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Task-Specific Expert Pruning for Sparse Mixture-of-Experts
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The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity. However, with trillions of parameters, MoE is hard to be deployed on cloud or mobile environment. The inference of MoE requires expert parallelism, which is not hardware-friendly and communication expensive. Especially for resource-limited downstream tasks, such sparse structure has to sacrifice a lot of computing efficiency for limited performance gains. In this work, we observe most experts contribute scarcely little to the MoE fine-tuning and inference. We further propose a general method to progressively drop the non-professional experts for the target downstream task, which preserves the benefits of MoE while reducing the MoE model into one single-expert dense model. Our experiments reveal that the fine-tuned single-expert model could preserve 99.3% benefits from MoE across six different types of tasks while enjoying 2x inference speed with free communication cost.
Forward citations
Cited by 4 Pith papers
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Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference
Communication-aware expert placement plus device-level pruning yields 1.23–1.86× MoE inference throughput and better accuracy at equal speedup than load-balance or sequential baselines.
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Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
DERN prunes SMoE LLMs by decomposing removed experts into neuron segments, reassigning the best-matching ones to kept experts, and clustering them into compact replacements, beating prior pruning baselines without retraining.
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Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation
PSP-Seg prunes redundant modules during training to make 3D segmentation networks much smaller and faster without losing accuracy.
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Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
Deleting syntax- or semantics-specialized experts in a Mixture-of-Experts language model reproduces Broca's- and Wernicke's-like aphasia, and retraining the remaining experts models functional recovery.
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