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体验新版 GitCode,发现更多精彩内容 >>
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0e816260
编写于
8月 24, 2020
作者:
S
ShenLiang
提交者:
GitHub
8月 24, 2020
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add div, floor_div, remainder (#26562)
* add div, floor_div, remainder
上级
973fdd67
变更
13
隐藏空白更改
内联
并排
Showing
13 changed file
with
601 addition
and
83 deletion
+601
-83
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc
...le/fluid/operators/elementwise/elementwise_floordiv_op.cc
+2
-0
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu
...le/fluid/operators/elementwise/elementwise_floordiv_op.cu
+2
-0
paddle/fluid/operators/elementwise/elementwise_floordiv_op.h
paddle/fluid/operators/elementwise/elementwise_floordiv_op.h
+20
-3
python/paddle/fluid/dygraph/math_op_patch.py
python/paddle/fluid/dygraph/math_op_patch.py
+29
-10
python/paddle/fluid/layers/math_op_patch.py
python/paddle/fluid/layers/math_op_patch.py
+29
-12
python/paddle/fluid/tests/unittests/test_dist_transpiler_async_decay.py
...fluid/tests/unittests/test_dist_transpiler_async_decay.py
+2
-2
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_div_op.py
+115
-16
python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py
...dle/fluid/tests/unittests/test_elementwise_floordiv_op.py
+123
-17
python/paddle/fluid/tests/unittests/test_elementwise_mod_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_mod_op.py
+117
-17
python/paddle/fluid/tests/unittests/test_math_op_patch.py
python/paddle/fluid/tests/unittests/test_math_op_patch.py
+4
-4
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py
+2
-1
python/paddle/nn/functional/loss.py
python/paddle/nn/functional/loss.py
+2
-1
python/paddle/tensor/math.py
python/paddle/tensor/math.py
+154
-0
未找到文件。
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc
浏览文件 @
0e816260
...
...
@@ -49,6 +49,8 @@ REGISTER_OP_WITHOUT_GRADIENT(elementwise_floordiv, ops::ElementwiseOp,
REGISTER_OP_CPU_KERNEL
(
elementwise_floordiv
,
ops
::
ElementwiseFloorDivKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ElementwiseFloorDivKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
ElementwiseFloorDivKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
ElementwiseFloorDivKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu
浏览文件 @
0e816260
...
...
@@ -19,5 +19,7 @@ namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL
(
elementwise_floordiv
,
ops
::
ElementwiseFloorDivKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseFloorDivKernel
<
plat
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseFloorDivKernel
<
plat
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseFloorDivKernel
<
plat
::
CUDADeviceContext
,
int64_t
>
);
paddle/fluid/operators/elementwise/elementwise_floordiv_op.h
浏览文件 @
0e816260
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <math.h>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
...
...
@@ -24,7 +25,16 @@ namespace operators {
template
<
typename
T
>
struct
FloorDivFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
a
/
b
;
}
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
static_cast
<
T
>
(
floor
(
a
/
b
));
}
};
template
<
typename
T
>
struct
InverseFloorDivFunctor
{
inline
HOSTDEVICE
T
operator
()(
T
a
,
T
b
)
const
{
return
static_cast
<
T
>
(
floor
(
b
/
a
));
}
};
template
<
typename
DeviceContext
,
typename
T
>
...
...
@@ -32,8 +42,15 @@ void elementwise_floor_div(const framework::ExecutionContext &ctx,
const
framework
::
Tensor
*
x
,
const
framework
::
Tensor
*
y
,
framework
::
Tensor
*
z
)
{
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
ElementwiseComputeEx
<
FloorDivFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
FloorDivFunctor
<
T
>
(),
z
);
auto
x_dims
=
x
->
dims
();
auto
y_dims
=
y
->
dims
();
if
(
x_dims
.
size
()
>=
y_dims
.
size
())
{
ElementwiseComputeEx
<
FloorDivFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
FloorDivFunctor
<
T
>
(),
z
);
}
else
{
ElementwiseComputeEx
<
InverseFloorDivFunctor
<
T
>
,
DeviceContext
,
T
>
(
ctx
,
x
,
y
,
axis
,
InverseFloorDivFunctor
<
T
>
(),
z
);
}
}
template
<
typename
DeviceContext
,
typename
T
>
...
...
python/paddle/fluid/dygraph/math_op_patch.py
浏览文件 @
0e816260
...
...
@@ -19,6 +19,7 @@ from ..framework import Variable, convert_np_dtype_to_dtype_, _varbase_creator
from
..layers.layer_function_generator
import
OpProtoHolder
from
..layers
import
common_methods
from
.
import
to_variable
,
no_grad
import
paddle
import
numpy
as
np
import
six
...
...
