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1# Copyright 2021 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15import numpy as np
16import pytest
17
18import mindspore as ms
19from mindspore import context, Tensor, Parameter
20from mindspore.nn import Cell, Momentum
21from mindspore.ops import operations as P
22from mindspore.train import Model
23from tests.dataset_mock import MindData
24
25
26class Dataset(MindData):
27    def __init__(self, predict, label, length=3):
28        super(Dataset, self).__init__(size=length)
29        self.predict = predict
30        self.label = label
31        self.index = 0
32        self.length = length
33
34    def __iter__(self):
35        return self
36
37    def __next__(self):
38        if self.index >= self.length:
39            raise StopIteration
40        self.index += 1
41        return self.predict, self.label
42
43    def reset(self):
44        self.index = 0
45
46
47class Net(Cell):
48    def __init__(self, w1, strategy1=None, strategy2=None):
49        super().__init__()
50        self.mul = P.Mul().shard(strategy1)
51        self.w1 = Parameter(w1, "w1")
52        self.topk = P.TopK().shard(strategy2)
53
54    def construct(self, x, b):
55        out = self.mul(x, self.w1)
56        out, _ = self.topk(out, 8)
57        return out
58
59
60_x = Tensor(np.ones([16, 64]), dtype=ms.float32)
61_b = Tensor(np.ones([16, 64]), dtype=ms.float32)
62_w1 = Tensor(np.ones([128, 64]), dtype=ms.float32)
63
64
65def compile_net(net):
66    learning_rate = 0.1
67    momentum = 0.9
68    epoch_size = 2
69    dataset = Dataset(_x, _b)
70    opt = Momentum(net.trainable_params(), learning_rate, momentum)
71    model = Model(net, optimizer=opt)
72    model.train(epoch_size, dataset, dataset_sink_mode=False)
73    context.reset_auto_parallel_context()
74
75
76def test_topk_data_parallel():
77    context.set_auto_parallel_context(
78        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
79    strategy1 = ((8, 1), (8, 1))
80    strategy2 = ((8, 1),)
81    net = Net(_w1, strategy1, strategy2)
82    compile_net(net)
83
84
85def test_topk_model_parallel():
86    context.set_auto_parallel_context(
87        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
88    strategy1 = ((2, 4), (2, 4))
89    strategy2 = ((2, 1),)
90    net = Net(_w1, strategy1, strategy2)
91    compile_net(net)
92
93
94def test_topk_auto_parallel():
95    context.set_auto_parallel_context(
96        parallel_mode="auto_parallel", device_num=8, global_rank=0)
97    net = Net(_w1)
98    compile_net(net)
99
100
101def test_topk_strategy_error():
102    context.set_auto_parallel_context(
103        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
104    strategy1 = ((8, 1), (8, 1))
105    strategy2 = ((1, 8),)
106    net = Net(_w1, strategy1, strategy2)
107    with pytest.raises(RuntimeError):
108        compile_net(net)
109