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519ef7f7
编写于
6月 13, 2019
作者:
T
Tensor Tang
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'mul' into 'develop'
enable Mul See merge request paddlepaddle/paddlelitearmbackend!16
上级
02029900
85487210
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
347 addition
and
101 deletion
+347
-101
paddle/fluid/lite/core/cpu_info.cc
paddle/fluid/lite/core/cpu_info.cc
+5
-5
paddle/fluid/lite/kernels/arm/CMakeLists.txt
paddle/fluid/lite/kernels/arm/CMakeLists.txt
+2
-1
paddle/fluid/lite/kernels/arm/conv_compute_test.cc
paddle/fluid/lite/kernels/arm/conv_compute_test.cc
+15
-0
paddle/fluid/lite/kernels/arm/fc_compute.cc
paddle/fluid/lite/kernels/arm/fc_compute.cc
+6
-8
paddle/fluid/lite/kernels/arm/fc_compute.h
paddle/fluid/lite/kernels/arm/fc_compute.h
+2
-3
paddle/fluid/lite/kernels/arm/mul_compute.cc
paddle/fluid/lite/kernels/arm/mul_compute.cc
+38
-38
paddle/fluid/lite/kernels/arm/mul_compute.h
paddle/fluid/lite/kernels/arm/mul_compute.h
+39
-0
paddle/fluid/lite/kernels/arm/mul_compute_test.cc
paddle/fluid/lite/kernels/arm/mul_compute_test.cc
+152
-0
paddle/fluid/lite/kernels/arm/pool_compute_test.cc
paddle/fluid/lite/kernels/arm/pool_compute_test.cc
+1
-1
paddle/fluid/lite/kernels/arm/scale_compute_test.cc
paddle/fluid/lite/kernels/arm/scale_compute_test.cc
+11
-0
paddle/fluid/lite/operators/op_params.h
paddle/fluid/lite/operators/op_params.h
+1
-0
paddle/fluid/lite/tools/build.sh
paddle/fluid/lite/tools/build.sh
+75
-45
未找到文件。
paddle/fluid/lite/core/cpu_info.cc
浏览文件 @
519ef7f7
...
...
@@ -54,15 +54,15 @@ void DeviceInfo::InitInternal(DeviceInfo* dev) {
<<
", cluster ID: "
<<
dev
->
cluster_ids_
[
dev
->
core_ids_
[
i
]]
<<
", CPU ARCH: A"
<<
dev
->
archs_
[
i
];
}
LOG
(
INFO
)
<<
"L1 DataCache size is: "
;
VLOG
(
1
)
<<
"L1 DataCache size is: "
;
for
(
int
i
=
0
;
i
<
dev
->
compute_core_num_
;
++
i
)
{
LOG
(
INFO
)
<<
dev
->
L1_cache_
[
i
]
/
1024
<<
" KB"
;
VLOG
(
1
)
<<
dev
->
L1_cache_
[
i
]
/
1024
<<
" KB"
;
}
LOG
(
INFO
)
<<
"L2 Cache size is: "
;
VLOG
(
1
)
<<
"L2 Cache size is: "
;
for
(
int
i
=
0
;
i
<
dev
->
compute_core_num_
;
++
i
)
{
LOG
(
INFO
)
<<
dev
->
L2_cache_
[
i
]
/
1024
<<
" KB"
;
VLOG
(
1
)
<<
dev
->
L2_cache_
[
i
]
/
1024
<<
" KB"
;
}
LOG
(
INFO
)
<<
"Total memory: "
<<
dev
->
max_memory_
<<
"KB"
;
VLOG
(
1
)
<<
"Total memory: "
<<
dev
->
max_memory_
<<
"KB"
;
dev
->
max_freq_
=
max_freq
[
0
];
for
(
int
j
=
1
;
j
<
dev
->
compute_core_num_
;
++
j
)
{
...
...
paddle/fluid/lite/kernels/arm/CMakeLists.txt
浏览文件 @
519ef7f7
...
...
