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体验新版 GitCode,发现更多精彩内容 >>
提交
b3ac51ff
编写于
6月 03, 2017
作者:
D
dangqingqing
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
GPU implementation of row conv.
上级
a1815867
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
432 addition
and
4 deletion
+432
-4
paddle/function/CMakeLists.txt
paddle/function/CMakeLists.txt
+1
-0
paddle/function/RowConvOp.cpp
paddle/function/RowConvOp.cpp
+33
-4
paddle/function/RowConvOpGpu.cu
paddle/function/RowConvOpGpu.cu
+329
-0
paddle/function/RowConvOpTest.cpp
paddle/function/RowConvOpTest.cpp
+69
-0
未找到文件。
paddle/function/CMakeLists.txt
浏览文件 @
b3ac51ff
...
...
@@ -28,6 +28,7 @@ if(WITH_TESTING)
add_simple_unittest
(
PadOpTest
)
add_simple_unittest
(
MulOpTest
)
add_simple_unittest
(
CosSimOpTest
)
add_simple_unittest
(
RowConvOpTest
)
endif
()
endif
()
...
...
paddle/function/RowConvOp.cpp
浏览文件 @
b3ac51ff
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "RowConvOp.h"
#include <iostream>
#include "paddle/math/Vector.h"
namespace
paddle
{
...
...
@@ -127,10 +128,8 @@ public:
RowConv
<
Device
>
(
outMat
,
inMat
,
wMat
,
seqId
);
}
};
/**
* \brief The backward propagation of padding Function. Remove the elements
* in the padding positions of forward.
* \brief TODO(qingqing)
*
* Argument in this Function:
*/
...
...
@@ -158,7 +157,37 @@ public:
:
typename
Tensor
<
real
,
Device
>::
Matrix
(
nullptr
,
0
,
0
);
const
auto
seqId
=
in
.
getSequenceId
().
vector
<
int
,
Device
>
();
std
::
cout
<<
"in:"
<<
std
::
endl
;
for
(
int
i
=
0
;
i
<
inMat
.
getHeight
();
++
i
)
{
for
(
int
j
=
0
;
j
<
inMat
.
getWidth
();
++
j
)
{
std
::
cout
<<
outGMat
.
getElement
(
i
,
j
)
<<
" "
;
}
std
::
cout
<<
std
::
endl
;
}
std
::
cout
<<
"w:"
<<
std
::
endl
;
for
(
int
i
=
0
;
i
<
wMat
.
getHeight
();
++
i
)
{
for
(
int
j
=
0
;
j
<
wMat
.
getWidth
();
++
j
)
{
std
::
cout
<<
wMat
.
getElement
(
i
,
j
)
<<
" "
;
}
std
::
cout
<<
std
::
endl
;
}
std
::
cout
<<
"w:"
<<
std
::
endl
;
for
(
int
i
=
0
;
i
<
seqId
.
getSize
();
++
i
)
{
std
::
cout
<<
seqId
.
getElement
(
i
)
<<
" "
;
}
std
::
cout
<<
std
::
endl
;
RowConvGrad
<
Device
>
(
outGMat
,
inMat
,
wMat
,
inGMat
,
wGMat
,
seqId
);
std
::
cout
<<
std
::
endl
<<
"out:"
<<
std
::
endl
;
for
(
int
i
=
0
;
i
<
inGMat
.
getHeight
();
++
i
)
{
for
(
int
j
=
0
;
j
<
inGMat
.
getWidth
();
++
j
)
{
std
::
cout
<<
inGMat
.
getElement
(
i
,
j
)
<<
" "
;
}
std
::
cout
<<
std
::
endl
;
}
}
};
...
...
@@ -166,7 +195,7 @@ REGISTER_TYPED_FUNC(RowConv, CPU, RowConvFunc);
REGISTER_TYPED_FUNC
(
RowConvGrad
,
CPU
,
RowConvGradFunc
);
#ifndef PADDLE_ONLY_CPU
REGISTER_TYPED_FUNC
(
RowConv
,
GPU
,
RowConvFunc
);
REGISTER_TYPED_FUNC
(
RowConvGrad
,
GPU
,
Pad
GradFunc
);
REGISTER_TYPED_FUNC
(
RowConvGrad
,
GPU
,
RowConv
GradFunc
);
#endif
}
// namespace paddle
paddle/function/RowConvOpGpu.cu
0 → 100644
浏览文件 @
b3ac51ff
/* 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_base.h"
#include "RowConvOp.h"
namespace
paddle
{
template
<
int
BLOCK_H
,
int
BLOCK_W
>
__global__
void
KeRowConv
(
real
*
y
,
const
real
*
x
,
const
real
*
w
,
const
int
*
starts
,
const
int
height
,
const
int
width
,
const
int
numSeq
,
const
int
context
)
{
const
int
tidx
=
threadIdx
.
