forked from mindspore-Ecosystem/mindspore
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
# Copyright 2020 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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import numpy as np
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import mindspore as ms
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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, mul_weight, strategy=None):
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super(Net, self).__init__()
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self.reluv2 = P.ReLUV2().shard(strategy)
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self.mul = P.Mul()
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self.weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out = self.mul(x, self.weight)
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output, _ = self.reluv2(out)
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return output
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_w1 = Tensor(np.ones([32, 16, 48, 64]), dtype=ms.float32)
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_x = Tensor(np.ones([32, 16, 48, 64]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(mode=context.GRAPH_MODE)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_cell_graph_executor.compile(train_net, _x)
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context.reset_auto_parallel_context()
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def test_reluv2():
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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, 1, 2, 2),)
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net = Net(_w1, strategy)
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compile_net(net)
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def test_reluv2_no_full():
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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, 1, 2, 1),)
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net = Net(_w1, strategy)
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compile_net(net)
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def test_reluv2_no_strategy():
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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 = None
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net = Net(_w1, strategy)
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compile_net(net)
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def test_reluv2_auto_parallel():
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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(_w1)
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compile_net(net)
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