IntelPython / dpctl / 5649912819

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Implements logaddexp and hypot (#1272)

* Implements logaddexp and hypot

* BinaryElementwiseFunc get acceptance_fn parameter, set individually for tensor.divide

The _find_buf_dtype2 must stay consistent with _find_buf_dtype in that for
binary_fn that can be expressed via
    unary_fn(other_binary_fn(unary_fn(in1), unary_fn(in2))),
e.g. logaddexp(x1, x2) == log(exp(x1) + exp(x2))
the promotion of integral types to fp type must be consistence with both
evaluation.

The special-casing, necessary for proper working of dpt.divide, where
dpt.divide(dpt.asarray(1, dtype="i1"), dpt.asarray(1, dtype="u1")) should
give default fp type resulted in
 logaddexp(dpt.asarray(1, dtype="i1"), dpt.asarray(1, dtype="u1"))
returning array of different data-type than if evaluated alternatively,
since dpt.exp(dpt.asarray(1, dtype="i1")) and dpt.exp(dpt.asarray(1, dtype="u1"))
both gave float16 arrays, and the result was of float16 data type.

Now, both behaviors are fixed:

```
In [5]: dpt.log(dpt.add(dpt.exp(dpt.asarray(1, dtype="u1")), dpt.exp(dpt.asarray(1, dtype="i1"))))
Out[5]: usm_ndarray(1.693, dtype=float16)

In [6]: dpt.logaddexp(dpt.asarray(1, dtype="u1"), dpt.asarray(1, dtype="i1"))
Out[6]: usm_ndarray(1.693, dtype=float16)

In [7]: dpt.divide(dpt.asarray(1, dtype="u1"), dpt.asarray(1, dtype="i1"))
Out[7]: usm_ndarray(1., dtype=float32)
```

* Only try calling _inplace if inplace function is defined

* Since logaddexp is not defined for complex types, do not try complex Python scalar

* Implement logaddexp in numerically stable way

* Added missing argument

* Fixed computing allclose testing tolerance in test_dtype_matrix test

When NumPy's result has coarser dtype than that of DPCTL we must use
the largest resolution from each dtype.

* Calls of the type log(1 + X) changed to log1p(X) in logaddexp

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Co-authored-by: Oleksandr Pavlyk <oleksandr.pavlyk@intel.com>

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