wehub-resource-sync
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
# Copyright (c) 2025 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 get_device_place, is_custom_device
|
|
|
|
import paddle
|
|
from paddle import base
|
|
|
|
|
|
def get_places():
|
|
places = []
|
|
if base.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device_place())
|
|
places.append(paddle.CPUPlace())
|
|
return places
|
|
|
|
|
|
class TestCeilAPI_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2025)
|
|
self.places = get_places()
|
|
self.shape = [50]
|
|
self.dtype = "float64"
|
|
self.init_data()
|
|
|
|
def init_data(self):
|
|
self.np_x = np.random.rand(*self.shape).astype(self.dtype)
|
|
|
|
def test_dygraph_Compatibility(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.np_x)
|
|
paddle_dygraph_out = []
|
|
# Numpy reference output
|
|
ref_out = np.ceil(self.np_x)
|
|
# Position args (args)
|
|
out1 = paddle.ceil(x)
|
|
paddle_dygraph_out.append(out1)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.ceil(x=x)
|
|
paddle_dygraph_out.append(out2)
|
|
# Keywords args for torch compatibility
|
|
out3 = paddle.ceil(input=x)
|
|
paddle_dygraph_out.append(out3)
|
|
# Tensor method args
|
|
out4 = x.ceil()
|
|
paddle_dygraph_out.append(out4)
|
|
# Test 'out' parameter for torch compatibility
|
|
out5 = paddle.empty(ref_out.shape, dtype=x.dtype)
|
|
paddle.ceil(x, out=out5)
|
|
paddle_dygraph_out.append(out5)
|
|
# Check all dygraph results
|
|
for out in paddle_dygraph_out:
|
|
np.testing.assert_allclose(ref_out, out.numpy(), rtol=1e-05)
|
|
paddle.enable_static()
|
|
|
|
def test_static_Compatibility(self):
|
|
paddle.enable_static()
|
|
main = paddle.static.Program()
|
|
startup = paddle.static.Program()
|
|
with base.program_guard(main, startup):
|
|
# Define static data placeholders
|
|
x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
|
|
# Position args (args)
|
|
out1 = paddle.ceil(x)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.ceil(x=x)
|
|
# Keywords args for torch compatibility
|
|
out3 = paddle.ceil(input=x)
|
|
# Tensor method args
|
|
out4 = x.ceil()
|
|
# Numpy reference output
|
|
ref_out = np.ceil(self.np_x)
|
|
fetch_list = [out1, out2, out3, out4]
|
|
for place in self.places:
|
|
exe = base.Executor(place)
|
|
fetches = exe.run(
|
|
main,
|
|
feed={"x": self.np_x},
|
|
fetch_list=fetch_list,
|
|
)
|
|
for out in fetches:
|
|
np.testing.assert_allclose(out, ref_out, rtol=1e-05)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|