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体验新版 GitCode,发现更多精彩内容 >>
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03e11f3f
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 vscal jitcode
上级
5b7a9dd7
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
150 addition
and
85 deletion
+150
-85
paddle/fluid/operators/math/jit_code.cc
paddle/fluid/operators/math/jit_code.cc
+35
-0
paddle/fluid/operators/math/jit_code.h
paddle/fluid/operators/math/jit_code.h
+28
-2
paddle/fluid/operators/math/jit_kernel.h
paddle/fluid/operators/math/jit_kernel.h
+1
-2
paddle/fluid/operators/math/jit_kernel_blas.cc
paddle/fluid/operators/math/jit_kernel_blas.cc
+72
-71
paddle/fluid/operators/math/jit_kernel_exp.cc
paddle/fluid/operators/math/jit_kernel_exp.cc
+9
-6
paddle/fluid/operators/math/jit_kernel_test.cc
paddle/fluid/operators/math/jit_kernel_test.cc
+5
-4
未找到文件。
paddle/fluid/operators/math/jit_code.cc
浏览文件 @
03e11f3f
...
...
@@ -96,6 +96,41 @@ void VVVJitCode::generate() {
}
ret
();
}
bool
VScalJitCode
::
init
(
int
d
)
{
return
MayIUse
(
avx
);
}
void
VScalJitCode
::
generate
()
{
int
offset
=
0
;
vbroadcastss
(
ymm_src1
,
ptr
[
param1
]);
for
(
int
i
=
0
;
i
<
num_
/
AVX_FLOAT_BLOCK
;
++
i
)
{
vmovups
(
ymm_src2
,
ptr
[
param2
+
offset
]);
vmulps
(
ymm_dst
,
ymm_src1
,
ymm_src2
);
vmovups
(
ptr
[
param3
+
offset
],
ymm_dst
);
offset
+=
sizeof
(
float
)
*
AVX_FLOAT_BLOCK
;
}
int
rest
=
num_
%
AVX_FLOAT_BLOCK
;
if
(
rest
>=
4
)
{
vmovups
(
xmm_src2
,
ptr
[
param2
+
offset
]);
vmulps
(
xmm_dst
,
xmm_src1
,
xmm_src2
);
vmovups
(
ptr
[
param3
+
offset
],
xmm_dst
);
offset
+=
sizeof
(
float
)
*
4
;
rest
-=
4
;
}
if
(
rest
>=
2
)
{
vmovq
(
xmm_src2
,
ptr
[
param2
+
offset
]);
vmulps
(
xmm_dst
,
xmm_src1
,
xmm_src2
);
vmovq
(
ptr
[
param3
+
offset
],
xmm_dst
);
offset
+=
sizeof
(
float
)
*
2
;
rest
-=
2
;
}
if
(
rest
>
0
)
{
vmovss
(
xmm_src2
,
ptr
[
param2
+
offset
]);
vmulss
(
xmm_dst
,
xmm_src1
,
xmm_src2
);
vmovss
(
ptr
[
param3
+
offset
],
xmm_dst
);
}
ret
();
}
}
// namespace gen
}
// namespace jitkernel
}
// namespace math
...
...
paddle/fluid/operators/math/jit_code.h
浏览文件 @
03e11f3f
...
...
@@ -29,9 +29,9 @@ using ymm_t = const Xbyak::Ymm;
using
zmm_t
=
const
Xbyak
::
Zmm
;
using
Label
=
Xbyak
::
Label
;
// function: vec = Operand(vec, vec) (maybe with relu)
typedef
enum
{
mul
=
0
,
add
}
operand_type
;
// function: vec = Operand(vec, vec) (maybe with relu)
class
VVVJitCode
:
public
JitCode
{
public:
const
char
*
name
()
const
override
{
...
...
@@ -41,7 +41,7 @@ class VVVJitCode : public JitCode {
}
else
if
(
type_
==
operand_type
::
add
)
{
base
+=
"_Add"
;
}
base
+=
(
with_relu_
?
"_
r
elu"
:
""
);
base
+=
(
with_relu_
?
"_
R
elu"
:
""
);
return
base
.
c_str
();
}
explicit
VVVJitCode
(
int
d
,
operand_type
type
,
bool
with_relu
,
...
...
@@ -72,6 +72,32 @@ class VVVJitCode : public JitCode {
ymm_t
ymm_zero
=
ymm_t
(
2
);
};
class
VScalJitCode
:
public
JitCode
{
public:
DECLARE_JIT_CODE
(
VScalJitCode
);
explicit
VScalJitCode
(
int
d
,
size_t
code_size
=
256
*
1024
,
void
*
code_ptr
=
nullptr
)
:
JitCode
(
code_size
,
code_ptr
),
num_
(
d
)
{}
static
bool
init
(
int
d
);
void
generate
()
override
;
private:
int
num_
;
reg64_t
param1
{
abi_param1
};
reg64_t
param2
{
abi_param2
};
reg64_t
param3
{
abi_param3
};
xmm_t
xmm_src1
=
xmm_t
(
0
);
xmm_t
xmm_src2
=
xmm_t
(
1
);
xmm_t
xmm_dst
=
xmm_t
(
1
);
xmm_t
xmm_zero
=
xmm_t
(
2
);
ymm_t
ymm_src1
=
ymm_t
(
0
);
ymm_t
ymm_src2
=
ymm_t
(
1
);
ymm_t
ymm_dst
=
ymm_t
(
1
);
ymm_t
ymm_zero
=
ymm_t
(
2
);
};
}
// namespace gen
}
// namespace jitkernel
}
// namespace math
...
