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体验新版 GitCode,发现更多精彩内容 >>
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3c2420a3
编写于
12月 29, 2022
作者:
Y
ykkk2333
提交者:
GitHub
12月 29, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
xpu kernels support api int64 vector inputs, test=kunlun (#49336)
上级
418edae5
变更
16
显示空白变更内容
内联
并排
Showing
16 changed file
with
249 addition
and
115 deletion
+249
-115
cmake/external/xpu.cmake
cmake/external/xpu.cmake
+1
-1
paddle/phi/kernels/xpu/compare_kernel.cc
paddle/phi/kernels/xpu/compare_kernel.cc
+24
-16
paddle/phi/kernels/xpu/elementwise_add_kernel.cc
paddle/phi/kernels/xpu/elementwise_add_kernel.cc
+11
-2
paddle/phi/kernels/xpu/elementwise_divide_grad_kernel.cc
paddle/phi/kernels/xpu/elementwise_divide_grad_kernel.cc
+15
-9
paddle/phi/kernels/xpu/elementwise_divide_kernel.cc
paddle/phi/kernels/xpu/elementwise_divide_kernel.cc
+10
-2
paddle/phi/kernels/xpu/elementwise_grad_kernel.cc
paddle/phi/kernels/xpu/elementwise_grad_kernel.cc
+30
-18
paddle/phi/kernels/xpu/elementwise_kernel.cc
paddle/phi/kernels/xpu/elementwise_kernel.cc
+50
-10
paddle/phi/kernels/xpu/elementwise_multiply_grad_kernel.cc
paddle/phi/kernels/xpu/elementwise_multiply_grad_kernel.cc
+14
-9
paddle/phi/kernels/xpu/elementwise_multiply_kernel.cc
paddle/phi/kernels/xpu/elementwise_multiply_kernel.cc
+10
-2
paddle/phi/kernels/xpu/elementwise_subtract_grad_kernel.cc
paddle/phi/kernels/xpu/elementwise_subtract_grad_kernel.cc
+16
-9
paddle/phi/kernels/xpu/elementwise_subtract_kernel.cc
paddle/phi/kernels/xpu/elementwise_subtract_kernel.cc
+10
-2
paddle/phi/kernels/xpu/prod_kernel.cc
paddle/phi/kernels/xpu/prod_kernel.cc
+12
-7
paddle/phi/kernels/xpu/reduce_max_kernel.cc
paddle/phi/kernels/xpu/reduce_max_kernel.cc
+11
-7
paddle/phi/kernels/xpu/reduce_mean_kernel.cc
paddle/phi/kernels/xpu/reduce_mean_kernel.cc
+12
-7
paddle/phi/kernels/xpu/reduce_min_kernel.cc
paddle/phi/kernels/xpu/reduce_min_kernel.cc
+12
-7
paddle/phi/kernels/xpu/reduce_sum_kernel.cc
paddle/phi/kernels/xpu/reduce_sum_kernel.cc
+11
-7
未找到文件。
cmake/external/xpu.cmake
浏览文件 @
3c2420a3
...
...
@@ -10,7 +10,7 @@ set(XPU_RT_LIB_NAME "libxpurt.so")
if
(
NOT DEFINED XPU_BASE_URL
)
set
(
XPU_BASE_URL_WITHOUT_DATE
"https://baidu-kunlun-product.su.bcebos.com/KL-SDK/klsdk-dev"
)
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/202212
15
"
)
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/202212
27
"
)
else
()
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL
}
"
)
endif
()
...
...
paddle/phi/kernels/xpu/compare_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -60,7 +60,15 @@ void XPUCompareKernelImpl(const Context& dev_ctx,
int axis, \
DenseTensor* out) { \
using XPUType = typename XPUTypeTrait<T>::Type; \
XPUCompareKernelImpl<T, XPUType, Context>(dev_ctx, x, y, out, functor); \
auto f = [](xpu::Context* ctx, \
const XPUType* x, \
const XPUType* y, \
bool* z, \
const std::vector<int>& xshape, \
const std::vector<int>& yshape) { \
return functor(ctx, x, y, z, xshape, yshape); \
}; \
XPUCompareKernelImpl<T, XPUType, Context>(dev_ctx, x, y, out, f); \
} \
template <typename T, typename Context> \
void name##Kernel(const Context& dev_ctx, \
...
