[Docs][NNCF] TorchFX backend docs

docs/articles_en/openvino-workflow/model-optimization-guide/quantizing-models-post-training/basic-quantization-flow.rst

@@ -63,6 +63,13 @@ The transformation function is a function that takes a sample from the dataset a
          :language: python
          :fragment: [dataset]
 
+   .. tab-item:: TorchFX
+      :sync: torch_fx
+
+      .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_torch_fx.py
+         :language: python
+         :fragment: [dataset]
+
 If there is no framework dataset object, you can create your own entity that implements the ``Iterable`` interface in Python, for example the list of images, and returns data samples feasible for inference. In this case, a transformation function is not required.
 
 
@@ -102,6 +109,12 @@ See the `example section <#examples-of-how-to-apply-nncf-post-training-quantizat
          :language: python
          :fragment: [quantization]
 
+   .. tab-item:: TorchFX
+      :sync: torch_fx
+
+      .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_torch_fx.py
+         :language: python
+         :fragment: [quantization]
 
 After that the model can be converted into the OpenVINO Intermediate Representation (IR) if needed, compiled and run with OpenVINO.
 If you have not already installed OpenVINO developer tools, install it with ``pip install openvino``.
@@ -136,6 +149,17 @@ If you have not already installed OpenVINO developer tools, install it with ``pi
          :language: python
          :fragment:  [inference]
 
+TorchFX models can be compiled and run with OpenVINO optimizations by `torch.compile(..., backend="openvino") <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`__ functionality:
+
+.. tab-set::
+
+   .. tab-item:: TorchFX
+      :sync: torch_fx
+
+      .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_torch_fx.py
+         :language: python
+         :fragment:  [inference]
+
 Tune quantization parameters
 ############################
 

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

@@ -0,0 +1,44 @@
+# 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 images
+
+calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)
+#! [dataset]
+
+#! [quantization]
+import torchvision
+from nncf.torch import disable_patching
+
+input_fp32 = torch.ones((1, 3, 224, 224)) # FP32 model input
+model = torchvision.models.resnet50(pretrained=True)
+
+with disable_patching():
+    exported_model = torch.export.export_for_training(model, args=(input_fp32,)).module()
+    quantized_model = nncf.quantize(exported_model, calibration_dataset)
+#! [quantization]
+
+#! [inference]
+import openvino.torch
+
+input_fp32 = ... # FP32 model input
+
+# compile quantized model using torch.compile API
+with disable_patching():
+    compiled_model_int8 = torch.compile(quantized_model, backend="openvino")
+    # OpenVINO backend compiles the model during the first call,
+    # so the first call is expected to be slower than the following calls
+    res = compiled_model_int8(input_fp32)
+
+    # save the model
+    exported_program = torch.export.export(compiled_model_int8, args=(input_fp32,))
+    torch.export.save(exported_program, 'exported_program.pt2')
+#! [inference]