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84c3523c
编写于
6月 30, 2017
作者:
H
hedaoyuan
提交者:
GitHub
6月 30, 2017
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差异文件
Merge pull request #2625 from hedaoyuan/nnpack_lib
NNPACKConvFunction
上级
a71ed273
47f1031f
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
402 addition
and
20 deletion
+402
-20
CMakeLists.txt
CMakeLists.txt
+5
-0
paddle/function/CMakeLists.txt
paddle/function/CMakeLists.txt
+8
-0
paddle/function/nnpack/NNPACKConvOp.cpp
paddle/function/nnpack/NNPACKConvOp.cpp
+238
-0
paddle/function/nnpack/NNPACKConvOpTest.cpp
paddle/function/nnpack/NNPACKConvOpTest.cpp
+99
-0
paddle/function/nnpack/nnpack.cmake
paddle/function/nnpack/nnpack.cmake
+16
-0
paddle/gserver/layers/ExpandConvLayer.cpp
paddle/gserver/layers/ExpandConvLayer.cpp
+36
-20
未找到文件。
CMakeLists.txt
浏览文件 @
84c3523c
...
...
@@ -49,6 +49,7 @@ option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
option
(
ON_TRAVIS
"Exclude special unit test on Travis CI"
OFF
)
option
(
WITH_C_API
"Compile PaddlePaddle with C-API(Prediction)"
OFF
)
option
(
WITH_GOLANG
"Compile PaddlePaddle with GOLANG"
OFF
)
option
(
USE_NNPACK
"Compile PaddlePaddle with NNPACK library"
OFF
)
# CMAKE_BUILD_TYPE
if
(
NOT CMAKE_BUILD_TYPE
)
...
...
@@ -129,6 +130,10 @@ if(WITH_GPU)
endif
(
NOT WITH_DSO
)
endif
(
WITH_GPU
)
if
(
USE_NNPACK
)
list
(
APPEND EXTERNAL_LIBS
${
NNPACK_LIB
}
${
PTHREADPOOL_LIB
}
"rt"
)
endif
(
USE_NNPACK
)
add_subdirectory
(
proto
)
# "add_subdirectory(paddle)" and "add_subdirectory(python)" should be
...
...
paddle/function/CMakeLists.txt
浏览文件 @
84c3523c
...
...
@@ -10,6 +10,14 @@ if(WITH_GPU)
cuda_compile
(
cu_objs
${
cu_files
}
)
endif
()
if
(
USE_NNPACK
)
include
(
nnpack/nnpack.cmake
)
list
(
APPEND cpp_files nnpack/NNPACKConvOp.cpp
)
if
(
WITH_TESTING
)
add_unittest
(
NNPACKConvOpTest nnpack/NNPACKConvOpTest.cpp
)
endif
()
endif
()
add_library
(
paddle_function STATIC
${
cpp_files
}
${
cu_objs
}
)
add_dependencies
(
paddle_function
${
external_project_dependencies
}
)
add_dependencies
(
paddle_function paddle_proto
)
...
...
paddle/function/nnpack/NNPACKConvOp.cpp
0 → 100644
浏览文件 @
84c3523c
/* 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 "nnpack.h"
#include "paddle/function/ConvOp.h"
DEFINE_bool
(
nnpack_allocate_outside
,
false
,
"Allocate and free workspace memory outside the NNPACK interface."
);
DEFINE_int32
(
nnpack_num_threads
,
0
,
"The number of nnpack threads"
"default: 0; 0 to disable threadpool."
);
namespace
paddle
{
nnp_convolution_algorithm
get_nnp_convolution_algorithm
(
const
std
::
string
&
algorithm
)
{
if
(
algorithm
==
"auto"
)
{
return
nnp_convolution_algorithm_auto
;
}
else
if
(
algorithm
==
"ft8x8"
)
{
return
nnp_convolution_algorithm_ft8x8
;
}
else
if
(
algorithm
==
"ft16x16"
)
{
return
nnp_convolution_algorithm_ft16x16
;
}
else
if
(
algorithm
==
"wt8x8"
)
{
return
nnp_convolution_algorithm_wt8x8
;
}
else
if
(
algorithm
==
"implicit-gemm"
)
{
return
nnp_convolution_algorithm_implicit_gemm
;
}
else
if
(
algorithm
==
"direct"
)
{
return
nnp_convolution_algorithm_direct
;
}
else
{
return
nnp_convolution_algorithm_auto
;
}
}
template
<
DeviceType
Device
>
class
NNPACKConvFunction
:
public
ConvFunctionBase
{
public:
void
init
(
const
FuncConfig
&
config
)
override
{
ConvFunctionBase
::
init
(
config
);
CHECK_EQ
(
groups_
,
(
size_t
)
1
);
algorithm_
=
get_nnp_convolution_algorithm
(
config
.
