What’s a Transformer in 3 Steps?

A transformer in code and process – in 3 steps

Here’s a simplified 3-step explanation:

Step 1: Input Process (Tokenization and Embedding)

1. Tokenization: The input (such as a sentence or image) is broken down into smaller parts (tokens). In text-based models, these can be words or subword units.
2. Embedding: Each token is transformed into a continuous vector (embedding) that represents its meaning in a high-dimensional space.
3. Positional Encoding: Since transformers don’t inherently process sequences in order, positional encodings are added to the token embeddings to give information about their position in the sequence.

Step 2: Attention Mechanism (Self-Attention)

1. Query, Key, Value Matrices: The input vectors are transformed into three matrices: Query, Key, and Value. These help the model decide which tokens to focus on at each step.
2. Scaled Dot-Product Attention: The Query matrix is multiplied by the Key matrix, and the resulting scores are scaled and passed through a softmax function to determine which parts of the sequence the model should attend to.
3. Weighted Sum: The attention weights are applied to the Value matrix to generate a context-aware representation of the input token, capturing relationships between all tokens in the sequence.

Step 3: Output Generation (Decoding)

1. Layer Stacking: Multiple layers of the attention mechanism and feed-forward networks are stacked together to refine the representation of each token.
2. Decoding (for generation tasks): In decoder-only transformers (like GPT), the model generates one token at a time, using previous tokens to predict the next one until the task is complete.
3. Final Output: The final output is generated in the form of a probability distribution over the possible output tokens, and the most probable token is selected at each step to produce the final result (e.g., a sentence or prediction).

This 3-step process represents the core mechanics of a transformer, combining tokenization, attention, and decoding to achieve powerful results in natural language processing, machine translation, and many other tasks!

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