March 2026 monthly highlights for the TT/XLA and TT/MLIR workstreams, focusing on delivering features, fixing critical issues, and strengthening upstream integration to drive reliability and performance in production deployments.

February 2026 Monthly Summary for Tenstorrent development work across tt-xla, tt-mlir, tt-forge-models, tt-forge. This month emphasized keeping dependencies current, strengthening CI reliability, and delivering value through platform-wide improvements. The team uplifted critical third-party components, hardened test infrastructure, and fixed targeted issues to reduce risk in production pipelines while enabling faster experimentation and tighter feedback loops from CI to model/ML deployments.

January 2026: Delivered upstream alignment, CI stabilization, and demo reliability improvements across TT-XLA, TT-Forge-Models, and TT-Forge. Key outcomes include multiple third_party uplifts, CI/test stability enhancements, resilient uplift workflow, logging deadlock fixes, and improved demo/benchmark reliability for updated models.

December 2025 monthly summary for tt-xla, tt-forge-models, and tt-mlir. Focused on delivering major feature uplifts, stabilizing CI, and accelerating feedback loops to drive business value for model training and inference workloads. Key features and code deliveries spanned multiple third_party uplifts and runtime improvements, while major bug fixes improved nightly stability and test reliability across the stack. The month culminated in stronger end-to-end run reliability, better resource utilization in CI, and clearer instrumentation for failure analysis. 1) Key features delivered - Uplifted third_party/tt_forge_models to latest revisions across batch 1 (Dec 2–12), enabling end-to-end transfuser/torch single-device-full-inference paths and aligning with Forge-model-based test scenarios; revs included 2794c318, 1cedf78c, 919f42c7, 6723438c, 34ea72f6, ebe4603d, 844c9be3 (illustrative representative revisions). - Uplifted third_party/tt-mlir to latest revisions across batch 1 (Dec 3–5 and subsequent days), aligning with nightly/testing requirements; representative revisions include 2977ed60, 421fc7b0, 5b727af9, 608cc56f, 5009f476, among others. - Added device compute option support in jax.jit to improve device mapping and reduce runtime errors in multi-device configurations. - Expanded CI capabilities: added more workers to xfail nightly CI runs to improve parallelization and restructured nightly/weekly CI pipelines for faster, more reliable feedback. - Updated test durations and failure handling to reflect latest workflows, improving predictability of CI outcomes. 2) Major bugs fixed - Nightly CI stability fixes and related test duration alignment to reduce false positives and flakiness. - Fixes to nightly CI for issues such as alexnet/YOLO test handling, data-parallel test saturation, and parallelism-related flakiness. - Torch accelerator integration fix to ensure tests do not fail due to non-registered accelerators. - Serialization and output capturing fixes: corrected --serialize behavior for torch op tests and fixed output capturing fixture under varied pytest conditions. - Memory-related and configuration fixes in data-parallel training, including addressing GPT-2 memory footprint and RED model training config adjustments. 3) Overall impact and accomplishments - Significantly improved end-to-end reliability for model uplift and testing scenarios, enabling more frequent feedback and faster iteration cycles. - Reduced CI noise and flakiness, shortening cycle times for validation of new features and third_party upgrades. - Strengthened cross-repo collaboration with tt-forge-models and tt-mlir teams, aligning revisions with nightly/test requirements and improving compatibility across components. 4) Technologies/skills demonstrated - Proficiency with cross-repo dependency management, continuous integration orchestration, and prioritization of reliability in ML workloads. - Advanced usage of JAX/JIT, PyTorch, XLA, and MLIR integration to enable scalable and robust model execution. - Expertise in debugging CI infra, memory management for data-parallel pipelines, and resilient test infrastructure (xfails, skips, and multi-result reporting).

November 2025 monthly summary for TT-XLA/Forge-Models/TT-MLIR focused on upstream alignment, CI reliability, and testing infrastructure across three repos. Key work included extensive third_party uplift work on tt-mlir and tt_forge_models, stabilization of nightly CI/test reliability, and targeted model integration improvements. Also delivered a critical bug fix for bfloat16 tensor creation in TT-MLIR. The combined efforts reduced integration risk, improved production reliability, and accelerated feature delivery by strengthening testing and upstream alignment.

October 2025: Stabilized Yolox and ONNX Runtime dependency management for tt-forge-models to eliminate nightly build failures and improve environment reproducibility, enabling faster iterations and more reliable model tooling.

