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体验新版 GitCode,发现更多精彩内容 >>
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
显示空白变更内容
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 @@
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/operator.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/functors.h"
#include "paddle/fluid/operators/math/math_function.h"
...
...
@@ -89,7 +92,6 @@ struct PowFunctor {
};
static
std
::
vector
<
int
>
GetBroadcastShape
(
InTensors
ins
)
{
// TODO(xiongkun03) check the operators and output
PADDLE_ENFORCE_EQ
(
ins
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"GetBroadcastShape Receive 2 tensors"
"but got [%d]"
,
...
...
@@ -125,6 +127,19 @@ static std::vector<int> GetBroadcastShape(InTensors ins) {
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
>
struct
DeviceIndependenceTensorOperations
{
// 1. Device indenpendence, for kernel reuse.
...
...
@@ -153,20 +168,25 @@ struct DeviceIndependenceTensorOperations {
framework
::
Tensor
Matmul
(
const
framework
::
Tensor
&
mat_a
,
const
framework
::
Tensor
&
mat_b
,
bool
trans_a
=
false
,
bool
trans_b
=
false
)
{
framework
::
AttributeMap
attrs
;
attrs
[
"trans_x"
]
=
trans_a
;
attrs
[
"trans_y"
]
=
trans_b
;
NameInTensorMap
inputs
({{
"X"
,
{
&
mat_a
}},
{
"Y"
,
{
&
mat_b
}}});
framework
::
Tensor
ret
;
auto
a_dim
=
mat_a
.
dims
();
auto
b_dim
=
mat_b
.
dims
();
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
()
-
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
out
;
// transpose the last two dimision
framework
::
Tensor
ret
;
auto
x_dim
=
x
.
dims
();
auto
x_vec
=
framework
::
vectorize
<
int
>
(
x_dim
);
int
rank
=
x_vec
.
size
();
...
...
@@ -177,26 +197,42 @@ struct DeviceIndependenceTensorOperations {
axis
[
i
]
=
i
;
}
std
::
swap
(
axis
[
rank
-
1
],
axis
[
rank
-
2
]);
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
axis
;
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
return
CreateOpRunAndReturnTensor
(
"transpose2"
,
inputs
,
attrs
,
out_shape
,
{
"Out"
,
"XShape"
});
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
ret
.
Resize
(
framework
::
make_ddim
(
x_vec
));
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
switch
(
rank
)
{
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
,
// FIXME link error
int
padding_value
=
0
)
{
framework
::
AttributeMap
attrs
;
attrs
[
"offset"
]
=
offset
;
attrs
[
"padding_value"
]
=
padding_value
;
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}}});
PADDLE_ENFORCE_EQ
(
padding_value
,
0
,
platform
::
errors
::
InvalidArgument
(
"Current diag only support padding_value = 0"
));
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
();
std
::
vector
<
int
>
out_shape
;
if
(
x_rank
==
2
)
{
PADDLE_
ENFORCE_EQ
(
x
.
dims
()[
0
],
x
.
dims
()[
1
],
platform
::
errors
::
InvalidArgument
(
"if X is a Matrix, then X must be square"
));
out_shape
.
push_back
(
x
.
dims
()[
0
]
);
PADDLE_
THROW
(
platform
::
errors
::
InvalidArgument
(
"Current diag only support vector"
"-> diagonalized matrix, not support matrix -> vector,"
" Use DiagOp instead."
)
);
}
else
if
(
x_rank
==
1
)
{
out_shape
.
push_back
(
x
.
dims
()[
0
]);
out_shape
.
push_back
(
x
.
dims
()[
0
]);
...
...
