June 2026 monthly summary for tenstorrent repositories tt-xla and tt-mlir. Focused on delivering scalable model inference, expanding MoE support, and stabilizing CI/regressions to improve reliability and business value. Key features delivered: - DeepSeek-V4 streaming inference: implemented layer-by-layer streaming for DeepSeek-V4-Flash with end-to-end tests; added input shard pinning to stabilize warmup and execution phases. - MoE backend integration from HuggingFace: introduced modular HF-backed MOE routing with 2D mesh support; added new tt_torch modules for moe_dense and attention backends; removed legacy monkey patches to enable HF dispatching. - Gemma-4 26B-A4B MoE support for vLLM on a 2D mesh: added TT-fused MoE routing, 2D mesh partitioning, and relevant closures/patch fixes to enable real-expert parallelism. - Mesh topology stability and tokenizer loading fixes: opened a 2D (4,8) parent mesh for BH Galaxy to mitigate MGD topology failures; updated tokenizer loading to prevent crashes in DeepSeek-V4 flows. - Test stability and validation improvements: removed xfail in mistral vLLM tests; reduced DeepSeek-V4 PCC end-to-end depth to 10 layers to avoid CI timeouts; added a CPU-reference PCC check and verbose decoding logs to surface output quality in CI. Major impact: enabled memory-efficient streaming for large models, broadened MoE support across HF and vLLM stacks, and reduced CI churn with targeted stability improvements. Demonstrated expertise in distributed runtime design, MoE routing, tokenizer resilience, and test automation. Technologies/skills demonstrated: - Layered streaming inference, end-to-end testing, and input shard management. - HuggingFace MoE backend integration, 2D mesh routing, and module modularization. - TT-Metal/tt-xla integration considerations with 2D mesh partitioning and global mesh registration. - BH Galaxy topology resilience and tokenizer robustness. - CI stabilization, performance regression analysis, and PCC validation.

May 2026 monthly summary focusing on stability, scale-up readiness, and model support; delivered memory-management fixes, MoE/DeepSeek enhancements, and compile-time optimizations across tt-xla and tt-mlir to boost reliability and business value. Key features include A2aSparseMLP padding for small batches, DeepSeek-V4 MoE integration, a build-time memory option for const-eval inputs, Gemma-4 text-only inference with cross-layer KV sharing, and enhanced MoE testing with real weights. In tt-mlir, L1-overflow handling for fused concatenate_heads, a performance-oriented ConstevalForwardAnalysis, and a stability workaround for large head_dim in SDPA decode. These changes reduce runtime failures, enable larger models, speed up compilation, and improve test fidelity.

April 2026 highlights: Implemented foundational improvements across TT MLIR and XLA backends to boost performance, stability, and model scalability. Replaced custom CCL canonicalization with SDY built-in patterns and added a post-canonicalization dead-op elimination in tt-mlir, reducing unnecessary work and preventing pattern-matching failures in StableHLO-to-TTIR. Propagated output_shard_dim through the full pipeline for AllToAllCombineOp, enabling consistent shard control from frontend to runtime. Introduced optional per-operation device synchronization for debugging asynchronous execution, with zero-overhead in release builds. In tt-xla, extended sparse MoE support to GLM-4, DeepSeek V3, and Kimi K2, including router unification, deduplication, and a new GLM-4 MoE layer test, setting the stage for improved model throughput and scalability.

March 2026 highlights: Delivered scalable Sparse MoE support across TT-MLIR and TT-XLA, enabling sparse computations on multi-device meshes and end-to-end paths from StableHLO to runtime. Key features include Sparse MoE ops (sparse_matmul, all_to_all dispatch/combine, moe_expert_remap) and the SparseMLP module with extended sharding and a testing flag for dynamic MoE layer replacement. Implemented memory-efficient path for GPT OSS 120B nightly tests by delegating CPU golden path to the original expert implementation, reducing peak memory. Improved reliability and thread-safety with a PjrtTensorPool race-condition fix. Stabilized runtime by disabling approximate exponential mode for Blackhole SDPA decode to avoid compile failures. These workstreams collectively improve model scale, test reliability, and performance in production-like workloads while reducing risk in large-scale MoE deployments.

February 2026 monthly summary for tt-mlir: Implemented Safe Sharding for Periodic Non-Splat Tensors, enabling safe sharding of periodic constants along the sharding axis and addressing a prior limitation in GlobalToLocalShape. This work is captured in commit d02b89115ebc4d76a15a9746d9cf0c011d993bf3 and related to the issue #6928. Resulting changes set the foundation for more scalable distributed workloads by ensuring correctness when tensor values are periodic and broadcasted, reducing unnecessary data duplication while maintaining identical data slices across devices.

