Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
机器未来
Paddle
提交
76e188e5
P
Paddle
项目概览
机器未来
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
76e188e5
编写于
2月 02, 2018
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add layer norm [GPU]
上级
71a70f20
变更
15
隐藏空白更改
内联
并排
Showing
15 changed file
with
393 addition
and
13 deletion
+393
-13
paddle/operators/compare_op.h
paddle/operators/compare_op.h
+1
-1
paddle/operators/elementwise_add_op.h
paddle/operators/elementwise_add_op.h
+2
-1
paddle/operators/elementwise_div_op.h
paddle/operators/elementwise_div_op.h
+2
-1
paddle/operators/elementwise_max_op.h
paddle/operators/elementwise_max_op.h
+2
-1
paddle/operators/elementwise_min_op.h
paddle/operators/elementwise_min_op.h
+2
-1
paddle/operators/elementwise_mul_op.h
paddle/operators/elementwise_mul_op.h
+2
-1
paddle/operators/elementwise_op_function.h
paddle/operators/elementwise_op_function.h
+2
-2
paddle/operators/elementwise_pow_op.h
paddle/operators/elementwise_pow_op.h
+2
-1
paddle/operators/elementwise_sub_op.h
paddle/operators/elementwise_sub_op.h
+2
-1
paddle/operators/layer_norm_op.cc
paddle/operators/layer_norm_op.cc
+6
-3
paddle/operators/layer_norm_op.cu
paddle/operators/layer_norm_op.cu
+245
-0
paddle/operators/math/math_function.cc
paddle/operators/math/math_function.cc
+6
-0
paddle/operators/math/math_function.cu
paddle/operators/math/math_function.cu
+25
-0
paddle/operators/math/math_function.h
paddle/operators/math/math_function.h
+12
-0
paddle/operators/math/math_function_impl.h
paddle/operators/math/math_function_impl.h
+82
-0
未找到文件。
paddle/operators/compare_op.h
浏览文件 @
76e188e5
...
...
@@ -62,7 +62,7 @@ class CompareOpKernel
z
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
axis
=
context
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
Functor
,
DeviceContext
,
T
,
bool
>
(
context
,
x
,
y
,
axis
,
z
);
Functor
(),
z
);
}
};
...
...
paddle/operators/elementwise_add_op.h
浏览文件 @
76e188e5
...
...
@@ -35,7 +35,8 @@ class ElementwiseAddKernel : public framework::OpKernel<T> {
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
AddFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
z
);
ElementwiseComputeEx
<
AddFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
AddFunctor
<
T
>
(),
z
);
}
};
...
...
paddle/operators/elementwise_div_op.h
浏览文件 @
76e188e5
...
...
@@ -35,7 +35,8 @@ class ElementwiseDivKernel : public framework::OpKernel<T> {
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
DivFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
z
);
ElementwiseComputeEx
<
DivFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
DivFunctor
<
T
>
(),
z
);
}
};
...
...
paddle/operators/elementwise_max_op.h
浏览文件 @
76e188e5
...
...
@@ -35,7 +35,8 @@ class ElementwiseMaxKernel : public framework::OpKernel<T> {
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
MaxFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
z
);
ElementwiseComputeEx
<
MaxFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
MaxFunctor
<
T
>
(),
z
);
}
};
...
...
paddle/operators/elementwise_min_op.h
浏览文件 @
76e188e5
...
...
@@ -35,7 +35,8 @@ class ElementwiseMinKernel : public framework::OpKernel<T> {
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
MinFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
z
);
ElementwiseComputeEx
<
MinFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
MinFunctor
<
T
>
(),
z
);
}
};
...
...
paddle/operators/elementwise_mul_op.h
浏览文件 @
76e188e5
...
...
@@ -34,7 +34,8 @@ class ElementwiseMulKernel : public framework::OpKernel<T> {
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
z
);
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
MulFunctor
<
T
>
(),
z
);
}
};
...
...
paddle/operators/elementwise_op_function.h
浏览文件 @
76e188e5
...
...
