Hydragen: High-Throughput LLM Inference with Shared Prefixes
Jordan Juravsky Bradley Brown Ryan Ehrlich Daniel Y. Fu Christopher Ré Azalia Mirhoseini
Transformer-based large language models (LLMs) are now deployed to hundreds
of millions of users. LLM inference is commonly performed on batches of
sequences that share a prefix, such as few-shot examples or a chatbot system
prompt. Decoding in this large-batch setting can be bottlenecked by the
attention operation, which reads large key-value (KV) caches from memory and
computes inefficient matrix-vector products for every sequence in the batch. In
this work, we introduce Hydragen, a hardware-aware exact implementation of
attention with shared prefixes. Hydragen computes attention over the shared
prefix and unique suffixes separately. This decomposition enables efficient
prefix attention by batching queries together across sequences, reducing
redundant memory reads and enabling the use of hardware-friendly matrix
multiplications. Our method can improve end-to-end LLM throughput by up to 32x
against competitive baselines, with speedup growing with the batch size and
shared prefix length. Hydragen also enables the use of very long shared
contexts: with a high batch size, increasing the prefix length from 1K to 16K
tokens decreases Hydragen throughput by less than 15%, while the throughput of
baselines drops by over 90%. Hydragen generalizes beyond simple prefix-suffix
decomposition and can be applied to tree-based prompt sharing patterns,
allowing us to further reduce inference time on competitive programming
problems by 55%.