quic/aimet
Apr 2025 – Jun 2026
14 Months active
Languages Used
C++PythonShellRSTrstonnxtorchreStructuredText
Technical Skills
Code OrganizationEnd-to-End Pipeline DevelopmentGQA ModelsLarge Language ModelsLoRAModel Optimization
Mukherjee, Ananya
PROFILE
Mukherjee, Ananya
Over 14 months, contributed to the quic/aimet repository by building and optimizing end-to-end quantization and model conversion workflows for large language models and transformer architectures. Developed ONNX-to-PyTorch model converters, integrated AdaScale and LoRA support, and enhanced quantized inference for models like Llama, Qwen, and Gemma using Python and PyTorch. Improved deployment reliability through CUDA and CPU memory optimizations, robust testing, and release management. Refactored codebases for maintainability, expanded documentation, and streamlined CI pipelines. Addressed cross-framework compatibility, containerization, and DevOps tooling, enabling efficient onboarding and validation of new models while reducing manual effort and supporting scalable, production-ready deployments.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
35Total
Bugs
7
Commits
35
Features
19
Lines of code
6,851
Activity Months14
Your Network
280 peopleShared Repositories
47
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Gajula, Sai ChaitanyaMember
Mehta, HitarthMember
Garg, KeshavMember
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for quic/aimet focusing on AdaScale integration efforts.
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for quic/aimet focusing on AdaScale integration efforts.
June 2026
May 2026
6 Commits • 3 Features
May 1, 2026In May 2026, delivered key AdaScale and ONNX pipeline enhancements for quic/aimet, with memory-safe rebuilds, robust tooling, and improved multi-adapter support. These changes increase model deployment efficiency, reliability, and developer productivity while maintaining strong code quality and extensibility.
May 2026
6 Commits • 3 Features
May 1, 2026In May 2026, delivered key AdaScale and ONNX pipeline enhancements for quic/aimet, with memory-safe rebuilds, robust tooling, and improved multi-adapter support. These changes increase model deployment efficiency, reliability, and developer productivity while maintaining strong code quality and extensibility.
April 2026
4 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for quic/aimet: Focus on expanding ONNX2Torch compatibility, stabilizing cross-platform tests, and improving release documentation. Delivered Opset 18-21 support in ONNX2Torch with ReduceMean and Reshape (allowzero=1), and fixed pylint issues; stabilized Windows ARM64 CI by disabling dynamo-based tests and addressing nightly failures; updated SpinQuant ONNX to 2.30 with release notes and improved docs. These changes broaden model conversion coverage, reduce platform-related flakiness, and accelerate release readiness for SpinQuant.
4 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for quic/aimet: Focus on expanding ONNX2Torch compatibility, stabilizing cross-platform tests, and improving release documentation. Delivered Opset 18-21 support in ONNX2Torch with ReduceMean and Reshape (allowzero=1), and fixed pylint issues; stabilized Windows ARM64 CI by disabling dynamo-based tests and addressing nightly failures; updated SpinQuant ONNX to 2.30 with release notes and improved docs. These changes broaden model conversion coverage, reduce platform-related flakiness, and accelerate release readiness for SpinQuant.
April 2026
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 performance summary for quic/aimet: - Key features delivered: Integrated Qwen3 MoE quantized modules into the AIMET base directory to enable quantized MoE support; Onboarded Qwen3 MoE models to the GenAI test framework with adaptations for model integration and transformer compatibility. - Major bugs fixed/quality improvements: Code cleanup and stability enhancements in the GenAI onboarding flow, including removal of unnecessary prints and timers, Ruff formatting enforcement, and transformer-version gating (support window 4.51–4.57) to prevent incompatibilities. - Overall impact and accomplishments: Expanded MoE model support within AIMET and GenAI workflows, enabling faster testing cycles, broader model coverage, and improved reliability for GenAI-based MoE validation. Demonstrated end-to-end integration, maintainability, and CI-readiness. - Technologies/skills demonstrated: Python, PyTorch MoE workflows, quantized model handling, GenAI test framework integration, code quality tooling (Ruff), and version gating for compatibility.
