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11a838dc
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11a838dc
编写于
12月 07, 2017
作者:
L
liuqi
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add tuning code for opencl kernel.
上级
3ce4322d
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
288 addition
and
94 deletion
+288
-94
mace/kernels/opencl/addn.cc
mace/kernels/opencl/addn.cc
+42
-8
mace/kernels/opencl/batch_norm_opencl.cc
mace/kernels/opencl/batch_norm_opencl.cc
+6
-1
mace/kernels/opencl/concat.cc
mace/kernels/opencl/concat.cc
+50
-13
mace/kernels/opencl/conv_2d_opencl_1x1.cc
mace/kernels/opencl/conv_2d_opencl_1x1.cc
+6
-1
mace/kernels/opencl/conv_2d_opencl_3x3.cc
mace/kernels/opencl/conv_2d_opencl_3x3.cc
+6
-1
mace/kernels/opencl/conv_2d_opencl_general.cc
mace/kernels/opencl/conv_2d_opencl_general.cc
+6
-1
mace/kernels/opencl/pooling_opencl.cc
mace/kernels/opencl/pooling_opencl.cc
+55
-18
mace/kernels/opencl/relu_opencl.cc
mace/kernels/opencl/relu_opencl.cc
+6
-1
mace/kernels/opencl/resize_bilinear_opencl.cc
mace/kernels/opencl/resize_bilinear_opencl.cc
+46
-12
mace/python/tools/tf_converter_lib.py
mace/python/tools/tf_converter_lib.py
+50
-30
tools/validate.py
tools/validate.py
+6
-2
tools/validate_gcn.sh
tools/validate_gcn.sh
+9
-6
未找到文件。
mace/kernels/opencl/addn.cc
浏览文件 @
11a838dc
...
...
@@ -6,6 +6,7 @@
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/utils.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
kernels
{
...
...
@@ -33,8 +34,6 @@ static void AddN(const std::vector<const Tensor *> &input_tensors,
built_options
.
emplace
(
"-DINPUT_NUM="
+
ToString
(
input_tensors
.
size
()));
auto
addn_kernel
=
runtime
->
BuildKernel
(
"addn"
,
"addn"
,
built_options
);
const
uint32_t
lws
=
runtime
->
GetKernelMaxWorkGroupSize
(
addn_kernel
);
uint32_t
idx
=
0
;
for
(
auto
input
:
input_tensors
)
{
addn_kernel
.
setArg
(
idx
++
,
...
...
@@ -42,12 +41,47 @@ static void AddN(const std::vector<const Tensor *> &input_tensors,
}
addn_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
addn_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
width_pixels
,
batch_height_pixels
),
cl
::
NDRange
(
64
,
16
),
// TODO fix this
nullptr
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"error code: "
<<
error
;
const
uint32_t
gws
[
2
]
=
{
static_cast
<
uint32_t
>
(
width_pixels
),
static_cast
<
uint32_t
>
(
batch_height_pixels
)
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
addn_kernel
);
std
::
vector
<
uint32_t
>
lws
=
{
64
,
16
};
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
uint32_t
local_ws
[
2
];
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
width_pixels
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
batch_height_pixels
,
kwg_size
/
local_ws
[
0
]);
return
{{
local_ws
[
0
],
local_ws
[
1
]},
{
kwg_size
/
16
,
16
},
{
kwg_size
/
32
,
32
},
{
kwg_size
/
64
,
64
},
{
kwg_size
/
128
,
128
},
{
kwg_size
/
256
,
256
},
{
kwg_size
,
1
},
{
1
,
kwg_size
}
};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
addn_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
return
error
;
};
std
::
stringstream
ss
;
ss
<<
"addn_opencl_kernel_"
<<
output
->
dim
(
0
)
<<
"_"
<<
output
->
dim
(
1
)
<<
"_"
<<
output
->
dim
(
2
)
<<
"_"
<<
output
->
dim
(
3
);
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(),
lws
,
params_generator
,
func
);
}
template
<
typename
T
>
...
...
mace/kernels/opencl/batch_norm_opencl.cc
浏览文件 @
11a838dc
...
...
@@ -48,8 +48,13 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
bm_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
8
,
128
,
1
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/concat.cc
浏览文件 @
11a838dc
...
...
@@ -6,6 +6,7 @@
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/utils.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
kernels
{
...
...
