From 25b7f147d1f10c7907d3678b6d05fecdeba4c996 Mon Sep 17 00:00:00 2001
From: Justin Chu <justinchuby@users.noreply.github.com>
Date: Thu, 23 Jan 2025 16:22:36 -0800
Subject: [PATCH 1/5] Update onnx_registry_tutorial.py

---
 beginner_source/onnx/onnx_registry_tutorial.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/beginner_source/onnx/onnx_registry_tutorial.py b/beginner_source/onnx/onnx_registry_tutorial.py
index 0f64ba9c4d4..5e4ad216b88 100644
--- a/beginner_source/onnx/onnx_registry_tutorial.py
+++ b/beginner_source/onnx/onnx_registry_tutorial.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
 `Introduction to ONNX <intro_onnx.html>`_ ||
 `Exporting a PyTorch model to ONNX <export_simple_model_to_onnx_tutorial.html>`_ ||

From 4ea18e1428b95d9161c846858cde2e4dfe88fe4e Mon Sep 17 00:00:00 2001
From: Justin Chu <justinchuby@users.noreply.github.com>
Date: Fri, 24 Jan 2025 12:52:17 -0800
Subject: [PATCH 2/5] update

---
 .../img/onnx/custom_addandround_function.png  | Bin 34769 -> 0 bytes
 _static/img/onnx/custom_addandround_model.png | Bin 7544 -> 0 bytes
 _static/img/onnx/custom_aten_add_function.png | Bin 16332 -> 0 bytes
 _static/img/onnx/custom_aten_add_model.png    | Bin 8613 -> 0 bytes
 _static/img/onnx/custom_aten_gelu_model.png   | Bin 26439 -> 0 bytes
 .../onnx/onnx_registry_tutorial.py            | 358 ++++++++----------
 6 files changed, 158 insertions(+), 200 deletions(-)
 delete mode 100644 _static/img/onnx/custom_addandround_function.png
 delete mode 100644 _static/img/onnx/custom_addandround_model.png
 delete mode 100644 _static/img/onnx/custom_aten_add_function.png
 delete mode 100644 _static/img/onnx/custom_aten_add_model.png
 delete mode 100644 _static/img/onnx/custom_aten_gelu_model.png

diff --git a/_static/img/onnx/custom_addandround_function.png b/_static/img/onnx/custom_addandround_function.png
deleted file mode 100644
index a0c7000161e9585859c331b73497557814345855..0000000000000000000000000000000000000000
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diff --git a/beginner_source/onnx/onnx_registry_tutorial.py b/beginner_source/onnx/onnx_registry_tutorial.py
index 5e4ad216b88..9f9b028fc42 100644
--- a/beginner_source/onnx/onnx_registry_tutorial.py
+++ b/beginner_source/onnx/onnx_registry_tutorial.py
@@ -1,12 +1,12 @@
 """
 `Introduction to ONNX <intro_onnx.html>`_ ||
 `Exporting a PyTorch model to ONNX <export_simple_model_to_onnx_tutorial.html>`_ ||
-**Extending the ONNX Registry**
+**Extending the ONNX Exporter Operator Support**
 
-Extending the ONNX Registry
-===========================
+Extending the ONNX Exporter Operator Support
+============================================
 
-**Authors:** Ti-Tai Wang (titaiwang@microsoft.com)
+**Authors:** Ti-Tai Wang (titaiwang@microsoft.com), Justin Chu (justinchu@microsoft.com)
 """
 
