Visualizing Transformers and Attention

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This is the summary note from Grant Sanderson’s talk at TNG Big Tech 2024. My earlir article on transformers can be found here

Transformers and Their Flexibility

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• 📜 Origin: Introduced in 2017 in the “Attention is All You Need” paper, originally for machine translation.
• 🌍 Applications Beyond Translation: Used in transcription (e.g., Whisper), text-to-speech, and even image classification.
• 🤖 Chatbot Models: Focused on models trained to predict the next token in a sequence, generating text iteratively one token at a time.

Next Token Prediction and Creativity

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• 🔮 Prediction Process: Predicts probabilities for possible next tokens, selects one, and repeats the process.
• 🌡️ Temperature Control: Adjusting randomness in token selection affects creativity vs. predictability in outputs.

Tokens and Tokenization

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• 🧩 What are Tokens? Subdivisions of input data (words, subwords, punctuation, or image patches).
• 🔡 Why Not Characters? Using characters increases context size and computational complexity; tokens balance meaning and computational efficiency.
• 📖 Byte Pair Encoding (BPE): A common method for tokenization.

Embedding Tokens into Vectors

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• 📏 Embedding: Tokens are mapped to high-dimensional vectors representing their meaning.
• 🗺️ Contextual Meaning: Vectors evolve through the network to capture context, disambiguate meaning, and encode relationships.

The Attention Mechanism

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• 🔍 Purpose: Enables tokens to “attend” to others, updating their vectors based on relevance.
• 🔑 Key Components:
  • Query Matrix: Encodes what a token is “looking for.”
  • Key Matrix: Encodes how a token responds to queries.
  • Value Matrix: Encodes information passed between tokens.
• 🧮 Calculations:
  • Dot Product: Measures alignment between keys and queries.
  • Softmax: Converts dot products into normalized weights for updates.
• ⛓️ Masked Attention: Ensures causality by blocking future tokens from influencing past ones.

Multi-Headed Attention

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• 💡 Parallel Heads: Multiple attention heads allow different types of relationships (e.g., grammar, semantic context) to be processed simultaneously.
• 🚀 Efficiency on GPUs: Designed to maximize parallelization for faster computation.

Multi-Layer Perceptrons (MLPs)

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• 🤔 Role in Transformers:
  • Add capacity for general knowledge and non-contextual reasoning.
  • Store facts learned during training, e.g., associations like “Michael Jordan plays basketball.”
• 🔢 Parameters: MLPs hold the majority of the model’s parameters.

Training Transformers

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• 📚 Learning Framework:
  • Models are trained on vast datasets using next-token prediction, requiring no manual labels.
  • Cost Function: Measures prediction accuracy using negative log probabilities, guiding parameter updates.
• 🏔️ Optimization: Gradient descent navigates a high-dimensional cost surface to minimize error.
• 🌐 Pretraining: Allows large-scale unsupervised learning before fine-tuning with human feedback.

Embedding Space and High Dimensions

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• 🔄 Semantic Clusters: Similar words cluster together; directions in the space encode relationships (e.g., gender: King - Male + Female = Queen).
• 🌌 High Dimensionality: Embedding spaces have thousands of dimensions, enabling distinct representations of complex concepts.
• 📈 Scaling Efficiency: High-dimensional spaces allow exponentially more “almost orthogonal” directions for encoding meanings.

Practical Applications

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• ✍️ Language Models: Effective for chatbots, summarization, and more due to their generality and parallel processing.
• 🖼️ Multimodal Models: Transformers can integrate text, images, and sound by treating all as tokens in a unified framework.

Challenges and Limitations

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• 📏 Context Size Limitations: Attention grows quadratically with context size, requiring optimization for large contexts.
• ♻️ Inference Redundancy: Token-by-token generation can involve redundant computations; caching mitigates this at inference time.

Engineering and Design

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• 🛠️ Hardware Optimization: Transformers are designed to exploit GPUs’ parallelism for efficient matrix multiplication.
• 🔗 Residual Connections: Baked into the architecture to enhance stability and ease of training.

The Power of Scale

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• 📈 Scaling Laws: Larger models and more data improve performance, often qualitatively.
• 🔄 Self-Supervised Pretraining: Enables training on vast unlabeled datasets before fine-tuning.

BPE (Byte Pair Encoding)

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BPE is a widely used tokenization method in natural language processing (NLP) and machine learning. It is designed to balance between breaking text into characters and full words by representing text as a sequence of subword units. This approach helps models handle rare and unseen words effectively while keeping the vocabulary size manageable.

How BPE Works:

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1. Start with Characters:

   • Initially, every character in the text is treated as a separate token.

2. Merge Frequent Pairs:

   • BPE repeatedly identifies the most frequent pair of adjacent tokens in the training corpus and merges them into a single token. This process is iteratively applied.
   • For example:
     • Input: low, lower, lowest
     • Output Vocabulary: {low_, e, r, s, t}

3. Build Vocabulary:

   • The merging process stops after a predefined number of merges, resulting in a vocabulary of subwords, characters, and some common full words.

Visualizing transformers and attention