quic/aimet
Oct 2024 – Jan 2025
4 Months active
Languages Used
PythonRSTC++
Technical Skills
Deep LearningPyTorchQuantizationDocumentationMachine LearningTechnical Writing
Priyanka Dangi
PROFILE
Priyanka Dangi
Over four months, Pranav Dangi enhanced the quic/aimet repository by expanding quantization workflows and unifying core training operations for PyTorch models. He delivered quantization support for custom normalization modules and additional mathematical operations, improving model compatibility and configurability. By refactoring FakeQuantizedBatchNorm and migrating training extensions from C++ to Python, he reduced code complexity and improved maintainability. Pranav also updated AdaRound documentation and streamlined image assets, supporting clearer onboarding and documentation quality. His work leveraged Python, C++, and PyTorch, demonstrating depth in code refactoring, quantization, and technical writing while addressing both engineering challenges and user-facing documentation needs.
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
200 people
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
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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
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