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体验新版 GitCode,发现更多精彩内容 >>
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提交
c9f7cff0
编写于
9月 16, 2021
作者:
Z
zhangkaihuo
提交者:
GitHub
9月 16, 2021
浏览文件
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差异文件
Add a new op: paddle.linalg.multi_dot (#35224)
上级
72b07726
变更
7
展开全部
隐藏空白更改
内联
并排
Showing
7 changed file
with
929 addition
and
13 deletion
+929
-13
paddle/fluid/operators/multi_dot_op.cc
paddle/fluid/operators/multi_dot_op.cc
+567
-0
python/paddle/__init__.py
python/paddle/__init__.py
+1
-0
python/paddle/fluid/tests/unittests/test_multi_dot_op.py
python/paddle/fluid/tests/unittests/test_multi_dot_op.py
+263
-0
python/paddle/fluid/tests/unittests/white_list/check_shape_white_list.py
...luid/tests/unittests/white_list/check_shape_white_list.py
+1
-0
python/paddle/linalg.py
python/paddle/linalg.py
+2
-0
python/paddle/tensor/__init__.py
python/paddle/tensor/__init__.py
+2
-0
python/paddle/tensor/linalg.py
python/paddle/tensor/linalg.py
+93
-13
未找到文件。
paddle/fluid/operators/multi_dot_op.cc
0 → 100644
浏览文件 @
c9f7cff0
此差异已折叠。
点击以展开。
python/paddle/__init__.py
浏览文件 @
c9f7cff0
...
@@ -99,6 +99,7 @@ from .tensor.linalg import cholesky # noqa: F401
...
@@ -99,6 +99,7 @@ from .tensor.linalg import cholesky # noqa: F401
from
.tensor.linalg
import
bmm
# noqa: F401
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
multi_dot
# noqa: F401
from
.tensor.linalg
import
matrix_power
# noqa: F401
from
.tensor.linalg
import
matrix_power
# 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
...
...
python/paddle/fluid/tests/unittests/test_multi_dot_op.py
0 → 100644
浏览文件 @
c9f7cff0
# Copyright (c) 2021 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
,
skip_check_grad_ci
from
numpy.linalg
import
multi_dot
from
op_test
import
OpTest
import
paddle
paddle
.
enable_static
()
#the unittest of multi_dot
#compare the result of paddle multi_dot and numpy multi_dot
class
TestMultiDotOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"multi_dot"
self
.
dtype
=
self
.
get_dtype
()
self
.
get_inputs_and_outputs
()
def
get_dtype
(
self
):
return
"float64"
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
2
,
8
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
8
,
4
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
self
.
check_grad
([
'x1'
],
'Out'
)
#(A*B)*C
class
TestMultiDotOp3Mat
(
TestMultiDotOp
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
2
,
10
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
10
,
4
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
4
,
3
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
])}
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
self
.
check_grad
([
'x1'
],
'Out'
)
self
.
check_grad
([
'x2'
],
'Out'
)
#A*(B*C)
class
TestMultiDotOp3Mat2
(
TestMultiDotOp
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
3
,
4
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
4
,
8
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
8
,
2
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
])}
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
self
.
check_grad
([
'x1'
],
'Out'
)
self
.
check_grad
([
'x2'
],
'Out'
)
class
TestMultiDotOp4Mat
(
TestMultiDotOp
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
8
,
6
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
6
,
3
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
3
,
4
)).
astype
(
self
.
dtype
)
self
.
D
=
np
.
random
.
random
((
4
,
5
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
),
(
'x3'
,
self
.
D
)]
}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
,
self
.
D
])}
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
self
.
check_grad
([
'x1'
],
'Out'
)
self
.
check_grad
([
'x2'
],
'Out'
)
self
.
check_grad
([
'x3'
],
'Out'
)
class
TestMultiDotOpFirst1D
(
TestMultiDotOp
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
4
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
4
,
3
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
])}
class
TestMultiDotOp3MatFirst1D
(
TestMultiDotOp3Mat
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
4
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
4
,
3
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
3
,
3
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
])}
class
TestMultiDotOp4MatFirst1D
(
TestMultiDotOp4Mat
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
4
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
4
,
3
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
3
,
4
)).
astype
(
self
.
dtype
)
self
.
