CodeLinaro/onnxruntime
Jul 2025 – Aug 2025
2 Months active
Languages Used
C++
Technical Skills
C++ developmentError handlingGraph algorithmsPlugin architecturememory managementperformance optimization
Chunye Wang@AMD
PROFILE
Chunye Wang@amd
Worked on the CodeLinaro/onnxruntime repository, focusing on enhancing the stability and performance of the Execution Provider (EP) path. Addressed a shape inference failure in Graph::Resolve by introducing explicit type information for EP context nodes, allowing the graph resolution process to proceed even when type inference was incomplete. Improved the VitisAI EP integration by optimizing model cloning, enabling tensor raw data sharing for externalized tensor proto to boost memory management and runtime efficiency. Utilized C++ for development, applying expertise in error handling, graph algorithms, and performance optimization to reduce runtime failures and streamline large-scale model deployment workflows.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
31
Activity Months2
Your Network
1625 peopleSame Organization
@amd.com1561
7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for CodeLinaro/onnxruntime focusing on performance and stability improvements in the VitisAI EP integration, specifically around model cloning, memory management, and tensor proto handling. The work enhances efficiency for large-scale model deployments and prepares the codebase for future optimizations.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for CodeLinaro/onnxruntime focusing on performance and stability improvements in the VitisAI EP integration, specifically around model cloning, memory management, and tensor proto handling. The work enhances efficiency for large-scale model deployments and prepares the codebase for future optimizations.
August 2025
July 2025
1 Commits
Jul 1, 2025July 2025 focused on stabilizing the Execution Provider path in ONNX Runtime by addressing a shape inference failure in Graph::Resolve when type information is missing. The fix ensures Graph::Resolve can proceed without errors when type inference isn’t explicitly defined, improving EP reliability across varying models and deployments.
July 2025
1 Commits
Jul 1, 2025July 2025 focused on stabilizing the Execution Provider path in ONNX Runtime by addressing a shape inference failure in Graph::Resolve when type information is missing. The fix ensures Graph::Resolve can proceed without errors when type inference isn’t explicitly defined, improving EP reliability across varying models and deployments.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ developmentError handlingGraph algorithmsPlugin architecturememory managementperformance optimization
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