This work proposes a BP-free algorithm, called ZeroLock, that decouples the model updates into independent chunk updates by local objective construction and provides the first theoretical framework for such local objective construction-based approaches under general model chunk division by mapping local objectives to the global objective.
Abstract
Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy. Although existing studies proposed pipeline parallelism to address the limited memory and computing resources of edge devices, they commonly rely on backpropagation (BP) training, which has a fundamental limitation of update locking and could experience severe throughput and memory bottlenecks. In this work, we propose a BP-free algorithm, called ZeroLock, that decouples the model updates into independent chunk updates by local objective construction. It breaks the update locking of BP and hence can improve throughput at the algorithm level and lower memory usage by reducing activation storage. To the best of our knowledge, we provide the first theoretical framework for such local objective construction-based approaches under general model chunk division by mapping local objectives to the global objective. We prove that ZeroLock has a convergence rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, which differs from BP only by polylogarithmic factors. We design a system for ZeroLock and build real-world prototypes, incorporating techniques such as early forwarding and failure recovery for efficient and robust implementation. Experiments on the prototype show that compared to BP-based baselines, ZeroLock reduces the memory by 26.5% and improves throughput by 4.9%.
The demand for efficient training of Transformer language models is rapidly increasing, yet the resource-intensive attention mechanism severely bottlenecks the process. Previous attempts to fuse attention operations managed to reduce off-chip memory access, thereby improving memory bandwidth. Unfortunately, these techniques are impractical in restricted environments, as they exhibit resource utilization that scales with model dimensions and do not consider the entire training dataflow. To address this, we propose operation fusion for the attention training dataflow using local safe softmax and model-independent tiling. By taking both the forward and the backward passes into account, local safe softmax reduces I/O access under small-cache conditions. Furthermore, model-independent tiling ensures that the required on-chip memory footprint remains independent of model dimensions, enabling scalability across diverse models. Software simulations and hardware evaluation show that our method reduces off-chip access, on-chip memory usage, FLOPs, training runtime, and energy cost compared to conventional approaches, confirming its suitability for efficiently training attention in resource-restricted hardware.
Joonseok Kim, Daeheon Lee, DongHwan Yoon et al.· Proceedings of the ACM/IEEE...· 0 citations
Modern large language model (LLM) inference suffers from severe Time-To-First-Token (TTFT) bottlenecks. Existing prefix KV caching mechanisms are inherently stateless, forcing a trade-off between cross-chunk attention accuracy and online recomputation overhead. To address this issue, we propose Pegasus, a novel stateful prefix KV caching system that aims to achieve full-context attention accuracy while avoiding costly recomputation. To handle the exponential growth of context states under limited memory capacity, Pegasus employs a Recursive Path-Pruning Caching (RPPC) algorithm to selectively cache high-value states based on access frequency, memory footprint, and asymmetric latency benefit. In addition, Pegasus introduces a transition-based KV management mechanism to mitigate cache-miss overhead. By exploiting the sparsity of state-dependent KV variations, it replaces expensive attention recomputation and I/O-intensive tensor reloading with lightweight sparse state transitions. Extensive experiments show that Pegasus improves end-to-end serving throughput by 45.9% on average, reduces TTFT by up to 78.5%, and lowers cache-miss recovery overhead by more than 72%.
Fahao Chen, Peng Li, Dongxiao Yu et al.· Fall Joint Computer Conferen...· 0 citations
This work proposes Turbo, a first-of-its-kind in-network aggregation system that accelerates long-context inference by offloading query broadcast and attention aggregation to switches and introduces a rolling forward scheme that propagates states to enable cross-stage updates.
Ying Wan, Yuchen Xu, Chuwen Zhang et al.· Proceedings of the ACM SIGCO...· 0 citations
The rapid growth of Large Language Models has outpaced GPU memory capacity, making state-of-the-art models unfit for most GPUs. This necessitates evicting model parameters to CPU memory or disk, further away from the processing elements. In these scenarios, inference throughput is restricted by interconnect bandwidth, and while quantization schemes like FP8 reduce memory usage and enable larger models to fit smaller memories, they compromise generation quality and rely on specialized hardware support for efficient inference. To this end, we propose an inference pipeline that utilizes lossless entropy encoding (rANS) alleviating the bottleneck while maintaining standard floating-point portability or output precision. We demonstrate the potential of this approach by integrating the pipeline into vLLM, with a hardware-aware parallelization strategy. Our method increases the effective GPU memory capacity up to 30% in inference workloads with evicted weights, with no loss of quality and at the same time achieving speed up of 2.5–4.4%.
Väinö-Waltteri Granat, Jakub Žádník, M. Mäkitalo et al.· IEEE International Conferenc...· 0 citations
PFM (PIM-as-Flexible-Memory), a dual-view memory system that decouples physical data layout from accessor-visible logical views, is presented, demonstrating its effectiveness and broad applicability as a unified memory management solution for NPU-PIM systems.
Shixin Zhao, Lian Liu, Tian Han et al.· 0 citations