def compute_loss(self, model, inputs, return_outputs=False): """ How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. """ 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())}." ) # We don't use .loss here since the model may return tuples instead of ModelOutput. loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] return (loss, outputs) if return_outputs else loss详细解释一下上面的代码

这段代码是在Trainer类中的compute_loss方法中实现的。该方法用于计算模型的损失，其默认行为是从模型的输出中获取第一个元素作为损失值。在这段代码中，首先判断是否使用了标签平滑器（label_smoother），如果使用了则将标签从输入中弹出，否则将标签置为None。接着，调用模型的forward方法获取输出，如果模型具有过去的状态，则将其保存在Trainer对象中。如果标签不为None，则使用标签平滑器计算损失值。最后，返回损失值和输出，或者仅返回损失值，具体取决于return_outputs参数的值。如果模型返回的是一个字典，并且其中不包含"loss"键，则会抛出ValueError异常。