Resolve dpt.asarray failed to copy input numpy.ndarray #2055

adds _from_numpy_empty_like_orderK utility function

Author: ndgrigorian

dpctl/tensor/_copy_utils.py

@@ -116,7 +116,9 @@ def _copy_from_numpy_into(dst, np_ary):
     )
 
 
-def from_numpy(np_ary, /, *, device=None, usm_type="device", sycl_queue=None):
+def from_numpy(
+    np_ary, /, *, device=None, usm_type="device", sycl_queue=None, order="K"
+):
     """
     from_numpy(arg, device=None, usm_type="device", sycl_queue=None)
 
@@ -349,6 +351,31 @@ def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
     _copy_same_shape(dst, src_same_shape)
 
 
+def _make_empty_like_orderK(X, dt, usm_type, dev):
+    st = list(X.strides)
+    perm = sorted(
+        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
+    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:
+        st_sorted = [st[i] for i in perm]
+        sl = tuple(
+            (
+                slice(None, None, -1)
+                if st_sorted[i] < 0
+                else slice(None, None, None)
+            )
+            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'
 
@@ -371,28 +398,28 @@ def _empty_like_orderK(X, dt, usm_type=None, dev=None):
         return dpt.empty_like(
             X, dtype=dt, usm_type=usm_type, device=dev, order="F"
         )
-    st = list(X.strides)
-    perm = sorted(
-        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
-    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:
-        st_sorted = [st[i] for i in perm]
-        sl = tuple(
-            (
-                slice(None, None, -1)
-                if st_sorted[i] < 0
-                else slice(None, None, None)
-            )
-            for i in range(X.ndim)
+    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 np.ndarray, got {type(X)}")
+    fl = X.flags
+    if fl["C"] or X.size <= 1:
+        return dpt.usm_ndarray(
+            X.shape, dtype=dt, usm_type=usm_type, device=dev, order="C"
         )
-        R = R[sl]
-    return dpt.permute_dims(R, inv_perm)
+    elif fl["F"]:
+        return dpt.usm_ndarray(
+            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):

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
@@ -243,24 +246,7 @@ def _asarray_from_numpy_ndarray(
     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(