[antonwolfy]

dpnp.append with axis=None converts a Python numeric scalar in values to an array before choosing the result dtype, so the scalar can upcast arr. NumPy 2.6 deprecates this behavior: a future release will use the Python scalar promotion rules instead, as concatenate does. This PR mirrors that deprecation.

a = dpnp.ones(3, dtype=dpnp.int8)

dpnp.append(a, 1).dtype     # int64, now emits FutureWarning (int8 in the future)
dpnp.append(a, [1]).dtype   # int64, no warning
dpnp.append(a, dpnp.asarray(1, dtype=a.dtype, device=a.device)).dtype  # int8, no warning

The result is unchanged, but the call now emits:

dpnp.append converted Python numeric scalars to arrays before choosing the result dtype, producing dtype int64. This behavior will change in a future release, these inputs will use dtype int8, which may round values differently or raise an error. Wrap the scalar in a list to keep the current result, or convert it to an array of dtype 'int8' for the future behavior.

The warning is emitted only when the dtype will actually change, i.e. when dpnp.result_type(arr, values) differs from the current result dtype. As in NumPy, only exact Python int, float and complex scalars are affected; Python bool, NumPy scalars, lists and arrays don't warn. Values out of range for the future dtype (e.g. 300 with a uint8 array) will raise OverflowError once the change lands.

On devices without fp64 support, a Python float or complex scalar already converts to a single-precision array, so e.g. a float32 array keeps its dtype and no warning is emitted there.

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• Have you added your changes to the changelog?

[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/pull/3125/index.html

[coveralls]

coverage: 82.642% (+0.02%) from 82.627% — deprecate-append-scalar-promotion into master