Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

• URL: http://arxiv.org/abs/2402.14800v2
• Date: Thu, 30 May 2024 16:24:16 GMT
• Title: Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
• Authors: Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li,
• Abstract summary: MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. This paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques.
• Score: 90.14693869269519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weight pruning methods that rely on specifically designed hardware, this paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques. Specifically, we propose, for the first time to our best knowledge, post-training approaches for task-agnostic and task-specific expert pruning and skipping of MoE LLMs, tailored to improve deployment efficiency while maintaining model performance across a wide range of tasks. Extensive experiments show that our proposed methods can simultaneously reduce model sizes and increase the inference speed, while maintaining satisfactory performance. Data and code will be available at https://github.com/Lucky-Lance/Expert_Sparsity.

Related papers

• CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference [33.871080938643566]
  Large language models (LLMs) achieve impressive performance by scaling model parameters, but this comes with significant inference overhead. We propose CMoE, a novel framework to efficiently carve MoE models from dense models. CMoE achieves remarkable performance through efficient expert grouping and lightweight adaptation.
  arXiv  Detail & Related papers  (2025-02-06T14:05:30Z)
• Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks [39.820621967837205]
  Inference-time methods to boost Large Language Models performance have been shown effective in past works, though they largely rely on sequential queries. We propose a novel, training-free LLM ensemble framework where a single model is fed an optimized, diverse set of prompts in parallel. We empirically demonstrate that our method leads to significant gains on math reasoning tasks, e.g., on MATH.
  arXiv  Detail & Related papers  (2024-12-12T17:49:05Z)
• 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)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models [53.638791265113625]
  Sparsity-Preserved efficient fine-tuning method for large language models. Code will be made available at https://github.com/Lucky-Lance/SPP.
  arXiv  Detail & Related papers  (2024-05-25T04:55:27Z)
• Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers [2.1165011830664673]
  Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. The sheer size of these models poses challenges in terms of storage, training and inference due to the inclusion of billions of parameters through layer stacking. We show that even with fewer layers, LLMs maintain similar or better performance levels, particularly in prompt-based fine-tuning for text classification tasks.
  arXiv  Detail & Related papers  (2024-02-18T20:47:10Z)
• Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks [5.536630285985836]
  We introduce parameter-efficient sparsity crafting (PESC) PESC crafts dense models into sparse models using the mixture-of-experts (MoE) architecture. Our best sparse model outperforms other sparse and dense models and exhibits superior general capabilities compared to GP3.5.
  arXiv  Detail & Related papers  (2024-01-05T09:58:09Z)
• Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts [74.40198929049959]
  Large multi-modal models (LMMs) exhibit remarkable performance across numerous tasks. generalist LMMs often suffer from performance degradation when tuned over a large collection of tasks. We propose Omni-SMoLA, an architecture that uses the Soft MoE approach to mix many multimodal low rank experts.
  arXiv  Detail & Related papers  (2023-12-01T23:04:27Z)
• Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression [64.07696663255155]
  Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
  arXiv  Detail & Related papers  (2023-10-24T07:58:20Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

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.