def step()

in Dassl.pytorch/dassl/optim/radam.py [0:0]

• 55 lines of code
• 11 McCabe index (conditional complexity)

    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.float()
                if grad.is_sparse:
                    raise RuntimeError(
                        "RAdam does not support sparse gradients"
                    )

                p_data_fp32 = p.data.float()

                state = self.state[p]

                if len(state) == 0:
                    state["step"] = 0
                    state["exp_avg"] = torch.zeros_like(p_data_fp32)
                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
                else:
                    state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
                    state["exp_avg_sq"] = state["exp_avg_sq"].type_as(
                        p_data_fp32
                    )

                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                beta1, beta2 = group["betas"]

                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                state["step"] += 1
                beta2_t = beta2**state["step"]
                N_sma_max = 2 / (1-beta2) - 1
                N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1-beta2_t)

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    if group["weight_decay"] != 0:
                        p_data_fp32.add_(
                            -group["weight_decay"] * group["lr"], p_data_fp32
                        )
                    step_size = (
                        group["lr"] * math.sqrt(
                            (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
                            (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
                        ) / (1 - beta1**state["step"])
                    )
                    denom = exp_avg_sq.sqrt().add_(group["eps"])
                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
                    p.data.copy_(p_data_fp32)
                elif self.degenerated_to_sgd:
                    if group["weight_decay"] != 0:
                        p_data_fp32.add_(
                            -group["weight_decay"] * group["lr"], p_data_fp32
                        )
                    step_size = group["lr"] / (1 - beta1**state["step"])
                    p_data_fp32.add_(-step_size, exp_avg)
                    p.data.copy_(p_data_fp32)

        return loss