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
f521a30d
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 2 年 前同步成功
通知
2325
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
f521a30d
编写于
9月 13, 2021
作者:
X
xiongkun
提交者:
GitHub
9月 13, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine svd; unexpose tensor.svd; fix english document; set timeout=40 (#35635)
上级
86a6be1a
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
208 addition
and
123 deletion
+208
-123
paddle/fluid/operators/svd_helper.h
paddle/fluid/operators/svd_helper.h
+179
-92
paddle/fluid/operators/svd_op.h
paddle/fluid/operators/svd_op.h
+3
-6
python/paddle/__init__.py
python/paddle/__init__.py
+0
-2
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+1
-1
python/paddle/tensor/__init__.py
python/paddle/tensor/__init__.py
+0
-2
python/paddle/tensor/linalg.py
python/paddle/tensor/linalg.py
+25
-20
未找到文件。
paddle/fluid/operators/svd_helper.h
浏览文件 @
f521a30d
...
@@ -20,6 +20,9 @@
...
@@ -20,6 +20,9 @@
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/diag_op.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/functors.h"
#include "paddle/fluid/operators/math/functors.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/math_function.h"
...
@@ -89,7 +92,6 @@ struct PowFunctor {
...
@@ -89,7 +92,6 @@ struct PowFunctor {
};
};
static
std
::
vector
<
int
>
GetBroadcastShape
(
InTensors
ins
)
{
static
std
::
vector
<
int
>
GetBroadcastShape
(
InTensors
ins
)
{
// TODO(xiongkun03) check the operators and output
PADDLE_ENFORCE_EQ
(
ins
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
PADDLE_ENFORCE_EQ
(
ins
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"GetBroadcastShape Receive 2 tensors"
"GetBroadcastShape Receive 2 tensors"
"but got [%d]"
,
"but got [%d]"
,
...
@@ -125,6 +127,19 @@ static std::vector<int> GetBroadcastShape(InTensors ins) {
...
@@ -125,6 +127,19 @@ static std::vector<int> GetBroadcastShape(InTensors ins) {
return
broadcast_shape
;
return
broadcast_shape
;
}
}
#define DITO_TRANSPOSE_RANK_CASE(N) \
case N: { \
math::Transpose<DeviceContext, T, N> trans; \
trans(dev_ctx, x, &ret, axis); \
break; \
}
#define DITO_SLICE_RANK_CASE(N) \
case N: { \
EigenSliceWrapper<N>(&x, offset, extends, &ret); \
break; \
}
template
<
typename
DeviceContext
,
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
struct
DeviceIndependenceTensorOperations
{
struct
DeviceIndependenceTensorOperations
{
// 1. Device indenpendence, for kernel reuse.
// 1. Device indenpendence, for kernel reuse.
...
@@ -153,20 +168,25 @@ struct DeviceIndependenceTensorOperations {
...
@@ -153,20 +168,25 @@ struct DeviceIndependenceTensorOperations {
framework
::
Tensor
Matmul
(
const
framework
::
Tensor
&
mat_a
,
framework
::
Tensor
Matmul
(
const
framework
::
Tensor
&
mat_a
,
const
framework
::
Tensor
&
mat_b
,
bool
trans_a
=
false
,
const
framework
::
Tensor
&
mat_b
,
bool
trans_a
=
false
,
bool
trans_b
=
false
)
{
bool
trans_b
=
false
)
{
framework
::
AttributeMap
attrs
;
framework
::
Tensor
ret
;
attrs
[
"trans_x"
]
=
trans_a
;
attrs
[
"trans_y"
]
=
trans_b
;
NameInTensorMap
inputs
({{
"X"
,
{
&
mat_a
}},
{
"Y"
,
{
&
mat_b
}}});
auto
a_dim
=
mat_a
.
dims
();
auto
a_dim
=
mat_a
.
dims
();
auto
b_dim
=
mat_b
.
dims
();
auto
b_dim
=
mat_b
.
dims
();
std
::
vector
<
int
>
x_vec
=
framework
::
vectorize
<
int
>
(
a_dim
);
std
::
vector
<
int
>
x_vec
=
framework
::
vectorize
<
int
>
(
a_dim
);
x_vec
[
x_vec
.
size
()
-
2
]
=
a_dim
[
a_dim
.
size
()
-
(
trans_a
?
1
:
2
)];
x_vec
[
x_vec
.
size
()
-
2
]
=
a_dim
[
a_dim
.
size
()
-
(
trans_a
?
1
:
2
)];
x_vec
[
x_vec
.
size
()
-
1
]
=
b_dim
[
b_dim
.
size
()
-
(
trans_b
?
2
:
1
)];
x_vec
[
x_vec
.
size
()
-
1
]
=
b_dim
[
b_dim
.
size
()
-
(
trans_b
?
