In August 2025, the Param project at facebookresearch focused on enhancing benchmarking flexibility by introducing a configurable PyTorch profiler scope. The new capability enables precise control over profiler iterations during benchmarking, improving the signal-to-noise ratio of performance data and speeding up optimization cycles.

July 2025 monthly summary for ROCm/pytorch: Delivered Custom Communication API Enhancements enabling two new APIs, set_custom_all_gather and set_custom_reduce_scatter, to tailor all-gather and reduce-scatter behavior, improving flexibility, memory allocation control, and performance in distributed training. Implemented API safety by restricting set_allocate_memory_from_process_group when using custom communication hooks, with assertions and tests to prevent conflicts. These changes increase configurability and reliability for large-scale distributed training workloads. Core commits: 0364db7cd14ffa67b48ef8c27fefbb3eed2b065d; 8c2e45008282cf5202b72a0ecb0c2951438abeea.

June 2025 monthly summary for facebookresearch/param. Delivered NCCLx Benchmarking Enhancements to the ncclx backend, including all_gather_p support, bus bandwidth calculation, and upfront memory pool setup via set_up(). This work enhances benchmarking capabilities, provides actionable metrics, and accelerates performance tuning for distributed training. No major bugs fixed reported for this repository this month.

Summary for May 2025 (pytorch/torchrec): Delivered 2D embedding integration into the TorchRec training pipeline with configuration options for synchronizing distributed model parameters, including new methods for syncing embeddings and adjustments to existing classes to support this functionality. Fixed a stability issue by removing the instance-level pipelined forward type to prevent assertion errors in the training pipeline. These changes improve scalability and reliability for embedding-heavy, distributed recommender workloads and lay groundwork for future 2D embedding features.

March 2025: TorchRec work focused on enhancing distributed model parallelism with improved observability. Implemented multi-tensor All-Reduce in DMPCollection and added profiling annotations to track 2D weight and optimizer synchronization, enabling better performance tuning and troubleshooting. Also addressed type-safety and robustness in the DMPC integration.