def step()

in training/optimizer.py [0:0]

• 20 lines of code
• 6 McCabe index (conditional complexity)

    def step(self, closure=None):
        """Performs a single optimization step.
        Args:
          closure (callable, optional): A closure that reevaluates the model
            and returns the loss.
        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

                # Perform stepweight decay
                p.data.mul_(1 - group["lr"] * group["weight_decay"])

                grad = p.grad
                state = self.state[p]
                # State initialization
                if len(state) == 0:
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(p)

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

                # Weight update
                update = exp_avg * beta1 + grad * (1 - beta1)
                p.add_(torch.sign(update), alpha=-group["lr"])
                # Decay the momentum running average coefficient
                exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)

        return loss