May 2026 monthly summary focusing on MLX bitwise operator support and interpreter enhancements in pytorch/executorch. Delivered integer-tensor support for aten.bitwise_and via a dedicated BitwiseAndNode, updated the MLX interpreter to lower integer tensors correctly, and expanded op coverage with table-driven registrations for aten.bitwise_and.Tensor and aten.bitwise_and.Scalar. Preserved the existing bool-path for aten.logical_and to minimize disruption while extending integer support. Implemented comprehensive tests across bool, integer, and scalar cases to validate correctness and robustness across tensor types. This work closes a critical gap in MLX lowering for bitwise operations, enabling correct, performant execution for mixed-type inputs in real-world workloads. Key outcomes include delivering a schema change (BitwiseAndNode), interpreter runtime support (exec_bitwise_and), op registrations, and cross-type test coverage, driving reliability and business value by expanding MLX capabilities and ensuring accurate vectorized operations on integer tensors.

April 2026: Delivered a reliability-focused bug fix for Flux2 non-diffusers guidance LoRA conversion in huggingface/diffusers. The change corrects the conversion logic and properly handles expanded Flux2 LoRA block names, incorporating prior PR feedback to improve functionality and reliability. This work, associated with PR #13486 and co-authored by Sayak Paul, enhances Flux2 compatibility and reduces downstream deployment issues for LoRA workflows.