Have you ever wondered how machines understand and process human language?
Do you know what's behind ChatGPT, GPT, Llama and any modern LLM?
The key lies in a groundbreaking technology called Transformers, central to which is the self-attention mechanism.
Let's dive deep into this fascinating world and unravel the secrets of how machines learn to pay attention.
Understanding the Core: Self-Attention in Transformers
What is a Transformer?
Transformers have revolutionized the field of natural language processing.
But what exactly are they?
A Transformer model comprises multiple blocks, each containing layers of attention and feed-forward networks.
These models excel in tasks requiring understanding of context and relationships within the text.
The Heart of the Transformer: Self-Attention
Self-attention, the core mechanism of Transformers, determines the relevance of each word in a sequence to every other word.
It answers a fundamental question: "How much should a word attend to another word in the sequence for a specific task?"
Step 1: Preprocessing Inputs
In a Transformer, inputs like words or tokens are first converted into embeddings.
These embeddings represent each word in a high-dimensional space.
Step 2: Calculating Interaction Scores
Each input embedding passes through linear/dense layers to form queries (Wq), keys (Wk), and values (Wv).
Wq, Wk, and Wv projects each of the input embedding vectors into 3 different vectors: the Queries, the Keys, and the Values.
The keys and queries are used to compute a score matrix that represents the interaction between each token. This is done by taking the dot product of the queries with the keys.
A score matrix is computed, representing interactions between each token. This gives us a sense of how similar the Queries and the Keys are
Step 3: Generating Hidden States
The score matrix from previous step is passed through a softmax function, which normalizes the scores so they can be interpreted as probabilities.
This softmax step is often referred to as Self-attentions.
Output is one vector for each word.
For each of the resulting vectors, we now compute the dot products to the Value vectors of all the other words.
Output is hidden states or context vectors
Multiple Attention Heads
Transformers utilize multiple attention heads, each focusing on different input aspects.
The outputs of these heads are combined, allowing the model to capture varied input data facets.
Practical Application: Attention module in Code
The following Python code demonstrates a basic Attention module using PyTorch:
This code encapsulates the essence of attention mechanisms in neural networks. It's a crucial part of larger architectures, such as Transformers.
Conclusion: The Power of Attention
Transformers, powered by self-attention, have transformed how machines understand language.
By mimicking human attention, they have unlocked new potentials in machine learning and AI.
The journey of understanding these technologies is as exciting as their applications, which continue to evolve and shape the future of AI.
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