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
PaddlePaddle
Paddle
提交
95474815
P
Paddle
项目概览
PaddlePaddle
/
Paddle
接近 2 年 前同步成功
通知
2323
Star
20933
Fork
5424
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
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
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
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
未验证
提交
95474815
编写于
7月 13, 2022
作者:
R
Ruibiao Chen
提交者:
GitHub
7月 13, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Move eigvals OP to PHI (#44183)
* Move eigvals OP to PHI * Fix CI errors * Fix CI errors
上级
0a5d625b
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
383 addition
and
50 deletion
+383
-50
paddle/fluid/operators/eigvals_op.cc
paddle/fluid/operators/eigvals_op.cc
+8
-49
paddle/phi/api/yaml/legacy_api.yaml
paddle/phi/api/yaml/legacy_api.yaml
+8
-0
paddle/phi/core/utils/data_type.h
paddle/phi/core/utils/data_type.h
+17
-0
paddle/phi/infermeta/unary.cc
paddle/phi/infermeta/unary.cc
+33
-0
paddle/phi/infermeta/unary.h
paddle/phi/infermeta/unary.h
+4
-0
paddle/phi/kernels/cpu/eigvals_kernel.cc
paddle/phi/kernels/cpu/eigvals_kernel.cc
+260
-0
paddle/phi/kernels/eigvals_kernel.h
paddle/phi/kernels/eigvals_kernel.h
+25
-0
paddle/phi/ops/compat/eigvals_sig.cc
paddle/phi/ops/compat/eigvals_sig.cc
+25
-0
python/paddle/tensor/linalg.py
python/paddle/tensor/linalg.py
+3
-1
未找到文件。
paddle/fluid/operators/eigvals_op.cc
浏览文件 @
95474815
...
...
@@ -12,9 +12,10 @@ 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/eigvals_op.h"
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/unary.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -36,59 +37,17 @@ class EigvalsOpMaker : public framework::OpProtoAndCheckerMaker {
class
EigvalsOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"Eigvals"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"Out"
),
"Output"
,
"Out"
,
"Eigvals"
);
DDim
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"The dimensions of Input(X) for Eigvals operator "
"should be at least 2, "
"but received X's dimension = %d, X's shape = [%s]."
,
x_dims
.
size
(),
x_dims
));
if
(
ctx
->
IsRuntime
()
||
!
phi
::
contain_unknown_dim
(
x_dims
))
{
int
last_dim
=
x_dims
.
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
x_dims
[
last_dim
],
x_dims
[
last_dim
-
1
],
platform
::
errors
::
InvalidArgument
(
"The last two dimensions of Input(X) for Eigvals "
"operator should be equal, "
"but received X's shape = [%s]."
,
x_dims
));
}
auto
output_dims
=
vectorize
(
x_dims
);
output_dims
.
resize
(
x_dims
.
size
()
-
1
);
ctx
->
SetOutputDim
(
"Out"
,
phi
::
make_ddim
(
output_dims
));
}
};
class
EigvalsOpVarTypeInference
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
framework
::
InferVarTypeContext
*
ctx
)
const
{
auto
input_dtype
=
ctx
->
GetInputDataType
(
"X"
);
auto
output_dtype
=
framework
::
IsComplexType
(
input_dtype
)
?
input_dtype
:
framework
::
ToComplexType
(
input_dtype
);
ctx
->
SetOutputDataType
(
"Out"
,
output_dtype
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
DECLARE_INFER_SHAPE_FUNCTOR
(
eigvals
,
EigvalsInferShapeFunctor
,
PD_INFER_META
(
phi
::
EigvalsInferMeta
));
REGISTER_OPERATOR
(
eigvals
,
ops
::
EigvalsOp
,
ops
::
EigvalsOpMaker
,
ops
::
EigvalsOpVarTypeInference
);
REGISTER_OP_CPU_KERNEL
(
eigvals
,
ops
::
EigvalsKernel
<
phi
::
CPUContext
,
float
>
,
ops
::
EigvalsKernel
<
phi
::
CPUContext
,
double
>
,
ops
::
EigvalsKernel
<
phi
::
CPUContext
,
paddle
::
platform
::
complex
<
float
>>
,
ops
::
EigvalsKernel
<
phi
::
CPUContext
,
paddle
::
platform
::
complex
<
double
>>
);
EigvalsInferShapeFunctor
);
paddle/phi/api/yaml/legacy_api.yaml
浏览文件 @
95474815
...
