Transformer Paper Review: Attention is All You Need

Table of Contents

Introduction #

Google’s 2017 paper “Attention is All You Need” revolutionized the NLP field. This post analyzes the core ideas and structure of the Transformer.

Limitations of Previous Approaches #

Problems with RNN/LSTM #

Sequential Processing: Cannot parallelize
Long-range Dependencies: Gradient vanishing issues
Training Speed: Very slow for long sequences

Core Ideas of Transformer #

Self-Attention Mechanism #

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def scaled_dot_product_attention(Q, K, V, mask=None):
    """
    Q: Query matrix
    K: Key matrix
    V: Value matrix
    """
    d_k = K.shape[-1]
    scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_k)
    
    if mask is not None:
        scores = scores.masked_fill(mask == 0, -1e9)
    
    attention_weights = F.softmax(scores, dim=-1)
    output = torch.matmul(attention_weights, V)
    
    return output, attention_weights

Multi-Head Attention #

Uses multiple attention heads in parallel to extract information from different perspectives.

Architecture Structure #

Encoder: Processes input sequence
Decoder: Generates output sequence
Positional Encoding: Adds position information

Why Revolutionary? #

Parallel Processing: Maximizes GPU efficiency
Long-range Dependencies: Direct connections
Scalability: Foundation for BERT, GPT, etc.

Practical Application Tips #

Memory Optimization #

When training large models, attention memory usage scales quadratically with sequence length. Use gradient checkpointing and mixed precision.

Training Stabilization #

Warm-up learning rate schedule and layer normalization are essential.

Conclusion #

Transformer is not just a new architecture—it has become the foundation for various fields beyond NLP, including computer vision (ViT) and multimodal (CLIP).