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import os
import random
import torch
from d2l import torch as d2l
#@save
d2l.DATA_HUB['wikitext-2'] = (
'https://s3.amazonaws.com/research.metamind.io/wikitext/'
'wikitext-2-v1.zip', '3c914d17d80b1459be871a5039ac23e752a53cbe')
#@save
def _read_wiki(data_dir):
file_name = os.path.join(data_dir, 'wiki.train.tokens')
with open(file_name, 'r', encoding = 'utf-8') as f:
lines = f.readlines()
# 大写字母转换为小写字母
paragraphs = [line.strip().lower().split(' . ')
for line in lines if len(line.split(' . ')) >= 2]
random.shuffle(paragraphs)
return paragraphs
# 生成下一句预测任务的数据
#@save
def _get_next_sentence(sentence, next_sentence, paragraphs):
# 预测一个句子对中两个句子是不是相邻
# 50%概率选择相邻句子对，50%概率选择随机句子对
# print('type(paragraphs) : ', type(paragraphs))
# print('len(paragraphs) : ', len(paragraphs))
# print('paragraphs : ', paragraphs)
# print('random.choice(paragraphs) : ', random.choice(paragraphs))
# print('random.choice(random.choice(paragraphs)) : ', random.choice(random.choice(paragraphs)))
if random.random() < 0.5:
is_next = True
else:
# paragraphs是三重列表的嵌套
next_sentence = random.choice(random.choice(paragraphs))
is_next = False
return sentence, next_sentence, is_next
# 下一个句子预测
#@save
def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
nsp_data_from_paragraph = []
for i in range(len(paragraph) - 1):
tokens_a, tokens_b, is_next = _get_next_sentence(paragraph[i], paragraph[i + 1], paragraphs)
# 考虑1个'<cls>'词元和2个'<sep>'词元
# 只会终止执行本次循环中剩下的代码，直接从下一次循环继续执行。
if len(tokens_a) + len(tokens_b) + 3 > max_len:
continue
# 转换为BERT输入
tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
# 生产下一个句子预测数据
nsp_data_from_paragraph.append((tokens, segments, is_next))
return nsp_data_from_paragraph
# 生成遮蔽语言模型任务的数据
# 从BERT输入序列生成遮蔽语言模型的训练样本
#@save
def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds, vocab):
"""
:param tokens: BERT输入序列的词元的列表
:param candidate_pred_positions:不包括特殊词元的BERT输入序列的词元索引的列表（特殊词元在遮蔽语言模型任务中不被预测）
:param num_mlm_preds:预测的数量（选择15%要预测的随机词元）
:param vocab:
:return:替换后的输入词元、发生预测的词元索引和这些预测的标签
"""
# 为遮蔽语言模型的输入创建新的词元副本，其中输入可能包含替换的“<mask>”或随机词元
mlm_input_tokens = [token for token in tokens]
pred_positions_and_labels = []
# 打乱后用于在遮蔽语言模型任务中获取15%的随机词元进行预测
random.shuffle(candidate_pred_positions)
for mlm_pred_position in candidate_pred_positions:
if len(pred_positions_and_labels) >= num_mlm_preds:
break
masked_token = None
# 80%的概率：将词替换为“<mask>”词元
if random.random() < 0.8:
masked_token = '<mask>'
else:
# 10%的概率：保持词不变
if random.random() < 0.5:
masked_token = tokens[mlm_pred_position]
# 10%的概率：用随机词替换该词
else:
masked_token = random.choice(vocab.idx_to_token)
mlm_input_tokens[mlm_pred_position] = masked_token
pred_positions_and_labels.append((mlm_pred_position, tokens[mlm_pred_position]))
return mlm_input_tokens, pred_positions_and_labels
# 将BERT输入序列（tokens）作为输入，并返回输入词元的索引、发生预测的词元索引以及这些预测的标签索引。
#@save
def _get_mlm_data_from_tokens(tokens, vocab):
candidate_pred_positions = []
# tokens是一个字符串列表
for i, token in enumerate(tokens):
# 在遮蔽语言模型任务中不会预测特殊词元
if token in ['<cls>', '<sep>']:
continue
candidate_pred_positions.append(i)
# 遮蔽语言模型任务中预测15%的随机词元
num_mlm_preds = max(1, round(len(tokens) * 0.15))
mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens(
tokens, candidate_pred_positions, num_mlm_preds, vocab)
# pred_positions_and_labels.append((mlm_pred_position, tokens[mlm_pred_position]))
pred_positions_and_labels = sorted(pred_positions_and_labels,key=lambda x: x[0])
pred_positions = [v[0] for v in pred_positions_and_labels]
mlm_pred_labels = [v[1] for v in pred_positions_and_labels]
# vocab[mlm_input_tokens] - > mask
return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]
# 将文本转换为预训练数据集
#@save
def _pad_bert_inputs(examples, max_len, vocab):
max_num_mlm_preds = round(max_len * 0.15)
all_token_ids, all_segments, valid_lens, = [], [], []
