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ea259c63
编写于
12月 18, 2018
作者:
T
tensor-tang
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电子邮件补丁
差异文件
enable layer norm intrinsic code
上级
b1516783
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
209 addition
and
0 deletion
+209
-0
paddle/fluid/operators/jit/more/intrinsic/CMakeLists.txt
paddle/fluid/operators/jit/more/intrinsic/CMakeLists.txt
+1
-0
paddle/fluid/operators/jit/more/intrinsic/layer_norm.cc
paddle/fluid/operators/jit/more/intrinsic/layer_norm.cc
+168
-0
paddle/fluid/operators/jit/more/intrinsic/layer_norm.h
paddle/fluid/operators/jit/more/intrinsic/layer_norm.h
+40
-0
未找到文件。
paddle/fluid/operators/jit/more/intrinsic/CMakeLists.txt
浏览文件 @
ea259c63
...
...
@@ -6,3 +6,4 @@ set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} jit_kernel_intrinsic PARENT_SCOPE)
# use mkl kernels by name and type
USE_JITKERNEL_MORE
(
crfdecoding, intrinsic
)
USE_JITKERNEL_MORE
(
layernorm, intrinsic
)
paddle/fluid/operators/jit/more/intrinsic/layer_norm.cc
0 → 100644
浏览文件 @
ea259c63
/* 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/jit/more/intrinsic/layer_norm.h"
#include <limits>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace
paddle
{
namespace
operators
{
namespace
jit
{
namespace
more
{
namespace
intrinsic
{
void
LayerNorm
(
float
*
x
,
float
*
out
,
float
*
mean
,
float
*
var
,
const
float
*
scale
,
const
float
*
bias
,
int
height
,
const
float
epsilon
,
int
right
)
{
__m256
sum
;
__m256
mean_vec
,
var_vec
;
__m128
hi
,
lo
;
__m256
tmp
;
size_t
offset
;
size_t
j
;
int
block
=
YMM_FLOAT_BLOCK
;
const
int
rest
=
right
%
block
;
const
int
end
=
right
-
rest
;
__m256
reverse_num_vec
=
_mm256_div_ps
(
_mm256_set1_ps
(
1.0
),
_mm256_set1_ps
(
right
));
__m256
epsilon_vec
=
_mm256_set1_ps
(
epsilon
);
int
rest_mask
=
((
-
1
)
&
(
~
((
~
0U
)
>>
(
sizeof
(
int
)
*
8
-
(
block
-
rest
)))))
&
0x0ff
;
__m256i
mask_vec
=
_mm256_set_epi32
(
rest_mask
&
0x80
?
0xffffffff
:
0
,
rest_mask
&
0x40
?
0xffffffff
:
0
,
rest_mask
&
0x20
?
0xffffffff
:
0
,
rest_mask
&
0x10
?
0xffffffff
:
0
,
rest_mask
&
0x8
?
0xffffffff
:
0
,
rest_mask
&
0x4
?
0xffffffff
:
0
,
rest_mask
&
0x2
?
0xffffffff
:
0
,
rest_mask
&
0x1
?
0xffffffff
:
0
);
for
(
int
i
=
0
;
i
<
height
;
++
i
)
{
offset
=
i
*
right
;
/* get mean */
sum
=
_mm256_setzero_ps
();
for
(
j
=
offset
;
j
<
end
+
offset
;
j
+=
block
)
{
sum
=
_mm256_add_ps
(
sum
,
_mm256_loadu_ps
((
const
float
*
)
x
+
j
));
}
if
(
rest
!=
0
)
{
j
=
offset
+
right
-
block
;
tmp
=
_mm256_loadu_ps
((
const
float
*
)
x
+
j
);
tmp
=
_mm256_blendv_ps
(
_mm256_setzero_ps
(),
tmp
,
*
(
__m256
*
)
&
mask_vec
);
// NOLINT
sum
=
_mm256_add_ps
(
sum
,
tmp
);
}
hi
=
_mm256_extractf128_ps
(
sum
,
1
);
lo
=
_mm256_extractf128_ps
(
sum
,
0
);
sum
=
_mm256_add_ps
(
sum
,
_mm256_insertf128_ps
(
_mm256_insertf128_ps
(
_mm256_setzero_ps
(),
hi
,
0
),
lo
,
1
));
sum
=
_mm256_hadd_ps
(
sum
,
sum
);
sum
=
_mm256_hadd_ps
(
sum
,
sum
);
mean_vec
=
_mm256_mul_ps
(
sum
,
reverse_num_vec
);
mean
[
i
]
=
*
reinterpret_cast
<
float
*>
(
&
mean_vec
);
/* get variance */
sum
=
_mm256_setzero_ps
();
for
(
j
=
offset
;
j
<
end
+
offset
;
j
+=
block
)
{
tmp
=
_mm256_sub_ps
(
_mm256_loadu_ps
((
const
float
*
)
x
+
j
),
mean_vec
);
tmp
=
_mm256_mul_ps
(
tmp
,
tmp
);
sum
=
_mm256_add_ps
(
sum
,
tmp
);
}
if
(
rest
!=
0
)
{
j
=
offset
+
right
-
block
;
tmp
=
_mm256_sub_ps
(
_mm256_loadu_ps
((
const
float
*
)
x
+
j
),
mean_vec
);
tmp
=
_mm256_mul_ps
