CI clearup

.github/workflows/numpy.yml

@@ -19,7 +19,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install git+https://github.com/numpy/numpy
+        python -m pip install numpy==1.22.1
         python -m pip install -r requirements.txt
     - name: Run the test suite
       env:
@@ -28,26 +28,18 @@ jobs:
         # Mark some known issues as XFAIL
         cat << EOF >> xfails.txt
 
+        # copy not implemented
+        array_api_tests/test_creation_functions.py::test_asarray_arrays
         # https://github.com/numpy/numpy/issues/18881
         array_api_tests/test_creation_functions.py::test_linspace
-        # einsum is not yet completed in the spec
-        array_api_tests/test_signatures.py::test_has_names[einsum]
-        # dlpack support is not yet implemented in NumPy
-        # See https://github.com/numpy/numpy/pull/19083
-        array_api_tests/test_signatures.py::test_function_positional_args[__dlpack__]
-        array_api_tests/test_signatures.py::test_function_positional_args[__dlpack_device__]
-        array_api_tests/test_signatures.py::test_function_positional_args[from_dlpack]
-        array_api_tests/test_signatures.py::test_function_positional_args[to_device]
-        array_api_tests/test_signatures.py::test_function_keyword_only_args[__dlpack__]
-        # floor_divide has an issue related to https://github.com/data-apis/array-api/issues/264
-        array_api_tests/test_elementwise_functions.py::test_floor_divide
-        # mesgrid doesn't return all arrays as the promoted dtype
-        array_api_tests/test_type_promotion.py::test_meshgrid
-        # https://github.com/numpy/numpy/pull/20066#issuecomment-947056094
-        array_api_tests/test_type_promotion.py::test_where
-        # shape mismatches are not handled
-        array_api_tests/test_type_promotion.py::test_tensordot
+        # https://github.com/numpy/numpy/issues/20870
+        array_api_tests/test_data_type_functions.py::test_can_cast
+        # linalg tests generally need more mulling over
+        array_api_tests/test_linalg.py
+        # waiting on NumPy to allow/revert distinct NaNs for np.unique
+        # https://github.com/numpy/numpy/issues/20326#issuecomment-1012380448
+        array_api_tests/test_set_functions.py
 
         EOF
 
-        pytest -v -rxXfE
+        pytest -v -rxXfE --ci

array_api_tests/dtype_helpers.py

@@ -216,7 +216,7 @@ def result_type(*dtypes: DataType):
     "ceil": numeric_dtypes,
     "cos": float_dtypes,
     "cosh": float_dtypes,
-    "divide": numeric_dtypes,
+    "divide": float_dtypes,
     "equal": all_dtypes,
     "exp": float_dtypes,
     "expm1": float_dtypes,

array_api_tests/test_array_object.py

@@ -167,7 +167,7 @@ def test_setitem_masking(shape, data):
     scalar_type = dh.get_scalar_type(x.dtype)
     for idx in sh.ndindex(x.shape):
         if key[idx]:
-            if isinstance(value, Scalar):
+            if isinstance(value, get_args(Scalar)):
                 ph.assert_scalar_equals(
                     "__setitem__",
                     scalar_type,
@@ -200,7 +200,7 @@ def make_param(method_name: str, dtype: DataType, stype: ScalarType) -> Param:
     + [make_param("__float__", d, float) for d in dh.float_dtypes],
 )
 @given(data=st.data())
-def test_duck_typing(method_name, dtype, stype, data):
+def test_scalar_casting(method_name, dtype, stype, data):
     x = data.draw(xps.arrays(dtype, shape=()), label="x")
     method = getattr(x, method_name)
     out = method()

array_api_tests/test_creation_functions.py

@@ -479,27 +479,26 @@ def test_linspace(num, dtype, endpoint, data):
         ah.assert_exactly_equal(out, expected)
 
 
-@given(
+@given(dtype=xps.numeric_dtypes(), data=st.data())
+def test_meshgrid(dtype, data):
     # The number and size of generated arrays is arbitrarily limited to prevent
     # meshgrid() running out of memory.
-    dtypes=hh.mutually_promotable_dtypes(5, dtypes=dh.numeric_dtypes),
-    data=st.data(),
-)
-def test_meshgrid(dtypes, data):
-    arrays = []
     shapes = data.draw(
-        hh.mutually_broadcastable_shapes(
-            len(dtypes), min_dims=1, max_dims=1, max_side=5
+        st.integers(1, 5).flatmap(
+            lambda n: hh.mutually_broadcastable_shapes(
+                n, min_dims=1, max_dims=1, max_side=5
+            )
         ),
         label="shapes",
     )
-    for i, (dtype, shape) in enumerate(zip(dtypes, shapes), 1):
+    arrays = []
+    for i, shape in enumerate(shapes, 1):
         x = data.draw(xps.arrays(dtype=dtype, shape=shape), label=f"x{i}")
         arrays.append(x)
     assert math.prod(x.size for x in arrays) <= hh.MAX_ARRAY_SIZE  # sanity check
     out = xp.meshgrid(*arrays)
     for i, x in enumerate(out):
-        ph.assert_dtype("meshgrid", dtypes, x.dtype, repr_name=f"out[{i}].dtype")
+        ph.assert_dtype("meshgrid", dtype, x.dtype, repr_name=f"out[{i}].dtype")
 
 
 def make_one(dtype: DataType) -> Scalar: