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# TorchDynamo-based ONNX Exporter

Warning

The ONNX exporter for TorchDynamo is a rapidly evolving beta technology.

## Overview

The ONNX exporter leverages TorchDynamo engine to hook into Python’s frame evaluation API and dynamically rewrite its bytecode into an FX Graph. The resulting FX Graph is then polished before it is finally translated into an ONNX graph.

The main advantage of this approach is that the FX graph is captured using bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.

The exporter is designed to be modular and extensible. It is composed of the following components:

> - ONNX Exporter: `Exporter` main class that orchestrates the export process.

> - ONNX Export Options: `ExportOptions` has a set of options that control the export process.

> - ONNX Registry: `OnnxRegistry` is the registry of ONNX operators and functions.

> - FX Graph Extractor: `FXGraphExtractor` extracts the FX graph from the PyTorch model.

> - Fake Mode: `ONNXFakeContext` is a context manager that enables fake mode for large scale models.

> - ONNX Export Output: `ExportOutput` is the output of the exporter that contains the exported ONNX graph and diagnostics.

> - ONNX Export Output Serializer: `ExportOutputSerializer` serializes the exported model to a file.

> - ONNX Diagnostic Options: `DiagnosticOptions` has a set of options that control the diagnostics emitted by the exporter.

## Dependencies

The ONNX exporter depends on extra Python packages:

> - ONNX

> - ONNX Script

They can be installed through pip:

    pip install --upgrade onnx onnxscript

## A simple example

See below a demonstration of exporter API in action with a simple Multilayer Perceptron (MLP) as example:

    import torch

    class MLPModel(nn.Module):
      def __init__(self):
          super().__init__()
          self.fc0 = nn.Linear(8, 8, bias=True)
          self.fc1 = nn.Linear(8, 4, bias=True)
          self.fc2 = nn.Linear(4, 2, bias=True)
          self.fc3 = nn.Linear(2, 2, bias=True)

      def forward(self, tensor_x: torch.Tensor):
          tensor_x = self.fc0(tensor_x)
          tensor_x = torch.sigmoid(tensor_x)
          tensor_x = self.fc1(tensor_x)
          tensor_x = torch.sigmoid(tensor_x)
          tensor_x = self.fc2(tensor_x)
          tensor_x = torch.sigmoid(tensor_x)
          output = self.fc3(tensor_x)
          return output

    model = MLPModel()
    tensor_x = torch.rand((97, 8), dtype=torch.float32)
    export_output = torch.onnx.dynamo_export(model, tensor_x)

As the code above shows, all you need is to provide `torch.onnx.dynamo_export()` with an instance of the model and its input. The exporter will then return an instance of `torch.onnx.ExportOutput` that contains the exported ONNX graph along with extra information.

The in-memory model available through `export_output.model_proto` is an `onnx.ModelProto` object in compliance with the ONNX IR spec. The ONNX model may then be serialized into a Protobuf file using the `torch.onnx.ExportOutput.save()` API.

    export_output.save("mlp.onnx")

## Inspecting the ONNX model using GUI

You can view the exported model using Netron.

Note that each layer is represented in a rectangular box with a f icon in the top right corner.

By expanding it, the function body is shown.

The function body is a sequence of ONNX operators or other functions.

## Diagnosing issues with SARIF

ONNX diagnostics goes beyond regular logs through the adoption of Static Analysis Results Interchange Format (aka SARIF) to help users debug and improve their model using a GUI, such as Visual Studio Code’s SARIF Viewer.

The main advantages are:

> - The diagnostics are emitted in machine parseable Static Analysis Results Interchange Format (SARIF).

> - A new clearer, structured way to add new and keep track of diagnostic rules.

> - Serve as foundation for more future improvements consuming the diagnostics.

ONNX Diagnostic SARIF Rules

- FXE0007:fx-graph-to-onnx
- FXE0008:fx-node-to-onnx
- FXE0010:fx-pass
- FXE0011:no-symbolic-function-for-call-function
- FXE0012:unsupported-fx-node-analysis
- FXE0013:op-level-debugging
- FXE0014:find-opschema-matched-symbolic-function
- FXE0015:fx-node-insert-type-promotion
- FXE0016:find-operator-overloads-in-onnx-registry

## API Reference

torch.onnx.dynamo_export(model, /, *model_args, export_options=None, **model_kwargs)

  Export a torch.nn.Module to an ONNX graph.

  Parameters:

    - model (Union[Module, Callable]) – The PyTorch model to be exported to ONNX.

    - model_args – Positional inputs to `model`.

    - model_kwargs – Keyword inputs to `model`.