@@ -162,6 +163,26 @@ def monkey_patch_math_varbase():
def
_scalar_div_
(
var
,
value
):
return
_scalar_elementwise_op_
(
var
,
1.0
/
value
,
0.0
)
# TODO(shenliang03): currently, it supports divide, floor_divide, remainder
# for binary operator by using the api to achieve the type promotion
def
_binary_method_creator_
(
op_type
,
reverse
=
False
):
import
paddle
def
__impl__
(
self
,
other_var
):
import
paddle
op
=
getattr
(
paddle
,
op_type
)
if
reverse
:
return
op
(
other_var
,
self
)
else
:
return
op
(
self
,
other_var
)
__impl__
.
__doc__
=
"""
See paddle.{}"""
.
format
(
op_type
)
__impl__
.
__name__
=
op_type
return
__impl__
# for binary operator such as elementwise, compare
def
_binary_creator_
(
method_name
,
op_type
,
...
...
@@ -260,22 +281,20 @@ def monkey_patch_math_varbase():
## a*b == b*a. Do not need to reverse explicitly
(
'__rmul__'
,
_binary_creator_
(
'__rmul__'
,
'elementwise_mul'
,
False
,
_scalar_mul_
)),
(
'__div__'
,
_binary_creator_
(
'__div__'
,
'elementwise_div'
,
False
,
_scalar_div_
)),
(
'__truediv__'
,
_binary_creator_
(
'__truediv__'
,
'elementwise_div'
,
False
,
_scalar_div_
)),
(
'__rdiv__'
,
_binary_creator_
(
'__rdiv__'
,
'elementwise_div'
,
True
,
None
)),
(
'__rtruediv__'
,
_binary_creator_
(
'rtruediv__'
,
'elementwise_div'
,
True
,
None
)),
(
'__pow__'
,
_binary_creator_
(
'__pow__'
,
'elementwise_pow'
,
False
,
None
)),
(
'__rpow__'
,
_binary_creator_
(
'__rpow__'
,
'elementwise_pow'
,
True
,
None
)),
(
'__floordiv__'
,
_binary_creator_
(
'__floordiv__'
,
'elementwise_floordiv'
,
False
,
None
)),
(
'__mod__'
,
_binary_creator_
(
'__mod__'
,
'elementwise_mod'
,
False
,
None
)),
# These binary use paddle.optype
(
'__div__'
,
_binary_method_creator_
(
'divide'
,
False
)),
(
'__truediv__'
,
_binary_method_creator_
(
'divide'
,
False
)),
(
'__rtruediv__'
,
_binary_method_creator_
(
'divide'
,
True
)),
(
'__rdiv__'
,
_binary_method_creator_
(
'divide'
,
True
)),
(
'__floordiv__'
,
_binary_method_creator_
(
'floor_divide'
,
False
)),
(
'__rfloordiv__'
,
_binary_method_creator_
(
'floor_divide'
,
True
)),
(
'__mod__'
,
_binary_method_creator_
(
'remainder'
,
False
)),
## for logical compare
(
'__eq__'
,
_binary_creator_
(
'__eq__'
,
'equal'
,
False
,
None
)),
(
'__ne__'
,
_binary_creator_
(
'__ne__'
,
'not_equal'
,
False
,
None
)),
...
...
python/paddle/fluid/layers/math_op_patch.py
浏览文件 @
0e816260
...
...
@@ -16,6 +16,7 @@ from __future__ import print_function
import
warnings
import
inspect
import
paddle
from
..
import
core
from
..framework
import
Variable
,
unique_name
...
...
@@ -45,6 +46,7 @@ EXPRESSION_MAP = {
"__pow__"
:
"A ** B"
,
"__rpow__"
:
"A **= B"
,
"__floordiv__"
:
"A //B"
,
"__rfloordiv__"
:
"A //= B"
,
"__mod__"
:
"A % B"
,
"__eq__"
:
"A == B"
,
"__ne__"
:
"A != B"
,
...
...
@@ -233,6 +235,25 @@ def monkey_patch_variable():
def
_scalar_div_
(
var
,
value
):
return
_scalar_op_
(
var
,
1.0
/
value
,
0.0
)
# TODO(shenliang03): currently, it supports divide, floor_divide, remainder
# for binary operator by using the api to achieve the type promotion
def
_binary_method_creator_
(
op_type
,
reverse
=
False
):
import
paddle
def
__impl__
(
self
,
other_var
):
op
=
getattr
(
paddle
,
op_type
)
if
reverse
:
return
op
(
other_var
,
self
)
else
:
return
op
(
self
,
other_var
)
__impl__
.
__doc__
=
"""
See paddle.{}"""
.
format
(
op_type
)
__impl__
.
__name__
=
op_type
return
__impl__
def
_binary_creator_
(
method_name
,
op_type
,
reverse
=
False
,
...
...
