TaskForSQuADQuestionAnswering.py

import collections
import sys

sys.path.append('../')
from model import BertConfig
from model import BertForQuestionAnswering
from utils import LoadSQuADQuestionAnsweringDataset
from utils import logger_init
from transformers import BertTokenizer
from transformers import get_scheduler
import logging
import torch
import os
import time
from tqdm import tqdm


class ModelConfig:
    def __init__(self):
        self.project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        self.dataset_dir = os.path.join(self.project_dir, 'data', 'SQuAD')
        self.pretrained_model_dir = os.path.join(self.project_dir, "bert_base_uncased_english")
        self.vocab_path = os.path.join(self.pretrained_model_dir, 'vocab.txt')
        self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
        self.train_file_path = os.path.join(self.dataset_dir, 'train-v1.1.json')
        self.test_file_path = os.path.join(self.dataset_dir, 'dev-v1.1.json')
        self.model_save_dir = os.path.join(self.project_dir, 'cache')
        self.logs_save_dir = os.path.join(self.project_dir, 'logs')
        self.model_save_path = os.path.join(self.model_save_dir, 'model.pt')
        self.n_best_size = 10  # 对预测出的答案近后处理时，选取的候选答案数量
        self.max_answer_len = 30  # 在对候选进行筛选时，对答案最大长度的限制
        self.is_sample_shuffle = True  # 是否对训练集进行打乱
        self.use_torch_multi_head = False  # 是否使用PyTorch中的multihead实现
        self.batch_size = 12
        self.max_sen_len = 384  # 最大句子长度，即 [cls] + question ids + [sep] +  context ids + [sep] 的长度
        self.max_query_len = 64  # 表示问题的最大长度，超过长度截取
        self.learning_rate = 3.5e-5
        self.doc_stride = 128  # 滑动窗口一次滑动的长度
        self.epochs = 2
        self.model_val_per_epoch = 1
        logger_init(log_file_name='qa', log_level=logging.DEBUG,
                    log_dir=self.logs_save_dir)
        if not os.path.exists(self.model_save_dir):
            os.makedirs(self.model_save_dir)

        # 把原始bert中的配置参数也导入进来
        bert_config_path = os.path.join(self.pretrained_model_dir, "config.json")
        bert_config = BertConfig.from_json_file(bert_config_path)
        for key, value in bert_config.__dict__.items():
            self.__dict__[key] = value
        # 将当前配置打印到日志文件中
        logging.info(" ### 将当前配置打印到日志文件中 ")
        for key, value in self.__dict__.items():
            logging.info(f"### {key} = {value}")


def train(config):
    model = BertForQuestionAnswering(config,
                                     config.pretrained_model_dir)
    if os.path.exists(config.model_save_path):
        loaded_paras = torch.load(config.model_save_path,weights_only=True)
        model.load_state_dict(loaded_paras)
        logging.info("## 成功载入已有模型，进行追加训练......")
    model = model.to(config.device)

