[ONNX] Update API to torch.onnx.export(..., dynamo=True)

beginner_source/onnx/README.txt

@@ -10,5 +10,9 @@ ONNX
     https://pytorch.org/tutorials/beginner/onnx/export_simple_model_to_onnx_tutorial.html
 
 3. onnx_registry_tutorial.py
-    Extending the ONNX Registry
+    Extending the ONNX exporter operator support
     https://pytorch.org/tutorials/beginner/onnx/onnx_registry_tutorial.html
+
+4. export_control_flow_model_to_onnx_tutorial.py
+    Export a model with control flow to ONNX
+    https://pytorch.org/tutorials/beginner/onnx/export_control_flow_model_to_onnx_tutorial.html

beginner_source/onnx/export_control_flow_model_to_onnx_tutorial.py

@@ -0,0 +1,184 @@
+# -*- coding: utf-8 -*-
+"""
+`Introduction to ONNX <intro_onnx.html>`_ ||
+`Exporting a PyTorch model to ONNX <export_simple_model_to_onnx_tutorial.html>`_ ||
+`Extending the ONNX exporter operator support <onnx_registry_tutorial.html>`_ ||
+**`Export a model with control flow to ONNX**
+
+Export a model with control flow to ONNX
+========================================
+
+**Author**: `Xavier Dupré <https://github.com/xadupre>`_.
+"""
+
+
+###############################################################################
+# Overview
+# --------
+# 
+# This tutorial demonstrates how to handle control flow logic while exporting
+# a PyTorch model to ONNX. It highlights the challenges of exporting
+# conditional statements directly and provides solutions to circumvent them.
+#
+# Conditional logic cannot be exported into ONNX unless they refactored
+# to use :func:`torch.cond`. Let's start with a simple model
+# implementing a test.
+# 
+# What you will learn:
+#
+# - How to refactor the model to use :func:`torch.cond` for exporting.
+# - How to export a model with control flow logic to ONNX.
+# - How to optimize the exported model using the ONNX optimizer.
+#
+# Prerequisites
+# ~~~~~~~~~~~~~
+#
+# * ``torch >= 2.6``
+
+
+import torch
+
+###############################################################################
+# Define the Models
+# -----------------
+#
+# Two models are defined:
+#
+# ForwardWithControlFlowTest: A model with a forward method containing an
+# if-else conditional.
+#
+# ModelWithControlFlowTest: A model that incorporates ForwardWithControlFlowTest
+# as part of a simple multi-layer perceptron (MLP). The models are tested with
+# a random input tensor to confirm they execute as expected.
+
+class ForwardWithControlFlowTest(torch.nn.Module):
+    def forward(self, x):
+        if x.sum():
+            return x * 2
+        return -x
+
+
+class ModelWithControlFlowTest(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.mlp = torch.nn.Sequential(
+            torch.nn.Linear(3, 2),
+            torch.nn.Linear(2, 1),
+            ForwardWithControlFlowTest(),
+        )
+
+    def forward(self, x):
+        out = self.mlp(x)
+        return out
+
+
+model = ModelWithControlFlowTest()
+
+
+###############################################################################
+# Exporting the Model: First Attempt
+# ----------------------------------
+#
+# Exporting this model using torch.export.export fails because the control
+# flow logic in the forward pass creates a graph break that the exporter cannot
+# handle. This behavior is expected, as conditional logic not written using
+# torch.cond is unsupported.
+# 
+# A try-except block is used to capture the expected failure during the export
+# process. If the export unexpectedly succeeds, an AssertionError is raised.
+
+x = torch.randn(3)
+model(x)
+
+try:
+    torch.export.export(model, (x,), strict=False)
+    raise AssertionError("This export should failed unless PyTorch now supports this model.")
+except Exception as e:
+    print(e)
+
+###############################################################################
+# Using torch.onnx.export with JIT Tracing
+# ----------------------------------------
+#
+# When exporting the model using torch.onnx.export with the dynamo=True
+# argument, the exporter defaults to using JIT tracing. This fallback allows
+# the model to export, but the resulting ONNX graph may not faithfully represent
+# the original model logic due to the limitations of tracing.
+
+
+onnx_program = torch.onnx.export(model, (x,), dynamo=True) 
+print(onnx_program.model)
+
+
+###############################################################################
+# Suggested Patch: Refactoring with torch.cond
+# --------------------------------------------
+#
+# To make the control flow exportable, the tutorial demonstrates replacing the
+# forward method in ForwardWithControlFlowTest with a refactored version that
+# uses torch.cond.
+#
+# Details of the Refactoring:
+#
+# Two helper functions (identity2 and neg) represent the branches of the conditional logic:
+# * torch.cond is used to specify the condition and the two branches along with the input arguments.
+# * The updated forward method is then dynamically assigned to the ForwardWithControlFlowTest instance within the model. A list of submodules is printed to confirm the replacement.
+
+def new_forward(x):
+    def identity2(x):
+        return x * 2
+
+    def neg(x):
+        return -x
+
+    return torch.cond(x.sum() > 0, identity2, neg, (x,))
+
+
+print("the list of submodules")
+for name, mod in model.named_modules():
+    print(name, type(mod))
+    if isinstance(mod, ForwardWithControlFlowTest):
+        mod.forward = new_forward
+
+###############################################################################
+# Let's see what the fx graph looks like.
+
+print(torch.export.export(model, (x,), strict=False))  
+
+###############################################################################
+# Let's export again.
+
+onnx_program = torch.onnx.export(model, (x,), dynamo=True)  
+print(onnx_program.model) 
+
+
+###############################################################################
+# We can optimize the model and get rid of the model local functions created to capture the control flow branches.  
+
+onnx_program.optimize()  
+print(onnx_program.model)  
+
+###############################################################################
+# Conclusion
+# ----------
+#
+# This tutorial demonstrates the challenges of exporting models with conditional
+# logic to ONNX and presents a practical solution using torch.cond.
+# While the default exporters may fail or produce imperfect graphs, refactoring the
+# model's logic ensures compatibility and generates a faithful ONNX representation.
+#
+# By understanding these techniques, we can overcome common pitfalls when
+# working with control flow in PyTorch models and ensure smooth integration with ONNX workflows.
+#
+# Further reading
+# ---------------
+#
+# The list below refers to tutorials that ranges from basic examples to advanced scenarios,
+# not necessarily in the order they are listed.
+# Feel free to jump directly to specific topics of your interest or
+# sit tight and have fun going through all of them to learn all there is about the ONNX exporter.
+#
+# .. include:: /beginner_source/onnx/onnx_toc.txt
+#
+# .. toctree::
+#    :hidden:

