October 2025 monthly summary for active ONNX-related development across microsoft/onnxscript and onnx/onnx. Focused on robustness, flexibility, and clarity to reduce deployment risk and improve cross-repo interoperability.

September 2025 focused on expanding the ONNX Script Rewriter's fusion capabilities to boost model optimization and opset 23 compatibility. Delivered two key features with solid test coverage: (1) Group Query Attention (GQA) Fusion added to ONNX Script Rewriter, including a new fusion rule, pattern, rewrite logic, and tests (commit f54cf47749ab7ffbe424c6e736ec4d74aa4c15b2). (2) Rotary Embedding Fusion for opset 23: refactored the rewriter to support opset 23 input argument changes, updated fusion patterns, renamed tests, and added a dedicated Rotary Embedding fusion test to ensure compatibility with the latest ONNX opset (commits dd df0c2f97c4839b5fbcdbd1c0509562a922a7fe, 9b54ad549aa927469e666404437c706d43c43f92). Additionally, utilities were extended to support scalar value checks to strengthen test reliability (commit 9b54ad549aa927469e666404437c706d43c43f92). Impact: Expanded fusion coverage unlocks additional optimization opportunities in production models, reduces manual rewriting, and improves forward compatibility with ONNX opset 23. The introduced tests increase confidence in rewriter behavior across ONNX versions, supporting faster iteration and safer deployments.

August 2025 (2025-08) monthly summary for microsoft/onnxscript focused on expanding and stabilizing fusion rules and graph rewriting to enable higher-performance ONNX graphs and broader model coverage. Delivered a cohesive set of feature enhancements across fusion rules, improved pattern matching for nested graphs, and converter support, complemented by tests to ensure reliability across diverse ONNX graphs. Stabilization work included module cleanup for better maintainability and easier future enhancements.

July 2025 monthly summary focused on delivering performance-oriented features and reliability improvements across ONNX-based tooling, with measurable business value through faster inference and broader dtype support, plus enhanced code quality controls.

June 2025, microsoft/onnxscript focused on strengthening fusion reliability, expanding test coverage for fused models, and simplifying rewrite paths to improve performance and debuggability. Efforts delivered concrete improvements in SDPA/GQA fusion, MHA fusion testing, and graph rewrite optimizations, with a clear uplift in maintainability and deployment readiness.

May 2025 monthly summary for microsoft/onnxscript and onnx repositories. Highlights include delivery of Advanced ONNX Script Pattern Matching with OrValue support (non-backtracking and backtracking) and codebase reorganization for maintainability; Multi-Head Attention Fusion improvements using disjunction-based rules and mask optimizations with tests; ONNX Script Rewriter versioning and shape optimization enhancements with updated tests; expansion of ONNX function inliner to support schema-defined functions with new C++/Python interfaces; and accompanying quality work including tests, docs, and small context/rename fixes.

April 2025 monthly summary for microsoft/onnxscript focusing on feature delivery, stability improvements, and business value realized through ONNX Runtime optimizations.

March 2025 performance-focused update for microsoft/onnxscript. Implemented a 1D Squeeze to Identity rewrite to simplify 1D Squeeze-Reshape patterns and reduce runtime overhead; added tests for 1D inputs and non-application to multi-D. Generalized ONNX Runtime transformer fusion to optimize MHA paths, remove initial MatMuls, and support packed MatMul and partial rotary embeddings, with new get_dim utility and enhanced cos-sin-cache handling for 1D position-ids; introduced rotary embedding tests. Added GELU fusion via a Tanh expansion, refactored transformer fusions, enabled Stable Diffusion Attention (SDPA) fusion, and aligned with FastGelu usage. Cleaned up ONNXScript by removing two warning messages to reduce noise. Results: faster inference for transformer models, broader rotary embedding support, improved testing reliability, and cleaner logs.

February 2025 monthly performance summary for microsoft/onnxscript: Delivered a trio of high-impact feature initiatives that enhance optimization capabilities, improve maintainability, and enable more flexible rewrite workflows. Key work includes ORT Fusion Rules refactor and rewrite rule consolidation, enabling model-local subgraph reuse for multi-step rewrites, and expanding ORT fusion pattern variants for Cosine-Sine Cache and SDPA. No explicit major bugs fixed this month; maintenance and refactor work contributed to stability and code quality.

January 2025 monthly performance summary for microsoft/onnxscript. Focused on expanding ONNX Script optimizer and rewriter capabilities and advancing attention fusion for ONNX Runtime. Deliverables emphasize reliability, performance, and maintainability, enabling broader optimization coverage for production models and safer, faster inference paths.

December 2024: Delivered robust debugging and conversion tooling for ONNX Script, controlled and consolidated performance improvements in ONNX Runtime, and enhanced reliability through shape propagation fixes and improved documentation across two repositories (microsoft/onnxscript and onnx/onnx). Focused on business value by enabling faster debugging, more reliable model serialization to ONNX-Script, and measurable runtime performance gains, while expanding developer usability and test coverage.

November 2024 monthly summary: Core work focused on optimizer quality and dynamic shape support across microsoft/onnxscript and onnx/onnx. Key deliverables: - microsoft/onnxscript: optimizer/rewriter enhancements across five commits (32090a8d, 5a359588, d81480b5, e6e3d525, 88dca666) delivering performance optimizations, richer pattern matching, identity replacements for Concat/Dropout, constant folding correctness improvements, and inliner naming stabilization (PRs #1937, #1944, #1945, #1947, #1953). - onnx/onnx: dynamic shape-aware data propagation using tensor rank to improve shape inference under dynamic shapes, including a new DynamicConcatTest (96a0ca43, #6557).