[bug] fix to_table for a non-array input when a low-precision queue is used

onedal/basic_statistics/basic_statistics.py

@@ -83,12 +83,7 @@ def fit(self, data, sample_weight=None, queue=None):
 
         is_single_dim = data.ndim == 1
 
-        data_table = to_table(data, queue=queue)
-        weights_table = (
-            to_table(sample_weight, queue=queue)
-            if sample_weight is not None
-            else to_table(None)
-        )
+        data_table, weights_table = to_table(data, sample_weight, queue=queue)
 
         dtype = data_table.dtype
         raw_result = self._compute_raw(data_table, weights_table, policy, dtype, is_csr)

onedal/datatypes/data_conversion.cpp

@@ -154,8 +154,16 @@ inline csr_table_t convert_to_csr_impl(PyObject *py_data,
 
 dal::table convert_to_table(py::object inp_obj, py::object queue) {
     dal::table res;
+
+    PyObject *obj = inp_obj.ptr();
+
+    if (obj == nullptr || obj == Py_None) {
+        return res;
+    }
+
 #ifdef ONEDAL_DATA_PARALLEL
-    if (!queue.is(py::none()) && !queue.attr("sycl_device").attr("has_aspect_fp64").cast<bool>()) {
+    if (!queue.is(py::none()) && !queue.attr("sycl_device").attr("has_aspect_fp64").cast<bool>() &&
+        hasattr(inp_obj, "dtype")) {
         // If the queue exists, doesn't have the fp64 aspect, and the data is float64
         // then cast it to float32
         int type = reinterpret_cast<PyArray_Descr *>(inp_obj.attr("dtype").ptr())->type_num;
@@ -173,11 +181,6 @@ dal::table convert_to_table(py::object inp_obj, py::object queue) {
     }
 #endif // ONEDAL_DATA_PARALLEL
 
-    PyObject *obj = inp_obj.ptr();
-
-    if (obj == nullptr || obj == Py_None) {
-        return res;
-    }
     if (is_array(obj)) {
         PyArrayObject *ary = reinterpret_cast<PyArrayObject *>(obj);
 

onedal/datatypes/tests/test_data.py

@@ -442,3 +442,44 @@ class DummySyclDevice:
     assert X_table.dtype == np.float32
     if dtype == np.float32 and not sparse:
         assert_allclose(X, from_table(X_table))
+
+
+@pytest.mark.parametrize("X", [None, 5, "test", True, [], np.pi, lambda: None])
+@pytest.mark.parametrize("queue", get_queues())
+def test_non_array(X, queue):
+    # Verify that to and from table doesn't raise errors
+    # no guarantee is made about type or content
+    err_str = ""
+
+    if np.isscalar(X):
+        if np.atleast_2d(X).dtype not in [np.float64, np.float32, np.int64, np.int32]:
+            err_str = "Found unsupported array type"
+    elif not (X is None or isinstance(X, np.ndarray)):
+        err_str = r"\[convert_to_table\] Not available input format for convert Python object to onedal table."
+
+    if err_str:
+        with pytest.raises(ValueError, match=err_str):
+            to_table(X)
+    else:
+        X_table = to_table(X, queue=queue)
+        from_table(X_table)
+
+
+@pytest.mark.skipif(
+    not _is_dpc_backend, reason="Requires DPC backend for dtype conversion"
+)
+@pytest.mark.parametrize("X", [None, 5, "test", True, [], np.pi, lambda: None])
+def test_low_precision_non_array(X):
+    # Use a dummy queue as fp32 hardware is not in public testing
+
+    class DummySyclQueue:
+        """This class is designed to act like dpctl.SyclQueue
+        to force dtype conversion"""
+
+        class DummySyclDevice:
+            has_aspect_fp64 = False
+
+        sycl_device = DummySyclDevice()
+
+    queue = DummySyclQueue()
+    test_non_array(X, queue)