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fbeb3e20
编写于
8月 10, 2018
作者:
qnqinan
浏览文件
操作
浏览文件
下载
差异文件
Merge remote-tracking branch 'origin/develop' into develop
上级
45cedd40
86a8196a
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
233 addition
and
29 deletion
+233
-29
src/common/enforce.h
src/common/enforce.h
+8
-1
src/operators/kernel/central-arm-func/conv_add_arm_func.h
src/operators/kernel/central-arm-func/conv_add_arm_func.h
+1
-6
src/operators/kernel/central-arm-func/conv_add_relu_arm_func.h
...perators/kernel/central-arm-func/conv_add_relu_arm_func.h
+1
-6
src/operators/kernel/central-arm-func/fusion_fc_arm_func.h
src/operators/kernel/central-arm-func/fusion_fc_arm_func.h
+6
-5
src/operators/math/gemm.cpp
src/operators/math/gemm.cpp
+199
-3
src/operators/math/gemm.h
src/operators/math/gemm.h
+10
-1
src/operators/math/math_function.cpp
src/operators/math/math_function.cpp
+3
-2
src/operators/math/math_function.h
src/operators/math/math_function.h
+2
-2
test/common/test_gemm_perf.cpp
test/common/test_gemm_perf.cpp
+3
-3
未找到文件。
src/common/enforce.h
浏览文件 @
fbeb3e20
...
@@ -61,7 +61,14 @@ struct PaddleMobileException : public std::exception {
...
@@ -61,7 +61,14 @@ struct PaddleMobileException : public std::exception {
}
}
#else
#else
#define PADDLE_MOBILE_THROW_EXCEPTION(...)
#define PADDLE_MOBILE_THROW_EXCEPTION(...)
#define PADDLE_MOBILE_ENFORCE(stat, ...)
#define PADDLE_MOBILE_ENFORCE(stat, ...) \
{ \
if (stat) { \
} else { \
} \
}
#endif
#endif
}
// namespace paddle_mobile
}
// namespace paddle_mobile
src/operators/kernel/central-arm-func/conv_add_arm_func.h
浏览文件 @
fbeb3e20
...
@@ -31,12 +31,7 @@ void ConvAddBasic(const FusionConvAddParam ¶m) {
...
@@ -31,12 +31,7 @@ void ConvAddBasic(const FusionConvAddParam ¶m) {
Tensor
bias
=
*
param
.
Bias
();
Tensor
bias
=
*
param
.
Bias
();
int
axis
=
param
.
Axis
();
int
axis
=
param
.
Axis
();
Tensor
*
output
=
param
.
Output
();
Tensor
*
output
=
param
.
Output
();
math
::
expand_bias
(
bias
,
axis
,
output
->
dims
());
float
*
output_data
=
output
->
data
<
float
>
();
float
*
biase_data
=
bias
.
data
<
float
>
();
float
*
biase_data
=
bias
.
data
<
float
>
();
for
(
int
k
=
0
;
k
<
output
->
numel
();
++
k
)
{
output_data
[
k
]
=
biase_data
[
k
];
}
int
groups
=
param
.
Groups
();
int
groups
=
param
.
Groups
();
std
::
vector
<
int
>
strides
=
param
.
Strides
();
std
::
vector
<
int
>
strides
=
param
.
Strides
();
...
@@ -113,7 +108,7 @@ void ConvAddBasic(const FusionConvAddParam ¶m) {
...
@@ -113,7 +108,7 @@ void ConvAddBasic(const FusionConvAddParam ¶m) {
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
float
>
(
filter_slice
,
false
,
col_matrix
,
false
,
math
::
matmul
<
float
>
(
filter_slice
,
false
,
col_matrix
,
false
,
static_cast
<
float
>
(
1
),
&
out_slice
,
static_cast
<
float
>
(
1
),
&
out_slice
,
static_cast
<
float
>
(
1
));
static_cast
<
float
>
(
1
)
,
false
,
biase_data
);
}
}
}
}
}
}
...
...
src/operators/kernel/central-arm-func/conv_add_relu_arm_func.h
浏览文件 @
fbeb3e20
...
