quic/aimet
Apr 2025 – Jul 2026
16 Months active
Languages Used
C++CMakeCUDAPythonShellTextYAMLrst
Technical Skills
Build System ConfigurationBuild SystemsC++C++ DevelopmentCI/CDCMake
Mehta, Hitarth
PROFILE
Mehta, Hitarth
Over 16 months, contributed to the quic/aimet repository by building and optimizing advanced quantization, model evaluation, and CI/CD automation for deep learning workflows. Leveraging Python, C++, and ONNX, developed features such as sequential MSE optimization, SpinQuant ONNX enhancements, and AdaScale training improvements, while expanding support for large language models and visual language models. Addressed deployment reliability by implementing robust testing, memory management, and cross-platform compatibility, including CUDA and Windows environments. Automated release pipelines and improved documentation, enabling faster iteration and safer releases. The work demonstrated depth in model optimization, quantization techniques, and workflow automation, supporting scalable, production-ready AI deployments.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
118Total
Bugs
18
Commits
118
Features
37
Lines of code
23,078
Activity Months16
Your Network
280 peopleShared Repositories
47
Khobare, AbhijitMember
Mukherjee, AnanyaMember
Kumar, AshvinMember
Sonawane, BhushanMember
Xu, BozheMember
Yuan, ChaoMember
Dinh, Dao Lan VyMember
Gajula, Sai ChaitanyaMember
Garg, KeshavMember
Work History
July 2026
1 Commits • 1 Features
Jul 1, 2026In July 2026, focused on expanding validation coverage for decoder blocks in ONNX models within the quic/aimet repository. Delivered architecture-coverage tests integrated into the testing framework, with structural checks to ensure decoder block integrity. This work strengthens ONNX deployment reliability by catching architectural regressions earlier in CI and QA workflows.
1 Commits • 1 Features
Jul 1, 2026In July 2026, focused on expanding validation coverage for decoder blocks in ONNX models within the quic/aimet repository. Delivered architecture-coverage tests integrated into the testing framework, with structural checks to ensure decoder block integrity. This work strengthens ONNX deployment reliability by catching architectural regressions earlier in CI and QA workflows.
July 2026
June 2026
9 Commits • 3 Features
Jun 1, 2026June 2026 (quic/aimet): Delivered AdaScale training improvements, ONNX graph pass and quantization enhancements, and Release/CI/CD infra upgrades. AdaScale now supports opt-in early stopping, configurable loss function, and sequence-dimension loss calculations with a default scaled sum-over-seq-dim loss, enabling more efficient training and potentially better model quality with less compute. ONNX graph passes gained rsqrt-based RMSNorm support and a Gather output quantizer override for the HTP V68 config, improving quantization fidelity and deployment consistency. Release/CI/CD infra improvements migrated docs hosting and release workflows to the Qualcomm organization, fixed docs push email rules, and added curl-based retries for CI dependency installation to improve pipeline resilience. Overall impact: reduced training compute, improved reliability and quality of quantized deployments, and a more maintainable release pipeline. Technologies/skills demonstrated: PyTorch AdaScale, ONNX graph optimizations, RMSNorm, quantization, CI/CD automation, release engineering, and cross-org collaboration.
June 2026
9 Commits • 3 Features
Jun 1, 2026June 2026 (quic/aimet): Delivered AdaScale training improvements, ONNX graph pass and quantization enhancements, and Release/CI/CD infra upgrades. AdaScale now supports opt-in early stopping, configurable loss function, and sequence-dimension loss calculations with a default scaled sum-over-seq-dim loss, enabling more efficient training and potentially better model quality with less compute. ONNX graph passes gained rsqrt-based RMSNorm support and a Gather output quantizer override for the HTP V68 config, improving quantization fidelity and deployment consistency. Release/CI/CD infra improvements migrated docs hosting and release workflows to the Qualcomm organization, fixed docs push email rules, and added curl-based retries for CI dependency installation to improve pipeline resilience. Overall impact: reduced training compute, improved reliability and quality of quantized deployments, and a more maintainable release pipeline. Technologies/skills demonstrated: PyTorch AdaScale, ONNX graph optimizations, RMSNorm, quantization, CI/CD automation, release engineering, and cross-org collaboration.
May 2026
13 Commits • 3 Features
May 1, 2026Month: 2026-05 – Delivered robust CI/CD and testing improvements, expanded model/architecture support, and strengthened release readiness for quic/aimet. These efforts reduced pipeline variability, accelerated validation, and broadened hardware compatibility, enabling safer, faster releases and stronger business value.