@@ -6,7 +6,7 @@ message(STATUS "compile with lite ARM kernels")
cc_library
(
fc_compute_arm SRCS fc_compute.cc DEPS
${
lite_kernel_deps
}
math_arm
)
cc_library
(
relu_compute_arm SRCS relu_compute.cc DEPS
${
lite_kernel_deps
}
)
cc_library
(
mul_compute_arm SRCS mul_compute.cc DEPS
${
lite_kernel_deps
}
eigen3
)
cc_library
(
mul_compute_arm SRCS mul_compute.cc DEPS
${
lite_kernel_deps
}
math_arm
)
cc_library
(
scale_compute_arm SRCS scale_compute.cc DEPS
${
lite_kernel_deps
}
math_arm
)
cc_library
(
softmax_compute_arm SRCS softmax_compute.cc DEPS
${
lite_kernel_deps
}
math_arm
)
cc_library
(
conv_compute_arm SRCS conv_compute.cc DEPS
${
lite_kernel_deps
}
math_arm
)
...
...
@@ -20,6 +20,7 @@ lite_cc_test(test_softmax_compute_arm SRCS softmax_compute_test.cc DEPS softmax_
lite_cc_test
(
test_conv_compute_arm SRCS conv_compute_test.cc DEPS conv_compute_arm
)
lite_cc_test
(
test_elementwise_add_compute_arm SRCS elementwise_add_compute_test.cc DEPS elementwise_add_compute_arm
)
lite_cc_test
(
test_pool_compute_arm SRCS pool_compute_test.cc DEPS pool_compute_arm
)
lite_cc_test
(
test_mul_compute_arm SRCS mul_compute_test.cc DEPS mul_compute_arm
)
lite_cc_test
(
test_split_compute_arm SRCS split_compute_test.cc DEPS split_compute_arm
)
set
(
arm_kernels
...
...
paddle/fluid/lite/kernels/arm/conv_compute_test.cc
浏览文件 @
519ef7f7
...
...
@@ -124,6 +124,20 @@ TEST(conv_arm, init) {
TEST
(
conv_arm
,
compute
)
{
DeviceInfo
::
Init
();
#if 1
for
(
auto
n
:
{
2
})
{
for
(
auto
ic
:
{
6
})
{
for
(
auto
oc
:
{
6
})
{
for
(
auto
ih
:
{
9
})
{
for
(
auto
iw
:
{
9
})
{
for
(
auto
flag_bias
:
{
false
,
true
})
{
for
(
auto
flag_relu
:
{
false
,
true
})
{
for
(
auto
depthwise
:
{
false
,
true
})
{
for
(
auto
dilation
:
{
1
})
{
for
(
auto
stride
:
{
1
,
2
})
{
for
(
auto
padding
:
{
0
,
1
,
2
})
{
for
(
auto
ks
:
{
1
,
3
,
5
})
{
#else
for
(
auto
n
:
{
1
,
2
})
{
for
(
auto
ic
:
{
6
,
32
/*, 128*/
})
{
for
(
auto
oc
:
{
6
,
32
/*, 128*/
})
{
...
...
@@ -136,6 +150,7 @@ TEST(conv_arm, compute) {
for
(
auto
stride
:
{
1
,
2
})
{
for
(
auto
padding
:
{
0
,
1
,
2
})
{
for
(
auto
ks
:
{
1
,
3
,
5
})
{
#endif
int
group
=
1
;
if
(
depthwise
)
{
// depthwise convolution ?
group
=
oc
=
ic
;
...
...
paddle/fluid/lite/kernels/arm/fc_compute.cc
浏览文件 @
519ef7f7
...
...
@@ -22,6 +22,10 @@ namespace lite {
namespace
kernels
{
namespace
arm
{
void
FcCompute
::
PrepareForRun
()
{
// TODO(TJ): transpose weight
}
void
FcCompute
::
Run
()
{
auto
&
param
=
this
->
Param
<
operators
::
FcParam
>
();
auto
x_dims
=
param
.
input
->
dims
();
...
...
