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5f2e8378
编写于
10月 12, 2018
作者:
D
Dun
提交者:
qingqing01
10月 12, 2018
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电子邮件补丁
差异文件
optimize depthwise conv by register memory (#13778)
* optimize depthwise conv by register memory * test=develop
上级
5428cb99
变更
1
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Showing
1 changed file
with
210 addition
and
65 deletion
+210
-65
paddle/fluid/operators/math/depthwise_conv.cu
paddle/fluid/operators/math/depthwise_conv.cu
+210
-65
未找到文件。
paddle/fluid/operators/math/depthwise_conv.cu
浏览文件 @
5f2e8378
...
@@ -46,17 +46,20 @@ __forceinline__ __device__ unsigned warp_id() {
...
@@ -46,17 +46,20 @@ __forceinline__ __device__ unsigned warp_id() {
return
ret
;
return
ret
;
}
}
#define ARG_DEFINE_KernelDepthwiseConv \
const T *const input_data, const T *const filter_data, const int batch_size, \
const int output_channels, const int output_height, \
const int output_width, const int input_channels, \
const int input_height, const int input_width, \
const int filter_multiplier, const int filter_height, \
const int filter_width, const int stride_height, const int stride_width, \
const int padding_height, const int padding_width, \
const int dilate_height, const int dilate_width, T *const output_data
// A Cuda kernel to compute the depthwise convolution forward pass
// A Cuda kernel to compute the depthwise convolution forward pass
// in NCHW format.
// in NCHW format.
template
<
typename
T
>
template
<
typename
T
>
__device__
__inline__
void
KernelDepthwiseConv
(
__device__
__inline__
void
KernelDepthwiseConv
(
ARG_DEFINE_KernelDepthwiseConv
)
{
const
T
*
const
input_data
,
const
T
*
const
filter_data
,
const
int
batch_size
,
const
int
output_channels
,
const
int
output_height
,
const
int
output_width
,
const
int
input_channels
,
const
int
input_height
,
const
int
input_width
,
const
int
filter_multiplier
,
const
int
filter_height
,
const
int
filter_width
,
const
int
stride_height
,
const
int
stride_width
,
const
int
padding_height
,
const
int
padding_width
,
const
int
dilate_height
,
const
int
dilate_width
,
T
*
const
output_data
)
{
for
(
int
w_out
=
threadIdx
.
x
;
w_out
<
output_width
;
w_out
+=
blockDim
.
x
)
{
for
(
int
w_out
=
threadIdx
.
x
;
w_out
<
output_width
;
w_out
+=
blockDim
.
x
)
{
for
(
int
h_out
=
threadIdx
.
y
;
h_out
<
output_height
;
h_out
+=
blockDim
.
y
)
{
for
(
int
h_out
=
threadIdx
.
y
;
h_out
<
output_height
;
h_out
+=
blockDim
.
y
)
{
const
int
batch
=
blockIdx
.
y
;
const
int
batch
=
blockIdx
.
y
;
...
@@ -97,42 +100,105 @@ __device__ __inline__ void KernelDepthwiseConv(
...
@@ -97,42 +100,105 @@ __device__ __inline__ void KernelDepthwiseConv(
}
}
}
}
template
<
typename
T
,
int
c_filter_multiplier
,
int
c_stride
>
template
<
typename
T
,
int
c_filter
>
__global__
void
KernelDepthwiseConvSp
(
__device__
__inline__
void
KernelDepthwiseConvCFilter
(
const
T
*
const
input_data
,
const
T
*
const
filter_data
,
const
int
batch_size
,
ARG_DEFINE_KernelDepthwiseConv
)
{
const
int
output_channels
,
const
int
output_height
,
const
int
output_width
,
const
int
kWeghtSize
=
c_filter
*
c_filter
;
const
int
input_channels
,
const
int
input_height
,
const
int
input_width
,
T
r_weight
[
kWeghtSize
];
const
int
filter_multiplier
,
const
int
filter_height
,
const
int
batch
=
blockIdx
.
y
;
const
int
filter_width
,
const
int
stride_height
,
const
int
stride_width
,
const
int
c_out
=
blockIdx
.
x
;
const
int
padding_height
,
const
int
padding_width
,
const
int
dilate_height
,
const
T
*
weight
=
filter_data
+
c_out
*
c_filter
*
c_filter
;
const
int
dilate_width
,
T
*
const
output_data
)
{
for
(
int
i
=
0
;
i
<
c_filter
*
c_filter
;
i
++
)
r_weight
[
i
]
=
weight
[
i
];
if
(
c_filter_multiplier
==
0
)
KernelDepthwiseConv
<
T
>
(
input_data
,
filter_data
,
batch_size
,
output_channels
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
filter_multiplier
,
filter_height
,
filter_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
output_data
);
else
for
(
int
w_out
=
threadIdx
.
