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

General-purpose, long-context autoregressive modeling with Perceiver AR