get
<
std
::
string
>
(
"algo"
));
// algorithm_ = nnp_convolution_algorithm_auto;
transform_strategy_
=
nnp_convolution_transform_strategy_compute
;
nnp_status
status
=
nnp_initialize
();
CHECK_EQ
(
status
,
nnp_status_success
);
workspaceBuffer_
=
nullptr
;
workspaceSize_
=
0
;
threadpool_
=
nullptr
;
if
(
FLAGS_nnpack_num_threads
)
{
threadpool_
=
pthreadpool_create
(
FLAGS_nnpack_num_threads
);
VLOG
(
3
)
<<
"Number of threads "
<<
pthreadpool_get_threads_count
(
threadpool_
);
}
}
~
NNPACKConvFunction
()
{
if
(
threadpool_
)
{
pthreadpool_destroy
(
threadpool_
);
}
if
(
workspaceBuffer_
)
{
free
(
workspaceBuffer_
);
}
}
virtual
void
check
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
const
TensorShape
&
input
=
inputs
[
0
].
shape
();
const
TensorShape
&
filter
=
inputs
[
1
].
shape
();
const
TensorShape
&
output
=
outputs
[
0
].
shape
();
checkShape
(
input
,
filter
,
output
);
}
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
numInputs_
,
inputs
.
size
());
CHECK_EQ
(
numOutputs_
,
outputs
.
size
());
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ASSIGN_TO
);
check
(
inputs
,
outputs
);
const
TensorShape
&
input
=
inputs
[
0
].
shape
();
const
TensorShape
&
filter
=
inputs
[
1
].
shape
();
const
TensorShape
&
output
=
outputs
[
0
].
shape
();
size_t
batchSize
=
input
[
0
];
size_t
inputChannels
=
input
[
1
];
size_t
inputHeight
=
input
[
2
];
size_t
inputWidth
=
input
[
3
];
size_t
filterHeight
=
getFilterHeight
(
filter
);
size_t
filterWidth
=
getFilterWidth
(
filter
);
size_t
outputChannels
=
output
[
1
];
// size_t outputHeight = output[2];
// size_t outputWidth = output[3];
nnp_size
inputSize
=
{.
width
=
inputWidth
,
.
height
=
inputHeight
};
nnp_padding
padding
=
{.
top
=
(
size_t
)
paddingH
(),
.
right
=
(
size_t
)
paddingW
(),
.
bottom
=
(
size_t
)
paddingH
(),
.
left
=
(
size_t
)
paddingW
()};
nnp_size
kernelSize
=
{.
width
=
filterWidth
,
.
height
=
filterHeight
};
nnp_size
outputSubsampling
=
{.
width
=
(
size_t
)
strideW
(),
.
height
=
(
size_t
)
strideH
()};
float
*
inputData
=
inputs
[
0
].
data
<
float
>
();
float
*
filterData
=
inputs
[
1
].
data
<
float
>
();
float
*
outputData
=
outputs
[
0
].
