Use the val argument in mask_nan_or_inf_with_val_inplace

[vineethsaivs]

What

mask_nan_or_inf_with_val_inplace(input, device=None, val=-1.) accepts val and then builds the fill tensor from a hardcoded -1.0, so the argument is dead:

t = torch.tensor([float('nan'), 2.0])
mask_nan_or_inf_with_val_inplace(t, device=t.device, val=0.0)
# t -> tensor([-1.,  2.])   expected tensor([0., 2.])

Why it is there

val arrived with the helper in #7184, which folded three copies of the inf/nan masking (runtime/utils.py, zero/stage3.py, zero/stage_1_and_2.py) into one function. The parameter is the knob that refactor introduced, and the body kept the literal from the code it replaced.

Fix

One line: build err from val.

All three in-tree callers use the default, and -1. is the same float the body hardcoded, so the gradient-norm paths are bit-identical.

Test

New test_mask_nan_or_inf_with_val_inplace_honors_val in tests/unit/runtime/test_runtime_utils.py, covering nan / +inf / -inf for several vals plus the unchanged default.

$ python -m pytest unit/runtime/test_runtime_utils.py -k "mask_nan_or_inf or call_to_str or count_used_parameters"
# before: 1 failed, 2 passed
#   E  assert [-1.0, -1.0, -1.0, 2.0] == [0.0, 0.0, 0.0, 2.0]
# after:  3 passed

The TestClipGradNorm* / TestCheckOverflow cases in the same file are DistributedTest and were deselected on this CPU-only box; they do not touch val.