x
;
const
int
tidy
=
threadIdx
.
y
;
const
int
blky
=
blockDim
.
y
;
const
int
gidx
=
blockIdx
.
x
*
blockDim
.
x
;
__shared__
real
sw
[
BLOCK_H
][
BLOCK_W
];
for
(
int
i
=
tidy
;
i
<
context
;
i
+=
blky
)
{
sw
[
i
][
tidx
]
=
gidx
+
tidx
<
width
?
w
[
i
*
width
+
gidx
+
tidx
]
:
0.0
;
}
__syncthreads
();
for
(
int
i
=
0
;
i
<
numSeq
;
++
i
)
{
const
int
start
=
starts
[
i
];
const
int
end
=
starts
[
i
+
1
];
const
int
steps
=
end
-
start
;
for
(
int
j
=
tidy
;
j
<
steps
;
j
+=
blky
)
{
real
sum
=
0
;
int
off
=
(
start
+
j
)
*
width
;
for
(
int
t
=
0
;
t
<
context
;
++
t
)
{
if
((
start
+
j
+
t
)
<
end
)
{
int
xoff
=
off
+
t
*
width
;
real
xVal
=
gidx
+
tidx
<
width
?
x
[
xoff
+
gidx
+
tidx
]
:
0.0
;
sum
+=
sw
[
t
][
tidx
]
*
xVal
;
}
}
if
(
gidx
+
tidx
<
width
)
{
y
[
off
+
gidx
+
tidx
]
+=
sum
;
}
}
}
}
__global__
void
KeRowConv2
(
real
*
y
,
const
real
*
x
,
const
real
*
w
,
const
int
*
starts
,
const
int
height
,
const
int
width
,
const
int
numSeq
,
const
int
context
)
{
const
int
tidx
=
threadIdx
.
x
;
const
int
tidy
=
threadIdx
.
y
;
const
int
blky
=
blockDim
.
y
;
const
int
gidx
=
blockIdx
.
x
*
blockDim
.
x
;
for
(
int
i
=
0
;
i
<
numSeq
;
++
i
)
{
const
int
start
=
starts
[
i
];
const
int
end
=
starts
[
i
+
1
];
const
int
steps
=
end
-
start
;
for
(
int
j
=
tidy
;
j
<
steps
;
j
+=
blky
)
{
int
off
=
(
start
+
j
)
*
width
;
real
sum
=
0
;
for
(
int
t
=
0
;
t
<
context
&&
(
start
+
j
+
t
)
<
end
;
++
t
)
{
int
xoff
=
off
+
t
*
width
;
real
xd
=
gidx
+
tidx
<
width
?
x
[
xoff
+
gidx
+
tidx
]
:
0.0
;
real
wd
=
gidx
+
tidx
<
width
?
w
[
t
*
width
+
gidx
+
tidx
]
:
0.0
;
sum
+=
wd
*
xd
;
}
if
(
gidx
+
tidx
<
width
)
{
y
[
off
+
gidx
+
tidx
]
+=
sum
;
}
}
}
}
template
<
>
void
RowConv
<
DEVICE_TYPE_GPU
>
(
GpuMatrix
&
out
,
const
GpuMatrix
&
in
,
const
GpuMatrix
&
filter
,
const
GpuIVector
&
seq
)
{
const
size_t
numSeq
=
seq
.
getSize
()
-
1
;
const
size_t
contextLength
=
filter
.
getHeight
();
const
size_t
height
=
in
.
getHeight
();
const
size_t
width
=
in
.
getWidth
();
LOG
(
INFO
)
<<
numSeq
;
LOG
(
INFO
)
<<
contextLength
;
LOG
(
INFO
)
<<
height
;
LOG
(
INFO
)
<<
width
;
real
*
y
=
out
.
getData
();
const
real
*
x
=
in
.
getData
();
const
real
*
w
=
filter
.
getData
();
const
int
*
starts
=
seq
.
getData
();
dim3
dimBlock
(
32
,
32
);
dim3
dimGrid
(
DIVUP
(
width
,
dimBlock
.
x
),
1
);
LOG
(
INFO
)
<<
dimGrid
.
