Q-Sparse: All Large Language Models can be Fully Sparsely-Activated

• URL: http://arxiv.org/abs/2407.10969v3
• Date: Wed, 24 Jul 2024 14:57:48 GMT
• Title: Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
• Authors: Hongyu Wang, Shuming Ma, Ruiping Wang, Furu Wei,
• Abstract summary: We introduce Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs) Q-Sparse enables full sparsity of activations in LLMs which can bring significant efficiency gains in inference. We also introduce Block Q-Sparse for batch training and inference.
• Score: 93.45300714803429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can bring significant efficiency gains in inference. This is achieved by applying top-K sparsification to the activations and the straight-through-estimator to the training. We also introduce Block Q-Sparse for batch training and inference. The key results from this work are, (1) Q-Sparse can achieve results comparable to those of baseline LLMs while being much more efficient at inference time; (2) We present an inference-optimal scaling law for sparsely-activated LLMs; (3) Q-Sparse is effective in different settings, including training-from-scratch, continue-training of off-the-shelf LLMs, and finetuning; (4) Q-Sparse works for both full-precision and 1-bit LLMs (e.g., BitNet b1.58). Particularly, the synergy of BitNet b1.58 and Q-Sparse (can be equipped with MoE) provides the cornerstone and a clear path to revolutionize the efficiency, including cost and energy consumption, of future LLMs.

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