Transformer Architecture
Visualize the core components and connectivity of a Transformer encoder block.
Transformer Architecture Theory
The Transformer architecture, introduced in the seminal paper "Attention Is All You Need" (Vaswani et al., 2017), revolutionized natural language processing by abandoning recurrence in favor of a purely attention-based mechanism.
Core Equation: Scaled Dot-Product Attention
Attention(Q, K, V) = softmax(QKT / √dk)V
Key Components
1. Multi-Head Attention
Instead of performing a single attention function, the Transformer maps the queries, keys, and values h times with different, learned linear projections. This allows the model to jointly attend to information from different representation subspaces at different positions.
2. Feed-Forward Networks
Each layer contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between: FFN(x) = max(0, xW1 + b1)W2 + b2.
3. Residual Connections & Normalization
Each sub-layer (attention and feed-forward) has a residual connection around it, followed by layer normalization. The output of each sub-layer is LayerNorm(x + Sublayer(x)). This helps mitigate the vanishing gradient problem in deep networks.
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