"""Adapted from https://github.com/NUS-HPC-AI-Lab/LARS-ImageNet-PyTorch/blob/main/lars.py""" from typing import Iterable import torch from torch.optim import Optimizer class Lars(Optimizer): r"""Implements the LARS optimizer from `"Large batch training of convolutional networks" `_. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) momentum (float, optional): momentum factor (default: 0) eeta (float, optional): LARS coefficient as used in the paper (default: 1e-3) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) """ def __init__(self, params: Iterable[torch.nn.Parameter], lr=1e-3, momentum=0, eeta=1e-3, weight_decay=0, epsilon=0.0) -> None: if not isinstance(lr, float) or lr < 0.0: raise ValueError("Invalid learning rate: {}".format(lr)) if momentum < 0.0: raise ValueError("Invalid momentum value: {}".format(momentum)) if weight_decay < 0.0: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if eeta <= 0 or eeta > 1: raise ValueError("Invalid eeta value: {}".format(eeta)) if epsilon < 0: raise ValueError("Invalid epsilon value: {}".format(epsilon)) defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay, eeta=eeta, epsilon=epsilon, lars=True) super().__init__(params, defaults) @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: weight_decay = group['weight_decay'] momentum = group['momentum'] eeta = group['eeta'] lr = group['lr'] lars = group['lars'] eps = group['epsilon'] for p in group['params']: if p.grad is None: continue decayed_grad = p.grad scaled_lr = lr if lars: w_norm = torch.norm(p) g_norm = torch.norm(p.grad) trust_ratio = torch.where(w_norm > 0 and g_norm > 0, eeta * w_norm / (g_norm + weight_decay * w_norm + eps), torch.ones_like(w_norm)) trust_ratio.clamp_(0.0, 50) scaled_lr *= trust_ratio.item() if weight_decay != 0: decayed_grad = decayed_grad.add(p, alpha=weight_decay) decayed_grad = torch.clamp(decayed_grad, -10.0, 10.0) if momentum != 0: param_state = self.state[p] if 'momentum_buffer' not in param_state: buf = param_state['momentum_buffer'] = torch.clone(decayed_grad).detach() else: buf = param_state['momentum_buffer'] buf.mul_(momentum).add_(decayed_grad) decayed_grad = buf p.add_(decayed_grad, alpha=-scaled_lr) return loss