Accelerating on-device ML on Meta’s family of apps with ExecuTorch

What this post added

This post details the adoption and impact of ExecuTorch, Meta's open-source PyTorch inference framework for edge devices, across Instagram, WhatsApp, Messenger, and Facebook. It highlights how ExecuTorch has accelerated on-device ML by improving model performance, reducing latency, and enhancing user privacy. Specific examples include migrating Instagram's Cutouts feature to ExecuTorch for faster sticker generation, optimizing WhatsApp's bandwidth estimation models for improved video call quality and reduced ANRs, enabling end-to-end encryption on Messenger by moving server-side models like language identification on-device, and enhancing Facebook Stories with background music recommendations powered by the SceneX model. The post also mentions the integration of fuzzing tests for enhanced security in WhatsApp and the ongoing migration of other key WhatsApp and Messenger models to ExecuTorch.