Xilinx/onnx-mlir
Oct 2025 – Apr 2026
4 Months active
Languages Used
C++MLIRPythonYAML
Technical Skills
Compiler DevelopmentMLIRONNXONNX RuntimeRefactoringTesting
ayanshul-amd
PROFILE
Ayanshul-amd
Worked on the Xilinx/onnx-mlir repository to enhance ONNX-MLIR transformation capabilities, focusing on configurable normalization, flexible tensor manipulation, and robust result naming. Delivered user-facing features such as InstanceNormalization to LayerNormalization transformation with flag control, and enabled ONNX Split decomposition into Slice operations for improved tensor operations. Refactored transformation passes for maintainability, introduced listeners for monitoring and result metadata, and improved quantization handling. Leveraged C++, MLIR, and Python scripting to implement pass-based transformations, CLI options, and code quality improvements. These efforts streamlined optimization pathways, enhanced code readability, and ensured stable downstream processing across compiler passes without introducing regressions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
7
Lines of code
343,483
Activity Months4
Your Network
1727 peopleSame Organization
@amd.com1657
7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Shared Repositories
70
Abhishek BishtMember
Alexandre EichenbergerMember
Ananya AmancherlaMember
Ananya AmancherlaMember
AnanyaMember
Anil Kumar MarthaMember
anup kumar singhMember
Ayush AnshulMember
chaitanyMember
Work History
April 2026
4 Commits • 1 Features
Apr 1, 2026April 2026: Delivered ResultNames preservation and inference during ONNX-MLIR transformations in Xilinx/onnx-mlir, enabling stable result naming across op rewrites and improving downstream processing and optimization. Refactored ResultNames handling to avoid manual copying/stamping and to rely on inference through transpose, reducing maintenance burden and potential for errors. Performed targeted code quality improvements including lint cleanup and minor comment cleanups.
4 Commits • 1 Features
Apr 1, 2026April 2026: Delivered ResultNames preservation and inference during ONNX-MLIR transformations in Xilinx/onnx-mlir, enabling stable result naming across op rewrites and improving downstream processing and optimization. Refactored ResultNames handling to avoid manual copying/stamping and to rely on inference through transpose, reducing maintenance burden and potential for errors. Performed targeted code quality improvements including lint cleanup and minor comment cleanups.
April 2026
February 2026
7 Commits • 3 Features
Feb 1, 2026February 2026 - Xilinx/onnx-mlir (2026-02) monthly summary focusing on business value and technical delivery across observability, quantization fidelity, and optimization behavior.
February 2026
7 Commits • 3 Features
Feb 1, 2026February 2026 - Xilinx/onnx-mlir (2026-02) monthly summary focusing on business value and technical delivery across observability, quantization fidelity, and optimization behavior.
November 2025
3 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — This month focused on delivering a tangible enhancement to ONNX-MLIR transformation capabilities and strengthening code quality for maintainability and faster iteration. The changes are aligned with business goals of enabling more flexible model transformations with safer configurability, and reducing long-term maintenance costs through improved code standards.
3 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — This month focused on delivering a tangible enhancement to ONNX-MLIR transformation capabilities and strengthening code quality for maintainability and faster iteration. The changes are aligned with business goals of enabling more flexible model transformations with safer configurability, and reducing long-term maintenance costs through improved code standards.
November 2025
October 2025
3 Commits • 1 Features
Oct 1, 2025Month: 2025-10 Overview: Implemented a user-facing transformation to convert InstanceNormalization to LayerNormalization by relocating the logic from the Canonicalize pass to the Decompose pass, and exposing it via a enable/disable flag. Updated API and tests to reflect the new location and flag semantics. This refactor provides business value by giving users control over normalization behavior and enabling optimization pathways, while simplifying maintenance of the pass pipeline. Key features delivered: - InstanceNorm → LayerNorm transformation moved from Canonicalize to Decompose, with a new enable/disable flag to control its activation. - API and test updates to align with the new pass location and flag semantics, ensuring consistent behavior across user flows. - End-to-end test adjustments to reflect the new decomposition path and invocation semantics. Major bugs fixed: - Stabilized end-to-end testing by correcting the decomposition pass invocation and ensuring correct use of the EmitONNXIR flag in tests, reducing flake and CI failures. Overall impact and accomplishments: - Provides business value by giving users explicit control over normalization behavior and enabling performance/compatibility optimization paths. - Improves code maintainability by consolidating normalization transformation logic into a single, reusable Decompose pass. Technologies/skills demonstrated: - Pass-based transformation design and refactoring, flag-driven feature toggles, API evolution, and test strategy (including e2e tests and test invocation flags).
October 2025
3 Commits • 1 Features
Oct 1, 2025Month: 2025-10 Overview: Implemented a user-facing transformation to convert InstanceNormalization to LayerNormalization by relocating the logic from the Canonicalize pass to the Decompose pass, and exposing it via a enable/disable flag. Updated API and tests to reflect the new location and flag semantics. This refactor provides business value by giving users control over normalization behavior and enabling optimization pathways, while simplifying maintenance of the pass pipeline. Key features delivered: - InstanceNorm → LayerNorm transformation moved from Canonicalize to Decompose, with a new enable/disable flag to control its activation. - API and test updates to align with the new pass location and flag semantics, ensuring consistent behavior across user flows. - End-to-end test adjustments to reflect the new decomposition path and invocation semantics. Major bugs fixed: - Stabilized end-to-end testing by correcting the decomposition pass invocation and ensuring correct use of the EmitONNXIR flag in tests, reducing flake and CI failures. Overall impact and accomplishments: - Provides business value by giving users explicit control over normalization behavior and enabling performance/compatibility optimization paths. - Improves code maintainability by consolidating normalization transformation logic into a single, reusable Decompose pass. Technologies/skills demonstrated: - Pass-based transformation design and refactoring, flag-driven feature toggles, API evolution, and test strategy (including e2e tests and test invocation flags).
Activity
Loading activity data...
Quality Metrics
Correctness95.2%
Maintainability88.2%
Architecture90.6%
Performance85.8%
AI Usage21.2%
Skills & Technologies
Programming Languages
C++MLIRPythonYAML
Technical Skills
C++C++ developmentCompiler DesignCompiler DevelopmentCompiler designContinuous IntegrationDevOpsDockerMLIRONNXONNX RuntimeONNX optimizationOptimizationPython ScriptingRefactoring
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline