Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
magicwindyyd
mindspore
提交
cd8b664f
M
mindspore
项目概览
magicwindyyd
/
mindspore
与 Fork 源项目一致
Fork自
MindSpore / mindspore
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
M
mindspore
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
cd8b664f
编写于
4年前
作者:
Z
zhanyuan
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Optimize the post process of arm64 matmul int8
上级
25bbf5c6
变更
11
展开全部
隐藏空白更改
内联
并排
Showing
11 changed file
with
664 addition
and
435 deletion
+664
-435
mindspore/lite/nnacl/assembly/arm64/MatmulInt8.S
mindspore/lite/nnacl/assembly/arm64/MatmulInt8.S
+128
-13
mindspore/lite/nnacl/common_func.c
mindspore/lite/nnacl/common_func.c
+0
-16
mindspore/lite/nnacl/int8/matmul_int8.c
mindspore/lite/nnacl/int8/matmul_int8.c
+41
-25
mindspore/lite/nnacl/int8/matmul_int8.h
mindspore/lite/nnacl/int8/matmul_int8.h
+6
-7
mindspore/lite/src/runtime/kernel/arm/int8/fullconnection_int8.cc
...e/lite/src/runtime/kernel/arm/int8/fullconnection_int8.cc
+45
-38
mindspore/lite/src/runtime/kernel/arm/int8/fullconnection_int8.h
...re/lite/src/runtime/kernel/arm/int8/fullconnection_int8.h
+22
-14
mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc
mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc
+35
-76
mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.h
mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.h
+17
-42
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/deconv_int8_tests.cc
.../test/ut/src/runtime/kernel/arm/int8/deconv_int8_tests.cc
+0
-48
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/fullconnection_int8_tests.cc
.../src/runtime/kernel/arm/int8/fullconnection_int8_tests.cc
+110
-81
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/matmul_int8_tests.cc
.../test/ut/src/runtime/kernel/arm/int8/matmul_int8_tests.cc
+260
-75
未找到文件。
mindspore/lite/nnacl/assembly/arm64/MatmulInt8.S
浏览文件 @
cd8b664f
...
...
@@ -24,7 +24,7 @@
//
void
MatmulInt8Neon64
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int8_t
*
dst
,
int
row4
,
int
col4
,
int
deep16
,
//
const
int
*
a_sums
,
const
int
*
bias
,
int
act_min
,
int
act_max
,
int
out_zp
,
//
int
multiplier
,
int
left_shift
,
int
right_shift
)
;
//
int
multiplier
,
int
left_shift
,
int
right_shift
,
int
row
,
int
col
,
int
stride
)
;
//
x0
:
a
(
left
matrix
ptr
)
//
x1
:
b
(
right
matrix
ptr
)
...
...
@@ -40,13 +40,18 @@
//
w11
:
multiplier
//
w12
:
left_shift
//
w13
:
right_shift
//
w14
:
row
//
w15
:
col
//
w24
:
stride
MatmulInt8Neon64
:
sub
sp
,
sp
,
#
1
60
sub
sp
,
sp
,
#
1
92
st1
{
v8
.4
s
,
v9
.4
s
,
v10
.4
s
,
v11
.4
s
},
[
sp
],
#
64
st1
{
v12
.4
s
,
v13
.4
s
,
v14
.4
s
,
v15
.4
s
},
[
sp
],
#
64
stp
x19
,
x20
,
[
sp
],
#
16
stp
x21
,
x22
,
[
sp
],
#
16
stp
x23
,
x24
,
[
sp
],
#
16
stp
x25
,
x26
,
[
sp
],
#
16
ldr
w8
,
[
sp
]
ldr
w9
,
[
sp
,
#
8
]
...
...
@@ -54,25 +59,28 @@ MatmulInt8Neon64:
ldr
w11
,
[
sp
,
#
24
]
ldr
w12
,
[
sp
,
#
32
]
ldr
w13
,
[
sp
,
#
40
]
ldr
w14
,
[
sp
,
#
48
]
ldr
w15
,
[
sp
,
#
56
]
ldr
w24
,
[
sp
,
#
64
]
mov
w15
,
#
0
//
b
col
index
mov
w16
,
#
0
//
a
row
index
mov
w17
,
#
4
//
sizeof
(
int8
)*
4
mul
w21
,
w5
,
w17
//
the
stride
of
a
/
b
:
sizeof
(
int8
)*
4
*
deep16
mov
w17
,
#
1
mov
x25
,
x2
L1
:
cmp
w
15
,
w4
cmp
w
4
,
#
0
//
if
at
the
end
of
col4
beq
End1
mov
w16
,
#
0
//
reset
a
row
index
mov
w16
,
w3
//
reset
a
row4
counter
mov
w23
,
w14
//
reset
a
row
counter
mov
x17
,
x0
//
reload
a
ptr
mov
x22
,
x6
//
reload
a_sums
ptr
L2
:
cmp
w16
,
w3
cmp
w16
,
#
0
beq
End2
mov
x18
,
x1
//
reload
b
ptr
mov
x19
,
x7
//
reload
bias
ptr
mov
x19
,
x7
//
reload
bias
ptr
mov
w20
,
w5
//
reload
depth
dup
v16
.4
s
,
wzr
dup
v17
.4
s
,
wzr
...
...
@@ -256,21 +264,128 @@ End3:
sqxtn
v15
.8
b
,
v13
.8
h
sqxtn2
v15
.16
b
,
v14
.8
h
st1
{
v15
.16
b
},
[
x2
],
#
16
add
w16
,
w16
,
#
4
//
a
row
index
+
4
cmp
w23
,
#
4
blt
Write
//
if
rows
<
4
cmp
w15
,
#
4
blt
Write
//
if
cols
<
4
st1
{
v15
.
s
}[
0
],
[
x2
],
x24
st1
{
v15
.
s
}[
1
],
[
x2
],
x24
st1
{
v15
.
s
}[
2
],
[
x2
],
x24
st1
{
v15
.
s
}[
3
],
[
x2
],
x24
b
Endwrite
Write
:
cmp
w15
,
#
4
beq
WriteCol4
cmp
w15
,
#
3
beq
WriteCol3
cmp
w15
,
#
2
beq
WriteCol2
cmp
w15
,
#
1
beq
WriteCol1
WriteCol4
:
st1
{
v15
.
s
}[
0
],
[
x2
],
x24
cmp
w23
,
#
1
beq
Endwrite
st1
{
v15
.
s
}[
1
],
[
x2
],
x24
cmp
w23
,
#
2
beq
Endwrite
st1
{
v15
.
s
}[
2
],
[
x2
],
x24
cmp
w23
,
#
3
beq
Endwrite
st1
{
v15
.
s
}[
3
],
[
x2
],
x24
b
Endwrite
WriteCol3
:
mov
x26
,
x2
st1
{
v15
.
b
}[
0
],
[
x26
],
#
1
st1
{
v15
.
b
}[
1
],
[
x26
],
#
1
st1
{
v15
.
b
}[
2
],
[
x26
],
#
1
add
x2
,
x2
,
x24
cmp
w23
,
#
1
beq
Endwrite
mov
x26
,
x2
st1
{
v15
.
b
}[
4
],
[
x26
],
#
1
st1
{
v15
.
b
}[
5
],
[
x26
],
#
1
st1
{
v15
.
b
}[
6
],
[
x26
],
#
1
add
x2
,
x2
,
x24
cmp
w23
,
#
2
beq
Endwrite
mov
x26
,
x2
st1
{
v15
.
b
}[
8
],
[
x26
],
#
1
st1
{
v15
.
b
}[
9
],
[
x26
],
#
1
st1
{
v15
.
b
}[
10
],
[
x26
],
#
1
add
x2
,
x2
,
x24
cmp
w23
,
#
3
beq
Endwrite
mov
x26
,
x2
st1
{
v15
.
b
}[
12
],
[
x26
],
#
1
st1
{
v15
.
b
}[
13
],
[
x26
],
#
1
st1
{
v15
.
b
}[
14
],
[
x26
],
#
1
add
x2
,
x2
,
x24
b
Endwrite
WriteCol2
:
mov
x26
,
x2
st1
{
v15
.
b
}[
0
],
[
x26
],
#
1
st1
{
v15
.
b
}[
1
],
[
x26
],
#
1
add
x2
,
x2
,
x24
cmp
w23
,
#
1
beq
Endwrite
mov
x26
,
x2
st1
{
v15
.
b
}[
4
],
[
x26
],
#
1
st1
{
v15
.
b
}[
5
],
[
x26
],
#
1
add
x2
,
x2
,
x24
cmp
w23
,
#
2
beq
Endwrite
mov
x26
,
x2
st1
{
v15
.
b
}[
8
],
[
x26
],
#
1
st1
{
v15
.
b
}[
9
],
[
x26
],
#
1
add
x2
,
x2
,
x24
cmp
w23
,
#
3
beq
Endwrite
mov
x26
,
x2
st1
{
v15
.
b
}[
12
],
[
x26
],
#
1
st1
{
v15
.
b
}[
13
],
[
x26
],
#
1
add
x2
,
x2
,
x24
b
Endwrite
WriteCol1
:
st1
{
v15
.
b
}[
0
],
[
x2
],
x24
cmp
w23
,
#
1
beq
Endwrite
st1
{
v15
.
b
}[
4
],
[
x2
],
x24
cmp
w23
,
#
2
beq
Endwrite
st1
{
v15
.
