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Source code for catalyst.contrib.optimizers.adamp

import math

import torch
import torch.nn.functional as F
from torch.optim.optimizer import Optimizer


[docs]class AdamP(Optimizer): """Implements AdamP algorithm. The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. The AdamP variant was proposed in `Slowing Down the Weight Norm Increase in Momentum-based Optimizers`_. Arguments: params: iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay coefficient (default: 0) delta: threshold that determines whether a set of parameters is scale invariant or not (default: 0.1) wd_ratio: relative weight decay applied on scale-invariant parameters compared to that applied on scale-variant parameters (default: 0.1) nesterov (boolean, optional): enables Nesterov momentum (default: False) .. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 .. _Slowing Down the Weight Norm Increase in Momentum-based Optimizers: https://arxiv.org/abs/2006.08217 Original source code: https://github.com/clovaai/AdamP """
[docs] 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, ): """ Args: params: iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay coefficient (default: 1e-2) delta: threshold that determines whether a set of parameters is scale invariant or not (default: 0.1) wd_ratio: relative weight decay applied on scale-invariant parameters compared to that applied on scale-variant parameters (default: 0.1) nesterov (boolean, optional): enables Nesterov momentum (default: False) """ defaults = dict( # noqa: C408 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) return F.cosine_similarity(x, y, dim=1, eps=eps).abs_() 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): """ Performs a single optimization step (parameter update). Arguments: closure: A closure that reevaluates the model and returns the loss. Optional for most optimizers. Returns: computed loss """ 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_(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 = 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_(perturb, alpha=-step_size) return loss
__all__ = ["AdamP"]