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3bf1ae9b
编写于
2月 20, 2019
作者:
D
dengkaipeng
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差异文件
add spectral_norm forwarn kenel
上级
cccde65b
变更
3
隐藏空白更改
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3 changed file
with
335 addition
and
0 deletion
+335
-0
paddle/fluid/operators/spectral_norm_op.cc
paddle/fluid/operators/spectral_norm_op.cc
+143
-0
paddle/fluid/operators/spectral_norm_op.h
paddle/fluid/operators/spectral_norm_op.h
+128
-0
python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
+64
-0
未找到文件。
paddle/fluid/operators/spectral_norm_op.cc
0 → 100644
浏览文件 @
3bf1ae9b
/* 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"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
SpectralNormOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Weight"
),
"Input(Weight) of SpectralNormOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"U"
),
"Input(U) of SpectralNormOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"V"
),
"Input(V) of SpectralNormOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SpectralNormOp should not be null."
);
auto
dim_weight
=
ctx
->
GetInputDim
(
"Weight"
);
auto
weight_dimsize
=
dim_weight
.
size
();
PADDLE_ENFORCE
(
weight_dimsize
>=
2
&&
weight_dimsize
<=
5
,
"The size of dims of Input(Weights) can only be 2, 3,"
"4, 5 for fc, conv1d, conv2d, conv3d layers."
);
int
dim
=
ctx
->
Attrs
().
Get
<
int
>
(
"dim"
);
int
power_iters
=
ctx
->
Attrs
().
Get
<
int
>
(
"power_iters"
);
PADDLE_ENFORCE
(
dim
>=
0
&&
dim
<
weight_dimsize
-
1
,
"Attr(dim) should be larger equal 0 and less then the"
"size of dims of Input(Weights) - 1,"
);
PADDLE_ENFORCE
(
power_iters
>=
0
,
"Attr(power_iters) should be larger equal then 0"
);
ctx
->
SetOutputDim
(
"Out"
,
dim_weight
);
ctx
->
ShareLoD
(
"Weight"
,
/*->*/
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
Tensor
>
(
"Weight"
)
->
type
(),
ctx
.
GetPlace
());
}
};
class
SpectralNormOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"Weight"
,
"The input weight tensor of spectral_norm operator, "
"This can be a 2-D, 3-D, 4-D, 5-D tensor which is the"
"weights of fc, conv1d, conv2d, conv3d layer."
);
AddInput
(
"U"
,
"The weight_u tensor of spectral_norm operator, "
"This can be a 1-D tensor in shape [H, 1],"
"H is the 1st dimentions of Weight after reshape"
"corresponding by Attr(dim)."
);
AddInput
(
"V"
,
"The weight_u tensor of spectral_norm operator, "
"This can be a 1-D tensor in shape [W, 1],"
"W is the 2nd dimentions of Weight after reshape"
"corresponding by Attr(dim)."
);
AddOutput
(
"Out"
,
"The output weight tensor of spectral_norm operator, "
"This tensor is in same shape with Input(Weight)."
);
AddAttr
<
int
>
(
"dim"
,
"dimension corresponding to number of outputs,"
"default 0 for fc layer, and 1 for conv1d, conv2d, conv3d"
"layers"
)
.
SetDefault
(
0
);
AddAttr
<
int
>
(
"power_iters"
,
"number of power iterations to calculate"
"spectral norm, default is 1."
)
.
SetDefault
(
1
);
AddAttr
<
float
>
(
"eps"
,
"epsilob for numerical stability in"
"calculating norms"
)
.
SetDefault
(
1e-12
);
AddComment
(
R"DOC(
This operator samples input X to given output shape by using specified
)DOC"
);
}
};
class
SpectralNormOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Weight"
),
"Input(Weight) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"U"
),
"Input(U) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"V"
),
"Input(V) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
dim_x
=
ctx
->
GetInputDim
(
"Weight"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Weight"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Weight"
),
dim_x
);
}
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
Tensor
>
(
"Weight"
)
->
type
(),
ctx
.
