PHOTON: Hierarchical Autoregressive Model for Efficient Language Generation
By
PaulHoule
A good honest bake. Not flashy, but you'll finish the whole bagel.
Summary
PHOTON is a new hierarchical autoregressive model architecture that addresses the memory and latency limitations of traditional Transformers in language generation. Unlike Transformers that scan tokens horizontally, PHOTON uses a vertical, multi-resolution approach with hierarchical latent streams. It features a bottom-up encoder that compresses tokens into low-rate contextual states and lightweight top-down decoders for token reconstruction. This architecture significantly reduces KV-cache traffic during decoding, achieving up to 1000x higher throughput per unit memory while maintaining competitive quality, especially in long-context and multi-query tasks.
Key quotes
· 4 pulledTransformers operate as horizontal token-by-token scanners; at each generation step, the model attends to an ever-growing sequence of token-level states.
We propose Parallel Hierarchical Operation for Top-down Networks (PHOTON), a hierarchical autoregressive model that replaces flat scanning with vertical, multi-resolution context access.
PHOTON maintains a hierarchy of latent streams: a bottom-up encoder progressively compresses tokens into low-rate contextual states, while lightweight top-down decoders reconstruct fine-grained token representations.
This reduces decode-time KV-cache traffic, yielding up to $10^{3} imes$ higher throughput per unit memory.
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