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
Grigory Vartanyan contributed to the Xilinx/onnx-mlir repository by developing features that enhanced both build system flexibility and data-path robustness. He introduced an optional Pybind11 and Python runtime path, allowing builds to exclude Python components and reducing dependency overhead, with corresponding updates to CI and test gating for reliable validation. In a separate effort, Grigory improved the handling of splat attributes in raw byte accesses, refining index calculations and ensuring correct data expansion for both splat and non-splat cases. His work demonstrated depth in C++ development, CMake-based build configuration, and MLIR-driven data processing, resulting in more robust workflows.
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.
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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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