Mixed Sparsity Training: Achieving 4$\times$ FLOP Reduction for Transformer Pretraining

• URL: http://arxiv.org/abs/2408.11746v1
• Date: Wed, 21 Aug 2024 16:13:16 GMT
• Title: Mixed Sparsity Training: Achieving 4$\times$ FLOP Reduction for Transformer Pretraining
• Authors: Pihe Hu, Shaolong Li, Longbo Huang,
• Abstract summary: Mixed Sparsity Training (MST) is an efficient pretraining method that can reduce about $75%$ of Floating Point Operations (FLOPs) while maintaining performance. Our experiment on GPT-2 showcases a FLOP reduction of $4times$ without compromising performance.
• Score: 32.925150708409205
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have made significant strides in complex tasks, yet their widespread adoption is impeded by substantial computational demands. With hundreds of billion parameters, transformer-based LLMs necessitate months of pretraining across a high-end GPU cluster. However, this paper reveals a compelling finding: transformers exhibit considerable redundancy in pretraining computations, which motivates our proposed solution, Mixed Sparsity Training (MST), an efficient pretraining method that can reduce about $75\%$ of Floating Point Operations (FLOPs) while maintaining performance. MST integrates dynamic sparse training (DST) with Sparsity Variation (SV) and Hybrid Sparse Attention (HSA) during pretraining, involving three distinct phases: warm-up, ultra-sparsification, and restoration. The warm-up phase transforms the dense model into a sparse one, and the restoration phase reinstates connections. Throughout these phases, the model is trained with a dynamically evolving sparse topology and an HSA mechanism to maintain performance and minimize training FLOPs concurrently. Our experiment on GPT-2 showcases a FLOP reduction of $4\times$ without compromising performance.

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