quic/aimet
Dec 2024 – Feb 2025
2 Months active
Languages Used
C++Python
Technical Skills
Backend IntegrationCode RefactoringDeep Learning FrameworksMixed PrecisionModel OptimizationPyTorch
Yathindra Kota
PROFILE
Yathindra Kota
Yuki Kota contributed to the quic/aimet repository by developing and refining mixed-precision and quantization workflows over a two-month period. He enhanced backend integration and model optimization in PyTorch, focusing on robust configuration management and improved runtime reliability. His work included refactoring quantization configuration to a config-driven approach, adding utilities for channel axis detection, and standardizing quantizer usage to ensure consistent behavior. He also exposed batch norm folding APIs across versions and fixed a critical bug in quantizer group identification for AMP. These efforts deepened the reliability and flexibility of quantization pipelines, demonstrating strong proficiency in Python, C++, and deep learning frameworks.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
3
Lines of code
1,335
Activity Months2
Your Network
200 people
Work History
February 2025
3 Commits • 2 Features
Feb 1, 2025Concise monthly summary for 2025-02 focusing on developer delivery for quic/aimet. Highlights include a bug fix for AMP Phase-3 QG identification to ensure the correct Quantizer Group detection and AMP behavior, a refactor of the quantization configuration to be read from a config file with a new channel axis utility, and an API exposure enhancement for batch norm folding across v1 and v2. These changes improve configurability, correctness, and API stability, enabling more reliable quantization workflows and easier cross-version usage.
3 Commits • 2 Features
Feb 1, 2025Concise monthly summary for 2025-02 focusing on developer delivery for quic/aimet. Highlights include a bug fix for AMP Phase-3 QG identification to ensure the correct Quantizer Group detection and AMP behavior, a refactor of the quantization configuration to be read from a config file with a new channel axis utility, and an API exposure enhancement for batch norm folding across v1 and v2. These changes improve configurability, correctness, and API stability, enabling more reliable quantization workflows and easier cross-version usage.
February 2025
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for quic/aimet focused on delivering robust MMP/MP enhancements, stabilizing the mixed-precision workflow, and improving developer and runtime reliability. Key work consolidated configuration improvements with better correctness checks and operational visibility, directly contributing to performance, reliability, and ease of use in production settings.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for quic/aimet focused on delivering robust MMP/MP enhancements, stabilizing the mixed-precision workflow, and improving developer and runtime reliability. Key work consolidated configuration improvements with better correctness checks and operational visibility, directly contributing to performance, reliability, and ease of use in production settings.
Activity
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Quality Metrics
Correctness90.0%
Maintainability85.0%
Architecture88.4%
Performance78.4%
AI Usage23.4%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
API DesignBackend IntegrationCode ExportCode RefactoringConfiguration ManagementDeep LearningDeep Learning FrameworksMixed PrecisionMixed Precision TrainingModel OptimizationPyTorchQuantizationSoftware DevelopmentTesting
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