[DOCS] torch.compile documentation updates for 2024.2 (#24908)

Port from https://github.com/openvinotoolkit/openvino/pull/24803

### Details:
 - Updated 'How to Use' and 'Options' sections
 - Added quantization and torchserve sections

---------

Co-authored-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>

docs/articles_en/openvino-workflow/torch-compile.rst

@@ -22,20 +22,33 @@ By default, Torch code runs in eager-mode, but with the use of ``torch.compile``
 How to Use
 ####################
 
-To use ``torch.compile``, you need to add an import statement and define the ``openvino`` backend.
+To use ``torch.compile``, you need to define the ``openvino`` backend in your PyTorch application.
 This way Torch FX subgraphs will be directly converted to OpenVINO representation without
 any additional PyTorch-based tracing/scripting.
+This approach works only for the **package distributed via pip**, as it is now configured with
+`torch_dynamo_backends entrypoint <https://pytorch.org/docs/stable/torch.compiler_custom_backends.html#registering-custom-backends>`__.
 
+.. code-block:: python
 
-.. code-block:: sh
-
-   import openvino.torch
    ...
    model = torch.compile(model, backend='openvino')
+   ...
+
+For OpenVINO installed via channels other than pip, such as conda, and versions older than
+2024.1, an additional import statement is needed:
+
+.. code-block:: python
+
+   import openvino.torch
+
+   ...
+   model = torch.compile(model, backend='openvino')
+   ...
 
 Execution diagram:
 
 .. image:: ../assets/images/torch_compile_backend_openvino.svg
+   :alt: torch.compile execution diagram
    :width: 992px
    :height: 720px
    :scale: 60%
@@ -51,6 +64,9 @@ enable model caching, set the cache directory etc. You can use a dictionary of t
   By default, the OpenVINO backend for ``torch.compile`` runs PyTorch applications
   on CPU. If you set this variable to ``GPU.0``, for example, the application will
   use the integrated graphics processor instead.
+* ``aot_autograd`` - enables a graph capture needed for dynamic shapes or to finetune a
+  model. For models with dynamic shapes, it is recommended to set this option to ``True``.
+  By default, aot_autograd is set to ``False``.
 * ``model_caching`` - enables saving the optimized model files to a hard drive,
   after the first application run. This makes them available for the following
   application executions, reducing the first-inference latency. By default, this
@@ -58,6 +74,15 @@ enable model caching, set the cache directory etc. You can use a dictionary of t
 * ``cache_dir`` - enables defining a custom directory for the model files (if
   ``model_caching`` is set to ``True``). By default, the OpenVINO IR is saved
   in the cache sub-directory, created in the application's root directory.
+* ``decompositions`` - enables defining additional operator decompositions. By
+  default, this is an empty list. For example, to add a decomposition for
+  an operator ``my_op``, add ``'decompositions': [torch.ops.aten.my_op.default]``
+  to the options.
+* ``disabled_ops`` - enables specifying operators that can be disabled from
+  openvino execution and make it fall back to native PyTorch runtime. For
+  example, to disable an operator ``my_op`` from OpenVINO execution, add
+  ``'disabled_ops': [torch.ops.aten.my_op.default]`` to the options. By
+  default, this is an empty list.
 * ``config`` - enables passing any OpenVINO configuration option as a dictionary
   to this variable. For details on the various options, refer to the
   :ref:`OpenVINO Advanced Features <openvino-advanced-features>`.
@@ -79,8 +104,10 @@ You can also set OpenVINO specific configuration options by adding them as a dic
 Windows support
 +++++++++++++++++++++
 
-Currently, PyTorch does not support ``torch.compile`` feature on Windows officially. However, it can be accessed by running
-the below instructions:
+PyTorch supports ``torch.compile`` officially on Windows from version 2.3.0 onwards.
+
+For PyTorch versions below 2.3.0, the ``torch.compile`` feature is not supported on Windows
+officially. However, it can be accessed by running the following instructions:
 
 1. Install the PyTorch nightly wheel file - `2.1.0.dev20230713 <https://download.pytorch.org/whl/nightly/cpu/torch-2.1.0.dev20230713%2Bcpu-cp38-cp38-win_amd64.whl>`__ ,
 2. Update the file at ``<python_env_root>/Lib/site-packages/torch/_dynamo/eval_frames.py``
@@ -104,6 +131,50 @@ the below instructions:
        if sys.version_info >= (3, 11):
            `raise RuntimeError("Python 3.11+ not yet supported for torch.compile")
 
+Support for PyTorch 2 export quantization (Preview)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+PyTorch 2 export quantization is supported by OpenVINO backend in ``torch.compile``. To be able
+to access this feature, follow the steps provided in
+`PyTorch 2 Export Post Training Quantization with X86 Backend through Inductor <https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_x86_inductor.html>`__
+and update the provided sample as explained below.
+
+1. If you are using PyTorch version 2.3.0 or later, disable constant folding in quantization to
+   be able to benefit from the optimization in the OpenVINO backend. This can be done by passing
+   ``fold_quantize=False`` parameter into the ``convert_pt2e`` function. To do so, change this
+   line:
+
+   .. code-block:: python
+
+      converted_model = convert_pt2e(prepared_model)
+
+   to the following:
+
+   .. code-block:: python
+
+      converted_model = convert_pt2e(prepared_model, fold_quantize=False)
+
+2. Set ``torch.compile`` backend as OpenVINO and execute the model.
+
+   Update this line below:
+
+   .. code-block:: python
+
+      optimized_model = torch.compile(converted_model)
+
+   As below:
+
+   .. code-block:: python
+
+      optimized_model = torch.compile(converted_model, backend="openvino")
+
+TorchServe Integration
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+TorchServe is a performant, flexible, and easy to use tool for serving PyTorch models in production. For more information on the details of TorchServe,
+you can refer to `TorchServe github repository. <https://github.com/pytorch/serve>`__. With OpenVINO ``torch.compile`` integration into TorchServe you can serve
+PyTorch models in production and accelerate them with OpenVINO on various Intel hardware. Detailed instructions on how to use OpenVINO with TorchServe are
+available in `TorchServe examples. <https://github.com/pytorch/serve/tree/master/examples/pt2/torch_compile_openvino>`__
 
 Support for Automatic1111 Stable Diffusion WebUI
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++