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81c31368
编写于
8月 07, 2017
作者:
Y
Yi Wang
提交者:
GitHub
8月 07, 2017
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Merge pull request #3309 from qingqing01/bn_infer
add a batch norm inference kernel.
上级
5a7a3c4d
06984239
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
213 addition
and
18 deletion
+213
-18
paddle/cuda/CMakeLists.txt
paddle/cuda/CMakeLists.txt
+1
-0
paddle/cuda/include/hl_batch_norm.h
paddle/cuda/include/hl_batch_norm.h
+48
-0
paddle/cuda/src/hl_batch_norm.cu
paddle/cuda/src/hl_batch_norm.cu
+66
-0
paddle/cuda/src/hl_cuda_cudnn.cc
paddle/cuda/src/hl_cuda_cudnn.cc
+0
-8
paddle/gserver/layers/CudnnBatchNormLayer.cpp
paddle/gserver/layers/CudnnBatchNormLayer.cpp
+28
-10
paddle/gserver/tests/test_BatchNorm.cpp
paddle/gserver/tests/test_BatchNorm.cpp
+70
-0
未找到文件。
paddle/cuda/CMakeLists.txt
浏览文件 @
81c31368
...
...
@@ -39,6 +39,7 @@ set(CUDA_CU_SOURCES
src/hl_cuda_lstm.cu
src/hl_top_k.cu
src/hl_batch_transpose.cu
src/hl_batch_norm.cu
src/hl_cuda_sequence.cu
src/hl_table_apply.cu
)
...
...
paddle/cuda/include/hl_batch_norm.h
0 → 100644
浏览文件 @
81c31368
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
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. */
#ifndef HL_BATCH_NORM_H_
#define HL_BATCH_NORM_H_
#include "hl_base.h"
/**
* @brief batch norm inferece.
*
* @param[in] input input data.
* @param[out] output output data.
* @param[in] scale batch normalization scale parameter (in original
* paper scale is referred to as gamma).
* @param[in] bias batch normalization bias parameter (in original
* paper scale is referred to as beta).
* @param[in] estimatedMean
* @param[in] estimatedVar The moving mean and variance
* accumulated during the training phase are passed
* as inputs here.
* @param[in] epsilon Epsilon value used in the batch
* normalization formula.
*/
extern
void
hl_batch_norm_cuda_inference
(
const
real
*
input
,
real
*
output
,
const
real
*
scale
,
const
real
*
bias
,
const
real
*
estimatedMean
,
const
real
*
estimatedVar
,
const
double
epsilon
,
size_t
batchSize
,
size_t
channel
,
size_t
height
,
size_t
width
);
#endif // HL_BATCH_NORM_H_
paddle/cuda/src/hl_batch_norm.cu
0 → 100644
浏览文件 @
81c31368
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
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 "hl_batch_norm.h"
__global__
void
batchNormInference
(
real
*
output
,
const
real
*
input
,
const
real
*
scale
,
const
real
*
bias
,
const
real
*
estimatedMean
,
const
real
*
estimatedVar
,
const
double
epsilon
,
size_t
batchSize
,
size_t
channel
,
size_t
height
,
size_t
width
)
{
const
int
tid
=
threadIdx
.
x
;
const
int
num
=
channel
*
height
*
width
;
const
int
batch
=
blockIdx
.
x
;
for
(
int
i
=
tid
;
i
<
num
;
i
+=
blockDim
.
x
)
{
const
int
c
=
i
/
(
height
*
width
);
const
int
id
=
batch
*
num
+
i
;
real
val
=
input
[
id
]
-
estimatedMean
[
c
];
val
/=
sqrt
(
estimatedVar
[
c
]
+
epsilon
);
val
*=
scale
[
c
];
val
+=
bias
[
c
];
output
[
id
]
=
val
;
}
}
void
hl_batch_norm_cuda_inference
(
const
real
*
input
,
real
*
output
,
const
real
*
scale
,
const
real
*
bias
,
const
real
*
estimatedMean
,
const
real
*
estimatedVar
,
const
double
epsilon
,
size_t
batchSize
,
size_t
channel
,
size_t
height
,
size_t
width
)
{
batchNormInference
<<<
batchSize
,
256
,
0
,
STREAM_DEFAULT
>>>
(
output
,
input
,
scale
,
bias
,
estimatedMean
,
estimatedVar
,
epsilon
,
batchSize
,
channel
,
height
,
width
);
CHECK_SYNC
(
"hl_batch_norm_cuda_inference failed!"
);
}
paddle/cuda/src/hl_cuda_cudnn.cc
浏览文件 @
81c31368
...
...
@@ -1023,14 +1023,6 @@ void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
real
beta
=
1.0
f
;
cudnnBatchNormMode_t
mode
=
CUDNN_BATCHNORM_SPATIAL
;
int
batch_size
=
((
cudnn_tensor_descriptor
)
inputDesc
)
->
batch_size
;
if
(
batch_size
>
1024
&&
g_cudnn_lib_version
<
6000
)
{
LOG
(
INFO
)
<<
" To process current batch data with size "
<<
batch_size
<<
" (>1024), cudnnBatchNorm requires cuDNN version >= 6000."
<<
" If there is an error complaining CUDNN_STATUS_NOT_SUPPORTED,"
<<
" just recompile PaddlePaddle with cuDNN >= 6000, replacing"
<<
" current version "
<<
g_cudnn_lib_version
;
}
CHECK_CUDNN
(
dynload
::
cudnnBatchNormalizationForwardInference
(
t_resource
.
cudnn_handle
,
mode
,
...
