python.assert
=================
dynamic_shape_assert
^^^^^^^^^^^^^^^^^^^^

.. note::

    Tags: :doc:`python.assert <python.assert>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    # mypy: allow-untyped-defs
    import torch
    
    class DynamicShapeAssert(torch.nn.Module):
        """
        A basic usage of python assertion.
        """
    
        def forward(self, x):
            # assertion with error message
            assert x.shape[0] > 2, f"{x.shape[0]} is greater than 2"
            # assertion without error message
            assert x.shape[0] > 1
            return x
    
    example_args = (torch.randn(3, 2),)
    tags = {"python.assert"}
    model = DynamicShapeAssert()
    

    torch.export.export(model, example_args)

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, x: "f32[3, 2]"):
                return (x,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='x'), target=None)])
    Range constraints: {}
    


list_contains
^^^^^^^^^^^^^

.. note::

    Tags: :doc:`python.data-structure <python.data-structure>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.assert <python.assert>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    # mypy: allow-untyped-defs
    import torch
    
    class ListContains(torch.nn.Module):
        """
        List containment relation can be checked on a dynamic shape or constants.
        """
    
        def forward(self, x):
            assert x.size(-1) in [6, 2]
            assert x.size(0) not in [4, 5, 6]
            assert "monkey" not in ["cow", "pig"]
            return x + x
    
    example_args = (torch.randn(3, 2),)
    tags = {"torch.dynamic-shape", "python.data-structure", "python.assert"}
    model = ListContains()
    

    torch.export.export(model, example_args)

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, x: "f32[3, 2]"):
                     add: "f32[3, 2]" = torch.ops.aten.add.Tensor(x, x);  x = None
                return (add,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
    Range constraints: {}