Version 2.11.0

• New Feature

  • PyTorch

    • SpinQuant (experimental) - implement SpinQuant PTQ technique (https://arxiv.org/pdf/2308.13137) for Llama, Qwen2, and Mistral families (R1 rotation w/o optimization) (7364b37)
    • Enable Adascale and Omniquant for Mistral (d33e98c)

  • ONNX

    • Enable llm_configurator for Llama (Experimental) (08c17b8)

• Bug fixes and Improvements

  • Common

    • Represent LPBQ as DequantizeLinear in onnx QDQ (a967b8f)
    • Add additional sanity checks in LPBQ export logic (45c2a65)
    • Allow negative block axis in LPBQ QDQ export (6f670a4)
    • Add support for enabling param bw=2 in QuantSim (2d4e0eb)
    • Fix tanh output encoding range to [-1, 1] (3c92bb7)

  • ONNX

    • Apply matmul exception rule only for integer quantization (bb93c76)
    • Optimize blockwise min-max encoding analyzer (4febdd4)
    • Remove explicit FP32 model creation inside AdaRound and optimize building sessions during the optimization process (b1415bd_)
    • Make Concat output quantizer inherit fixed input range (50f35dd)
    • Enable output quantizers to inherit input encoding when tying encodings (3750526)
    • Fix bug in CLE with bn_conv groups (654f4b1)

  • PyTorch

    • Guarantee positive scale during aimet-torch QAT (2ed8305)
    • Add secondary progress bars to Adascale and Omniquant (6c92a97)

• Documentation Updates

  • Update Quick Start example and PTQ section (6c9f584)
  • Add missing workflow images (f961ed4)

• Known Issues

  • Keras
    • Accuracy drop observed with AIMET Keras for certain models. Fix is planned for the next release.
    • Skipping 2.11 aimet-keras release due to regression