@@ -204,42 +240,73 @@ struct DeviceIndependenceTensorOperations {
PADDLE_THROW
(
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
,
const
framework
::
Tensor
&
y
)
{
InTensors
ins
({
&
x
,
&
y
});
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
-
1
;
// element wise add, support numpy broadcast.
framework
::
Tensor
ret
;
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}},
{
"Y"
,
{
&
y
}}});
return
CreateOpRunAndReturnTensor
(
"elementwise_add"
,
inputs
,
attrs
,
out_shape
);
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
ElementwiseComputeEx
<
AddFunctor
<
T
>
,
DeviceContext
,
T
>
(
context
,
&
x
,
&
y
,
-
1
,
AddFunctor
<
T
>
(),
&
ret
);
return
ret
;
}
framework
::
Tensor
Mul
(
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
y
)
{
InTensors
ins
({
&
x
,
&
y
});
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
-
1
;
framework
::
Tensor
ret
;
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}},
{
"Y"
,
{
&
y
}}});
return
CreateOpRunAndReturnTensor
(
"elementwise_mul"
,
inputs
,
attrs
,
out_shape
);
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
ElementwiseComputeEx
<
MulFunctor
<
T
>
,
DeviceContext
,
T
>
(
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
,
const
framework
::
Tensor
&
y
)
{
InTensors
ins
({
&
x
,
&
y
});
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
-
1
;
framework
::
Tensor
ret
;
std
::
vector
<
int
>
out_shape
=
GetBroadcastShape
({
&
x
,
&
y
});
NameInTensorMap
inputs
({{
"X"
,
{
&
x
}},
{
"Y"
,
{
&
y
}}});
return
CreateOpRunAndReturnTensor
(
"elementwise_sub"
,
inputs
,
attrs
,
out_shape
);
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
if
(
x
.
dims
().
size
()
>=
y
.
dims
().
size
())
{
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
)
{
// don't copy data, only change the dims
framework
::
Tensor
out
;
...
...
@@ -255,40 +322,29 @@ struct DeviceIndependenceTensorOperations {
out
.
Resize
(
framework
::
make_ddim
(
out_shape
));
return
out
;
}
framework
::
Tensor
Zeros
(
std
::
vector
<
int
>
shape
,
framework
::
proto
::
VarType
::
Type
dtype
,
float
fill_value
)
{
framework
::
AttributeMap
attrs
;
attrs
[
"dtype"
]
=
dtype
;
attrs
[
"shape"
]
=
shape
;
attrs
[
"value"
]
=
fill_value
;
NameInTensorMap
inputs
({});
return
CreateOpRunAndReturnTensor
(
"fill_constant"
,
inputs
,
attrs
,
shape
);
framework
::
Tensor
Fill
(
std
::
vector
<
int
>
shape
,
float
fill_value
)
{
framework
::
Tensor
ret
;
ret
.
Resize
(
framework
::
make_ddim
(
shape
));
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
SetConstant
<
DeviceContext
,
T
>
()(
dev_ctx
,
&
ret
,
T
(
fill_value
));
return
ret
;
}
framework
::
Tensor
Infinits
(
std
::
vector
<
int
>
shape
,
framework
::
proto
::
VarType
::
Type
dtype
)
{
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
Infinits
(
std
::
vector
<
int
>
shape
)
{
auto
value
=
static_cast
<
T
>
(
std
::
numeric_limits
<
double
>::
infinity
());
return
Fill
(
shape
,
value
);
}
framework
::
Tensor
Eye
(
int
n
,
framework
::
proto
::
VarType
::
Type
dtype
)
{
auto
output
=
Zeros
({
n
},
dtype
,
1
);
framework
::
Tensor
Eye
(
int
n
)
{
auto
output
=
Fill
({
n
},
1
);
auto
ret
=
Diag
(
output
);
return
ret
;
}
framework
::
Tensor
Slice
(
const
framework
::
Tensor
&
x
,
std
::
vector
<
int
>
axes
,
std
::
vector
<
int
>
starts
,
std
::
vector
<
int
>
ends
)
{
framework
::
Tensor
ret
;
std
::
vector
<
int
>
new_axes
=
axes
;
NameInTensorMap
inputs
({{
"Input"
,
{
&
x
}}});
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
(
axes
.
size
(),
starts
.
size
(),
platform
::
errors
::
InvalidArgument
(
"Slice Operator Argument Invalided"
));
...