 
@@ -14,262 +14,211 @@
 # Overview
 # --------
 #
-# This tutorial is an introduction to ONNX registry, which empowers users to implement new ONNX operators
-# or even replace existing operators with a new implementation.
+# This tutorial describes how you can create ONNX implementation for unsupported Torch operators
+# or replace existing implementation with your own.
 #
-# During the model export to ONNX, the PyTorch model is lowered to an intermediate
-# representation composed of `ATen operators <https://pytorch.org/docs/stable/torch.compiler_ir.html>`_.
-# While ATen operators are maintained by PyTorch core team, it is the responsibility of the ONNX exporter team
-# to independently implement each of these operators to ONNX through `ONNX Script <https://onnxscript.ai/>`_.
-# The users can also replace the behavior implemented by the ONNX exporter team with their own implementation
-# to fix bugs or improve performance for a specific ONNX runtime.
+# We will cover three scenarios that require extending the ONNX registry with custom operators:
 #
-# The ONNX Registry manages the mapping between PyTorch operators and the ONNX operators counterparts and provides
-# APIs to extend the registry.
-#
-# In this tutorial, we will cover three scenarios that require extending the ONNX registry with custom operators:
-#
-# * Unsupported ATen operators
+# * Unsupported Torch operators
 # * Custom operators with existing ONNX Runtime support
 # * Custom operators without ONNX Runtime support
 #
-# Unsupported ATen operators
+# Unsupported Torch operators
 # --------------------------
 #
-# Although the ONNX exporter team does their best efforts to support all ATen operators, some of them
+# Although the ONNX exporter team does their best efforts to support all Torch operators, some of them
 # might not be supported yet. In this section, we will demonstrate how you can add
-# unsupported ATen operators to the ONNX Registry.
+# unsupported Torch operators to the ONNX Registry.
 #
 # .. note::
-#       The steps to implement unsupported ATen operators are the same to replace the implementation of an existing
-#       ATen operator with a custom implementation.
-#       Because we don't actually have an unsupported ATen operator to use in this tutorial, we are going to leverage
-#       this and replace the implementation of ``aten::add.Tensor`` with a custom implementation the same way we would
-#       if the operator was not present in the ONNX Registry.
+#       The steps to implement unsupported Torch operators are the same to replace the implementation of an existing
+#       Torch operator with a custom implementation.
+#       Because we don't actually have an unsupported Torch operator to use in this tutorial, we are going to leverage
+#       this and replace the implementation of ``torch.ops.aten.add.Tensor`` with a custom implementation the same way we would
+#       if the operator was not implemented by the ONNX exporter.
 #
 # When a model cannot be exported to ONNX due to an unsupported operator, the ONNX exporter will show an error message
 # similar to:
 #
 # .. code-block:: python
 #
-#   RuntimeErrorWithDiagnostic: Unsupported FX nodes: {'call_function': ['aten.add.Tensor']}.
+#   No decompositions registered for [...]
 #
-# The error message indicates that the fully qualified name of unsupported ATen operator is ``aten::add.Tensor``.
-# The fully qualified name of an operator is composed of the namespace, operator name, and overload following
-# the format ``namespace::operator_name.overload``.
+# The error message indicates that the unsupported Torch operator is ``torch.ops.aten.add.Tensor``.
+# The operator is of type ``<class 'torch._ops.OpOverload'>``, and this operator is what we will use as the
+# target to register our custom implementation.
 #
-# To add support for an unsupported ATen operator or to replace the implementation for an existing one, we need:
+# To add support for an unsupported Torch operator or to replace the implementation for an existing one, we need:
 #
-# * The fully qualified name of the ATen operator (e.g. ``aten::add.Tensor``).
-#   This information is always present in the error message as show above.
+# * The target Torch operator.
 # * The implementation of the operator using `ONNX Script <https://github.com/microsoft/onnxscript>`__.
 #   ONNX Script is a prerequisite for this tutorial. Please make sure you have read the
 #   `ONNX Script tutorial <https://github.com/microsoft/onnxscript/blob/main/docs/tutorial/index.md>`_
 #   before proceeding.
-#
-# Because ``aten::add.Tensor`` is already supported by the ONNX Registry, we will demonstrate how to replace it with a
-# custom implementation, but keep in mind that the same steps apply to support new unsupported ATen operators.
-#
-# This is possible because the :class:`OnnxRegistry` allows users to override an operator registration.
-# We will override the registration of ``aten::add.Tensor`` with our custom implementation and verify it exists.
-#
 
 import torch
 import onnxruntime
 import onnxscript
-from onnxscript import opset18  # opset 18 is the latest (and only) supported version for now
 
-class Model(torch.nn.Module):
-    def forward(self, input_x, input_y):
-        return torch.ops.aten.add(input_x, input_y)  # generates a aten::add.Tensor node
+# Opset 18 is the standard supported version as of PyTorch 2.6
+from onnxscript import opset18 as op
 
-input_add_x = torch.randn(3, 4)
-input_add_y = torch.randn(3, 4)
-aten_add_model = Model()
 
+# Create a model that uses the operator torch.ops.aten.add.Tensor
+class Model(torch.nn.Module):
+    def forward(self, input_x, input_y):
+        return torch.ops.aten.add.Tensor(input_x, input_y)
 
-# Now we create a ONNX Script function that implements ``aten::add.Tensor``.
-# The function name (e.g. ``custom_aten_add``) is displayed in the ONNX graph, so we recommend to use intuitive names.
-custom_aten = onnxscript.values.Opset(domain="custom.aten", version=1)
 
-# NOTE: The function signature must match the signature of the unsupported ATen operator.
-# https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml
+# NOTE: The function signature (including param names) must match the signature of the unsupported Torch operator.
+# https://github.com/pytorch/pytorch/blob/main/aten/src/Torch/native/native_functions.yaml
 # NOTE: All attributes must be annotated with type hints.
-@onnxscript.script(custom_aten)
-def custom_aten_add(input_x, input_y, alpha: float = 1.0):
-    input_y = opset18.Mul(input_y, alpha)
-    return opset18.Add(input_x, input_y)
-
-
-# Now we have everything we need to support unsupported ATen operators.
-# Let's register the ``custom_aten_add`` function to ONNX registry, and export the model to ONNX again.
-onnx_registry = torch.onnx.OnnxRegistry()
-onnx_registry.register_op(
-    namespace="aten", op_name="add", overload="Tensor", function=custom_aten_add
-    )
-print(f"aten::add.Tensor is supported by ONNX registry: \
-      {onnx_registry.is_registered_op(namespace='aten', op_name='add', overload='Tensor')}"
-      )
-export_options = torch.onnx.ExportOptions(onnx_registry=onnx_registry)
-onnx_program = torch.onnx.dynamo_export(
-    aten_add_model, input_add_x, input_add_y, export_options=export_options
-    )
+def custom_aten_add(self, other, alpha: float = 1.0):
+    if alpha != 1.0:
+        alpha = op.CastLike(alpha, other)
+        other = op.Mul(other, alpha)
+    # To distinguish the custom implementation from the builtin one, we switch the order of the inputs
+    return op.Add(other, self)
+
+
+x = torch.tensor([1.0])
+y = torch.tensor([2.0])
+
+# Then we provide the custom implementation to the ONNX exporter as a ``custom_translation_table``.
+onnx_program = torch.onnx.export(
+    Model().eval(),
+    (x, y),
+    dynamo=True,
+    custom_translation_table={
+        torch.ops.aten.add.Tensor: custom_aten_add,
+    },
+)
+# Optimize the ONNX graph to remove redundant nodes
+onnx_program.optimize()
 
