MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models (2408.11743v1)

Abstract: As inference on LLMs emerges as an important workload in machine learning applications, weight quantization has become a standard technique for efficient GPU deployment. Quantization not only reduces model size, but has also been shown to yield substantial speedups for single-user inference, due to reduced memory movement, with low accuracy impact. Yet, it remains open whether speedups are achievable also in \emph{batched} settings with multiple parallel clients, which are highly relevant for practical serving. It is unclear whether GPU kernels can be designed to remain practically memory-bound, while supporting the substantially increased compute requirements of batched workloads. This paper resolves this question positively by describing the design of Mixed-precision Auto-Regressive LINear kernels, called MARLIN. Concretely, given a model whose weights are compressed via quantization to, e.g., 4 bits per element, MARLIN shows that batchsizes up to 16-32 can be supported with close to maximum ($4\times$) quantization speedup, and larger batchsizes up to 64-128 with gradually decreasing, but still significant, acceleration. MARLIN accomplishes this via a combination of techniques, such as asynchronous memory access, complex task scheduling and pipelining, and bespoke quantization support. Our experiments show that MARLIN's near-optimal performance on individual LLM layers across different scenarios can also lead to end-to-end LLM inference speedups (of up to $2.8\times$) when integrated with the popular vLLM serving engine. Finally, MARLIN is extensible to further compression techniques, like NVIDIA 2:4 sparsity, leading to additional speedups.

Overview of MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on LLMs

The paper "MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on LLMs," authored by Elias Frantar, Roberto L. Castro, Jiale Chen, Torsten Hoefler, and Dan Alistarh, introduces MARLIN, a specialized kernel designed to accelerate the inference of LLMs while maintaining high performance even under mixed-precision settings. The MARLIN kernel primarily addresses the challenges associated with batch parallelism and manages to achieve significant speedups while supporting various compression techniques.

Key Contributions and Techniques

The authors propose MARLIN (Mixed-precision Auto-Regressive LINear kernels), aiming to resolve the inefficiencies that arise when employing weight quantization in batched settings. Traditional weight quantization saves memory and accelerates single-user inference by minimizing memory movements, but achieving similar speedups for multi-user, batched inference has been a challenge due to increased arithmetic intensity. MARLIN addresses this issue through several innovative techniques:

1. Asynchronous Memory Access: By employing asynchronous memory access, MARLIN ensures that memory transfers and computations are overlapped, thus hiding latencies and boosting overall throughput.
2. Complex Task Scheduling and Pipelining: MARLIN utilizes advanced task scheduling and pipelining to manage dependencies effectively, ensuring that GPU utilization remains high even under mixed-precision constraints.
3. Bespoke Quantization Support: The kernel is designed to work efficiently with quantized data, specifically 4-bit weights, ensuring that the reduced memory movement provides tangible speedups.

The techniques culminate in a kernel capable of supporting batch sizes up to 128 with significant speedups, close to the maximum potential offered by 4-bit quantization (up to $4\times$), although the speedups decrease gradually with larger batch sizes.

Numerical Results and Performance Analysis

The paper presents robust experimental results demonstrating MARLIN's capability to provide near-optimal performance in various scenarios. Specifically, the fast matrix multiplication benchmarks show:

• Up to $3.9\times$ speedup for batch sizes up to 16-32, with performance decreasing to $1.5\times$ at batch size 128 on an NVIDIA A10 GPU.
• End-to-end inference speedups of up to $2.8\times$ when using the MARLIN kernel integrated with the vLLM serving engine.
• Further improvements in speedup to $3.2\times$ with the Sparse-MARLIN variant, leveraging NVIDIA's 2:4 sparsity format on Tensor Cores.

These results highlight MARLIN's efficacy in memory-bound and mixed-precision operations, which are critical for scalable LLM deployment.

Implications and Future Directions

The MARLIN kernel significantly impacts the practical deployment of LLMs by addressing a core issue in scalable batch inference. By achieving high efficiency even with reduced precision, it facilitates cost-effective and faster LLM servicing in real-world applications. The kernel's design ensures that it leverages modern GPU capabilities fully, setting a benchmark for future kernel designs.

From a theoretical perspective, MARLIN offers insights into overlapping memory operations with computation to hide latencies effectively, which could be extended to other compute-bound and memory-bound hybrid workloads. This approach can be particularly beneficial in environments constrained by memory bandwidth but unbounded by compute capabilities.

Speculations on Future Developments in AI and Related Technologies

The research paves the way for broader applications of mixed-precision and sparsity techniques in other areas requiring high computational throughput but limited by memory bandwidth. As LLMs continue to grow in size and complexity, efficient utilization of hardware through techniques like those proposed in MARLIN will become increasingly critical.

Future research could focus on:

• Extending Mixed-Precision Support: Beyond weight quantization, exploring further compression techniques like activation quantization could further enhance computational efficiency across various neural network architectures.
• Enhancing Multi-GPU Performance: Given the increasing prevalence of multi-GPU systems for training and inference, optimizing kernels like MARLIN for seamless multi-GPU parallelism could lead to even greater performance improvements.
• Exploring Novel Quantization Methods: Techniques combining vector quantization or extreme compression methods could be integrated into MARLIN-like kernels, further pushing the envelope of efficient LLM inference.

Conclusion

The paper on MARLIN presents a well-crafted solution to the persistent issue of efficient batched inference in LLMs under mixed-precision settings. With the successful demonstration of significant speedups and practical applicability, the kernel offers both immediate and long-term benefits to the field of machine learning. The implementation and results underscore the importance of specialized hardware-aware optimization techniques in deploying large-scale AI models effectively.