@@ -48,22 +52,16 @@ void FcCompute::Run() {
&
ctx
);
lite
::
arm
::
math
::
sgemm_prepack
(
packed_in
,
w_data
,
b_data
,
o_data
,
x_h
,
n
,
x_w
,
false
,
false
,
false
,
&
ctx
);
if
(
param
.
bias
)
{
CHECK_EQ
(
param
.
bias
->
numel
(),
n
);
lite
::
arm
::
math
::
fill_bias_fc
(
o_data
,
b_data
,
x_h
,
n
);
}
}
else
{
// use sgemmv
// sgemv((const float*)weights, (const float*)din, (float*)dout,
// false, n, x_w, _param->_flag_bias, (float*)bias, false);
lite
::
arm
::
math
::
sgemv
(
w_data
,
i_data
,
o_data
,
false
,
n
,
x_w
,
b_data
!=
nullptr
,
b_data
,
false
);
}
}
TargetType
FcCompute
::
target
()
const
{
return
TARGET
(
kARM
);
}
PrecisionType
FcCompute
::
precision
()
const
{
return
PRECISION
(
kFloat
);
}
}
// namespace arm
}
// namespace kernels
}
// namespace lite
...
...
paddle/fluid/lite/kernels/arm/fc_compute.h
浏览文件 @
519ef7f7
...
...
@@ -25,10 +25,9 @@ class FcCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
using
param_t
=
operators
::
FcParam
;
void
Run
()
override
;
void
PrepareFor
Run
()
override
;
TargetType
target
()
const
override
;
PrecisionType
precision
()
const
override
;
void
Run
()
override
;
virtual
~
FcCompute
()
=
default
;
};
...
...
paddle/fluid/lite/kernels/arm/mul_compute.cc
浏览文件 @
519ef7f7
...
...
@@ -12,57 +12,57 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include
<Eigen/Core>
#include "paddle/fluid/lite/
core/kernel
.h"
#include
"paddle/fluid/lite/kernels/arm/mul_compute.h"
#include "paddle/fluid/lite/
arm/math/funcs
.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/lite/core/type
s
.h"
#include "paddle/fluid/lite/core/type
_system
.h"
namespace
paddle
{
namespace
lite
{
namespace
kernels
{
namespace
arm
{
template
<
typename
T
>
void
mul_compute_eigen
(
const
T
*
x
,
int
x_h
,
int
x_w
,
const
T
*
y
,
int
y_h
,
int
y_w
,
T
*
out
)
{
using
matrix_t
=
Eigen
::
Matrix
<
T
,
Eigen
::
Dynamic
,
Eigen
::
Dynamic
,
Eigen
::
RowMajor
>
;
void
MulCompute
::
PrepareForRun
()
{
// TODO(TJ): transpose x or y if necessary
}
Eigen
::
Map
<
const
matrix_t
>
X
(
x
,
x_h
,
x_w
);
Eigen
::
Map
<
const
matrix_t
>
Y
(
y
,
y_h
,
y_w
);
Eigen
::
Map
<
matrix_t
>
Out
(
out
,
x_h
,
y_w
);
void
MulCompute
::
Run
()
{
auto
&
param
=
Param
<
param_t
>
();
Out
=
X
*
Y
;
}
const
auto
*
x_data
=
param
.
x
->
data
<
float
>
();
const
auto
*
y_data
=
param
.
y
->
data
<
float
>
();
auto
*
o_data
=
param
.
output
->
mutable_data
<
float
>
();
class
MulCompute
:
public
KernelLite
<
TARGET
(
kARM
),
PRECISION
(
kFloat
)
>
{
public:
using
param_t
=
operators
::
MulParam
;
int
m
=
static_cast
<
int
>
(
param
.
x
->
dims
().
Slice
(
0
,
param
.
x_num_col_dims
).
production
());
int
x_w
=
static_cast
<
int
>
(
param
.
x
->
dims
()
.
Slice
(
param
.
x_num_col_dims
,
param
.
x
->
dims
().
size
())
.
production
());
int
y_h
=
static_cast
<
int
>
(
param
.
y
->
dims
().
