# Copyright (c) 2022 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 dygraph_to_static_utils import ( Dy2StTestBase, test_ast_only, ) import paddle class TestInplaceAssign(Dy2StTestBase): @test_ast_only def test_case0(self): a = paddle.ones((1024, 2)) * 1 b = paddle.ones((1024, 3)) * 2 c = paddle.ones((1024, 4)) * 3 a._inplace_assign(b) np.testing.assert_array_equal(a.numpy(), b.numpy()) b._inplace_assign(c) np.testing.assert_array_equal(b.numpy(), c.numpy()) @test_ast_only def test_case1(self): def func(x): a = 1 * x b = 2 * x a._inplace_assign(b) return a x = paddle.ones((1,)) a = paddle.randn((1,)) x.stop_gradient = False a.stop_gradient = False y = func(x) y.mean().backward() np.testing.assert_array_equal(x.grad.numpy(), np.array([2.0])) def test_case2(self): def func(a, x): x = 2 * x x[:] = a * 2.0 return x def forward(a, x): output = paddle.jit.to_static(func)(a, x) x._inplace_assign(output) return x x = paddle.ones((1,)) a = paddle.randn((1,)) x.stop_gradient = False a.stop_gradient = False y = forward(a, x) y.mean().backward() np.testing.assert_array_equal(a.grad.numpy(), np.array([2.0])) if __name__ == "__main__": unittest.main()