GetPlace
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
spectral_norm
,
ops
::
SpectralNormOp
,
ops
::
SpectralNormOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
spectral_norm_grad
,
ops
::
SpectralNormOpGrad
);
REGISTER_OP_CPU_KERNEL
(
spectral_norm
,
ops
::
SpectralNormKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SpectralNormKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
spectral_norm_grad
,
ops
::
SpectralNormGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SpectralNormGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/spectral_norm_op.h
0 → 100644
浏览文件 @
3bf1ae9b
/* 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
,
size_t
D
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
using
Tensor
=
framework
::
Tensor
;
using
Array1
=
Eigen
::
DSizes
<
int64_t
,
1
>
;
using
Array2
=
Eigen
::
DSizes
<
int64_t
,
2
>
;
using
IndexPair
=
Eigen
::
IndexPair
<
int
>
;
static
inline
void
ResizeWeight
(
Tensor
*
weight_mat
,
const
int
dim
)
{
auto
weight_dims
=
weight_mat
->
dims
();
int
h
=
1
;
int
w
=
1
;
for
(
int
i
=
0
;
i
<
weight_dims
.
size
();
i
++
)
{
if
(
i
<=
dim
)
{
h
*=
weight_dims
[
i
];
}
else
{
w
*=
weight_dims
[
i
];
}
}
*
weight_mat
=
weight_mat
->
Resize
({
h
,
w
});
}
template
<
typename
DeviceContext
,
typename
T
>
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
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
);
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
;
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
;
auto
v_t_norm
=
v_t
.
square
().
sum
().
sqrt
().
eval
().
reshape
(
Array1
(
1
)).
broadcast
(
Array1
(
w
));
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
;
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
)
<<
"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
))
.
sum
()
.
eval
()
.
reshape
(
Array2
(
1
,
1
))
.
broadcast
(
Array2
(
h
,
w
));
LOG
(
ERROR
)
<<
"weight: "
<<
weight_t
;
LOG
(
ERROR
)
<<
"sigma: "
<<
sigma_t
;
weight_t
.
device
(
place
)
=
weight_t
/
sigma_t
;
}
template
<
typename
DeviceContext
,
typename
T
>
class
SpectralNormKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
weight
=
ctx
.
Input
<
Tensor
>
(
"Weight"
);
auto
u
=
ctx
.
Input
<
Tensor
>
(
"U"
);
auto
v
=
ctx
.
Input
<
Tensor
>
(
"V"
);
auto
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
int
dim
=
ctx
.
Attr
<
int
>
(
"dim"
);
int
power_iters
=
ctx
.
Attr
<
int
>
(
"power_iters"
);
float
eps
=
ctx
.
Attr
<
float
>
(
"eps"
);
Tensor
weight_mat
;
TensorCopySync
(
*
weight
,
ctx
.
GetPlace
(),
&
weight_mat
);
ResizeWeight
(
&
weight_mat
,
dim
);
Tensor
sigma
;
sigma
.
mutable_data
<
T
>
(
weight
->
dims
(),
ctx
.
GetPlace
());
Tensor
uu
,
vv
;
TensorCopySync
(
*
u
,
ctx
.
GetPlace
(),
&
uu
);
TensorCopySync
(
*
v
,
ctx
.
GetPlace
(),
&
vv
);
CalcMatrixSigmaAndNormWeight
<
DeviceContext
,
T
>
(
&
sigma
,
&
uu
,
&
vv
,
&
weight_mat
,
power_iters
,
eps
,
ctx
);
TensorCopySync
(
weight_mat
,
ctx
.
GetPlace
(),
out
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SpectralNormGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
0 → 100644
浏览文件 @
3bf1ae9b
# Copyright (c) 2018 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.
# 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.
from
__future__
import
division
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
from
paddle.fluid
import
core
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'
)
self
.
attrs
=
{
"dim"
:
self
.
dim
,
"power_iters"
:
self
.
power_iters
,
"eps"
:
self
.
eps
,
}
self
.
inputs
=
{
"Weight"
:
weight
,
"U"
:
u
,
"V"
:
v
,
}
output
=
weight
self
.
outputs
=
{
"Out"
:
weight
,
}
def
test_check_output
(
self
):
self
.
check_output
()
def
initTestCase
(
self
):
self
.
weight_shape
=
(
2
,
3
)
self
.
u_shape
=
(
2
,
)
self
.
v_shape
=
(
3
,
)
self
.
dim
=
0
self
.
power_iters
=
1
self
.
eps
=
1e-12
if
__name__
==
"__main__"
:
unittest
.
main
()
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