pytorch/executorch
Oct 2024 – Feb 2026
8 Months active
Languages Used
PythonC++
Technical Skills
Machine LearningNeural NetworksPyTorchPythonQuantizationUnit Testing
Oscar Andersson
PROFILE
Oscar Andersson
Oscar Andersson contributed to the pytorch/executorch repository by engineering backend features and optimizations for Arm, focusing on quantization, tensor operations, and TOSA dialect integration. He implemented quantized AvgPool2d and unified resize operations, expanded data type support, and introduced shape operation materialization for TOSA 1.1, enhancing model flexibility and backend compatibility. Using C++ and Python, Oscar addressed issues such as dimension order propagation and integer type handling, while maintaining robust unit testing and modular code structure. His work improved performance, reliability, and maintainability, enabling efficient on-device inference and streamlined deployment across diverse hardware and evolving machine learning workflows.
Overall Statistics
Feature vs Bugs
69%Features
Repository Contributions
16Total
Bugs
4
Commits
16
Features
9
Lines of code
2,831
Activity Months8
Your Network
832 peopleSame Organization
@arm.com497
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Arm backend dim_order propagation for TOSA shape ops was implemented in pytorch/executorch. The change adds new shape-node handling methods and ensures propagation of dim_order to TOSA shape operations. It also ensures that tosa_dim_order is propagated via to_tosa_memory_format_pass, preventing dimension-order conflicts. The shape-rank logic is clarified so rank is derived from output.shape[0] to correctly support dynamic shapes in TOSA ops. The work is captured under Change-Id: Id5861e4dc018c56ca95cdbe358507dfc7f706b78, co-authored by Per Åstrand and signed off by Oscar Andersson (Arm).
1 Commits • 1 Features
Feb 1, 2026February 2026: Arm backend dim_order propagation for TOSA shape ops was implemented in pytorch/executorch. The change adds new shape-node handling methods and ensures propagation of dim_order to TOSA shape operations. It also ensures that tosa_dim_order is propagated via to_tosa_memory_format_pass, preventing dimension-order conflicts. The shape-rank logic is clarified so rank is derived from output.shape[0] to correctly support dynamic shapes in TOSA ops. The work is captured under Change-Id: Id5861e4dc018c56ca95cdbe358507dfc7f706b78, co-authored by Per Åstrand and signed off by Oscar Andersson (Arm).
February 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on pytorch/executorch. Highlights include delivering TOSA 1.1 Shape Operations and Symbolic Integers Materialization in the Arm backend, with validation tests and downstream tooling readiness. Emphasis on business value and technical achievement with a focus on reliability, flexibility, and collaboration.
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on pytorch/executorch. Highlights include delivering TOSA 1.1 Shape Operations and Symbolic Integers Materialization in the Arm backend, with validation tests and downstream tooling readiness. Emphasis on business value and technical achievement with a focus on reliability, flexibility, and collaboration.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for pytorch/executorch focusing on Arm backend improvements, combining a bug fix and a feature to boost performance and maintainability.
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for pytorch/executorch focusing on Arm backend improvements, combining a bug fix and a feature to boost performance and maintainability.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for pytorch/executorch: Key feature delivered - TOSA Tensor Resize Operation: Introduced a TOSA dialect op for RESIZE, consolidating the existing upsample path into a single resize operation. Implemented in the ARM backend (commit 20f906b24f9b0e838956019ec35e04faa6f1b851). Improves performance and maintainability. Major bugs fixed - None reported for this month. Overall impact and accomplishments - Streamlined upsampling flow in executorch, enabling faster execution and easier maintenance. - Establishes groundwork for MLIR/TOSA-based optimizations and cross-backend portability. Technologies/skills demonstrated - TOSA dialect, MLIR integration, ARM backend engineering - Version control discipline, focused feature delivery
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for pytorch/executorch: Key feature delivered - TOSA Tensor Resize Operation: Introduced a TOSA dialect op for RESIZE, consolidating the existing upsample path into a single resize operation. Implemented in the ARM backend (commit 20f906b24f9b0e838956019ec35e04faa6f1b851). Improves performance and maintainability. Major bugs fixed - None reported for this month. Overall impact and accomplishments - Streamlined upsampling flow in executorch, enabling faster execution and easier maintenance. - Establishes groundwork for MLIR/TOSA-based optimizations and cross-backend portability. Technologies/skills demonstrated - TOSA dialect, MLIR integration, ARM backend engineering - Version control discipline, focused feature delivery
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025: Contributions to pytorch/executorch focused on expanding VelaIO capabilities and stabilizing the test suite. Delivered cross-backend 6D input/output shape support, updated data structures, and added 5D test coverage. Improved CI reliability through package version alignment and removal of flaky xfails.
3 Commits • 1 Features
Jun 1, 2025June 2025: Contributions to pytorch/executorch focused on expanding VelaIO capabilities and stabilizing the test suite. Delivered cross-backend 6D input/output shape support, updated data structures, and added 5D test coverage. Improved CI reliability through package version alignment and removal of flaky xfails.
