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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# ============================================================================
15""" test lamb """
16import numpy as np
17
18import mindspore.nn as nn
19from mindspore import Tensor, Parameter
20from mindspore.common.api import _cell_graph_executor
21from mindspore.nn import TrainOneStepCell, WithLossCell
22from mindspore.nn.optim import Lamb
23from mindspore.ops import operations as P
24import mindspore.common.dtype as mstype
25from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR
26
27
28class LambLearningRate(LearningRateSchedule):
29    def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
30        super(LambLearningRate, self).__init__()
31        self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
32        self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
33        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
34
35        self.greater = P.Greater()
36        self.one = Tensor(np.array([1.0]).astype(np.float32))
37        self.cast = P.Cast()
38
39    def construct(self, global_step):
40        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
41        warmup_lr = self.warmup_lr(global_step)
42        decay_lr = self.decay_lr(global_step)
43        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
44        return lr
45
46
47class Net(nn.Cell):
48    """ Net definition """
49
50    def __init__(self):
51        super(Net, self).__init__()
52        self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
53        self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))), name="bias")
54        self.matmul = P.MatMul()
55        self.biasAdd = P.BiasAdd()
56
57    def construct(self, x):
58        x = self.biasAdd(self.matmul(x, self.weight), self.bias)
59        return x
60
61
62class NetWithoutWeight(nn.Cell):
63    """ NetWithoutWeight definition """
64
65    def __init__(self):
66        super(NetWithoutWeight, self).__init__()
67        self.matmul = P.MatMul()
68
69    def construct(self, x):
70        x = self.matmul(x, x)
71        return x
72
73
74def test_lamb_compile_dynamic_lr():
75    """ test_Lamb_compile """
76    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
77    label = Tensor(np.zeros([1, 10]).astype(np.float32))
78    net = Net()
79    net.set_train()
80    loss = nn.SoftmaxCrossEntropyWithLogits()
81    warmup_decay_lr = LambLearningRate(0.01, 0.0001, 10, 20, 1.0)
82    optimizer = Lamb(net.trainable_params(), warmup_decay_lr)
83
84    net_with_loss = WithLossCell(net, loss)
85    train_network = TrainOneStepCell(net_with_loss, optimizer)
86    _cell_graph_executor.compile(train_network, inputs, label)
87
88
89def test_lamb_compile():
90    """ test_Lamb_compile """
91    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
92    label = Tensor(np.zeros([1, 10]).astype(np.float32))
93    net = Net()
94    net.set_train()
95    loss = nn.SoftmaxCrossEntropyWithLogits()
96
97    optimizer = Lamb(net.trainable_params(), 0.02, 0.9)
98
99    net_with_loss = WithLossCell(net, loss)
100    train_network = TrainOneStepCell(net_with_loss, optimizer)
101    _cell_graph_executor.compile(train_network, inputs, label)
102
103
104def test_lamb_group():
105    """ test_Lamb_group_compile """
106    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
107    label = Tensor(np.zeros([1, 10]).astype(np.float32))
108    net = Net()
109    net.set_train()
110    loss = nn.SoftmaxCrossEntropyWithLogits()
111    warmup_decay_lr = LambLearningRate(0.01, 0.0001, 10, 20, 1.0)
112    all_params = net.trainable_params()
113    group_params = [{'params': [all_params[0]], 'lr': warmup_decay_lr, 'weight_decay': 0.9},
114                    {'params': [all_params[1]]}]
115    optimizer = Lamb(group_params, 0.02)
116
117    net_with_loss = WithLossCell(net, loss)
118    train_network = TrainOneStepCell(net_with_loss, optimizer)
119    _cell_graph_executor.compile(train_network, inputs, label)
120