pytorch/executorch
Mar 2026 – Apr 2026
2 Months active
Languages Used
Python
Technical Skills
AI and machine learningAI integrationPyTorchbackend developmentmachine learningquantum neural networks
qti-horodnic
PROFILE
Qti-horodnic
During a two-month period, Horodnic contributed to the pytorch/executorch repository by expanding hardware-accelerated operation support for the Qualcomm AI Engine Direct backend. He implemented new mathematical operations such as isInf, isNan, log2, log10, log1p, trunc, acos, and avg_pool_1d, using decomposition passes and backend integration to improve numerical robustness and inference throughput. His work involved Python and C++ for backend development, leveraging PyTorch and machine learning expertise. Comprehensive test coverage and alignment with adaptive pooling workflows ensured maintainability and regression safety, positioning executorch to accelerate more workloads and reduce latency on Qualcomm hardware platforms.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
4
Lines of code
946
Activity Months2
Your Network
506 peopleSame Organization
@qti.qualcomm.com171
Work History
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026: Delivered two new Qualcomm AI Engine Direct backend capabilities in executorch (acos via decomposition and avg_pool_1d with tests), expanded hardware-accelerated operation coverage, and strengthened tests and maintainability. These changes unlock faster inference on Qualcomm devices for additional ops and lay groundwork for future expansions.
2 Commits • 1 Features
Apr 1, 2026April 2026: Delivered two new Qualcomm AI Engine Direct backend capabilities in executorch (acos via decomposition and avg_pool_1d with tests), expanded hardware-accelerated operation coverage, and strengthened tests and maintainability. These changes unlock faster inference on Qualcomm devices for additional ops and lay groundwork for future expansions.
April 2026
March 2026
5 Commits • 3 Features
Mar 1, 2026March 2026 monthly summary for pytorch/executorch focusing on Qualcomm AI Engine Direct (QCE Direct) backend integration and enhancements. What was delivered: - QNN backend: Added isInf and isNan operations support with tests to detect infinite and NaN values in tensors, improving numeric robustness on Qualcomm hardware. - QNN backend math operations: Implemented support for log2, log10, log1p via a decomposition pass and enhanced by trunc operation optimization, all with test coverage. - RAND support: Introduced RAND operation for the ATen core under QCE Direct with a new backend/operator class and tests, expanding stochastic capabilities in the QNN backend. Key outcomes: - Expanded hardware-accelerated operation coverage for the Qualcomm AI Engine Direct backend. - Strengthened numerical stability and correctness in QNN-backed tensors, reducing edge-case failures. - Added comprehensive tests ensuring reliability across isInf/isNan, log-related ops, trunc, and RAND. Technologies and skills demonstrated: - Deepening C++/ATen backend development, backend integration with QCE Direct, and test-driven development. - Backend decomposition passes for mathematical operations, operator class design, and cross-repo collaboration with PyTorch contributors. - Emphasis on performance value: expanding hardware-accelerated op support translates to improved throughput and reduced CPU fallback for users on Qualcomm hardware.
March 2026
5 Commits • 3 Features
Mar 1, 2026March 2026 monthly summary for pytorch/executorch focusing on Qualcomm AI Engine Direct (QCE Direct) backend integration and enhancements. What was delivered: - QNN backend: Added isInf and isNan operations support with tests to detect infinite and NaN values in tensors, improving numeric robustness on Qualcomm hardware. - QNN backend math operations: Implemented support for log2, log10, log1p via a decomposition pass and enhanced by trunc operation optimization, all with test coverage. - RAND support: Introduced RAND operation for the ATen core under QCE Direct with a new backend/operator class and tests, expanding stochastic capabilities in the QNN backend. Key outcomes: - Expanded hardware-accelerated operation coverage for the Qualcomm AI Engine Direct backend. - Strengthened numerical stability and correctness in QNN-backed tensors, reducing edge-case failures. - Added comprehensive tests ensuring reliability across isInf/isNan, log-related ops, trunc, and RAND. Technologies and skills demonstrated: - Deepening C++/ATen backend development, backend integration with QCE Direct, and test-driven development. - Backend decomposition passes for mathematical operations, operator class design, and cross-repo collaboration with PyTorch contributors. - Emphasis on performance value: expanding hardware-accelerated op support translates to improved throughput and reduced CPU fallback for users on Qualcomm hardware.
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Quality Metrics
Correctness97.2%
Maintainability80.0%
Architecture88.6%
Performance80.0%
AI Usage45.8%
Skills & Technologies
Programming Languages
Python
Technical Skills
AI and machine learningAI integrationPyTorchbackend developmentmachine learningquantum neural networks
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