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Let's build the GPT Tokenizer

Andrej Karpathy2024-02-20
31K views|5 months ago
💫 Short Summary

The video discusses the process of tokenization in large language models, covering the introduction of the byte pair encoding algorithm and its implementation to compress byte sequences. It demonstrates how to find the most common byte pair and replace it with a new token, and provides code examples for implementing the algorithm.The video explains the Byte Pair Encoding (BPE) algorithm, its implementation in Python, and the process of merging tokens using BPE. It also demonstrates the encoding and decoding of text into token sequences using BPE.The video delves into the details of SentencePiece, a subword tokenization algorithm, and its implementation, covering topics such as vocabulary size, model training, and inspection, as well as the handling of unknown tokens and special characters. The speaker also discusses the potential limitations and issues that may arise from the tokenization process.The video explores the complexities of tokenization in natural language processing, discussing issues with partial tokens, the phenomenon of "solid gold Magikarp," and the importance of efficient encoding schemes. It also delves into the code from OpenAI on GPT-2 and provides recommendations for using GPT-4 tokens and vocabulary in applications.

✨ Highlights
📊 Transcript
The speaker discusses the necessity of understanding tokenization in large language models.
00:00
Tokenization is essential to understand in detail, as it is complex and has hidden pitfalls.
In the previous video 'Let's Build GPT from scratch,' a simple tokenization process was used.
A vocabulary of 65 possible characters was created, and a lookup table for converting each character string piece into a token integer was also generated.
The speaker demonstrates the tokenization process using a web app.
03:02
Shows a web app where tokenization is running live in the browser using JavaScript.
Types 'hello world' into the app, and the string is tokenized into 300 tokens.
The app displays the tokens with different colors for each token for clarity.
The speaker explains the tokenization of mathematical expressions and numbers in the GPT-3 model.
06:11
In the GPT-3 model, numbers are tokenized, but the way they are broken up into tokens is arbitrary.
The speaker provides examples of how numbers are tokenized into separate tokens.
The tokenization of the string 'egg' and 'Egg' is also shown, demonstrating that case sensitivity affects the tokens.
The impact of tokenization on non-English languages and code indentation in Python is discussed.
09:03
Non-English languages may have longer token sequences due to the training set imbalance.
Python code with indentation using spaces is inefficiently tokenized, leading to longer sequences.
Tokenization of individual spaces as separate tokens is demonstrated in the Python code.
The video discusses the improvement in handling white spaces for Python in the GPT-4 tokenizer.
12:08
The GPT-4 tokenizer groups more spaces into a single token, which densifies Python code.
The improvement in Python coding ability from GPT-2 to GPT-4 is attributed to the design of the tokenizer.
The video also mentions the desire to feed raw byte sequences into language models, but for now, they have to be compressed using the byte pair encoding algorithm.
The process of encoding text into Unicode and the use of Unicode code points is explained.
15:42
Strings in Python are immutable sequences of Unicode code points.
Unicode code points are defined by the Unicode Consortium, representing over 150,000 characters across 161 scripts.
The 'ord' function in Python is used to access the Unicode code point of a single character.
Unicode text is translated into binary data by UTF-8, UTF-16, and UTF-32 encodings.
UTF-8 is preferred due to its variable-length encoding, which can represent characters using 1 to 4 bytes.
The video discusses the process of merging tokens using a specific function in Python.
00:00
The function takes a list of IDs and a pair to replace, and the pair is replaced with a new index.
It iterates through the IDs, swaps out the pair for the index, and creates a new list.
The function checks for equality at the current position with the pair and appends the replacement index if a match is found.
The speaker demonstrates the process of creating a new vocabulary by merging tokens using the Byte Pair Encoding (BPE) algorithm.
05:47
The video mentions that longer text yields more representative statistics for BPE.
The raw text is encoded into bytes using the UTF-8 encoding.
A function is used to merge two bytes at a time to create a new token integer.
The process is repeated for a specified number of merges to build a vocabulary.
The instructor highlights the code for merging tokens and discusses the implementation in Python.
10:40
The instructor explains the code for merging tokens using the BPE algorithm.
A new token integer is minted for each merge, and the old tokens are replaced with the new token.
The instructor demonstrates the code's output, showing the merge of '101' and '32' into '256'.
The section covers the encoding and decoding process of token sequences using byte pair encoding (BPE) algorithm.
15:20
The speaker explains the implementation of encoding and decoding functions for token sequences.
An issue with decoding single or empty tokens is mentioned, and the solution involves checking the length of the tokens list.
The importance of ensuring valid UTF-8 encoding for decoding is emphasized.
The video discusses the implementation of the Byte Pair Encoding (BPE) algorithm in Python for encoding and decoding text.
20:15
The speaker introduces the Byte Pair Encoding (BPE) algorithm for tokenization.
A Python implementation for encoding and decoding text using BPE is demonstrated.
The speaker explains the process step by step, including tokenization, merging, and decoding.
An example is given to illustrate how the algorithm works in practice.
The tutor explains the BPE algorithm and how to avoid merging tokens that shouldn't be merged by enforcing rules on certain characters.
25:06
The tutor introduces the Byte Pair Encoding (BPE) algorithm and its application in NLP.
An example is provided to illustrate the issue of naively merging tokens that have different semantics due to surrounding characters.
The tutor mentions that OpenAI's GPT-2 uses a modified version of the BPE algorithm to manually enforce merging rules for certain characters.
