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[DOCS] fix pytorch link port 24.2 (#24743)
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.. {#openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch}
Converting a PyTorch Model
==========================

Expand Down Expand Up @@ -30,11 +28,19 @@ Here is the simplest example of PyTorch model conversion using a model from ``to
* ``torch.jit.ScriptFunction``
* ``torch.export.ExportedProgram``

When using ``torch.nn.Module`` as an input model, ``openvino.convert_model`` often requires the ``example_input`` parameter to be specified. Internally, it triggers the model tracing during the model conversion process, using the capabilities of the ``torch.jit.trace`` function.
When using ``torch.nn.Module`` as an input model, ``openvino.convert_model`` often requires the
``example_input`` parameter to be specified. Internally, it triggers the model tracing during
the model conversion process, using the capabilities of the ``torch.jit.trace`` function.

The use of ``example_input`` can lead to a better quality OpenVINO model in terms of correctness and performance compared to converting the same original model without specifying ``example_input``. While the necessity of ``example_input`` depends on the implementation details of a specific PyTorch model, it is recommended to always set the ``example_input`` parameter when it is available.
The use of ``example_input`` can lead to a better quality OpenVINO model in terms of correctness
and performance compared to converting the same original model without specifying
``example_input``. While the necessity of ``example_input`` depends on the implementation
details of a specific PyTorch model, it is recommended to always set the ``example_input``
parameter when it is available.

The value for the ``example_input`` parameter can be easily derived from knowing the input tensor's element type and shape. While it may not be suitable for all cases, random numbers can frequently serve this purpose effectively:
The value for the ``example_input`` parameter can be easily derived from knowing the input
tensor's element type and shape. While it may not be suitable for all cases, random numbers can
frequently serve this purpose effectively:

.. code-block:: py
:force:
Expand All @@ -46,7 +52,11 @@ The value for the ``example_input`` parameter can be easily derived from knowing
model = torchvision.models.resnet50(weights='DEFAULT')
ov_model = ov.convert_model(model, example_input=torch.rand(1, 3, 224, 224))
In practice, the code to evaluate or test the PyTorch model is usually provided with the model itself and can be used to generate a proper ``example_input`` value. A modified example of using ``resnet50`` model from ``torchvision`` is presented below. It demonstrates how to switch inference in the existing PyTorch application to OpenVINO and how to get value for ``example_input``:
In practice, the code to evaluate or test the PyTorch model is usually provided with the model
itself and can be used to generate a proper ``example_input`` value. A modified example of using
``resnet50`` model from ``torchvision`` is presented below. It demonstrates how to switch
inference in the existing PyTorch application to OpenVINO and how to get value for
``example_input``:

.. code-block:: py
:force:
Expand Down Expand Up @@ -89,7 +99,9 @@ Check out more examples in :doc:`interactive Python tutorials <../../learn-openv

.. note::

In the examples above the ``openvino.save_model`` function is not used because there are no PyTorch-specific details regarding the usage of this function. In all examples, the converted OpenVINO model can be saved to IR by calling ``ov.save_model(ov_model, 'model.xml')`` as usual.
In the examples above the ``openvino.save_model`` function is not used because there are no
PyTorch-specific details regarding the usage of this function. In all examples, the converted
OpenVINO model can be saved to IR by calling ``ov.save_model(ov_model, 'model.xml')`` as usual.

Supported Input Parameter Types
###############################
Expand All @@ -100,44 +112,75 @@ If the model has a single input, the following input types are supported in ``ex
* ``torch.Tensor``
* ``tuple`` or any nested combination of tuples

If a model has multiple inputs, the input values are combined in a ``list``, a ``tuple``, or a ``dict``:
If a model has multiple inputs, the input values are combined in a ``list``, a ``tuple``, or a
``dict``:

* values in a ``list`` or ``tuple`` should be passed in the same order as the original model specifies,
* values in a ``list`` or ``tuple`` should be passed in the same order as the original model
specifies,
* ``dict`` has keys from the names of the original model argument names.

Enclosing in ``list``, ``tuple`` or ``dict`` can be used for a single input as well as for multiple inputs.
Enclosing in ``list``, ``tuple`` or ``dict`` can be used for a single input as well as for
multiple inputs.