@@ -365,10 +365,10 @@ template <typename Functor, typename DeviceContext, typename T,
typename
OutType
=
T
>
void
ElementwiseComputeEx
(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
Tensor
*
x
,
const
framework
::
Tensor
*
y
,
int
axis
,
const
framework
::
Tensor
*
y
,
int
axis
,
Functor
func
,
framework
::
Tensor
*
z
)
{
TransformFunctor
<
Functor
,
T
,
DeviceContext
,
OutType
>
functor
(
x
,
y
,
z
,
ctx
.
template
device_context
<
DeviceContext
>(),
Functor
()
);
x
,
y
,
z
,
ctx
.
template
device_context
<
DeviceContext
>(),
func
);
auto
x_dims
=
x
->
dims
();
auto
y_dims
=
y
->
dims
();
...
...
paddle/operators/elementwise_pow_op.h
浏览文件 @
76e188e5
...
...
@@ -36,7 +36,8 @@ class ElementwisePowKernel : public framework::OpKernel<T> {
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
PowFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
z
);
ElementwiseComputeEx
<
PowFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
PowFunctor
<
T
>
(),
z
);
}
};
...
...
paddle/operators/elementwise_sub_op.h
浏览文件 @
76e188e5
...
...
@@ -34,7 +34,8 @@ class ElementwiseSubKernel : public framework::OpKernel<T> {
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
z
);
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
SubFunctor
<
T
>
(),
z
);
}
};
...
...
paddle/operators/layer_norm_op.cc
浏览文件 @
76e188e5
...
...
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/layer_norm_op.h"
#include "paddle/operators/elementwise_op_function.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -353,8 +355,9 @@ namespace ops = paddle::operators;
REGISTER_OP
(
layer_norm
,
ops
::
LayerNormOp
,
ops
::
LayerNormOpMaker
,
layer_norm_grad
,
ops
::
LayerNormGradOp
);
REGISTER_OP_CPU_KERNEL
(
layer_norm
,
ops
::
LayerNormKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
layer_norm
,
ops
::
LayerNormKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
LayerNormKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
layer_norm_grad
,
ops
::
LayerNormGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
ops
::
LayerNormGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
LayerNormGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/operators/layer_norm_op.cu
0 → 100644
浏览文件 @
76e188e5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/elementwise_op_function.h"
#include "paddle/operators/layer_norm_op.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
using
DataLayout
=
framework
::
DataLayout
;
namespace
{
template
<
typename
T
>
struct
SubAndSquareFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
(
a
-
b
)
*
(
a
-
b
);
}
};
template
<
typename
T
>
struct
DivAndSqrtFunctor
{
explicit
DivAndSqrtFunctor
(
T
epsilon
)
{
epsilon_
=
epsilon
;
}
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
/
(
sqrt
(
b
)
+
epsilon_
);
}
private:
T
epsilon_
;
};
template
<
typename
T
>
struct
MulFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
*
b
;
}
};
template
<
typename
T
>
struct
AddFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
+
b
;
}
};
template
<
typename
T
>
struct
SubFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
-
b
;
}
};
template
<
typename
T
>
struct
MulInvVarFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
*
std
::
sqrt
(
1.0
/
b
);
}
};
}
// namespace
template
<
typename
DeviceContext
,
typename
T
>
class
LayerNormCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
float
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
x
=
*
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
auto
*
mean
=
ctx
.
Output
<
Tensor
>
(
"Mean"
);
auto
*
var
=
ctx
.
Output
<
Tensor
>
(
"Variance"
);
const
auto
begin_norm_axis
=
ctx
.
Attr
<
int
>
(
"begin_norm_axis"
);
const
auto
&
x_dims
=
x
.
dims
();
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
mean
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
var
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
matrix_dim
=
framework
::
flatten_to_2d
(
x_dims
,
begin_norm_axis
);
int
left
=
static_cast
<
int
>
(
matrix_dim
[
0
]);
int
right
=
static_cast
<
int
>
(
matrix_dim
[
1
]);
framework
::
DDim
matrix_shape
({
left
,
right
});
x
.
