Merge branch 'master' into github_actions/local_caches

.github/workflows/build_doc.yml

@@ -21,7 +21,7 @@ jobs:
           lfs: 'true'
 
       - name: Install apt-get dependencies
-        uses: awalsh128/[email protected].1
+        uses: awalsh128/[email protected].2
         with:
           packages: graphviz texlive liblua5.2-0 libclang1-9 libclang-cpp9
           version: 3.0

.github/workflows/code_snippets.yml

@@ -30,7 +30,7 @@ jobs:
           submodules: 'true'
 
       - name: Install OpenCL
-        uses: awalsh128/[email protected].1
+        uses: awalsh128/[email protected].2
         if: runner.os == 'Linux'
         with:
           packages: ocl-icd-opencl-dev opencl-headers

.github/workflows/job_pytorch_models_tests.yml

@@ -106,6 +106,8 @@ jobs:
 
       - name: Install OpenVINO Python wheels
         run: |
+          # To enable pytest parallel features
+          python3 -m pip install pytest-xdist[psutil]
           python3 -m pip install ${INSTALL_DIR}/tools/openvino-*
           python3 -m pip install ${INSTALL_DIR}/openvino_tokenizers-*
 
@@ -118,10 +120,20 @@ jobs:
         env:
           CPLUS_INCLUDE_PATH: ${{ env.Python_ROOT_DIR }}/include/python${{ env.PYTHON_VERSION }}
 
-      - name: PyTorch Models Tests
+      - name: PyTorch Models Tests Timm and Torchvision
         run: |
           export PYTHONPATH=${MODEL_HUB_TESTS_INSTALL_DIR}:$PYTHONPATH
-          python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/pytorch -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-torch_model_tests.html --self-contained-html -v
+          python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/pytorch/ -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-torch_model_timm_tv_tests.html --self-contained-html -v -n 4 -k "TestTimmConvertModel or TestTorchHubConvertModel"
+        env:
+          TYPE: ${{ inputs.event == 'schedule' && 'nightly' || 'precommit'}}
+          TEST_DEVICE: CPU
+          OP_REPORT_FILE: ${{ env.INSTALL_TEST_DIR }}/TEST-torch_unsupported_ops.log
+
+      - name: PyTorch Models Tests Not Timm or Torchvision
+        if: always()
+        run: |
+          export PYTHONPATH=${MODEL_HUB_TESTS_INSTALL_DIR}:$PYTHONPATH
+          python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/pytorch -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-torch_model_tests.html --self-contained-html -v -k "not (TestTimmConvertModel or TestTorchHubConvertModel)"
         env:
           TYPE: ${{ inputs.event == 'schedule' && 'nightly' || 'precommit'}}
           TEST_DEVICE: CPU

.github/workflows/linux.yml

@@ -318,7 +318,7 @@ jobs:
 
   Conformance:
     needs: [ Build, Smart_CI ]
-    timeout-minutes: ${{ matrix.TEST_TYPE == 'API' && 5 || 30 }}
+    timeout-minutes: ${{ matrix.TEST_TYPE == 'API' && 5 || 20 }}
     defaults:
       run:
         shell: bash
@@ -511,18 +511,23 @@ jobs:
       runner: 'ubuntu-20.04-8-cores'
       model_scope: 'precommit'
 
-  TensorFlow_Models_Tests_Nightly:
-    name: TensorFlow Models tests
+  TensorFlow_Models_Tests_Nightly_TF_HUB:
+    name: TensorFlow TF Hub Models tests
+    if: ${{ github.event_name == 'schedule' }}
+    needs: [ Build, Smart_CI, Openvino_tokenizers ]
+    uses: ./.github/workflows/job_tensorflow_models_tests.yml
+    with:
+      runner: 'ubuntu-20.04-16-cores'
+      model_scope: 'nightly_tf_hub'
+
+  TensorFlow_Models_Tests_Nightly_HF:
+    name: TensorFlow Hugging Face Models tests
     if: ${{ github.event_name == 'schedule' }}
     needs: [ Build, Smart_CI, Openvino_tokenizers ]
-    strategy:
-      max-parallel: 2
-      matrix:
-        MODEL_SCOPE: ['nightly_hf', 'nightly_tf_hub']
     uses: ./.github/workflows/job_tensorflow_models_tests.yml
     with:
       runner: 'ubuntu-20.04-16-cores'
-      model_scope: ${{ matrix.MODEL_SCOPE }}
+      model_scope: 'nightly_hf'
 
   # TODO: Switch back to self-hosted runners
   # container:

docs/articles_en/get-started/install-openvino-overview/install-openvino-linux-header.rst

@@ -5,8 +5,8 @@ Install OpenVINO™ Runtime on Linux
 
 
 .. meta::
-   :description: Learn how to install OpenVINO™ Runtime on Linux operating system. 
-                 You can use an archive, a PyPi package, APT, YUM, Conda Forge, 
+   :description: Learn how to install OpenVINO™ Runtime on Linux operating system.
+                 You can use an archive, a PyPi package, npm package, APT, YUM, Conda Forge,
                  Homebrew or a Docker image.
 