@@ -41,21 +42,57 @@ static void Concat2(const Tensor *input0,
concat_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
input0
->
dim
(
3
)));
concat_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
const
uint32_t
gws
[
3
]
=
{
static_cast
<
uint32_t
>
(
channel_blk
),
static_cast
<
uint32_t
>
(
width
),
static_cast
<
uint32_t
>
(
batch
*
height
),
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
concat_kernel
);
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blk
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
64
,
8
,
8
},
{
kwg_size
/
64
,
16
,
4
},
{
kwg_size
/
128
,
8
,
16
},
{
kwg_size
/
128
,
16
,
8
},
{
kwg_size
/
128
,
32
,
4
},
{
1
,
kwg_size
/
32
,
32
},
{
1
,
kwg_size
/
64
,
64
},
{
1
,
kwg_size
/
128
,
128
},
{
3
,
15
,
9
},
{
7
,
15
,
9
},
{
9
,
7
,
15
},
{
15
,
7
,
9
},
{
1
,
kwg_size
,
1
}};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
concat_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
],
params
[
2
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
uint32_t
lws
[
3
]
=
{
8
,
16
,
8
}
;
// lws[0] = std::min<uint32_t>(channel_blk, kwg_size)
;
// lws[1] = std::min<uint32_t>(width, kwg_size / lws[0])
;
// lws[2] = std::min<uint32_t>(height * batch, kwg_size / (lws[0] * lws[1]))
;
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
concat_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
static_cast
<
uint32_t
>
(
channel_blk
),
static_cast
<
uint32_t
>
(
width
),
static_cast
<
uint32_t
>
(
height
*
batch
)
),
cl
::
NDRange
(
lws
[
0
],
lws
[
1
],
lws
[
2
])
,
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
);
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
return
error
;
}
;
std
::
stringstream
ss
;
ss
<<
"concat_opencl_kernel_"
<<
output
->
dim
(
0
)
<<
"_"
<<
output
->
dim
(
1
)
<<
"_"
<<
output
->
dim
(
2
)
<<
"_"
<<
output
->
dim
(
3
);
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(
),
lws
,
params_generator
,
func
);
}
template
<
typename
T
>
...
...
mace/kernels/opencl/conv_2d_opencl_1x1.cc
浏览文件 @
11a838dc
...
...
@@ -68,8 +68,13 @@ void Conv1x1(const Tensor *input,
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
15
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width_blocks
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/conv_2d_opencl_3x3.cc
浏览文件 @
11a838dc
...
...
@@ -60,8 +60,13 @@ static void Conv2d3x3S12(const Tensor *input, const Tensor *filter,
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
4
,
15
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width_blocks
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/conv_2d_opencl_general.cc
浏览文件 @
11a838dc
...
...
@@ -62,8 +62,13 @@ void Conv2dOpencl(const Tensor *input, const Tensor *filter,
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width_blocks
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/pooling_opencl.cc
浏览文件 @
11a838dc
...
...
@@ -6,6 +6,7 @@
#include "mace/core/runtime/opencl/cl2_header.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
kernels
{
...
...
@@ -23,11 +24,6 @@ static void Pooling(const Tensor *input,
index_t
channels
=
output
->
dim
(
3
);
index_t
channel_blocks
=
(
channels
+
3
)
/
4
;
const
uint32_t
gws
[
3
]
=
{
static_cast
<
uint32_t
>
(
channel_blocks
),
static_cast
<
uint32_t
>
(
out_width
),
static_cast
<
uint32_t
>
(
batch
*
out_height
),
};
auto
runtime
=
OpenCLRuntime
::
Get
();
std
::
set
<
std
::
string
>
built_options
;
...
...
@@ -44,13 +40,6 @@ static void Pooling(const Tensor *input,
}
auto
pooling_kernel
=
runtime
->
BuildKernel
(
"pooling"
,
"pooling"
,
built_options
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
pooling_kernel
);
uint32_t
lws
[
3
];
lws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
lws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
lws
[
0
]);
lws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
lws
[
0
]
*
lws
[
1
]));
uint32_t
idx
=
0
;
pooling_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
const
cl
::
Image2D
*>
(
input
->
buffer
())));
pooling_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
input
->
dim
(
1
)));
...
...