@@ -339,22 +360,18 @@ def monkey_patch_variable():
# a*b == b*a. Do not need to reverse explicitly
(
'__rmul__'
,
_binary_creator_
(
'__rmul__'
,
'elementwise_mul'
,
False
,
_scalar_mul_
)),
(
'__div__'
,
_binary_creator_
(
'__div__'
,
'elementwise_div'
,
False
,
_scalar_div_
)),
(
'__truediv__'
,
_binary_creator_
(
'__truediv__'
,
'elementwise_div'
,
False
,
_scalar_div_
)),
(
'__rdiv__'
,
_binary_creator_
(
'__rdiv__'
,
'elementwise_div'
,
True
,
None
)),
(
'__rtruediv__'
,
_binary_creator_
(
'__rtruediv__'
,
'elementwise_div'
,
True
,
None
)),
(
'__pow__'
,
_binary_creator_
(
'__pow__'
,
'elementwise_pow'
,
False
,
None
)),
(
'__rpow__'
,
_binary_creator_
(
'__rpow__'
,
'elementwise_pow'
,
True
,
None
)),
(
'__floordiv__'
,
_binary_creator_
(
'__floordiv__'
,
'elementwise_floordiv'
,
False
,
None
)),
(
'__mod__'
,
_binary_creator_
(
'__mod__'
,
'elementwise_mod'
,
False
,
None
)),
# These binary use paddle.optype
(
'__div__'
,
_binary_method_creator_
(
'divide'
,
False
)),
(
'__rdiv__'
,
_binary_method_creator_
(
'divide'
,
True
)),
(
'__truediv__'
,
_binary_method_creator_
(
'divide'
,
False
)),
(
'__rtruediv__'
,
_binary_method_creator_
(
'divide'
,
True
)),
(
'__floordiv__'
,
_binary_method_creator_
(
'floor_divide'
,
False
)),
(
'__rfloordiv__'
,
_binary_method_creator_
(
'floor_divide'
,
True
)),
(
'__mod__'
,
_binary_method_creator_
(
'remainder'
,
False
)),
# for logical compare
(
'__eq__'
,
_binary_creator_
(
'__eq__'
,
'equal'
,
False
,
None
)),
(
'__ne__'
,
_binary_creator_
(
'__ne__'
,
'not_equal'
,
False
,
None
)),
...
...
python/paddle/fluid/tests/unittests/test_dist_transpiler_async_decay.py
浏览文件 @
0e816260
...
...
@@ -113,8 +113,8 @@ class TranspilerAsyncLRDecayTest(unittest.TestCase):
[
"listen_and_serv"
])
# block1: sum,cast,scale,floor,fill_constant,elementwise_pow,scale
self
.
assertEqual
([
op
.
type
for
op
in
pserver
.
blocks
[
1
].
ops
],
[
"sum"
,
"cast"
,
"
scale"
,
"floor"
,
"fill_constant"
,
"elementwise_pow
"
,
"scale"
"sum"
,
"cast"
,
"
fill_constant"
,
"elementwise_div"
,
"floor
"
,
"
fill_constant"
,
"elementwise_pow"
,
"
scale"
])
# block1~2: optimize pass
...
...
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
浏览文件 @
0e816260
...
...
@@ -240,25 +240,124 @@ class TestElementwiseDivBroadcast(unittest.TestCase):
self
.
assertEqual
((
out_result
==
(
2
/
x
)).
all
(),
True
)
class
TestDivideOp
(
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'
)
class
TestDivideAPI
(
unittest
.
TestCase
):
def
setUp
(
self
):
paddle
.
set_default_dtype
(
"float64"
)
self
.
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
self
.
places
.
append
(
fluid
.
CUDAPlace
(
0
))
def
check_static_result
(
self
,
place
):
# rule 1
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
np
.
array
([
1
,
2
,
3
])
self
.
assertRaises
(
TypeError
,
paddle
.
divide
,
x
=
x
,
y
=
y
)
# rule 2: both the inputs are not Tensor
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
2
y
=
4
res
=
paddle
.
divide
(
x
,
y
)
exe
=
fluid
.
Executor
(
place
)
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{},
fetch_list
=
[
res
])
self
.
assertEqual
(
np_z
[
0
]
==
0.5
,
True
)
# rule 3:
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
3
],
dtype
=
"float32"
)
self
.
assertRaises
(
TypeError
,
paddle
.
divide
,
x
=
x
,
y
=
y
)
# rule 4: x is Tensor, y is scalar
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
2
exe
=
fluid
.
Executor
(
place
)
res
=
x
/
y
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
2
,
3
,
4
]).
astype
(
'float64'
)},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
1.
,
1.5
,
2.
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
# rule 5: y is Tensor, x is scalar
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
2
exe
=
fluid
.
Executor
(
place
)
res
=
y
/
x
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
2
,
8
,
4
]).
astype
(
'float64'
)},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
1.
,
0.25
,
0.5
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
# rule 6: y is Tensor, x is Tensor
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
3
],
dtype
=
"float64"
)
exe
=
fluid
.
Executor
(
place
)
res
=
x
/
y
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
2
,
3
,
4
]).
astype
(
'float64'
),
"y"
:
np
.
array
([
1
,
5
,
2
]).
astype
(
'float64'
)
},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
2.
