MoM: Linear Sequence Modeling with Mixture-of-Memories

↗ arXiv ↗ Hugging Face

TL;DR

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Key Takeaways

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Why does it matter?

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This paper is important as it introduces a novel architecture that addresses the limitations of existing linear sequence models. By overcoming challenges related to limited memory capacity and memory interference, this research paves the way for more efficient and effective handling of long-range dependencies in various NLP tasks, opening new avenues for exploration.

Visual Insights

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🔼 The Mixture-of-Memories (MoM) model uses multiple independent memory states to process input tokens. A router network directs each token to the top K most relevant memory states, where it updates the memory state. Non-selected memories remain unchanged to prevent interference. A shared memory is continuously active and accessible to all tokens. The diagram shows this memory routing and updating process, illustrating a basic update mechanism. More complex mechanisms, such as gating, would follow a similar approach.

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Figure 1: Framework of MoM. Each input token selectively activates and updates K𝐾Kitalic_K memory states, leaving non-activated memory states unchanged to avoid interference from current input. Additionally, we introduce a continuously activated shared memory. This figure presents the basic memory update mechanism; other mechanisms involving gating or more complex updates follow a similar approach.

🔼 This table lists various memory update rules used in different linear sequence models, showing how they can be interpreted within a recurrent model framework. Each rule describes how the current memory state (M_t) is updated based on the previous state (M_t-1), key (k_t), and value (v_t) vectors. The parameters α_t and β_t are data-dependent scalars (between 0 and 1), a_t is a data-dependent vector, and γ is a data-independent constant. This clarifies the relationship between seemingly different linear models and their underlying memory update mechanisms.

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Table 1: Memory Update Rules. We demonstrate that several current linear sequence models can be viewed as recurrent models in terms of memory updates, where αt,βt∈(0,1)subscript𝛼𝑡subscript𝛽𝑡01\alpha_{t},\beta_{t}\in(0,1)italic_α start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT , italic_β start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT ∈ ( 0 , 1 ) are data-dependent scaler, 𝒂tsubscript𝒂𝑡\bm{a}_{t}bold_italic_a start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT is data-dependent vector, and γ𝛾\gammaitalic_γ is a data-independent constant.

In-depth insights

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MoM: Interference

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The Mixture-of-Memories (MoM) architecture tackles the challenge of memory interference in sequence modeling. Traditional linear models compress the entire input into a single memory state, leading to information loss during recall-intensive tasks. Inspired by neuroscience, MoM utilizes multiple independent memory states, with a router network directing input tokens to specific memories. This greatly expands the overall memory capacity while minimizing interference. Tokens that introduce new or orthogonal information are directed to separate memory states, preserving diverse information without overwriting existing content. By isolating memory updates, MoM concurrently manages distinct types of information, improving performance on tasks that require extensive contextual understanding. The MoM model effectively narrows the performance gap with Transformer models.

Linear w/MoM

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The concept of “Linear w/MoM” seems to be a novel approach to combine the efficiency of linear sequence models with the enhanced memory capabilities of the Mixture-of-Memories (MoM) architecture. Traditional linear models often struggle with recall-intensive tasks due to their limited capacity to retain long-range dependencies. “Linear w/MoM” likely addresses this by leveraging multiple independent memory states, allowing the model to capture and retain diverse aspects of the input sequence without the interference that plagues single-memory linear models. The integration of a routing mechanism is also critical, directing input tokens to the most relevant memory states. This selective activation ensures that each memory specializes in encoding specific types of information. By mixing the memories through weighed summation, the model is better able to retrieve info.

Exp.: LongBench

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While “Exp.: LongBench” isn’t explicitly present, it’s reasonable to infer its purpose: evaluating performance on tasks requiring long-range dependency handling. The ‘LongBench’ benchmark (Bai et al., 2024) measures a model’s ability to process extended contexts, crucial for tasks like summarization and code completion. Models with limited memory often struggle, making ‘LongBench’ a critical test. The paper likely presents results showing the model’s performance across various LongBench categories (summarization, few-shot, code), highlighting its capacity to capture and utilize information over long input sequences. It demonstrates whether proposed architecture can effectively overcome the limitations of existing methods and maintain performance with increasing sequence length.

Memory Routing

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The research paper introduces a novel approach to sequence modeling called Mixture-of-Memories (MoM), drawing inspiration from neuroscience to address the limitations of existing linear sequence models. The core idea is to utilize multiple independent memory states, akin to how the brain handles multi-item memory, to mitigate memory interference. A key component for achieving this is a router network which directs input tokens to specific memory states, ensuring that relevant information is stored and retrieved effectively. The router acts as an information bottleneck, guiding each token to the most appropriate memory. This approach allows MoM to increase memory capacity and maintain diverse information without overwriting. This memory routing mechanism helps MoM perform well on recall-intensive tasks.

Neuro-Inspired

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The paper’s title, “MoM: Linear Sequence Modeling with Mixture-of-Memories,” immediately suggests a neuro-inspired approach. The mention of “memories” directly connects to cognitive functions of the brain. The abstract explicitly states inspiration from neuroscience, particularly the brain’s ability to maintain long-term memory while reducing “memory interference.” The Mixture-of-Memories architecture mirrors how the brain segregates and processes information. This is evident in the hippocampus, where theta-gamma oscillations manage multi-item memory. Each token gets routed to specific memory states, mimicking specialized neural circuits. The design to mitigate memory interference reflects an understanding of how the brain avoids overwriting or corrupting stored information. The approach mirrors how the brain handles complexity and information overload. This is by dividing tasks among specialized areas or circuits, ensuring that each piece of information is handled efficiently without causing interference with other processes. The explicit reference to theta-gamma oscillations indicates a sophisticated understanding of neural coding and memory consolidation. The MoM architecture is designed to emulate the brain’s efficient memory management system.

More visual insights

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More on figures

🔼 This figure compares the inference time and GPU memory usage of MoM and Transformer++ with flash attention when generating 1K tokens at various sequence lengths. It visually demonstrates the linear time and space complexity of MoM compared to the quadratic complexity of Transformer++, highlighting MoM’s efficiency advantage, especially for longer sequences.

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Figure 2: Efficiency of MoM. We demonstrate the inference time and GPU memory consumption required to generate 1K tokens at specific sequence lengths.

🔼 This figure displays the training loss curves for 340 million parameter models trained on 15 billion tokens. A fixed random seed of 42 was used to ensure reproducibility. The graph allows for a comparison of training loss across different models, showing the rate at which each model’s loss decreases over the course of training.

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Figure 3: Training Loss. Loss curves for training 340M models on 15B tokens with a fixed random seed of 42.

Full paper

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