AIMET Release Notes

Release Notes for Qualcomm AI Model Efficiency ToolKit (AIMET)

1.22.2

Tensorflow

  • Added support for supergroups : MatMul + Add

  • Added support for TF-Slim BN name with backslash

  • Added support for Depthwise + Conv in CLS

Documentation

1.22.1

  • Added support for QuantizableMultiHeadAttention for PyTorch nn.transformer layers by @quic-kyuykim

  • Support functional conv2d in model preparer by @quic-kyuykim

  • Enable qat with multi gpu by @quic-mangal

  • Optimize forward pass logic of PyTorch QAT 2.0 by @quic-geunlee

  • Fix functional depthwise conv support on model preparer by @quic-kyuykim

  • Fix bug in model validator to correctly identify functional ops in leaf module by @quic-klhsieh

  • Support dynamic functional conv2d in model preparer by @quic-kyuykim

  • Added updated default runtime config, also a per-channel one. Fixed n… by @quic-akhobare

  • Include residing module info in model validator by @quic-klhsieh

  • Support for Keras MultiHeadAttention Layer by @quic-ashvkuma

Documentation

1.22.0

  • Support for simulation and QAT for PyTorch transformer models (including support for torch.nn mha and encoder layers)

Documentation

1.21.0

  • New feature: PyTorch QuantAnalyzer - Visualize per-layer sensitivity and per-quantizer PDF histograms

  • New feature: TensorFlow AutoQuant - Automatically apply various AIMET post-training quantization techniques

  • PyTorch QAT with Range Learning: Added support for Per Channel Quantization

  • PyTorch: Enabled exporting of encodings for multi-output leaf module

  • TensorFlow Adaround

    • Added ability to use configuration file in API to adapt to a specific runtime target

    • Added Per-Channel Quantization support

  • TensorFlow QuantSim: Added support for FP16 inference and QAT

  • TensorFlow Per Channel Quantization

    • Fixed speed and accuracy issues

    • Fixed zero accuracy for 16-bits per channel quantization

    • Added support for DepthWise Conv2d Op

  • Multiple other bug fixes

Documentation

1.19.1.py37

  • PyTorch: Added CLE support for Conv1d, ConvTranspose1d and Depthwise Separable Conv1d layers

  • PyTorch: Added High-Bias Fold support for Conv1D layer

  • PyTorch: Modified Elementwise Concat Op to support any number of tensors

  • Minor dependency fixes

Documentation

1.19.1

  • PyTorch: Added CLE support for Conv1d, ConvTranspose1d and Depthwise Separable Conv1d layers

  • PyTorch: Added High-Bias Fold support for Conv1D layer

  • PyTorch: Modified Elementwise Concat Op to support any number of tensors

  • Minor dependency fixes

Documentation

1.18.0.py37

  • Multiple bug fixes

  • Additional feature examples for PyTorch and TensorFlow

Documentation

1.18.0

  • Multiple bug fixes

  • Additional feature examples for PyTorch and TensorFlow

Documentation

1.17.0.py37

  • Add Adaround TF feature

  • Added Examples for Torch quantization, and Channel Pruning & Spatial SVD compression

Documentation

1.17.0

  • Add Adaround TF feature

  • Added Examples for Torch quantization, and Channel Pruning & Spatial SVD compression

Documentation

1.16.2.py37

  • Added a new post-training quantization feature called AdaRound, which stands for AdaptiveRounding

  • Quantization simulation and QAT now also support recurrent layers (RNN, LSTM, GRU)

Documentation

1.16.2

  • Added a new post-training quantization feature called AdaRound, which stands for AdaptiveRounding

  • Quantization simulation and QAT now also support recurrent layers (RNN, LSTM, GRU)

Documentation

1.16.1

  • Added separate packages for CPU and GPU models. This allows users with CPU-only hosts to run AIMET.

  • Added separate packages for PyTorch and TensorFlow. Reduces the number of dependencies that users would need to install.

Documentation