Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs

• URL: http://arxiv.org/abs/2405.20835v3
• Date: Wed, 5 Jun 2024 09:53:18 GMT
• Title: Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs
• Authors: Davide Paglieri, Saurabh Dash, Tim Rocktäschel, Jack Parker-Holder,
• Abstract summary: Post-Training Quantization (PTQ) enhances the efficiency of Large Language Models (LLMs) We explore the role of calibration sets in PTQ, specifically their effect on hidden activations. Our analysis reveals a marked contrast in quantization effectiveness across accessible models.
• Score: 27.38239289662178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-Training Quantization (PTQ) enhances the efficiency of Large Language Models (LLMs) by enabling faster operation and compatibility with more accessible hardware through reduced memory usage, at the cost of small performance drops. We explore the role of calibration sets in PTQ, specifically their effect on hidden activations in various notable open-source LLMs. Calibration sets are crucial for evaluating activation magnitudes and identifying outliers, which can distort the quantization range and negatively impact performance. Our analysis reveals a marked contrast in quantization effectiveness across models. The older OPT model, upon which much of the quantization literature is based, shows significant performance deterioration and high susceptibility to outliers with varying calibration sets. In contrast, newer models like Llama-2 7B, Llama-3 8B, Command-R 35B, and Mistral 7B demonstrate strong robustness, with Mistral 7B showing near-immunity to outliers and stable activations. These findings suggest a shift in PTQ strategies might be needed. As advancements in pre-training methods reduce the relevance of outliers, there is an emerging need to reassess the fundamentals of current quantization literature. The emphasis should pivot towards optimizing inference speed, rather than primarily focusing on outlier preservation, to align with the evolving characteristics of state-of-the-art LLMs.

Related papers

• A deeper look at depth pruning of LLMs [49.30061112976263]
  Large Language Models (LLMs) are resource-intensive to train but more costly to deploy in production. Recent work has attempted to prune blocks of LLMs based on cheap proxies for estimating block importance. We show that adaptive metrics exhibit a trade-off in performance between tasks.
  arXiv  Detail & Related papers  (2024-07-23T08:40:27Z)
• Low-rank finetuning for LLMs: A fairness perspective [54.13240282850982]
  Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. We show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors.
  arXiv  Detail & Related papers  (2024-05-28T20:43:53Z)
• Investigating Automatic Scoring and Feedback using Large Language Models [46.1232919707345]
  This paper explores the efficacy of PEFT-based quantized models, employing classification or regression head, to fine-tune language models for automatic grading and feedback generation. The results show that prediction of grade scores via finetuned LLMs are highly accurate, achieving less than 3% error in grade percentage on average.
  arXiv  Detail & Related papers  (2024-05-01T16:13:54Z)
• What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation [55.153595212571375]
  Quantization is a technique for improving the memory and computational efficiency of large language models (LLMs) We propose a new perspective on quantization, viewing it as perturbations added to the weights and activations of LLMs. We conduct experiments with various artificial perturbations to explore their impact on LLM performance.
  arXiv  Detail & Related papers  (2024-03-11T03:42:51Z)
• ApiQ: Finetuning of 2-Bit Quantized Large Language Model [12.328293460903911]
  ApiQ is designed to restore the lost information from quantization by concurrently initializing the LoRA components and quantizing the weights of LLMs. It consistently achieves superior finetuning results across various bit-widths.
  arXiv  Detail & Related papers  (2024-02-07T09:36:54Z)
• On the Impact of Calibration Data in Post-training Quantization and Pruning [36.1039389951318]
  Quantization and pruning form the foundation of compression for neural networks. We present the first empirical study on the effect of calibration data upon model compression methods.
  arXiv  Detail & Related papers  (2023-11-16T10:30:00Z)
• LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses [14.77013588561901]
  We present LitCab, a lightweight calibration mechanism consisting of a single linear layer that takes the input text representation and predicts a bias term. For evaluation, we construct CaT, a benchmark consisting of eight text generation tasks, covering responses ranging from short phrases to paragraphs. We test LitCab with Llama2-7B, where it improves calibration across all tasks, reducing the average ECE score by as large as 30%.
  arXiv  Detail & Related papers  (2023-10-30T00:30:34Z)
• CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration [59.48235003469116]
  We show that data augmentation consistently enhances OOD performance. We also show that CF augmented models which are easier to calibrate also exhibit much lower entropy when assigning importance.
  arXiv  Detail & Related papers  (2023-09-14T16:16:40Z)
• Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study [90.34226812493083]
  This work aims to investigate the impact of quantization on emphemergent abilities, which are important characteristics that distinguish LLMs from small language models. Our empirical experiments show that these emergent abilities still exist in 4-bit quantization models, while 2-bit models encounter severe performance degradation. To improve the performance of low-bit models, we conduct two special experiments: (1) fine-gained impact analysis that studies which components (or substructures) are more sensitive to quantization, and (2) performance compensation through model fine-tuning.
  arXiv  Detail & Related papers  (2023-07-16T15:11:01Z)
• To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
  Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs. We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting. Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
  arXiv  Detail & Related papers  (2023-05-22T17:02:15Z)

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.