13 Commits • 3 Features
May 1, 2026Month: 2026-05 – Delivered robust CI/CD and testing improvements, expanded model/architecture support, and strengthened release readiness for quic/aimet. These efforts reduced pipeline variability, accelerated validation, and broadened hardware compatibility, enabling safer, faster releases and stronger business value.
May 2026
April 2026
12 Commits • 2 Features
Apr 1, 2026Concise monthly summary for 2026-04 (quic/aimet): Delivered SpinQuant ONNX optimization and quantization enhancements, improved Windows test reliability, and modernized CI/dependency management, contributing to higher inference performance, reliability, and developer velocity. Key features delivered: - SpinQuant ONNX optimization and quantization enhancements across the ONNX path, including Hadamard rotation absorption into ONNX weights, LM head precision simplification, sequence chunking for block-quantized subgraphs, and extended support for Visual Language Models (VLM); documentation migrated to native aimet-onnx SpinQuant. - Documentation and examples migrated to native aimet-onnx SpinQuant, reducing maintenance burden and aligning with current ONNX integration. Major bugs fixed: - Windows test environment fix: ensured necessary directories exist and skipped non-applicable tests on certain Windows architectures to stabilize tests. CI, dependencies, and build improvements: - Dependency management: unpinned torch, added onnx_ir, updated transformers for Gemma4 support. - CI enhancements: use Ephemeral runners for build-wheels; updated CI pipelines for faster, more reliable runs. Overall impact and accomplishments: - Improved inference performance and model quantization quality for SpinQuant ONNX paths, enabling better throughput on VLM workloads. - More reliable CI and test results across Windows and Linux/CI environments, accelerating iteration cycles. - Streamlined maintenance with native ONNX SpinQuant docs and updated examples. Technologies/skills demonstrated: - SpinQuant, ONNX, quantization, Hadamard rotation, sequence chunking, VLM support, Python tooling, CI/CD, ephemeral runners, dependency management (torch, onnx_ir, transformers).
April 2026
12 Commits • 2 Features
Apr 1, 2026Concise monthly summary for 2026-04 (quic/aimet): Delivered SpinQuant ONNX optimization and quantization enhancements, improved Windows test reliability, and modernized CI/dependency management, contributing to higher inference performance, reliability, and developer velocity. Key features delivered: - SpinQuant ONNX optimization and quantization enhancements across the ONNX path, including Hadamard rotation absorption into ONNX weights, LM head precision simplification, sequence chunking for block-quantized subgraphs, and extended support for Visual Language Models (VLM); documentation migrated to native aimet-onnx SpinQuant. - Documentation and examples migrated to native aimet-onnx SpinQuant, reducing maintenance burden and aligning with current ONNX integration. Major bugs fixed: - Windows test environment fix: ensured necessary directories exist and skipped non-applicable tests on certain Windows architectures to stabilize tests. CI, dependencies, and build improvements: - Dependency management: unpinned torch, added onnx_ir, updated transformers for Gemma4 support. - CI enhancements: use Ephemeral runners for build-wheels; updated CI pipelines for faster, more reliable runs. Overall impact and accomplishments: - Improved inference performance and model quantization quality for SpinQuant ONNX paths, enabling better throughput on VLM workloads. - More reliable CI and test results across Windows and Linux/CI environments, accelerating iteration cycles. - Streamlined maintenance with native ONNX SpinQuant docs and updated examples. Technologies/skills demonstrated: - SpinQuant, ONNX, quantization, Hadamard rotation, sequence chunking, VLM support, Python tooling, CI/CD, ephemeral runners, dependency management (torch, onnx_ir, transformers).
March 2026
10 Commits • 4 Features
Mar 1, 2026March 2026 monthly summary for quic/aimet: Key features and robustness improvements delivered across AdaScale documentation, RMSNorm optimizations, and transformer quantization tooling; CI/CD reliability enhancements, and stability-focused dependency pinning. These efforts enhance quantization accuracy, workflow reliability, and performance visibility for customers and internal users.
10 Commits • 4 Features
Mar 1, 2026March 2026 monthly summary for quic/aimet: Key features and robustness improvements delivered across AdaScale documentation, RMSNorm optimizations, and transformer quantization tooling; CI/CD reliability enhancements, and stability-focused dependency pinning. These efforts enhance quantization accuracy, workflow reliability, and performance visibility for customers and internal users.