2
:
1
)];
return
CreateOpRunAndReturnTensor
(
"matmul_v2"
,
inputs
,
attrs
,
x_vec
);
ret
.
Resize
(
framework
::
make_ddim
(
x_vec
));
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
blas
=
GetBlas
();
auto
mat_a_discrib
=
math
::
CreateMatrixDescriptor
(
a_dim
,
0
,
trans_a
);
auto
mat_b_discrib
=
math
::
CreateMatrixDescriptor
(
b_dim
,
0
,
trans_b
);
blas
.
MatMul
(
mat_a
,
mat_a_discrib
,
mat_b
,
mat_b_discrib
,
T
(
1.0
),
&
ret
,
T
(
0.0
));
return
ret
;
}
}
// transpose the last two dimision
framework
::
Tensor
Transpose
(
const
framework
::
Tensor
&
x
)
{
framework
::
Tensor
Transpose
(
const
framework
::
Tensor
&
x
)
{
framework
::
Tensor
out
;
// transpose the last two dimision
framework
::
Tensor
ret
;
auto
x_dim
=
x
.
dims
();
auto
x_dim
=
x
.
dims
();
auto
x_vec
=
framework
::
vectorize
<
int
>
(
x_dim
);
auto
x_vec
=
framework
::
vectorize
<
int
>
(
x_dim
);
int
rank
=
x_vec
.
size
();
int
rank
=
x_vec
.
size
();
...
@@ -177,26 +197,42 @@ struct DeviceIndependenceTensorOperations {
...
@@ -177,26 +197,42 @@ struct DeviceIndependenceTensorOperations {
axis
[
i
]
=
i
;
axis
[
i
]
=
i
;
}
}
std
::
swap
(
axis
[
rank
-
1
],
axis
[
rank
-
2
]);
std
::
swap
(
axis
[
rank
-
1
],
axis
[
rank
-
2
]);
framework
::
AttributeMap
attrs
;
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
attrs
[
"axis"
]
=
axis
;
ret
.
Resize
(
framework
::
make_ddim
(
x_vec
));
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
return
CreateOpRunAndReturnTensor
(
"transpose2"
,
inputs
,
attrs
,
out_shape
,
switch
(
rank
)
{
{
"Out"
,
"XShape"
});
DITO_TRANSPOSE_RANK_CASE
(
2
);
DITO_TRANSPOSE_RANK_CASE
(
3
);
DITO_TRANSPOSE_RANK_CASE
(
4
);
DITO_TRANSPOSE_RANK_CASE
(
5
);
DITO_TRANSPOSE_RANK_CASE
(
6
);
default:
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Invalid Rank number, "
"currently only support rank between 2~6"
));
}
}
return
ret
;
}
}
framework
::
Tensor
Diag
(
const
framework
::
Tensor
&
x
,
int
offset
=
0
,
framework
::
Tensor
Diag
(
const
framework
::
Tensor
&
x
,
int
offset
=
0
,
// FIXME link error
int
padding_value
=
0
)
{
int
padding_value
=
0
)
{
framework
::
AttributeMap
attrs
;
PADDLE_ENFORCE_EQ
(
padding_value
,
0
,
attrs
[
"offset"
]
=
offset
;
platform
::
errors
::
InvalidArgument
(
attrs
[
"padding_value"
]
=
padding_value
;
"Current diag only support padding_value = 0"
));
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
PADDLE_ENFORCE_EQ
(
offset
,
0
,
platform
::
errors
::
InvalidArgument
(
"Current diag only support offset = 0,"
"you can use DiagOp instead(not recommend)"
));
framework
::
Tensor
ret
;
int
x_rank
=
x
.
dims
().
size
();
int
x_rank
=
x
.
dims
().
size
();
std
::
vector
<
int
>
out_shape
;
std
::
vector
<
int
>
out_shape
;
if
(
x_rank
==
2
)
{
if
(
x_rank
==
2
)
{
PADDLE_
ENFORCE_EQ
(
x
.
dims
()[
0
],
x
.
dims
()[
1
],
PADDLE_
THROW
(
platform
::
errors
::
InvalidArgument
(
platform
::
errors
::
InvalidArgument
(
"Current diag only support vector"
"if X is a Matrix, then X must be square"
));
"-> diagonalized matrix, not support matrix -> vector,"
out_shape
.
push_back
(
x
.
dims
()[
0
]
);
" Use DiagOp instead."
)
);
}
else
if
(
x_rank
==
1
)
{
}
else
if
(
x_rank
==
1
)
{
out_shape
.
push_back
(
x
.
dims
()[
0
]);
out_shape
.
push_back
(
x
.
dims
()[
0
]);
out_shape
.
push_back
(
x
.
dims
()[
0
]);
out_shape
.
push_back
(
x
.
dims
()[
0
]);
...
