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Fix dpnp.median/dpnp.nanmedian result shape for empty and size-1 kept dimensions - #3081
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`_flatten_array_along_axes` merged the reduced axes with a trailing `-1`, which `reshape` cannot infer once the array has size 0 and a kept axis is also 0. Reducing e.g. an array of shape `(0, 3, 4)` over `axis=(1, 2)` raised `ValueError: cannot reshape array of size 0 into shape (0,newaxis)`. Compute the merged length explicitly so the empty case returns the expected empty result.
antonwolfy
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October 1, 2026 12:00
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View rendered docs @ https://intelpython.github.io/dpnp/index.html |
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coverage: 78.624% (+0.002%) from 78.622% — fix-median-empty-kept-axis into master |
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Array API standard conformance tests for dpnp=0.21.0dev11=np2py314h8d9cdd5_19 ran successfully. |
`_calc_nanmedian` squeezed every size-1 axis, which also dropped kept (non-reduced) dimensions of size 1 and produced a wrong result shape (e.g. a (1, 5) array reduced over axis=1 returned shape () instead of (1,)). Squeeze only the reduced trailing axis.
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dpnp.median/dpnp.nanmedian for a tuple axis with an empty kept dimensiondpnp.median/dpnp.nanmedian result shape for empty and size-1 kept dimensions
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…kept dimensions (#3081) This PR fixes two kept-dimension shape bugs in `dpnp.median`/`dpnp.nanmedian`. **Empty kept dimension.** `dpnp.median` and `dpnp.nanmedian` raised `ValueError` when called with a tuple (or list) `axis` and one of the kept, non-reduced dimensions has size 0, e.g. ``` dpnp.median(dpnp.empty((0, 3, 4)), axis=(1, 2)) ``` The sequence-axis path flattens the reduced axes in `_flatten_array_along_axes` with `a.reshape(kept_shape + (-1,))`. The `-1` cannot be inferred once the array has size 0 and a kept axis is also 0 (every merged length yields a size-0 array, so the dimension is ambiguous), which makes `reshape` raise. The merged length is now computed explicitly with `math.prod(...)`, so the tuple-axis path returns the same empty result as the single-axis path. **Size-1 kept dimension.** `dpnp.nanmedian` dropped kept dimensions of size 1 and returned the wrong shape, e.g. a `(1, 5)` array reduced over `axis=1` returned shape `()` instead of `(1,)`. `_calc_nanmedian` ended with an unconditional `dpnp.squeeze(res)`, which also removed non-reduced size-1 axes; it now squeezes only the reduced trailing axis with `dpnp.squeeze(res, axis=-1)`. eec0aeb
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This PR fixes two kept-dimension shape bugs in
dpnp.median/dpnp.nanmedian.Empty kept dimension.
dpnp.mediananddpnp.nanmedianraisedValueErrorwhen called with a tuple (or list)axisand one of the kept, non-reduced dimensions has size 0, e.g.The sequence-axis path flattens the reduced axes in
_flatten_array_along_axeswitha.reshape(kept_shape + (-1,)). The-1cannot be inferred once the array has size 0 and a kept axis is also 0 (every merged length yields a size-0 array, so the dimension is ambiguous), which makesreshaperaise. The merged length is now computed explicitly withmath.prod(...), so the tuple-axis path returns the same empty result as the single-axis path.Size-1 kept dimension.
dpnp.nanmediandropped kept dimensions of size 1 and returned the wrong shape, e.g. a(1, 5)array reduced overaxis=1returned shape()instead of(1,)._calc_nanmedianended with an unconditionaldpnp.squeeze(res), which also removed non-reduced size-1 axes; it now squeezes only the reduced trailing axis withdpnp.squeeze(res, axis=-1).