Transformer KV Cache Compression Using Gauge Canonicalization
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Solution Overview
Problem
The incompressibility of KV cache states in transformer models leads to a memory bottleneck, limiting deployment scalability and context lengths, and existing compression techniques either require retraining, sacrifice accuracy, or provide unreliable improvements.
Innovation Solution
GaugeKV, a KV cache compression technique based on gauge transformation, reparametrizes model weights using gauge canonicalization and entropy encoding to achieve exact preservation of model function with controlled accuracy-memory tradeoffs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If KV cache compression is applied to reduce memory usage, then memory bottleneck is alleviated and deployment scalability improves, but model accuracy may be compromised
Solution Approach 1:
The patent applies parameter changes by transforming the KV cache from its original representation to a compressed representation through gauge transformation. This involves changing the parameter space of the KV cache while maintaining the essential information needed for accurate predictions, thus reducing memory usage without significantly compromising model accuracy.
Solution Approach 2:
The patent creates a compressed copy of the KV cache that retains the essential information. By making a transformed copy rather than working with the original full-precision KV cache, the system reduces memory requirements while preserving the functional equivalence needed for accurate model predictions.
2Quantity of substance
If existing compression techniques are used, then memory usage is reduced, but they require retraining which increases complexity and time cost
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing the gauge transformation matrices during model initialization. This allows the compression to be applied at inference time without requiring retraining, as the transformation parameters are already prepared in advance.
Solution Approach 2:
The patent introduces an intermediary transformation layer that bridges the original model and the compressed representation. The gauge transformation matrices serve as intermediaries that enable compression without requiring changes to the original model weights, thus avoiding retraining while achieving memory reduction.
3Length of moving object
If KV cache is compressed to serve longer contexts, then context length is extended, but memory access patterns become less predictable
Solution Approach 1:
The patent changes the parameter representation of KV cache through gauge transformation, which reorganizes the data in a way that maintains access pattern predictability even for longer contexts. The transformation preserves the structural relationships that enable efficient memory access while extending the usable context length.
Data Source
AI summary
KV cache for transformer models may be compressed through gauge transformation, entropy encoding, or rank-r approximation. Transformation matrices may be determined for gauge transformation of an attention layer. The query weight matrix and key weight matrix of the head may be transformed using a transformation matrix. The value weight matrix and output weight matrix of the head may be transformed using another transformation matrix. The gauge transformation may produce canonicalized weights. The attention layer may be updated with the canonicalized weights. The canonicalized model may be executed, and canonicalized KV data may be produced during the execution. A portion of the canonicalized KV data may be further compressed entropy encoding and then stored in a cold tail cache. The rest of the canonicalized KV data may be stored in a hot window cache. The canonicalized KV data may be further compressed based on rank-r approximation before or after gauge transformation.