quic/aimet
Dec 2024 – Jan 2025
2 Months active
Languages Used
PythonC++onnxtorch
Technical Skills
Code OrganizationCode RefactoringMachine LearningObject-Oriented ProgrammingPyTorchRefactoring
Seokjun An
PROFILE
Seokjun An
Seokan worked on the quic/aimet repository, focusing on architectural refactoring and performance improvements for quantization and model export workflows. Over two months, Seokan reorganized quantization modules under a new base directory, introduced a shared QuantAnalyzerBase class to reduce code duplication, and aligned module structures for maintainability. In addition, Seokan addressed a performance issue in AMP v2 by refining quantizer logic and overhauled the ONNX export process to simplify graph structure, precompute encodings, and improve test coverage. The work leveraged Python, C++, and PyTorch, demonstrating depth in code organization, debugging, and software architecture to enhance long-term project stability.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
4,504
Activity Months2
Your Network
200 people
Work History
January 2025
3 Commits • 1 Features
Jan 1, 2025Month 2025-01: Focused on stabilizing AMP v2 performance and simplifying the ONNX export workflow for quic/aimet. Delivered a targeted performance fix addressing unintended compute_param_encodings slowdown and a major export refactor that reduces graph complexity, precomputes encodings before export, and improves test coverage. These efforts enhanced runtime stability, export reliability, and long-term maintainability.
3 Commits • 1 Features
Jan 1, 2025Month 2025-01: Focused on stabilizing AMP v2 performance and simplifying the ONNX export workflow for quic/aimet. Delivered a targeted performance fix addressing unintended compute_param_encodings slowdown and a major export refactor that reduces graph complexity, precomputes encodings before export, and improves test coverage. These efforts enhanced runtime stability, export reliability, and long-term maintainability.
January 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024: Architecture-focused refactor in quic/aimet to improve maintainability and prepare for future feature work. Reorganized quant-related modules under a new _base directory, introduced QuantAnalyzerBase to consolidate shared functionality across v1 and v2 analyzers, and reduced duplication. The changes position the project for faster feature delivery and easier maintenance while preserving existing behavior.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024: Architecture-focused refactor in quic/aimet to improve maintainability and prepare for future feature work. Reorganized quant-related modules under a new _base directory, introduced QuantAnalyzerBase to consolidate shared functionality across v1 and v2 analyzers, and reduced duplication. The changes position the project for faster feature delivery and easier maintenance while preserving existing behavior.
Activity
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Quality Metrics
Correctness92.0%
Maintainability88.0%
Architecture88.0%
Performance72.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Pythononnxtorch
Technical Skills
Code OrganizationCode RefactoringDebuggingGraph ManipulationMachine LearningModel ExportONNXONNX ExportObject-Oriented ProgrammingPerformance OptimizationPyTorchQuantizationRefactoringSoftware ArchitectureTesting
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