Slice
(
0
,
param
.
y_num_col_dims
).
production
());
int
n
=
static_cast
<
int
>
(
param
.
y
->
dims
()
.
Slice
(
param
.
y_num_col_dims
,
param
.
y
->
dims
().
size
())
.
production
());
void
Run
()
override
{
auto
&
param
=
Param
<
operators
::
MulParam
>
();
core
::
dim2
x_shape
(
{
static_cast
<
int
>
(
param
.
x
->
dims
().
Slice
(
0
,
param
.
x_num_col_dims
).
production
()),
static_cast
<
int
>
(
param
.
x
->
dims
()
.
Slice
(
param
.
x_num_col_dims
,
param
.
x
->
dims
().
size
())
.
production
())});
core
::
dim2
y_shape
(
{
static_cast
<
int
>
(
param
.
y
->
dims
().
Slice
(
0
,
param
.
y_num_col_dims
).
production
()),
static_cast
<
int
>
(
param
.
y
->
dims
()
.
Slice
(
param
.
y_num_col_dims
,
param
.
y
->
dims
().
size
())
.
production
())});
CHECK_EQ
(
x_w
,
y_h
)
<<
"x_w must be equal with y_h"
;
auto
k
=
x_w
;
if
(
n
==
1
)
{
lite
::
arm
::
math
::
sgemv
(
x_data
,
y_data
,
o_data
,
false
,
m
,
k
,
false
,
nullptr
,
false
);
mul_compute_eigen
(
param
.
x
->
data
<
float
>
(),
x_shape
.
x
,
x_shape
.
y
,
//
param
.
y
->
data
<
float
>
(),
y_shape
.
x
,
y_shape
.
y
,
//
param
.
output
->
mutable_data
<
float
>
());
}
}
else
{
constexpr
bool
is_tranposed_y
=
false
;
auto
&
ctx
=
this
->
ctx_
->
template
As
<
ARMContext
>();
virtual
~
MulCompute
()
=
default
;
};
float
*
packed_x
=
static_cast
<
float
*>
(
ctx
.
workspace_data
<
float
>
())
+
ctx
.
l2_cache_size
()
/
sizeof
(
float
);
lite
::
arm
::
math
::
prepackA
(
packed_x
,
x_data
,
k
,
0
,
m
,
0
,
k
,
false
,
&
ctx
);
lite
::
arm
::
math
::
sgemm_prepack
(
packed_x
,
y_data
,
nullptr
,
o_data
,
m
,
n
,
k
,
false
,
false
,
is_tranposed_y
,
&
ctx
);
}
}
}
// namespace arm
}
// namespace kernels
...
...
paddle/fluid/lite/kernels/arm/mul_compute.h
0 → 100644
浏览文件 @
519ef7f7
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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 once
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/lite/core/types.h"
namespace
paddle
{
namespace
lite
{
namespace
kernels
{
namespace
arm
{
class
MulCompute
:
public
KernelLite
<
TARGET
(
kARM
),
PRECISION
(
kFloat
)
>
{
public:
using
param_t
=
operators
::
MulParam
;
void
PrepareForRun
()
override
;
void
Run
()
override
;
virtual
~
MulCompute
()
=
default
;
};
}
// namespace arm
}
// namespace kernels
}
// namespace lite
}
// namespace paddle
paddle/fluid/lite/kernels/arm/mul_compute_test.cc
0 → 100644
浏览文件 @
519ef7f7
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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 "paddle/fluid/lite/kernels/arm/mul_compute.h"
#include <gtest/gtest.h>
#include <algorithm>
#include <iostream>
#include <memory>
#include <random>
#include <utility>
#include <vector>
#include "paddle/fluid/lite/arm/math/funcs.h"
#include "paddle/fluid/lite/core/op_registry.h"
namespace
paddle
{
namespace
lite
{
namespace
kernels
{
namespace
arm
{
template
<
typename
T
>
void
FillData
(
T
*
a
,
const
int
n
,
const
T
lower
=
static_cast
<
T
>
(
-
2.
f
),
const
T
upper
=
static_cast
<
T
>
(
2.
f
))
{
static
unsigned
int
seed
=
100
;
std
::
mt19937
rng
(
seed
++
);
std
::
uniform_real_distribution
<
double
>
uniform_dist
(
0
,
1
);
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
a
[
i
]
=
static_cast
<
T
>
(
uniform_dist
(
rng
)
*
(
upper
-
lower
)
+
lower
);
}
}
TEST
(
mul_arm
,
retrive_op
)
{
auto
mul
=
KernelRegistry
::
Global
().
