基于 HuggingFace的Transformer库，在Colab或Kaggle进行预训练。
鉴于算力限制，选用了较小的英文数据集wikitext-2
目的：跑通Mask语言模型的预训练流程
一、准备
1.1 安装依赖
!pip3 install --upgrade pip
!pip install -U datasets
!pip install accelerate -U
注意：在Kaggle上训练时，最好将datasets更新到最新版（再重启kernel），避免版本低报错
colab和kaggle已经预安装transformers库
1.2 数据准备
加载数据
from datasets import concatenate_datasets, load_dataset
wikitext2 = load_dataset("liuyanchen1015/VALUE_wikitext2_been_done", split="train")
# concatenate_datasets可以合并数据集，这里为了减少训练数据量，只用1个数据集
dataset = concatenate_datasets([wikitext2])
# 将数据集合切分为 90% 用于训练，10% 用于测试
d = dataset.train_test_split(test_size=0.1)
type(d["train"])
可以看到d的类型是
datasets.arrow_dataset.Dataset
def __init__(arrow_table: Table, info: Optional[DatasetInfo]=None, split: Optional[NamedSplit]=None, indices_table: Optional[Table]=None, fingerprint: Optional[str]=None)
A Dataset backed by an Arrow table.
训练集和测试集数量
print(len(d["train"]), len(d["test"]))
5391 600
查看数据
d["train"][0]
sentence、idx和score构成的字典，sentence为文本
{'sentence': "Rowley dismissed this idea given the shattered state of Africaine and instead towed the frigate back to Île Bourbon , shadowed by Astrée and Iphigénie on the return journey . The French frigates did achieve some consolation in pursuing Rowley from a distance , running into and capturing the Honourable East India Company 's armed brig Aurora , sent from India to reinforce Rowley . On 15 September , Boadicea , Africaine and the brigs done arrived at Saint Paul , Africaine sheltering under the fortifications of the harbour while the others put to sea , again seeking to drive away the French blockade but unable to bring them to action . Bouvet returned to Port Napoleon on 18 September , and thus was not present when Rowley attacked and captured the French flagship Vénus and Commodore Hamelin at the Action of 18 September 1810 .",
'idx': 1867,
'score': 1}
接下来将训练和测试数据，分别保存在本地文件中（后续用于训练tokenizer）
def dataset_to_text(dataset, output_filename="data.txt", num=1000):
"""Utility function to save dataset text to disk,
useful for using the texts to train the tokenizer
(as the tokenizer accepts files)"""
cnt = 0
with open(output_filename, "w") as f:
for t in dataset["sentence"]:
print(t, file=f) # 重定向到文件
# print(len(t), t)
cnt += 1
if cnt == num:
break
print("Write {} sample in {}".format(num, output_filename))
# save the training set to train.txt
dataset_to_text(d["train"], "train.txt", len(d["train"]))
# save the testing set to test.txt
dataset_to_text(d["test"], "test.txt", len(d["test"]))
二、训练分词器（Tokenizer）
BERT采用了WordPiece分词，即
根据训练语料先训练一个语言模型，迭代每次在合并词元对时，会对所有词元进行评分，然后选择使训练数据似然增加最多的token对。直到，到达预定的词表大小。
相比BPE的选取频词最高的token对，WordPiece使用了语言模型来计算似然值：
score = 词元对出现的概率 / （第1个词元出现的概率 * 第1个词元出现的概率）。
因此，需要首先这里使用transformers库中的BertWordPieceTokenizer 类来完成分词器（Tokenizer）训练
import os, json
from transformers import BertTokenizerFast
from tokenizers import BertWordPieceTokenizer
special_tokens = [
"[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]", "<S>", "<T>"
]
# 加载之前存储的训练语料（可同时选择训练集和测试集）
# files = ["train.txt", "test.txt"]
# 在training set上训练tokenizer
files = ["train.txt"]
# 设定词表大小，BERT模型vocab size为30522，可自行更改
vocab_size = 30_522
# 最大序列长度, 较小的长度可以增加batch数量、提升训练速度
# 最大序列长度同时也决定了，BERT预训练的PositionEmbedding的大小，决定了其最大推理长度
max_length = 512
