python.builtin

dynamic_shape_round

Note

Tags: python.builtin, torch.dynamic-shape

Support Level: NOT_SUPPORTED_YET

Original source code:

# mypy: allow-untyped-defs
import torch

from torch._export.db.case import SupportLevel
from torch.export import Dim

class DynamicShapeRound(torch.nn.Module):
    """
    Calling round on dynamic shapes is not supported.
    """

    def forward(self, x):
        return x[: round(x.shape[0] / 2)]

x = torch.randn(3, 2)
dim0_x = Dim("dim0_x")
example_args = (x,)
tags = {"torch.dynamic-shape", "python.builtin"}
support_level = SupportLevel.NOT_SUPPORTED_YET
dynamic_shapes = {"x": {0: dim0_x}}
model = DynamicShapeRound()


torch.export.export(model, example_args, dynamic_shapes=dynamic_shapes)

Result:

AssertionError: RoundToInt(IntTrueDiv(dim0_x, 2)) <= dim0_x

tensor_setattr

Note

Tags: python.builtin

Support Level: SUPPORTED

Original source code:

# mypy: allow-untyped-defs
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

example_args = (torch.randn(3, 2), "attr")
tags = {"python.builtin"}
model = TensorSetattr()


torch.export.export(model, example_args)

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]", attr):
                 add: "f32[3, 2]" = torch.ops.aten.add.Tensor(x, 4);  x = None
            return (add,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=ConstantArgument(name='attr', 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: python.builtin

Support Level: SUPPORTED

Original source code:

# mypy: allow-untyped-defs
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 forward(self, x):
        a = A()
        return type(a).func(x)


example_args = (torch.randn(3, 4),)
tags = {"python.builtin"}
model = TypeReflectionMethod()


torch.export.export(model, example_args)

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 4]"):
                 add: "f32[3, 4]" = torch.ops.aten.add.Tensor(x, 1);  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: {}