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PaddleDetection
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b3c63f40
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PaddleDetection
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b3c63f40
编写于
9月 28, 2018
作者:
T
tensor-tang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add vscal and unit test
上级
0987f2b4
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
193 addition
and
1 deletion
+193
-1
paddle/fluid/operators/math/jit_kernel.h
paddle/fluid/operators/math/jit_kernel.h
+7
-0
paddle/fluid/operators/math/jit_kernel_blas.cc
paddle/fluid/operators/math/jit_kernel_blas.cc
+76
-0
paddle/fluid/operators/math/jit_kernel_test.cc
paddle/fluid/operators/math/jit_kernel_test.cc
+110
-1
未找到文件。
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @
b3c63f40
...
...
@@ -75,6 +75,13 @@ class VAddKernel : public Kernel {
virtual
void
Compute
(
const
int
n
,
const
T
*
x
,
const
T
*
y
,
T
*
z
)
=
0
;
};
template
<
typename
T
>
class
VScalKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
int
n
,
const
T
a
,
const
T
*
x
,
T
*
y
)
=
0
;
virtual
void
Compute
(
const
int
n
,
const
T
a
,
T
*
x
)
=
0
;
};
template
<
typename
T
>
class
LSTMKernel
:
public
Kernel
{
public:
...
...
paddle/fluid/operators/math/jit_kernel_blas.cc
浏览文件 @
b3c63f40
...
...
@@ -206,8 +206,84 @@ VADD_INTRI8_FLOAT(jit::avx512f);
#undef VADD_MKL_FLOAT
#undef VADD_MKL_DOUBLE
/* VSCAL JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VScalKernelImpl
:
public
VScalKernel
<
T
>
{
public:
void
Compute
(
const
int
n
,
const
T
a
,
const
T
*
x
,
T
*
y
)
override
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
a
*
x
[
i
];
}
}
void
Compute
(
const
int
n
,
const
T
a
,
T
*
x
)
override
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
x
[
i
]
=
a
*
x
[
i
];
}
}
};
#ifdef PADDLE_WITH_MKLML
#define VSCAL_MKL_FLOAT(isa, block) \
template <> \
void VScalKernelImpl<float, isa, block>::Compute(const int n, const float a, \
float* x) { \
platform::dynload::cblas_sscal(n, a, x, 1); \
}
#define VSCAL_MKL_DOUBLE(isa, block) \
template <> \
void VScalKernelImpl<double, isa, block>::Compute( \
const int n, const double a, double* x) { \
platform::dynload::cblas_dscal(n, a, x, 1); \
}
FOR_EACH_ISA
(
VSCAL_MKL_FLOAT
,
kGT16
);
FOR_EACH_ISA_BLOCK
(
VSCAL_MKL_DOUBLE
);
#endif
#define VSCAL_INTRI8(isa) \
template <> \
void VScalKernelImpl<float, isa, kEQ8>::Compute(const int n, const float a, \
const float* x, float* y) { \
__m256 tmp; \
__m256 scalar = _mm256_set1_ps(a); \
tmp = _mm256_loadu_ps(x); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(y, tmp); \
}
#define VSCAL_INTRI8_INPLACE(isa) \
template <> \
void VScalKernelImpl<float, isa, kEQ8>::Compute(const int n, const float a, \
float* x) { \
__m256 tmp; \
__m256 scalar = _mm256_set1_ps(a); \
tmp = _mm256_loadu_ps(x); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(x, tmp); \
}
#ifdef __AVX__
VSCAL_INTRI8
(
jit
::
avx
);
VSCAL_INTRI8_INPLACE
(
jit
::
avx
);
#endif
#ifdef __AVX2__
VSCAL_INTRI8
(
jit
::
avx2
);
VSCAL_INTRI8_INPLACE
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
VSCAL_INTRI8
(
jit
::
avx512f
);
VSCAL_INTRI8_INPLACE
(
jit
::
avx512f
);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef VSCAL_INTRI8
#undef VSCAL_INTRI8_INPLACE
#undef VSCAL_MKL_FLOAT
#undef VSCAL_MKL_DOUBLE
REGISTER_BLAS_JITKERNEL
(
vmul
,
VMulKernel
);
REGISTER_BLAS_JITKERNEL
(
vadd
,
VAddKernel
);
REGISTER_BLAS_JITKERNEL
(
vscal
,
VScalKernel
);
#undef FOR_EACH_ISA
#undef FOR_EACH_BLOCK
...
...
paddle/fluid/operators/math/jit_kernel_test.cc
浏览文件 @
b3c63f40
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <sys/time.h>
#include <cstring>
#include <string>
#include <vector>
#include "gflags/gflags.h"
...
...
@@ -28,6 +29,8 @@ limitations under the License. */
#include <immintrin.h>
#endif
constexpr
int
repeat
=
20000
;
inline
double
GetCurrentUS
()
{
struct
timeval
time
;
gettimeofday
(
&
time
,
NULL
);
...
