Optimize attention for long sequences
Overview
This question evaluates understanding of efficient attention mechanisms for long-sequence Transformers in the ML System Design domain, including sparse/sliding-window, low-rank/kernelized, recurrent/state-space, KV-cache families, and kernel-level optimizations like FlashAttention, with attention to accuracy–throughput–memory trade-offs and numerical stability. It is commonly asked to assess system-level ML engineering skills for optimizing GPU memory, bandwidth, and throughput under long-context constraints, testing both conceptual understanding of algorithmic trade-offs and practical application knowledge of performance and numerics.
System Design: Efficient Attention for Long Sequences
Context
You are designing or optimizing sequence models that must process long contexts under tight GPU memory and throughput constraints. Full softmax attention scales quadratically with sequence length, which is often impractical beyond a few thousand tokens.
Task
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Survey the main families of efficient attention or long-context mechanisms:
- Sparse/sliding-window patterns
- Low-rank/kernelized approximations
- Recurrent/state-space approaches
- KV-cache optimizations for autoregressive decoding
- Compare accuracy–throughput–memory trade-offs across these families. Give high-level guidance on when each is appropriate.
- Explain at a high level why FlashAttention improves performance (I/O-aware tiling that reduces HBM traffic, fused kernels, selective recomputation) and where it helps most.
- Discuss key constraints and considerations: sequence length, head dimension, memory bandwidth, and numerical/numerics stability.
Assume the audience is familiar with standard Transformer attention but not with specialized kernels or approximate methods.
Solution
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