@@ -62,12 +51,60 @@ static void Pooling(const Tensor *input,
pooling_kernel
.
setArg
(
idx
++
,
pooling_size
);
pooling_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
pooling_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
cl
::
NDRange
(
lws
[
0
],
lws
[
1
],
lws
[
2
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
error
;
const
uint32_t
gws
[
3
]
=
{
static_cast
<
uint32_t
>
(
channel_blocks
),
static_cast
<
uint32_t
>
(
out_width
),
static_cast
<
uint32_t
>
(
batch
*
out_height
),
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
pooling_kernel
);
std
::
vector
<
uint32_t
>
lws
(
3
,
0
);
lws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
lws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
lws
[
0
]);
lws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
lws
[
0
]
*
lws
[
1
]));
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
64
,
8
,
8
},
{
kwg_size
/
64
,
16
,
4
},
{
kwg_size
/
128
,
8
,
16
},
{
kwg_size
/
128
,
16
,
8
},
{
kwg_size
/
128
,
32
,
4
},
{
1
,
kwg_size
/
32
,
32
},
{
1
,
kwg_size
/
64
,
64
},
{
1
,
kwg_size
/
128
,
128
},
{
3
,
15
,
9
},
{
7
,
15
,
9
},
{
9
,
7
,
15
},
{
15
,
7
,
9
},
{
1
,
kwg_size
,
1
}};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
pooling_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
],
params
[
2
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
return
error
;
};
std
::
stringstream
ss
;
ss
<<
"pooling_opencl_kernel_"
<<
output
->
dim
(
0
)
<<
"_"
<<
output
->
dim
(
1
)
<<
"_"
<<
output
->
dim
(
2
)
<<
"_"
<<
output
->
dim
(
3
);
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(),
lws
,
params_generator
,
func
);
}
template
<
typename
T
>
...
...
mace/kernels/opencl/relu_opencl.cc
浏览文件 @
11a838dc
...
...
@@ -50,8 +50,13 @@ void ReluFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
relu_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/resize_bilinear_opencl.cc
浏览文件 @
11a838dc
...
...
@@ -7,6 +7,7 @@
#include "mace/kernels/resize_bilinear.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/utils.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
kernels
{
...
...
@@ -44,8 +45,6 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
built_options
.
emplace
(
"-DCMD_DATA_TYPE="
+
DtToUpstreamCLCMDDt
(
dt
));
auto
rb_kernel
=
runtime
->
BuildKernel
(
"resize_bilinear"
,
"resize_bilinear_nocache"
,
built_options
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
rb_kernel
);
uint32_t
idx
=
0
;
rb_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
const
cl
::
Image2D
*>
(
input
->
buffer
())));
rb_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
...
...
@@ -55,17 +54,52 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
rb_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
in_width
));
rb_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
out_height
));
auto
command_queue
=
runtime
->
command_queue
();
const
uint32_t
gws
[
3
]
=
{
static_cast
<
uint32_t
>
(
channel_blocks
),
static_cast
<
uint32_t
>
(
out_width
),
static_cast
<
uint32_t
>
(
out_height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
rb_kernel
);
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
64
,
8
,
8
},
{
kwg_size
/
64
,
16
,
4
},
{
kwg_size
/
128
,
8
,
16
},
{
kwg_size
/
128
,
16
,
8
},
{
kwg_size
/
128
,
32
,
4
},
{
1
,
kwg_size
/
32
,
32
},
{
1
,
kwg_size
/
64
,
64
},
{
1
,
kwg_size
/
128
,
128
},
{
1
,
kwg_size
,
1
}};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
rb_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
],
params
[
2
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
return
error
;
};
std
::
stringstream
ss
;
ss
<<
"resize_bilinear_opencl_kernel_"
<<
output
->
dim
(
0
)
<<
"_"
<<
output
->
dim
(
1
)
<<
"_"
<<
output
->
dim
(
2
)
<<
"_"
<<
output
->
dim
(
3
);
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(),
lws
,
params_generator
,
func
);
cl_int
error
=
command_queue
.
enqueueNDRangeKernel
(
rb_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
static_cast
<
int32_t
>
(
channel_blocks
),
static_cast
<
int32_t
>
(
out_width
),
static_cast
<
int32_t
>
(
out_height
*
batch
)),
// TODO tuning
cl
::
NDRange
(
1
,
static_cast
<
int32_t
>
(
out_width
>
kwg_size
?
kwg_size
:
out_width
),
1
),
nullptr
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
,
error
);
}
template
struct
ResizeBilinearFunctor
<
DeviceType
::
OPENCL
,
float
>;
...
...
mace/python/tools/tf_converter_lib.py
浏览文件 @
11a838dc
from
mace.proto
import
mace_pb2
import
tensorflow
as
tf
import
numpy
as
np
from
mace.python.tools.convert_util
import
tf_dtype_2_mace_dtype
# TODO: support NCHW formt, now only support NHWC.
padding_mode
=
{
...