...
paddle/gserver/layers/CudnnBatchNormLayer.cpp
浏览文件 @
81c31368
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#include "CudnnBatchNormLayer.h"
#include "Layer.h"
#include "paddle/cuda/include/hl_batch_norm.h"
#include "paddle/utils/Stat.h"
namespace
paddle
{
...
...
@@ -79,16 +80,33 @@ void CudnnBatchNormLayer::forward(PassType passType) {
savedInvVar
);
}
else
{
// used movingMean and movingVar in testing
hl_batch_norm_forward_inference
(
ioDesc_
,
input
,
ioDesc_
,
output
,
bnParamDesc_
,
gamma
,
beta
,
movingMean
,
movingVar
,
EPS
);
if
(
batchSize
<=
1024
)
{
hl_batch_norm_forward_inference
(
ioDesc_
,
input
,
ioDesc_
,
output
,
bnParamDesc_
,
gamma
,
beta
,
movingMean
,
movingVar
,
EPS
);
}
else
{
// There is a limitation in cudnn library.
// When the batch size is larger than 1024 in cuDNN v5.1,
// the cudnnBatchNormalizationForwardInference will fail.
hl_batch_norm_cuda_inference
(
input
,
output
,
gamma
,
beta
,
movingMean
,
movingVar
,
EPS
,
batchSize
,
channels_
,
imageH_
,
imageW_
);
}
}
/* activation */
{
...
...
paddle/gserver/tests/test_BatchNorm.cpp
浏览文件 @
81c31368
...
...
@@ -21,6 +21,8 @@ limitations under the License. */
#include "paddle/utils/GlobalConstants.h"
#include "LayerGradUtil.h"
#include "paddle/cuda/include/hl_batch_norm.h"
#include "paddle/math/tests/TensorCheck.h"
#include "paddle/testing/TestUtil.h"
using
namespace
paddle
;
// NOLINT
...
...
@@ -117,6 +119,74 @@ TEST(Layer, batchNorm) {
CHECK_EQ
(
static_cast
<
int
>
(
convLayer
->
getOutputValue
()
->
getWidth
()),
576
);
}
#ifndef PADDLE_ONLY_CPU
void
batchNormInference
(
int
n
,
int
c
,
int
h
,
int
w
)
{
MatrixPtr
input
=
std
::
make_shared
<
GpuMatrix
>
(
n
,
c
*
h
*
w
);
MatrixPtr
cudnnOut
=
std
::
make_shared
<
GpuMatrix
>
(
n
,
c
*
h
*
w
);
MatrixPtr
cudaOut
=
std
::
make_shared
<
GpuMatrix
>
(
n
,
c
*
h
*
w
);
MatrixPtr
cudnnCheck
=
std
::
make_shared
<
CpuMatrix
>
(
n
,
c
*
h
*
w
);
MatrixPtr
cudaCheck
=
std
::
make_shared
<
CpuMatrix
>
(
n
,
c
*
h
*
w
);
input
->
randomizeUniform
();
cudnnOut
->
zeroMem
();
cudaOut
->
zeroMem
();
MatrixPtr
scale
=
std
::
make_shared
<
GpuMatrix
>
(
1
,
c
);
scale
->
randomizeUniform
();
MatrixPtr
bias
=
std
::
make_shared
<
GpuMatrix
>
(
1
,
c
);
bias
->
randomizeUniform
();
MatrixPtr
movingMean
=
std
::
make_shared
<
GpuMatrix
>
(
1
,
c
);
movingMean
->
randomizeUniform
();
MatrixPtr
movingVar
=
std
::
make_shared
<
GpuMatrix
>
(
1
,
c
);
movingVar
->
randomizeUniform
();
movingVar
->
clip
(
0.01
,
50
);
hl_tensor_descriptor
ioDesc
;
hl_tensor_descriptor
bnDesc
;
hl_create_tensor_descriptor
(
&
ioDesc
);
hl_create_tensor_descriptor
(
&
bnDesc
);
hl_tensor_reshape
(
ioDesc
,
n
,
c
,
h
,
w
);
hl_tensor_reshape
(
bnDesc
,
1
,
c
,
1
,
1
);
double
EPS
=
1E-5
;
hl_batch_norm_forward_inference
(
ioDesc
,
input
->
getData
(),
ioDesc
,
cudnnOut
->
getData
(),
bnDesc
,
scale
->
getData
(),
bias
->
getData
(),
movingMean
->
getData
(),
movingVar
->
getData
(),
EPS
);
hl_batch_norm_cuda_inference
(
input
->
getData
(),
cudaOut
->
getData
(),
scale
->
getData
(),
bias
->
getData
(),
movingMean
->
getData
(),
movingVar
->
getData
(),
EPS
,
n
,
c
,
h
,
w
);
cudnnCheck
->
copyFrom
(
*
cudnnOut
);
cudaCheck
->
copyFrom
(
*
cudaOut
);
autotest
::
TensorCheckErr
(
*
cudnnCheck
,
*
cudaCheck
);
hl_destroy_tensor_descriptor
(
ioDesc
);
hl_destroy_tensor_descriptor
(
bnDesc
);
}
TEST
(
BatchNorm
,
Inference
)
{
batchNormInference
(
33
,
267
,
1
,
1
);
batchNormInference
(
19
,
105
,
4
,
4
);
}
#endif
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
...
...
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