Refactor Where operator to avoid generation of NaN tensors

[kamalrajkannan78]

In pybuda where op is decomposed in a below way

elif type == "where":

    condition = inputs[0]
    x = inputs[1]
    y = inputs[2]
    one = dc.tensor(torch.ones((1,)))
    not_condition = dc.op("subtract", [one, condition])

    t0 = dc.op("multiply", [condition, x])
    t1 = dc.op("multiply", [not_condition, y])

    add = dc.op("add", [t0, t1])
    dc.fuse(add)

In some cases, mask tensor(inputs[0]) may contain values (example : 3.4028234663852886e+38 -> torch.finfo(torch.float32).min -> highest representable value for a float32 ) other than 0s and 1s. subtraction of even very small values to this type of mask values should generate -infinity values which will lead to NaN tensor. To avoid that,

• Added preprocessing steps to convert condition(mask) into binary tensor:
  • Cast the condition to float32.
  • Applied absolute value and clipped it between 0 and 1.0.
  • Converted the condition to binary (0 or 1) by comparing it to 0.

Fill values are getting replaced with ±1e4 without checking whether it is inf/-inf or not. To avoid that,

• Enhanced the handling of -inf/inf values in the fill value
  • Added a condition to check if the value is infinity/ -Infinity .If it is true, then only replacement done with ±1e4 based on the sign.