Merge pull request #2058 from IntelPython/resolve-gh-2055

ndgrigorian · web-flow · commit 8dcc44fe8315 · 2025-04-23T17:05:20.000-07:00
Resolve gh-2055
diff --git a/dpctl/tensor/_copy_utils.py b/dpctl/tensor/_copy_utils.py
@@ -349,36 +349,19 @@ def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
     _copy_same_shape(dst, src_same_shape)
 
 
-def _empty_like_orderK(X, dt, usm_type=None, dev=None):
-    """Returns empty array like `x`, using order='K'
-
-    For an array `x` that was obtained by permutation of a contiguous
-    array the returned array will have the same shape and the same
-    strides as `x`.
+def _make_empty_like_orderK(x, dt, usm_type, dev):
     """
-    if not isinstance(X, dpt.usm_ndarray):
-        raise TypeError(f"Expected usm_ndarray, got {type(X)}")
-    if usm_type is None:
-        usm_type = X.usm_type
-    if dev is None:
-        dev = X.device
-    fl = X.flags
-    if fl["C"] or X.size <= 1:
-        return dpt.empty_like(
-            X, dtype=dt, usm_type=usm_type, device=dev, order="C"
-        )
-    elif fl["F"]:
-        return dpt.empty_like(
-            X, dtype=dt, usm_type=usm_type, device=dev, order="F"
-        )
-    st = list(X.strides)
+    Returns empty array with shape and strides like `x`, with dtype `dt`,
+    USM type `usm_type`, on device `dev`.
+    """
+    st = list(x.strides)
     perm = sorted(
-        range(X.ndim),
-        key=lambda d: builtins.abs(st[d]) if X.shape[d] > 1 else 0,
+        range(x.ndim),
+        key=lambda d: builtins.abs(st[d]) if x.shape[d] > 1 else 0,
         reverse=True,
     )
-    inv_perm = sorted(range(X.ndim), key=lambda i: perm[i])
-    sh = X.shape
+    inv_perm = sorted(range(x.ndim), key=lambda i: perm[i])
+    sh = x.shape
     sh_sorted = tuple(sh[i] for i in perm)
     R = dpt.empty(sh_sorted, dtype=dt, usm_type=usm_type, device=dev, order="C")
     if min(st) < 0:
@@ -389,12 +372,60 @@ def _empty_like_orderK(X, dt, usm_type=None, dev=None):
                 if st_sorted[i] < 0
                 else slice(None, None, None)
             )
-            for i in range(X.ndim)
+            for i in range(x.ndim)
         )
         R = R[sl]
     return dpt.permute_dims(R, inv_perm)
 