,
0.6
,
2.
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
y_1
=
paddle
.
divide
(
x
,
y
,
name
=
'div_res'
)
self
.
assertEqual
((
'div_res'
in
y_1
.
name
),
True
)
def
test_static
(
self
):
for
place
in
self
.
places
:
self
.
check_static_result
(
place
=
place
)
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
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
paddle
.
divide
(
x
,
y
)
np_z
=
z
.
numpy
()
z_expected
=
np
.
array
([
2.
,
0.6
,
2.
])
self
.
assertEqual
((
np_z
==
z_expected
).
all
(),
True
)
for
place
in
self
.
places
:
with
fluid
.
dygraph
.
guard
(
place
):
# rule 1 : avoid numpy.ndarray
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
)
self
.
assertRaises
(
TypeError
,
paddle
.
divide
,
x
=
x
,
y
=
np_y
)
# rule 2: both the inputs are not Tensor
z
=
paddle
.
divide
(
3
,
2
)
self
.
assertEqual
(
z
.
numpy
()[
0
]
==
1.5
,
True
)
# rule 3: both the inputs are Tensor
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"float32"
)
y
=
paddle
.
to_tensor
(
np_y
,
dtype
=
"float64"
)
self
.
assertRaises
(
TypeError
,
paddle
.
divide
,
x
=
x
,
y
=
y
)
# rule 4: x is Tensor, y is scalar
np_x
=
np
.
array
([
2
,
3
,
4
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"int32"
)
y
=
2
z
=
x
/
y
z_expected
=
np
.
array
([
1.
,
1.5
,
2.
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
# rule 5: y is Tensor, x is scalar
np_x
=
np
.
array
([
2
,
1
,
4
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"int32"
)
y
=
2
z
=
y
/
x
z_expected
=
np
.
array
([
1.
,
2.
,
0.5
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
# rule 6: y is Tensor, x is Tensor
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
x
/
y
z_expected
=
np
.
array
([
2.
,
0.6
,
2.
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py
浏览文件 @
0e816260
...
...
@@ -58,6 +58,13 @@ class TestElementwiseModOp(OpTest):
pass
class
TestElementwiseModOpInverse
(
TestElementwiseModOp
):
def
init_input_output
(
self
):
self
.
x
=
np
.
random
.
uniform
(
0
,
10000
,
[
10
]).
astype
(
self
.
dtype
)
self
.
y
=
np
.
random
.
uniform
(
0
,
1000
,
[
10
,
10
]).
astype
(
self
.
dtype
)
self
.
out
=
np
.
floor_divide
(
self
.
x
,
self
.
y
)
class
TestElementwiseModOp_scalar
(
TestElementwiseModOp
):
def
init_input_output
(
self
):
scale_x
=
random
.
randint
(
0
,
100000000
)
...
...
@@ -67,25 +74,124 @@ 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
)
class
TestFloorDivideAPI
(
unittest
.
TestCase
):
def
setUp
(
self
):
paddle
.
set_default_dtype
(
"float64"
)
self
.
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
self
.
places
.
append
(
fluid
.
CUDAPlace
(
0
))
def
check_static_result
(
self
,
place
):
# rule 1
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
np
.
array
([
1
,
2
,
3
])
self
.
assertRaises
(
TypeError
,
paddle
.
floor_divide
,
x
=
x
,
y
=
y
)
# rule 2: both the inputs are not Tensor
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
2
y
=
4
res
=
paddle
.
floor_divide
(
x
,
y
)
exe
=
fluid
.
Executor
(
place
)
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{},
fetch_list
=
[
res
])
self
.
assertEqual
(
np_z
[
0
]
==
0.
,
True
)
# rule 3:
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
3
],
dtype
=
"float32"
)
self
.
assertRaises
(
TypeError
,
paddle
.
floor_divide
,
x
=
x
,
y
=
y
)
# rule 4: x is Tensor, y is scalar
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
2
exe
=
fluid
.
Executor
(
place
)
res
=
x
//
y
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
2
,
3
,
4
]).
astype
(
'float64'
)},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
1.
,
1.
,
2.
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
# rule 5: y is Tensor, x is scalar
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
2
exe
=
fluid
.
Executor
(
place
)
res
=
y
//
x
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
2
,
8
,
4
]).
astype
(
'float64'
)},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
1.
,
0.
,
0.
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
# rule 6: y is Tensor, x is Tensor
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
3
],
dtype
=
"float64"
)
exe
=
fluid
.
Executor
(
place
)
res
=
x
//
y
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
2
,
3
,
4
]).
astype
(
'float64'
),
"y"
:
np
.
array
([
1
,
5
,
2
]).
astype
(
'float64'
)
},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
2.
,
0.
,
2.