(
tmp
,
tmp
);
tmp
=
_mm256_blendv_ps
(
_mm256_setzero_ps
(),
tmp
,
*
(
__m256
*
)
&
mask_vec
);
// NOLINT
sum
=
_mm256_add_ps
(
sum
,
tmp
);
}
hi
=
_mm256_extractf128_ps
(
sum
,
1
);
lo
=
_mm256_extractf128_ps
(
sum
,
0
);
sum
=
_mm256_add_ps
(
sum
,
_mm256_insertf128_ps
(
_mm256_insertf128_ps
(
_mm256_setzero_ps
(),
hi
,
0
),
lo
,
1
));
sum
=
_mm256_hadd_ps
(
sum
,
sum
);
sum
=
_mm256_hadd_ps
(
sum
,
sum
);
var_vec
=
_mm256_mul_ps
(
sum
,
reverse_num_vec
);
var
[
i
]
=
*
reinterpret_cast
<
float
*>
(
&
var_vec
);
/* get x_norm and calculate output*/
for
(
j
=
offset
;
j
<
end
+
offset
;
j
+=
block
)
{
tmp
=
_mm256_sub_ps
(
_mm256_loadu_ps
((
const
float
*
)
x
+
j
),
mean_vec
);
tmp
=
_mm256_div_ps
(
tmp
,
_mm256_sqrt_ps
(
_mm256_add_ps
(
var_vec
,
epsilon_vec
)));
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
out
)
+
j
,
tmp
);
}
if
(
rest
!=
0
)
{
j
=
offset
+
right
-
block
;
tmp
=
_mm256_sub_ps
(
_mm256_loadu_ps
((
const
float
*
)
x
+
j
),
mean_vec
);
tmp
=
_mm256_div_ps
(
tmp
,
_mm256_sqrt_ps
(
_mm256_add_ps
(
var_vec
,
epsilon_vec
)));
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
out
)
+
j
,
tmp
);
}
if
(
scale
)
{
if
(
rest
!=
0
)
{
j
=
offset
+
right
-
block
;
tmp
=
_mm256_loadu_ps
((
const
float
*
)
out
+
j
);
}
for
(
j
=
offset
;
j
<
end
+
offset
;
j
+=
block
)
{
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
out
)
+
j
,
_mm256_mul_ps
(
_mm256_loadu_ps
((
const
float
*
)
out
+
j
),
_mm256_loadu_ps
((
const
float
*
)
scale
+
j
-
offset
)));
}
if
(
rest
!=
0
)
{
j
=
offset
+
right
-
block
;
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
out
)
+
j
,
_mm256_mul_ps
(
tmp
,
_mm256_loadu_ps
((
const
float
*
)
scale
+
j
-
offset
)));
}
}
if
(
bias
)
{
if
(
rest
!=
0
)
{
j
=
offset
+
right
-
block
;
tmp
=
_mm256_loadu_ps
((
const
float
*
)
out
+
j
);
}
for
(
j
=
offset
;
j
<
end
+
offset
;
j
+=
block
)
{
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
out
)
+
j
,
_mm256_add_ps
(
_mm256_loadu_ps
((
const
float
*
)
out
+
j
),
_mm256_loadu_ps
((
const
float
*
)
bias
+
j
-
offset
)));
}
if
(
rest
!=
0
)
{
j
=
offset
+
right
-
block
;
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
out
)
+
j
,
_mm256_add_ps
(
tmp
,
_mm256_loadu_ps
((
const
float
*
)
bias
+
j
-
offset
)));
}
}
}
}
bool
LayerNormKernel
::
UseMe
(
int
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
);
}
}
// namespace intrinsic
}
// namespace more
}
// namespace jit
}
// namespace operators
}
// namespace paddle
namespace
intrinsic
=
paddle
::
operators
::
jit
::
more
::
intrinsic
;
REGISTER_JITKERNEL_MORE
(
layernorm
,
intrinsic
,
intrinsic
::
LayerNormKernel
);
paddle/fluid/operators/jit/more/intrinsic/layer_norm.h
0 → 100644
浏览文件 @
ea259c63
/* 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 <type_traits>
#include "paddle/fluid/operators/jit/kernel_base.h"
namespace
paddle
{
namespace
operators
{
namespace
jit
{
namespace
more
{
namespace
intrinsic
{
void
LayerNorm
(
float
*
x
,
float
*
out
,
float
*
mean
,
float
*
var
,
const
float
*
scale
,
const
float
*
bias
,
int
height
,
const
float
epsilon
,
int
right
);
class
LayerNormKernel
:
public
KernelImpl
<
LayerNormTuples
<
float
>>
{
public:
LayerNormKernel
()
{
this
->
func
=
LayerNorm
;
}
bool
UseMe
(
typename
LayerNormTuples
<
float
>::
attr_type
)
const
override
;
};
}
// namespace intrinsic
}
// namespace more
}
// namespace jit
}
// namespace operators
}
// namespace paddle
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