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# ============================================================================ 15import numpy as np 16import mindspore as ms 17import mindspore.context as context 18from mindspore import Tensor, Parameter 19import mindspore.nn as nn 20from mindspore.common.api import _cell_graph_executor 21from mindspore.nn import TrainOneStepCell, Momentum 22from mindspore.ops import operations as P 23 24 25class Net(nn.Cell): 26 def __init__(self, mul_weight, strategy=None): 27 super(Net, self).__init__() 28 self.reluv2 = P.ReLUV2().shard(strategy) 29 self.mul = P.Mul() 30 self.weight = Parameter(mul_weight, "w1") 31 32 def construct(self, x): 33 out = self.mul(x, self.weight) 34 output, _ = self.reluv2(out) 35 return output 36 37 38_w1 = Tensor(np.ones([32, 16, 48, 64]), dtype=ms.float32) 39_x = Tensor(np.ones([32, 16, 48, 64]), dtype=ms.float32) 40 41 42def compile_net(net): 43 context.set_context(mode=context.GRAPH_MODE) 44 optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9) 45 train_net = TrainOneStepCell(net, optimizer) 46 train_net.set_auto_parallel() 47 train_net.set_train() 48 _cell_graph_executor.compile(train_net, _x) 49 context.reset_auto_parallel_context() 50 51 52def test_reluv2(): 53 context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) 54 strategy = ((2, 1, 2, 2),) 55 net = Net(_w1, strategy) 56 compile_net(net) 57 58 59def test_reluv2_no_full(): 60 context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) 61 strategy = ((2, 1, 2, 1),) 62 net = Net(_w1, strategy) 63 compile_net(net) 64 65 66def test_reluv2_no_strategy(): 67 context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) 68 strategy = None 69 net = Net(_w1, strategy) 70 compile_net(net) 71 72 73def test_reluv2_auto_parallel(): 74 context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0) 75 net = Net(_w1) 76 compile_net(net) 77