1# Copyright 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 numpy as np 16import pytest 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20from mindspore.ops import operations as P 21 22class Net(nn.Cell): 23 def __init__(self, axis=-1): 24 super(Net, self).__init__() 25 self.Softmax = P.Softmax(axis) 26 27 def construct(self, x): 28 return self.Softmax(x) 29 30def get_output(x, enable_graph_kernel=False): 31 context.set_context(enable_graph_kernel=enable_graph_kernel) 32 opt = Net() 33 output = opt(Tensor(x)) 34 return output 35 36def test_softmax(shape, dtype): 37 np.random.seed(0) 38 x = np.random.normal(0, 1, shape).astype(dtype) 39 40 expect = get_output(x, False) 41 output = get_output(x, True) 42 43 rtol = 1.e-4 44 atol = 1.e-4 45 if dtype == "float16": 46 rtol = 1.e-3 47 atol = 1.e-3 48 49 assert np.allclose(expect.asnumpy(), output.asnumpy(), rtol, atol, equal_nan=True) 50 51@pytest.mark.level0 52@pytest.mark.platform_x86_gpu_training 53@pytest.mark.env_onecard 54def test_softmax_gpu(): 55 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 56 test_softmax([4, 32, 48], np.float32) 57 58@pytest.mark.level1 59@pytest.mark.platform_arm_ascend_training 60@pytest.mark.platform_x86_ascend_training 61@pytest.mark.env_onecard 62def test_softmax_ascend(): 63 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") 64 test_softmax([2, 32, 48, 64], np.float32) 65