def step()

in benchmarks/experimental/experimental_async_approaches.py [0:0]

• 45 lines of code
• 13 McCabe index (conditional complexity)

    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        for group in self.param_groups:
            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data

                amsgrad = group.get("amsgrad", False)

                p_data = p.data

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state["step"] = 0
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(p_data)
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros_like(p_data)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state["max_exp_avg_sq"] = torch.zeros_like(p_data)
                else:
                    state["exp_avg"] = state["exp_avg"].to(p_data)
                    state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data)
                    if amsgrad:
                        state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(p_data)

                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

                exp_avg_data = exp_avg.data
                exp_avg_sq_data = exp_avg_sq.data

                # Decay the first and second moment running average coefficient
                exp_avg_data.mul_(beta1).add_(grad, alpha=1 - beta1)
                exp_avg_sq_data.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq_data, out=max_exp_avg_sq_data)
                    # Use the max. for normalizing running avg. of gradient
                    denom = max_exp_avg_sq.sqrt().add_(group["eps"])
                else:
                    denom = exp_avg_sq_data.sqrt().add_(group["eps"])

                bias_correction1 = 1 - beta1 ** state["step"]
                bias_correction2 = 1 - beta2 ** state["step"]
                step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1

                if group["weight_decay"] != 0:
                    p_data.add_(p_data, alpha=-group["weight_decay"] * group["lr"])
                p_data.addcdiv_(exp_avg_data, denom, value=-step_size)

        return loss