...
@@ -536,6 +536,14 @@
func
:
eigh
backward
:
eigh_grad
-
api
:
eigvals
args
:
(Tensor x)
output
:
Tensor
infer_meta
:
func
:
EigvalsInferMeta
kernel
:
func
:
eigvals
-
api
:
einsum
args
:
(Tensor[] x, str equation)
output
:
Tensor, Tensor[]{x.size()}, Tensor[]{x.size()}
...
...
paddle/phi/core/utils/data_type.h
浏览文件 @
95474815
...
...
@@ -80,4 +80,21 @@ inline void VisitDataTypeTiny(phi::DataType type, Visitor visitor) {
"Not supported phi::DataType(%d) as data type."
,
static_cast
<
int
>
(
type
)));
}
inline
bool
IsComplexType
(
const
DataType
&
type
)
{
return
(
type
==
DataType
::
COMPLEX64
||
type
==
DataType
::
COMPLEX128
);
}
inline
DataType
ToComplexType
(
const
DataType
&
type
)
{
switch
(
type
)
{
case
DataType
::
FLOAT32
:
return
DataType
::
COMPLEX64
;
case
DataType
::
FLOAT64
:
return
DataType
::
COMPLEX128
;
default:
PADDLE_THROW
(
errors
::
Unimplemented
(
"Can not transform data type (%s) to complex type, now only support "
"float32 and float64 real value."
,
type
));
}
}
}
// namespace phi
paddle/phi/infermeta/unary.cc
浏览文件 @
95474815
...
...
@@ -399,6 +399,39 @@ void EighInferMeta(const MetaTensor& x,
out_v
->
set_dims
(
input_dim
);
}
void
EigvalsInferMeta
(
const
MetaTensor
&
x
,
MetaTensor
*
out
,
MetaConfig
config
)
{
auto
x_dims
=
x
.
dims
();
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
errors
::
InvalidArgument
(
"The dimensions of Input(X) for Eigvals operator "
"should be at least 2, "
"but received X's dimension = %d, X's shape = [%s]."
,
x_dims
.
size
(),
x_dims
));
if
(
config
.
is_runtime
||
!
phi
::
contain_unknown_dim
(
x_dims
))
{
int
last_dim
=
x_dims
.
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
x_dims
[
last_dim
],
x_dims
[
last_dim
-
1
],
errors
::
InvalidArgument
(
"The last two dimensions of Input(X) for Eigvals "
"operator should be equal, "
"but received X's shape = [%s]."
,
x_dims
));
}
auto
out_dims
=
vectorize
(
x_dims
);
out_dims
.
resize
(
x_dims
.
size
()
-
1
);
const
DataType
&
x_dtype
=
x
.
dtype
();
const
DataType
&
out_dtype
=
IsComplexType
(
x_dtype
)
?
x_dtype
:
ToComplexType
(
x_dtype
);
out
->
set_dims
(
make_ddim
(
out_dims
));
out
->
set_dtype
(
out_dtype
);
}
void
EinsumInferMeta
(
const
std
::
vector
<
const
MetaTensor
*>&
inputs
,
const
std
::
string
&
equation
,
MetaTensor
*
out
,
...
...
paddle/phi/infermeta/unary.h
浏览文件 @
95474815
...
...
