src/nli/training.py [868:913]:
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                outputs = model(batch['input_ids'],
                                attention_mask=batch['attention_mask'],
                                labels=batch['y'])
            else:
                outputs = model(batch['input_ids'],
                                attention_mask=batch['attention_mask'],
                                token_type_ids=batch['token_type_ids'],
                                labels=batch['y'])

            loss, logits = outputs[:2]

            uid_list.extend(list(batch['uid']))
            y_list.extend(batch['y'].tolist())
            pred_list.extend(torch.max(logits, 1)[1].view(logits.size(0)).tolist())
            logits_list.extend(logits.tolist())

    assert len(pred_list) == len(logits_list)
    assert len(pred_list) == len(logits_list)

    result_items_list = []
    for i in range(len(uid_list)):
        r_item = dict()
        r_item['uid'] = uid_list[i]
        r_item['logits'] = logits_list[i]
        r_item['predicted_label'] = id2label[pred_list[i]]

        result_items_list.append(r_item)

    return result_items_list


def debug_node_info(args):
    names = ['global_rank', 'local_rank', 'node_rank']
    values = []

    for name in names:
        if name in args:
            values.append(getattr(args, name))
        else:
            return "Pro:No node info "

    return "Pro:" + '|'.join([f"{name}:{value}" for name, value in zip(names, values)]) + "||Print:"


if __name__ == '__main__':
    main()
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src/nli/training_extra.py [854:899]:
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                outputs = model(batch['input_ids'],
                                attention_mask=batch['attention_mask'],
                                labels=batch['y'])
            else:
                outputs = model(batch['input_ids'],
                                attention_mask=batch['attention_mask'],
                                token_type_ids=batch['token_type_ids'],
                                labels=batch['y'])

            loss, logits = outputs[:2]

            uid_list.extend(list(batch['uid']))
            y_list.extend(batch['y'].tolist())
            pred_list.extend(torch.max(logits, 1)[1].view(logits.size(0)).tolist())
            logits_list.extend(logits.tolist())

    assert len(pred_list) == len(logits_list)
    assert len(pred_list) == len(logits_list)

    result_items_list = []
    for i in range(len(uid_list)):
        r_item = dict()
        r_item['uid'] = uid_list[i]
        r_item['logits'] = logits_list[i]
        r_item['predicted_label'] = id2label[pred_list[i]]

        result_items_list.append(r_item)

    return result_items_list


def debug_node_info(args):
    names = ['global_rank', 'local_rank', 'node_rank']
    values = []

    for name in names:
        if name in args:
            values.append(getattr(args, name))
        else:
            return "Pro:No node info "

    return "Pro:" + '|'.join([f"{name}:{value}" for name, value in zip(names, values)]) + "||Print:"


if __name__ == '__main__':
    main()
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