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2632d77d
编写于
9月 09, 2022
作者:
5
5u13
提交者:
GitHub
9月 09, 2022
浏览文件
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电子邮件补丁
差异文件
optimization of max_pool3d forward (#45820)
上级
a001f263
变更
1
显示空白变更内容
内联
并排
Showing
1 changed file
with
91 addition
and
58 deletion
+91
-58
paddle/phi/kernels/funcs/pooling.cu
paddle/phi/kernels/funcs/pooling.cu
+91
-58
未找到文件。
paddle/phi/kernels/funcs/pooling.cu
浏览文件 @
2632d77d
...
@@ -38,6 +38,24 @@ struct FastDivModForPooling {
...
@@ -38,6 +38,24 @@ struct FastDivModForPooling {
}
}
};
};
struct
FastDivModForPooling3D
{
public:
paddle
::
platform
::
FastDivMod
channel
;
paddle
::
platform
::
FastDivMod
width
;
paddle
::
platform
::
FastDivMod
height
;
paddle
::
platform
::
FastDivMod
depth
;
explicit
HOSTDEVICE
FastDivModForPooling3D
(
const
int
channels
,
const
int
output_width
,
const
int
output_height
,
const
int
output_depth
)
{
channel
=
paddle
::
platform
::
FastDivMod
(
channels
);
width
=
paddle
::
platform
::
FastDivMod
(
output_width
);
height
=
paddle
::
platform
::
FastDivMod
(
output_height
);
depth
=
paddle
::
platform
::
FastDivMod
(
output_depth
);
}
};
struct
FastDivModForPoolingWithMoreStaff
{
struct
FastDivModForPoolingWithMoreStaff
{
public:
public:
paddle
::
platform
::
FastDivMod
channel
;
paddle
::
platform
::
FastDivMod
channel
;
...
@@ -2003,7 +2021,7 @@ template class MaxPool2dWithIndexFunctor<phi::GPUContext, double, int>;
...
@@ -2003,7 +2021,7 @@ template class MaxPool2dWithIndexFunctor<phi::GPUContext, double, int>;
template
class
MaxPool2dWithIndexGradFunctor
<
phi
::
GPUContext
,
double
,
int
>;
template
class
MaxPool2dWithIndexGradFunctor
<
phi
::
GPUContext
,
double
,
int
>;
template
<
typename
T1
,
typename
T2
>
template
<
typename
T1
,
typename
T2
>
__global__
void
KernelMaxPool3DWithIdx
(
const
int
n
threads
,
__global__
void
KernelMaxPool3DWithIdx
(
const
int
n
cd
,
const
T1
*
input_data
,
const
T1
*
input_data
,
const
int
channels
,
const
int
channels
,
const
int
input_depth
,
const
int
input_depth
,
...
@@ -2023,32 +2041,41 @@ __global__ void KernelMaxPool3DWithIdx(const int nthreads,
...
@@ -2023,32 +2041,41 @@ __global__ void KernelMaxPool3DWithIdx(const int nthreads,
const
int
padding_width
,
const
int
padding_width
,
bool
adaptive
,
bool
adaptive
,
T1
*
output_data
,
T1
*
output_data
,
T2
*
mask_data
)
{
T2
*
mask_data
,
for
(
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
index
<
nthreads
;
FastDivModForPooling3D
divmods_output
)
{
index
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
w_offset
,
h_offset
,
d_offset
,
nc_offset
;
int
pw
=
index
%
output_width
;
int
dstart
,
dend
,
hstart
,
hend
,
wstart
,
wend
;
int
ph
=
(
index
/
output_width
)
%
output_height
;
const
T1
*
input_data_cur
;
int
pd
=
(
index
/
output_width
/
output_height
)
%
output_depth
;
int
c
=
(
index
/
output_width
/
output_height
/
output_depth
)
%
channels
;
w_offset
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
batch_idx
=
h_offset
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
index
/
output_width
/
output_height
/
output_depth
/
channels
;
if
(
w_offset
<
output_width
&&
h_offset
<
output_height
)
{
for
(
int
index_z
=
blockIdx
.
z
*
blockDim
.
z
+
threadIdx
.
z
;
index_z
<
ncd
;
index_z
+=
gridDim
.
z
*
blockDim
.
z
)
{
auto
output_depth_divmod
=
divmods_output
.
depth
.
Divmod
(
index_z
);
d_offset
=
output_depth_divmod
.
val
[
1
];
nc_offset
=
output_depth_divmod
.
