Month 2025-10: FP8 acceleration work in pytorch/FBGEMM, including a correctness/performance fix for FP8 Blockwise GEMM with CUTLASS scaling and the initial Blackwell FP8 convolution kernel for SM100. Establishes FP8 data-paths, improves correctness, and lays groundwork for higher-throughput FP8 inference.

September 2025 monthly summary for repository pytorch/FBGEMM. Focused on robustness and reliability in the attention path. Delivered a critical bug fix to prevent integer overflow in the attention workspace calculation, using size_t and ElementAccumulator sizing to ensure valid arithmetic and robust workspace allocation for attention mechanisms. This work strengthens stability for large-scale models and longer sequences, reducing risk of invalid allocations and potential runtime errors in production deployments.

In August 2025, the FP8 strategy for pytorch/FBGEMM advanced with two focused deliverables and strengthened test coverage. Key features delivered include FP8 Embedding Support Enhancements enabling FP8 (E4M3) embedding weights in the FBGEMM training backend and Native FP8 (NFP8) support in Split Table Batched Embeddings, with GPU Adagrad optimizations and new tests. Major bug fixes include ROCm FP8 format handling: ensured the correct FP8 format (FNUZ) is used when OCP is allowed but not preferred; refined FP8 type selection and extended unit tests for AMD row-wise quantization. Overall impact: improved memory efficiency and training throughput for FP8 workflows, broader ROCm/AMD compatibility, and stronger test coverage. Technologies/skills demonstrated: FP8 formats (E4M3, FNUZ, NFP8), FBGEMM backend/frontend integration, Split Table Batched Embeddings, Adagrad optimization on GPUs, ROCm 6.4 handling, AMD row-wise quantization, and expanded unit testing.

July 2025 (2025-07) — PyTorch FB-GEMM monthly summary focused on FP8 quantization and 3D/batched GEMM improvements. Key features delivered include FP8 Groupwise Quantization and Groupwise Kernel Enhancements, and GEMM Kernel 3D/Batched Support. Minor bug fix implemented for MX4 quantize dtype alignment.

June 2025: Delivered preshuffled tensor support and quantization robustness for FP8/INT4 GEMMs in FBGEMM with VLLM integration. Expanded quantization to handle non-divisible K, added preshuffled CK FP8 Rowwise GEMM variants with multiple kernel options and heuristic dispatch for memory-bound vs compute-bound workloads, and introduced a Python utility (shuffle_slice) for preshuffled int4 tensors with improved error checking to support VLLM integrations. Stabilized kernels by relaxing CUTLASS checks and improving error handling. Prepared groundwork for broader deployment with VLLM and demonstrated strong performance-oriented kernel design and tooling improvements.

May 2025 monthly summary for pytorch/FBGEMM focusing on KV cache work and code hygiene. Delivered a dedicated KV Cache Operators header file with C++ API access for custom functions, and refactored function stub declarations to improve modularity and header practices. These changes streamline integration, reduce header churn, and set a solid foundation for future KV cache enhancements.

April 2025: Implemented preshuffled BF16I4 GEMM kernel family using CUTLASS, delivering 1.5–2× speedups for many shapes and a preshuffled BF16I4 Grouped GEMM variant when zero-points are not supported by CUTLASS. Updated DeepGemm with the latest performance improvements and re-enabled rowwise scaling, including CUDA property adjustments for better portability. Modernized FP8 GEMM tuning with new kernel configurations and cleanup, and removed an experimental carveout that caused build issues. Strengthened correctness and stability with explicit tests for shuffled mixed-precision GEMMs and compatibility fixes for BF16 grouped GEMM tests. Fixed large-sequence grouped GEMM integer overflow by switching to int64 indexing and updating related hash functions. Improved OSS usability for quantize_bench and added GPU shared memory initialization utilities to aid debugging. Overall, these efforts deliver meaningful business value through faster compute, more reliable mixed-precision workflows, and improved OSS support.

