My distillation of how Transformer Models work
My distillation of how Transformer Models work
Five months of tinkering, trying to to undertand the black box phenomenon in transformer models by exploring GPT2 - its Tokens, Activation Values, and the Interconnected Web
GPT-2, developed by OpenAI, is a transformer-based model widely used for text generation. In this blog post, we will explore the inner workings of GPT-2 and unravel how tokens, activation values, and the attention mechanism interplay to produce coherent and contextually relevant text.
Tokenization and Vocabulary Library
GPT-2 has a vocabulary library of approximately 52,257 tokens. The text input to the model is broken down into these tokens, which represent the basic units of meaning in language.
For example, the sentence "I love programming" might be tokenized into ["I", " love", " programming"]. Each of these tokens corresponds to an integer based on the model's 52k vocabulary.
The Vocabulary Library and Interconnected Web
The vocabulary library is not just a static list of words. It's the starting point of an interconnected web of transformations that ultimately leads to the understanding of the text input.
Embedding Layer: The integer representation of tokens is mapped to continuous vectors through the embedding layer. This mapping connects the discrete tokens to a higher-dimensional space, enabling the model to capture more complex relationships.
Hidden Layers and Activation Values: As the vectors pass through the model's hidden layers, they undergo transformations that produce activation values. These activation values, ranging from large numbers to 14 decimal places, represent the abstract essence of the tokens, capturing their contextual information.
Attention Mechanism: The attention mechanism further connects the tokens, allowing the model to consider the relationship between different tokens in the sequence. This mechanism can highlight important relationships and modulate the activation values accordingly.
The interconnected web of 52k tokens and the corresponding transformations within the neural network enables GPT-2 to generate text that mirrors human-like understanding and creativity.
Exploring Activation Values
The activation values are not isolated numbers corresponding to individual tokens. They are part of a network where each value is influenced by the token's context, the attention mechanism, and the model's learned understanding of language.
These values are computed through the model's layers and are dynamic, meaning that the same token might have different activation values depending on its context within the input text.
Attention Weights
The attention mechanism allows the model to focus on different parts of the input text when processing it. The Q, K, V weights in the transformer layers facilitate this focus, enabling the model to selectively emphasize or ignore certain tokens.
This selective attention modifies the activation values, allowing the model to capture the nuanced relationships between tokens, such as syntactic dependencies or semantic meanings.
Conclusion
GPT-2's ability to understand and generate human-like text is a result of the intricate interplay between its vocabulary of 52k tokens, the activation values, and the attention mechanism. This interconnected web of components forms a dynamic and adaptable system that mirrors human language processing, making GPT-2 a powerful tool for various natural language processing tasks.
To recap:
Tokenization: The input text is tokenized into discrete units, and each token corresponds to an integer based on the model's vocabulary of 52k tokens.
Embedding Layer: These integers are then mapped to continuous vectors in the embedding layer. This layer essentially represents a 52k x d matrix, where d is the dimension of the embeddings (e.g., 768 for GPT-2). This transformation connects the discrete tokens to continuous space, allowing for a richer representation.
Hidden Layers and Activation Values: As the continuous vectors pass through the hidden layers of the neural network, they undergo a series of linear transformations and non-linear activations. The activation values in these layers represent the token's information in a higher-dimensional, abstract space.
Attention Mechanism: The attention mechanism within the transformer layers allows for relationships between different tokens to be captured. This further connects the tokens into an interconnected web, where the understanding of one token may depend on the context of others.
Final Outputs: The interconnected web of activation values is finally used to make predictions, such as generating the next token in a sequence. The relationship between tokens and the interconnected web of activation values is dynamic, allowing the model to generate coherent and contextually relevant text.
From the embedding layer to the hidden states and attention weights, every step in the process contributes to the model's ability to comprehend and generate coherent text. The seamless integration of these components showcases the elegance and complexity of transformer models like GPT-2.
In closing, it seems to me that this technology is one of the most sophisticated data compression and retrieval systems I have ever encountered. It resonates more with biology than computer science to me. I am still in the process of implementing these concepts through code in my alignment work, and I plan to write more about this revolutionary technology as I move forward.