 
@@ -23,6 +23,7 @@ Install OpenVINO™ Runtime on Linux
    Use Homebrew <openvino_docs_install_guides_installing_openvino_brew>
    Use Conan <openvino_docs_install_guides_installing_openvino_conan>
    Use Docker <openvino_docs_install_guides_installing_openvino_docker>
+   Use npm <openvino_docs_install_guides_installing_openvino_npm>
 
 
 If you want to install OpenVINO™ Runtime on Linux, you have the following options:
@@ -36,8 +37,5 @@ If you want to install OpenVINO™ Runtime on Linux, you have the following opti
 * :doc:`Install OpenVINO using Homebrew <openvino_docs_install_guides_installing_openvino_brew>`
 * :doc:`Install OpenVINO using Docker <openvino_docs_install_guides_installing_openvino_docker>`
 * :doc:`Install OpenVINO using Conan Package Manager <openvino_docs_install_guides_installing_openvino_conan>`
-
-
-
-
+* :doc:`Install OpenVINO using npm <openvino_docs_install_guides_installing_openvino_npm>`
 

docs/articles_en/get-started/install-openvino-overview/install-openvino-macos-header.rst

@@ -5,8 +5,8 @@ Install OpenVINO™ Runtime for macOS
 
 
 .. meta::
-   :description: Learn how to install OpenVINO™ Runtime on macOS operating 
-                 system. You can use an archive, a PyPi package, Conda Forge 
+   :description: Learn how to install OpenVINO™ Runtime on macOS operating
+                 system. You can use an archive, a PyPi package, npm package, Conda Forge
                  or Homebrew.
 
 
@@ -20,7 +20,7 @@ Install OpenVINO™ Runtime for macOS
    Use Conda Forge <openvino_docs_install_guides_installing_openvino_conda>
    Use vcpkg <openvino_docs_install_guides_installing_openvino_vcpkg>
    Use Conan <openvino_docs_install_guides_installing_openvino_conan>
-
+   Use npm <openvino_docs_install_guides_installing_openvino_npm>
 
 If you want to install OpenVINO™ Runtime on macOS, you have the following options:
 
@@ -31,6 +31,5 @@ If you want to install OpenVINO™ Runtime on macOS, you have the following opti
 * :doc:`Install OpenVINO using Homebrew <openvino_docs_install_guides_installing_openvino_brew>`
 * :doc:`Install OpenVINO using vcpkg <openvino_docs_install_guides_installing_openvino_vcpkg>`
 * :doc:`Install OpenVINO using Conan Package Manager <openvino_docs_install_guides_installing_openvino_conan>`
-
-
+* :doc:`Install OpenVINO using npm <openvino_docs_install_guides_installing_openvino_npm>`
 

docs/articles_en/get-started/install-openvino-overview/install-openvino-npm.rst

@@ -0,0 +1,61 @@
+.. {#openvino_docs_install_guides_installing_openvino_npm}
+
+Install Intel® Distribution of OpenVINO™ Toolkit from npm Registry
+==================================================================
+
+.. meta::
+   :description: Learn how to install OpenVINO™ Runtime on Windows, Linux, and
+                 macOS operating systems, using the npm registry.
+
+
+.. note::
+
+   Note that the npm distribution:
+
+   * offers the JavaScript API only
+   * is dedicated to users of all major OSes: Windows, Linux, and macOS
+     (all x86_64 / arm64 architectures)
+   * macOS offers support only for CPU inference
+
+.. tab-set::
+
+   .. tab-item:: System Requirements
+      :sync: system-requirements
+
+      - Windows, Linux, macOS
+      - x86, ARM (Windows ARM not supported)
+
+   .. tab-item:: Software Requirements
+      :sync: software-requirements
+
+      `Node.js version 20.5.1 and higher <https://nodejs.org/en/download/>`__
+
+
+Installing OpenVINO Node.js
+###########################
+
+1. Make sure that you have installed `Node.js and npm <https://nodejs.org/en/download>`__
+   on your system.
+2. Navigate to your project directory and run the following command in the terminal:
+
+   .. code-block:: sh
+
+      npm install openvino-node
+
+.. note::
+
+   The *openvino-node* npm package runs in Node.js environment only and provides
+   a subset of :doc:`OpenVINO Runtime C++ API <../../api/c_cpp_api/group__ov__cpp__api>`.
+
+What's Next?
+####################
+
+Now that you’ve installed OpenVINO npm package, you’re ready to run your own machine
+learning applications! Explore :doc:`OpenVINO Node.js API <../../api/nodejs_api/nodejs_api>`
+to learn more about how to integrate a model in Node.js applications.
+
+Additional Resources
+####################
+
+- Intel® Distribution of OpenVINO™ toolkit home page: https://software.intel.com/en-us/openvino-toolkit
+- For IoT Libraries & Code Samples, see `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__.

