Xilinx/onnx-mlir
Aug 2025 – Feb 2026
2 Months active
Languages Used
C++CMakeMLIRPythonYAML
Technical Skills
Build System ConfigurationBuild SystemsCI/CDCMakeConditional CompilationPython Scripting
Grigory Vartanyan
PROFILE
Grigory Vartanyan
Worked on the Xilinx/onnx-mlir repository to enhance build system flexibility and data-path robustness over a two-month period. Developed an optional Pybind11 and Python runtime integration, allowing builds to exclude Python components and reducing dependency overhead. This involved CMake-based conditional compilation, CI pipeline adjustments, and test gating to ensure reliable validation across configurations. Additionally, improved the handling of splat attributes in raw byte accesses, refining C++ and MLIR-based data processing logic to ensure correct data expansion and robust index calculations. These contributions strengthened both the build process and core data-path reliability, supporting more flexible deployment and reducing potential defects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
257
Activity Months2
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Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for Xilinx/onnx-mlir focused on strengthening data-path robustness in attribute handling. Delivered a targeted feature to improve splat attribute processing in raw byte accesses, ensured correct data expansion for both splat and non-splat cases, and refined index calculations to support splat scenarios. The work reduces defect risk in critical data-paths and improves reliability for downstream ML workloads.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for Xilinx/onnx-mlir focused on strengthening data-path robustness in attribute handling. Delivered a targeted feature to improve splat attribute processing in raw byte accesses, ensured correct data expansion for both splat and non-splat cases, and refined index calculations to support splat scenarios. The work reduces defect risk in critical data-paths and improves reliability for downstream ML workloads.
February 2026
August 2025
4 Commits • 1 Features
Aug 1, 2025Monthly work summary for 2025-08 focusing on build system flexibility and CI/test reliability for Xilinx/onnx-mlir. Implemented an optional Pybind11/Python runtime path to enable lean builds, with corresponding CI/test gating to ensure stable validation and broader deployment options.
August 2025
4 Commits • 1 Features
Aug 1, 2025Monthly work summary for 2025-08 focusing on build system flexibility and CI/test reliability for Xilinx/onnx-mlir. Implemented an optional Pybind11/Python runtime path to enable lean builds, with corresponding CI/test gating to ensure stable validation and broader deployment options.
Activity
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Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture92.0%
Performance92.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeMLIRPythonYAML
Technical Skills
Build System ConfigurationBuild SystemsC++ DevelopmentCI/CDCMakeConditional CompilationData ProcessingMLIRPython ScriptingTesting
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