Lookahead Optimizer

Description

The lookahead optimizer is an innovative optimization technique that enhances the training stability and convergence of traditional optimizers, such as Adam or SGD, by maintaining two sets of parameters:

Fast weights: Are updated frequently using a standard optimizer
Slow weights: Are updated less frequently by synchronizing them with the fast weights

This approach allows for better exploration of the loss landscape, as the optimizer can escape local minima and smooth out oscillations in the optimization trajectory.

Info

By leveraging the strengths of both the base optimizer and the lookahead mechanism, this optimizer leads to faster convergence and improved generalization, making it a valuable addition to deep learning model training.

Example

import torch

class Lookahead(torch.optim.Optimizer):
    def __init__(self, optimizer, k=5, alpha=0.5):
        self.optimizer = optimizer
        self.k = k
        self.alpha = alpha
        self.step_counter = 0
        self.slow_weights = [
            [p.clone().detach() for p in group["params"]]
            for group in optimizer.param_groups
        ]

    def step(self):
        self.step_counter += 1
        self.optimizer.step()

        if self.step_counter % self.k == 0:
            for group, slow_weights in zip(self.optimizer.param_groups, self.slow_weights):
                for p, q in zip(group["params"], slow_weights):
                    # q = slow weights, p = fast weights
                    p.data.mul_(self.alpha).add_(q, alpha=1.0 - self.alpha)
                    q.data.copy_(p.data)