Merge branch 'main' into no-dependencies

Author: asmeurer

array_api_compat/common/_aliases.py

@@ -325,31 +325,30 @@ def _asarray(
     else:
         COPY_FALSE = (False,)
         COPY_TRUE = (True,)
-    if copy in COPY_FALSE:
+    if copy in COPY_FALSE and namespace != "dask.array":
         # 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 (hasattr(xp, "ndarray") and isinstance(obj, xp.ndarray)):
         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 xp.array(obj, copy=True, dtype=dtype)
         return obj
+    elif namespace == "dask.array":
+        if copy in COPY_TRUE:
+            if dtype is None:
+                return obj.copy()
+            # Go through numpy, since dask copy is no-op by default
+            import numpy as np
+            obj = np.array(obj, dtype=dtype, copy=True)
+            return xp.array(obj, dtype=dtype)
+        else:
+            import dask.array as da
+            import numpy as np
+            if not isinstance(obj, da.Array):
+                obj = np.asarray(obj, dtype=dtype)
+                return da.from_array(obj)
+            return obj
 
     return xp.asarray(obj, dtype=dtype, **kwargs)
 

array_api_compat/dask/array/linalg.py

@@ -16,10 +16,11 @@
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from ...common._typing import Array
+    from typing import Literal
 
-# cupy.linalg doesn't have __all__. If it is added, replace this with
+# dask.array.linalg doesn't have __all__. If it is added, replace this with
 #
-# from cupy.linalg import __all__ as linalg_all
+# from dask.array.linalg import __all__ as linalg_all
 _n = {}
 exec('from dask.array.linalg import *', _n)
 del _n['__builtins__']
@@ -32,7 +33,15 @@
 QRResult = _linalg.QRResult
 SlogdetResult = _linalg.SlogdetResult
 SVDResult = _linalg.SVDResult
-qr = get_xp(da)(_linalg.qr)
+# TODO: use the QR wrapper once dask
+# supports the mode keyword on QR
+# https://github.com/dask/dask/issues/10388
+#qr = get_xp(da)(_linalg.qr)
+def qr(x: Array, mode: Literal['reduced', 'complete'] = 'reduced',
+       **kwargs) -> QRResult:
+    if mode != "reduced":
+        raise ValueError("dask arrays only support using mode='reduced'")
+    return QRResult(*da.linalg.qr(x, **kwargs))
 cholesky = get_xp(da)(_linalg.cholesky)
 matrix_rank = get_xp(da)(_linalg.matrix_rank)
 matrix_norm = get_xp(da)(_linalg.matrix_norm)
@@ -44,7 +53,7 @@
 def svd(x: Array, full_matrices: bool = True, **kwargs) -> SVDResult:
     if full_matrices:
         raise ValueError("full_matrics=True is not supported by dask.")
-    return da.linalg.svd(x, **kwargs)
+    return da.linalg.svd(x, coerce_signs=False, **kwargs)
 
 def svdvals(x: Array) -> Array:
     # TODO: can't avoid computing U or V for dask

dask-xfails.txt

@@ -37,6 +37,9 @@ array_api_tests/test_data_type_functions.py::test_finfo[float32]
 # (I think the test is not forcing the op to be computed?)
 array_api_tests/test_creation_functions.py::test_linspace
 
+# out.shape=(2,) but should be (1,)
+array_api_tests/test_indexing_functions.py::test_take
+
 # out=-0, but should be +0
 array_api_tests/test_special_cases.py::test_binary[__pow__(x1_i is -0 and x2_i > 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +0]
 array_api_tests/test_special_cases.py::test_iop[__ipow__(x1_i is -0 and x2_i > 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +0]

numpy-1-21-xfails.txt

@@ -89,6 +89,7 @@ array_api_tests/test_set_functions.py::test_unique_values
 # The test suite is incorrectly checking sums that have loss of significance
 # (https://github.com/data-apis/array-api-tests/issues/168)
 array_api_tests/test_statistical_functions.py::test_sum
+array_api_tests/test_statistical_functions.py::test_prod
 
 # NumPy 1.21 doesn't support NPY_PROMOTION_STATE=weak, so many tests fail with
 # type promotion issues
@@ -121,21 +122,24 @@ array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_xor[bi
 array_api_tests/test_operators_and_elementwise_functions.py::test_divide[__truediv__(x, s)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_divide[__truediv__(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_divide[divide(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_equal[equal(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_equal[__eq__(x1, x2)]
+array_api_tests/test_operators_and_elementwise_functions.py::test_equal[equal(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__floordiv__(x, s)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__floordiv__(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__ifloordiv__(x, s)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[floor_divide(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_greater[__gt__(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_greater[greater(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_less[__lt__(x1, x2)]
+array_api_tests/test_operators_and_elementwise_functions.py::test_less[less(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_less_equal[less_equal(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_logaddexp
 array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[__imul__(x, s)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[__mul__(x, s)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[__mul__(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[multiply(x1, x2)]
+array_api_tests/test_operators_and_elementwise_functions.py::test_not_equal[__ne__(x1, x2)]
+array_api_tests/test_operators_and_elementwise_functions.py::test_not_equal[not_equal(x1, x2)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_pow[__ipow__(x, s)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_pow[__pow__(x, s)]
 array_api_tests/test_operators_and_elementwise_functions.py::test_pow[__pow__(x1, x2)]

numpy-dev-xfails.txt

@@ -42,3 +42,4 @@ array_api_tests/meta/test_hypothesis_helpers.py::test_symmetric_matrices
 # The test suite is incorrectly checking sums that have loss of significance
 # (https://github.com/data-apis/array-api-tests/issues/168)
 array_api_tests/test_statistical_functions.py::test_sum
+array_api_tests/test_statistical_functions.py::test_prod

numpy-xfails.txt

@@ -44,3 +44,4 @@ array_api_tests/meta/test_hypothesis_helpers.py::test_symmetric_matrices
 # The test suite is incorrectly checking sums that have loss of significance
 # (https://github.com/data-apis/array-api-tests/issues/168)
 array_api_tests/test_statistical_functions.py::test_sum
+array_api_tests/test_statistical_functions.py::test_prod

tests/test_common.py

@@ -60,3 +60,23 @@ def test_to_device_host(library):
     # here is that we can test portably after calling
     # to_device(x, "cpu") to return to host
     assert_allclose(x, expected)
+
+
+@pytest.mark.parametrize("target_library,func", is_functions.items())
+@pytest.mark.parametrize("source_library", is_functions.keys())
+def test_asarray(source_library, target_library, func, request):
+    if source_library == "dask.array" and target_library == "torch":
+        # Allow rest of test to execute instead of immediately xfailing
+        # xref https://github.com/pandas-dev/pandas/issues/38902
+
+        # TODO: remove xfail once
+        # https://github.com/dask/dask/issues/8260 is resolved
+        request.node.add_marker(pytest.mark.xfail(reason="Bug in dask raising error on conversion"))
+    src_lib = import_(source_library, wrapper=True)
+    tgt_lib = import_(target_library, wrapper=True)
+    is_tgt_type = globals()[func]
+
+    a = src_lib.asarray([1, 2, 3])
+    b = tgt_lib.asarray(a)
+
+    assert is_tgt_type(b), f"Expected {b} to be a {tgt_lib.ndarray}, but was {type(b)}"