Optimizing Speculative Decoding for Serving Large Language Models Using Goodput

• URL: http://arxiv.org/abs/2406.14066v2
• Date: Tue, 25 Jun 2024 20:53:16 GMT
• Title: Optimizing Speculative Decoding for Serving Large Language Models Using Goodput
• Authors: Xiaoxuan Liu, Cade Daniel, Langxiang Hu, Woosuk Kwon, Zhuohan Li, Xiangxi Mo, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang,
• Abstract summary: speculative decoding is one of the most effective techniques for large language models. We develop a dynamic framework SmartSpec to determine the best speculation length for each request. We show that SmartSpec consistently reduces average request latency by up to 3.2x compared to non-speculative decoding baselines.
• Score: 32.479057822334354
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Reducing the inference latency of large language models (LLMs) is crucial, and speculative decoding (SD) stands out as one of the most effective techniques. Rather than letting the LLM generate all tokens directly, speculative decoding employs effective proxies to predict potential outputs, which are then verified by the LLM without compromising the generation quality. Yet, deploying SD in real online LLM serving systems (with continuous batching) does not always yield improvement -- under higher request rates or low speculation accuracy, it paradoxically increases latency. Furthermore, there is no best speculation length work for all workloads under different system loads. Based on the observations, we develop a dynamic framework SmartSpec. SmartSpec dynamically determines the best speculation length for each request (from 0, i.e., no speculation, to many tokens) -- hence the associated speculative execution costs -- based on a new metric called goodput, which characterizes the current observed load of the entire system and the speculation accuracy. We show that SmartSpec consistently reduces average request latency by up to 3.2x compared to non-speculative decoding baselines across different sizes of target models, draft models, request rates, and datasets. Moreover, SmartSpec can be applied to different styles of speculative decoding, including traditional, model-based approaches as well as model-free methods like prompt lookup and tree-style decoding.

Related papers

• SpecServe: Efficient and SLO-Aware Large Language Model Serving with Adaptive Speculative Decoding [18.45994543035372]
  Speculative decoding has emerged as a compelling technique to accelerate Large Language Model inference. Existing speculative decoding solutions often fail to adapt to varying workloads and system environments. We introduce SpecServe, an efficient LLM inference system that dynamically adjusts speculative strategies according to real-time request loads.
  arXiv  Detail & Related papers  (2025-03-07T02:27:51Z)
• TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs [11.615399679746675]
  Specializing large language models (LLMs) for local deployment in domain-specific use cases is necessary for strong performance. We develop TrimLLM based on the layer-wise specialization phenomenon we empirically observed and verified on contemporary LLMs. We show it retains LLMs' capacity in specific domains and inference speedup achieves irrespective of hardware and deep learning frameworks.
  arXiv  Detail & Related papers  (2024-12-15T16:47:16Z)
• SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration [10.970637831760136]
  Speculative decoding (SD) has emerged as a widely used paradigm to accelerate the inference of large language models (LLMs) We introduce SWIFT, an on-the-fly self-speculative decoding algorithm that adaptively selects intermediate layers of LLMs to skip during inference. We show that SWIFT can achieve over a 1.3x-1.6x speedup while preserving the original distribution of the generated text.
  arXiv  Detail & Related papers  (2024-10-09T14:15:30Z)
• ParallelSpec: Parallel Drafter for Efficient Speculative Decoding [62.68430939686566]
  We present ParallelSpec, an alternative to auto-regressive drafting strategies in state-of-the-art speculative decoding approaches. In contrast to auto-regressive drafting in the speculative stage, we train a parallel drafter to serve as an efficient speculative model.
  arXiv  Detail & Related papers  (2024-10-08T01:05:08Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Adaptive Draft-Verification for Efficient Large Language Model Decoding [24.347886232342862]
  Large language model (LLM) decoding involves generating a sequence of tokens based on a given context. The typical autoregressive decoding method requires a separate forward pass through the model for each token generated. We introduce ADED, which accelerates LLM decoding without requiring fine-tuning.
  arXiv  Detail & Related papers  (2024-06-27T22:20:39Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• Speculative Streaming: Fast LLM Inference without Auxiliary Models [21.454206732725563]
  Speculative Streaming is a single-model speculative decoding method. It fuses drafting into the target model by changing the fine-tuning objective from next token prediction to future n-gram prediction. It speeds up decoding by 1.8 - 3.1X in a diverse set of tasks.
  arXiv  Detail & Related papers  (2024-02-16T23:36:43Z)
• Extreme Compression of Large Language Models via Additive Quantization [59.3122859349777]
  Our algorithm, called AQLM, generalizes the classic Additive Quantization (AQ) approach for information retrieval. We provide fast GPU and CPU implementations of AQLM for token generation, which enable us to match or outperform optimized FP16 implementations for speed.
  arXiv  Detail & Related papers  (2024-01-11T18:54:44Z)
• The Synergy of Speculative Decoding and Batching in Serving Large Language Models [3.3849225405083336]
  We propose a new speculative decoding strategy that chooses the optimal speculation length for different batch sizes. Our evaluations show that our proposed method can achieve equal or better performance than the state-of-the-art speculation decoding schemes with fixed speculation length.
  arXiv  Detail & Related papers  (2023-10-28T20:36:36Z)
• DistillSpec: Improving Speculative Decoding via Knowledge Distillation [70.61777015900272]
  Speculative decoding (SD) accelerates large language model inference by employing a faster draft model for generating multiple tokens. We propose DistillSpec that uses knowledge distillation to better align the draft model with the target model, before applying SD. We show that DistillSpec yields impressive 10 - 45% speedups over standard SD on a range of standard benchmarks.
  arXiv  Detail & Related papers  (2023-10-12T16:21:04Z)
• SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification [13.174386920965107]
  SpecInfer is a system that accelerates generative large language model (LLM) serving with tree-based speculative inference and verification. The correctness of all candidate token sequences represented by a token tree is verified against the LLM in parallel using a novel tree-based parallel decoding mechanism.
  arXiv  Detail & Related papers  (2023-05-16T20:12:59Z)
• Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation [80.2267931231335]
  We propose Speculative Decoding (SpecDec) to study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding. SpecDec has two innovations: Spec-Drafter -- an independent model specially optimized for efficient drafting, and Spec-Verification -- a reliable method for verifying the drafted tokens efficiently.
  arXiv  Detail & Related papers  (2022-03-30T17:27:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.