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mmpretrain.engine.optimizers.lars 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Iterable

import torch
from torch.optim.optimizer import Optimizer

from mmpretrain.registry import OPTIMIZERS


[文档]@OPTIMIZERS.register_module() class LARS(Optimizer): """Implements layer-wise adaptive rate scaling for SGD. Based on Algorithm 1 of the following paper by You, Gitman, and Ginsburg. `Large Batch Training of Convolutional Networks: <https://arxiv.org/abs/1708.03888>`_. Args: params (Iterable): Iterable of parameters to optimize or dicts defining parameter groups. lr (float): Base learning rate. momentum (float): Momentum factor. Defaults to 0. weight_decay (float): Weight decay (L2 penalty). Defaults to 0. dampening (float): Dampening for momentum. Defaults to 0. eta (float): LARS coefficient. Defaults to 0.001. nesterov (bool): Enables Nesterov momentum. Defaults to False. eps (float): A small number to avoid dviding zero. Defaults to 1e-8. Example: >>> optimizer = LARS(model.parameters(), lr=0.1, momentum=0.9, >>> weight_decay=1e-4, eta=1e-3) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() """ def __init__(self, params: Iterable, lr: float, momentum: float = 0, weight_decay: float = 0, dampening: float = 0, eta: float = 0.001, nesterov: bool = False, eps: float = 1e-8) -> None: if not isinstance(lr, float) and lr < 0.0: raise ValueError(f'Invalid learning rate: {lr}') if momentum < 0.0: raise ValueError(f'Invalid momentum value: {momentum}') if weight_decay < 0.0: raise ValueError(f'Invalid weight_decay value: {weight_decay}') if eta < 0.0: raise ValueError(f'Invalid LARS coefficient value: {eta}') defaults = dict( lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov, eta=eta) if nesterov and (momentum <= 0 or dampening != 0): raise ValueError( 'Nesterov momentum requires a momentum and zero dampening') self.eps = eps super().__init__(params, defaults) def __setstate__(self, state) -> None: super().__setstate__(state) for group in self.param_groups: group.setdefault('nesterov', False)
[文档] @torch.no_grad() def step(self, closure=None) -> torch.Tensor: """Performs a single optimization step. Args: 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'] dampening = group['dampening'] eta = group['eta'] nesterov = group['nesterov'] lr = group['lr'] lars_exclude = group.get('lars_exclude', False) for p in group['params']: if p.grad is None: continue d_p = p.grad if lars_exclude: local_lr = 1. else: weight_norm = torch.norm(p).item() grad_norm = torch.norm(d_p).item() if weight_norm != 0 and grad_norm != 0: # Compute local learning rate for this layer local_lr = eta * weight_norm / \ (grad_norm + weight_decay * weight_norm + self.eps) else: local_lr = 1. actual_lr = local_lr * lr d_p = d_p.add(p, alpha=weight_decay).mul(actual_lr) if momentum != 0: param_state = self.state[p] if 'momentum_buffer' not in param_state: buf = param_state['momentum_buffer'] = \ torch.clone(d_p).detach() else: buf = param_state['momentum_buffer'] buf.mul_(momentum).add_(d_p, alpha=1 - dampening) if nesterov: d_p = d_p.add(buf, alpha=momentum) else: d_p = buf p.add_(-d_p) return loss
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