Revision history [back]

Yes, it is possible to utilize libtorch for training models with quantization awareness. Libtorch has in-built support for quantization-aware training (QAT) using techniques like Post-Training Static Quantization (PTSQ), Dynamic Quantization, and Quantization-aware Fine-tuning. These techniques enable models to be optimized for deployment on low-power hardware by reducing their memory footprint and computational requirements while maintaining accuracy.

To utilize libtorch for QAT, one needs to define the model architecture and data input pipeline, set the precision for the model (8-bit, 16-bit, etc.), and define the quantization-aware training functions using the provided libraries. Once these functions are defined, the model can be trained using backpropagation and the gradients can be used to update the network weights. Additionally, one can monitor the QAT training process with built-in performance metrics like FLOPS, memory footprint, and accuracy.

In conclusion, libtorch is a powerful tool for QAT and enables developers to create efficient deep learning models that can be easily deployed on resource-constrained devices without sacrificing accuracy.