October 2025 monthly summary for pytorch/FBGEMM focusing on the FP4 grouped API and performance tuning enhancements for Torch. Delivered a new FP4 grouped API with MX/NV FP4 format support, added comprehensive unit tests, and introduced a generic NVFP4 grouped tuning heuristic to replace llama-specific implementations. Stabilized benchmarks, removed unnecessary kernel instances, and merged MX/NV instance files to simplify configuration and improve maintainability. These changes enable faster quantized model inference, broader hardware support, and improved overall reliability across FP4 grouped workflows.

September 2025 monthly performance summary: Key features delivered: - FBGEMM ROCm FP8 stability and build fixes: improved assertions for ROCm fp8_rowwise_grouped_gemm, tuned tuning cache for SM100, corrected AMD test routing on NVIDIA, and resolved ROCm build regressions. - MXFP8 Grouped GEMM tuning: tuned the MXFP8 grouped GEMM path to improve performance. - FP4 quantization refactor and BF16 removal: split quantize_ops_gpu, FP4 grouped refactor, removal of CK BF16 gemm, and related tests; accompanied by binary size reduction. - GenAI enablement and heuristic generation script upgrade: enabled USE_FBGEMM_GENAI and refreshed the heuristic generation workflow. - PyTorch ROCm FP8 scaled_grouped_mm support for gfx942: enabling improved performance on gfx942 ROCm deployments. Major bugs fixed: - ROCm FP8 grouped GEMM stability: improvements to assertions and stability across the stack. - Tuning cache issue for f8f8bf16_rowwise_grouped on SM100. - Corrected AMD test routing when running on NVIDIA hardware. - Fixed ROCm build regressions introduced earlier. Overall impact and accomplishments: - Increased reliability and performance of FP8 GEMM paths across ROCm/NVIDIA environments, reducing production risk and enabling more robust benchmarking and deployment. Preparatory work completed for next-gen autotuning via GENAI, and dropout BF16 paths help streamline future maintenance. Cross-repo validation in PyTorch ensures ROCm/GFX942 readiness. Technologies/skills demonstrated: - GPU kernel tuning and stability engineering for FP8 paths; ROCm/NVIDIA interoperability debugging; codebase refactors in quantization and BF16 removal; benchmarking and device-property tooling enhancements; GENAI integration for heuristic generation; ATen API usage for device architecture detection.

July 2025 focused on advancing FP8-based GEMM for FBGEMM within the PyTorch ecosystem, delivering core enhancements and PyTorch integration, expanding support across 2D/3D input layouts, and strengthening tooling and stability. Key outcomes include FP8 rowwise GEMM core enhancements with KPadding and a PyTorch-compliant grouped GEMM API, plus build/autotuning scaffolding enabling robust experimentation. Quantize Benchmark tooling and test enhancements introduced a pair_NK mode, clarified output paths, and improved scaling benchmarks with torch.compile. Stability and reliability improvements addressed AMD CUDA test gating and added an assertion to masked_select_jagged_1d to prevent runtime errors. These efforts increase performance, reliability, and user-facing tooling, accelerating experimentation and deployment of FP8 GEMM in production workflows.

In June 2025, focused on pytorch/FBGEMM: FP8 bias handling consistency, kernel footprint reduction, auto-tuning and tuning caches for FP8/BF16 grouped GEMM, and CI/build reliability. Implementations delivered simplify configurations, reduce redundant kernel variants, boost FP8/BF16 performance through targeted tuning, and improve stability across accelerators. These workstream improvements position the project for faster performance iteration and more reliable deployments.

May 2025 (pytorch/FBGEMM): Delivered two key features that enhance benchmarking coverage and BF16 performance for grouped GEMM. Grouped GEMM Benchmarking Enhancement enables benchmarking across multiple group sizes by accepting a comma-separated list of group sizes in quantize_bench.py, expanding configuration coverage and improving benchmarking fidelity. BF16 Grouped GEMM Performance Improvements and Groundwork includes a smarter kernel selection heuristic for Cutlass BF16 Grouped GEMM across diverse group sizes and matrix dimensions, plus a structural refactor to enable parallel kernel compilation for future performance gains. These efforts improve performance analysis, enable data-driven optimizations, and lay the foundation for further scalability across hardware.