Cross-Format Model Comparison Using Layer Weight Matching
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Solution Overview
Problem
Existing technologies struggle to accurately compare machine learning models represented in different formats or file conventions, leading to difficulties in ensuring model identity and integrity, particularly during format conversions or sharing, which can result in security risks.
Innovation Solution
A method is developed to extract and compare weight layers, identifiers, and biases from machine learning models across different formats, using deterministic reordering and fuzzy matching to determine model identity, ensuring compatibility and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simple hash comparisons are used on model files, then comparison speed is improved, but accuracy deteriorates because tensor weights are stored differently across formats
Solution Approach 1:
The patent segments the model file into distinct components (tensor weights, metadata, framework-specific data) and compares only the essential tensor weight segments across different formats, ignoring format-specific variations. This allows accurate comparison without being affected by format differences.
Solution Approach 2:
The patent extracts tensor weight data from model files in different formats and compares only these extracted weights, separating them from format-specific metadata and structural elements. This extraction approach enables accurate comparison while maintaining speed.
2Measurement precision
If detailed component-by-component comparison is performed, then model identity accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential tensor weight components from model files, ignoring format-specific metadata and structural elements. This selective extraction simplifies the comparison process while maintaining accuracy by focusing only on the components that define model identity.
Solution Approach 2:
The patent applies different comparison strategies to different components of the model file: exact matching for tensor weights, selective ignoring for metadata, and format-specific handling for framework data. This local quality approach optimizes the comparison process by treating each component according to its importance.
3Adaptability or versatility
If model format conversion is performed, then compatibility is improved, but security risks increase due to potential hijacking
Solution Approach 1:
The patent implements a verification feedback mechanism that compares the original model's tensor weights with the converted model's weights to ensure they match. This feedback loop detects potential hijacking or corruption during format conversion, maintaining security while enabling compatibility.
Solution Approach 2:
The patent performs preliminary comparison of model components before and after format conversion to verify integrity. By checking tensor weight equivalence in advance, the system prevents security issues from propagating to the converted model.
Data Source
AI summary
A machine learning model representation is obtained from a model source and information characterizing the layers of the model representation is extracted to result in extracted model information. This extracted model information can be compared to information characterizing one or more known (i.e., previously characterized) machine learning models in order to determine whether there is a match based on layer information. A match can, in some cases, be used to determine an identity of the underlying machine learning model for the model representation. Information regarding the comparison (i.e., the model matching determination) can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.