docs/articles_en/get-started/install-openvino-overview/install-openvino-windows-header.rst

@@ -5,8 +5,8 @@ Install OpenVINO™ Runtime on Windows
 
 
 .. meta::
-   :description: Learn how to install OpenVINO™ Runtime on Windows operating 
-                 system. You can use an archive, a PyPi package, Conda Forge, 
+   :description: Learn how to install OpenVINO™ Runtime on Windows operating
+                 system. You can use an archive, a PyPi package, npm package, Conda Forge,
                  or a Docker image.
 
 
@@ -20,6 +20,7 @@ Install OpenVINO™ Runtime on Windows
    Use vcpkg <openvino_docs_install_guides_installing_openvino_vcpkg>
    Use Docker <openvino_docs_install_guides_installing_openvino_docker>
    Use Conan <openvino_docs_install_guides_installing_openvino_conan>
+   Use npm <openvino_docs_install_guides_installing_openvino_npm>
 
 
 
@@ -31,5 +32,5 @@ If you want to install OpenVINO™ Runtime on Windows, you have the following op
 * :doc:`Install OpenVINO using vcpkg <openvino_docs_install_guides_installing_openvino_vcpkg>`
 * :doc:`Install OpenVINO using Docker <openvino_docs_install_guides_installing_openvino_docker>`
 * :doc:`Install OpenVINO using Conan Package Manager <openvino_docs_install_guides_installing_openvino_conan>`
-
+* :doc:`Install OpenVINO using npm <openvino_docs_install_guides_installing_openvino_npm>`
 

docs/articles_en/openvino-workflow/model-optimization-guide/quantizing-models-post-training/quantizing-with-accuracy-control.rst

@@ -14,7 +14,7 @@ This is the advanced quantization flow that allows to apply 8-bit quantization t
 * Since accuracy validation is run several times during the quantization process, quantization with accuracy control can take more time than the :doc:`Basic 8-bit quantization <basic_quantization_flow>` flow.
 * The resulted model can provide smaller performance improvement than the :doc:`Basic 8-bit quantization <basic_quantization_flow>` flow because some of the operations are kept in the original precision.
 
-.. note:: Currently, 8-bit quantization with accuracy control is available only for models in OpenVINO representation.
+.. note:: Currently, 8-bit quantization with accuracy control is available only for models in OpenVINO and onnx.ModelProto representation.
 
 The steps for the quantization with accuracy control are described below.
 
@@ -38,10 +38,18 @@ This step is similar to the :doc:`Basic 8-bit quantization <basic_quantization_f
          :language: python
          :fragment: [dataset]
 
+   .. tab-item:: ONNX
+      :sync: onnx
+
+      .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_aa_onnx.py
+         :language: python
+         :fragment: [dataset]
+
 Prepare validation function
 ############################
 
-Validation function receives ``openvino.CompiledModel`` object and validation dataset and returns accuracy metric value. The following code snippet shows an example of validation function for OpenVINO model:
+The validation function takes two arguments: a model object and a validation dataset, and it returns the accuracy metric value. The type of the model object varies for different frameworks. In OpenVINO, it is an ``openvino.CompiledModel``. In ONNX, it is an ``onnx.ModelProto``.
+The following code snippet shows an example of a validation function for OpenVINO and ONNX framework:
 
 .. tab-set::
 
@@ -52,10 +60,17 @@ Validation function receives ``openvino.CompiledModel`` object and validation da
          :language: python
          :fragment: [validation]
 
+   .. tab-item:: ONNX
+      :sync: onnx
+
+      .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_aa_onnx.py
+         :language: python
+         :fragment: [validation]
+
 Run quantization with accuracy control
 #######################################
 