...
@@ -306,27 +362,31 @@ struct DeviceIndependenceTensorOperations {
"C++ Slice Operation Not Support End < Start"
));
out_shape
[
axis
]
=
ed
-
st
;
}
framework
::
AttributeMap
attrs
;
attrs
[
"axes"
]
=
new_axes
;
attrs
[
"starts"
]
=
starts
;
attrs
[
"ends"
]
=
ends
;
return
CreateOpRunAndReturnTensor
(
"slice"
,
inputs
,
attrs
,
out_shape
);
std
::
vector
<
int
>
offset
(
rank
),
extends
(
rank
);
for
(
size_t
i
=
0
;
i
<
rank
;
++
i
)
{
offset
[
i
]
=
0
;
extends
[
i
]
=
x
.
dims
()[
i
];
}
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
);
for
(
size_t
i
=
0
;
i
<
new_axes
.
size
();
++
i
)
{
offset
[
new_axes
[
i
]]
=
starts
[
i
];
extends
[
new_axes
[
i
]]
=
ends
[
i
]
-
starts
[
i
];
}
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
);
ret
.
Resize
(
framework
::
make_ddim
(
out_shape
));
ret
.
mutable_data
<
T
>
(
context
.
GetPlace
());
switch
(
rank
)
{
DITO_SLICE_RANK_CASE
(
1
);
DITO_SLICE_RANK_CASE
(
2
);
DITO_SLICE_RANK_CASE
(
3
);
DITO_SLICE_RANK_CASE
(
4
);
DITO_SLICE_RANK_CASE
(
5
);
DITO_SLICE_RANK_CASE
(
6
);
default:
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Invalid Rank number, "
"currently only support rank between 2~6"
));
}
}
return
ret
;
}
private:
...
...
@@ -338,14 +398,40 @@ struct DeviceIndependenceTensorOperations {
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
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
(
const
std
::
string
&
type
,
const
NameInTensorMap
&
inputs
,
const
framework
::
AttributeMap
&
attrs
,
std
::
vector
<
int
>
out_shape
,
NameOutTensor
out_str
=
{
"Out"
})
{
// varialble set dims must be LoDTensor / SelectedRowTensor
framework
::
Scope
&
local_scope
=
context
.
scope
().
NewScope
();
framework
::
VariableNameMap
op_outputs
;
for
(
auto
out_name
:
out_str
)
{
local_scope
.
Var
(
"tmp_"
+
out_name
)
->
GetMutable
<
framework
::
LoDTensor
>
();
...
...
@@ -373,6 +459,7 @@ struct DeviceIndependenceTensorOperations {
}
op_inputs
[
item
.
first
]
=
name_vector
;
}
auto
op
=
framework
::
OpRegistry
::
CreateOp
(
type
,
op_inputs
,
op_outputs
,
attrs
);
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> {
size_t
(
batches
*
col_v
*
cols
*
sizeof
(
math
::
Real
<
T
>
)));
auto
*
S_out
=
S
->
mutable_data
<
math
::
Real
<
T
>>
(
context
.
GetPlace
(),
size_t
(
batches
*
k
*
sizeof
(
math
::
Real
<
T
>
)));
/*SVD Use the Eigen Library*/
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> {
auto
s_square
=
dito
.
Pow
(
S
,
2
);
auto
F
=
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
);
Tensor
sigma_term
;
Tensor
u_term
;
...
...
@@ -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
.
Matmul
(
U
,
u_term
);
if
(
m
>
k
)
{
auto
project
=
dito
.
Sub
(
dito
.
Eye
(
m
,
U
.
type
()),
dito
.
Matmul
(
U
,
U
,
false
,
true
));
auto
project
=
dito
.
