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体验新版 GitCode,发现更多精彩内容 >>
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0d28edfa
编写于
4月 01, 2022
作者:
X
xiongkun
提交者:
GitHub
4月 01, 2022
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差异文件
add yaml for ele_max ele_min. (#41161)
* add yaml for ele_max ele_min * fig * push * xxx
上级
01724b1a
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
121 addition
and
19 deletion
+121
-19
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+1
-1
python/paddle/fluid/tests/unittests/op_test.py
python/paddle/fluid/tests/unittests/op_test.py
+24
-9
python/paddle/fluid/tests/unittests/test_elementwise_max_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_max_op.py
+26
-4
python/paddle/fluid/tests/unittests/test_elementwise_min_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_min_op.py
+16
-2
python/paddle/tensor/math.py
python/paddle/tensor/math.py
+16
-3
python/paddle/utils/code_gen/api.yaml
python/paddle/utils/code_gen/api.yaml
+18
-0
python/paddle/utils/code_gen/backward.yaml
python/paddle/utils/code_gen/backward.yaml
+20
-0
未找到文件。
python/paddle/fluid/layers/nn.py
浏览文件 @
0d28edfa
...
...
@@ -40,6 +40,7 @@ from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, c
import paddle
from paddle.utils import deprecated
from paddle import _C_ops
from paddle.fluid.framework import in_dygraph_mode, _in_legacy_dygraph
__all__ = [
'fc',
...
...
@@ -204,7 +205,6 @@ def _elementwise_op_in_dygraph(x,
op_name=None):
op = getattr(_C_ops, op_name)
out = op(x, y, 'axis', axis, 'use_mkldnn', use_mkldnn)
return dygraph_utils._append_activation_in_dygraph(
out, act, use_mkldnn=use_mkldnn)
...
...
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @
0d28edfa
...
...
@@ -781,10 +781,12 @@ class OpTest(unittest.TestCase):
if
arg_name
in
api_ignore_param_list
:
results
.
append
(
get_default
(
idx
,
api_defaults
))
else
:
assert
idx_of_op_proto_arguments
<
len
(
input_arguments
),
"Assert False."
tmp
=
input_arguments
[
idx_of_op_proto_arguments
]
idx_of_op_proto_arguments
+=
1
if
(
idx_of_op_proto_arguments
<
len
(
input_arguments
)):
tmp
=
input_arguments
[
idx_of_op_proto_arguments
]
idx_of_op_proto_arguments
+=
1
else
:
tmp
=
Empty
()
# use the default value
if
isinstance
(
tmp
,
Empty
):
results
.
append
(
get_default
(
idx
,
api_defaults
))
else
:
...
...
@@ -1356,6 +1358,9 @@ class OpTest(unittest.TestCase):
self
.
op_test
=
op_test
# stop the op_test object.
self
.
op_type
=
op_test
.
op_type
def
init
(
self
):
pass
def
convert_uint16_to_float
(
self
,
actual_np
,
expect_np
):
raise
NotImplementedError
(
"base class, not implement!"
)
...
...
@@ -1387,7 +1392,7 @@ class OpTest(unittest.TestCase):
rtol
=
self
.
rtol
if
hasattr
(
self
,
'rtol'
)
else
1e-5
,
equal_nan
=
equal_nan
),
"Output ("
+
name
+
") has diff at "
+
str
(
place
)
+
" in "
+
self
.
checker_name
+
" checker"
)
self
.
checker_name
)
def
_compare_list
(
self
,
name
,
actual
,
expect
):
""" if expect is a tuple, we need to compare list.
...
...
@@ -1403,7 +1408,7 @@ class OpTest(unittest.TestCase):
# NOTE(zhiqiu): np.allclose([], [1.]) returns True
# see details: https://stackoverflow.com/questions/38331703/why-does-numpys-broadcasting-sometimes-allow-comparing-arrays-of-different-leng
if
expect_np
.
size
==
0
:
self
.
op_test
.
assertTrue
(
actual_np
.
size
==
0
)
self
.
op_test
.
assertTrue
(
actual_np
.
size
==
0
)
# }}}
self
.
_compare_numpy
(
name
,
actual_np
,
expect_np
)
if
isinstance
(
expect
,
tuple
):
self
.
_compare_list
(
name
,
actual
,
expect
)
...
...