@@ -204,42 +240,73 @@ struct DeviceIndependenceTensorOperations {
...
@@ -204,42 +240,73 @@ struct DeviceIndependenceTensorOperations {
PADDLE_THROW
(
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Rank must less or equal than 2"
));
platform
::
errors
::
InvalidArgument
(
"Rank must less or equal than 2"
));
}
}
return
CreateOpRunAndReturnTensor
(
"diag_v2"
,
inputs
,
attrs
,
out_shape
);
ret
=
Fill
({
out_shape
[
0
],
out_shape
[
0
]},
0.0
);
T
*
output
=
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
for_range
=
GetForRange
(
x
.
numel
());
for_range
(
DiagFunctor
<
T
>
(
x
.
data
<
T
>
(),
x
.
numel
(),
output
));
return
ret
;
}
framework
::
Tensor
Div
(
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
y
)
{
framework
::
Tensor
ret
;
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
ElementwiseComputeEx
<
DivFunctor
<
T
>
,
DeviceContext
,
T
>
(
context
,
&
x
,
&
y
,
-
1
,
DivFunctor
<
T
>
(),
&
ret
);
return
ret
;
}
}
framework
::
Tensor
Add
(
const
framework
::
Tensor
&
x
,
framework
::
Tensor
Add
(
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
y
)
{
const
framework
::
Tensor
&
y
)
{
InTensors
ins
({
&
x
,
&
y
});
// element wise add, support numpy broadcast.
framework
::
AttributeMap
attrs
;
framework
::
Tensor
ret
;
attrs
[
"axis"
]
=
-
1
;
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}},
{
"Y"
,
{
&
y
}}});
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
return
CreateOpRunAndReturnTensor
(
"elementwise_add"
,
inputs
,
attrs
,
ElementwiseComputeEx
<
AddFunctor
<
T
>
,
DeviceContext
,
T
>
(
out_shape
);
context
,
&
x
,
&
y
,
-
1
,
AddFunctor
<
T
>
(),
&
ret
);
return
ret
;
}
}
framework
::
Tensor
Mul
(
const
framework
::
Tensor
&
x
,
framework
::
Tensor
Mul
(
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
y
)
{
const
framework
::
Tensor
&
y
)
{
InTensors
ins
({
&
x
,
&
y
});
framework
::
Tensor
ret
;
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
-
1
;
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}},
{
"Y"
,
{
&
y
}}});
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
return
CreateOpRunAndReturnTensor
(
"elementwise_mul"
,
inputs
,
attrs
,
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
out_shape
);
context
,
&
x
,
&
y
,
-
1
,
MulFunctor
<
T
>
(),
&
ret
);
return
ret
;
}
framework
::
Tensor
ReduceSum
(
const
framework
::
Tensor
&
x
,
std
::
vector
<
int
>
out_dim
)
{
framework
::
AttributeMap
attrs
;
attrs
[
"dim"
]
=
std
::
vector
<
int
>
{
-
1
};
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
return
CreateOpRunAndReturnTensor
(
"reduce_sum"
,
inputs
,
attrs
,
out_dim
);
}
framework
::
Tensor
ReduceMax
(
const
framework
::
Tensor
&
x
,
std
::
vector
<
int
>
out_dim
)
{
framework
::
AttributeMap
attrs
;
attrs
[
"dim"
]
=
std
::
vector
<
int
>
{
-
1
};
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
return
CreateOpRunAndReturnTensor
(
"reduce_max"
,
inputs
,
attrs
,
out_dim
);
}
}
framework
::
Tensor
Sub
(
const
framework
::
Tensor
&
x
,
framework
::
Tensor
Sub
(
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
y
)
{
const
framework
::
Tensor
&
y
)
{
InTensors
ins
({
&
x
,
&
y
});
framework
::
Tensor
ret
;
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
-
1
;
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}},
{
"Y"
,
{
&
y
}}});
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
return
CreateOpRunAndReturnTensor
(
"elementwise_sub"
,
inputs
,
attrs
,
if
(
x
.
dims
().
size
()
>=
y
.
dims
().
size
())
{
out_shape
);
ElementwiseComputeEx
<
SubFunctor
<
T
>
,
DeviceContext
,
T
>
(
context
,
&
x
,
&
y
,
-
1
,
SubFunctor
<
T
>
(),
&
ret
);
}
else
{
ElementwiseComputeEx
<
InverseSubFunctor
<
T
>
,
DeviceContext
,
T
>
(
// This is copyed from elementwise_sub, which means we
// need reverse will xrank < yrank
context
,
&
x
,
&
y
,
-
1
,
InverseSubFunctor
<
T
>
(),
&
ret
);
}
return
ret
;
}
}
const
framework
::
Tensor
Unsqueeze
(
const
framework
::
Tensor
&
x
,
int
axis
=
0
)
{
const
framework
::
Tensor
Unsqueeze
(
const
framework
::
Tensor
&
x
,
int
axis
=
0
)
{
// don't copy data, only change the dims
// don't copy data, only change the dims
framework
::
Tensor
out
;
framework
::
Tensor
out
;
...