    - export_options (Optional[ExportOptions]) – Options to influence the export to ONNX.

  Returns:

    An in-memory representation of the exported ONNX model.

  Return type:

    ExportOutput

  Example 1 - Simplest export

      class MyModel(torch.nn.Module):
          def __init__(self) -> None:
              super().__init__()
              self.linear = torch.nn.Linear(2, 2)
          def forward(self, x, bias=None):
              out = self.linear(x)
              out = out + bias
              return out
      model = MyModel()
      kwargs = {"bias": 3.}
      args = (torch.randn(2, 2, 2),)
      export_output = torch.onnx.dynamo_export(
          model,
          *args,
          **kwargs).save("my_simple_model.onnx")

  Example 2 - Exporting with dynamic shapes

      # The previous model can be exported with dynamic shapes
      export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
      export_output = torch.onnx.dynamo_export(
          model,
          *args,
          **kwargs,
          export_options=export_options)
      export_output.save("my_dynamic_model.onnx")

  By printing input dynamic dimensions we can see the input shape is no longer (2,2,2)

      >>> print(export_output.model_proto.graph.input[0])
      name: "arg0"
      type {
        tensor_type {
          elem_type: 1
          shape {
            dim {
              dim_param: "arg0_dim_0"
            }
            dim {
              dim_param: "arg0_dim_1"
            }
            dim {
              dim_param: "arg0_dim_2"
            }
          }
        }
      }

class torch.onnx.ExportOptions(*, dynamic_shapes=None, op_level_debug=None, fake_context=None, onnx_registry=None, diagnostic_options=None)

  Options to influence the TorchDynamo ONNX exporter.

  Variables:

    - dynamic_shapes (Optional[bool]) – Shape information hint for input/output tensors. When `None`, the exporter determines the most compatible setting. When `True`, all input shapes are considered dynamic. When `False`, all input shapes are considered static.

    - op_level_debug (Optional[bool]) – Whether to export the model with op-level debug information

    - diagnostic_options (DiagnosticOptions) – The diagnostic options for the exporter.

    - fake_context (Optional[ONNXFakeContext]) – The fake context used for symbolic tracing.

    - onnx_registry (Optional[OnnxRegistry]) – The ONNX registry used to register ATen operators to ONNX functions.

torch.onnx.enable_fake_mode()

  Enable fake mode for the duration of the context.

  Internally it instantiates a `torch._subclasses.fake_tensor.FakeTensorMode` context manager that converts user input and model parameters into `torch._subclasses.fake_tensor.FakeTensor`.

  A `torch._subclasses.fake_tensor.FakeTensor` is a `torch.Tensor` with the ability to run PyTorch code without having to actually do computation through tensors allocated on a `meta` device. Because there is no actual data being allocated on the device, this API allows for exporting large models without the actual memory footprint needed for executing it.

  It is highly recommended to enable fake mode when exporting models that are too large to fit into memory.

  Returns:

    A `ONNXFakeContext` object that must be passed to `dynamo_export()` through the `ExportOptions.fake_context` argument.

  Example:

      # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
      >>> import torch
      >>> import torch.onnx
      >>> class MyModel(torch.nn.Module):  # Dummy model
      ...     def __init__(self) -> None:
      ...         super().__init__()
      ...         self.linear = torch.nn.Linear(2, 2)
      ...     def forward(self, x):
      ...         out = self.linear(x)
      ...         return out
      >>> with torch.onnx.enable_fake_mode() as fake_context:
      ...     my_nn_module = MyModel()
      ...     arg1 = torch.randn(2, 2, 2)  # positional input 1
      >>> export_options = torch.onnx.ExportOptions(fake_context=fake_context)
      >>> export_output = torch.onnx.dynamo_export(
      ...     my_nn_module,
      ...     arg1,
      ...     export_options=export_options
      ... )
      >>> # Saving model WITHOUT initializers
      >>> export_output.save("my_model_without_initializers.onnx")
      >>> # Saving model WITH initializers
      >>> export_output.save("my_model_with_initializers.onnx", model_state_dict=MyModel().state_dict())

  Warning

  This API is experimental and is NOT backward-compatible.

class torch.onnx.ExportOutput(model_proto, input_adapter, output_adapter, diagnostic_context, *, fake_context=None, export_exception=None)

  An in-memory representation of a PyTorch model that has been exported to ONNX.

  adapt_torch_inputs_to_onnx(*model_args, **model_kwargs)

    Converts the PyTorch model inputs to exported ONNX model inputs format.

    Due to design differences, input/output format between PyTorch model and exported ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model, but only flattened tensors are supported by ONNX, etc.

