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Machine Learning
December 15, 2023
20 min read

Understanding Transformer Architecture in NLP

Deep dive into the transformer architecture that powers modern natural language processing models.

H

HumbleBabs

Data Scientist & AI Engineer

Introduction

The transformer architecture has revolutionized natural language processing, enabling models like BERT, GPT, and T5 to achieve unprecedented performance on a wide range of language tasks.

In this comprehensive guide, we'll explore the transformer architecture in detail, understanding its key components, how it works, and why it has become the foundation for modern NLP systems.

The Evolution of NLP

Understanding the transformer requires context about previous approaches:

RNNs & LSTMs

Sequential processing with memory, but limited by vanishing gradients and parallelization.

CNNs

Parallel processing with local feature extraction, but limited context window.

Transformers

Parallel processing with global attention, enabling long-range dependencies.

Key Components of Transformers

The transformer architecture consists of several key components:

Core Components:

1

Self-Attention Mechanism

Allows tokens to attend to all other tokens in the sequence

2

Multi-Head Attention

Multiple attention heads capture different types of relationships

3

Positional Encoding

Adds position information to token embeddings

4

Feed-Forward Networks

Process attention outputs with non-linear transformations

Self-Attention Mechanism

The heart of the transformer is the self-attention mechanism:

Attention Formula

Self-attention computes attention weights using Query, Key, and Value matrices.

Attention(Q,K,V) = softmax(QK^T/√d_k)V

Where:
- Q: Query matrix
- K: Key matrix  
- V: Value matrix
- d_k: Dimension of keys

Multi-Head Attention

Multiple attention heads allow the model to focus on different aspects of the input.

MultiHead(Q,K,V) = Concat(head_1,...,head_h)W^O

Where each head_i = Attention(QW_i^Q, KW_i^K, VW_i^V)

Scaled Dot-Product Attention

The scaling factor prevents gradients from becoming too large.

def scaled_dot_product_attention(Q, K, V, mask=None):
    scores = torch.matmul(Q, K.transpose(-2, -1))
    scores = scores / math.sqrt(K.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)

Positional Encoding

Since transformers process all tokens in parallel, they need explicit position information:

Sinusoidal Positional Encoding:

•Unique Encoding: Each position gets a unique encoding vector
•Generalization: Can generalize to sequences longer than training data
•Relative Positions: Encodes relative position information through trigonometric functions

Transformer Architecture

The complete transformer architecture combines these components:

Encoder-Decoder Structure

The original transformer uses an encoder-decoder architecture for sequence-to-sequence tasks.

class Transformer(nn.Module):
    def __init__(self, d_model, n_heads, n_layers):
        super().__init__()
        self.encoder = Encoder(d_model, n_heads, n_layers)
        self.decoder = Decoder(d_model, n_heads, n_layers)
        
    def forward(self, src, tgt):
        enc_output = self.encoder(src)
        dec_output = self.decoder(tgt, enc_output)
        return dec_output

Encoder Layer

Each encoder layer consists of self-attention and feed-forward networks with residual connections.

class EncoderLayer(nn.Module):
    def __init__(self, d_model, n_heads):
        super().__init__()
        self.self_attn = MultiHeadAttention(d_model, n_heads)
        self.feed_forward = FeedForward(d_model)
        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
        
    def forward(self, x):
        attn_output = self.self_attn(x)
        x = self.norm1(x + attn_output)
        ff_output = self.feed_forward(x)
        return self.norm2(x + ff_output)

Modern Transformer Variants

Several variants have emerged to address different challenges:

BERT

Bidirectional encoder with masked language modeling and next sentence prediction.

GPT

Decoder-only architecture with autoregressive language modeling.

T5

Unified text-to-text framework for all NLP tasks.

Efficient Transformers

Variants like Linformer and Performer for linear complexity.

Challenges and Limitations

Despite their success, transformers have several limitations:

•Computational Complexity: O(n²) complexity with sequence length
•Memory Requirements: High memory usage for long sequences
•Training Data: Requires massive amounts of training data
•Interpretability: Black-box nature makes debugging difficult

Future Directions

Research continues to address transformer limitations:

✓

Efficient Attention

Linear attention mechanisms and sparse attention

✓

Multimodal Models

Integrating vision, audio, and text

✓

Few-Shot Learning

Learning from minimal examples

Conclusion

The transformer architecture has fundamentally changed the landscape of natural language processing, enabling models that can understand and generate human language with unprecedented accuracy.

Understanding the transformer architecture is essential for anyone working in modern NLP. While challenges remain, ongoing research continues to push the boundaries of what's possible with these powerful models.

Tags:
NLPTransformersDeep LearningBERTAttentionMachine Learning