Embedding Representation Versioning for Efficient Training Rollback
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Solution Overview
Problem
Existing embedding representation management methods face high management costs and poor efficiency due to the continuous increase in pre-training manners and data scales, making them unsuitable for multi-version management.
Innovation Solution
A method involving loading an embedding representation from disk into memory, training it based on preset data, determining a new version number, and storing it back on disk, with features like daily dumps, hourly/minute backups, and multi-level differential storage to manage multiple versions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If embedding representations are managed in file form with continuous increase in pre-training manners and data scales, then more embedding versions can be stored, but management costs increase and management efficiency deteriorates
Solution Approach 1:
The patent segments embedding representations by version numbers and organizes them in a structured directory format (e.g., embedding_version1.bin, embedding_version2.bin). This segmentation allows independent management of each version while reducing overall management complexity through systematic organization.
Solution Approach 2:
The patent introduces an embedding management system as an intermediary layer between storage and usage. This system handles version tracking, loading, and management operations automatically, reducing direct management complexity for users while supporting multiple embedding versions.
2Reliability
If all intermediate versions during training are stored, then rollback capability is improved, but storage consumption increases
Solution Approach 1:
The patent applies local quality by storing intermediate versions selectively based on evaluation metrics. Only intermediate versions that meet specific quality thresholds or evaluation conditions are preserved, while others are discarded. This ensures rollback capability for quality versions while minimizing storage consumption.
Solution Approach 2:
The patent implements a strategy of discarding intermediate versions that do not meet evaluation criteria during training, while recovering and storing those that do. This selective retention approach maintains necessary rollback capability while reducing overall storage requirements compared to storing all intermediate versions.
Data Source
AI summary
An embedding representation management method and apparatus are provided. The method includes: loading, in response to a first version number input by a user, a first embedding representation corresponding to the first version number from a disk into a memory; training the first embedding representation based on preset training data to obtain a second embedding representation; determining a second version number of the second embedding representation based on the first version number and a scenario of the training data; and storing the second embedding representation and the second version number on the disk.