Xilinx/onnx-mlir
Dec 2024 – Feb 2026
8 Months active
Languages Used
C++MLIRMarkdownPythonShell
Technical Skills
Build SystemsC++Compiler DevelopmentIntermediate Representation TransformationLow-Level OptimizationONNX Runtime
Pablo Lanza
PROFILE
Pablo Lanza
Pablo Lanza Serrano contributed to the Xilinx/onnx-mlir repository by developing and refining ONNX-to-TOSA conversion features, focusing on attention mechanisms and rotary embeddings to expand model compatibility on Xilinx platforms. He implemented robust shape inference, validation, and attribute handling for new ONNX operations, and decomposed custom Microsoft attention patterns into standard ONNX ops to improve interoperability. Using C++, MLIR, and Python, Pablo enhanced test coverage, addressed edge cases in tensor operations, and strengthened cross-platform reliability. His work emphasized maintainability through code cleanup, type safety, and modular design, resulting in a more reliable and production-ready model conversion pipeline for machine learning workflows.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
30Total
Bugs
4
Commits
30
Features
10
Lines of code
2,916
Activity Months8
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1467 peopleSame Organization
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Work History
February 2026
4 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for repository Xilinx/onnx-mlir focusing on feature delivery, bug fixes, and code quality improvements. Key outcomes include enabling causal attention configuration in GQA decomposition, introducing a configurable MatmulNBits decomposition into ONNX operations with a feature flag, and comprehensive code cleanup with stronger type checks. These efforts advance language-modeling capabilities, improve pipeline modularity, and enhance maintainability with robust type safety.
4 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for repository Xilinx/onnx-mlir focusing on feature delivery, bug fixes, and code quality improvements. Key outcomes include enabling causal attention configuration in GQA decomposition, introducing a configurable MatmulNBits decomposition into ONNX operations with a feature flag, and comprehensive code cleanup with stronger type checks. These efforts advance language-modeling capabilities, improve pipeline modularity, and enhance maintainability with robust type safety.
February 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12. December 2025 delivered ONNX Export Robustness for Rotary Embedding in Xilinx/onnx-mlir. Strengthened attribute handling and added validation to prevent unsupported attributes during ONNX export, improving reliability for rotary embedding deployments. The changes enhance production readiness and reduce downstream integration issues across inference backends.
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12. December 2025 delivered ONNX Export Robustness for Rotary Embedding in Xilinx/onnx-mlir. Strengthened attribute handling and added validation to prevent unsupported attributes during ONNX export, improving reliability for rotary embedding deployments. The changes enhance production readiness and reduce downstream integration issues across inference backends.
November 2025
2 Commits • 2 Features
Nov 1, 2025Month 2025-11 — Xilinx/onnx-mlir Delivered two key ONNX integration features, improving interoperability, standard compliance, and maintenance for production workflows. No major bugs fixed this month as focus remained on feature delivery and code quality improvements. Overall impact: Expanded ONNX compatibility within the mlir pipeline, enabling broader adoption and easier export of Microsoft-originated attention patterns into standard ONNX ops. Strengthened validation and attribute handling to reduce integration risk in downstream models and tooling.
2 Commits • 2 Features
Nov 1, 2025Month 2025-11 — Xilinx/onnx-mlir Delivered two key ONNX integration features, improving interoperability, standard compliance, and maintenance for production workflows. No major bugs fixed this month as focus remained on feature delivery and code quality improvements. Overall impact: Expanded ONNX compatibility within the mlir pipeline, enabling broader adoption and easier export of Microsoft-originated attention patterns into standard ONNX ops. Strengthened validation and attribute handling to reduce integration risk in downstream models and tooling.
November 2025
October 2025
7 Commits • 2 Features
Oct 1, 2025Concise monthly summary for 2025-10 focused on ONNX-MLIR work in Xilinx/onnx-mlir. Delivered core feature and ecosystem enhancements to expand model compatibility, robustness, and maintainability, enabling broader deployment of attention-based models and rotary embeddings on Xilinx platforms.
October 2025
7 Commits • 2 Features
Oct 1, 2025Concise monthly summary for 2025-10 focused on ONNX-MLIR work in Xilinx/onnx-mlir. Delivered core feature and ecosystem enhancements to expand model compatibility, robustness, and maintainability, enabling broader deployment of attention-based models and rotary embeddings on Xilinx platforms.
