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100 lines
3.7 KiB
100 lines
3.7 KiB
3 years ago
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"""Adapted from https://github.com/NUS-HPC-AI-Lab/LARS-ImageNet-PyTorch/blob/main/lars.py"""
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from typing import Iterable
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import torch
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from torch.optim import Optimizer
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from colossalai.registry import OPTIMIZERS
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@OPTIMIZERS.register_module
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class Lars(Optimizer):
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r"""Implements the LARS optimizer from `"Large batch training of convolutional networks"
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<https://arxiv.org/pdf/1708.03888.pdf>`_.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate
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momentum (float, optional): momentum factor (default: 0)
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eeta (float, optional): LARS coefficient as used in the paper (default: 1e-3)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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"""
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def __init__(
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self,
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params: Iterable[torch.nn.Parameter],
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lr=1e-3,
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momentum=0,
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eeta=1e-3,
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weight_decay=0,
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epsilon=0.0
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) -> None:
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if not isinstance(lr, float) or lr < 0.0:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if momentum < 0.0:
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raise ValueError("Invalid momentum value: {}".format(momentum))
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if weight_decay < 0.0:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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if eeta <= 0 or eeta > 1:
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raise ValueError("Invalid eeta value: {}".format(eeta))
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if epsilon < 0:
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raise ValueError("Invalid epsilon value: {}".format(epsilon))
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defaults = dict(lr=lr, momentum=momentum,
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weight_decay=weight_decay, eeta=eeta, epsilon=epsilon, lars=True)
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super().__init__(params, defaults)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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weight_decay = group['weight_decay']
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momentum = group['momentum']
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eeta = group['eeta']
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lr = group['lr']
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lars = group['lars']
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eps = group['epsilon']
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for p in group['params']:
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if p.grad is None:
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continue
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decayed_grad = p.grad
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scaled_lr = lr
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if lars:
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w_norm = torch.norm(p)
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g_norm = torch.norm(p.grad)
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trust_ratio = torch.where(
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w_norm > 0 and g_norm > 0,
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eeta * w_norm / (g_norm + weight_decay * w_norm + eps),
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torch.ones_like(w_norm)
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)
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trust_ratio.clamp_(0.0, 50)
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scaled_lr *= trust_ratio.item()
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if weight_decay != 0:
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decayed_grad = decayed_grad.add(p, alpha=weight_decay)
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decayed_grad = torch.clamp(decayed_grad, -10.0, 10.0)
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if momentum != 0:
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param_state = self.state[p]
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if 'momentum_buffer' not in param_state:
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buf = param_state['momentum_buffer'] = torch.clone(
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decayed_grad).detach()
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else:
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buf = param_state['momentum_buffer']
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buf.mul_(momentum).add_(decayed_grad)
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decayed_grad = buf
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p.add_(decayed_grad, alpha=-scaled_lr)
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return loss
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