September 2025: Focused on stability and CUDA compatibility for graphcore/pytorch-fork. Key action was updating the FBGEMM submodule to address CUDA 13 compatibility issues, preventing runtime errors on CUDA 13 environments. Commit e310cc5e06b1c7d6d3be423976a5ee9f9a5e5bc3 ("Update fbgemm submodule (#163411)" ) was applied. This work reduces the risk of production outages and supports deployment on newer GPUs, laying groundwork for future CUDA updates.

July 2025 performance engineering highlights FP8 kernel optimization and AMD parity across two repositories. In pytorch/FBGEMM, addressed FP8 AMD kernel performance degradation by introducing hipcc compiler flags for the fbgemm_gpu/experimental/gen_ai path, reducing OSS FP8 kernel slowdowns. In graphcore/pytorch-fork, added FP8 rowwise scaling support to the ROCm/AMD path for the _scaled_grouped_mm API, including CMake configuration, kernel implementations, and unit tests to validate functionality and performance metrics. These changes improve cross-platform FP8 performance parity with Nvidia capabilities and broaden AMD hardware support, enabling faster inference/training on AMD GPUs. Key tech include HIP/ROCm, CMake, kernel optimization, and unit testing to raise performance and reliability.

April 2025 monthly summary for HabanaAI/vllm-fork: Focused on stabilizing the model evaluation workflow through targeted dependency management and CI improvements. Upgraded evaluation tooling to stay aligned with latest features and fixes, enabling faster, more reliable benchmarking.