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 performance summary for quic/aimet: - Key features delivered: Integrated Qwen3 MoE quantized modules into the AIMET base directory to enable quantized MoE support; Onboarded Qwen3 MoE models to the GenAI test framework with adaptations for model integration and transformer compatibility. - Major bugs fixed/quality improvements: Code cleanup and stability enhancements in the GenAI onboarding flow, including removal of unnecessary prints and timers, Ruff formatting enforcement, and transformer-version gating (support window 4.51–4.57) to prevent incompatibilities. - Overall impact and accomplishments: Expanded MoE model support within AIMET and GenAI workflows, enabling faster testing cycles, broader model coverage, and improved reliability for GenAI-based MoE validation. Demonstrated end-to-end integration, maintainability, and CI-readiness. - Technologies/skills demonstrated: Python, PyTorch MoE workflows, quantized model handling, GenAI test framework integration, code quality tooling (Ruff), and version gating for compatibility.
February 2026
3 Commits • 1 Features
Feb 1, 2026February 2026 focused on delivering optimized Qwen3 inference path and stabilizing deployment runtime. Delivered AdaScale input handling refinements and quantized MoE modules, plus a critical CPU memory leak fix in the Aimet wrapper to enable memory reuse during ONNX inference sessions, resulting in faster, more memory-efficient deployments.
3 Commits • 1 Features
Feb 1, 2026February 2026 focused on delivering optimized Qwen3 inference path and stabilizing deployment runtime. Delivered AdaScale input handling refinements and quantized MoE modules, plus a critical CPU memory leak fix in the Aimet wrapper to enable memory reuse during ONNX inference sessions, resulting in faster, more memory-efficient deployments.
February 2026
January 2026
3 Commits • 2 Features
Jan 1, 2026Month: 2026-01 — quic/aimet monthly summary highlighting key features, major fixes, impact, and skills demonstrated. 1) Key features delivered - Release notes and documentation updates for Version 2.22.0: added support for loading 2.0.0 encoding format; included ONNX/encoding export bug fixes; LLM recipes docs updated with performance metrics and CUDA memory settings. Commit activity: 59ca0660990b3d6ab7572b1bb9c079ef8519d684; af3eae7bd73963753356e2b2af19e9282eabd55d. - AdaScale performance optimization: reduced rebuild overhead by rebuilding the sim session only once after applying adascale across all blocks. Commit: 1d17866efef6ffa34f6a1952b3541153b576ec29. 2) Major bugs fixed - Release notes commit includes bug fixes for 2.0.0 float encoding export and AffineEncoding; documentation reflects these fixes as part of Version 2.22.0 release. Commits: 59ca0660990b3d6ab7572b1bb9c079ef8519d684. 3) Overall impact and accomplishments - Improved release readiness and product reliability with explicit support for newer encoding formats and corrected encoding export paths. Documentation improvements enabled clearer performance benchmarking and CUDA memory guidance for LL models. AdaScale optimization reduced per-session rebuild overhead, accelerating iteration cycles. 4) Technologies/skills demonstrated - Release engineering and documentation across repositories, encoding formats (2.0.0, ONNX), AdaScale algorithm optimization, performance benchmarking, LL M/PCQ recipe data communication, CUDA memory tuning. Top 3-5 achievements: - Release notes/docs for Version 2.22.0 updated to include 2.0.0 encoding loading support and ONNX/export fixes; LLM recipes docs updated for performance metrics and CUDA memory settings (commits 59ca0660990b3d6ab7572b1bb9c079ef8519d684; af3eae7bd73963753356e2b2af19e9282eabd55d). - AdaScale optimization: rebuilt sim session only once after applying adascale across all blocks (commit 1d17866efef6ffa34f6a1952b3541153b576ec29). - Documentation updates for llm_recipies numbers and CUDA mem adjustments supporting PCQ_SpinQuant_Adascale (commit af3eae7bd73963753356e2b2af19e9282eabd55d).
