# 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 paddle import unittest import numpy as np from paddle.fluid import core from paddle.fluid.framework import _test_eager_guard, _in_legacy_dygraph class TestTensorCopyFrom(unittest.TestCase): def func_main(self): if paddle.fluid.core.is_compiled_with_cuda(): place = paddle.CPUPlace() np_value = np.random.random(size=[10, 30]).astype('float32') tensor = paddle.to_tensor(np_value, place=place) tensor._uva() self.assertTrue(tensor.place.is_gpu_place()) def test_main(self): with _test_eager_guard(): self.func_main() self.func_main() class TestUVATensorFromNumpy(unittest.TestCase): def func_uva_tensor_creation(self): if paddle.fluid.core.is_compiled_with_cuda(): dtype_list = [ "int32", "int64", "float32", "float64", "float16", "int8", "int16", "bool" ] for dtype in dtype_list: data = np.random.randint(10, size=[4, 5]).astype(dtype) if _in_legacy_dygraph(): tensor = paddle.fluid.core.to_uva_tensor(data, 0) else: tensor = core.eager.to_uva_tensor(data, 0) self.assertTrue(tensor.place.is_gpu_place()) self.assertTrue(np.allclose(tensor.numpy(), data)) def test_uva_tensor_creation(self): with _test_eager_guard(): self.func_uva_tensor_creation() self.func_uva_tensor_creation() if __name__ == "__main__": unittest.main()