Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

• URL: http://arxiv.org/abs/2403.09636v2
• Date: Tue, 23 Jul 2024 17:55:30 GMT
• Title: Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference
• Authors: Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski, David Tarjan, Edoardo M. Ponti,
• Abstract summary: Transformers have emerged as the backbone of large language models (LLMs) We propose Dynamic Memory Compression (DMC), a method for online key-value cache compression at inference time. We retrofit pre-trained LLMs such as Llama 2 (7B, 13B and 70B) into DMC Transformers, achieving up to 7x throughput increase during auto-regressive inference on an NVIDIA H100 GPU.
• Score: 1.9639467358416092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers have emerged as the backbone of large language models (LLMs). However, generation remains inefficient due to the need to store in memory a cache of key-value representations for past tokens, whose size scales linearly with the input sequence length and batch size. As a solution, we propose Dynamic Memory Compression (DMC), a method for online key-value cache compression at inference time. Most importantly, the model learns to apply different compression ratios in different heads and layers. We retrofit pre-trained LLMs such as Llama 2 (7B, 13B and 70B) into DMC Transformers, achieving up to 7x throughput increase during auto-regressive inference on an NVIDIA H100 GPU. DMC is applied via continued pre-training on a negligible percentage of the original data without adding any extra parameters. DMC preserves the original downstream performance with up to 4x cache compression, outperforming up-trained grouped-query attention (GQA) and key-value eviction policies (H$_2$O, TOVA). GQA and DMC can be even combined to obtain compounded gains. Hence, DMC can serve as a drop-in replacement for KV caching in existing LLMs to fit longer contexts and larger batches within any given memory budget.

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