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体验新版 GitCode,发现更多精彩内容 >>
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314e150f
编写于
9月 10, 2018
作者:
H
Haihao Shen
浏览文件
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差异文件
Merge branch 'public-dev' into develop
上级
fbcecddf
a39eba77
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
169 addition
and
7 deletion
+169
-7
paddle/fluid/operators/norm_op.cu
paddle/fluid/operators/norm_op.cu
+143
-6
paddle/fluid/operators/norm_op.h
paddle/fluid/operators/norm_op.h
+4
-1
python/paddle/fluid/tests/unittests/test_norm_op.py
python/paddle/fluid/tests/unittests/test_norm_op.py
+22
-0
未找到文件。
paddle/fluid/operators/norm_op.cu
浏览文件 @
314e150f
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 201
8
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.
...
...
@@ -11,14 +11,151 @@ 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. */
#define EIGEN_USE_GPU
#include <algorithm>
#include "cub/cub.cuh"
#include "paddle/fluid/operators/norm_op.h"
namespace
paddle
{
namespace
operators
{
__device__
__forceinline__
float
square_root
(
float
x
)
{
return
sqrtf
(
x
);
}
__device__
__forceinline__
double
square_root
(
double
x
)
{
return
sqrt
(
x
);
}
template
<
typename
T
,
int
BlockDim
>
__global__
void
Normalize
(
const
T
*
x
,
const
int
pre
,
const
int
axis_n
,
// dim in axis
const
int
post
,
const
T
eps
,
T
*
y
,
T
*
out_norm
)
{
typedef
cub
::
BlockReduce
<
T
,
BlockDim
>
BlockReduce
;
__shared__
typename
BlockReduce
::
TempStorage
temp_storage
;
int
num
=
pre
*
post
;
for
(
int
i
=
blockIdx
.
x
;
i
<
num
;
i
+=
gridDim
.
x
)
{
int
base
=
(
i
/
post
)
*
post
*
axis_n
+
(
i
%
post
);
T
sum
=
0.0
;
__shared__
T
norm
;
for
(
int
j
=
threadIdx
.
x
;
j
<
axis_n
;
j
+=
blockDim
.
x
)
{
const
T
x_ij
=
x
[
base
+
j
*
post
];
sum
+=
x_ij
*
x_ij
;
}
T
reduce_result
=
BlockReduce
(
temp_storage
).
Sum
(
sum
);
if
(
threadIdx
.
x
==
0
)
{
norm
=
square_root
(
reduce_result
+
eps
);
out_norm
[
i
]
=
norm
;
}
__syncthreads
();
for
(
int
j
=
threadIdx
.
x
;
j
<
axis_n
;
j
+=
blockDim
.
x
)
{
const
int
index
=
base
+
j
*
post
;
y
[
index
]
=
x
[
index
]
/
norm
;
}
}
}
template
<
typename
DeviceContext
,
typename
T
>
class
NormCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in_x
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out_y
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
out_norm
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Norm"
);
const
T
*
x
=
in_x
->
data
<
T
>
();
T
*
y
=
out_y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
norm
=
out_norm
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
xdim
=
in_x
->
dims
();
auto
ndim
=
out_norm
->
dims
();
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
T
eps
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
));
if
(
axis
<
0
)
axis
=
xdim
.
size
()
+
axis
;
int
pre
,
n
,
post
;
GetDims
(
xdim
,
axis
,
&
pre
,
&
n
,
&
post
);
auto
&
dev_ctx
=
ctx
.
cuda_device_context
();
const
int
block
=
512
;
int
max_threads
=
dev_ctx
.
GetMaxPhysicalThreadCount
();
const
int
max_blocks
=
std
::
max
(
max_threads
/
block
,
1
);
int
grid
=
std
::
min
(
max_blocks
,
pre
*
post
);
Normalize
<
T
,
block
><<<
grid
,
block
,
0
,
dev_ctx
.
stream
()
>>>
(
x
,
pre
,
n
,
post
,
eps
,
y
,
norm
);
}
};
template
<
typename
T
,
int
BlockDim
>
__global__
void
NormalizeGradient
(
const
T
*
x
,
const
T
*
x_norm
,
const
T
*
y_grad
,
const
int
pre
,
const
int
axis_n
,
const
int
post
,
T
*
x_grad
)
{
typedef
cub
::
BlockReduce
<
T
,
BlockDim
>
BlockReduce
;
__shared__
typename
BlockReduce
::
TempStorage
temp_storage_sum
;
int
num
=
pre
*
post
;
for
(
int
i
=
blockIdx
.
x
;
i
<
num
;
i
+=
gridDim
.
x
)
{
T
sum
=
0.0
;
__shared__
T
row_sum
;
__shared__
T
row_sqrt_norm
;
__shared__
T
row_norm
;
auto
base
=
(
i
/
post
)
*
post
*
axis_n
+
(
i
%
post
);
for
(
int
j
=
threadIdx
.
x
;
j
<
axis_n
;
j
+=
blockDim
.
x
)
{
int
index
=
base
+
j
*
post
;
sum
+=
x
[
index
]
*
y_grad
[
index
];
}
T
reduce_result
=
BlockReduce
(
temp_storage_sum
).
