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2747503b
编写于
10月 15, 2018
作者:
Y
yangfei
浏览文件
操作
浏览文件
下载
差异文件
add some function
上级
640a1ab5
408407f4
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
215 addition
and
67 deletion
+215
-67
src/framework/cl/cl_engine.h
src/framework/cl/cl_engine.h
+2
-2
src/framework/cl/cl_image.h
src/framework/cl/cl_image.h
+15
-18
src/framework/executor.cpp
src/framework/executor.cpp
+1
-1
src/operators/kernel/cl/batchnorm_kernel.cpp
src/operators/kernel/cl/batchnorm_kernel.cpp
+8
-3
src/operators/kernel/cl/cl_kernel/batchnorm_kernel.cl
src/operators/kernel/cl/cl_kernel/batchnorm_kernel.cl
+5
-5
src/operators/kernel/cl/cl_kernel/conv_kernel.cl
src/operators/kernel/cl/cl_kernel/conv_kernel.cl
+130
-1
src/operators/kernel/cl/conv_kernel.cpp
src/operators/kernel/cl/conv_kernel.cpp
+45
-37
src/operators/kernel/cl/feed_kernel.cpp
src/operators/kernel/cl/feed_kernel.cpp
+4
-0
tools/android-debug-script/push2android.sh
tools/android-debug-script/push2android.sh
+5
-0
未找到文件。
src/framework/cl/cl_engine.h
浏览文件 @
2747503b
...
...
@@ -40,8 +40,8 @@ class CLEngine {
return
std
::
move
(
context_ptr
);
}
std
::
unique_ptr
<
_cl_command_queue
,
CLCommQueueDeleter
>
CreateClCommandQueue
(
cl_context
context
)
{
std
::
unique_ptr
<
_cl_command_queue
,
CLCommQueueDeleter
>
CreateClCommandQueue
(
cl_context
context
)
{
cl_int
status
;
cl_command_queue
queue
=
clCreateCommandQueue
(
context
,
devices_
[
0
],
0
,
&
status
);
...
...
src/framework/cl/cl_image.h
浏览文件 @
2747503b
...
...
@@ -182,28 +182,25 @@ class CLImage {
DLOG
<<
" image width: "
<<
width
;
DLOG
<<
" image height: "
<<
height
;
cl_image_format
cf
=
{
.
image_channel_order
=
CL_RGBA
,
.
image_channel_data_type
=
CL_HALF_FLOAT
};
cl_image_format
cf
=
{.
image_channel_order
=
CL_RGBA
,
.
image_channel_data_type
=
CL_HALF_FLOAT
};
cl_image_desc
cid
=
{
.
image_type
=
CL_MEM_OBJECT_IMAGE2D
,
.
image_width
=
width
,
.
image_height
=
height
,
.
image_depth
=
1
,
.
image_array_size
=
1
,
.
image_row_pitch
=
0
,
.
image_slice_pitch
=
0
,
.
num_mip_levels
=
0
,
.
num_samples
=
0
,
// .buffer = nullptr
.
image_type
=
CL_MEM_OBJECT_IMAGE2D
,
.
image_width
=
width
,
.
image_height
=
height
,
.
image_depth
=
1
,
.
image_array_size
=
1
,
.
image_row_pitch
=
0
,
.
image_slice_pitch
=
0
,
.
num_mip_levels
=
0
,
.
num_samples
=
0
,
// .buffer = nullptr
};
cid
.
buffer
=
nullptr
;
cl_image_
=
clCreateImage
(
context
,
CL_MEM_READ_WRITE
|
(
imageData
?
CL_MEM_COPY_HOST_PTR
:
0
),
&
cf
,
// const cl_image_format *image_format
&
cid
,
// const cl_image_desc *image_desc
context
,
CL_MEM_READ_WRITE
|
(
imageData
?
CL_MEM_COPY_HOST_PTR
:
0
),
&
cf
,
// const cl_image_format *image_format
&
cid
,
// const cl_image_desc *image_desc
reinterpret_cast
<
void
*>
(
imageData
.
get
()),
// void *host_ptr
&
err
);
...
