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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 as nn | |
| from torch.optim.optimizer import Optimizer, required | |
| import math | |
| 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 _channel_view(self, x): | |
| return x.view(x.size(0), -1) | |
| def _layer_view(self, x): | |
| return x.view(1, -1) | |
| def _cosine_similarity(self, x, y, eps, view_func): | |
| x = view_func(x) | |
| y = view_func(y) | |
| x_norm = x.norm(dim=1).add_(eps) | |
| y_norm = y.norm(dim=1).add_(eps) | |
| dot = (x * y).sum(dim=1) | |
| return dot.abs() / x_norm / y_norm | |
| def _projection(self, p, grad, perturb, delta, wd_ratio, eps): | |
| wd = 1 | |
| expand_size = [-1] + [1] * (len(p.shape) - 1) | |
| for view_func in [self._channel_view, self._layer_view]: | |
| cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func) | |
| if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)): | |
| p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps) | |
| perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size) | |
| wd = wd_ratio | |
| return perturb, wd | |
| return perturb, wd | |
| def step(self, closure=None): | |
| loss = None | |
| if closure is not None: | |
| loss = closure() | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| grad = p.grad.data | |
| 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.data) | |
| state['exp_avg_sq'] = torch.zeros_like(p.data) | |
| # 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_(1 - beta1, grad) | |
| exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) | |
| 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 = self._projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps']) | |
| # Weight decay | |
| if group['weight_decay'] > 0: | |
| p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio) | |
| # Step | |
| p.data.add_(-step_size, perturb) | |
| return loss | |