test readonly with numpy; rename use_stream parameter

cuda_core/examples/strided_memory_view.py

@@ -107,6 +107,7 @@
 # Below, as a user we want to perform the said in-place operation on either CPU
 # or GPU, by calling the corresponding function implemented "elsewhere" (done above).
 
+
 # We assume the 0-th argument supports either DLPack or CUDA Array Interface (both
 # of which are supported by StridedMemoryView).
 @args_viewable_as_strided_memory((0,))

cuda_core/tests/test_utils.py

@@ -27,6 +27,14 @@ def convert_strides_to_counts(strides, itemsize):
         np.empty(3, dtype=np.int32),
         np.empty((6, 6), dtype=np.float64)[::2, ::2],
         np.empty((3, 4), order="F"),
+        np.empty((), dtype=np.float16),
+        # readonly is fixed recently (numpy/numpy#26501)
+        pytest.param(
+            np.frombuffer(b""),
+            marks=pytest.mark.skipif(
+                tuple(int(i) for i in np.__version__.split(".")[:2]) < (2, 1), reason="need numpy 2.1.0+"
+            ),
+        ),
     ),
 )
 class TestViewCPU:
@@ -57,22 +65,23 @@ def _check_view(self, view, in_arr):
         assert view.device_id == -1
         assert view.is_device_accessible is False
         assert view.exporting_obj is in_arr
+        assert view.readonly is not in_arr.flags.writeable
 
 
 def gpu_array_samples():
     # TODO: this function would initialize the device at test collection time
     samples = []
     if cp is not None:
         samples += [
-            (cp.empty(3, dtype=cp.complex64), None),
+            (cp.empty(3, dtype=cp.complex64), False),
             (cp.empty((6, 6), dtype=cp.float64)[::2, ::2], True),
             (cp.empty((3, 4), order="F"), True),
         ]
     # Numba's device_array is the only known array container that does not
     # support DLPack (so that we get to test the CAI coverage).
     if numba_cuda is not None:
         samples += [
-            (numba_cuda.device_array((2,), dtype=np.int8), None),
+            (numba_cuda.device_array((2,), dtype=np.int8), False),
             (numba_cuda.device_array((4, 2), dtype=np.float32), True),
         ]
     return samples
@@ -86,14 +95,14 @@ def gpu_array_ptr(arr):
     raise NotImplementedError(f"{arr=}")
 
 
-@pytest.mark.parametrize("in_arr,stream", (*gpu_array_samples(),))
+@pytest.mark.parametrize("in_arr,use_stream", (*gpu_array_samples(),))
 class TestViewGPU:
-    def test_args_viewable_as_strided_memory_gpu(self, in_arr, stream):
+    def test_args_viewable_as_strided_memory_gpu(self, in_arr, use_stream):
         # TODO: use the device fixture?
         dev = Device()
         dev.set_current()
         # This is the consumer stream
-        s = dev.create_stream() if stream else None
+        s = dev.create_stream() if use_stream else None
 
         @args_viewable_as_strided_memory((0,))
         def my_func(arr):
@@ -102,12 +111,12 @@ def my_func(arr):
 
         my_func(in_arr)
 
-    def test_strided_memory_view_cpu(self, in_arr, stream):
+    def test_strided_memory_view_cpu(self, in_arr, use_stream):
         # TODO: use the device fixture?
         dev = Device()
         dev.set_current()
         # This is the consumer stream
-        s = dev.create_stream() if stream else None
+        s = dev.create_stream() if use_stream else None
 
         view = StridedMemoryView(in_arr, stream_ptr=s.handle if s else -1)
         self._check_view(view, in_arr, dev)
@@ -125,3 +134,4 @@ def _check_view(self, view, in_arr, dev):
         assert view.device_id == dev.device_id
         assert view.is_device_accessible is True
         assert view.exporting_obj is in_arr
+        # can't test view.readonly with CuPy or Numba...