...
src/framework/executor.cpp
浏览文件 @
2747503b
...
...
@@ -37,7 +37,7 @@ limitations under the License. */
#include "framework/cl/cl_image.h"
#endif
int
debug_to
=
2
;
int
debug_to
=
4
;
namespace
paddle_mobile
{
namespace
framework
{
...
...
src/operators/kernel/cl/batchnorm_kernel.cpp
浏览文件 @
2747503b
...
...
@@ -47,15 +47,20 @@ bool BatchNormKernel<GPU_CL, float>::Init(BatchNormParam<GPU_CL> *param) {
new_bias_ptr
[
i
]
=
bias_ptr
[
i
]
-
mean_ptr
[
i
]
*
inv_std_ptr
[
i
]
*
scale_ptr
[
i
];
}
delete
[](
new_scale_ptr
);
delete
[](
new_bias_ptr
);
framework
::
CLImage
*
new_scale
=
new
framework
::
CLImage
();
new_scale
->
SetTensorData
(
new_scale_ptr
,
variance
->
dims
());
new_scale
->
InitCLImage
(
this
->
cl_helper_
.
CLContext
());
framework
::
CLImage
*
new_bias
=
new
framework
::
CLImage
();
new_bias
->
SetTensorData
(
new_bias_ptr
,
variance
->
dims
());
new_bias
->
InitCLImage
(
this
->
cl_helper_
.
CLContext
());
param
->
SetNewScale
(
new_scale
);
param
->
SetNewBias
(
new_bias
);
delete
[](
new_scale_ptr
);
delete
[](
new_bias_ptr
);
return
true
;
}
...
...
src/operators/kernel/cl/cl_kernel/batchnorm_kernel.cl
浏览文件 @
2747503b
...
...
@@ -3,8 +3,8 @@
__kernel
void
batchnorm
(
__private
const
int
out_height,
__private
const
int
out_width,
__read_only
image2d_t
input,
__read_only
image2d_t
new_scale,
__read_only
image2d_t
new_bias,
__read_only
image2d_t
new_scale
_image
,
__read_only
image2d_t
new_bias
_image
,
__write_only
image2d_t
output
)
{
const
int
out_c
=
get_global_id
(
0
)
;
const
int
out_w
=
get_global_id
(
1
)
;
...
...
@@ -13,12 +13,12 @@ __kernel void batchnorm(__private const int out_height,
const
sampler_t
sampler
=
CLK_NORMALIZED_COORDS_TRUE
| CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST
;
half4
new_scale
=
read_imageh
(
bn_scal
e,
sampler,
(
int2
)(
out_c,
0
))
;
half4
new_bias
=
read_imageh
(
bn_bias
,
sampler,
(
int2
)(
out_c,
0
))
;
half4
new_scale
=
read_imageh
(
new_scale_imag
e,
sampler,
(
int2
)(
out_c,
0
))
;
half4
new_bias
=
read_imageh
(
new_bias_image
,
sampler,
(
int2
)(
out_c,
0
))
;
int
pos_x
=
mad24
(
out_c,
out_width,
out_w
)
;
half4
in
=
read_imageh
(
input,
sampler,
(
int2
)(
pos_x,
out_nh
))
;
half4
out
=
mad
(
in,
new_scale,
new_bias
)
;
write_imageh
(
output,
(
int2
)(
pos_x,
nh
)
,
out
)
;
write_imageh
(
output,
(
int2
)(
pos_x,
out_
nh
)
,
out
)
;
}
src/operators/kernel/cl/cl_kernel/conv_kernel.cl
浏览文件 @
2747503b
...
...
@@ -12,10 +12,139 @@ 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.
