quic/aimet
Oct 2024 – Jan 2025
4 Months active
Languages Used
PythonRSTC++
Technical Skills
Deep LearningPyTorchQuantizationDocumentationMachine LearningTechnical Writing
Priyanka Dangi
PROFILE
Priyanka Dangi
Worked on the quic/aimet repository to expand and refine quantization workflows for deep learning models, focusing on PyTorch and C++. Delivered quantization support for custom normalization modules and additional numerical operations, improving model compatibility and flexibility. Unified core training extensions under Python implementations, removing legacy C++ code paths to simplify maintenance and enhance testability. Enhanced documentation by updating AdaRound guides and streamlining image assets, supporting clearer onboarding and repository hygiene. Emphasized code refactoring, model compression, and technical writing throughout, ensuring consistent, maintainable solutions that address both engineering complexity and user-facing clarity across AIMET’s quantization and training extension features.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
5
Lines of code
1,374
Activity Months4
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
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01. Key accomplishment: Updated AIMET documentation image references and assets to streamline visuals, improve clarity, and ensure asset integrity.
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01. Key accomplishment: Updated AIMET documentation image references and assets to streamline visuals, improve clarity, and ensure asset integrity.
January 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered unified Python implementations for AIMET PyTorch training extensions in quic/aimet by removing the USE_PYTHON_IMPL flag, thereby eliminating the C++ MO path and standardizing all core operations (batch norm folding, cross-layer scaling, weight SVD pruning) under Python implementations. This reduces complexity, improves consistency and maintainability, and enhances testability across the PyTorch training extensions.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered unified Python implementations for AIMET PyTorch training extensions in quic/aimet by removing the USE_PYTHON_IMPL flag, thereby eliminating the C++ MO path and standardizing all core operations (batch norm folding, cross-layer scaling, weight SVD pruning) under Python implementations. This reduces complexity, improves consistency and maintainability, and enhances testability across the PyTorch training extensions.
November 2024
2 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for quic/aimet focused on expanding quantization coverage and updating AdaRound documentation to improve model efficiency, reliability, and developer onboarding.
2 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for quic/aimet focused on expanding quantization coverage and updating AdaRound documentation to improve model efficiency, reliability, and developer onboarding.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10: Focused on expanding AIMET's quantization workflow to support custom normalization modules. Delivered quantization support for custom normalization (BatchNorm, GroupNorm, Normalize) in quic/aimet, refactored FakeQuantizedBatchNorm to properly detach running_mean and running_var, and added tests to validate these modules. These changes broaden model compatibility, improve configurability, and strengthen the reliability of quantization across custom normalization scenarios.
October 2024
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10: Focused on expanding AIMET's quantization workflow to support custom normalization modules. Delivered quantization support for custom normalization (BatchNorm, GroupNorm, Normalize) in quic/aimet, refactored FakeQuantizedBatchNorm to properly detach running_mean and running_var, and added tests to validate these modules. These changes broaden model compatibility, improve configurability, and strengthen the reliability of quantization across custom normalization scenarios.
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability88.0%
Architecture88.0%
Performance78.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonRST
Technical Skills
Code RefactoringDeep LearningDocumentationMachine LearningModel CompressionPyTorchQuantizationSoftware EngineeringTechnical Writing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline