count
;
break
;
break
;
}
}
}
}
}
}
*
accuracy
=
static_cast
<
float
>
(
correct
)
/
static_cast
<
float
>
(
N
);
total
[
threadIdx
.
x
]
=
count
;
__syncthreads
();
// reduce the count with init value 0, and output accuracy.
int
result
=
thrust
::
reduce
(
thrust
::
device
,
total
,
total
+
BlockSize
,
0
);
if
(
threadIdx
.
x
==
0
)
{
*
accuracy
=
static_cast
<
float
>
(
result
)
/
static_cast
<
float
>
(
N
);
}
}
}
template
<
typename
T
>
template
<
typename
T
>
...
@@ -57,8 +69,8 @@ class AccuracyOpCUDAKernel : public framework::OpKernel {
...
@@ -57,8 +69,8 @@ class AccuracyOpCUDAKernel : public framework::OpKernel {
return
;
return
;
}
}
Accuracy
SingleKernel
<<<
1
,
1
>>>
(
num_samples
,
infer_width
,
1
,
inference_data
,
Accuracy
CudaKernel
<
PADDLE_CUDA_NUM_THREADS
><<<
1
,
PADDLE_CUDA_NUM_THREADS
>>>
(
label_data
,
accuracy_data
);
num_samples
,
infer_width
,
inference_data
,
label_data
,
accuracy_data
);
}
}
};
};
...
...
paddle/platform/cuda_helper.h
浏览文件 @
8580dce3
...
@@ -24,6 +24,11 @@ namespace platform {
...
@@ -24,6 +24,11 @@ namespace platform {
#define USE_CUDA_ATOMIC(op, T) \
#define USE_CUDA_ATOMIC(op, T) \
CUDA_ATOMIC_WRAPPER(op, T) { return atomic##op(address, val); }
CUDA_ATOMIC_WRAPPER(op, T) { return atomic##op(address, val); }
// Default thread count per block(or block size).
// TODO(typhoonzero): need to benchmark against setting this value
// to 1024.
constexpr
int
PADDLE_CUDA_NUM_THREADS
=
512
;
// For atomicAdd.
// For atomicAdd.
USE_CUDA_ATOMIC
(
Add
,
float
);
USE_CUDA_ATOMIC
(
Add
,
float
);
...
...
python/paddle/v2/framework/tests/test_accuracy_op.py
浏览文件 @
8580dce3
...
@@ -6,16 +6,17 @@ from op_test import OpTest
...
@@ -6,16 +6,17 @@ from op_test import OpTest
class
TestAccuracyOp
(
OpTest
):
class
TestAccuracyOp
(
OpTest
):
def
setUp
(
self
):
def
setUp
(
self
):
self
.
op_type
=
"accuracy"
self
.
op_type
=
"accuracy"
infer
=
np
.
random
.
randint
(
0
,
2
,
(
32
,
1
)).
astype
(
"int"
)
n
=
8192
label
=
np
.
random
.
randint
(
0
,
2
,
(
32
,
)).
astype
(
"int"
)
infer
=
np
.
random
.
randint
(
0
,
2
,
(
n
,
1
)).
astype
(
"int"
)
label
=
np
.
random
.
randint
(
0
,
2
,
(
n
,
)).
astype
(
"int"
)
self
.
inputs
=
{
'Inference'
:
infer
,
"Label"
:
label
}
self
.
inputs
=
{
'Inference'
:
infer
,
"Label"
:
label
}
num_correct
=
0
num_correct
=
0
for
rowid
in
xrange
(
32
):
for
rowid
in
xrange
(
n
):
for
ele
in
infer
[
rowid
]:
for
ele
in
infer
[
rowid
]:
if
ele
==
label
[
rowid
]:
if
ele
==
label
[
rowid
]:
num_correct
+=
1
num_correct
+=
1
break
break
self
.
outputs
=
{
'Accuracy'
:
[
num_correct
/
32.0
]}
self
.
outputs
=
{
'Accuracy'
:
[
num_correct
/
float
(
n
)
]}
def
test_check_output
(
self
):
def
test_check_output
(
self
):
self
.
check_output
()
self
.
check_output
()
...
...
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