Scaling Laws for Fine-Grained Mixture of Experts
- URL: http://arxiv.org/abs/2402.07871v1
- Date: Mon, 12 Feb 2024 18:33:47 GMT
- Title: Scaling Laws for Fine-Grained Mixture of Experts
- Authors: Jakub Krajewski, Jan Ludziejewski, Kamil Adamczewski, Maciej Pi\'oro,
Micha{\l} Krutul, Szymon Antoniak, Kamil Ciebiera, Krystian Kr\'ol, Tomasz
Odrzyg\'o\'zd\'z, Piotr Sankowski, Marek Cygan, Sebastian Jaszczur
- Abstract summary: Mixture of Experts (MoE) models have emerged as a primary solution for reducing the computational cost of Large Language Models.
In this work, we analyze their scaling properties, incorporating an expanded range of variables.
We establish scaling laws for fine-grained MoE, taking into account the number of training tokens, model size, and granularity.
- Score: 4.412803924115907
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mixture of Experts (MoE) models have emerged as a primary solution for
reducing the computational cost of Large Language Models. In this work, we
analyze their scaling properties, incorporating an expanded range of variables.
Specifically, we introduce a new hyperparameter, granularity, whose adjustment
enables precise control over the size of the experts. Building on this, we
establish scaling laws for fine-grained MoE, taking into account the number of
training tokens, model size, and granularity. Leveraging these laws, we derive
the optimal training configuration for a given computational budget. Our
findings not only show that MoE models consistently outperform dense
Transformers but also highlight that the efficiency gap between dense and MoE
models widens as we scale up the model size and training budget. Furthermore,
we demonstrate that the common practice of setting the size of experts in MoE
to mirror the feed-forward layer is not optimal at almost any computational
budget.
Related papers
- Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient [4.34286535607654]
We present joint scaling laws for dense and MoE models, incorporating key factors such as the number of active parameters, dataset size, and the number of experts.
Surprisingly, we show that MoE models can be more memory-efficient than dense models, contradicting conventional wisdom.
arXiv Detail & Related papers (2025-02-07T18:55:38Z) - Scalable Language Models with Posterior Inference of Latent Thought Vectors [52.63299874322121]
Latent-Thought Language Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space.
LTMs possess additional scaling dimensions beyond traditional LLMs, yielding a structured design space.
LTMs significantly outperform conventional autoregressive models and discrete diffusion models in validation perplexity and zero-shot language modeling.
arXiv Detail & Related papers (2025-02-03T17:50:34Z) - Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models [34.79589443380606]
The scaling of large language models (LLMs) is a critical research area for the efficiency and effectiveness of model training and deployment.
Our work investigates the transferability and discrepancies of scaling laws between Dense Models and MoE models.
arXiv Detail & Related papers (2024-10-08T03:21:56Z) - Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning [78.72226641279863]
Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling.
Our research explores task-specific model pruning to inform decisions about designing SMoE architectures.
We introduce an adaptive task-aware pruning technique UNCURL to reduce the number of experts per MoE layer in an offline manner post-training.
arXiv Detail & Related papers (2024-09-02T22:35:03Z) - More Compute Is What You Need [3.184416958830696]
We propose a new scaling law that suggests model performance depends mostly on the amount of compute spent for transformer-based models.
We predict that (a) for inference efficiency, training should prioritize smaller model sizes and larger training datasets, and (b) assuming the exhaustion of available web datasets, scaling the model size might be the only way to further improve model performance.
arXiv Detail & Related papers (2024-04-30T12:05:48Z) - SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts [49.01990048827639]
We introduce SEER-MoE, a framework for reducing both the memory footprint and compute requirements of pre-trained MoE models.
The first stage involves pruning the total number of experts using a heavy-hitters counting guidance, while the second stage employs a regularization-based fine-tuning strategy to recover accuracy loss.
Our empirical studies demonstrate the effectiveness of our method, resulting in a sparse MoEs model optimized for inference efficiency with minimal accuracy trade-offs.
arXiv Detail & Related papers (2024-04-07T22:13:43Z) - Toward Inference-optimal Mixture-of-Expert Large Language Models [55.96674056805708]
We study the scaling law of MoE-based large language models (LLMs)
We find that MoEs with a few (4/8) experts are the most serving efficient solution under the same performance, but costs 2.5-3.5x more in training.
We propose to amend the scaling law of MoE by introducing inference efficiency as another metric besides the validation loss.
arXiv Detail & Related papers (2024-04-03T16:33:42Z) - Mixtures of Experts Unlock Parameter Scaling for Deep RL [54.26191237981469]
In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules into value-based networks results in more parameter-scalable models.
This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.
arXiv Detail & Related papers (2024-02-13T17:18:56Z) - Efficient Large Scale Language Modeling with Mixtures of Experts [61.45159383372181]
Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation.
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings.
arXiv Detail & Related papers (2021-12-20T17:05:11Z)
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.