@@ -80,6 +80,10 @@ void EighInferMeta(const MetaTensor& x,
MetaTensor
*
out_w
,
MetaTensor
*
out_v
);
void
EigvalsInferMeta
(
const
MetaTensor
&
x
,
MetaTensor
*
out
,
MetaConfig
config
=
MetaConfig
());
void
EinsumInferMeta
(
const
std
::
vector
<
const
MetaTensor
*>&
inputs
,
const
std
::
string
&
equation
,
MetaTensor
*
out
,
...
...
paddle/
fluid/operators/eigvals_op.h
→
paddle/
phi/kernels/cpu/eigvals_kernel.cc
浏览文件 @
95474815
// Copyright (c) 202
1
PaddlePaddle Authors. All Rights Reserved.
// Copyright (c) 202
2
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.
...
...
@@ -12,23 +12,17 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#
pragma once
#
include "paddle/phi/kernels/eigvals_kernel.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/platform/for_range.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/complex.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/utils/data_type.h"
#include "paddle/phi/kernels/funcs/complex_functors.h"
#include "paddle/phi/kernels/funcs/for_range.h"
#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
namespace
phi
{
template
<
typename
T
,
typename
enable
=
void
>
struct
PaddleComplex
;
...
...
@@ -37,79 +31,60 @@ template <typename T>
struct
PaddleComplex
<
T
,
typename
std
::
enable_if
<
std
::
is_floating_point
<
T
>::
value
>::
type
>
{
using
type
=
paddle
::
platform
::
complex
<
T
>
;
using
type
=
dtype
::
complex
<
T
>
;
};
template
<
typename
T
>
struct
PaddleComplex
<
T
,
typename
std
::
enable_if
<
std
::
is_same
<
T
,
platform
::
complex
<
float
>>::
value
||
std
::
is_same
<
T
,
platform
::
complex
<
double
>>::
value
>::
type
>
{
std
::
is_same
<
T
,
dtype
::
complex
<
float
>>::
value
||
std
::
is_same
<
T
,
dtype
::
complex
<
double
>>::
value
>::
type
>
{
using
type
=
T
;
};
template
<
typename
T
>
using
PaddleCType
=
typename
PaddleComplex
<
T
>::
type
;
template
<
typename
T
>
using
Real
=
typename
phi
::
dtype
::
Real
<
T
>
;
static
void
SpiltBatchSquareMatrix
(
const
Tensor
&
input
,
std
::
vector
<
Tensor
>*
output
)
{
DDim
input_dims
=
input
.
dims
();
int
last_dim
=
input_dims
.
size
()
-
1
;
int
n_dim
=
input_dims
[
last_dim
];
DDim
flattened_input_dims
,
flattened_output_dims
;
if
(
input_dims
.
size
()
>
2
)
{
flattened_input_dims
=
phi
::
flatten_to_3d
(
input_dims
,
last_dim
-
1
,
last_dim
);
}
else
{
flattened_input_dims
=
phi
::
make_ddim
({
1
,
n_dim
,
n_dim
});
}
using
Real
=
typename
dtype
::
Real
<
T
>
;
Tensor
flattened_input
;
flattened_input
.
ShareDataWith
(
input
);
flattened_input
.
Resize
(
flattened_input_dims
);
(
*
output
)
=
flattened_input
.
Split
(
1
,
0
);
}
static
void
CheckLapackEigResult
(
const
int
info
,
const
std
::
string
&
name
)
{
PADDLE_ENFORCE_LE
(
info
,
0
,
platform
::
errors
::
PreconditionNotMet
(
"The QR algorithm failed to compute all the "
"eigenvalues in function %s."
,
name
.
c_str
()));
inline
void
CheckLapackEigResult
(
const
int
info
,
const
std
::
string
&
name
)
{
PADDLE_ENFORCE_LE
(
info
,
0
,
errors
::
PreconditionNotMet
(
"The QR algorithm failed to compute all the "
"eigenvalues in function %s."