January 2026
3 Commits • 2 Features
Jan 1, 2026Month: 2026-01 — quic/aimet monthly summary highlighting key features, major fixes, impact, and skills demonstrated. 1) Key features delivered - Release notes and documentation updates for Version 2.22.0: added support for loading 2.0.0 encoding format; included ONNX/encoding export bug fixes; LLM recipes docs updated with performance metrics and CUDA memory settings. Commit activity: 59ca0660990b3d6ab7572b1bb9c079ef8519d684; af3eae7bd73963753356e2b2af19e9282eabd55d. - AdaScale performance optimization: reduced rebuild overhead by rebuilding the sim session only once after applying adascale across all blocks. Commit: 1d17866efef6ffa34f6a1952b3541153b576ec29. 2) Major bugs fixed - Release notes commit includes bug fixes for 2.0.0 float encoding export and AffineEncoding; documentation reflects these fixes as part of Version 2.22.0 release. Commits: 59ca0660990b3d6ab7572b1bb9c079ef8519d684. 3) Overall impact and accomplishments - Improved release readiness and product reliability with explicit support for newer encoding formats and corrected encoding export paths. Documentation improvements enabled clearer performance benchmarking and CUDA memory guidance for LL models. AdaScale optimization reduced per-session rebuild overhead, accelerating iteration cycles. 4) Technologies/skills demonstrated - Release engineering and documentation across repositories, encoding formats (2.0.0, ONNX), AdaScale algorithm optimization, performance benchmarking, LL M/PCQ recipe data communication, CUDA memory tuning. Top 3-5 achievements: - Release notes/docs for Version 2.22.0 updated to include 2.0.0 encoding loading support and ONNX/export fixes; LLM recipes docs updated for performance metrics and CUDA memory settings (commits 59ca0660990b3d6ab7572b1bb9c079ef8519d684; af3eae7bd73963753356e2b2af19e9282eabd55d). - AdaScale optimization: rebuilt sim session only once after applying adascale across all blocks (commit 1d17866efef6ffa34f6a1952b3541153b576ec29). - Documentation updates for llm_recipies numbers and CUDA mem adjustments supporting PCQ_SpinQuant_Adascale (commit af3eae7bd73963753356e2b2af19e9282eabd55d).
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for quic/aimet focusing on AdaScale improvements, correctness fixes, and CUDA memory optimizations. Implemented a critical AdaScale correctness fix by computing scales and offsets from min/max and freezing parameter quantizer encodings after updates to restore accuracy (referencing commit e29e3b7a6ddd25114feebf86855447f8a9b172d0). Added CUDA memory optimizations for AdaScale ONNX path, including deleting training artifacts post-completion, moving intermediate tensors to CPU, and improved device management (references to commit 878a240333a9065ec6e4e774c17942c3d028c21f), plus adopting torch.no_grad() for output collection and replacing inference_mode where appropriate. These changes reduce memory footprint, improve throughput, and stabilize the AdaScale workflow across ONNX paths.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for quic/aimet focusing on AdaScale improvements, correctness fixes, and CUDA memory optimizations. Implemented a critical AdaScale correctness fix by computing scales and offsets from min/max and freezing parameter quantizer encodings after updates to restore accuracy (referencing commit e29e3b7a6ddd25114feebf86855447f8a9b172d0). Added CUDA memory optimizations for AdaScale ONNX path, including deleting training artifacts post-completion, moving intermediate tensors to CPU, and improved device management (references to commit 878a240333a9065ec6e4e774c17942c3d028c21f), plus adopting torch.no_grad() for output collection and replacing inference_mode where appropriate. These changes reduce memory footprint, improve throughput, and stabilize the AdaScale workflow across ONNX paths.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025: Delivered architecture improvements and testing enhancements for quic/aimet to enable Adascale integration and more robust ONNX handling. Key refinements include removing the FP model dependency in model_converter by switching to Extractor for ONNX model splitting, expanding the GenAI test framework to support Adascale, and updating related unit tests. Refined adascale_model_config_dict to use model_type as the key, improved model-type handling, and added developer documentation. Also completed code quality improvements (ruff lint changes) and updated tests to align with API changes. Business impact: faster onboarding of Adascale-enabled models, reduced integration risk, and improved test coverage for GenAI workflows.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025: Delivered architecture improvements and testing enhancements for quic/aimet to enable Adascale integration and more robust ONNX handling. Key refinements include removing the FP model dependency in model_converter by switching to Extractor for ONNX model splitting, expanding the GenAI test framework to support Adascale, and updating related unit tests. Refined adascale_model_config_dict to use model_type as the key, improved model-type handling, and added developer documentation. Also completed code quality improvements (ruff lint changes) and updated tests to align with API changes. Business impact: faster onboarding of Adascale-enabled models, reduced integration risk, and improved test coverage for GenAI workflows.
