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72509ec3
编写于
2月 21, 2019
作者:
D
dengkaipeng
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add unittest for spectral_norm. test=develop
上级
3bf1ae9b
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
82 addition
and
32 deletion
+82
-32
paddle/fluid/operators/spectral_norm_op.cu
paddle/fluid/operators/spectral_norm_op.cu
+22
-0
paddle/fluid/operators/spectral_norm_op.h
paddle/fluid/operators/spectral_norm_op.h
+30
-22
python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
+30
-10
未找到文件。
paddle/fluid/operators/spectral_norm_op.cu
0 → 100644
浏览文件 @
72509ec3
/* Copyright (c) 2018 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/fluid/operators/spectral_norm_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
spectral_norm
,
ops
::
SpectralNormKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SpectralNormKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
spectral_norm_grad
,
ops
::
SpectralNormGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SpectralNormGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/spectral_norm_op.h
浏览文件 @
72509ec3
...
...
@@ -46,47 +46,51 @@ static inline void CalcMatrixSigmaAndNormWeight(
Tensor
*
sigma
,
Tensor
*
u
,
Tensor
*
v
,
Tensor
*
weight
,
const
int
power_iters
,
const
float
eps
,
const
framework
::
ExecutionContext
&
ctx
)
{
auto
&
place
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
ctx
);
auto
sigma_t
=
EigenTensor
<
T
,
2
>::
From
(
*
sigma
);
auto
weight_t
=
EigenTensor
<
T
,
2
>::
From
(
*
weight
);
auto
u_t
=
EigenTensor
<
T
,
1
>::
From
(
*
u
);
auto
v_t
=
EigenTensor
<
T
,
1
>::
From
(
*
v
);
auto
u_t
=
EigenTensor
<
T
,
2
>::
From
(
*
u
);
auto
v_t
=
EigenTensor
<
T
,
2
>::
From
(
*
v
);
const
int
h
=
weight
->
dims
()[
0
];
const
int
w
=
weight
->
dims
()[
1
];
Eigen
::
array
<
int
,
2
>
perm
=
{
1
,
0
};
Eigen
::
array
<
IndexPair
,
1
>
product_dims
=
{
IndexPair
(
1
,
0
)};
auto
weight_trans_t
=
weight_t
.
shuffle
(
perm
);
LOG
(
ERROR
)
<<
"weight: "
<<
weight_t
;
LOG
(
ERROR
)
<<
"weight_trans: "
<<
weight_trans_t
;
// LOG(ERROR) << "weight: " << weight_t;
// LOG(ERROR) << "weight_trans: " << weight_trans_t;
for
(
int
i
=
0
;
i
<
power_iters
;
i
++
)
{
v_t
.
device
(
place
)
=
weight_trans_t
.
contract
(
u_t
,
product_dims
);
LOG
(
ERROR
)
<<
"iter v: "
<<
v_t
;
// v_t.device(place) = weight_trans_t.contract(u_t, product_dims);
blas
.
MatMul
(
*
weight
,
true
,
*
u
,
false
,
T
(
1
),
v
,
T
(
0
));
// LOG(ERROR) << "iter v: " << v_t;
auto
v_t_norm
=
v_t
.
square
().
sum
().
sqrt
().
eval
().
reshape
(
Array1
(
1
)).
broadcast
(
Array1
(
w
));
LOG
(
ERROR
)
<<
"iter v_norm: "
<<
v_t_norm
;
//
LOG(ERROR) << "iter v_norm: " << v_t_norm;
v_t
.
device
(
place
)
=
v_t
/
(
v_t_norm
+
v_t_norm
.
constant
(
eps
));
LOG
(
ERROR
)
<<
"iter norm v: "
<<
v_t
;
u_t
.
device
(
place
)
=
weight_t
.
contract
(
v_t
,
product_dims
);
LOG
(
ERROR
)
<<
"iter u: "
<<
u_t
;
// LOG(ERROR) << "iter norm v: " << v_t;
// u_t.device(place) = weight_t.contract(v_t, product_dims);
blas
.
MatMul
(
*
weight
,
false
,
*
v
,
false
,
T
(
1
),
u
,
T
(
0
));
// LOG(ERROR) << "iter u: " << u_t;
auto
u_t_norm
=
u_t
.
square
().
sum
().
sqrt
().
eval
().
reshape
(
Array1
(
1
)).
broadcast
(
Array1
(
h
));
u_t
.
device
(
place
)
=
u_t
/
(
u_t_norm
+
u_t_norm
.
constant
(
eps
));
LOG
(
ERROR
)
<<
"iter norm u: "
<<
u_t
;
//
LOG(ERROR) << "iter norm u: " << u_t;
}
LOG
(
ERROR
)
<<
"h"
<<
h
<<
"w"
<<
w
;
LOG
(
ERROR
)
<<
"u: "
<<
u_t
;
LOG
(
ERROR
)
<<
"v: "
<<
v_t
;
LOG
(
ERROR
)
<<
"weight_v: "
<<
weight_t
.
contract
(
v_t
,
product_dims
);
sigma_t
.
device
(
place
)
=
(
u_t
*
weight_t
.
contract
(
v_t
,
product_dims
))
// LOG(ERROR) << "h" << h << "w" << w;
// LOG(ERROR) << "u: " << u_t;
// LOG(ERROR) << "v: " << v_t;
Tensor
weight_v
;
weight_v
.
mutable_data
<
T
>
({
h
,
1
},
ctx
.
