January 09, 2024
We propose an implementation of an efficient fused matrix multiplication kernel for W4A16 quantized inference, where we perform dequantization and GEMM in a fused kernel using a SplitK work decomposition. Our implementation shows improvement for the type of skinny matrix-matrix multiplications found in foundation model inference workloads. In particular, this paper surveys the type of matrix multiplication between a skinny activation matrix and a square weight matrix. Our results show an average of 65\% speed improvement on A100, and an average of 124\% speed improvement on H100 (with a peak of 295\%) for a range of matrix dimensions including those found in a llama-style model, where m < n = k.
Written by
Less Wright
Adnan Hoque
Publisher
arxiv.org
Research Topics
Core Machine Learning
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