# 是否截断处理, 这边开启截断处理
# truncate_longer_samples = False
truncate_longer_samples = True
# 初始化WordPiece tokenizer
tokenizer = BertWordPieceTokenizer()
# 训练tokenizer
tokenizer.train(files=files,
vocab_size=vocab_size,
special_tokens=special_tokens)
# 截断至最大序列长度-512 tokens
tokenizer.enable_truncation(max_length=max_length)
model_path = "pretrained-bert"
# make the directory if not already there
if not os.path.isdir(model_path):
os.mkdir(model_path)
# 存储tokenizer（其实就是词表文件vocab.txt）
tokenizer.save_model(model_path)
# 存储tokenizer的配置信息到config文件,
# including special tokens, whether to lower case and the maximum sequence length
with open(os.path.join(model_path, "config.json"), "w") as f:
tokenizer_cfg = {
"do_lower_case": True,
"unk_token": "[UNK]",
"sep_token": "[SEP]",
"pad_token": "[PAD]",
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"model_max_length": max_length,
"max_len": max_length,
}
json.dump(tokenizer_cfg, f)
# when the tokenizer is trained and configured, load it as BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained(model_path)
vocab.txt内容，包括特殊7个token以及训练得到的tokens
[PAD]
[UNK]
[CLS]
[SEP]
[MASK]
<S>
<T>
!
"
#
$
%
&
'
(
config.json文件
{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "model_max_length": 512, "max_len": 512}
config.json和vocab.txt文件都是我们平时微调BERT或GPT经常见到的文件，其来源就是这个。
三 预处理语料集合
在开始BERT预训前，还需要将预训练语料根据训练好的 Tokenizer进行处理，将文本转换为分词后的结果。
如果文档长度超过512个Token），那么就直接进行截断。
数据处理代码如下所示：
def encode_with_truncation(examples):
"""Mapping function to tokenize the sentences passed with truncation"""
return tokenizer(examples["sentence"], truncation=True, padding="max_length",
max_length=max_length, return_special_tokens_mask=True)
def encode_without_truncation(examples):
"""Mapping function to tokenize the sentences passed without truncation"""
return tokenizer(examples["sentence"], return_special_tokens_mask=True)
# 上述encode函数的使用，取决于truncate_longer_samples变量
encode = encode_with_truncation if truncate_longer_samples else encode_without_truncation
# tokenizing训练集
train_dataset = d["train"].map(encode, batched=True)
# tokenizing测试集
test_dataset = d["test"].map(encode, batched=True)
if truncate_longer_samples:
# remove other columns and set input_ids and attention_mask as PyTorch tensors
train_dataset.set_format(type="torch", columns=["input_ids", "attention_mask"])
test_dataset.set_format(type="torch", columns=["input_ids", "attention_mask"])
else:
# remove other columns, and remain them as Python lists
test_dataset.set_format(columns=["input_ids", "attention_mask", "special_tokens_mask"])
train_dataset.set_format(columns=["input_ids", "attention_mask", "special_tokens_mask"])
truncate_longer_samples变量控制对数据集进行词元处理的 encode() 函数。
- 如果设置为True，则会截断超过最大序列长度（max_length）的句子。否则不截断。
- 如果truncate_longer_samples设为False，需要将没有截断的样本连接起来，并组合成固定长度的向量。
这边开启截断处理（ 不截断处理,代码有为解决的bug ）。便于拼成chunk，batch处理。
Map: 100%
5391/5391 [00:15<00:00, 352.72 examples/s]
Map: 100%
600/600 [00:01<00:00, 442.19 examples/s]
观察分词结果
x = d["train"][10]['sentence'].split(".")[0]
x
In his foreword to John Marriott 's book , Thunderbirds Are Go ! , Anderson put forward several explanations for the series ' enduring popularity : it " contains elements that appeal to most children – danger , jeopardy and destruction
按max_length=33进行截断处理
tokenizer(x, truncation=True, padding="max_length", max_length=33, return_special_tokens_mask=True)