March 2026
February 2026
10 Commits • 4 Features
Feb 1, 2026February 2026 monthly highlights for quic/aimet: delivered a set of performance, memory, and stability improvements across model evaluation, quantization, ONNX export, and CI/CD pipelines. Key changes include wrapping evaluation in torch.no_grad to prevent gradient calculations and enabling CUDAExecutionProvider for ONNX evaluation when available, resulting in lower memory usage and faster inference. Quantization and training memory optimizations reduced peak memory by removing the HardSwish supergroup and freeing intermediate tensors after each batch. Memory leak fixes address ONNX ModelProto leaks during save/load and GPU memory leaks in the AdaScale optimization loop, with added stability tests. ONNX export utilities were enhanced for latest model specs with clarified input/output naming, centralized value-info utilities, and hadamard utility refactors, alongside a temporary ONNX-SIM pin during the transition. Pipeline reliability was improved by adding a dependency to the docker-push-latest step in the nightly CI workflow. These changes collectively improve runtime performance, stability, deployment reliability, and developer experience.
February 2026
10 Commits • 4 Features
Feb 1, 2026February 2026 monthly highlights for quic/aimet: delivered a set of performance, memory, and stability improvements across model evaluation, quantization, ONNX export, and CI/CD pipelines. Key changes include wrapping evaluation in torch.no_grad to prevent gradient calculations and enabling CUDAExecutionProvider for ONNX evaluation when available, resulting in lower memory usage and faster inference. Quantization and training memory optimizations reduced peak memory by removing the HardSwish supergroup and freeing intermediate tensors after each batch. Memory leak fixes address ONNX ModelProto leaks during save/load and GPU memory leaks in the AdaScale optimization loop, with added stability tests. ONNX export utilities were enhanced for latest model specs with clarified input/output naming, centralized value-info utilities, and hadamard utility refactors, alongside a temporary ONNX-SIM pin during the transition. Pipeline reliability was improved by adding a dependency to the docker-push-latest step in the nightly CI workflow. These changes collectively improve runtime performance, stability, deployment reliability, and developer experience.
January 2026
6 Commits • 2 Features
Jan 1, 2026January 2026: Stabilized ONNX export and release readiness (2.24), advanced quantization work for large language models, extended matrix operations in sequential MSE to support transposed MatMul, and hardened SpinQuant state loading with improved documentation. These efforts reduce nightly instability, unlock potential gains in model throughput and memory efficiency, and provide clearer integration guidelines for SpinQuant.
6 Commits • 2 Features
Jan 1, 2026January 2026: Stabilized ONNX export and release readiness (2.24), advanced quantization work for large language models, extended matrix operations in sequential MSE to support transposed MatMul, and hardened SpinQuant state loading with improved documentation. These efforts reduce nightly instability, unlock potential gains in model throughput and memory efficiency, and provide clearer integration guidelines for SpinQuant.
January 2026
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 – quic/aimet: Key technical achievements focused on GenAI test reliability and ONNX quantization accuracy. Delivered three concrete items: 1) GenAI Tests Framework: Fixed QuantScheme import path to align with updated aimet_torch structure, reducing import errors. 2) ONNX Quantization Axis Correction for Transposed MatMul: Corrected quantization axis handling to ensure correct inference quantization. 3) GenAI Testing Workflow: Added conditional artifact downloading based on build success, increasing test stability. Business impact: more reliable CI, faster feedback, and improved end-to-end quantization accuracy enabling safer model deployment.
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 – quic/aimet: Key technical achievements focused on GenAI test reliability and ONNX quantization accuracy. Delivered three concrete items: 1) GenAI Tests Framework: Fixed QuantScheme import path to align with updated aimet_torch structure, reducing import errors. 2) ONNX Quantization Axis Correction for Transposed MatMul: Corrected quantization axis handling to ensure correct inference quantization. 3) GenAI Testing Workflow: Added conditional artifact downloading based on build success, increasing test stability. Business impact: more reliable CI, faster feedback, and improved end-to-end quantization accuracy enabling safer model deployment.
November 2025
7 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for quic/aimet: Key features delivered include SeqMSE Enhancements with LPBQ Support delivering user-facing progress UI, memory-safe loss computation, predictive batched matmul with memory fallback, and LPBQ integration; cross-repo work across aimet-onnx and aimet-torch to enable robust SeqMSE with LPBQ and sequential fallback. Also delivered Self-contained model extraction with embedded weights to fix ValidationError and remove external data dependencies; and improved CPU memory usage reporting accuracy by using RSS. Major bugs fixed include ValidationError during LazyExtractor due to missing external data, memory reporting inaccuracies, and quantization input handling reliability by ensuring all prepared inputs are moved to the target device. Overall impact: higher reliability, reduced data dependency, improved memory/quantization behavior, enabling smoother deployments. Technologies/skills demonstrated: Python, ONNX, Torch, LPBQ, SeqMSE, memory management, device-aware data movement, cross-repo collaboration.