March 2025: Delivered a consolidated FP8/INT4 grouped GEMM kernel ecosystem for pytorch/FBGEMM, with preshuffled inputs, stacked/grouped kernels, and targeted optimizations enabling efficient sparse M and large N/K workloads. Implemented FP8I4 quantization enhancements (columnwise weight scaling and quantization helpers) to broaden low-precision deployment options. Achieved robustness and tuning improvements, including fixes for edge cases (empty input views, empty groups) and input transformations for better performance. Updated Cutlass to v3.8-2 and integrated kernel-level optimizations (cumulative sum fusion, input handling refinements, and groupwise/rowwise scaling support). The work was delivered over 15 commits across two features, reflecting sustained focus on performance, accuracy, and production readiness.

February 2025 performance snapshot: Delivered targeted FP8 quantization workflows and pointer-based data broadcasting enhancements across two key repos (intel/sycl-tla and pytorch/FBGEMM). Key features include EVT Pointer Array broadcasting support for Row/Column operations, end-to-end quantization benchmarking enhancements (preprocessing, GPU tracing, and a fast-accumulation toggle), and integration of the DeepGEMM library into the quantize benchmark suite. Major bugs fixed include FP8 grouped GEMM correctness when zero_start_index_M is omitted and corrected dimension handling in Triton rowwise quantization for jagged tensors, improving MoE model correctness and overall reliability. Overall, these changes enhance model accuracy, benchmarking realism, and FP8 performance potential, reinforcing business value through more robust inference paths and more credible performance metrics.

January 2025 performance-focused contributions for pytorch/FBGEMM. Delivered kernel-level GEMM optimizations to reduce launch overhead, introduced FP8 and BF16 grouped GEMM variants (dynamic/static) for improved performance across varying input shapes and CUDA graph usage, and enhanced FP8 rowwise quantization with optional zero_start_index_M. Implementations include edge-case handling (empty inputs, zero_start_index_M omissions) and improved sparsity handling for MOE rows, contributing to better end-to-end throughput and robustness of production workloads.

December 2024 (pytorch/FBGEMM) focused on delivering high-impact FP8 quantization features, broader cross-hardware compatibility, and robustness improvements. Key features delivered include FP8 Rowwise GEMM with output preallocation mutability and cross-version/hardware compatibility, Torch.compile meta registrations for KV cache operators and attention/dequantization paths, and substantial BF16/FP8 grouped GEMM enhancements with dynamic M support, CK-based AMD kernel paths, and benchmarking integration. To stabilize the pipeline, a targeted FP8 non-contiguous quantization reversion fix was applied to address NaNs in the llama4 model, followed by quantization stability improvements to prevent integer overflow on extremely large inputs. These contributions were complemented by API standardization efforts to support multiple outputs in grouped GEMMs and broader AMD support for BF16 workflows. Overall, the month delivered concrete, business-value improvements in performance, portability, and correctness across FP8/BF16 quantization paths, enabling faster inference/training for large models and reducing cross-hardware friction.

November 2024: Focused on FP8 performance and stability in FBGEMM. Delivered CK FP8 Grouped and Batched GEMM improvements with fused row-wise scaling, FP8 batched GEMM with fused epilogue scaling, heuristic dispatch for kernel selection, and CUDA graph-compatible configurations, including production-ready settings. Reverted Python-based shape registration of custom operators to CPP to improve stability and Torch export compatibility. These changes increased FP8 compute throughput, reduced kernel dispatch overhead, and improved deployment reliability across Torch pipelines.

Month 2024-10 focused on stabilizing CI for the FBGEMM OSS build and laying groundwork for FP8 kernel expansion. Key improvements include CI unblock through targeted test gating and a kernel directory reorganization to enable FP8 kernel growth. Overall, improved CI reliability, clearer code organization, and readiness for future FP8 work.