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import torch | |
@torch.jit.script | |
def foo(x, y): | |
for i in range(100): | |
if torch.fmod(_to_tensor(i), 3) == 0: | |
y += x | |
else: | |
x += y | |
return x, y | |
print(foo.__getattr__('forward').graph) | |
==== | |
graph(%x.1 : Dynamic | |
%y.1 : Dynamic) { | |
%2 : int = prim::Constant[value=100]() | |
%3 : int = prim::Constant[value=1]() | |
%y : Dynamic, %x : Dynamic = prim::Loop(%2, %3, %y.1, %x.1) | |
block0(%i : int, %11 : Dynamic, %12 : Dynamic) { | |
%5 : Long() = prim::NumToTensor(%i) | |
%6 : int = prim::Constant[value=3]() | |
%7 : Dynamic = aten::fmod(%5, %6) | |
%8 : int = prim::Constant[value=0]() | |
%9 : Dynamic = aten::eq(%7, %8) | |
%10 : int = prim::TensorToNum(%9) | |
%x.3 : Dynamic, %y.3 : Dynamic = prim::If(%10) | |
block0() { | |
%13 : int = prim::Constant[value=1]() | |
%y.2 : Dynamic = aten::add(%11, %12, %13) | |
-> (%12, %y.2) | |
} | |
block1() { | |
%15 : int = prim::Constant[value=1]() | |
%x.2 : Dynamic = aten::add(%12, %11, %15) | |
-> (%x.2, %11) | |
} | |
%19 : int = prim::Constant[value=1]() | |
-> (%19, %y.3, %x.3) | |
} | |
return (%x, %y); | |
} | |
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