 
+def _empty_like_orderK(x, dt, usm_type=None, dev=None):
+    """
+    Returns empty array like `x`, using order='K'
+
+    For an array `x` that was obtained by permutation of a contiguous
+    array the returned array will have the same shape and the same
+    strides as `x`.
+    """
+    if not isinstance(x, dpt.usm_ndarray):
+        raise TypeError(f"Expected usm_ndarray, got {type(x)}")
+    if usm_type is None:
+        usm_type = x.usm_type
+    if dev is None:
+        dev = x.device
+    fl = x.flags
+    if fl["C"] or x.size <= 1:
+        return dpt.empty_like(
+            x, dtype=dt, usm_type=usm_type, device=dev, order="C"
+        )
+    elif fl["F"]:
+        return dpt.empty_like(
+            x, dtype=dt, usm_type=usm_type, device=dev, order="F"
+        )
+    return _make_empty_like_orderK(x, dt, usm_type, dev)
+
+
+def _from_numpy_empty_like_orderK(x, dt, usm_type, dev):
+    """
+    Returns empty usm_ndarray like NumPy array `x`, using order='K'
+
+    For an array `x` that was obtained by permutation of a contiguous
+    array the returned array will have the same shape and the same
+    strides as `x`.
+    """
+    if not isinstance(x, np.ndarray):
+        raise TypeError(f"Expected numpy.ndarray, got {type(x)}")
+    fl = x.flags
+    if fl["C"] or x.size <= 1:
+        return dpt.empty(
+            x.shape, dtype=dt, usm_type=usm_type, device=dev, order="C"
+        )
+    elif fl["F"]:
+        return dpt.empty(
+            x.shape, dtype=dt, usm_type=usm_type, device=dev, order="F"
+        )
+    return _make_empty_like_orderK(x, dt, usm_type, dev)
+
+
 def _empty_like_pair_orderK(X1, X2, dt, res_shape, usm_type, dev):
     if not isinstance(X1, dpt.usm_ndarray):
         raise TypeError(f"Expected usm_ndarray, got {type(X1)}")
@@ -732,7 +763,7 @@ def _extract_impl(ary, ary_mask, axis=0):
     if exec_q is None:
         raise dpctl.utils.ExecutionPlacementError(
             "arrays have different associated queues. "
-            "Use `Y.to_device(X.device)` to migrate."
+            "Use `y.to_device(x.device)` to migrate."
         )
     ary_nd = ary.ndim
     pp = normalize_axis_index(operator.index(axis), ary_nd)
diff --git a/dpctl/tensor/_ctors.py b/dpctl/tensor/_ctors.py
@@ -24,7 +24,10 @@
 import dpctl.tensor as dpt
 import dpctl.tensor._tensor_impl as ti
 import dpctl.utils
-from dpctl.tensor._copy_utils import _empty_like_orderK
+from dpctl.tensor._copy_utils import (
+    _empty_like_orderK,
+    _from_numpy_empty_like_orderK,
+)
 from dpctl.tensor._data_types import _get_dtype
 from dpctl.tensor._device import normalize_queue_device
 from dpctl.tensor._usmarray import _is_object_with_buffer_protocol
@@ -233,6 +236,7 @@ def _asarray_from_numpy_ndarray(
     if dtype is None:
         # deduce device-representable output data type
         dtype = _map_to_device_dtype(ary.dtype, copy_q)
+    _ensure_native_dtype_device_support(dtype, copy_q.sycl_device)
     f_contig = ary.flags["F"]
     c_contig = ary.flags["C"]
     fc_contig = f_contig or c_contig
@@ -242,27 +246,8 @@ def _asarray_from_numpy_ndarray(
         order = "C" if c_contig else "F"
     if order == "K":
         # new USM allocation
-        _ensure_native_dtype_device_support(dtype, copy_q.sycl_device)
-        res = dpt.usm_ndarray(
-            ary.shape,
-            dtype=dtype,
-            buffer=usm_type,
-            order="C",
-            buffer_ctor_kwargs={"queue": copy_q},
-        )
-        original_strides = ary.strides
-        ind = sorted(
-            range(ary.ndim),
-            key=lambda i: abs(original_strides[i]),
-            reverse=True,
-        )
-        new_strides = tuple(res.strides[ind[i]] for i in ind)
-        # reuse previously made USM allocation
-        res = dpt.usm_ndarray(
-            res.shape, dtype=res.dtype, buffer=res.usm_data, strides=new_strides
-        )
+        res = _from_numpy_empty_like_orderK(ary, dtype, usm_type, copy_q)
     else:
-        _ensure_native_dtype_device_support(dtype, copy_q.sycl_device)
         res = dpt.usm_ndarray(
             ary.shape,
             dtype=dtype,
diff --git a/dpctl/tests/elementwise/test_type_utils.py b/dpctl/tests/elementwise/test_type_utils.py
@@ -19,7 +19,6 @@
 
 import dpctl
 import dpctl.tensor as dpt
-import dpctl.tensor._copy_utils as cu
 import dpctl.tensor._type_utils as tu
 
 from .utils import _all_dtypes, _map_to_device_dtype
@@ -70,50 +69,6 @@ def test_type_util_can_cast():
                     assert isinstance(r, bool)
 
