Exposing to_default_precision as a backend_array_api.

cubed/array_api/creation_functions.py

@@ -1,7 +1,7 @@
 import math
 from typing import TYPE_CHECKING, Iterable, List
 
-from cubed.backend_array_api import namespace as nxp
+from cubed.backend_array_api import namespace as nxp, to_default_precision
 from cubed.backend_array_api import default_dtypes
 from cubed.core import Plan, gensym
 from cubed.core.ops import map_blocks
@@ -18,13 +18,6 @@
     from .array_object import Array
 
 
-def _to_default_precision(dtype, *, device=None):
-    """Returns a dtype of the same kind with the default precision."""
-    for k, dtype_ in default_dtypes(device=device).items():
-        if nxp.isdtype(dtype, k):
-            return dtype_
-
-
 def arange(
     start, /, stop=None, step=1, *, dtype=None, device=None, chunks="auto", spec=None
 ) -> "Array":
@@ -35,7 +28,7 @@ def arange(
         # TODO: Use inspect API
         dtype = nxp.arange(start, stop, step * num if num else step).dtype
         # the default nxp call does not adjust the data type to the default precision.
-        dtype = _to_default_precision(dtype, device=device)
+        dtype = to_default_precision(dtype, device=device)
 
     chunks = normalize_chunks(chunks, shape=(num,), dtype=dtype)
     chunksize = chunks[0][0]
@@ -77,7 +70,7 @@ def asarray(
         # ensure blocks are arrays
         a = nxp.asarray(a, dtype=dtype)
     if dtype is None:
-        dtype = _to_default_precision(a.dtype, device=device)
+        dtype = to_default_precision(a.dtype, device=device)
         a = a.astype(dtype)
 
     chunksize = to_chunksize(normalize_chunks(chunks, shape=a.shape, dtype=dtype))

cubed/backend_array_api.py

@@ -64,3 +64,10 @@ def numpy_array_to_backend_array(arr, *, dtype=None):
     if isinstance(arr, dict):
         return {k: namespace.asarray(v, dtype=dtype) for k, v in arr.items()}
     return namespace.asarray(arr, dtype=dtype)
+
+
+def to_default_precision(dtype, *, device=None):
+    """Returns a dtype of the same kind with the default precision."""
+    for k, dtype_ in default_dtypes(device=device).items():
+        if namespace.isdtype(dtype, k):
+            return dtype_

cubed/core/ops.py

@@ -15,7 +15,7 @@
 from toolz import accumulate, map
 
 from cubed import config
-from cubed.backend_array_api import backend_array_to_numpy_array
+from cubed.backend_array_api import backend_array_to_numpy_array, to_default_precision
 from cubed.backend_array_api import namespace as nxp
 from cubed.backend_array_api import numpy_array_to_backend_array
 from cubed.core.array import CoreArray, check_array_specs, compute, gensym
@@ -41,17 +41,21 @@
     from cubed.array_api.array_object import Array
 
 
-def from_array(x, chunks="auto", asarray=None, spec=None) -> "Array":
+def from_array(x, chunks="auto", asarray=None, spec=None, device=None) -> "Array":
     """Create a Cubed array from an array-like object."""
 
     if isinstance(x, CoreArray):
         raise ValueError(
             "Array is already a Cubed array. Use 'asarray' or 'rechunk' instead."
         )
 
+    dtype = to_default_precision(x.dtype)
+    if x.dtype != dtype:
+        x = x.astype(dtype)
+
     previous_chunks = getattr(x, "chunks", None)
     outchunks = normalize_chunks(
-        chunks, x.shape, dtype=x.dtype, previous_chunks=previous_chunks
+        chunks, x.shape, dtype=dtype, previous_chunks=previous_chunks
     )
 
     if isinstance(x, zarr.Array):  # zarr fast path