Ring Attention with Blockwise Transformers for Near-Infinite Context
Hao Liu, Matei Zaharia, Pieter Abbeel2023arXiv 2023
Read it on arxiv.org(opens in a new tab)Why this one
Read this after FlashAttention and FlashAttention-2, then keep The Google File System in the back of your mind. The trick here is not a new attention approximation or a bigger GPU wishlist. It treats a long context window like a distributed dataflow problem: split the sequence into blocks, move key and value blocks around a ring, and hide that movement behind useful compute. People often hear “near-infinite context” and think modeling breakthrough. The useful lesson is stricter: exact attention is still possible when memory is the limit, if your schedule respects where bytes live and when they move. For an engineer building long-context serving or training systems, this paper is worth the evening because it turns context length from a magic model property into a layout, communication, and overlap problem you can reason about.
What to take away
- Sequence length can scale with device count when tokens are partitioned across machines instead of replicated everywhere.
- The ring schedule overlaps KV block communication with attention compute, so networking is hidden rather than paid as a separate phase.
- This preserves exact attention, unlike sparse or approximate long-context methods that change model behavior.
Reads with
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
ring attention extends its exact blockwise attention from one gpu to a device ring
- FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
it inherits the same io aware kernel concerns before adding inter gpu scheduling
- The Google File System
the same chunking and placement instinct shows up here inside long context attention