...
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @
03e11f3f
...
...
@@ -83,8 +83,7 @@ class VAddReluKernel : public Kernel {
template
<
typename
T
>
class
VScalKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
a
,
const
T
*
x
,
T
*
y
)
const
=
0
;
virtual
void
Compute
(
const
T
a
,
T
*
x
)
const
=
0
;
void
(
*
Compute
)(
const
T
*
,
const
T
*
,
T
*
,
int
);
};
template
<
typename
T
>
...
...
paddle/fluid/operators/math/jit_kernel_blas.cc
浏览文件 @
03e11f3f
...
...
@@ -57,6 +57,13 @@ void VAddReluRefer(const T* x, const T* y, T* z, int n) {
}
}
template
<
typename
T
>
void
VScalRefer
(
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
);
...
...
@@ -83,6 +90,28 @@ template <>
void
VAddMKL
<
double
>
(
const
double
*
x
,
const
double
*
y
,
double
*
z
,
int
n
)
{
platform
::
dynload
::
vdAdd
(
n
,
x
,
y
,
z
);
}
template
<
typename
T
>
void
VScalMKL
(
const
T
*
a
,
const
T
*
x
,
T
*
y
,
int
n
);
template
<
>
void
VScalMKL
<
float
>
(
const
float
*
a
,
const
float
*
x
,
float
*
y
,
int
n
)
{
if
(
x
==
y
)
{
platform
::
dynload
::
cblas_sscal
(
n
,
*
a
,
y
,
1
);
}
else
{
VScalRefer
<
float
>
(
a
,
x
,
y
,
n
);
}
}
template
<
>
void
VScalMKL
<
double
>
(
const
double
*
a
,
const
double
*
x
,
double
*
y
,
int
n
)
{
if
(
x
==
y
)
{
platform
::
dynload
::
cblas_dscal
(
n
,
*
a
,
y
,
1
);
}
else
{
VScalRefer
<
double
>
(
a
,
x
,
y
,
n
);
}
}
#endif
#define DECLARE_STATIC_FUNC \
...
...
@@ -226,87 +255,60 @@ bool VAddReluKernelImpl<float>::useJIT(int d) {
}
#endif
#undef DECLARE_STATIC_FUNC
REGISTER_JITKERNEL
(
vmul
,
VMulKernel
);
REGISTER_JITKERNEL
(
vadd
,
VAddKernel
);
REGISTER_JITKERNEL
(
vaddrelu
,
VAddReluKernel
);
/* VSCAL JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
/* VScal JitKernel */
template
<
typename
T
>
class
VScalKernelImpl
:
public
VScalKernel
<
T
>
{
public:
explicit
VScalKernelImpl
(
int
d
)
:
VScalKernel
<
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
]
=
a
*
x
[
i
];
}
}
void
Compute
(
const
T
a
,
T
*
x
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
x
[
i
]
=
a
*
x
[
i
]
;
DECLARE_STATIC_FUNC
;
explicit
VScalKernelImpl
(
int
d
)
:
VScalKernel
<
T
>
()
{
#ifdef PADDLE_WITH_XBYAK
if
(
useJIT
(
d
))
{
size_t
sz
=
96
+
d
/
AVX_FLOAT_BLOCK
*
4
*
8
;
jitcode_
.
reset
(
new
gen
::
VScalJitCode
(
d
,
sz
>
4096
?