@@ -1431,10 +1436,14 @@ class OpTest(unittest.TestCase):
the main enter point of Checker class
"""
self
.
init
()
self
.
calculate_output
()
self
.
compare_outputs_with_expects
()
class
StaticChecker
(
Checker
):
def
init
(
self
):
self
.
checker_name
=
"static checker"
def
calculate_output
(
self
):
outs
,
fetch_list
=
self
.
op_test
.
_calc_output
(
place
,
no_check_set
=
no_check_set
)
...
...
@@ -1474,6 +1483,9 @@ class OpTest(unittest.TestCase):
"Output ("
+
name
+
") has different lod at "
+
str
(
place
))
class
DygraphChecker
(
Checker
):
def
init
(
self
):
self
.
checker_name
=
"dygraph checker"
def
calculate_output
(
self
):
self
.
outputs
=
self
.
op_test
.
_calc_dygraph_output
(
place
,
no_check_set
=
no_check_set
)
...
...
@@ -1519,18 +1531,21 @@ class OpTest(unittest.TestCase):
rtol
=
self
.
rtol
if
hasattr
(
self
,
'rtol'
)
else
1e-5
,
equal_nan
=
equal_nan
),
"Output ("
+
name
+
") has diff at "
+
str
(
place
)
+
" in "
+
self
.
checker_name
+
" checker"
)
" in "
+
self
.
checker_name
)
class
EagerChecker
(
DygraphChecker
):
def
init
(
self
):
self
.
checker_name
=
"eager checker"
def
calculate_output
(
self
):
# we only check end2end api when check_eager=True
self
.
is_python_api_test
=
True
with
_test_eager_guard
():
self
.
is_python_api_test
=
True
eager_dygraph_outs
=
self
.
op_test
.
_calc_python_api_output
(
place
)
if
eager_dygraph_outs
is
None
:
# missing KernelSignature, fall back to eager middle output.
self
.
is_python_api_test
=
False
# missing KernelSignature, fall back to eager middle output.
eager_dygraph_outs
=
self
.
op_test
.
_calc_dygraph_output
(
place
,
no_check_set
=
no_check_set
)
self
.
outputs
=
eager_dygraph_outs
...
...
python/paddle/fluid/tests/unittests/test_elementwise_max_op.py
浏览文件 @
0d28edfa
...
...
@@ -20,11 +20,13 @@ from op_test import OpTest, skip_check_grad_ci, convert_float_to_uint16
import
os
import
re
import
paddle.fluid.core
as
core
import
paddle
class
TestElementwiseOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
# If x and y have the same value, the max() is not differentiable.
# So we generate test data by the following method
# to avoid them being too close to each other.
...
...
@@ -35,10 +37,16 @@ class TestElementwiseOp(OpTest):
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
if
hasattr
(
self
,
'attrs'
):
self
.
check_output
(
check_eager
=
False
)
else
:
self
.
check_output
(
check_eager
=
True
)
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
)
if
hasattr
(
self
,
'attrs'
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
check_eager
=
False
)
else
:
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
check_eager
=
True
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
...
...
@@ -55,6 +63,7 @@ class TestElementwiseOp(OpTest):
class
TestElementwiseBF16Op
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
self
.
dtype
=
np
.
uint16
# If x and y have the same value, the max() is not differentiable.
# So we generate test data by the following method
...
...
@@ -69,10 +78,16 @@ class TestElementwiseBF16Op(OpTest):
self
.
outputs
=
{
'Out'
:
convert_float_to_uint16
(
np
.
maximum
(
x
,
y
))}
def
test_check_output
(
self
):
self
.
check_output
()
if
hasattr
(
self
,
'attrs'
):
self
.
check_output
(
check_eager
=
False
)
else
:
self
.
check_output
(
check_eager
=
True
)
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
)
if
hasattr
(
self
,
'attrs'
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
check_eager
=
False
)
else
:
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
check_eager
=
True
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
([
'Y'
],
'Out'
,
no_grad_set
=
set
(
"X"
))
...
...
@@ -86,6 +101,7 @@ class TestElementwiseBF16Op(OpTest):
class
TestElementwiseMaxOp_scalar
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
x
=
np
.
random
.
random_integers
(
-
5
,
5
,
[
2
,
3
,
20
]).
astype
(
"float64"
)
y
=
np
.
array
([
0.5
]).
astype
(
"float64"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
...
...