...
paddle/phi/kernels/xpu/elementwise_add_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -54,8 +54,17 @@ void AddRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_add
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_add
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_divide_grad_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -35,15 +35,21 @@ void DivideGradKernel(const Context& dev_ctx,
DenseTensor
*
dy
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
funcs
::
ElementwiseGradPreProcess
(
dout
,
dx
);
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
xpu
::
broadcast_div_grad
<
XPUType
>
,
true
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
const
XPUType
*
z
,
const
XPUType
*
dz
,
XPUType
*
dy
,
XPUType
*
dx
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_div_grad
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
dz
,
dy
,
dx
,
xshape
,
yshape
);
};
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
f
,
true
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_divide_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -31,8 +31,16 @@ void DivideRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_div
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_div
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_grad_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -29,15 +29,21 @@ void MaximumGradKernel(const Context& dev_ctx,
DenseTensor
*
dx
,
DenseTensor
*
dy
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
xpu
::
broadcast_max_grad
<
XPUType
>
,
true
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
const
XPUType
*
z
,
const
XPUType
*
dz
,
XPUType
*
dy
,
XPUType
*
dx
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_max_grad
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
dz
,
dy
,
dx
,
xshape
,
yshape
);
};
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
f
,
true
);
}
template
<
typename
T
,
typename
Context
>
...
...
@@ -49,15 +55,21 @@ void MinimumGradKernel(const Context& dev_ctx,
DenseTensor
*
dx
,
DenseTensor
*
dy
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
xpu
::
broadcast_min_grad
<
XPUType
>
,
true
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
const
XPUType
*
z
,
const
XPUType
*
dz
,
XPUType
*
dy
,
XPUType
*
dx
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_min_grad
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
dz
,
dy
,
dx
,
xshape
,
yshape
);
};
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
f
,
true
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -27,8 +27,16 @@ void FloorDivideRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_floordiv
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_floordiv
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
template
<
typename
T
,
typename
Context
>
...
...
@@ -38,8 +46,16 @@ void MaximumRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_max
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_max
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
template
<
typename
T
,
typename
Context
>
...
...
@@ -49,8 +65,16 @@ void MinimumRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_min
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_min
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
template
<
typename
T
,
typename
Context
>
...
...
@@ -60,8 +84,16 @@ void RemainderRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_mod
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_mod
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
template
<
typename
T
,
typename
Context
>
...
...
@@ -71,8 +103,16 @@ void ElementwisePowRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_pow
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_pow
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_multiply_grad_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -34,15 +34,20 @@ void MultiplyGradKernel(const Context& dev_ctx,
DenseTensor
*
dy
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
funcs
::
ElementwiseGradPreProcess
(
dout
,
dx
);
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
xpu
::
broadcast_mul_grad
<
XPUType
>
,
true
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
const
XPUType
*
z
,
const
XPUType
*
dz
,
XPUType
*
dy
,
XPUType
*
dx
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_mul_grad
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
dz
,
dy
,
dx
,
xshape
,
yshape
);
};
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
f
,
true
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_multiply_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -31,8 +31,16 @@ void MultiplyRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_mul
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_mul
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_subtract_grad_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -28,15 +28,22 @@ void SubtractGradKernel(const Context& dev_ctx,
DenseTensor
*
dx
,
DenseTensor
*
dy
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
phi
::
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
xpu
::
broadcast_sub_grad
<
XPUType
>
,
false
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
const
XPUType
*
z
,
const
XPUType
*
dz
,
XPUType
*
dy
,
XPUType
*
dx
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_sub_grad
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
dz
,
dy
,
dx
,
xshape
,
yshape
);
};
phi
::
XPUElementwiseGrad
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
dout
,
axis
,
dx
,
dy
,
f
,
false
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/elementwise_subtract_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -26,8 +26,16 @@ void SubtractRawKernel(const Context& dev_ctx,
int
axis
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
phi
::
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
xpu
::
broadcast_sub
<
XPUType
>
);
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
const
XPUType
*
y
,
XPUType
*
z
,
const
std
::
vector
<
int
>&
xshape
,
const
std
::
vector
<
int
>&
yshape
)
{
return
xpu
::
broadcast_sub
<
XPUType
>
(
ctx
,
x
,
y
,
z
,
xshape
,
yshape
);
};
phi
::
XPUElementwise
<
T
,
XPUType
>
(
dev_ctx
,
x
,
y
,
axis
,
out
,
f
);
}
}
// namespace phi
...