    The actual adapting steps are associated with each individual export. It depends on the PyTorch model, the particular set of model_args and model_kwargs used for the export, and export options.

    This method replays the adapting steps recorded during export.

    Parameters:

      - model_args – The PyTorch model inputs.

      - model_kwargs – The PyTorch model keyword inputs.

    Returns:

      A sequence of tensors converted from PyTorch model inputs.

    Return type:

      Sequence[Union[Tensor, int, float, bool]]

    Example:

        # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
        >>> import torch
        >>> import torch.onnx
        >>> from typing import Dict, Tuple
        >>> def func_with_nested_input_structure(
        ...     x_dict: Dict[str, torch.Tensor],
        ...     y_tuple: Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
        ... ):
        ...     if "a" in x_dict:
        ...         x = x_dict["a"]
        ...     elif "b" in x_dict:
        ...         x = x_dict["b"]
        ...     else:
        ...         x = torch.randn(3)
        ...
        ...     y1, (y2, y3) = y_tuple
        ...
        ...     return x + y1 + y2 + y3
        >>> x_dict = {"a": torch.tensor(1.)}
        >>> y_tuple = (torch.tensor(2.), (torch.tensor(3.), torch.tensor(4.)))
        >>> export_output = torch.onnx.dynamo_export(func_with_nested_input_structure, x_dict, y_tuple)
        >>> print(x_dict, y_tuple)
        {'a': tensor(1.)} (tensor(2.), (tensor(3.), tensor(4.)))
        >>> print(export_output.adapt_torch_inputs_to_onnx(x_dict, y_tuple))
        (tensor(1.), tensor(2.), tensor(3.), tensor(4.))

    Warning

    This API is experimental and is NOT backward-compatible.

  adapt_torch_outputs_to_onnx(model_outputs)

    Converts the PyTorch model outputs to exported ONNX model outputs format.

    Due to design differences, input/output format between PyTorch model and exported ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model, but only flattened tensors are supported by ONNX, etc.

    The actual adapting steps are associated with each individual export. It depends on the PyTorch model, the particular set of model_args and model_kwargs used for the export, and export options.

    This method replays the adapting steps recorded during export.

    Parameters:

      model_outputs (Any) – The PyTorch model outputs.

    Returns:

      PyTorch model outputs in exported ONNX model outputs format.

    Return type:

      Sequence[Union[Tensor, int, float, bool]]

    Example:

        # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
        >>> import torch
        >>> import torch.onnx
        >>> def func_returning_tuples(x, y, z):
        ...     x = x + y
        ...     y = y + z
        ...     z = x + y
        ...     return (x, (y, z))
        >>> x = torch.tensor(1.)
        >>> y = torch.tensor(2.)
        >>> z = torch.tensor(3.)
        >>> export_output = torch.onnx.dynamo_export(func_returning_tuples, x, y, z)
        >>> pt_output = func_returning_tuples(x, y, z)
        >>> print(pt_output)
        (tensor(3.), (tensor(5.), tensor(8.)))
        >>> print(export_output.adapt_torch_outputs_to_onnx(pt_output))
        [tensor(3.), tensor(5.), tensor(8.)]

    Warning

    This API is experimental and is NOT backward-compatible.

  property diagnostic_context: diagnostics.DiagnosticContext

    The diagnostic context associated with the export.

  property fake_context: Optional[ONNXFakeContext]

    The fake context associated with the export.

  property model_proto: onnx.ModelProto

    The exported ONNX model as an `onnx.ModelProto`.

  save(destination, *, model_state_dict=None, serializer=None)

    Saves the in-memory ONNX model to `destination` using specified `serializer`.

    Parameters:

      - destination (Union[str, BufferedIOBase]) – The destination to save the ONNX model. It can be either a string or a file-like object. When used with `model_state_dict`, it must be a string with a full path to the destination. In that case, besides saving the ONNX model, a folder with “_initializers” suffix (without extension) will be created to store the each initializer of the ONNX model in a separate file. For example, if the destination is “/path/model.onnx”, the initializers will be saved in “/path/model_initializers/” folder.

      - model_state_dict (Optional[Union[Dict[str, Any], str]]) – The state_dict of the PyTorch model containing all weights on it. It can be either a dict as returned by `model.state_dict()`, or a string with a file name. Required when `enable_fake_mode()` is used but real initializers are needed on the ONNX graph. It can be either a string with the path to a checkpoint or a dictionary with the actual model state.

