ai_platform_nlu/nlp/reading.py

#! -*- coding: utf-8 -*-
# 10个epoch后在valid上能达到约0.77的分数
# (Accuracy=0.7282149325820084  F1=0.8207266829447049   Final=0.7744708077633566)

import json, os, re

os.environ['TF_KERAS'] = '1'
import numpy as np
from bert4keras.backend import keras, K
from bert4keras.models import build_transformer_model
from bert4keras.tokenizers import Tokenizer, load_vocab
from bert4keras.optimizers import Adam
from bert4keras.snippets import sequence_padding, DataGenerator
from bert4keras.snippets import open
from keras.layers import Lambda
from keras.models import Model
from tqdm import tqdm

import tensorflow as tf

config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True  # 按需分配显存
tf_session = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=config)
tf.compat.v1.keras.backend.set_session(tf_session)

max_p_len = 256
max_q_len = 64
max_a_len = 32
batch_size = 32
epochs = 10


# # bert配置
# config_path = '../models/nezha_gpt/config.json'
# checkpoint_path = '../models/nezha_gpt/gpt.ckpt'
# dict_path = '../models/tokenizer/vocab.txt'

# # 标注数据
# webqa_data = json.load(open('../data/qa/WebQA.json'))
# sogou_data = json.load(open('../data/qa/SogouQA.json'))

# # 保存一个随机序（供划分valid用）
# if not os.path.exists('../random_order.json'):
#     random_order = list(range(len(sogou_data)))
#     np.random.shuffle(random_order)
#     json.dump(random_order, open('../random_order.json', 'w'), indent=4)
# else:
#     random_order = json.load(open('../random_order.json'))

# # 划分valid
# train_data = [sogou_data[j] for i, j in enumerate(random_order) if i % 3 != 0]
# valid_data = [sogou_data[j] for i, j in enumerate(random_order) if i % 3 == 0]
# train_data.extend(train_data)
# train_data.extend(webqa_data)  # 将SogouQA和WebQA按2:1的比例混合

# # 加载并精简词表，建立分词器
# token_dict, keep_tokens = load_vocab(
#     dict_path=dict_path,
#     simplified=True,
#     startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]'],
# )
# tokenizer = Tokenizer(token_dict, do_lower_case=True)


# class data_generator(DataGenerator):
#     """数据生成器
#     """
#     def __iter__(self, random=False):
#         """单条样本格式为
#         输入：[CLS][MASK][MASK][SEP]问题[SEP]篇章[SEP]
#         输出：答案
#         """
#         batch_token_ids, batch_segment_ids, batch_a_token_ids = [], [], []
#         for is_end, D in self.sample(random):
#             question = D['question']
#             answers = [p['answer'] for p in D['passages'] if p['answer']]
#             passage = np.random.choice(D['passages'])['passage']
#             passage = re.sub(u' |、|；|，', ',', passage)
#             final_answer = ''
#             for answer in answers:
#                 if all([
#                     a in passage[:max_p_len - 2] for a in answer.split(' ')
#                 ]):
#                     final_answer = answer.replace(' ', ',')
#                     break
#             a_token_ids, _ = tokenizer.encode(
#                 final_answer, maxlen=max_a_len + 1
#             )
#             q_token_ids, _ = tokenizer.encode(question, maxlen=max_q_len + 1)
#             p_token_ids, _ = tokenizer.encode(passage, maxlen=max_p_len + 1)
#             token_ids = [tokenizer._token_start_id]
#             token_ids += ([tokenizer._token_mask_id] * max_a_len)
#             token_ids += [tokenizer._token_end_id]
#             token_ids += (q_token_ids[1:] + p_token_ids[1:])
#             segment_ids = [0] * len(token_ids)
#             batch_token_ids.append(token_ids)
#             batch_segment_ids.append(segment_ids)
#             batch_a_token_ids.append(a_token_ids[1:])
#             if len(batch_token_ids) == self.batch_size or is_end:
#                 batch_token_ids = sequence_padding(batch_token_ids)
#                 batch_segment_ids = sequence_padding(batch_segment_ids)
#                 batch_a_token_ids = sequence_padding(
#                     batch_a_token_ids, max_a_len
#                 )
#                 yield [batch_token_ids, batch_segment_ids], batch_a_token_ids
#                 batch_token_ids, batch_segment_ids, batch_a_token_ids = [], [], []


def masked_cross_entropy(y_true, y_pred):
    """交叉熵作为loss，并mask掉padding部分的预测
    """
    y_true = K.reshape(y_true, [K.shape(y_true)[0], -1])
    y_mask = K.cast(K.not_equal(y_true, 0), K.floatx())
    cross_entropy = K.sparse_categorical_crossentropy(y_true, y_pred)
    cross_entropy = K.sum(cross_entropy * y_mask) / K.sum(y_mask)
    return cross_entropy