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
def
test_static
(
self
):
for
place
in
self
.
places
:
self
.
check_static_result
(
place
=
place
)
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
)
for
place
in
self
.
places
:
with
fluid
.
dygraph
.
guard
(
place
):
# rule 1 : avoid numpy.ndarray
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
)
self
.
assertRaises
(
TypeError
,
paddle
.
floor_divide
,
x
=
x
,
y
=
np_y
)
# rule 2: both the inputs are not Tensor
z
=
paddle
.
floor_divide
(
3
,
2
)
self
.
assertEqual
(
z
.
numpy
()[
0
]
==
1.
,
True
)
# rule 3: both the inputs are Tensor
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"float32"
)
y
=
paddle
.
to_tensor
(
np_y
,
dtype
=
"float64"
)
self
.
assertRaises
(
TypeError
,
paddle
.
floor_divide
,
x
=
x
,
y
=
y
)
# rule 4: x is Tensor, y is scalar
np_x
=
np
.
array
([
2
,
3
,
4
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"int32"
)
y
=
2
z
=
x
//
y
z_expected
=
np
.
array
([
1
,
1
,
2
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
# rule 5: y is Tensor, x is scalar
np_x
=
np
.
array
([
2
,
1
,
4
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"int32"
)
y
=
2
z
=
y
//
x
z_expected
=
np
.
array
([
1
,
2
,
0
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
# rule 6: y is Tensor, x is Tensor
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
x
//
y
z_expected
=
np
.
array
([
2.
,
0.
,
2.
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/unittests/test_elementwise_mod_op.py
浏览文件 @
0e816260
...
...
@@ -84,25 +84,125 @@ 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
)
class
TestRemainderAPI
(
unittest
.
TestCase
):
def
setUp
(
self
):
paddle
.
set_default_dtype
(
"float64"
)
self
.
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
self
.
places
.
append
(
fluid
.
CUDAPlace
(
0
))
def
check_static_result
(
self
,
place
):
# rule 1
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
np
.
array
([
1
,
2
,
3
])
self
.
assertRaises
(
TypeError
,
paddle
.
remainder
,
x
=
x
,
y
=
y
)
# rule 3:
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
3
],
dtype
=
"float32"
)
self
.
assertRaises
(
TypeError
,
paddle
.
remainder
,
x
=
x
,
y
=
y
)
# rule 4: x is Tensor, y is scalar
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
2
exe
=
fluid
.
Executor
(
place
)
res
=
x
%
y
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
2
,
3
,
4
]).
astype
(
'float64'
)},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
0.
,
1.
,
0.
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
# rule 5: y is Tensor, x is scalar
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
3
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
3
],
dtype
=
"float32"
)
self
.
assertRaises
(
TypeError
,
paddle
.
remainder
,
x
=
x
,
y
=
y
)
# rule 6: y is Tensor, x is Tensor
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
3
],
dtype
=
"float64"
)
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
1
],
dtype
=
"float64"
)
exe
=
fluid
.
Executor
(
place
)
res
=
x
%
y
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
1.
,
2.
,
4
]).
astype
(
'float64'
),
"y"
:
np
.
array
([
1.5
]).
astype
(
'float64'
)
},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
1.
,
0.5
,
1.0
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
# rule 6: y is Tensor, x is Tensor
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
x
=
fluid
.
data
(
name
=
"x"
,
shape
=
[
6
],
dtype
=
"float64"
)
y
=
fluid
.
data
(
name
=
"y"
,
shape
=
[
1
],
dtype
=
"float64"
)
exe
=
fluid
.
Executor
(
place
)
res
=
x
%
y
np_z
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"x"
:
np
.
array
([
-
3.
,
-
2
,
-
1
,
1
,
2
,
3
]).
astype
(
'float64'
),
"y"
:
np
.
array
([
2
]).
astype
(
'float64'
)
},
fetch_list
=
[
res
])
z_expected
=
np
.
array
([
1.
,
0.
,
1.
,
1.
,
0.
,
1.
])
self
.
assertEqual
((
np_z
[
0
]
==
z_expected
).
all
(),
True
)
def
test_static
(
self
):
for
place
in
self
.
places
:
self
.
check_static_result
(
place
=
place
)
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
)
for
place
in
self
.
places
:
with
fluid
.
dygraph
.
guard
(
place
):
# rule 1 : avoid numpy.ndarray
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
)
self
.
assertRaises
(
TypeError
,
paddle
.
remainder
,
x
=
x
,
y
=
np_y
)
# rule 3: both the inputs are Tensor
np_x
=
np
.
array
([
2
,
3
,
4
])
np_y
=
np
.
array
([
1
,
5
,
2
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"float32"
)
y
=
paddle
.
to_tensor
(
np_y
,
dtype
=
"float64"
)
self
.
assertRaises
(
TypeError
,
paddle
.
remainder
,
x
=
x
,
y
=
y
)
# rule 4: x is Tensor, y is scalar
np_x
=
np
.
array
([
2
,
3
,
4
])
x
=
paddle
.
to_tensor
(
np_x
,
dtype
=
"int32"
)
y
=
2
z
=
x
%
y
z_expected
=
np
.
array
([
0
,
1
,
0
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
# rule 5: y is Tensor, x is scalar
np_x
=
np
.
array
([
2
,
3
,
4
])
x
=
paddle
.
to_tensor
(
np_x
)
self
.
assertRaises
(
TypeError
,
paddle
.
remainder
,
x
=
3
,
y
=
x
)
# rule 6: y is Tensor, x is Tensor
np_x
=
np
.
array
([
1.