...
@@ -111,18 +110,14 @@ def add_output_transform(name, net_def):
epsilon_arg
.
name
=
'buffer_type'
epsilon_arg
.
i
=
buffer_type_map
[
'IN_OUT'
]
def
convert_op_outputs
(
mace_op_def
,
tf_op
):
mace_op_def
.
output
.
extend
([
output
.
name
for
output
in
tf_op
.
outputs
])
mace_op_def
.
output_type
.
extend
([
tf_dtype_2_mace_dtype
(
output
.
dtype
)
for
output
in
tf_op
.
outputs
])
def
add_output_shape
(
outputs
,
op
):
output_shapes
=
[]
for
output
in
tf_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
()
)
output_shapes
.
append
(
output_shape
)
mace_op_def
.
output_shape
.
extend
(
output_shapes
)
for
output
in
outputs
:
if
output
.
shape
is
not
None
and
not
output
.
shape
:
output_shape
=
mace_pb2
.
OutputShape
(
)
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
()
)
output_shapes
.
append
(
output_shape
)
op
.
output_shape
.
extend
(
output_shapes
)
def
convert_ops
(
unresolved_ops
,
dt
,
net_def
,
device
):
ops_count
=
len
(
unresolved_ops
)
...
...
@@ -185,7 +180,8 @@ def convert_ops(unresolved_ops, dt, net_def, device):
final_op
=
relu_op
resolved_count
=
4
convert_op_outputs
(
op_def
,
final_op
)
op_def
.
output
.
extend
([
output
.
name
for
output
in
final_op
.
outputs
])
add_output_shape
(
final_op
.
outputs
,
op_def
)
elif
first_op
.
type
==
'FusedBatchNorm'
:
op_def
.
name
=
first_op
.
name
...
...
@@ -199,9 +195,7 @@ def convert_ops(unresolved_ops, dt, net_def, device):
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
op_def
.
output
.
extend
([
first_op
.
outputs
[
0
].
name
])
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
first_op
.
outputs
[
0
].
shape
.
as_list
())
op_def
.
output_shape
.
extend
([
output_shape
])
add_output_shape
(
first_op
.
outputs
,
op_def
)
epsilon_arg
=
op_def
.
arg
.
add
()
epsilon_arg
.
name
=
'epsilon'
...
...
@@ -217,31 +211,42 @@ def convert_ops(unresolved_ops, dt, net_def, device):
mul_2_op
=
unresolved_ops
[
4
]
sub_op
=
unresolved_ops
[
5
]
add_1_op
=
unresolved_ops
[
6
]
#
print (mul_op.type, mul_2_op.type, mul_1_op.type, sub_op.type)
print
(
mul_op
.
type
,
mul_2_op
.
type
,
mul_1_op
.
type
,
sub_op
.
type
)
if
mul_op
.
type
!=
'Mul'
or
mul_2_op
.
type
!=
'Mul'
or
\
mul_1_op
.
type
!=
'Mul'
or
sub_op
.
type
!=
'Sub'
or
add_1_op
.
type
!=
'Add'
:
raise
Exception
(
'Invalid BatchNorm Op'
)
get_input_tensor
(
mul_1_op
,
0
)
input_name
=
get_input_tensor
(
mul_1_op
,
0
).
name
gamma
=
get_input_tensor
(
mul_op
,
1
).
name
beta
=
get_input_tensor
(
sub_op
,
0
).
name
mean
=
get_input_tensor
(
mul_2_op
,
0
).
name
variance
=
get_input_tensor
(
add_op
,
0
).
name
epsilon
=
get_input_tensor
(
add_op
,
1
).
name
op_def
.
name
=
first_op
.
name
[:
-
4
]
# remove /add
op_def
.
type
=
'BatchNorm'
op_def
.
input
.
extend
([
input_name
,
gamma
,
beta
,
mean
,
variance
,
epsilon
])
convert_op_outputs
(
op_def
,
add_1_op
)
if
device
==
'gpu'
:
op_def
.
input
.
extend
([
input_name
])
for
tensor_name
in
[
gamma
,
beta
,
mean
,
variance
]:
output_name
=
add_buffer_to_image
(
tensor_name
,
"ARGUMENT"
,
dt
,
net_def
)
op_def
.
input
.
extend
([
output_name
])
else
:
op_def
.
input
.
extend
([
input_name
,
gamma
,
beta
,
mean
,
variance
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
add_1_op
.