,
name
.
c_str
()));
PADDLE_ENFORCE_GE
(
info
,
0
,
platform
::
errors
::
InvalidArgument
(
errors
::
InvalidArgument
(
"The %d-th argument has an illegal value in function %s."
,
-
info
,
name
.
c_str
()));
}
template
<
typename
DeviceContext
,
typename
T
>
static
typename
std
::
enable_if
<
std
::
is_floating_point
<
T
>::
value
>::
type
LapackEigvals
(
const
framework
::
Execution
Context
&
ctx
,
const
Tensor
&
input
,
Tensor
*
output
,
Tensor
*
work
,
Tensor
*
rwork
/*unused*/
)
{
Tensor
a
;
// will be overwritten when lapackEig exit
framework
::
TensorCopy
(
input
,
input
.
place
()
,
&
a
);
Tensor
w
;
template
<
typename
T
,
typename
Context
>
typename
std
::
enable_if
<
std
::
is_floating_point
<
T
>::
value
>::
type
LapackEigvals
(
const
Context
&
ctx
,
const
Dense
Tensor
&
input
,
Dense
Tensor
*
output
,
Dense
Tensor
*
work
,
Dense
Tensor
*
rwork
/*unused*/
)
{
Dense
Tensor
a
;
// will be overwritten when lapackEig exit
Copy
(
ctx
,
input
,
input
.
place
(),
/*blocking=*/
true
,
&
a
);
Dense
Tensor
w
;
int64_t
n_dim
=
input
.
dims
()[
1
];
auto
*
w_data
=
w
.
mutable_data
<
T
>
(
phi
::
make_ddim
({
n_dim
<<
1
}),
ctx
.
GetPlace
()
);
w
.
Resize
(
make_ddim
({
n_dim
<<
1
}));
T
*
w_data
=
ctx
.
template
Alloc
<
T
>(
&
w
);
int64_t
work_mem
=
work
->
memory_size
();
int64_t
required_work_mem
=
3
*
n_dim
*
sizeof
(
T
);
PADDLE_ENFORCE_GE
(
work_mem
,
3
*
n_dim
*
sizeof
(
T
),
platform
::
errors
::
InvalidArgument
(
errors
::
InvalidArgument
(
"The memory size of the work tensor in LapackEigvals function "
"should be at least %"
PRId64
" bytes, "
"but received work
\'
s memory size = %"
PRId64
" bytes."
,
...
...
@@ -132,30 +107,28 @@ LapackEigvals(const framework::ExecutionContext& ctx,
static_cast
<
T
*>
(
NULL
),
&
info
);
std
::
string
name
=
"framework::platform::dynload::dgeev_"
;
if
(
framework
::
TransToProtoVarType
(
input
.
dtype
())
==
framework
::
proto
::
VarType
::
FP64
)
{
name
=
"framework::platform::dynload::sgeev_"
;
std
::
string
name
=
"phi::backend::dynload::dgeev_"
;
if
(
input
.
dtype
()
==
DataType
::
FLOAT64
)
{
name
=
"phi::backend::dynload::sgeev_"
;
}
CheckLapackEigResult
(
info
,
name
);
platform
::
ForRange
<
DeviceContext
>
for_range
(
ctx
.
template
device_context
<
DeviceContext
>(),
n_dim
);
phi
::
funcs
::
RealImagToComplexFunctor
<
PaddleCType
<
T
>>
functor
(
funcs
::
ForRange
<
Context
>
for_range
(
ctx
,
n_dim
);
funcs
::
RealImagToComplexFunctor
<
PaddleCType
<
T
>>
functor
(
w_data
,
w_data
+
n_dim
,
output
->
template
data
<
PaddleCType
<
T
>
>
(),
n_dim
);
for_range
(
functor
);
}
template
<
typename
DeviceContext
,
typename
T
>
typename
std
::
enable_if
<
std
::
is_same
<
T
,
platform
::
complex
<
float
>>::
value
||
std
::
is_same
<
T
,
platform
::
complex
<
double
>>::
value
>::
type
LapackEigvals
(
const
framework
::
Execution
Context
&
ctx
,
const
Tensor
&
input
,
Tensor
*
output
,
Tensor
*
work
,
Tensor
*
rwork
)
{
Tensor
a
;
// will be overwritten when lapackEig exit
framework
::
TensorCopy
(
input
,
input
.