D
=
np
.
random
.
random
((
4
,
5
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
),
(
'x3'
,
self
.
D
)]
}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
,
self
.
D
])}
class
TestMultiDotOpLast1D
(
TestMultiDotOp
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
3
,
6
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
6
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
])}
class
TestMultiDotOp3MatLast1D
(
TestMultiDotOp3Mat
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
2
,
4
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
4
,
3
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
3
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
])}
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
self
.
check_grad
([
'x1'
],
'Out'
)
self
.
check_grad
([
'x2'
],
'Out'
)
class
TestMultiDotOp4MatLast1D
(
TestMultiDotOp4Mat
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
2
,
3
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
3
,
2
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
2
,
3
)).
astype
(
self
.
dtype
)
self
.
D
=
np
.
random
.
random
((
3
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
),
(
'x3'
,
self
.
D
)]
}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
,
self
.
D
])}
class
TestMultiDotOpFirstAndLast1D
(
TestMultiDotOp
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
4
,
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
4
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
])}
class
TestMultiDotOp3MatFirstAndLast1D
(
TestMultiDotOp3Mat
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
6
,
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
6
,
4
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
4
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
)]}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
])}
class
TestMultiDotOp4MatFirstAndLast1D
(
TestMultiDotOp4Mat
):
def
get_inputs_and_outputs
(
self
):
self
.
A
=
np
.
random
.
random
((
3
,
)).
astype
(
self
.
dtype
)
self
.
B
=
np
.
random
.
random
((
3
,
4
)).
astype
(
self
.
dtype
)
self
.
C
=
np
.
random
.
random
((
4
,
2
)).
astype
(
self
.
dtype
)
self
.
D
=
np
.
random
.
random
((
2
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
[(
'x0'
,
self
.
A
),
(
'x1'
,
self
.
B
),
(
'x2'
,
self
.
C
),
(
'x3'
,
self
.
D
)]
}
self
.
outputs
=
{
'Out'
:
multi_dot
([
self
.
A
,
self
.
B
,
self
.
C
,
self
.
D
])}
#####python API test#######
class
TestMultiDotOpError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
(),
paddle
.
static
.
Program
()):
# The inputs type of multi_dot must be list matrix.
input1
=
12
self
.
assertRaises
(
TypeError
,
paddle
.
multi_dot
,
[
input1
,
input1
])
# The inputs dtype of multi_dot must be float64, float64 or float16.
input2
=
paddle
.
static
.
data
(
name
=
'input2'
,
shape
=
[
10
,
10
],
dtype
=
"int32"
)
self
.
assertRaises
(
TypeError
,
paddle
.
multi_dot
,
[
input2
,
input2
])
# the number of tensor must be larger than 1
x0
=
paddle
.
static
.
data
(
name
=
'x0'
,
shape
=
[
3
,
2
],
dtype
=
"float64"
)
self
.
assertRaises
(
ValueError
,
paddle
.
multi_dot
,
[
x0
])
#the first tensor must be 1D or 2D
x1
=
paddle
.
static
.
data
(
name
=
'x1'
,
shape
=
[
3
,
2
,
3
],
dtype
=
"float64"
)
x2
=
paddle
.
static
.
data
(
name
=
'x2'
,
shape
=
[
3
,
2
],
dtype
=
"float64"
)
self
.
assertRaises
(
ValueError
,
paddle
.
multi_dot
,
[
x1
,
x2
])
#the last tensor must be 1D or 2D
x3
=
paddle
.
static
.
data
(
name
=
'x3'
,
shape
=
[
3
,
2
],
dtype
=
"float64"
)
x4
=
paddle
.
static
.