 ######################################################################
-# Now let's inspect the model and verify the model has a ``custom_aten_add`` instead of ``aten::add.Tensor``.
-# The graph has one graph node for ``custom_aten_add``, and inside of it there are four function nodes, one for each
-# operator, and one for constant attribute.
-#
-
-# graph node domain is the custom domain we registered
-assert onnx_program.model_proto.graph.node[0].domain == "custom.aten"
-assert len(onnx_program.model_proto.graph.node) == 1
-# graph node name is the function name
-assert onnx_program.model_proto.graph.node[0].op_type == "custom_aten_add"
-# function node domain is empty because we use standard ONNX operators
-assert {node.domain for node in onnx_program.model_proto.functions[0].node} == {""}
-# function node name is the standard ONNX operator name
-assert {node.op_type for node in onnx_program.model_proto.functions[0].node} == {"Add", "Mul", "Constant"}
+# Now let's inspect the model and verify the model is using the custom implementation.
 
+print(onnx_program.model)
 
-######################################################################
-# This is how ``custom_aten_add_model`` looks in the ONNX graph using Netron:
-#
-# .. image:: /_static/img/onnx/custom_aten_add_model.png
-#    :width: 70%
-#    :align: center
+# We get
 #
-# Inside the ``custom_aten_add`` function, we can see the three ONNX nodes we
-# used in the function (``CastLike``, ``Add``, and ``Mul``), and one ``Constant`` attribute:
-#
-# .. image:: /_static/img/onnx/custom_aten_add_function.png
-#    :width: 70%
-#    :align: center
-#
-# This was all that we needed to register the new ATen operator into the ONNX Registry.
-# As an additional step, we can use ONNX Runtime to run the model, and compare the results with PyTorch.
-#
-
-
-# Use ONNX Runtime to run the model, and compare the results with PyTorch
-onnx_program.save("./custom_add_model.onnx")
-ort_session = onnxruntime.InferenceSession(
-    "./custom_add_model.onnx", providers=['CPUExecutionProvider']
-    )
-
-def to_numpy(tensor):
-    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
-
-onnx_input = onnx_program.adapt_torch_inputs_to_onnx(input_add_x, input_add_y)
-onnxruntime_input = {k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)}
-onnxruntime_outputs = ort_session.run(None, onnxruntime_input)
-
-torch_outputs = aten_add_model(input_add_x, input_add_y)
-torch_outputs = onnx_program.adapt_torch_outputs_to_onnx(torch_outputs)
-
-assert len(torch_outputs) == len(onnxruntime_outputs)
-for torch_output, onnxruntime_output in zip(torch_outputs, onnxruntime_outputs):
-    torch.testing.assert_close(torch_output, torch.tensor(onnxruntime_output))
+# .. code-block:: python
+#     <
+#         ir_version=10,
+#         opset_imports={'pkg.onnxscript.torch_lib.common': 1, '': 18},
+#         producer_name='pytorch',
+#         producer_version='2.7.0.dev20250124+cu124',
+#         domain=None,
+#         model_version=None,
+#     >
+#     graph(
+#         name=main_graph,
+#         inputs=(
+#             %"input_x"<FLOAT,[1]>,
+#             %"input_y"<FLOAT,[1]>
+#         ),
+#         outputs=(
+#             %"add"<FLOAT,[1]>
+#         ),
+#     ) {
+#         0 |  # node_Add_0
+#             %"add"<FLOAT,[1]> ⬅️ ::Add(%"input_y", %"input_x")
+#         return %"add"<FLOAT,[1]>
+#     }
+#
+# The translation is using our custom implementation: In node ``node_Add_0``, ``input_y`` now
+# comes first, and ``input_x`` comes second.
+#
+# We can use ONNX Runtime to run the model and verify the results by calling
+# the ONNXProgram directly on the input tensors.
+
+result = onnx_program(x, y)[0]
+torch.testing.assert_close(result, torch.tensor([3.0]))
 
 
 ######################################################################
 # Custom operators with existing ONNX Runtime support
 # ---------------------------------------------------
 #
-# In this case, the user creates a model with standard PyTorch operators, but the ONNX runtime
+# In this case, the user creates a model with standard PyTorch operators, but the runtime
 # (e.g. Microsoft's ONNX Runtime) can provide a custom implementation for that kernel, effectively replacing the
-# existing implementation in the ONNX Registry. Another use case is when the user wants to use a custom implementation
-# of an existing ONNX operator to fix a bug or improve performance of a specific operator.
-# To achieve this, we only need to register the new implementation with the existing ATen fully qualified name.
-#
-# In the following example, we use the ``com.microsoft.Gelu`` from ONNX Runtime,
-# which is not the same ``Gelu`` from ONNX spec. Thus, we register the Gelu with
-# the namespace ``com.microsoft`` and operator name ``Gelu``.
+# existing implementation.
 #
-# Before we begin, let's check whether ``aten::gelu.default`` is really supported by the ONNX registry.
+# In the following example, we use the ``com.microsoft.Gelu`` operator provided by ONNX Runtime,
+# which is not the same ``Gelu`` from ONNX spec.
 