b
}[
8
],
[
x2
],
x24
cmp
w23
,
#
3
beq
Endwrite
st1
{
v15
.
b
}[
12
],
[
x2
],
x24
b
Endwrite
Endwrite
:
sub
w16
,
w16
,
#
4
//
a
row4
counter
-
4
sub
w23
,
w23
,
#
4
//
a
row
counter
-
4
b
L2
End2
:
add
w15
,
w15
,
#
4
//
b
col
index
+
4
sub
w4
,
w4
,
#
4
//
b
col4
counter
-
4
sub
w15
,
w15
,
#
4
//
b
col
counter
-
4
add
x1
,
x1
,
x21
//
b
ptr
+
stride
add
x7
,
x7
,
#
16
//
bias
ptr
+
stride
add
x25
,
x25
,
#
4
//
output
+
stride
(
4
*
sizeof
(
int8
))
mov
x2
,
x25
b
L1
End1
:
sub
sp
,
sp
,
#
1
60
sub
sp
,
sp
,
#
1
92
ld1
{
v8
.4
s
,
v9
.4
s
,
v10
.4
s
,
v11
.4
s
},
[
sp
],
#
64
ld1
{
v12
.4
s
,
v13
.4
s
,
v14
.4
s
,
v15
.4
s
},
[
sp
],
#
64
ldp
x19
,
x20
,
[
sp
],
#
16
ldp
x21
,
x22
,
[
sp
],
#
16
ldp
x23
,
x24
,
[
sp
],
#
16
ldp
x25
,
x26
,
[
sp
],
#
16
ret
#endif
This diff is collapsed.
Click to expand it.
mindspore/lite/nnacl/common_func.c
浏览文件 @
cd8b664f
...
...
@@ -228,19 +228,3 @@ void IndirectGemmFp32_Comm(float *output, const float *input, const float *weigh
return
;
}
void
SimplePostFuncInt8
(
const
int
*
in
,
int8_t
*
out
,
int
oc
,
int
plane
,
int
plane8
,
int32_t
multiplier
,
int32_t
left_shift
,
int32_t
right_shift
,
int32_t
zp
)
{
/* (int32_t)row8x8-major * multiplier => (int8_t)row-major */
for
(
int
r
=
0
;
r
<
plane
;
r
++
)
{
for
(
int
c
=
0
;
c
<
oc
;
c
++
)
{
int
c8div
=
c
/
8
,
c8mod
=
c
%
8
;
int
src_index
=
c8div
*
plane8
*
8
+
r
*
8
+
c8mod
;
int
dst_index
=
r
*
oc
+
c
;
int32_t
value
=
in
[
src_index
];
value
=
MultiplyByQuantizedMultiplier
(
value
,
multiplier
,
left_shift
,
right_shift
)
+
zp
;
value
=
MSMIN
(
CHAR_MAX
,
value
);
value
=
MSMAX
(
CHAR_MIN
,
value
);
out
[
dst_index
]
=
(
int8_t
)
value
;
}
}
}
This diff is collapsed.
Click to expand it.
mindspore/lite/nnacl/int8/matmul_int8.c
浏览文件 @
cd8b664f
...
...
@@ -117,25 +117,6 @@ void RowMajor2Col8MajorInt8(int8_t *src_ptr, int8_t *dst_ptr, int row, int col)
}
}
void
MatMulInt8
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int32_t
*
c
,
const
int
row8
,
const
int
col8
,
const
int
deep
,
const
int32_t
a_zp
,
const
int32_t
b_zp
)
{
/* col8-major * row8-major => row8x8-major */
for
(
int
row
=
0
;
row
<
row8
;
row
++
)
{
for
(
int
col
=
0
;
col
<
col8
;
col
++
)
{
int
r8div
=
row
/
8
,
r8mod
=
row
%
8
;
int
c8div
=
col
/
8
,
c8mod
=
col
%
8
;
size_t
ci
=
c8div
*
row8
*
8
+
row
*
8
+
c8mod
;
int32_t
value
=
0
;
for
(
int
d
=
0
;
d
<
deep
;
d
++
)
{
size_t
ai
=
r8div
*
deep
*
8
+
d
*
8
+
r8mod
;
size_t
bi
=
c8div
*
deep
*
8
+
d
*
8
+
c8mod
;
value
=
value
+
((
int32_t
)
a
[
ai
]
-
a_zp
)
*
((
int32_t
)
b
[
bi
]
-
b_zp
);
}
c
[
ci
]
=
value
;
}
}
}
void
MatMulInt8_16x4
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int
*
dst
,
int
row_4
,
int
col_4
,
int
deep_16
,
const
int
*
input_sum
,
const
int
*
bias
)
{
/* row4x16-major * row16x4-major => row4x4-major */
...
...
@@ -191,6 +172,36 @@ void MatMulInt8_16x4_r(const int8_t *a, const int8_t *b, int8_t *dst, size_t row
return
;
}
/* row4x16-major * col16x4-major => row4x4-major */
void
MatmulInt8
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int8_t
*
dst
,
const
int
*
a_sums
,
const
int
*
bias
,
int
act_min
,
int
act_max
,
int
out_zp
,
int
multiplier
,
int
left_shift
,
int
right_shift
,
int
row
,
int
col
,
int
deep16
,
int
stride
)
{
int8_t
*
output
=
dst
;
for
(
int
r
=
0
;
r
<
row
;
r
++
)
{
for
(
int
c
=
0
;
c
<
col
;
c
++
)
{
int
r4div
=
r
/
C4NUM
;
int
r4mod
=
r
%
C4NUM
;
int
c4div
=
c
/
C4NUM
;
int
c4mod
=
c
%
C4NUM
;
int
value
=
0
;
for
(
int
d
=
0
;
d
<
deep16
;
d
++
)
{
int
d16div
=
d
/
C16NUM
;
int
d16mod
=
d
%
C16NUM
;
size_t
ai
=
r4div
*
deep16
*
C4NUM
+
d16div
*
C4NUM
*
C16NUM
+
r4mod
*
C16NUM
+
d16mod
;
size_t
bi
=
c4div
*
deep16
*
C4NUM
+
d16div
*
C4NUM
*
C16NUM
+
c4mod
*
C16NUM
+
d16mod
;
value
+=
a
[
ai
]
*
b
[
bi
];
}
value
-=
a_sums
[
r
];
value
+=
bias
[
c
];
value
=
MultiplyByQuantizedMultiplier
(
value
,
multiplier
,
left_shift
,
right_shift
)
+
out_zp
;
value
=
MSMIN
(
INT8_MAX
,
value
);
value
=
MSMAX
(
INT8_MIN
,
value
);
output
[
c
]
=
(
int8_t
)
value
;
}
output
+=
stride
;
}
}
void
RowMajor2Row4x16Major
(
int8_t
*
src
,
int
row
,
int
col
,
int8_t
*
dst
,
int
col_16
)
{
int
stride
=
sizeof
(
int8_t
)
*
16
*
4
;
for
(
int
r
=
0
;
r
<
row
;
++
r
)
{
...
...
@@ -213,23 +224,28 @@ void RowMajor2Col16x4Major(int8_t *src, int row, int col, int8_t *dst, int row_1
}
}
void
RowMajor2Asums
(
int8_t
*
a
,
int
row
,
int
col
,
int
b_zp
,
int
*
dst
)
{
// dst: weight_zp * input_row_sums
void
CalcInputSums
(
int8_t
*
input
,
int
row
,
int
col
,
int
weight_zp
,
int
*
dst
)
{
for
(
int
r
=
0
;
r
<
row
;
++
r
)
{
int
sum
=
0
;
for
(
int
c
=
0
;
c
<
col
;
++
c
)
{
int
src_idx
=
r
*
col
+
c
;
dst
[
r
]
+=
a
[
src_idx
];
sum
+=
input
[
src_idx
];
}
dst
[
r
]
*=
b_zp
;
sum
*=
weight_zp
;
dst
[
r
]
=
sum
;
}
}
void
RowMajor2Bbias
(
int8_t
*
b
,
int
row
,
int
col
,
int
a_zp
,
int
b_zp
,
int
*
bias
,
int
*
dst
)
{
// dst: bias + depth*input_zp*weight_zp - input_zp*weight_col_sums
void
CalcWeightBiasSums
(
int8_t
*
weight
,
int
row
,
int
col
,
int
input_zp
,
int
weight_zp
,
int
*
bias
,
int
*
dst
)
{
for
(
int
c
=
0
;
c
<
col
;
++
c
)
{
int
sum
=
0
;
for
(
int
r
=
0
;
r
<
row
;
++
r
)
{
int
src_idx
=
r
*
col
+
c
;
dst
[
c
]
+=
b
[
src_idx
];
sum
+=
weight
[
src_idx
];
}
dst
[
c
]
=
row
*
a_zp
*
b_zp
-
a_zp
*
dst
[
c
]
;
dst
[
c
]
=
row
*
input_zp
*
weight_zp
-
input_zp
*
sum
;
if
(
bias
)
{
dst
[
c
]
+=
bias
[
c
];
}
...
...
This diff is collapsed.
Click to expand it.
mindspore/lite/nnacl/int8/matmul_int8.h
浏览文件 @
cd8b664f
...
...
