vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training

• URL: http://arxiv.org/abs/2312.12391v2
• Date: Tue, 10 Sep 2024 04:16:28 GMT
• Title: vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training
• Authors: Jehyeon Bang, Yujeong Choi, Myeongwoo Kim, Yongdeok Kim, Minsoo Rhu,
• Abstract summary: This paper presents our profiling-driven simulator called vTrain to determine an efficient and cost-effective training system configuration. We demonstrate vTrain's practicality through several case studies, e.g., effectively evaluating optimal training parallelization strategies.
• Score: 3.0051215935332505
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As large language models (LLMs) become widespread in various application domains, a critical challenge the AI community is facing is how to train these large AI models in a cost-effective manner. Existing LLM training plans typically employ a heuristic based parallel training strategy which is based on empirical observations rather than grounded upon a thorough examination of the search space of LLM parallelization. Such limitation renders existing systems to leave significant performance left on the table, wasting millions of dollars worth of training cost. This paper presents our profiling-driven simulator called vTrain, providing AI practitioners a fast yet accurate software framework to determine an efficient and cost-effective LLM training system configuration. We demonstrate vTrain's practicality through several case studies, e.g., effectively evaluating optimal training parallelization strategies that balances training time and its associated training cost, efficient multi-tenant GPU cluster schedulers targeting multiple LLM training jobs, and determining a compute-optimal LLM model architecture given a fixed compute budget.

Related papers

• A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs [74.35290684163718]
  A primary challenge in large language model (LLM) development is their onerous pre-training cost. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by leveraging a small language model (SLM)
  arXiv  Detail & Related papers  (2024-10-24T14:31:52Z)
• Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate [118.37653302885607]
  We present the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs) MIR is indicative about training data selection, training strategy schedule, and model architecture design to get better pre-training results.
  arXiv  Detail & Related papers  (2024-10-09T17:59:04Z)
• Exploring Scaling Laws for Local SGD in Large Language Model Training [4.125418728284004]
  We show that local SGD achieves competitive results compared to conventional methods, given equivalent model parameters, datasets, and computational resources. This demonstrates its viability as an alternative to single large-cluster training.
  arXiv  Detail & Related papers  (2024-09-20T04:02:48Z)
• Achieving Peak Performance for Large Language Models: A Systematic Review [0.0]
  Large language models (LLMs) have achieved remarkable success in natural language processing (NLP) As models grow into the trillion- parameter range, computational and memory costs increase significantly. This makes it difficult for many researchers to access the resources needed to train or apply these models.
  arXiv  Detail & Related papers  (2024-09-07T13:57:41Z)
• Understanding the Performance and Estimating the Cost of LLM Fine-Tuning [9.751868268608675]
  Fine-tuning Large Language Models (LLMs) for specific tasks in a cost-effective manner. In this paper, we characterize sparse Mixture of Experts (MoE) based LLM fine-tuning to understand their accuracy and runtime performance. We also develop and validate an analytical model to estimate the cost of LLM fine-tuning on the cloud.
  arXiv  Detail & Related papers  (2024-08-08T16:26:07Z)
• Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• SoupLM: Model Integration in Large Language and Multi-Modal Models [51.12227693121004]
  Training large language models (LLMs) requires significant computing resources. Existing publicly available LLMs are typically pre-trained on diverse, privately curated datasets spanning various tasks.
  arXiv  Detail & Related papers  (2024-07-11T05:38:15Z)
• One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments [43.107261545706415]
  Large Language Models (LLMs) have advanced rapidly but face significant memory demands. Current methods typically require lengthy training to alleviate the performance degradation from quantization loss. We make an initial attempt to extend the once-for-all framework to large language models.
  arXiv  Detail & Related papers  (2024-05-30T16:05:15Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)

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.