多余token被截断了，没有padding。
- 句子首部加上了（token id为2的[CLS]）
- 句子尾部加上了（token id为3的[SEP]）
{'input_ids': [2, 502, 577, 23750, 649, 511, 1136, 29823, 13, 59, 1253, 18, 4497, 659, 762, 7, 18, 3704, 2041, 3048, 1064, 26668, 534, 492, 1005, 13, 12799, 4281, 32, 555, 8, 4265, 3],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'special_tokens_mask': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]}
按max_length=70进行截断处理
tokenizer(x, truncation=True, padding="max_length", max_length=70, return_special_tokens_mask=True)
不足max_length的token进行了padding（token id为0， 即[PAD]）， 对应attention_mask为0（注意力系数会变成无穷小，即mask掉）
{'input_ids': [2, 11045, 13, 61, 1248, 3346, 558, 8, 495, 4848, 1450, 8, 18, 981, 1146, 578, 17528, 558, 18652, 513, 13102, 18, 749, 1660, 819, 1985, 548, 566, 11279, 579, 826, 3881, 8079, 505, 495, 28263, 360, 18, 16273, 505, 570, 4145, 1644, 18, 505, 652, 757, 662, 2903, 1334, 791, 543, 3008, 810, 495, 817, 8079, 4436, 505, 579, 1558, 3, 0, 0, 0, 0, 0, 0, 0, 0],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
'special_tokens_mask': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
如果，设置为不截断
tokenizer(x, truncation=False, return_special_tokens_mask=True)
比较正常的结果
{'input_ids': [2, 502, 577, 23750, 649, 511, 1136, 29823, 13, 59, 1253, 18, 4497, 659, 762, 7, 18, 3704, 2041, 3048, 1064, 26668, 534, 492, 1005, 13, 12799, 4281, 32, 555, 8, 4265, 2603, 545, 3740, 511, 843, 1970, 178, 8021, 18, 19251, 362, 510, 6681, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'special_tokens_mask': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]}
反tokenize
y = tokenizer.decode(y["input_ids"])
y
直观看到，加入了[CLS]和[SEP]，而且这个例子是无损解码的。
[CLS] in his foreword to john marriott's book, thunderbirds are go!, anderson put forward several explanations for the series'enduring popularity : it " contains elements that appeal to most children – danger, jeopardy and destruction [SEP]
四、模型训练
在构建了处理好的预训练语料后，可以开始模型训练。
直接使用transformers的trainer类型，其代码如下所示：
from transformers import BertConfig, BertForMaskedLM
from transformers import DataCollatorForLanguageModeling, TrainingArguments, Trainer
# 使用config初始化model
model_config = BertConfig(vocab_size=vocab_size,
max_position_embeddings=max_length)
model = BertForMaskedLM(config=model_config)
# 初始化BERT的data collator, 随机Mask 20% (default is 15%)的token
# 掩码语言模型建模(Masked Language Modeling ，MLM)。预测[Mask]token对应的原始token
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer, mlm=True, mlm_probability=0.2
)
# 训练参数
training_args = TrainingArguments(
output_dir=model_path, # output directory to where save model checkpoint
evaluation_strategy="steps", # evaluate each `logging_steps` steps
overwrite_output_dir=True,
num_train_epochs=10, # number of training epochs, feel free to tweak
per_device_train_batch_size=10, # the training batch size, put it as high as your GPU memory fits
gradient_accumulation_steps=8, # accumulating the gradients before updating the weights
per_device_eval_batch_size=64, # evaluation batch size
logging_steps=1000, # evaluate, log and save model checkpoints every 1000 step
save_steps=1000,
# load_best_model_at_end=True, # whether to load the best model (in terms of loss)
# at the end of training
# save_total_limit=3, # whether you don't have much space so you
# let only 3 model weights saved in the disk
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=test_dataset,
