∞-former: Infinite Memory Transformer (aka Infty-Former / Infinity-Former, Research Paper Explained)

#inftyformer #infinityformer #transformer

Vanilla Transformers are excellent sequence models, but suffer from very harsch constraints on the length of the sequences they can process. Several attempts have been made to extend the Transformer's sequence length, but few have successfully gone beyond a constant factor improvement. This paper presents a method, based on continuous attention mechanisms, to attend to an unbounded past sequence by representing the past as a continuous signal, rather than a sequence. This enables the Infty-Former to effectively enrich the current context with global information, which increases performance on long-range dependencies in sequence tasks. Further, the paper presents the concept of sticky memories, which highlight past events that are of particular importance and elevates their representation in the long-term memory.

OUTLINE:

0:00 - Intro & Overview

1:10 - Sponsor Spot: Weights & Biases

3:35 - Problem Statement

8:00 - Continuous Attention Mechanism

16:25 - Unbounded Memory via concatenation & contraction

18:05 - Does this make sense?

20:25 - How the Long-Term Memory is used in an attention layer

27:40 - Entire Architecture Recap

29:30 - Sticky Memories by Importance Sampling

31:25 - Commentary: Pros and cons of using heuristics

32:30 - Experiments & Results

Paper: https://arxiv.org/abs/2109.00301

Sponsor: Weights & Biases

https://wandb.me/start

Abstract:

Transformers struggle when attending to long contexts, since the amount of computation grows with the context length, and therefore they cannot model long-term memories effectively. Several variations have been proposed to alleviate this problem, but they all have a finite memory capacity, being forced to drop old information. In this paper, we propose the ∞-former, which extends the vanilla transformer with an unbounded long-term memory. By making use of a continuous-space attention mechanism to attend over the long-term memory, the ∞-former's attention complexity becomes independent of the context length. Thus, it is able to model arbitrarily long contexts and maintain "sticky memories" while keeping a fixed computation budget. Experiments on a synthetic sorting task demonstrate the ability of the ∞-former to retain information from long sequences. We also perform experiments on language modeling, by training a model from scratch and by fine-tuning a pre-trained language model, which show benefits of unbounded long-term memories.

Authors: Pedro Henrique Martins, Zita Marinho, André F. T. Martins

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