quic/aimet
Apr 2025 – Mar 2026
8 Months active
Languages Used
INIPythonrsttxtC++RSTCSSText
Technical Skills
DocumentationModel ConversionONNXPythonQuantizationRelease Management
Khobare, Abhijit
PROFILE
Khobare, Abhijit
Worked on the quic/aimet repository to deliver quantization, model optimization, and deployment tooling for deep learning workflows. Over eight months, developed APIs for mixed-precision and quantizer management, enhanced ONNX export compatibility, and improved quantization performance for large models. Used Python and ONNX to refactor export logic, optimize graph manipulation, and implement robust unit testing. Addressed licensing compliance and improved documentation, including release notes and workflow guides, to support onboarding and deployment. Enhanced data visualization with Bokeh for sensitivity analysis and resolved front-end issues for documentation readability. The work emphasized maintainability, scalability, and reliability across quantization and model export pipelines.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
16Total
Bugs
5
Commits
16
Features
9
Lines of code
2,244
Activity Months8
Your Network
233 peopleSame Organization
@qti.qualcomm.com186
Shared Repositories
47
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Sonawane, BhushanMember
Xu, BozheMember
Yuan, ChaoMember
Dinh, Dao Lan VyMember
Gajula, Sai ChaitanyaMember
Mehta, HitarthMember
Garg, KeshavMember
Work History
March 2026
1 Commits
Mar 1, 2026March 2026 monthly summary for quic/aimet focused on cross-framework reliability and release management. Delivered essential documentation updates for the 2.25.1 release, clarifying bug fixes for ONNX and Torch, which reduces support load and accelerates downstream adoption.
1 Commits
Mar 1, 2026March 2026 monthly summary for quic/aimet focused on cross-framework reliability and release management. Delivered essential documentation updates for the 2.25.1 release, clarifying bug fixes for ONNX and Torch, which reduces support load and accelerates downstream adoption.
March 2026
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for quic/aimet focusing on key features delivered, major bugs fixed, and overall impact. Highlighted work includes AdaScale Quantizer Conv2d support, QDQ export handling improvements post MatMul-Add, and release notes/versioning improvements for ONNX/Torch quantization. The work delivered strengthens quantization reliability, determinism, and documentation, with measurable business value in model efficiency and deployment readiness.
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for quic/aimet focusing on key features delivered, major bugs fixed, and overall impact. Highlighted work includes AdaScale Quantizer Conv2d support, QDQ export handling improvements post MatMul-Add, and release notes/versioning improvements for ONNX/Torch quantization. The work delivered strengthens quantization reliability, determinism, and documentation, with measurable business value in model efficiency and deployment readiness.
September 2025
3 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for quic/aimet. Key outcomes include improved documentation readability under dark system themes through a CSS fix in code snippet rendering; expanded test coverage with a unit test for set_fixed_encoding_range ensuring correct quantization ranges for model inputs/outputs; and release readiness with the 2.16.0 version bump. These efforts deliver business value by enhancing developer experience, increasing reliability of quantization features, and enabling a smooth public release.
3 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for quic/aimet. Key outcomes include improved documentation readability under dark system themes through a CSS fix in code snippet rendering; expanded test coverage with a unit test for set_fixed_encoding_range ensuring correct quantization ranges for model inputs/outputs; and release readiness with the 2.16.0 version bump. These efforts deliver business value by enhancing developer experience, increasing reliability of quantization features, and enabling a smooth public release.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025In August 2025, delivered quantization performance and reliability improvements in quic/aimet, focusing on scalability for large models. Achievements include speeding up QuantSim initialization, optimizing the large_model_qdq_export test for runtime and precision, and refactoring the set_quantizer to remove O(N^2) complexity while preserving correct quantizer-to-node associations. These changes reduce setup and test times, improve production readiness, and enhance determinism of quantized models. Commits f7e700f98973bdf39907482d3092349ceae2047e and c0bdb466f0e26b5757f473308af0c41c47a50fb1 were merged to implement these improvements.
August 2025
2 Commits • 1 Features
Aug 1, 2025In August 2025, delivered quantization performance and reliability improvements in quic/aimet, focusing on scalability for large models. Achievements include speeding up QuantSim initialization, optimizing the large_model_qdq_export test for runtime and precision, and refactoring the set_quantizer to remove O(N^2) complexity while preserving correct quantizer-to-node associations. These changes reduce setup and test times, improve production readiness, and enhance determinism of quantized models. Commits f7e700f98973bdf39907482d3092349ceae2047e and c0bdb466f0e26b5757f473308af0c41c47a50fb1 were merged to implement these improvements.
