June 2025 monthly summary for microsoft/onnxruntime focusing on key features delivered, major bugs fixed, impact, and technologies demonstrated. The main deliverable this month was a stability improvement in the static quantization path. Bug fix implemented to ensure opset version updates do not invalidate models during static quantization. Key changes: - Replaced direct graph edits with a version-conversion based approach to preserve model validity when updating opset versions in the static quantization runner. - Commit: 0e4bf97c12d3e4a8667282f15887225b72a818cf (message: Fix illegal update model opset version in static_quantize_runner (#24949)). Impact and business value: - Prevents generation of invalid quantized models, reducing downstream inference failures and support burden. - Improves stability and release readiness of the quantization flow in ONNX Runtime, particularly for models relying on static quantization. - Aligns with ongoing quality goals for ONNX Runtime quantization compatibility with ONNX opset evolution. Technologies/skills demonstrated: - ONNX/ONNX Runtime quantization flow, static quantization path, opset version handling. - Usage of onnx.version_converter.convert_version to preserve model validity. - Code quality and maintainability improvements by isolating changes to the version-conversion path. Overall accomplishment: - A surgical, release-ready fix that increases reliability of quantized models and reduces risk for production deployments.

April 2025: Delivered key QNN-EP enhancements in mozilla/onnxruntime, expanding compatibility and operator coverage. Implemented Softmax support for ONNX opset < 13 with flexible axis handling and robust tensor shape management, and added Upsample operator support in the QNN Execution Provider with op builder updates and dedicated unit tests. These changes broaden model compatibility, improve runtime reliability, and demonstrate end-to-end engineering discipline from code changes to tests.