x
;
w_out
<
output_width
;
w_out
+=
blockDim
.
x
)
{
KernelDepthwiseConv
<
T
>
(
input_data
,
filter_data
,
batch_size
,
output_channels
,
for
(
int
h_out
=
threadIdx
.
y
;
h_out
<
output_height
;
h_out
+=
blockDim
.
y
)
{
output_height
,
output_width
,
input_channels
,
const
int
batch
=
blockIdx
.
y
;
input_height
,
input_width
,
c_filter_multiplier
,
const
int
c_out
=
blockIdx
.
x
;
filter_height
,
filter_height
,
c_stride
,
c_stride
,
padding_height
,
padding_width
,
dilate_height
,
const
int
c_in
=
c_out
/
filter_multiplier
;
dilate_width
,
output_data
);
T
value
=
0
;
const
int
h_in_start
=
-
padding_height
+
h_out
*
stride_height
;
const
int
w_in_start
=
-
padding_width
+
w_out
*
stride_width
;
const
int
h_in_end
=
h_in_start
+
c_filter
*
dilate_height
;
const
int
w_in_end
=
w_in_start
+
c_filter
*
dilate_width
;
const
int
in_offset
=
((
batch
*
input_channels
+
c_in
)
*
input_height
)
*
input_width
;
const
int
h_end
=
h_in_end
<
input_height
?
h_in_end
:
input_height
;
const
int
w_end
=
w_in_end
<
input_width
?
w_in_end
:
input_width
;
const
int
h_start
=
h_in_start
>
0
?
h_in_start
:
0
;
const
int
w_start
=
w_in_start
>
0
?
w_in_start
:
0
;
for
(
int
h_in
=
h_in_start
,
h_f
=
0
;
h_f
<
c_filter
;
h_in
+=
dilate_height
,
h_f
++
)
{
for
(
int
w_in
=
w_in_start
,
w_f
=
0
;
w_f
<
c_filter
;
w_in
+=
dilate_width
,
w_f
++
)
{
if
(
h_in
>=
0
&&
h_in
<
input_height
&&
w_in
>=
0
&&
w_in
<
input_width
)
{
const
int
offset
=
in_offset
+
h_in
*
input_width
+
w_in
;
value
+=
r_weight
[
h_f
*
c_filter
+
w_f
]
*
input_data
[
offset
];
}
}
}
int
index
=
((
batch
*
gridDim
.
x
+
c_out
)
*
output_height
+
h_out
)
*
output_width
+
w_out
;
output_data
[
index
]
=
value
;
}
}
}
template
<
typename
T
,
int
c_filter_multiplier
,
int
c_stride
,
int
c_filter
>
__global__
void
KernelDepthwiseConvSp
(
ARG_DEFINE_KernelDepthwiseConv
)
{
if
(
c_filter_multiplier
==
0
)
{
if
(
c_filter
==
-
1
)
KernelDepthwiseConv
<
T
>
(
input_data
,
filter_data
,
batch_size
,
output_channels
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
filter_multiplier
,
filter_height
,
filter_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
output_data
);
else
KernelDepthwiseConvCFilter
<
T
,
c_filter
>
(
input_data
,
filter_data
,
batch_size
,
output_channels
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
filter_multiplier
,
filter_height
,
filter_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
output_data
);
}
else
{
if
(
c_filter
==
-
1
)
KernelDepthwiseConv
<
T
>
(
input_data
,
filter_data
,
batch_size
,
output_channels
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
c_filter_multiplier
,
filter_height
,
filter_height
,
c_stride
,
c_stride
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
output_data
);
else
KernelDepthwiseConvCFilter
<
T
,
c_filter
>
(
input_data
,
filter_data
,
batch_size
,
output_channels
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
c_filter_multiplier
,
filter_height
,
filter_height
,
c_stride
,
c_stride
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
output_data
);
}
}
}
// CUDA kernel to compute the depthwise convolution backprop w.r.t input.
// CUDA kernel to compute the depthwise convolution backprop w.r.t input.
