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120 lines
4.3 KiB
120 lines
4.3 KiB
# 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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""" test lamb """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import Lamb
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from mindspore.ops import operations as P
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import mindspore.common.dtype as mstype
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from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR
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class LambLearningRate(LearningRateSchedule):
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def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
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super(LambLearningRate, self).__init__()
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self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
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self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
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self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
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self.greater = P.Greater()
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self.one = Tensor(np.array([1.0]).astype(np.float32))
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self.cast = P.Cast()
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def construct(self, global_step):
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is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
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warmup_lr = self.warmup_lr(global_step)
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decay_lr = self.decay_lr(global_step)
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lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
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return lr
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
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self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))), name="bias")
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self.matmul = P.MatMul()
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self.biasAdd = P.BiasAdd()
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def construct(self, x):
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x = self.biasAdd(self.matmul(x, self.weight), self.bias)
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return x
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class NetWithoutWeight(nn.Cell):
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""" NetWithoutWeight definition """
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def __init__(self):
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super(NetWithoutWeight, self).__init__()
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self.matmul = P.MatMul()
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def construct(self, x):
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x = self.matmul(x, x)
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return x
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def test_lamb_compile_dynamic_lr():
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""" test_Lamb_compile """
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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warmup_decay_lr = LambLearningRate(0.01, 0.0001, 10, 20, 1.0)
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optimizer = Lamb(net.trainable_params(), warmup_decay_lr)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_executor.compile(train_network, inputs, label)
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def test_lamb_compile():
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""" test_Lamb_compile """
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Lamb(net.trainable_params(), 0.02, 0.9)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_executor.compile(train_network, inputs, label)
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def test_lamb_group():
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""" test_Lamb_group_compile """
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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warmup_decay_lr = LambLearningRate(0.01, 0.0001, 10, 20, 1.0)
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all_params = net.trainable_params()
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group_params = [{'params': [all_params[0]], 'lr': warmup_decay_lr, 'weight_decay': 0.9},
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{'params': [all_params[1]]}]
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optimizer = Lamb(group_params, 0.02)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_executor.compile(train_network, inputs, label)
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