Transformer Architectures: Beyond the Basics

Transformers have revolutionized natural language processing and are now making waves in computer vision, audio, and multimodal applications. This deep dive explores the architectural innovations that make transformers so powerful.

Fig. 01 — Transformer architectures power modern AI systems through attention mechanisms

The Attention Mechanism

At the heart of every transformer lies the self-attention mechanism, which allows the model to weigh the importance of different parts of the input sequence.

def scaled_dot_product_attention(Q, K, V, mask=None):
    scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(Q.size(-1))
    if mask is not None:
        scores = scores.masked_fill(mask == 0, -1e9)
    attention_weights = F.softmax(scores, dim=-1)
    return torch.matmul(attention_weights, V)

Multi-Head Attention

Multi-head attention allows the model to attend to information from different representation subspaces simultaneously:

• Query (Q): What information are we looking for?
• Key (K): What information is available?
• Value (V): What is the actual information?

Positional Encoding

Since transformers don't have inherent sequence order, positional encodings are crucial:

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1).float()
        div_term = torch.exp(torch.arange(0, d_model, 2).float() *
                           -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe.unsqueeze(0))

Vision Transformers (ViT)

The application of transformers to computer vision has opened new possibilities:

"Vision Transformers treat image patches as sequences, enabling the same powerful attention mechanisms used in NLP to understand visual relationships."

Recent Innovations

Modern transformer variants like Swin Transformers, Perceiver, and CLIP demonstrate the continued evolution of these architectures across domains.

Conclusion

Understanding transformer architectures is essential for anyone working in modern machine learning. The flexibility and power of attention mechanisms continue to drive innovation across AI domains.