S3: Structured Sparsity Specification
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Abstract (arXiv)
We introduce the Structured Sparsity Specification (S3), an algebraic framework for defining, composing, and implementing structured sparse patterns. S3 specifies sparsity through three components: a View that reshapes the tensor via layout composition, a Block specification that defines the atomic pruning unit, and the sparsity decision Scope. Both Block and Scope support Coupling across tensors for coordinated sparsification. S3 enables precise specification of diverse sparsity structures, from fine-grained N:M patterns to coarse channel pruning, and integrates seamlessly with Optimal Brain Damage (OBD) and Surgeon (OBS). We formalize the framework mathematically, demonstrate its expressiveness on canonical patterns, and validate it experimentally via structured OBS and OBD implementations built entirely on S3, which surpasses well-established second order heuristics on output reconstruction across common configurations.
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References · 10
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2016Pointer Sentinel Mixture Modelscited by 2,992
- 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,250
- 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
- 2016Pruning Filters for Efficient ConvNetscited by 1,129
- 2023SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shotcited by 1,090
- 2019Are Sixteen Heads Really Better than One?cited by 803
- 2023A Simple and Effective Pruning Approach for Large Language Modelscited by 776
- 2019Rigging the Lottery: Making All Tickets Winnerscited by 491
- 2023Structured Pruning for Deep Convolutional Neural Networks: A surveycited by 122
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