1# Copyright 2023 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 as ms 20import mindspore.nn as nn 21 22 23class Net(nn.Cell): 24 def __init__(self): 25 super(Net, self).__init__() 26 self.rnn = nn.RNN(10, 16, 2, has_bias=True, batch_first=True, bidirectional=False, dtype=ms.float16) 27 28 def construct(self, x, y): 29 out = self.rnn(x, y) 30 return out 31 32 33@pytest.mark.level1 34@pytest.mark.platform_x86_cpu 35@pytest.mark.platform_arm_cpu 36@pytest.mark.platform_x86_gpu_training 37@pytest.mark.platform_arm_ascend_training 38@pytest.mark.platform_x86_ascend_training 39@pytest.mark.env_onecard 40@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) 41def test_rnn_para_customed_dtype(mode): 42 """ 43 Feature: RNN 44 Description: Verify the result of RNN specifying customed para dtype. 45 Expectation: success 46 """ 47 ms.set_context(mode=mode) 48 net = Net() 49 x = ms.Tensor(np.ones([3, 5, 10]).astype(np.float16)) 50 h0 = ms.Tensor(np.ones([1 * 2, 3, 16]).astype(np.float16)) 51 output, _ = net(x, h0) 52 expect_output_shape = (3, 5, 16) 53 assert np.allclose(expect_output_shape, output.shape) 54