,
2.
,
4
])
np_y
=
np
.
array
([
1.5
])
x
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
x
%
y
z_expected
=
np
.
array
([
1.
,
0.5
,
1.0
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
# rule 6: y is Tensor, x is Tensor
np_x
=
np
.
array
([
-
3.
,
-
2
,
-
1
,
1
,
2
,
3
])
np_y
=
np
.
array
([
2.
])
x
=
paddle
.
to_tensor
(
np_x
)
y
=
paddle
.
to_tensor
(
np_y
)
z
=
x
%
y
z_expected
=
np
.
array
([
1.
,
0.
,
1.
,
1.
,
0.
,
1.
])
self
.
assertEqual
((
z_expected
==
z
.
numpy
()).
all
(),
True
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/unittests/test_math_op_patch.py
浏览文件 @
0e816260
...
...
@@ -189,15 +189,15 @@ class TestMathOpPatches(unittest.TestCase):
@
prog_scope
()
def
test_integer_div
(
self
):
a
=
fluid
.
layers
.
data
(
name
=
"a"
,
shape
=
[
1
],
dtype
=
'int64'
)
b
=
a
/
7
b
=
a
/
2
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
a_np
=
numpy
.
array
([
3
,
4
,
10
,
14
,
9
,
18
])
.
astype
(
'int64'
)
a_np
=
numpy
.
array
([
3
,
4
,
10
,
14
,
9
,
18
])
b_np
,
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"a"
:
a_np
},
fetch_list
=
[
b
])
b_np_actual
=
(
a_np
/
7
).
astype
(
'int64'
)
# for paddle2.0, use true_divide
b_np_actual
=
(
a_np
/
2.0
)
self
.
assertTrue
(
numpy
.
array_equal
(
b_np
,
b_np_actual
))
@
prog_scope
()
...
...
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py
浏览文件 @
0e816260
...
...
@@ -248,7 +248,8 @@ class PolicyGradient(object):
func
=
reward_func
,
x
=
[
action
,
length
],
out
=
reward
)
neg_log_prob
=
layers
.
cross_entropy
(
act_prob
,
action
)
cost
=
neg_log_prob
*
reward
cost
=
(
layers
.
reduce_sum
(
cost
)
/
layers
.
reduce_sum
(
length
)
cost
=
(
layers
.
reduce_sum
(
cost
)
/
layers
.
cast
(
layers
.
reduce_sum
(
length
),
"float32"
)
)
if
length
is
not
None
else
layers
.
reduce_mean
(
cost
)
optimizer
=
fluid
.
optimizer
.
Adam
(
self
.
lr
)
optimizer
.
minimize
(
cost
)
...
...
python/paddle/nn/functional/loss.py
浏览文件 @
0e816260
...
...
@@ -1034,7 +1034,8 @@ def ctc_loss(log_probs,
loss_out
=
fluid
.
layers
.
squeeze
(
loss_out
,
[
-
1
])
assert
reduction
in
[
'mean'
,
'sum'
,
'none'
]
if
reduction
==
'mean'
:
loss_out
=
paddle
.
mean
(
loss_out
/
label_lengths
)
loss_out
=
paddle
.
mean
(
loss_out
/
paddle
.
cast
(
label_lengths
,
loss_out
.
dtype
))
elif
reduction
==
'sum'
:
loss_out
=
paddle
.
sum
(
loss_out
)
return
loss_out
...
...
python/paddle/tensor/math.py
浏览文件 @
0e816260
...
...
@@ -62,6 +62,7 @@ from ..fluid.layers import increment #DEFINE_ALIAS
from
..fluid.layers
import
multiplex
#DEFINE_ALIAS
from
..fluid.layers
import
sums
#DEFINE_ALIAS
from
..fluid
import
layers
import
paddle
__all__
=
[
'abs'
,
...
...
@@ -133,6 +134,19 @@ __all__ = [
]
# yapf: enable.
_supported_int_dtype_
=
[
VarDesc
.
VarType
.
UINT8
,
VarDesc
.
VarType
.
INT8
,
VarDesc
.
VarType
.
INT16
,
VarDesc
.
VarType
.
INT32
,
VarDesc
.
VarType
.
INT64
,
]
_supported_float_dtype_
=
[
VarDesc
.
VarType
.
FP32
,
VarDesc
.
VarType
.
FP64
,
]
@
templatedoc
()
def
pow
(
input
,
exponent
,
name
=
None
):
"""
...
...
