version 1.23.0

What's New

• TF-enhanced calibration scheme has been accelerated using a custom CUDA kernel. Runs significantly faster now.
• Installation instructions are now combined with rest of the documentation (User-Guide and API docs)

PyTorch

• Fixed backward pass of the fake-quantize (QcQuantizeWrapper) nodes to handle symmetric mode correctly
• Per-channel quantization is now enabled on a per-op-type basis
• Support for recursively excluding module from a root module in QuantSim
• Support for excluding layers when running model validator and model preparer
• Reduced memory usage in AdaRound
• Fixed bugs in AdaRound for per-channel quantization
• Made ConnectedGraph more robust when identifying custom layers
• Added jupyter notebook-based examples for the following features
• AutoQuant: Added support for sparse conv layers in QuantSim (experimental)

Keras

• Added support for Keras per-channel quantization
• Changed interface to CLE to accept a pre-compiled model
• Added jupyter notebook-based examples for the following features: Transformer quantization

TensorFlow

• Fix to avoid unnecessary indexing in AdaRound

Documentation

• Release main page: https://github.com/quic/aimet/releases/tag/1.23.0
• Installation guide: https://quic.github.io/aimet-pages/releases/latest/install/index.html
• User guide: https://quic.github.io/aimet-pages/releases/1.23.0/user_guide/index.html
• API documentation: https://quic.github.io/aimet-pages/releases/1.23.0/api_docs/index.html
• Documentation main page: https://quic.github.io/aimet-pages/index.html