1.2. Transformer Internals
🪄 Step 1: Intuition & Motivation
Core Idea: Transformers are like massive communication networks inside your brain — every word (token) can “talk” to every other word, deciding which ones are most relevant for understanding context.
Simple Analogy: Imagine a classroom of students (tokens). Each student listens to everyone else before deciding what they understand. Some students (attention heads) specialize — one focuses on grammar, another on topic, another on emotion. Together, they make sense of the conversation.
🌱 Step 2: Core Concept
Let’s now open the Transformer’s “engine” and peek inside.
Multi-Head Self-Attention — The Heart of the Transformer
Self-attention allows each token to focus on other tokens based on relevance. Instead of processing words sequentially (like RNNs), Transformers process all tokens in parallel and learn which words depend on which.
Each token is projected into three vectors:
- Query (Q) – “What am I looking for?”
- Key (K) – “What do I have to offer?”
- Value (V) – “What information do I carry?”
For every token, the attention mechanism measures how well its Query matches every other token’s Key. This produces an attention map — like a heatmap of “who’s paying attention to whom.”
Then, the values (V) are combined using these attention weights — letting each token “borrow” meaning from others.
And since we have multiple heads (multi-head attention), the model can learn different types of relationships simultaneously — syntactic, semantic, and positional.
Feed-Forward Networks — The Thinking Stage
After attending to other tokens, each token passes through a Feed-Forward Network (FFN) — a small MLP applied independently to every token.
It’s like the model thinking:
“Now that I’ve gathered context, let me process it through my internal logic before I pass it forward.”
The FFN transforms the attended information into richer representations before the next layer begins.
Residual Connections & Layer Normalization — The Stabilizers
Deep models can easily “forget” earlier information or suffer from exploding/vanishing gradients.
Residual connections let each layer add its new insights on top of previous knowledge instead of replacing it. ($\text{Output} = \text{Input} + \text{Layer Output}$)
Layer Normalization ensures that activations remain in a stable numerical range, preventing training divergence.
Think of it like running a marathon with energy drinks (residuals) and regular hydration breaks (layer norms).
Causal Masking — Why GPT Thinks One Word at a Time
GPT is a decoder-only Transformer, meaning it generates text from left to right. Causal masks prevent the model from “cheating” — each token can only attend to previous tokens, never the future ones.
In a 5-word sentence, when generating the 4th word, the model only sees words 1–3. This maintains the natural flow of language and enables generation.
FlashAttention — The Memory Saver
Attention mechanisms normally require $O(n^2)$ memory — every token compares itself to every other token. This quickly explodes with long sequences.
FlashAttention is a clever optimization that:
- Computes attention in chunks to fit GPU memory.
- Uses mathematical tricks to fuse softmax + matrix multiplications efficiently.
- Keeps results exact (unlike approximations).
Result: same output, drastically faster and leaner memory usage — crucial for training 100B+ parameter models.
Rotary Positional Embeddings (RoPE) — Giving Tokens a Sense of Order
Transformers process all tokens simultaneously — meaning they naturally don’t know which token came first. To fix this, we add positional embeddings that inject sequence order.
RoPE (Rotary Positional Embedding) elegantly encodes positions by rotating each token’s vector in a continuous way. It allows the model to generalize better to longer contexts than fixed sinusoidal embeddings.
Think of it like teaching a robot not just who is speaking, but when they spoke in the conversation.
Parallel Transformer Blocks — Making it Lightning Fast
Modern GPT variants (like GPT-4 and Mistral) parallelize the attention and FFN computations, reducing sequential dependencies between them.
Instead of waiting for attention to finish before FFN starts, both are computed in a pipeline — similar to how CPU instruction pipelines overlap tasks for efficiency.
This design improves throughput and lowers latency, enabling faster generation speeds without architectural compromise.
📐 Step 3: Mathematical Foundation
Scaled Dot-Product Attention
- $Q$: Query matrix (what we’re looking for).
- $K$: Key matrix (what each token represents).
- $V$: Value matrix (the information each token carries).
- $d_k$: Dimensionality of the key vectors (used to scale dot products).
🧠 Step 4: Key Ideas & Assumptions
- Every token is context-aware. The meaning of a word depends on all others before it.
- Parallel computation replaces recurrence. All tokens process together, removing sequential bottlenecks.
- Stability is critical. Residuals and normalization make deep models trainable at scale.
- Order matters. Positional encodings ensure the model remembers sequence structure.
⚖️ Step 5: Strengths, Limitations & Trade-offs
- Captures long-range dependencies without recurrence.
- Highly parallelizable — fast on modern GPUs.
- Modular — easy to stack and extend.
- Flexible enough to handle text, code, or multimodal data.
- Attention cost grows quadratically with sequence length.
- Positional embeddings may still fail at extreme context sizes.
- Interpretability remains limited — attention ≠ explanation.
🚧 Step 6: Common Misunderstandings
🚨 Common Misunderstandings (Click to Expand)
- “Self-attention means the model looks at itself.” No — it means each token attends to other tokens in the same sequence.
- “Layer normalization is just rescaling.” It’s also centering — ensuring the distribution of activations stays balanced across layers.
- “Attention shows reasoning.” Not exactly — attention weights show focus, but not logical inference.
🧩 Step 7: Mini Summary
🧠 What You Learned: Transformers use self-attention, normalization, and feed-forward networks to process tokens in parallel, building context-aware representations.
⚙️ How It Works: Each token communicates with others through queries, keys, and values — refined by attention weights and stabilized by residuals and normalization.
🎯 Why It Matters: Understanding this internal structure is crucial — it’s the “engine room” powering GPT’s ability to reason, generate, and scale.