1# Copyright 2019 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 16 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20from mindspore.common.api import ms_function 21from mindspore.ops.composite import GradOperation 22 23context.set_context(device_target="Ascend") 24 25 26class Grad(nn.Cell): 27 def __init__(self, network): 28 super(Grad, self).__init__() 29 self.grad = GradOperation(get_all=True, sens_param=True) 30 self.network = network 31 32 @ms_function 33 def construct(self, input_, output_grad): 34 return self.grad(self.network)(input_, output_grad) 35 36 37class Net(nn.Cell): 38 def __init__(self): 39 super(Net, self).__init__() 40 self.dense = nn.Dense(2048, 1001) 41 42 def construct(self, x): 43 return self.dense(x) 44 45 46def test_net(): 47 x = np.random.randn(32, 2048).astype(np.float32) 48 sens = np.random.randn(32, 1001).astype(np.float32) 49 net = Grad(Net()) 50 output = net(Tensor(x), Tensor(sens)) 51 print(output.asnumpy()) 52 53def test_net_ND(): 54 x = np.random.randn(2, 32, 2048).astype(np.float32) 55 sens = np.random.randn(2, 32, 1001).astype(np.float32) 56 net = Grad(Net()) 57 output = net(Tensor(x), Tensor(sens)) 58 print(output.asnumpy()) 59