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Accelerating Transformer Inference and Training with 2:4 Activation Sparsity
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In this paper, we demonstrate how to leverage 2:4 sparsity, a popular hardware-accelerated GPU sparsity pattern, to activations to accelerate large language model training and inference. Crucially we exploit the intrinsic sparsity found in Squared-ReLU activations to provide this acceleration with no accuracy loss. Our approach achieves up to 1.3x faster Feed Forward Network (FFNs) in both the forwards and backwards pass. This work highlights the potential for sparsity to play a key role in accelerating large language model training and inference.
Forward citations
Cited by 2 Pith papers
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Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models
Amber Pruner proposes training-free N:M activation sparsity for LLM prefill; however, the supplied manuscript body is an unrelated paper.
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TorchAO: PyTorch-Native Training-to-Serving Model Optimization
TorchAO delivers a PyTorch-native, end-to-end workflow for FP8 training, QAT, PTQ, and sparsity, with benchmarked speedups and production use in quantized Llama releases.
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