@@ -95,6 +111,7 @@ class TestElementwiseMaxOp_scalar(TestElementwiseOp):
class
TestElementwiseMaxOp_Vector
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
x
=
np
.
random
.
random
((
100
,
)).
astype
(
"float64"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
"float64"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
(
100
,
)).
astype
(
"float64"
)
...
...
@@ -105,6 +122,7 @@ class TestElementwiseMaxOp_Vector(TestElementwiseOp):
class
TestElementwiseMaxOp_broadcast_0
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
100
,
5
,
2
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
np
.
float64
)
y
=
x
[:,
0
,
0
]
+
sgn
*
\
...
...
@@ -121,6 +139,7 @@ class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp):
class
TestElementwiseMaxOp_broadcast_1
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
100
,
3
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
np
.
float64
)
y
=
x
[
0
,
:,
0
]
+
sgn
*
\
...
...
@@ -137,6 +156,7 @@ class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp):
class
TestElementwiseMaxOp_broadcast_2
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
1
,
3
,
100
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
np
.
float64
)
y
=
x
[
0
,
0
,
:]
+
sgn
*
\
...
...
@@ -152,6 +172,7 @@ class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
class
TestElementwiseMaxOp_broadcast_3
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
50
,
2
,
1
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
50
,
2
)).
astype
(
np
.
float64
)
y
=
x
[
0
,
:,
:,
0
]
+
sgn
*
\
...
...
@@ -168,6 +189,7 @@ class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp):
class
TestElementwiseMaxOp_broadcast_4
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
self
.
python_api
=
paddle
.
maximum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
,
5
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
2
,
3
,
1
,
5
)).
astype
(
np
.
float64
)
y
=
x
+
sgn
*
\
...
...
python/paddle/fluid/tests/unittests/test_elementwise_min_op.py
浏览文件 @
0d28edfa
...
...
@@ -27,6 +27,7 @@ paddle.enable_static()
class
TestElementwiseOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
# If x and y have the same value, the min() is not differentiable.
# So we generate test data by the following method
# to avoid them being too close to each other.
...
...
@@ -37,10 +38,16 @@ class TestElementwiseOp(OpTest):
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
if
hasattr
(
self
,
'attrs'
):
self
.
check_output
(
check_eager
=
False
)
else
:
self
.
check_output
(
check_eager
=
True
)
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
)
if
hasattr
(
self
,
'attrs'
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
check_eager
=
False
)
else
:
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
check_eager
=
True
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
...
...
@@ -56,6 +63,7 @@ class TestElementwiseOp(OpTest):
class
TestElementwiseMinOp_scalar
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
x
=
np
.
random
.
random_integers
(
-
5
,
5
,
[
10
,
3
,
4
]).
astype
(
"float64"
)
y
=
np
.
array
([
0.5
]).
astype
(
"float64"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
...
...
@@ -65,6 +73,7 @@ class TestElementwiseMinOp_scalar(TestElementwiseOp):
class
TestElementwiseMinOp_Vector
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
x
=
np
.
random
.
random
((
100
,
)).
astype
(
"float64"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
"float64"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
(
100
,
)).
astype
(
"float64"
)
...
...
@@ -75,6 +84,7 @@ class TestElementwiseMinOp_Vector(TestElementwiseOp):
class
TestElementwiseMinOp_broadcast_0
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
100
,
3
,
2
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
np
.
float64
)
y
=
x
[:,
0
,
0
]
+
sgn
*
\
...
...
@@ -91,6 +101,7 @@ class TestElementwiseMinOp_broadcast_0(TestElementwiseOp):
class
TestElementwiseMinOp_broadcast_1
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
100
,
3
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
np
.
float64
)
y
=
x
[
0
,
:,
0
]
+
sgn
*
\
...
...
@@ -107,6 +118,7 @@ class TestElementwiseMinOp_broadcast_1(TestElementwiseOp):
class
TestElementwiseMinOp_broadcast_2
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
100
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
100
,
)).
astype
(
np
.
float64
)
y
=
x
[
0
,
0
,
:]
+
sgn
*
\
...
...
@@ -122,6 +134,7 @@ class TestElementwiseMinOp_broadcast_2(TestElementwiseOp):
class
TestElementwiseMinOp_broadcast_3
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
25
,
4
,
1
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
25
,
4
)).
astype
(
np
.
float64
)
y
=
x
[
0
,
:,
:,
0
]
+
sgn
*
\
...
...