)
# 训练模型
trainer.train()
训练日志
wandb: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)
wandb: You can find your API key in your browser here: https://wandb.ai/authorize
wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:
········································
wandb: Appending key for api.wandb.ai to your netrc file: /root/.netrc
Tracking run with wandb version 0.16.3
Run data is saved locally in /kaggle/working/wandb/run-20240303_135646-j2ki48iz
Syncing run crimson-microwave-1 to Weights & Biases (docs)
View project at https://wandb.ai/1506097817gm/huggingface
View run at https://wandb.ai/1506097817gm/huggingface/runs/j2ki48iz
[ 19/670 1:27:00 < 55:32:12, 0.00 it/s, Epoch 0.27/10]
这里我尝试在kaggle CPU上训练了下的，耗时太长
GPU训练时, 切换更大的数据集
开始训练后，可以如下输出结果：
[ 19/670 1:27:00 < 55:32:12, 0.00 it/s, Epoch 0.27/10]
Step Training Loss Validation Loss
1000 6.904000 6.558231
2000 6.498800 6.401168
3000 6.362600 6.277831
4000 6.251000 6.172856
5000 6.155800 6.071129
6000 6.052800 5.942584
7000 5.834900 5.546123
8000 5.537200 5.248503
9000 5.272700 4.934949
10000 4.915900 4.549236
五、模型使用
5.1 直接推理
# 加载模型参数 checkpoint
model = BertForMaskedLM.from_pretrained(os.path.join(model_path, "checkpoint-10000"))
# 加载tokenizer
tokenizer = BertTokenizerFast.from_pretrained(model_path)
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
# 模型推理
examples = [
"Today's most trending hashtags on [MASK] is Donald Trump",
"The [MASK] was cloudy yesterday, but today it's rainy.",
]
for example in examples:
for prediction in fill_mask(example):
print(f"{prediction['sequence']}, confidence: {prediction['score']}")
print("="*50)
进行，mask的token预测（完形填空😺），可以得到如下输出：
```python
today's most trending hashtags on twitter is donald trump, confidence: 0.1027069091796875
today's most trending hashtags on monday is donald trump, confidence: 0.09271949529647827
today's most trending hashtags on tuesday is donald trump, confidence: 0.08099588006734848
today's most trending hashtags on facebook is donald trump, confidence: 0.04266013577580452
today's most trending hashtags on wednesday is donald trump, confidence: 0.04120611026883125
==================================================
the weather was cloudy yesterday, but today it's rainy., confidence: 0.04445931687951088
the day was cloudy yesterday, but today it's rainy., confidence: 0.037249673157930374
the morning was cloudy yesterday, but today it's rainy., confidence: 0.023775646463036537
the weekend was cloudy yesterday, but today it's rainy., confidence: 0.022554103285074234
the storm was cloudy yesterday, but today it's rainy., confidence: 0.019406016916036606
==================================================
参考资料
- 参考《大规模语言模型：从理论到实践》第2.2节预训练代码，将其调通
实现代码：文章来源：https://www.toymoban.com/news/detail-839510.html
- github: https://github.com/DayDreamChaser/llm-todo/blob/main/BERT预训练脚本.ipynb
- kaggle: https://www.kaggle.com/code/leonyiyi/bert-pretraining-on-wikitext2/edit
ToDo
这次的BERT预训练只做了MLM，类似Roberta。文章来源地址https://www.toymoban.com/news/detail-839510.html
- 后续可以根据预训练的模型进行各种任务微调
- 可以将BERT的Trainer内部实现解析
- 详解WordPiece和BPE
- 下一个: GPT2预训练和SFT
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