place
()
,
&
a
);
template
<
typename
T
,
typename
Context
>
typename
std
::
enable_if
<
std
::
is_same
<
T
,
dtype
::
complex
<
float
>>::
value
||
std
::
is_same
<
T
,
dtype
::
complex
<
double
>>::
value
>::
type
LapackEigvals
(
const
Context
&
ctx
,
const
Dense
Tensor
&
input
,
Dense
Tensor
*
output
,
Dense
Tensor
*
work
,
Dense
Tensor
*
rwork
)
{
Dense
Tensor
a
;
// will be overwritten when lapackEig exit
Copy
(
ctx
,
input
,
input
.
place
(),
/*blocking=*/
true
,
&
a
);
int64_t
work_mem
=
work
->
memory_size
();
int64_t
n_dim
=
input
.
dims
()[
1
];
...
...
@@ -163,7 +136,7 @@ LapackEigvals(const framework::ExecutionContext& ctx,
PADDLE_ENFORCE_GE
(
work_mem
,
3
*
n_dim
*
sizeof
(
T
),
platform
::
errors
::
InvalidArgument
(
errors
::
InvalidArgument
(
"The memory size of the work tensor in LapackEigvals function "
"should be at least %"
PRId64
" bytes, "
"but received work
\'
s memory size = %"
PRId64
" bytes."
,
...
...
@@ -171,11 +144,11 @@ LapackEigvals(const framework::ExecutionContext& ctx,
work_mem
));
int64_t
rwork_mem
=
rwork
->
memory_size
();
int64_t
required_rwork_mem
=
(
n_dim
<<
1
)
*
sizeof
(
phi
::
dtype
::
Real
<
T
>
);
int64_t
required_rwork_mem
=
(
n_dim
<<
1
)
*
sizeof
(
dtype
::
Real
<
T
>
);
PADDLE_ENFORCE_GE
(
rwork_mem
,
required_rwork_mem
,
platform
::
errors
::
InvalidArgument
(
errors
::
InvalidArgument
(
"The memory size of the rwork tensor in LapackEigvals function "
"should be at least %"
PRId64
" bytes, "
"but received rwork
\'
s memory size = %"
PRId64
" bytes."
,
...
...
@@ -183,7 +156,7 @@ LapackEigvals(const framework::ExecutionContext& ctx,
rwork_mem
));
int
info
=
0
;
phi
::
funcs
::
lapackEig
<
T
,
phi
::
dtype
::
Real
<
T
>>
(
phi
::
funcs
::
lapackEig
<
T
,
dtype
::
Real
<
T
>>
(
'N'
,
'N'
,
static_cast
<
int
>
(
n_dim
),
...
...
@@ -196,78 +169,92 @@ LapackEigvals(const framework::ExecutionContext& ctx,
1
,
work
->
template
data
<
T
>(),
static_cast
<
int
>
(
work_mem
/
sizeof
(
T
)),
rwork
->
template
data
<
phi
::
dtype
::
Real
<
T
>
>
(),
rwork
->
template
data
<
dtype
::
Real
<
T
>
>
(),
&
info
);
std
::
string
name
=
"framework::platform::dynload::cgeev_"
;
if
(
framework
::
TransToProtoVarType
(
input
.
dtype
())
==
framework
::
proto
::
VarType
::
COMPLEX64
)
{
name
=
"framework::platform::dynload::zgeev_"
;
std
::
string
name
=
"phi::backend::dynload::cgeev_"
;
if
(
input
.
dtype
()
==
DataType
::
COMPLEX128
)
{
name
=
"phi::backend::dynload::zgeev_"
;
}
CheckLapackEigResult
(
info
,
name
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
EigvalsKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
Tensor
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
Tensor
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
output
->
mutable_data
<
PaddleCType
<
T
>>
(
ctx
.
