arxiv_ml 95% Match Research Paper ML engineers,AI researchers,Deep learning practitioners,LLM developers 1 month ago

Randomized Gradient Subspaces for Efficient Large Language Model Training

large-language-models › training-methods

📄 Abstract

Abstract: Training large language models (LLMs) is often bottlenecked by extreme memory demands, with optimizer states dominating the footprint. Recent works mitigates this cost by projecting gradients into low-dimensional subspaces using sophisticated update strategies. In this paper, we analyze the dynamics of gradient space and its underlying subspaces. We find that while a small subspace captures most gradient energy, a significant portion still resides in the residual bulk; moreover, the influence of the core subspace diminishes over time and in deeper layers. We also observe that the gradient space exhibits near-flat curvature, calling for algorithms that explicitly account for this geometry. Motivated by these insights, we introduce a suite of randomized algorithms, GrassWalk and GrassJump, which exploit subspace and achieve state-of-the-art memory savings while improving performance on LLaMA-1B and LLaMA-7B pretraining.

Key Contributions

This paper introduces GrassWalk and GrassJump, randomized algorithms that exploit gradient subspaces for efficient LLM training, achieving significant memory savings and improving performance on LLaMA models. It provides novel insights into gradient space dynamics and curvature.

Business Value

Enables training of larger and more capable language models on existing hardware, reducing computational costs and democratizing access to advanced AI.

Paper Metadata

Innovation Type

Algorithmic/Methodological

Deployment Feasibility

High, as it's an optimization technique for training.

Limitations Addressed

Extreme memory demands during LLM training, particularly from optimizer states, and the diminishing influence of core gradient subspaces over time.

Performance Gains

State-of-the-art memory savings,Improved performance on LLaMA models.

View Code on GitHub

Technical Tags

Large Language Models (LLMs)Gradient subspacesMemory efficiencyOptimizer statesRandomized algorithmsGrassWalkGrassJumpLLaMAPretrainingCurvature

Research Topics

Large Language Model TrainingDeep Learning OptimizationModel CompressionComputational EfficiencyNeural Network Architectures

Methods & Architectures

Randomized gradient subspace projectionGrassWalk algorithmGrassJump algorithm LLaMA-1BLLaMA-7B

Applications & Tasks

Natural Language Processing Machine Learning Infrastructure Reducing memory demands during LLM trainingImproving training efficiency Training large language models with reduced memory footprintAchieving state-of-the-art memory savings

Datasets & Benchmarks

Benchmarks

State-of-the-art memory savings on LLaMA-1B and LLaMA-7B pretraining.

Memory savingsTraining speedModel performance (e.g., perplexity)

Related Fields

Deep LearningNatural Language ProcessingOptimizationComputer Architecture

Keywords

Large Language ModelsLLM TrainingMemory EfficiencyGradient SubspacesRandomized AlgorithmsGrassWalkGrassJumpLLaMAPretrainingOptimizer StatesDeep LearningOptimizationComputational Efficiency

Academic Context

#Large Language Model Training#Deep Learning Optimization#Model Compression#Computational Efficiency#Neural Network Architectures

Companies & Organizations

Companies Mentioned

Microsoft

Technology Stack

ML Infrastructure

Distributed training frameworks

Commercial Potential

Potential Products

More efficient LLM training librariesCloud services for optimized LLM training

Target Industries

TechnologyAI ResearchCloud Computing

Use Case Examples

Training foundation models with significantly less GPU memoryEnabling fine-tuning of large models on consumer hardware

Competitive Edge

Offers a novel algorithmic approach to memory efficiency in LLM training, outperforming existing subspace projection methods.

Market Opportunity

Vast, related to the growing market for LLMs and AI infrastructure.

Revenue Models

Integration into ML platformsconsulting services.

Resource Requirements

Compute Needs

High, but reduced compared to standard training.

Data Requirements

Large text corpora for pretraining.

Deployment Constraints

Requires integration into existing training pipelines.

Scalability

Designed for large-scale LLM training, aiming to improve scalability.

Production Readiness

Maturity Level

Implemented/Published

Time to Market

Short-term for adoption by researchers, medium-term for industry integration.

Licensing

Likely permissive (e.g., MIT, Apache) given the GitHub link.

Patent Potential

Moderate, for the specific algorithms and techniques.

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