    model.train()
    bert_tokenize = BertTokenizer.from_pretrained(config.pretrained_model_dir).tokenize
    data_loader = LoadSQuADQuestionAnsweringDataset(vocab_path=config.vocab_path,
                                                    tokenizer=bert_tokenize,
                                                    batch_size=config.batch_size,
                                                    max_sen_len=config.max_sen_len,
                                                    max_query_length=config.max_query_len,
                                                    max_position_embeddings=config.max_position_embeddings,
                                                    pad_index=config.pad_token_id,
                                                    is_sample_shuffle=config.is_sample_shuffle,
                                                    doc_stride=config.doc_stride)
    train_iter, test_iter, val_iter = \
        data_loader.load_train_val_test_data(train_file_path=config.train_file_path,
                                             test_file_path=config.test_file_path,
                                             only_test=False)
    optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
    lr_scheduler = get_scheduler(name='linear',
                                 optimizer=optimizer,
                                 num_warmup_steps=int(len(train_iter) * 0),
                                 num_training_steps=int(config.epochs * len(train_iter)))
    max_acc = 0
    for epoch in range(config.epochs):
        losses = 0
        start_time = time.time()
        for idx, (batch_input, batch_seg, batch_label, _, _, _, _) in enumerate(train_iter):
            batch_input = batch_input.to(config.device)  # [src_len, batch_size]
            batch_seg = batch_seg.to(config.device)
            batch_label = batch_label.to(config.device)
            padding_mask = (batch_input == data_loader.PAD_IDX).transpose(0, 1)
            loss, start_logits, end_logits = model(input_ids=batch_input,
                                                   attention_mask=padding_mask,
                                                   token_type_ids=batch_seg,
                                                   position_ids=None,
                                                   start_positions=batch_label[:, 0],
                                                   end_positions=batch_label[:, 1])
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            losses += loss.item()
            acc_start = (start_logits.argmax(1) == batch_label[:, 0]).float().mean()
            acc_end = (end_logits.argmax(1) == batch_label[:, 1]).float().mean()
            acc = (acc_start + acc_end) / 2
            if idx % 10 == 0:
                logging.info(f"Epoch: {epoch}, Batch[{idx}/{len(train_iter)}], "
                             f"Train loss :{loss.item():.3f}, Train acc: {acc:.3f}")
            if idx % 100 == 0:
                y_pred = [start_logits.argmax(1), end_logits.argmax(1)]
                y_true = [batch_label[:, 0], batch_label[:, 1]]
                show_result(batch_input, data_loader.vocab.itos,
                            y_pred=y_pred, y_true=y_true)
        end_time = time.time()
        train_loss = losses / len(train_iter)
        logging.info(f"Epoch: {epoch}, Train loss: "
                     f"{train_loss:.3f}, Epoch time = {(end_time - start_time):.3f}s")
        if (epoch + 1) % config.model_val_per_epoch == 0:
            acc = evaluate(val_iter, model,
                           config.device,
                           data_loader.PAD_IDX,
                           inference=False)
            logging.info(f" ### Accuracy on val: {round(acc, 4)} max :{max_acc}")
            if acc > max_acc:
                max_acc = acc
            torch.save(model.state_dict(), config.model_save_path)


def evaluate(data_iter, model, device, PAD_IDX, inference=False):
    model.eval()
    with torch.no_grad():
        acc_sum, n = 0.0, 0
        all_results = collections.defaultdict(list)
        for batch_input, batch_seg, batch_label, batch_qid, _, batch_feature_id, _ in tqdm(data_iter):
            batch_input = batch_input.to(device)  # [src_len, batch_size]
            batch_seg = batch_seg.to(device)
            batch_label = batch_label.to(device)
            padding_mask = (batch_input == PAD_IDX).transpose(0, 1)
            start_logits, end_logits = model(input_ids=batch_input,
                                             attention_mask=padding_mask,
                                             token_type_ids=batch_seg,
                                             position_ids=None)
            # 将同一个问题下的所有预测样本的结果保存到一个list中，这里只对batchsize=1时有用
            all_results[batch_qid[0]].append([batch_feature_id[0],
                                              start_logits.cpu().numpy().reshape(-1),
                                              end_logits.cpu().numpy().reshape(-1)])
            if not inference:
                acc_sum_start = (start_logits.argmax(1) == batch_label[:, 0]).float().sum().item()
                acc_sum_end = (end_logits.argmax(1) == batch_label[:, 1]).float().sum().item()
                acc_sum += (acc_sum_start + acc_sum_end)
                n += len(batch_label)
        model.train()
        if inference:
            return all_results
        return acc_sum / (2 * n)


def show_result(batch_input, itos, num_show=5, y_pred=None, y_true=None):
    """
    本函数的作用是在训练模型的过程中展示相应的结果
    :param batch_input:
    :param itos:
    :param num_show:
    :param y_pred:
    :param y_true:
    :return:
    """
    count = 0
    batch_input = batch_input.transpose(0, 1)  # 转换为[batch_size, seq_len]形状
    for i in range(len(batch_input)):  # 取一个batch所有的原始文本
        if count == num_show:
            break
        input_tokens = [itos[s] for s in batch_input[i]]  # 将question+context 的ids序列转为字符串
        start_pos, end_pos = y_pred[0][i], y_pred[1][i]
        answer_text = " ".join(input_tokens[start_pos:(end_pos + 1)]).replace(" ##", "")
        input_text = " ".join(input_tokens).replace(" ##", "").split('[SEP]')
        question_text, context_text = input_text[0], input_text[1]

        logging.info(f"### Question: {question_text}")
        logging.info(f"  ## Predicted answer: {answer_text}")
        start_pos, end_pos = y_true[0][i], y_true[1][i]
        true_answer_text = " ".join(input_tokens[start_pos:(end_pos + 1)])
        true_answer_text = true_answer_text.replace(" ##", "")
        logging.info(f"  ## True answer: {true_answer_text}")
        logging.info(f"  ## True answer idx: {start_pos.cpu(), end_pos.cpu()}")
        count += 1


