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106 lines
3.5 KiB
106 lines
3.5 KiB
"""
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AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
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Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
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Code: https://github.com/clovaai/AdamP
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Copyright (c) 2020-present NAVER Corp.
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MIT license
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"""
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import torch
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import torch.nn.functional as F
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from torch.optim.optimizer import Optimizer
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import math
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def _channel_view(x) -> torch.Tensor:
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return x.reshape(x.size(0), -1)
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def _layer_view(x) -> torch.Tensor:
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return x.reshape(1, -1)
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def projection(p, grad, perturb, delta: float, wd_ratio: float, eps: float):
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wd = 1.
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expand_size = (-1,) + (1,) * (len(p.shape) - 1)
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for view_func in [_channel_view, _layer_view]:
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param_view = view_func(p)
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grad_view = view_func(grad)
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cosine_sim = F.cosine_similarity(grad_view, param_view, dim=1, eps=eps).abs_()
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# FIXME this is a problem for PyTorch XLA
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if cosine_sim.max() < delta / math.sqrt(param_view.size(1)):
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p_n = p / param_view.norm(p=2, dim=1).add_(eps).reshape(expand_size)
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perturb -= p_n * view_func(p_n * perturb).sum(dim=1).reshape(expand_size)
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wd = wd_ratio
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return perturb, wd
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return perturb, wd
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class AdamP(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False):
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defaults = dict(
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lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
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delta=delta, wd_ratio=wd_ratio, nesterov=nesterov)
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super(AdamP, self).__init__(params, defaults)
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@torch.no_grad()
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def step(self, closure=None):
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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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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad
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beta1, beta2 = group['betas']
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nesterov = group['nesterov']
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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state['exp_avg'] = torch.zeros_like(p)
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state['exp_avg_sq'] = torch.zeros_like(p)
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# Adam
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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state['step'] += 1
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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step_size = group['lr'] / bias_correction1
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if nesterov:
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perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom
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else:
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perturb = exp_avg / denom
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# Projection
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wd_ratio = 1.
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if len(p.shape) > 1:
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perturb, wd_ratio = projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps'])
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# Weight decay
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if group['weight_decay'] > 0:
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p.mul_(1. - group['lr'] * group['weight_decay'] * wd_ratio)
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# Step
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p.add_(perturb, alpha=-step_size)
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return loss
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