inductor: align baddbmm behavior with eager mode for beta=0 and input has nan value (#96087)
For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value:
```
def fn_test(input, mat1, mat2):
return torch.baddbmm(input, mat1, mat2, beta=0.0)
opt_fn = torch._dynamo.optimize("inductor")(fn_test)
a, b, c = [torch.rand((3,2,2)) for _ in range(3)]
real_out = fn_test(a, b, c)
a[:] = torch.nan
compiled_out = opt_fn(a, b,c)
print(compiled_out)
print(real_out)
```
before this PR, the output will be like this:
```
tensor([[[0.4272, 0.6037],
[0.4279, 0.4219]],
[[0.0838, 0.4873],
[0.1210, 0.5516]],
[[ nan, nan],
[ nan, nan]]])
tensor([[[0.4272, 0.6037],
[0.4279, 0.4219]],
[[0.0838, 0.4873],
[0.1210, 0.5516]],
[[0.4985, 0.1072],
[0.0857, 0.0186]]])
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96087
Approved by: https://github.com/jansel, https://github.com/ngimel, https://github.com/jgong5