-``nncf.quantize_with_accuracy_control()`` function is used to run the quantization with accuracy control. The following code snippet shows an example of quantization with accuracy control for OpenVINO model:
+``nncf.quantize_with_accuracy_control()`` function is used to run the quantization with accuracy control. The following code snippet shows an example of quantization with accuracy control for OpenVINO and ONNX framework:
 
 .. tab-set::
 
@@ -66,6 +81,13 @@ Run quantization with accuracy control
          :language: python
          :fragment: [quantization]
 
+   .. tab-item:: ONNX
+      :sync: onnx
+
+      .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_aa_onnx.py
+         :language: python
+         :fragment: [quantization]
+
 * ``max_drop`` defines the accuracy drop threshold. The quantization process stops when the degradation of accuracy metric on the validation dataset is less than the ``max_drop``. The default value is 0.01. NNCF will stop the quantization and report an error if the ``max_drop`` value can't be reached.
 
 * ``drop_type`` defines how the accuracy drop will be calculated: ``ABSOLUTE`` (used by default) or ``RELATIVE``.
@@ -81,6 +103,13 @@ After that the model can be compiled and run with OpenVINO:
          :language: python
          :fragment: [inference]
 
+   .. tab-item:: ONNX
+      :sync: onnx
+
+      .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_aa_onnx.py
+         :language: python
+         :fragment: [inference]
+
 To save the model in the OpenVINO Intermediate Representation (IR), use ``openvino.save_model()``. When dealing with an original model in FP32 precision, it's advisable to preserve FP32 precision in the most impactful model operations that were reverted from INT8 to FP32. To do this, consider using compress_to_fp16=False during the saving process. This recommendation is based on the default functionality of ``openvino.save_model()``, which saves models in FP16, potentially impacting accuracy through this conversion.
 
 .. tab-set::
@@ -101,6 +130,7 @@ Examples of NNCF post-training quantization with control of accuracy metric:
 
 * `Post-Training Quantization of Anomaly Classification OpenVINO model with control of accuracy metric <https://github.com/openvinotoolkit/nncf/blob/develop/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control>`__
 * `Post-Training Quantization of YOLOv8 OpenVINO Model with control of accuracy metric <https://github.com/openvinotoolkit/nncf/blob/develop/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control>`__
+* `Post-Training Quantization of YOLOv8 ONNX Model with control of accuracy metric <https://github.com/openvinotoolkit/nncf/blob/develop/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control>`__
 
 See also
 ####################

docs/optimization_guide/nncf/ptq/code/ptq_aa_onnx.py

@@ -0,0 +1,75 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+#! [dataset]
+import nncf
+import torch
+
+calibration_loader = torch.utils.data.DataLoader(...)
+
+def transform_fn(data_item):
+    images, _ = data_item
+    return {input_name: images.numpy()} # input_name should be taken from the model, 
+                                        # e.g. model.graph.input[0].name
+
+calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)
+validation_dataset = nncf.Dataset(calibration_loader, transform_fn)
+#! [dataset]
+
+#! [validation]
+import numpy as np
+import torch
+from sklearn.metrics import accuracy_score
+
+import onnx
+import onnxruntime
+
+
+def validate(model: onnx.ModelProto,
+             validation_loader: torch.utils.data.DataLoader) -> float:
+    predictions = []
+    references = []
+
+    input_name = model.graph.input[0].name
+    serialized_model = model.SerializeToString()
+    session = onnxruntime.InferenceSession(serialized_model, providers=["CPUExecutionProvider"])
+    output_names = [output.name for output in session.get_outputs()]
+
+    for images, target in validation_loader:
+        pred = session.run(output_names, input_feed={input_name: images.numpy()})[0]
+        predictions.append(np.argmax(pred, axis=1))
+        references.append(target)
+
+    predictions = np.concatenate(predictions, axis=0)
+    references = np.concatenate(references, axis=0)
+    return accuracy_score(predictions, references)
+#! [validation]
+
+#! [quantization]
+import onnx
+
+model = onnx.load("model_path")
+
+quantized_model = nncf.quantize_with_accuracy_control(
+    model,
+    calibration_dataset=calibration_dataset,
+    validation_dataset=validation_dataset,
+    validation_fn=validate,
+    max_drop=0.01,
+    drop_type=nncf.DropType.ABSOLUTE,
+)
+#! [quantization]
+
+#! [inference]
+import openvino as ov
+
+# convert ONNX model to OpenVINO model
+ov_quantized_model = ov.convert_model(quantized_model)
+
+# compile the model to transform quantized operations to int8
+model_int8 = ov.compile_model(ov_quantized_model)
+
+input_fp32 = ... # FP32 model input
+res = model_int8(input_fp32)
+
+#! [inference]