Understanding Transformer Architecture

Introduction

The transformer architecture is the backbone of modern NLP and generative models, including BERT, GPT, and Vision Transformers.

Introduced in Attention Is All You Need (Vaswani et al., 2017), transformers enable parallel processing of sequences and capture long-range dependencies efficiently.

Why Transformers?

✅ Overcome the limitations of RNNs in sequence modeling.
✅ Enable parallel computation, speeding up training.
✅ Utilize self-attention to capture dependencies regardless of distance in a sequence.

Core Components of Transformer Architecture

1️⃣ Self-Attention

Allows each position in the input sequence to attend to all other positions, enabling the model to weigh the importance of words relative to others dynamically.

Scaled Dot-Product Attention:
Attention(Q, K, V) = softmax((QKᵀ) / sqrt(dₖ)) V

• Q (Query), K (Key), V (Value) are projections of the input.
• The scaling by the square root of the key dimension (denoted as dₖ, the dimensionality of the key/query vectors) prevents large dot products, stabilizing gradients during training.

2️⃣ Multi-Head Attention

Multiple attention heads allow the model to learn information from different representation subspaces jointly.

MultiHead(Q, K, V) = Concat(head₁, …, headₕ) Wᴼ

where each head:
headᵢ = Attention(QWᵢᵠ, KWᵢᴷ, VWᵢⱽ)

3️⃣ Positional Encoding

Since transformers lack recurrence, positional encoding is added to the input embeddings to incorporate sequence order information using sinusoidal or learned embeddings.

4️⃣ Feedforward Networks

Each position’s output from the attention layer passes through a position-wise feedforward neural network.

5️⃣ Layer Normalization and Residual Connections

Each sublayer in the transformer (attention and feedforward) is wrapped with residual connections followed by layer normalization to stabilize and speed up training.

Building a Simple Transformer Block with PyTorch

import torch
import torch.nn as nn

class TransformerBlock(nn.Module):
    def __init__(self, embed_dim, heads, ff_hidden_dim, dropout=0.1):
        super().__init__()
        self.attention = nn.MultiheadAttention(embed_dim, heads, dropout=dropout)
        self.norm1 = nn.LayerNorm(embed_dim)
        self.ff = nn.Sequential(
            nn.Linear(embed_dim, ff_hidden_dim),
            nn.ReLU(),
            nn.Linear(ff_hidden_dim, embed_dim)
        )
        self.norm2 = nn.LayerNorm(embed_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        attn_output, _ = self.attention(x, x, x)
        x = self.norm1(x + self.dropout(attn_output))
        ff_output = self.ff(x)
        x = self.norm2(x + self.dropout(ff_output))
        return x

Applications of Transformers

✅ Natural Language Processing (BERT, GPT, T5).
✅ Computer Vision (Vision Transformers - ViT).
✅ Generative models (Stable Diffusion, DALL-E).

Conclusion

✅ Transformers revolutionized deep learning with efficient, scalable, and powerful architectures.
✅ Self-attention allows for capturing dependencies in sequences without recurrence.
✅ Building blocks like multi-head attention, positional encoding, and feedforward layers are reusable in many architectures.

What’s Next?

✅ Experiment with transformer blocks for text classification and translation tasks.
✅ Learn about pre-trained transformers and fine-tuning them for your tasks.
✅ Explore Vision Transformers for computer vision projects.

Join our SuperML Community to share your transformer experiments and collaborate with others in advanced deep learning.

Happy Learning! 🚀