Run more tests on array-api-strict and sparse

Author: crusaderky

array_api_compat/common/_helpers.py

@@ -631,13 +631,9 @@ def device(x: Array, /) -> Device:
         return "cpu"
     elif is_dask_array(x):
         # Peek at the metadata of the jax array to determine type
-        try:
-            import numpy as np
-            if isinstance(x._meta, np.ndarray):
-                # Must be on CPU since backed by numpy
-                return "cpu"
-        except ImportError:
-            pass
+        if is_numpy_array(x._meta):
+            # Must be on CPU since backed by numpy
+            return "cpu"
         return _DASK_DEVICE
     elif is_jax_array(x):
         # JAX has .device() as a method, but it is being deprecated so that it

docs/supported-array-libraries.md

@@ -137,3 +137,8 @@ The minimum supported Dask version is 2023.12.0.
 ## [Sparse](https://sparse.pydata.org/en/stable/)
 
 Similar to JAX, `sparse` Array API support is contained directly in `sparse`.
+
+(array-api-strict-support)=
+## [array-api-strict](https://data-apis.org/array-api-strict/)
+
+array-api-strict exists only to test support for the Array API, so it does not need any wrappers.

tests/_helpers.py

@@ -1,14 +1,10 @@
 from importlib import import_module
-import sys
 
 import pytest
 
 wrapped_libraries = ["numpy", "cupy", "torch", "dask.array"]
-all_libraries = wrapped_libraries + ["jax.numpy"]
+all_libraries = wrapped_libraries + ["array_api_strict", "jax.numpy", "sparse"]
 
-# `sparse` added array API support as of Python 3.10.
-if sys.version_info >= (3, 10):
-    all_libraries.append('sparse')
 
 def import_(library, wrapper=False):
     if library == 'cupy':
@@ -20,9 +16,7 @@ def import_(library, wrapper=False):
             jax_numpy = import_module("jax.numpy")
             if not hasattr(jax_numpy, "__array_api_version__"):
                 library = 'jax.experimental.array_api'
-        elif library.startswith('sparse'):
-            library = 'sparse'
-        else:
+        elif library in wrapped_libraries:
             library = 'array_api_compat.' + library
 
     return import_module(library)

tests/test_all.py

@@ -18,7 +18,10 @@
 
 @pytest.mark.parametrize("library", ["common"] + wrapped_libraries)
 def test_all(library):
-    import_(library, wrapper=True)
+    if library == "common":
+        import array_api_compat.common  # noqa: F401
+    else:
+        import_(library, wrapper=True)
 
     for mod_name in sys.modules:
         if not mod_name.startswith('array_api_compat.' + library):

tests/test_array_namespace.py

@@ -14,12 +14,12 @@
 
 @pytest.mark.parametrize("use_compat", [True, False, None])
 @pytest.mark.parametrize("api_version", [None, "2021.12", "2022.12", "2023.12"])
-@pytest.mark.parametrize("library", all_libraries + ['array_api_strict'])
+@pytest.mark.parametrize("library", all_libraries)
 def test_array_namespace(library, api_version, use_compat):
     xp = import_(library)
 
     array = xp.asarray([1.0, 2.0, 3.0])
-    if use_compat is True and library in {'array_api_strict', 'jax.numpy', 'sparse'}:
+    if use_compat and library not in wrapped_libraries:
         pytest.raises(ValueError, lambda: array_namespace(array, use_compat=use_compat))
         return
     namespace = array_api_compat.array_namespace(array, api_version=api_version, use_compat=use_compat)

tests/test_common.py

@@ -3,6 +3,7 @@
     is_dask_array, is_jax_array, is_pydata_sparse_array,
     is_numpy_namespace, is_cupy_namespace, is_torch_namespace,
     is_dask_namespace, is_jax_namespace, is_pydata_sparse_namespace,
+    is_array_api_strict_namespace,
 )
 
 from array_api_compat import device, is_array_api_obj, is_writeable_array, to_device
@@ -30,6 +31,7 @@
     'dask.array': 'is_dask_namespace',
     'jax.numpy': 'is_jax_namespace',
     'sparse': 'is_pydata_sparse_namespace',
+    'array_api_strict': 'is_array_api_strict_namespace',
 }
 
 
@@ -71,7 +73,12 @@ def test_xp_is_array_generics(library):
         is_func = globals()[func]
         if is_func(x0):
             matches.append(library2)
-    assert matches in ([library], ["numpy"])
+
+    if library == "array_api_strict":
+        # There is no is_array_api_strict_array() function
+        assert matches == []
+    else:
+        assert matches in ([library], ["numpy"])
 
 
 @pytest.mark.parametrize("library", all_libraries)
@@ -128,26 +135,33 @@ def test_to_device_host(library):
 @pytest.mark.parametrize("target_library", is_array_functions.keys())
 @pytest.mark.parametrize("source_library", is_array_functions.keys())
 def test_asarray_cross_library(source_library, target_library, request):
-    if source_library == "dask.array" and target_library == "torch":
+    def _xfail(reason: str) -> None:
         # Allow rest of test to execute instead of immediately xfailing
         # xref https://github.com/pandas-dev/pandas/issues/38902
+        request.node.add_marker(pytest.mark.xfail(reason=reason))
 
+    if source_library == "dask.array" and target_library == "torch":
         # 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"))
-    if source_library == "cupy" and target_library != "cupy":
+        _xfail(reason="Bug in dask raising error on conversion")
+    elif source_library == "jax.numpy" and target_library == "torch":
+        _xfail(reason="casts int to float")
+    elif source_library == "cupy" and target_library != "cupy":
         # cupy explicitly disallows implicit conversions to CPU
         pytest.skip(reason="cupy does not support implicit conversion to CPU")
     elif source_library == "sparse" and target_library != "sparse":
         pytest.skip(reason="`sparse` does not allow implicit densification")
+
     src_lib = import_(source_library, wrapper=True)
     tgt_lib = import_(target_library, wrapper=True)
     is_tgt_type = globals()[is_array_functions[target_library]]
 
-    a = src_lib.asarray([1, 2, 3])
+    a = src_lib.asarray([1, 2, 3], dtype=src_lib.int32)
     b = tgt_lib.asarray(a)
 
     assert is_tgt_type(b), f"Expected {b} to be a {tgt_lib.ndarray}, but was {type(b)}"
+    assert b.dtype == tgt_lib.int32
+
 
 @pytest.mark.parametrize("library", wrapped_libraries)
 def test_asarray_copy(library):