GetPlace
());
std
::
vector
<
Tensor
>
input_matrices
;
SpiltBatchSquareMatrix
(
*
input
,
/*->*/
&
input_matrices
);
int64_t
n_dim
=
input_matrices
[
0
].
dims
()[
1
];
int64_t
n_batch
=
input_matrices
.
size
();
DDim
output_dims
=
output
->
dims
();
output
->
Resize
(
phi
::
make_ddim
({
n_batch
,
n_dim
}));
std
::
vector
<
Tensor
>
output_vectors
=
output
->
Split
(
1
,
0
);
// query workspace size
T
qwork
;
int
info
;
phi
::
funcs
::
lapackEig
<
T
,
phi
::
dtype
::
Real
<
T
>>
(
'N'
,
'N'
,
static_cast
<
int
>
(
n_dim
),
input_matrices
[
0
].
template
data
<
T
>(),
static_cast
<
int
>
(
n_dim
),
NULL
,
NULL
,
1
,
NULL
,
1
,
&
qwork
,
-
1
,
static_cast
<
phi
::
dtype
::
Real
<
T
>*>
(
NULL
),
&
info
);
int64_t
lwork
=
static_cast
<
int64_t
>
(
qwork
);
Tensor
work
,
rwork
;
try
{
work
.
mutable_data
<
T
>
(
phi
::
make_ddim
({
lwork
}),
ctx
.
GetPlace
());
}
catch
(
memory
::
allocation
::
BadAlloc
&
)
{
LOG
(
WARNING
)
<<
"Failed to allocate Lapack workspace with the optimal "
<<
"memory size = "
<<
lwork
*
sizeof
(
T
)
<<
" bytes, "
<<
"try reallocating a smaller workspace with the minimum "
<<
"required size = "
<<
3
*
n_dim
*
sizeof
(
T
)
<<
" bytes, "
<<
"this may lead to bad performance."
;
lwork
=
3
*
n_dim
;
work
.
mutable_data
<
T
>
(
phi
::
make_ddim
({
lwork
}),
ctx
.
GetPlace
());
}
if
(
framework
::
IsComplexType
(
framework
::
TransToProtoVarType
(
input
->
dtype
())))
{
rwork
.
mutable_data
<
phi
::
dtype
::
Real
<
T
>>
(
phi
::
make_ddim
({
n_dim
<<
1
}),
ctx
.
GetPlace
());
}
for
(
int64_t
i
=
0
;
i
<
n_batch
;
++
i
)
{
LapackEigvals
<
DeviceContext
,
T
>
(
ctx
,
input_matrices
[
i
],
&
output_vectors
[
i
],
&
work
,
&
rwork
);
}
output
->
Resize
(
output_dims
);
void
SpiltBatchSquareMatrix
(
const
DenseTensor
&
input
,
std
::
vector
<
DenseTensor
>*
output
)
{
DDim
input_dims
=
input
.
dims
();
int
last_dim
=
input_dims
.
size
()
-
1
;
int
n_dim
=
input_dims
[
last_dim
];
DDim
flattened_input_dims
,
flattened_output_dims
;
if
(
input_dims
.
size
()
>
2
)
{
flattened_input_dims
=
phi
::
flatten_to_3d
(
input_dims
,
last_dim
-
1
,
last_dim
);
}
else
{
flattened_input_dims
=
phi
::
make_ddim
({
1
,
n_dim
,
n_dim
});
}
};
}
// namespace operators
}
// namespace paddle
DenseTensor
flattened_input
;
flattened_input
.
ShareDataWith
(
input
);
flattened_input
.
Resize
(
flattened_input_dims
);
(
*
output
)
=
flattened_input
.