@@ -138,6 +151,7 @@ class TestElementwiseMinOp_broadcast_3(TestElementwiseOp):
class
TestElementwiseMinOp_broadcast_4
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
self
.
python_api
=
paddle
.
minimum
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
10
,
2
,
5
)).
astype
(
np
.
float64
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
2
,
10
,
1
,
5
)).
astype
(
np
.
float64
)
y
=
x
+
sgn
*
\
...
...
python/paddle/tensor/math.py
浏览文件 @
0d28edfa
...
...
@@ -177,6 +177,12 @@ def pow(x, y, name=None):
raise
TypeError
(
'y must be scalar or tensor type, but received: %s '
%
(
type
(
y
)))
OP_NAMEMAPPING
=
{
'elementwise_max'
:
'final_state_maximum'
,
'elementwise_min'
:
'final_state_minimum'
,
'elementwise_pow'
:
'final_state_elementwise_pow'
,
'elementwise_floordiv'
:
'final_state_floor_divide'
,
}
@
dygraph_only
def
_elementwise_op_in_dygraph
(
x
,
...
...
@@ -185,13 +191,20 @@ def _elementwise_op_in_dygraph(x,
act
=
None
,
use_mkldnn
=
False
,
op_name
=
None
):
op
=
getattr
(
_C_ops
,
op_name
)
out
=
op
(
x
,
y
,
'axis'
,
axis
,
'use_mkldnn'
,
use_mkldnn
)
def
is_inplace
(
op_name
):
return
op_name
[
-
1
]
==
"_"
if
in_dygraph_mode
():
op
=
getattr
(
_C_ops
,
OP_NAMEMAPPING
[
op_name
]
if
not
is_inplace
(
op_name
)
else
op_name
)
out
=
op
(
x
,
y
)
if
_in_legacy_dygraph
():
op
=
getattr
(
_C_ops
,
op_name
)
out
=
op
(
x
,
y
,
'axis'
,
axis
,
'use_mkldnn'
,
use_mkldnn
)
return
dygraph_utils
.
_append_activation_in_dygraph
(
out
,
act
,
use_mkldnn
=
use_mkldnn
)
def
_elementwise_op
(
helper
):
op_type
=
helper
.
layer_type
original_op_type
=
helper
.
kwargs
.
get
(
'original_op_type'
,
op_type
)
...
...
python/paddle/utils/code_gen/api.yaml
浏览文件 @
0d28edfa
...
...
@@ -744,6 +744,15 @@
func
:
matrix_power
backward
:
matrix_power_grad
-
api
:
maximum
args
:
(Tensor x, Tensor y)
output
:
Tensor(out)
infer_meta
:
func
:
ElementwiseInferMeta
kernel
:
func
:
maximum
backward
:
maximum_grad
-
api
:
mean
args
:
(Tensor x, int64_t[] axis={}, bool keep_dim=false)
output
:
Tensor
...
...
@@ -752,6 +761,15 @@
kernel
:
func
:
mean
-
api
:
minimum
args
:
(Tensor x, Tensor y)
output
:
Tensor(out)
infer_meta
:
func
:
ElementwiseInferMeta
kernel
:
func
:
minimum
backward
:
minimum_grad
-
api
:
modulo
args
:
(Tensor x, Tensor y)
output
:
Tensor
...
...
python/paddle/utils/code_gen/backward.yaml
浏览文件 @
0d28edfa
...
...
@@ -408,6 +408,26 @@
kernel
:
func
:
matrix_power_grad
-
backward_api
:
maximum_grad
forward
:
maximum(Tensor x, Tensor y) -> Tensor(out)
args
:
(Tensor x, Tensor y, Tensor out_grad, int axis=-1)
output
:
Tensor(x_grad), Tensor(y_grad)
infer_meta
:
func
:
GeneralBinaryGradInferMeta
param
:
[
x
,
y
]
kernel
:
func
:
maximum_grad
-
backward_api
:
minimum_grad
forward
:
minimum(Tensor x, Tensor y) -> Tensor(out)
args
:
(Tensor x, Tensor y, Tensor out_grad, int axis=-1)
output
:
Tensor(x_grad), Tensor(y_grad)
infer_meta
:
func
:
GeneralBinaryGradInferMeta
param
:
[
x
,
y
]
kernel
:
func
:
minimum_grad
-
backward_api
:
modulo_grad
forward
:
add (Tensor x, Tensor y) -> Tensor(out)
args
:
(Tensor x, Tensor y, Tensor out_grad, int axis = -1)
...
...
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