Create
<
TARGET
(
kARM
),
PRECISION
(
kFloat
)
>
(
"mul"
);
ASSERT_FALSE
(
mul
.
empty
());
ASSERT_TRUE
(
mul
.
front
());
}
TEST
(
mul_arm
,
init
)
{
MulCompute
mul
;
ASSERT_EQ
(
mul
.
precision
(),
PRECISION
(
kFloat
));
ASSERT_EQ
(
mul
.
target
(),
TARGET
(
kARM
));
}
TEST
(
mul_arm
,
compare_test
)
{
using
T
=
float
;
for
(
int
m
:
{
1
,
2
,
3
,
4
})
{
for
(
int
n
:
{
1
,
2
,
3
,
4
})
{
for
(
int
k
:
{
1
,
2
,
3
,
4
})
{
VLOG
(
3
)
<<
"m: "
<<
m
<<
", n: "
<<
n
<<
", k: "
<<
k
;
lite
::
Tensor
x
,
y
,
out
,
ref
;
x
.
Resize
({
m
,
k
});
y
.
Resize
({
k
,
n
});
out
.
Resize
({
m
,
n
});
ref
.
Resize
({
m
,
n
});
auto
*
x_data
=
x
.
mutable_data
<
T
>
();
auto
*
y_data
=
y
.
mutable_data
<
T
>
();
auto
*
out_data
=
out
.
mutable_data
<
T
>
();
auto
*
ref_data
=
ref
.
mutable_data
<
T
>
();
FillData
<
T
>
(
x_data
,
x
.
dims
().
production
());
FillData
<
T
>
(
y_data
,
y
.
dims
().
production
());
FillData
<
T
>
(
out_data
,
out
.
dims
().
production
(),
0
,
0
);
FillData
<
T
>
(
ref_data
,
out
.
dims
().
production
(),
0
,
0
);
MulCompute
mul
;
operators
::
MulParam
param
;
param
.
x
=
&
x
;
param
.
y
=
&
y
;
param
.
output
=
&
out
;
DeviceInfo
::
Init
();
std
::
unique_ptr
<
KernelContext
>
ctx
(
new
KernelContext
);
ctx
->
As
<
ARMContext
>
();
mul
.
SetParam
(
param
);
mul
.
SetContext
(
std
::
move
(
ctx
));
mul
.
PrepareForRun
();
mul
.
Run
();
lite
::
arm
::
math
::
mul_compute_eigen
(
x_data
,
m
,
k
,
y_data
,
k
,
n
,
ref_data
);
for
(
int
i
=
0
;
i
<
out
.
dims
().
production
();
i
++
)
{
EXPECT_NEAR
(
out_data
[
i
],
ref_data
[
i
],
1e-3
);
}
}
}
}
}
TEST
(
mul_arm
,
num_col_dims
)
{
using
T
=
float
;
lite
::
Tensor
x
,
y
,
out
,
ref
;
x
.
Resize
({
2
,
3
,
4
});
y
.
Resize
({
3
,
4
,
5
});
out
.
Resize
({
2
,
5
});
ref
.
