python.assert

dynamic_shape_assert

Note

Tags: python.assert

Support Level: SUPPORTED

Original source code:

import torch



def dynamic_shape_assert(x):
    """
    A basic usage of python assertion.
    """
    # 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

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, l_x_: "f32[3, 2]"):
            return (l_x_,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)])
Range constraints: {}
Equality constraints: []

list_contains

Note

Tags: torch.dynamic-shape, python.data-structure, python.assert

Support Level: SUPPORTED

Original source code:

import torch



def list_contains(x):
    """
    List containment relation can be checked on a dynamic shape or constants.
    """
    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

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, l_x_: "f32[3, 2]"):
                add: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, l_x_);  l_x_ = None
            return (add,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}
Equality constraints: []