GetPlace
());
blas
.
MatMul
(
*
weight
,
false
,
*
v
,
false
,
T
(
1
),
&
weight_v
,
T
(
0
));
auto
weight_v_t
=
EigenTensor
<
T
,
2
>::
From
(
weight_v
);
// LOG(ERROR) << "weight_v: " << weight_v_t;
sigma_t
.
device
(
place
)
=
(
u_t
*
weight_v_t
)
.
sum
()
.
eval
()
.
reshape
(
Array2
(
1
,
1
))
.
broadcast
(
Array2
(
h
,
w
));
LOG
(
ERROR
)
<<
"weight: "
<<
weight_t
;
LOG
(
ERROR
)
<<
"sigma: "
<<
sigma_t
;
//
LOG(ERROR) << "weight: " << weight_t;
//
LOG(ERROR) << "sigma: " << sigma_t;
weight_t
.
device
(
place
)
=
weight_t
/
sigma_t
;
}
...
...
@@ -103,6 +107,9 @@ class SpectralNormKernel : public framework::OpKernel<T> {
int
power_iters
=
ctx
.
Attr
<
int
>
(
"power_iters"
);
float
eps
=
ctx
.
Attr
<
float
>
(
"eps"
);
const
int
h
=
weight
->
dims
()[
0
];
const
int
w
=
weight
->
dims
()[
1
];
Tensor
weight_mat
;
TensorCopySync
(
*
weight
,
ctx
.
GetPlace
(),
&
weight_mat
);
ResizeWeight
(
&
weight_mat
,
dim
);
...
...
@@ -113,7 +120,8 @@ class SpectralNormKernel : public framework::OpKernel<T> {
TensorCopySync
(
*
u
,
ctx
.
GetPlace
(),
&
uu
);
TensorCopySync
(
*
v
,
ctx
.
GetPlace
(),
&
vv
);
CalcMatrixSigmaAndNormWeight
<
DeviceContext
,
T
>
(
&
sigma
,
&
uu
,
&
vv
,
&
weight_mat
,
power_iters
,
eps
,
ctx
);
&
sigma
,
&
(
uu
.
Resize
({
h
,
1
})),
&
(
vv
.
Resize
({
w
,
1
})),
&
weight_mat
,
power_iters
,
eps
,
ctx
);
TensorCopySync
(
weight_mat
,
ctx
.
GetPlace
(),
out
);
}
};
...
...
python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
浏览文件 @
72509ec3
...
...
@@ -21,17 +21,36 @@ from op_test import OpTest
from
paddle.fluid
import
core
def
spectral_norm
(
weight
,
u
,
v
,
dim
,
power_iters
,
eps
):
h
=
w
=
1
for
i
,
d
in
enumerate
(
weight
.
shape
):
if
i
<=
dim
:
h
*=
d
else
:
w
*=
d
weight_mat
=
weight
.
reshape
((
h
,
w
))
u
=
u
.
reshape
((
h
,
1
))
v
=
v
.
reshape
((
w
,
1
))
for
i
in
range
(
power_iters
):
v
=
np
.
matmul
(
weight_mat
.
T
,
u
)
v_norm
=
np
.
sqrt
((
v
*
v
).
sum
())
v
=
v
/
(
v_norm
+
eps
)
u
=
np
.
matmul
(
weight_mat
,
v
)
u_norm
=
np
.
sqrt
((
u
*
u
).
sum
())
u
=
u
/
(
u_norm
+
eps
)
sigma
=
(
u
*
np
.
matmul
(
weight_mat
,
v
)).
sum
()
return
(
weight_mat
/
sigma
).
reshape
(
weight
.
shape
)
class
TestSpectralNormOp
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
op_type
=
'spectral_norm'
# weight = np.random.random(self.weight_shape).astype('float32')
# u = np.random.random(self.u_shape).astype('float32')
# v = np.random.random(self.u_shape).astype('float32')
weight
=
np
.
ones
(
self
.
weight_shape
).
astype
(
'float32'
)
weight
[
1
,
:]
=
2.
u
=
np
.
ones
(
self
.
u_shape
).
astype
(
'float32'
)
v
=
np
.
ones
(
self
.
v_shape
).
astype
(
'float32'
)
weight
=
np
.
random
.
random
(
self
.
weight_shape
).
astype
(
'float32'
)
u
=
np
.
random
.
random
(
self
.
u_shape
).
astype
(
'float32'
)
v
=
np
.
random
.
random
(
self
.
v_shape
).
astype
(
'float32'
)
self
.
attrs
=
{
"dim"
:
self
.
dim
,
...
...
@@ -45,8 +64,9 @@ class TestSpectralNormOp(OpTest):
"V"
:
v
,
}
output
=
weight
self
.
outputs
=
{
"Out"
:
weight
,
}
output
=
spectral_norm
(
weight
,
u
,
v
,
self
.
dim
,
self
.
power_iters
,
self
.
eps
)
self
.
outputs
=
{
"Out"
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -56,7 +76,7 @@ class TestSpectralNormOp(OpTest):
self
.
u_shape
=
(
2
,
)
self
.
v_shape
=
(
3
,
)
self
.
dim
=
0
self
.
power_iters
=
1
self
.
power_iters
=
2
self
.
eps
=
1e-12
...
...
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