@@ -32,12 +32,7 @@ void ConvAddReluCompute(const FusionConvAddReluParam ¶m) {
...
@@ -32,12 +32,7 @@ void ConvAddReluCompute(const FusionConvAddReluParam ¶m) {
Tensor
bias
=
*
param
.
Bias
();
Tensor
bias
=
*
param
.
Bias
();
int
axis
=
param
.
Axis
();
int
axis
=
param
.
Axis
();
Tensor
*
output
=
param
.
Output
();
Tensor
*
output
=
param
.
Output
();
math
::
expand_bias
(
bias
,
axis
,
output
->
dims
());
float
*
output_data
=
output
->
data
<
float
>
();
float
*
biase_data
=
bias
.
data
<
float
>
();
float
*
biase_data
=
bias
.
data
<
float
>
();
for
(
int
k
=
0
;
k
<
output
->
numel
();
++
k
)
{
output_data
[
k
]
=
biase_data
[
k
];
}
int
groups
=
param
.
Groups
();
int
groups
=
param
.
Groups
();
std
::
vector
<
int
>
strides
=
param
.
Strides
();
std
::
vector
<
int
>
strides
=
param
.
Strides
();
...
@@ -115,7 +110,7 @@ void ConvAddReluCompute(const FusionConvAddReluParam ¶m) {
...
@@ -115,7 +110,7 @@ void ConvAddReluCompute(const FusionConvAddReluParam ¶m) {
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
float
>
(
filter_slice
,
false
,
col_matrix
,
false
,
math
::
matmul
<
float
>
(
filter_slice
,
false
,
col_matrix
,
false
,
static_cast
<
float
>
(
1
),
&
out_slice
,
static_cast
<
float
>
(
1
),
&
out_slice
,
static_cast
<
float
>
(
1
),
true
);
static_cast
<
float
>
(
1
),
true
,
biase_data
);
}
}
}
}
}
}
...
...
src/operators/kernel/central-arm-func/fusion_fc_arm_func.h
浏览文件 @
fbeb3e20
...
@@ -30,6 +30,7 @@ void FusionFcCompute(const FusionFcParam ¶m) {
...
@@ -30,6 +30,7 @@ void FusionFcCompute(const FusionFcParam ¶m) {
int
axis
=
param
.
Axis
();
int
axis
=
param
.
Axis
();
Tensor
*
out
=
param
.
Out
();
Tensor
*
out
=
param
.
Out
();
auto
*
out_data
=
out
->
mutable_data
<
float
>
();
auto
*
out_data
=
out
->
mutable_data
<
float
>
();
float
*
bias_data
=
out
->
mutable_data
<
float
>
();
const
Tensor
x_matrix
=
const
Tensor
x_matrix
=
input_x
->
dims
().
size
()
>
2
input_x
->
dims
().
size
()
>
2
?
framework
::
ReshapeToMatrix
(
*
input_x
,
param
.
XNumColDims
())
?
framework
::
ReshapeToMatrix
(
*
input_x
,
param
.
XNumColDims
())
...
@@ -47,18 +48,18 @@ void FusionFcCompute(const FusionFcParam ¶m) {
...
@@ -47,18 +48,18 @@ void FusionFcCompute(const FusionFcParam ¶m) {
PADDLE_MOBILE_ENFORCE
(
out_dim
[
1
]
==
input_z
->
dims
()[
0
],
PADDLE_MOBILE_ENFORCE
(
out_dim
[
1
]
==
input_z
->
dims
()[
0
],
" out_dim.size must be 2."
);
" out_dim.size must be 2."
