MobileQuant: Mobile-friendly Quantization for On-device Language Models

• URL: http://arxiv.org/abs/2408.13933v2
• Date: Fri, 4 Oct 2024 12:03:03 GMT
• Title: MobileQuant: Mobile-friendly Quantization for On-device Language Models
• Authors: Fuwen Tan, Royson Lee, Łukasz Dudziak, Shell Xu Hu, Sourav Bhattacharya, Timothy Hospedales, Georgios Tzimiropoulos, Brais Martinez,
• Abstract summary: Large language models (LLMs) have revolutionized language processing, delivering outstanding results across multiple applications. deploying LLMs on edge devices poses several challenges with respect to memory, energy, and compute costs. We introduce a simple post-training quantization method, named MobileQuant, that extends previous weight equivalent transformation works.
• Score: 31.75012542498791
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have revolutionized language processing, delivering outstanding results across multiple applications. However, deploying LLMs on edge devices poses several challenges with respect to memory, energy, and compute costs, limiting their widespread use in devices such as mobile phones. A promising solution is to reduce the number of bits used to represent weights and activations. While existing works have found partial success at quantizing LLMs to lower bitwidths, e.g. 4-bit weights, quantizing activations beyond 16 bits often leads to large computational overheads due to poor on-device quantization support, or a considerable accuracy drop. Yet, 8-bit activations are very attractive for on-device deployment as they would enable LLMs to fully exploit mobile-friendly hardware, e.g. Neural Processing Units (NPUs). In this work, we make a first attempt to facilitate the on-device deployment of LLMs using integer-only quantization. We first investigate the limitations of existing quantization methods for on-device deployment, with a special focus on activation quantization. We then address these limitations by introducing a simple post-training quantization method, named MobileQuant, that extends previous weight equivalent transformation works by jointly optimizing the weight transformation and activation range parameters in an end-to-end manner. MobileQuant demonstrates superior capabilities over existing methods by 1) achieving near-lossless quantization on a wide range of LLM benchmarks, 2) reducing latency and energy consumption by 20\%-50\% compared to current on-device quantization strategies, 3) requiring limited compute budget, 4) being compatible with mobile-friendly compute units, e.g. NPU.

Related papers

• SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models [67.67135738642547]
  Post-training quantization (PTQ) is a powerful compression technique investigated in large language models (LLMs) Existing PTQ methods are not ideal in terms of accuracy and efficiency, especially with below 4 bit-widths. This paper presents a Salience-Driven Mixed-Precision Quantization scheme for LLMs, namely SliM-LLM.
  arXiv  Detail & Related papers  (2024-05-23T16:21:48Z)
• Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge [45.690907522226794]
  Large Language Models (LLMs) stand out for their impressive performance in intricate language modeling tasks. Recent works show that 8-bit or lower weight quantization is feasible with minimal impact on end-to-end task performance. We propose Agile-Quant, an activation-guided quantization framework for popular Large Language Models.
  arXiv  Detail & Related papers  (2023-12-09T22:12:52Z)
• Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization [12.655230451207956]
  This paper focuses on post-training quantization (PTQ) in Large Language Models (LLMs) We present two innovative techniques: activation-quantization-aware scaling (AQAS) and sequence-length-aware calibration (SLAC) We demonstrate that our techniques significantly boost task accuracies to levels comparable with full-precision models.
  arXiv  Detail & Related papers  (2023-11-09T06:19:51Z)
• QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models [85.02796681773447]
  We propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation. QA-LoRA is easily implemented with a few lines of code.
  arXiv  Detail & Related papers  (2023-09-26T07:22:23Z)
• OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models [57.27101446992148]
  Large language models (LLMs) have revolutionized natural language processing tasks. Recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM. We introduce an Omnidirectionally calibrated Quantization technique for LLMs, which achieves good performance in diverse quantization settings.
  arXiv  Detail & Related papers  (2023-08-25T02:28:35Z)
• AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration [54.692405042065815]
  We propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for LLM low-bit weight-only quantization. AWQ protects only 1% salient weights and achieves excellent quantization performance for instruction-tuned LMs and, for the first time, multi-modal LMs. We also implement TinyChat, an efficient and flexible inference framework tailored for 4-bit on-device LLM/VLMs.
  arXiv  Detail & Related papers  (2023-06-01T17:59:10Z)
• DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables [49.965024476651706]
  DeepGEMM is a lookup table based approach for the execution of ultra low-precision convolutional neural networks on SIMD hardware. Our implementation outperforms corresponding 8-bit integer kernels by up to 1.74x on x86 platforms.
  arXiv  Detail & Related papers  (2023-04-18T15:13:10Z)
• SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models [14.929695160346276]
  Large language models (LLMs) show excellent performance but are compute- and memory-intensive. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization solution. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy.
  arXiv  Detail & Related papers  (2022-11-18T18:59:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.