val
[
0
];
int
output_index
=
nc_offset
*
output_depth
*
output_height
*
output_width
+
d_offset
*
output_height
*
output_width
+
h_offset
*
output_width
+
w_offset
;
int
input_offset
=
nc_offset
*
input_depth
*
input_height
*
input_width
;
input_data_cur
=
input_data
+
input_offset
;
int
dstart
,
dend
;
int
hstart
,
hend
;
int
wstart
,
wend
;
if
(
adaptive
)
{
if
(
adaptive
)
{
dstart
=
AdaptStartIndex
(
pd
,
input_depth
,
output_depth
);
dstart
=
AdaptStartIndex
(
d_offset
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
pd
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
d_offset
,
input_depth
,
output_depth
);
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hstart
=
AdaptStartIndex
(
h_offset
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
h_offset
,
input_height
,
output_height
);
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wstart
=
AdaptStartIndex
(
w_offset
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
w_offset
,
input_width
,
output_width
);
}
else
{
}
else
{
dstart
=
pd
*
stride_depth
-
padding_depth
;
dstart
=
d_offset
*
stride_depth
-
padding_depth
;
hstart
=
ph
*
stride_height
-
padding_height
;
hstart
=
h_offset
*
stride_height
-
padding_height
;
wstart
=
pw
*
stride_width
-
padding_width
;
wstart
=
w_offset
*
stride_width
-
padding_width
;
dend
=
min
(
dstart
+
ksize_depth
,
input_depth
);
dend
=
min
(
dstart
+
ksize_depth
,
input_depth
);
hend
=
min
(
hstart
+
ksize_height
,
input_height
);
hend
=
min
(
hstart
+
ksize_height
,
input_height
);
wend
=
min
(
wstart
+
ksize_width
,
input_width
);
wend
=
min
(
wstart
+
ksize_width
,
input_width
);
...
@@ -2059,21 +2086,20 @@ __global__ void KernelMaxPool3DWithIdx(const int nthreads,
...
@@ -2059,21 +2086,20 @@ __global__ void KernelMaxPool3DWithIdx(const int nthreads,
T1
ele
=
-
FLT_MAX
;
T1
ele
=
-
FLT_MAX
;
int
max_index
=
-
1
;
int
max_index
=
-
1
;
input_data
+=
(
batch_idx
*
channels
+
c
)
*
input_depth
*
input_height
*
input_width
;
for
(
int
d
=
dstart
;
d
<
dend
;
++
d
)
{
for
(
int
d
=
dstart
;
d
<
dend
;
++
d
)
{
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
if
(
ele
<
input_data
[(
d
*
input_height
+
h
)
*
input_width
+
w
])
{
if
(
ele
<
input_data_cur
[(
d
*
input_height
+
h
)
*
input_width
+
w
])
{
max_index
=
(
d
*
input_height
+
h
)
*
input_width
+
w
;
max_index
=
(
d
*
input_height
+
h
)
*
input_width
+
w
;
ele
=
input_data
[
max_index
];
ele
=
input_data_cur
[
max_index
];
}
}
}
}
}
}
}
}
output_data
[
index
]
=
ele
;
output_data
[
output_index
]
=
ele
;
mask_data
[
index
]
=
max_index
;
mask_data
[
output_index
]
=
max_index
;
}
}
}
}
}
...
@@ -2201,19 +2227,25 @@ class MaxPool3dWithIndexFunctor<phi::GPUContext, T1, T2> {
...
@@ -2201,19 +2227,25 @@ class MaxPool3dWithIndexFunctor<phi::GPUContext, T1, T2> {
T1
*
output_data
=
context
.
template
Alloc
<
T1
>(
output
);
T1
*
output_data
=
context
.
template
Alloc
<
T1
>(
output
);
T2
*
mask_data
=
context
.
template
Alloc
<
T2
>(
mask
);
T2
*
mask_data
=
context
.
template
Alloc
<
T2
>(
mask
);
int
nthreads
=
batch_size
*
output_channels
*
output_depth
*
output_height
*
int
ncd
=
batch_size
*
input_channels
*
output_depth
;
output_width
;
int
thread_num
=
1024
;
#ifdef WITH_NV_JETSON
backends
::
gpu
::
ChangeThreadNum
(
context
,
&
thread_num
);
#endif
int
blocks
=
(
nthreads
+
thread_num
-
1
)
/
thread_num
;
int
thread_x
=
32
;
dim3
threads
(
thread_num
,
1
);
int
thread_y
=
8
;
dim3
grid
(
blocks
,
1
);
int
thread_z
=
1
;
dim3
threads
(
thread_x
,
thread_y
,
thread_z
);
std
::
array
<
int
,
3
>
max_grid_dim
=
context
.
GetCUDAMaxGridDimSize
();
int
block_x
=
(
output_width
+
threads
.
x
-
1
)
/
threads
.
x
;
int
block_y
=
(
output_height
+
threads
.
y
-
1
)
/
threads
.
y
;
int
block_z
=
(
ncd
>
max_grid_dim
[
2
]
*
threads
.
z
)
?
max_grid_dim
[
2
]
:
(
ncd
+
threads
.
z
-
1
)
/
threads
.
z
;
dim3
grid
(
block_x
,
block_y
,
block_z
);
auto
pool_divmods_output
=
FastDivModForPooling3D
(
input_channels
,
output_width
,
output_height
,
output_depth
);
KernelMaxPool3DWithIdx
<
T1
,
T2
>
KernelMaxPool3DWithIdx
<
T1
,
T2
>
<<<
grid
,
threads
,
0
,
context
.
stream
()
>>>
(
n
threads
,
<<<
grid
,
threads
,
0
,
context
.
stream
()
>>>
(
n
cd
,
input_data
,
input_data
,
input_channels
,
input_channels
,
input_depth
,
input_depth
,
...
@@ -2233,7 +2265,8 @@ class MaxPool3dWithIndexFunctor<phi::GPUContext, T1, T2> {
...
@@ -2233,7 +2265,8 @@ class MaxPool3dWithIndexFunctor<phi::GPUContext, T1, T2> {
padding_width
,
padding_width
,
adaptive
,
adaptive
,
output_data
,
output_data
,
mask_data
);
mask_data
,
pool_divmods_output
);
}
}
};
};
...
...
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