DéjàVu

Overview

With DéjàVu, we aim to achieve fault-tolerant and resource-efficient serving of LLMs. We observe that distributed LLM serving is costly and often underutilizes hardware accelerators due to three key challenges:

1. Bubbles in pipeline-parallel deployments caused by the bimodal latency of prompt and token processing
2. GPU memory overprovisioning
3. Long recovery times in case of failures

DéjàVu addresses all these challenges using a versatile and efficient KV cache streaming library: DéjàVuLib. Using DéjàVuLib, we propose and implement:

1. Efficient prompt-token disaggregation to reduce pipeline bubbles
2. Microbatch swapping for efficient GPU memory management
3. State replication for fault-tolerance

DéjàVu is implemented on top of NVIDIA FasterTransformer. Like the original FasterTransformer implementation, it supports both tensor and pipeline parallelism.

Supported Features - DéjàVuLib

DéjàVuLib is a library built to handle KV cache streaming to and from GPU We support the following: (currently tested for the GPT, OPT and BLOOM models)

• Streaming of the KV cache to/from CPU memory and flushing local disk
• Streaming of KV cache to/from another GPU (in a different machine) via NCCL
• Streaming of KV cache to local CPU, and then flushing to another machine's CPU over the network, via MPI or BOOST

Supported Features - DéjàVu

• Disaggregation of Prompt and Token processing
• Fault Tolerance support with cache replication
• Swapping to CPU for pipeline parallelism

Documentation

1. Installation: Check docs/install.md
2. DéjàVuLib documentation and microbenchmarks: Check docs/dejavulib.md
3. DéjàVu serving system documentation and benchmarks: Check docs/dejavu.md
4. DéjàVu Planner documentation: Check docs/dejavu_planner.md
5. DéjàVu simulator: Check docs/dejavu_simulator.md
6. For FasterTransformer original documentation: Check docs/original_ft

Paper

If you use DéjàVu or DéjàVuLib in your research, please cite our paper:

@misc{strati2024dejavu,
      title={D\'ej\`aVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving},
      author={Foteini Strati and Sara Mcallister and Amar Phanishayee and Jakub Tarnawski and Ana Klimovic},
      year={2024},
      eprint={2403.01876},
      archivePrefix={arXiv},
      primaryClass={cs.DC}
}