Split
(
1
,
0
);
}
template
<
typename
T
,
typename
Context
>
void
EigvalsKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
x
,
DenseTensor
*
out
)
{
ctx
.
template
Alloc
<
PaddleCType
<
T
>
>
(
out
);
std
::
vector
<
DenseTensor
>
x_matrices
;
SpiltBatchSquareMatrix
(
x
,
/*->*/
&
x_matrices
);
int64_t
n_dim
=
x_matrices
[
0
].
dims
()[
1
];
int64_t
n_batch
=
x_matrices
.
size
();
DDim
out_dims
=
out
->
dims
();
out
->
Resize
(
make_ddim
({
n_batch
,
n_dim
}));
std
::
vector
<
DenseTensor
>
out_vectors
=
out
->
Split
(
1
,
0
);
// query workspace size
T
qwork
;
int
info
;
funcs
::
lapackEig
<
T
,
dtype
::
Real
<
T
>>
(
'N'
,
'N'
,
static_cast
<
int
>
(
n_dim
),
x_matrices
[
0
].
template
data
<
T
>(),
static_cast
<
int
>
(
n_dim
),
NULL
,
NULL
,
1
,
NULL
,
1
,
&
qwork
,
-
1
,
static_cast
<
dtype
::
Real
<
T
>*>
(
NULL
),
&
info
);
int64_t
lwork
=
static_cast
<
int64_t
>
(
qwork
);
DenseTensor
work
,
rwork
;
work
.
Resize
(
make_ddim
({
lwork
}));
ctx
.
template
Alloc
<
T
>(
&
work
);
if
(
IsComplexType
(
x
.
dtype
()))
{
rwork
.
Resize
(
make_ddim
({
n_dim
<<
1
}));
ctx
.
template
Alloc
<
dtype
::
Real
<
T
>
>
(
&
rwork
);
}
for
(
int64_t
i
=
0
;
i
<
n_batch
;
++
i
)
{
LapackEigvals
<
T
,
Context
>
(
ctx
,
x_matrices
[
i
],
&
out_vectors
[
i
],
&
work
,
&
rwork
);
}
out
->
Resize
(
out_dims
);
}
}
// namespace phi
PD_REGISTER_KERNEL
(
eigvals
,
CPU
,
ALL_LAYOUT
,
phi
::
EigvalsKernel
,
float
,
double
,
phi
::
dtype
::
complex
<
float
>
,
phi
::
dtype
::
complex
<
double
>
)
{}
paddle/phi/kernels/eigvals_kernel.h
0 → 100644
浏览文件 @
95474815
// Copyright (c) 2022 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.
#pragma once
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/device_context.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
EigvalsKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
x
,
DenseTensor
*
out
);
}
// namespace phi
paddle/phi/ops/compat/eigvals_sig.cc
0 → 100644
浏览文件 @
95474815
// Copyright (c) 2022 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.
#include "paddle/phi/core/compat/op_utils.h"
namespace
phi
{
KernelSignature
EigvalsOpArgumentMapping
(
const
ArgumentMappingContext
&
ctx
)
{
return
KernelSignature
(
"eigvals"
,
{
"X"
},
{},
{
"Out"
});
}
}
// namespace phi
PD_REGISTER_ARG_MAPPING_FN
(
eigvals
,
phi
::
EigvalsOpArgumentMapping
);
python/paddle/tensor/linalg.py
浏览文件 @
95474815
...
...
@@ -2339,7 +2339,9 @@ def eigvals(x, name=None):
"The last two dimensions of Input(x) should be equal, but received x's shape = {}"
.
format
(
x_shape
))
if
paddle
.
in_dynamic_mode
():
if
in_dygraph_mode
():
return
_C_ops
.
final_state_eigvals
(
x
)
elif
paddle
.
in_dynamic_mode
():
return
_C_ops
.
eigvals
(
x
)
helper
=
LayerHelper
(
'eigvals'
,
**
locals
())
...
...
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