MatFormer: Nested Transformer for Elastic Inference
作者: Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham Kakade, Ali Farhadi, Prateek Jain
分类: cs.LG, cs.CL, cs.CV
发布日期: 2023-10-11 (更新: 2024-12-15)
备注: 30 pages, 11 figures, first three authors contributed equally. NeurIPS, 2024
🔗 代码/项目: PROJECT_PAGE
💡 一句话要点
提出MatFormer以解决弹性推理问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 弹性推理 Transformer 嵌套前馈网络 模型压缩 多模态学习
📋 核心要点
- 现有基础模型在推理时面临高成本和资源限制，导致选择不匹配的模型。
- MatFormer通过嵌套FFN块结构优化Transformer，支持弹性推理，适应不同部署需求。
- 实验表明，MatFormer能够提取多个小型模型，且在验证损失和下游任务评估中表现优于独立训练的模型。
📝 摘要（中文）
基础模型在不同推理约束下的应用广泛，从大型多加速器集群到资源受限的移动设备。然而，训练这些模型的高昂成本限制了可提供的独特模型规模数量。因此，实践者往往不得不选择与其特定延迟和成本要求不完全匹配的模型。本文提出了MatFormer，这是一种新型的Transformer架构，旨在提供跨多种部署约束的弹性推理。MatFormer通过在标准Transformer模型中引入嵌套的前馈网络（FFN）块结构来实现这一目标。在训练过程中，我们优化多个不同大小的嵌套FFN块的参数，使得能够提取出数百个准确的小型模型，而无需额外的计算成本。我们在不同模型类别（解码器和编码器）和模态（语言和视觉）上验证了MatFormer的有效性，展示了其在实际部署中的潜力。
🔬 方法详解
问题定义：本文旨在解决基础模型在不同推理约束下的适应性问题。现有方法由于训练成本高，导致可用模型规模有限，无法满足特定的延迟和成本要求。
核心思路：MatFormer的核心思路是通过在标准Transformer中引入嵌套的前馈网络（FFN）块结构，优化多个不同大小的FFN块参数，从而实现弹性推理。这样的设计使得在不增加计算成本的情况下，能够提取出多个小型模型。
技术框架：MatFormer的整体架构包括多个嵌套的FFN块，这些块在训练过程中被优化以适应不同的模型大小。模型训练时，能够根据需求动态调整FFN块的大小，从而实现不同的推理性能。
关键创新：MatFormer的主要创新在于其嵌套FFN块结构，允许在一个模型中提取出多个小型模型，显著提高了模型的灵活性和适应性。这与传统的单一模型训练方法形成了鲜明对比。
关键设计：在设计上，MatFormer采用了多层嵌套FFN结构，优化了参数设置，并在损失函数中引入了适应性调整机制，以确保不同规模模型的有效性和准确性。
🖼️ 关键图片
📊 实验亮点
实验结果显示，850M参数的MatFormer语言模型能够提取出582M到850M参数的多个小型模型，这些模型在验证损失和一次性下游评估中表现优于独立训练的模型。此外，从MatViT编码器提取的小型编码器在大规模检索中保持了度量空间结构，显示出显著的推理延迟减少。
🎯 应用场景
MatFormer的研究成果在多个领域具有广泛的应用潜力，尤其是在需要高效推理的场景，如移动设备、边缘计算和实时系统。其弹性推理能力使得开发者能够根据具体需求选择合适的模型规模，从而优化资源使用和响应时间，提升用户体验。
📄 摘要（原文）
Foundation models are applied in a broad spectrum of settings with different inference constraints, from massive multi-accelerator clusters to resource-constrained standalone mobile devices. However, the substantial costs associated with training these models often limit the number of unique model sizes that can be offered. Consequently, practitioners are compelled to select a model that may not be optimally aligned with their specific latency and cost requirements. We present MatFormer, a novel Transformer architecture designed to provide elastic inference across diverse deployment constraints. MatFormer achieves this by incorporating a nested Feed Forward Network (FFN) block structure within a standard Transformer model. During training, we optimize the parameters of multiple nested FFN blocks with varying sizes, enabling the extraction of hundreds of accurate smaller models without incurring additional computational costs. We empirically validate the efficacy of MatFormer across different model classes (decoders and encoders) and modalities (language and vision), demonstrating its potential for real-world deployment. We show that a 850M decoder-only MatFormer language model (MatLM) allows us to extract multiple smaller models spanning from 582M to 850M parameters, each exhibiting better validation loss and one-shot downstream evaluations than independently trained counterparts. Furthermore, we observe that smaller encoders extracted from a universal MatFormer-based ViT (MatViT) encoder preserve the metric-space structure for adaptive large-scale retrieval. Finally, we showcase that speculative decoding with the accurate and consistent submodels extracted from MatFormer can lead to significant reduction in inference latency. Project website: https://devvrit.github.io/matformer/