@@ -24,8 +24,6 @@
#ifdef __cplusplus
extern
"C"
{
#endif
void
MatMulInt8
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int
*
c
,
const
int
row8
,
const
int
col8
,
const
int
deep
,
const
int
a_zp
,
const
int
b_zp
);
void
MatMulInt8_16x4
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int
*
dst
,
int
row_4
,
int
col_4
,
int
deep_16
,
const
int
*
input_sum
,
const
int
*
bias
);
void
MatMulInt8_16x4_r
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int8_t
*
dst
,
size_t
row
,
size_t
col
,
size_t
deep_16
,
...
...
@@ -39,15 +37,16 @@ void RowMajor2Row16x4MajorInt8(void *src_ptr, void *dst_ptr, int row, int col);
void
RowMajor2Row4x16Major
(
int8_t
*
src
,
int
row
,
int
col
,
int8_t
*
dst
,
int
col_16
);
void
RowMajor2Col16x4Major
(
int8_t
*
src
,
int
row
,
int
col
,
int8_t
*
dst
,
int
row_16
);
void
RowMajor2Asums
(
int8_t
*
a
,
int
row
,
int
col
,
int
b_zp
,
int
*
dst
);
void
RowMajor2Bbias
(
int8_t
*
b
,
int
row
,
int
col
,
int
a_zp
,
int
b_zp
,
int
*
bias
,
int
*
dst
);
void
Row4x4Major2RowMajor
(
int8_t
*
src
,
int
row4
,
int8_t
*
dst
,
int
row
,
int
cow
);
void
CalcInputSums
(
int8_t
*
a
,
int
row
,
int
col
,
int
b_zp
,
int
*
dst
);
void
CalcWeightBiasSums
(
int8_t
*
b
,
int
row
,
int
col
,
int
a_zp
,
int
b_zp
,
int
*
bias
,
int
*
dst
);
void
MatmulInt8
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int8_t
*
dst
,
const
int
*
a_sums
,
const
int
*
bias
,
int
act_min
,
int
act_max
,
int
out_zp
,
int
multiplier
,
int
left_shift
,
int
right_shift
,
int
row
,
int
col
,
int
deep16
,
int
stride
);
#ifdef ENABLE_ARM64
// bias = bias + depth * a_zp * b_zp - a_zp * b_sums
void
MatmulInt8Neon64
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int8_t
*
dst
,
int
row4
,
int
col4
,
int
deep16
,
const
int
*
a_sums
,
const
int
*
bias
,
int
act_min
,
int
act_max
,
int
out_zp
,
int
multiplier
,
int
left_shift
,
int
right_shift
);
int
right_shift
,
int
row
,
int
col
,
int
stride
);
void
MatMulR4Int8Neon64
(
const
int8_t
*
a
,
const
int8_t
*
b
,
int32_t
*
dst
,
int
row4
,
int
col4
,
int
deep16
,
const
int
*
input_sum
,
const
int
*
bias
);
...
...
This diff is collapsed.
Click to expand it.
mindspore/lite/src/runtime/kernel/arm/int8/fullconnection_int8.cc
浏览文件 @
cd8b664f
...
...
@@ -39,36 +39,32 @@ int FullconnectionInt8CPUKernel::ReSize() {
fc_param_
->
row_8_
=
UP_ROUND
(
fc_param_
->
row_
,
8
);
fc_param_
->
col_8_
=
UP_ROUND
(
fc_param_
->
col_
,
8
);
thread_count_
=
MSMIN
(
thread_count_
,
UP_DIV
(
fc_param_
->
col_8_
,
8
));
thread_stride_
=
UP_DIV
(
UP_DIV
(
fc_param_
->
col_8_
,
8
),
thread_count_
);
a_c8_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
fc_param_
->
row_8_
*
fc_param_
->
deep_
*
sizeof
(
int8_t
)));
if
(
!
a_c8_ptr_
)
{
return
RET_MEMORY_FAILED
;
}
memset
(
a_c8_ptr_
,
0
,
fc_param_
->
row_8_
*
fc_param_
->
deep_
*
sizeof
(
int8_t
));
b_r8_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
fc_param_
->
col_8_
*
fc_param_
->
deep_
*
sizeof
(
int8_t
)));
if
(
!
b_r8_ptr_
)
{
return
RET_MEMORY_FAILED
;
}
memset
(
b_r8_ptr_
,
0
,
fc_param_
->
col_8_
*
fc_param_
->
deep_
*
sizeof
(
int8_t
));
r4_
=
UP_ROUND
(
fc_param_
->
row_
,
4
);
c4_
=
UP_ROUND
(
fc_param_
->
col_
,
4
);
d16_
=
UP_ROUND
(
fc_param_
->
deep_
,
16
);
thread_count_
=
MSMIN
(
thread_count_
,
UP_DIV
(
c4_
,
4
));
thread_stride_
=
UP_DIV
(
UP_DIV
(
c4_
,
4
),
thread_count_
);
a_r4x16_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
r4_
*
d16_
*
sizeof
(
int8_t
)));
if
(
!
a_r4x16_ptr_
)
return
RET_MEMORY_FAILED
;
memset
(
a_r4x16_ptr_
,
0
,
r4_
*
d16_
*
sizeof
(
int8_t
));
b_c16x4_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
c4_
*
d16_
*
sizeof
(
int8_t
)));
if
(
!
b_c16x4_ptr_
)
return
RET_MEMORY_FAILED
;
memset
(
b_c16x4_ptr_
,
0
,
c4_
*
d16_
*
sizeof
(
int8_t
));
input_sums_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
r4_
*
sizeof
(
int
)));
if
(
!
input_sums_
)
return
RET_MEMORY_FAILED
;
memset
(
input_sums_
,
0
,
r4_
*
sizeof
(
int
));
weight_bias_sums_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
c4_
*
sizeof
(
int
)));
if
(
!
weight_bias_sums_
)
return
RET_MEMORY_FAILED
;
memset
(
weight_bias_sums_
,
0
,
c4_
*
sizeof
(
int
));
auto
weight_data
=
reinterpret_cast
<
int8_t
*>
(
in_tensors_
[
1
]
->
Data
());
RowMajor2Col8MajorInt8
(
weight_data
,
b_r8_ptr_
,
fc_param_
->
col_
,
fc_param_
->
deep_
);
c_r8x8_ptr_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
fc_param_
->
row_8_
*
fc_param_
->
col_8_
*
sizeof
(
int
)));
if
(
!
c_r8x8_ptr_
)
{
return
RET_MEMORY_FAILED
;
}
memset
(
c_r8x8_ptr_
,
0
,
fc_param_
->
row_8_
*
fc_param_
->
col_8_
*
sizeof
(
int
));
auto
bias_len
=
fc_param_
->
col_8_
*
sizeof
(
int
);
bias_ptr_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
bias_len
));
if
(
!
bias_ptr_
)
{
return
RET_MEMORY_FAILED
;
}
memset
(
bias_ptr_
,
0
,
bias_len
);
RowMajor2Row4x16Major
(
weight_data
,
fc_param_
->
col_
,
fc_param_
->
deep_
,
b_c16x4_ptr_
,
d16_
);
if
(
in_tensors_
.
size
()
==
3
)
{
auto
bias_len
=
fc_param_
->
col_8_
*
sizeof
(
int
);
bias_ptr_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
bias_len
));
if
(
!
bias_ptr_
)
return
RET_MEMORY_FAILED
;
memcpy
(
bias_ptr_
,
in_tensors_
[
2
]
->
Data
(),
bias_len
);
}
else
{
bias_ptr_
=
NULL
;
}
auto
input_tensor
=
in_tensors_
[
0
];
...
...
@@ -93,18 +89,32 @@ int FullconnectionInt8CPUKernel::ReSize() {
CalculateActivationRangeQuantized
(
fc_param_
->
act_type_
==
ActType_Relu
,
fc_param_
->
act_type_
==
ActType_Relu6
,
quant_params_
.
output
.
zp_
,
quant_params_
.
output
.
scale_
,
&
quant_params_
.
out_act_min
,
&
quant_params_
.
out_act_max
);
CalcWeightBiasSums
(
weight_data
,
fc_param_
->
deep_
,
fc_param_
->
col_
,
quant_params_
.
input
.
zp_
,
quant_params_
.
weight
.
zp_
,
bias_ptr_
,
weight_bias_sums_
);
return
RET_OK
;
}
int
FullconnectionInt8CPUKernel
::
RunImpl
(
int
task_id
)
{
int
cur_oc
=
MSMIN
(
thread_stride_
,
UP_DIV
(
fc_param_
->
col_8_
,
8
)
-
task_id
*
thread_stride_
);
int
cur_oc
=
MSMIN
(
thread_stride_
,
UP_DIV
(
c4_
,
4
)
-
task_id
*
thread_stride_
);
if
(
cur_oc
<=
0
)
{
return
RET_OK
;
}
auto
&
p
=
quant_params_
;
auto
cur_b
=
b_r8_ptr_
+
task_id
*
thread_stride_
*
C8NUM
*
fc_param_
->
deep_
;
auto
cur_c
=
c_r8x8_ptr_
+
task_id
*
thread_stride_
*
C8NUM
*
fc_param_
->
row_8_
;
MatMulInt8
(
a_c8_ptr_
,
cur_b
,
cur_c
,
fc_param_
->
row_8_
,
cur_oc
*
8
,
fc_param_
->
deep_
,
p
.
input
.
zp_
,
p
.
weight
.
