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45711dad
编写于
8月 22, 2020
作者:
W
WangXi
提交者:
GitHub
8月 22, 2020
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电子邮件补丁
差异文件
【API】rename div to divide, add floor_divide, remainder (#26434)
上级
f5d13498
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
175 addition
and
86 deletion
+175
-86
paddle/fluid/operators/elementwise/elementwise_mod_op.cc
paddle/fluid/operators/elementwise/elementwise_mod_op.cc
+4
-4
python/paddle/__init__.py
python/paddle/__init__.py
+5
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+3
-0
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_div_op.py
+5
-5
python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py
...dle/fluid/tests/unittests/test_elementwise_floordiv_op.py
+23
-0
python/paddle/fluid/tests/unittests/test_elementwise_mod_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_mod_op.py
+23
-0
python/paddle/tensor/__init__.py
python/paddle/tensor/__init__.py
+5
-1
python/paddle/tensor/math.py
python/paddle/tensor/math.py
+107
-75
未找到文件。
paddle/fluid/operators/elementwise/elementwise_mod_op.cc
浏览文件 @
45711dad
...
...
@@ -25,14 +25,14 @@ class ElementwiseModOpMaker : public ElementwiseOpMaker {
void
AddInputX
()
override
{
AddInput
(
"X"
,
"(
Variable), Tensor or LoD
Tensor of any dimensions. Its dtype "
"should be int32, int64."
);
"(
Tensor),
Tensor of any dimensions. Its dtype "
"should be int32, int64
, float32 or float64
."
);
}
void
AddInputY
()
override
{
AddInput
(
"Y"
,
"(
Variable), Tensor or LoD
Tensor of any dimensions. Its dtype "
"should be int32, int64."
);
"(
Tensor),
Tensor of any dimensions. Its dtype "
"should be int32, int64
, float32 or float64
."
);
}
std
::
string
GetOpFuntionality
()
const
override
{
...
...
python/paddle/__init__.py
浏览文件 @
45711dad
...
...
@@ -176,7 +176,11 @@ from .tensor.math import maximum #DEFINE_ALIAS
from
.tensor.math
import
min
#DEFINE_ALIAS
from
.tensor.math
import
minimum
#DEFINE_ALIAS
from
.tensor.math
import
mm
#DEFINE_ALIAS
from
.tensor.math
import
div
#DEFINE_ALIAS
from
.tensor.math
import
divide
#DEFINE_ALIAS
from
.tensor.math
import
floor_divide
#DEFINE_ALIAS
from
.tensor.math
import
remainder
#DEFINE_ALIAS
from
.tensor.math
import
mod
#DEFINE_ALIAS
from
.tensor.math
import
floor_mod
#DEFINE_ALIAS
from
.tensor.math
import
multiply
#DEFINE_ALIAS
from
.tensor.math
import
add
#DEFINE_ALIAS
from
.tensor.math
import
atan
#DEFINE_ALIAS
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
45711dad
...
...
@@ -11484,6 +11484,7 @@ Examples:
return _elementwise_op(LayerHelper('elementwise_add', **locals()))
@deprecated(since="2.0.0", update_to="paddle.divide")
def elementwise_div(x, y, axis=-1, act=None, name=None):
"""
:alias_main: paddle.elementwise_div
...
...
@@ -11907,6 +11908,7 @@ Examples:
return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
@deprecated(since="2.0.0", update_to="paddle.remainder")
def elementwise_mod(x, y, axis=-1, act=None, name=None):
"""
:alias_main: paddle.elementwise_mod
...
...
@@ -11944,6 +11946,7 @@ Examples:
return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
@deprecated(since="2.0.0", update_to="paddle.floor_divide")
def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
"""
:alias_main: paddle.elementwise_floordiv
...
...
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
浏览文件 @
45711dad
...
...
@@ -240,22 +240,22 @@ class TestElementwiseDivBroadcast(unittest.TestCase):
self
.
assertEqual
((
out_result
==
(
2
/
x
)).
all
(),
True
)
class
TestDivOp
(
unittest
.
TestCase
):
class
TestDiv
ide
Op
(
unittest
.
TestCase
):
def
test_name
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
2
,
3
],
dtype
=
"float32"
)
y
=
fluid
.
data
(
name
=
'y'
,
shape
=
[
2
,
3
],
dtype
=
'float32'
)
y_1
=
paddle
.
div
(
x
,
y
,
name
=
'div_res'
)
y_1
=
paddle
.
div
ide
(
x
,
y
,
name
=
'div_res'
)
self
.
assertEqual
((
'div_res'
in
y_1
.
name
),
True
)
def
test_dygraph
(
self
):
with
fluid
.
dygraph
.
guard
():
np_x
=
np
.
array
([
2
,
3
,
4
]).
astype
(
'float64'
)
np_y
=
np
.
array
([
1
,
5
,
2
]).
astype
(
'float64'
)
x
=
fluid
.
dygraph
.
to_variable
(
np_x
)
y
=
fluid
.
dygraph
.
to_variable
(
np_y
)
z
=
paddle
.
div
(
x
,
y
)
x
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
paddle
.
div
ide
(
x
,
y
)
np_z
=
z
.
numpy
()
z_expected
=
np
.
array
([
2.
