Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
<|reference_start|>Q-Sparse: All Large Language Models can be Fully Sparsely-Activated: 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.<|reference_end|>
@article{wang2024q-sparse:,
    title={Q-Sparse: All Large Language Models can be Fully Sparsely-Activated},
    author={Hongyu Wang, Shuming Ma, Ruiping Wang, Furu Wei},
    journal={arXiv preprint arXiv:2407.10969},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10969},
    primaryClass={cs.CL cs.LG}
}