@@ -308,9 +322,69 @@ def divide(x, y, name=None):
axis
=
-
1
act
=
None
if
in_dygraph_mode
():
# rule 1 : avoid numpy.ndarray
if
isinstance
(
x
,
numpy
.
ndarray
)
or
isinstance
(
y
,
numpy
.
ndarray
):
raise
TypeError
(
"divide(): arguments must be Tensor or scalar, not numpy.ndarray."
)
# rule 2: both the inputs are not Tensor
elif
not
isinstance
(
x
,
paddle
.
Tensor
)
and
not
isinstance
(
y
,
paddle
.
Tensor
):
x
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
fill_value
=
x
)
y
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
fill_value
=
y
)
# rule 3: both the inputs are Tensor
elif
isinstance
(
x
,
paddle
.
Tensor
)
and
isinstance
(
y
,
paddle
.
Tensor
):
if
y
.
dtype
!=
x
.
dtype
:
raise
TypeError
(
"divide(): argument position 1 and argument position 2 must have the same dtype."
"But x is {}, y is {}"
.
format
(
x
.
dtype
,
y
.
dtype
))
elif
x
.
dtype
in
_supported_int_dtype_
:
x
=
x
.
astype
(
paddle
.
get_default_dtype
())
y
=
y
.
astype
(
paddle
.
get_default_dtype
())
# rule 4: x is Tensor, y is scalar
elif
isinstance
(
x
,
paddle
.
Tensor
)
and
not
isinstance
(
y
,
paddle
.
Tensor
):
if
x
.
dtype
in
_supported_int_dtype_
:
x
=
x
.
astype
(
paddle
.
get_default_dtype
())
y
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
x
.
dtype
,
fill_value
=
y
)
# rule 5: x is scalar, y is Tensor
elif
not
isinstance
(
x
,
paddle
.
Tensor
)
and
isinstance
(
y
,
paddle
.
Tensor
):
if
y
.
dtype
in
_supported_int_dtype_
:
y
=
y
.
astype
(
paddle
.
get_default_dtype
())
x
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
y
.
dtype
,
fill_value
=
x
)
return
_elementwise_op_in_dygraph
(
x
,
y
,
axis
=
axis
,
act
=
act
,
op_name
=
op_type
)
# rule 1 : avoid numpy.ndarray
if
isinstance
(
x
,
numpy
.
ndarray
)
or
isinstance
(
y
,
numpy
.
ndarray
):
raise
TypeError
(
"divide(): arguments must be Tensor or scalar, not numpy.ndarray."
)
# rule 2: both the inputs are not Tensor
elif
not
isinstance
(
x
,
Variable
)
and
not
isinstance
(
y
,
Variable
):
x
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
value
=
x
)
y
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
value
=
y
)
# rule 3: both the inputs are Tensor
elif
isinstance
(
x
,
Variable
)
and
isinstance
(
y
,
Variable
):
if
y
.
dtype
!=
x
.
dtype
:
raise
TypeError
(
"divide(): argument position 1 and argument position 2 must have the same dtype."
"But x is {}, y is {}"
.
format
(
x
.
dtype
,
y
.
dtype
))
elif
x
.
dtype
in
_supported_int_dtype_
:
x
=
paddle
.
cast
(
x
,
paddle
.
get_default_dtype
())
y
=
paddle
.
cast
(
y
,
paddle
.
get_default_dtype
())
# rule 4: x is Tensor, y is scalar
elif
isinstance
(
x
,
Variable
)
and
not
isinstance
(
y
,
Variable
):
if
x
.
dtype
in
_supported_int_dtype_
:
x
=
paddle
.
cast
(
x
,
paddle
.
get_default_dtype
())
y
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
x
.
dtype
,
value
=
y
)
# rule 5: x is scalar, y is Tensor
elif
not
isinstance
(
x
,
Variable
)
and
isinstance
(
y
,
Variable
):
if
y
.
dtype
in
_supported_int_dtype_
:
y
=
paddle
.
cast
(
y
,
paddle
.
get_default_dtype
())
x
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
y
.
dtype
,
value
=
x
)
return
_elementwise_op
(
LayerHelper
(
op_type
,
**
locals
()))
...
...
@@ -352,9 +426,55 @@ def floor_divide(x, y, name=None):
op_type
=
'elementwise_floordiv'
axis
=
-
1
if
in_dygraph_mode
():
# rule 1 : avoid numpy.ndarray
if
isinstance
(
x
,
numpy
.
ndarray
)
or
isinstance
(
y
,
numpy
.
ndarray
):
raise
TypeError
(
"floor_divide(): arguments must be Tensor or scalar, not numpy.ndarray."
)
# rule 2: both the inputs are not Tensor
elif
not
isinstance
(
x
,
paddle
.
Tensor
)
and
not
isinstance
(
y
,
paddle
.
Tensor
):
x
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
fill_value
=
x
)
y
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
fill_value
=
y
)
# rule 3: both the inputs are Tensor
elif
isinstance
(
x
,
paddle
.
