# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ import numpy as np import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter from mindspore.communication.management import init, get_rank, get_group_size from mindspore.ops import operations as P context.set_context(mode=context.GRAPH_MODE, device_target='GPU') init() rank = get_rank() size = get_group_size() x = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1) class Net(nn.Cell): def __init__(self): super(Net, self).__init__() self.x1 = Parameter(initializer(Tensor(x), x.shape), name='x1') self.x2 = Parameter(initializer(Tensor(x), x.shape), name='x2') self.x3 = Parameter(initializer(Tensor(x), x.shape), name='x3') self.broadcast1 = P.Broadcast(0) self.broadcast2 = P.Broadcast(1) self.broadcast3 = P.Broadcast(2) def construct(self): return (self.broadcast1((self.x1,)), self.broadcast2((self.x2,)), self.broadcast3((self.x3,))) def test_Broadcast(): broadcast = Net() output = broadcast() expect0 = np.ones([3, 1, 3, 3]).astype(np.float32) * 1 expect1 = np.ones([3, 1, 3, 3]).astype(np.float32) * 2 expect2 = np.ones([3, 1, 3, 3]).astype(np.float32) * 3 diff0 = output[0][0].asnumpy() - expect0 error0 = np.ones(shape=expect0.shape) * 1.0e-5 assert np.all(diff0 < error0) assert output[0][0].shape == expect0.shape diff1 = output[1][0].asnumpy() - expect1 error1 = np.ones(shape=expect1.shape) * 1.0e-5 assert np.all(diff1 < error1) assert output[1][0].shape == expect1.shape diff2 = output[2][0].asnumpy() - expect2 error2 = np.ones(shape=expect2.shape) * 1.0e-5 assert np.all(diff2 < error2) assert output[2][0].shape == expect2.shape