Hierarchical Embedding Cache for Partial Long-Context Updates
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Solution Overview
Problem
Existing machine-learned models face inefficiencies in updating cached latent values due to the need for full recomputation when context data changes, especially in long contexts or dynamic workflows, leading to high computational costs and latency.
Innovation Solution
A hierarchical embedding tree structure is used to cache context data, allowing partial updates by reusing unaffected embeddings, reducing the need for full cache rebuilds and optimizing memory allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full recomputation is performed when context data changes, then cache accuracy is maintained, but computational cost and latency increase significantly
Solution Approach 1:
The context data is divided into multiple chunks, each with its own embedding stored in the cache. When a change occurs, only the affected chunk's embedding needs to be recomputed and updated, rather than recomputing all embeddings. This segmentation allows maintaining cache accuracy for updated portions while avoiding the high computational cost of full recomputation.
Solution Approach 2:
Different parts of the context data are treated differently based on their update status. The system maintains a mapping between context data and cached embeddings, allowing selective updates only to embeddings corresponding to changed chunks. This local quality approach ensures that updated regions have fresh embeddings while unchanged regions retain their cached values, balancing accuracy and efficiency.
2Reliability
If full cache rebuild is performed when context data changes, then cache consistency is ensured, but time consumption increases
Solution Approach 1:
The cache is structured to store embeddings for individual context chunks separately. When context data changes, the system identifies which chunks are affected and updates only those specific embeddings in the cache, rather than rebuilding the entire cache. This maintains cache consistency for updated portions while significantly reducing update latency.
Solution Approach 2:
The system pre-establishes a mapping between context data and cached embeddings before changes occur. This preliminary organization allows the system to quickly identify which embeddings need updating when changes happen, avoiding the need to scan or rebuild the entire cache structure and thereby reducing update time.
3Quantity of substance
If hierarchical embedding tree is used for caching, then memory efficiency improves, but device complexity increases
Solution Approach 1:
The embedding tree is segmented into multiple levels, with each level representing a different granularity of context data. This segmentation allows the system to store embeddings for various scopes (individual chunks, groups of chunks, etc.) in a hierarchical manner, improving memory efficiency by organizing data structureurally while managing complexity through systematic organization.
Solution Approach 2:
The hierarchical embedding tree uses a nested structure where child nodes represent more detailed context chunks and parent nodes represent aggregated representations. This nesting allows compact storage of embeddings at multiple levels, improving memory usage efficiency while the structured nature of the nesting provides a clear framework for managing the complexity.
Data Source
AI summary
An example method includes obtaining a hierarchical embedding tree that embeds context data in a plurality of embedded representations respectively associated with a plurality of nodes. In the example method, each respective embedded representation associated with each respective terminal node of a plurality of terminal nodes of the plurality of nodes is generated based on a respective chunk of the context data. In the example method, each respective embedded representation associated with each respective non-terminal node of a plurality of non-terminal nodes of the plurality of nodes is generated based on the embedded representations associated with two or more of the plurality of nodes that are child nodes of the respective non-terminal node. The example method includes generating, based on an update to a chunk of the context data, updated embedded representations respectively for a plurality of nodes that are intersected by a path from a terminal node associated with the updated chunk to an ancestor node for the terminal node. The example method includes updating the hierarchical embedding tree using the updated embedded representations. The example method includes caching the updated hierarchical embedding tree for providing inputs to a machine-learned decoder model to generate responses to queries based on the updated context data.