Source code for lmflow.optim.adabound

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import math

import torch
from torch.optim.optimizer import Optimizer

[docs] class AdaBound(Optimizer): r"""Implements AdaBound algorithm. It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate https://arxiv.org/abs/1902.09843 Note: Reference code: https://github.com/Luolc/AdaBound """ def __init__( self, params, lr: float = 1e-3, betas = (0.9, 0.999), final_lr: float = 0.1, gamma: float = 1e-3, eps: float = 1e-8, weight_decay: float = 0, amsbound: bool = False, ) -> None: if lr <= 0.0: raise ValueError("Invalid learning rate: {}".format(lr)) if eps < 0.0: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError( "Invalid beta parameter at index 0: {}".format(betas[0]) ) if not 0.0 <= betas[1] < 1.0: raise ValueError( "Invalid beta parameter at index 1: {}".format(betas[1]) ) if final_lr < 0.0: raise ValueError( "Invalid final learning rate: {}".format(final_lr) ) if not 0.0 <= gamma < 1.0: raise ValueError("Invalid gamma parameter: {}".format(gamma)) if weight_decay < 0: raise ValueError( "Invalid weight_decay value: {}".format(weight_decay) ) defaults = dict( lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps, weight_decay=weight_decay, amsbound=amsbound, ) super(AdaBound, self).__init__(params, defaults)
[docs] self.base_lrs = [group["lr"] for group in self.param_groups]
[docs] def __setstate__(self, state) -> None: super(AdaBound, self).__setstate__(state) for group in self.param_groups: group.setdefault("amsbound", False)
[docs] def step(self, closure = None): r"""Performs a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group, base_lr in zip(self.param_groups, self.base_lrs): for p in group["params"]: if p.grad is None: continue grad = p.grad.data if grad.is_sparse: msg = ( "AdaBound does not support sparse gradients, " "please consider SparseAdam instead" ) raise RuntimeError(msg) amsbound = group["amsbound"] state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 # Exponential moving average of gradient values state["exp_avg"] = torch.zeros_like( p, memory_format=torch.preserve_format ) # Exponential moving average of squared gradient values state["exp_avg_sq"] = torch.zeros_like( p, memory_format=torch.preserve_format ) if amsbound: # Maintains max of all exp. moving avg. of # sq. grad. values state["max_exp_avg_sq"] = torch.zeros_like( p, memory_format=torch.preserve_format ) exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] if amsbound: max_exp_avg_sq = state["max_exp_avg_sq"] beta1, beta2 = group["betas"] state["step"] += 1 if group["weight_decay"] != 0: grad = grad.add(p.data, alpha=group["weight_decay"]) # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) if amsbound: # Maintains the maximum of all 2nd moment running # avg. till now torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) # Use the max. for normalizing running avg. of gradient denom = max_exp_avg_sq.sqrt().add_(group["eps"]) else: denom = exp_avg_sq.sqrt().add_(group["eps"]) bias_correction1 = 1 - beta1 ** state["step"] bias_correction2 = 1 - beta2 ** state["step"] step_size = ( group["lr"] * math.sqrt(bias_correction2) / bias_correction1 ) # Applies bounds on actual learning rate # lr_scheduler cannot affect final_lr, this is a workaround # to apply lr decay final_lr = group["final_lr"] * group["lr"] / base_lr lower_bound = final_lr * ( 1 - 1 / (group["gamma"] * state["step"] + 1) ) upper_bound = final_lr * ( 1 + 1 / (group["gamma"] * state["step"]) ) step_size = torch.full_like(denom, step_size) step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_( exp_avg ) p.data.add_(-step_size) return loss