"Entropy-Guided Attention for Private LLMs"

Private LLM inference gets a speed boost through clever entropy management.

The paper introduces an entropy-guided framework that reduces nonlinear operations in LLMs while maintaining model stability and attention head diversity for efficient private inference.

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https://arxiv.org/abs/2501.03489

🔍 Original Problem:

→ Private inference for LLMs faces major performance bottlenecks due to expensive nonlinear operations like GELU and LayerNorm, causing high latency and communication costs.

→ A single GELU activation requires 3.9M operations with 1-2KB communication per operation, making private inference impractical.

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🛠️ Solution in this Paper:

→ The paper introduces an information-theoretic framework using Shannon's entropy to analyze nonlinearities in transformer models.

→ They develop an entropy-guided attention mechanism with learnable thresholds for each attention head.

→ The solution replaces LayerNorm with static normalization techniques like weight and spectral normalization.

→ A novel entropy regularization approach prevents entropic overload while maintaining attention head diversity.

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💡 Key Insights:

→ Nonlinearities serve dual purpose: ensuring training stability and maintaining attention head diversity

→ Removing nonlinearities causes entropy collapse in deeper layers

→ Entropy regularization with headwise learnable thresholds effectively mitigates entropic overload

→ Static normalization can replace LayerNorm while avoiding nonlinear operation overheads

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📊 Results:

→ Achieved 3.94x reduction in communication overhead

→ 1.72x speedup in end-to-end private inference latency

→ Entropy regularization improved perplexity by 7.8% in simplified Softmax-only models

→ Demonstrated scalability across different model depths and context lengths

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