BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers

• URL: http://arxiv.org/abs/2507.03117v1
• Date: Thu, 03 Jul 2025 18:53:54 GMT
• Title: BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers
• Authors: Patrik Okanovic, Sameer Deshmukh, Grzegorz Kwasniewski, Kentaro Katayama, Takumi Honda, Maciej Besta, Torsten Hoefler,
• Abstract summary: (Bl)ock (a)nd (S)parse (T)ransformers) (BLaST)<n>We introduce (Bl)ock (a)nd (S)parse (T)ransformers) (BLaST)<n>BLaST achieves up to 95% sparsity in sparse weights with negligible accuracy loss.
• Score: 16.72390519245507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The energy consumption of large-scale ML models is dominated by data movement - shuffling billions of parameters across memory hierarchies and data centers. Effective sparsification to prune redundant parameters is still challenging: existing methods incur significant accuracy degradation, performance overhead, or both. We introduce (Bl)ock (a)nd (S)parse (T)ransformers (BLaST), a general, robust, and reliable sparsification method applicable to linear layers in all settings. Our method iteratively sparsifies weight matrices into a block sparsity pattern suitable for efficient sparse matrix-matrix (SpMM) multiplication. BLaST achieves up to 95% sparsity in MLP weights with negligible accuracy loss. Our fused, highly optimized Sparse MLP kernel delivers up to 16.7x speedup over dense MLPs across 9 architectures and 8 datasets, resulting in up to 1.6x inference speedup, 1.11x pretraining speedup and up to 3.12x inference memory usage reduction. BLaST enables the next generation of large-scale AI systems by reducing energy use, memory footprint, and latency.

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