- Keywords: sparsity, efficient training and inference.
- Abstract: Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into unstructured fine-grained sparsity that zeroes out multiple individual weights distributed across the neural network, and structured coarse-grained sparsity which prunes blocks of sub-networks of a neural network. Fine-grained sparsity can achieve a high compression ratio but is not hardware friendly and hence receives limited speed gains. On the other hand, coarse-grained sparsity cannot simultaneously achieve both apparent acceleration on modern GPUs and decent performance. In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network, which can maintain the advantages of both unstructured fine-grained sparsity and structured coarse-grained sparsity simultaneously on specifically designed GPUs. Specifically, a 2 : 4 sparse network could achieve 2× speed-up without performance drop on Nvidia A100 GPUs. Furthermore, we propose a novel and effective ingredient, sparse-refined straight-through estimator (SR-STE), to alleviate the negative influence of the approximated gradients computed by vanilla STE during optimization. We also define a metric, Sparse Architecture Divergence (SAD), to measure the sparse network’s topology change during the training process. Finally, We justify SR-STE’s advantages with SAD and demonstrate the effectiveness of SR-STE by performing comprehensive experiments on various tasks. Anonymous code and model will be at available at https://github.com/anonymous-NM-sparsity/NM-sparsity.
- One-sentence Summary: a simple yet universal recipe to learn N:M sparse neural networks from scratch
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- Code: [![github](/images/github_icon.svg) anonymous-NM-sparsity/NM-sparsity](https://github.com/anonymous-NM-sparsity/NM-sparsity) + [![Papers with Code](/images/pwc_icon.svg) 2 community implementations](https://paperswithcode.com/paper/?openreview=K9bw7vqp_s)
- Data: [COCO](https://paperswithcode.com/dataset/coco), [FlyingChairs](https://paperswithcode.com/dataset/flyingchairs), [ImageNet](https://paperswithcode.com/dataset/imagenet)