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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.ops import operations as P
- 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
- context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
-
- init('nccl')
- rank = get_rank()
- size = get_group_size()
- x = np.ones([size, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.x = Parameter(initializer(Tensor(x), x.shape), name='x')
-
- self.op0 = "sum"
- self.op1 = "max"
- self.op2 = "min"
- self.op3 = "prod"
-
- self.reduce_scatter1 = P.ReduceScatter(self.op0, group=NCCL_WORLD_COMM_GROUP)
- self.reduce_scatter2 = P.ReduceScatter(self.op1, group=NCCL_WORLD_COMM_GROUP)
- self.reduce_scatter3 = P.ReduceScatter(self.op2, group=NCCL_WORLD_COMM_GROUP)
-
- def construct(self):
- return (self.reduce_scatter1(self.x),
- self.reduce_scatter2(self.x),
- self.reduce_scatter3(self.x))
-
-
- def test_ReduceScatter():
- reduce_scatter = Net()
- output = reduce_scatter()
-
- sum = np.ones([size, 1, 3, 3]).astype(np.float32) * 0
- for i in range(size):
- sum += np.ones([size, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
- expect0 = sum[rank: rank + 1]
- diff0 = output[0].asnumpy() - expect0
- error0 = np.ones(shape=expect0.shape) * 1.0e-5
- assert np.all(diff0 < error0)
- assert (output[0].shape() == expect0.shape)
-
- expect1 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * size
- diff1 = output[1].asnumpy() - expect1
- error1 = np.ones(shape=expect1.shape) * 1.0e-5
- assert np.all(diff1 < error1)
- assert (output[1].shape() == expect1.shape)
-
- expect2 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * 1
- diff2 = output[2].asnumpy() - expect2
- error2 = np.ones(shape=expect2.shape) * 1.0e-5
- assert np.all(diff2 < error2)
- assert (output[2].shape() == expect2.shape)
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