Xilinx/onnx-mlir
Jan 2026 – Jan 2026
1 Month active
Languages Used
C++YAML
Technical Skills
C++C++ developmentCompiler DesignMLIRMLIR DevelopmentONNX
pragyam
PROFILE
Pragyam
Pragya Musal contributed to the Xilinx/onnx-mlir repository by developing new compiler features focused on tensor operations and layout flexibility. She implemented a channel-last (NHWC) resize operation in the ONNX dialect, supporting dynamic shape inference and robust verification to facilitate seamless NCHW to NHWC conversion. Pragya migrated and enhanced core compiler passes, including 3D to 2D conversion and depthwise convolution with channel multiplier, from flexml to onnx-mlir. Her work, primarily in C++ and MLIR, included schema and test updates, as well as code formatting improvements, resulting in broader model compatibility, improved maintainability, and strengthened test coverage.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
4,348
Activity Months1
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Work History
January 2026
6 Commits • 3 Features
Jan 1, 2026January 2026 (2026-01) – Delivered critical feature and reliability enhancements to Xilinx/onnx-mlir. Key accomplishments include enabling a channel-last (NHWC) resize operation in the ONNX dialect with dynamic shape inference and verification, updating tests and schemas, and ensuring smooth NCHW↔NHWC conversion. Migrated essential compiler passes from flexml to onnx-mlir, including 3D→2D conversion, depthwise convolution, and pooling, and added depthwise convolution with channel multiplier. Also applied code formatting improvements for readability and maintainability. Overall, these changes broaden model compatibility, improve performance potential, and strengthen code quality and test coverage.
6 Commits • 3 Features
Jan 1, 2026January 2026 (2026-01) – Delivered critical feature and reliability enhancements to Xilinx/onnx-mlir. Key accomplishments include enabling a channel-last (NHWC) resize operation in the ONNX dialect with dynamic shape inference and verification, updating tests and schemas, and ensuring smooth NCHW↔NHWC conversion. Migrated essential compiler passes from flexml to onnx-mlir, including 3D→2D conversion, depthwise convolution, and pooling, and added depthwise convolution with channel multiplier. Also applied code formatting improvements for readability and maintainability. Overall, these changes broaden model compatibility, improve performance potential, and strengthen code quality and test coverage.
January 2026
Activity
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Quality Metrics
Correctness93.4%
Maintainability86.6%
Architecture93.4%
Performance86.6%
AI Usage26.8%
Skills & Technologies
Programming Languages
C++YAML
Technical Skills
C++C++ developmentCompiler DesignMLIRMLIR DevelopmentONNXONNX OperationsTensor Manipulationcode formattingcompiler designmachine learningschema designsoftware maintenancetensor manipulation
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