forked from mindspore-Ecosystem/mindspore
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# Copyright 2019 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, NCCL_WORLD_COMM_GROUP, 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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init()
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rank = get_rank()
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size = get_group_size()
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x = np.ones([1, 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.all_gather = P.AllGather(group=NCCL_WORLD_COMM_GROUP)
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self.x = Parameter(initializer(Tensor(x), x.shape), name='x')
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def construct(self):
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return self.all_gather(self.x)
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def test_AllGather():
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all_gather = Net()
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output = all_gather()
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expect = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * (0 + 1)
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for i in range(size - 1):
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tmp = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 2)
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expect = np.concatenate((expect, tmp))
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diff = np.absolute(output.asnumpy() - expect)
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error = np.ones(shape=expect.shape) * 1.0e-5
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assert np.all(diff < error)
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assert output.shape == expect.shape
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