Multi-Head Latent Attention – Latent KV-Cache Interpretation

This image explains the Multi-Head Latent Attention (MLA) mechanism and Latent KV-Cache technique for efficient inference in transformer models.

Core Concepts

1. Latent and Residual Split

Q, K, V are decomposed into two components:

• Latent (C): Compressed representation shared across heads (q^c, k^c, v^c)
• Residual (R): Contains detailed information of individual tokens (q^R, k^R)

2. KV Cache Compression

Instead of traditional approach, stores only in compressed form:

• k^R (Latent Key): Stores only Latent Space features
• Achieves significant reduction in KV cache size compared to GQA models

3. Operation Flow

1. Generate Latent c_t^Q from Input Hidden h_t (using FP8)
2. Create q_{t,i}^C, q_{t,i}^R through Latent
3. k^R and v^c are concatenated and fed to Multi-Head Attention
4. Caching during inference: Only k^R and compressed Value stored (shown with checkered icon)
5. Apply RoPE (Rotary Position Embedding) for position information

4. FP8/FP32 Mixed Precision

• FP8: Applied to most matrix multiplications (increases computational efficiency)
• FP32: Applied to critical operations like RoPE (maintains numerical stability)

Key Advantages

• Memory Efficiency: Caches only compressed representations instead of full K, V
• Computational Efficiency: Fast inference using FP8
• Long Sequence Processing: Enables understanding of long contexts through relative position information

Residual & RoPE Explanation

• Residual: The difference between predicted and actual values (“difference between expected and measured values”)
• RoPE: A technique that rotates Q and K vectors based on position, allowing attention scores to be calculated using only relative distances

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Summary

This technique represents a cutting-edge optimization for LLM inference that dramatically reduces memory footprint by storing only compressed latent representations in the KV cache while maintaining model quality. The combination of latent-residual decomposition and mixed precision (FP8/FP32) enables both faster computation and longer context handling. RoPE further enhances the model’s ability to understand relative positions in extended sequences.

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With Cluade