The tutor showcases the regular expression pattern used in GPT-2's tokenizer to enforce merging rules for specific characters.
The instructor demonstrates a regular expression pattern for matching parts of a string and explains how it enforces separation between tokens.
30:07
The instructor uses the findall method to match the pattern against a string and organize the occurrences into a list.
The pattern consists of optional spaces and letters, matching words in the string.
The pattern allows for the separation of tokens by ensuring that spaces are not considered part of the letters.
The Byte Pair Encoding (BPE) algorithm is implemented to tokenize a string by finding the longest match in the merges dictionary and applying the merge until no more matches are found.
35:08
The implementation checks for the longest match in the merges dictionary and applies the merge.
An issue may arise if there is nothing to merge, in which case the implementation returns the first element of the stats list.
The implementation encodes the text into tokens by finding consecutive pairs of bytes that are allowed to merge according to the merges dictionary.
The speaker demonstrates the functionality of the implementation using test cases.
The speaker discusses how sentences are treated as raw data and explores the challenges of defining an actual sentence in different languages.
01:34:03
Sentences are like individual training examples in the context of language models.
It's difficult to define what an actual sentence is due to the variation in languages.
The concept of 'sentence' may have different meanings in different languages.
The video discusses the training of a file using the 'talk 400. model' and 'talk 400. wab' extensions, and demonstrates how to inspect the vocabulary.
01:35:40
The model file and vocabulary are inspected after training.
The video shows the creation of the file with 'talk 400. model' and 'talk 400. wab' extensions.
The vocabulary size is 400 for the trained text.
The section explores the concept of vocabulary size in the transformer model and the importance of adding new tokens.
01:40:57
The vocabulary size affects the number of rows in the token embedding table.
More tokens require additional rows in the embedding table.
The linear layer at the end of the transformer produces probabilities for the next token in the sequence.
The video discusses the process of introducing new tokens into a vocabulary and the application of gist tokens for parameter efficient fine-tuning.
01:46:29
New tokens can be added to the vocabulary by resizing the embedding.
Parameters for new tokens are trained using distillation.
Gist tokens are used for compressing long prompts and maintaining model performance.
The speaker delves into the tokenization process and its impact on the model's ability to spell words and perform other related tasks.
01:51:50
The model's inability to spell words well is attributed to the tokenization process, where characters are chunked into tokens.
The model struggles with character-level tasks such as reversing strings due to the tokenization method.
Tokenizing numbers also affects the model's ability to perform simple arithmetic.
The speaker demonstrates how the language model handles special tokens and discusses the issue of trailing white space affecting the model's behavior.
01:57:22
The language model may treat special tokens as individual tokens if not handled properly.
Adding space at the end of a string can cause the model to see it as a separate token, leading to unpredictable behavior.
The model's training data may not have seen certain token combinations, causing issues in completing the sequence.
The speaker discusses the issues with partial tokens and mentions the presence of code for dealing with unstable tokens in the TToken repository.
02:03:21
The issues with partial tokens include incomplete characters at the beginning of the next token and having some characters missing in long tokens.
There is a lot of undocumented code dealing with unstable tokens in the TToken repository.
The "solid gold Magikarp" phenomenon is explained as a result of certain tokens not being present in the training data for the language model, leading to undefined behavior when evoked.
02:04:02
The strange tokens in the embedding cluster were found to be associated with specific Reddit user mentions.
These tokens were not a part of the training data for the language model, resulting in them being untrained and leading to undefined behavior when used.
The video suggests that the tokenization dataset may have contained mentions of the Reddit user, but this data was not present in the actual language model training set.
The speaker discusses the behavior of the model when prompted with certain "trigger words" that result in the model exhibiting strange and sometimes inappropriate responses.
02:06:32
The behavior is attributed to tokenization, where certain tokens may not have been present in the training data for the language model.
The speaker explains that the tokens associated with the behavior were from a Reddit user mentioned in the tokenization dataset but not the training data for the language model.
At test time, evoking these tokens leads to undefined behavior in the model.
The speaker talks about the efficiency of different formats and representations in GPT tokenizers, highlighting the differences between JSON and YAML in terms of token density.
02:08:25
JSON is more dense in tokens compared to YAML.
The speaker emphasizes the importance of tokenization density and efficient encoding schemes.
Suggests spending time measuring the token efficiencies of different formats and settings.
The speaker warns about the potential security issues and AI safety concerns related to tokenization but also recommends using GPT-4 tokens and the TockToken library for application and inference.
02:10:34
Acknowledgment of potential security issues and AI safety concerns related to tokenization.
Recommendation to consider using GPT-4 tokens and the TockToken library for application and inference.
The speaker discusses different tokenization methods and recommends using BPE with SentencePiece for training vocabulary, but expresses preference for waiting for an efficient implementation in the future.
02:11:51
Cautioning about the complexity of settings and potential mis-calibration in SentencePiece.
Suggestion to wait for a more efficient implementation in the future.
Mention of the current lack of an ideal training code implementation for tokenization.