#define ARG_DEFINE_KernelDepthwiseConvInputGrad \
const T *const output_grad_data, const T *const filter_data, \
const int batch_size, const int output_channels, \
const int output_height, const int output_width, \
const int input_channels, const int input_height, const int input_width, \
const int filter_multiplier, const int filter_height, \
const int filter_width, const int stride_height, const int stride_width, \
const int padding_height, const int padding_width, \
const int dilate_height, const int dilate_width, \
T *const input_grad_data
template
<
typename
T
>
template
<
typename
T
>
__device__
__inline__
void
KernelDepthwiseConvInputGrad
(
__device__
__inline__
void
KernelDepthwiseConvInputGrad
(
const
T
*
const
output_grad_data
,
const
T
*
const
filter_data
,
ARG_DEFINE_KernelDepthwiseConvInputGrad
)
{
const
int
batch_size
,
const
int
output_channels
,
const
int
output_height
,
const
int
output_width
,
const
int
input_channels
,
const
int
input_height
,
const
int
input_width
,
const
int
filter_multiplier
,
const
int
filter_height
,
const
int
filter_width
,
const
int
stride_height
,
const
int
stride_width
,
const
int
padding_height
,
const
int
padding_width
,
const
int
dilate_height
,
const
int
dilate_width
,
T
*
const
input_grad_data
)
{
for
(
int
w_in
=
threadIdx
.
x
;
w_in
<
input_width
;
w_in
+=
blockDim
.
x
)
{
for
(
int
w_in
=
threadIdx
.
x
;
w_in
<
input_width
;
w_in
+=
blockDim
.
x
)
{
for
(
int
h_in
=
threadIdx
.
y
;
h_in
<
input_height
;
h_in
+=
blockDim
.
y
)
{
for
(
int
h_in
=
threadIdx
.
y
;
h_in
<
input_height
;
h_in
+=
blockDim
.
y
)
{
const
int
batch
=
blockIdx
.
y
;
const
int
batch
=
blockIdx
.
y
;
...
@@ -184,15 +250,67 @@ __device__ __inline__ void KernelDepthwiseConvInputGrad(
...
@@ -184,15 +250,67 @@ __device__ __inline__ void KernelDepthwiseConvInputGrad(
}
}
}
}
template
<
typename
T
,
int
c_filter_multiplier
,
int
c_stride
>
template
<
typename
T
,
int
c_filter
,
int
c_filter_multiplier
>
__device__
__inline__
void
KernelDepthwiseConvInputGradCFilter
(
ARG_DEFINE_KernelDepthwiseConvInputGrad
)
{
const
int
kWeghtSize
=
c_filter
*
c_filter
*
c_filter_multiplier
+
1
;
T
r_weight
[
kWeghtSize
];
const
int
batch
=
blockIdx
.
y
;
const
int
c_in
=
blockIdx
.
x
;
for
(
int
c_i
=
0
;
c_i
<
filter_multiplier
;
c_i
++
)
{
int
c_out
=
c_in
*
filter_multiplier
+
c_i
;
const
T
*
weight
=
filter_data
+
c_out
*
c_filter
*
c_filter
;
for
(
int
i
=
0
;
i
<
c_filter
*
c_filter
;
i
++
)
r_weight
[
i
+
c_i
*
c_filter
*
c_filter
]
=
weight
[
c_filter
*
c_filter
-
i
-
1
];
}
for
(
int
w_in
=
threadIdx
.
x
;
w_in
<
input_width
;
w_in
+=
blockDim
.
x
)
{
for
(
int
h_in
=
threadIdx
.
y
;
h_in
<
input_height
;
h_in
+=
blockDim
.
y
)
{
const
int
batch
=
blockIdx
.
y
;
const
int
c_in
=
blockIdx
.
x
;
int
h_out_start
=
h_in
-
(
c_filter
-
1
)
*
dilate_height
+
padding_height
;
int
w_out_start
=
w_in
-
(
c_filter
-
1
)
*
dilate_width
+
padding_width
;
T
value
=
0
;
for
(
int
c_i
=
0
;
c_i
<
filter_multiplier
;
c_i
++
)
{
int
c_out
=
c_in
*
filter_multiplier
+
c_i
;
for
(
int
h_out
=
h_out_start
,
h_f
=
0
;
h_f
<
c_filter
;
h_out
+=
dilate_height
,
h_f
++
)
{
for
(
int
w_out
=
w_out_start
,
w_f
=
0
;
w_f
<
c_filter
;
w_out
+=
dilate_width
,
w_f
++
)
{
int
s_h_out
=
h_out
/
stride_height
;
int
s_w_out
=
w_out
/
stride_width
;
if
(
h_out
%
stride_height
==
0
&&
w_out
%
stride_width
==
0
&&
s_h_out
>=
0
&&
s_h_out
<
output_height
&&
s_w_out
>=
0
&&
s_w_out
<
output_width
)
{
const
int
output_grad_offset
=
((
batch
*
output_channels
+
c_out
)
*
output_height
+
s_h_out
)
*
output_width
+
s_w_out
;
value
+=
output_grad_data
[
output_grad_offset
]
*
r_weight
[
h_f
*
c_filter
+
w_f
+
c_i
*
c_filter
*
c_filter
];
}
}
}
}
int
index
=
((
batch
*
gridDim
.