# 拼接 all_mlm_weights = 0 否则为1
all_pred_positions, all_mlm_weights, all_mlm_labels = [], [], []
nsp_labels = []
# _get_nsp_data_from_paragraph
# return nsp_data_from_paragraph -- (tokens, segments, is_next)
# _get_mlm_data_from_tokens
# return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]
for (token_ids, pred_positions, mlm_pred_label_ids, segments, is_next) in examples:
all_token_ids.append(torch.tensor(token_ids + [vocab['<pad>']] * (max_len - len(token_ids)), dtype=torch.long))
all_segments.append(torch.tensor(segments + [0] * (max_len - len(segments)), dtype=torch.long))
# valid_lens不包括'<pad>'的计数
valid_lens.append(torch.tensor(len(token_ids), dtype=torch.float32))
all_pred_positions.append(torch.tensor(pred_positions + [0] * (max_num_mlm_preds - len(pred_positions)), dtype=torch.long))
# 填充词元的预测将通过乘以0权重在损失中过滤掉
all_mlm_weights.append(torch.tensor([1.0] * len(mlm_pred_label_ids) + [0.0] * (max_num_mlm_preds - len(pred_positions)),
dtype=torch.float32))
all_mlm_labels.append(torch.tensor(mlm_pred_label_ids + [0] * (max_num_mlm_preds - len(mlm_pred_label_ids)), dtype=torch.long))
nsp_labels.append(torch.tensor(is_next, dtype=torch.long))
return (all_token_ids, all_segments, valid_lens, all_pred_positions, all_mlm_weights, all_mlm_labels, nsp_labels)
#@save
class _WikiTextDataset(torch.utils.data.Dataset):
def __init__(self, paragraphs, max_len):
# 输入paragraphs[i]是代表段落的句子字符串列表；
# 而输出paragraphs[i]是代表段落的句子列表，其中每个句子都是词元列表
paragraphs = [d2l.tokenize(paragraph, token='word') for paragraph in paragraphs]
sentences = [sentence for paragraph in paragraphs for sentence in paragraph]
self.vocab = d2l.Vocab(sentences, min_freq=5, reserved_tokens=['<pad>', '<mask>', '<cls>', '<sep>'])
# 获取下一句子预测任务的数据
examples = []
for paragraph in paragraphs:
# def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
# nsp_data_from_paragraph.append((tokens, segments, is_next))
# return nsp_data_from_paragraph
# extend() 函数用于在列表末尾一次性追加另一个序列中的多个值（用新列表扩展原来的列表）。
examples.extend(_get_nsp_data_from_paragraph(paragraph, paragraphs, self.vocab, max_len))
# 获取遮蔽语言模型任务的数据
# def _get_mlm_data_from_tokens(tokens, vocab):
# return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]
examples = [(_get_mlm_data_from_tokens(tokens, self.vocab) + (segments, is_next)) for tokens, segments, is_next in examples]
# 填充输入
# for (token_ids, pred_positions, mlm_pred_label_ids, segments, is_next) in examples:
(self.all_token_ids, self.all_segments, self.valid_lens, self.all_pred_positions, self.all_mlm_weights,
self.all_mlm_labels, self.nsp_labels) = _pad_bert_inputs(examples, max_len, self.vocab)
def __getitem__(self, idx):
return (self.all_token_ids[idx], self.all_segments[idx],
self.valid_lens[idx], self.all_pred_positions[idx],
self.all_mlm_weights[idx], self.all_mlm_labels[idx],
self.nsp_labels[idx])
def __len__(self):
return len(self.all_token_ids)
#@save
def load_data_wiki(batch_size, max_len):
"""加载WikiText-2数据集"""
num_workers = 0
data_dir = d2l.download_extract('wikitext-2', 'wikitext-2')
paragraphs = _read_wiki(data_dir)
train_set = _WikiTextDataset(paragraphs, max_len)
train_iter = torch.utils.data.DataLoader(train_set, batch_size, shuffle=True, num_workers=num_workers)
return train_iter, train_set.vocab
batch_size, max_len = 512, 64
train_iter, vocab = load_data_wiki(batch_size, max_len)
# tokens_X torch.Size([512, 64])
# segments_X torch.Size([512, 64])
# valid_lens_x torch.Size([512])
# pred_positions_X torch.Size([512, 10]) 预测多少个位置 64*0.15
# mlm_weights_X torch.Size([512, 10])
# mlm_Y torch.Size([512, 10])
# nsp_y torch.Size([512])
for (tokens_X, segments_X, valid_lens_x, pred_positions_X, mlm_weights_X, mlm_Y, nsp_y) in train_iter:
print(tokens_X.shape, segments_X.shape, valid_lens_x.shape, pred_positions_X.shape, mlm_weights_X.shape, mlm_Y.shape, nsp_y.shape)
break