Sub
(
dito
.
Eye
(
m
),
dito
.
Matmul
(
U
,
U
,
false
,
true
));
u_term
=
dito
.
Add
(
u_term
,
dito
.
Mul
(
dito
.
Matmul
(
project
,
dU
),
dito
.
Unsqueeze
(
s_inverse
,
-
2
)));
}
...
...
@@ -129,8 +127,7 @@ class SvdGradKernel : public framework::OpKernel<T> {
v_term
=
dito
.
Mul
(
dito
.
Matmul
(
dito
.
Mul
(
dito
.
Sub
(
UTG
,
GTU
),
F
),
VH
),
dito
.
Unsqueeze
(
S
,
-
1
));
if
(
n
>
k
)
{
auto
project
=
dito
.
Sub
(
dito
.
Eye
(
n
,
U
.
type
()),
dito
.
Matmul
(
VH
,
VH
,
true
,
false
));
auto
project
=
dito
.
Sub
(
dito
.
Eye
(
n
),
dito
.
Matmul
(
VH
,
VH
,
true
,
false
));
v_term
=
dito
.
Add
(
v_term
,
dito
.
Mul
(
dito
.
Matmul
(
dVH
,
project
),
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
from
.tensor.linalg
import
histogram
# noqa: F401
from
.tensor.linalg
import
mv
# 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
greater_equal
# noqa: F401
from
.tensor.logic
import
greater_than
# noqa: F401
...
...
@@ -498,7 +497,6 @@ __all__ = [ # noqa
'sqrt'
,
'cholesky'
,
'matrix_power'
,
'svd'
,
'randperm'
,
'linspace'
,
'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
set_tests_properties
(
test_lstm_cudnn_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_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_trilinear_interp_v2_op 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
from
.linalg
import
histogram
# noqa: F401
from
.linalg
import
mv
# noqa: F401
from
.linalg
import
matrix_power
# noqa: F401
from
.linalg
import
svd
# noqa: F401
from
.logic
import
equal
# noqa: F401
from
.logic
import
greater_equal
# noqa: F401
from
.logic
import
greater_than
# noqa: F401
...
...
@@ -226,7 +225,6 @@ tensor_method_func = [ #noqa
'histogram'
,
'mv'
,
'matrix_power'
,
'svd'
,
'abs'
,
'acos'
,
'all'
,
...
...
python/paddle/tensor/linalg.py
浏览文件 @
f521a30d
...
...
@@ -1036,46 +1036,51 @@ def mv(x, vec, name=None):
def
svd
(
x
,
full_matrices
=
False
,
name
=
None
):
r
"""
Computes the singular value decomposition of one
matrix or batches of regular matrice.
Computes the singular value decomposition of one matrix or a batch of regular matrices.
Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
.. math::
X = U * diag(S) * VT
Args:
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
instability. The data type of x should be float32 or float64.
full_matrices(bool): A flag to control the behavor of svd.
instable. The data type of x should be float32 or float64.
full_matrices (bool): A flag to control the behavor of svd.
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.
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:
Tensor: Tensor U, the shape of U is controlled by full_matrices flag.
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.
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]`
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 = x.reshape([3, 2])
u, s, v
t
= paddle.linalg.svd(x)
u, s, v
h
= paddle.linalg.svd(x)
print (u)
print (s)
print (vt)
#U = [[ 0.27364809, -0.21695147 ],
# [ 0.37892198, -0.87112408 ],
# [ 0.8840446 , 0.44053933 ]]
print (s)
#S = [8.14753743, 0.78589688]
print (vh)
#VT= [[ 0.51411221, 0.85772294],
# [ 0.85772294, -0.51411221]]
# one can verify : U * S * VT =
X ;
# U * UH =
I ;
# V * VH = I
# one can verify : U * S * VT =
= X
# U * UH =
= I
# V * VH =
=
I
"""
if
in_dygraph_mode
():
...
...
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