      - serializer (Optional[ExportOutputSerializer]) – The serializer to use. If not specified, the model will be serialized as Protobuf.

  save_diagnostics(destination)

    Saves the export diagnostics as a SARIF log to the specified destination path.

    Parameters:

      destination (str) – The destination to save the diagnostics SARIF log. It must have a .sarif extension.

    Raises:

      ValueError – If the destination path does not end with .sarif extension.

class torch.onnx.ExportOutputSerializer(*args, **kwargs)

  Protocol for serializing an ONNX graph into a specific format (e.g. Protobuf). Note that this is an advanced usage scenario.

  serialize(export_output, destination)

    Protocol method that must be implemented for serialization.

    Parameters:

      - export_output (ExportOutput) – Represents the in-memory exported ONNX model

      - destination (BufferedIOBase) – A binary IO stream or pre-allocated buffer into which the serialized model should be written.

    Example

    A simple serializer that writes the exported `onnx.ModelProto` in Protobuf format to `destination`:

        # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
        >>> import io
        >>> import torch
        >>> import torch.onnx
        >>> class MyModel(torch.nn.Module):  # Dummy model
        ...     def __init__(self) -> None:
        ...         super().__init__()
        ...         self.linear = torch.nn.Linear(2, 2)
        ...     def forward(self, x):
        ...         out = self.linear(x)
        ...         return out
        >>> class ProtobufExportOutputSerializer:
        ...     def serialize(
        ...         self, export_output: torch.onnx.ExportOutput, destination: io.BufferedIOBase
        ...     ) -> None:
        ...         destination.write(export_output.model_proto.SerializeToString())
        >>> model = MyModel()
        >>> arg1 = torch.randn(2, 2, 2)  # positional input 1
        >>> torch.onnx.dynamo_export(model, arg1).save(
        ...     destination="exported_model.onnx",
        ...     serializer=ProtobufExportOutputSerializer(),
        ... )

class torch.onnx.OnnxExporterError(export_output, message)

  Raised when an ONNX exporter error occurs.

  This exception is thrown when there’s an error during the ONNX export process. It encapsulates the `ExportOutput` object generated until the failure, allowing access to the partial export results and associated metadata.

class torch.onnx.OnnxRegistry

  Registry for ONNX functions.

  The registry maintains a mapping from qualified names to symbolic functions under a fixed opset version. It supports registering custom onnx-script functions and for dispatcher to dispatch calls to the appropriate function.

  get_op_functions(namespace, op_name, overload=None)

    Returns a list of ONNXFunctions for the given op: torch.ops.<namespace>.<op_name>.<overload>.

    The list is ordered by the time of registration. The custom operators should be in the second half of the list.

    Parameters:

      - namespace (str) – The namespace of the operator to get.

      - op_name (str) – The name of the operator to get.

      - overload (Optional[str]) – The overload of the operator to get. If it’s default overload, leave it to None.

    Returns:

      A list of ONNXFunctions corresponding to the given name, or None if the name is not in the registry.

    Return type:

      Optional[List[ONNXFunction]]

  is_registered_op(namespace, op_name, overload=None)

    Returns whether the given op is registered: torch.ops.<namespace>.<op_name>.<overload>.

    Parameters:

      - namespace (str) – The namespace of the operator to check.

      - op_name (str) – The name of the operator to check.

      - overload (Optional[str]) – The overload of the operator to check. If it’s default overload, leave it to None.

    Returns:

      True if the given op is registered, otherwise False.

    Return type:

      bool

  property opset_version: int

    The ONNX opset version the exporter should target. Defaults to the latest supported ONNX opset version: 18. The default version will increment over time as ONNX continues to evolve.

  register_op(function, namespace, op_name, overload=None, is_complex=False)

    Registers a custom operator: torch.ops.<namespace>.<op_name>.<overload>.

    Parameters:

      - function (Union['onnxscript.OnnxFunction', 'onnxscript.TracedOnnxFunction']) – The onnx-sctip function to register.

      - namespace (str) – The namespace of the operator to register.

      - op_name (str) – The name of the operator to register.

      - overload (Optional[str]) – The overload of the operator to register. If it’s default overload, leave it to None.

      - is_complex (bool) – Whether the function is a function that handles complex valued inputs.

    Raises:

      ValueError – If the name is not in the form of ‘namespace::op’.

class torch.onnx.DiagnosticOptions(verbosity_level=20, warnings_as_errors=False)

  Options for diagnostic context.

  Variables:

    - verbosity_level (int) – Set the amount of information logged for each diagnostics, equivalent to the ‘level’ in Python logging module.

    - warnings_as_errors (bool) – When True, warning diagnostics are treated as error diagnostics.

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