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
Worked on the quic/aimet repository to enhance maintainability and performance in quantization workflows using Python, C++, and PyTorch. Over two months, restructured AutoQuant and Quant Analyzer modules by introducing a shared QuantAnalyzerBase and reorganizing dependencies under a new base directory, reducing code duplication and aligning architecture for future features. Addressed a performance issue in AMP v2 by refining quantizer logic, and refactored the ONNX export workflow to simplify graph structure, precompute encodings, and improve test coverage. These efforts improved runtime stability, export reliability, and positioned the codebase for easier maintenance and faster feature development.
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
232 peopleShared Repositories
47
Khobare, AbhijitMember
Mukherjee, AnanyaMember
Kumar, AshvinMember
Sonawane, BhushanMember
Xu, BozheMember
Yuan, ChaoMember
Dinh, Dao Lan VyMember
Gajula, Sai ChaitanyaMember
Mehta, HitarthMember
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.
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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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