NeuralSeq/utils/training_utils.py

from utils.hparams import hparams


class RSQRTSchedule(object):
    def __init__(self, optimizer):
        super().__init__()
        self.optimizer = optimizer
        self.constant_lr = hparams['lr']
        self.warmup_updates = hparams['warmup_updates']
        self.hidden_size = hparams['hidden_size']
        self.lr = hparams['lr']
        for param_group in optimizer.param_groups:
            param_group['lr'] = self.lr
        self.step(0)

    def step(self, num_updates):
        constant_lr = self.constant_lr
        warmup = min(num_updates / self.warmup_updates, 1.0)
        rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5
        rsqrt_hidden = self.hidden_size ** -0.5
        self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-7)
        for param_group in self.optimizer.param_groups:
            param_group['lr'] = self.lr
        return self.lr

    def get_lr(self):
        return self.optimizer.param_groups[0]['lr']