Tensor
)
and
isinstance
(
y
,
paddle
.
Tensor
):
if
y
.
dtype
!=
x
.
dtype
:
raise
TypeError
(
"floor_divide(): argument position 1 and argument position 2 must have the same dtype."
"But x is {}, y is {}"
.
format
(
x
.
dtype
,
y
.
dtype
))
# rule 4: x is Tensor, y is scalar
elif
isinstance
(
x
,
paddle
.
Tensor
)
and
not
isinstance
(
y
,
paddle
.
Tensor
):
y
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
x
.
dtype
,
fill_value
=
y
)
# rule 5: x is scalar, y is Tensor
elif
not
isinstance
(
x
,
paddle
.
Tensor
)
and
isinstance
(
y
,
paddle
.
Tensor
):
x
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
y
.
dtype
,
fill_value
=
x
)
return
_elementwise_op_in_dygraph
(
x
,
y
,
axis
=
axis
,
op_name
=
op_type
)
# rule 1 : avoid numpy.ndarray
if
isinstance
(
x
,
numpy
.
ndarray
)
or
isinstance
(
y
,
numpy
.
ndarray
):
raise
TypeError
(
"divide(): arguments must be Tensor or scalar, not numpy.ndarray."
)
# rule 2: both the inputs are not Tensor
elif
not
isinstance
(
x
,
Variable
)
and
not
isinstance
(
y
,
Variable
):
x
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
value
=
x
)
y
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
paddle
.
get_default_dtype
(),
value
=
y
)
# rule 3: both the inputs are Tensor
elif
isinstance
(
x
,
Variable
)
and
isinstance
(
y
,
Variable
):
if
y
.
dtype
!=
x
.
dtype
:
raise
TypeError
(
"divide(): argument position 1 and argument position 2 must have the same dtype."
"But x is {}, y is {}"
.
format
(
x
.
dtype
,
y
.
dtype
))
# rule 4: x is Tensor, y is scalar
elif
isinstance
(
x
,
Variable
)
and
not
isinstance
(
y
,
Variable
):
y
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
x
.
dtype
,
value
=
y
)
# rule 5: x is scalar, y is Tensor
elif
not
isinstance
(
x
,
Variable
)
and
isinstance
(
y
,
Variable
):
x
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
y
.
dtype
,
value
=
x
)
return
_elementwise_op
(
LayerHelper
(
op_type
,
**
locals
()))
...
...
@@ -396,9 +516,43 @@ def remainder(x, y, name=None):
op_type
=
'elementwise_mod'
axis
=
-
1
if
in_dygraph_mode
():
# rule 1 : avoid numpy.ndarray
if
isinstance
(
x
,
numpy
.
ndarray
)
or
isinstance
(
y
,
numpy
.
ndarray
):
raise
TypeError
(
"remainder(): arguments must be Tensor or scalar, not numpy.ndarray."
)
elif
not
isinstance
(
x
,
paddle
.
Tensor
):
raise
TypeError
(
"remainder(): arguments position 1 must be Tensor, not {}"
.
format
(
type
(
x
)))
# rule 3: both the inputs are Tensor
elif
isinstance
(
y
,
paddle
.
Tensor
):
if
y
.
dtype
!=
x
.
dtype
:
raise
TypeError
(
"remainder(): argument position 1 and argument position 2 must have the same dtype."
"But x is {}, y is {}"
.
format
(
x
.
dtype
,
y
.
dtype
))
# rule 4: x is Tensor, y is scalar
elif
not
isinstance
(
y
,
paddle
.
Tensor
):
y
=
paddle
.
full
(
shape
=
[
1
],
dtype
=
x
.
dtype
,
fill_value
=
y
)
return
_elementwise_op_in_dygraph
(
x
,
y
,
axis
=
axis
,
op_name
=
op_type
)
# rule 1 : avoid numpy.ndarray
if
isinstance
(
x
,
numpy
.
ndarray
)
or
isinstance
(
y
,
numpy
.
ndarray
):
raise
TypeError
(
"remainder(): arguments must be Tensor or scalar, not numpy.ndarray."
)
elif
not
isinstance
(
x
,
Variable
):
raise
TypeError
(
"remainder(): arguments position 1 must be Tensor, not {}"
.
format
(
type
(
x
)))
# rule 3: both the inputs are Tensor
elif
isinstance
(
y
,
Variable
):
if
y
.
dtype
!=
x
.
dtype
:
raise
TypeError
(
"remainder(): argument position 1 and argument position 2 must have the same dtype."
"But x is {}, y is {}"
.
format
(
x
.
dtype
,
y
.
dtype
))
# rule 4: x is Tensor, y is scalar
elif
not
isinstance
(
y
,
paddle
.
Tensor
):
y
=
paddle
.
fill_constant
(
shape
=
[
1
],
dtype
=
x
.
dtype
,
value
=
y
)
return
_elementwise_op
(
LayerHelper
(
op_type
,
**
locals
()))
...
...
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