
Worked on the Xilinx/onnx-mlir repository to enhance model compilation and deployment workflows by delivering GELU shape inference for the FrontendDialectTransformer, improving output element type accuracy and aligning inference logic with existing DequantizeLinear and QuantizeLinear paths. Developed a new command-line interface option in C++ to decouple ONNX initializer file locations from model directories, streamlining asset management and deployment reproducibility. Addressed CI instability by removing unstable ONNX MLIR rewriter configurations from the CMake build system, reducing flaky tests and accelerating integration cycles. Demonstrated expertise in compiler development, build systems, and machine learning operations, with a focus on maintainable, production-grade solutions.
August 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 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: 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: 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.
Concise 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.
Concise 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.

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