pytorch/executorch
Oct 2024 – Jun 2026
11 Months active
Languages Used
PythonShell
Technical Skills
PyTorchPythonPython programmingTOSATorchback end development
Per Åstrand
PROFILE
Per Åstrand
Worked extensively on the pytorch/executorch repository, delivering backend features and quantization improvements for Arm and Ethos-U hardware. Focused on TOSA dialect integration, multi-version specification handling, and dynamic shape support, the work enabled robust model conversion and deployment across evolving hardware targets. Leveraged Python and Shell scripting to implement quantization folding passes, operator support, and serialization utilities, while enhancing test infrastructure for maintainability. Addressed compatibility and reliability by upgrading toolchains, refining subprocess management, and introducing flexible spec utilities. The engineering approach emphasized modularity, cross-backend portability, and regression safety, resulting in scalable, production-ready backend workflows for machine learning inference.
Overall Statistics
Feature vs Bugs
74%Features
Repository Contributions
32Total
Bugs
5
Commits
32
Features
14
Lines of code
4,834
Activity Months11
Your Network
950 peopleSame Organization
@arm.com569
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for pytorch/executorch: Primary activity focused on upgrading Ethos-U backend tooling via tosa-tools 2026.5.0 to enhance compatibility and enable features. This aligns the Arm/Ethos-U backend with the latest toolchain and reduces integration risk. No explicit bug fixes were recorded this month as the main effort was a dependency upgrade and preparation for upcoming backend enhancements.
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for pytorch/executorch: Primary activity focused on upgrading Ethos-U backend tooling via tosa-tools 2026.5.0 to enhance compatibility and enable features. This aligns the Arm/Ethos-U backend with the latest toolchain and reduces integration risk. No explicit bug fixes were recorded this month as the main effort was a dependency upgrade and preparation for upcoming backend enhancements.
June 2026
March 2026
5 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for pytorch/executorch: Delivered ARM backend quantization enhancements and IO improvements, focusing on uint8 IO support, compatibility, and maintainability. Implemented uint8 IO quantization, updated toolchain to tosa-tools 2026.05 for serialization compatibility with older model-converter tools, and refined QSpec handling to respect TOSA constraints. Introduced preserve_io_quantization option and extended +FP/+INT support for VGF tensors, strengthening cross-device portability and performance of quantized inference on ARM hardware. These changes reduce conversion friction, improve correctness, and lay groundwork for further optimization and robustness across the ARM backend.
March 2026
5 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for pytorch/executorch: Delivered ARM backend quantization enhancements and IO improvements, focusing on uint8 IO support, compatibility, and maintainability. Implemented uint8 IO quantization, updated toolchain to tosa-tools 2026.05 for serialization compatibility with older model-converter tools, and refined QSpec handling to respect TOSA constraints. Introduced preserve_io_quantization option and extended +FP/+INT support for VGF tensors, strengthening cross-device portability and performance of quantized inference on ARM hardware. These changes reduce conversion friction, improve correctness, and lay groundwork for further optimization and robustness across the ARM backend.
January 2026
5 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01) focused on strengthening the Arm backend (pytorch/executorch) with multi-version TOSA support, reliability improvements, and new dialect capabilities to future-proof for evolving TOSA specs. Key features delivered include: 1) TOSA 1.1 multi-version support and utilities in the Arm backend, introducing a new Tosa_1_1 specification class, version-agnostic operator registration, and related helpers to manage specifications across versions; 2) centralized and simplified spec handling via spec list helpers and a TosaSpecification list method to ensure consistent operator registration across all supported TOSA versions; 3) New TOSA dialect operation for CONST_SHAPE to create constant-shape tensors, enabling improved shape management in tensor operations; 4) Broader changes to support the new dialect and spec flow with a dedicated ArmPass integration for CONST_SHAPE creation; 5) Reliability improvement in command execution by fixing subprocess invocation in the VGF backend to avoid stdin/read path issues. Impact: these changes reduce maintenance cost for multi-version TOSA support, improve backend reliability and shape-aware tensor operations, and enhance overall production readiness for Arm backend workloads. Technologies/skills demonstrated: ARM backend development, TOSA dialects and specification management, Python subprocess handling, code refactoring for maintainability, and commits-based traceability in a large-scale ML backend.
5 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01) focused on strengthening the Arm backend (pytorch/executorch) with multi-version TOSA support, reliability improvements, and new dialect capabilities to future-proof for evolving TOSA specs. Key features delivered include: 1) TOSA 1.1 multi-version support and utilities in the Arm backend, introducing a new Tosa_1_1 specification class, version-agnostic operator registration, and related helpers to manage specifications across versions; 2) centralized and simplified spec handling via spec list helpers and a TosaSpecification list method to ensure consistent operator registration across all supported TOSA versions; 3) New TOSA dialect operation for CONST_SHAPE to create constant-shape tensors, enabling improved shape management in tensor operations; 4) Broader changes to support the new dialect and spec flow with a dedicated ArmPass integration for CONST_SHAPE creation; 5) Reliability improvement in command execution by fixing subprocess invocation in the VGF backend to avoid stdin/read path issues. Impact: these changes reduce maintenance cost for multi-version TOSA support, improve backend reliability and shape-aware tensor operations, and enhance overall production readiness for Arm backend workloads. Technologies/skills demonstrated: ARM backend development, TOSA dialects and specification management, Python subprocess handling, code refactoring for maintainability, and commits-based traceability in a large-scale ML backend.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for pytorch/executorch: Focused on strengthening serialization support and spec management for TOSA on the Arm backend, enabling future hardware backends and improved portability. Delivered experimental TOSA 1.1 serialization support, deprecated outdated TOSA 1.0 specs, and established a flexible spec handling approach with alignment to tosa-tools main branch.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for pytorch/executorch: Focused on strengthening serialization support and spec management for TOSA on the Arm backend, enabling future hardware backends and improved portability. Delivered experimental TOSA 1.1 serialization support, deprecated outdated TOSA 1.0 specs, and established a flexible spec handling approach with alignment to tosa-tools main branch.
October 2025
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on business value and technical achievement in pytorch/executorch. Deliverables center on Arm TOSA backend improvements enabling dynamic shapes and enhanced shape handling, with targeted fixes and tests to improve stability and compatibility.
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on business value and technical achievement in pytorch/executorch. Deliverables center on Arm TOSA backend improvements enabling dynamic shapes and enhanced shape handling, with targeted fixes and tests to improve stability and compatibility.
October 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on structural improvements to the Arm testing path within executorch and laying groundwork for scalable Arm backend validation. The key change centralized in a common test harness, reducing maintenance overhead and enabling consistent test execution across Arm-related tests. No major bugs recorded this month; changes are infrastructure and maintainability oriented.
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on structural improvements to the Arm testing path within executorch and laying groundwork for scalable Arm backend validation. The key change centralized in a common test harness, reducing maintenance overhead and enabling consistent test execution across Arm-related tests. No major bugs recorded this month; changes are infrastructure and maintainability oriented.
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
Correctness89.4%
Maintainability83.2%
Architecture86.8%
Performance83.2%
AI Usage27.6%
Skills & Technologies
Programming Languages
PythonShell
Technical Skills
Backend DevelopmentDeep LearningMachine LearningPyTorchPythonPython programmingTOSATOSA dialectTOSA dialect integrationTOSA specification handlingTensor operationsTorchback end developmentbackend developmentdata processing
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