Release notes update

ReleaseNotes.md

@@ -1,5 +1,52 @@
 # Release Notes
 
+## New in Release 2.7.0
+
+Post-training Quantization:
+
+- Features:
+  - (OpenVINO) Added support for data-free 4-bit weights compression through NF4 and INT4 data types (`compress_weights(…)` pipeline).
+  - (OpenVINO) Added support for [IF operation](https://docs.openvino.ai/latest/openvino_docs_ops_infrastructure_If_8.html) quantization.
+  - (OpenVINO) Added `dump_intermediate_model` parameter support for AccuracyAwareAlgorithm (`quantize_with_accuracy_control(…)` pipeline).
+  - (OpenVINO) Added support for SmoothQuant and ChannelAlignment algorithms for HyperparameterTuner algorithm (`quantize_with_tune_hyperparams(…)` pipeline).
+  - (PyTorch) Post-training Quantization is now supported with `quantize(…)` pipeline and the common implementation of quantization algorithms. Deprecated `create_compressed_model()` method for Post-training Quantization.
+  - Added new types (AvgPool, GroupNorm, LayerNorm) to the ignored scope for `ModelType.Transformer` scheme.
+  - `QuantizationPreset.Mixed` was set as the default for `ModelType.Transformer` scheme.
+- Fixes:
+  - (OpenVINO, ONNX, PyTorch) Aligned/added patterns between backends (SE block, MVN layer, multiple activations, etc.) to restore performance/metrics.
+  - Fixed patterns for `ModelType.Transformer` to align with the [quantization scheme](https://docs.openvino.ai/latest/openvino_docs_OV_UG_lpt.html).
+- Improvements:
+  - Improved UX with the new progress bar for pipeline, new exceptions, and .dot graph visualization updates.
+  - (OpenVINO) Optimized WeightsCompression algorithm (`compress_weights(…)` pipeline) execution time for LLM's quantization, added ignored scope support.
+  - (OpenVINO) Optimized AccuracyAwareQuantization algorithm execution time with multi-threaded approach while calculating ranking score (`quantize_with_accuracy_control(…)` pipeline).
+  - (OpenVINO) Added [extract_ov_subgraph tool](tools/extract_ov_subgraph.py) for large IR subgraph extraction.
+  - (ONNX) Optimized quantization pipeline (up to 1.15x speed up).
+- Tutorials:
+  - [Post-Training Optimization of BLIP Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/233-blip-visual-language-processing)
+  - [Post-Training Optimization of DeepFloyd IF Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/238-deepfloyd-if)
+  - [Post-Training Optimization of Grammatical Error Correction Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/214-grammar-correction)
+  - [Post-Training Optimization of Dolly 2.0 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/240-dolly-2-instruction-following)
+  - [Post-Training Optimization of Massively Multilingual Speech Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/255-mms-massively-multilingual-speech)
+  - [Post-Training Optimization of OneFormer Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/249-oneformer-segmentation)
+  - [Post-Training Optimization of InstructPix2Pix Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/231-instruct-pix2pix-image-editing)
+  - [Post-Training Optimization of LLaVA Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/257-llava-multimodal-chatbot)
+  - [Post-Training Optimization of Latent Consistency Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/263-latent-consistency-models-image-generation)
+  - [Post-Training Optimization of Distil-Whisper Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/267-distil-whisper-asr)
+  - [Post-Training Optimization of FastSAM Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/261-fast-segment-anything)
+- Known issues:
+  - (ONNX) `quantize(...)` method can generate inaccurate int8 results for models with the BatchNormalization layer that contains biases. To get the best accuracy, use the `do_constant_folding=True` option during export from PyTorch to ONNX.
+
+Compression-aware training:
+
+- Fixes:
+  - (PyTorch) Fixed Hessian trace calculation to solve [#2155](https://github.com/openvinotoolkit/nncf/issues/2155) issue.
+- Requirements:
+  - Updated PyTorch version (2.1.0).
+  - Updated numpy version (<1.27).
+- Deprecations/Removals:
+  - (PyTorch) Removed legacy external quantizer storage names.
+  - (PyTorch) Removed torch < 2.0 version support.
+
 ## New in Release 2.6.0
 
 Post-training Quantization:

docs/Installation.md

@@ -69,7 +69,8 @@ as well as the supported versions of Python:
 
 | NNCF      | OpenVINO   | PyTorch  | ONNX     | TensorFlow | Python |
 |-----------|------------|----------|----------|------------|--------|
-| `develop` | `2023.1.0` | `2.1`    | `1.13.1` | `2.12.0`   | `3.8`  |
+| `develop` | `2023.2.0` | `2.1`    | `1.13.1` | `2.12.0`   | `3.8`  |
+| `2.7.0`   | `2023.2.0` | `2.1`    | `1.13.1` | `2.12.0`   | `3.8`  |
 | `2.6.0`   | `2023.1.0` | `2.0.1`  | `1.13.1` | `2.12.0`   | `3.8`  |
 | `2.5.0`   | `2023.0.0` | `1.13.1` | `1.13.1` | `2.11.1`   | `3.8`  |
 | `2.4.0`   | `2022.1.0` | `1.12.1` | `1.12.0` | `2.8.2`    | `3.8`  |

nncf/version.py

@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "2.6.0"
+__version__ = "2.7.0"
 
 BKC_TORCH_VERSION = "2.1.0"
 BKC_TORCHVISION_VERSION = "0.16.0"