-onnx_registry = torch.onnx.OnnxRegistry()
-print(f"aten::gelu.default is supported by ONNX registry: \
-    {onnx_registry.is_registered_op(namespace='aten', op_name='gelu', overload='default')}")
-
-
-######################################################################
-# In our example, ``aten::gelu.default`` operator is supported by the ONNX registry,
-# so :meth:`onnx_registry.is_registered_op` returns ``True``.
-
-class CustomGelu(torch.nn.Module):
+class GeluModel(torch.nn.Module):
     def forward(self, input_x):
         return torch.ops.aten.gelu(input_x)
 
-# com.microsoft is an official ONNX Runtime namspace
-custom_ort = onnxscript.values.Opset(domain="com.microsoft", version=1)
+# Create a namespace for the custom operator using ONNX Script
+# com.microsoft is an official ONNX Runtime namespace
+microsoft_op = onnxscript.values.Opset(domain="com.microsoft", version=1)
 
-# NOTE: The function signature must match the signature of the unsupported ATen operator.
-# https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml
+# NOTE: The function signature (including param names) must match the signature of the unsupported Torch operator.
+# https://github.com/pytorch/pytorch/blob/main/aten/src/Torch/native/native_functions.yaml
 # NOTE: All attributes must be annotated with type hints.
-@onnxscript.script(custom_ort)
-def custom_aten_gelu(input_x, approximate: str = "none"):
-    # We know com.microsoft::Gelu is supported by ONNX Runtime
-    # It's only not supported by ONNX
-    return custom_ort.Gelu(input_x)
+# The function must be scripted using the ``@onnxscript.script()`` decorator when
+# using operators from custom domains. This may be improved in future versions.
+from onnxscript import FLOAT
 
+@onnxscript.script(microsoft_op)
+def custom_aten_gelu(self: FLOAT, approximate: str = "none") -> FLOAT:
+    return microsoft_op.Gelu(self)
 
-onnx_registry = torch.onnx.OnnxRegistry()
-onnx_registry.register_op(
-    namespace="aten", op_name="gelu", overload="default", function=custom_aten_gelu)
-export_options = torch.onnx.ExportOptions(onnx_registry=onnx_registry)
+onnx_program = torch.onnx.export(
+    GeluModel().eval(),
+    (x,),
+    dynamo=True,
+    custom_translation_table={
+        torch.ops.aten.gelu.default: custom_aten_gelu,
+    },
+)
 
-aten_gelu_model = CustomGelu()
-input_gelu_x = torch.randn(3, 3)
-
-onnx_program = torch.onnx.dynamo_export(
-    aten_gelu_model, input_gelu_x, export_options=export_options
-    )
+# Optimize the ONNX graph to remove redundant nodes
+onnx_program.optimize()
 
 
 ######################################################################
 # Let's inspect the model and verify the model uses op_type ``Gelu``
 # from namespace ``com.microsoft``.
 #
-# .. note::
-#     :func:`custom_aten_gelu` does not exist in the graph because
-#     functions with fewer than three operators are inlined automatically.
-#
-
-# graph node domain is the custom domain we registered
-assert onnx_program.model_proto.graph.node[0].domain == "com.microsoft"
-# graph node name is the function name
-assert onnx_program.model_proto.graph.node[0].op_type == "Gelu"
 
+print(onnx_program.model)
 
-######################################################################
-# The following diagram shows ``custom_aten_gelu_model`` ONNX graph using Netron,
-# we can see the ``Gelu`` node from module ``com.microsoft`` used in the function:
-#
-# .. image:: /_static/img/onnx/custom_aten_gelu_model.png
+# We get
 #
-# That is all we need to do. As an additional step, we can use ONNX Runtime to run the model,
-# and compare the results with PyTorch.
-#
-
-onnx_program.save("./custom_gelu_model.onnx")
-ort_session = onnxruntime.InferenceSession(
-    "./custom_gelu_model.onnx", providers=['CPUExecutionProvider']
-    )
+# .. code-block:: python
+#     <
+#         ir_version=10,
+#         opset_imports={'pkg.onnxscript.torch_lib.common': 1, 'com.microsoft': 1, '': 18},
+#         producer_name='pytorch',
+#         producer_version='2.7.0.dev20250124+cu124',
+#         domain=None,
+#         model_version=None,
+#     >
+#     graph(
+#         name=main_graph,
+#         inputs=(
+#             %"input_x"<FLOAT,[1]>
+#         ),
+#         outputs=(
+#             %"gelu"<FLOAT,[1]>
+#         ),
+#     ) {
+#         0 |  # n0
+#              %"gelu"<FLOAT,[1]> ⬅️ com.microsoft::Gelu(%"input_x")
+#         return %"gelu"<FLOAT,[1]>
+#     }
 
-def to_numpy(tensor):
-    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
 
-onnx_input = onnx_program.adapt_torch_inputs_to_onnx(input_gelu_x)
-onnxruntime_input = {k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)}
-onnxruntime_outputs = ort_session.run(None, onnxruntime_input)
+######################################################################
+# Similar to the previous example, we can use ONNX Runtime to run the model and verify the results.
 
-torch_outputs = aten_gelu_model(input_gelu_x)
-torch_outputs = onnx_program.adapt_torch_outputs_to_onnx(torch_outputs)
+result = onnx_program(x)[0]
+torch.testing.assert_close(result, torch.ops.aten.gelu(x))
 
-assert len(torch_outputs) == len(onnxruntime_outputs)
-for torch_output, onnxruntime_output in zip(torch_outputs, onnxruntime_outputs):
-    torch.testing.assert_close(torch_output, torch.tensor(onnxruntime_output))
 
 ######################################################################
 # Custom operators without ONNX Runtime support
@@ -298,24 +247,29 @@ def to_numpy(tensor):
 # NOTE: This is a beta feature in PyTorch, and is subject to change.
 from torch._custom_op import impl as custom_op
 
