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| """ | |
| AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py | |
| Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217 | |
| Code: https://github.com/clovaai/AdamP | |
| Copyright (c) 2020-present NAVER Corp. | |
| MIT license | |
| """ | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.optim.optimizer import Optimizer | |
| import math | |
| def _channel_view(x) -> torch.Tensor: | |
| return x.reshape(x.size(0), -1) | |
| def _layer_view(x) -> torch.Tensor: | |
| return x.reshape(1, -1) | |
| def projection(p, grad, perturb, delta: float, wd_ratio: float, eps: float): | |
| wd = 1. | |
| expand_size = (-1,) + (1,) * (len(p.shape) - 1) | |
| for view_func in [_channel_view, _layer_view]: | |
| param_view = view_func(p) | |
| grad_view = view_func(grad) | |
| cosine_sim = F.cosine_similarity(grad_view, param_view, dim=1, eps=eps).abs_() | |
| # FIXME this is a problem for PyTorch XLA | |
| if cosine_sim.max() < delta / math.sqrt(param_view.size(1)): | |
| p_n = p / param_view.norm(p=2, dim=1).add_(eps).reshape(expand_size) | |
| perturb -= p_n * view_func(p_n * perturb).sum(dim=1).reshape(expand_size) | |
| wd = wd_ratio | |
| return perturb, wd | |
| return perturb, wd | |
| class AdamP(Optimizer): | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, | |
| weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False): | |
| defaults = dict( | |
| lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, | |
| delta=delta, wd_ratio=wd_ratio, nesterov=nesterov) | |
| super(AdamP, self).__init__(params, defaults) | |
| def step(self, closure=None): | |
| loss = None | |
| if closure is not None: | |
| with torch.enable_grad(): | |
| loss = closure() | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| grad = p.grad | |
| beta1, beta2 = group['betas'] | |
| nesterov = group['nesterov'] | |
| state = self.state[p] | |
| # State initialization | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| state['exp_avg'] = torch.zeros_like(p) | |
| state['exp_avg_sq'] = torch.zeros_like(p) | |
| # Adam | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| state['step'] += 1 | |
| bias_correction1 = 1 - beta1 ** state['step'] | |
| bias_correction2 = 1 - beta2 ** state['step'] | |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) | |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) | |
| denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) | |
| step_size = group['lr'] / bias_correction1 | |
| if nesterov: | |
| perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom | |
| else: | |
| perturb = exp_avg / denom | |
| # Projection | |
| wd_ratio = 1. | |
| if len(p.shape) > 1: | |
| perturb, wd_ratio = projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps']) | |
| # Weight decay | |
| if group['weight_decay'] > 0: | |
| p.mul_(1. - group['lr'] * group['weight_decay'] * wd_ratio) | |
| # Step | |
| p.add_(perturb, alpha=-step_size) | |
| return loss | |