zp_
);
int
cur_oc_res
=
MSMIN
(
thread_stride_
*
C4NUM
,
fc_param_
->
col_
-
task_id
*
thread_stride_
*
C4NUM
);
auto
&
q
=
quant_params_
;
auto
&
p
=
fc_param_
;
auto
cur_b
=
b_c16x4_ptr_
+
task_id
*
thread_stride_
*
C4NUM
*
d16_
;
auto
cur_bias
=
weight_bias_sums_
+
task_id
*
thread_stride_
*
C4NUM
;
auto
output_ptr
=
reinterpret_cast
<
int8_t
*>
(
out_tensors_
[
0
]
->
Data
());
auto
cur_c
=
output_ptr
+
task_id
*
thread_stride_
*
C4NUM
;
#ifdef ENABLE_ARM64
MatmulInt8Neon64
(
a_r4x16_ptr_
,
cur_b
,
cur_c
,
r4_
,
cur_oc
*
C4NUM
,
d16_
,
input_sums_
,
cur_bias
,
q
.
out_act_min
,
q
.
out_act_max
,
q
.
output
.
zp_
,
q
.
quant_multiplier
,
q
.
left_shift
,
q
.
right_shift
,
p
->
row_
,
cur_oc_res
,
p
->
col_
*
sizeof
(
int8_t
));
#else
MatmulInt8
(
a_r4x16_ptr_
,
cur_b
,
cur_c
,
input_sums_
,
cur_bias
,
q
.
out_act_min
,
q
.
out_act_max
,
q
.
output
.
zp_
,
q
.
quant_multiplier
,
q
.
left_shift
,
q
.
right_shift
,
p
->
row_
,
cur_oc_res
,
d16_
,
p
->
col_
);
#endif
return
RET_OK
;
}
...
...
@@ -124,13 +134,10 @@ int FullconnectionInt8CPUKernel::Run() {
MS_LOG
(
ERROR
)
<<
"Prepare failed."
;
return
RET_ERROR
;
}
auto
a_ptr
=
reinterpret_cast
<
int8_t
*>
(
in_tensors_
[
0
]
->
Data
());
auto
output_ptr
=
reinterpret_cast
<
int8_t
*>
(
out_tensors_
[
0
]
->
Data
());
auto
&
p
=
quant_params_
;
RowMajor2Col8MajorInt8
(
a_ptr
,
a_c8_ptr_
,
fc_param_
->
row_
,
fc_param_
->
deep_
);
auto
input_ptr
=
reinterpret_cast
<
int8_t
*>
(
in_tensors_
[
0
]
->
Data
());
RowMajor2Row4x16Major
(
input_ptr
,
fc_param_
->
row_
,
fc_param_
->
deep_
,
a_r4x16_ptr_
,
d16_
);
CalcInputSums
(
input_ptr
,
fc_param_
->
row_
,
fc_param_
->
deep_
,
quant_params_
.
weight
.
zp_
,
input_sums_
);
LiteBackendParallelLaunch
(
FcInt8Run
,
this
,
thread_count_
);
PostFuncInt8C8
(
c_r8x8_ptr_
,
bias_ptr_
,
output_ptr
,
fc_param_
->
col_
,
fc_param_
->
row_
,
p
.
quant_multiplier
,
p
.
left_shift
,
p
.
right_shift
,
p
.
output
.
zp_
,
p
.
out_act_min
,
p
.
out_act_max
);
return
RET_OK
;
}
...
...
This diff is collapsed.
Click to expand it.
mindspore/lite/src/runtime/kernel/arm/int8/fullconnection_int8.h
浏览文件 @
cd8b664f
...
...
@@ -41,28 +41,36 @@ class FullconnectionInt8CPUKernel : public FullconnectionBaseCPUKernel {
private:
void
FreeTmpBuffer
()
{
if
(
a_
c8
_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
a_
c8
_ptr_
);
a_
c8
_ptr_
=
nullptr
;
if
(
a_
r4x16
_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
a_
r4x16
_ptr_
);
a_
r4x16
_ptr_
=
nullptr
;
}
if
(
b_
r8
_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
b_
r8
_ptr_
);
b_
r8
_ptr_
=
nullptr
;
if
(
b_
c16x4
_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
b_
c16x4
_ptr_
);
b_
c16x4
_ptr_
=
nullptr
;
}
if
(
c_r8x8_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
c_r8x8_ptr_
);
c_r8x8_ptr_
=
nullptr
;
if
(
input_sums_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
input_sums_
);
input_sums_
=
nullptr
;
}
if
(
weight_bias_sums_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
weight_bias_sums_
);
weight_bias_sums_
=
nullptr
;
}
if
(
bias_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
bias_ptr
_
);
bias_ptr
_
=
nullptr
;
ctx_
->
allocator
->
Free
(
weight_bias_sums
_
);
weight_bias_sums
_
=
nullptr
;
}
}
MatmulQuantArg
quant_params_
;
int8_t
*
a_c8_ptr_
=
nullptr
;
int8_t
*
b_r8_ptr_
=
nullptr
;
int
*
c_r8x8_ptr_
=
nullptr
;
int8_t
*
a_r4x16_ptr_
=
nullptr
;
int8_t
*
b_c16x4_ptr_
=
nullptr
;
int
*
input_sums_
=
nullptr
;
int
*
weight_bias_sums_
=
nullptr
;
int
*
bias_ptr_
=
nullptr
;
int
r4_
;
int
c4_
;
int
d16_
;
};
}
// namespace mindspore::kernel
...
...
This diff is collapsed.
Click to expand it.
mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc
浏览文件 @
cd8b664f
...
...
@@ -48,46 +48,23 @@ int MatmulInt8CPUKernel::ReSize() {
params_
->
row_8_
=
UP_ROUND
(
params_
->
row_
,
8
);
params_
->
col_8_
=
UP_ROUND
(
params_
->
col_
,
8
);
#ifdef ENABLE_ARM64
r4_
=
UP_ROUND
(
params_
->
row_
,
4
);
c4_
=
UP_ROUND
(
params_
->
col_
,
4
);
d16_
=
UP_ROUND
(
params_
->
deep_
,
16
);
a_r4d16_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
r4_
*
d16_
*
sizeof
(
int8_t
)));
if
(
!
a_r4d16_ptr_
)
return
RET_MEMORY_FAILED
;
memset
(
a_r4d16_ptr_
,
0
,
r4_
*
d16_
*
sizeof
(
int8_t
));
b_c4d16_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
c4_
*
d16_
*
sizeof
(
int8_t
)));
if
(
!
b_c4d16_ptr_
)
return
RET_MEMORY_FAILED
;
memset
(
b_c4d16_ptr_
,
0
,
c4_
*
d16_
*
sizeof
(
int8_t
));
c_r4c4_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
r4_
*
c4_
*
sizeof
(
int8_t
)));
if
(
!
c_r4c4_ptr_
)
return
RET_MEMORY_FAILED
;
memset
(
c_r4c4_ptr_
,
0
,
r4_
*
c4_
*
sizeof
(
int8_t
));
a_sums_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
r4_
*
sizeof
(
int
)));
if
(
!
a_sums_
)
return
RET_MEMORY_FAILED
;
memset
(
a_sums_
,
0
,
r4_
*
sizeof
(
int
));
b_bias_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
c4_
*
sizeof
(
int
)));
if
(
!
b_bias_
)
return
RET_MEMORY_FAILED
;
memset
(
b_bias_
,
0
,
c4_
*
sizeof
(
int
));
a_r4x16_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
r4_
*
d16_
*
sizeof
(
int8_t
)));
if
(
!
a_r4x16_ptr_
)
return
RET_MEMORY_FAILED
;
memset
(
a_r4x16_ptr_
,
0
,
r4_
*
d16_
*
sizeof
(
int8_t
));
b_c16x4_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
c4_
*
d16_
*
sizeof
(
int8_t
)));
if
(
!
b_c16x4_ptr_
)
return
RET_MEMORY_FAILED
;
memset
(
b_c16x4_ptr_
,
0
,
c4_
*
d16_
*
sizeof
(
int8_t
));
input_sums_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
r4_
*
sizeof
(
int
)));
if
(
!
input_sums_
)
return
RET_MEMORY_FAILED
;
memset
(
input_sums_
,
0
,
r4_
*
sizeof
(
int
));
weight_bias_sums_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
c4_
*
sizeof
(
int
)));
if
(
!
weight_bias_sums_
)
return
RET_MEMORY_FAILED
;
memset
(
weight_bias_sums_
,
0
,
c4_
*
sizeof
(
int
));
thread_count_
=
MSMIN
(
thread_count_
,
UP_DIV
(
c4_
,
4
));
thread_stride_
=
UP_DIV
(
UP_DIV
(
c4_
,
4
),
thread_count_
);
#else
a_c8_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
params_
->
row_8_
*
params_
->
deep_
*
sizeof
(
int8_t
)));
if
(
!
a_c8_ptr_
)
{
return
RET_MEMORY_FAILED
;
}
memset
(
a_c8_ptr_
,
0
,
params_
->
row_8_
*
params_
->
deep_
*
sizeof
(
int8_t
));
b_r8_ptr_
=
reinterpret_cast
<
int8_t
*>
(
ctx_
->
allocator
->
Malloc
(
params_
->
col_8_
*
params_
->
deep_
*
sizeof
(
int8_t
)));
if
(
!
b_r8_ptr_
)
{
return
RET_MEMORY_FAILED
;
}
memset
(
b_r8_ptr_
,
0
,
params_
->
col_8_
*
params_
->
deep_
*
sizeof
(
int8_t
));
c_r8x8_ptr_
=
reinterpret_cast
<
int
*>
(
ctx_
->
allocator
->
Malloc
(
params_
->
row_8_
*
params_
->
col_8_
*
sizeof
(
int
)));
if
(
!
c_r8x8_ptr_
)
{
return
RET_MEMORY_FAILED
;
}
memset
(
c_r8x8_ptr_
,
0
,
params_
->
row_8_
*
params_
->
col_8_
*
sizeof
(
int
));
thread_count_
=
MSMIN
(
thread_count_
,
UP_DIV
(
params_
->
col_8_
,
8
));
thread_stride_
=
UP_DIV
(
UP_DIV
(
params_
->
col_8_
,
8
),
thread_count_
);
#endif
auto
input_tensor
=
in_tensors_
[
0
];
auto
params
=
input_tensor
->
GetQuantParams
();
...
