ENH: Break the assumption that all ufuncs and gufuncs want is element-wise loop aliasing #11416
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I'm not sure, but my initial impression is that the two relevant cases are: (1) the loop can tolerate some core inputs being identical to some core outputs, (2) the loop can't tolerate overlap at all. For cases where there's partial overlap between core dimensions, or overlap between different loop iterations, then maybe we should unconditionally copy? Are there any other interesting cases? |
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The intended meaning of the ASSUME_ELEMENTWISE flag is that given the
input arrays, for each iterator outer loop index, the inner loop is
guaranteed to not touch memory associated with other iterator outer
indices, so that the overlap detection can do reasoning on the outer
loop level.
The current implementation of ELEMENTWISE is only a special case that's
easy to reason about, and further refinement could be done later.
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In particular, the use of the iterator in ufunc_at is not "elementwise"
in this sense.
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Allowed overriding the ufunc flags in #11580 |
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The current implementation of both ufuncs and gufuncs (those with core-dimensions and an inner-loop signature) use the
NPY_ITER_OVERLAP_ASSUME_ELEMENTWISEflag to prevent allocating buffer memory if it can be determined that:solve_may_have_internal_overlap()for single-byte overlap returns `0Let's call this element-wise aliasing, since it is intended for elementwise ufuncs like
np.sin.For all other cases, output ndarrays will use writeback semantics to allocate temporary memory.
There should be a point in the gufunc call that a gufunc can say "element-wise aliasing is OK", or "leave all aliasing to the inner loop" or "always copy-on-any-overlap". We need a flag to indicate these (and maybe other, like contiguous) strategies. See also PR #11381 (closed) which proposed unilaterally changing the default.
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