Compressed Proxy Models for Efficient Unit Testing of Large Machine Learning Systems
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Solution Overview
Problem
Training and testing very large machine learning models (VLMs) are costly and time-consuming, making it challenging to ensure that changes to the dataset, model pipeline, or codebase do not adversely impact the model's behavior, particularly in critical applications like self-driving vehicles.
Innovation Solution
The implementation of a framework for automatic or semi-automatic unit testing using compressed models, which generates a proxy version of the VLM through neural network compression, allowing for cost-effective retraining and retesting while ensuring that the model's behavior remains valid by comparing metadata before and after changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full-scale retraining and retesting of VLMs is performed to ensure model validity after changes, then model behavior reliability is improved, but economic cost and time consumption increase significantly
Solution Approach 1:
The patent creates compressed proxy versions of the original VLM that replicate its behavior for testing purposes. These compressed models serve as copies that can be tested rapidly and cheaply, eliminating the need to retest the full-scale original model while still providing validity guarantees through behavioral comparison
Solution Approach 2:
The patent employs compressed models that are computationally inexpensive and can be rapidly created and discarded. These temporary proxy models are used solely for validation purposes after changes, then discarded once their testing function is complete, avoiding the high cost of repeatedly training and testing full-scale VLMs
2Reliability
If full-scale retraining and retesting of VLMs is performed to ensure model validity after changes, then model behavior reliability is improved, but economic cost increases significantly
Solution Approach 1:
The patent creates compressed proxy versions of the original VLM that replicate its behavior for testing purposes. These compressed models serve as copies that can be tested rapidly and cheaply, eliminating the need to retest the full-scale original model while still providing validity guarantees through behavioral comparison
Solution Approach 2:
The patent employs compressed models that are computationally inexpensive and can be rapidly created and discarded. These temporary proxy models are used solely for validation purposes after changes, then discarded once their testing function is complete, avoiding the high cost of repeatedly training and testing full-scale VLMs
3Productivity
If compressed proxy models are used for unit testing instead of original VLMs, then testing efficiency is improved, but model compression complexity increases
Solution Approach 1:
The patent introduces compressed models as intermediary objects that mediate between the change detection system and the original VLM. These intermediaries perform the actual testing work and can be rapidly created and discarded, protecting the original expensive model from repeated testing while still providing valid results
Solution Approach 2:
The patent transforms the original VLM into compressed proxy models by changing key parameters such as model size, precision, or architecture while preserving essential behavioral characteristics. This parameter transformation enables rapid creation of testable proxies without requiring full model retraining
Data Source
AI summary
Testing very large machine models is disclosed. A framework is provided that allows changes to very large machine learning models to be evaluated using compressed machine learning models and automatic or semi-automatic unit testing.