Transformer Architecture Deep Dive

Introduction

In 2017, Google's team published the groundbreaking paper "Attention Is All You Need", introducing the Transformer architecture. This model not only revolutionized the NLP field but also became the cornerstone of modern large language models.

Self-Attention Mechanism

Self-Attention is the core of Transformer, allowing the model to attend to different positions when processing sequences.

QKV Calculation

def scaled_dot_product_attention(Q, K, V, d_k):
    scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_k)
    attention = F.softmax(scores, dim=-1)
    output = torch.matmul(attention, V)
    return output

Token Generation Process

Tokens are the basic units of information processing in Transformer. During generation:

1. Input Encoding: Tokenize text into token sequences
2. Positional Encoding: Add position information
3. Multi-layer Attention: Extract features through multiple Self-Attention layers
4. Output Prediction: Generate probability distribution for the next token

Practical Applications

Transformer architecture has been widely applied in:
- Machine Translation
- Text Generation
- Code Generation
- Multimodal Understanding

Conclusion

Understanding how Transformer works is crucial for mastering modern AI technology. Tokens, as the basic units of information, play a central role throughout the process.