A Hardware-Friendly Tiled Singular-Value Decomposition-Based Matrix Multiplication for Transformer-Based Models
LI Hai-long, Jaewan Choi, Yongsuk Kwon, Jung Ho Ahn · IEEE Computer Architecture Letters · 2023
Transformer-based models have become the backbone of numerous state-of-the-art natural language processing (NLP) tasks, including large language models. Matrix multiplication, a fundamental operation in the Transformer-based models, accounts for most of the execution time. While singular value decomposition (SVD) can accelerate this operation by reducing the amount of computation and memory footprints through rank size reduction, it leads to degraded model quality due to challenges in preserving important information. Moreover, this method does not effectively utilize the resources of modern GPUs. In this paper, we propose a hardware-friendly approach: matrix multiplication based on tiled singular value decomposition (TSVD). TSVD divides a matrix into multiple tiles and performs matrix factorization on each tile using SVD. By breaking down the process into smaller regions, TSVD mitigates the loss of important data. We apply the matrices decomposed by TSVD for matrix multiplication, and our TSVD-based matrix multiplication (TSVD-matmul) method exhibits more efficiency on GPUs compared to the SVD approach, especially for small problem sizes or matrices with a tall-and-skinny shape. It is because TSVD-matmul leverages GPU resources more efficiently. As a result, TSVD-matmul achieved a speedup of 1.03× to 3.24× compared to the SVD approach at compression ratios ranging from 2 to 8. When deployed to GPT-2, TSVD not only performs competitively with a full fine-tuning on the E2E NLG task but also achieves a speedup of 1.06× to 1.24× at 2 to 8 compression ratios while increasing accuracy by up to 1.5 BLEU score.