Mitigating Outlier Channels in Language Model Quantization with Activation Regularization

Core Concepts

Outlier channels in language models emerge early in training and are prevalent in layers with residual streams. Regularizing both the input and output activations via quantization-aware training and kurtosis regularization can enable efficient 4-bit quantization of language models.

Abstract

The authors conduct an empirical study on the emergence of outlier channels in language models during pretraining. They find that outlier channels, which are feature dimensions with values orders of magnitude higher than others, tend to appear early in training and are more prevalent in layers with residual streams. To mitigate the impact of these outlier channels, the authors propose a two-pronged approach: Quantization-aware training (QAT) on the input activations: The authors use a QAT strategy that learns the clipping values for each activation layer, effectively controlling the number of outlier channels and mitigating their effect through clipping. Kurtosis regularization on the output activations: The authors additionally regularize the kurtosis (heaviness of the tails) of the layer's output distribution, which discourages the creation of outliers in the first place. This prevents the model from "migrating" the difficulty of quantizing outlier channels to the weights, which makes post-training weight quantization more challenging. The authors show that combining these two techniques allows them to train a 1 billion parameter language model with 4-bit activations and 4-bit weights that performs competitively to a standard-precision 16-bit baseline. They also find that the benefits of kurtosis regularization become more pronounced as the weights are quantized to lower bitwidths.

Stats

"Outlier channels are known to be crucial for strong model performance (Kovaleva et al., 2021; Puccetti et al., 2022), but pose significant challenges from a model compression perspective, for instance via post-training quantization (PTQ) (Dettmers et al., 2022; Xiao et al., 2023; Wei et al., 2022)." "We find that dimensions with outlier channels emerge relatively early in training (see fig. 1(a), top), suggesting that their mitigation requires early intervention." "These outlier channels are particularly prevalent in the output projection layer of the first layer, as well as the query-key-value projection layers of the other layers."

Quotes

"Outlier channels are known to be crucial for strong model performance (Kovaleva et al., 2021; Puccetti et al., 2022), but pose significant challenges from a model compression perspective, for instance via post-training quantization (PTQ) (Dettmers et al., 2022; Xiao et al., 2023; Wei et al., 2022)." "We find that dimensions with outlier channels emerge relatively early in training (see fig. 1(a), top), suggesting that their mitigation requires early intervention."

Key Insights Distilled From

Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization

by Aniruddha Nr... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03605.pdf

Deeper Inquiries

How would the proposed techniques perform on even larger language models, such as those with tens of billions of parameters

The proposed techniques for mitigating outlier channels and improving quantization in language models could potentially perform well on even larger models with tens of billions of parameters. The key lies in the early intervention strategies employed, such as activation regularization through Quantization-Aware Training (QAT) and kurtosis regularization. By addressing outlier channels early in the training process, these techniques could scale effectively to larger models by controlling the emergence and impact of outlier channels across a greater number of parameters. Additionally, the insights gained from studying outlier channels and their effects on quantization could be leveraged to fine-tune the regularization strategies for larger models, ensuring efficient and accurate quantization even at scale.

What other types of activation regularization techniques could be explored to further improve the quantization of language models

To further improve the quantization of language models, additional activation regularization techniques could be explored. One potential approach could involve incorporating techniques that focus on distributional properties of activations, such as entropy regularization or sparsity-inducing penalties. By encouraging activations to follow specific distributions or exhibit certain sparsity patterns, these techniques could complement existing methods like QAT and kurtosis regularization, enhancing the overall quantization process. Additionally, exploring adaptive regularization schemes that dynamically adjust regularization strengths based on the characteristics of the model or training data could offer a more flexible and effective way to control outlier channels and optimize quantization performance.

How might the insights from this work on outlier channels apply to other types of neural networks beyond language models

The insights gained from studying outlier channels in language models could be applicable to other types of neural networks beyond just language models. For instance, in computer vision tasks, outlier channels in convolutional neural networks (CNNs) could similarly impact the quantization process and overall model performance. By applying similar activation regularization techniques like QAT and kurtosis regularization, it may be possible to mitigate the effects of outlier channels in CNNs and improve the quantization of these models. Additionally, the concept of outlier channels and their impact on quantization could be relevant to various domains where neural networks are used, such as reinforcement learning or speech recognition, highlighting the broader applicability of the findings from this work.