Resize
({
2
,
5
});
auto
*
x_data
=
x
.
mutable_data
<
T
>
();
auto
*
y_data
=
y
.
mutable_data
<
T
>
();
auto
*
out_data
=
out
.
mutable_data
<
T
>
();
auto
*
ref_data
=
ref
.
mutable_data
<
T
>
();
FillData
<
T
>
(
x_data
,
x
.
dims
().
production
());
FillData
<
T
>
(
y_data
,
y
.
dims
().
production
());
FillData
<
T
>
(
out_data
,
out
.
dims
().
production
());
FillData
<
T
>
(
ref_data
,
out
.
dims
().
production
());
MulCompute
mul
;
operators
::
MulParam
param
;
param
.
x
=
&
x
;
param
.
y
=
&
y
;
param
.
output
=
&
out
;
param
.
x_num_col_dims
=
1
;
param
.
y_num_col_dims
=
2
;
DeviceInfo
::
Init
();
std
::
unique_ptr
<
KernelContext
>
ctx
(
new
KernelContext
);
ctx
->
As
<
ARMContext
>
();
mul
.
SetParam
(
param
);
mul
.
SetContext
(
std
::
move
(
ctx
));
mul
.
PrepareForRun
();
mul
.
Run
();
lite
::
arm
::
math
::
mul_compute_eigen
(
x_data
,
2
,
12
,
y_data
,
12
,
5
,
ref_data
);
for
(
int
i
=
0
;
i
<
out
.
dims
().
production
();
i
++
)
{
EXPECT_NEAR
(
out_data
[
i
],
ref_data
[
i
],
1e-3
);
}
}
}
// namespace arm
}
// namespace kernels
}
// namespace lite
}
// namespace paddle
USE_LITE_KERNEL
(
mul
,
kARM
,
kFloat
,
kNCHW
,
def
);
paddle/fluid/lite/kernels/arm/pool_compute_test.cc
浏览文件 @
519ef7f7
...
...
@@ -182,7 +182,7 @@ TEST(pool_arm, compute) {
for
(
auto
stride
:
{
2
})
{
for
(
auto
pad
:
{
0
})
{
for
(
auto
n
:
{
1
,
3
,
4
,
11
})
{
for
(
auto
c
:
{
1
,
3
,
11
,
4
,
1024
})
{
for
(
auto
c
:
{
1
,
3
,
11
/* ,1024 */
})
{
// speedup for ci
for
(
auto
h
:
{
3
,
1
,
11
,
4
,
1
})
{
for
(
auto
w
:
{
1
,
3
,
4
,
12
,
1
})
{
VLOG
(
3
)
<<
"n:"
<<
n
<<
" c:"
<<
c
<<
" h:"
<<
h
<<
" w:"
<<
w
...
...
paddle/fluid/lite/kernels/arm/scale_compute_test.cc
浏览文件 @
519ef7f7
...
...
@@ -54,6 +54,15 @@ TEST(scale_arm, compute) {
lite
::
Tensor
output
;
lite
::
Tensor
output_ref
;
#if 1 // for ci speedup
for
(
auto
n
:
{
1
,
3
})
{
for
(
auto
c
:
{
1
,
3
})
{
for
(
auto
h
:
{
3
,
4
})
{
for
(
auto
w
:
{
4
,
3
})
{
for
(
auto
bias_after_scale
:
{
true
,
false
})
{
for
(
auto
s
:
{
-
1.0
f
,
0.13
f
})
{
for
(
auto
b
:
{
-
15.
f
,
0.11234
f
})
{
#else
for
(
auto
n
:
{
1
,
3
,
4
,
11
})
{
for
(
auto
c
:
{
1
,
3
,
11
,
4
})
{
for
(
auto
h
:
{
3
,
1
,
11
,
4
})
{
...
...
@@ -61,6 +70,8 @@ TEST(scale_arm, compute) {
for
(
auto
bias_after_scale
:
{
true
,
false
})
{
for
(
auto
s
:
{
-
100.25
f
,
-
1.0
f
,
0.13
f
,
3840.975
f
})
{
for
(
auto
b
:
{
-
3075.495
f
,
-
15.
f
,
0.11234
f
,
128.15
f
})
{
#endif
x
.
Resize
(
DDim
(
std
::
vector
<
int64_t
>
({
n
,
c
,
h
,
w
})));
output
.
Resize
(
DDim
(
std
::
vector
<
int64_t
>
({
n
,
c
,
h
,
w
})));
output_ref
.
Resize
(
DDim
(
std
::
vector
<
int64_t
>
({
n
,
c
,
h
,
w
})));
...
