Fall back when casting a timestamp to numeric in cudf-polars (rapidsa…

…i#16232)

This PR adds logic that falls back to CPU when a cudf-polars query would cast a timestamp column to a numeric type, an unsupported operation in libcudf, which should fix a few polars tests. It could be cleaned up a bit with some of the utilities that will be added in rapidsai#16150.

Authors:
  - https://github.com/brandon-b-miller

Approvers:
  - Lawrence Mitchell (https://github.com/wence-)

URL: rapidsai#16232

python/cudf_polars/cudf_polars/dsl/expr.py

@@ -1188,6 +1188,10 @@ class Cast(Expr):
     def __init__(self, dtype: plc.DataType, value: Expr) -> None:
         super().__init__(dtype)
         self.children = (value,)
+        if not plc.unary.is_supported_cast(self.dtype, value.dtype):
+            raise NotImplementedError(
+                f"Can't cast {self.dtype.id().name} to {value.dtype.id().name}"
+            )
 
     def do_evaluate(
         self,

python/cudf_polars/tests/expressions/test_casting.py

@@ -0,0 +1,52 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import pytest
+
+import polars as pl
+
+from cudf_polars.testing.asserts import (
+    assert_gpu_result_equal,
+    assert_ir_translation_raises,
+)
+
+_supported_dtypes = [(pl.Int8(), pl.Int64())]
+
+_unsupported_dtypes = [
+    (pl.String(), pl.Int64()),
+]
+
+
+@pytest.fixture
+def dtypes(request):
+    return request.param
+
+
+@pytest.fixture
+def tests(dtypes):
+    fromtype, totype = dtypes
+    if fromtype == pl.String():
+        data = ["a", "b", "c"]
+    else:
+        data = [1, 2, 3]
+    return pl.DataFrame(
+        {
+            "a": pl.Series(data, dtype=fromtype),
+        }
+    ).lazy(), totype
+
+
+@pytest.mark.parametrize("dtypes", _supported_dtypes, indirect=True)
+def test_cast_supported(tests):
+    df, totype = tests
+    q = df.select(pl.col("a").cast(totype))
+    assert_gpu_result_equal(q)
+
+
+@pytest.mark.parametrize("dtypes", _unsupported_dtypes, indirect=True)
+def test_cast_unsupported(tests):
+    df, totype = tests
+    assert_ir_translation_raises(
+        df.select(pl.col("a").cast(totype)), NotImplementedError
+    )