x
;
if
(
contextLength
<=
32
)
{
KeRowConv
<
32
,
32
><<<
dimGrid
,
dimBlock
,
0
,
STREAM_DEFAULT
>>>
(
y
,
x
,
w
,
starts
,
height
,
width
,
numSeq
,
contextLength
);
}
else
{
KeRowConv2
<<<
dimGrid
,
dimBlock
,
0
,
STREAM_DEFAULT
>>>
(
y
,
x
,
w
,
starts
,
height
,
width
,
numSeq
,
contextLength
);
}
CHECK_SYNC
(
"RowConv"
);
}
template
<
int
BLOCK_H
,
int
BLOCK_W
,
int
CONTEXT
>
__global__
void
KeRowConvBwWeight
(
real
*
dw
,
const
real
*
x
,
const
real
*
dy
,
const
int
*
starts
,
const
int
height
,
const
int
width
,
const
int
numSeq
,
const
int
context
)
{
const
int
tidx
=
threadIdx
.
x
;
const
int
tidy
=
threadIdx
.
y
;
const
int
blky
=
blockDim
.
y
;
const
int
gidx
=
blockIdx
.
x
*
blockDim
.
x
;
__shared__
real
sh_x
[
BLOCK_H
][
BLOCK_W
];
__shared__
real
sh_dy
[
BLOCK_H
][
BLOCK_W
];
__shared__
real
sh_dw
[
CONTEXT
][
BLOCK_W
];
for
(
int
t
=
tidy
;
t
<
context
;
t
+=
blky
)
{
sh_dw
[
t
][
tidx
]
=
0.0
;
}
__syncthreads
();
for
(
int
i
=
0
;
i
<
numSeq
;
++
i
)
{
const
int
start
=
starts
[
i
];
const
int
end
=
starts
[
i
+
1
];
const
int
steps
=
end
-
start
;
for
(
int
j
=
tidy
;
j
<
steps
;
j
+=
BLOCK_H
)
{
int
xoff
=
gidx
+
tidx
;
int
yoff
=
start
+
j
;
// transpose
sh_x
[
tidx
][
tidy
]
=
xoff
<
width
&&
yoff
<
end
?
x
[
yoff
*
width
+
xoff
]
:
0.0
;
sh_dy
[
tidx
][
tidy
]
=
xoff
<
width
&&
yoff
<
end
?
dy
[
yoff
*
width
+
xoff
]
:
0.0
;
__syncthreads
();
for
(
int
t
=
0
;
t
<
context
;
t
++
)
{
real
val
=
tidx
+
t
<
blockDim
.
x
?
sh_x
[
tidy
][
tidx
+
t
]
*
sh_dy
[
tidy
][
tidx
]
:
0.0
;
// warp size and blockDim.x is 32.
for
(
int
offset
=
16
;
offset
>
0
;
offset
/=
2
)
{
val
+=
__shfl_down
(
val
,
offset
);
}
if
(
tidx
==
0
)
{
sh_dw
[
t
][
tidy
]
+=
val
;
}
__syncthreads
();
}
}
}
for
(
int
t
=
tidy
;
t
<
context
&&
(
gidx
+
tidx
)
<
width
;
t
+=
blky
)
{
dw
[
t
*
width
+
gidx
+
tidx
]
+=
sh_dw
[
t
][
tidx
];
}
}
template
<
int
BLOCK_H
,
int
BLOCK_W
>
__global__
void
KeRowConvBwWeight2
(
real
*
dw
,
const
real
*
x
,
const
real
*
dy
,
const
int
*
starts
,
const
int
height
,
const
int
width
,
const
int
numSeq
,
const
int
context
)
{
const
int
tidx
=
threadIdx
.
x
;
const
int
tidy
=
threadIdx
.
y
;
const
int
gidx
=
blockIdx
.
x
*
blockDim
.
x
;
__shared__
real
sh_x
[
BLOCK_H
][
BLOCK_W
];
__shared__
real
sh_dy
[
BLOCK_H
][
BLOCK_W
];
for
(
int
i
=
0
;
i
<
numSeq
;
++
i
)
{
const
int
start
=
starts
[
i
];
const
int
end
=
starts
[
i
+
1
];
const
int
steps
=
end
-
start
;
for
(
int
j
=
0
;
j
<
steps
;
j
+=
BLOCK_H
)
{
int
xoff
=
gidx
+
tidx
;
int
yoff
=
start
+
j
;
// transpose
sh_x
[
tidx
][
tidy
]
=
xoff
<
width
&&
yoff
<
end
?
