April 2026 monthly summary for sgl-project/sglang. Focused on back-end reliability and type-safety for Aiter attention. Delivered a critical fix to ensure data-type consistency across activations by casting the fp8bf16 prefill kernel output back to the model's input dtype, improving stability and correctness on ROCm deployments. No new user-facing features this month; major bug fix reduces runtime dtype errors in inference/training pipelines. The change aligns kernel outputs with the model dtype and enhances cross-hardware compatibility.

March 2026 (2026-03) performance summary for AMD-AGI/Primus: Delivered targeted improvements to Primus-Turbo for faster FP8 grouped GEMM and added precision control options, along with environment and testing enhancements to streamline Aiter installation and validation. Also fixed a Docker build issue to ensure reliable image creation with the correct Primus Turbo Aiter commit.

February 2026 monthly summary for pytorch/ao: Delivered FP8 support for ROCm MI300/MI350 in scaled grouped matrix multiplication, including device capability checks and adjusted FP8 quantization to improve usability and performance for FP8 workflows. Fixed gradient return values in _Float8GroupedMM to ensure correct backpropagation. These efforts broaden FP8 adoption on ROCm devices, improve training reliability, and demonstrate proficiency in ROCm-capable kernels, quantization pipelines, and PyTorch extension development.

January 2026 monthly summary for pytorch/ao focusing on delivering gfx942 architecture support with FP8 in the scaled_grouped_mm function, including robustness improvements, testing enhancements, and code quality fixes. This work extends hardware coverage to gfx942 GPUs and FP8 precision, contributing to performance, memory efficiency, and reliability across the PyTorch AO module.

Month: 2025-11 — AMD-AGI/Primus delivered performance-focused FP8 optimization and compatibility updates to accelerate matrix operations and enable FP8 quantization. Implemented Megatron FP8 turbo grouped GEMM and updated dependencies, including renaming the float8 module to low_precision (primus_turbo) with adjusted imports to preserve compatibility. These changes improve throughput and reduce latency for FP8 workloads and lay groundwork for future FP8 optimizations across model training and inference.

October 2025 monthly summary for AMD-AGI/Primus focusing on performance improvements and CI reliability. Delivered Turbo integration for CI and model configuration to optimize llama3.1_8B throughput by enabling turbo attention and grouped MLP, with dependency pinning to ensure consistent builds.

August 2025 monthly summary for AMD-AGI/Primus. Focused on delivering a high-impact feature to enhance matrix multiplication performance and flexibility. No major bug fixes were recorded in the provided data.

Month: 2025-07 — Key features delivered: Primus-Turbo backend integration for Torchtitan in AMD-AGI/Primus, enabling Turbo-specific model processing workflows. Configuration options updated to toggle Primus-Turbo features for enhanced processing capabilities. Overall monthly focus was on delivering scalable backend support with minimal disruption to existing pipelines.

June 2025 – AMD-AGI/Primus: Delivered kernel benchmark enhancements expanding model coverage and improving reporting. Implemented Llama3.1_405B configuration, refactored parameter combination generation with itertools, and added JSON output for benchmark results to support CI pipelines and flexible analytics. No major bugs fixed this month. Impact: broader benchmarking reach, faster and more robust experiments, and easier integration with dashboards. Technologies demonstrated: Python, itertools, JSON, benchmarking tooling, config-driven refactor.

May 2025 — Delivered a Comprehensive Benchmarking Suite for Large Model Training Operators (AMD-AGI/Primus). Implemented scripts and configurations to benchmark GEMM, Attention, and RCCL paths across multiple models and configurations, with automated data collection and detailed performance metrics. Established an initial baseline and reporting framework to guide optimization and hardware decisions. Commit ff715167a38496df8aac6700004fd7925d992001 (Primus benchmark #43) ensures traceability and reproducibility. Major bugs fixed: none documented this month. This work enables data-driven performance improvements, reduces deployment risk, and accelerates optimization cycles across hardware/software stacks.

April 2025 monthly summary for AMD-AGI/Primus. Focused on performance engineering and tooling for GEMM workloads. Delivered a comprehensive Hipblaslt GEMM tuning workflow enhancement, including an offline tuning example with a README detailing shape dumping, tuning steps, and applying tuned results, plus an automation Python script. Extended the tuning tool to support multi-device tuning via multiprocessing, enabling faster, parallel experiments and scalable optimization across devices. Overall impact: reduced time-to-insight for GEMM performance tuning, improved repeatability, and a foundation for broader adoption across teams. Technologies demonstrated include Python automation, multiprocessing for parallel tuning, and thorough documentation. Note: there were no major bugs fixed this month; stabilization efforts were focused on tooling and workflow reliability.