@@ -255,40 +322,29 @@ struct DeviceIndependenceTensorOperations {
...
@@ -255,40 +322,29 @@ struct DeviceIndependenceTensorOperations {
out
.
Resize
(
framework
::
make_ddim
(
out_shape
));
out
.
Resize
(
framework
::
make_ddim
(
out_shape
));
return
out
;
return
out
;
}
}
framework
::
Tensor
Fill
(
std
::
vector
<
int
>
shape
,
float
fill_value
)
{
framework
::
Tensor
Zeros
(
std
::
vector
<
int
>
shape
,
framework
::
Tensor
ret
;
framework
::
proto
::
VarType
::
Type
dtype
,
ret
.
Resize
(
framework
::
make_ddim
(
shape
));
float
fill_value
)
{
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
framework
::
AttributeMap
attrs
;
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
attrs
[
"dtype"
]
=
dtype
;
SetConstant
<
DeviceContext
,
T
>
()(
dev_ctx
,
&
ret
,
T
(
fill_value
));
attrs
[
"shape"
]
=
shape
;
return
ret
;
attrs
[
"value"
]
=
fill_value
;
NameInTensorMap
inputs
({});
return
CreateOpRunAndReturnTensor
(
"fill_constant"
,
inputs
,
attrs
,
shape
);
}
}
framework
::
Tensor
Infinits
(
std
::
vector
<
int
>
shape
)
{
framework
::
Tensor
Infinits
(
std
::
vector
<
int
>
shape
,
auto
value
=
static_cast
<
T
>
(
std
::
numeric_limits
<
double
>::
infinity
());
framework
::
proto
::
VarType
::
Type
dtype
)
{
return
Fill
(
shape
,
value
);
framework
::
AttributeMap
attrs
;
attrs
[
"dtype"
]
=
dtype
;
attrs
[
"shape"
]
=
shape
;
attrs
[
"str_value"
]
=
std
::
string
(
"inf"
);
NameInTensorMap
inputs
({});
return
CreateOpRunAndReturnTensor
(
"fill_constant"
,
inputs
,
attrs
,
shape
);
}
}
framework
::
Tensor
Eye
(
int
n
)
{
framework
::
Tensor
Eye
(
int
n
,
framework
::
proto
::
VarType
::
Type
dtype
)
{
auto
output
=
Fill
({
n
},
1
);
auto
output
=
Zeros
({
n
},
dtype
,
1
);
auto
ret
=
Diag
(
output
);
auto
ret
=
Diag
(
output
);
return
ret
;
return
ret
;
}
}
framework
::
Tensor
Slice
(
const
framework
::
Tensor
&
x
,
std
::
vector
<
int
>
axes
,
framework
::
Tensor
Slice
(
const
framework
::
Tensor
&
x
,
std
::
vector
<
int
>
axes
,
std
::
vector
<
int
>
starts
,
std
::
vector
<
int
>
ends
)
{
std
::
vector
<
int
>
starts
,
std
::
vector
<
int
>
ends
)
{
framework
::
Tensor
ret
;
std
::
vector
<
int
>
new_axes
=
axes
;
std
::
vector
<
int
>
new_axes
=
axes
;
NameInTensorMap
inputs
({{
"Input"
,
{
&
x
}}});
std
::
vector
<
int
>
out_shape
=
framework
::
vectorize
<
int
>
(
x
.
dims
());
std
::
vector
<
int
>
out_shape
=
framework
::
vectorize
<
int
>
(
x
.
dims
());
in
t
rank
=
out_shape
.
size
();
size_
t
rank
=
out_shape
.
size
();
PADDLE_ENFORCE_EQ
(
PADDLE_ENFORCE_EQ
(
axes
.
size
(),
starts
.
size
(),
axes
.
size
(),
starts
.
size
(),
platform
::
errors
::
InvalidArgument
(
"Slice Operator Argument Invalided"
));
platform
::
errors
::
InvalidArgument
(
"Slice Operator Argument Invalided"
));
...
@@ -306,27 +362,31 @@ struct DeviceIndependenceTensorOperations {
...
