Source code for lmflow.optim.sophia

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

import torch
from torch.optim.optimizer import Optimizer

[docs] class SophiaG(Optimizer): """ Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training. Code from: https://github.com/Liuhong99/Sophia/ """ def __init__(self, params, lr=1e-4, betas=(0.965, 0.99), rho = 0.04, weight_decay=1e-1, *, maximize: bool = False, capturable: bool = False): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) 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 not 0.0 <= rho: raise ValueError("Invalid rho parameter at index 1: {}".format(rho)) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) defaults = dict(lr=lr, betas=betas, rho=rho, weight_decay=weight_decay, maximize=maximize, capturable=capturable) super(SophiaG, self).__init__(params, defaults)
[docs] def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault('maximize', False) group.setdefault('capturable', False) state_values = list(self.state.values()) step_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['step']) if not step_is_tensor: for s in state_values: s['step'] = torch.tensor(float(s['step']))
@torch.no_grad()
[docs] def update_hessian(self): for group in self.param_groups: beta1, beta2 = group['betas'] for p in group['params']: if p.grad is None: continue state = self.state[p] if len(state) == 0: state['step'] = torch.zeros((1,), dtype=torch.float, device=p.device) \ if self.defaults['capturable'] else torch.tensor(0.) state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['hessian'] = torch.zeros_like(p, memory_format=torch.preserve_format) if 'hessian' not in state.keys(): state['hessian'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['hessian'].mul_(beta2).addcmul_(p.grad, p.grad, value=1 - beta2)
@torch.no_grad()
[docs] def step(self, closure=None, bs=5120): loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params_with_grad = [] grads = [] exp_avgs = [] state_steps = [] hessian = [] beta1, beta2 = group['betas'] for p in group['params']: if p.grad is None: continue params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError('SophiaG does not support sparse gradients') grads.append(p.grad) state = self.state[p] # State initialization if len(state) == 0: state['step'] = torch.zeros((1,), dtype=torch.float, device=p.device) \ if self.defaults['capturable'] else torch.tensor(0.) state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['hessian'] = torch.zeros_like(p, memory_format=torch.preserve_format) if 'hessian' not in state.keys(): state['hessian'] = torch.zeros_like(p, memory_format=torch.preserve_format) exp_avgs.append(state['exp_avg']) state_steps.append(state['step']) hessian.append(state['hessian']) if self.defaults['capturable']: bs = torch.ones((1,), dtype=torch.float, device=p.device) * bs # Perform the actual update step here instead of calling SophiaG again for p, grad, exp_avg, h, step in zip(params_with_grad, grads, exp_avgs, hessian, state_steps): if group['weight_decay'] != 0: grad = grad.add(p, alpha=group['weight_decay']) # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) step.add_(1) # Compute the update using the hessian information update = exp_avg.div(1 - beta1 ** step.item()) h_sqrt = h.sqrt().add_(group['rho']) p.addcdiv_(update, h_sqrt, value=-group['lr']) return loss