QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference (2402.10076v1)

Abstract: We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized LLMs. QUICK addresses the shared memory bank-conflict problem of state-of-the-art mixed precision matrix multiplication kernels. Our method interleaves the quantized weight matrices of LLMs offline to skip the shared memory write-back after the dequantization. We demonstrate up to 1.91x speedup over existing kernels of AutoAWQ on larger batches and up to 1.94x throughput gain on representative LLM models on various NVIDIA GPU devices.

• The paper introduces QUICK, a novel CUDA kernel framework that interleaves quantized weight matrices and eliminates shared memory conflicts in matrix multiplication.
• It leverages weight-only quantization to reduce memory usage and boosts inference throughput by up to 1.91–1.94 times on NVIDIA GPUs.
• The performance improvements are especially significant for large batch processing, enabling more efficient real-time LLM deployments.

Overview of "QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference"

The paper "QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference" addresses a critical challenge in deploying LLMs efficiently in real-world applications. The driving motivation behind this research is the substantial computational and memory demands posed by LLMs, particularly those with parameter counts in the order of hundreds of billions. These demands propel the need for optimized inference strategies, such as weight-only quantization, which has emerged as a prominent method to reduce memory footprint while maintaining computational efficacy.

The QUICK Methodology

The authors propose QUICK, a novel set of CUDA kernels designed to optimize the inference of quantized LLMs by resolving shared memory bank-conflicts inherent in contemporary mixed precision matrix multiplication kernels. The essence of the QUICK approach lies in the offline interleaving of quantized weight matrices. This reordering circumvents the shared memory write-back requirement post-dequantization, directly enhancing computation throughput.

The paper extensively explores quantization, particularly weight-only quantization, which remains critical for compressing the overall model size. Despite its benefits in reducing memory usage and supporting efficient LLM inference, weight-only quantization introduces a bottleneck at the mixed precision General Matrix Multiplication (GEMM) stage, primarily due to the overhead of dequantization. The shared memory bank conflicts that arise during this step significantly hinder throughput, especially in large batch processing.

Enhanced Performance with Tensor Cores

The paper also explores GEMM kernels based on NVIDIA's Tensor Cores, which inherently boost performance due to their architectural design but still suffer from limitations when implementing mixed precision operations. The QUICK method seeks to overcome these limitations by eliminating shared memory write-backs and increasing tile size, thus optimizing Tensor Core usage.

Experimental Validation and Results

Empirical evaluations highlight the superior performance of QUICK kernels. The paper reports that QUICK achieves speedups of up to 1.91 times over existing AutoAWQ-Kernels on larger batches and throughput gains up to 1.94 times on various NVIDIA GPU devices. These results signify notable efficiency improvements in the inference process, particularly with larger batch sizes where traditional GEMM kernels underperform relative to directly floating-point-based implementations.

Practical Implications and Future Directions

The introduction of QUICK offers significant practical benefits, particularly for real-time applications and services relying on LLMs, such as conversational agents and code generation systems, where latency and efficiency are paramount. The methodology paves the way for further explorations into optimizing GEMM operations in CUDA environments without sacrificing precision or imposing excessive computational overhead.

While QUICK has demonstrated impressive gains, the paper acknowledges limitations in handling extremely large batch sizes (over 512), where fp16 kernels still hold efficiency advantages. Future research directions proposed include further streamlining the dequantization process and exploring software optimizations, such as automated split-k parameter optimization, to maximize throughput across diverse hardware configurations and model architectures.

Conclusion

In conclusion, the research presents an adept contribution to the domain of AI inference optimizations, providing an effective solution to a longstanding performance bottleneck in LLM deployment scenarios. QUICK emerges as a promising framework, enhancing the efficiency of weight-only quantized LLM inference and opening pathways for further refinements and advancements in AI computation techniques.