v2.9.0

Post-training Quantization:

Features:

• (OpenVINO) Added modified AWQ algorithm for 4-bit data-aware weights compression. This algorithm applied only for patterns MatMul->Multiply->Matmul. For that awq optional parameter has been added to nncf.compress_weights() and can be used to minimize accuracy degradation of compressed models (note that this option increases the compression time).
• (ONNX) Introduced support for the ONNX backend in the nncf.quantize_with_accuracy_control() method. Users can now perform quantization with accuracy control for onnx.ModelProto. By leveraging this feature, users can enhance the accuracy of quantized models while minimizing performance impact.
• (ONNX) Added an example based on the YOLOv8n-seg model for demonstrating the usage of quantization with accuracy control for the ONNX backend.
• (PT) Added SmoothQuant algorithm for PyTorch backend in nncf.quantize().
• (OpenVINO) Added an example with the hyperparameters tuning for the TinyLLama model.
• Introduced the nncf.AdvancedAccuracyRestorerParameters.
• Introduced the subset_size option for the nncf.compress_weights().
• Introduced TargetDevice.NPU as the replacement for TargetDevice.VPU.

Fixes:

• Fixed API Enums serialization/deserialization issue.
• Fixed issue with required arguments for revert_operations_to_floating_point_precision method.

Improvements:

• (ONNX) Aligned statistics collection with OpenVINO and PyTorch backends.
• Extended nncf.compress_weights() with Convolution & Embeddings compression in order to reduce memory footprint.

Deprecations/Removals:

• (OpenVINO) Removed outdated examples with nncf.quantize() for BERT and YOLOv5 models.
• (OpenVINO) Removed outdated example with nncf.quantize_with_accuracy_control() for SSD MobileNetV1 FPN model.
• (PyTorch) Deprecated the binarization algorithm.
• Removed Post-training Optimization Tool as OpenVINO backend.
• Removed Dockerfiles.
• TargetDevice.VPU was replaced by TargetDevice.NPU.

Tutorials:

• Post-Training Optimization of Stable Diffusion v2 Model
• Post-Training Optimization of DeciDiffusion Model
• Post-Training Optimization of DepthAnything Model
• Post-Training Optimization of Stable Diffusion ControlNet Model

Compression-aware training:

Fixes

• (PyTorch) Fixed issue with NNCFNetworkInterface.get_clean_shallow_copy missed arguments.

Acknowledgements

Thanks for contributions from the OpenVINO developer community:
@AishwaryaDekhane
@UsingtcNower
@Om-Doiphode