Let's build the GPT Tokenizer
The Tokenizer is a necessary and pervasive component of Large Language Models (LLMs), where it translates between strings and tokens (text chunks). Tokenizers are a completely separate stage of the LLM pipeline: they have their own training sets, training algorithms (Byte Pair Encoding), and after training implement two fundamental functions: encode() from strings to tokens, and decode() back from tokens to strings. In this lecture we build from scratch the Tokenizer used in the GPT series from OpenAI. In the process, we will see that a lot of weird behaviors and problems of LLMs actually trace back to tokenization. We'll go through a number of these issues, discuss why tokenization is at fault, and why someone out there ideally finds a way to delete this stage entirely.
Chapters:
00:00:00 intro: Tokenization, GPT-2 paper, tokenization-related issues
00:05:50 tokenization by example in a Web UI (tiktokenizer)
00:14:56 strings in Python, Unicode code points
00:18:15 Unicode byte encodings, ASCII, UTF-8, UTF-16, UTF-32
00:22:47 daydreaming: deleting tokenization
00:23:50 Byte Pair Encoding (BPE) algorithm walkthrough
00:27:02 starting the implementation
00:28:35 counting consecutive pairs, finding most common pair
00:30:36 merging the most common pair
00:34:58 training the tokenizer: adding the while loop, compression ratio
00:39:20 tokenizer/LLM diagram: it is a completely separate stage
00:42:47 decoding tokens to strings
00:48:21 encoding strings to tokens
00:57:36 regex patterns to force splits across categories
01:11:38 tiktoken library intro, differences between GPT-2/GPT-4 regex
01:14:59 GPT-2 encoder.py released by OpenAI walkthrough
01:18:26 special tokens, tiktoken handling of, GPT-2/GPT-4 differences
01:25:28 minbpe exercise time! write your own GPT-4 tokenizer
01:28:42 sentencepiece library intro, used to train Llama 2 vocabulary
01:43:27 how to set vocabulary set? revisiting gpt.py transformer
01:48:11 training new tokens, example of prompt compression
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