);
axis
=
(
axis
==
-
1
?
out_dim
.
size
()
-
input_z
->
dims
().
size
()
:
axis
);
axis
=
(
axis
==
-
1
?
out_dim
.
size
()
-
input_z
->
dims
().
size
()
:
axis
);
PADDLE_MOBILE_ENFORCE
(
axis
==
1
,
" to fit broadcast, axis = 1. "
)
PADDLE_MOBILE_ENFORCE
(
axis
==
1
,
" to fit broadcast, axis = 1. "
)
;
int64_t
classes
=
input_z
->
numel
();
int64_t
classes
=
input_z
->
numel
();
for
(
int
i
=
0
;
i
<
out_dim
[
0
];
i
++
)
{
for
(
int
i
=
0
;
i
<
out_dim
[
0
];
i
++
)
{
memory
::
Copy
(
out_data
+
i
*
classes
,
input_z_data
,
sizeof
(
float
)
*
classes
);
memory
::
Copy
(
out_data
+
i
*
classes
,
input_z_data
,
sizeof
(
float
)
*
classes
);
}
}
for
(
int
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
//
for (int i = 0; i < out->numel(); i++) {
DLOG
<<
out_data
[
i
];
//
DLOG << out_data[i];
}
//
}
math
::
matmul
<
float
>
(
x_matrix
,
false
,
y_matrix
,
false
,
static_cast
<
float
>
(
1
),
math
::
matmul
<
float
>
(
x_matrix
,
false
,
y_matrix
,
false
,
static_cast
<
float
>
(
1
),
out
,
static_cast
<
float
>
(
1
));
out
,
static_cast
<
float
>
(
1
)
,
false
,
bias_data
);
PADDLE_MOBILE_ENFORCE
(
out_dim
.
size
()
==
2
,
" out_dim.size must be 2."
);
PADDLE_MOBILE_ENFORCE
(
out_dim
.
size
()
==
2
,
" out_dim.size must be 2."
);
// if (out_dim.size() != 2) {
// if (out_dim.size() != 2) {
// out->Resize(out_dim);
// out->Resize(out_dim);
...
...
src/operators/math/gemm.cpp
浏览文件 @
fbeb3e20
...
@@ -392,6 +392,42 @@ void InnerKernel(int mc, int nc, float alpha, const float *a, const float *b,
...
@@ -392,6 +392,42 @@ void InnerKernel(int mc, int nc, float alpha, const float *a, const float *b,
}
}
}
}
// 分块矩阵乘法
void
InnerKernelWithBias
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
const
float
*
b
,
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
bool
relu
,
float
*
bias
)
{
#pragma omp parallel for
for
(
int
j
=
0
;
j
<
nc
;
j
+=
NR
)
{
for
(
int
i
=
0
;
i
<
mc
;
i
+=
MR
)
{
#if __aarch64__
// AddDot8x12(KC, a + i * KC, b + j * KC, c + i * NC + j, NC);
AddDot6x16
(
KC
,
a
+
i
*
KC
,
b
+
j
*
KC
,
c
+
i
*
NC
+
j
,
NC
);
#else
// AddDot4x4(KC, a + i * KC, b + j * KC, c + i * NC + j, NC);
// AddDot4x8(KC, a + i * KC, b + j * KC, c + i * NC + j, NC);
AddDot6x8
(
KC
,
a
+
i
*
KC
,
b
+
j
*
KC
,
c
+
i
*
NC
+
j
,
NC
);
#endif
}
}
if
(
alpha
!=
1
)
{
WriteWithAlphaBeta
(
mc
,
nc
,
c
,
C
,
ldc
);
return
;
}
if
(
beta
==
0
)
{
WriteBasic
(
mc
,
nc
,
c
,
C
,
ldc
);
return
;
}
if
(
beta
==
1
&&
!
relu
)
{
WriteWithAddV1
(
mc
,
nc
,
c
,
C
,
ldc
,
bias
);
return
;
}
if
(
beta
==
1
&&
relu
)
{
WriteWithAddReluV1
(
mc
,
nc
,
c
,
C
,
ldc
,
bias
);
return
;
}
}
// 分块矩阵乘法
// 分块矩阵乘法
void
InnerKernelWithBn
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
void
InnerKernelWithBn
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
const
float
*
b
,
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
const
float
*
b
,
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
...
@@ -577,6 +613,43 @@ void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc) {
...