Sum
(
sum
);
if
(
threadIdx
.
x
==
0
)
{
row_sum
=
reduce_result
;
row_sqrt_norm
=
x_norm
[
i
];
row_norm
=
row_sqrt_norm
*
row_sqrt_norm
;
}
__syncthreads
();
for
(
int
j
=
threadIdx
.
x
;
j
<
axis_n
;
j
+=
blockDim
.
x
)
{
int
index
=
base
+
j
*
post
;
const
T
x_ij
=
x
[
index
];
const
T
dy_ij
=
y_grad
[
index
];
x_grad
[
index
]
=
(
dy_ij
-
x_ij
*
row_sum
/
row_norm
)
/
row_sqrt_norm
;
}
}
}
template
<
typename
DeviceContext
,
typename
T
,
typename
AttrType
=
T
>
class
NormGradCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in_x
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
in_norm
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Norm"
);
auto
*
in_dy
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out_dx
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
T
*
dx
=
out_dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
x
=
in_x
->
data
<
T
>
();
const
T
*
x_norm
=
in_norm
->
data
<
T
>
();
const
T
*
dy
=
in_dy
->
data
<
T
>
();
auto
xdim
=
in_x
->
dims
();
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
if
(
axis
<
0
)
axis
=
xdim
.
size
()
+
axis
;
int
pre
,
n
,
post
;
GetDims
(
xdim
,
axis
,
&
pre
,
&
n
,
&
post
);
auto
&
dev_ctx
=
ctx
.
cuda_device_context
();
const
int
block
=
512
;
int
max_threads
=
dev_ctx
.
GetMaxPhysicalThreadCount
();
const
int
max_blocks
=
std
::
max
(
max_threads
/
block
,
1
);
int
grid
=
std
::
min
(
max_blocks
,
pre
*
post
);
NormalizeGradient
<
T
,
block
><<<
grid
,
block
,
0
,
dev_ctx
.
stream
()
>>>
(
x
,
x_norm
,
dy
,
pre
,
n
,
post
,
dx
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
using
CUDA
=
paddle
::
platform
::
CUDADeviceContext
;
REGISTER_OP_CUDA_KERNEL
(
norm
,
ops
::
NormKernel
<
CUDA
,
float
>
,
ops
::
NormKernel
<
CUDA
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
norm_grad
,
ops
::
NormGradKernel
<
CUDA
,
float
>
,
ops
::
NormGradKernel
<
CUDA
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
norm
,
ops
::
Norm
CUDA
Kernel
<
CUDA
,
float
>
,
ops
::
Norm
CUDA
Kernel
<
CUDA
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
norm_grad
,
ops
::
NormGrad
CUDA
Kernel
<
CUDA
,
float
>
,
ops
::
NormGrad
CUDA
Kernel
<
CUDA
,
double
>
);
paddle/fluid/operators/norm_op.h
浏览文件 @
314e150f
...
...
@@ -65,14 +65,17 @@ class NormKernel : public framework::OpKernel<T> {
Eigen
::
DSizes
<
int
,
1
>
rdim
(
1
);
// y = x / sqrt((sum(x * x) + epsilon))
// norm = sqrt(sum(x * x) + epsilon)
auto
sum
=
x
.
pow
(
2
).
sum
(
rdim
)
+
eps
;
auto
x2
=
x
*
x
;
auto
sum
=
x2
.
sum
(
rdim
)
+
eps
;
norm
.
device
(
*
place
)
=
sum
.
sqrt
();
// y = x / norm
Eigen
::
DSizes
<
int
,
3
>
rshape
(
pre
,
1
,
post
);
Eigen
::
DSizes
<
int
,
3
>
bcast
(
1
,
n
,
1
);
y
.
device
(
*
place
)
=
x
/
norm
.
reshape
(
rshape
).
broadcast
(
bcast
);
}
};
template
<
typename
DeviceContext
,
typename
T
,
typename
AttrType
=
T
>
class
NormGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
python/paddle/fluid/tests/unittests/test_norm_op.py
浏览文件 @
314e150f
...
...
@@ -63,5 +63,27 @@ class TestNormOp3(TestNormOp):
self
.
epsilon
=
1e-8
class
TestNormOp4
(
TestNormOp
):
def
init_test_case
(
self
):
self
.
shape
=
[
128
,
1024
,
14
,
14
]
self
.
axis
=
2
self
.
epsilon
=
1e-8
def
test_check_grad
(
self
):
# since the gradient check is very slow in large shape, so skip check_grad
pass
class
TestNormOp5
(
TestNormOp
):
def
init_test_case
(
self
):
self
.
shape
=
[
2048
,
2048
]
self
.
axis
=
1
self
.
epsilon
=
1e-8
def
test_check_grad
(
self
):
# since the gradient check is very slow in large shape, so skip check_grad
pass
if
__name__
==
'__main__'
:
unittest
.
main
()
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