September 2025 monthly summary for PyTorch quantization work across torchrec and FBGEMM. Delivered configurable rounding_mode exposure in quantization paths to enable flexible and precise quantization, updated MTIA integration in embedding compute kernels with corrected stats for accurate performance metrics, and exposed rounding_mode in MX4 quantization with updates to QuantizationContext and QuantizedCommCodec. These changes improve performance tunability, device utilization visibility, and cross-repo consistency, setting the stage for QPS improvements and better resource planning.

Month 2025-08 focused on clarifying CFF naming and expanding MTIA device support in distributed training stacks. Completed standardization in tlparse to reduce ambiguity and improve consistency, and integrated MTIA as a recognized device type in TorchRec's sharding plan and estimator, including a device type utility function. No major bug fixes recorded in the provided data; the work delivered tangible features enabling broader hardware support and improved codebase clarity, maintainability, and scalability.