1# Copyright 2020 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 18 19import mindspore.context as context 20from mindspore import Tensor 21import mindspore.ops.operations._grad_ops as P 22context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") 23np.random.seed(1) 24 25@pytest.mark.level0 26@pytest.mark.platform_x86_gpu_training 27@pytest.mark.env_onecard 28def test_atangrad_fp32(): 29 x_np = np.random.rand(4, 2).astype(np.float32) * 10 30 dout_np = np.random.rand(4, 2).astype(np.float32) * 10 31 output_ms = P.AtanGrad()(Tensor(x_np), Tensor(dout_np)) 32 output_np = dout_np / (1 + np.square(x_np)) 33 assert np.allclose(output_ms.asnumpy(), output_np, 1e-4, 1e-4) 34 35@pytest.mark.level0 36@pytest.mark.platform_x86_gpu_training 37@pytest.mark.env_onecard 38def test_atangrad_fp16(): 39 x_np = np.random.rand(4, 2).astype(np.float16) * 10 40 dout_np = np.random.rand(4, 2).astype(np.float16) * 10 41 output_ms = P.AtanGrad()(Tensor(x_np), Tensor(dout_np)) 42 output_np = dout_np.astype(np.float32) / (1 + np.square(x_np.astype(np.float32))) 43 assert np.allclose(output_ms.asnumpy(), output_np.astype(np.float16), 1e-3, 1e-3) 44