def build_reading_model(config_path: str, ckpt_path: str, keep_tokens: str, weight_path: str):
    model = build_transformer_model(
        config_path,
        ckpt_path,
        with_mlm=True,
        keep_tokens=keep_tokens,  # 只保留keep_tokens中的字，精简原字表
    )
    output = Lambda(lambda x: x[:, 1:max_a_len + 1])(model.output)
    model = Model(model.input, output)
    model.compile(loss=masked_cross_entropy, optimizer=Adam(1e-5))
    model.load_weights(weight_path)
    return model


def get_ngram_set(x, n):
    """生成ngram合集，返回结果格式是:
    {(n-1)-gram: set([n-gram的第n个字集合])}
    """
    result = {}
    for i in range(len(x) - n + 1):
        k = tuple(x[i:i + n])
        if k[:-1] not in result:
            result[k[:-1]] = set()
        result[k[:-1]].add(k[-1])
    return result


def gen_answer(question, passages, model, tokenizer):
    """由于是MLM模型，所以可以直接argmax解码。
    """
    all_p_token_ids, token_ids, segment_ids = [], [], []
    for passage in passages:
        passage = re.sub(u' |、|；|，', ',', passage)
        p_token_ids, _ = tokenizer.encode(passage, maxlen=max_p_len + 1)
        q_token_ids, _ = tokenizer.encode(question, maxlen=max_q_len + 1)
        all_p_token_ids.append(p_token_ids[1:])
        token_ids.append([tokenizer._token_start_id])
        token_ids[-1] += ([tokenizer._token_mask_id] * max_a_len)
        token_ids[-1] += [tokenizer._token_end_id]
        token_ids[-1] += (q_token_ids[1:] + p_token_ids[1:])
        segment_ids.append([0] * len(token_ids[-1]))
    token_ids = sequence_padding(token_ids)
    segment_ids = sequence_padding(segment_ids)
    probas = model.predict([token_ids, segment_ids])
    results = {}
    for t, p in zip(all_p_token_ids, probas):
        a, score = tuple(), 0.
        for i in range(max_a_len):
            idxs = list(get_ngram_set(t, i + 1)[a])
            if tokenizer._token_end_id not in idxs:
                idxs.append(tokenizer._token_end_id)
            # pi是将passage以外的token的概率置零
            pi = np.zeros_like(p[i])
            pi[idxs] = p[i, idxs]
            a = a + (pi.argmax(),)
            score += pi.max()
            if a[-1] == tokenizer._token_end_id:
                break
        score = score / (i + 1)
        a = tokenizer.decode(a)
        if a:
            results[a] = results.get(a, []) + [score]
    results = {
        k: (np.array(v) ** 2).sum() / (sum(v) + 1)
        for k, v in results.items()
    }
    return results


def max_in_dict(d):
    if d:
        return sorted(d.items(), key=lambda s: -s[1])[0][0]


# def predict_to_file(data, filename):
#     """将预测结果输出到文件，方便评估
#     """
#     with open(filename, 'w', encoding='utf-8') as f:
#         for d in tqdm(iter(data), desc=u'正在预测(共%s条样本)' % len(data)):
#             q_text = d['question']
#             p_texts = [p['passage'] for p in d['passages']]
#             a = gen_answer(q_text, p_texts)
#             a = max_in_dict(a)
#             if a:
#                 s = u'%s\t%s\n' % (d['id'], a)
#             else:
#                 s = u'%s\t\n' % (d['id'])
#             f.write(s)
#             f.flush()


# class Evaluator(keras.callbacks.Callback):
#     """评估与保存
#     """
#     def __init__(self):
#         self.lowest = 1e10

#     def on_epoch_end(self, epoch, logs=None):
#         # 保存最优
#         if logs['loss'] <= self.lowest:
#             self.lowest = logs['loss']
#             model.save_weights('../models/qa/best_model.weights')


if __name__ == '__main__':
    model = build_reading_model()
    model.load_weights('../models/qa/best_model.weights')
    questions = "嬴政出生在哪里？"
    passages = [
        "秦始皇嬴政（前259年—前210年），嬴姓，赵氏 ，名政（一说名“正”），又称赵政 、祖龙 ，也有吕政一说（详见“人物争议-姓名之争”目录）。秦庄襄王和赵姬之子。中国古代杰出的政治家、战略家、改革家，首次完成中国大一统的政治人物，也是中国第一个称皇帝的君主。",
        "公元前221年，秦统一六国之后，秦王嬴政认为自己“德兼三皇，功过五帝”，遂采用三皇之“皇”、五帝之“帝”构成“皇帝”的称号，是中国历史上第一个使用“皇帝”称号的君主，所以自称“始皇帝”。",
        "秦始皇有二十余子。长子扶苏，少子胡亥。",
        "嬴政出生在当时赵国的邯郸廓城（在今城内中街以东，丛台西南的朱家巷一带），是当时的秦国王孙异人之子。"]
    print(gen_answer(questions, passages))