...
paddle/fluid/lite/operators/op_params.h
浏览文件 @
519ef7f7
...
...
@@ -57,6 +57,7 @@ struct FcParam {
lite
::
Tensor
*
output
{};
lite
::
DDim
in_mat_dims
;
int
in_num_col_dims
{
1
};
bool
weight_transposed
{
false
};
};
struct
ReluParam
{
...
...
paddle/fluid/lite/tools/build.sh
浏览文件 @
519ef7f7
...
...
@@ -43,10 +43,14 @@ function cmake_arm {
-DARM_TARGET_OS
=
$1
-DARM_TARGET_ARCH_ABI
=
$2
}
function
build_single
{
make
$1
-j
$(
expr
$(
nproc
)
- 2
)
}
function
build
{
file
=
$1
for
_test
in
$(
cat
$file
)
;
do
make
$_test
-j
$(
expr
$(
nproc
)
- 2
)
build_single
$_test
done
}
...
...
@@ -63,39 +67,6 @@ function test_lite {
done
}
port_armv8
=
5554
port_armv7
=
5556
# Run test on android
function
test_lite_android
{
local
file
=
$1
local
adb_abi
=
$2
local
port
=
if
[[
${
adb_abi
}
==
"armeabi-v7a"
]]
;
then
port
=
${
port_armv7
}
fi
if
[[
${
adb_abi
}
==
"arm64-v8a"
]]
;
then
port
=
${
port_armv8
}
fi
if
[[
"
${
port
}
x"
==
"x"
]]
;
then
echo
"Port can not be empty"
exit
1
fi
echo
"file:
${
file
}
"
# push all to adb and test
adb_work_dir
=
"/data/local/tmp"
skip_list
=
"test_model_parser_lite"
for
_test
in
$(
cat
$file
)
;
do
[[
$skip_list
=
~
(
^|[[:space:]]
)
$_test
(
$|
[[
:space:]]
)
]]
&&
continue
||
echo
'skip $_test'
testpath
=
$(
find ./paddle/fluid
-name
${
_test
}
)
adb
-s
emulator-
${
port
}
push
${
testpath
}
${
adb_work_dir
}
adb
-s
emulator-
${
port
}
shell
chmod
+x
"
${
adb_work_dir
}
/
${
_test
}
"
adb
-s
emulator-
${
port
}
shell
"./
${
adb_work_dir
}
/
${
_test
}
"
done
}
# Build the code and run lite server tests. This is executed in the CI system.
function
build_test_server
{
mkdir
-p
./build
...
...
@@ -108,8 +79,34 @@ function build_test_server {
build
$LIBS_FILE
}
# Build the code and run lite server tests. This is executed in the CI system.
# test_arm_android <some_test_name> <adb_port_number>
function
test_arm_android
{
test_name
=
$1
port
=
$2
if
[[
"
${
test_name
}
x"
==
"x"
]]
;
then
echo
"test_name can not be empty"
exit
1
fi
if
[[
"
${
port
}
x"
==
"x"
]]
;
then
echo
"Port can not be empty"
exit
1
fi
echo
"test name:
${
test_name
}
"
adb_work_dir
=
"/data/local/tmp"
skip_list
=
"test_model_parser_lite"
# add more with space
[[
$skip_list
=
~
(
^|[[:space:]]
)
$test_name
(
$|
[[
:space:]]
)
]]
&&
continue
||
echo
'skip $test_name'
testpath
=
$(
find ./paddle/fluid
-name
${
test_name
}
)
adb
-s
emulator-
${
port
}
push
${
testpath
}
${
adb_work_dir
}
adb
-s
emulator-
${
port
}
shell
chmod
+x
"
${
adb_work_dir
}
/
${
test_name
}
"
adb
-s
emulator-
${
port
}
shell
"./
${
adb_work_dir
}
/
${
test_name
}
"
}
# Build the code and run lite arm tests. This is executed in the CI system.
function
build_test_arm
{
port_armv8
=
5554
port_armv7
=
5556
adb kill-server
adb devices |
grep
emulator |
cut
-f1
|
while
read
line
;
do
adb
-s
$line
emu
kill
;
done
# start android arm64-v8a armeabi-v7a emulators first
...
