The Core of Transformers

Self-Attention (also known as Intra-Attention) is the mechanism that allows a model to look at other words in an input sequence to get a better encoding for the word it is currently processing.

Unlike RNNs, which process words one by one, Self-Attention allows every word to "talk" to every other word simultaneously, regardless of their distance.

1. Why do we need Self-Attention?

Consider the sentence: "The animal didn't cross the street because it was too tired."

When a model processes the word "it", it needs to know what "it" refers to. Is it the animal or the street?

• In a standard RNN, if the sentence is long, the model might "forget" about the animal by the time it reaches "it".
• In Self-Attention, the model calculates a score that links "it" strongly to "animal" and weakly to "street".

2. The Three Vectors: Query, Key, and Value

To calculate self-attention, we create three vectors from every input word (embedding) by multiplying it by three weight matrices ($W^Q, W^K, W^V$) that are learned during training.

Vector	Analogy (The Library)	Purpose
Query ($Q$)	The topic you are searching for.	Represents the current word looking at other words.
Key ($K$)	The label on the spine of the book.	Represents the "relevance" tag of all other words.
Value ($V$)	The information inside the book.	Represents the actual content of the word.

3. The Calculation Process

The attention score is calculated through a series of matrix operations:

1. Dot Product: We multiply the Query of the current word by the Keys of all other words.
2. Scaling: We divide by the square root of the dimension of the key ($\sqrt{d_k}$) to keep gradients stable.
3. Softmax: We apply a Softmax function to turn scores into probabilities (weights) that sum to 1.
4. Weighted Sum: We multiply the weights by the Value vectors to get the final output for that word.

$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$

4. Advanced Flow Logic (Mermaid)

The following diagram represents how an input embedding is transformed into an Attention output.

5. Multi-Head Attention

In practice, we don't just use one self-attention mechanism. We use Multi-Head Attention. This involves running several self-attention calculations (heads) in parallel.

• One head might focus on the subject-verb relationship.
• Another head might focus on adjectives.
• Another head might focus on contextual references.

By combining these, the model gets a much richer understanding of the text.

6. Implementation with PyTorch

Modern deep learning frameworks provide highly optimized modules for this.

import torch
import torch.nn as nn

# Embedding dim = 512, Number of heads = 8
multihead_attn = nn.MultiheadAttention(embed_dim=512, num_heads=8)

# Input shape: (sequence_length, batch_size, embed_dim)
query = torch.randn(10, 1, 512)
key = torch.randn(10, 1, 512)
value = torch.randn(10, 1, 512)

attn_output, attn_weights = multihead_attn(query, key, value)

print(f"Output shape: {attn_output.shape}") # [10, 1, 512]

References

• Original Paper: Attention Is All You Need (2017)
• The Illustrated Transformer: Jay Alammar's Blog
• Harvard NLP: The Annotated Transformer

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Self-Attention allows the model to understand the context of a sequence. But how do we stack these layers to build the most powerful models in AI today?