...
@@ -46,7 +49,113 @@ void RandomVec(const int n, T* a) {
}
}
constexpr
int
repeat
=
20000
;
void
vscal_ref
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
a
*
x
[
i
];
}
}
void
vscal_inp_ref
(
const
int
n
,
const
float
a
,
float
*
x
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
x
[
i
]
=
a
*
x
[
i
];
}
}
#if defined __AVX__ || defined __AVX2__
void
vscal_intri8
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
__m256
tmp
;
__m256
scalar
=
_mm256_set1_ps
(
a
);
tmp
=
_mm256_loadu_ps
(
x
);
tmp
=
_mm256_mul_ps
(
tmp
,
scalar
);
_mm256_storeu_ps
(
y
,
tmp
);
}
void
vscal_inp_intri8
(
const
int
n
,
const
float
a
,
float
*
x
)
{
__m256
tmp
;
__m256
scalar
=
_mm256_set1_ps
(
a
);
tmp
=
_mm256_loadu_ps
(
x
);
tmp
=
_mm256_mul_ps
(
tmp
,
scalar
);
_mm256_storeu_ps
(
x
,
tmp
);
}
#endif
#ifdef PADDLE_WITH_MKLML
void
vscal_inp_mkl
(
const
int
n
,
const
float
a
,
float
*
x
)
{
paddle
::
platform
::
dynload
::
cblas_sscal
(
n
,
a
,
x
,
1
);
}
#endif
TEST
(
JitKernel
,
vscal
)
{
namespace
jit
=
paddle
::
operators
::
math
::
jitkernel
;
for
(
int
d
:
{
7
,
8
,
15
,
16
,
30
,
256
,
512
})
{
std
::
vector
<
float
>
x
(
d
),
y
(
d
);
std
::
vector
<
float
>
zref
(
d
),
ztgt
(
d
);
RandomVec
<
float
>
(
d
,
x
.
data
());
std
::
memcpy
(
y
.
data
(),
x
.
data
(),
sizeof
(
float
)
*
d
);
float
a
=
2.
f
;
const
auto
&
ker
=
jit
::
KernelPool
::
Instance
().
template
Get
<
jit
::
VScalKernel
<
float
>
>
(
d
);
const
float
*
x_data
=
x
.
data
();
float
*
y_data
=
y
.
data
();
float
*
ztgt_data
=
ztgt
.
data
();
float
*
zref_data
=
zref
.
data
();
auto
trefs
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
vscal_ref
(
d
,
a
,
x_data
,
zref_data
);
}
auto
trefe
=
GetCurrentUS
();
auto
trefs1
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
vscal_inp_ref
(
d
,
a
,
y_data
);
}
auto
trefe1
=
GetCurrentUS
();
#ifdef PADDLE_WITH_MKLML
auto
tmkls
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
vscal_inp_mkl
(
d
,
a
,
y_data
);
}
auto
tmkle
=
GetCurrentUS
();
#endif
#if defined __AVX__ || defined __AVX2__
if
(
d
==
8
)
{
auto
si0
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
vscal_intri8
(
d
,
a
,
x_data
,
zref_data
);
}
auto
si1
=
GetCurrentUS
();
auto
si2
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
vscal_inp_intri8
(
d
,
a
,
y_data
);
}
auto
si3
=
GetCurrentUS
();
VLOG
(
3
)
<<
"Vec size 8 intr takes: "
<<
(
si1
-
si0
)
/
repeat
<<
" us, inplace: "
<<
(
si3
-
si2
)
/
repeat
;
}
#endif
auto
ttgts
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
ker
->
Compute
(
d
,
a
,
x_data
,
ztgt_data
);
}
auto
ttgte
=
GetCurrentUS
();
auto
ttgts1
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
ker
->
Compute
(
d
,
a
,
y_data
);
}
auto
ttgte1
=
GetCurrentUS
();
VLOG
(
3
)
<<
"Vec size "
<<
d
<<
": refer takes: "
<<
(
trefe
-
trefs
)
/
repeat
<<
" us, inplace takes: "
<<
(
trefe1
-
trefs1
)
/
repeat
#ifdef PADDLE_WITH_MKLML
<<
" us, mkl inplace takes: "
<<
(
tmkle
-
tmkls
)
/
repeat
<<
" us, "
#else
<<
" us, "
#endif
<<
"tgt takes: "
<<
(
ttgte
-
ttgts
)
/
repeat
<<
"us, tgt inplace takes: "
<<
(
ttgte1
-
ttgts1
)
/
repeat
;
for
(
int
i
=
0
;
i
<
d
;
++
i
)
{
EXPECT_NEAR
(
ztgt_data
[
i
],
zref_data
[
i
],
1e-3
);
}
}
}
void
vmul_ref
(
const
int
n
,
const
float
*
x
,
const
float
*
y
,
float
*
z
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
...
...
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