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77 lines
2.6 KiB
77 lines
2.6 KiB
# 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 pytest
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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 import dtype as mstype
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from mindspore.ops import operations as P
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import mindspore._ms_mpi as mpi
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# run comand:
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# mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_reduce_scatter.py
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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context.set_mpi_config(enable_mpi=True)
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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.op = "sum"
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self.reducescatter = P.HostReduceScatter(op=self.op, group=[0,1,2])
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def construct(self, x):
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return self.reducescatter(x)
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class AllGatherNet(nn.Cell):
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def __init__(self):
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super(AllGatherNet, self).__init__()
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self.hostallgather = P.HostAllGather(group=(0, 1, 2))
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def construct(self, x):
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return self.hostallgather(x)
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def test_net_reduce_scatter():
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x = np.arange(12).astype(np.float32) * 0.1
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reducescatter = Net()
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rankid = mpi.get_rank_id()
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print("self rankid:", rankid)
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output = reducescatter(Tensor(x, mstype.float32))
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print("output:\n", output)
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if rankid == 0:
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expect_result = np.arange(4).astype(np.float32) * 0.3
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if rankid == 1:
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expect_result = np.arange(4, 8).astype(np.float32) * 0.3
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if rankid == 2:
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expect_result = np.arange(8, 12).astype(np.float32) * 0.3
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diff = abs(output.asnumpy() - expect_result)
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error = np.ones(shape=expect_result.shape) * 1.0e-6
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assert np.all(diff < error)
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allgather = AllGatherNet()
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allgather_output = allgather(output)
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print("allgather result:\n", allgather_output)
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expect_allgather_result = np.arange(12).astype(np.float32) * 0.3
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diff = abs(allgather_output.asnumpy() - expect_allgather_result)
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error = np.ones(shape=expect_allgather_result.shape) * 1.0e-6
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assert np.all(diff < error)
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if __name__ == '__main__':
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test_net_reduce_scatter()
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