Noam Optimizer

This is the PyTorch implementation of optimizer introduced in the paper Attention Is All You Need.

14from typing import Dict
16from labml_nn.optimizers import WeightDecay
17from labml_nn.optimizers.amsgrad import AMSGrad

Noam Optimizer

This class extends from Adam optimizer defined in

20class Noam(AMSGrad):

Initialize the optimizer

  • params is the list of parameters
  • lr is the learning rate $\alpha$
  • betas is a tuple of ($\beta_1$, $\beta_2$)
  • eps is $\hat{\epsilon}$ or $\epsilon$ based on optimized_update
  • weight_decay is an instance of class WeightDecay defined in
  • ‘optimized_update’ is a flag whether to optimize the bias correction of the second moment by doing it after adding $\epsilon$
  • amsgrad is a flag indicating whether to use AMSGrad or fallback to plain Adam
  • warmup number of warmup steps
  • d_model model size; i.e. number of dimensions in the transformer
  • defaults is a dictionary of default for group values. This is useful when you want to extend the class AdamWarmup.
27    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
28                 weight_decay: WeightDecay = WeightDecay(),
29                 optimized_update: bool = True,
30                 amsgrad=False,
31                 warmup=0, d_model=512, defaults=None):
49        defaults = {} if defaults is None else defaults
50        defaults.update(dict(warmup=warmup))
51        super().__init__(params, lr, betas, eps, weight_decay, optimized_update, amsgrad, defaults)
52        self.d_model = d_model

Get learning-rate

where $w$ is the number of warmup steps.

54    def get_lr(self, state: Dict[str, any], group: Dict[str, any]):

62        factor = min(state['step'] ** (-0.5), state['step'] * group['warmup'] ** (-1.5))

64        return group['lr'] * self.d_model ** (-0.5) * factor

Plot learning rate for different warmups and model sizes

Plot of learning rate

67def _test_noam_lr():
73    import matplotlib.pyplot as plt
74    import numpy as np
75    from torch import nn
77    model = nn.Linear(10, 10)
78    opts = [Noam(model.parameters(), d_model=512, warmup=4000, lr=1),
79            Noam(model.parameters(), d_model=512, warmup=8000, lr=1),
80            Noam(model.parameters(), d_model=2048, warmup=2000, lr=1)]
81    plt.plot(np.arange(1, 20000), [[opt.get_lr({'step': i}, opt.defaults) for opt in opts] for i in range(1, 20000)])
82    plt.legend(["512:4000", "512:8000", "2048:2000"])
83    plt.title("Learning Rate")
87if __name__ == '__main__':
88    _test_noam_lr()