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- # Copyright 2019 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, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
- from mindspore.ops import operations as P
-
- context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
-
- init('nccl')
- rank = get_rank()
- size = get_group_size()
- x = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.all_gather = P.AllGather(group=NCCL_WORLD_COMM_GROUP)
- self.x = Parameter(initializer(Tensor(x), x.shape), name='x')
-
- def construct(self):
- return self.all_gather(self.x)
-
-
- def test_AllGather():
- all_gather = Net()
- output = all_gather()
-
- expect = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * (0 + 1)
- for i in range(size - 1):
- tmp = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 2)
- expect = np.concatenate((expect, tmp))
- diff = output.asnumpy() - expect
- error = np.ones(shape=expect.shape) * 1.0e-5
- assert np.all(diff < error)
- assert output.shape == expect.shape
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