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1# Copyright 2020 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 mindspore as ms
17import mindspore.context as context
18from mindspore.common.api import _cell_graph_executor
19from mindspore import Tensor, Parameter
20import mindspore.nn as nn
21from mindspore.nn import Cell, TrainOneStepCell, Momentum
22from mindspore.ops import operations as P
23
24
25class TwoInputBpropOperator(Cell):
26    def __init__(self):
27        super().__init__()
28        self.op = P.Mul()
29        self.bp = P.Add()
30
31    def construct(self, x, y):
32        return self.op(x, y)
33
34    def bprop(self, x, y, out, dout):
35        return self.bp(5, x), self.bp(y, 8)
36
37
38class ParallelFloorDivBpropNet(Cell):
39    def __init__(self, mul_size, test_size, strategy=None, strategy2=None):
40        super().__init__()
41        mul_np = np.full(mul_size, 0.5, dtype=np.float32)
42        floordiv_np = np.full(test_size, 0.1, dtype=np.float32)
43        self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
44        self.floordiv_weight = Parameter(Tensor(floordiv_np), name="floordiv_weight")
45        self.mul = TwoInputBpropOperator()
46        self.floor_div = P.FloorDiv()
47        self.bn = nn.BatchNorm1d(num_features=96)
48        if strategy is not None:
49            self.mul.op.shard(strategy2)
50            self.mul.bp.shard(strategy2)
51            self.floor_div.shard(strategy)
52
53    def construct(self, inputs, label):
54        x = self.mul(inputs, self.mul_weight)
55        x = self.floor_div(x, self.floordiv_weight)
56        x = self.bn(x)
57        return x
58
59
60inputs_ = Tensor(np.random.randn(128, 96).astype(np.float32), dtype=ms.float32)
61label_ = Tensor(np.random.randn(128, 96).astype(np.float32), dtype=ms.float32)
62
63
64def compile_net(net):
65    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
66    train_net = TrainOneStepCell(net, optimizer)
67    train_net.set_auto_parallel()
68    train_net.set_train()
69    _cell_graph_executor.compile(train_net, inputs_, label_)
70    context.reset_auto_parallel_context()
71
72
73def test_net():
74    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
75    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=4, global_rank=0)
76    strategy = ((4, 1), (4, 1))
77    net = ParallelFloorDivBpropNet(mul_size=(128, 96), test_size=(128, 96), strategy=strategy, strategy2=strategy)
78    compile_net(net)
79