sz
:
4096
));
this
->
Compute
=
jitcode_
->
getCode
<
void
(
*
)(
const
T
*
,
const
T
*
,
T
*
,
int
)
>
();
return
;
}
}
};
#endif
#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block) \
template <> \
void VScalKernelImpl<float, isa, block>::Compute(const float a, float* x) \
const { \
platform::dynload::cblas_sscal(this->num_, a, x, 1); \
}
#define MKL_DOUBLE(isa, block) \
template <> \
void VScalKernelImpl<double, isa, block>::Compute(const double a, double* x) \
const { \
platform::dynload::cblas_dscal(this->num_, a, x, 1); \
}
FOR_EACH_ISA
(
MKL_FLOAT
,
kGT16
);
FOR_EACH_ISA_BLOCK
(
MKL_DOUBLE
);
if
(
useMKL
(
d
))
{
this
->
Compute
=
VScalMKL
<
T
>
;
return
;
}
#endif
#define INTRI8_FLOAT(isa) \
template <> \
void VScalKernelImpl<float, isa, kEQ8>::Compute( \
const float a, const float* x, float* y) const { \
__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 INTRI8_INPLACE_FLOAT(isa) \
template <> \
void VScalKernelImpl<float, isa, kEQ8>::Compute(const float a, float* x) \
const { \
__m256 tmp; \
__m256 scalar = _mm256_set1_ps(a); \
tmp = _mm256_loadu_ps(x); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(x, tmp); \
this
->
Compute
=
VScalRefer
<
T
>
;
}
#ifdef PADDLE_WITH_XBYAK
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI8_INPLACE_FLOAT
(
jit
::
avx
);
private:
std
::
unique_ptr
<
gen
::
VScalJitCode
>
jitcode_
{
nullptr
};
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI8_INPLACE_FLOAT
(
jit
::
avx2
);
};
#ifdef PADDLE_WITH_XBYAK
template
<
>
bool
VScalKernelImpl
<
float
>::
useJIT
(
int
d
)
{
return
gen
::
VScalJitCode
::
init
(
d
);
}
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI8_INPLACE_FLOAT
(
jit
::
avx512f
);
#ifdef PADDLE_WITH_MKLML
template
<
>
bool
VScalKernelImpl
<
float
>::
useMKL
(
int
d
)
{
return
d
>
512
;
}
template
<
>
bool
VScalKernelImpl
<
double
>::
useMKL
(
int
d
)
{
return
true
;
}
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef INTRI8_INPLACE_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE
#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
>
...
...
@@ -467,7 +469,6 @@ class VIdentityKernelImpl : public VIdentityKernel<T> {
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{}
};
REGISTER_JITKERNEL_DEPRECATED
(
vscal
,
VScalKernel
);
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 @
03e11f3f
...
...
@@ -409,9 +409,10 @@ class VTanhKernelImpl : public VTanhKernel<T> {
vaddbias_
=
KernelPool
::
Instance
().
template
Get
<
VAddBiasKernel
<
T
>
>
(
d
);
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{
vscal_
->
Compute
(
static_cast
<
T
>
(
2
),
x
,
y
);
const
T
a
=
static_cast
<
T
>
(
2
);
vscal_
->
Compute
(
&
a
,
x
,
y
,
this
->
num_
);
vsigmoid_
->
Compute
(
y
,
y
);
vscal_
->
Compute
(
static_cast
<
T
>
(
2
),
y
);
vscal_
->
Compute
(
&
a
,
y
,
y
,
this
->
num_
);
vaddbias_
->
Compute
(
static_cast
<
T
>
(
-
1
),
y
,
y
);
}
...
...
@@ -472,9 +473,10 @@ class VTanhKernelImpl : public VTanhKernel<T> {
_mm256_storeu_ps(y, tmp); \
x += AVX_FLOAT_BLOCK; \
y += AVX_FLOAT_BLOCK; \
vscal_->Compute(2.f, x, y); \
const float a = 2.f; \
vscal_->Compute(&a, x, y, this->num_); \
vsigmoid_->Compute(y, y); \
vscal_->Compute(
2.f, y);
\
vscal_->Compute(
&a, y, y, this->num_);
\
vaddbias_->Compute(-1.f, y, y); \
}
...
...
@@ -502,9 +504,10 @@ class VTanhKernelImpl : public VTanhKernel<T> {
} \
x += this->end_; \
y += this->end_; \
vscal_->Compute(2.f, x, y); \
const float a = 2.f; \
vscal_->Compute(&a, x, y, this->num_); \
vsigmoid_->Compute(y, y); \
vscal_->Compute(
2.f, y);
\
vscal_->Compute(
&a, y, y, this->num_);
\
vaddbias_->Compute(-1.f, y, y); \
}
...
...
paddle/fluid/operators/math/jit_kernel_test.cc
浏览文件 @
03e11f3f
...
...
@@ -281,9 +281,10 @@ void vtanh_better(
const
paddle
::
operators
::
math
::
jitkernel
::
VAddBiasKernel
<
float
>>&
vaddbias
,
const
int
n
,
const
float
*
x
,
float
*
y
)
{
vscal
->
Compute
(
2.
f
,
x
,
y
);
const
float
tmp1
=
2.
f
;
vscal
->
Compute
(
&
tmp1
,
x
,
y
,
n
);
vsigmoid
->
Compute
(
y
,
y
);
vscal
->
Compute
(
2.
f
,
y
);
vscal
->
Compute
(
&
tmp1
,
y
,
y
,
n
);
vaddbias
->
Compute
(
-
1.
f
,
y
,
y
);
}
...
...
@@ -531,12 +532,12 @@ TEST(JitKernel, vscal) {
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
();
auto
ttgts1
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
ker
->
Compute
(
a
,
y_data
);
ker
->
Compute
(
&
a
,
y_data
,
y_data
,
d
);
}
auto
ttgte1
=
GetCurrentUS
();
VLOG
(
3
)
<<
"Vec size "
<<
d
<<
": refer takes: "
<<
(
trefe
-
trefs
)
/
repeat
...
...
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