@@ -577,6 +613,43 @@ void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc) {
}
}
}
}
}
}
// C = A * B + bias
void
WriteWithAddV1
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
bias
)
{
int
nc1
=
nc
/
4
;
int
_nc1
=
nc
%
4
;
float
*
c_ptr
,
*
C_ptr
;
float32x4_t
cv
;
float32x4_t
biasv
;
for
(
int
i
=
0
;
i
<
mc
;
++
i
)
{
c_ptr
=
c
+
i
*
NC
;
C_ptr
=
C
+
i
*
ldc
;
biasv
=
vld1q_dup_f32
(
bias
+
i
);
for
(
int
j
=
0
;
j
<
nc1
;
++
j
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
vst1q_f32
(
C_ptr
,
cv
);
c_ptr
+=
4
;
C_ptr
+=
4
;
}
if
(
_nc1
!=
0
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
if
(
_nc1
>=
1
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
0
);
C_ptr
++
;
}
if
(
_nc1
>=
2
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
1
);
C_ptr
++
;
}
if
(
_nc1
>=
3
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
2
);
C_ptr
++
;
}
}
}
}
// C = A * B + C, relu(C)
// C = A * B + C, relu(C)
void
WriteWithAddRelu
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
)
{
void
WriteWithAddRelu
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
)
{
...
@@ -619,6 +692,48 @@ void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc) {
...
@@ -619,6 +692,48 @@ void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc) {
}
}
}
}
// C = A * B + bias, relu(C)
void
WriteWithAddReluV1
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
bias
)
{
int
nc1
=
nc
/
4
;
int
_nc1
=
nc
%
4
;
float
*
c_ptr
,
*
C_ptr
;
float32x4_t
cv
;
float32x4_t
biasv
;
float32x4_t
zero
=
vdupq_n_f32
(
0.0
);
for
(
int
i
=
0
;
i
<
mc
;
++
i
)
{
c_ptr
=
c
+
i
*
NC
;
C_ptr
=
C
+
i
*
ldc
;
biasv
=
vld1q_dup_f32
(
bias
+
i
);
for
(
int
j
=
0
;
j
<
nc1
;
++
j
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
cv
=
vmaxq_f32
(
cv
,
zero
);
vst1q_f32
(
C_ptr
,
cv
);
c_ptr
+=
4
;
C_ptr
+=
4
;
}
if
(
_nc1
!=
0
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
cv
=
vmaxq_f32
(
cv
,
zero
);
if
(
_nc1
>=
1
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
0
);
C_ptr
++
;
}
if
(
_nc1
>=
2
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
1
);
C_ptr
++
;
}
if
(
_nc1
>=
3
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
2
);
C_ptr
++
;
}
}
}
}
// C = A * B, batchnorm(C)
// C = A * B, batchnorm(C)
void
WriteWithBn
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
new_scale
,
void
WriteWithBn
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
new_scale
,
float
*
new_bias
)
{
float
*
new_bias
)
{
...
@@ -1448,6 +1563,44 @@ void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc) {
...
@@ -1448,6 +1563,44 @@ void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc) {
}
}
}
}
// C = A * B + bias
void
WriteWithAddV1
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
bias
)
{
int
nc1
=
nc
/
4
;
int
_nc1
=
nc
%
4
;
float
*
c_ptr
,
*
C_ptr
;
float32x4_t
cv
;
float32x4_t
biasv
;
for
(
int
i
=
0
;
i
<
mc
;
++
i
)
{
c_ptr
=
c
+
i
*
NC
;
C_ptr
=
C
+
i
*
ldc
;
biasv
=
vld1q_dup_f32
(
bias
+
i
);
for
(
int
j
=
0
;
j
<
nc1
;
++
j
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
vst1q_f32
(
C_ptr
,
cv
);
c_ptr
+=
4
;
C_ptr
+=
4
;
}
if
(
_nc1
!=
0
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
if
(
_nc1
>=
1
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
0
);
C_ptr
++
;
}
if
(
_nc1
>=
2
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
1
);
C_ptr
++
;
}
if
(
_nc1
>=
3
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
2
);
C_ptr
++
;
}
}
}
}
// C = A * B + C, relu(C)
// C = A * B + C, relu(C)
void
WriteWithAddRelu
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
)
{
void
WriteWithAddRelu
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
)
{
int
nc1
=
nc
/
16
;
int
nc1
=
nc
/
16
;
...