If a model has a single input parameter and the type of this input is a ``tuple``, it should be always passed enclosed into an extra ``list``, ``tuple`` or ``dict`` as in the case of multiple inputs. It is required to eliminate ambiguity between ``model((a, b))`` and ``model(a, b)`` in this case.
If a model has a single input parameter and the type of this input is a ``tuple``, it should be
always passed enclosed into an extra ``list``, ``tuple`` or ``dict`` as in the case of multiple
inputs. It is required to eliminate ambiguity between ``model((a, b))`` and ``model(a, b)`` in
this case.

Non-tensor Data Types
#####################

When a non-tensor data type, such as a ``tuple`` or ``dict``, appears in a model input or output, it is flattened. The flattening means that each element within the ``tuple`` will be represented as a separate input or output. The same is true for ``dict`` values, where the keys of the ``dict`` are used to form a model input/output name. The original non-tensor input or output is replaced by one or multiple new inputs or outputs resulting from this flattening process. This flattening procedure is applied recursively in the case of nested ``tuples``, ``lists``, and ``dicts`` until it reaches the assumption that the most nested data type is a tensor.

For example, if the original model is called with ``example_input=(a, (b, c, (d, e)))``, where ``a``, ``b``, ... ``e`` are tensors, it means that the original model has two inputs. The first is a tensor ``a``, and the second is a tuple ``(b, c, (d, e))``, containing two tensors ``b`` and ``c`` and a nested tuple ``(d, e)``. Then the resulting OpenVINO model will have signature ``(a, b, c, d, e)``, which means it will have five inputs, all of type tensor, instead of two in the original model.

If your model has a ``dict`` input, such as, ``{"x": a, "y": b, "z": c}``, it will be decomposed into multiple inputs of the OpenVINO model signature: ``(a, b, c)``, where inputs assume the names of ``x``, ``y``, and ``z`` respectively.
When a non-tensor data type, such as a ``tuple`` or ``dict``, appears in a model input or output,
it is flattened. The flattening means that each element within the ``tuple`` will be represented
as a separate input or output. The same is true for ``dict`` values, where the keys of the
``dict`` are used to form a model input/output name. The original non-tensor input or output is
replaced by one or multiple new inputs or outputs resulting from this flattening process. This
flattening procedure is applied recursively in the case of nested ``tuples``, ``lists``, and
``dicts`` until it reaches the assumption that the most nested data type is a tensor.

For example, if the original model is called with ``example_input=(a, (b, c, (d, e)))``, where
``a``, ``b``, ... ``e`` are tensors, it means that the original model has two inputs. The first
is a tensor ``a``, and the second is a tuple ``(b, c, (d, e))``, containing two tensors ``b``
and ``c`` and a nested tuple ``(d, e)``. Then the resulting OpenVINO model will have signature
``(a, b, c, d, e)``, which means it will have five inputs, all of type tensor, instead of two in
the original model.

If your model has a ``dict`` input, such as, ``{"x": a, "y": b, "z": c}``, it will be decomposed
into multiple inputs of the OpenVINO model signature: ``(a, b, c)``, where inputs assume the
names of ``x``, ``y``, and ``z`` respectively.

.. note::

An important consequence of flattening is that only ``tuple`` and ``dict`` with a fixed number of elements and key values are supported. The structure of such inputs should be fully described in the ``example_input`` parameter of ``convert_model``. The flattening on outputs should be reproduced with the given ``example_input`` and cannot be changed once the conversion is done.
An important consequence of flattening is that only ``tuple`` and ``dict`` with a fixed number
of elements and key values are supported. The structure of such inputs should be fully
described in the ``example_input`` parameter of ``convert_model``. The flattening on outputs
should be reproduced with the given ``example_input`` and cannot be changed once the
conversion is done.

