Xilinx/onnx-mlir
Jan 2025 – May 2026
4 Months active
Languages Used
C++
Technical Skills
Compiler DevelopmentMachine Learning OperationsCommand-Line InterfaceONNX RuntimeBuild SystemCMake
Liam Fitzpatrick
PROFILE
Liam Fitzpatrick
Worked on Xilinx/onnx-mlir to deliver features and fixes that improved model compilation, deployment, and runtime efficiency. Developed GELU shape inference for the FrontendDialectTransformer, aligning it with existing inference paths to enhance type accuracy during model execution. Added a command-line interface option for flexible ONNX initializer file management, supporting reproducible deployments. Addressed CI instability by removing problematic ONNX MLIR rewriters from the CMake build system, reducing test flakiness. Implemented efficient in-memory external data handling for ONNX TensorProto, optimizing memory usage for large models. Demonstrated expertise in C++, CMake, and MLIR, focusing on maintainable, production-grade compiler and runtime enhancements.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
3
Lines of code
388
Activity Months4
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Work History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 — Xilinx/onnx-mlir delivered a feature focused on Efficient In-Memory External Data Handling for ONNX TensorProto. The change enables treating the location marker */_ORT_MEM_ADDR_/* as in-memory external data, reducing data copies and improving memory management for large models. This work strengthens the ONNX-MLIR data path and lays groundwork for better performance in model loading and processing. Major bugs fixed: none reported in this scope; the month was primarily feature-driven and prepared for integration. The work reflects collaboration and a commitment to scalable data handling in ONNX workflows.
1 Commits • 1 Features
May 1, 2026May 2026 — Xilinx/onnx-mlir delivered a feature focused on Efficient In-Memory External Data Handling for ONNX TensorProto. The change enables treating the location marker */_ORT_MEM_ADDR_/* as in-memory external data, reducing data copies and improving memory management for large models. This work strengthens the ONNX-MLIR data path and lays groundwork for better performance in model loading and processing. Major bugs fixed: none reported in this scope; the month was primarily feature-driven and prepared for integration. The work reflects collaboration and a commitment to scalable data handling in ONNX workflows.
May 2026
August 2025
1 Commits
Aug 1, 2025August 2025 monthly summary for Xilinx/onnx-mlir focused on CI stabilization by removing unstable ONNX MLIR rewriters from the build. This change reduces flakiness and accelerates integration cycles. Commit 8547acb2a48626d2e9a4386f27789a774af5d511 implements the fix by removing rewriter configurations in the CMake build system.
August 2025
1 Commits
Aug 1, 2025August 2025 monthly summary for Xilinx/onnx-mlir focused on CI stabilization by removing unstable ONNX MLIR rewriters from the build. This change reduces flakiness and accelerates integration cycles. Commit 8547acb2a48626d2e9a4386f27789a774af5d511 implements the fix by removing rewriter configurations in the CMake build system.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a new CLI option to specify the directory for ONNX initializer files in Xilinx/onnx-mlir, decoupling initializer assets from the model directory to improve asset management and deployment reproducibility. The change simplifies asset organization for downstream users and aligns with our ongoing efficiency initiatives.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a new CLI option to specify the directory for ONNX initializer files in Xilinx/onnx-mlir, decoupling initializer assets from the model directory to improve asset management and deployment reproducibility. The change simplifies asset organization for downstream users and aligns with our ongoing efficiency initiatives.
February 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025Concise monthly summary for 2025-01 focused on delivering GELU shape inference for FrontendDialectTransformer in Xilinx/onnx-mlir, improving accuracy of output element types during model compilation and execution, and aligning GELU inference with existing paths for DequantizeLinear and QuantizeLinear. The work supports reliable model deployment and reduces runtime type errors for GELU-enabled models.
January 2025
1 Commits • 1 Features
Jan 1, 2025Concise monthly summary for 2025-01 focused on delivering GELU shape inference for FrontendDialectTransformer in Xilinx/onnx-mlir, improving accuracy of output element types during model compilation and execution, and aligning GELU inference with existing paths for DequantizeLinear and QuantizeLinear. The work supports reliable model deployment and reduces runtime type errors for GELU-enabled models.
Activity
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Quality Metrics
Correctness90.0%
Maintainability85.0%
Architecture85.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
Build SystemC++CMakeCommand-Line InterfaceCompiler DevelopmentMLIRMachine Learning OperationsONNXONNX RuntimeUnit Testing
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