Merge pull request #21995 from eirrgang/mei-1468

BUG: Distinguish exact vs. equivalent dtype for C type aliases.

4 files changed, 107 insertions, 3 deletions

diff --git a/doc/release/upcoming_changes/21995.compatibility.rst b/doc/release/upcoming_changes/21995.compatibility.rst
new file mode 100644
index 000000000..d3ff9c1bd
--- /dev/null
+++ b/doc/release/upcoming_changes/21995.compatibility.rst
@@ -0,0 +1,21 @@
+Returned arrays respect uniqueness of dtype kwarg objects
+---------------------------------------------------------
+When the ``dtype`` keyword argument is used with :py:func:`np.array()`
+or :py:func:`asarray()`, the dtype of the returned array now
+always exactly matches the dtype provided by the caller.
+
+In some cases this change means that a *view* rather than the
+input array is returned.
+The following is an example for this on 64bit Linux where ``long``
+and ``longlong`` are the same precision::
+
+    >>> arr = np.array([1, 2, 3], dtype="long")
+    >>> new_dtype = np.dtype("longlong")
+    >>> new = np.asarray(arr, dtype=new_dtype)
+    >>> new.dtype is dtype
+    True
+    >>> new is arr
+    False
+
+Before the change, the ``dtype`` did not match because ``new is arr``
+was true.
diff --git a/numpy/array_api/tests/test_asarray.py b/numpy/array_api/tests/test_asarray.py
new file mode 100644
index 000000000..5c269823f
--- /dev/null
+++ b/numpy/array_api/tests/test_asarray.py
@@ -0,0 +1,64 @@
+import itertools
+
+import numpy as np
+
+
+def test_dtype_identity():
+    """Confirm the intended behavior for ``asarray`` results.
+
+    The result of ``asarray()`` should have the dtype provided through the
+    keyword argument, when used. This forces unique array handles to be
+    produced for unique np.dtype objects, but (for equivalent dtypes), the
+    underlying data (the base object) is shared with the original array object.
+
+    Ref https://github.com/numpy/numpy/issues/1468
+    """
+    int_array = np.array([1, 2, 3], dtype='i')
+    assert np.asarray(int_array) is int_array
+
+    # The character code resolves to the singleton dtype object provided
+    # by the numpy package.
+    assert np.asarray(int_array, dtype='i') is int_array
+
+    # Derive a dtype from n.dtype('i'), but add a metadata object to force
+    # the dtype to be distinct.
+    unequal_type = np.dtype('i', metadata={'spam': True})
+    annotated_int_array = np.asarray(int_array, dtype=unequal_type)
+    assert annotated_int_array is not int_array
+    assert annotated_int_array.base is int_array
+    # Create an equivalent descriptor with a new and distinct dtype instance.
+    equivalent_requirement = np.dtype('i', metadata={'spam': True})
+    annotated_int_array_alt = np.asarray(annotated_int_array,
+                                         dtype=equivalent_requirement)
+    assert unequal_type == equivalent_requirement
+    assert unequal_type is not equivalent_requirement
+    assert annotated_int_array_alt is not annotated_int_array
+    assert annotated_int_array_alt.dtype is equivalent_requirement
+
+    # Check the same logic for a pair of C types whose equivalence may vary
+    # between computing environments.
+    # Find an equivalent pair.
+    integer_type_codes = ('i', 'l', 'q')
+    integer_dtypes = [np.dtype(code) for code in integer_type_codes]
+    typeA = None
+    typeB = None
+    for typeA, typeB in itertools.permutations(integer_dtypes, r=2):
+        if typeA == typeB:
+            assert typeA is not typeB
+            break
+    assert isinstance(typeA, np.dtype) and isinstance(typeB, np.dtype)
+
+    # These ``asarray()`` calls may produce a new view or a copy,
+    # but never the same object.
+    long_int_array = np.asarray(int_array, dtype='l')
+    long_long_int_array = np.asarray(int_array, dtype='q')
+    assert long_int_array is not int_array
+    assert long_long_int_array is not int_array
+    assert np.asarray(long_int_array, dtype='q') is not long_int_array
+    array_a = np.asarray(int_array, dtype=typeA)
+    assert typeA == typeB
+    assert typeA is not typeB
+    assert array_a.dtype is typeA
+    assert array_a is not np.asarray(array_a, dtype=typeB)
+    assert np.asarray(array_a, dtype=typeB).dtype is typeB
+    assert array_a is np.asarray(array_a, dtype=typeB).base
diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c
index 18a572e51..f01752431 100644
--- a/numpy/core/src/multiarray/multiarraymodule.c
+++ b/numpy/core/src/multiarray/multiarraymodule.c
@@ -1627,12 +1627,31 @@ _array_fromobject_generic(
                 goto finish;
             }
         }
-        /* One more chance */
+        /* One more chance for faster exit if user specified the dtype. */
         oldtype = PyArray_DESCR(oparr);
         if (PyArray_EquivTypes(oldtype, type)) {
             if (copy != NPY_COPY_ALWAYS && STRIDING_OK(oparr, order)) {
                 Py_INCREF(op);
-                ret = oparr;
+                if (oldtype == type) {
+                    ret = oparr;
+                }
+                else {
+                    /* Create a new PyArrayObject from the caller's
+                     * PyArray_Descr. Use the reference `op` as the base
+                     * object. */
+                    Py_INCREF(type);
+                    ret = (PyArrayObject *)PyArray_NewFromDescrAndBase(
+                            Py_TYPE(op),
+                            type,
+                            PyArray_NDIM(oparr),
+                            PyArray_DIMS(oparr),
+                            PyArray_STRIDES(oparr),
+                            PyArray_DATA(oparr),
+                            PyArray_FLAGS(oparr),
+                            op,
+                            op
+                            );
+                }
                 goto finish;
             }
             else {
diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py
index b056d5169..3997f44e3 100644
--- a/numpy/ma/tests/test_core.py
+++ b/numpy/ma/tests/test_core.py
@@ -5387,7 +5387,7 @@ def test_ufunc_with_out_varied():
 
 
 def test_astype_mask_ordering():
-    descr = [('v', int, 3), ('x', [('y', float)])]
+    descr = np.dtype([('v', int, 3), ('x', [('y', float)])])
     x = array([
         [([1, 2, 3], (1.0,)),  ([1, 2, 3], (2.0,))],
         [([1, 2, 3], (3.0,)),  ([1, 2, 3], (4.0,))]], dtype=descr)