From d3a3fe96190721c778156b14001e92de7dc594c2 Mon Sep 17 00:00:00 2001 From: Nikita Malinin Date: Mon, 22 Apr 2024 08:50:37 +0200 Subject: [PATCH] Release notes 2.10 (#2640) ### Changes - Added v2.10.0 template; ### Reason for changes - Upcoming release; ### Related tickets - 138131; #### For the contributors: 1. Please add your changes (as the commit to the branch) to the list according to the template and previous notes; 2. Do not add tests-related notes; 3. Provide the list of the PRs (for all your notes) in the comment for the discussion; --------- Co-authored-by: Liubov Talamanova Co-authored-by: Daniil Lyakhov Co-authored-by: Nikita Savelyev --- ReleaseNotes.md | 52 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) diff --git a/ReleaseNotes.md b/ReleaseNotes.md index 58b37cd0a1a..cb49903134d 100644 --- a/ReleaseNotes.md +++ b/ReleaseNotes.md @@ -1,5 +1,57 @@ # Release Notes +## New in Release 2.10.0 + +Post-training Quantization: + +- Features: + - Introduced the subgraph defining functionality for the `nncf.IgnoredScope()` option. + - Introduced limited support for the batch size of more than 1. MobilenetV2 [PyTorch example](examples/post_training_quantization/torch/mobilenet_v2) was updated with batch support. +- Fixes: + - Fixed issue with the `nncf.OverflowFix` parameter absence in some scenarios. + - Aligned the list of correctable layers for the FastBiasCorrection algorithm between PyTorch, OpenVINO and ONNX backends. + - Fixed issue with the `nncf.QuantizationMode` parameters combination. + - Fixed MobilenetV2 ([PyTorch](examples/post_training_quantization/torch/mobilenet_v2), [ONNX](examples/post_training_quantization/onnx/mobilenet_v2), [OpenVINO](examples/post_training_quantization/openvino/mobilenet_v2)) examples for the Windows platform. + - (OpenVINO) Fixed [Anomaly Classification example](examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control) for the Windows platform. + - (PyTorch) Fixed bias shift magnitude calculation for fused layers. + - (OpenVINO) Fixed removing the ShapeOf graph which led to an error in the `nncf.quantize_with_accuracy_control()` method. +- Improvements: + - `OverflowFix`, `AdvancedSmoothQuantParameters` and `AdvancedBiasCorrectionParameters` were exposed into the `nncf.*` namespace. + - (OpenVINO, PyTorch) Introduced scale compression to FP16 for weights in `nncf.compress_weights()` method, regardless of model weights precision. + - (PyTorch) Modules that NNCF inserted were excluded from parameter tracing. + - (OpenVINO) Extended the list of correctable layers for the BiasCorrection algorithm. + - (ONNX) Aligned BiasCorrection algorithm behaviour with OpenVINO in specific cases. +- Tutorials: + - [Post-Training Optimization of PhotoMaker Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/photo-maker/photo-maker.ipynb) + - [Post-Training Optimization of Stable Diffusion XL Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-diffusion-xl/stable-diffusion-xl.ipynb) + - [Post-Training Optimization of KerasCV Stable Diffusion Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-diffusion-keras-cv/stable-diffusion-keras-cv.ipynb) + - [Post-Training Optimization of Paint By Example Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/paint-by-example/paint-by-example.ipynb) + - [Post-Training Optimization of aMUSEd Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/amused-lightweight-text-to-image/amused-lightweight-text-to-image.ipynb) + - [Post-Training Optimization of InstantID Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/instant-id/instant-id.ipynb) + - [Post-Training Optimization of LLaVA Next Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llava-next-multimodal-chatbot/llava-next-multimodal-chatbot.ipynb) + - [Post-Training Optimization of AnimateAnyone Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/animate-anyone/animate-anyone.ipynb) + - [Post-Training Optimization of YOLOv8-OBB Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov8-optimization/yolov8-obb.ipynb) + - [Post-Training Optimization of LLM Agent](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-agent-langchain/llm-agent-langchain.ipynb) + +Compression-aware training: + +- Features: + - (PyTorch) `nncf.quantize` method now may be used as quantization initialization for Quantization-Aware Training. Added a [Resnet18-based example](examples/quantization_aware_training/torch/resnet18) with the transition from the Post-Training Quantization to a Quantization-Aware Training algorithm. + - (PyTorch) Introduced extractors for the fused Convolution, Batch-/GroupNorm, and Linear functions. +- Fixes: + - (PyTorch) Fixed `apply_args_defaults` function issue. + - (PyTorch) Fixed `dtype` handling for the compressed `torch.nn.Parameter`. + - (PyTorch) Fixed `is_shared` parameter propagation. +- Improvements: + - (PyTorch) Updated command creation behaviour to reduce the number of adapters. + - (PyTorch) Added option to insert point for models that wrapped with `replace_modules=False`. +- Deprecations/Removals: + - (PyTorch) Removed the `binarization` algorithm. + - NNCF installation via `pip install nncf[]` option is now deprecated. +- Requirements: + - Updated PyTorch (2.2.1) and CUDA (12.1) versions. + - Updated ONNX (1.16.0) and ONNXRuntime (1.17.1) versions. + ## New in Release 2.9.0 Post-training Quantization: