1# Copyright 2020-2021 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15import pytest 16import numpy as np 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20from mindspore.ops import operations as P 21 22 23class Net(nn.Cell): 24 def __init__(self, multiples): 25 super(Net, self).__init__() 26 self.tile = P.Tile() 27 self.multiples = multiples 28 29 def construct(self, x): 30 return self.tile(x, self.multiples) 31 32 33def get_output(x, multiples, enable_graph_kernel=False): 34 context.set_context(enable_graph_kernel=enable_graph_kernel) 35 net = Net(multiples) 36 output = net(x) 37 return output 38 39 40def test_tile(shape, dtype, multiples): 41 x = Tensor(np.random.normal(0, 1, shape).astype(dtype)) 42 expect = get_output(x, multiples, False) 43 output = get_output(x, multiples, True) 44 45 expect_np = expect.asnumpy().copy() 46 output_np = output.asnumpy().copy() 47 48 assert np.allclose(expect_np, output_np, 0.0001, 0.0001) 49 50 51@pytest.mark.level0 52@pytest.mark.platform_arm_ascend_training 53@pytest.mark.platform_x86_ascend_training 54@pytest.mark.env_onecard 55def test_tile_ascend(): 56 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") 57 test_tile((24, 1), np.float16, (2, 2, 2)) 58 test_tile((24, 1), np.float32, (1, 2)) 59