Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models
- URL: http://arxiv.org/abs/2410.10846v1
- Date: Tue, 01 Oct 2024 16:10:21 GMT
- Title: Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models
- Authors: Keivan Alizadeh, Iman Mirzadeh, Hooman Shahrokhi, Dmitry Belenko, Frank Sun, Minsik Cho, Mohammad Hossein Sekhavat, Moin Nabi, Mehrdad Farajtabar,
- Abstract summary: Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget.
We propose a novel framework that integrates smaller auxiliary modules within each Feed-Forward Network layer of the LLM.
We show that trained routers operate differently from oracles and often yield suboptimal solutions.
- Score: 16.16372459671255
- License:
- Abstract: Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative decoding, and early exit strategies leverage the insight that computational demands can vary significantly based on the complexity and nature of the input. However, identifying optimal routing patterns for dynamic execution remains an open challenge, limiting the full potential of these adaptive methods. To address this need, we study adaptive computation in LLMs more systematically. We propose a novel framework that integrates smaller auxiliary modules within each Feed-Forward Network layer of the LLM. This design enables dynamic routing of tokens based on task complexity: tokens can be processed by either the small or big modules at each layer, or even bypass certain layers entirely. This allows us to introduce a novel notion of a token's difficulty, defined by its potential to benefit from additional computational resources. Importantly, by employing oracles to identify optimal patterns of adaptive computations, we gain valuable insights into the internal workings of LLMs and the routing processes in a simplified heterogeneous MoE setup. We show that trained routers operate differently from oracles and often yield suboptimal solutions. Notably, activating a large module in just one layer outperforms models that use large modules across all layers, underscoring the gap between practical implementations of routing in MoE models and theoretical optima for adaptive computation.
Related papers
- DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks.
We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge.
Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
arXiv Detail & Related papers (2025-02-18T02:37:26Z) - Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.
However, they still struggle with problems requiring multi-step decision-making and environmental feedback.
We propose a framework that can automatically learn a reward model from the environment without human annotations.
arXiv Detail & Related papers (2025-02-17T18:49:25Z) - AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment [13.977849745488339]
AmoebaLLM is a novel framework designed to enable the instant derivation of large language models of arbitrary shapes.
AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications.
arXiv Detail & Related papers (2024-11-15T22:02:28Z) - Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities.
In-Context Learning (ICL) and.
Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting.
LLMs to downstream tasks.
We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
arXiv Detail & Related papers (2024-09-30T10:48:20Z) - Adaptive Draft-Verification for Efficient Large Language Model Decoding [24.347886232342862]
Large language model (LLM) decoding involves generating a sequence of tokens based on a given context.
The typical autoregressive decoding method requires a separate forward pass through the model for each token generated.
We introduce ADED, which accelerates LLM decoding without requiring fine-tuning.
arXiv Detail & Related papers (2024-06-27T22:20:39Z) - Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion [53.33473557562837]
Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost.
We propose a practical and scalable approach to solve this problem via mixture of experts (MoE) based model fusion.
By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives.
arXiv Detail & Related papers (2024-06-14T07:16:18Z) - Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference [12.371152982808914]
We introduce the Adaptive Computation Module (ACM), a generic module that dynamically adapts its computational load to match the estimated difficulty of the input on a per-token basis.
An ACM consists of a sequence of learners that progressively refine the output of their preceding counterparts. An additional gating mechanism determines the optimal number of learners to execute for each token.
Our evaluation of transformer models in computer vision and speech recognition demonstrates that substituting layers with ACMs significantly reduces inference costs without degrading the downstream accuracy for a wide interval of user-defined budgets.
arXiv Detail & Related papers (2023-12-15T20:39:43Z) - Amortizing intractable inference in large language models [56.92471123778389]
We use amortized Bayesian inference to sample from intractable posterior distributions.
We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training.
As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
arXiv Detail & Related papers (2023-10-06T16:36:08Z) - A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical
Computation Offloading [62.34538208323411]
We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs)
MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
arXiv Detail & Related papers (2023-09-02T11:01:16Z)
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.