September 2025
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly performance summary for Xilinx/onnx-mlir focusing on business value and technical achievements. Key improvements center on correctness of ONNX dialect shape operations and robustness of the ONNX-to-TOSA conversion path. The work enhances reliability for model conversion pipelines and reduces downstream debugging efforts in production environments.
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly performance summary for Xilinx/onnx-mlir focusing on business value and technical achievements. Key improvements center on correctness of ONNX dialect shape operations and robustness of the ONNX-to-TOSA conversion path. The work enhances reliability for model conversion pipelines and reduces downstream debugging efforts in production environments.
September 2025
July 2025
7 Commits
Jul 1, 2025July 2025 focused on stabilizing ONNX-MLIR slice conversion and improving cross-platform reliability in Xilinx/onnx-mlir. Key efforts delivered robust edge-case handling for ONNX→TOSA Slice conversion, updates to padding logic, expanded test coverage, and a Windows portability fix. These changes reduce risk in model conversions, broaden platform support, and strengthen test infrastructure for regression safety and maintainability.
July 2025
7 Commits
Jul 1, 2025July 2025 focused on stabilizing ONNX-MLIR slice conversion and improving cross-platform reliability in Xilinx/onnx-mlir. Key efforts delivered robust edge-case handling for ONNX→TOSA Slice conversion, updates to padding logic, expanded test coverage, and a Windows portability fix. These changes reduce risk in model conversions, broaden platform support, and strengthen test infrastructure for regression safety and maintainability.
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for Xilinx/onnx-mlir: Implemented ONNX to TOSA Slice operation enhancements with non-unit steps, including padding and reshaping to handle steps greater than one. Expanded test coverage to validate uneven slicing and added validation to prevent negative steps, improving correctness and stability. Overall impact includes broader model compatibility on Xilinx targets, higher reliability of slice conversions, and stronger test coverage. Demonstrated technologies/skills include ONNX-MLIR integration, shape calculation logic, test-driven development, and robust input validation.
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for Xilinx/onnx-mlir: Implemented ONNX to TOSA Slice operation enhancements with non-unit steps, including padding and reshaping to handle steps greater than one. Expanded test coverage to validate uneven slicing and added validation to prevent negative steps, improving correctness and stability. Overall impact includes broader model compatibility on Xilinx targets, higher reliability of slice conversions, and stronger test coverage. Demonstrated technologies/skills include ONNX-MLIR integration, shape calculation logic, test-driven development, and robust input validation.
June 2025
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for Xilinx/onnx-mlir focused on delivering a concrete enhancement to the ONNX-MLIR lowering pipeline and solidifying its test and maintenance posture. Key feature delivered: the ONNX Where operation lowering to TOSA Select, enabling correct broadcast semantics and support for multiple data types, effectively representing ONNX Where in the TOSA dialect. This work included the addition of tests to validate the pattern and ensure long-term stability within the TOSA backend workflow. Additionally, the month included import-related fixes and responses to code-review feedback that improved build reliability and integration with existing lowering patterns. The work is backed by a set of incremental commits that finalized the feature and addressed review comments, keeping changes focused and well-scoped.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for Xilinx/onnx-mlir focused on delivering a concrete enhancement to the ONNX-MLIR lowering pipeline and solidifying its test and maintenance posture. Key feature delivered: the ONNX Where operation lowering to TOSA Select, enabling correct broadcast semantics and support for multiple data types, effectively representing ONNX Where in the TOSA dialect. This work included the addition of tests to validate the pattern and ensure long-term stability within the TOSA backend workflow. Additionally, the month included import-related fixes and responses to code-review feedback that improved build reliability and integration with existing lowering patterns. The work is backed by a set of incremental commits that finalized the feature and addressed review comments, keeping changes focused and well-scoped.
Activity
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Quality Metrics
Correctness89.0%
Maintainability86.0%
Architecture86.6%
Performance80.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
C++MLIRMarkdownPythonShell
Technical Skills
Build SystemBuild SystemsC++C++ developmentCode DocumentationCompiler DevelopmentCross-Platform DevelopmentDependency ManagementIntermediate Representation (IR) ManipulationIntermediate Representation TransformationLow-Level OptimizationMLIRMachine LearningNeural NetworksONNX
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