Xilinx/onnx-mlir
Aug 2025 – Feb 2026
3 Months active
Languages Used
C++CMakeMLIR
Technical Skills
Build SystemBuild System ConfigurationC++Code FormattingCode RefactoringCompiler Development
xiaohan
PROFILE
Xiaohan
Xiao Han contributed to the Xilinx/onnx-mlir repository by developing and refining quantization and dataflow optimization features for ONNX-MLIR compiler infrastructure. Over three months, Xiao implemented robust support for QDQ and qOp replacement, dynamic cast handling, and enhanced compiler passes to improve model reliability and maintainability. Using C++, MLIR, and CMake, Xiao expanded test coverage with lit-based tests, strengthened validation for quantization paths, and improved error handling in ONNX dialect optimization. The work included code refactoring, formatting, and maintenance, resulting in deeper correctness guarantees, broader model compatibility, and a more reliable, maintainable codebase for machine learning compiler workflows.
Overall Statistics
Feature vs Bugs
44%Features
Repository Contributions
39Total
Bugs
10
Commits
39
Features
8
Lines of code
1,582
Activity Months3
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1467 peopleSame Organization
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Work History
February 2026
6 Commits • 1 Features
Feb 1, 2026February 2026 (2026-02) focused on strengthening quantization correctness and expanding test coverage in Xilinx/onnx-mlir. Key deliveries include: 1) Quantization equality checks improvements: refactor to support scalar scale/zero-point comparisons and to ignore axis and block for non-scalar cases; commits 3e25c674ef28056e0ef5cd30e4fc5d3d0e583768, 3a0e43595406903907ac0114518eee7d7343a900, 66afa51c0fa2c79dd6954c0cb9d065daa33476c0. 2) Testing: expand per-axis and per-block dequantization/quantization tests; commit 83986a53c565eeb7a607fc70c616dc855677d2ec. 3) Maintenance/fixes: revert slice lit test and update remove_slice tests to rely on canonicalization for improved shape inference; commits 9b9f8e7157c94514d78cacf8fdd70629cb2659b2, b00c6b9f30e1fc62f00a72dd796154426076993f. Overall impact: stronger correctness guarantees for quantization paths, expanded validation, and more robust test infrastructure. Technologies/skills demonstrated: C++, MLIR/ONNX-MLIR, lit-based testing, scalar-value refactoring, and canonicalization.
6 Commits • 1 Features
Feb 1, 2026February 2026 (2026-02) focused on strengthening quantization correctness and expanding test coverage in Xilinx/onnx-mlir. Key deliveries include: 1) Quantization equality checks improvements: refactor to support scalar scale/zero-point comparisons and to ignore axis and block for non-scalar cases; commits 3e25c674ef28056e0ef5cd30e4fc5d3d0e583768, 3a0e43595406903907ac0114518eee7d7343a900, 66afa51c0fa2c79dd6954c0cb9d065daa33476c0. 2) Testing: expand per-axis and per-block dequantization/quantization tests; commit 83986a53c565eeb7a607fc70c616dc855677d2ec. 3) Maintenance/fixes: revert slice lit test and update remove_slice tests to rely on canonicalization for improved shape inference; commits 9b9f8e7157c94514d78cacf8fdd70629cb2659b2, b00c6b9f30e1fc62f00a72dd796154426076993f. Overall impact: stronger correctness guarantees for quantization paths, expanded validation, and more robust test infrastructure. Technologies/skills demonstrated: C++, MLIR/ONNX-MLIR, lit-based testing, scalar-value refactoring, and canonicalization.
February 2026
September 2025
1 Commits
Sep 1, 2025Concise monthly summary for 2025-09 focusing on business value and technical achievements in Xilinx/onnx-mlir. Delivered robustness improvements and corrected behavior in QDQAroundOpOpt Pass; improved error handling and code correctness in ONNX dialect optimization; contributed to reliability and maintainability of the ONNX-MLIR integration.
September 2025
1 Commits
Sep 1, 2025Concise monthly summary for 2025-09 focusing on business value and technical achievements in Xilinx/onnx-mlir. Delivered robustness improvements and corrected behavior in QDQAroundOpOpt Pass; improved error handling and code correctness in ONNX dialect optimization; contributed to reliability and maintainability of the ONNX-MLIR integration.
August 2025
32 Commits • 7 Features
Aug 1, 2025August 2025 performance summary for Xilinx/onnx-mlir: focused on dataflow support enhancements, expanded op coverage, and code quality improvements that collectively increase model reliability, performance, and maintainability. Delivered concrete features, strengthened validation, and improved maintainability for future iterations.
32 Commits • 7 Features
Aug 1, 2025August 2025 performance summary for Xilinx/onnx-mlir: focused on dataflow support enhancements, expanded op coverage, and code quality improvements that collectively increase model reliability, performance, and maintainability. Delivered concrete features, strengthened validation, and improved maintainability for future iterations.
August 2025
Activity
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Quality Metrics
Correctness87.4%
Maintainability86.6%
Architecture85.2%
Performance80.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeMLIR
Technical Skills
Build SystemBuild System ConfigurationC++C++ developmentCode FormattingCode RefactoringCompiler DevelopmentCompiler OptimizationEmbedded SystemsLLVMLow-Level OptimizationMLIRMLIR Optimization PassesMachine LearningONNX
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