,
0.6
,
2.
])
self
.
assertEqual
((
np_z
==
z_expected
).
all
(),
True
)
...
...
python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py
浏览文件 @
45711dad
...
...
@@ -15,6 +15,8 @@
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
...
...
@@ -65,5 +67,26 @@ class TestElementwiseModOp_scalar(TestElementwiseModOp):
self
.
out
=
np
.
floor_divide
(
self
.
x
,
self
.
y
)
class
TestFloorDivideOp
(
unittest
.
TestCase
):
def
test_name
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
2
,
3
],
dtype
=
"int64"
)
y
=
fluid
.
data
(
name
=
'y'
,
shape
=
[
2
,
3
],
dtype
=
'int64'
)
y_1
=
paddle
.
floor_divide
(
x
,
y
,
name
=
'div_res'
)
self
.
assertEqual
((
'div_res'
in
y_1
.
name
),
True
)
def
test_dygraph
(
self
):
with
fluid
.
dygraph
.
guard
():
np_x
=
np
.
array
([
2
,
3
,
8
,
7
]).
astype
(
'int64'
)
np_y
=
np
.
array
([
1
,
5
,
3
,
3
]).
astype
(
'int64'
)
x
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
paddle
.
floor_divide
(
x
,
y
)
np_z
=
z
.
numpy
()
z_expected
=
np
.
array
([
2
,
0
,
2
,
2
])
self
.
assertEqual
((
np_z
==
z_expected
).
all
(),
True
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_elementwise_mod_op.py
浏览文件 @
45711dad
...
...
@@ -15,6 +15,8 @@
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
...
...
@@ -82,5 +84,26 @@ class TestElementwiseModOpDouble(TestElementwiseModOpFloat):
self
.
dtype
=
np
.
float64
class
TestRemainderOp
(
unittest
.
TestCase
):
def
test_name
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
2
,
3
],
dtype
=
"int64"
)
y
=
fluid
.
data
(
name
=
'y'
,
shape
=
[
2
,
3
],
dtype
=
'int64'
)
y_1
=
paddle
.
remainder
(
x
,
y
,
name
=
'div_res'
)
self
.
assertEqual
((
'div_res'
in
y_1
.
name
),
True
)
def
test_dygraph
(
self
):
with
fluid
.
dygraph
.
guard
():
np_x
=
np
.
array
([
2
,
3
,
8
,
7
]).
astype
(
'int64'
)
np_y
=
np
.
array
([
1
,
5
,
3
,
3
]).
astype
(
'int64'
)
x
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
paddle
.
remainder
(
x
,
y
)
np_z
=
z
.
numpy
()
z_expected
=
np
.
array
([
0
,
3
,
2
,
1
])
self
.
assertEqual
((
np_z
==
z_expected
).
all
(),
True
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/tensor/__init__.py
浏览文件 @
45711dad
...
...
@@ -144,7 +144,11 @@ from .math import maximum #DEFINE_ALIAS
from
.math
import
min
#DEFINE_ALIAS
from
.math
import
minimum
#DEFINE_ALIAS
from
.math
import
mm
#DEFINE_ALIAS
from
.math
import
div
#DEFINE_ALIAS
from
.math
import
divide
#DEFINE_ALIAS
from
.math
import
floor_divide
#DEFINE_ALIAS
from
.math
import
remainder
#DEFINE_ALIAS
from
.math
import
mod
#DEFINE_ALIAS
from
.math
import
floor_mod
#DEFINE_ALIAS
from
.math
import
multiply
#DEFINE_ALIAS
from
.math
import
add
#DEFINE_ALIAS
from
.math
import
atan
#DEFINE_ALIAS
...
...
python/paddle/tensor/math.py
浏览文件 @
45711dad
...
...
@@ -111,7 +111,11 @@ __all__ = [
'min'
,
'minimum'
,
'mm'
,
'div'
,
'divide'
,
'floor_divide'
,
'remainder'
,
'mod'
,
'floor_mod'
,
'multiply'
,
'add'
,
'atan'
,
...
...
@@ -263,114 +267,143 @@ Examples:
return
_elementwise_op
(
LayerHelper
(
op_type
,
**
locals
()))
def
div
(
x
,
y
,
name
=
None
):
def
div
ide
(
x
,
y
,
name
=
None
):
"""
Example
s:
Divide two tensors element-wise. The equation i
s:
.. code-block:: python
.. math::
out = x / y
import paddle
import paddle.fluid as fluid
import numpy as np
**Note**:
``paddle.divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
def gen_data():
return {
"x": np.array([2, 3, 4]).astype('float32'),
"y": np.array([1, 5, 2]).astype('float32')
}
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
x = fluid.data(name="x", shape=[3], dtype='float32')
y = fluid.data(name="y", shape=[3], dtype='float32')
z = paddle.div(x, y)
# z = x / y
Returns:
N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
Examples:
print(z_value) # [2., 0.6, 2.]