Month: 2026-01 — Key accomplishment: fix gather sharding axis conflict in tenstorrent/tt-mlir by changing the gather sharding rule from all-reduce to all-gather before the gather operation for collapsed_slice_dims, ensuring correctness and stability of sharded paths. Impact: prevents runtime errors caused by axis overlap, improves reliability of distributed training workloads, and aligns sharding behavior with replication expectations. Technical notes: commit 918ff6ebd0cb8f8873d8674280b1e4a063a9c8d0 implements the change (kReduction -> kNeedReplication) for collapsed_slice_dims to perform all-gather before the gather.

December 2025 monthly summary: Implemented cross-repo improvements that boost performance, scalability, and interoperability. Key work includes sharding system enhancements and accurate sharding propagation in tt-mlir, an explicit CSE pass in StableHLO to TTIR lowering, vLLM multi-batch support with AscendScheduler in tt-xla, and a user-facing sharding constraints API for intermediate tensors. These changes improve distributed execution, reduce redundant ops, and enable more predictable multi-user workloads across Torch/Torch-XLA integrations.

November 2025 focused on strengthening multi-device compute capabilities and ensuring test reliability across tt-mlir and tt-xla. Delivered foundational sharding infrastructure: per-consumer broadcasting decoupling to avoid cross-path sharding constraints, sharding propagation through composite operations, and the ability to register custom sharding rules for non-built-in ops via the Shardy framework. In parallel, TT-XLA CI tests were stabilized by fixing a missing argument in nightly tests for llama_3_1_70b models.

Month: 2025-10 This month focused on backend reliability and performance improvements for PyTorch/XLA and TT-XLA, delivering a key feature for PJRT backend customization, addressing critical dtype-promotion edge-cases in bfloat16 multiplications, and stabilizing multi-device execution workflows. The work enhances performance tuning capabilities, reduces runtime errors in distributed graphs, and improves test coverage to prevent regressions.

September 2025: Delivered a targeted test-backend fix for tenstorrent/tt-xla to run on TT PJRT XLA backend by adding explicit device conversion for both the model and its inputs. The change ensures tests execute on TT PJRT instead of CPU, producing accurate performance signals and reliable benchmarking. This work strengthened CI validation and cross-hardware compatibility, demonstrating proficiency with PyTorch XLA, TT PJRT, and test automation.

Performance-focused month for tenstorrent/tt-metal (2025-08). Delivered a fast-path optimization for concat_ndim to accelerate single-shard tensor concatenation by bypassing unnecessary shape and dimension checks, reducing per-call overhead in common workloads. Implemented via a focused set of commits (four) with messages describing the minimal-dimensional fast path. No major bugs fixed documented for this repo this month; effort concentrated on performance enhancement, code-path robustness, and maintainability. Overall impact includes improved throughput and lower latency for single-shard concat operations, contributing to better real-time and batch workloads. Skills demonstrated include low-level optimization, careful performance profiling, and disciplined commit hygiene.

July 2025 Monthly Summary for tenstorrent/tt-torch: Delivered enhancements to the PCC (Precision Consistency Check) validation for Blackhole models, extended test coverage, and refined per-model PCC requirements to tolerate minor diffs without compromising correctness. Resolved key device-level issues related to bfloat16 usage and utilization, stabilizing validation on target hardware. These changes improve model validation reliability, reduce test flakiness, and enable safer, faster deployments across production workloads including AlbertMaskedLM and ResNet50.

June 2025 monthly summary for tenstorrent/tt-torch focusing on test suite reliability and CI stability. Key outcomes include re-enabling clamp tests for integer bounds, adding explicit tests for integer and float bounds, and hardening the nightly pipeline by skipping PCC checks on select architectures and validating executor output shapes before reshaping. These improvements reduced CI flakiness, improved edge-case coverage, and accelerated safe release readiness. Technologies involved include Python, PyTorch, CI pipelines, and test frameworks. Business value: more reliable builds, faster feedback, and safer progress toward feature releases.

May 2025 performance summary for repository tenstorrent/tt-torch focusing on reliability, throughput, and computation graph correctness. Key features delivered include PCC-based model accuracy safety with IR/NN fusing stability and program export optimization with intermediate caching. Major bugs fixed address data casting correctness and golden outputs fidelity across multi-chip models. This work delivers measurable business value by improving model reliability, reducing the risk of regressions, and increasing throughput for model deployment pipelines.