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体验新版 GitCode,发现更多精彩内容 >>
提交
6e1ee7fb
编写于
1月 29, 2019
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
cache softmax kernel func
test=develop
上级
c7449227
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
28 addition
and
53 deletion
+28
-53
paddle/fluid/operators/jit/helper.h
paddle/fluid/operators/jit/helper.h
+15
-8
paddle/fluid/operators/jit/more/mix/mix.cc
paddle/fluid/operators/jit/more/mix/mix.cc
+10
-43
paddle/fluid/operators/math/softmax_impl.h
paddle/fluid/operators/math/softmax_impl.h
+3
-2
未找到文件。
paddle/fluid/operators/jit/helper.h
浏览文件 @
6e1ee7fb
...
...
@@ -118,26 +118,33 @@ typename KernelTuples::func_type Get(
return
GetRefer
<
KT
,
KernelTuples
>
();
}
template
<
KernelType
KT
,
typename
KernelTuples
>
class
KernelFuncs
Cache
{
template
<
KernelType
KT
,
typename
KernelTuples
,
typename
PlaceType
>
class
KernelFuncs
{
public:
KernelFuncs
Cache
()
=
default
;
static
KernelFuncs
Cache
&
Instanc
e
()
{
static
thread_local
KernelFuncs
Cache
<
KT
,
KernelTuples
>
g_func_cache
;
KernelFuncs
()
=
default
;
static
KernelFuncs
&
Cach
e
()
{
static
thread_local
KernelFuncs
<
KT
,
KernelTuples
,
PlaceType
>
g_func_cache
;
return
g_func_cache
;
}
bool
Has
(
int
key
)
const
{
return
funcs_
.
find
(
key
)
!=
funcs_
.
end
();
}
typename
KernelTuples
::
func_type
At
(
int
key
)
{
return
funcs_
.
at
(
key
);
}
void
Insert
(
int
key
,
typename
KernelTuples
::
func_type
func
)
{
funcs_
.
emplace
(
key
,
func
);
}
typename
KernelTuples
::
func_type
At
(
int
key
)
{
if
(
Has
(
key
))
{
return
funcs_
.
at
(
key
);
}
auto
func
=
Get
<
KT
,
KernelTuples
,
PlaceType
>
(
key
);
Insert
(
key
,
func
);
return
func
;
}
private:
std
::
unordered_map
<
int
,
typename
KernelTuples
::
func_type
>
funcs_
;
DISABLE_COPY_AND_ASSIGN
(
KernelFuncs
Cache
);
DISABLE_COPY_AND_ASSIGN
(
KernelFuncs
);
};
const
char
*
to_string
(
KernelType
kt
);
...
...
paddle/fluid/operators/jit/more/mix/mix.cc
浏览文件 @
6e1ee7fb
...
...
@@ -49,49 +49,16 @@ void VTanh(const T* x, T* y, int n) {
}
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
)
{
typename
XRNTuples
<
T
>::
func_type
compute_hmax
{
nullptr
};
typename
XRNTuples
<
T
>::
func_type
compute_hsum
{
nullptr
};
typename
AXYNTuples
<
T
>::
func_type
compute_vscal
{
nullptr
};
typename
AXYNTuples
<
T
>::
func_type
compute_vaddbias
{
nullptr
};
typename
XYNTuples
<
T
>::
func_type
compute_vexp
{
nullptr
};
if
(
!
KernelFuncsCache
<
kHMax
,
XRNTuples
<
T
>>::
Instance
().
Has
(
n
))
{
compute_hmax
=
Get
<
kHMax
,
XRNTuples
<
T
>
,
platform
::
CPUPlace
>
(
n
);
KernelFuncsCache
<
kHMax
,
XRNTuples
<
T
>>::
Instance
().
Insert
(
n
,
compute_hmax
);
}
else
{
compute_hmax
=
KernelFuncsCache
<
kHMax
,
XRNTuples
<
T
>>::
Instance
().