...
@@ -122,6 +119,7 @@ function build_test_arm {
for
os
in
"android"
"armlinux"
;
do
for
abi
in
"arm64-v8a"
"armeabi-v7a"
"armeabi-v7a-hf"
;
do
# TODO(TJ): enable compile on v7-hf on andorid and all v7 on armlinux
if
[[
${
abi
}
==
"armeabi-v7a-hf"
]]
;
then
echo
"armeabi-v7a-hf is not supported on both android and armlinux"
continue
...
...
@@ -138,17 +136,30 @@ function build_test_arm {
cmake_arm
${
os
}
${
abi
}
build
$TESTS_FILE
# armlinux need in another docker
# TODO(TJ): enable test with armlinux
if
[[
${
os
}
==
"android"
]]
;
then
adb_abi
=
${
abi
}
if
[[
${
adb_abi
}
==
"armeabi-v7a-hf"
]]
;
then
adb_abi
=
"armeabi-v7a"
fi
if
[[
${
adb_abi
}
==
"armeabi-v7a"
]]
;
then
# skip v7 tests
# skip all armv7 tests
# TODO(TJ): enable test with armv7
continue
fi
test_lite_android
$TESTS_FILE
${
adb_abi
}
# armlinux need in another docker
local
port
=
if
[[
${
adb_abi
}
==
"armeabi-v7a"
]]
;
then
port
=
${
port_armv7
}
fi
if
[[
${
adb_abi
}
==
"arm64-v8a"
]]
;
then
port
=
${
port_armv8
}
fi
echo
"test file:
${
TESTS_FILE
}
"
for
_test
in
$(
cat
$TESTS_FILE
)
;
do
test_arm_android
$_test
$port
done
fi
cd
-
done
...
...
@@ -164,12 +175,13 @@ function print_usage {
echo
"----------------------------------------"
echo
-e
"cmake_x86: run cmake with X86 mode"
echo
-e
"cmake_cuda: run cmake with CUDA mode"
echo
-e
"cmake_arm: run cmake with ARM mode"
echo
-e
"
--arm_os=<os> --arm_abi=<abi>
cmake_arm: run cmake with ARM mode"
echo
echo
-e
"build: compile the tests"
echo
-e
"--test_name=<test_name> build_single: compile single test"
echo
echo
-e
"test_server: run server tests"
echo
-e
"
test_mobile: run mobile tests
"
echo
-e
"
--test_name=<test_name> --adb_port_number=<adb_port_number> test_arm_android: run arm test
"
echo
"----------------------------------------"
echo
}
...
...
@@ -182,11 +194,31 @@ function main {
TESTS_FILE
=
"
${
i
#*=
}
"
shift
;;
--test_name
=
*
)
TEST_NAME
=
"
${
i
#*=
}
"
shift
;;
--arm_os
=
*
)
ARM_OS
=
"
${
i
#*=
}
"
shift
;;
--arm_abi
=
*
)
ARM_ABI
=
"
${
i
#*=
}
"
shift
;;
--arm_port
=
*
)
ARM_PORT
=
"
${
i
#*=
}
"
shift
;;
build
)
build
$TESTS_FILE
build
$LIBS_FILE
shift
;;
build_single
)
build_single
$TEST_NAME
shift
;;
cmake_x86
)
cmake_x86
shift
...
...
@@ -196,15 +228,15 @@ function main {
shift
;;
cmake_arm
)
cmake_arm
$
2
$3
cmake_arm
$
ARM_OS
$ARM_ABI
shift
;;
test_server
)
test_lite
$TESTS_FILE
shift
;;
test_
mobile
)
test_
lite
$TESTS_FILE
test_
arm_android
)
test_
arm_android
$TEST_NAME
$ARM_PORT
shift
;;
build_test_server
)
...
...
@@ -224,7 +256,5 @@ function main {
done
}
print_usage
main
$@
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