outputs
])
add_output_shape
(
add_1_op
.
outputs
,
op_def
)
epsilon_arg
=
op_def
.
arg
.
add
()
epsilon_arg
.
name
=
'epsilon'
epsilon_arg
.
f
=
get_input_tensor
(
add_op
,
1
).
eval
().
astype
(
np
.
float
)
data_format_arg
=
op_def
.
arg
.
add
()
data_format_arg
.
name
=
'data_format'
data_format_arg
.
s
=
'NHWC'
resolved_count
=
7
elif
first_op
.
type
==
'Relu6'
:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
'Relu'
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
convert_op_outputs
(
op_def
,
first_op
)
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
]
)
add_output_shape
(
first_op
.
outputs
,
op_def
)
max_limit_arg
=
op_def
.
arg
.
add
()
max_limit_arg
.
name
=
'max_limit'
max_limit_arg
.
f
=
6
...
...
@@ -249,8 +254,8 @@ def convert_ops(unresolved_ops, dt, net_def, device):
op_def
.
name
=
first_op
.
name
op_def
.
type
=
'Pooling'
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
convert_op_outputs
(
op_def
,
first_op
)
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
]
)
add_output_shape
(
first_op
.
outputs
,
op_def
)
pooling_type_arg
=
op_def
.
arg
.
add
()
pooling_type_arg
.
name
=
'pooling_type'
pooling_type_arg
.
i
=
pooling_type_mode
[
first_op
.
type
]
...
...
@@ -270,31 +275,46 @@ def convert_ops(unresolved_ops, dt, net_def, device):
op_def
.
name
=
first_op
.
name
op_def
.
type
=
"AddN"
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
convert_op_outputs
(
op_def
,
first_op
)
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
add_output_shape
(
first_op
.
outputs
,
op_def
)
elif
first_op
.
type
==
'ConcatV2'
:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
"Concat"
op_def
.
input
.
extend
([
first_op
.
inputs
[
i
].
name
for
i
in
xrange
(
2
)])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
axis_arg
=
op_def
.
arg
.
add
()
axis_arg
.
name
=
'axis'
axis_arg
.
i
=
get_input_tensor
(
first_op
,
2
).
eval
().
astype
(
np
.
int32
)
convert_op_outputs
(
op_def
,
first_op
)
add_output_shape
(
first_op
.
outputs
,
op_def
)
elif
first_op
.
type
==
'ResizeBilinear'
:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
"ResizeBilinear"
op_def
.
input
.
extend
([
first_op
.
inputs
[
0
].
name
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
size_arg
=
op_def
.
arg
.
add
()
size_arg
.
name
=
'size'
size_arg
.
ints
.
extend
(
get_input_tensor
(
first_op
,
1
).
eval
().
astype
(
np
.
int32
).
flat
)
size_arg
=
op_def
.
arg
.
add
()
size_arg
.
name
=
'align_corners'
size_arg
.
i
=
first_op
.
get_attr
(
'align_corners'
)
convert_op_outputs
(
op_def
,
first_op
)
elif
first_op
.
type
in
[
'Relu'
,
'SpaceToBatchND'
,
'BatchToSpaceND'
,
'BiasAdd'
]:
add_output_shape
(
first_op
.
outputs
,
op_def
)
elif
first_op
.
type
==
'BiasAdd'
:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
first_op
.
type
op_def
.
input
.
extend
([
first_op
.
inputs
[
0
].
name
])
if
device
==
'gpu'
:
output_name
=
add_buffer_to_image
(
first_op
.
inputs
[
1
].
name
,
"ARGUMENT"
,
dt
,
net_def
)
op_def
.
input
.
extend
([
output_name
])
else
:
op_def
.
input
.
extend
([
first_op
.
inputs
[
1
].
name
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
add_output_shape
(
first_op
.
outputs
,
op_def
)
elif
first_op
.
type
in
[
'Relu'
,
'SpaceToBatchND'
,
'BatchToSpaceND'
]:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
first_op
.
type
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
convert_op_outputs
(
op_def
,
first_op
)
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
add_output_shape
(
first_op
.
outputs
,
op_def
)
else
:
raise
Exception
(
'Unknown Op: %s, type: %s'
%
(
first_op
.
name
,
first_op
.
type
))
pass
...
...
tools/validate.py
浏览文件 @
11a838dc
...
...
@@ -4,6 +4,7 @@ import os
import
os.path
import
tensorflow
as
tf
import
numpy
as
np
from
scipy
import
spatial
from
tensorflow
import
gfile
...
