Quantize neural upscaling models with ExecuTorch

About this Learning Path

Who is this for?

This is an advanced topic for ML developers who want to reduce latency and memory bandwidth by exporting INT8 models to the `.vgf` file format using the ExecuTorch Arm backend.

What will you learn?

Upon completion of this Learning Path, you will be able to:

• Explain when to use post-training quantization (PTQ) vs quantization-aware training (QAT)
• Prepare and quantize a PyTorch model using TorchAO PT2E quantization APIs
• Export the quantized model to TOSA and generate a model artifact with the ExecuTorch Arm backend
• Validate the exported graph by visualizing it using Google's Model Explorer

Prerequisites

Before starting, you will need the following:

• Basic PyTorch model training and evaluation experience
• A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch