Transformer architecture explained

8 messages · 2 min ago

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You13:00

Can you explain transformer architecture in neural networks? I want to understand the attention mechanism.

Novanova-4o31213:00

Great question! Transformer architecture is the foundation of modern large language models. Let me break it down clearly.

What is a Transformer?

A Transformer is a deep learning architecture introduced in the 2017 paper "Attention is All You Need" by Vaswani et al. It replaced recurrent neural networks (RNNs) for sequence modeling tasks.

The Self-Attention Mechanism

The core innovation is self-attention, which allows the model to weigh the importance of different tokens when processing each token:

python

import torch
import torch.nn.functional as F

def scaled_dot_product_attention(Q, K, V, d_k):
    # Q, K, V are query, key, value matrices
    scores = torch.matmul(Q, K.transpose(-2, -1)) / (d_k ** 0.5)
    attention_weights = F.softmax(scores, dim=-1)
    return torch.matmul(attention_weights, V)

Key Components

1.Multi-Head Attention — runs attention in parallel across multiple "heads" to capture different relationship patterns

2.Positional Encoding — injects sequence order information since attention is order-agnostic

3.Feed-Forward Layers — processes each position independently after attention

4.Layer Normalization — stabilizes training across deep networks

Why It Works

Unlike RNNs, transformers process all tokens in parallel, enabling:

•Better long-range dependencies — attention can connect any two tokens directly

•Parallelization — dramatically faster training on modern GPUs

•Scalability — larger models consistently outperform smaller ones

Would you like me to dive deeper into multi-head attention or the encoder-decoder architecture specifically?

You13:02

Yes, please explain multi-head attention in more detail. How does it differ from single-head attention?

Novanova-4o42813:02

Multi-head attention is one of the most elegant ideas in the transformer. Here's the intuition and implementation:

Single-Head vs Multi-Head

Single-head attention computes one set of attention weights — essentially asking one question about relationships between tokens.

Multi-head attention runs h attention operations in parallel, each with its own learned projection matrices. This lets different heads specialize:

•Head 1 might focus on syntactic relationships (subject-verb agreement)

•Head 2 might focus on coreference (pronouns linking to nouns)

•Head 3 might capture semantic similarity between concepts

python

class MultiHeadAttention(torch.nn.Module):
    def __init__(self, d_model=512, num_heads=8):
        super().__init__()
        self.d_k = d_model // num_heads
        self.num_heads = num_heads
        
        # Learned projections for each head
        self.W_Q = torch.nn.Linear(d_model, d_model)
        self.W_K = torch.nn.Linear(d_model, d_model)
        self.W_V = torch.nn.Linear(d_model, d_model)
        self.W_O = torch.nn.Linear(d_model, d_model)
    
    def forward(self, x):
        batch_size = x.size(0)
        
        # Project and reshape to (batch, heads, seq_len, d_k)
        Q = self.W_Q(x).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        K = self.W_K(x).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        V = self.W_V(x).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        
        # Attention across all heads simultaneously
        attn = scaled_dot_product_attention(Q, K, V, self.d_k)
        
        # Concatenate heads and project back
        attn = attn.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.d_k)
        return self.W_O(attn)

The Key Insight

The total computation is the same as single-head attention (since d_k = d_model / h), but you get h different "perspectives" on the data. GPT-4 uses 96 attention heads — each attending to different patterns in the 128K context window.

This is why transformers are so powerful at understanding nuanced language — they literally look at the same sentence through dozens of different lenses simultaneously.

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