x
[
yoff
*
width
+
xoff
]
:
0.0
;
sh_dy
[
tidx
][
tidy
]
=
xoff
<
width
&&
yoff
<
end
?
dy
[
yoff
*
width
+
xoff
]
:
0.0
;
__syncthreads
();
for
(
int
t
=
0
;
t
<
context
;
t
++
)
{
real
val
=
tidx
+
t
<
blockDim
.
x
?
sh_x
[
tidy
][
tidx
+
t
]
*
sh_dy
[
tidy
][
tidx
]
:
0.0
;
// warp size and blockDim.x is 32.
for
(
int
offset
=
16
;
offset
>
0
;
offset
/=
2
)
{
val
+=
__shfl_down
(
val
,
offset
);
}
if
(
tidx
==
0
&&
(
gidx
+
tidy
)
<
width
)
{
dw
[
t
*
width
+
gidx
+
tidy
]
+=
val
;
}
}
}
}
}
template
<
int
BLOCK_H
,
int
BLOCK_W
>
__global__
void
KeRowConvBwData
(
real
*
dx
,
const
real
*
w
,
const
real
*
dy
,
const
int
*
starts
,
const
int
height
,
const
int
width
,
const
int
numSeq
,
const
int
context
)
{
const
int
tidx
=
threadIdx
.
x
;
const
int
tidy
=
threadIdx
.
y
;
const
int
blky
=
blockDim
.
y
;
const
int
gidx
=
blockIdx
.
x
*
blockDim
.
x
;
__shared__
real
sw
[
BLOCK_H
][
BLOCK_W
];
for
(
int
i
=
tidy
;
i
<
context
;
i
+=
blky
)
{
sw
[
i
][
tidx
]
=
gidx
+
tidx
<
width
?
w
[
i
*
width
+
gidx
+
tidx
]
:
0.0
;
}
__syncthreads
();
for
(
int
i
=
0
;
i
<
numSeq
;
++
i
)
{
const
int
start
=
starts
[
i
];
const
int
end
=
starts
[
i
+
1
];
const
int
steps
=
end
-
start
;
for
(
int
j
=
tidy
;
j
<
steps
;
j
+=
blky
)
{
real
sum
=
0
;
int
off
=
(
start
+
j
)
*
width
;
for
(
int
t
=
0
;
t
<
context
&&
(
j
-
t
)
>=
0
;
++
t
)
{
int
dyOff
=
off
-
t
*
width
;
real
dyVal
=
gidx
+
tidx
<
width
?
dy
[
dyOff
+
gidx
+
tidx
]
:
0.0
;
sum
+=
sw
[
t
][
tidx
]
*
dyVal
;
}
if
(
gidx
+
tidx
<
width
)
{
dx
[
off
+
gidx
+
tidx
]
+=
sum
;
}
}
}
}
__global__
void
KeRowConvBwData2
(
real
*
dx
,
const
real
*
w
,
const
real
*
dy
,
const
int
*
starts
,
const
int
height
,
const
int
width
,
const
int
numSeq
,
const
int
context
)
{
const
int
tidx
=
threadIdx
.
x
;
const
int
tidy
=
threadIdx
.
y
;
const
int
blky
=
blockDim
.
y
;
const
int
gidx
=
blockIdx
.
x
*
blockDim
.
x
;
for
(
int
i
=
0
;
i
<
numSeq
;
++
i
)
{
const
int
start
=
starts
[
i
];
const
int
end
=
starts
[
i
+
1
];
const
int
steps
=
end
-
start
;
for
(
int
j
=
tidy
;
j
<
steps
;
j
+=
blky
)
{
real
sum
=
0
;
int
off
=
(
start
+
j
)
*
width
;
for
(
int
t
=
0
;
t
<
context
&&
(
j
-
t
)
>=
0
;
++
t
)
{
int
dyOff
=
off
-
t
*
width
;
real
dyVal
=
gidx
+
tidx
<
width
?
dy
[
dyOff
+
gidx
+
tidx
]
:
0.0
;
real
wVal
=
gidx
+
tidx
<
width
?
w
[
t
*
width
+
gidx
+
tidx
]
:
0.0
;
sum
+=
wVal
*
dyVal
;
}
if
(
gidx
+
tidx
<
width
)
{
dx
[
off
+
gidx
+
tidx
]
+=
sum
;
}
}
}
}
template
<
>
void
RowConvGrad
<
DEVICE_TYPE_GPU
>
(
const
GpuMatrix
&
outG
,
const
GpuMatrix
&
in
,
const
GpuMatrix
&
filter
,
GpuMatrix
&
inG
,
GpuMatrix
&
filterG
,
const
GpuIVector
&
seq
)
{
const
size_t
numSeq
=
seq
.
