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88c0ed3b
编写于
10月 15, 2018
作者:
L
liuruilong
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix cl image error
上级
ac1c2581
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
82 addition
and
82 deletion
+82
-82
src/framework/cl/cl_image.h
src/framework/cl/cl_image.h
+17
-20
src/framework/operator.cpp
src/framework/operator.cpp
+1
-0
src/operators/kernel/cl/cl_kernel/conv_kernel.cl
src/operators/kernel/cl/cl_kernel/conv_kernel.cl
+3
-1
src/operators/kernel/cl/conv_kernel.cpp
src/operators/kernel/cl/conv_kernel.cpp
+61
-61
未找到文件。
src/framework/cl/cl_image.h
浏览文件 @
88c0ed3b
...
...
@@ -101,7 +101,7 @@ class CLImage {
T
*
data
()
const
{
if
(
initialized_
)
{
PADDLE_MOBILE_THROW_EXCEPTION
(
" cl image has initialized, tensor data has been deleted
"
);
" cl image has initialized, tensor data has been deleted
, can't use tensor data
"
);
}
return
reinterpret_cast
<
T
*>
(
tensor_data_
);
}
...
...
@@ -118,6 +118,7 @@ class CLImage {
private:
void
InitCLImage
(
cl_context
context
,
float
*
tensor_data
,
const
DDim
&
dim
)
{
DLOG
<<
" tensor dim: "
<<
dim
;
cl_image_format
cf
=
{.
image_channel_order
=
CL_RGBA
,
.
image_channel_data_type
=
CL_HALF_FLOAT
};
// NCHW -> [W * (C+3)/4, H * N]
...
...
@@ -135,29 +136,23 @@ class CLImage {
tensor_data_
[
i
]
=
0
;
}
}
size_t
N
,
C
,
H
,
W
;
if
(
tensor_dims_
.
size
()
==
4
)
{
N
=
tensor_dims_
[
0
];
if
(
N
<
0
)
{
N
=
1
;
}
C
=
tensor_dims_
[
1
];
H
=
tensor_dims_
[
2
];
W
=
tensor_dims_
[
3
];
width_of_one_block_
=
W
;
height_of_one_block_
=
H
;
size_t
new_dims
[]
=
{
1
,
1
,
1
,
1
};
}
else
if
(
tensor_dims_
.
size
()
==
1
)
{
N
=
1
;
C
=
tensor_dims_
[
0
];
H
=
1
;
W
=
1
;
width_of_one_block_
=
W
;
height_of_one_block_
=
H
;
for
(
int
j
=
0
;
j
<
dim
.
size
();
++
j
)
{
new_dims
[
4
-
dim
.
size
()
+
j
]
=
dim
[
j
];
}
size_t
N
,
C
,
H
,
W
;
N
=
new_dims
[
0
];
C
=
new_dims
[
1
];
H
=
new_dims
[
2
];
W
=
new_dims
[
3
];
width_of_one_block_
=
W
;
height_of_one_block_
=
H
;
size_t
width
=
W
*
((
C
+
3
)
/
4
);
size_t
height
=
H
*
N
;
...
...
@@ -196,6 +191,8 @@ class CLImage {
}
}
cl_int
err
;
DLOG
<<
" image width: "
<<
width
;
DLOG
<<
" image height: "
<<
height
;
cl_image_
=
clCreateImage2D
(
context
,
// cl_context context
CL_MEM_READ_WRITE
|
(
imageData
?
CL_MEM_COPY_HOST_PTR
:
0
),
// cl_mem_flags flags
...
...
src/framework/operator.cpp
浏览文件 @
88c0ed3b
...
...
@@ -60,6 +60,7 @@ void OperatorBase<Dtype>::Run() {
DLOG
<<
" begin run "
<<
type_
;
RunImpl
();
DLOG
<<
" end run "
<<
type_
;
#ifdef PADDLE_MOBILE_DEBUG
DLOG
<<
"-------------"
<<
type_
<<
"----------------------------"
;
vector
<
string
>
input_keys
=
GetInputKeys
();
...
...
src/operators/kernel/cl/cl_kernel/conv_kernel.cl
浏览文件 @
88c0ed3b
...
...
@@ -12,4 +12,6 @@ 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
"conv_kernel.inc.cl"
//#include
"conv_kernel.inc.cl"
__kernel
void
conv_3x3
()
{}
\ No newline at end of file
src/operators/kernel/cl/conv_kernel.cpp
浏览文件 @
88c0ed3b
...
...
@@ -42,18 +42,18 @@ bool ConvKernel<GPU_CL, float>::Init(ConvParam<GPU_CL> *param) {
param
->
Filter
()
->
HeightOfOneBlock
()
==
1
)
{
DLOG
<<
" here1 "
;
this
->
cl_helper_
.
AddKernel
(
"conv_1x1"
,
"conv_
add_bn_relu_
kernel.cl"
);
this
->
cl_helper_
.
AddKernel
(
"conv_1x1"
,
"conv_kernel.cl"
);
}
else
if
(
param
->
Filter
()
->
dims
()[
1
]
==
1
)
{
DLOG
<<
" here2 "
;
this
->
cl_helper_
.
AddKernel
(
"depth_conv_3x3"
,
"conv_
add_bn_relu_
kernel.cl"
);
this
->
cl_helper_
.
AddKernel
(
"depth_conv_3x3"
,
"conv_kernel.cl"
);
}
else
if
(
param
->
Filter
()
->
WidthOfOneBlock
()
==
3
&&
param
->
Filter
()
->
HeightOfOneBlock
()
==
3
)
{
DLOG
<<
" here3 "
;
this
->
cl_helper_
.
AddKernel
(
"conv_3x3"
,
"conv_
add_bn_relu_
kernel.cl"
);
this
->
cl_helper_
.