7 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for quic/aimet: Key features delivered include SeqMSE Enhancements with LPBQ Support delivering user-facing progress UI, memory-safe loss computation, predictive batched matmul with memory fallback, and LPBQ integration; cross-repo work across aimet-onnx and aimet-torch to enable robust SeqMSE with LPBQ and sequential fallback. Also delivered Self-contained model extraction with embedded weights to fix ValidationError and remove external data dependencies; and improved CPU memory usage reporting accuracy by using RSS. Major bugs fixed include ValidationError during LazyExtractor due to missing external data, memory reporting inaccuracies, and quantization input handling reliability by ensuring all prepared inputs are moved to the target device. Overall impact: higher reliability, reduced data dependency, improved memory/quantization behavior, enabling smoother deployments. Technologies/skills demonstrated: Python, ONNX, Torch, LPBQ, SeqMSE, memory management, device-aware data movement, cross-repo collaboration.
November 2025
October 2025
13 Commits • 2 Features
Oct 1, 2025October 2025 (2025-10) monthly summary for quic/aimet. Focused on delivering advanced Sequential MSE optimization capabilities and sweeping maintenance to improve quantization accuracy, performance, and developer experience. Key achievements include CUDA-accelerated loss computation with dynamic ONNX Runtime I/O binding, vectorized delta-offset calculations, improved candidate initialization, and the ability to exclude specific nodes during optimization, all complemented by a calibration step post-SeqMSE to boost quantization accuracy.
October 2025
13 Commits • 2 Features
Oct 1, 2025October 2025 (2025-10) monthly summary for quic/aimet. Focused on delivering advanced Sequential MSE optimization capabilities and sweeping maintenance to improve quantization accuracy, performance, and developer experience. Key achievements include CUDA-accelerated loss computation with dynamic ONNX Runtime I/O binding, vectorized delta-offset calculations, improved candidate initialization, and the ability to exclude specific nodes during optimization, all complemented by a calibration step post-SeqMSE to boost quantization accuracy.
September 2025
4 Commits • 3 Features
Sep 1, 2025Month: 2025-09 | Project: quic/aimet. This month focused on delivering the 2.14.0 release plan, improving measurement capabilities, documenting Lite-MP, and stabilizing quantization logic. Business value was delivered via clearer release communication, enhanced quality metrics, and robust per-block quantization handling, contributing to a more reliable AIMET offering and smoother customer adoption.
4 Commits • 3 Features
Sep 1, 2025Month: 2025-09 | Project: quic/aimet. This month focused on delivering the 2.14.0 release plan, improving measurement capabilities, documenting Lite-MP, and stabilizing quantization logic. Business value was delivered via clearer release communication, enhanced quality metrics, and robust per-block quantization handling, contributing to a more reliable AIMET offering and smoother customer adoption.
September 2025
August 2025
3 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for quic/aimet: strengthened quantization reliability and CI/build efficiency to support more reliable releases and faster iteration cycles. Key features include automatic adjustment of weight scales to prevent int32 bias overflow in W16A16 quantization and UV-based dependency management for Docker builds and AIMET wheel installations. CI stability improvements temporarily disabled Bokeh-server-dependent tests to address ongoing CI issues while investigations continue. These efforts improved quantization accuracy and stability, reduced build times, and lowered CI noise, delivering tangible business value through more dependable tooling and release readiness.
August 2025
3 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for quic/aimet: strengthened quantization reliability and CI/build efficiency to support more reliable releases and faster iteration cycles. Key features include automatic adjustment of weight scales to prevent int32 bias overflow in W16A16 quantization and UV-based dependency management for Docker builds and AIMET wheel installations. CI stability improvements temporarily disabled Bokeh-server-dependent tests to address ongoing CI issues while investigations continue. These efforts improved quantization accuracy and stability, reduced build times, and lowered CI noise, delivering tangible business value through more dependable tooling and release readiness.
July 2025
5 Commits • 2 Features
Jul 1, 2025July 2025 highlights for quic/aimet: Key features delivered: - CI/CD pipeline reliability and environment management improvements: Remove CUDA dependencies for tf-gpu tests; decouple test execution from CUDA; add reusable Conda environment workflow with retry logic; gating nightly pipelines to non-GitHub.com servers. Quantization capabilities: - Quantization: Python binding for quantize-dequantize in QcQuantizeOp; tests across per-tensor, per-channel, and per-block schemes. Bug fixes: - Quantization: Bias quantization handling correctness fix; exclude bias from quantization when weights are not quantized; updated tests. Impact and accomplishments: - Reduced CUDA dependencies, enabling more reliable and reproducible CI/CD pipelines, which shortens feedback cycles and stabilizes nightly runs. - Expanded quantization capabilities with a Python binding and comprehensive tests, improving deployment readiness and model accuracy. Technologies/skills demonstrated: - CI/CD workflows and environment management (Conda), GitHub Actions, test automation. - Python bindings and multi-scheme quantization (per-tensor, per-channel, per-block). - Debugging and correctness validation in quantization workflows.
