Monthly performance summary for 2025-10 focused on delivering performance enhancements and resource-management improvements for pytorch/FBGEMM. The work centered on refining grouped GEMM kernel selection and enabling explicit SM-level control during pretraining to optimize hardware utilization and throughput for large-scale models.

September 2025 monthly summary for pytorch/FBGEMM focused on accelerating BF16 grouped GEMM paths for llama4x pretraining and enhancing benchmarking capabilities. Key developments include memory-flexible BF16 grouped GEMM support for pretraining forward/gradient/outputs, hardware-specific optimizations for GB200/H100, and integration of robust benchmarking tooling with multi-parameter tuning. A regression involving relocation issues prompted a controlled revert, followed by targeted fixes to stabilize wgrad paths. The work delivers meaningful business value through faster pretraining throughput, improved memory efficiency, and deeper performance insights.

August 2025 performance summary for repository pytorch/FBGEMM focusing on FP8 quantization acceleration and benchmarking stability. Delivered MXFP8 grouped GEMM support with to_mxfp8 conversion and MXFP8StackedGroupedGemm integration, along with a tested OSS FBGEMM compatibility workaround to address versioning differences in test environments. Implemented MXFP4 quantization performance improvements and a bug fix for the scaling factor handling, complemented by adding inline PTX to the kernel to boost throughput. Conducted comprehensive quantization benchmark code cleanup, removing NVFP4 references and simplifying global scale logic to streamline measurements. Collectively, these efforts improved performance, reduced test fragility, and enhanced maintainability of FP8 quantization and benchmarking workflows.

July 2025 (2025-07): Delivered FP4 GEMM improvements for Llama4 in pytorch/FBGEMM, including new kernels and dispatch logic tailored to Llama4 shapes, robustness for zero-dimension tensors, and an enhanced quantization pipeline in grouped GEMM with correct scaling propagation. Implemented broad performance enhancements via CUDA kernels and heuristics, and completed a refactor to improve maintainability. This work enhances inference speed and stability for Llama4-based models, reduces edge-case errors, and lays groundwork for future FP4 optimizations.

June 2025: Delivered FP8 and BF16 GEMM robustness and performance improvements for pytorch/FBGEMM, focusing on memory-bound workloads and cross-architecture portability. Implemented memory-safe FP8 batched GEMM with tensor validity checks and Llama4-specific FP8 grouped GEMM kernels with heuristic kernel selection, achieving 13–30% performance gains on memory-bound shapes and improved stability for edge cases. Added BF16 GEMM performance optimizations on Blackwell, including a kernel selection refactor and SM100-optimized kernels; introduced BF16I4ShuffledBatchedGemm (BF16 x INT4 mixed-precision) to boost throughput on memory-bound workloads and facilitate broader system integration. These changes strengthen throughput, stability, and integration readiness across platforms, delivering measurable business value in model scaling and deployment.

May 2025 performance summary for pytorch/FBGEMM focused on end-to-end quantization and GEMM optimizations for Nvidia Blackwell. Delivered MXFP4 quantization support (FP32 to MXFP4) with packed tensor support and scaling, generalized FP4/NVFP4 GEMM implementations, and introduced a PyTorch reference kernel for MXFP4 GEMM numeric verification along with a Triton kernel for MXFP4 quantization. Implemented MXFP4/NVFP4 CUTLASS grouped GEMM to enable high-throughput inference on Blackwell.

April 2025 summary: Targeted AI quantization improvements across two core repos to enable more memory-efficient inference and higher throughput for production workloads. Delivered FP4 4-bit quantization with CUTLASS/CUDA GEMM acceleration in FBGEMM and stabilized on-the-fly Int4 quantization in llama-models.

March 2025 summary for pytorch/FBGEMM: Delivered FP8 dequantization kernel and INT32 M_sizes compatibility for grouped GEMM, enabling upcasting in FP8 workflows and improving correctness and performance. This work includes Triton-based kernel implementation and accompanying unit tests, laying groundwork for faster FP8 inference and broader FP8 adoption.

February 2025 monthly summary for pytorch/FBGEMM focused on FP8 Grouped GEMM Enhancements and Benchmarks. Delivered rowwise scaling for FP8 grouped GEMM to accelerate MoE models, unified FP8 grouped GEMM implementations across CUTLASS and CK, and tile shape tuning to optimize throughput. Introduced new benchmarks for FP8 tensorwise and blockwise GEMM in cuBLAS-based quantization benches, enabling better performance visibility and comparisons.

January 2025 monthly summary for pytorch/FBGEMM focusing on FP8 GEMM performance paths and AMD support enhancements.

December 2024 monthly summary focusing on key accomplishments in pytorch/FBGEMM: implemented stability enhancements for FP8 CUDA quantization when inputs are zero-sized, added tests, and strengthened pipeline reliability for FP8 quantization.

November 2024 monthly summary for pytorch/FBGEMM: Delivered performance-focused MoE enhancements with FP8 and BF16 grouped GEMM, including CUDA graph acceleration, dynamic shape support for token-choice MoE, and end-to-end performance improvements. Refactored FP8 grouped GEMM to enable cudagraph readiness and integrated CUDA graph support, while introducing BF16 grouped GEMM kernels for CUDA 12.0+ with CUTLASS. These changes improve throughput and reduce latency for large MoE models, and optimize resource utilization across CUDA-enabled GPUs.