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# Owner(s): ["oncall: package/deploy"]
import torch
from torch.fx import wrap
wrap("a_non_torch_leaf")
class ModWithSubmod(torch.nn.Module):
def __init__(self, script_mod):
super().__init__()
self.script_mod = script_mod
def forward(self, x):
return self.script_mod(x)
class ModWithTensor(torch.nn.Module):
def __init__(self, tensor):
super().__init__()
self.tensor = tensor
def forward(self, x):
return self.tensor * x
class ModWithSubmodAndTensor(torch.nn.Module):
def __init__(self, tensor, sub_mod):
super().__init__()
self.tensor = tensor
self.sub_mod = sub_mod
def forward(self, x):
return self.sub_mod(x) + self.tensor
class ModWithTwoSubmodsAndTensor(torch.nn.Module):
def __init__(self, tensor, sub_mod_0, sub_mod_1):
super().__init__()
self.tensor = tensor
self.sub_mod_0 = sub_mod_0
self.sub_mod_1 = sub_mod_1
def forward(self, x):
return self.sub_mod_0(x) + self.sub_mod_1(x) + self.tensor
class ModWithMultipleSubmods(torch.nn.Module):
def __init__(self, mod1, mod2):
super().__init__()
self.mod1 = mod1
self.mod2 = mod2
def forward(self, x):
return self.mod1(x) + self.mod2(x)
class SimpleTest(torch.nn.Module):
def forward(self, x):
x = a_non_torch_leaf(x, x)
return torch.relu(x + 3.0)
def a_non_torch_leaf(a, b):
return a + b
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