Researchers Accelerate Arbitrary Precision Large Language Models, Overcoming Computational Limits with Novel Methods

The increasing demand for large language models (LLMs) is hampered by their substantial computational requirements, limiting their widespread use in real-time applications. Shaobo Ma, alongside Chao Fang from the Shanghai Qi Zhi Institute, and Haikuo Shao and Zhongfeng Wang from Nanjing University, address this challenge with a new acceleration scheme called APT-LLM. Their work focuses on optimising LLM performance at arbitrary precision, a difficult task for current graphics processing units (GPUs) due to limitations in hardware support and inefficient data handling. APT-LLM introduces a novel data format and a bit-level matrix multiplication method, alongside a refined memory management system and dynamic kernel mapping, to unlock the full potential of GPU Tensor Cores. The results demonstrate significant speedups, up to 3. 99x faster than standard floating-point calculations and improvements over existing integer-based acceleration techniques, paving the way for more efficient and accessible LLM inference.

To address these challenges, researchers propose a comprehensive acceleration scheme for arbitrary precision LLMs, named APT-LLM. The approach introduces a novel data format, bipolar-INT, which enables efficient and lossless conversion with signed integers, while also improving suitability for parallel computation. Key contributions and findings include the ability to trade-off between accuracy and computational efficiency, crucial for LLMs where lower precision can significantly speed up inference without substantial quality loss. The approach involves both algorithmic optimizations and careful utilization of the GPU’s tensor core capabilities, designed to maximize throughput and minimize latency. Experiments demonstrate that APT achieves substantial speedups compared to existing quantization and inference techniques, particularly for low-bit (4-bit and even lower) LLMs.

The paper reports significant improvements in throughput and latency across various LLMs and hardware configurations. By utilizing lower precision, APT reduces memory footprint, allowing for larger models to be loaded and processed. The technique is applicable to a wide range of LLMs, including Llama 2, OPT, Bloom, and Qwen, and can be integrated with existing inference frameworks. APT cleverly manipulates the data representation and computation within the GPU’s tensor cores, dynamically adjusting the number of bits used to represent weights and activations, allowing for fine-grained control over the trade-off between accuracy and performance.

The research highlights the importance of precision in LLM inference and demonstrates that arbitrary precision can unlock significant performance gains. Effective LLM acceleration requires a co-design approach that leverages both algorithmic optimizations and hardware capabilities. The research opens up new avenues for exploring more efficient and scalable LLM inference techniques.

Bipolar-INT Format Accelerates Low-Bit Language Models

Researchers have developed a new acceleration scheme, named APT-LLM, to significantly improve the performance of large language models (LLMs) on graphics processing units (GPUs). This breakthrough addresses the substantial computational demands that currently limit the deployment and real-time operation of these powerful AI systems. The team focused on enabling efficient operation with ultra-low-bit quantized LLMs, a technique for reducing computational cost, while overcoming limitations in existing GPU hardware and software. To further enhance performance, the team implemented a memory management system focused on data recovery, strategically employing fast shared memory to substantially increase kernel execution speed and reduce memory access latency. They also created a kernel mapping method that dynamically selects optimal configurable hyperparameters for varying matrix sizes, ensuring peak performance across different LLM architectures and precision settings.

Experiments demonstrate that APT-LLM achieves up to a 3. 99 speedup compared to FP16 baselines and a 2. 16 speedup over existing CUTLASS INT4 acceleration on RTX 3090 GPUs. On more powerful RTX 4090 and H800 GPUs, APT-LLM delivers up to a 2. 44 speedup over FP16 and a 1. 65 speedup over CUTLASS integer baselines. These results demonstrate a substantial advancement in LLM acceleration, paving the way for more efficient and accessible AI applications.

APT-LLM Accelerates Low-Bit Language Models

This research introduces APT-LLM, a new acceleration scheme designed to improve the efficiency of large language models on GPU hardware. The system addresses limitations in current methods related to GPU Tensor Core support, memory management, and inflexible kernel optimisation, all of which hinder the deployment of ultra-low-bit quantized LLMs. APT-LLM achieves this through a novel data format, bipolar-INT, which enhances parallel processing, and a bit-level matrix multiplication technique that flexibly utilises GPU resources. Evaluations across several popular language models demonstrate significant speedups with APT-LLM compared to standard FP16 baselines and existing integer acceleration methods like CUTLASS.

Specifically, the system achieves up to a 3. 99x speedup on an RTX 3090 and up to 2. 44x on RTX 4090 and H800 GPUs. The authors acknowledge that while their algorithm is mathematically lossless, adjustments to model quantization parameters are necessary, which can introduce minor errors to model perplexity, though these remain within acceptable limits.