data
(
name
=
'x4'
,
shape
=
[
3
,
2
,
2
],
dtype
=
"float64"
)
self
.
assertRaises
(
ValueError
,
paddle
.
multi_dot
,
[
x3
,
x4
])
#the tensor must be 2D, except first and last tensor
x5
=
paddle
.
static
.
data
(
name
=
'x5'
,
shape
=
[
3
,
2
],
dtype
=
"float64"
)
x6
=
paddle
.
static
.
data
(
name
=
'x6'
,
shape
=
[
2
],
dtype
=
"float64"
)
x7
=
paddle
.
static
.
data
(
name
=
'x7'
,
shape
=
[
2
,
2
],
dtype
=
"float64"
)
self
.
assertRaises
(
ValueError
,
paddle
.
multi_dot
,
[
x5
,
x6
,
x7
])
class
APITestMultiDot
(
unittest
.
TestCase
):
def
test_out
(
self
):
paddle
.
enable_static
()
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
x0
=
paddle
.
static
.
data
(
name
=
'x0'
,
shape
=
[
3
,
2
],
dtype
=
"float64"
)
x1
=
paddle
.
static
.
data
(
name
=
'x1'
,
shape
=
[
2
,
3
],
dtype
=
'float64'
)
result
=
paddle
.
multi_dot
([
x0
,
x1
])
exe
=
paddle
.
static
.
Executor
(
paddle
.
CPUPlace
())
data1
=
np
.
random
.
rand
(
3
,
2
).
astype
(
"float64"
)
data2
=
np
.
random
.
rand
(
2
,
3
).
astype
(
"float64"
)
np_res
=
exe
.
run
(
feed
=
{
'x0'
:
data1
,
'x1'
:
data2
},
fetch_list
=
[
result
])
expected_result
=
np
.
linalg
.
multi_dot
([
data1
,
data2
])
self
.
assertTrue
(
np
.
allclose
(
np_res
,
expected_result
,
atol
=
1e-5
),
"two value is
\
{}
\n
{}, check diff!"
.
format
(
np_res
,
expected_result
))
def
test_dygraph_without_out
(
self
):
paddle
.
disable_static
()
device
=
paddle
.
CPUPlace
()
input_array1
=
np
.
random
.
rand
(
3
,
4
).
astype
(
"float64"
)
input_array2
=
np
.
random
.
rand
(
4
,
3
).
astype
(
"float64"
)
data1
=
paddle
.
to_tensor
(
input_array1
)
data2
=
paddle
.
to_tensor
(
input_array2
)
out
=
paddle
.
multi_dot
([
data1
,
data2
])
expected_result
=
np
.
linalg
.
multi_dot
([
input_array1
,
input_array2
])
self
.
assertTrue
(
np
.
allclose
(
expected_result
,
out
.
numpy
()))
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/white_list/check_shape_white_list.py
浏览文件 @
c9f7cff0
...
@@ -28,4 +28,5 @@ NEED_TO_FIX_OP_LIST = [
...
@@ -28,4 +28,5 @@ NEED_TO_FIX_OP_LIST = [
'cvm'
,
'cvm'
,
'cudnn_lstm'
,
'cudnn_lstm'
,
'rnn'
,
'rnn'
,
'multi_dot'
,
]
]
python/paddle/linalg.py
浏览文件 @
c9f7cff0
...
@@ -16,6 +16,7 @@ from .tensor.linalg import cholesky # noqa: F401
...
@@ -16,6 +16,7 @@ from .tensor.linalg import cholesky # noqa: F401
from
.tensor.linalg
import
norm
# noqa: F401
from
.tensor.linalg
import
norm
# noqa: F401
from
.tensor.linalg
import
matrix_power
# noqa: F401
from
.tensor.linalg
import
matrix_power
# noqa: F401
from
.tensor
import
inverse
as
inv
# noqa: F401
from
.tensor
import
inverse
as
inv
# noqa: F401
from
.tensor.linalg
import
multi_dot
# noqa: F401
from
.tensor.linalg
import
matrix_rank
from
.tensor.linalg
import
matrix_rank
from
.tensor.linalg
import
svd
from
.tensor.linalg
import
svd
...
