Source code for lmflow.optim.yogi

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

import math
import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer

[docs] class Yogi(Optimizer): r"""Implements Yogi Optimizer Algorithm. It has been proposed in `Adaptive methods for Nonconvex Optimization`. https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization # noqa Note: Reference code: https://github.com/4rtemi5/Yogi-Optimizer_Keras """ def __init__( self, params, lr: float = 1e-2, betas = (0.9, 0.999), eps: float = 1e-3, initial_accumulator: float = 1e-6, weight_decay: float = 0, ) -> 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 weight_decay < 0: raise ValueError( "Invalid weight_decay value: {}".format(weight_decay) ) defaults = dict( lr=lr, betas=betas, eps=eps, initial_accumulator=initial_accumulator, weight_decay=weight_decay, ) super(Yogi, self).__init__(params, defaults)
[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 in self.param_groups: for p in group["params"]: if p.grad is None: continue grad = p.grad.data if grad.is_sparse: raise RuntimeError( "Yogi does not support sparse gradients, " "please consider SparseAdam instead" ) state = self.state[p] # State initialization # Followed from official implementation in tensorflow addons: # https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/yogi.py#L118 # noqa # For more details refer to the discussion: # https://github.com/jettify/pytorch-optimizer/issues/77 if len(state) == 0: state["step"] = 0 # Exponential moving average of gradient values state["exp_avg"] = nn.init.constant_( torch.empty_like( p.data, memory_format=torch.preserve_format ), group["initial_accumulator"], ) # Exponential moving average of squared gradient values state["exp_avg_sq"] = nn.init.constant_( torch.empty_like( p.data, memory_format=torch.preserve_format ), group["initial_accumulator"], ) exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] beta1, beta2 = group["betas"] state["step"] += 1 bias_correction1 = 1 - beta1 ** state["step"] bias_correction2 = 1 - beta2 ** state["step"] 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) grad_squared = grad.mul(grad) exp_avg_sq.addcmul_( torch.sign(exp_avg_sq - grad_squared), grad_squared, value=-(1 - beta2), ) denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_( group["eps"] ) step_size = group["lr"] / bias_correction1 p.data.addcdiv_(exp_avg, denom, value=-step_size) return loss