March 2026 (2026-03) focused on delivering a faster, more scalable GEMM backend for TransformerEngine on ROCm. Implemented a CK Tile-based grouped GEMM backend with accumulation support, replacing the previous hipBlasLt-based path, including configuration options and test updates. Re-enabled in-place accumulation with numerical stability improvements and enhanced epilogue traits to differentiate accumulation vs non-accumulation scenarios. These changes establish a foundation for higher throughput grouped GEMM workloads and more reliable Transformer workloads on AMD GPUs.

February 2026: Implemented GPU-accelerated test data generation using hiprand for ROCm/TransformerEngine, added safeguards to ensure GPU data remains consistent with CPU data, and optimized CI workflow by switching to default checkout depth. Updated CMake to link hiprand for AMD GPUs, improving test reliability and CI throughput.

January 2026 monthly summary for ROCm/TransformerEngine: Delivered FP8-Optimized GEMM reference implementation, enabling an FP8 path for GEMM and offloading computation to improve GPU throughput for transformer workloads. This work enhances performance and scalability for FP8-based training and inference, and establishes groundwork for broader FP8 adoption in TransformerEngine.

Monthly performance summary for 2025-12 focusing on ROCm/TransformerEngine: Key feature delivered (Two-stage Amax kernel performance enhancements across HIP, Transformer Engine, and Triton for quantization) with cross-framework optimization; tests, docs, and code cleanups. No separate bug fixes reported; improvements include reliability and performance gains. Impact includes improved AMD GPU compatibility and faster tensor quantization workflows, enabling more efficient training/inference. Technologies demonstrated include HIP, Transformer Engine, Triton, quantization, test automation, and documentation.

November 2025 monthly summary for ROCm/TransformerEngine. Focused on delivering FP8 current scaling integration for Triton in Transformer Engine, enabling improved FP8 tensor quantization and performance for Triton-based inference. Implemented the current scaling path and updated tests and related functions to support the new scaling method. This work centers on a single feature with direct impact on inference speed, accuracy, and Triton compatibility. No major bugs were reported this month; bug-fix efforts were minimal and carried forward to stabilization in the next cycle.

October 2025 monthly summary for ROCm/triton focusing on documentation reliability and developer onboarding. Fixed Tune GEMM Documentation Link Fix to ensure the tune_gemm README hyperlinks resolve to the correct resources, improving user navigation and reducing potential confusion. The change is captured in commit 76076e1d7d16a988a61a66264845990acd1244ab (Correct links in tune_gemm README (#886)).