beginner_source/onnx/export_simple_model_to_onnx_tutorial.py

@@ -2,26 +2,27 @@
 """
 `Introduction to ONNX <intro_onnx.html>`_ ||
 **Exporting a PyTorch model to ONNX** ||
-`Extending the ONNX Registry <onnx_registry_tutorial.html>`_
+`Extending the ONNX exporter operator support <onnx_registry_tutorial.html>`_ ||
+`Export a model with control flow to ONNX <export_control_flow_model_to_onnx_tutorial.html>`_
 
 Export a PyTorch model to ONNX
 ==============================
 
-**Author**: `Ti-Tai Wang <https://github.com/titaiwangms>`_ and `Xavier Dupré <https://github.com/xadupre>`_
+**Author**: `Ti-Tai Wang <https://github.com/titaiwangms>`_, Justin Chu ([email protected]) and Thiago Crepaldi <https://github.com/thiagocrepaldi>`_.
 
 .. note::
-    As of PyTorch 2.1, there are two versions of ONNX Exporter.
+    As of PyTorch 2.5, there are two versions of ONNX Exporter.
 
-    * ``torch.onnx.dynamo_export`` is the newest (still in beta) exporter based on the TorchDynamo technology released with PyTorch 2.0
-    * ``torch.onnx.export`` is based on TorchScript backend and has been available since PyTorch 1.2.0
+    * ``torch.onnx.export(..., dynamo=True)`` is the newest (still in beta) exporter using ``torch.export`` and Torch FX to capture the graph. It was released with PyTorch 2.5
+    * ``torch.onnx.export`` uses TorchScript and has been available since PyTorch 1.2.0
 
 """
 
 ###############################################################################
 # In the `60 Minute Blitz <https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`_,
 # we had the opportunity to learn about PyTorch at a high level and train a small neural network to classify images.
 # In this tutorial, we are going to expand this to describe how to convert a model defined in PyTorch into the
-# ONNX format using TorchDynamo and the ``torch.onnx.dynamo_export`` ONNX exporter.
+# ONNX format using the ``torch.onnx.export(..., dynamo=True)`` ONNX exporter.
 #
 # While PyTorch is great for iterating on the development of models, the model can be deployed to production
 # using different formats, including `ONNX <https://onnx.ai/>`_ (Open Neural Network Exchange)!
@@ -47,8 +48,7 @@
 #
 #  .. code-block:: bash
 #
-#   pip install onnx
-#   pip install onnxscript
+#   pip install --upgrade onnx onnxscript
 #
 # 2. Author a simple image classifier model
 # -----------------------------------------
@@ -62,17 +62,16 @@
 import torch.nn.functional as F
 