*/
#
pragma
OPENCL
EXTENSION
cl_khr_fp16
:
enable
__kernel
void
conv_3x3
()
{
__kernel
void
conv_3x3
(
__private
const
int
global_size_dim0,
__private
const
int
global_size_dim1,
__private
const
int
global_size_dim2,
__read_only
image2d_t
input_image,
__read_only
image2d_t
filter,
#
ifdef
BIASE
__read_only
image2d_t
bias,
#
endif
#
ifdef
BATCH_NORM
__read_only
image2d_t
new_scale,
__read_only
image2d_t
new_biase,
#
endif
__write_only
image2d_t
output_image,
__private
const
int
stride,
__private
const
int
offset,
__private
const
int
input_c,
__private
const
int
dilation,
__private
const
int
input_width,/*
of
one
block
*/
__private
const
int
input_height,/*
of
one
block
*/
__private
const
int
output_width,
__private
const
int
output_height
)
{
const
int
out_c
=
get_global_id
(
0
)
;
const
int
out_w
=
get_global_id
(
1
)
;
const
int
out_nh
=
get_global_id
(
2
)
;
int2
stride_xy
;
stride_xy.x
=
stride
;
stride_xy.y
=
stride
;
int2
ouput_pos_in_one_block
;
ouput_pos_in_one_block.x
=
out_w
;
ouput_pos_in_one_block.y
=
out_nh
;
int2
in_pos_in_one_block
;
in_pos_in_one_block.x
=
ouput_pos_in_one_block.x
*
stride
+
offset
;
in_pos_in_one_block.y
=
ouput_pos_in_one_block.y
*
stride
+
offset
;
#
ifdef
BIASE
half4
output
=
read_imageh
(
bias,
sampler,
int2
(
out_c,
0
))
;
#
else
half4
output
=
0.0
;
#
endif
half4
input[9]
;
const
sampler_t
sampler
=
CLK_NORMALIZED_COORDS_TRUE
|
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST
;
for
(
int
i
=
0
; i < input_c; ++i) {
int2
pos_in
=
(
int2
)(
i
*
input_width
+
in_pos_in_one_block.x,
in_pos_in_one_block.y
)
;
input[0]
=
select
(
read_imageh
(
input_image,
sampler,
(
int2
)(
pos_in.x
-
dilation,
pos_in.y
-
dilation
))
,
(
half4
)(
0.0
)
,
(
ushort4
)(
in_pos_in_one_block.x
-
dilation
<
0
|
| in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[1] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y - dilation)),
(half4)(0.0),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[2] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y - dilation)),
(half4)(0.0),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[3] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y)),
(half4)(0.0),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[4] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y)),
(half4)(0.0),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y >= input_height));
input[5] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y)),
(half4)(0.0),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[6] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y + dilation)),
(half4)(0.0),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[7] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y + dilation)),
(half4)(0.0),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[8] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y + dilation)),
(half4)(0.0),
(ushort4)(pos_in.x + dilation < 0 || in_pos_in_one_block.y + dilation < 0 || pos_in.x + dilation >= input_width |
|
in_pos_in_one_block.y
+
dilation
>=
input_height
))
;
for
(
int
j
=
0
; j < 9; ++j) {
int2
fuck
;
fuck.x
=
i
*
3
+
j
%
3
;
fuck.y
=
out_c
*
4
*
3
+
0
*
out_c
*
3
+
j
/
3
;
half4
weight_x
=
read_imageh
(
filter,
sampler,
fuck
)
;
output.x
+=
dot
(
input[j],
weight_x
)
;
fuck.y
=
out_c
*
4
*
3
+
1
*
out_c
*
3
+
j
/
3
;
half4
weight_y
=
read_imageh
(
filter,
sampler,
fuck
)
;
output.y
+=
dot
(
input[j],
weight_y
)
;
fuck.y
=
out_c
*
4
*
3
+
2
*
out_c
*
3
+
j
/
3
;
half4
weight_z
=
read_imageh
(
filter,
sampler,
fuck
)
;
output.z
+=
dot
(
input[j],
weight_z
)
;
fuck.y
=
out_c
*
4
*
3
+
3
*
out_c
*
3
+
j
/
3
;
half4
weight_w
=
read_imageh
(
filter,
sampler,
fuck
)
;
output.w
+=
dot
(
input[j],
weight_w
)
;
}
}
#
ifdef
BATCH_NORM
output
=
output
*
read_imageh
(
new_scale,
sampler,
int2
(
out_c,
0
))
+
read_imageh
(
new_biase,
sampler,
int2
(
out_c,
0
))
#
endif
#
ifdef
RELU
output
=
activation
(
output
)
;
#
endif
write_imageh
(
output_image,
(
int2
)(
out_c
*
global_size_dim1
+
out_w,
out_nh
)
,
output
)
;
}
src/operators/kernel/cl/conv_kernel.cpp
浏览文件 @
2747503b
...
