Merge pull request #20729 from seberg/relax-reduce-dtype-identity-check

BUG: Relax dtype identity check in reductions

2 files changed, 17 insertions, 2 deletions

diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c
index 52b354353..83e18a363 100644
--- a/numpy/core/src/umath/ufunc_object.c
+++ b/numpy/core/src/umath/ufunc_object.c
@@ -2764,9 +2764,13 @@ reducelike_promote_and_resolve(PyUFuncObject *ufunc,
      * The first operand and output should be the same array, so they should
      * be identical.  The second argument can be different for reductions,
      * but is checked to be identical for accumulate and reduceat.
+     * Ideally, the type-resolver ensures that all are identical, but we do
+     * not enforce this here strictly.  Otherwise correct handling of
+     * byte-order changes (or metadata) requires a lot of care; see gh-20699.
      */
-    if (out_descrs[0] != out_descrs[2] || (
-            enforce_uniform_args && out_descrs[0] != out_descrs[1])) {
+    if (!PyArray_EquivTypes(out_descrs[0], out_descrs[2]) || (
+            enforce_uniform_args && !PyArray_EquivTypes(
+                    out_descrs[0], out_descrs[1]))) {
         PyErr_Format(PyExc_TypeError,
                 "the resolved dtypes are not compatible with %s.%s. "
                 "Resolved (%R, %R, %R)",
diff --git a/numpy/core/tests/test_ufunc.py b/numpy/core/tests/test_ufunc.py
index 76e4cdcfd..9a9d46da0 100644
--- a/numpy/core/tests/test_ufunc.py
+++ b/numpy/core/tests/test_ufunc.py
@@ -2148,6 +2148,17 @@ class TestUfunc:
             # It would be safe, but not equiv casting:
             ufunc(a, c, out=out, casting="equiv")
 
+    def test_reducelike_byteorder_resolution(self):
+        # See gh-20699, byte-order changes need some extra care in the type
+        # resolution to make the following succeed:
+        arr_be = np.arange(10, dtype=">i8")
+        arr_le = np.arange(10, dtype="<i8")
+
+        assert np.add.reduce(arr_be) == np.add.reduce(arr_le)
+        assert_array_equal(np.add.accumulate(arr_be), np.add.accumulate(arr_le))
+        assert_array_equal(
+            np.add.reduceat(arr_be, [1]), np.add.reduceat(arr_le, [1]))
+
     def test_reducelike_out_promotes(self):
         # Check that the out argument to reductions is considered for
         # promotion.  See also gh-20455.