Approximating Two-Layer Feedforward Networks for Efficient Transformers

• URL: http://arxiv.org/abs/2310.10837v3
• Date: Tue, 21 Nov 2023 13:58:00 GMT
• Title: Approximating Two-Layer Feedforward Networks for Efficient Transformers
• Authors: R\'obert Csord\'as, Kazuki Irie, J\"urgen Schmidhuber
• Abstract summary: We present a general framework that unifies various methods to approximate two-layer NNs, including product-key memories (PKMs) We show that our MoEs are competitive with the dense Transformer-XL on both the WikiText-103 and enwiki8 datasets at two different scales. This demonstrates that MoEs are relevant not only to extremely large LMs but also to any-scale resource-efficient LMs.
• Score: 15.793406740545024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How to reduce compute and memory requirements of neural networks (NNs) without sacrificing performance? Many recent works use sparse Mixtures of Experts (MoEs) to build resource-efficient large language models (LMs). Here we introduce several novel perspectives on MoEs, presenting a general framework that unifies various methods to approximate two-layer NNs (e.g., feedforward blocks of Transformers), including product-key memories (PKMs). Leveraging insights from this framework, we propose methods to improve both MoEs and PKMs. Unlike prior work that compares MoEs with dense baselines under the compute-equal condition, our evaluation condition is parameter-equal, which is crucial to properly evaluate LMs. We show that our MoEs are competitive with the dense Transformer-XL on both the WikiText-103 and enwiki8 datasets at two different scales, while being much more resource efficient. This demonstrates that MoEs are relevant not only to extremely large LMs but also to any-scale resource-efficient LMs. Our code is public.

Related papers

• ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration [61.579842548990754]
  Mixture-of-Experts (MoE) Transformer, the backbone of multiple phenomenal language models, leverages sparsity by activating only a fraction of model parameters for each input token. We introduce ResMoE, an innovative MoE approximation framework that utilizes Wasserstein barycenter to extract a common expert (barycenter expert) and approximate the residuals between this barycenter expert and the original ones.
  arXiv  Detail & Related papers  (2025-03-10T03:15:54Z)
• Continual Pre-training of MoEs: How robust is your router? [25.438359533860954]
  MoEs benefit from improved sample efficiency at training time and achieve much stronger performance. Many closed-source and open-source frontier language models have adopted an MoE architecture. We show that MoE LLMs maintain their sample efficiency (relative to a FLOP-matched dense model) during CPT and that they can match the performance of a fully re-trained MoE at a fraction of the cost.
  arXiv  Detail & Related papers  (2025-03-06T22:55:01Z)
• Rethinking Data: Towards Better Performing Domain-Specific Small Language Models [0.0]
  This paper presents our approach to finetuning a small Language Models (LM) We achieve this by improving data quality at each stage of the LM training pipeline. We improve the model generalization ability by merging the models fine-tuned with different parameters on different data subsets.
  arXiv  Detail & Related papers  (2025-03-03T12:19:12Z)
• MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference [0.8388591755871735]
  Homomorphic Encryption (HE) enables us to perform machine learning tasks over encrypted data. We propose MOFHEI, a framework that optimize the model to make HE-based neural network inference, fast and efficient. Our framework achieves up to 98% pruning ratio on LeNet, eliminating up to 93% of the required HE operations for performing PI.
  arXiv  Detail & Related papers  (2024-12-10T22:44:54Z)
• $γ-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models [87.43596173378913]
  We propose an innovative strategy for existing MLLMs called $gamma$-MoD. In $gamma$-MoD, a novel metric is proposed to guide the deployment of MoDs in the MLLM. Based on ARank, we propose two novel designs to maximize the computational sparsity of MLLM.
  arXiv  Detail & Related papers  (2024-10-17T17:59:53Z)
• EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference [49.94169109038806]
  This paper introduces EPS-MoE, a novel expert pipeline scheduler for MoE that surpasses the existing parallelism schemes. Our results demonstrate at most 52.4% improvement in prefill throughput compared to existing parallel inference methods.
  arXiv  Detail & Related papers  (2024-10-16T05:17:49Z)
• Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization [0.0]
  We present Double Sparse Factorization (DSF), where we factorize each weight matrix into two sparse matrices. Our method achieves state-of-the-art results, enabling unprecedented sparsification of neural networks.
  arXiv  Detail & Related papers  (2024-09-27T15:48:39Z)
• Layerwise Recurrent Router for Mixture-of-Experts [42.36093735411238]
  Mixture-of-Experts (MoE) architecture stands out for its ability to scale model size without significantly increasing training costs. Current MoE models often display parameter inefficiency. We introduce the Layerwise Recurrent Router for Mixture-of-Experts (RMoE)
  arXiv  Detail & Related papers  (2024-08-13T10:25:13Z)
• Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules [96.21649779507831]
  We propose a novel architecture dubbed mixture-of-modules (MoM) MoM is motivated by an intuition that any layer, regardless of its position, can be used to compute a token. We show that MoM provides not only a unified framework for Transformers but also a flexible and learnable approach for reducing redundancy.
  arXiv  Detail & Related papers  (2024-07-09T08:50:18Z)
• Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark [46.72960840801211]
  Mixture-of-Experts(MoE) approach offers a promising way to scale Large Language Models(LLMs) MoE suffers from significant memory overheads, necessitating model compression techniques. This paper explores several MoE structure-aware quantizations, ranging from coarse to fine granularity, from MoE block to individual linear weight.
  arXiv  Detail & Related papers  (2024-06-12T12:44:48Z)
• MoEUT: Mixture-of-Experts Universal Transformers [75.96744719516813]
  Universal Transformers (UTs) have advantages over standard Transformers in learning compositional generalizations. Layer-sharing drastically reduces the parameter count compared to the non-shared model with the same dimensionality. No previous work has succeeded in proposing a shared-layer Transformer design that is competitive in parameter count-dominated tasks such as language modeling.
  arXiv  Detail & Related papers  (2024-05-25T03:24:32Z)
• Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model [58.9100867327305]
  Large and sparse feed-forward layers (S-FFN) have proven effective in scaling up Transformers model size for textitpretraining large language models. We analyzed two major design choices of S-FFN: the memory block (a.k.a. expert) size and the memory block selection method. We found a simpler selection method -- textbftextttAvg-K that selects blocks through their mean aggregated hidden states, achieving lower perplexity in language model pretraining.
  arXiv  Detail & Related papers  (2023-05-23T12:28:37Z)
• AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation [104.0979785739202]
  Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing MoE models mostly consider a homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. We develop AutoMoE -- a framework for designing heterogeneous MoE's under computational constraints.
  arXiv  Detail & Related papers  (2022-10-14T05:32:17Z)
• CPM-2: Large-scale Cost-effective Pre-trained Language Models [71.59893315671997]
  We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference. We introduce knowledge inheritance to accelerate the pre-training process by exploiting existing PLMs instead of training models from scratch. We implement a new inference toolkit, namely InfMoE, for using large-scale PLMs with limited computational resources.
  arXiv  Detail & Related papers  (2021-06-20T15:43:54Z)

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.