...
@@ -34,9 +35,12 @@ def load_data(file):
def
valid_output
(
out_shape
,
mace_out_file
,
tf_out_value
):
mace_out_value
=
load_data
(
mace_out_file
)
if
mace_out_value
.
size
!=
0
:
similarity
=
(
1
-
spatial
.
distance
.
cosine
(
tf_out_value
.
flat
,
mace_out_value
))
print
'MACE VS TF similarity: '
,
similarity
if
similarity
>
0.999
:
print
'=======================Passed! Haha======================'
mace_out_value
=
mace_out_value
.
reshape
(
out_shape
)
np
.
testing
.
assert_allclose
(
mace_out_value
,
tf_out_value
,
rtol
=
0.05
)
print
'=======================Passed! Haha======================'
else
:
print
'=======================Skip empty node==================='
...
...
@@ -62,7 +66,7 @@ def run_model(input_shape):
input_value
=
input_value
.
reshape
(
input_shape
)
output_value
=
session
.
run
(
output_node
,
feed_dict
=
{
input_node
:
[
input_value
]})
# output_value.astype(np.float32).tofile( os.path.dirname(FLAGS.input_file) + '/tf_weigh
t')
output_value
.
astype
(
np
.
float32
).
tofile
(
os
.
path
.
dirname
(
FLAGS
.
input_file
)
+
'/tf_ou
t'
)
return
output_value
def
main
(
unused_args
):
...
...
tools/validate_gcn.sh
浏览文件 @
11a838dc
...
...
@@ -2,10 +2,10 @@
# Must run at root dir of mace project.
set
+x
Usage
()
{
echo
'Usage: bash tools/validate_gcn.sh tf_model_
fil
e'
echo
'Usage: bash tools/validate_gcn.sh tf_model_
path image_siz
e'
}
if
[
$#
!=
1
]
;
then
if
[
$#
!=
2
]
;
then
Usage
exit
-1
fi
...
...
@@ -19,12 +19,13 @@ OUTPUT_FILE_NAME='gcn.out'
OUTPUT_LIST_FILE
=
'gcn.list'
PHONE_DATA_DIR
=
"/data/local/tmp/
${
MACE_MODEL_NAME
}
"
KERNEL_DIR
=
"
${
PHONE_DATA_DIR
}
/cl/"
IMAGE_SIZE
=
$2
# Step 1: Generate input data
echo
"Step 1: Generate input data"
python tools/validate.py
--generate_data
true
--random_seed
1
\
--input_file
=
${
MODEL_DIR
}
/
${
INPUT_FILE_NAME
}
\
--input_shape
=
512,512,3
--input_shape
=
"
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,3"
# Step 2: convert tf model to mace model
echo
"Step 2: convert tf model to mace model and optimize memory"
...
...
@@ -56,14 +57,15 @@ adb push bazel-bin/mace/examples/mace_run ${PHONE_DATA_DIR}
num_threads
=
${
1
:-
4
}
adb </dev/null shell
MACE_RUN_PARAMETER_PATH
=
${
PHONE_DATA_DIR
}
/mace_run.config
\
adb </dev/null shell
MACE_CPP_MIN_VLOG_LEVEL
=
0
\
MACE_RUN_PARAMETER_PATH
=
${
PHONE_DATA_DIR
}
/mace_run.config
\
MACE_KERNEL_PATH
=
$KERNEL_DIR
\
OMP_NUM_THREADS
=
$num_threads
\
${
PHONE_DATA_DIR
}
/mace_run
\
--model
=
${
PHONE_DATA_DIR
}
/
${
MACE_OPT_MODEL_NAME
}
\
--input
=
mace_input_node
\
--output
=
mace_output_node
\
--input_shape
=
1,512,512,3
\
--input_shape
=
"1,
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,3"
\
--input_file
=
${
PHONE_DATA_DIR
}
/
${
INPUT_FILE_NAME
}
\
--output_file
=
${
PHONE_DATA_DIR
}
/
${
OUTPUT_FILE_NAME
}
\
--device
=
OPENCL
\
...
...
@@ -81,4 +83,5 @@ python tools/validate.py --model_file ${TF_MODEL_FILE_PATH} \
--mace_out_file
${
MODEL_DIR
}
/
${
OUTPUT_FILE_NAME
}
\
--input_node
input
\
--output_node
GCN/br_result_2/fcn_br
\
--output_shape
1,512,512,2
--input_shape
"
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,3"
\
--output_shape
"1,
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,2"
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