@@ -23,6 +24,7 @@ __all__ = [
...
@@ -23,6 +24,7 @@ __all__ = [
'cholesky'
,
#noqa
'cholesky'
,
#noqa
'norm'
,
'norm'
,
'inv'
,
'inv'
,
'multi_dot'
,
'matrix_rank'
,
'matrix_rank'
,
'svd'
,
'svd'
,
'matrix_power'
'matrix_power'
...
...
python/paddle/tensor/__init__.py
浏览文件 @
c9f7cff0
...
@@ -45,6 +45,8 @@ from .linalg import bmm # noqa: F401
...
@@ -45,6 +45,8 @@ 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
multi_dot
# 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
...
...
python/paddle/tensor/linalg.py
浏览文件 @
c9f7cff0
...
@@ -789,25 +789,25 @@ def matrix_rank(x, tol=None, hermitian=False, name=None):
...
@@ -789,25 +789,25 @@ def matrix_rank(x, tol=None, hermitian=False, name=None):
r
"""
r
"""
Computes the rank of a matrix.
Computes the rank of a matrix.
The rank of a matrix is the number of singular values that are greater than the specified tol threshold when hermitian=False,
The rank of a matrix is the number of singular values that are greater than the specified tol threshold when hermitian=False,
or the number of eigenvalues in absolute value that are greater than the specified tol threshold when hermitian=True.
or the number of eigenvalues in absolute value that are greater than the specified tol threshold when hermitian=True.
Args:
Args:
x (Tensor): The input tensor.
x (Tensor): The input tensor.
Its shape should be [..., m, n], where ... is zero or more batch dimensions. If x is a batch of matrices then the output
Its shape should be [..., m, n], where ... is zero or more batch dimensions. If x is a batch of matrices then the output
has the same batch dimensions. The data type of x should be float32 or float64.
has the same batch dimensions. The data type of x should be float32 or float64.
tol (float,Tensor,optional): the tolerance value. Default: None.
tol (float,Tensor,optional): the tolerance value. Default: None.
If tol is not specified, and sigma is the largest singular value (or eigenvalue in absolute value), and eps is the
If tol is not specified, and sigma is the largest singular value (or eigenvalue in absolute value), and eps is the
epsilon value for the dtype of x, then tol is computed with formula tol=sigma * max(m,n) * eps. Note that if x is
epsilon value for the dtype of x, then tol is computed with formula tol=sigma * max(m,n) * eps. Note that if x is
a batch of matrices, tol is computed this way for every batch.
a batch of matrices, tol is computed this way for every batch.
hermitian (bool,optional): indicates whether x is Hermitian. Default: False.
hermitian (bool,optional): indicates whether x is Hermitian. Default: False.
When hermitian=True, x is assumed to be Hermitian, but x is not checked inside the function. Instead, We just use the
When hermitian=True, x is assumed to be Hermitian, but x is not checked inside the function. Instead, We just use the
lower triangular of the matrix to compute.
lower triangular of the matrix to compute.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Returns:
Tensor: Rank of tensor x.
Tensor: Rank of tensor x.
Examples:
Examples:
.. code-block:: python
.. code-block:: python
...
@@ -824,7 +824,7 @@ def matrix_rank(x, tol=None, hermitian=False, name=None):
...
@@ -824,7 +824,7 @@ def matrix_rank(x, tol=None, hermitian=False, name=None):
# d = [[1, 1, 1, 1],
# d = [[1, 1, 1, 1],
# [1, 1, 1, 1],
# [1, 1, 1, 1],
# [1, 1, 1, 1]]
# [1, 1, 1, 1]]
"""
"""
if
in_dygraph_mode
():
if
in_dygraph_mode
():
...
