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General-purpose, long-context autoregressive modeling with Perceiver AR (2202.07765v2)

Published 15 Feb 2022 in cs.LG, cs.AI, cs.CV, cs.SD, and eess.AS

Abstract: Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive to scale to the number of inputs and layers needed to capture this long-range structure. We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms. When trained on images or music, Perceiver AR generates outputs with clear long-term coherence and structure. Our architecture also obtains state-of-the-art likelihood on long-sequence benchmarks, including 64 x 64 ImageNet images and PG-19 books.

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Authors (15)
  1. Curtis Hawthorne (17 papers)
  2. Andrew Jaegle (26 papers)
  3. Cătălina Cangea (16 papers)
  4. Sebastian Borgeaud (19 papers)
  5. Charlie Nash (10 papers)
  6. Mateusz Malinowski (41 papers)
  7. Sander Dieleman (29 papers)
  8. Oriol Vinyals (116 papers)
  9. Matthew Botvinick (30 papers)
  10. Ian Simon (16 papers)
  11. Hannah Sheahan (5 papers)
  12. Neil Zeghidour (39 papers)
  13. Jean-Baptiste Alayrac (38 papers)
  14. João Carreira (49 papers)
  15. Jesse Engel (30 papers)
Citations (60)

Summary

  • The paper introduces Perceiver AR, an innovative architecture that projects lengthy inputs into compact latents to efficiently handle over 100k tokens.
  • It integrates causally masked cross- and self-attention to preserve autoregressive dependencies while significantly reducing computational overhead.
  • Demonstrated across language, image, and music tasks, Perceiver AR outperforms conventional models with state-of-the-art metrics on datasets like ImageNet 64×64 and PG-19.

An Analysis of the Perceiver AR Model for Long-Context Autoregressive Modeling

The paper explores Perceiver AR, a novel architecture crafted for general-purpose, long-context autoregressive modeling. It tackles the computational challenges associated with scaling conventional autoregressive models like Transformers, especially for contexts that span hundreds of thousands of elements in data such as books, images, or musical performances. By leveraging cross-attention mechanisms to map extended sequences into a reduced number of latents, Perceiver AR maintains end-to-end causal masking while allowing scalability to long input sequences without necessitating hand-crafted sparsity or additional memory mechanisms.

Core Innovations

  1. Efficiency in Handling Long Contexts: Perceiver AR decouples the input length from the model's computational requirements. Unlike conventional Transformers, which scale quadratically with input length, Perceiver AR employs an initial cross-attention mechanism to project inputs into a smaller latent space, followed by deep self-attention processing. This separation enables efficient handling of contexts with over 100k tokens.
  2. Causal Dependency Structure: By assigning an ordering to the latents and integrating causally masked cross- and self-attention, Perceiver AR ensures that each model output respects autoregressive dependency structures. This critical adjustment allows the model's outputs to be decoded sequentially while maintaining the computational benefits of the Perceiver architecture.
  3. Domain Agnosticism: The architecture demonstrates utility across diverse modalities, including language, image, and audio generation. For instance, it performs exceptionally well on the 64×64 ImageNet and Project Gutenberg's PG-19 datasets, achieving state-of-the-art likelihoods.

Numerical Results and Comparative Performance

The paper provides strong numerical evidence supporting Perceiver AR's efficacy:

  • ImageNet 64×64: The model achieved 3.40 bits per dimension, outperforming several other autoregressive models like PixelCNN and Sparse Transformer.
  • PG-19 LLMing: Perceiver AR excels with a test perplexity of 28.9, surpassing models such as Transformer-XL and Compressive Transformer.
  • Symbolic Music on MAESTRO: On both MAESTRO v1 and v3, the model demonstrates lower negative log-likelihoods compared to the Music Transformer.

Practical Implications and Theoretical Impacts

Perceiver AR's decoupling of input size and computational burden represents a significant step forward for scalable autoregressive models, providing a framework that is both efficient and versatile across tasks. Practically, this allows for more extensive contextual information to be used during model training and inference, improving the ability to capture long-range dependencies commonly found in complex real-world tasks. This versatility paves the way for the development of more robust generative models across domains previously constrained by input length restrictions.

Future Directions and Speculations

As AI research continues to push the boundaries of context size and multidimensional data, Perceiver AR opens multiple avenues for exploration. Future endeavors could include combining Perceiver AR's efficiency with other architectural improvements, such as those focused on enhancing memory or incorporating hybrid attention mechanisms. Additionally, expanding Perceiver AR's application to more diverse datasets and exploring its integration with tokenization strategies or other dimensionality reduction techniques could further amplify its utility and applicability.

In conclusion, Perceiver AR offers a compelling architecture for autoregressive modeling, maintaining computational feasibility while embracing the increasing need for long-context dependencies. As such, it stands as a catalyst for innovation in context-aware generative models, drawing significant interest from researchers aiming to extend contextual boundaries in AI applications.

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