.. code-block:: python
import paddle
import numpy as np
.. code-block:: python
paddle.disable_static()
import paddle
import paddle.fluid as fluid
import numpy as np
np_x = np.array([2, 3, 4]).astype('float64')
np_y = np.array([1, 5, 2]).astype('float64')
x = paddle.to_tensor(np_x)
y = paddle.to_tensor(np_y)
z = paddle.divide(x, y)
print(z.numpy()) # [2., 0.6, 2.]
def gen_data():
return {
"x": np.ones((2, 3, 4, 5)).astype('float32'),
"y": np.zeros((4, 5)).astype('float32')
}
"""
op_type
=
'elementwise_div'
axis
=
-
1
act
=
None
if
in_dygraph_mode
():
return
_elementwise_op_in_dygraph
(
x
,
y
,
axis
=
axis
,
act
=
act
,
op_name
=
op_type
)
x = fluid.data(name="x", shape=[2, 3, 4, 5], dtype='float32')
y = fluid.data(name="y", shape=[4, 5], dtype='float32')
z = paddle.div(x, y, name='z')
# z = x / y
return
_elementwise_op
(
LayerHelper
(
op_type
,
**
locals
()))
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
def
floor_divide
(
x
,
y
,
name
=
None
):
"""
Floor divide two tensors element-wise. The equation is:
print(z_value[0])
print(z_value[0].shape) # z.shape=[2,3,4,5]
.. math::
out = x // y
**Note**:
``paddle.floor_divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
.. code-block:: python
Args:
x (Tensor): the input tensor, it's data type should be int32, int64.
y (Tensor): the input tensor, it's data type should be int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
import paddle
import paddle.fluid as fluid
import numpy as np
Returns:
N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
def gen_data():
return {
"x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
"y": np.random.randint(1, 5, size=[5]).astype('float32')
}
Examples:
x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.data(name="y", shape=[5], dtype='float32')
z = paddle.div(x, y)
# z = x / y
.. code-block:: python
place = fluid.CPUPlace()
exe = fluid.Executor(place)
import paddle
import numpy as np
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
print(z_value[0])
print(z_value[0].shape) # z.shape=[2,3,4,5]
paddle.disable_static()
np_x = np.array([2, 3, 8, 7])
np_y = np.array([1, 5, 3, 3])
x = paddle.to_tensor(np_x)
y = paddle.to_tensor(np_y)
z = paddle.floor_divide(x, y)
print(z.numpy()) # [2, 0, 2, 2]
.. code-block:: python
"""
op_type
=
'elementwise_floordiv'
axis
=
-
1
if
in_dygraph_mode
():
return
_elementwise_op_in_dygraph
(
x
,
y
,
axis
=
axis
,
op_name
=
op_type
)
import paddle
import paddle.fluid as fluid
import numpy as np
return
_elementwise_op
(
LayerHelper
(
op_type
,
**
locals
()))
with fluid.dygraph.guard(fluid.CPUPlace()):
np_x = np.array([2, 3, 4]).astype('float64')
np_y = np.array([1, 5, 2]).astype('float64')
x = fluid.dygraph.to_variable(np_x)
y = fluid.dygraph.to_variable(np_y)
z = paddle.div(x, y)
np_z = z.numpy()
print(np_z) # [2., 0.6, 2.]
def
remainder
(
x
,
y
,
name
=
None
):
"""
op_type
=
'elementwise_div'
Mod two tensors element-wise. The equation is:
.. math::
out = x \% y
**Note**:
``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
Args:
x (Tensor): the input tensor, it's data type should be int32, int64.
y (Tensor): the input tensor, it's data type should be int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
np_x = np.array([2, 3, 8, 7])
np_y = np.array([1, 5, 3, 3])
x = paddle.to_tensor(np_x)
y = paddle.to_tensor(np_y)
z = paddle.remainder(x, y)
print(z.numpy()) # [0, 3, 2, 1]
"""
op_type
=
'elementwise_mod'
axis
=
-
1
act
=
None
if
in_dygraph_mode
():
return
_elementwise_op_in_dygraph
(
x
,
y
,
axis
=
axis
,
act
=
act
,
op_name
=
op_type
)
x
,
y
,
axis
=
axis
,
op_name
=
op_type
)
return
_elementwise_op
(
LayerHelper
(
op_type
,
**
locals
()))
mod
=
remainder
#DEFINE_ALIAS
floor_mod
=
remainder
#DEFINE_ALIAS
def
multiply
(
x
,
y
,
axis
=-
1
,
name
=
None
):
"""
:alias_main: paddle.multiply
...
...
@@ -512,7 +545,6 @@ Examples:
for
func
in
[
add
,
div
,
maximum
,
minimum
,
multiply
...
...
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