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The PR updates the documentation to reflect the changes introduced in pytorch 2.5 and related to onnx exporter. Pull Request resolved: https://github.com/pytorch/pytorch/pull/135372 Approved by: https://github.com/justinchuby Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
115 lines
4.5 KiB
ReStructuredText
115 lines
4.5 KiB
ReStructuredText
torch.onnx
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==========
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Overview
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--------
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`Open Neural Network eXchange (ONNX) <https://onnx.ai/>`_ is an open standard
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format for representing machine learning models. The ``torch.onnx`` module captures the computation graph from a
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native PyTorch :class:`torch.nn.Module` model and converts it into an
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`ONNX graph <https://github.com/onnx/onnx/blob/main/docs/IR.md>`_.
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The exported model can be consumed by any of the many
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`runtimes that support ONNX <https://onnx.ai/supported-tools.html#deployModel>`_, including
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Microsoft's `ONNX Runtime <https://www.onnxruntime.ai>`_.
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**There are two flavors of ONNX exporter API that you can use, as listed below.**
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Both can be called through function :func:`torch.onnx.export`.
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Next example shows how to export a simple model.
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.. code-block:: python
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import torch
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class MyModel(torch.nn.Module):
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def __init__(self):
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super(MyModel, self).__init__()
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self.conv1 = torch.nn.Conv2d(1, 128, 5)
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def forward(self, x):
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return torch.relu(self.conv1(x))
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input_tensor = torch.rand((1, 1, 128, 128), dtype=torch.float32)
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model = MyModel()
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torch.onnx.export(
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model, # model to export
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(input_tensor,), # inputs of the model,
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"my_model.onnx", # filename of the ONNX model
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input_names=["input"], # Rename inputs for the ONNX model
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dynamo=True # True or False to select the exporter to use
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)
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Next sections introduces the two versions of the exporter.
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TorchDynamo-based ONNX Exporter
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-------------------------------
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*The TorchDynamo-based ONNX exporter is the newest (and Beta) exporter for PyTorch 2.1 and newer*
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TorchDynamo engine is leveraged to hook into Python's frame evaluation API and dynamically rewrite its
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bytecode into an FX Graph. The resulting FX Graph is then polished before it is finally translated into an
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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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:doc:`Learn more about the TorchDynamo-based ONNX Exporter <onnx_dynamo>`
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TorchScript-based ONNX Exporter
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-------------------------------
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*The TorchScript-based ONNX exporter is available since PyTorch 1.2.0*
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`TorchScript <https://pytorch.org/docs/stable/jit.html>`_ is leveraged to trace (through :func:`torch.jit.trace`)
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the model and capture a static computation graph.
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As a consequence, the resulting graph has a couple limitations:
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* It does not record any control-flow, like if-statements or loops;
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* Does not handle nuances between ``training`` and ``eval`` mode;
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* Does not truly handle dynamic inputs
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As an attempt to support the static tracing limitations, the exporter also supports TorchScript scripting
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(through :func:`torch.jit.script`), which adds support for data-dependent control-flow, for example. However, TorchScript
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itself is a subset of the Python language, so not all features in Python are supported, such as in-place operations.
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:doc:`Learn more about the TorchScript-based ONNX Exporter <onnx_torchscript>`
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Contributing / Developing
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-------------------------
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The ONNX exporter is a community project and we welcome contributions. We follow the
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`PyTorch guidelines for contributions <https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md>`_, but you might
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also be interested in reading our `development wiki <https://github.com/pytorch/pytorch/wiki/PyTorch-ONNX-exporter>`_.
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.. toctree::
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:hidden:
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onnx_dynamo
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onnx_dynamo_onnxruntime_backend
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onnx_torchscript
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.. This module needs to be documented. Adding here in the meantime
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.. for tracking purposes
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.. py:module:: torch.onnx.errors
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.. py:module:: torch.onnx.operators
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.. py:module:: torch.onnx.symbolic_caffe2
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.. py:module:: torch.onnx.symbolic_helper
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.. py:module:: torch.onnx.symbolic_opset10
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.. py:module:: torch.onnx.symbolic_opset11
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.. py:module:: torch.onnx.symbolic_opset13
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.. py:module:: torch.onnx.symbolic_opset14
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.. py:module:: torch.onnx.symbolic_opset15
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.. py:module:: torch.onnx.symbolic_opset16
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.. py:module:: torch.onnx.symbolic_opset17
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.. py:module:: torch.onnx.symbolic_opset18
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.. py:module:: torch.onnx.symbolic_opset19
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.. py:module:: torch.onnx.symbolic_opset20
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.. py:module:: torch.onnx.symbolic_opset7
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.. py:module:: torch.onnx.symbolic_opset8
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.. py:module:: torch.onnx.symbolic_opset9
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.. py:module:: torch.onnx.utils
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.. py:module:: torch.onnx.verification
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.. py:module:: torch.onnx.symbolic_opset12 |