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for quic/aimet: Improved licensing compliance and clarified deployment workflows. Key deliverables include standardizing SPDX license identifiers and updating quantization workflow documentation to cover model conversion, graph optimization, post-training quantization, and QDQ export, with notes on accuracy-vs-performance tradeoffs and introduction of Lite Mixed Precision and Automatic Mixed Precision. These changes reduce licensing risk, accelerate onboarding for new users, and provide clearer guidance for developers and customers deploying AIMET quantization.
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for quic/aimet: Improved licensing compliance and clarified deployment workflows. Key deliverables include standardizing SPDX license identifiers and updating quantization workflow documentation to cover model conversion, graph optimization, post-training quantization, and QDQ export, with notes on accuracy-vs-performance tradeoffs and introduction of Lite Mixed Precision and Automatic Mixed Precision. These changes reduce licensing risk, accelerate onboarding for new users, and provide clearer guidance for developers and customers deploying AIMET quantization.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for quic/aimet: Key features delivered include a lite mp API for sensitivity-guided layer precision toggling (int16/float16) and visualization improvements for per-layer sensitivity analysis. The first commit 537ec10d7b9998049388b93bcc405ee2c154fdf9 adds API and implementation; the second commit c96894f3795e1b0986ba0c2b6f0b04464d003d0f adds hover tooltips and switches x-axis to layer indices. No major bugs reported. Overall impact: more efficient ONNX training through targeted precision changes and improved UX for sensitivity analysis, enabling faster experimentation and better resource usage. Technologies demonstrated: API design for lite-precision workflows, ONNX training extensions, data visualization UX (tooltips), Python tooling, and commit-quality documentation.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for quic/aimet: Key features delivered include a lite mp API for sensitivity-guided layer precision toggling (int16/float16) and visualization improvements for per-layer sensitivity analysis. The first commit 537ec10d7b9998049388b93bcc405ee2c154fdf9 adds API and implementation; the second commit c96894f3795e1b0986ba0c2b6f0b04464d003d0f adds hover tooltips and switches x-axis to layer indices. No major bugs reported. Overall impact: more efficient ONNX training through targeted precision changes and improved UX for sensitivity analysis, enabling faster experimentation and better resource usage. Technologies demonstrated: API design for lite-precision workflows, ONNX training extensions, data visualization UX (tooltips), Python tooling, and commit-quality documentation.
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05 — Delivered a robust ONNX QDQ export feature for quic/aimet, improving compatibility across opset versions 10, 13, and 21. Refactored export logic to handle quantization parameters (precision and per-channel quantization) and added checks for non-standard integer precisions to prevent export errors. Result: fewer export failures, better interoperability with downstream inference pipelines, and a solid foundation for future opset support.
1 Commits • 1 Features
May 1, 2025Month: 2025-05 — Delivered a robust ONNX QDQ export feature for quic/aimet, improving compatibility across opset versions 10, 13, and 21. Refactored export logic to handle quantization parameters (precision and per-channel quantization) and added checks for non-standard integer precisions to prevent export errors. Result: fewer export failures, better interoperability with downstream inference pipelines, and a solid foundation for future opset support.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 (quic/aimet) — Key release and interoperability work that adds business value and shows strong technical execution. AIMET 2.5.0 was released with a new set_quantizers() API and experimental PyTorch techniques; release notes, docs versions, and core version were updated (commit c0bcbbd63f84555d8e9b9968b330099380e3adae). ONNX quantization workflow was hardened with Opset compatibility improvements, enabling QuantizeLinear/DequantizeLinear export across opsets (>=10/13/21 depending on per-channel or blockwise quantization) and INT16 support, plus graceful logging for unsupported lower opsets (commit 9c77fbab8e6520f7c4de2391d8854cb2f36be29c).
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 (quic/aimet) — Key release and interoperability work that adds business value and shows strong technical execution. AIMET 2.5.0 was released with a new set_quantizers() API and experimental PyTorch techniques; release notes, docs versions, and core version were updated (commit c0bcbbd63f84555d8e9b9968b330099380e3adae). ONNX quantization workflow was hardened with Opset compatibility improvements, enabling QuantizeLinear/DequantizeLinear export across opsets (>=10/13/21 depending on per-channel or blockwise quantization) and INT16 support, plus graceful logging for unsupported lower opsets (commit 9c77fbab8e6520f7c4de2391d8854cb2f36be29c).
Activity
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Quality Metrics
Correctness93.8%
Maintainability91.2%
Architecture91.2%
Performance90.6%
AI Usage21.2%
Skills & Technologies
Programming Languages
C++CSSINIPythonRSTTextrsttxt
Technical Skills
API DevelopmentBokehData VisualizationDeep LearningDocumentationFront-end DevelopmentGraph ManipulationLicensing ManagementModel ConversionModel ExportModel OptimizationONNXOptimizationPerformance TuningPyTorch
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