...
@@ -112,27 +89,25 @@ int MatmulInt8CPUKernel::ReSize() {
}
int
MatmulInt8CPUKernel
::
RunImpl
(
int
task_id
)
{
#ifdef ENABLE_ARM64
int
cur_oc
=
MSMIN
(
thread_stride_
,
UP_DIV
(
c4_
,
4
)
-
task_id
*
thread_stride_
);
if
(
cur_oc
<=
0
)
{
return
RET_OK
;
}
auto
cur_b
=
b_c4d16_ptr_
+
task_id
*
thread_stride_
*
4
*
d16_
;
auto
cur_c
=
c_r4c4_ptr_
+
task_id
*
thread_stride_
*
4
*
r4_
;
int
cur_oc_res
=
MSMIN
(
thread_stride_
*
C4NUM
,
params_
->
col_
-
task_id
*
thread_stride_
*
C4NUM
);
auto
cur_b
=
b_c16x4_ptr_
+
task_id
*
thread_stride_
*
4
*
d16_
;
auto
cur_bias
=
weight_bias_sums_
+
task_id
*
thread_stride_
*
4
;
auto
cur_c
=
c_ptr_
+
task_id
*
thread_stride_
*
4
;
auto
&
p
=
quant_params_
;
MatmulInt8Neon64
(
a_r4d16_ptr_
,
cur_b
,
cur_c
,
r4_
,
c4_
,
d16_
,
a_sums_
,
b_bias_
,
INT_MIN
,
INT_MAX
,
p
.
output
.
zp_
,
p
.
quant_multiplier
,
p
.
left_shift
,
p
.
right_shift
);
#ifdef ENABLE_ARM64
MatmulInt8Neon64
(
a_r4x16_ptr_
,
cur_b
,
cur_c
,
r4_
,
cur_oc
*
C4NUM
,
d16_
,
input_sums_
,
cur_bias
,
INT8_MIN
,
INT8_MAX
,
p
.
output
.
zp_
,
p
.
quant_multiplier
,
p
.
left_shift
,
p
.
right_shift
,
params_
->
row_
,
cur_oc_res
,
params_
->
col_
*
sizeof
(
int8_t
));
#else
int
cur_oc
=
MSMIN
(
thread_stride_
,
UP_DIV
(
params_
->
col_8_
,
8
)
-
task_id
*
thread_stride_
);
if
(
cur_oc
<=
0
)
{
return
RET_OK
;
}
auto
cur_b
=
b_r8_ptr_
+
task_id
*
thread_stride_
*
C8NUM
*
params_
->
deep_
;
auto
cur_c
=
c_r8x8_ptr_
+
task_id
*
thread_stride_
*
C8NUM
*
params_
->
row_8_
;
MatMulInt8
(
a_c8_ptr_
,
cur_b
,
cur_c
,
params_
->
row_8_
,
cur_oc
*
8
,
params_
->
deep_
,
quant_params_
.
input
.
zp_
,
quant_params_
.
weight
.
zp_
);
MatmulInt8
(
a_r4x16_ptr_
,
cur_b
,
cur_c
,
input_sums_
,
cur_bias
,
INT8_MIN
,
INT8_MAX
,
p
.
output
.
zp_
,
p
.
quant_multiplier
,
p
.
left_shift
,
p
.
right_shift
,
params_
->
row_
,
cur_oc_res
,
d16_
,
params_
->
col_
);
#endif
return
RET_OK
;
}
...
...
@@ -162,43 +137,27 @@ int MatmulInt8CPUKernel::Run() {
for
(
int
i
=
0
;
i
<
params_
->
batch
;
++
i
)
{
auto
cur_a_ptr
=
a_ptr
+
i
*
a_stride
;
auto
cur_b_ptr
=
b_ptr
+
i
*
b_stride
;
auto
cur_c_ptr
=
c_ptr
+
i
*
c_stride
;
#ifdef ENABLE_ARM64
if
(
params_
->
a_transpose_
)
{
RowMajor2Col16x4Major
(
cur_a_ptr
,
params_
->
deep_
,
params_
->
row_
,
a_r4
d
16_ptr_
,
d16_
);
RowMajor2Col16x4Major
(
cur_a_ptr
,
params_
->
deep_
,
params_
->
row_
,
a_r4
x
16_ptr_
,
d16_
);
}
else
{
RowMajor2Row4x16Major
(
cur_a_ptr
,
params_
->
row_
,
params_
->
deep_
,
a_r4
d
16_ptr_
,
d16_
);
RowMajor2Row4x16Major
(
cur_a_ptr
,
params_
->
row_
,
params_
->
deep_
,
a_r4
x
16_ptr_
,
d16_
);
}
if
(
params_
->
b_transpose_
)
{
RowMajor2Row4x16Major
(
cur_b_ptr
,
params_
->
col_
,
params_
->
deep_
,
b_c
4d16
_ptr_
,
d16_
);
RowMajor2Row4x16Major
(
cur_b_ptr
,
params_
->
col_
,
params_
->
deep_
,
b_c
16x4
_ptr_
,
d16_
);
}
else
{
RowMajor2Col16x4Major
(
cur_b_ptr
,
params_
->
deep_
,
params_
->
col_
,
b_c
4d16
_ptr_
,
d16_
);
RowMajor2Col16x4Major
(
cur_b_ptr
,
params_
->
deep_
,
params_
->
col_
,
b_c
16x4
_ptr_
,
d16_
);
}
c_ptr_
=
c_ptr
+
i
*
c_stride
;
auto
&
q
=
quant_params_
;
RowMajor2Asums
(
cur_a_ptr
,
params_
->
row_
,
params_
->
deep_
,
q
.
weight
.
zp_
,
a_sums_
);
RowMajor2Bbias
(
cur_b_ptr
,
params_
->
deep_
,
params_
->
col_
,
q
.
input
.
zp_
,
q
.
weight
.
zp_
,
NULL
,
b_bias_
);
LiteBackendParallelLaunch
(
MatmulInt8Run
,
this
,
thread_count_
);
Row4x4Major2RowMajor
(
c_r4c4_ptr_
,
r4_
,
cur_c_ptr
,
params_
->
row_
,
params_
->
col_
);
#else
if
(
params_
->
a_transpose_
)
{
RowMajor2Row8MajorInt8
(
cur_a_ptr
,
a_c8_ptr_
,
params_
->
deep_
,
params_
->
row_
);
}
else
{
RowMajor2Col8MajorInt8
(
cur_a_ptr
,
a_c8_ptr_
,
params_
->
row_
,
params_
->
deep_
);
}
if
(
params_
->
b_transpose_
)
{
RowMajor2Col8MajorInt8
(
cur_b_ptr
,
b_r8_ptr_
,
params_
->
col_
,
params_
->
deep_
);
}
else
{
RowMajor2Row8MajorInt8
(
cur_b_ptr
,
b_r8_ptr_
,
params_
->
deep_
,
params_
->
col_
);
CalcInputSums
(
cur_a_ptr
,
params_
->
row_
,
params_
->
deep_
,
q
.
weight
.
zp_
,
input_sums_
);
CalcWeightBiasSums
(
cur_b_ptr
,
params_
->
deep_
,
params_
->
col_
,
q
.
input
.
zp_
,
q
.
weight
.
zp_
,
NULL
,
weight_bias_sums_
);
ret
=
LiteBackendParallelLaunch
(
MatmulInt8Run
,
this
,
thread_count_
);
if
(
ret
!=
RET_OK
)
{
MS_LOG
(
ERROR
)
<<
"MatmulInt8Run error: ["
<<
ret
<<
"]"
;
return
ret
;
}
LiteBackendParallelLaunch
(
MatmulInt8Run
,
this
,
thread_count_
);
auto
&
q
=
quant_params_
;
SimplePostFuncInt8
(
c_r8x8_ptr_
,
cur_c_ptr
,
params_
->
col_
,
params_
->
row_
,
params_
->
row_8_
,
q
.
quant_multiplier
,
q
.
left_shift
,
q
.
right_shift
,
q
.
output
.
zp_
);
#endif
}
return
RET_OK
;
}
}
// namespace mindspore::kernel
This diff is collapsed.
Click to expand it.
mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.h
浏览文件 @
cd8b664f
...
...
