forked from mindspore-Ecosystem/mindspore
73 lines
2.8 KiB
Python
73 lines
2.8 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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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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import numpy as np
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import mindspore.context as context
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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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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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init('nccl')
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rank = get_rank()
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size = get_group_size()
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x = np.ones([size, 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.x = Parameter(initializer(Tensor(x), x.shape), name='x')
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self.op0 = "sum"
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self.op1 = "max"
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self.op2 = "min"
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self.op3 = "prod"
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self.reduce_scatter1 = P.ReduceScatter(self.op0, group=NCCL_WORLD_COMM_GROUP)
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self.reduce_scatter2 = P.ReduceScatter(self.op1, group=NCCL_WORLD_COMM_GROUP)
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self.reduce_scatter3 = P.ReduceScatter(self.op2, group=NCCL_WORLD_COMM_GROUP)
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def construct(self):
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return (self.reduce_scatter1(self.x),
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self.reduce_scatter2(self.x),
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self.reduce_scatter3(self.x))
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def test_ReduceScatter():
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reduce_scatter = Net()
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output = reduce_scatter()
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sum = np.ones([size, 1, 3, 3]).astype(np.float32) * 0
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for i in range(size):
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sum += np.ones([size, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
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expect0 = sum[rank : rank + 1]
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diff0 = output[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].shape() == expect0.shape)
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expect1 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * size
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diff1 = output[1].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].shape() == expect1.shape)
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expect2 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * 1
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diff2 = output[2].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].shape() == expect2.shape)
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