2026-03 monthly summary for pytorch-labs/tritonbench: Implemented Signal-to-Noise Ratio (SNR) computation in the benchmark metrics to improve evaluation of model performance against baselines, enabling robust comparisons and better decision-making on model robustness. The work includes integration into the evaluation pipeline and is backed by commit 1bf6980bf3d0024fe7d5b1573e0110330d7b2a45 and related PR 931 (Differential Revision: D95460199). In addition, groundwork for performance optimizations was advanced through a fused projection rotary mxfp8 GEMM forward kernel, aiming to increase benchmarking throughput. This month’s changes deliver tangible business value by improving model robustness assessment, reducing analysis time, and facilitating smoother PR integrations.

February 2026 – Focused benchmarking enhancements for TritonBench in meta-pytorch/tritonbench. Implemented Cosine Similarity Benchmarking Enhancement, expanding evaluation metrics to include cosine similarity between outputs for more nuanced model comparisons. Also fixed a critical accuracy issue in fp4 GEMMs, stabilizing results and improving trust in benchmarking outputs. These changes increase decision-support quality for model selection and performance evaluation, with minimal overhead. Key PRs: Differential Revision D92888980; Pull Request resolved: https://github.com/meta-pytorch/tritonbench/pull/862.

Monthly summary for 2025-11 (pytorch/FBGEMM). Delivered a critical fix and tests for Blackwell FMHA, significantly improving reliability and performance of the fused multi-head attention path. Key work included: - Fixed cutlass_blackwell_fmha_custom_op.py to restore full functionality with corrected parameter handling, types, and default values. - Added comprehensive tests for Blackwell FMHA forward and backward passes, using BF16 and comparing against jagged_flash_attention_v2 (Triton JFA v2) for validation. - Created a blackwell_fmha.py test harness following the blackwell_gdpa.py pattern, executing 10 randomized configurations with varying batch sizes, sequence lengths, and heads. - Implemented data generation with generate_jagged_data to ensure realistic test inputs. - Updated BUCK dependencies to triton_jfa_v2 and added jfa_utils for data generation, and switched Python bindings to blackwell_attention.