quic/aimet
Apr 2025 – Jun 2026
11 Months active
Languages Used
INIPythonrsttxtC++RSTCSSText
Technical Skills
DocumentationModel ConversionONNXPythonQuantizationRelease Management
Khobare, Abhijit
PROFILE
Khobare, Abhijit
Over 11 months, contributed to the quic/aimet repository by building and optimizing quantization, model export, and calibration workflows for deep learning models. Developed APIs and modularized SpinQuant rotation logic, enabling flexible R1/R2/R3 support and robust ONNX integration. Enhanced quantization reliability through graph optimization, quantizer folding, and improved export compatibility across ONNX opsets. Addressed deployment and onboarding challenges by refining documentation, licensing, and CI/CD pipelines. Leveraged Python, ONNX, and YAML configuration to streamline model optimization, data processing, and release management. The work emphasized reproducibility, scalability, and efficient experimentation, supporting both production and research use cases in machine learning.
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
37Total
Bugs
6
Commits
37
Features
19
Lines of code
317,300
Activity Months11
Your Network
280 peopleShared Repositories
47
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Kumar, AshvinMember
Sonawane, BhushanMember
Xu, BozheMember
Yuan, ChaoMember
Dinh, Dao Lan VyMember
Gajula, Sai ChaitanyaMember
Mehta, HitarthMember
Garg, KeshavMember
Work History
June 2026
11 Commits • 5 Features
Jun 1, 2026June 2026 performance summary for quic/aimet. Key contributions focused on SpinQuant integration, ONNX quantization, and pipeline reliability, with a clear emphasis on reducing model size and improving deployment readiness. Major enhancements included upstream SpinQuant R3 online rotation support for ONNX, QDQ folding into initializers, and reinforced ONNX recipe handling to ensure reproducibility. Added a calibration dataset for robust evaluation and strengthened CI/docs release processes.
11 Commits • 5 Features
Jun 1, 2026June 2026 performance summary for quic/aimet. Key contributions focused on SpinQuant integration, ONNX quantization, and pipeline reliability, with a clear emphasis on reducing model size and improving deployment readiness. Major enhancements included upstream SpinQuant R3 online rotation support for ONNX, QDQ folding into initializers, and reinforced ONNX recipe handling to ensure reproducibility. Added a calibration dataset for robust evaluation and strengthened CI/docs release processes.
June 2026
May 2026
7 Commits • 3 Features
May 1, 20262026-05 Monthly Summary – quic/aimet Key features delivered: - SpinQuant rotation and analysis enhancements: added R2 rotation support for per-head Hadamard transforms and enabled selecting between R1 and R2 rotations. Substantially refactored ONNX SpinQuant into modular layers (model_analysis, transforms, passes) to enable future rotations without touching R1 logic, and introduced a thin apply_spinquant orchestrator with rotation validation and single-session rebuild per sim. Exposed R1/R2 selection via apply_spinquant flags and GenAI Lab YAML. - Floating-point weights support for LLM experiments: added FP weights alongside INT activations to expand experimentation options in GenAI Lab (GPL) without breaking existing INT paths. - CI/CD and documentation improvements for Gen AI Lab: removed redundant QuIC org repolinter workflow and added YAML configuration documentation (CONFIG.md guidance) to streamline setup and governance. Major bugs fixed: - SpinQuant embed tokens filter tightened to exclude non-embedding Gathers, preventing IndexError in infer_hidden_size when ONNX exports emit static 2-D embedding tables. Overall impact and accomplishments: - Accelerated experimentation with SpinQuant by enabling robust R1/R2 support and modularization, reducing brittleness and rebuild overhead per simulation. Improved stability and correctness of embedding/token processing in ONNX exports. Enhanced governance and onboarding with clearer GenAI Lab YAML configuration and streamlined CI checks. Technologies/skills demonstrated: - ONNX, SpinQuant architecture, and modular refactor (model_analysis, transforms, passes) - Rotation management with R1/R2, and exposure via flags/YAML - Embedding/token processing safety checks and defensive programming - FP weights integration with INT activations for LLM experiments - CI/CD optimization and documentation practices (Gen AI Lab YAML, CONFIG.md)
May 2026
7 Commits • 3 Features
May 1, 20262026-05 Monthly Summary – quic/aimet Key features delivered: - SpinQuant rotation and analysis enhancements: added R2 rotation support for per-head Hadamard transforms and enabled selecting between R1 and R2 rotations. Substantially refactored ONNX SpinQuant into modular layers (model_analysis, transforms, passes) to enable future rotations without touching R1 logic, and introduced a thin apply_spinquant orchestrator with rotation validation and single-session rebuild per sim. Exposed R1/R2 selection via apply_spinquant flags and GenAI Lab YAML. - Floating-point weights support for LLM experiments: added FP weights alongside INT activations to expand experimentation options in GenAI Lab (GPL) without breaking existing INT paths. - CI/CD and documentation improvements for Gen AI Lab: removed redundant QuIC org repolinter workflow and added YAML configuration documentation (CONFIG.md guidance) to streamline setup and governance. Major bugs fixed: - SpinQuant embed tokens filter tightened to exclude non-embedding Gathers, preventing IndexError in infer_hidden_size when ONNX exports emit static 2-D embedding tables. Overall impact and accomplishments: - Accelerated experimentation with SpinQuant by enabling robust R1/R2 support and modularization, reducing brittleness and rebuild overhead per simulation. Improved stability and correctness of embedding/token processing in ONNX exports. Enhanced governance and onboarding with clearer GenAI Lab YAML configuration and streamlined CI checks. Technologies/skills demonstrated: - ONNX, SpinQuant architecture, and modular refactor (model_analysis, transforms, passes) - Rotation management with R1/R2, and exposure via flags/YAML - Embedding/token processing safety checks and defensive programming - FP weights integration with INT activations for LLM experiments - CI/CD optimization and documentation practices (Gen AI Lab YAML, CONFIG.md)
April 2026
3 Commits • 2 Features
Apr 1, 2026April 2026 focused on stabilizing and streamlining ONNX-based optimization workflows for quic/aimet, delivering safety guarantees for BN folding with shared weights, improving ONNX export fidelity for Torch Dynamo, and pruning non-essential LoRA adapter branches to optimize graph performance. These changes reduce risk during optimization, improve deployment readiness, and enhance runtime efficiency across AC/production scenarios.
3 Commits • 2 Features
Apr 1, 2026April 2026 focused on stabilizing and streamlining ONNX-based optimization workflows for quic/aimet, delivering safety guarantees for BN folding with shared weights, improving ONNX export fidelity for Torch Dynamo, and pruning non-essential LoRA adapter branches to optimize graph performance. These changes reduce risk during optimization, improve deployment readiness, and enhance runtime efficiency across AC/production scenarios.
April 2026
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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).
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
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Quality Metrics
Correctness94.6%
Maintainability89.2%
Architecture92.0%
Performance89.0%
AI Usage29.8%
Skills & Technologies
Programming Languages
C++CSSINIMarkdownPythonRSTTextYAMLreStructuredTextrst
Technical Skills
AI Model CalibrationAPI DevelopmentAWS S3BokehCI/CDCSS stylingData ProcessingData VisualizationDeep LearningDevOpsDocumentationFront-end DevelopmentGitHub ActionsGraph ManipulationGraph Optimization
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