+
 @custom_op.custom_op("mylibrary::addandround_op")
-def addandround_op(tensor_x: torch.Tensor) -> torch.Tensor:
-    ...
+def addandround_op(tensor_x: torch.Tensor) -> torch.Tensor: ...
+
 
 @addandround_op.impl_abstract()
 def addandround_op_impl_abstract(tensor_x):
     return torch.empty_like(tensor_x)
 
+
 @addandround_op.impl("cpu")
 def addandround_op_impl(tensor_x):
     return torch.round(tensor_x + tensor_x)  # add x to itself, and round the result
 
+
 torch._dynamo.allow_in_graph(addandround_op)
 
+
 class CustomFoo(torch.nn.Module):
     def forward(self, tensor_x):
         return addandround_op(tensor_x)
 
+
 input_addandround_x = torch.randn(3)
 custom_addandround_model = CustomFoo()
 
@@ -335,8 +289,9 @@ def forward(self, tensor_x):
 
 custom_opset = onnxscript.values.Opset(domain="test.customop", version=1)
 
-# NOTE: The function signature must match the signature of the unsupported ATen operator.
-# https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml
+
+# NOTE: The function signature must match the signature of the unsupported Torch operator.
+# https://github.com/pytorch/pytorch/blob/main/aten/src/Torch/native/native_functions.yaml
 # NOTE: All attributes must be annotated with type hints.
 @onnxscript.script(custom_opset)
 def custom_addandround(input_x):
@@ -350,13 +305,16 @@ def custom_addandround(input_x):
 
 onnx_registry = torch.onnx.OnnxRegistry()
 onnx_registry.register_op(
-    namespace="mylibrary", op_name="addandround_op", overload="default", function=custom_addandround
-    )
+    namespace="mylibrary",
+    op_name="addandround_op",
+    overload="default",
+    function=custom_addandround,
+)
 
 export_options = torch.onnx.ExportOptions(onnx_registry=onnx_registry)
 onnx_program = torch.onnx.dynamo_export(
     custom_addandround_model, input_addandround_x, export_options=export_options
-    )
+)
 onnx_program.save("./custom_addandround_model.onnx")
 
 
@@ -436,7 +394,7 @@ def custom_addandround(input_x):
 # ----------
 #
 # Congratulations! In this tutorial, we explored the :class:`ONNXRegistry` API and
-# discovered how to create custom implementations for unsupported or existing ATen operators
+# discovered how to create custom implementations for unsupported or existing Torch operators
 # using ONNX Script.
 # Finally, we leveraged ONNX Runtime to execute the model and compare the results with PyTorch,
 # providing us with a comprehensive understanding of handling unsupported

From 152b8e4e1af2b3ca1f548fbc3af08e22e37cdc6f Mon Sep 17 00:00:00 2001
From: Justin Chu <justinchuby@users.noreply.github.com>
Date: Fri, 24 Jan 2025 13:52:03 -0800
Subject: [PATCH 3/5] done

---
 .../onnx/onnx_registry_tutorial.py            | 265 +++++++-----------
 1 file changed, 102 insertions(+), 163 deletions(-)

diff --git a/beginner_source/onnx/onnx_registry_tutorial.py b/beginner_source/onnx/onnx_registry_tutorial.py
index 9f9b028fc42..5cfb7635d00 100644
--- a/beginner_source/onnx/onnx_registry_tutorial.py
+++ b/beginner_source/onnx/onnx_registry_tutorial.py
@@ -14,26 +14,26 @@
 # Overview
 # --------
 #
-# This tutorial describes how you can create ONNX implementation for unsupported Torch operators
+# This tutorial describes how you can create ONNX implementation for unsupported PyTorch operators
 # or replace existing implementation with your own.
 #
-# We will cover three scenarios that require extending the ONNX registry with custom operators:
+# We will cover three scenarios that require extending the ONNX exporter's operator support:
 #
-# * Unsupported Torch operators
-# * Custom operators with existing ONNX Runtime support
-# * Custom operators without ONNX Runtime support
+# * Overriding the implementation of an existing PyTorch operator
+# * Using custom ONNX operators
+# * Supporting a custom PyTorch operator
 #
-# Unsupported Torch operators
-# --------------------------
+# Overriding the implementation of an existing PyTorch operator
+# -------------------------------------------------------------
 #
-# Although the ONNX exporter team does their best efforts to support all Torch operators, some of them
+# Although the ONNX exporter team does their best efforts to support all PyTorch operators, some of them
 # might not be supported yet. In this section, we will demonstrate how you can add
-# unsupported Torch operators to the ONNX Registry.
+# unsupported PyTorch operators to the ONNX Registry.
 #
 # .. note::
-#       The steps to implement unsupported Torch operators are the same to replace the implementation of an existing
-#       Torch operator with a custom implementation.
-#       Because we don't actually have an unsupported Torch operator to use in this tutorial, we are going to leverage
+#       The steps to implement unsupported PyTorch operators are the same to replace the implementation of an existing
+#       PyTorch operator with a custom implementation.
+#       Because we don't actually have an unsupported PyTorch operator to use in this tutorial, we are going to leverage
 #       this and replace the implementation of ``torch.ops.aten.add.Tensor`` with a custom implementation the same way we would
 #       if the operator was not implemented by the ONNX exporter.
 #
@@ -44,13 +44,13 @@
 #
 #   No decompositions registered for [...]
 #
-# The error message indicates that the unsupported Torch operator is ``torch.ops.aten.add.Tensor``.
+# The error message indicates that the unsupported PyTorch operator is ``torch.ops.aten.add.Tensor``.
 # The operator is of type ``<class 'torch._ops.OpOverload'>``, and this operator is what we will use as the
 # target to register our custom implementation.
 #
-# To add support for an unsupported Torch operator or to replace the implementation for an existing one, we need:
+# To add support for an unsupported PyTorch operator or to replace the implementation for an existing one, we need:
 #
-# * The target Torch operator.
+# * The target PyTorch operator.
 # * The implementation of the operator using `ONNX Script <https://github.com/microsoft/onnxscript>`__.
 #   ONNX Script is a prerequisite for this tutorial. Please make sure you have read the
 #   `ONNX Script tutorial <https://github.com/microsoft/onnxscript/blob/main/docs/tutorial/index.md>`_
@@ -70,8 +70,8 @@ def forward(self, input_x, input_y):
         return torch.ops.aten.add.Tensor(input_x, input_y)
 