...
paddle/phi/kernels/xpu/prod_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -29,13 +29,18 @@ void ProdRawKernel(const Context& dev_ctx,
bool
reduce_all
,
DenseTensor
*
out
)
{
reduce_all
=
recompute_reduce_all
(
x
,
dims
,
reduce_all
);
int
r
=
XPUReduce
<
Context
,
T
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
xpu
::
reduce_prod
<
T
>
);
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
XPUType
*
y
,
const
std
::
vector
<
int
>&
xdims
,
const
std
::
vector
<
int
>&
reduce_dims
)
{
return
xpu
::
reduce_prod
<
XPUType
>
(
ctx
,
x
,
y
,
xdims
,
reduce_dims
);
};
int
r
=
XPUReduce
<
Context
,
T
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
f
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"reduce_prod"
);
}
...
...
paddle/phi/kernels/xpu/reduce_max_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -29,13 +29,17 @@ void MaxRawKernel(const Context& dev_ctx,
bool
reduce_all
,
DenseTensor
*
out
)
{
reduce_all
=
recompute_reduce_all
(
x
,
dims
,
reduce_all
);
int
r
=
XPUReduce
<
Context
,
T
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
xpu
::
reduce_max
<
T
>
);
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
XPUType
*
y
,
const
std
::
vector
<
int
>&
xdims
,
const
std
::
vector
<
int
>&
reduce_dims
)
{
return
xpu
::
reduce_max
<
XPUType
>
(
ctx
,
x
,
y
,
xdims
,
reduce_dims
);
};
int
r
=
XPUReduce
<
Context
,
XPUType
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
f
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"reduce_max"
);
}
...
...
paddle/phi/kernels/xpu/reduce_mean_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -29,13 +29,18 @@ void MeanRawKernel(const Context& dev_ctx,
bool
reduce_all
,
DenseTensor
*
out
)
{
reduce_all
=
recompute_reduce_all
(
x
,
dims
,
reduce_all
);
int
r
=
XPUReduce
<
Context
,
T
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
xpu
::
reduce_mean
<
T
>
);
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
XPUType
*
y
,
const
std
::
vector
<
int
>&
xdims
,
const
std
::
vector
<
int
>&
reduce_dims
)
{
return
xpu
::
reduce_mean
<
XPUType
>
(
ctx
,
x
,
y
,
xdims
,
reduce_dims
);
};
int
r
=
XPUReduce
<
Context
,
XPUType
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
f
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"reduce_mean"
);
}
...
...
paddle/phi/kernels/xpu/reduce_min_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -29,13 +29,18 @@ void MinRawKernel(const Context& dev_ctx,
bool
reduce_all
,
DenseTensor
*
out
)
{
reduce_all
=
recompute_reduce_all
(
x
,
dims
,
reduce_all
);
int
r
=
XPUReduce
<
Context
,
T
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
xpu
::
reduce_min
<
T
>
);
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
XPUType
*
y
,
const
std
::
vector
<
int
>&
xdims
,
const
std
::
vector
<
int
>&
reduce_dims
)
{
return
xpu
::
reduce_min
<
XPUType
>
(
ctx
,
x
,
y
,
xdims
,
reduce_dims
);
};
int
r
=
XPUReduce
<
Context
,
XPUType
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
f
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"reduce_min"
);
}
...
...
paddle/phi/kernels/xpu/reduce_sum_kernel.cc
浏览文件 @
3c2420a3
...
...
@@ -30,13 +30,17 @@ void SumRawKernel(const Context& dev_ctx,
DataType
out_dtype
,
DenseTensor
*
out
)
{
reduce_all
=
recompute_reduce_all
(
x
,
dims
,
reduce_all
);
int
r
=
XPUReduce
<
Context
,
T
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
xpu
::
reduce_sum
<
T
>
);
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
auto
f
=
[](
xpu
::
Context
*
ctx
,
const
XPUType
*
x
,
XPUType
*
y
,
const
std
::
vector
<
int
>&
xdims
,
const
std
::
vector
<
int
>&
reduce_dims
)
{
return
xpu
::
reduce_sum
<
XPUType
>
(
ctx
,
x
,
y
,
xdims
,
reduce_dims
);
};
int
r
=
XPUReduce
<
Context
,
XPUType
>
(
dev_ctx
,
x
,
dims
.
GetData
(),
keep_dim
,
reduce_all
,
out
,
f
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"reduce_sum"
);
}
...
...
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