@@ -1522,6 +1675,48 @@ void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc) {
...
@@ -1522,6 +1675,48 @@ void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc) {
}
}
}
}
// C = A * B + bias, relu(C)
void
WriteWithAddReluV1
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
bias
)
{
int
nc1
=
nc
/
4
;
int
_nc1
=
nc
%
4
;
float
*
c_ptr
,
*
C_ptr
;
float32x4_t
cv
;
float32x4_t
biasv
;
float32x4_t
zero
=
vdupq_n_f32
(
0.0
);
for
(
int
i
=
0
;
i
<
mc
;
++
i
)
{
c_ptr
=
c
+
i
*
NC
;
C_ptr
=
C
+
i
*
ldc
;
biasv
=
vld1q_dup_f32
(
bias
+
i
);
for
(
int
j
=
0
;
j
<
nc1
;
++
j
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
cv
=
vmaxq_f32
(
cv
,
zero
);
vst1q_f32
(
C_ptr
,
cv
);
c_ptr
+=
4
;
C_ptr
+=
4
;
}
if
(
_nc1
!=
0
)
{
cv
=
vld1q_f32
(
c_ptr
);
cv
=
vaddq_f32
(
cv
,
biasv
);
cv
=
vmaxq_f32
(
cv
,
zero
);
if
(
_nc1
>=
1
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
0
);
C_ptr
++
;
}
if
(
_nc1
>=
2
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
1
);
C_ptr
++
;
}
if
(
_nc1
>=
3
)
{
vst1q_lane_f32
(
C_ptr
,
cv
,
2
);
C_ptr
++
;
}
}
}
}
// C = A * B, batchnorm(C)
// C = A * B, batchnorm(C)
void
WriteWithBn
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
scale
,
void
WriteWithBn
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
scale
,
float
*
bias
)
{
float
*
bias
)
{
...
@@ -2053,7 +2248,8 @@ void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc) {
...
@@ -2053,7 +2248,8 @@ void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc) {
// 32位 float 矩阵乘法
// 32位 float 矩阵乘法
void
Sgemm
(
int
m
,
int
n
,
int
k
,
float
alpha
,
const
float
*
A
,
int
lda
,
void
Sgemm
(
int
m
,
int
n
,
int
k
,
float
alpha
,
const
float
*
A
,
int
lda
,
const
float
*
B
,
int
ldb
,
float
beta
,
float
*
C
,
int
ldc
,
bool
relu
)
{
const
float
*
B
,
int
ldb
,
float
beta
,
float
*
C
,
int
ldc
,
bool
relu
,
float
*
bias
)
{
// L1 data cache is 32 kib (Per Contex-A57, Contex-A72, Contex-A73)
// L1 data cache is 32 kib (Per Contex-A57, Contex-A72, Contex-A73)
// L2 cache is 0.5~4 Mib (Contex-A72 cluster)
// L2 cache is 0.5~4 Mib (Contex-A72 cluster)
int
L1
=
32
*
1024
;
int
L1
=
32
*
1024
;
...
@@ -2103,8 +2299,8 @@ void Sgemm(int m, int n, int k, float alpha, const float *A, int lda,
...
@@ -2103,8 +2299,8 @@ void Sgemm(int m, int n, int k, float alpha, const float *A, int lda,
#else
#else
PackMatrixA_6r
(
mc
,
KC
,
mc
%
MR
,
&
A
(
i
,
0
),
lda
,
packedA
);
PackMatrixA_6r
(
mc
,
KC
,
mc
%
MR
,
&
A
(
i
,
0
),
lda
,
packedA
);
#endif
#endif
InnerKernel
(
mc
,
nc
,
alpha
,
packedA
,
packedB
,
beta
,
packedC
,
&
C
(
i
,
j
),
ldc
,
InnerKernel
WithBias
(
mc
,
nc
,
alpha
,
packedA
,
packedB
,
beta
,
packedC
,
relu
);
&
C
(
i
,
j
),
ldc
,
relu
,
bias
+
i
);
}
}
}
}
...
