def step()

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

• 48 lines of code
• 9 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(
                        "Adam does not support sparse gradients, please consider SparseAdam instead"
                    )

                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"]

                state["step"] += 1

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

                denom = exp_avg_sq.sqrt().add_(group["eps"])
                bias_correction1 = 1 - beta1**state["step"]
                bias_correction2 = 1 - beta2**state["step"]

                if group["warmup"] > state["step"]:
                    scheduled_lr = 1e-8 + state["step"] * group["lr"] / group[
                        "warmup"]
                else:
                    scheduled_lr = group["lr"]

                step_size = (
                    scheduled_lr * math.sqrt(bias_correction2) /
                    bias_correction1
                )

                if group["weight_decay"] != 0:
                    p_data_fp32.add_(
                        -group["weight_decay"] * scheduled_lr, p_data_fp32
                    )

                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)

                p.data.copy_(p_data_fp32)

        return loss