ENH: ndonnx device() support; TST: better ndonnx test coverage

array_api_compat/common/_helpers.py

@@ -776,7 +776,7 @@ def to_device(x: Array, device: Device, /, *, stream: Optional[Union[int, Any]]
     device : Hardware device the array data resides on.
 
     """
-    if is_numpy_array(x):
+    if is_numpy_array(x) or is_ndonnx_array(x):
         if stream is not None:
             raise ValueError("The stream argument to to_device() is not supported")
         if device == 'cpu':

docs/supported-array-libraries.md

@@ -138,6 +138,11 @@ The minimum supported Dask version is 2023.12.0.
 
 Similar to JAX, `sparse` Array API support is contained directly in `sparse`.
 
+(ndonnx-support)=
+## [ndonnx](https://github.com/quantco/ndonnx)
+
+Similar to JAX, `ndonnx` Array API support is contained directly in `ndonnx`.
+
 (array-api-strict-support)=
 ## [array-api-strict](https://data-apis.org/array-api-strict/)
 

tests/_helpers.py

@@ -3,11 +3,12 @@
 import pytest
 
 wrapped_libraries = ["numpy", "cupy", "torch", "dask.array"]
-all_libraries = wrapped_libraries + ["array_api_strict", "jax.numpy", "sparse"]
-
+all_libraries = wrapped_libraries + [
+    "array_api_strict", "jax.numpy", "ndonnx", "sparse"
+]
 
 def import_(library, wrapper=False):
-    if library == 'cupy':
+    if library in ('cupy', 'ndonnx'):
         pytest.importorskip(library)
     if wrapper:
         if 'jax' in library:
@@ -20,3 +21,14 @@ def import_(library, wrapper=False):
             library = 'array_api_compat.' + library
 
     return import_module(library)
+
+
+def xfail(request: pytest.FixtureRequest, reason: str) -> None:
+    """
+    XFAIL the currently running test.
+
+    Unlike ``pytest.xfail``, allow rest of test to execute instead of immediately
+    halting it, so that it may result in a XPASS.
+    xref https://github.com/pandas-dev/pandas/issues/38902
+    """
+    request.node.add_marker(pytest.mark.xfail(reason=reason))

tests/test_array_namespace.py

@@ -22,6 +22,9 @@ def test_array_namespace(library, api_version, use_compat):
     if use_compat and library not in wrapped_libraries:
         pytest.raises(ValueError, lambda: array_namespace(array, use_compat=use_compat))
         return
+    if library == "ndonnx" and api_version in ("2021.12", "2022.12"):
+        pytest.skip("Unsupported API version")
+
     namespace = array_namespace(array, api_version=api_version, use_compat=use_compat)
 
     if use_compat is False or use_compat is None and library not in wrapped_libraries:

tests/test_common.py

@@ -8,15 +8,17 @@
 from array_api_compat import (  # noqa: F401
     is_numpy_array, is_cupy_array, is_torch_array,
     is_dask_array, is_jax_array, is_pydata_sparse_array,
+    is_ndonnx_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,
+    is_array_api_strict_namespace, is_ndonnx_namespace,
 )
 
 from array_api_compat import (
     device, is_array_api_obj, is_lazy_array, is_writeable_array, size, to_device
 )
-from ._helpers import import_, wrapped_libraries, all_libraries
+from ._helpers import all_libraries, import_, wrapped_libraries, xfail
+
 
 is_array_functions = {
     'numpy': 'is_numpy_array',
@@ -25,6 +27,7 @@
     'dask.array': 'is_dask_array',
     'jax.numpy': 'is_jax_array',
     'sparse': 'is_pydata_sparse_array',
+    'ndonnx': 'is_ndonnx_array',
 }
 
 is_namespace_functions = {
@@ -35,6 +38,7 @@
     'jax.numpy': 'is_jax_namespace',
     'sparse': 'is_pydata_sparse_namespace',
     'array_api_strict': 'is_array_api_strict_namespace',
+    'ndonnx': 'is_ndonnx_namespace',
 }
 
 
@@ -185,7 +189,10 @@ class C:
 
 
 @pytest.mark.parametrize("library", all_libraries)
-def test_device(library):
+def test_device(library, request):
+    if library == "ndonnx":
+        xfail(request, reason="Needs ndonnx >=0.9.4")
+
     xp = import_(library, wrapper=True)
 
     # We can't test much for device() and to_device() other than that
@@ -223,17 +230,19 @@ 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):
-    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
-        _xfail(reason="Bug in dask raising error on conversion")
+        xfail(request, reason="Bug in dask raising error on conversion")
+    elif (
+        source_library == "ndonnx" 
+        and target_library not in ("array_api_strict", "ndonnx", "numpy")
+    ):
+        xfail(request, reason="The truth value of lazy Array Array(dtype=Boolean) is unknown")
+    elif source_library == "ndonnx" and target_library == "numpy":
+        xfail(request, reason="produces numpy array of ndonnx scalar arrays")
     elif source_library == "jax.numpy" and target_library == "torch":
-        _xfail(reason="casts int to float")
+        xfail(request, 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")