[Bugfix] Fix common_broadcastable_dtype returning a lossy dtype

[UgaTheDev]

common_broadcastable_dtype is documented to return a dtype that every input
can be cast to without losing information. The implementation was

max(dtypes, key=lambda dtype: sum(is_lossless_cast(dt, dtype) for dt in dtypes))

That is an argmax over the input collection, so it can only ever return a dtype
that was passed in. When no input is a valid target for all the others, it still
returns one of them, silently lossy.

{float16, bfloat16} is the case that matters in practice. Neither is a lossless
cast target for the other, so both score 1 and max returns whichever the
iterable yields first:

common_broadcastable_dtype([torch.float16, torch.bfloat16])  # torch.float16
common_broadcastable_dtype([torch.bfloat16, torch.float16])  # torch.bfloat16

The correct answer is float32, which is in neither input. The result also
depends on iteration order of the collection the caller passes in.

Fix is to use torch.promote_types, which is defined over the full dtype
lattice rather than only the inputs:

functools.reduce(torch.promote_types, dtypes)

This returns float32 for both orderings above and matches the previous
behaviour on the existing test cases.

Scope note: this does not make the function match its docstring in every case.
torch.promote_types(torch.int64, torch.float16) is float16, which loses
precision. That is pre-existing and left alone here.

Added the [float16, bfloat16] -> float32 case to
test_common_broadcastable_dtype.

Test:

pytest tests/utils_/test_torch_utils.py --noconftest \
  -k "common_broadcastable_dtype or is_lossless_cast"

29 passed. Full-file collection fails in my environment on an unrelated missing
xgrammar import from the tests conftest.

[claude]

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[UgaTheDev]

This is blocked on pre-run-check, which gates CI until an author has 4 merged PRs. #51627 merged yesterday so the count is 1, and this cannot run its tests until someone starts them.

Could a maintainer run /ci run? The defect is reproducible in a few lines:

common_broadcastable_dtype([float16, bfloat16]) -> torch.float16
common_broadcastable_dtype([bfloat16, float16]) -> torch.bfloat16

Neither is a lossless target for the other, so the function returns a dtype that violates its own docstring, and which of the two you get depends on iteration order. The cause is that max(dtypes, key=...) is an argmax over the input set, so it can only return a dtype that was passed in, while the correct answer here is float32. The fix is functools.reduce(torch.promote_types, dtypes), and I verified it matches current behaviour on all four existing parametrised cases before adding the new one.