forked from mindspore-Ecosystem/mindspore
114 lines
3.6 KiB
Python
114 lines
3.6 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 math
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import numpy as np
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import mindspore as ms
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from mindspore.common import dtype as mstype
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from mindspore import context, Tensor, Parameter
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from mindspore.nn import Cell, Momentum
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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from mindspore.train import Model
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from tests.dataset_mock import MindData
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class Dataset(MindData):
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def __init__(self, predict, label, length=3):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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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.predict, self.label
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def reset(self):
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self.index = 0
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class Net(Cell):
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def __init__(self, weight, start, limit, delta, strategy1=None, strategy2=None, strategy3=None):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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if isinstance(start, float):
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self.type = mstype.float32
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else:
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self.type = mstype.int32
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limit = float(limit)
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start = float(start)
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delta = float(delta)
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length_input = math.ceil((limit - start) / delta)
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self.input_tensor = Tensor(list(range(int(length_input))), self.type)
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self.range = inner.Range(start, limit, delta)
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self.range.shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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self.weight = Parameter(weight, "w")
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def construct(self, x, b):
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r_out = self.range(self.input_tensor)
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out = self.mul(x, self.weight)
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out = self.mul2(out, r_out)
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return out
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dev_num = 4
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_x = Tensor(np.ones([64 // dev_num, 8]), dtype=ms.float32)
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_b = Tensor(np.ones([8]), dtype=ms.float32)
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_w1 = Tensor(np.ones([64, 8]), dtype=ms.float32)
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def compile_net(net):
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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dataset = Dataset(_x, _b)
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, optimizer=opt)
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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context.reset_auto_parallel_context()
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def test_range():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=dev_num, global_rank=2)
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((2,),)
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strategy3 = ((2, 2), (2,))
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net = Net(_w1, 0, 8, 1, strategy1, strategy2, strategy3)
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compile_net(net)
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def test_range2():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=dev_num, global_rank=0)
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strategy1 = ((4, 1), (4, 1))
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strategy2 = ((1,),)
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strategy3 = ((4, 1), (1,))
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net = Net(_w1, 0.0, 4.0, 0.5, strategy1, strategy2, strategy3)
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compile_net(net)
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def test_range3():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=dev_num, global_rank=2)
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net = Net(_w1, 0.0, 4.0, 0.5)
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compile_net(net)
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