pytorch/executorch
Oct 2024 – Mar 2025
5 Months active
Languages Used
PythonShell
Technical Skills
PyTorchPythonPython programmingTOSATorchback end development
Per Åstrand
PROFILE
Per Åstrand
Per Åstrand contributed to the pytorch/executorch repository by developing and optimizing quantization workflows and backend support for TOSA-based models. He implemented quantization folding passes, enhanced operator support, and improved graph optimization, focusing on accurate handling of quantized and dequantized operations. Using Python and PyTorch, Per introduced features such as per-channel scaling, robust input/output quantization parameter retrieval, and debugging enhancements like improved object representations. His work addressed cross-backend compatibility, reduced runtime errors, and increased test coverage, resulting in more reliable deployment of quantized models. The depth of his contributions reflects strong backend development and machine learning engineering skills.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
17Total
Bugs
4
Commits
17
Features
7
Lines of code
1,761
Activity Months5
Your Network
786 people
Work History
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for pytorch/executorch focused on delivering features that enhance debugging clarity and per-channel scaling flexibility, with emphasis on business value and robust technical execution.
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for pytorch/executorch focused on delivering features that enhance debugging clarity and per-channel scaling flexibility, with emphasis on business value and robust technical execution.
March 2025
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary focused on stabilizing quantization for the Arm backend in pytorch/executorch. Delivered a compatibility fix for module-type filtering in the quantizer when porting from xnnpack, reducing mis-filtering and improving cross-backend portability.
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary focused on stabilizing quantization for the Arm backend in pytorch/executorch. Delivered a compatibility fix for module-type filtering in the quantizer when porting from xnnpack, reducing mis-filtering and improving cross-backend portability.
December 2024
4 Commits • 1 Features
Dec 1, 2024December 2024: Delivered Quantization Folding Pass Enhancements and Testing for pytorch/executorch. Implemented input/output quantization parameter retrieval helpers, expanded tests to validate folding and annotation during quantization, and relaxed quantization parameter requirements for TOSA tests to improve robustness and deployment reliability of quantized models. Also introduced helper functions for the Q/DQ folding pass and updated tests to reflect a sequence of ops, increasing test coverage, reliability, and regression safety across backends. This work enhances model quantization fidelity across backends, accelerates QA, and supports broader hardware compatibility.
4 Commits • 1 Features
Dec 1, 2024December 2024: Delivered Quantization Folding Pass Enhancements and Testing for pytorch/executorch. Implemented input/output quantization parameter retrieval helpers, expanded tests to validate folding and annotation during quantization, and relaxed quantization parameter requirements for TOSA tests to improve robustness and deployment reliability of quantized models. Also introduced helper functions for the Q/DQ folding pass and updated tests to reflect a sequence of ops, increasing test coverage, reliability, and regression safety across backends. This work enhances model quantization fidelity across backends, accelerates QA, and supports broader hardware compatibility.
December 2024
November 2024
4 Commits • 1 Features
Nov 1, 2024Concise monthly summary for 2024-11 focused on delivering quantization and stability improvements in pytorch/executorch, highlighting business value and technical achievements.
November 2024
4 Commits • 1 Features
Nov 1, 2024Concise monthly summary for 2024-11 focused on delivering quantization and stability improvements in pytorch/executorch, highlighting business value and technical achievements.
October 2024
6 Commits • 3 Features
Oct 1, 2024October 2024: Delivered major TOSA-based enhancements and quantization improvements in executorch. Key features include TOSA Specification integration in ArmPartitioner to guide operator support, implementation of TOSA.MIN and TOSA.MAX with tests, and DQ/Q folding rescaling; addressed critical qdq ADD handling, and improved dequantization safety and TOSA reference model output handling. These changes extend operator coverage, improve quantization accuracy, prevent overflow in BI workloads, and strengthen path for deployment and performance.
6 Commits • 3 Features
Oct 1, 2024October 2024: Delivered major TOSA-based enhancements and quantization improvements in executorch. Key features include TOSA Specification integration in ArmPartitioner to guide operator support, implementation of TOSA.MIN and TOSA.MAX with tests, and DQ/Q folding rescaling; addressed critical qdq ADD handling, and improved dequantization safety and TOSA reference model output handling. These changes extend operator coverage, improve quantization accuracy, prevent overflow in BI workloads, and strengthen path for deployment and performance.
October 2024
Activity
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Quality Metrics
Correctness87.0%
Maintainability82.4%
Architecture84.8%
Performance82.4%
AI Usage28.2%
Skills & Technologies
Programming Languages
PythonShell
Technical Skills
Backend DevelopmentDeep LearningMachine LearningPyTorchPythonPython programmingTOSATorchback end developmentbackend developmentdata processingdebuggingdeep learninggraph optimizationmachine learning
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