Dynamic KV Cache Quantization for LLM Inference Memory
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Solution Overview
Problem
The high computational demands of deep neural networks (DNNs), particularly transformer-based neural networks used in large language models, lead to significant power consumption and runtime costs during training and inference.
Innovation Solution
Implementing a KV cache paging scheme with dynamic variable quantization, where the quantization level of KV cache pages is adjusted based on their importance scores and system load, allowing for different precision levels across KV cache pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform quantization is applied to all KV cache pages, then implementation is simple, but memory footprint cannot be optimized
Solution Approach 1:
The patent applies different quantization levels to different KV cache pages based on their importance scores. Critical pages use higher precision (e.g., FP16) while non-critical pages use lower precision (e.g., FP8), optimizing memory usage without uniformly applying quantization across the board.
Solution Approach 2:
The system dynamically adjusts quantization levels based on runtime importance scores calculated from attention weights. The quantization strategy adapts to the actual usage patterns of different KV cache pages during inference, rather than using a fixed uniform approach.
2Measurement precision
If higher precision is used for all KV cache pages, then inference accuracy is maintained, but memory footprint increases
Solution Approach 1:
High precision is selectively applied only to critical KV cache pages that have high importance scores, while lower precision is applied to less critical pages. This localized approach maintains accuracy where needed while reducing overall memory consumption.
Solution Approach 2:
The system changes the precision parameter dynamically based on the importance score of each KV cache page. Pages with higher importance scores maintain higher precision parameters, while pages with lower scores use reduced precision parameters.
3Quantity of substance
If KV cache pages are evicted to reduce memory usage, then memory footprint decreases, but service availability decreases
Solution Approach 1:
The system continuously monitors importance scores of KV cache pages and uses this feedback to dynamically adjust quantization levels. When memory pressure is detected, the system can safely evict or down-quantize pages with lower importance scores, while maintaining high precision for critical pages that affect service quality.
Solution Approach 2:
The eviction strategy is dynamic and adapts to runtime conditions. The system adjusts which pages to evict or retain based on their importance scores and current system state, optimizing the balance between memory usage and service availability.
Data Source
AI summary
Key-value (KV) cache paging schemes can improve memory management for KV caches by storing a KV cache page having key tensors and value tensors for a fixed number of tokens in a fixed-sized block in the KV cache of a worker. To further improve memory management, the schemes can be modified to implement dynamic variable quantization. Quantization level of a KV cache page can be set based on a runtime importance score of the KV cache page. In addition, the quantization level of the KV cache page can be set based on the system load. The end result is a scheme that can achieve a high compression ratio of KV cache pages in the KV cache. Fitting more KV cache pages in the KV cache can lead to higher inference throughput, higher system-level user capacity, and higher end-to-end service availability.