Xilinx/onnx-mlir
Aug 2025 – Sep 2025
2 Months active
Languages Used
C++MLIR
Technical Skills
C++C++ DevelopmentCode DocumentationCompiler DevelopmentMLIRONNX
Kosh Rai
PROFILE
Kosh Rai
Kosh Rai contributed to the Xilinx/onnx-mlir repository by enhancing quantization correctness and test coverage for ONNX-MLIR workflows. He implemented quantized exponent handling in ONNX Pow canonicalization, enabling accurate Mul-based transformations when exponents originate from DequantizeLinear, and enforced scalar requirements for scale and zero-point in ONNX operations to improve robustness. Using C++ and MLIR, Kosh also updated the ONNX to TOSA conversion tests to align with the evolving TOSA dialect API, ensuring that padding values and expectations matched new semantics. His work demonstrated depth in compiler development and maintained stability across quantized and test-driven code paths.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
78
Activity Months2
Your Network
1333 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Xilinx/onnx-mlir focusing on aligning MLIR ONNX to TOSA padding test coverage with the updated TOSA dialect API. Implemented test updates to define padding values using tosa.const_shape and adjusted expectations so tosa.pad accepts a shape type, ensuring test suite parity with the updated padding semantics. This work strengthens the ONNX-MLIR -> TOSA path and reduces future maintenance due to API changes.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Xilinx/onnx-mlir focusing on aligning MLIR ONNX to TOSA padding test coverage with the updated TOSA dialect API. Implemented test updates to define padding values using tosa.const_shape and adjusted expectations so tosa.pad accepts a shape type, ensuring test suite parity with the updated padding semantics. This work strengthens the ONNX-MLIR -> TOSA path and reduces future maintenance due to API changes.
September 2025
August 2025
4 Commits • 1 Features
Aug 1, 2025August 2025 milestone for Xilinx/onnx-mlir focused on quantization correctness and robustness. Implemented quantized exponent handling for ONNX Pow canonicalization, enabling correct Mul-based transformation when exponents come from DequantizeLinear, and added tests and clearer dequantization notes. Also fixed scalar enforcement for scale and zero-point in ONNX ops to improve robustness. Delivered targeted tests and enhanced comments to aid maintenance. Result: improved quantization accuracy, reduced edge-case failures, and stronger compatibility with quantized ONNX workflows.
August 2025
4 Commits • 1 Features
Aug 1, 2025August 2025 milestone for Xilinx/onnx-mlir focused on quantization correctness and robustness. Implemented quantized exponent handling for ONNX Pow canonicalization, enabling correct Mul-based transformation when exponents come from DequantizeLinear, and added tests and clearer dequantization notes. Also fixed scalar enforcement for scale and zero-point in ONNX ops to improve robustness. Delivered targeted tests and enhanced comments to aid maintenance. Result: improved quantization accuracy, reduced edge-case failures, and stronger compatibility with quantized ONNX workflows.
Activity
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Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture92.0%
Performance84.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
C++C++ DevelopmentCode DocumentationCompiler DevelopmentMLIRONNXONNX RuntimeQuantizationTesting
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