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1# Copyright 2019 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
16
17import mindspore as ms
18import mindspore.nn as nn
19from mindspore import Tensor
20from mindspore import context
21from mindspore.common.api import _cell_graph_executor
22from mindspore.ops import composite as C
23from mindspore.ops import operations as P
24
25
26grad_all = C.GradOperation(get_all=True)
27
28
29class NetWithLoss(nn.Cell):
30    def __init__(self, network, strategy3):
31        super(NetWithLoss, self).__init__()
32        self.loss = P.SoftmaxCrossEntropyWithLogits().shard(strategy3)
33        self.network = network
34
35    def construct(self, x, y, bias, label):
36        predict = self.network(x, y, bias)
37        return self.loss(predict, label)[0]
38
39
40class GradWrap(nn.Cell):
41    def __init__(self, network):
42        super(GradWrap, self).__init__()
43        self.network = network
44
45    def construct(self, x, y, bias, label):
46        return grad_all(self.network)(x, y, bias, label)
47
48
49def test_linear():
50    class Net(nn.Cell):
51        def __init__(self, strategy0, strategy1, strategy2):
52            super().__init__()
53            self.fc_nobias = P.MatMul(transpose_b=True).shard(strategy0)
54            self.add = P.Add().shard(strategy1)
55            self.gelu = P.GeLU().shard(strategy2)
56
57        def construct(self, x, y, bias):
58            out = self.fc_nobias(x, y)
59            out = self.add(out, bias)
60            out = self.gelu(out)
61            return out
62
63    context.set_auto_parallel_context(device_num=16, global_rank=0)
64    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
65    strategy0 = ((2, 4), (2, 4))
66    strategy1 = ((2, 4), (4,))
67    strategy2 = ((2, 8),)
68    strategy3 = ((16, 1), (16, 1))
69    net = GradWrap(NetWithLoss(Net(strategy0, strategy1, strategy2), strategy3))
70    net.set_auto_parallel()
71
72    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
73    y = Tensor(np.ones([64, 32]), dtype=ms.float32)
74    bias = Tensor(np.ones([64]), dtype=ms.float32)
75    label = Tensor(np.ones([64, 64]), dtype=ms.float32)
76    net.set_train()
77    _cell_graph_executor.compile(net, x, y, bias, label)
78