Efficient Infinite Context Transformers with Infini-attention

核心概念

This work introduces an efficient attention mechanism called Infini-attention that enables Transformer-based Large Language Models (LLMs) to effectively process infinitely long inputs with bounded memory and computation.

摘要

The paper introduces a novel attention mechanism called Infini-attention that enables Transformer-based LLMs to efficiently process infinitely long input sequences.

Key highlights:

• Infini-attention incorporates a compressive memory into the standard attention mechanism, allowing it to maintain the entire context history with bounded memory.
• It combines both masked local attention and long-term linear attention in a single Transformer block, enabling effective modeling of both short-range and long-range dependencies.
• The authors demonstrate the effectiveness of their approach on long-context language modeling, 1M sequence length passkey retrieval, and 500K length book summarization tasks.
• Infini-Transformers, models using the Infini-attention, outperform baseline models while introducing minimal additional parameters and enabling fast streaming inference.

The paper makes the following key contributions:

1. Introduces a practical and powerful attention mechanism - Infini-attention - that efficiently models long and short-range contextual dependencies.
2. Infini-attention requires minimal changes to the standard attention layer, enabling plug-and-play continual pre-training and long-context adaptation.
3. The approach allows Transformer LLMs to scale to infinitely long contexts with bounded memory and compute resources by processing inputs in a streaming fashion.

統計資料

The grass is green.
The sky is blue.
The sun is yellow.
The pass key is 9054.

引述

None

從以下內容提煉的關鍵洞見

Leave No Context Behind

by Tsendsuren M... 於 arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07143.pdf

深入探究

How can the Infini-attention mechanism be extended or adapted to other sequence modeling tasks beyond language modeling?

The Infini-attention mechanism can be extended to various sequence modeling tasks beyond language modeling by leveraging its ability to efficiently process infinitely long contexts with bounded memory and computation resources. For tasks such as time series forecasting, the Infini-attention can be adapted to capture long-range dependencies in temporal data. By incorporating compressive memory and a combination of local and global attention mechanisms, the model can effectively learn patterns and relationships in sequential data points. Additionally, in tasks like image captioning or video analysis, the Infini-attention can be modified to handle long sequences of visual information by integrating spatial and temporal dependencies. This adaptation would enable the model to capture context across frames or image regions, enhancing its understanding of visual sequences.

What are the potential limitations or drawbacks of the compressive memory approach used in Infini-attention, and how could they be addressed?

One potential limitation of the compressive memory approach in Infini-attention is the trade-off between memory efficiency and information retention. As the model compresses historical information into a fixed set of memory parameters, there is a risk of losing detailed context from past segments. To address this limitation, techniques such as incorporating a more sophisticated memory update mechanism or implementing a dynamic memory allocation strategy could be explored. By dynamically allocating memory based on the relevance or importance of past information, the model can better retain crucial context while still maintaining efficiency.
Another drawback could be the complexity of training and fine-tuning the model with compressive memory. The training process may require careful optimization to ensure that the model effectively learns to update and retrieve information from the compressive memory. Techniques like regularization, adaptive learning rates, or advanced optimization algorithms can help mitigate these challenges and improve the training stability of the model.

What other types of long-range dependencies or contextual information, beyond just textual content, could be effectively captured by an Infini-attention-based model?

In addition to textual content, an Infini-attention-based model could effectively capture long-range dependencies and contextual information in various domains such as:

Audio Processing: Infini-attention could be applied to tasks like speech recognition or music generation to capture long-term dependencies in audio sequences. The model could learn patterns in audio signals over time, enabling more accurate transcription or generation of audio data.

Biomedical Data Analysis: In the field of healthcare, Infini-attention could be utilized to analyze longitudinal patient data, capturing dependencies in medical records or time-series data. This could aid in predicting patient outcomes, identifying trends in health data, or optimizing treatment plans.

Financial Forecasting: Infini-attention could be employed in financial forecasting tasks to analyze long sequences of market data and capture complex dependencies in stock prices or economic trends. By considering historical context, the model could improve the accuracy of financial predictions and risk assessments.

By adapting Infini-attention to these domains, the model can effectively capture diverse types of long-range dependencies and contextual information, enhancing its applicability across a wide range of sequence modeling tasks.