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This work proposes NOSA, a trainable sparse attention mechanism natively designed for KV cache offloading that explicitly constrains the volume of CPU-GPU KV transfers, thereby achieving low communication overhead and high decoding throughput.
This paper proposes DPP, which transforms PP granularity from a static design choice into a workload-adaptive optimization space over packed, split, and hybrid chunks and further introduces a new coupling between heterogeneous pipeline scheduling and gradient checkpointing.
StateFlow is presented, a sequence pipeline parallelism system for models with linear recurrence that partitions each sequence into chunks and schedules their execution while propagating boundary states and gradients across chunks, thereby reducing activation lifetimes and improving training throughput.
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