Xilinx/onnx-mlir
Aug 2025 – Apr 2026
4 Months active
Languages Used
C++CMakeMLIR
Technical Skills
Build SystemBuild System ConfigurationC++Code FormattingCode RefactoringCompiler Development
xiaohan
PROFILE
Xiaohan
Worked on the Xilinx/onnx-mlir repository, delivering compiler optimizations and reliability improvements for ONNX-to-MLIR workflows. Developed features such as configurable lowering of GlobalAveragePool to ReduceMean, enhanced quantization correctness, and expanded test coverage for dataflow and tensor operations. Addressed robustness in quantization passes by refining equality checks and error handling, and improved code maintainability through refactoring and formatting. Utilized C++, MLIR, and CMake to implement optimization passes, test automation, and code quality enhancements. The work focused on maintainable, tunable compiler infrastructure, aligning with downstream requirements and ensuring stable, validated transformations for machine learning and embedded systems applications.
Overall Statistics
Feature vs Bugs
45%Features
Repository Contributions
45Total
Bugs
11
Commits
45
Features
9
Lines of code
1,695
Activity Months4
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Work History
April 2026
6 Commits • 1 Features
Apr 1, 2026In April 2026, delivered a configurable optimization in the ONNX-to-MLIR path for Xilinx/onnx-mlir that lowers GlobalAveragePool to ReduceMean, introduced a new enablGAPToReduceMean flag, and refined tests and formatting to ensure consistency. Reverted GreedyRewriteConfig changes in shape simplification to restore prior stable behavior. These changes deliver tangible performance benefits, tunable optimization control, and strengthened test coverage and code quality, aligning with downstream dmac flow requirements and long-term maintainability.
6 Commits • 1 Features
Apr 1, 2026In April 2026, delivered a configurable optimization in the ONNX-to-MLIR path for Xilinx/onnx-mlir that lowers GlobalAveragePool to ReduceMean, introduced a new enablGAPToReduceMean flag, and refined tests and formatting to ensure consistency. Reverted GreedyRewriteConfig changes in shape simplification to restore prior stable behavior. These changes deliver tangible performance benefits, tunable optimization control, and strengthened test coverage and code quality, aligning with downstream dmac flow requirements and long-term maintainability.
April 2026
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.
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.
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.
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
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.
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.
Activity
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Quality Metrics
Correctness87.8%
Maintainability86.6%
Architecture85.4%
Performance81.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeMLIR
Technical Skills
Build SystemBuild System ConfigurationC++C++ developmentCode FormattingCode RefactoringCompiler DesignCompiler DevelopmentCompiler OptimizationCompiler designEmbedded SystemsLLVMLow-Level OptimizationMLIRMLIR Optimization Passes
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