5 Commits • 2 Features
Jul 1, 2025July 2025 highlights for quic/aimet: Key features delivered: - CI/CD pipeline reliability and environment management improvements: Remove CUDA dependencies for tf-gpu tests; decouple test execution from CUDA; add reusable Conda environment workflow with retry logic; gating nightly pipelines to non-GitHub.com servers. Quantization capabilities: - Quantization: Python binding for quantize-dequantize in QcQuantizeOp; tests across per-tensor, per-channel, and per-block schemes. Bug fixes: - Quantization: Bias quantization handling correctness fix; exclude bias from quantization when weights are not quantized; updated tests. Impact and accomplishments: - Reduced CUDA dependencies, enabling more reliable and reproducible CI/CD pipelines, which shortens feedback cycles and stabilizes nightly runs. - Expanded quantization capabilities with a Python binding and comprehensive tests, improving deployment readiness and model accuracy. Technologies/skills demonstrated: - CI/CD workflows and environment management (Conda), GitHub Actions, test automation. - Python bindings and multi-scheme quantization (per-tensor, per-channel, per-block). - Debugging and correctness validation in quantization workflows.
July 2025
June 2025
6 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for quic/aimet focusing on stabilizing releases, enabling GPU builds, and modernizing the installation/build workflow. Actions centered on restoring stable CPU-based releases, enabling onnx-gpu support for PyPI wheels, gating CUDA-dependent tests to improve release robustness, and a docs overhaul to streamline setup.
June 2025
6 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for quic/aimet focusing on stabilizing releases, enabling GPU builds, and modernizing the installation/build workflow. Actions centered on restoring stable CPU-based releases, enabling onnx-gpu support for PyPI wheels, gating CUDA-dependent tests to improve release robustness, and a docs overhaul to streamline setup.
May 2025
4 Commits • 2 Features
May 1, 2025May 2025 monthly summary for quic/aimet. Delivered key enhancements to ONNX integration focusing on more predictable runtime behavior and robust packaging, enabling GPU deployment options and improved cross-platform compatibility. No major bugs reported this period; all changes are feature/packaging oriented with clear business value. Demonstrated strong automation and collaboration across runtime, packaging, and plugin components, leading to smoother user deployment and broader hardware support.
4 Commits • 2 Features
May 1, 2025May 2025 monthly summary for quic/aimet. Delivered key enhancements to ONNX integration focusing on more predictable runtime behavior and robust packaging, enabling GPU deployment options and improved cross-platform compatibility. No major bugs reported this period; all changes are feature/packaging oriented with clear business value. Demonstrated strong automation and collaboration across runtime, packaging, and plugin components, leading to smoother user deployment and broader hardware support.
May 2025
April 2025
12 Commits • 4 Features
Apr 1, 2025Month: 2025-04 summary for quic/aimet. This month delivered key CI/CD reliability and security improvements, strengthened CUDA/ONNX build and runtime compatibility, and improved sequential MSE performance. Documentation and versioning updates ensure clear installation guidance and version awareness. The work demonstrates strong CI discipline, GPU-accelerated ONNX workflow stability, and maintainability improvements across the codebase.
April 2025
12 Commits • 4 Features
Apr 1, 2025Month: 2025-04 summary for quic/aimet. This month delivered key CI/CD reliability and security improvements, strengthened CUDA/ONNX build and runtime compatibility, and improved sequential MSE performance. Documentation and versioning updates ensure clear installation guidance and version awareness. The work demonstrates strong CI discipline, GPU-accelerated ONNX workflow stability, and maintainability improvements across the codebase.
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Quality Metrics
Correctness93.0%
Maintainability89.0%
Architecture89.8%
Performance87.8%
AI Usage26.4%
Skills & Technologies
Programming Languages
BashC++CMakeCUDADockerfileJSONMarkdownPythonRSTShell
Technical Skills
AIMETAlgorithmsBuild AutomationBuild System ConfigurationBuild SystemsC++C++ DevelopmentCI/CDCMakeCUDACode RefactoringContainerizationData EvaluationData ProcessingData Structures
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