version 2.1.0

What's New

• New Features

  • PyTorch and ONNX
    • [BREAKING CHANGE]: AIMET QuantSim by default uses per-channel quantization for weights instead of per-tensor
    • AIMET QuantSim exports encoding json schema version 1.0.0 by default
  • PyTorch
    • AIMET now quantizes scalar inputs of type torch.nn.Parameter - these were not quantized in prior releases
    • Published recipe for performing LoRA QAT - using LoRA adapters to recover quantized accuracy of the base model. Includes recipes for weight-only (WQ) and weight-and-activation (QWA) QAT

• Bug Fixes

  • PyTorch
    • Fixed a bug that prevented Adaround from caching data samples with PyTorch versions 2.6 and later

Documentation

• Release main page: https://github.com/quic/aimet/releases/tag/2.1.0
• Documentation: https://quic.github.io/aimet-pages/releases/2.1.0/index.html

Packages

• aimet_torch-2.1.0+cu121-cp310-none-any.whl
  • PyTorch 2.1 GPU package with Python 3.10 and CUDA 12.x
• aimet_torch-2.1.0+cpu-cp310-none-any.whl
  • PyTorch 2.1 CPU package with Python 3.10 - If installing on a machine without CUDA
• aimet_onnx-2.1.0+cu118-cp310-cp310-manylinux_2_34_x86_64.whl
  • ONNX 1.16 GPU package with Python 3.10 - Recommended for use with ONNX models
• aimet_onnx-2.1.0+cpu-cp310-cp310-manylinux_2_34_x86_64.whl
  • ONNX 1.16 CPU package with Python 3.10 - If installing on a machine without CUDA
• aimet_tensorflow-2.1.0+cu118-cp310-cp310-manylinux_2_34_x86_64.whl
  • TensorFlow 2.10 GPU package with Python 3.10 - Recommended for use with TensorFlow models
• aimet_tensorflow-2.1.0+cpu-cp310-cp310-manylinux_2_34_x86_64.whl
  • TensorFlow 2.10 CPU package with Python 3.10 - If installing on a machine without CUDA