Multi-Head Latent Attention (MLA) Interpretation

This image is a technical diagram explaining the structure of Multi-Head Latent Attention (MLA).

🎯 Core Concept

MLA is a mechanism that improves the memory efficiency of traditional Multi-Head Attention.

Traditional Approach (Before) vs MLA

Traditional Approach:

• Stores K, V vectors of all past tokens
• Memory usage increases linearly with sequence length

MLA:

• Summarizes past information with a fixed-size Latent vector (c^KV)
• Maintains constant memory usage regardless of sequence length

📊 Architecture Explanation

1. Input Processing

• Starts from Input Hidden State (h_t)

2. Latent Vector Generation

• Latent c_t^Q: For Query of current token (compressed representation)
• Latent c_t^KV: For Key-Value (cached and reused)

3. Query, Key, Value Generation

• Query (q): Generated from current token (h_t)
• Key-Value: Generated from Latent c_t^KV
  • Creates Compressed (C) and Recent (R) versions from c_t^KV
  • Concatenates both for use

4. Multi-Head Attention Execution

• Performs attention computation with generated Q, K, V
• Uses BF16 (Mixed Precision)

✅ Key Advantages

1. Memory Efficiency: Compresses past information into fixed-size vectors
2. Faster Inference: Reuses cached Latent vectors
3. Information Preservation: Maintains performance by combining compressed and recent information
4. Mixed Precision Support: Utilizes FP8, FP32, BF16

🔑 Key Differences

• v_t^R from Latent c_t^KV is not used (purple box on the right side of diagram)
• Value of current token is directly generated from h_t
• This enables efficient combination of compressed past information and current information

This architecture is an innovative approach to solve the KV cache memory problem during LLM inference.

---

Summary

MLA replaces the linearly growing KV cache with fixed-size latent vectors, dramatically reducing memory consumption during inference. It combines compressed past information with current token data through an efficient attention mechanism. This innovation enables faster and more memory-efficient LLM inference while maintaining model performance.

#MultiHeadLatentAttention #MLA #TransformerOptimization #LLMInference #KVCache #MemoryEfficiency #AttentionMechanism #DeepLearning #NeuralNetworks #AIArchitecture #ModelCompression #EfficientAI #MachineLearning #NLP #LargeLanguageModels

With Claude