ParallelSpec: Parallel Drafter for Efficient Speculative Decoding

• URL: http://arxiv.org/abs/2410.05589v1
• Date: Tue, 8 Oct 2024 01:05:08 GMT
• Title: ParallelSpec: Parallel Drafter for Efficient Speculative Decoding
• Authors: Zilin Xiao, Hongming Zhang, Tao Ge, Siru Ouyang, Vicente Ordonez, Dong Yu,
• Abstract summary: 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.
• Score: 62.68430939686566
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Speculative decoding has proven to be an efficient solution to large language model (LLM) inference, where the small drafter predicts future tokens at a low cost, and the target model is leveraged to verify them in parallel. However, most existing works still draft tokens auto-regressively to maintain sequential dependency in language modeling, which we consider a huge computational burden in speculative decoding. 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. ParallelSpec learns to efficiently predict multiple future tokens in parallel using a single model, and it can be integrated into any speculative decoding framework that requires aligning the output distributions of the drafter and the target model with minimal training cost. Experimental results show that ParallelSpec accelerates baseline methods in latency up to 62% on text generation benchmarks from different domains, and it achieves 2.84X overall speedup on the Llama-2-13B model using third-party evaluation criteria.

Related papers

• Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding [55.2480439325792]
  In arbitrary-order language models, it is an open question how to sample tokens in parallel from the correct joint distribution. We find that a different class of models, any-subset autoregressive models (AS-ARMs), holds the solution. We show that AS-ARMs achieve state-of-the-art performance among sub-200M parameter models on infilling benchmark tasks, and nearly match the performance of models 50X larger on code generation.
  arXiv  Detail & Related papers  (2025-04-29T06:33:13Z)
• Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE [15.003006630308517]
  Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to predict multiple tokens. We propose Jakiro, leveraging Mixture of Experts (MoE), where independent experts generate diverse predictions. Our method significantly boosts prediction accuracy and achieves higher inference speedups.
  arXiv  Detail & Related papers  (2025-02-10T09:24:06Z)
• Parallelized Autoregressive Visual Generation [65.9579525736345]
  We propose a simple yet effective approach for parallelized autoregressive visual generation. Our method achieves a 3.6x speedup with comparable quality and up to 9.5x speedup with minimal quality degradation across both image and video generation tasks.
  arXiv  Detail & Related papers  (2024-12-19T17:59:54Z)
• Parallel Speculative Decoding with Adaptive Draft Length [10.36819001596531]
  We propose a conceptually simple, flexible, and general framework to boost speculative decoding. PEARL proposes textitpre-verify to verify the first draft token in advance during the drafting phase, and textitpost-verify to generate more draft tokens during the verification phase. PEARL parallels the drafting phase and the verification phase via applying the two strategies, and achieves adaptive draft length for different scenarios.
  arXiv  Detail & Related papers  (2024-08-13T08:32:06Z)
• Graph-Structured Speculative Decoding [52.94367724136063]
  Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models. We introduce an innovative approach utilizing a directed acyclic graph (DAG) to manage the drafted hypotheses. We observe a remarkable speedup of 1.73$times$ to 1.96$times$, significantly surpassing standard speculative decoding.
  arXiv  Detail & Related papers  (2024-07-23T06:21:24Z)
• Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines [74.42485647685272]
  We focus on Generative Masked Language Models (GMLMs) We train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. We adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality.
  arXiv  Detail & Related papers  (2024-07-22T18:00:00Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• Multi-Candidate Speculative Decoding [82.05519287513444]
  Large language models have shown impressive capabilities across a variety of NLP tasks, yet their generating text autoregressively is time-consuming. One way to speed them up is speculative decoding, which generates candidate segments from a fast draft model that is then verified in parallel by the target model. This paper proposes sampling multiple candidates from a draft model and then organising them in batches for verification. We design algorithms for efficient multi-candidate verification while maintaining the distribution of the target model.
  arXiv  Detail & Related papers  (2024-01-12T17:15:23Z)
• PaSS: Parallel Speculative Sampling [29.23180061749074]
  Scaling the size of language models to tens of billions of parameters has led to impressive performance on a wide range of tasks. At generation, these models are used auto-regressively, requiring a forward pass for each generated token, and thus reading the full set of parameters from memory. We show promising performance (up to $30%$ speed-up) while requiring only as few as $O(d_emb)$ additional parameters.
  arXiv  Detail & Related papers  (2023-11-22T18:37:27Z)
• SPEED: Speculative Pipelined Execution for Efficient Decoding [35.45955948053644]
  We propose SPEED, which improves inference efficiency by speculatively executing multiple future tokens in parallel with the current token. For Transformer decoders that employ parameter sharing, the memory operations for the tokens executing in parallel can be amortized. We demonstrate the efficiency of our method in terms of latency reduction relative to model accuracy and demonstrate how speculation allows for training deeper decoders with parameter sharing with minimal runtime overhead.
  arXiv  Detail & Related papers  (2023-10-18T16:07:01Z)
• 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)

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.