2020-08-17 20:11:09 +08:00
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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2020-08-27 15:11:02 +08:00
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init()
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2020-08-17 20:11:09 +08:00
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rank = get_rank()
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size = get_group_size()
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x = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.x1 = Parameter(initializer(Tensor(x), x.shape), name='x1')
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self.x2 = Parameter(initializer(Tensor(x), x.shape), name='x2')
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self.x3 = Parameter(initializer(Tensor(x), x.shape), name='x3')
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self.broadcast1 = P.Broadcast(0)
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self.broadcast2 = P.Broadcast(1)
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self.broadcast3 = P.Broadcast(2)
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def construct(self):
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return (self.broadcast1((self.x1,)),
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self.broadcast2((self.x2,)),
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self.broadcast3((self.x3,)))
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def test_Broadcast():
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broadcast = Net()
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output = broadcast()
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expect0 = np.ones([3, 1, 3, 3]).astype(np.float32) * 1
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expect1 = np.ones([3, 1, 3, 3]).astype(np.float32) * 2
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expect2 = np.ones([3, 1, 3, 3]).astype(np.float32) * 3
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diff0 = output[0][0].asnumpy() - expect0
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error0 = np.ones(shape=expect0.shape) * 1.0e-5
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assert np.all(diff0 < error0)
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assert output[0][0].shape == expect0.shape
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diff1 = output[1][0].asnumpy() - expect1
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error1 = np.ones(shape=expect1.shape) * 1.0e-5
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assert np.all(diff1 < error1)
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assert output[1][0].shape == expect1.shape
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diff2 = output[2][0].asnumpy() - expect2
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error2 = np.ones(shape=expect2.shape) * 1.0e-5
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assert np.all(diff2 < error2)
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assert output[2][0].shape == expect2.shape
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