@@ -1112,12 +1112,12 @@ def matrix_power(x, n, name=None):
...
@@ -1112,12 +1112,12 @@ def matrix_power(x, n, name=None):
.. math::
.. math::
Out = X ^ {n}
Out = X ^ {n}
Specifically,
Specifically,
- If `n > 0`, it returns the matrix or a batch of matrices raised to the power
- If `n > 0`, it returns the matrix or a batch of matrices raised to the power
of `n`.
of `n`.
- If `n = 0`, it returns the identity matrix or a batch of identity matrices.
- If `n = 0`, it returns the identity matrix or a batch of identity matrices.
- If `n < 0`, it returns the inverse of each matrix (if invertible) raised to
- If `n < 0`, it returns the inverse of each matrix (if invertible) raised to
...
@@ -1128,7 +1128,7 @@ def matrix_power(x, n, name=None):
...
@@ -1128,7 +1128,7 @@ def matrix_power(x, n, name=None):
to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or
to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or
more batch dimensions. Its data type should be float32 or float64.
more batch dimensions. Its data type should be float32 or float64.
n (int): The exponent. It can be any positive, negative integer or zero.
n (int): The exponent. It can be any positive, negative integer or zero.
name (str, optional): Name for the operation (optional, default is None).
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Returns:
...
@@ -1171,3 +1171,83 @@ def matrix_power(x, n, name=None):
...
@@ -1171,3 +1171,83 @@ def matrix_power(x, n, name=None):
outputs
=
{
'Out'
:
out
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'n'
:
n
})
attrs
=
{
'n'
:
n
})
return
out
return
out
def
multi_dot
(
x
,
name
=
None
):
"""
Multi_dot is an operator that calculates multiple matrix multiplications.
Supports inputs of float, double and float16 dtypes. This function does not
support batched inputs.
The input tensor in [x] must be 2-D except for the first and last can be 1-D.
If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector
of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it
is treated as a column vector of shape(n, 1).
If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,
otherwise the output is a 1-D vector.
Multi_dot will select the lowest cost multiplication order for calculation. The
cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.
Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,
we can calculate the cost of different multiplication orders as follows:
- Cost((AB)C) = 20x5x100 + 20x100x10 = 30000
- Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000
In this case, multiplying B and C first, then multiply A, which is 5 times faster
than sequential calculation.
Args:
x ([Tensor]): The input tensors which is a list Tensor.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Tensor: The output Tensor.
Examples:
.. code-block:: python
import paddle
import numpy as np
# A * B
A_data = np.random.random([3, 4]).astype(np.float32)
B_data = np.random.random([4, 5]).astype(np.float32)
A = paddle.to_tensor(A_data)
B = paddle.to_tensor(B_data)
out = paddle.multi_dot([A, B])
print(out.numpy().shape)
# [3, 5]
# A * B * C
A_data = np.random.random([10, 5]).astype(np.float32)
B_data = np.random.random([5, 8]).astype(np.float32)
C_data = np.random.random([8, 7]).astype(np.float32)
A = paddle.to_tensor(A_data)
B = paddle.to_tensor(B_data)
C = paddle.to_tensor(C_data)
out = paddle.multi_dot([A, B, C])
print(out.numpy().shape)
# [10, 7]
"""
if
in_dygraph_mode
():
return
_C_ops
.
multi_dot
(
x
)
check_type
(
x
,
'x'
,
(
list
,
tuple
),
'multi_dot'
)
for
id
,
item
in
enumerate
(
x
):
check_variable_and_dtype
(
item
,
'x['
+
str
(
id
)
+
']'
,
[
'float16'
,
'float32'
,
'float64'
],
'multi_dot'
)
if
item
.
dtype
!=
x
[
0
].
dtype
:
raise
TypeError
(
"All the Tensors in the input must have the same data type."
)
helper
=
LayerHelper
(
'multi_dot'
,
**
locals
())
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
)
helper
.
append_op
(
type
=
'multi_dot'
,
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
out
})
return
out
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