@@ -39,57 +39,32 @@ class MatmulInt8CPUKernel : public MatmulBaseCPUKernel {
private:
void
FreeTmpBuffer
()
{
#ifdef ENABLE_ARM64
if
(
a_r4d16_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
a_r4d16_ptr_
);
a_r4d16_ptr_
=
nullptr
;
if
(
a_r4x16_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
a_r4x16_ptr_
);
a_r4x16_ptr_
=
nullptr
;
}
if
(
b_c
4d16
_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
b_c
4d16
_ptr_
);
b_c
4d16
_ptr_
=
nullptr
;
if
(
b_c
16x4
_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
b_c
16x4
_ptr_
);
b_c
16x4
_ptr_
=
nullptr
;
}
if
(
c_r4c4_ptr
_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
c_r4c4_ptr
_
);
c_r4c4_ptr
_
=
nullptr
;
if
(
input_sums
_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
input_sums
_
);
input_sums
_
=
nullptr
;
}
if
(
a
_sums_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
a
_sums_
);
a
_sums_
=
nullptr
;
if
(
weight_bias
_sums_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
weight_bias
_sums_
);
weight_bias
_sums_
=
nullptr
;
}
if
(
b_bias_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
b_bias_
);
b_bias_
=
nullptr
;
}
#else
if
(
a_c8_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
a_c8_ptr_
);
a_c8_ptr_
=
nullptr
;
}
if
(
b_r8_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
b_r8_ptr_
);
b_r8_ptr_
=
nullptr
;
}
if
(
c_r8x8_ptr_
!=
nullptr
)
{
ctx_
->
allocator
->
Free
(
c_r8x8_ptr_
);
c_r8x8_ptr_
=
nullptr
;
}
#endif
}
MatmulQuantArg
quant_params_
;
#ifdef ENABLE_ARM64
int8_t
*
a_r4d16_ptr_
=
nullptr
;
int8_t
*
b_c4d16_ptr_
=
nullptr
;
int8_t
*
c_r4c4_ptr_
=
nullptr
;
int
*
a_sums_
=
nullptr
;
int
*
b_bias_
=
nullptr
;
int8_t
*
a_r4x16_ptr_
=
nullptr
;
int8_t
*
b_c16x4_ptr_
=
nullptr
;
int8_t
*
c_ptr_
=
nullptr
;
int
*
input_sums_
=
nullptr
;
int
*
weight_bias_sums_
=
nullptr
;
int
r4_
;
int
c4_
;
int
d16_
;
#else
int8_t
*
a_c8_ptr_
=
nullptr
;
int8_t
*
b_r8_ptr_
=
nullptr
;
int
*
c_r8x8_ptr_
=
nullptr
;
#endif
};
// namespace mindspore::kernel
}
// namespace mindspore::kernel
...
...
This diff is collapsed.
Click to expand it.
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/deconv_int8_tests.cc
浏览文件 @
cd8b664f
...
...
@@ -134,54 +134,6 @@ TEST_F(TestDeconvInt8, PackInputTest1) {
CompareOutputData
(
dst
,
co
,
8
*
32
,
1
);
}
TEST_F
(
TestDeconvInt8
,
MatMulTest1
)
{
int8_t
a_row_major_10_12
[]
=
{
-
6
,
76
,
32
,
80
,
-
73
,
8
,
-
85
,
-
3
,
114
,
80
,
30
,
42
,
-
41
,
117
,
62
,
-
76
,
-
77
,
-
111
,
88
,
105
,
68
,
105
,
-
74
,
13
,
51
,
94
,
31
,
-
52
,
-
92
,
-
4
,
-
35
,
-
71
,
101
,
-
93
,
46
,
-
65
,
57
,
-
41
,
-
51
,
77
,
1
,
9
,
73
,
-
19
,
-
36
,
57
,
81
,
-
24
,
40
,
103
,
112
,
109
,
-
41
,
-
68
,
57
,
61
,
55
,
-
20
,
3
,
2
,
17
,
-
16
,
-
31
,
58
,
-
4
,
67
,
-
4
,
-
95
,
-
5
,
-
72
,
81
,
15
,
-
7
,
-
16
,
-
47
,
112
,
114
,
-
26
,
-
98
,
53
,
15
,
-
49
,
26
,
19
,
19
,
8
,
-
57
,
-
35
,
-
79
,
118
,
29
,
21
,
37
,
-
48
,
83
,
7
,
124
,
113
,
-
5
,
15
,
-
8
,
107
,
-
65
,
-
88
,
50
,
-
47
,
-
80
,
-
84
,
3
,
-
45
,
92
,
42
,
-
20
,
-
101
,
106
,
-
10
,
89
,
67
,
55
,
10
};
int32_t
zp_a
=
15
;
int8_t
a_col8_major
[
16
*
12
]
=
{
0
};
int8_t
b_col_major_12_18
[]
=
{
92
,
27
,
22
,
52
,
-
112
,
-
20
,
-
57
,
-
2
,
89
,
32
,
93
,
-
66
,
-
25
,
-
54
,
94
,
-
97
,
-
119
,
-
98
,
101
,
-
99
,
77
,
-
83
,
76
,
95
,
59
,
97
,
8
,
40
,
-
109
,
-
20
,
67
,
-
107
,
37
,
-
6
,
-
54
,
-
20
,
-
30
,
36
,
-
106
,
-
103
,
-
3
,
-
86
,
-
82
,
59
,
4
,
-
75
,
-
50
,
-
106
,
55
,
104
,
-
117
,
-
71
,
-
20
,
-
85
,
-
77
,
16
,
-
25
,
-
58
,
4
,
80
,
-
75
,
94
,
32
,
-
68
,
2
,
40
,
56
,
-
103
,
11
,
-
98
,
-
70
,
-
69
,
0
,
57
,
-
6
,
82
,
66
,
-
112
,
-
61
,
33
,
-
77
,
-
53
,
95
,
-
38
,
87
,
-
46
,
-
3
,
81
,
-
47
,
43
,
21
,
26
,
-
45
,
-
57
,
50
,
-
24
,
-
82
,
-
114
,
61
,
46
,
-
53
,
78
,
-
24
,
31
,
-
7
,
37
,
29
,
38
,
45
,
106
,
52
,
-
42
,
31
,
-
6
,
-
61
,
-
87
,
2
,
79
,
-
5
,
-
42
,
43
,
-
106
,
-
104
,
7
,
91
,
-
63
,
58
,
97
,
-
15
,
74
,
-
96
,
15
,
-
23
,
-
3
,
-
47
,
-
97
,
100
,
-
54
,
26
,
-
46
,
35
,
26
,
100
,
-
80
,
34
,
-
25
,
96
,
-
67
,
-
80
,
-
27
,
66
,
41
,
41
,
-
43
,
-
43
,
-
38
,
-
4
,
-
64
,
31
,
7
,
-
8
,
6
,
-
2
,
39
,
-
119
,
53
,
75
,
-
91
,
-
44
,
77
,
-
62
,
22
,
-
44
,
78
,
-
67
,
-
48
,
-
115
,
-
4
,
43
,
81
,
40
,
-
20
,
-
5
,
-
89
,
60
,
-
62
,
-
4
,
-
48
,
66
,
-
64
,
-
69
,
62
,
17
,
-
89
,
1
,
87
,
81
,
32
,
-
29
,
51
,
40
,
27
,
66
,
67
,
11
,
-
69
,
85
,
-
79
,
-
106
,
55
,
22
,
-
23
,
62
,
69
,
-
74
,
49
};
int32_t
zp_b
=
-
20
;
int8_t
b_row8_major
[
12
*
24
]
=
{
0
};
int32_t
co_row_major_10_18
[]
=
{
32005
,
3597
,
16595
,
-
3458
,
6627
,
-
6663
,
818
,
-
3910
,
10228
,
15079
,
-
19205
,
-
10203
,
-
3178
,
-
10046
,
10374
,
-
6199
,
5330
,
12163
,
1819
,
20533
,
17382
,
18283
,
9778
,
9185
,
-
12623
,
-
26234
,
-
11987
,
7904
,
8144
,
-
1603
,
27611
,
-
10190
,
-
20053
,
4999
,
-
28389
,
21852
,
24680
,
25858
,
23506
,
17944
,
11768
,
24378
,
-
6102
,
-
4675
,
-
23460
,
10434
,
-
47579
,
1986
,
12018
,
-
19418
,
-
7248
,
4938
,
-
32613
,
-
941
,
8171
,
-
4788
,
3325
,
-
11310
,
-
8351
,
-
14786
,
6909
,
16401
,
2017
,
-
6456
,
11242
,
7393
,
-
9119
,
17312
,
2646
,
-
14402
,
7201
,
-
9949
,
23986
,
17607
,
27461
,
-
1547
,
2783
,
7558
,
19487
,
11158
,
-
2686
,
6328
,
-
8225
,
-
11668
,
21858
,
-
2079
,
-
8671
,
-
639
,
-
1544
,
1235
,
1156
,
6582
,
2829
,
-
10311
,
-
2692
,
5154
,
1527
,
10870
,
106
,
-
8189
,
-
24174
,
-
1846
,
-
15399
,
-
3598
,
14874
,
-
5591
,
-
619
,
-
13667
,
-
6053
,
-
31103
,
-
24499
,
13008
,
9143
,
-
17982
,
28437
,
2176
,
-
2114
,
-
11631
,
10779
,
-
1032
,
-
24690
,
-
3112
,
2125
,
432
,
20270
,
-
33859
,
8907
,
10063
,
1603
,
3761
,
4805
,
4904
,
-
15594
,
10786
,
4287
,
-
13591
,
-
18777
,
-
1679
,
2109
,
-
2243
,
12051
,
-
8504
,
-
6558
,
4209
,
13606
,
-
25803
,
27922
,
12092
,
7140
,
27142
,
-
12267
,
2339
,
-
26224
,
23674
,
-
26579
,
-
11398
,
-
1823
,
-
18976
,
3641
,
4415
,
-
24878
,
-
2045
,
15937
,
41465
,
12601
,
-
14513
,
-
17619
,
-
5728
,
334
,
-
424
,
8147
,
-
1369
,
5984
,
11000
,
19016
,
4456
,
-
25920
,
4506
,
5930
,
15458
};
int32_t
c_row8x8_major
[
16
*
24
]
=
{
0
};
int32_t
out_row_major
[
180
]
=
{
0
};
RowMajor2Col8MajorInt8
(
a_row_major_10_12
,
a_col8_major
,
10
,
12
);
RowMajor2Col8MajorInt8
(
b_col_major_12_18
,
b_row8_major
,
18
,
12
);
MatMulInt8
(
a_col8_major
,
b_row8_major
,
c_row8x8_major
,
16
,
24
,
12
,
zp_a
,
zp_b
);
Row8x8Major2RowMajor
(
reinterpret_cast
<
float
*>
(
c_row8x8_major
),
reinterpret_cast
<
float
*>
(
out_row_major
),
10
,
18
,
18
);
CompareOutputData
(
out_row_major
,
co_row_major_10_18
,
180
,
1
);
}
TEST_F
(
TestDeconvInt8
,
InputSumTest1
)
{
int8_t
packed_a
[]
=
{
-
6
,
76
,
32
,
80
,
-
73
,
8
,
-
85
,
-
3
,
114
,
80
,
30
,
42
,
15
,
15
,
15
,
15
,
-
41
,
117
,
62
,
-
76
,
-
77
,
-
111
,
...
