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

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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.
# Note:
# 0D Tensor indicates that the tensor's dimension is 0
# 0D Tensor's shape is always [], numel is 1
# which can be created by paddle.rand([])
import unittest
import paddle
unary_apis_with_complex_input = [
paddle.real,
paddle.imag,
paddle.angle,
paddle.conj,
]
class AssertShapeEqualMixin:
def assertShapeEqual(self, out, target_tuple):
if not paddle.framework.in_pir_mode():
out_shape = list(out.shape)
else:
out_shape = out.shape
self.assertEqual(out_shape, target_tuple)
class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase):
def test_dygraph_unary(self):
paddle.disable_static()
for api in unary_apis_with_complex_input:
x = paddle.rand([]) + 1j * paddle.rand([])
x.stop_gradient = False
x.retain_grads()
out = api(x)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [])
paddle.enable_static()
def test_static_unary(self):
paddle.enable_static()
for api in unary_apis_with_complex_input:
main_prog = paddle.static.Program()
block = main_prog.global_block()
exe = paddle.static.Executor()
with paddle.static.program_guard(
main_prog, paddle.static.Program()
):
x = paddle.complex(paddle.rand([]), paddle.rand([]))
x.stop_gradient = False
out = api(x)
[(_, x_grad), (_, out_grad)] = paddle.static.append_backward(
out, parameter_list=[x, out]
)
res = exe.run(main_prog, fetch_list=[x, out, x_grad, out_grad])
for item in res:
self.assertEqual(item.shape, ())
paddle.disable_static()
class TestAsReal(unittest.TestCase, AssertShapeEqualMixin):
def test_dygraph(self):
paddle.disable_static()
x = paddle.rand([]) + 1j * paddle.rand([])
x.stop_gradient = False
x.retain_grads()
out = paddle.as_real(x)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [2])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [2])
paddle.enable_static()
def test_static(self):
paddle.enable_static()
main_prog = paddle.static.Program()
block = main_prog.global_block()
exe = paddle.static.Executor()
with paddle.static.program_guard(main_prog, paddle.static.Program()):
x = paddle.complex(paddle.rand([]), paddle.rand([]))
x.stop_gradient = False
out = paddle.as_real(x)
self.assertShapeEqual(x, [])
self.assertShapeEqual(out, [2])
[(_, x_grad), (_, out_grad)] = paddle.static.append_backward(
out.sum(), parameter_list=[x, out]
)
res = exe.run(main_prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (2,))
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, (2,))
paddle.disable_static()
class TestAsComplex(unittest.TestCase, AssertShapeEqualMixin):
def test_dygraph(self):
paddle.disable_static()
x = paddle.rand([2])
x.stop_gradient = False
x.retain_grads()
out = paddle.as_complex(x)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [2])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [2])
self.assertEqual(out.grad.shape, [])
paddle.enable_static()
def test_static(self):
paddle.enable_static()
main_prog = paddle.static.Program()
block = main_prog.global_block()
exe = paddle.static.Executor()
with paddle.static.program_guard(main_prog, paddle.static.Program()):
x = paddle.rand([2])
x.stop_gradient = False
out = paddle.as_complex(x)
self.assertShapeEqual(x, [2])
self.assertShapeEqual(out, [])
[(_, x_grad), (_, out_grad)] = paddle.static.append_backward(
out.sum(), parameter_list=[x, out]
)
res = exe.run(main_prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, (2,))
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, (2,))
self.assertEqual(res[3].shape, ())
paddle.disable_static()
if __name__ == "__main__":
unittest.main()