October 2025
4 Commits • 1 Features
Oct 1, 2025October 2025: Delivered a complete ONNX-to-PyTorch model converter integration in quic/aimet, with cleanup and modularization, expanded test coverage, removal of the v1 implementation, and added type hints for maintainability. Focused on enabling seamless cross-framework model conversion, improving testability, and reducing future maintenance costs.
4 Commits • 1 Features
Oct 1, 2025October 2025: Delivered a complete ONNX-to-PyTorch model converter integration in quic/aimet, with cleanup and modularization, expanded test coverage, removal of the v1 implementation, and added type hints for maintainability. Focused on enabling seamless cross-framework model conversion, improving testability, and reducing future maintenance costs.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered an ONNX-to-PyTorch Transformer Weights Converter for aimet-onnx, enabling efficient transfer of decoder weights from ONNX models to custom PyTorch transformer blocks (Llama, Qwen). Implemented a reusable converter utility, expanded tests, and refactored ONNX modules to support dynamic decoder block mapping. This work reduces manual re-implementation, accelerates experimentation, and improves cross-framework interoperability between ONNX-exported models and PyTorch implementations.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered an ONNX-to-PyTorch Transformer Weights Converter for aimet-onnx, enabling efficient transfer of decoder weights from ONNX models to custom PyTorch transformer blocks (Llama, Qwen). Implemented a reusable converter utility, expanded tests, and refactored ONNX modules to support dynamic decoder block mapping. This work reduces manual re-implementation, accelerates experimentation, and improves cross-framework interoperability between ONNX-exported models and PyTorch implementations.
August 2025
2 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — quic/aimet: Release 2.13.0 readiness and Adaround testing enhancements. This month focused on release engineering for a timely, production-ready 2.13.0, plus strengthening test alignment with API changes to improve reliability across ONNX, PyTorch, and Keras. Key features delivered: - Release 2.13.0 preparation completed: version bump across release notes and version-tracking files; release notes populated with bug fixes and improvements for ONNX and PyTorch; known issues for Keras documented. Major bugs fixed: - Adaround test API update and validation: updated ONNX Adaround tests to align with the new Adaround API; refactored test setup; simplified quantization simulation; validated Adaround correctness by comparing model outputs before and after application. Overall impact and accomplishments: - Enabled release readiness with clear cross-framework changes, improving predictability for downstream users and CI. - Strengthened test reliability for Adaround, reducing risk of regressions in production workflows. - Established baseline documentation for known issues and cross-framework expectations, improving engineering communication and issue tracking. Technologies/skills demonstrated: - Release engineering and version management across multi-repo scope; test-driven validation and API-driven test refactor; cross-framework validation (ONNX, PyTorch, Adaround) and quantization workflows; basic documentation maintenance for known issues.