AddKernel
(
"conv_3x3"
,
"conv_kernel.cl"
);
}
else
{
PADDLE_MOBILE_THROW_EXCEPTION
(
" not support "
);
...
...
@@ -64,64 +64,64 @@ bool ConvKernel<GPU_CL, float>::Init(ConvParam<GPU_CL> *param) {
template
<
>
void
ConvKernel
<
GPU_CL
,
float
>::
Compute
(
const
ConvParam
<
GPU_CL
>
&
param
)
{
DLOG
<<
" Compute helper: "
<<
&
cl_helper_
;
DLOG
<<
" begin compute "
;
auto
kernel
=
this
->
cl_helper_
.
KernelAt
(
0
);
DLOG
<<
" get work size "
;
auto
default_work_size
=
this
->
cl_helper_
.
DefaultWorkSize
(
*
param
.
Output
());
DLOG
<<
" end work size "
;
int
c_block
=
default_work_size
[
0
];
int
w
=
default_work_size
[
1
];
int
nh
=
default_work_size
[
2
];
auto
input
=
param
.
Input
()
->
GetCLImage
();
DLOG
<<
" get Input "
;
auto
filter
=
param
.
Filter
()
->
GetCLImage
();
DLOG
<<
" get Filter "
;
auto
output
=
param
.
Output
();
DLOG
<<
" get Output "
;
int
stride
=
param
.
Strides
()[
0
];
int
offset
=
param
.
Offset
();
int
input_c
=
param
.
Input
()
->
CBlock
();
int
dilation
=
param
.
Dilations
()[
0
];
int
input_width
=
param
.
Input
()
->
WidthOfOneBlock
();
int
input_height
=
param
.
Input
()
->
HeightOfOneBlock
();
cl_int
status
;
DLOG
<<
" begin set kernel arg "
;
status
=
clSetKernelArg
(
kernel
,
0
,
sizeof
(
int
),
&
c_block
);
status
=
clSetKernelArg
(
kernel
,
1
,
sizeof
(
int
),
&
w
);
status
=
clSetKernelArg
(
kernel
,
2
,
sizeof
(
int
),
&
nh
);
status
=
clSetKernelArg
(
kernel
,
3
,
sizeof
(
cl_mem
),
&
input
);
status
=
clSetKernelArg
(
kernel
,
4
,
sizeof
(
cl_mem
),
&
filter
);
status
=
clSetKernelArg
(
kernel
,
5
,
sizeof
(
cl_mem
),
&
output
);
status
=
clSetKernelArg
(
kernel
,
6
,
sizeof
(
int
),
&
stride
);
status
=
clSetKernelArg
(
kernel
,
7
,
sizeof
(
int
),
&
offset
);
status
=
clSetKernelArg
(
kernel
,
8
,
sizeof
(
int
),
&
input_c
);
status
=
clSetKernelArg
(
kernel
,
9
,
sizeof
(
int
),
&
dilation
);
status
=
clSetKernelArg
(
kernel
,
10
,
sizeof
(
int
),
&
input_width
);
status
=
clSetKernelArg
(
kernel
,
11
,
sizeof
(
int
),
&
input_height
);
DLOG
<<
" end set kernel arg "
;
CL_CHECK_ERRORS
(
status
);
DLOG
<<
" begin enqueue "
;
status
=
clEnqueueNDRangeKernel
(
this
->
cl_helper_
.
CLCommandQueue
(),
kernel
,
3
,
NULL
,
default_work_size
.
data
(),
NULL
,
0
,
NULL
,
NULL
);
DLOG
<<
" end enqueue "
;
CL_CHECK_ERRORS
(
status
);
//
DLOG << " Compute helper: " << &cl_helper_;
//
DLOG << " begin compute ";
//
auto kernel = this->cl_helper_.KernelAt(0);
//
DLOG << " get work size ";
//
auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.Output());
//
DLOG << " end work size ";
//
int c_block = default_work_size[0];
//
int w = default_work_size[1];
//
int nh = default_work_size[2];
//
auto input = param.Input()->GetCLImage();
//
//
DLOG << " get Input ";
//
//
auto filter = param.Filter()->GetCLImage();
//
//
DLOG << " get Filter ";
//
//
auto output = param.Output();
//
//
DLOG << " get Output ";
//
//
int stride = param.Strides()[0];
//
int offset = param.Offset();
//
int input_c = param.Input()->CBlock();
//
int dilation = param.Dilations()[0];
//
int input_width = param.Input()->WidthOfOneBlock();
//
int input_height = param.Input()->HeightOfOneBlock();
//
//
cl_int status;
//
//
DLOG << " begin set kernel arg ";
//
//
status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
//
status = clSetKernelArg(kernel, 1, sizeof(int), &w);
//
status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
//
status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
//
status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
//
status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
//
status = clSetKernelArg(kernel, 6, sizeof(int), &stride);
//
status = clSetKernelArg(kernel, 7, sizeof(int), &offset);
//
status = clSetKernelArg(kernel, 8, sizeof(int), &input_c);
//
status = clSetKernelArg(kernel, 9, sizeof(int), &dilation);
//
status = clSetKernelArg(kernel, 10, sizeof(int), &input_width);
//
status = clSetKernelArg(kernel, 11, sizeof(int), &input_height);
//
//
DLOG << " end set kernel arg ";
//
//
CL_CHECK_ERRORS(status);
//
//
DLOG << " begin enqueue ";
//
//
status =
//
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
//
default_work_size.data(), NULL, 0, NULL, NULL);
//
//
DLOG << " end enqueue ";
//
//
CL_CHECK_ERRORS(status);
}
template
class
ConvKernel
<
GPU_CL
,
float
>;
...
...
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