 
-# NOTE: The function signature (including param names) must match the signature of the unsupported Torch operator.
-# https://github.com/pytorch/pytorch/blob/main/aten/src/Torch/native/native_functions.yaml
+# NOTE: The function signature (including param names) must match the signature of the unsupported PyTorch operator.
+# https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml
 # NOTE: All attributes must be annotated with type hints.
 def custom_aten_add(self, other, alpha: float = 1.0):
     if alpha != 1.0:
@@ -101,6 +101,7 @@ def custom_aten_add(self, other, alpha: float = 1.0):
 
 print(onnx_program.model)
 
+######################################################################
 # We get
 #
 # .. code-block:: python
@@ -138,8 +139,8 @@ def custom_aten_add(self, other, alpha: float = 1.0):
 
 
 ######################################################################
-# Custom operators with existing ONNX Runtime support
-# ---------------------------------------------------
+# Using custom ONNX operators
+# ---------------------------
 #
 # In this case, the user creates a model with standard PyTorch operators, but the runtime
 # (e.g. Microsoft's ONNX Runtime) can provide a custom implementation for that kernel, effectively replacing the
@@ -148,25 +149,29 @@ def custom_aten_add(self, other, alpha: float = 1.0):
 # In the following example, we use the ``com.microsoft.Gelu`` operator provided by ONNX Runtime,
 # which is not the same ``Gelu`` from ONNX spec.
 
+
 class GeluModel(torch.nn.Module):
     def forward(self, input_x):
         return torch.ops.aten.gelu(input_x)
 
+
 # Create a namespace for the custom operator using ONNX Script
 # com.microsoft is an official ONNX Runtime namespace
 microsoft_op = onnxscript.values.Opset(domain="com.microsoft", version=1)
 
-# NOTE: The function signature (including param names) must match the signature of the unsupported Torch operator.
-# https://github.com/pytorch/pytorch/blob/main/aten/src/Torch/native/native_functions.yaml
+# NOTE: The function signature (including param names) must match the signature of the unsupported PyTorch operator.
+# https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml
 # NOTE: All attributes must be annotated with type hints.
 # The function must be scripted using the ``@onnxscript.script()`` decorator when
 # using operators from custom domains. This may be improved in future versions.
 from onnxscript import FLOAT
 
+
 @onnxscript.script(microsoft_op)
 def custom_aten_gelu(self: FLOAT, approximate: str = "none") -> FLOAT:
     return microsoft_op.Gelu(self)
 
+
 onnx_program = torch.onnx.export(
     GeluModel().eval(),
     (x,),
@@ -187,6 +192,7 @@ def custom_aten_gelu(self: FLOAT, approximate: str = "none") -> FLOAT:
 
 print(onnx_program.model)
 
+######################################################################
 # We get
 #
 # .. code-block:: python
@@ -221,181 +227,114 @@ def custom_aten_gelu(self: FLOAT, approximate: str = "none") -> FLOAT:
 
 
 ######################################################################
-# Custom operators without ONNX Runtime support
-# ---------------------------------------------
-#
-# In this case, the operator is not supported by any ONNX runtime, but we
-# would like to use it as custom operator in ONNX graph. Therefore, we need to implement
-# the operator in three places:
+# Supporting a custom PyTorch operator
+# ------------------------------------
 #
-# 1. PyTorch FX graph
-# 2. ONNX Registry
-# 3. ONNX Runtime
+# In this case, the operator is an operator that is user implemented and registered to PyTorch.
 #
 # In the following example, we would like to use a custom operator
 # that takes one tensor input, and returns one output. The operator adds
 # the input to itself, and returns the rounded result.
 #
-#
-# Custom Ops Registration in PyTorch FX Graph (Beta)
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-#
-# Firstly, we need to implement the operator in PyTorch FX graph.
-# This can be done by using ``torch._custom_op``.
-#
+# Firstly, we assume the custom operator is implemented and registered with ``torch.library.custom_op()``.
+# You can refer to `Creating new custom ops in Python <https://pytorch.org/docs/stable/library.html#torch.library.custom_op>`_
+# for a detailed guide on how to create custom operators.
 
-# NOTE: This is a beta feature in PyTorch, and is subject to change.
-from torch._custom_op import impl as custom_op
 
+# Define and use the operator in PyTorch
+@torch.library.custom_op("mylibrary::add_and_round_op", mutates_args=())
+def add_and_round_op(input: torch.Tensor) -> torch.Tensor:
+    return torch.round(input + input)
 
-@custom_op.custom_op("mylibrary::addandround_op")
-def addandround_op(tensor_x: torch.Tensor) -> torch.Tensor: ...
 
