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体验新版 GitCode,发现更多精彩内容 >>
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5e64244f
编写于
11月 08, 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 vaddbias jitcode
test=develop
上级
5f7956ae
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
62 addition
and
60 deletion
+62
-60
paddle/fluid/operators/math/jit_code.h
paddle/fluid/operators/math/jit_code.h
+11
-1
paddle/fluid/operators/math/jit_kernel.h
paddle/fluid/operators/math/jit_kernel.h
+3
-1
paddle/fluid/operators/math/jit_kernel_blas.cc
paddle/fluid/operators/math/jit_kernel_blas.cc
+37
-47
paddle/fluid/operators/math/jit_kernel_exp.cc
paddle/fluid/operators/math/jit_kernel_exp.cc
+6
-6
paddle/fluid/operators/math/jit_kernel_test.cc
paddle/fluid/operators/math/jit_kernel_test.cc
+5
-5
未找到文件。
paddle/fluid/operators/math/jit_code.h
浏览文件 @
5e64244f
...
...
@@ -31,16 +31,26 @@ using Label = Xbyak::Label;
typedef
enum
{
mul
=
0
,
add
}
operand_type
;
// function: vec = Operand(vec(
scalar), vec(
scalar)) (maybe with relu)
// function: vec = Operand(vec(
or scalar), vec(or
scalar)) (maybe with relu)
class
VXXJitCode
:
public
JitCode
{
public:
const
char
*
name
()
const
override
{
std
::
string
base
=
"VXXJitCode"
;
if
(
scalar_index_
==
1
)
{
base
+=
"_Scalar"
;
}
else
{
base
+=
"_Vec"
;
}
if
(
type_
==
operand_type
::
mul
)
{
base
+=
"_Mul"
;
}
else
if
(
type_
==
operand_type
::
add
)
{
base
+=
"_Add"
;
}
if
(
scalar_index_
==
2
)
{
base
+=
"_Scalar"
;
}
else
{
base
+=
"_Vec"
;
}
base
+=
(
with_relu_
?
"_Relu"
:
""
);
return
base
.
c_str
();
}
...
...
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @
5e64244f
...
...
@@ -83,13 +83,15 @@ class VAddReluKernel : public Kernel {
template
<
typename
T
>
class
VScalKernel
:
public
Kernel
{
public:
// y = a.*x
void
(
*
Compute
)(
const
T
*
,
const
T
*
,
T
*
,
int
);
};
template
<
typename
T
>
class
VAddBiasKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
a
,
const
T
*
x
,
T
*
y
)
const
=
0
;
// y = a.+x
void
(
*
Compute
)(
const
T
*
,
const
T
*
,
T
*
,
int
);
};
template
<
typename
T
>
...
...
paddle/fluid/operators/math/jit_kernel_blas.cc
浏览文件 @
5e64244f
...
...
@@ -60,6 +60,13 @@ void VScalRefer(const T* a, const T* x, T* y, int n) {
}
}
template
<
typename
T
>
void
VAddBiasRefer
(
const
T
*
a
,
const
T
*
x
,
T
*
y
,
int
n
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
a
[
0
]
+
x
[
i
];
}
}
#ifdef PADDLE_WITH_MKLML
template
<
typename
T
>
void
VMulMKL
(
const
T
*
x
,
const
T
*
y
,
T
*
z
,
int
n
);
...
...
@@ -300,62 +307,46 @@ bool VScalKernelImpl<double>::useMKL(int d) {
}
#endif
#undef DECLARE_STATIC_FUNC
REGISTER_JITKERNEL
(
vmul
,
VMulKernel
);
REGISTER_JITKERNEL
(
vadd
,
VAddKernel
);
REGISTER_JITKERNEL
(
vscal
,
VScalKernel
);
REGISTER_JITKERNEL
(
vaddrelu
,
VAddReluKernel
);
/* VAddBias JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
template
<
typename
T
>
class
VAddBiasKernelImpl
:
public
VAddBiasKernel
<
T
>
{
public:
explicit
VAddBiasKernelImpl
(
int
d
)
:
VAddBiasKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
a
,
const
T
*
x
,
T
*
y
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
x
[
i
]
+
a
;
DECLARE_STATIC_FUNC
;
explicit
VAddBiasKernelImpl
(
int
d
)
:
VAddBiasKernel
<
T
>
()
{
#ifdef PADDLE_WITH_XBYAK
if
(
useJIT
(
d
))
{
size_t
sz
=
96
+
d
/
AVX_FLOAT_BLOCK
*
4
*
8
;
jitcode_
.
reset
(
new
gen
::
VXXJitCode
(
d
,
gen
::
operand_type
::
add
,
1
,
false
,
sz
>
4096
?