@@ -306,27 +362,31 @@ struct DeviceIndependenceTensorOperations {
"C++ Slice Operation Not Support End < Start"
));
"C++ Slice Operation Not Support End < Start"
));
out_shape
[
axis
]
=
ed
-
st
;
out_shape
[
axis
]
=
ed
-
st
;
}
}
framework
::
AttributeMap
attrs
;
std
::
vector
<
int
>
offset
(
rank
),
extends
(
rank
);
attrs
[
"axes"
]
=
new_axes
;
for
(
size_t
i
=
0
;
i
<
rank
;
++
i
)
{
attrs
[
"starts"
]
=
starts
;
offset
[
i
]
=
0
;
attrs
[
"ends"
]
=
ends
;
extends
[
i
]
=
x
.
dims
()[
i
];
return
CreateOpRunAndReturnTensor
(
"slice"
,
inputs
,
attrs
,
out_shape
);
}
}
for
(
size_t
i
=
0
;
i
<
new_axes
.
size
();
++
i
)
{
offset
[
new_axes
[
i
]]
=
starts
[
i
];
framework
::
Tensor
ReduceSum
(
const
framework
::
Tensor
&
x
,
extends
[
new_axes
[
i
]]
=
ends
[
i
]
-
starts
[
i
];
std
::
vector
<
int
>
out_dim
)
{
}
framework
::
AttributeMap
attrs
;
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
attrs
[
"dim"
]
=
std
::
vector
<
int
>
{
-
1
};
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
switch
(
rank
)
{
return
CreateOpRunAndReturnTensor
(
"reduce_sum"
,
inputs
,
attrs
,
out_dim
);
DITO_SLICE_RANK_CASE
(
1
);
}
DITO_SLICE_RANK_CASE
(
2
);
DITO_SLICE_RANK_CASE
(
3
);
framework
::
Tensor
ReduceMax
(
const
framework
::
Tensor
&
x
,
DITO_SLICE_RANK_CASE
(
4
);
std
::
vector
<
int
>
out_dim
)
{
DITO_SLICE_RANK_CASE
(
5
);
framework
::
AttributeMap
attrs
;
DITO_SLICE_RANK_CASE
(
6
);
attrs
[
"dim"
]
=
std
::
vector
<
int
>
{
-
1
};
default:
{
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
return
CreateOpRunAndReturnTensor
(
"reduce_max"
,
inputs
,
attrs
,
out_dim
);
"Invalid Rank number, "
"currently only support rank between 2~6"
));
}
}
return
ret
;
}
}
private:
private:
...
@@ -338,14 +398,40 @@ struct DeviceIndependenceTensorOperations {
...
@@ -338,14 +398,40 @@ struct DeviceIndependenceTensorOperations {
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
return
platform
::
ForRange
<
DeviceContext
>
(
dev_ctx
,
numel
);
return
platform
::
ForRange
<
DeviceContext
>
(
dev_ctx
,
numel
);
}
}
template
<
size_t
D
>
void
EigenSliceWrapper
(
const
framework
::
Tensor
*
in
,
const
std
::
vector
<
int
>&
start
,
const
std
::
vector
<
int
>&
end
,
framework
::
Tensor
*
out
)
{
// Slice by call Eigen Tensor Function `.slice()`
size_t
rank
=
in
->
dims
().
size
();
PADDLE_ENFORCE_EQ
(
start
.
size
(),
rank
,
platform
::
errors
::
InvalidArgument
(
"EigenSliceWrapper function start "
"argument must have the same length as input rank."
));
PADDLE_ENFORCE_EQ
(
end
.
size
(),
rank
,
platform
::
errors
::
InvalidArgument
(
"EigenSliceWrapper function end "
"argument must have the same length as input rank."
));
auto
eigen_place_ptr
=
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
eigen_place
=
*
eigen_place_ptr
;
auto
out_t
=
framework
::
EigenTensor
<
T
,
D
>::
From
(
*
out
,
out
->
dims
());
auto
in_t
=
framework
::
EigenTensor
<
T
,
D
>::
From
(
*
in
,
in
->
dims
());
Eigen
::
DSizes
<
int
,
D
>
offsets_32bit
,
extents_32bit
;
for
(
size_t
i
=
0
;
i
<
D
;
i
++
)
{
offsets_32bit
[
i
]
=
start
[
i
];
extents_32bit
[
i
]
=
end
[
i
];
}
EigenSlice
<
std
::
decay_t
<
decltype
(
eigen_place
)
>
,
T
,
D
>::
Eval
(
eigen_place
,
framework
::
To32BitIndex
(
out_t
),
framework
::
To32BitIndex
(
in_t
),
offsets_32bit
,
extents_32bit
);
}
framework
::
Tensor
CreateOpRunAndReturnTensor
(
framework
::
Tensor
CreateOpRunAndReturnTensor
(
const
std
::
string
&
type
,
const
NameInTensorMap
&
inputs
,
const
std
::
string
&
type
,
const
NameInTensorMap
&
inputs
,
const
framework
::
AttributeMap
&
attrs
,
std
::
vector
<
int
>
out_shape
,
const
framework
::
AttributeMap
&
attrs
,
std
::
vector
<
int
>
out_shape
,
NameOutTensor
out_str
=
{
"Out"
})
{
NameOutTensor
out_str
=
{
"Out"
})
{
// varialble set dims must be LoDTensor / SelectedRowTensor
// varialble set dims must be LoDTensor / SelectedRowTensor
framework
::
Scope
&
local_scope
=
context
.
scope
().
