def step()

in bitsandbytes/optim/optimizer.py [0:0]

• 19 lines of code
• 8 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()

        overflows = []

        if not self.initialized:
            self.check_overrides()
            self.to_gpu() # needed for fairseq pure fp16 training
            self.initialized = True

        for gindex, group in enumerate(self.param_groups):
            for pindex, p in enumerate(group['params']):
                if p.grad is None:
                    continue
                state = self.state[p]
                if len(state) == 0:
                    self.init_state(group, p, gindex, pindex)

                self.update_step(group, p, gindex, pindex)

        return loss