April 2026 monthly highlights focused on aligning Shardy with MLIR SDY across two major Intel-tensorflow repositories (xla and tensorflow). Key efforts delivered structural migrations, pass integrations, and tooling improvements that enable a stable, MLIR-centered sharding pipeline and pave the path for future performance optimizations. Key outcomes: - Shardy migration: In both xla and tensorflow repos, the Shardy inliner/outliner were migrated into the mlir::sdy namespace with integration through core passes (ShardMapExport, ExportOps, ManualReductionCollectives) and inliner/outliner integration sequencing. This included multi-part migrations, pass adjustments, and cross-repo coordination. Representative commits show porting of passes and alignment to mlir::sdy implementations. - Shardy flattener/unflattener migration: The Shardy flattener was moved from xla::sdy to mlir::sdy and integrated through the pipeline (ShardMapImport, ImportSdyCustomCallsPass, OpenWhileFreeVarsSharding, LiftInlinedMeshes, DedupMeshes). The unflattener moved to mlir namespace and integrated into the mlir::sdy pipeline as part of the drop, with associated pass reordering and namespace relocation. - Dedup/refactor and integration improvements: Added original function names for outliner, moved deduplication to unflattenner, and removed explicit dedup on the outliner. This reduces duplication and consolidates dedup logic where it belongs, improving maintainability. - Utilities and reshards enhancements: Centralized in/outliner utilities within shardy and added reshards for cases where a function has no arg shardings but the call has; introduced call-graph walking utilities to support robust sharding analysis. - Integration tooling and defaults: Updated integration scripts to pull the latest shardy changes, introduced a default late inlining option, and refined the integration workflow to ensure consistent updates across both repos. Impact and value: - Technical: Established a cohesive MLIR SDY-centric sharding pipeline, reduced cross-repo fragmentation, and enabled future optimizations with clearer dependencies and pass ordering. - Business value: Accelerated feature delivery cycles, reduced risk in large-scale refactors, and laid groundwork for performance improvements through consistent sharding semantics and easier verification via tooling. Technologies and skills demonstrated: - MLIR/StableHLO/SDY pass orchestration, cross-repo coordination, large-scale refactoring, unit-test alignment, and integration tooling.

March 2026 performance and development recap across XLA backends (Intel-tensorflow/xla, ROCm/tensorflow-upstream, openxla/xla, and Intel-tensorflow/tensorflow). Key focus areas included sharding propagation, import pipeline reliability, and call-graph deduplication under the Shardy/XLA integration workstream. Delivered cross-repo features that improve correctness, scalability, and debugging visibility for shardings, while maintaining production safety (no-op in prod where appropriate).

February 2026 monthly summary focusing on key accomplishments, features delivered, impact, and technologies demonstrated for Intel-tensorflow/xla and ROCm/tensorflow-upstream.