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https://github.com/zebrajr/pytorch.git
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Pull Request resolved: https://github.com/pytorch/pytorch/pull/152237 Approved by: https://github.com/huydhn, https://github.com/malfet
128 lines
4.0 KiB
ReStructuredText
128 lines
4.0 KiB
ReStructuredText
TorchDynamo-based ONNX Exporter
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===============================
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.. automodule:: torch.onnx
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:noindex:
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.. contents:: :local:
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:depth: 1
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Overview
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--------
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The ONNX exporter leverages TorchDynamo engine to hook into Python's frame evaluation API
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and dynamically rewrite its bytecode into an FX Graph.
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The resulting FX Graph is then polished before it is finally translated into an ONNX graph.
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The main advantage of this approach is that the `FX graph <https://pytorch.org/docs/stable/fx.html>`_ is captured using
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bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.
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In addition, during the export process, memory usage is significantly reduced compared to the TorchScript-enabled exporter.
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See the :doc:`memory usage documentation <onnx_dynamo_memory_usage>` for more information.
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Dependencies
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------------
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The ONNX exporter depends on extra Python packages:
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- `ONNX <https://onnx.ai>`_
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- `ONNX Script <https://microsoft.github.io/onnxscript>`_
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They can be installed through `pip <https://pypi.org/project/pip/>`_:
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.. code-block:: bash
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pip install --upgrade onnx onnxscript
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`onnxruntime <https://onnxruntime.ai>`_ can then be used to execute the model
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on a large variety of processors.
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A simple example
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----------------
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See below a demonstration of exporter API in action with a simple Multilayer Perceptron (MLP) as example:
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.. code-block:: python
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import torch
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import torch.nn as nn
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class MLPModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.fc0 = nn.Linear(8, 8, bias=True)
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self.fc1 = nn.Linear(8, 4, bias=True)
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self.fc2 = nn.Linear(4, 2, bias=True)
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self.fc3 = nn.Linear(2, 2, bias=True)
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def forward(self, tensor_x: torch.Tensor):
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tensor_x = self.fc0(tensor_x)
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tensor_x = torch.sigmoid(tensor_x)
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tensor_x = self.fc1(tensor_x)
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tensor_x = torch.sigmoid(tensor_x)
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tensor_x = self.fc2(tensor_x)
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tensor_x = torch.sigmoid(tensor_x)
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output = self.fc3(tensor_x)
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return output
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model = MLPModel()
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tensor_x = torch.rand((97, 8), dtype=torch.float32)
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onnx_program = torch.onnx.export(model, (tensor_x,), dynamo=True)
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As the code above shows, all you need is to provide :func:`torch.onnx.export` with an instance of the model and its input.
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The exporter will then return an instance of :class:`torch.onnx.ONNXProgram` that contains the exported ONNX graph along with extra information.
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``onnx_program.optimize()`` can be called to optimize the ONNX graph with constant folding and elimination of redundant operators. The optimization is done in-place.
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.. code-block:: python
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onnx_program.optimize()
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The in-memory model available through ``onnx_program.model_proto`` is an ``onnx.ModelProto`` object in compliance with the `ONNX IR spec <https://github.com/onnx/onnx/blob/main/docs/IR.md>`_.
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The ONNX model may then be serialized into a `Protobuf file <https://protobuf.dev/>`_ using the :meth:`torch.onnx.ONNXProgram.save` API.
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.. code-block:: python
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onnx_program.save("mlp.onnx")
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Inspecting the ONNX model using GUI
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-----------------------------------
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You can view the exported model using `Netron <https://netron.app/>`__.
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.. image:: _static/img/onnx/onnx_dynamo_mlp_model.png
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:width: 40%
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:alt: MLP model as viewed using Netron
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When the conversion fails
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-------------------------
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Function :func:`torch.onnx.export` should called a second time with
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parameter ``report=True``. A markdown report is generated to help the user
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to resolve the issue.
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.. toctree::
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:hidden:
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onnx_dynamo_memory_usage
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API Reference
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-------------
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.. autofunction:: torch.onnx.export
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.. autoclass:: torch.onnx.ONNXProgram
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:members:
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.. autofunction:: is_in_onnx_export
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.. autoclass:: torch.onnx.OnnxExporterError
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:members:
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.. autofunction:: torch.onnx.enable_fake_mode
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Deprecated
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----------
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The following classes and functions are deprecated and will be removed.
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.. autofunction:: torch.onnx.dynamo_export
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.. autoclass:: torch.onnx.ExportOptions
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