5 papers
PATCH: Learnable Tile-level Hybrid Sparsity for LLMs
Younes Hourri, Mohammad Mozaffari, Maryam Mehri Dehnavi
Large language models (LLMs) deliver impressive performance but incur prohibitive memory and compute costs at deployment. Model pruning is an effective way to reduce these overhead…
OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction
Mohammad Mozaffari, Samuel Kushnir, Maryam Mehri Dehnavi +1
Post-training model pruning is a promising solution, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principled joint optimizatio…
Optimal Software Pipelining and Warp Specialization for Tensor Core GPUs
Rupanshu Soi, Rohan Yadav, Fredrik Kjolstad +4
GPU architectures have continued to grow in complexity, with recent incarnations introducing increasingly powerful fixed-function units for matrix multiplication and data movement…
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Mohammad Mozaffari, Amir Yazdanbakhsh, Maryam Mehri Dehnavi
Conventional model compression techniques for LLMs address high memory consumption and slow inference challenges but typically require computationally expensive retraining to prese…
SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs
Mohammad Mozaffari, Amir Yazdanbakhsh, Zhao Zhang +1
We propose SLoPe, a Double-Pruned Sparse Plus Lazy Low-rank Adapter Pretraining method for LLMs that improves the accuracy of sparse LLMs while accelerating their pretraining and i…