def inference(config):
    bert_tokenize = BertTokenizer.from_pretrained(config.pretrained_model_dir).tokenize
    data_loader = LoadSQuADQuestionAnsweringDataset(vocab_path=config.vocab_path,
                                                    tokenizer=bert_tokenize,
                                                    batch_size=1,  # 只能是1
                                                    max_sen_len=config.max_sen_len,
                                                    doc_stride=config.doc_stride,
                                                    max_query_length=config.max_query_len,
                                                    max_answer_length=config.max_answer_len,
                                                    max_position_embeddings=config.max_position_embeddings,
                                                    pad_index=config.pad_token_id,
                                                    n_best_size=config.n_best_size)
    test_iter, all_examples = data_loader.load_train_val_test_data(test_file_path=config.test_file_path,
                                                                   only_test=True)
    model = BertForQuestionAnswering(config,
                                     config.pretrained_model_dir)
    if os.path.exists(config.model_save_path):
        loaded_paras = torch.load(config.model_save_path,weights_only=True)
        model.load_state_dict(loaded_paras)
        logging.info("## 成功载入已有模型，开始进行推理......")
    else:
        raise ValueError(f"## 模型{config.model_save_path}不存在，请检查路径或者先训练模型......")

    model = model.to(config.device)
    all_result_logits = evaluate(test_iter, model, config.device,
                                 data_loader.PAD_IDX, inference=True)
    data_loader.write_prediction(test_iter, all_examples,
                                 all_result_logits, config.dataset_dir)
    logging.info(f"## 推理完毕, 预测结果已经写入： {config.dataset_dir}")
    # 运行结束以后会在 SQuAD 数据集所在的目录下生成两个文件 best_result.json 和 best_n_result.json
    # 然后去该目录下 data/SQuAD 运行 python evaluate-v1.1.py dev-v1.1.json best_result.json
    # 就可以查看评估结果 {"exact_match": 80.82308420056765, "f1": 88.36083293486375}
    # 上述结果使用当前 config 中的参数即可，勿需任何修改！


def interaction(config):
    bert_tokenize = BertTokenizer.from_pretrained(config.pretrained_model_dir)

    model = BertForQuestionAnswering(config,
                                     config.pretrained_model_dir)
    if os.path.exists(config.model_save_path):
        loaded_paras = torch.load(config.model_save_path)
        model.load_state_dict(loaded_paras)
        logging.info("## 成功载入已有模型，开始进行推理......")
    else:
        logging.error(" \n ### -----------正在随机初始化一个模型进行测试使用  ------------ ###")
    while True:
        desc = input("####: 请输出文本描述：")
        question = input("####: 请输出问题：")

        # desc = "Architecturally, the school has a Catholic character. Atop the Main Building's gold dome is a"
        # question = "What is the school's name?"
        desc_token_ids = bert_tokenize(desc)["input_ids"]
        question_token_ids = bert_tokenize(question)["input_ids"]
        input_token = question_token_ids + desc_token_ids[1:]
        seg = [0] * len(question_token_ids) + [1] * (len(desc_token_ids) - 1)
        input_tensor = torch.tensor(input_token, dtype=torch.long).reshape(-1, 1)
        seg_tensor = torch.tensor(seg, dtype=torch.long).reshape(-1, 1)

        start_logits, end_logits = model(input_ids=input_tensor,
                                         attention_mask=None,
                                         token_type_ids=seg_tensor,
                                         position_ids=None)

        start_ids = torch.argsort(start_logits, dim=1).tolist()[0][::-1]
        end_ids = torch.argsort(end_logits, dim=1).tolist()[0][::-1]
        pretty_print(bert_tokenize, input_token, start_ids, end_ids)


def pretty_print(bert_tokenize, input_token, start_ids, end_ids):
    # print(input_token)
    first_sep_idx = input_token.index(102)
    for start_id in start_ids:
        for end_id in end_ids:
            if start_id <= first_sep_idx or end_id <= first_sep_idx or start_id >= end_id \
                    or start_id >= len(input_token) or end_id >= len(input_token):
                continue
            print(start_id, end_id)
            answer_text_idx = input_token[start_id:(end_id + 1)]
            answer_text = bert_tokenize.decode(answer_text_idx)
            print("答案：", answer_text)
            return


if __name__ == '__main__':
    model_config = ModelConfig()
    train(config=model_config)
    inference(model_config)
    # interaction(model_config)