NewScope
();
framework
::
Scope
&
local_scope
=
context
.
scope
().
NewScope
();
framework
::
VariableNameMap
op_outputs
;
framework
::
VariableNameMap
op_outputs
;
for
(
auto
out_name
:
out_str
)
{
for
(
auto
out_name
:
out_str
)
{
local_scope
.
Var
(
"tmp_"
+
out_name
)
->
GetMutable
<
framework
::
LoDTensor
>
();
local_scope
.
Var
(
"tmp_"
+
out_name
)
->
GetMutable
<
framework
::
LoDTensor
>
();
...
@@ -373,6 +459,7 @@ struct DeviceIndependenceTensorOperations {
...
@@ -373,6 +459,7 @@ struct DeviceIndependenceTensorOperations {
}
}
op_inputs
[
item
.
first
]
=
name_vector
;
op_inputs
[
item
.
first
]
=
name_vector
;
}
}
auto
op
=
auto
op
=
framework
::
OpRegistry
::
CreateOp
(
type
,
op_inputs
,
op_outputs
,
attrs
);
framework
::
OpRegistry
::
CreateOp
(
type
,
op_inputs
,
op_outputs
,
attrs
);
op
->
Run
(
local_scope
,
context
.
GetPlace
());
op
->
Run
(
local_scope
,
context
.
GetPlace
());
...
...
paddle/fluid/operators/svd_op.h
浏览文件 @
f521a30d
...
@@ -54,7 +54,6 @@ class SvdCPUKernel : public framework::OpKernel<T> {
...
@@ -54,7 +54,6 @@ class SvdCPUKernel : public framework::OpKernel<T> {
size_t
(
batches
*
col_v
*
cols
*
sizeof
(
math
::
Real
<
T
>
)));
size_t
(
batches
*
col_v
*
cols
*
sizeof
(
math
::
Real
<
T
>
)));
auto
*
S_out
=
S
->
mutable_data
<
math
::
Real
<
T
>>
(
auto
*
S_out
=
S
->
mutable_data
<
math
::
Real
<
T
>>
(
context
.
GetPlace
(),
size_t
(
batches
*
k
*
sizeof
(
math
::
Real
<
T
>
)));
context
.
GetPlace
(),
size_t
(
batches
*
k
*
sizeof
(
math
::
Real
<
T
>
)));
/*SVD Use the Eigen Library*/
/*SVD Use the Eigen Library*/
math
::
BatchSvd
<
T
>
(
x_data
,
U_out
,
VH_out
,
S_out
,
rows
,
cols
,
batches
,
full
);
math
::
BatchSvd
<
T
>
(
x_data
,
U_out
,
VH_out
,
S_out
,
rows
,
cols
,
batches
,
full
);
}
}
...
@@ -96,7 +95,7 @@ class SvdGradKernel : public framework::OpKernel<T> {
...
@@ -96,7 +95,7 @@ class SvdGradKernel : public framework::OpKernel<T> {
auto
s_square
=
dito
.
Pow
(
S
,
2
);
auto
s_square
=
dito
.
Pow
(
S
,
2
);
auto
F
=
auto
F
=
dito
.
Sub
(
dito
.
Unsqueeze
(
s_square
,
-
2
),
dito
.
Unsqueeze
(
s_square
,
-
1
));
dito
.
Sub
(
dito
.
Unsqueeze
(
s_square
,
-
2
),
dito
.
Unsqueeze
(
s_square
,
-
1
));
F
=
dito
.
Add
(
F
,
dito
.
Diag
(
dito
.
Infinits
({
k
}
,
U
.
type
()
)));
F
=
dito
.
Add
(
F
,
dito
.
Diag
(
dito
.
Infinits
({
k
})));
F
=
dito
.
Pow
(
F
,
-
1
);
F
=
dito
.
Pow
(
F
,
-
1
);
Tensor
sigma_term
;
Tensor
sigma_term
;
Tensor
u_term
;
Tensor
u_term
;
...
@@ -115,8 +114,7 @@ class SvdGradKernel : public framework::OpKernel<T> {
...
@@ -115,8 +114,7 @@ class SvdGradKernel : public framework::OpKernel<T> {
u_term
=
dito
.
Mul
(
dito
.
Mul
(
dito
.
Sub
(
UTG
,
GTU
),
F
),
dito
.
Unsqueeze
(
S
,
-
2
));
u_term
=
dito
.
