Tokenization is a critical preprocessing step in working with large language models (LLMs). It involves converting raw text into a sequence of tokens that a model can understand. In this section, we focus on training a tokenizer using the Byte Pair Encoding (BPE) algorithm, which is commonly used in state-of-the-art language models like GPT-2 and GPT-4.
Text data comes in the form of strings, which are sequences of Unicode code points. Before feeding this text into an LLM, we must convert it into a sequence of integers representing tokens. Directly using Unicode code points as tokens isn’t practical due to their vast number and variability. Instead, we use tokenization algorithms to create a more manageable and stable set of tokens.
The BPE algorithm works by iteratively merging the most frequent pairs of tokens in the training data. Initially, the tokens are individual characters or bytes from the UTF-8 encoding of the text. As the algorithm proceeds, it creates new tokens representing frequent pairs of characters, thereby compressing the text and reducing the sequence length.
For our example, we use a dataset containing English text, such as a collection of Shakespeare’s works. We start by encoding this text into UTF-8, resulting in a sequence of bytes. We then convert these bytes into a list of integers to facilitate processing.
We define a function getStats to count the frequency of each pair of tokens in our sequence. Using this function, we identify the most common pair to merge next.
Once we have the most frequent pair, we replace every occurrence of this pair with a new token. We define a function to perform this replacement, which takes the list of current tokens and the pair to merge, creating a new list with the pair replaced by the new token index.
We set a target vocabulary size and iteratively perform the merging process until we reach this size. Each merge reduces the sequence length and adds a new token to our vocabulary.
The training process involves applying the BPE algorithm to our entire dataset. We keep track of the merges in a dictionary, effectively building a binary tree representing the tokenization process. This tree will later allow us to encode new text into tokens and decode token sequences back into text.
With the tokenizer trained, we implement functions to encode raw text into token sequences and decode token sequences back into text. These functions rely on the merges dictionary and the vocabulary we created during training.
In addition to the tokens created through BPE, we can introduce special tokens to represent specific concepts or delimiters, such as the end-of-text token used in GPT-2. These special tokens are manually added to the vocabulary and handled separately from the BPE-generated tokens.
Tokenization is a nuanced process with many complexities. It affects the performance and capabilities of LLMs in various tasks, including spelling, arithmetic, and handling non-English languages. Understanding tokenization is essential for anyone working with LLMs, as it influences the model’s behavior and its interaction with different types of data.