 
-class MyModel(nn.Module):
-
+class ImageClassifierModel(nn.Module):
     def __init__(self):
-        super(MyModel, self).__init__()
+        super().__init__()
         self.conv1 = nn.Conv2d(1, 6, 5)
         self.conv2 = nn.Conv2d(6, 16, 5)
         self.fc1 = nn.Linear(16 * 5 * 5, 120)
         self.fc2 = nn.Linear(120, 84)
         self.fc3 = nn.Linear(84, 10)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor):
         x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
         x = F.max_pool2d(F.relu(self.conv2(x)), 2)
         x = torch.flatten(x, 1)
@@ -81,16 +80,27 @@ def forward(self, x):
         x = self.fc3(x)
         return x
 
+
 ######################################################################
 # 3. Export the model to ONNX format
 # ----------------------------------
 #
 # Now that we have our model defined, we need to instantiate it and create a random 32x32 input.
 # Next, we can export the model to ONNX format.
 
-torch_model = MyModel()
-torch_input = torch.randn(1, 1, 32, 32)
-onnx_program = torch.onnx.dynamo_export(torch_model, torch_input)
+torch_model = ImageClassifierModel()
+# Create example inputs for exporting the model. The inputs should be a tuple of tensors.
+example_inputs = (torch.randn(1, 1, 32, 32),)
+onnx_program = torch.onnx.export(torch_model, example_inputs, dynamo=True)
+
+######################################################################
+# 3.5. (Optional) Optimize the ONNX model
+# ---------------------------------------
+#
+# The ONNX model can be optimized with constant folding, and elimination of redundant nodes.
+# The optimization is done in-place, so the original ONNX model is modified.
+
+onnx_program.optimize()
 
 ######################################################################
 # As we can see, we didn't need any code change to the model.
@@ -102,13 +112,14 @@ def forward(self, x):
 # Although having the exported model loaded in memory is useful in many applications,
 # we can save it to disk with the following code:
 
-onnx_program.save("my_image_classifier.onnx")
+onnx_program.save("image_classifier_model.onnx")
 
 ######################################################################
 # You can load the ONNX file back into memory and check if it is well formed with the following code:
 
 import onnx
-onnx_model = onnx.load("my_image_classifier.onnx")
+
+onnx_model = onnx.load("image_classifier_model.onnx")
 onnx.checker.check_model(onnx_model)
 
 ######################################################################
@@ -124,7 +135,7 @@ def forward(self, x):
 #   :align: center
 #
 #
-# Once Netron is open, we can drag and drop our ``my_image_classifier.onnx`` file into the browser or select it after
+# Once Netron is open, we can drag and drop our ``image_classifier_model.onnx`` file into the browser or select it after
 # clicking the **Open model** button.
 #
 # .. image:: ../../_static/img/onnx/image_classifier_onnx_model_on_netron_web_ui.png
@@ -155,16 +166,15 @@ def forward(self, x):
 
 import onnxruntime
 
-onnx_input = [torch_input]
-print(f"Input length: {len(onnx_input)}")
-print(f"Sample input: {onnx_input}")
+onnx_inputs = [tensor.numpy(force=True) for tensor in example_inputs]
+print(f"Input length: {len(onnx_inputs)}")
+print(f"Sample input: {onnx_inputs}")
 
-ort_session = onnxruntime.InferenceSession("./my_image_classifier.onnx", providers=['CPUExecutionProvider'])
+ort_session = onnxruntime.InferenceSession(
+    "./image_classifier_model.onnx", providers=["CPUExecutionProvider"]
+)
 
-def to_numpy(tensor):
-    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
-
-onnxruntime_input = {k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)}
+onnxruntime_input = {input_arg.name: input_value for input_arg, input_value in zip(ort_session.get_inputs(), onnx_inputs)}
 
 # onnxruntime returns a list of outputs
 onnxruntime_outputs = ort_session.run(None, onnxruntime_input)[0]
@@ -179,7 +189,7 @@ def to_numpy(tensor):
 # For that, we need to execute the PyTorch model with the same input and compare the results with ONNX Runtime's.
 # Before comparing the results, we need to convert the PyTorch's output to match ONNX's format.
 
-torch_outputs = torch_model(torch_input)
+torch_outputs = torch_model(*example_inputs)
 
 assert len(torch_outputs) == len(onnxruntime_outputs)
 for torch_output, onnxruntime_output in zip(torch_outputs, onnxruntime_outputs):
@@ -209,4 +219,4 @@ def to_numpy(tensor):
 #
 # .. toctree::
 #    :hidden:
-#
+#