110 lines
3.2 KiB
Python
110 lines
3.2 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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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.train.model import Model
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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.ops import operations as P
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class DatasetLenet():
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def __init__(self, data, label, length=3):
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self.data = data
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self.label = label
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self.index = 1
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.data, self.label
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def reset(self):
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self.index = 0
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def get_dataset_size(self):
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return 32
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def get_repeat_count(self):
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return 1
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def get_batch_size(self):
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return 32
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def create_tuple_iterator(self, num_epochs=1, do_copy=True):
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return self
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class MatMulCell(nn.Cell):
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def __init__(self):
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super().__init__()
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self.matmul = P.MatMul()
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self.relu = P.ReLU()
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self.weight = Parameter(initializer("ones", [64, 64]), name="param1")
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def construct(self, x):
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out = self.matmul(x, self.weight)
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out = self.relu(out)
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return out
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.weight = Parameter(initializer("ones", [64, 64]), name="param")
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self.cell1 = MatMulCell()
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self.cell2 = MatMulCell()
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self.cell3 = MatMulCell()
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self.cell4 = MatMulCell()
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self.relu = P.ReLU().shard(strategy2)
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self.reduce = P.ReduceSum()
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def construct(self, x, y):
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out = self.matmul(x, self.weight)
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if self.reduce(y) == 1.0:
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out = self.cell1(out)
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elif self.reduce(y) == 2.0:
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out = self.cell2(out)
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elif self.reduce(y) == 3.0:
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out = self.cell3(out)
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else:
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out = self.cell4(out)
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out = self.relu(out)
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out = out + x
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return out
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def test_control_flow():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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strategy1 = ((2, 4), (4, 1))
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strategy2 = ((4, 1),)
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net = Net(strategy1, strategy2)
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data = Tensor(np.ones([128, 64]), dtype=ms.float32)
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label = Tensor(np.ones([8, 8]), dtype=ms.float32)
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dataset = DatasetLenet(data, label, 3)
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opt = nn.Lamb(net.trainable_params(), learning_rate=0.01)
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model = Model(net, optimizer=opt)
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model.train(2, dataset, dataset_sink_mode=False)
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