June 2025
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pytorch/executorch: Delivered Arm Backend Enhancements for TOSA 1.0, expanding rescale data type support and standardizing tensor naming to prevent clashes across constants, Conv2dVisitor, and rescale pathways. These changes improve model compatibility and reduce maintenance burden, enabling broader model deployment on Arm. No major bugs fixed were reported in this period. Overall impact: improved flexibility and reliability in Arm backend for TOSA 1.0.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pytorch/executorch: Delivered Arm Backend Enhancements for TOSA 1.0, expanding rescale data type support and standardizing tensor naming to prevent clashes across constants, Conv2dVisitor, and rescale pathways. These changes improve model compatibility and reduce maintenance burden, enabling broader model deployment on Arm. No major bugs fixed were reported in this period. Overall impact: improved flexibility and reliability in Arm backend for TOSA 1.0.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for pytorch/executorch. Delivered critical ARM backend enhancements and reliability fixes that strengthen deployment readiness and correctness. Key achievements include initial Arm backend right shift support, and a robust ScalarsToAttributePass with improved int64->int32 handling and safeguards against unintended scalar modifications. These efforts increased ARM compatibility, reduced runtime errors in attribute handling, and improved test coverage and specification alignment, delivering measurable business value in terms of broader hardware support and more maintainable backend code.
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for pytorch/executorch. Delivered critical ARM backend enhancements and reliability fixes that strengthen deployment readiness and correctness. Key achievements include initial Arm backend right shift support, and a robust ScalarsToAttributePass with improved int64->int32 handling and safeguards against unintended scalar modifications. These efforts increased ARM compatibility, reduced runtime errors in attribute handling, and improved test coverage and specification alignment, delivering measurable business value in terms of broader hardware support and more maintainable backend code.
November 2024
October 2024
4 Commits • 2 Features
Oct 1, 2024October 2024 monthly performance summary for pytorch/executorch (Arm backend) Key features delivered: - ArmQuantizer: Added quantization support for AvgPool2d to reduce precision and improve efficiency while preserving performance; includes unit tests for Conv2D and AvgPool2d. Commits: c35386f4740e09efa4508e18a556bf4c24f2fe34 (ArmQuantizer: Quantize AvgPool2d #5873). - Arm backend slice operation: Fixed handling when end index is missing by using min(end, dimension_size); updated tests to cover new behavior and prevent out-of-bounds errors. Commit: befc1a905b7226ffb8d6ef223b47f75721142d89 (Arm backend: Make slice-op work without end index #5782). - Arm backend: LayerNorm, mean, and variance decomposition and refactor to improve efficiency and modularity; removes circular dependencies. Commits: f93270a4c2c4c812e75796fde79e0472f7d4cbef (Arm backend: Add layer_norm decomposition #6288) and 9abc9f49f16c49751ce4ff04d23e780cc11b860a (Solve circular import error). Major bugs fixed: - Fixed slice operation end index handling to avoid out-of-bounds errors. - Resolved circular import issues introduced during decomposition refactor, enabling stable builds. Overall impact and accomplishments: - Substantial performance and efficiency gains on the Arm backend through quantization and operation decomposition, supporting faster on-device inference and reduced memory footprint. - Improved code quality and maintainability via modular refactors and expanded test coverage, reducing regression risk. Technologies/skills demonstrated: - Quantization techniques, backend optimization, modular refactoring, test-driven development, and dependency management. Business value: - Lower latency and memory usage for AvgPool2d paths on Arm, enabling better edge-device performance and scalability for larger models.
October 2024
4 Commits • 2 Features
Oct 1, 2024October 2024 monthly performance summary for pytorch/executorch (Arm backend) Key features delivered: - ArmQuantizer: Added quantization support for AvgPool2d to reduce precision and improve efficiency while preserving performance; includes unit tests for Conv2D and AvgPool2d. Commits: c35386f4740e09efa4508e18a556bf4c24f2fe34 (ArmQuantizer: Quantize AvgPool2d #5873). - Arm backend slice operation: Fixed handling when end index is missing by using min(end, dimension_size); updated tests to cover new behavior and prevent out-of-bounds errors. Commit: befc1a905b7226ffb8d6ef223b47f75721142d89 (Arm backend: Make slice-op work without end index #5782). - Arm backend: LayerNorm, mean, and variance decomposition and refactor to improve efficiency and modularity; removes circular dependencies. Commits: f93270a4c2c4c812e75796fde79e0472f7d4cbef (Arm backend: Add layer_norm decomposition #6288) and 9abc9f49f16c49751ce4ff04d23e780cc11b860a (Solve circular import error). Major bugs fixed: - Fixed slice operation end index handling to avoid out-of-bounds errors. - Resolved circular import issues introduced during decomposition refactor, enabling stable builds. Overall impact and accomplishments: - Substantial performance and efficiency gains on the Arm backend through quantization and operation decomposition, supporting faster on-device inference and reduced memory footprint. - Improved code quality and maintainability via modular refactors and expanded test coverage, reducing regression risk. Technologies/skills demonstrated: - Quantization techniques, backend optimization, modular refactoring, test-driven development, and dependency management. Business value: - Lower latency and memory usage for AvgPool2d paths on Arm, enabling better edge-device performance and scalability for larger models.
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Quality Metrics
Correctness90.0%
Maintainability82.6%
Architecture86.2%
Performance82.6%
AI Usage33.8%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ programmingGraph processingMachine LearningNeural NetworksPyTorchPythonPython programmingQuantizationTOSATOSA dialectTensor operationsTorchUnit Testingback end developmentbackend development
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