Transformers: Understanding the Architecture Behind Modern AI

Transformers are the backbone of modern artificial intelligence models. Since the introduction of the Transformer architecture, advancements in AI have accelerated, particularly in areas like translations and question-answering. This article aims to provide a comprehensive understanding of Transformers, from the foundational concepts to the entire architecture.

The Transformer model is not the first of its kind. Before its inception, Recurrent Neural Networks (RNNs) were popular, processing the current input along with previous inputs. However, RNNs struggled when dealing with long sequences as the impact of earlier inputs weakened over time. Attention mechanisms addressed this issue by focusing on relevant inputs, but they faced limitations when handling sentences with many words.

To tackle these issues, Transformers introduced multi-head attention, enabling the model to capture semantic similarities between tokens more effectively. A Transformer consists of two primary components: the Encoder and the Decoder. Both parts undergo multiple layers, with the original Transformer architecture featuring six encoder and six decoder layers.

Before delving into the main architecture, certain prerequisites must be met. The sentence is first tokenized and then converted into embeddings. For instance, "I am a boy" becomes [I, am, a, boy], which is then transformed into token IDs and embedding vectors. Each token becomes a vector of a specific dimension (e.g., (768, 1024), depending on the model). This numerical representation allows the Transformer to process the words instead of treating them as raw text.

Positional encodings are crucial in Transformers, as they provide information about the position of each token in the sequence. The original Transformer uses sine and cosine functions for this purpose: PE(pos, 2i) = sin(pos / 10000^(2i/d_model)) and PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model)). For example, given the sentence "I love AI" and d_model = 4, the positional encoding for position 1 would be approximately [0.8415, 0.5403, 0.0100, 0.99995].

The most critical component of a Transformer is the Self-Attention mechanism. In a given sentence, each token is projected into a coordinate system, breaking down into three parts: Query (Q), Key (K), and Value (V). Think of Q as "What am I looking for?", K as "See, this is how I look in the coordinate system," and V as "This is what my value in the system contains."

The Query vector for each word is multiplied with the Key vectors for each word using a dot product. The result is then divided by √d_k to prevent large dot products from dominating the probability distribution. Softmax is applied to generate attention weights, which are subsequently multiplied with the corresponding Value vectors. This process is repeated multiple times to capture semantic relationships between tokens in a technique known as multi-head attention.

Multi-head attention entails running several attention heads in parallel, allowing each head to learn distinct types of relationships. For example, one head may focus on subject-verb relationships, while another may concentrate on verb-object relationships. Afterward, the outputs of all heads are concatenated and passed through a linear transformation.

The final step in the Transformer architecture involves residual connections and layer normalization. After the attention operation, the output is added to the original input through a residual connection, followed by layer normalization. This approach ensures that the model maintains its performance during training and helps alleviate issues related to gradient disappearance or explosion.