HuggingFace 代码加速秘诀，专门用于针对解决numpy矩阵太大，程序中断的问题

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    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:print("predictions:", predictions)print("label_ids:", label_ids)# preds = np.argmax(predictions, axis=2)  # ori，注释preds = predictions   # 改，不用重复计算了print("preds:", preds)batch_size, seq_len = preds.shapeprint("batch_size:", batch_size)print("seq_len:", seq_len)out_label_list = [[] for _ in range(batch_size)]preds_list = [[] for _ in range(batch_size)]for i in range(batch_size):for j in range(seq_len):if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:out_label_list[i].append(label_map[label_ids[i][j]])preds_list[i].append(label_map[preds[i][j]])return preds_list, out_label_list

# 防止numpy矩阵过大，增加的函数，把数据提前运算def preprocess_logits_for_metrics(logits, labels):if isinstance(logits, tuple):# Depending on the model and config, logits may contain extra tensors,# like past_key_values, but logits always come firstlogits = logits[0]return logits.argmax(dim=-1)# Initialize our Trainertrainer = Trainer(model=model,args=training_args,train_dataset=train_dataset,eval_dataset=eval_dataset,compute_metrics=compute_metrics,preprocess_logits_for_metrics=preprocess_logits_for_metrics,)