...
This diff is collapsed.
Click to expand it.
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/fullconnection_int8_tests.cc
浏览文件 @
cd8b664f
...
...
@@ -29,99 +29,128 @@ class TestFcInt8 : public mindspore::CommonTest {
TestFcInt8
()
{}
};
int
FcInt8TestInit
(
std
::
vector
<
lite
::
tensor
::
Tensor
*>
*
inputs_
,
std
::
vector
<
lite
::
tensor
::
Tensor
*>
*
outputs_
,
MatMulParameter
*
matmal_param
,
float
**
correct
,
double
*
scale
,
int
*
zeropoint
)
{
float
input_max
=
20
;
float
input_min
=
-
20
;
float
weight_max
=
1
;
float
weight_min
=
-
1
;
float
output_max
=
20
;
float
output_min
=
-
20
;
struct
TensorInfo
{
float
*
data
;
int
*
data_int
;
float
min
;
float
max
;
int
len
;
std
::
vector
<
int
>
*
shape
;
}
;
double
input_scale
=
(
input_max
-
input_min
)
/
(
std
::
numeric_limits
<
int8_t
>::
max
()
-
std
::
numeric_limits
<
int8_t
>::
min
());
int
input_zp
=
std
::
numeric_limits
<
int8_t
>::
max
()
-
input_max
/
input_scale
;
double
weight_scale
=
(
weight_max
-
weight_min
)
/
(
std
::
numeric_limits
<
int8_t
>::
max
()
-
std
::
numeric_limits
<
int8_t
>::
min
());
int
weight_zp
=
std
::
numeric_limits
<
int8_t
>::
max
()
-
weight_max
/
weight_scale
;
double
output_scale
=
(
output_max
-
output_min
)
/
(
std
::
numeric_limits
<
int8_t
>::
max
()
-
std
::
numeric_limits
<
int8_t
>::
min
());
int
output_zp
=
std
::
numeric_limits
<
int8_t
>::
max
()
-
output_max
/
output_scale
;
*
scale
=
output_scale
;
*
zeropoint
=
output_zp
;
extern
void
QuantProcess
(
float
*
input
,
int
len
,
float
min
,
float
max
,
float
*
scale
,
int
*
zero_point
,
int8_t
*
output
);
extern
lite
::
tensor
::
Tensor
*
MakeQuantTensor
(
int8_t
*
data
,
int
len
,
std
::
vector
<
int
>
*
shape
,
float
scale
,
int
zp
);
Tensor
*
in_t
=
new
Tensor
(
kNumberTypeInt8
,
{
2
,
2
,
2
,
2
},
schema
::
Format_NHWC
,
static_cast
<
schema
::
NodeType
>
(
1
));
in_t
->
MallocData
();
float
in
[]
=
{
-
3.2366564
,
-
4.7733846
,
-
7.8329225
,
16.146885
,
5.060793
,
-
6.1471
,
-
1.7680453
,
-
6.5721383
,
17.87506
,
-
5.1192183
,
10.742863
,
1.4536934
,
19.693445
,
19.45783
,
5.063163
,
0.5234792
};
Quantize
(
in
,
in_t
->
ElementsNum
(),
input_scale
,
input_zp
,
reinterpret_cast
<
int8_t
*>
(
in_t
->
Data
()));
auto
in_quant_arg
=
new
mindspore
::
lite
::
tensor
::
QuantArg
();
in_quant_arg
->
zeroPoint
=
input_zp
;
in_quant_arg
->
scale
=
input_scale
;
in_t
->
AddQuantParam
(
*
in_quant_arg
);
inputs_
->
push_back
(
in_t
);
lite
::
tensor
::
Tensor
*
MakeIntTensor
(
int
*
data
,
int
len
,
std
::
vector
<
int
>
*
shape
)
{
auto
tensor
=
new
lite
::
tensor
::
Tensor
(
kNumberTypeInt32
,
*
shape
,
schema
::
Format_NHWC
,
static_cast
<
schema
::
NodeType
>
(
1
));
tensor
->
MallocData
();
auto
tensor_ptr
=
reinterpret_cast
<
int
*>
(
tensor
->
Data
());
memcpy
(
tensor_ptr
,
data
,
len
*
sizeof
(
int
));
return
tensor
;
}
Tensor
*
weight_t
=
new
Tensor
(
kNumberTypeInt8
,
{
3
,
8
},
schema
::
Format_NHWC
,
static_cast
<
schema
::
NodeType
>
(
1
));
weight_t
->
MallocData
();
float
weight
[]
=
{
-
0.24438887
,
0.06738146
,
-
0.8169129
,
0.21510671
,
-
0.012470592
,
-
0.053063435
,
0.6050155
,
0.8656233
,
0.12911413
,
-
0.028635843
,
-
0.034080597
,
-
0.10622552
,
-
0.012254699
,
-
0.01312836
,
0.25241964
,
-
0.4706142
,
0.2451482
,
-
0.9558459
,
0.4481974
,
0.33251503
,
-
0.011705584
,
-
0.1720293
,
-
0.39410214
,
-
0.73637343
};
Quantize
(
weight
,
weight_t
->
ElementsNum
(),
weight_scale
,
weight_zp
,
reinterpret_cast
<
int8_t
*>
(
weight_t
->
Data
()));
auto
weight_quant_arg
=
new
mindspore
::
lite
::
tensor
::
QuantArg
();
weight_quant_arg
->
zeroPoint
=
weight_zp
;
weight_quant_arg
->
scale
=
weight_scale
;
weight_t
->
AddQuantParam
(
*
weight_quant_arg
);
inputs_
->
push_back
(
weight_t
);
void
FcInt8TestInit
(
std
::
vector
<
lite
::
tensor
::
Tensor
*>
*
inputs
,
std
::
vector
<
lite
::
tensor
::
Tensor
*>
*
outputs
,
TensorInfo
*
in
,
TensorInfo
*
weight
,
TensorInfo
*
bias
,
TensorInfo
*
out
)
{
float
in_scale
,
weight_scale
,
out_scale
;
int
in_zp
,
weight_zp
,
out_zp
;
int8_t
*
in_data
=
new
int8_t
[
in
->
len
];
int8_t
*
weight_data
=
new
int8_t
[
weight
->
len
];
QuantProcess
(
in
->
data
,
in
->
len
,
in
->
min
,
in
->
max
,
&
in_scale
,
&
in_zp
,
in_data
);
auto
in_tensor
=
MakeQuantTensor
(
in_data
,
in
->
len
,
in
->
shape
,
in_scale
,
in_zp
);
inputs
->
push_back
(
in_tensor
);
QuantProcess
(
weight
->
data
,
weight
->
len
,
weight
->
min
,
weight
->
max
,
&
weight_scale
,
&
weight_zp
,
weight_data
);
auto
weight_tensor
=
MakeQuantTensor
(
weight_data
,
weight
->
len
,
weight
->
shape
,
weight_scale
,
weight_zp
);
inputs
->
push_back
(
weight_tensor
);
auto
bias_tensor
=
MakeIntTensor
(
bias
->
data_int
,
bias
->
len
,
bias
->
shape
);
inputs
->
push_back
(
bias_tensor
);
QuantProcess
(
out
->
data
,
out
->
len
,
out
->
min
,
out
->
max
,
&
out_scale
,
&
out_zp
,
nullptr
);
auto
out_tensor
=
MakeQuantTensor
(
nullptr
,
out
->
len
,
out
->
shape
,
out_scale
,
out_zp
);
outputs
->
push_back
(
out_tensor
);
delete
[]
in_data
;
delete
[]
weight_data
;
}
Tensor
*
bias_t
=
new
Tensor
(
kNumberTypeInt32
,
{
3
},
schema
::
Format_NHWC
,
static_cast
<
schema
::
NodeType
>
(
1
));
bias_t
->
MallocData
();
memset
(
bias_t
->
Data
(),
0
,
sizeof
(
int
)
*
bias_t
->
ElementsNum
());
inputs_
->
push_back
(
bias_t
);
TEST_F
(
TestFcInt8
,
fctest1
)
{
float
in
[]
=
{
4.259103407444801
,
5.992151035772917
,
-
9.495343223733581
,
3.0509999931426215
,
-
16.635707833991095
,
-
14.72005749234452
,
2.8290916795754093
,
-
15.827977973039049
,
-
16.98208477063347
,
2.8801101778935347
,
-
0.5905297521382735
,
18.042746010536085
,
3.913511213700396
,
11.571264917136105
,
19.084257392926148
,
8.571560238377568
,
17.58868010598305
,
12.433311533838427
,
4.548078598583526
,
15.609650071521138
,
6.663372887795717
,
17.581323475674594
,
1.453277207446778
,
-
6.119351424589654
,
-
16.87310296820285
,
11.906066592064796
,
-
13.290100998834653
,
19.627129875430548
,
16.034262583959162
,
10.255738135902781
,
12.134650347811792
,
-
5.5882066903433305
,
15.554050723026322
,
15.288481461776783
,
17.651080309797287
,
-
9.258779162183215
,
4.218532791445092
,
-
6.205309122668545
,
1.2220458021156908
,
1.6800736573947326
};
TensorInfo
in_params
;
in_params
.