getSize
()
-
1
;
const
size_t
contextLength
=
filter
.
getHeight
();
const
size_t
height
=
in
.
getHeight
();
const
size_t
width
=
in
.
getWidth
();
const
real
*
dy
=
outG
.
getData
();
const
real
*
x
=
in
.
getData
();
const
real
*
w
=
filter
.
getData
();
real
*
dx
=
inG
.
getData
();
real
*
dw
=
filterG
.
getData
();
const
int
*
starts
=
seq
.
getData
();
dim3
dimBlock
(
32
,
32
);
dim3
dimGrid
(
DIVUP
(
width
,
dimBlock
.
x
),
1
);
if
(
contextLength
<=
16
)
{
KeRowConvBwWeight
<
32
,
32
,
16
>
<<<
dimGrid
,
dimBlock
,
0
,
STREAM_DEFAULT
>>>
(
dw
,
x
,
dy
,
starts
,
height
,
width
,
numSeq
,
contextLength
);
}
else
{
KeRowConvBwWeight2
<
32
,
32
>
<<<
dimGrid
,
dimBlock
,
0
,
STREAM_DEFAULT
>>>
(
dw
,
x
,
dy
,
starts
,
height
,
width
,
numSeq
,
contextLength
);
}
dim3
dimBlock2
(
32
,
32
);
dim3
dimGrid2
(
DIVUP
(
width
,
dimBlock2
.
x
),
1
);
if
(
contextLength
<=
64
)
{
KeRowConvBwData
<
32
,
64
>
<<<
dimGrid2
,
dimBlock2
,
0
,
STREAM_DEFAULT
>>>
(
dx
,
w
,
dy
,
starts
,
height
,
width
,
numSeq
,
contextLength
);
}
else
{
KeRowConvBwData2
<<<
dimGrid2
,
dimBlock2
,
0
,
STREAM_DEFAULT
>>>
(
dx
,
w
,
dy
,
starts
,
height
,
width
,
numSeq
,
contextLength
);
}
CHECK_SYNC
(
"RowConvGrad"
);
}
}
// namespace paddle
paddle/function/RowConvOpTest.cpp
0 → 100644
浏览文件 @
b3ac51ff
/* 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 "FunctionTest.h"
namespace
paddle
{
void
testRowConvFw
(
size_t
batchSize
,
size_t
dim
,
size_t
contextLength
)
{
FunctionCompare
test
(
"RowConv"
,
FuncConfig
());
test
.
addSequence
(
SequenceIdArg
(
TensorShape
{
batchSize
}));
test
.
addInputs
(
SequenceArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
batchSize
,
dim
}));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
contextLength
,
dim
}));
test
.
addOutputs
(
SequenceArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
batchSize
,
dim
}),
ADD_TO
);
test
.
run
();
}
void
testRowConvBw
(
size_t
batchSize
,
size_t
dim
,
size_t
contextLength
)
{
FunctionCompare
test
(
"RowConvGrad"
,
FuncConfig
());
test
.
addSequence
(
SequenceIdArg
(
TensorShape
{
batchSize
}));
test
.
addInputs
(
SequenceArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
batchSize
,
dim
}));
test
.
addInputs
(
SequenceArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
batchSize
,
dim
}));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
contextLength
,
dim
}));
test
.
addOutputs
(
SequenceArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
batchSize
,
dim
}),
ADD_TO
);
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
TensorShape
{
contextLength
,
dim
}),
ADD_TO
);
test
.
run
();
}
TEST
(
RowConv
,
real
)
{
// for (size_t numSamples : {17, 129}) {
// for (size_t dim : {16, 248}) {
// for (size_t context: {3, 7, 65}) {
LOG
(
INFO
)
<<
"==========="
;
// for (size_t numSamples : {17}) {
// for (size_t dim : {16}) {
// for (size_t context: {3}) {
size_t
numSamples
=
17
;
size_t
dim
=
16
;
size_t
context
=
3
;
LOG
(
INFO
)
<<
" numSamples="
<<
numSamples
<<
" dim="
<<
dim
<<
" context length="
<<
context
;
testRowConvFw
(
numSamples
,
dim
,
context
);
// testRowConvBw(numSamples, dim, context);
// }
// }
// }
}
}
// namespace paddle
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