python.assert

dynamic_shape_assert

Note

Tags: python.assert

Support Level: SUPPORTED

Original source code:

import torch



class DynamicShapeAssert(torch.nn.Module):
    """
    A basic usage of python assertion.
    """
    def __init__(self):
        super().__init__()

    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

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "f32[3, 2]"):
            return (arg0_1,)

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='arg0_1'), target=None)])
Range constraints: {}

list_contains

Note

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

Support Level: SUPPORTED

Original source code:

import torch



class ListContains(torch.nn.Module):
    """
    List containment relation can be checked on a dynamic shape or constants.
    """
    def __init__(self):
        super().__init__()

    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

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "f32[3, 2]"):
                add: "f32[3, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg0_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: {}