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BUG: properly handle negative indexes in ufunc_at fast path #24150

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merged 1 commit into from
Jul 9, 2023
Merged

BUG: properly handle negative indexes in ufunc_at fast path #24150

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11 changes: 8 additions & 3 deletions numpy/core/src/umath/ufunc_object.c
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Original file line number Diff line number Diff line change
Expand Up @@ -5812,14 +5812,19 @@ trivial_at_loop(PyArrayMethodObject *ufuncimpl, NPY_ARRAYMETHOD_FLAGS flags,
}
}

npy_intp *inner_size = NpyIter_GetInnerLoopSizePtr(iter->outer);

if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) {
npy_clear_floatstatus_barrier((char *)context);
}

do {
args[1] = (char *) iter->outer_ptrs[0];
npy_intp *inner_size = NpyIter_GetInnerLoopSizePtr(iter->outer);
npy_intp * indxP = (npy_intp *)iter->outer_ptrs[0];
for (npy_intp i=0; i < *inner_size; i++) {
if (indxP[i] < 0) {
indxP[i] += iter->fancy_dims[0];
}
}
args[1] = (char *)indxP;
steps[1] = iter->outer_strides[0];

res = ufuncimpl->contiguous_indexed_loop(
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8 changes: 8 additions & 0 deletions numpy/core/tests/test_ufunc.py
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Expand Up @@ -2224,6 +2224,14 @@ def test_ufunc_at_advanced(self):
np.maximum.at(a, [0], 0)
assert_equal(a, np.array([1, 2, 3]))

def test_at_negative_indexes(self):
a = np.arange(10)
indxs = np.array([-1, 1, -1, 2])
np.add.at(a, indxs, 1)
assert a[-1] == 11 # issue 24147
assert a[1] == 2
assert a[2] == 3

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An bad merge (locally) brought me here, but then I noticed that this can modify the original index array:

assert indxs[0] == -1

would fail. I think it shows that passing pointers or full offsets rather than indices makes more sense here (we need a temporary array anyway), but I have to think about how a hot-fix should look like.
Probably either need a local buffer here, or push the fixing into the indexed loop.

(Maybe local buffer is good, since that is what I would want to do with that hot-fix anyway in some form, even if it may make sense to push the buffer for that case slightly further up.)

def test_at_not_none_signature(self):
# Test ufuncs with non-trivial signature raise a TypeError
a = np.ones((2, 2, 2))
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