python.builtin¶
dynamic_shape_round¶
Original source code:
# mypy: allow-untyped-defs
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:
AssertionError: RoundToInt(IntTrueDiv(dim0_x, 2)) <= dim0_x
tensor_setattr¶
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
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¶
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 __init__(self):
super().__init__()
def forward(self, x):
a = A()
return type(a).func(x)
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: {}
You can rewrite the example above to something like the following:
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)