bitsandbytes/optim/optimizer.py [341:363]:
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        if config['percentile_clipping'] < 100:
            state['gnorm_vec'] = torch.zeros((100,), device=p.device)

        if config['max_unorm'] > 0.0:
            state['unorm_vec'] = torch.zeros((1,), device=p.device)

    @torch.no_grad()
    def update_step(self, group, p, gindex, pindex):
        state = self.state[p]
        grad = p.grad

        config = self.get_config(gindex, pindex, group)

        state['step'] += 1
        step = state['step']

        if config['percentile_clipping'] < 100:
            current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(grad, state['gnorm_vec'], step, config['percentile_clipping'])
        else:
            gnorm_scale = 1.0

        if state['state1'].dtype == torch.float:
            F.optimizer_update_32bit(self.optimizer_name, grad, p, state['state1'], config['betas'][0], config['eps'], step, config['lr'],
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bitsandbytes/optim/optimizer.py [453:476]:
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        if config['percentile_clipping'] < 100:
            state['gnorm_vec'] = torch.zeros((100,), device=p.device)

        if config['max_unorm'] > 0.0:
            state['unorm_vec'] = torch.zeros((1,), device=p.device)


    @torch.no_grad()
    def update_step(self, group, p, gindex, pindex):
        state = self.state[p]
        grad = p.grad

        config = self.get_config(gindex, pindex, group)

        state['step'] += 1
        step = state['step']

        if config['percentile_clipping'] < 100:
            current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(grad, state['gnorm_vec'], step, config['percentile_clipping'])
        else:
            gnorm_scale = 1.0

        if state['state1'].dtype == torch.float:
            F.optimizer_update_32bit(self.optimizer_name, grad, p, state['state1'], config['betas'][0], config['eps'], step, config['lr'],
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