forked from huawei/mindspore2022
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
# Copyright 2022 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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import numpy as np
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from mindspore import Tensor, context
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from parallel.utils.utils import compile_net
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x_ = Tensor(np.random.normal(size=[32, 8, 8]).astype(np.float32))
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class Net(Cell):
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def __init__(self, strategy=None):
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super(Net, self).__init__()
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self.l2_loss = P.L2Loss().shard(strategy)
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def construct(self, x):
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return self.l2_loss(x)
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def test_l2_loss_auto_parallel():
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"""
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Feature: test L2Loss auto parallel
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Description: auto parallel
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net()
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compile_net(net, x_)
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def test_l2_loss_model_parallel():
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"""
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Feature: test L2Loss model parallel
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Description: model parallel
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy = ((2, 2, 2),)
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net = Net(strategy)
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compile_net(net, x_)
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