Utils for creating random dags and generating code for nn modules based on such dags.
In particular, this was used to do fuzz testing for unflatten, where the random dags instructed the generation of calls, const accesses, and buffer mutations in a system of nn modules.
Example of generated test:
```python
def test_unflatten_random_dag_const_preserving_3(self):
class N2(torch.nn.Module):
def __init__(self):
super().__init__()
self.const = torch.ones(1)
def forward(self, x):
return x + 1
class N1(torch.nn.Module):
def __init__(self):
super().__init__()
self.const = torch.ones(1)
self.n2 = N2()
def forward(self, x):
x = x + self.n2.const
x = self.n2(x + 1)
return x + 1
class N0(torch.nn.Module):
def __init__(self):
super().__init__()
self.const = torch.ones(1)
self.n1 = N1()
def forward(self, x):
x = x + self.n1.n2.const
x = self.n1(x + 1)
x = self.n1.n2(x + 1)
return x + 1
inp = (torch.ones(1),)
eager = N0()(*inp)
ep = torch.export.export(
N0(),
inp,
strict=False,
preserve_module_call_signature=(
"n1",
"n1.n2",
),
)
epm = ep.module()
ufm = torch.export.unflatten(ep)
assert torch.allclose(epm(*inp), eager)
assert torch.allclose(ufm(*inp), eager)
```
Differential Revision: [D66838348](https://our.internmc.facebook.com/intern/diff/D66838348/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142180
Approved by: https://github.com/angelayi, https://github.com/ydwu4
