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# ============================================================================ 15 16import numpy as np 17import pytest 18import mindspore.context as context 19from mindspore import Tensor 20from mindspore.nn import Cell 21import mindspore.ops.operations._grad_ops as G 22 23 24class TanhGradNet(Cell): 25 def __init__(self): 26 super(TanhGradNet, self).__init__() 27 self.tanh_grad = G.TanhGrad() 28 29 def construct(self, y, dy): 30 return self.tanh_grad(y, dy) 31 32 33def test_tanh_grad(): 34 np.random.seed(0) 35 input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32) 36 input_dy = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32) 37 net = TanhGradNet() 38 result = net(Tensor(input_y), Tensor(input_dy)) 39 expect = input_dy * (1.0 - input_y * input_y) 40 res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True) 41 assert res 42 43 44@pytest.mark.level0 45@pytest.mark.platform_x86_gpu_training 46@pytest.mark.env_onecard 47def test_tanh_grad_gpu(): 48 context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU") 49 test_tanh_grad() 50 51 52@pytest.mark.level0 53@pytest.mark.platform_arm_ascend_training 54@pytest.mark.platform_x86_ascend_training 55@pytest.mark.env_onecard 56def test_tanh_grad_ascend(): 57 context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend") 58 test_tanh_grad() 59