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REST: Retrieval-Based Speculative Decoding

If training's got you in a stew, take a REST and speed right through.

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News

🎉 2024-3-14: REST is accepted to NAACL 2024!

Introduction

REST is a retrieval-based speculative decoding method designed to boost generation speed of LLMs. Instead of relying on a draft language model like speculative decoding, REST utilizes a datastore to retrieve and employ draft tokens. Moreover, REST differs from blockwise parallel decoding and Medusa in that it doesn't require extra training steps. It functions as a plug-and-play solution capable of accelerating any pre-existing language model.

<div align="center"> <picture> <img src="assets/rest_overview.png" width="85%"> </picture> <br> <div align="left" width="80%"> <em>Overview of REST. During inference, the input context is utilized as the query to retrieve docs from the datastore that match the longest suffix of the input. A Trie is constructed using the continuations from the retrieved docs and low-frequency branches are pruned. Candidates from the pruned subtree will be further fed into the LLM with a tree attention mask for verification. All correct tokens from the start will be accepted, and the draft tokens after the first mistake will be rejected.</em> </div> <br> </div> <div align="center"> <picture> <img src="assets/rest_results.png" width="85%"> </picture> <br> <div align="left" width="40%"> <em>Speed on HumanEval and MT-Bench with standard autoregressive generation and REST. The temperature is set to 0.8 and the top-p to 0.95 for nucleus sampling in HumanEval. For MT-Bench, the settings are 0.7 for temperature and 0.8 for top-p. All the experiments are conducted on a single NVIDIA A6000 GPU and 96 CPU cores with a batch size of 1.</em> </div> <br> </div> <!-- <p align="center"> <picture> <img src="assets/rest_results.png" width="55%"> </picture> </p> -->

Contents

Installation

conda create -n rest python=3.9
conda activate rest
pip3 install -r requirements.txt # pay attention to Pytorch CUDA version
pip3 install DraftRetriever/wheels/draftretriever-0.1.0-cp39-cp39-manylinux_2_34_x86_64.whl

Build datastore

Build a small one

Build a chat datastore using data from ShareGPT within 10 minutes (requires 465MB disk storage)

cd datastore
python3 get_datastore_chat.py --model-path lmsys/vicuna-7b-v1.5 # get datastore_chat_small.idx in this folder

Build a Python code generation datastore from The Stack within 20 minutes (requires 924MB disk storage)

cd datastore
python3 get_datastore_code.py --model-path codellama/CodeLlama-7b-instruct-hf # get datastore_stack_small.idx in this folder

Build a large one

(optionally) Build a chat datastore using data from UltraChat (requires 12GB disk storage)

cd datastore
python3 get_datastore_chat.py --model-path lmsys/vicuna-7b-v1.5 --large-datastore True # get datastore_chat_large.idx in  this folder

(optionally) Build a Python code generation datastore from The Stack (requires 27GB disk storage)

cd datastore
python3 get_datastore_code.py --model-path codellama/CodeLlama-7b-instruct-hf --large-datastore True # get datastore_stack_large.idx in this folder

Inference

Inference on MT-Bench

cd llm_judge
RAYON_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0 python3 gen_model_answer_rest.py --model-path lmsys/vicuna-7b-v1.5 --model-id vicuna-7b-v1.5 --datastore-path ../datastore/datastore_chat_small.idx

Inference on HumanEval

cd human_eval
RAYON_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0 python3 rest_test.py --model-path codellama/CodeLlama-7b-instruct-hf --datastore-path ../datastore/datastore_stack_small.idx

Free Chat

RAYON_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0 python3 -m rest.inference.cli --datastore-path datastore/datastore_chat_small.idx --base-model lmsys/vicuna-7b-v1.5

Note that the RAYON_NUM_THREADS environment variable control the maximum number of threads for retrieval. You can adjust it based on your machine.

Other Models and Datastore

In the examples above, we default to use Vicuna and CodeLlama. But actually you can use any LLaMA-based models you like by simply changing the "--model-path" argument. You can also build the datastore from any data you like. If you want to use architectures other than LLaMA, you can also modify the file model/modeling_llama_kv.py to match the corresponding model.

Note: For models with a vocab size larger than 65535 (range of u16), you may change this line in writer from self.index_file.write_u16::<LittleEndian>(item as u16)?; to self.index_file.write_u32::<LittleEndian>(item as u32)?; Besides, change these two lines in Reader from for i in (0..data_u8.len()).step_by(2) { let int = LittleEndian::read_u16(&data_u8[i..i+2]) as i32; to for i in (0..data_u8.len()).step_by(4) { let int = LittleEndian::read_u32(&data_u8[i..i+4]) as i32; (Fixed by scandukuri)

Citation

@misc{he2023rest,
      title={REST: Retrieval-Based Speculative Decoding}, 
      author={Zhenyu He and Zexuan Zhong and Tianle Cai and Jason D Lee and Di He},
      year={2023},
      eprint={2311.08252},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Acknowledgements

The codebase is from Medusa and influenced by remarkable projects from the LLM community, including FastChat, TinyChat, vllm and many others.