 
-def test_type_utils_empty_like_orderK():
-    try:
-        a = dpt.empty((10, 10), dtype=dpt.int32, order="F")
-    except dpctl.SyclDeviceCreationError:
-        pytest.skip("No SYCL devices available")
-    X = cu._empty_like_orderK(a, dpt.int32, a.usm_type, a.device)
-    assert X.flags["F"]
-
-
-def test_type_utils_empty_like_orderK_invalid_args():
-    with pytest.raises(TypeError):
-        cu._empty_like_orderK([1, 2, 3], dpt.int32, "device", None)
-    with pytest.raises(TypeError):
-        cu._empty_like_pair_orderK(
-            [1, 2, 3],
-            (
-                1,
-                2,
-                3,
-            ),
-            dpt.int32,
-            (3,),
-            "device",
-            None,
-        )
-    try:
-        a = dpt.empty(10, dtype=dpt.int32)
-    except dpctl.SyclDeviceCreationError:
-        pytest.skip("No SYCL devices available")
-    with pytest.raises(TypeError):
-        cu._empty_like_pair_orderK(
-            a,
-            (
-                1,
-                2,
-                3,
-            ),
-            dpt.int32,
-            (10,),
-            "device",
-            None,
-        )
-
-
 def test_type_utils_find_buf_dtype():
     def _denier_fn(dt):
         return False
diff --git a/dpctl/tests/test_tensor_copy_utils.py b/dpctl/tests/test_tensor_copy_utils.py
@@ -0,0 +1,100 @@
+#                      Data Parallel Control (dpctl)
+#
+# Copyright 2020-2025 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import pytest
+
+import dpctl.tensor as dpt
+import dpctl.tensor._copy_utils as cu
+from dpctl.tests.helper import get_queue_or_skip
+
+
+def test_copy_utils_empty_like_orderK():
+    get_queue_or_skip()
+    a = dpt.empty((10, 10), dtype=dpt.int32, order="F")
+    X = cu._empty_like_orderK(a, dpt.int32, a.usm_type, a.device)
+    assert X.flags["F"]
+
+
+def test_copy_utils_empty_like_orderK_invalid_args():
+    get_queue_or_skip()
+    with pytest.raises(TypeError):
+        cu._empty_like_orderK([1, 2, 3], dpt.int32, "device", None)
+    with pytest.raises(TypeError):
+        cu._empty_like_pair_orderK(
+            [1, 2, 3],
+            (
+                1,
+                2,
+                3,
+            ),
+            dpt.int32,
+            (3,),
+            "device",
+            None,
+        )
+
+    a = dpt.empty(10, dtype=dpt.int32)
+    with pytest.raises(TypeError):
+        cu._empty_like_pair_orderK(
+            a,
+            (
+                1,
+                2,
+                3,
+            ),
+            dpt.int32,
+            (10,),
+            "device",
+            None,
+        )
+
+
+def test_copy_utils_from_numpy_empty_like_orderK():
+    q = get_queue_or_skip()
+
+    a = np.empty((10, 10), dtype=np.int32, order="C")
+    r0 = cu._from_numpy_empty_like_orderK(a, dpt.int32, "device", q)
+    assert r0.flags["C"]
+
+    b = np.empty((10, 10), dtype=np.int32, order="F")
+    r1 = cu._from_numpy_empty_like_orderK(b, dpt.int32, "device", q)
+    assert r1.flags["F"]
+
+    c = np.empty((2, 3, 4), dtype=np.int32, order="C")
+    c = np.transpose(c, (1, 0, 2))
+    r2 = cu._from_numpy_empty_like_orderK(c, dpt.int32, "device", q)
+    assert not r2.flags["C"] and not r2.flags["F"]
+
+
+def test_copy_utils_from_numpy_empty_like_orderK_invalid_args():
+    with pytest.raises(TypeError):
+        cu._from_numpy_empty_like_orderK([1, 2, 3], dpt.int32, "device", None)
+
+
+def test_gh_2055():
+    """
+    Test that `dpt.asarray` works on contiguous NumPy arrays with `order="K"`
+    when dimensions are permuted.
+
+    See: https://github.com/IntelPython/dpctl/issues/2055
+    """
+    get_queue_or_skip()
+
+    a = np.ones((2, 3, 4), dtype=dpt.int32)
+    a_t = np.transpose(a, (2, 0, 1))
+    r = dpt.asarray(a_t)
+    assert not r.flags["C"] and not r.flags["F"]
