June 2026 — pytorch/ao: Focused FP8 quantization deprecation work. Removed tensorwise-scaled MSLK support and enforced use of alternative kernel paths. Updated the testing framework to skip tests requiring the deprecated MSLK, and cleaned up code to guide users toward recommended alternatives. This reduces maintenance burden, lowers production risk, and improves clarity around the FP8 workflow. Technologies demonstrated include FP8 quantization, C++/Python code cleanup, and test-framework modernization. Commit b2b58d85ea5588ca4098211f0e962f2a68cb0b90 (Co-authored-by: Chris Thi).

Concise monthly summary for May 2026 focusing on key accomplishments, major fixes, impact, and technologies demonstrated. Feature delivered concentrated in repository pytorch/test-infra, with a version upgrade to ensure compatibility with Torch 2.12.

2026-04 monthly summary for pytorch/test-infra focusing on packaging automation and release workflow. Key feature delivered: MSLK Package Release 1.1.1 with an updated release script that bumps the MSLK version from 1.1.0 to 1.1.1 and triggers a dry-run followed by release. No critical bugs fixed this month. Overall impact: establishes a repeatable, reliable packaging release process, accelerating delivery of updated MSLK artifacts to downstream users. Technologies/skills demonstrated: release scripting, version management, CI/CD automation, and packaging workflows. Business value: reduced manual toil in releases, faster time-to-market for new MSLK releases, and clearer distribution of artifacts to wheels at the test-infra location.

March 2026 performance summary: Delivered a targeted release script compatibility update to support Torch 2.11 in the pytorch/test-infra pipeline, ensuring the release tooling remains robust amid the Torch upgrade. This work aligns with our ongoing goal of smooth, risk-free releases and reduced upgrade friction for downstream users.

January 2026: Delivered a cohesive migration to MSLK for GenAI and quantized GEMM kernels across core PyTorch components, with a focus on business value and cross-platform compatibility. Work spanned three repositories: meta-pytorch/tritonbench, pytorch/pytorch, and pytorch/ao. Key efforts unified kernel paths under MSLK, added MSLK as a submodule, and refactored builds to consume MSLK kernels for ROCm and CUDA. Updated tests, workflows, and documentation to reflect the transition. OSS export-related issues encountered during relands were resolved to ensure stable public releases and smoother onboarding for downstream projects.

December 2025 (Month: 2025-12) performance focus: stability and reliability of the FBGEMM MXFP4 path in PyTorch with targeted improvements to tensor handling and API clarity, enabling safer deployment of MXFP4 workloads. The effort centered on correcting integration issues and cementing test coverage to defend against regressions as the MXFP4 ecosystem evolves.

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.

June 2025 monthly summary for the pytorch/FBGEMM repository focused on enabling GenAI integration with PyTorch build. The work centered on updating the build system to treat FBGEMM GenAI as a PyTorch dependency, ensuring compatibility through CMake configuration tweaks, library naming adjustments, and installation property updates to align with PyTorch's build and packaging workflow.

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.