September 2025 Monthly Summary (tenstorrent/tt-xla and tenstorrent/tt-mlir) Overview: Delivered a substantial uplift of the core MLIR-based stack, stabilized CI, and enabled larger model workloads in the infra. Achieved cross-repo alignment with JAX 0.7.1 and StableHLO features, reducing risk in production training pipelines and improving developer velocity. Key achievements (top 6): - TT-MLIR uplift: executed a sequence of multi-commit uplifts for third_party/tt-mlir across Sep 1–30, aligning TT-XLA with the latest MLIR/JAX updates and bringing in numerous fixes and improvements. - Manual uplift to TT-MLIR: resolved build breakage after the uplift and restored patch/test stability (#1312 related), ensuring a clean baseline for downstream work. - Dependency upgrade: upgraded JAX to 0.7.1, enabling compatibility with the latest accelerator/runtime changes and improved stability. - CI/infra improvements: fixed nightly builds and optimizer tests; enabled large models on CIv2 runners; introduced infra changes to mark large models as PASSED, increasing CI coverage for large-scale workloads. - Frontend and code quality: implemented frontend default-argument as input (reducing unintended const-eval), and removed the destructor in JaxModelTester to improve lifecycle correctness and memory behavior. - tt-mlir and stability enhancements: added AnalyzeMesh round-trip shardy handling utilities for JAX compatibility; introduced stablehlo.optimization_barrier support across TTCore/TTIr/Runtime with barrier folding. Major bugs fixed (highlights): - Deleted the JaxModelTester destructor to prevent lifecycle/memory issues. - Fixed nightly/xfailing tests consolidation in CI, reducing flaky nightly behavior. - Addressed frontend pass semantics to avoid incorrect consteval behavior. Overall impact and business value: - Reduced build/release risk by keeping third_party/tt-mlir in lock-step with upstream MLIR/JAX changes. - Increased reliability and predictability of CI for large-model runs, accelerating validation cycles and enabling more aggressive release schedules. - Improved runtime correctness for training workloads through StableHLO integration and improved attribute handling in AnalyzeMesh, reducing the chance of silent regressions. Technologies and skills demonstrated: - MLIR/TTCore/TTIr/Runtime integration, third-party uplift management, and end-to-end patch orchestration. - JAX 0.7.1 compatibility and dependency management. - StableHLO barrier support, AnalyzeMesh pass improvements, and frontend pass adjustments. - CI optimization, test stability practices, and infra-level model-size handling.

August 2025 monthly summary for tenstorrent/tt-xla focused on stability, performance, and readiness for production-scale workloads. Delivered two waves of third_party/tt-mlir uplift across 23 commits to bring dependencies to current SHAs, enabled TT-MLIR optimizer via compile-time options, and implemented CI/test improvements to accelerate feedback for large-model scenarios. Added monkeypatching for flax.model.apply and a weight/input marking pipeline to stabilize model tooling. Fixed key reliability issues in large-model tests and reduce_scatter tests, reducing flaky test outcomes and improving overall CI reliability. Business impact includes faster upgrade cycles, improved build stability, and better support for large-model workloads across teams.

July 2025 monthly summary focusing on delivering business value through dependency management, testing reliability, packaging improvements, and cross-repo fixes across tt-xla and tt-mlir. Highlighted achievements include keeping TT-MLIR in sync with upstream revisions for batch 1, enabling broader hardware support (tt-metal), and improvements in observability and test stability to shorten debug cycles.

June 2025 Monthly Summary (tenstorrent/tt-xla and tt-mlir) focused on delivering business value through enhanced observability, robust multi-device support, and API/tooling improvements, while stabilizing builds and improving documentation delivery. The work contributed directly to reliable performance monitoring, scalable multi-device workloads, and smoother uplift to TT-XLA with improved tooling integration.

For May 2025, focused on enhancing the Tensor library stability and test coverage in tenstorrent/tt-metal. Delivered internal refactor for tensor handling with a namespaced multi-device host tensor check, expanded 2D convolution testing, and performed build/config cleanups. The work reduces maintenance burden, improves reliability, and provides better performance visibility for future optimizations.

April 2025 monthly summary for tenstorrent/tt-xla and tt-mlir focused on boosting testing infrastructure, CI stability, multi-device PJRT/XLA execution, and uplift readiness. The work delivered stronger release readiness, more reliable distributed execution, and demonstrated key technical capabilities across MLIR/XLA toolchains.

Performance/scale-focused month for 2025-03 across tt-xla and tt-mlir. Key features delivered include end-to-end multi-chip sharding integration across ModuleBuilder and PJRT with support for Shardy and GSPMD dialects, enabling consolidated sharding information flow and runtime strategies. Expanded testing infrastructure and CI for multi-chip workloads with virtualized CPU meshes, Shardy/GSPMD backends, updated test layouts, and centralized sharding logic to improve reliability and coverage. Refactored sharding strategy mapping by relocating fillStrategyMapFromSharding to tt-mlir for better maintainability and consistency. Added toHost support for multi-device sharded tensors on the host in tt-mlir, enabling multi-device workloads in frontends, and moved related utilities for centralized strategy mapping. Fixed runtime compatibility for toHost output after a tt-mlir dependency upgrade to ensure correct execution of LoadedExecutableInstance::Execute. Impact: reduces toil, increases scalability and reliability of multi-chip deployments; improves frontend capabilities and maintenance across tt-xla and tt-mlir; demonstrates proficiency in C++/Python/MLIR tooling, runtime integration, and CI engineering.

February 2025 performance summary focusing on expanding cross-device testing, multi-device validation groundwork, memory safety in PJRT, and frontend-runtime tensor ownership integration. Key items delivered across tt-xla and tt-mlir repos include dynamic TT device testing, multichip testing framework, PJRT tensor memory safeguards, and createOwnedTensor API exposure to the TTNN runtime, enabling frontend-owned data and multichip workflows. Business value realized includes improved test coverage across TT devices, safer memory management, and smoother frontend-runtime integration for multi-device workloads.

December 2024 monthly summary highlighting key features delivered across TT-MLIR and TT-XLA, major bug fixes, and overall impact. Delivered enhanced operator support, expanded shape handling, and improved test/dev workflow, driving broader model support and maintainability.

November 2024: Focused stabilization of the tensor reshape path in tt-metal (tenstorrent/tt-metal) with a targeted bug fix for rank-1 shapes. Delivered an extra validation check in the reshape operation to handle degenerate shapes, preventing indexing errors, crashes, and incorrect results. This work improves reliability for edge-case inputs and strengthens production stability for Metal-backed tensor operations.