Resize
(
matrix_shape
);
y
->
Resize
(
matrix_shape
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
math
::
RowwiseMean
<
DeviceContext
,
T
>
row_mean
;
// functor-> get mean
row_mean
(
dev_ctx
,
x
,
mean
);
// functor-> get variance
ElementwiseComputeEx
<
SubAndSquareFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
x
,
mean
,
/*axis*/
0
,
SubAndSquareFunctor
<
T
>
(),
y
);
row_mean
(
dev_ctx
,
*
y
,
var
);
// functor-> get norm_out
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
x
,
mean
,
/*axis*/
0
,
SubFunctor
<
T
>
(),
y
);
ElementwiseComputeEx
<
DivAndSqrtFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
y
,
var
,
/*axis*/
0
,
DivAndSqrtFunctor
<
T
>
(
static_cast
<
T
>
(
epsilon
)),
y
);
framework
::
DDim
scale_shape
({
right
});
if
(
scale
)
{
Tensor
scale_matrix
=
*
scale
;
scale_matrix
.
Resize
(
scale_shape
);
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
y
,
&
scale_matrix
,
/*axis*/
1
,
MulFunctor
<
T
>
(),
y
);
}
if
(
bias
)
{
Tensor
bias_matrix
=
*
bias
;
bias_matrix
.
Resize
(
scale_shape
);
ElementwiseComputeEx
<
AddFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
y
,
&
bias_matrix
,
/*axis*/
1
,
AddFunctor
<
T
>
(),
y
);
}
y
->
Resize
(
x_dims
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
LayerNormCUDAGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
float
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
auto
x
=
*
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
mean
=
*
ctx
.
Input
<
Tensor
>
(
"Mean"
);
auto
var
=
*
ctx
.
Input
<
Tensor
>
(
"Variance"
);
auto
scale
=
*
ctx
.
Input
<
Tensor
>
(
"Scale"
);
auto
d_y
=
*
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
const
auto
begin_norm_axis
=
ctx
.
Attr
<
int
>
(
"begin_norm_axis"
);
// init output
auto
*
d_x
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
d_scale
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Scale"
));
auto
*
d_bias
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Bias"
));
const
auto
&
x_dims
=
x
.
dims
();
auto
matrix_dim
=
framework
::
flatten_to_2d
(
x_dims
,
begin_norm_axis
);
int
left
=
static_cast
<
int
>
(
matrix_dim
[
0
]);
int
right
=
static_cast
<
int
>
(
matrix_dim
[
1
]);
framework
::
DDim
matrix_shape
({
left
,
right
});
d_y
.
Resize
(
matrix_shape
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
math
::
ColwiseSum
<
DeviceContext
,
T
>
colwise_sum
;
Tensor
temp
;
Tensor
temp_norm
;
if
(
d_scale
||
d_x
)
{
x
.
Resize
(
matrix_shape
);
temp
.
mutable_data
<
T
>
(
matrix_shape
,
ctx
.
GetPlace
());
temp_norm
.
mutable_data
<
T
>
(
matrix_shape
,
ctx
.
GetPlace
());
// get x_norm
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
x
,
&
mean
,
/*axis*/
0
,
SubFunctor
<
T
>
(),
&
temp_norm
);
ElementwiseComputeEx
<
DivAndSqrtFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
temp_norm
,
&
var
,
/*axis*/
0
,
DivAndSqrtFunctor
<
T
>
(
static_cast
<
T
>
(
epsilon
)),
&
temp_norm
);
}
if
(
d_bias
)
{
d_bias
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
colwise_sum
(
dev_ctx
,
d_y
,
d_bias
);
}
if
(
d_scale
)
{
d_scale
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
temp_norm
,
&
d_y
,
/*axis*/
0
,
MulFunctor
<
T
>
(),
&
temp
);
colwise_sum
(
dev_ctx
,
temp
,
d_scale
);
}
if
(
d_x
)
{
framework
::
DDim
vec_shape
({
left
});
d_x
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
Tensor
temp_vec
;
temp_vec
.
mutable_data
<
T
>
(
vec_shape
,
ctx
.