x
+
c_in
)
*
input_height
+
h_in
)
*
input_width
+
w_in
;
input_grad_data
[
index
]
=
value
;
}
}
}
template
<
typename
T
,
int
c_filter_multiplier
,
int
c_stride
,
int
c_filter
>
__global__
void
KernelDepthwiseConvInputGradSp
(
__global__
void
KernelDepthwiseConvInputGradSp
(
const
T
*
const
output_grad_data
,
const
T
*
const
filter_data
,
ARG_DEFINE_KernelDepthwiseConvInputGrad
)
{
const
int
batch_size
,
const
int
output_channels
,
const
int
output_height
,
const
int
output_width
,
const
int
input_channels
,
const
int
input_height
,
const
int
input_width
,
const
int
filter_multiplier
,
const
int
filter_height
,
const
int
filter_width
,
const
int
stride_height
,
const
int
stride_width
,
const
int
padding_height
,
const
int
padding_width
,
const
int
dilate_height
,
const
int
dilate_width
,
T
*
const
input_grad_data
)
{
if
(
c_filter_multiplier
==
0
)
if
(
c_filter_multiplier
==
0
)
KernelDepthwiseConvInputGrad
<
T
>
(
KernelDepthwiseConvInputGrad
<
T
>
(
output_grad_data
,
filter_data
,
batch_size
,
output_channels
,
output_grad_data
,
filter_data
,
batch_size
,
output_channels
,
...
@@ -200,13 +318,20 @@ __global__ void KernelDepthwiseConvInputGradSp(
...
@@ -200,13 +318,20 @@ __global__ void KernelDepthwiseConvInputGradSp(
filter_multiplier
,
filter_height
,
filter_width
,
stride_height
,
filter_multiplier
,
filter_height
,
filter_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
,
dilate_height
,
stride_width
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
input_grad_data
);
dilate_width
,
input_grad_data
);
else
else
if
(
c_filter
==
-
1
)
KernelDepthwiseConvInputGrad
<
T
>
(
KernelDepthwiseConvInputGrad
<
T
>
(
output_grad_data
,
filter_data
,
batch_size
,
output_channels
,
output_grad_data
,
filter_data
,
batch_size
,
output_channels
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
c_filter_multiplier
,
filter_height
,
filter_width
,
c_stride
,
c_stride
,
c_filter_multiplier
,
filter_height
,
filter_width
,
c_stride
,
c_stride
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
input_grad_data
);
input_grad_data
);
else
KernelDepthwiseConvInputGradCFilter
<
T
,
c_filter
,
c_filter_multiplier
>
(
output_grad_data
,
filter_data
,
batch_size
,
output_channels
,
output_height
,
output_width
,
input_channels
,
input_height
,
input_width
,
c_filter_multiplier
,
filter_height
,
filter_width
,
c_stride
,
c_stride
,
padding_height
,
padding_width
,
dilate_height
,
dilate_width
,
input_grad_data
);
}
}
// Cuda kernel to compute the depthwise convolution backprop w.r.t. filter.
// Cuda kernel to compute the depthwise convolution backprop w.r.t. filter.
...
@@ -325,12 +450,14 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
...
@@ -325,12 +450,14 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
dim3
threads
(
std
::
min
(
output_width
,
thread
),
blocks
,
1
);
dim3
threads
(
std
::
min
(
output_width
,
thread
),
blocks
,
1
);
dim3
grid
(
output_channels
,
batch_size
,
1
);
dim3
grid
(
output_channels
,
batch_size
,
1
);
int
filter_multiplier
=
output_channels
/
input_channels
;
int
filter_multiplier
=
output_channels
/
input_channels
;
#define check_case(c_filter_multiplier, c_stride
)
\
#define check_case(c_filter_multiplier, c_stride
, c_filter)
\
if (c_filter_multiplier == 0 || \
if (c_filter_multiplier == 0 || \
filter_multiplier == c_filter_multiplier && \
filter_multiplier == c_filter_multiplier && \
stride_height == stride_width && stride_height == c_stride) { \
stride_height == stride_width && stride_height == c_stride && \
KernelDepthwiseConvSp<T, c_filter_multiplier, \
(ksize_height == ksize_width && ksize_height == c_filter || \
c_stride><<<grid, threads, 0, context.stream()>>>( \
c_filter == -1)) { \
KernelDepthwiseConvSp<T, c_filter_multiplier, c_stride, \
c_filter><<<grid, threads, 0, context.stream()>>>( \
input_data, filter_data, batch_size, output_channels, output_height, \
input_data, filter_data, batch_size, output_channels, output_height, \
output_width, input_channels, input_height, input_width, \
output_width, input_channels, input_height, input_width, \
filter_multiplier, ksize_height, ksize_width, stride_height, \
filter_multiplier, ksize_height, ksize_width, stride_height, \
...
