reference/code/Fast-dLLM/third_party/lmflow/optim/novograd.py

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

import torch
import torch.optim as optim

class NovoGrad(optim.Optimizer):
    def __init__(self, params, lr=0.01, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, grad_averaging=False, amsgrad=False):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            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 not 0.0 <= weight_decay:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, grad_averaging=grad_averaging, amsgrad=amsgrad)
        super(NovoGrad, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(NovoGrad, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault('amsgrad', False)

    def step(self, closure=None):
        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('NovoGrad does not support sparse gradients')
                amsgrad = group['amsgrad']

                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([]).to(state['exp_avg'].device)
                    if amsgrad:
                        state['max_exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                if amsgrad:
                    max_exp_avg_sq = state['max_exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

                norm = torch.sum(torch.pow(grad, 2))

                if exp_avg_sq == 0:
                    exp_avg_sq.copy_(norm)
                else:
                    exp_avg_sq.mul_(beta2).add_(1 - beta2, norm)

                if amsgrad:
                    # 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'])

                grad.div_(denom)
                if group['weight_decay'] != 0:
                    grad.add_(group['weight_decay'], p.data)

                if group['grad_averaging']:
                    grad.mul_(1 - beta1)

                exp_avg.mul_(beta1).add_(grad)

                p.data.add_(-group['lr'], exp_avg)

        return loss