GetPlace
());
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
math
::
RowwiseMean
<
DeviceContext
,
T
>
row_mean
;
if
(
d_scale
)
{
// dy_dx
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
d_y
,
&
scale
,
/*axis*/
1
,
MulFunctor
<
T
>
(),
&
temp
);
framework
::
Copy
(
temp
,
ctx
.
GetPlace
(),
ctx
.
device_context
(),
d_x
);
// dy_dmean_dx
row_mean
(
dev_ctx
,
temp
,
&
temp_vec
);
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
d_x
,
&
temp_vec
,
/*axis*/
0
,
SubFunctor
<
T
>
(),
d_x
);
// dy_var_dx
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
temp
,
&
temp_norm
,
/*axis*/
0
,
MulFunctor
<
T
>
(),
&
temp
);
}
else
{
// dy_dx
framework
::
Copy
(
d_y
,
ctx
.
GetPlace
(),
ctx
.
device_context
(),
d_x
);
// dy_dmean_dx
row_mean
(
dev_ctx
,
d_y
,
&
temp_vec
);
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
d_x
,
&
temp_vec
,
/*axis*/
0
,
SubFunctor
<
T
>
(),
d_x
);
// dy_var_dx
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
d_y
,
&
temp_norm
,
/*axis*/
0
,
MulFunctor
<
T
>
(),
&
temp
);
}
// dy_var_dx
row_mean
(
dev_ctx
,
temp
,
&
temp_vec
);
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
&
temp_norm
,
&
temp_vec
,
/*axis*/
0
,
MulFunctor
<
T
>
(),
&
temp_norm
);
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
d_x
,
&
temp_norm
,
/*axis*/
0
,
SubFunctor
<
T
>
(),
d_x
);
ElementwiseComputeEx
<
DivAndSqrtFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
d_x
,
&
var
,
/*axis*/
0
,
DivAndSqrtFunctor
<
T
>
(
static_cast
<
T
>
(
epsilon
)),
d_x
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
layer_norm
,
ops
::
LayerNormCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
LayerNormCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
layer_norm_grad
,
ops
::
LayerNormCUDAGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
LayerNormCUDAGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/operators/math/math_function.cc
浏览文件 @
76e188e5
...
...
@@ -331,6 +331,12 @@ template struct RowwiseAdd<platform::CPUDeviceContext, double>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
double
>;
template
struct
RowwiseSum
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
RowwiseSum
<
platform
::
CPUDeviceContext
,
double
>;
template
struct
RowwiseMean
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
RowwiseMean
<
platform
::
CPUDeviceContext
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/math_function.cu
浏览文件 @
76e188e5
...
...
@@ -325,6 +325,31 @@ void ColwiseSum<platform::CUDADeviceContext, double>::operator()(
vector
->
data
<
double
>
());
}
template
struct
RowwiseSum
<
platform
::
CUDADeviceContext
,
float
>;
// template struct RowwiseSum<platform::CUDADeviceContext, double>;
// TODO(zcd): Following ColwiseSum format, need to confirm.
// The RowwiseSum<platform::CUDADeviceContext, double> failed in debug mode,
// and only failed for this case. So reimplemented it.
template
<
>
void
RowwiseSum
<
platform
::
CUDADeviceContext
,
double
>::
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
vector
)
{
auto
in_dims
=
input
.
dims
();
auto
size
=
input
.
numel
()
/
in_dims
[
0
];
PADDLE_ENFORCE_EQ
(
vector
->
numel
(),
in_dims
[
0
]);
framework
::
Tensor
one
;
one
.
mutable_data
<
double
>
({
size
},
context
.
GetPlace
());
SetConstant
<
platform
::
CUDADeviceContext
,
double
>
set
;
set
(
context
,
&
one
,
static_cast
<
double
>
(
1.0
));
gemv
<
platform
::
CUDADeviceContext
,
double
>
(
context
,
true
,
static_cast
<
int
>
(
in_dims
[
1
]),
static_cast
<
int
>
(
in_dims
[
0
]),
1.0
,
one
.
data
<
double
>
(),
input
.
data
<
double
>
(),
0.0
,
vector
->
data
<
double
>
());
}
template
struct
RowwiseMean
<
platform
::
CUDADeviceContext
,
float
>;
template
struct
RowwiseMean
<
platform
::
CUDADeviceContext
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/math_function.h
浏览文件 @
76e188e5
...