@@ -338,11 +465,17 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
...
@@ -338,11 +465,17 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
dilate_width, output_data); \
dilate_width, output_data); \
return; \
return; \
}
}
check_case
(
1
,
1
);
check_case
(
1
,
1
,
3
);
check_case
(
1
,
2
);
check_case
(
1
,
1
,
5
);
// NOTE(liangdun): 0,0 for other case
check_case
(
1
,
1
,
-
1
);
// add other case if needed, e.g. check_case(2^n,1)
check_case
(
1
,
2
,
3
);
check_case
(
0
,
0
);
check_case
(
1
,
2
,
5
);
check_case
(
1
,
2
,
-
1
);
check_case
(
0
,
0
,
3
);
check_case
(
0
,
0
,
5
);
check_case
(
0
,
0
,
-
1
);
// NOTE(liangdun): 0,0 for other case
// add other case if needed, e.g. check_case(2^n,1)
#undef check_case
#undef check_case
}
}
};
};
...
@@ -384,13 +517,15 @@ class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, T> {
...
@@ -384,13 +517,15 @@ class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, T> {
dim3
grid
(
input_channels
,
batch_size
,
1
);
dim3
grid
(
input_channels
,
batch_size
,
1
);
int
filter_multiplier
=
output_channels
/
input_channels
;
int
filter_multiplier
=
output_channels
/
input_channels
;
#define check_case(c_filter_multiplier, c_stride
)
\
#define check_case(c_filter_multiplier, c_stride
, c_filter)
\
if (c_filter_multiplier == 0 || \
if (c_filter_multiplier == 0 || \
filter_multiplier == c_filter_multiplier && \
filter_multiplier == c_filter_multiplier && \
stride_height == stride_width && stride_height == c_stride) { \
stride_height == stride_width && stride_height == c_stride && \
(ksize_height == ksize_width && ksize_height == c_filter || \
c_filter == -1)) { \
KernelDepthwiseConvInputGradSp< \
KernelDepthwiseConvInputGradSp< \
T, c_filter_multiplier,
\
T, c_filter_multiplier,
c_stride,
\
c_
stride
><<<grid, threads, 0, context.stream()>>>( \
c_
filter
><<<grid, threads, 0, context.stream()>>>( \
output_grad_data, filter_data, batch_size, output_channels, \
output_grad_data, filter_data, batch_size, output_channels, \
output_height, output_width, input_channels, input_height, \
output_height, output_width, input_channels, input_height, \
input_width, filter_multiplier, ksize_height, ksize_width, \
input_width, filter_multiplier, ksize_height, ksize_width, \
...
@@ -398,11 +533,21 @@ class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, T> {
...
@@ -398,11 +533,21 @@ class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, T> {
dilate_height, dilate_width, input_grad_data); \
dilate_height, dilate_width, input_grad_data); \
return; \
return; \
}
}
check_case
(
1
,
1
);
check_case
(
1
,
1
,
3
);
check_case
(
1
,
2
);
check_case
(
1
,
1
,
5
);
// NOTE(liangdun): 0,0 for other case
check_case
(
1
,
1
,
-
1
);
// add other case if needed, e.g. check_case(2^n,1)
check_case
(
1
,
2
,
3
);
check_case
(
0
,
0
);
check_case
(
1
,
2
,
5
);
check_case
(
1
,
2
,
-
1
);
check_case
(
2
,
1
,
3
);
check_case
(
2
,
1
,
5
);
check_case
(
2
,
1
,
-
1
);
check_case
(
2
,
2
,
3
);
check_case
(
2
,
2
,
5
);
check_case
(
2
,
2
,
-
1
);
check_case
(
0
,
0
,
-
1
);
// NOTE(liangdun): 0,0 for other case
// add other case if needed, e.g. check_case(2^n,1)
#undef check_case
#undef check_case
}
}
};
};
...
...
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