Version 2.43.0

• New Features

  • ONNX

    • Support bfloat16 graphs in QuantizationSimModel, including calibration, SeqMSE, AdaScale, AdaRound, AMP, cross-layer equalization, BatchNorm folding, SpinQuant, QuantAnalyzer, layer output dumping, PSNR/lite-mp evaluation, and LoRA adapters in aimet_onnx.peft (ff4f611, 901c4cf, 76e0b47, 28ace30, 36190d4, bea0d4e, fee0d31, 66e6e3b, 606ee2f, 46bbd5d, a3f7904, cfa85a5, c507684, e4092b7, e572b36, 7b28de8)
    • Quantize If branches in QuantizationSimModel behind the subgraph flag (e4f515b, 3019fd1, 6a25cb6)

• Bug fixes and Improvements

  • ONNX

    • Redesign aimet_onnx.experimental.lora: adapters are now prepared with aimet_onnx.peft.load_lora_adapters instead of prepare_lora_onnx, and quantized with a smaller, renamed helper set (f371064)
    • Skip unsafe encoding tying through Split (980d9d7)
    • Refactor to_onnx_qdq() helpers to use the onnx_ir API and fix a missing helper export (fd36575, f909daf)
    • Don't optimize frozen quantizers in AdaScale (9ca2127)
    • Add strict option to encodings_to_onnx_qdq to skip unmatched encodings instead of raising (78f0907)
    • Tie Scan body inputs to their boundary quantizers (94ad369)

  • Torch

    • Fix clipped QDQ scale gradients with zero-point shifts (aa91641)

  • Common

    • Upgrade onnxsim to 0.7.0 (4867813)
    • Windows (x64/arm64) and macOS aimet-onnx wheels are now available on PyPI (81d3a20)
    • Update quantization recipe docs for Gemma 4 and Qwen3-VL (af9c406)
    • Pin torch<2.15 and torchvision<0.29.2 in released wheels to avoid pulling CUDA 13 builds incompatible with CUDA 12.x drivers (cd33832)