Files
paddlepaddle--paddle/test/legacy_test/test_pow_op.py
T
2026-07-13 12:40:42 +08:00

375 lines
10 KiB
Python

# Copyright (c) 2024 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, get_places
import paddle
from paddle.framework import core
def pow_grad(x, y, dout):
dx = dout * y * np.power(x, (y - 1))
dy = dout * np.log(x) * np.power(x, y)
return dx, dy
class TestPowOp(OpTest):
def setUp(self):
self.op_type = "pow"
self.python_api = paddle.pow
self.public_python_api = paddle.pow
self.prim_op_type = "comp"
self.outputs = None
self.custom_setting()
if not self.outputs:
self.outputs = {
'Out': np.power(self.inputs['X'], self.attrs["factor"])
}
self.places = get_places()
def custom_setting(self):
self.inputs = {
'X': np.random.uniform(1, 2, [20, 5]).astype("float64"),
}
self.attrs = {"factor": 2.0}
def test_check_output(self):
self.check_output(check_pir=True, check_symbol_infer=False)
def test_check_grad(self):
self.check_grad(
['X'],
'Out',
check_prim_pir=True,
check_pir=True,
)
class TestPowOp_ZeroDim1(TestPowOp):
def custom_setting(self):
self.inputs = {
'X': np.random.uniform(1, 2, []).astype("float64"),
}
self.attrs = {
"factor": float(np.random.uniform(1, 2, []).astype(np.float32))
}
class TestPowOp_big_shape_1(TestPowOp):
def custom_setting(self):
self.inputs = {
'X': np.random.uniform(1, 2, [10, 10]).astype("float64"),
}
self.attrs = {
"factor": float(np.random.uniform(0, 10, []).astype(np.float32))
}
class TestPowOp_big_shape_2(TestPowOp):
def custom_setting(self):
self.inputs = {
'X': np.random.uniform(1, 2, [4, 6, 8]).astype("float64"),
}
self.attrs = {
"factor": float(np.random.uniform(0, 10, []).astype(np.float32))
}
class TestPowOpInt(TestPowOp):
def custom_setting(self):
self.inputs = {
'X': np.asarray([1, 3, 6]),
}
self.attrs = {"factor": 2.0}
def test_check_grad(self):
pass
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64(TestPowOp):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": 2.0}
def test_check_output(self):
for place in self.places:
self.check_output_with_place(
place, check_pir=True, check_symbol_infer=False
)
def test_check_grad(self):
for place in self.places:
self.check_grad_with_place(
place,
['X'],
'Out',
check_pir=True,
)
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64_1(TestPowOp_Complex64):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": -3.4}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64_2(TestPowOp_Complex64):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": -2}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64_3(TestPowOp_Complex64):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": -0.1}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64_4(TestPowOp_Complex64):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": 0}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64_5(TestPowOp_Complex64):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": 0.7}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64_6(TestPowOp_Complex64):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": 1}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex64_7(TestPowOp_Complex64):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex64),
}
self.attrs = {"factor": 5.4}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128(TestPowOp):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": 2.0}
def test_check_output(self):
for place in self.places:
self.check_output_with_place(
place, check_pir=True, check_symbol_infer=False
)
def test_check_grad(self):
for place in self.places:
self.check_grad_with_place(
place,
['X'],
'Out',
check_pir=True,
)
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128_1(TestPowOp_Complex128):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": -3.4}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128_2(TestPowOp_Complex128):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": -2}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128_3(TestPowOp_Complex128):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": -0.1}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128_4(TestPowOp_Complex128):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": 0}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128_5(TestPowOp_Complex128):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": 0.7}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128_6(TestPowOp_Complex128):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": 1}
@unittest.skipIf(
core.is_compiled_with_xpu(),
"Skip XPU for complex dtype is not fully supported",
)
class TestPowOp_Complex128_7(TestPowOp_Complex128):
def custom_setting(self):
self.inputs = {
'X': (
np.random.uniform(0.1, 1, [1, 3, 6])
+ 1j * np.random.uniform(0.1, 1, [1, 3, 6])
).astype(np.complex128),
}
self.attrs = {"factor": 5.4}
if __name__ == '__main__':
unittest.main()