data
=
in
;
in_params
.
len
=
40
;
std
::
vector
<
int
>
in_shape
{
5
,
2
,
2
,
2
};
in_params
.
shape
=
&
in_shape
;
in_params
.
min
=
-
20
;
in_params
.
max
=
20
;
Tensor
*
out_t
=
new
Tensor
(
kNumberTypeInt8
,
{
2
,
3
},
schema
::
Format_NHWC
,
static_cast
<
schema
::
NodeType
>
(
1
));
out_t
->
MallocData
();
auto
output_quant_arg
=
new
mindspore
::
lite
::
tensor
::
QuantArg
();
output_quant_arg
->
zeroPoint
=
output_zp
;
output_quant_arg
->
scale
=
output_scale
;
out_t
->
AddQuantParam
(
*
output_quant_arg
);
outputs_
->
push_back
(
out_t
);
float
weight
[]
=
{
-
0.586269014312498
,
0.10845796767603733
,
0.8455159907124523
,
0.20261291069007226
,
0.7564258582027543
,
0.4505005038790615
,
-
0.607259232240795
,
-
0.6962171798923924
,
0.7967573009922135
,
-
0.46069496925353715
,
-
0.2967638879316592
,
-
0.7025557337565955
,
-
0.5313515272071268
,
0.07584168670764102
,
-
0.6860034691410029
,
0.9218806800279316
,
-
0.07408538201953907
,
-
0.7933652717840096
,
0.6636691558029275
,
-
0.30198695606477477
,
0.790225747868754
,
-
0.9478140254555916
,
0.4537316306461665
,
0.1776848732022871
,
-
0.7492316745474277
,
-
0.5825825240770948
,
0.5680842804542614
,
-
0.9255552309192772
,
0.20866577718844725
,
0.9570928647172854
,
0.18172570688854406
,
-
0.26442830241827253
,
-
0.24765169216720873
,
-
0.19512285277145702
,
0.1120696020054861
,
0.7558578199370625
,
-
0.15032457481135109
,
-
0.08485585411928809
,
0.6343014796699504
,
0.026380085222785787
,
-
0.40516674259120444
,
-
0.7407588590646037
,
-
0.28521396461492454
,
0.2555841827858194
,
0.023640857478332444
,
-
0.6540694390119834
,
0.7439705499824205
,
-
0.7579774562590929
};
TensorInfo
weight_params
;
weight_params
.
data
=
weight
;
weight_params
.
len
=
48
;
std
::
vector
<
int
>
weight_shape
{
6
,
8
};
weight_params
.
shape
=
&
weight_shape
;
weight_params
.
min
=
-
1
;
weight_params
.
max
=
1
;
*
correct
=
reinterpret_cast
<
float
*>
(
malloc
(
out_t
->
ElementsNum
()
*
sizeof
(
float
)));
float
nchw_co
[]
=
{
3.84586822
,
0.93586633
,
12.16212629
,
-
10.93835061
,
2.46887183
,
8.61480108
};
memcpy
(
*
correct
,
nchw_co
,
out_t
->
ElementsNum
()
*
sizeof
(
float
));
int
bias
[
6
]
=
{
0
};
TensorInfo
bias_params
;
bias_params
.
data_int
=
bias
;
bias_params
.
len
=
6
;
std
::
vector
<
int
>
bias_shape
{
6
};
bias_params
.
shape
=
&
bias_shape
;
matmal_param
->
b_transpose_
=
true
;
matmal_param
->
a_transpose_
=
false
;
matmal_param
->
has_bias_
=
true
;
matmal_param
->
act_type_
=
ActType_No
;
return
out_t
->
ElementsNum
();
}
float
correct
[]
=
{
-
19.170732
,
-
7.5019627
,
-
13.015462
,
-
27.760283
,
4.1447954
,
20.660276
,
4.0412164
,
-
33.750015
,
-
4.560128
,
7.1035166
,
27.976341
,
9.75216
,
14.383608
,
-
12.87587
,
-
24.688887
,
-
12.185722
,
3.7933283
,
-
19.266382
,
17.193876
,
-
49.99205
,
-
15.480089
,
-
3.1659412
,
19.470417
,
13.758459
,
4.0713396
,
4.614437
,
11.296907
,
-
7.244551
,
-
11.143417
,
-
21.233654
};
TensorInfo
out_params
;
out_params
.
data
=
correct
;
out_params
.
len
=
30
;
std
::
vector
<
int
>
out_shape
{
5
,
6
};
out_params
.
shape
=
&
out_shape
;
out_params
.
min
=
-
50
;
out_params
.
max
=
50
;
TEST_F
(
TestFcInt8
,
fcint8
)
{
std
::
vector
<
lite
::
tensor
::
Tensor
*>
inputs_
;
std
::
vector
<
lite
::
tensor
::
Tensor
*>
outputs_
;
auto
matmul_param
=
new
MatMulParameter
()
;
f
loat
*
correct
;
double
output_scale
;
int
output_zp
;
int
total_size
=
FcInt8TestInit
(
&
inputs_
,
&
outputs_
,
matmul_param
,
&
correct
,
&
output_scale
,
&
output_zp
);
lite
::
Context
*
ctx
=
new
lite
::
Context
;
auto
fc_param
=
new
MatMulParameter
();
fc_param
->
a_transpose_
=
false
;
fc_param
->
b_transpose_
=
true
;
fc_param
->
has_bias_
=
true
;
f
c_param
->
act_type_
=
ActType_No
;
std
::
vector
<
lite
::
tensor
::
Tensor
*>
inputs
;
std
::
vector
<
lite
::
tensor
::
Tensor
*>
outputs
;
FcInt8TestInit
(
&
inputs
,
&
outputs
,
&
in_params
,
&
weight_params
,
&
bias_params
,
&
out_params
);
auto
ctx
=
new
lite
::
Context
;
ctx
->
thread_num_
=
2
;
kernel
::
FullconnectionInt8CPUKernel
*
fc
=
new
kernel
::
FullconnectionInt8CPUKernel
(
reinterpret_cast
<
OpParameter
*>
(
matmul_param
),
inputs_
,
outputs_
,
ctx
,
nullptr
);
kernel
::
FullconnectionInt8CPUKernel
*
fc
=
new
kernel
::
FullconnectionInt8CPUKernel
(
reinterpret_cast
<
OpParameter
*>
(
fc_param
),
inputs
,
outputs
,
ctx
,
nullptr
);
fc
->
Init
();
fc
->
Run
();
float
fout
[
6
]
=
{
0
};
Dequantize
(
reinterpret_cast
<
int8_t
*>
(
outputs_
[
0
]
->
Data
()),
outputs_
[
0
]
->
ElementsNum
(),
output_scale
,
output_zp
,
fout
);
CompareOutputData
(
fout
,
correct
,
6
,
0.2
);
delete
matmul_param
;
float
out_scale
;
int
out_zp
;
QuantProcess
(
correct
,
out_params
.
len
,
out_params
.
min
,
out_params
.
max
,
&
out_scale
,
&
out_zp
,
nullptr
);
float
*
out
=
new
float
[
out_params
.
len
];
Dequantize
(
reinterpret_cast
<
int8_t
*>
(
outputs
[
0
]
->
Data
()),
outputs
[
0
]
->
ElementsNum
(),
out_scale
,
out_zp
,
out
);
CompareOutputData
(
out
,
correct
,
6
,
0.3
);
delete
fc
;
for
(
auto
t
:
inputs
_
)
delete
t
;
for
(
auto
t
:
outputs
_
)
delete
t
;
free
(
correct
)
;
for
(
auto
t
:
inputs
)
delete
t
;
for
(
auto
t
:
outputs
)
delete
t
;
delete
[]
out
;
}
}
// namespace mindspore
This diff is collapsed.
Click to expand it.
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/matmul_int8_tests.cc
浏览文件 @
cd8b664f
此差异已折叠。
点击以展开。
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录
新手
引导
客服
返回
顶部