python.assert¶
dynamic_shape_assert¶
Original source code:
# mypy: allow-untyped-defs
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, 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¶
Original source code:
# mypy: allow-untyped-defs
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, 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: {}