...
src/operators/math/gemm.h
浏览文件 @
fbeb3e20
...
@@ -62,6 +62,9 @@ void PackMatrixB_16c(int k, int n, int n_tail, const float *B, int ldb,
...
@@ -62,6 +62,9 @@ void PackMatrixB_16c(int k, int n, int n_tail, const float *B, int ldb,
// 分块矩阵乘法
// 分块矩阵乘法
void
InnerKernel
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
const
float
*
b
,
void
InnerKernel
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
const
float
*
b
,
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
bool
relu
);
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
bool
relu
);
void
InnerKernelWithBias
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
const
float
*
b
,
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
bool
relu
,
float
*
bias
);
void
InnerKernelWithBn
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
void
InnerKernelWithBn
(
int
mc
,
int
nc
,
float
alpha
,
const
float
*
a
,
const
float
*
b
,
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
const
float
*
b
,
float
beta
,
float
*
c
,
float
*
C
,
int
ldc
,
...
@@ -91,8 +94,13 @@ void WriteBasic(int mc, int nc, float *c, float *C, int ldc);
...
@@ -91,8 +94,13 @@ void WriteBasic(int mc, int nc, float *c, float *C, int ldc);
void
WriteWithAlphaBeta
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
);
void
WriteWithAlphaBeta
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
);
// C = A * B + C
// C = A * B + C
void
WriteWithAdd
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
);
void
WriteWithAdd
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
);
// C = A * B + bias
void
WriteWithAddV1
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
bias
);
// C = A * B + C, relu(C)
// C = A * B + C, relu(C)
void
WriteWithAddRelu
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
);
void
WriteWithAddRelu
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
);
// C = A * B + bias ,relu(C)
void
WriteWithAddReluV1
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
bias
);
// C = A * B, batchnorm(C)
// C = A * B, batchnorm(C)
void
WriteWithBn
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
new_scale
,
void
WriteWithBn
(
int
mc
,
int
nc
,
float
*
c
,
float
*
C
,
int
ldc
,
float
*
new_scale
,
float
*
new_bias
);
float
*
new_bias
);
...
@@ -120,7 +128,8 @@ void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float *new_scale,
...
@@ -120,7 +128,8 @@ void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float *new_scale,
// 32位 float 矩阵乘法
// 32位 float 矩阵乘法
void
Sgemm
(
int
m
,
int
n
,
int
k
,
float
alpha
,
const
float
*
A
,
int
lda
,
void
Sgemm
(
int
m
,
int
n
,
int
k
,
float
alpha
,
const
float
*
A
,
int
lda
,
const
float
*
B
,
int
ldb
,
float
beta
,
float
*
C
,
int
ldc
,
bool
relu
);
const
float
*
B
,
int
ldb
,
float
beta
,
float
*
C
,
int
ldc
,
bool
relu
,
float
*
bias
);
// 32位 float 矩阵乘法, 并对结果进行 batchnrom
// 32位 float 矩阵乘法, 并对结果进行 batchnrom
void
SgemmWithBn
(
int
m
,
int
n
,
int
k
,
float
alpha
,
const
float
*
A
,
int
lda
,
void
SgemmWithBn
(
int
m
,
int
n
,
int
k
,
float
alpha
,
const
float
*
A
,
int
lda
,
...
...
src/operators/math/math_function.cpp
浏览文件 @
fbeb3e20
...
@@ -22,7 +22,8 @@ namespace math {
...
