Features

• Quantization
  • Supported QDQ as experimental quantization format for ONNX Runtime
  • Improved FX symbolic tracing for PyTorch
  • Supported multi-metrics for quantization tuning
• Knowledge distillation
  • Improved distillation algorithm for intermediate layer knowledge transfer
• Productivity
  • Improved quantization productivity for ONNX Runtime through GUI
  • Improved PyTorch INT8 model save/load methods
• Ecosystem
  • Upstreamed INC quantized Yolov3, DenseNet, Mask-Rcnn, Yolov4 models to ONNX Model Zoo
  • Became PyTorch ecosystem tool shortly after published PyTorch INC tutorial
• Examples
  • Added INC quantized ResNet50 v1.5 and BERT-Large model for IPEX
  • Supported dynamic quantization & weight sharing on bare metal reference engine