At
(
n
);
}
if
(
!
KernelFuncsCache
<
kHSum
,
XRNTuples
<
T
>>::
Instance
().
Has
(
n
))
{
compute_hsum
=
Get
<
kHSum
,
XRNTuples
<
T
>
,
platform
::
CPUPlace
>
(
n
);
KernelFuncsCache
<
kHSum
,
XRNTuples
<
T
>>::
Instance
().
Insert
(
n
,
compute_hsum
);
}
else
{
compute_hsum
=
KernelFuncsCache
<
kHSum
,
XRNTuples
<
T
>>::
Instance
().
At
(
n
);
}
if
(
!
KernelFuncsCache
<
kVScal
,
AXYNTuples
<
T
>>::
Instance
().
Has
(
n
))
{
compute_vscal
=
Get
<
kVScal
,
AXYNTuples
<
T
>
,
platform
::
CPUPlace
>
(
n
);
KernelFuncsCache
<
kVScal
,
AXYNTuples
<
T
>>::
Instance
().
Insert
(
n
,
compute_vscal
);
}
else
{
compute_vscal
=
KernelFuncsCache
<
kVScal
,
AXYNTuples
<
T
>>::
Instance
().
At
(
n
);
}
if
(
!
KernelFuncsCache
<
kVAddBias
,
AXYNTuples
<
T
>>::
Instance
().
Has
(
n
))
{
compute_vaddbias
=
Get
<
kVAddBias
,
AXYNTuples
<
T
>
,
platform
::
CPUPlace
>
(
n
);
KernelFuncsCache
<
kVAddBias
,
AXYNTuples
<
T
>>::
Instance
().
Insert
(
n
,
compute_vaddbias
);
}
else
{
compute_vaddbias
=
KernelFuncsCache
<
kVAddBias
,
AXYNTuples
<
T
>>::
Instance
().
At
(
n
);
}
if
(
!
KernelFuncsCache
<
kVExp
,
XYNTuples
<
T
>>::
Instance
().
Has
(
n
))
{
compute_vexp
=
Get
<
KernelType
::
kVExp
,
XYNTuples
<
T
>
,
platform
::
CPUPlace
>
(
n
);
KernelFuncsCache
<
kVExp
,
XYNTuples
<
T
>>::
Instance
().
Insert
(
n
,
compute_vexp
);
}
else
{
compute_vexp
=
KernelFuncsCache
<
kVExp
,
XYNTuples
<
T
>>::
Instance
().
At
(
n
);
}
auto
compute_hmax
=
KernelFuncs
<
kHMax
,
XRNTuples
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_hsum
=
KernelFuncs
<
kHSum
,
XRNTuples
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_vscal
=
KernelFuncs
<
kVScal
,
AXYNTuples
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_vaddbias
=
KernelFuncs
<
kVAddBias
,
AXYNTuples
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_vexp
=
KernelFuncs
<
kVExp
,
XYNTuples
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
n
);
for
(
int
i
=
0
;
i
<
bs
;
++
i
)
{
T
scalar
;
...
...
paddle/fluid/operators/math/softmax_impl.h
浏览文件 @
6e1ee7fb
...
...
@@ -82,8 +82,9 @@ class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
const
int
kClassDim
=
1
;
// 2D data. Batch x C
auto
compute_softmax
=
jit
::
Get
<
jit
::
kSoftmax
,
jit
::
SoftmaxTuples
<
float
>
,
platform
::
CPUPlace
>
(
in_dims
[
kClassDim
]);
jit
::
KernelFuncs
<
jit
::
kSoftmax
,
jit
::
SoftmaxTuples
<
float
>
,
platform
::
CPUPlace
>::
Cache
()
.
At
(
in_dims
[
kClassDim
]);
compute_softmax
(
in_data
,
out_data
,
in_dims
[
kClassDim
],
in_dims
[
kBatchDim
]);
}
};
...
...
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