if self.label_smoother is not None and "labels" in inputs: labels = inputs.pop("labels") else: labels = None outputs = model(**inputs) # Save past state if it exists # TODO: this needs to be fixed and made cleaner later. if self.args.past_index >= 0: self._past = outputs[self.args.past_index] if labels is not None: if unwrap_model(model)._get_name() in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values(): loss = self.label_smoother(outputs, labels, shift_labels=True) else: loss = self.label_smoother(outputs, labels) else: if isinstance(outputs, dict) and "loss" not in outputs: raise ValueError( "The model did not return a loss from the inputs, only the following keys: " f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}." )详细解释一个这个los

这段代码是在训练模型时计算损失函数的部分。首先判断是否有标签（labels）输入，若有则从输入中弹出标签并保存到labels变量中，否则labels为None。然后通过调用模型（model）的forward方法（model(**inputs)）获取模型的输出（outputs）。如果模型有过去状态（past state），则将其保存到self._past中。接下来，如果labels不为None，则使用标签平滑器（label smoother）计算损失（loss）。标签平滑器的作用是在训练过程中减少过拟合现象。如果labels为None，则判断模型的输出是否包含损失值，若不包含则抛出ValueError异常。