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""" |
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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 as nn |
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from torch.optim.optimizer import Optimizer, required |
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import math |
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class AdamP(Optimizer): |
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def __init__( |
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self, |
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params, |
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lr=1e-3, |
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betas=(0.9, 0.999), |
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eps=1e-8, |
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weight_decay=0, |
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delta=0.1, |
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wd_ratio=0.1, |
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nesterov=False, |
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): |
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defaults = dict( |
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lr=lr, |
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betas=betas, |
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eps=eps, |
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weight_decay=weight_decay, |
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delta=delta, |
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wd_ratio=wd_ratio, |
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nesterov=nesterov, |
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) |
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super(AdamP, self).__init__(params, defaults) |
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def _channel_view(self, x): |
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return x.view(x.size(0), -1) |
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def _layer_view(self, x): |
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return x.view(1, -1) |
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def _cosine_similarity(self, x, y, eps, view_func): |
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x = view_func(x) |
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y = view_func(y) |
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x_norm = x.norm(dim=1).add_(eps) |
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y_norm = y.norm(dim=1).add_(eps) |
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dot = (x * y).sum(dim=1) |
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return dot.abs() / x_norm / y_norm |
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def _projection(self, p, grad, perturb, delta, wd_ratio, eps): |
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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 [self._channel_view, self._layer_view]: |
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cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func) |
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if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)): |
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p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps) |
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perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(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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def step(self, closure=None): |
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loss = None |
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if closure is not None: |
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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.data |
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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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if len(state) == 0: |
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state["step"] = 0 |
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state["exp_avg"] = torch.zeros_like(p.data) |
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state["exp_avg_sq"] = torch.zeros_like(p.data) |
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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_(1 - beta1, grad) |
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) |
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denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_( |
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group["eps"] |
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) |
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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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wd_ratio = 1 |
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if len(p.shape) > 1: |
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perturb, wd_ratio = self._projection( |
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p, |
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grad, |
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perturb, |
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group["delta"], |
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group["wd_ratio"], |
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group["eps"], |
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) |
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if group["weight_decay"] > 0: |
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p.data.mul_(1 - group["lr"] * group["weight_decay"] * wd_ratio) |
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p.data.add_(-step_size, perturb) |
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return loss |
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