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Generative AI for Autonomous Driving
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Module 5
Efficient Attention: From MHA to MLA
1
KV Cache Memory Cost for Multi-Head Attention
Quantify the KV cache memory cost for standard multi-head attention as a function of layers, heads, and sequence length
2
Grouped Query Attention: Memory Efficiency Through K/V Head Sharing
Describe Grouped Query Attention (GQA) and how sharing key-value heads reduces memory usage
3
Multi-Head Latent Attention and KV Cache Compression
Explain Multi-head Latent Attention (MLA) and its use of low-rank compression for the KV cache
4
Down-projection and On-the-Fly Up-projection in MLA
Describe the down-projection and on-the-fly up-projection steps in MLA
5
Decoupled RoPE: Separating Content and Positional Components in MLA
Explain the decoupled RoPE strategy in MLA that separates content and positional components
6
MHA, GQA, and MLA: A Comparative Analysis
Compare MHA, GQA, and MLA in terms of memory efficiency, compute cost, and model quality trade-offs
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Modern Transformer Components
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Scaling with Mixture-of-Experts