sz
:
4096
));
this
->
Compute
=
jitcode_
->
getCode
<
void
(
*
)(
const
T
*
,
const
T
*
,
T
*
,
int
)
>
();
return
;
}
}
};
#define INTRI8_FLOAT(isa) \
template <> \
void VAddBiasKernelImpl<float, isa, kEQ8>::Compute( \
const float a, const float* x, float* y) const { \
__m256 tmp = _mm256_loadu_ps(x); \
tmp = _mm256_add_ps(tmp, _mm256_set1_ps(a)); \
_mm256_storeu_ps(y, tmp); \
}
#endif
#define INTRI16_FLOAT(isa) \
template <> \
void VAddBiasKernelImpl<float, isa, kEQ16>::Compute( \
const float a, const float* x, float* y) const { \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + 8); \
tmp0 = _mm256_add_ps(tmp0, _mm256_set1_ps(a)); \
tmp1 = _mm256_add_ps(tmp1, _mm256_set1_ps(a)); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + 8, tmp1); \
this
->
Compute
=
VAddBiasRefer
<
T
>
;
}
#ifdef PADDLE_WITH_XBYAK
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI16_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI16_FLOAT
(
jit
::
avx2
);
private:
std
::
unique_ptr
<
gen
::
VXXJitCode
>
jitcode_
{
nullptr
};
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI16_FLOAT
(
jit
::
avx512f
);
};
#ifdef PADDLE_WITH_XBYAK
template
<
>
bool
VAddBiasKernelImpl
<
float
>::
useJIT
(
int
d
)
{
return
gen
::
VXXJitCode
::
init
(
d
,
1
);
}
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef DECLARE_STATIC_FUNC
REGISTER_JITKERNEL
(
vmul
,
VMulKernel
);
REGISTER_JITKERNEL
(
vadd
,
VAddKernel
);
REGISTER_JITKERNEL
(
vaddrelu
,
VAddReluKernel
);
REGISTER_JITKERNEL
(
vscal
,
VScalKernel
);
REGISTER_JITKERNEL
(
vaddbias
,
VAddBiasKernel
);
/* VRelu JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
...
...
@@ -466,7 +457,6 @@ class VIdentityKernelImpl : public VIdentityKernel<T> {
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{}
};
REGISTER_JITKERNEL_DEPRECATED
(
vaddb
,
VAddBiasKernel
);
REGISTER_JITKERNEL_DEPRECATED
(
vrelu
,
VReluKernel
);
REGISTER_JITKERNEL_DEPRECATED
(
videntity
,
VIdentityKernel
);
...
...
paddle/fluid/operators/math/jit_kernel_exp.cc
浏览文件 @
5e64244f
...
...
@@ -409,11 +409,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
vaddbias_
=
KernelPool
::
Instance
().
template
Get
<
VAddBiasKernel
<
T
>
>
(
d
);
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{
const
T
a
=
static_cast
<
T
>
(
2
);
const
T
a
=
static_cast
<
T
>
(
2
)
,
b
=
static_cast
<
T
>
(
-
1
)
;
vscal_
->
Compute
(
&
a
,
x
,
y
,
this
->
num_
);
vsigmoid_
->
Compute
(
y
,
y
);
vscal_
->
Compute
(
&
a
,
y
,
y
,
this
->
num_
);
vaddbias_
->
Compute
(
static_cast
<
T
>
(
-
1
),
y
,
y
);
vaddbias_
->
Compute
(
&
b
,
y
,
y
,
this
->
num_
);
}
private:
...
...
@@ -473,11 +473,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
_mm256_storeu_ps(y, tmp); \
x += AVX_FLOAT_BLOCK; \
y += AVX_FLOAT_BLOCK; \
const float a = 2.f
;
\
const float a = 2.f
, b = -1.f;
\
vscal_->Compute(&a, x, y, this->num_); \
vsigmoid_->Compute(y, y); \
vscal_->Compute(&a, y, y, this->num_); \
vaddbias_->Compute(
-1.f, y, y);
\
vaddbias_->Compute(
&b, y, y, this->num_);
\
}
#define INTRI_GT16_FLOAT(isa, expisa) \
...
...
@@ -504,11 +504,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
} \
x += this->end_; \
y += this->end_; \
const float a = 2.f
;
\
const float a = 2.f
, b = -1.f;
\
vscal_->Compute(&a, x, y, this->num_); \
vsigmoid_->Compute(y, y); \
vscal_->Compute(&a, y, y, this->num_); \
vaddbias_->Compute(
-1.f, y, y);
\
vaddbias_->Compute(
&b, y, y, this->num_);
\
}
#ifndef __WIN32
...
...
paddle/fluid/operators/math/jit_kernel_test.cc
浏览文件 @
5e64244f
...
...
@@ -128,7 +128,7 @@ TEST(JitKernel, vaddbias) {
auto
trefe
=
GetCurrentUS
();
auto
ttgts
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
ker
->
Compute
(
a
,
x_data
,
ztgt_data
);
ker
->
Compute
(
&
a
,
x_data
,
ztgt_data
,
d
);
}
auto
ttgte
=
GetCurrentUS
();
...
...
@@ -281,11 +281,11 @@ void vtanh_better(
const
paddle
::
operators
::
math
::
jitkernel
::
VAddBiasKernel
<
float
>>&
vaddbias
,
const
int
n
,
const
float
*
x
,
float
*
y
)
{
const
float
tmp1
=
2
.
f
;
vscal
->
Compute
(
&
tmp1
,
x
,
y
,
n
);
const
float
a
=
2.
f
,
b
=
-
1
.
f
;
vscal
->
Compute
(
&
a
,
x
,
y
,
n
);
vsigmoid
->
Compute
(
y
,
y
);
vscal
->
Compute
(
&
tmp1
,
y
,
y
,
n
);
vaddbias
->
Compute
(
-
1.
f
,
y
,
y
);
vscal
->
Compute
(
&
a
,
y
,
y
,
n
);
vaddbias
->
Compute
(
&
b
,
y
,
y
,
n
);
}
TEST
(
JitKernel
,
vtanh
)
{
...
...
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