...
@@ -78,7 +78,7 @@ void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> ¶m) {
DLOG
<<
" get Filter "
;
auto
output
=
param
.
Output
();
auto
output
=
param
.
Output
()
->
GetCLImage
()
;
DLOG
<<
" get Output "
;
...
...
@@ -89,45 +89,54 @@ void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> ¶m) {
int
input_width
=
param
.
Input
()
->
WidthOfOneBlock
();
int
input_height
=
param
.
Input
()
->
HeightOfOneBlock
();
int
output_width
=
param
.
Output
()
->
WidthOfOneBlock
();
int
output_height
=
param
.
Output
()
->
HeightOfOneBlock
();
cl_int
status
;
DLOG
<<
" begin set kernel arg "
;
// status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 1, sizeof(int), &w);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 6, sizeof(int), &stride);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 7, sizeof(int), &offset);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 8, sizeof(int), &input_c);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 9, sizeof(int), &dilation);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 10, sizeof(int), &input_width);
// CL_CHECK_ERRORS(status);
//
// status = clSetKernelArg(kernel, 11, sizeof(int), &input_height);
// CL_CHECK_ERRORS(status);
status
=
clSetKernelArg
(
kernel
,
0
,
sizeof
(
int
),
&
c_block
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
1
,
sizeof
(
int
),
&
w
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
2
,
sizeof
(
int
),
&
nh
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
3
,
sizeof
(
cl_mem
),
&
input
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
4
,
sizeof
(
cl_mem
),
&
filter
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
5
,
sizeof
(
cl_mem
),
&
output
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
6
,
sizeof
(
int
),
&
stride
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
7
,
sizeof
(
int
),
&
offset
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
8
,
sizeof
(
int
),
&
input_c
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
9
,
sizeof
(
int
),
&
dilation
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
10
,
sizeof
(
int
),
&
input_width
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
11
,
sizeof
(
int
),
&
input_height
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
12
,
sizeof
(
int
),
&
output_width
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
13
,
sizeof
(
int
),
&
output_height
);
CL_CHECK_ERRORS
(
status
);
DLOG
<<
" end set kernel arg "
;
...
...
@@ -138,7 +147,6 @@ void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> ¶m) {
default_work_size
.
data
(),
NULL
,
0
,
NULL
,
NULL
);
CL_CHECK_ERRORS
(
status
);
DLOG
<<
" end enqueue "
;
}
template
class
ConvKernel
<
GPU_CL
,
float
>;
...
...
src/operators/kernel/cl/feed_kernel.cpp
浏览文件 @
2747503b
...
...
@@ -61,7 +61,11 @@ void FeedKernel<GPU_CL, float>::Compute(const FeedParam<GPU_CL> ¶m) {
size_t
region
[
3
]
=
{
height
,
width
,
1
};
clEnqueueReadImage
(
commandQueue
,
cl_image
,
CL_TRUE
,
origin
,
region
,
0
,
0
,
out
,
0
,
NULL
,
NULL
);
<<<<<<<
HEAD
for
(
int
i
=
0
;
i
<
numel
;
i
++
)
DLOG
<<
Half2Float
(
out
[
i
])
<<
","
<<
i
;
=======
// for (int i = 0; i < numel; i++) DLOG << Half2Float(out[i]);
>>>>>>>
289
b739de8517c21872107c16790b9cb2e7042d7
}
template
class
FeedKernel
<
GPU_CL
,
float
>;
...
...
tools/android-debug-script/push2android.sh
浏览文件 @
2747503b
#!/usr/bin/env sh
push_fn
()
{
cp
../../src/operators/kernel/cl/cl_kernel/
*
../../build/release/arm-v7a/build/cl_kernel/
MODELS_PATH
=
"../../test/models/*"
MODELS_SRC
=
"../../test/models"
IMAGE_PATH
=
"../../test/images/*"
...
...
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