@@ -22,7 +22,8 @@ namespace math {
template
<
>
template
<
>
void
matmul
<
float
>
(
const
framework
::
Tensor
&
matrix_a
,
bool
trans_a
,
void
matmul
<
float
>
(
const
framework
::
Tensor
&
matrix_a
,
bool
trans_a
,
const
framework
::
Tensor
&
matrix_b
,
bool
trans_b
,
float
alpha
,
const
framework
::
Tensor
&
matrix_b
,
bool
trans_b
,
float
alpha
,
framework
::
Tensor
*
matrix_out
,
float
beta
,
bool
relu
)
{
framework
::
Tensor
*
matrix_out
,
float
beta
,
bool
relu
,
float
*
bias
)
{
auto
dim_a
=
matrix_a
.
dims
();
auto
dim_a
=
matrix_a
.
dims
();
auto
dim_b
=
matrix_b
.
dims
();
auto
dim_b
=
matrix_b
.
dims
();
auto
dim_out
=
matrix_out
->
dims
();
auto
dim_out
=
matrix_out
->
dims
();
...
@@ -42,7 +43,7 @@ void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
...
@@ -42,7 +43,7 @@ void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
int
K
=
(
!
trans_a
)
?
dim_a
[
1
]
:
dim_a
[
0
];
int
K
=
(
!
trans_a
)
?
dim_a
[
1
]
:
dim_a
[
0
];
Sgemm
(
M
,
N
,
K
,
alpha
,
matrix_a
.
data
<
float
>
(),
K
,
matrix_b
.
data
<
float
>
(),
N
,
Sgemm
(
M
,
N
,
K
,
alpha
,
matrix_a
.
data
<
float
>
(),
K
,
matrix_b
.
data
<
float
>
(),
N
,
beta
,
matrix_out
->
data
<
float
>
(),
N
,
relu
);
beta
,
matrix_out
->
data
<
float
>
(),
N
,
relu
,
bias
);
}
}
template
<
>
template
<
>
...
...
src/operators/math/math_function.h
浏览文件 @
fbeb3e20
...
@@ -21,11 +21,11 @@ namespace paddle_mobile {
...
@@ -21,11 +21,11 @@ namespace paddle_mobile {
namespace
operators
{
namespace
operators
{
namespace
math
{
namespace
math
{
// matrix multiply with continuous memory
template
<
typename
T
>
template
<
typename
T
>
void
matmul
(
const
framework
::
Tensor
&
matrix_a
,
bool
trans_a
,
void
matmul
(
const
framework
::
Tensor
&
matrix_a
,
bool
trans_a
,
const
framework
::
Tensor
&
matrix_b
,
bool
trans_b
,
T
alpha
,
const
framework
::
Tensor
&
matrix_b
,
bool
trans_b
,
T
alpha
,
framework
::
Tensor
*
matrix_out
,
T
beta
,
bool
relu
=
false
);
framework
::
Tensor
*
matrix_out
,
T
beta
,
bool
relu
=
false
,
float
*
bias
=
nullptr
);
template
<
typename
T
>
template
<
typename
T
>
void
matmulWithBn
(
const
framework
::
Tensor
&
matrix_a
,
bool
trans_a
,
void
matmulWithBn
(
const
framework
::
Tensor
&
matrix_a
,
bool
trans_a
,
...
...
test/common/test_gemm_perf.cpp
浏览文件 @
fbeb3e20
...
@@ -49,9 +49,9 @@ int main() {
...
@@ -49,9 +49,9 @@ int main() {
auto
time1
=
time
();
auto
time1
=
time
();
for
(
int
j
=
0
;
j
<
10
;
++
j
)
{
for
(
int
j
=
0
;
j
<
10
;
++
j
)
{
paddle_mobile
::
operators
::
math
::
matmul
<
float
>
(
aa
,
false
,
bb
,
false
,
paddle_mobile
::
operators
::
math
::
matmul
<
float
>
(
static_cast
<
float
>
(
1
),
&
cc
,
aa
,
false
,
bb
,
false
,
static_cast
<
float
>
(
1
),
&
cc
,
static_cast
<
float
>
(
0
)
,
static_cast
<
float
>
(
0
),
false
);
false
,
biasptr
);
// paddle_mobile::operators::math::matmulWithBn<float>(
// paddle_mobile::operators::math::matmulWithBn<float>(
// aa, false, bb, false, static_cast<float>(1), &cc,
// aa, false, bb, false, static_cast<float>(1), &cc,
...
...
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