Efficient Instance-Aware Group Quantization for Vision Transformers

Core Concepts

To address the significant scale variations in activations and softmax attentions across channels and tokens in vision transformers, we introduce an instance-aware group quantization framework that dynamically splits the channels and tokens into multiple groups and applies separate quantizers for each group.

Abstract

The content discusses a novel post-training quantization (PTQ) method for vision transformers (ViTs) called Instance-Aware Group Quantization for ViTs (IGQ-ViT). The key insights are:

Activations and softmax attentions in ViTs exhibit significant scale variations across channels and tokens, respectively, which makes existing PTQ methods for convolutional neural networks (CNNs) inappropriate for ViTs.

IGQ-ViT addresses this issue by dynamically splitting the channels of activation maps and rows of softmax attentions into multiple groups, such that the values within each group share similar statistical properties. It then applies separate quantizers for each group.

The number of groups for each layer is adjusted to minimize the discrepancies between predictions from quantized and full-precision models, under a bit-operation (BOP) constraint.

IGQ-ViT can be applied to various components in ViTs, including input activations of fully-connected layers and softmax attentions, unlike previous methods that are limited to specific parts of transformer architectures.

Extensive experiments on image classification, object detection, and instance segmentation demonstrate the effectiveness of IGQ-ViT, setting new state-of-the-art results with various ViT architectures.

Stats

The distribution of activations in each channel varies significantly for individual input instances in vision transformers, unlike in convolutional neural networks.
The ranges of activations for each channel in vision transformers vary drastically among different input instances, in contrast to convolutional neural networks.

Quotes

"We have observed that activations and softmax attentions in ViTs have significant scale variations for individual channels and tokens, respectively, across different input instances."
"To address this, we introduce instance-aware group quantization for ViTs (IGQ-ViT)."

Key Insights Distilled From

Instance-Aware Group Quantization for Vision Transformers

by Jaehyeon Moo... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00928.pdf

Deeper Inquiries

How can the instance-aware grouping technique be extended to other types of neural network architectures beyond vision transformers

The instance-aware grouping technique can be extended to other types of neural network architectures by adapting the concept of dynamic grouping based on statistical properties of activations. For example, in recurrent neural networks (RNNs), the hidden states at each time step could be grouped dynamically based on their distributions. Similarly, in graph neural networks (GNNs), the node or edge features could be grouped based on their statistical properties. By applying the instance-aware grouping technique to these architectures, the networks can adapt to the varying distributions of activations or features across different instances, leading to more efficient and effective quantization.

What are the potential drawbacks or limitations of the instance-aware grouping approach, and how can they be addressed

One potential drawback of the instance-aware grouping approach is the computational overhead involved in dynamically assigning channels or tokens to groups at runtime. This process may introduce additional complexity during inference, especially on resource-constrained devices. To address this limitation, optimizations can be made to streamline the grouping process, such as precomputing group assignments based on a representative set of instances or implementing efficient algorithms for dynamic grouping.
Another limitation could be the sensitivity of the grouping technique to the choice of hyperparameters, such as the number of groups. Suboptimal group sizes may lead to subpar quantization results. To mitigate this, automated techniques like hyperparameter tuning or reinforcement learning can be employed to find the optimal group sizes for different layers or components of the neural network.

What other techniques, beyond quantization, could be explored to efficiently deploy vision transformers on resource-constrained devices

Beyond quantization, other techniques that could be explored to efficiently deploy vision transformers on resource-constrained devices include:

Knowledge Distillation: Transfer knowledge from a larger, well-trained model to a smaller, quantized model to maintain performance while reducing model size.
Pruning: Identify and remove redundant or less important parameters in the model to reduce the computational and memory footprint.
Low-Rank Approximation: Approximate weight matrices with low-rank matrices to reduce the number of parameters and computations required.
Sparsity: Introduce sparsity in the model by setting certain weights to zero, leading to more efficient computations.
Quantization-Aware Training: Train the model with quantization constraints from the beginning to ensure that the model is optimized for quantization during training.
Model Compression Techniques: Utilize techniques like weight sharing, tensor decomposition, or structured pruning to reduce the model size and complexity.

By combining these techniques with instance-aware grouping for quantization, a comprehensive approach can be developed to deploy vision transformers efficiently on devices with limited computational resources.