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2026-07-13 12:40:42 +08:00

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Python

# Copyright (c) 2021 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.static import Program
class TestDiagFlatAPI(unittest.TestCase):
def setUp(self):
self.input_np = np.random.random(size=(10, 10)).astype(np.float64)
self.expected0 = np.diagflat(self.input_np)
self.expected1 = np.diagflat(self.input_np, k=1)
self.expected2 = np.diagflat(self.input_np, k=-1)
self.input_np2 = np.random.random(size=(20)).astype(np.float64)
self.expected3 = np.diagflat(self.input_np2)
self.expected4 = np.diagflat(self.input_np2, k=1)
self.expected5 = np.diagflat(self.input_np2, k=-1)
def run_imperative(self):
x = paddle.to_tensor(self.input_np)
y = paddle.diagflat(x)
np.testing.assert_allclose(y.numpy(), self.expected0, rtol=1e-05)
y = paddle.diagflat(x, offset=1)
np.testing.assert_allclose(y.numpy(), self.expected1, rtol=1e-05)
y = paddle.diagflat(x, offset=-1)
np.testing.assert_allclose(y.numpy(), self.expected2, rtol=1e-05)
x = paddle.to_tensor(self.input_np2)
y = paddle.diagflat(x)
np.testing.assert_allclose(y.numpy(), self.expected3, rtol=1e-05)
y = paddle.diagflat(x, offset=1)
np.testing.assert_allclose(y.numpy(), self.expected4, rtol=1e-05)
y = paddle.diagflat(x, offset=-1)
np.testing.assert_allclose(y.numpy(), self.expected5, rtol=1e-05)
def run_static(self, use_gpu=False):
main = paddle.static.Program()
startup = paddle.static.Program()
with paddle.static.program_guard(main, startup):
x = paddle.static.data(
name='input', shape=[10, 10], dtype='float64'
)
x2 = paddle.static.data(name='input2', shape=[20], dtype='float64')
result0 = paddle.diagflat(x)
result3 = paddle.diagflat(x2)
place = get_device_place() if use_gpu else paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup)
res0, res3 = exe.run(
main,
feed={"input": self.input_np, 'input2': self.input_np2},
fetch_list=[result0, result3],
)
np.testing.assert_allclose(res0, self.expected0, rtol=1e-05)
np.testing.assert_allclose(res3, self.expected3, rtol=1e-05)
def test_cpu(self):
paddle.disable_static(place=paddle.CPUPlace())
self.run_imperative()
paddle.enable_static()
with paddle.static.program_guard(Program()):
self.run_static()
def test_gpu(self):
if not (paddle.is_compiled_with_cuda() or is_custom_device()):
return
paddle.disable_static(place=get_device_place())
self.run_imperative()
paddle.enable_static()
with paddle.static.program_guard(Program()):
self.run_static(use_gpu=True)
def test_fp16_with_gpu(self, use_gpu=False):
if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
place = get_device_place()
with paddle.static.program_guard(
paddle.static.Program(), paddle.static.Program()
):
input = np.random.random([10, 10]).astype("float16")
x = paddle.static.data(
name="x", shape=[10, 10], dtype="float16"
)
y = paddle.diagflat(x)
expected = np.diagflat(input)
exe = paddle.static.Executor(place)
res = exe.run(
paddle.static.default_main_program(),
feed={
"x": input,
},
fetch_list=[y],
)
if __name__ == "__main__":
unittest.main()