forked from mindspore-Ecosystem/mindspore
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
# Copyright 2021 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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"""test hccl allreduce with 8p"""
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import os
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import dtype as mstype
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from mindspore.ops import operations as P
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from mindspore.communication.management import init
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np.random.seed(1)
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os.environ['GRAPH_OP_RUN'] = str(1)
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os.environ['HCCL_WHITELIST_DISABLE'] = str(1)
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init()
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class AllReduceNet(nn.Cell):
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def __init__(self):
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super(AllReduceNet, self).__init__()
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self.mul = P.Mul()
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self.all_reduce = P.AllReduce()
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self.add = P.Add()
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self.y1 = Tensor(np.array([[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]])).astype(np.float32)
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self.y2 = Tensor(np.array([[-16, -16, -16, -16], [-16, -16, -16, -16], \
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[-16, -16, -16, -16]])).astype(np.float32)
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def construct(self, x):
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x = self.mul(x, 2)
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z = self.add(x, self.y1)
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z = self.all_reduce(z)
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out = self.add(z, self.y2)
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out = self.all_reduce(out)
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out = self.mul(out, 2)
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return out
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def test_hccl_allreduce_8p():
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net = AllReduceNet()
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input_x = np.ones([3, 4]).astype(np.float32)
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expect_output = [[256, 256, 256, 256], [256, 256, 256, 256], [256, 256, 256, 256]]
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output = net(Tensor(input_x, mstype.float32))
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assert np.allclose(output.asnumpy(), expect_output)
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