2 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — quic/aimet: Release 2.13.0 readiness and Adaround testing enhancements. This month focused on release engineering for a timely, production-ready 2.13.0, plus strengthening test alignment with API changes to improve reliability across ONNX, PyTorch, and Keras. Key features delivered: - Release 2.13.0 preparation completed: version bump across release notes and version-tracking files; release notes populated with bug fixes and improvements for ONNX and PyTorch; known issues for Keras documented. Major bugs fixed: - Adaround test API update and validation: updated ONNX Adaround tests to align with the new Adaround API; refactored test setup; simplified quantization simulation; validated Adaround correctness by comparing model outputs before and after application. Overall impact and accomplishments: - Enabled release readiness with clear cross-framework changes, improving predictability for downstream users and CI. - Strengthened test reliability for Adaround, reducing risk of regressions in production workflows. - Established baseline documentation for known issues and cross-framework expectations, improving engineering communication and issue tracking. Technologies/skills demonstrated: - Release engineering and version management across multi-repo scope; test-driven validation and API-driven test refactor; cross-framework validation (ONNX, PyTorch, Adaround) and quantization workflows; basic documentation maintenance for known issues.
August 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focused on improving OmniQuant usability and clarity through targeted documentation enhancements. Delivered a documentation update clarifying that blocks must be contiguous for optimization, and provided a concrete contiguous block example. This reduces onboarding time, mitigates misconfiguration risks, and supports reliable optimization workflows for OmniQuant.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focused on improving OmniQuant usability and clarity through targeted documentation enhancements. Delivered a documentation update clarifying that blocks must be contiguous for optimization, and provided a concrete contiguous block example. This reduces onboarding time, mitigates misconfiguration risks, and supports reliable optimization workflows for OmniQuant.
May 2025
2 Commits • 2 Features
May 1, 2025In May 2025, two high-impact features were delivered in quic/aimet that advance model compatibility and quantization workflows, with an emphasis on business value and maintainable documentation. The team updated support for Qwen 2.5 in OmniQuant, including decoder/utility changes and integration of OmniQuant recipes into the GenAI test framework to broaden model testing. Additionally, comprehensive OmniQuant PTQ documentation and usage guides were published, including API references and practical Python examples to improve quantized model accuracy and developer adoption. No major bugs fixed this month; the focus was on feature delivery, testing readiness, and clear guidance for users. These efforts collectively improve deployment readiness, reduce time-to-value for quantized models, and strengthen testing coverage for GenAI workflows.
2 Commits • 2 Features
May 1, 2025In May 2025, two high-impact features were delivered in quic/aimet that advance model compatibility and quantization workflows, with an emphasis on business value and maintainable documentation. The team updated support for Qwen 2.5 in OmniQuant, including decoder/utility changes and integration of OmniQuant recipes into the GenAI test framework to broaden model testing. Additionally, comprehensive OmniQuant PTQ documentation and usage guides were published, including API references and practical Python examples to improve quantized model accuracy and developer adoption. No major bugs fixed this month; the focus was on feature delivery, testing readiness, and clear guidance for users. These efforts collectively improve deployment readiness, reduce time-to-value for quantized models, and strengthen testing coverage for GenAI workflows.
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04 (quic/aimet). Focused on delivering an end-to-end Omniquant workflow integrated with large-language-model support, stabilizing model scaling features, and improving maintainability. Overall: Completed core integration work and bug fixes that enhance PTQ readiness for LLMs, reduce maintenance burden, and provide API access for model scaling. Delivered measurable improvements in quantization efficiency and accuracy for large models, enabling more reliable deployment pipelines.
April 2025
3 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04 (quic/aimet). Focused on delivering an end-to-end Omniquant workflow integrated with large-language-model support, stabilizing model scaling features, and improving maintainability. Overall: Completed core integration work and bug fixes that enhance PTQ readiness for LLMs, reduce maintenance burden, and provide API access for model scaling. Delivered measurable improvements in quantization efficiency and accuracy for large models, enabling more reliable deployment pipelines.
Activity
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Quality Metrics
Correctness92.0%
Maintainability86.8%
Architecture88.0%
Performance87.2%
AI Usage31.4%
Skills & Technologies
Programming Languages
BashC++CMakeMarkdownPythonRSTShellbashonnxreStructuredText
Technical Skills
CUDA OptimizationCode CleanupCode OrganizationCode RefactoringContainerizationDeep LearningDevOpsDocumentationEnd-to-End Pipeline DevelopmentGQA ModelsLarge Language ModelsLoRAMachine LearningMemory ManagementModel Conversion
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