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体验新版 GitCode,发现更多精彩内容 >>
提交
a3377f7b
编写于
11月 01, 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
refine jitcode and add vmul jitcode implementation
上级
f3badacd
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
123 addition
and
29 deletion
+123
-29
paddle/fluid/operators/math/CMakeLists.txt
paddle/fluid/operators/math/CMakeLists.txt
+1
-1
paddle/fluid/operators/math/jit_code.cc
paddle/fluid/operators/math/jit_code.cc
+53
-0
paddle/fluid/operators/math/jit_code.h
paddle/fluid/operators/math/jit_code.h
+63
-0
paddle/fluid/operators/math/jit_kernel_blas.cc
paddle/fluid/operators/math/jit_kernel_blas.cc
+6
-28
未找到文件。
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @
a3377f7b
...
...
@@ -76,6 +76,6 @@ endif()
cc_test
(
concat_test SRCS concat_test.cc DEPS concat_and_split
)
cc_test
(
cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info
)
cc_library
(
jit_kernel
SRCS jit_kernel.cc jit_gen.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
SRCS jit_kernel.cc jit_gen.cc jit_
code.cc jit_
kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
DEPS cpu_info cblas gflags enforce
)
cc_test
(
jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel
)
paddle/fluid/operators/math/jit_code.cc
0 → 100644
浏览文件 @
a3377f7b
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/jit_code.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
namespace
jitkernel
{
namespace
gen
{
using
namespace
platform
::
jit
;
// NOLINT
bool
VMulJitCode
::
init
(
int
d
)
{
// TODO(TJ): maybe one AVX is enough, AVX above would slow down freq
// try more with avx2 or avx512
if
(
MayIUse
(
avx
)
||
MayIUse
(
avx2
))
{
return
d
%
AVX_FLOAT_BLOCK
==
0
;
}
else
{
return
false
;
}
}
void
VMulJitCode
::
generate
()
{
preCode
();
int
stride
=
sizeof
(
float
)
*
AVX_FLOAT_BLOCK
;
for
(
int
i
=
0
;
i
<
num_
/
AVX_FLOAT_BLOCK
;
++
i
)
{
vmovups
(
ymm_src1
,
ptr
[
param1
+
i
*
stride
]);
vmovups
(
ymm_src2
,
ptr
[
param2
+
i
*
stride
]);
vmulps
(
ymm_dst
,
ymm_src1
,
ymm_src2
);
vmovups
(
ptr
[
param3
+
stride
*
i
],
ymm_dst
);
}
postCode
();
}
}
// namespace gen
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_code.h
0 → 100644
浏览文件 @
a3377f7b
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/operators/math/jit_gen.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
namespace
jitkernel
{
namespace
gen
{
using
reg64_t
=
const
Xbyak
::
Reg64
;
using
reg32_t
=
const
Xbyak
::
Reg32
;
using
xmm_t
=
const
Xbyak
::
Xmm
;
using
ymm_t
=
const
Xbyak
::
Ymm
;
using
zmm_t
=
const
Xbyak
::
Zmm
;
using
Label
=
Xbyak
::
Label
;
class
VMulJitCode
:
public
JitCode
{
public:
DECLARE_JIT_CODE
(
VMulJitCode
);
explicit
VMulJitCode
(
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
);
ymm_t
ymm_src1
=
ymm_t
(
0
);
zmm_t
zmm_src1
=
zmm_t
(
0
);
xmm_t
xmm_src2
=
xmm_t
(
1
);
ymm_t
ymm_src2
=
ymm_t
(
1
);
zmm_t
zmm_src2
=
zmm_t
(
1
);
xmm_t
xmm_dst
=
xmm_t
(
2
);
ymm_t
ymm_dst
=
ymm_t
(
2
);
zmm_t
zmm_dst
=
zmm_t
(
2
);
};
}
// namespace gen
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_kernel_blas.cc
浏览文件 @
a3377f7b
...
...
@@ -14,7 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <string>
#include "paddle/fluid/operators/math/jit_
gen
.h"
#include "paddle/fluid/operators/math/jit_
code
.h"
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#include "paddle/fluid/platform/enforce.h"
...
...
@@ -30,30 +30,7 @@ namespace paddle {
namespace
operators
{
namespace
math
{
namespace
jitkernel
{
namespace
jit
=
platform
::
jit
;
// remove me
using
namespace
platform
::
jit
;
// NOLINT
/* VMUL JitKernel */
struct
VMulJitCode
:
public
gen
::
JitCode
{
DECLARE_JIT_CODE
(
VMulJitCode
);
explicit
VMulJitCode
(
size_t
code_size
=
256
*
1024
,
void
*
code_ptr
=
nullptr
)
:
gen
::
JitCode
(
code_size
,
code_ptr
)
{}
static
bool
init
(
int
d
)
{
if
(
MayIUse
(
avx
)
||
MayIUse
(
avx2
))
{
return
d
%
AVX_FLOAT_BLOCK
==
0
;
}
else
if
(
MayIUse
(
avx512f
))
{
return
d
%
AVX512_FLOAT_BLOCK
==
0
;
}
else
{
return
false
;
}
}
void
generate
()
override
{
preCode
();
postCode
();
}
};
namespace
jit
=
platform
::
jit
;
template
<
typename
T
>
void
VMulRefer
(
const
T
*
x
,
const
T
*
y
,
T
*
z
,
int
n
)
{
...
...
@@ -76,6 +53,7 @@ void VMulMKL<double>(const double* x, const double* y, double* z, int n) {
}
#endif
/* VMUL JitKernel */
template
<
typename
T
>
class
VMulKernelImpl
:
public
VMulKernel
<
T
>
{
public:
...
...
@@ -88,7 +66,7 @@ class VMulKernelImpl : public VMulKernel<T> {
explicit
VMulKernelImpl
(
int
d
)
:
VMulKernel
<
T
>
()
{
if
(
useJIT
(
d
))
{
constexpr
size_t
sz
=
256
*
1024
;
// TODO(TJ): should be related with d
jitcode_
.
reset
(
new
VMulJitCode
(
sz
));
jitcode_
.
reset
(
new
gen
::
VMulJitCode
(
d
,
sz
));
this
->
Compute
=
jitcode_
->
getCode
<
void
(
*
)(
const
T
*
,
const
T
*
,
T
*
,
int
)
>
();
return
;
...
...
@@ -103,12 +81,12 @@ class VMulKernelImpl : public VMulKernel<T> {
}
private:
std
::
unique_ptr
<
VMulJitCode
>
jitcode_
{
nullptr
};
std
::
unique_ptr
<
gen
::
VMulJitCode
>
jitcode_
{
nullptr
};
};
template
<
>
bool
VMulKernelImpl
<
float
>::
useJIT
(
int
d
)
{
return
VMulJitCode
::
init
(
d
);
return
gen
::
VMulJitCode
::
init
(
d
);
}
template
<
>
...
...
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