...
@@ -128,6 +128,18 @@ struct ColwiseSum {
framework
::
Tensor
*
vec
);
};
template
<
typename
DeviceContext
,
typename
T
>
struct
RowwiseSum
{
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
vec
);
};
template
<
typename
DeviceContext
,
typename
T
>
struct
RowwiseMean
{
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
vec
);
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/math_function_impl.h
浏览文件 @
76e188e5
...
...
@@ -87,6 +87,88 @@ class ColwiseSum<platform::CPUDeviceContext, T> {
}
};
template
<
typename
DeviceContext
,
typename
T
>
void
RowwiseMean
<
DeviceContext
,
T
>::
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
out
)
{
auto
in_dims
=
input
.
dims
();
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
2U
);
PADDLE_ENFORCE_EQ
(
out
->
numel
(),
in_dims
[
0
]);
auto
in
=
framework
::
EigenMatrix
<
T
>::
From
(
input
);
auto
vec
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
out
);
vec
.
device
(
*
context
.
eigen_device
())
=
in
.
mean
(
Eigen
::
array
<
int
,
1
>
({{
1
}}));
}
// TODO(zcd): Following ColwiseSum format, need to confirm.
// Specialize for CPU, since Eigen implement a general reduce. However,
// rowwise-sum can be easily implemented. General reduce has a huge overhead in
// CPU
template
<
typename
T
>
class
RowwiseMean
<
platform
::
CPUDeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
out
)
{
auto
&
in_dims
=
input
.
dims
();
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
2U
);
auto
height
=
in_dims
[
0
];
auto
size
=
in_dims
[
1
];
PADDLE_ENFORCE_EQ
(
out
->
numel
(),
height
);
auto
inv_size
=
1.0
/
size
;
T
*
out_buf
=
out
->
mutable_data
<
T
>
(
out
->
place
());
const
T
*
in_buf
=
input
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
static_cast
<
size_t
>
(
height
);
++
i
)
{
T
sum
=
0
;
for
(
size_t
j
=
0
;
j
<
static_cast
<
size_t
>
(
size
);
++
j
)
{
sum
+=
in_buf
[
i
*
size
+
j
];
}
out_buf
[
i
]
=
sum
*
inv_size
;
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
void
RowwiseSum
<
DeviceContext
,
T
>::
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
out
)
{
auto
in_dims
=
input
.
dims
();
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
2U
);
PADDLE_ENFORCE_EQ
(
out
->
numel
(),
in_dims
[
0
]);
auto
in
=
framework
::
EigenMatrix
<
T
>::
From
(
input
);
auto
vec
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
out
);
vec
.
device
(
*
context
.
eigen_device
())
=
in
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
1
}}));
}
// TODO(zcd): Following ColwiseSum format, need to confirm.
// Specialize for CPU, since Eigen implement a general reduce. However,
// rowwise-sum can be easily implemented. General reduce has a huge overhead in
// CPU
template
<
typename
T
>
class
RowwiseSum
<
platform
::
CPUDeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
out
)
{
auto
&
in_dims
=
input
.
dims
();
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
2U
);
auto
height
=
in_dims
[
0
];
auto
size
=
in_dims
[
1
];
PADDLE_ENFORCE_EQ
(
out
->
numel
(),
size
);
T
*
out_buf
=
out
->
mutable_data
<
T
>
(
out
->
place
());
const
T
*
in_buf
=
input
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
static_cast
<
size_t
>
(
height
);
++
i
)
{
T
sum
=
0
;
for
(
size_t
j
=
0
;
j
<
static_cast
<
size_t
>
(
size
);
++
j
)
{
sum
+=
in_buf
[
i
*
size
+
j
];
}
out_buf
[
i
]
=
sum
;
}
}
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录