Low-Cost Deployment of Multiple Large Language Models with Varying Bit-Widths

מושגי ליבה

This paper introduces "any-precision LLM", a method for efficiently deploying multiple large language models (LLMs) with varying bit-widths, reducing the high memory costs associated with maintaining different-sized models.

תקציר

The paper addresses the challenges of deploying multiple, different-sized LLMs, which is a practical requirement for effectively handling queries with varied latency constraints and supporting techniques like speculative decoding. The key challenges are the high memory overhead and the training costs of acquiring multiple LLM variants.
To address these challenges, the paper proposes "any-precision LLM", which extends the concept of "any-precision DNN" to LLMs. The core idea is to generate a single large "parent" LLM model at the highest supported bit-width (e.g., 8-bit) and then derive smaller models at lower bit-widths (e.g., 3-bit, 4-bit) by taking the most significant bits of the parent model's parameters. This approach significantly reduces the memory footprint required to deploy multiple LLMs, as only the parent model and a set of quantization parameters need to be stored.
The paper makes two key contributions to enable effective any-precision LLM:

A lightweight method for any-precision quantization of LLMs, based on an incremental upscaling approach that leverages a post-training quantization (PTQ) framework. This method can generate a set of quantized LLMs at varying bit-widths, while maintaining state-of-the-art model quality.

A specialized software engine that supports efficient execution of any-precision LLMs. The engine adopts a bitplane-based weight representation and introduces optimizations like efficient bit-transpose and merged table lookups to maximize the performance benefits of reduced bit-widths.

Extensive experiments demonstrate that the proposed any-precision LLM solution significantly reduces the memory costs of deploying multiple different-sized LLMs, while matching or even outperforming the state-of-the-art quantization techniques at each bit-width in terms of model quality and inference throughput.

סטטיסטיקה

Deploying three tiers of LLMs (large base, half-sized, quarter-sized) nearly doubles the total memory requirement compared to a single large model.
Our any-precision LLM solution can pack LLMs of 3, 4, ..., 8-bit into a memory footprint comparable to a single 8-bit LLM, achieving up to 3.56x memory savings.

ציטוטים

"Any-precision LLM, an extension of the concept of any-precision DNN (Yu et al., 2021) to LLM, is a promising solution for the low-cost deployment of multiple, different-sized LLMs."
"Our solution efficiently packs LLMs quantized to varying bit-widths, such as 3, 4, ... up to n bits, into a memory footprint comparable to a single n-bit LLM."
"Our solution, despite having to adopt a bit-interleaved (bitplane) memory layout for the support of any-precision, showcases high inference throughput, matching or even outperforming that of state-of-the-art quantized matrix-vector multiplication engines that do not support any-precision (Kim et al., 2023b)."

תובנות מפתח מזוקקות מ:

Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs

by Yeonhong Par... ב- arxiv.org 05-07-2024

https://arxiv.org/pdf/2402.10517.pdf

שאלות מעמיקות

How can the any-precision LLM approach be extended to support dynamic bit-width selection during inference, allowing the system to adapt the model precision on-the-fly based on the current resource constraints and performance requirements?

The any-precision LLM approach can be extended to support dynamic bit-width selection during inference by implementing a mechanism that evaluates the current resource constraints and performance requirements in real-time. This dynamic adaptation can be achieved through a feedback loop that continuously monitors the system's resource utilization and inference performance. Based on this monitoring, the system can adjust the bit-width of the LLM model on-the-fly to optimize performance while staying within the resource constraints.
One approach to implementing dynamic bit-width selection is to incorporate a decision-making algorithm that considers factors such as available memory, computational resources, and latency requirements. This algorithm can analyze the trade-offs between model precision and performance metrics, dynamically selecting the optimal bit-width for the current inference task.
Furthermore, the system can utilize techniques like reinforcement learning or heuristics to learn and adapt the bit-width selection strategy based on historical performance data and real-time feedback. By continuously evaluating and adjusting the model precision during inference, the system can achieve an optimal balance between model accuracy and efficiency under varying conditions.

How can the any-precision LLM concept be applied to other types of large neural models beyond language models, such as large vision transformers or multimodal models? What are the potential challenges and trade-offs in doing so?

The any-precision LLM concept can be applied to other types of large neural models, such as vision transformers or multimodal models, by extending the principles of any-precision quantization and incremental upscaling to these domains. The key idea is to leverage the benefits of any-precision quantization to reduce the memory footprint and deployment costs of multiple different-sized models while maintaining high performance.
One potential challenge in applying the any-precision LLM concept to vision transformers or multimodal models is the difference in data characteristics and model architectures compared to language models. Vision transformers, for example, may have different weight distributions and sensitivity patterns that require specialized quantization techniques tailored to the specific domain.
Additionally, the trade-offs in implementing any-precision quantization for vision transformers or multimodal models include the potential impact on model accuracy and inference speed. Lowering the bit-width of the model may lead to a loss in precision, affecting the overall performance on tasks that require fine-grained details or complex interactions between modalities.
Furthermore, the computational complexity of vision transformers and multimodal models may introduce challenges in efficiently implementing any-precision quantization techniques, as these models often involve larger input dimensions and more complex computations compared to language models.

How can the any-precision LLM framework be integrated with other model compression techniques, such as pruning or distillation, to further reduce the memory footprint and deployment costs of multiple different-sized LLMs?

Integrating the any-precision LLM framework with other model compression techniques like pruning or distillation can further enhance the reduction of memory footprint and deployment costs for multiple different-sized LLMs. Here are some strategies for integrating these techniques:

Pruning: Pruning can be applied in conjunction with any-precision quantization to remove redundant or less important weights from the model. By combining pruning with any-precision quantization, the system can achieve even greater compression ratios while maintaining model accuracy. Pruning can help reduce the overall memory footprint of the LLMs, making them more memory-efficient.

Distillation: Distillation involves training a smaller student model to mimic the behavior of a larger teacher model. By distilling knowledge from a high-precision LLM to a lower-precision LLM within the any-precision framework, the system can transfer the essential information while reducing the model size. This approach can help improve the inference speed and memory efficiency of the LLMs.

Hybrid Approaches: Hybrid approaches that combine any-precision quantization with pruning and distillation techniques can offer a comprehensive solution for model compression. By leveraging the strengths of each technique, the system can achieve significant reductions in memory footprint and deployment costs while maintaining high model quality and performance.

Overall, integrating the any-precision LLM framework with other model compression techniques can provide a holistic approach to optimizing the deployment of multiple different-sized LLMs across various applications and use cases.