Extreme Compression of Large Language Models via Additive Quantization

• URL: http://arxiv.org/abs/2401.06118v3
• Date: Sat, 8 Jun 2024 10:55:52 GMT
• Title: Extreme Compression of Large Language Models via Additive Quantization
• Authors: Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh,
• Abstract summary: AQLM is first scheme that is optimal in terms of accuracy-vs-model-size when compressing to less than 3 bits per parameter. We provide fast GPU and CPU implementations of AQLM for token generation.
• Score: 59.3122859349777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of accurate open large language models (LLMs) has led to a race towards performant quantization techniques which can enable their execution on end-user devices. In this paper, we revisit the problem of ``extreme'' LLM compression -- defined as targeting extremely low bit counts, such as 2 to 3 bits per parameter -- from the point of view of classic methods in Multi-Codebook Quantization (MCQ). Our algorithm, called AQLM, generalizes the classic Additive Quantization (AQ) approach for information retrieval to advance the state-of-the-art in LLM compression, via two innovations: 1) learned additive quantization of weight matrices in input-adaptive fashion, and 2) joint optimization of codebook parameters across each transformer blocks. Broadly, AQLM is the first scheme that is Pareto optimal in terms of accuracy-vs-model-size when compressing to less than 3 bits per parameter, and significantly improves upon all known schemes in the extreme compression (2bit) regime. In addition, AQLM is practical: we provide fast GPU and CPU implementations of AQLM for token generation, which enable us to match or outperform optimized FP16 implementations for speed, while executing in a much smaller memory footprint.

Related papers

• SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching [32.4599581528901]
  "Two-tower" architecture is used for compressing pre-trained LLM parameters into compact representations and fine-tuning the additive full-precision adapter. We propose SpaLLM (Sketched Adapting of LLMs), a novel compressive adaptation approach for LLMs. We show that SpaLLM sketches pre-trained LLM weights into lookup tables and directly fine-tunes the values in these tables.
  arXiv  Detail & Related papers  (2024-10-08T20:58:24Z)
• EfficientQAT: Efficient Quantization-Aware Training for Large Language Models [50.525259103219256]
  quantization-aware training (QAT) offers a solution by reducing memory consumption through low-bit representations with minimal accuracy loss. We propose Efficient Quantization-Aware Training (EfficientQAT), a more feasible QAT algorithm. EfficientQAT involves two consecutive phases: Block-wise training of all parameters (Block-AP) and end-to-end training of quantization parameters (E2E-QP)
  arXiv  Detail & Related papers  (2024-07-10T17:53:30Z)
• GPTQT: Quantize Large Language Models Twice to Push the Efficiency [1.3149617027696827]
  This paper introduces a new post-training quantization method, GPTQT, to reduce memory usage and enhance processing speed. Practice has shown that minimizing the quantization error of weights is ineffective, leading to overfitting. GPTQT employs a progressive two-step approach: initially quantizing weights using Linear quantization to a relatively high bit, followed by converting obtained int weight to lower bit binary coding.
  arXiv  Detail & Related papers  (2024-07-03T08:08:01Z)
• Low-Rank Quantization-Aware Training for LLMs [8.535254310145005]
  Large language models (LLMs) are omnipresent, however their practical deployment is challenging due to their ever increasing computational and memory demands. We propose LR-QAT -- a lightweight and memory-efficient QAT algorithm for LLMs. Our method outperforms common post-training quantization (PTQ) approaches and reaches the same model performance as full-model QAT at the fraction of its memory usage.
  arXiv  Detail & Related papers  (2024-06-10T15:44:22Z)
• CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs [44.03692512352445]
  Column-Level Adaptive weight Quantization (CLAQ) is a novel and effective framework for Large Language Models (LLMs) quantization. In this paper, we present a novel and effective CLAQ framework by introducing three different types of adaptive strategies for LLM quantization. Experiments on various mainstream open source LLMs including LLaMA-1, LLaMA-2 and Yi demonstrate that our methods achieve the state-of-the-art results across different bit settings.
  arXiv  Detail & Related papers  (2024-05-27T14:49:39Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment [56.44025052765861]
  Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accurate, sparse foundational versions of performant LLMs. We show a total speedup on CPUs for sparse-quantized LLaMA models of up to 8.6x.
  arXiv  Detail & Related papers  (2024-05-06T16:03:32Z)
• ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers [38.16040503271727]
  We propose a memory-efficient finetuning algorithm for large language models (LLMs) lploraattains competitive performance on text classification, natural language inference, and instruction following tasks using significantly less memory than existing approaches. We also surpass the state-of-the-art ROUGE score on a popular summarization task.
  arXiv  Detail & Related papers  (2023-09-28T02:55:01Z)
• SqueezeLLM: Dense-and-Sparse Quantization [80.32162537942138]
  Main bottleneck for generative inference with LLMs is memory bandwidth, rather than compute, for single batch inference. We introduce SqueezeLLM, a post-training quantization framework that enables lossless compression to ultra-low precisions of up to 3-bit. Our framework incorporates two novel ideas: (i) sensitivity-based non-uniform quantization, which searches for the optimal bit precision assignment based on second-order information; and (ii) the Dense-and-Sparse decomposition that stores outliers and sensitive weight values in an efficient sparse format.
  arXiv  Detail & Related papers  (2023-06-13T08:57:54Z)
• SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression [76.73007709690306]
  We introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique. SpQR achieves relative accuracy losses of less than 1% in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run 33B parameter LLM on a single 24 GB consumer GPU without any performance degradation at 15% speedup.
  arXiv  Detail & Related papers  (2023-06-05T17:53:28Z)

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.