July 2026 monthly summary for tenstorrent/tt-mlir focusing on TTNN dialect correctness and extended test coverage. Key outcomes include stabilizing code emission for negative dimension handling and expanding test targets to improve coverage for new features.

June 2026 monthly summary for tenstorrent repositories (tt-xla and tt-mlir): Delivered key reliability, portability, and multi-device improvements across testing, serialization, and emission pipelines. Aligns tt-xla test infrastructure with tt-mlir changes, strengthens multi-device correctness, and enhances wheel compatibility and code cleanliness.

Month: 2026-05 — Tenstorrent/tt-mlir: Delivered a robust refactor of forward-input handling in the EmitPy codegen, stabilized test/export paths, and improved runtime reliability across multi-graph scenarios. Key outcomes include the introduction of a dedicated forward-input separation pass, targeted fixes to the test pipeline and module export paths, and a robust eviction of stale Python modules to avoid cross-graph cache issues. These changes reduce complexity in code generation, improve correctness of exports, and prevent runtime errors in production-like multi-graph environments.

April 2026: Concise monthly summary for tenstorrent/tt-mlir focusing on stability, test reliability, and trace operation conversion across EmitC/EmitPy pipelines.

Month: 2026-03 – Delivered core EmitPy/TTCore enhancements across the tenstorrent/tt-mlir project, focusing on conditional execution, flexible dictionary support, and architecture-friendly codegen improvements. These changes improve business value by enabling more expressive models, robust caching, and faster, more maintainable emission pipelines, supported by expanded test coverage.

In January 2026, delivered dictionary-based constant caching in EmitPy by consolidating multiple const-eval globals into a single cache, enabling more efficient caching of constant evaluations and smaller generated Python output. Implemented dictionary ops (CreateDictOp, SetValueForDictKeyOp, GetValueForDictKeyOp) to support this caching and introduced consolidated cache usage in the EmitPy flow. Simplified global variable handling by removing the redundant GetGlobalOp in favor of GlobalStatementOp, with updated tests. These changes reduce boilerplate, improve maintainability, and lay groundwork for future performance gains in constant evaluation.

November 2025 monthly summary for tenstorrent/tt-mlir: Delivered substantial feature work around Python interop and consteval performance. Implemented new EmitPy dialect operations to manage global variables with defined create/access/assign semantics, and added comprehensive tests. Introduced a caching mechanism for consteval results via LoadCachedOp using GlobalOps, with an updated conversion pattern to create and reuse global variables for caching. Expanded test coverage and ensured robust behavior across positive/negative scenarios. Result: improved runtime efficiency for consteval calls and stronger integration with Python workflows.

Month: 2025-09 — Focused on enabling Python code generation for ResNet models via EmitPy integration in the tt-mlir repository. Key work includes integrating a TTNN to EmitPy emitter and expanding operation coverage to translate more complex models, laying groundwork for future optimizer support. This work strengthens the bridge between model definitions and executable Python pipelines, accelerating deployment of ResNet-style architectures. No major bugs reported or fixed in this period.

July 2025 monthly summary for tenstorrent/tt-mlir. Key features delivered include the EmitPy Python code generation (MLIR dialect) for the MNIST TTIR pipeline, introducing the EmitPy dialect to encode Python syntax within MLIR and enabling direct Python code emission. This work includes a complete TTIR -> EmitPy conversion pipeline and a subsequent Python code emission step for the MNIST model. Additionally, TTNN-to-EmitPy conversion was refactored to use the EmitPyTTNNEmitter, consolidating the conversion pathway and improving maintainability.

Month 2025-05: Improved device initialization reliability in tenstorrent/tt-mlir by fixing the default L1 small size to 32 KB (1 << 15). This correction ensures alignment with the tt-mlir runtime configuration and prevents 0-sized defaults during device opening in the ttnn-standalone workflow. Result: more stable startup, fewer initialization failures, and smoother end-to-end workloads. Commit reference: 8b9e965d48b528808203515ed398ef777902676b (Fix set default l1 small size on opening the device, ##3214).