bitsandbytes/optim/optimizer.py [469:504]:
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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 [680:715]:
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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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