Check out more examples of model conversion with non-tensor data types in the following tutorials:

* `Video Subtitle Generation using Whisper and OpenVINO™ <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/whisper-subtitles-generation>`__
* `Visual Question Answering and Image Captioning using BLIP and OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/blip-visual-language-processing>`__
* `Video Subtitle Generation using Whisper and OpenVINO™
<https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/whisper-subtitles-generation>`__
* `Visual Question Answering and Image Captioning using BLIP and OpenVINO
<https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/blip-visual-language-processing>`__

Input and output names of the model
###################################

PyTorch doesn't produce relevant names for model inputs and outputs in the TorchScript representation. OpenVINO will assign input names based on the signature of models's ``forward`` method or ``dict`` keys provided in the ``example_input``. Output names will be assigned if there is a ``dict`` at the output or when there is some internal name available in the TorchScript model representation. In general, the output name is not assigned and stays empty. It is recommended to address model outputs by the index rather then the name.
PyTorch doesn't produce relevant names for model inputs and outputs in the TorchScript
representation. OpenVINO will assign input names based on the signature of models's ``forward``
method or ``dict`` keys provided in the ``example_input``. Output names will be assigned if
there is a ``dict`` at the output or when there is some internal name available in the
TorchScript model representation. In general, the output name is not assigned and stays empty.
It is recommended to address model outputs by the index rather then the name.

Support for torch.export
########################

`torch.export <https://pytorch.org/docs/2.2/export.html>` is the current way to get a graph representation of a model (since PyTorch 2.1). It produces ``ExportedProgram`` which includes the graph representation in the FX format. Refer to
`PyTorch documentation <https://pytorch.org/docs/stable/fx.html>`__
to see why it has advantage over the TorchScript representation.
`torch.export <https://pytorch.org/docs/2.2/export.html>`__ is the current way to get a graph
representation of a model (since PyTorch 2.1). It produces ``ExportedProgram`` which includes
the graph representation in the FX format. To see why it has an advantage over the TorchScript
representation, refer to `PyTorch documentation <https://pytorch.org/docs/stable/fx.html>`__.

Here is an example of how to convert a model obtained with ``torch.export``:

Expand All @@ -155,14 +198,20 @@ Here is an example of how to convert a model obtained with ``torch.export``:
.. note::

This is an experimental feature. Use it only if you know that you need to. PyTorch version 2.2 is recommended. Dynamic shapes are not supported yet.
This is an experimental feature. Use it only if you know that you need to. PyTorch version 2.2
is recommended. Dynamic shapes are not supported yet.

Exporting a PyTorch Model to ONNX Format
########################################

An alternative method of converting PyTorch models is exporting a PyTorch model to ONNX with ``torch.onnx.export`` first and then converting the resulting ``.onnx`` file to OpenVINO Model with ``openvino.convert_model``. It can be considered as a backup solution if a model cannot be converted directly from PyTorch to OpenVINO as described in the above chapters. Converting through ONNX can be more expensive in terms of code, conversion time, and allocated memory.
An alternative method of converting PyTorch models is exporting a PyTorch model to ONNX with
``torch.onnx.export`` first and then converting the resulting ``.onnx`` file to OpenVINO Model
with ``openvino.convert_model``. It can be considered as a backup solution if a model cannot be
converted directly from PyTorch to OpenVINO as described in the above chapters. Converting through
ONNX can be more expensive in terms of code, conversion time, and allocated memory.

1. Refer to the `Exporting PyTorch models to ONNX format <https://pytorch.org/docs/stable/onnx.html>`__ guide to learn how to export models from PyTorch to ONNX.
1. Refer to the `Exporting PyTorch models to ONNX format <https://pytorch.org/docs/stable/onnx.html>`__
guide to learn how to export models from PyTorch to ONNX.
2. Follow :doc:`Convert an ONNX model <convert-model-onnx>` chapter to produce OpenVINO model.

Here is an illustration of using these two steps together:
Expand All @@ -182,6 +231,9 @@ Here is an illustration of using these two steps together:
.. note::

As of version 1.8.1, not all PyTorch operations can be exported to ONNX opset 9 which is used by default.
It is recommended to export models to opset 11 or higher when export to default opset 9 is not working. In that case, use ``opset_version`` option of the ``torch.onnx.export``. For more information about ONNX opset, refer to the `Operator Schemas <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`__ page.
As of version 1.8.1, not all PyTorch operations can be exported to ONNX opset 9 which is
used by default. It is recommended to export models to opset 11 or higher when export to
default opset 9 is not working. In that case, use ``opset_version`` option of the
``torch.onnx.export``. For more information about ONNX opset, refer to the
`Operator Schemas <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`__ page.

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