-
-@addandround_op.impl_abstract()
-def addandround_op_impl_abstract(tensor_x):
+@add_and_round_op.register_fake
+def _add_and_round_op_fake(tensor_x):
     return torch.empty_like(tensor_x)
 
 
-@addandround_op.impl("cpu")
-def addandround_op_impl(tensor_x):
-    return torch.round(tensor_x + tensor_x)  # add x to itself, and round the result
-
-
-torch._dynamo.allow_in_graph(addandround_op)
-
-
-class CustomFoo(torch.nn.Module):
-    def forward(self, tensor_x):
-        return addandround_op(tensor_x)
+class AddAndRoundModel(torch.nn.Module):
+    def forward(self, input):
+        return add_and_round_op(input)
 
 
-input_addandround_x = torch.randn(3)
-custom_addandround_model = CustomFoo()
+# Implement the custom operator in ONNX using ONNX Script
+def onnx_add_and_round(input):
+    return op.Round(op.Add(input, input))
 
 
-######################################################################
-#
-# Custom Ops Registration in ONNX Registry
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-#
-# For the step 2 and 3, we need to implement the operator in ONNX registry.
-# In this example, we will implement the operator in ONNX registry
-# with the namespace ``test.customop`` and operator name ``CustomOpOne``,
-# and ``CustomOpTwo``. These two ops are registered and built in
-# `cpu_ops.cc <https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/testdata/custom_op_library/cpu/cpu_ops.cc>`__.
-#
-
-
-custom_opset = onnxscript.values.Opset(domain="test.customop", version=1)
-
-
-# NOTE: The function signature must match the signature of the unsupported Torch operator.
-# https://github.com/pytorch/pytorch/blob/main/aten/src/Torch/native/native_functions.yaml
-# NOTE: All attributes must be annotated with type hints.
-@onnxscript.script(custom_opset)
-def custom_addandround(input_x):
-    # The same as opset18.Add(x, x)
-    add_x = custom_opset.CustomOpOne(input_x, input_x)
-    # The same as opset18.Round(x, x)
-    round_x = custom_opset.CustomOpTwo(add_x)
-    # Cast to FLOAT to match the ONNX type
-    return opset18.Cast(round_x, to=1)
-
-
-onnx_registry = torch.onnx.OnnxRegistry()
-onnx_registry.register_op(
-    namespace="mylibrary",
-    op_name="addandround_op",
-    overload="default",
-    function=custom_addandround,
-)
-
-export_options = torch.onnx.ExportOptions(onnx_registry=onnx_registry)
-onnx_program = torch.onnx.dynamo_export(
-    custom_addandround_model, input_addandround_x, export_options=export_options
+onnx_program = torch.onnx.export(
+    AddAndRoundModel().eval(),
+    (x,),
+    dynamo=True,
+    custom_translation_table={
+        torch.ops.mylibrary.add_and_round_op.default: onnx_add_and_round,
+    },
 )
-onnx_program.save("./custom_addandround_model.onnx")
-
-
-######################################################################
-# The ``onnx_program`` exposes the exported model as protobuf through ``onnx_program.model_proto``.
-# The graph has one graph nodes for ``custom_addandround``, and inside ``custom_addandround``,
-# there are two function nodes, one for each operator.
-#
-
-assert onnx_program.model_proto.graph.node[0].domain == "test.customop"
-assert onnx_program.model_proto.graph.node[0].op_type == "custom_addandround"
-assert onnx_program.model_proto.functions[0].node[0].domain == "test.customop"
-assert onnx_program.model_proto.functions[0].node[0].op_type == "CustomOpOne"
-assert onnx_program.model_proto.functions[0].node[1].domain == "test.customop"
-assert onnx_program.model_proto.functions[0].node[1].op_type == "CustomOpTwo"
 
+# Optimize the ONNX graph to remove redundant nodes
+onnx_program.optimize()
+print(onnx_program)
 