Mul
(
dito
.
Mul
(
dito
.
Sub
(
UTG
,
GTU
),
F
),
dito
.
Unsqueeze
(
S
,
-
2
));
u_term
=
dito
.
Matmul
(
U
,
u_term
);
u_term
=
dito
.
Matmul
(
U
,
u_term
);
if
(
m
>
k
)
{
if
(
m
>
k
)
{
auto
project
=
auto
project
=
dito
.
Sub
(
dito
.
Eye
(
m
),
dito
.
Matmul
(
U
,
U
,
false
,
true
));
dito
.
Sub
(
dito
.
Eye
(
m
,
U
.
type
()),
dito
.
Matmul
(
U
,
U
,
false
,
true
));
u_term
=
dito
.
Add
(
u_term
,
dito
.
Mul
(
dito
.
Matmul
(
project
,
dU
),
u_term
=
dito
.
Add
(
u_term
,
dito
.
Mul
(
dito
.
Matmul
(
project
,
dU
),
dito
.
Unsqueeze
(
s_inverse
,
-
2
)));
dito
.
Unsqueeze
(
s_inverse
,
-
2
)));
}
}
...
@@ -129,8 +127,7 @@ class SvdGradKernel : public framework::OpKernel<T> {
...
@@ -129,8 +127,7 @@ class SvdGradKernel : public framework::OpKernel<T> {
v_term
=
dito
.
Mul
(
dito
.
Matmul
(
dito
.
Mul
(
dito
.
Sub
(
UTG
,
GTU
),
F
),
VH
),
v_term
=
dito
.
Mul
(
dito
.
Matmul
(
dito
.
Mul
(
dito
.
Sub
(
UTG
,
GTU
),
F
),
VH
),
dito
.
Unsqueeze
(
S
,
-
1
));
dito
.
Unsqueeze
(
S
,
-
1
));
if
(
n
>
k
)
{
if
(
n
>
k
)
{
auto
project
=
auto
project
=
dito
.
Sub
(
dito
.
Eye
(
n
),
dito
.
Matmul
(
VH
,
VH
,
true
,
false
));
dito
.
Sub
(
dito
.
Eye
(
n
,
U
.
type
()),
dito
.
Matmul
(
VH
,
VH
,
true
,
false
));
v_term
=
dito
.
Add
(
v_term
,
dito
.
Mul
(
dito
.
Matmul
(
dVH
,
project
),
v_term
=
dito
.
Add
(
v_term
,
dito
.
Mul
(
dito
.
Matmul
(
dVH
,
project
),
dito
.
Unsqueeze
(
s_inverse
,
-
1
)));
dito
.
Unsqueeze
(
s_inverse
,
-
1
)));
}
}
...
...
python/paddle/__init__.py
浏览文件 @
f521a30d
...
@@ -100,7 +100,6 @@ from .tensor.linalg import bmm # noqa: F401
...
@@ -100,7 +100,6 @@ from .tensor.linalg import bmm # noqa: F401
from
.tensor.linalg
import
histogram
# noqa: F401
from
.tensor.linalg
import
histogram
# noqa: F401
from
.tensor.linalg
import
mv
# noqa: F401
from
.tensor.linalg
import
mv
# noqa: F401
from
.tensor.linalg
import
matrix_power
# noqa: F401
from
.tensor.linalg
import
matrix_power
# noqa: F401
from
.tensor.linalg
import
svd
# noqa: F401
from
.tensor.logic
import
equal
# noqa: F401
from
.tensor.logic
import
equal
# noqa: F401
from
.tensor.logic
import
greater_equal
# noqa: F401
from
.tensor.logic
import
greater_equal
# noqa: F401
from
.tensor.logic
import
greater_than
# noqa: F401
from
.tensor.logic
import
greater_than
# noqa: F401
...
@@ -498,7 +497,6 @@ __all__ = [ # noqa
...
@@ -498,7 +497,6 @@ __all__ = [ # noqa
'sqrt'
,
'sqrt'
,
'cholesky'
,
'cholesky'
,
'matrix_power'
,
'matrix_power'
,
'svd'
,
'randperm'
,
'randperm'
,
'linspace'
,
'linspace'
,
'reshape'
,
'reshape'
,
...
...
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
f521a30d
...
@@ -889,7 +889,7 @@ set_tests_properties(test_multiprocess_dataloader_iterable_dataset_static PROPER
...
@@ -889,7 +889,7 @@ set_tests_properties(test_multiprocess_dataloader_iterable_dataset_static PROPER
set_tests_properties
(
test_lstm_cudnn_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_lstm_cudnn_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_stack_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_stack_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_bilinear_interp_v2_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_bilinear_interp_v2_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_svd_op PROPERTIES TIMEOUT
12
0
)
set_tests_properties
(
test_svd_op PROPERTIES TIMEOUT
4
0
)
set_tests_properties
(
test_deformable_psroi_pooling PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_deformable_psroi_pooling PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_trilinear_interp_v2_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_trilinear_interp_v2_op PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_imperative_static_runner_mnist PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_imperative_static_runner_mnist PROPERTIES TIMEOUT 120
)
...