data
<
float
>
();
void
*
bufferPtr
=
nullptr
;
size_t
*
sizePtr
=
nullptr
;
size_t
needSize
;
if
(
FLAGS_nnpack_allocate_outside
)
{
if
(
batchSize
==
1
)
{
nnp_status
status
=
nnp_convolution_inference
(
algorithm_
,
transform_strategy_
,
inputChannels
,
outputChannels
,
inputSize
,
padding
,
kernelSize
,
outputSubsampling
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
&
needSize
,
nnp_activation_identity
,
nullptr
,
nullptr
,
nullptr
);
CHECK_EQ
(
status
,
nnp_status_success
);
}
else
{
// only supports stride = 1
CHECK_EQ
(
strideH
(),
1
);
CHECK_EQ
(
strideW
(),
1
);
nnp_status
status
=
nnp_convolution_output
(
algorithm_
,
batchSize
,
inputChannels
,
outputChannels
,
inputSize
,
padding
,
kernelSize
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
&
needSize
,
nnp_activation_identity
,
nullptr
,
nullptr
,
nullptr
);
CHECK_EQ
(
status
,
nnp_status_success
);
}
VLOG
(
3
)
<<
"workspace size is "
<<
needSize
;
if
(
needSize
>
workspaceSize_
)
{
workspaceSize_
=
needSize
;
if
(
workspaceBuffer_
)
{
free
(
workspaceBuffer_
);
}
else
{
posix_memalign
(
&
workspaceBuffer_
,
64
,
needSize
);
}
}
if
(
needSize
)
{
bufferPtr
=
workspaceBuffer_
;
sizePtr
=
&
needSize
;
}
}
if
(
batchSize
==
1
)
{
nnp_status
status
=
nnp_convolution_inference
(
algorithm_
,
transform_strategy_
,
inputChannels
,
outputChannels
,
inputSize
,
padding
,
kernelSize
,
outputSubsampling
,
inputData
,
filterData
,
nullptr
,
/* bias */
outputData
,
bufferPtr
,
sizePtr
,
nnp_activation_identity
,
nullptr
,
threadpool_
,
/* threadpool */
nullptr
);
CHECK_EQ
(
status
,
nnp_status_success
);
}
else
{
// only supports stride = 1
CHECK_EQ
(
strideH
(),
1
);
CHECK_EQ
(
strideW
(),
1
);
nnp_status
status
=
nnp_convolution_output
(
algorithm_
,
batchSize
,
inputChannels
,
outputChannels
,
inputSize
,
padding
,
kernelSize
,
inputData
,
filterData
,
nullptr
,
/* bias */
outputData
,
bufferPtr
,
sizePtr
,
nnp_activation_identity
,
nullptr
,
threadpool_
,
/* threadpool */
nullptr
);
CHECK_EQ
(
status
,
nnp_status_success
);
}
}
private:
nnp_convolution_algorithm
algorithm_
;
nnp_convolution_transform_strategy
transform_strategy_
;
void
*
workspaceBuffer_
;
size_t
workspaceSize_
;
pthreadpool_t
threadpool_
;
};
REGISTER_TYPED_FUNC
(
NNPACKConv
,
CPU
,
NNPACKConvFunction
);
}
// namespace paddle
paddle/function/nnpack/NNPACKConvOpTest.cpp
0 → 100644
浏览文件 @
84c3523c
/* 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 <gtest/gtest.h>
#include "paddle/function/Function.h"
#include "paddle/function/FunctionTest.h"
DEFINE_string
(
algo
,
"auto"
,
"The algorithm (auto, ft8x8, ft16x16, wt8x8, "
"implicit-gemm, or direct) for computing convolution of NNPACK."
);
namespace
paddle
{
#define IS_NNPACK_SUPPORT(algo, filterSize, stride) \
if (algo == "direct" && filterSize != 1) continue; \
if (algo == "direct" && batchSize != 1) continue; \
if (algo == "wt8x8" && filterSize != 3) continue; \
if (algo == "implicit-gemm" && batchSize != 1) continue; \
if (algo != "auto" && algo != "implicit-gemm" && stride > 1) continue;
class
ConvolutionTest
{
public:
ConvolutionTest
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
std
::
string
algo
=
"auto"
)
{
for
(
size_t
batchSize
:
{
1
,
32
})
{
for
(
size_t
inputSize
:
{
7
,
14
,
54
})
{
for
(
size_t
filterSize
:
{
1
,
3
,
5
})
{
for
(
size_t
inputChannels
:
{
3
,
64
})
{
for
(
size_t
outputChannels
:
{
3
,
64
,
128
})
{
if
(
inputChannels
<
outputChannels
)
break
;
for
(
size_t
stride
:
{
1
,
2
})
{
// if batchSize > 1 NNPACKConv only supports stride = 1
if
(
batchSize
>
1
&&
stride
>
1
)
break
;
for
(
size_t
padding
:
{
0
,
1
})
{
if
(
padding
>=
filterSize
)
break
;
size_t
outputSize
=
(
inputSize
-
filterSize
+
2
*
padding
+
stride
)
/
stride
;
IS_NNPACK_SUPPORT
(
algo
,
filterSize
,
stride
);
LOG
(
INFO
)
<<
" batchSize="
<<
batchSize
<<
" inputChannels="
<<
inputChannels
<<
" inputHeight="
<<
inputSize
<<
" inputWidth="
<<
inputSize
<<
" outputChannels="
<<
outputChannels
<<
" filterHeight="
<<
filterSize
<<
" filterWidth="
<<
filterSize
<<
" outputHeight="
<<
outputSize
<<
" outputWidth="
<<
outputSize
<<
" stride="
<<
stride
<<
" padding="
<<
padding
;
std
::
vector
<
size_t
>
paddings
=
{
padding
,
padding
};
std
::
vector
<
size_t
>
strides
=
{
stride
,
stride
};
Compare2Function
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
test
(
conv1
,
conv2
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
1
)
.
