June 2026 monthly summary for PaddleFormers (PaddlePaddle/PaddleFormers). Focused on distributed training optimization to reduce resharding overhead and improve scalability in multi-node runs. Delivered a targeted performance optimization in the resharding path by refining tensor broadcasting and memory management, enabling faster distributed training workflows and more efficient resource usage.

September 2025 PaddleNLP monthly summary: Focused on enhancing training observability and stability. Delivered trainer module enhancements with TensorBoard visibility (timer logs and memory usage) and added backward operation for LayerNorm to improve training dynamics and monitoring. The changes include a cherry-pick from fleety (#11047) with commit 9c3ae1dbe656f7eccea69c66cb4e02c286bcbdb6. No explicit bug fixes were recorded this month; emphasis was on feature capability, reliability, and observability. Impact: faster diagnosis, better resource planning, and more reliable training runs across PaddleNLP.

March 2025 (PaddleNLP): Delivered DeepseekV2 MoE: Flex Token routing with OOM resilience, enabling dynamic token routing and safe operation under memory pressure. Implementations include MoEFlexTokenLayer gating refactor and FakeGate for OOM fallback, ensuring stable gradients and safe empty input dispatch.

For PaddleNLP in 2024-11, the team delivered memory management improvements for distributed training and a guard to prevent misconfigurations when using sharding stage1-v2 with AMP master grad. Key changes include ordered checkpoint saving to reduce OOM across processes and offloading/reloading optimizer states to lower GPU memory usage. These changes improved training stability, efficiency, and reliability for large-scale PaddleNLP experiments.