Memformer: A Memory-Augmented Transformer for Sequence Modeling

• URL: http://arxiv.org/abs/2010.06891v2
• Date: Tue, 12 Apr 2022 20:57:54 GMT
• Title: Memformer: A Memory-Augmented Transformer for Sequence Modeling
• Authors: Qingyang Wu, Zhenzhong Lan, Kun Qian, Jing Gu, Alborz Geramifard, Zhou Yu
• Abstract summary: We present Memformer, an efficient neural network for sequence modeling. Our model achieves linear time complexity and constant memory space complexity when processing long sequences.
• Score: 55.780849185884996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers have reached remarkable success in sequence modeling. However, these models have efficiency issues as they need to store all the history token-level representations as memory. We present Memformer, an efficient neural network for sequence modeling, that utilizes an external dynamic memory to encode and retrieve past information. Our model achieves linear time complexity and constant memory space complexity when processing long sequences. We also propose a new optimization scheme, memory replay back-propagation (MRBP), which promotes long-range back-propagation through time with a significantly reduced memory requirement. Experimental results show that Memformer has achieved comparable performance compared to the baselines by using 8.1x less memory space and 3.2x faster on inference. Analysis of the attention pattern shows that our external memory slots can encode and retain important information through timesteps.

Related papers

• MoM: Linear Sequence Modeling with Mixture-of-Memories [9.665802842933209]
  We introduce a novel architecture called Mixture-of-Memories (MoM) MoM utilizes multiple independent memory states, with a router network directing input tokens to specific memory states. MoM performs exceptionally well on recall-intensive tasks, surpassing existing linear sequence modeling techniques.
  arXiv  Detail & Related papers  (2025-02-19T12:53:55Z)
• Titans: Learning to Memorize at Test Time [20.12643072017223]
  We present a new neural long-term memory module that learns to memorize historical context. We show that this neural memory has the advantage of fast parallelizable training while maintaining a fast inference. We introduce a new family of architectures, called Titans, and present three variants to address how one can effectively incorporate memory into this architecture.
  arXiv  Detail & Related papers  (2024-12-31T22:32:03Z)
• Memory Layers at Scale [67.00854080570979]
  This work takes memory layers beyond proof-of-concept, proving their utility at contemporary scale. On downstream tasks, language models augmented with our improved memory layer outperform dense models with more than twice the budget, as well as mixture-of-expert models when matched for both compute and parameters. We provide a fully parallelizable memory layer implementation, demonstrating scaling laws with up to 128B memory parameters, pretrained to 1 trillion tokens, comparing to base models with up to 8B parameters.
  arXiv  Detail & Related papers  (2024-12-12T23:56:57Z)
• Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning [64.93848182403116]
  Current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term. We introduce the Stable Hadamard Memory, a novel memory model for reinforcement learning agents. Our approach significantly outperforms state-of-the-art memory-based methods on challenging partially observable benchmarks.
  arXiv  Detail & Related papers  (2024-10-14T03:50:17Z)
• HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing [33.720656946186885]
  Hierarchical Memory Transformer (HMT) is a novel framework that facilitates a model's long-context processing ability. HMT consistently improves the long-context processing ability of existing models.
  arXiv  Detail & Related papers  (2024-05-09T19:32:49Z)
• 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)
• Recurrent Memory Transformer [0.3529736140137003]
  We study a memory-augmented segment-level recurrent Transformer (Recurrent Memory Transformer) We implement a memory mechanism with no changes to Transformer model by adding special memory tokens to the input or output sequence. Our model performs on par with the Transformer-XL on language modeling for smaller memory sizes and outperforms it for tasks that require longer sequence processing.
  arXiv  Detail & Related papers  (2022-07-14T13:00:22Z)
• A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning [56.450090618578]
  Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. We show that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work. We propose a simple yet effective baseline, denoted as MEMO for Memory-efficient Expandable MOdel.
  arXiv  Detail & Related papers  (2022-05-26T08:24:01Z)
• LaMemo: Language Modeling with Look-Ahead Memory [50.6248714811912]
  We propose Look-Ahead Memory (LaMemo) that enhances the recurrence memory by incrementally attending to the right-side tokens. LaMemo embraces bi-directional attention and segment recurrence with an additional overhead only linearly proportional to the memory length. Experiments on widely used language modeling benchmarks demonstrate its superiority over the baselines equipped with different types of memory.
  arXiv  Detail & Related papers  (2022-04-15T06:11:25Z)

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.