set
(
"algo"
,
algo
));
TensorShape
shape0
{
batchSize
,
inputChannels
,
inputSize
,
inputSize
};
TensorShape
shape1
{
outputChannels
,
inputChannels
,
filterSize
,
filterSize
};
TensorShape
shape2
{
batchSize
,
outputChannels
,
outputSize
,
outputSize
};
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
shape0
));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
shape1
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
shape2
));
test
.
run
();
}
}
}
}
}
}
}
}
};
TEST
(
Convolution
,
NNPACK
)
{
// NNPACK only supports stride = 1
ConvolutionTest
test
(
"GemmConv-CPU"
,
"NNPACKConv-CPU"
,
FLAGS_algo
);
}
}
// namespace paddle
paddle/function/nnpack/nnpack.cmake
0 → 100644
浏览文件 @
84c3523c
# Find the NNPACK library
# NNPACK_ROOT - where to find NNPACK include and library.
#
set
(
NNPACK_FOUND OFF
)
set
(
NNPACK_ROOT $ENV{NNPACK_ROOT} CACHE PATH
"Folder contains NNPACK"
)
find_path
(
NNPACK_INC_DIR nnpack.h PATHS
${
NNPACK_ROOT
}
/include
)
find_library
(
NNPACK_LIB NAMES nnpack PATHS
${
NNPACK_ROOT
}
/lib
)
find_library
(
PTHREADPOOL_LIB NAMES pthreadpool PATHS
${
NNPACK_ROOT
}
/lib
)
if
(
NNPACK_INC_DIR AND NNPACK_LIB AND PTHREADPOOL_LIB
)
set
(
NNPACK_FOUND ON
)
INCLUDE_DIRECTORIES
(
${
NNPACK_INC_DIR
}
)
else
()
message
(
FATAL_ERROR
"Cannot find NNPACK in (
${
NNPACK_ROOT
}
)"
)
endif
()
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @
84c3523c
...
...
@@ -16,6 +16,10 @@ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
DEFINE_bool
(
use_nnpack
,
false
,
"Whether to use nnpack for convolution calculation."
);
namespace
paddle
{
/*
...
...
@@ -37,26 +41,38 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
for
(
int
i
=
0
;
i
<
config_
.
inputs_size
();
i
++
)
{
std
::
vector
<
size_t
>
paddings
=
{(
size_t
)
paddingY_
[
i
],
(
size_t
)
padding_
[
i
]};
std
::
vector
<
size_t
>
strides
=
{(
size_t
)
strideY_
[
i
],
(
size_t
)
stride_
[
i
]};
createFunction
(
forward_
,
!
isDeconv_
?
"GemmConv"
:
"GemmConvGradInput"
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
groups_
[
i
]));
createFunction
(
backward_
,
!
isDeconv_
?
"GemmConvGradInput"
:
"GemmConv"
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
groups_
[
i
]));
createFunction
(
backward_
,
"GemmConvGradFilter"
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
groups_
[
i
]));
if
(
FLAGS_use_nnpack
)
{
CHECK_EQ
(
isDeconv_
,
false
);
createFunction
(
forward_
,
"NNPACKConv"
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
groups_
[
i
])
.
set
(
"algo"
,
std
::
string
(
"auto"
)));
}
else
{
createFunction
(
forward_
,
!
isDeconv_
?
"GemmConv"
:
"GemmConvGradInput"
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
groups_
[
i
]));
createFunction
(
backward_
,
!
isDeconv_
?
"GemmConvGradInput"
:
"GemmConv"
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
groups_
[
i
]));
createFunction
(
backward_
,
"GemmConvGradFilter"
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
groups_
[
i
]));
}
}
return
true
;
}
...
...
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