PackMamba: Efficient Processing of Variable-Length Sequences in Mamba training

• URL: http://arxiv.org/abs/2408.03865v2
• Date: Wed, 21 Aug 2024 12:08:00 GMT
• Title: PackMamba: Efficient Processing of Variable-Length Sequences in Mamba training
• Authors: Haoran Xu, Ziqian Liu, Rong Fu, Zhongling Su, Zerui Wang, Zheng Cai, Zhilin Pei, Xingcheng Zhang,
• Abstract summary: Mamba, emerging as a groundbreaking architecture in the field of generative AI, demonstrates remarkable proficiency in handling elongated sequences. Existing training framework of Mamba presents inefficiency with variable-length sequence inputs. We propose PackMamba, a high- throughput Mamba that efficiently handles variable-length sequences.
• Score: 13.926804198202582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the evolution of large language models, traditional Transformer models become computationally demanding for lengthy sequences due to the quadratic growth in computation with respect to the sequence length. Mamba, emerging as a groundbreaking architecture in the field of generative AI, demonstrates remarkable proficiency in handling elongated sequences with reduced computational and memory complexity. Nevertheless, the existing training framework of Mamba presents inefficiency with variable-length sequence inputs. Either single-sequence training results in low GPU utilization, or batched processing of variable-length sequences to a maximum length incurs considerable memory and computational overhead. To address this problem, we analyze the performance of bottleneck operators in Mamba under diverse tensor shapes and proposed PackMamba, a high-throughput Mamba that efficiently handles variable-length sequences. Diving deep into state-space models (SSMs), we modify the parallel operators to avoid passing information between individual sequences while maintaining high performance. Experimental results on an NVIDIA A100 GPU demonstrate throughput exceeding the baseline single-sequence processing scheme: 3.06x speedup on the 1.4B model and 2.62x on the 2.8B model.

Related papers

• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• Bi-Mamba: Towards Accurate 1-Bit State Space Models [28.478762133816726]
  Bi-Mamba is a scalable and powerful 1-bit Mamba architecture designed for more efficient large language models. Bi-Mamba achieves performance comparable to its full-precision counterparts (e.g., FP16 or BF16) and much better accuracy than post-training-binarization (PTB) Mamba baselines.
  arXiv  Detail & Related papers  (2024-11-18T18:59:15Z)
• Taipan: Efficient and Expressive State Space Language Models with Selective Attention [100.16383527459429]
  Long-context language modeling is a significant challenge in Natural Language Processing (NLP) Recent State Space Models (SSMs) such as Mamba offer alternatives with constant memory usage, but they underperform in tasks requiring extensive in-context retrieval. We introduce Taipan, a novel hybrid architecture that combines Mamba-2 with Selective Attention Layers (SALs) Our experiments demonstrate Taipan's superior performance across various scales and tasks, offering a promising solution for efficient long-context language modeling.
  arXiv  Detail & Related papers  (2024-10-24T09:25:37Z)
• LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba [54.85262314960038]
  Local Attentional Mamba blocks capture both global contexts and local details with linear complexity. Our model exhibits exceptional scalability and surpasses the performance of DiT across various model scales on ImageNet at 256x256 resolution. Compared to state-of-the-art diffusion models on ImageNet 256x256 and 512x512, our largest model presents notable advantages, such as a reduction of up to 62% GFLOPs.
  arXiv  Detail & Related papers  (2024-08-05T16:39:39Z)
• DeciMamba: Exploring the Length Extrapolation Potential of Mamba [89.07242846058023]
  We introduce DeciMamba, a context-extension method specifically designed for Mamba. Experiments over real-world long-range NLP tasks show that DeciMamba can extrapolate to context lengths significantly longer than the ones seen during training.
  arXiv  Detail & Related papers  (2024-06-20T17:40:18Z)
• MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting [12.08746904573603]
  Mamba, based on selective state space models (SSMs), has emerged as a competitive alternative to Transformer. We propose four targeted improvements, leading to MambaTS. Experiments conducted on eight public datasets demonstrate that MambaTS achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-05-26T05:50:17Z)
• LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory [63.41820940103348]
  Self-attention mechanism's computational cost limits its practicality for long sequences. We propose a new method called LongVQ to compress the global abstraction as a length-fixed codebook. LongVQ effectively maintains dynamic global and local patterns, which helps to complement the lack of long-range dependency issues.
  arXiv  Detail & Related papers  (2024-04-17T08:26:34Z)
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces [31.985243136674146]
  Foundation models are almost universally based on the Transformer architecture and its core attention module. We identify that a key weakness of such models is their inability to perform content-based reasoning. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even blocks (Mamba) As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics.
  arXiv  Detail & Related papers  (2023-12-01T18:01:34Z)
• Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer [36.75562615596186]
  We propose MASFormer, an easy-to-implement transformer variant with Mixed Attention Spans. MASFormer is equipped with full attention to capture long-range dependencies, but only at a small number of layers. Experiments show that a decoder-only MASFormer model of 1.3B parameters can achieve competitive performance to vanilla transformers with full attention.
  arXiv  Detail & Related papers  (2023-10-19T03:32:05Z)
• Blockwise Parallel Transformer for Large Context Models [70.97386897478238]
  Blockwise Parallel Transformer (BPT) is a blockwise computation of self-attention and feedforward network fusion to minimize memory costs. By processing longer input sequences while maintaining memory efficiency, BPT enables training sequences 32 times longer than vanilla Transformers and up to 4 times longer than previous memory-efficient methods.
  arXiv  Detail & Related papers  (2023-05-30T19:25:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.