...
python/paddle/tensor/__init__.py
浏览文件 @
f521a30d
...
@@ -45,7 +45,6 @@ from .linalg import bmm # noqa: F401
...
@@ -45,7 +45,6 @@ from .linalg import bmm # noqa: F401
from
.linalg
import
histogram
# noqa: F401
from
.linalg
import
histogram
# noqa: F401
from
.linalg
import
mv
# noqa: F401
from
.linalg
import
mv
# noqa: F401
from
.linalg
import
matrix_power
# noqa: F401
from
.linalg
import
matrix_power
# noqa: F401
from
.linalg
import
svd
# noqa: F401
from
.logic
import
equal
# noqa: F401
from
.logic
import
equal
# noqa: F401
from
.logic
import
greater_equal
# noqa: F401
from
.logic
import
greater_equal
# noqa: F401
from
.logic
import
greater_than
# noqa: F401
from
.logic
import
greater_than
# noqa: F401
...
@@ -226,7 +225,6 @@ tensor_method_func = [ #noqa
...
@@ -226,7 +225,6 @@ tensor_method_func = [ #noqa
'histogram'
,
'histogram'
,
'mv'
,
'mv'
,
'matrix_power'
,
'matrix_power'
,
'svd'
,
'abs'
,
'abs'
,
'acos'
,
'acos'
,
'all'
,
'all'
,
...
...
python/paddle/tensor/linalg.py
浏览文件 @
f521a30d
...
@@ -1036,46 +1036,51 @@ def mv(x, vec, name=None):
...
@@ -1036,46 +1036,51 @@ def mv(x, vec, name=None):
def
svd
(
x
,
full_matrices
=
False
,
name
=
None
):
def
svd
(
x
,
full_matrices
=
False
,
name
=
None
):
r
"""
r
"""
Computes the singular value decomposition of one
Computes the singular value decomposition of one matrix or a batch of regular matrices.
matrix or batches of regular matrice.
Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
.. math::
X = U * diag(S) * VT
Args:
Args:
x (Tensor): The input tensor. Its shape should be `[..., N, M]`,
x (Tensor): The input tensor. Its shape should be `[..., N, M]`,
where
...
is zero or more batch dimensions. N and M can be arbitraty
where
`...`
is zero or more batch dimensions. N and M can be arbitraty
positive number. Note that if x is sigular matrices, the grad is numerical
positive number. Note that if x is sigular matrices, the grad is numerical
instability. The data type of x should be float32 or float64.
instable. The data type of x should be float32 or float64.
full_matrices (bool): A flag to control the behavor of svd.
full_matrices(bool): A flag to control the behavor of svd.
If full_matrices = True, svd op will compute full U and V matrics,
If full_matrices = True, svd op will compute full U and V matrics,
which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`.
which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`.
K = min(M, N).
If full_matrices = False, svd op will use a economic method to store U and V.
If full_matrices = False, svd op will use a economic method to store U and V.
which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`
which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Returns:
Tensor: Tensor U, the shape of U is controlled by full_matrices flag.
Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`
Tensor: Tensor S, the singular value of X. the shape of S is [..., K]
Tensor: Tensor VH, the conjugate transpose of V. the shape of V is controlled by full_matrices flag.
import numpy as np
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')
x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')
x = x.reshape([3, 2])
x = x.reshape([3, 2])
u, s, v
t
= paddle.linalg.svd(x)
u, s, v
h
= paddle.linalg.svd(x)
print (u)
print (u)
print (s)
print (vt)
#U = [[ 0.27364809, -0.21695147 ],
#U = [[ 0.27364809, -0.21695147 ],
# [ 0.37892198, -0.87112408 ],
# [ 0.37892198, -0.87112408 ],
# [ 0.8840446 , 0.44053933 ]]
# [ 0.8840446 , 0.44053933 ]]
print (s)
#S = [8.14753743, 0.78589688]
#S = [8.14753743, 0.78589688]
print (vh)
#VT= [[ 0.51411221, 0.85772294],
#VT= [[ 0.51411221, 0.85772294],
# [ 0.85772294, -0.51411221]]
# [ 0.85772294, -0.51411221]]
# one can verify : U * S * VT =
X ;
# one can verify : U * S * VT =
= X
# U * UH =
I ;
# U * UH =
= I
# V * VH = I
# V * VH =
=
I
"""
"""
if
in_dygraph_mode
():
if
in_dygraph_mode
():
...
...
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