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- # Copyright 2021 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 as ms
- from mindspore import context, Tensor, Parameter
- from mindspore.common.api import _cell_graph_executor
- from mindspore.nn import Cell, TrainOneStepCell, Momentum
- from mindspore.ops import operations as P
-
-
- class Net(Cell):
- def __init__(self, mul_weight, strategy1=None, strategy2=None):
- super().__init__()
- self.mul = P.Mul().shard(strategy1)
- self.dropout1 = P.Dropout(keep_prob=0.5).shard(strategy2)
- self.relu = P.ReLU().shard(strategy2)
- self.dropout2 = P.Dropout(keep_prob=0.5).shard(strategy2)
- self.relu2 = P.ReLU().shard(strategy2)
- self.mul_weight = Parameter(mul_weight, "w1")
-
- def construct(self, x, b):
- out = self.mul(x, self.mul_weight)
- out, _ = self.dropout1(out)
- out = self.relu(out)
- out, _ = self.dropout2(out)
- out = self.relu2(out)
- return out
-
-
- _x = Tensor(np.ones([128, 64]), dtype=ms.float32)
- _w1 = Tensor(np.ones([128, 64]), dtype=ms.float32)
- _b = Tensor(np.ones([128, 64]), dtype=ms.float32)
-
-
- def compile_net(net):
- optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
- train_net = TrainOneStepCell(net, optimizer)
- train_net.set_auto_parallel()
- train_net.set_train()
- _cell_graph_executor.compile(train_net, _x, _b)
- context.reset_auto_parallel_context()
-
-
- def test_dropout_data_parallel():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((16, 1), (16, 1))
- strategy2 = ((16, 1),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
-
-
- def test_dropout_model_parallel():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((1, 16), (1, 16))
- strategy2 = ((1, 16),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
-
-
- def test_dropout_mixed_parallel():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((4, 4), (4, 4))
- strategy2 = ((4, 4),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
-
-
- def test_dropout_auto_parallel():
- context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
- net = Net(_w1)
- compile_net(net)
-
-
- def test_dropout_repeat_calc():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((4, 4), (4, 4))
- strategy2 = ((2, 4),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
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