forked from mindspore-Ecosystem/mindspore
72 lines
2.3 KiB
Python
72 lines
2.3 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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import numpy as np
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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, Parameter
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from mindspore.communication.management import init
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from mindspore.ops import operations as P
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class DataParallelNet(nn.Cell):
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def __init__(self):
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super(DataParallelNet, self).__init__()
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weight_init = np.random.rand(512, 64).astype(np.float32)
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self.weight = Parameter(Tensor(weight_init), name="weight", layerwise_parallel=False)
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self.fc = P.MatMul()
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def construct(self, x):
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x = self.fc(x, self.weight)
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return x
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class ModelParallelNet(nn.Cell):
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def __init__(self):
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super(ModelParallelNet, self).__init__()
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weight_init = np.random.rand(512, 64).astype(np.float32)
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self.weight = Parameter(Tensor(weight_init), name="weight", layerwise_parallel=True)
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self.fc = P.MatMul()
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def construct(self, x):
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x = self.fc(x, self.weight)
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return x
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def test_param_broadcast():
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context.set_context(mode=context.GRAPH_MODE)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode="data_parallel", parameter_broadcast=True)
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init()
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network = DataParallelNet()
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network.set_train()
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predict = Tensor(np.ones([64, 512]).astype(np.float32) * 0.01)
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_ = network(predict)
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context.reset_auto_parallel_context()
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def test_param_not_broadcast():
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context.set_context(mode=context.GRAPH_MODE)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode="data_parallel", parameter_broadcast=False)
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init()
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network = ModelParallelNet()
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network.set_train()
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predict = Tensor(np.ones([64, 512]).astype(np.float32) * 0.01)
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_ = network(predict)
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context.reset_auto_parallel_context()
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