 ######################################################################
-# This is how ``custom_addandround_model`` ONNX graph looks using Netron:
-#
-# .. image:: /_static/img/onnx/custom_addandround_model.png
-#    :width: 70%
-#    :align: center
-#
-# Inside the ``custom_addandround`` function, we can see the two custom operators we
-# used in the function (``CustomOpOne``, and ``CustomOpTwo``), and they are from module
-# ``test.customop``:
-#
-# .. image:: /_static/img/onnx/custom_addandround_function.png
-#
-# Custom Ops Registration in ONNX Runtime
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-#
-# To link your custom op library to ONNX Runtime, you need to
-# compile your C++ code into a shared library and link it to ONNX Runtime.
-# Follow the instructions below:
-#
-# 1. Implement your custom op in C++ by following
-#    `ONNX Runtime instructions <`https://github.com/microsoft/onnxruntime/blob/gh-pages/docs/reference/operators/add-custom-op.md>`__.
-# 2. Download ONNX Runtime source distribution from
-#    `ONNX Runtime releases <https://github.com/microsoft/onnxruntime/releases>`__.
-# 3. Compile and link your custom op library to ONNX Runtime, for example:
-#
-# .. code-block:: bash
-#
-#    $ gcc -shared -o libcustom_op_library.so custom_op_library.cc -L /path/to/downloaded/ort/lib/ -lonnxruntime -fPIC
-#
-# 4. Run the model with ONNX Runtime Python API and compare the results with PyTorch.
+# We get
 #
 # .. code-block:: python
+#     <
+#         ir_version=10,
+#         opset_imports={'pkg.onnxscript.torch_lib.common': 1, '': 18},
+#         producer_name='pytorch',
+#         producer_version='2.7.0.dev20250124+cu124',
+#         domain=None,
+#         model_version=None,
+#     >
+#     graph(
+#         name=main_graph,
+#         inputs=(
+#             %"input"<FLOAT,[1]>
+#         ),
+#         outputs=(
+#             %"add_and_round_op"<FLOAT,[1]>
+#         ),
+#     ) {
+#         0 |  # node_Add_0
+#             %"val_0"<FLOAT,[1]> ⬅️ ::Add(%"input", %"input")
+#         1 |  # node_Round_1
+#             %"add_and_round_op"<FLOAT,[1]> ⬅️ ::Round(%"val_0")
+#         return %"add_and_round_op"<FLOAT,[1]>
+#     }
 #
-#    ort_session_options = onnxruntime.SessionOptions()
-#
-#    # NOTE: Link the custom op library to ONNX Runtime and replace the path
-#    # with the path to your custom op library
-#    ort_session_options.register_custom_ops_library(
-#        "/path/to/libcustom_op_library.so"
-#    )
-#    ort_session = onnxruntime.InferenceSession(
-#        "./custom_addandround_model.onnx", providers=['CPUExecutionProvider'], sess_options=ort_session_options)
+# And exported program
 #
-#    def to_numpy(tensor):
-#        return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
+# .. code-block:: python
+#     ExportedProgram:
+#         class GraphModule(torch.nn.Module):
+#             def forward(self, input: "f32[1]"):
+#                 input_1 = input
 #
-#    onnx_input = onnx_program.adapt_torch_inputs_to_onnx(input_addandround_x)
-#    onnxruntime_input = {k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)}
-#    onnxruntime_outputs = ort_session.run(None, onnxruntime_input)
+#                 add_and_round_op: "f32[1]" = torch.ops.mylibrary.add_and_round_op.default(input_1);  input_1 = None
+#                 return (add_and_round_op,)
 #
-#    torch_outputs = custom_addandround_model(input_addandround_x)
-#    torch_outputs = onnx_program.adapt_torch_outputs_to_onnx(torch_outputs)
+#     Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='input'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_and_round_op'), target=None)])
+#     Range constraints: {}
 #
-#    assert len(torch_outputs) == len(onnxruntime_outputs)
-#    for torch_output, onnxruntime_output in zip(torch_outputs, onnxruntime_outputs):
-#        torch.testing.assert_close(torch_output, torch.tensor(onnxruntime_output))
+# The translation is using our custom implementation to translate the ``torch.ops.mylibrary.add_and_round_op.default``
+# operator in the ExportedProgram to the ONNX operator ``Add`` and ``Round``.
 #
+
+######################################################################
+# Finally we verify the results.
+
+result = onnx_program(x)[0]
+torch.testing.assert_close(result, add_and_round_op(x))
+
+######################################################################
 # Conclusion
 # ----------
 #
-# Congratulations! In this tutorial, we explored the :class:`ONNXRegistry` API and
-# discovered how to create custom implementations for unsupported or existing Torch operators
+# Congratulations! In this tutorial, we explored the ``custom_translation_table`` option and
+# discovered how to create custom implementations for unsupported or existing PyTorch operators
 # using ONNX Script.
+#
 # Finally, we leveraged ONNX Runtime to execute the model and compare the results with PyTorch,
 # providing us with a comprehensive understanding of handling unsupported
 # operators in the ONNX ecosystem.

From 6a70faf346ea7ae8e705758193a5657560f53871 Mon Sep 17 00:00:00 2001
From: Justin Chu <justinchuby@users.noreply.github.com>
Date: Fri, 24 Jan 2025 13:55:56 -0800
Subject: [PATCH 4/5] you

---
 beginner_source/onnx/onnx_registry_tutorial.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/beginner_source/onnx/onnx_registry_tutorial.py b/beginner_source/onnx/onnx_registry_tutorial.py
index 5cfb7635d00..8c9bb2770f0 100644
--- a/beginner_source/onnx/onnx_registry_tutorial.py
+++ b/beginner_source/onnx/onnx_registry_tutorial.py
@@ -142,7 +142,7 @@ def custom_aten_add(self, other, alpha: float = 1.0):
 # Using custom ONNX operators
 # ---------------------------
 #
-# In this case, the user creates a model with standard PyTorch operators, but the runtime
+# In this case, you create a model with standard PyTorch operators, but the runtime
 # (e.g. Microsoft's ONNX Runtime) can provide a custom implementation for that kernel, effectively replacing the
 # existing implementation.
 #

From 2aae03039f9109cbf5b68cee9e0d22a1dba56e3f Mon Sep 17 00:00:00 2001
From: Justin Chu <justinchuby@users.noreply.github.com>
Date: Fri, 24 Jan 2025 13:57:55 -0800
Subject: [PATCH 5/5] we

---
 beginner_source/onnx/onnx_registry_tutorial.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/beginner_source/onnx/onnx_registry_tutorial.py b/beginner_source/onnx/onnx_registry_tutorial.py
index 8c9bb2770f0..8266f912a13 100644
--- a/beginner_source/onnx/onnx_registry_tutorial.py
+++ b/beginner_source/onnx/onnx_registry_tutorial.py
@@ -142,7 +142,7 @@ def custom_aten_add(self, other, alpha: float = 1.0):
 # Using custom ONNX operators
 # ---------------------------
 #
-# In this case, you create a model with standard PyTorch operators, but the runtime
+# In this case, we create a model with standard PyTorch operators, but the runtime
 # (e.g. Microsoft's ONNX Runtime) can provide a custom implementation for that kernel, effectively replacing the
 # existing implementation.
 #