Deep Learning Operator Registration Completeness Detection
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Solution Overview
Problem
The registration completeness of operators in deep learning frameworks relies on developer consciousness, lacking systematic detection and monitoring methods, leading to insufficient support for data types, data layouts, and execution devices.
Innovation Solution
A method and device for operator registration processing based on deep learning that automatically detects and monitors the completeness of operator registration by obtaining description and logic operation information, determining operator types, and using configuration information to ensure complete registration across data types, data layouts, and execution devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operator registration completeness relies on developer consciousness, then the system is simple to implement, but the registration completeness and support degree are insufficient
Solution Approach 1:
The system automatically detects operator registration completeness by itself without requiring external manual checking. The detection mechanism queries the operator library and compares registered operators against expected configurations, enabling the system to self-verify its own registration state and identify missing operators automatically
Solution Approach 2:
The system provides feedback about operator registration completeness by comparing actual registered operators with expected operator configurations. This feedback mechanism identifies missing operators and notifies developers, creating a closed-loop system that continuously monitors and reports registration status
2Productivity
If manual checking of operator registration is performed, then detection simplicity is maintained, but labor costs and time consumption increase
Solution Approach 1:
The patent replaces manual mechanical checking processes with an automated computational detection system. The system uses programmatic queries and automated comparisons instead of human developers manually reviewing operator registrations, thereby eliminating manual labor and significantly reducing detection time
3Reliability
If comprehensive operator registration detection is implemented, then registration completeness improves, but system complexity increases
Solution Approach 1:
The detection system is segmented into distinct functional modules: an operator library storing expected operator configurations, a detection mechanism that queries and compares, and a reporting system that identifies deficiencies. This segmentation allows each component to perform its specific function independently, making the overall complex system manageable and maintainable
Data Source
AI summary
The disclosure provides a method and a device for operator registration processing based on deep learning, an electronic device, and a storage medium. Description information and logic operation information of a registered operator are obtained. An operator type of the registered operator is determined based on the logic operation information. Configuration information corresponding to the operator type and preset in a deep learning framework is obtained. It is detected whether the registered operator is registered completely based on the description information and the configuration information.