python.builtin
==================
dynamic_shape_round
^^^^^^^^^^^^^^^^^^^

.. note::

    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.builtin <python.builtin>`

    Support Level: NOT_SUPPORTED_YET

Original source code:

.. code-block:: python

    import torch
    
    from torch.export import Dim
    
    x = torch.randn(3, 2)
    dim0_x = Dim("dim0_x")
    
    class DynamicShapeRound(torch.nn.Module):
        """
        Calling round on dynamic shapes is not supported.
        """
    
        def __init__(self):
            super().__init__()
    
        def forward(self, x):
            return x[: round(x.shape[0] / 2)]
    

Result:

.. code-block::

    AssertionError: 


tensor_setattr
^^^^^^^^^^^^^^

.. note::

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

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    
    
    class TensorSetattr(torch.nn.Module):
        """
        setattr() call onto tensors is not supported.
        """
        def forward(self, x, attr):
            setattr(x, attr, torch.randn(3, 2))
            return x + 4
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: "f32[3, 2]", arg1_1):
                    add: "f32[3, 2]" = torch.ops.aten.add.Tensor(arg0_1, 4);  arg0_1 = None
                return (add,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=ConstantArgument(value='attr'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
    Range constraints: {}
    


type_reflection_method
^^^^^^^^^^^^^^^^^^^^^^

.. note::

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

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    
    
    class A:
        @classmethod
        def func(cls, x):
            return 1 + x
    
    
    class TypeReflectionMethod(torch.nn.Module):
        """
        type() calls on custom objects followed by attribute accesses are not allowed
        due to its overly dynamic nature.
        """
    
        def __init__(self):
            super().__init__()
    
        def forward(self, x):
            a = A()
            return type(a).func(x)
    
    
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: "f32[3, 4]"):
                    add: "f32[3, 4]" = torch.ops.aten.add.Tensor(arg0_1, 1);  arg0_1 = None
                return (add,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
    Range constraints: {}
    


You can rewrite the example above to something like the following:

.. code-block:: python

    class TypeReflectionMethodRewrite(torch.nn.Module):
        """
        Custom object class methods will be inlined.
        """
    
        def __init__(self):
            super().__init__()
    
        def forward(self, x):
            return A.func(x)