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Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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Summary

This summary is machine-generated.

Task-KV optimizes large language model (LLM) inference by dynamically allocating KV cache memory. This method significantly reduces memory usage while maintaining performance comparable to full KV cache.

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Area of Science:

Artificial Intelligence
Computer Science

Background:

KV cache is crucial for LLM inference acceleration but demands significant memory with increasing input length.
Existing KV cache optimization methods lack adaptability across diverse tasks due to static attention head allocation.

Purpose of the Study:

To introduce Task-KV, a novel method for adaptive KV cache memory allocation in LLMs.
To leverage semantic differentiation of attention heads for task-specific KV cache budgeting.

Main Methods:

Task-KV dynamically allocates KV cache based on attention head semantic importance, distinguishing between heterogeneous and non-heterogeneous heads.
A semantic separator identifies heterogeneous heads contributing significantly to task outputs.
Middle activations are introduced to preserve contextual information from non-heterogeneous heads.

Main Results:

Task-KV significantly outperforms existing KV cache optimization baselines across various benchmarks and model architectures.
Task-KV achieves performance comparable to full KV cache with only 30% memory usage in full-context processing scenarios.

Conclusions:

Task-KV offers an effective and adaptive solution for LLM KV cache memory optimization.
The semantic differentiation approach enhances efficiency and performance in LLM inference.