address more comments

Author: lithomas1

array_api_compat/common/_aliases.py

@@ -325,17 +325,25 @@ def _asarray(
         # copy=False is not yet implemented in xp.asarray
         raise NotImplementedError("copy=False is not yet implemented")
     if (hasattr(xp, "ndarray") and isinstance(obj, xp.ndarray)) or hasattr(obj, "__array__"):
+        #print('hit me')
         if dtype is not None and obj.dtype != dtype:
             copy = True
+        #print(copy)
         if copy in COPY_TRUE:
             copy_kwargs = {}
             if namespace != "dask.array":
                 copy_kwargs["copy"] = True
             else:
                 # No copy kw in dask.asarray so we go thorugh np.asarray first
                 # (like dask also does) but copy after
+                if dtype is None:
+                    # Same dtype copy is no-op in dask
+                    #print("in here?")
+                    return obj.copy()
                 import numpy as np
+                #print(obj)
                 obj = np.asarray(obj).copy()
+                #print(obj)
             return xp.array(obj, dtype=dtype, **copy_kwargs)
         return obj
 

array_api_compat/common/_linalg.py

@@ -77,8 +77,10 @@ def matrix_rank(x: ndarray,
     # dimensional arrays.
     if x.ndim < 2:
         raise xp.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional")
-    S = xp.linalg.svdvals(x, **kwargs)
-    #S = xp.linalg.svd(x, compute_uv=False, **kwargs)
+    if hasattr(xp.linalg, "svdvals"):
+        S = xp.linalg.svdvals(x, **kwargs)
+    else:
+        S = xp.linalg.svd(x, compute_uv=False, **kwargs)
     if rtol is None:
         tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * xp.finfo(S.dtype).eps
     else:

array_api_compat/dask/array/_aliases.py

@@ -120,4 +120,9 @@ def dask_arange(
     concatenate as concat,
 )
 
-del da
+del da, partial
+
+__all__ = _aliases.__all__ + ['asarray', 'bool', 'acos',
+                              'acosh', 'asin', 'asinh', 'atan', 'atan2',
+                              'atanh', 'bitwise_left_shift', 'bitwise_invert',
+                              'bitwise_right_shift', 'concat', 'pow']

array_api_compat/dask/array/linalg.py

@@ -13,17 +13,13 @@
     from typing import Optional, Union, Tuple
     from ...common._typing import ndarray, Device, Dtype
 
-#cross = get_xp(da)(_linalg.cross)
-#outer = get_xp(da)(_linalg.outer)
 EighResult = _linalg.EighResult
 QRResult = _linalg.QRResult
 SlogdetResult = _linalg.SlogdetResult
 SVDResult = _linalg.SVDResult
 qr = get_xp(da)(_linalg.qr)
-#svd = get_xp(da)(_linalg.svd)
 cholesky = get_xp(da)(_linalg.cholesky)
 matrix_rank = get_xp(da)(_linalg.matrix_rank)
-#pinv = get_xp(da)(_linalg.pinv)
 matrix_norm = get_xp(da)(_linalg.matrix_norm)
 
 def svdvals(x: ndarray) -> Union[ndarray, Tuple[ndarray, ...]]:
@@ -34,9 +30,6 @@ def svdvals(x: ndarray) -> Union[ndarray, Tuple[ndarray, ...]]:
 vector_norm = get_xp(da)(_linalg.vector_norm)
 diagonal = get_xp(da)(_linalg.diagonal)
 
-#__all__ = linalg_all + _linalg.__all__
-
 del get_xp
 del da
-#del linalg_all
 del _linalg

dask-xfails.txt

@@ -9,9 +9,17 @@
 #| Draw 1 (key): (slice(None, None, None), slice(None, None, None))
 #| Draw 2 (value): dask.array<zeros_like, shape=(0, 2), dtype=bool, chunksize=(0, 2), chunktype=numpy.ndarray>
 
-# TODO: this also skips test_setitem_masking unnecessarily
+# Various shape mismatches e.g.
+ValueError: shape mismatch: value array of shape (0, 2) could not be broadcast to indexing result of shape (0, 2)
 array_api_tests/test_array_object.py::test_setitem
 
+# Fails since bad upcast from uint8 -> int64
+# MRE:
+# a = da.array(0, dtype="uint8")
+# b = da.array(False)
+# a[b] = 0
+array_api_tests/test_array_object.py::test_setitem_masking
+
 # Various indexing errors
 array_api_tests/test_array_object.py::test_getitem_masking