[REVIEW] Add support for make_meta_obj dispatch in dask-cudf

python/dask_cudf/dask_cudf/backends.py

@@ -6,7 +6,7 @@
 import pyarrow as pa
 
 from dask.dataframe.categorical import categorical_dtype_dispatch
-from dask.dataframe.core import get_parallel_type, make_meta, meta_nonempty
+from dask.dataframe.core import get_parallel_type, meta_nonempty
 from dask.dataframe.methods import (
     concat_dispatch,
     is_categorical_dtype_dispatch,
@@ -18,6 +18,8 @@
     _scalar_from_dtype,
     is_arraylike,
     is_scalar,
+    make_meta,
+    make_meta_obj,
 )
 
 import cudf
@@ -133,8 +135,8 @@ def _empty_series(name, dtype, index=None):
     return cudf.Series([], dtype=dtype, name=name, index=index)
 
 
-@make_meta.register(object)
-def make_meta_object(x, index=None):
+@make_meta_obj.register(object)
+def make_meta_object_cudf(x, index=None):
     """Create an empty cudf object containing the desired metadata.
 
     Parameters
@@ -244,7 +246,7 @@ def is_categorical_dtype_cudf(obj):
 
 try:
 
-    from dask.dataframe.utils import group_split_dispatch, hash_object_dispatch
+    from dask.dataframe.core import group_split_dispatch, hash_object_dispatch
 
     def safe_hash(frame):
         index = frame.index

python/dask_cudf/dask_cudf/core.py

@@ -72,7 +72,7 @@ def __init__(self, dsk, name, meta, divisions):
             dsk = HighLevelGraph.from_collections(name, dsk, dependencies=[])
         self.dask = dsk
         self._name = name
-        meta = dd.core.make_meta(meta)
+        meta = dd.utils.make_meta_util(meta)
         if not isinstance(meta, self._partition_type):
             raise TypeError(
                 f"Expected meta to specify type "
@@ -115,7 +115,7 @@ def assigner(df, k, v):
             out[k] = v
             return out
 
-        meta = assigner(self._meta, k, dd.core.make_meta(v))
+        meta = assigner(self._meta, k, dd.utils.make_meta_util(v))
         return self.map_partitions(assigner, k, v, meta=meta)
 
     def apply_rows(self, func, incols, outcols, kwargs=None, cache_key=None):
@@ -677,7 +677,7 @@ def reduction(
     if meta is None:
         meta_chunk = _emulate(apply, chunk, args, chunk_kwargs)
         meta = _emulate(apply, aggregate, [[meta_chunk]], aggregate_kwargs)
-    meta = dd.core.make_meta(meta)
+    meta = dd.utils.make_meta_util(meta)
 
     graph = HighLevelGraph.from_collections(b, dsk, dependencies=args)
     return dd.core.new_dd_object(graph, b, meta, (None, None))

python/dask_cudf/dask_cudf/tests/test_core.py

@@ -9,7 +9,8 @@
 
 import dask
 from dask import dataframe as dd
-from dask.dataframe.core import make_meta, meta_nonempty
+from dask.dataframe.core import meta_nonempty
+from dask.dataframe.utils import make_meta_util
 from dask.utils import M
 
 import cudf
@@ -585,20 +586,20 @@ def test_hash_object_dispatch(index):
     )
 
     # DataFrame
-    result = dd.utils.hash_object_dispatch(obj, index=index)
+    result = dd.core.hash_object_dispatch(obj, index=index)
     expected = dgd.backends.hash_object_cudf(obj, index=index)
     assert isinstance(result, cudf.Series)
     dd.assert_eq(result, expected)
 
     # Series
-    result = dd.utils.hash_object_dispatch(obj["x"], index=index)
+    result = dd.core.hash_object_dispatch(obj["x"], index=index)
     expected = dgd.backends.hash_object_cudf(obj["x"], index=index)
     assert isinstance(result, cudf.Series)
     dd.assert_eq(result, expected)
 
     # DataFrame with MultiIndex
     obj_multi = obj.set_index(["x", "z"], drop=True)
-    result = dd.utils.hash_object_dispatch(obj_multi, index=index)
+    result = dd.core.hash_object_dispatch(obj_multi, index=index)
     expected = dgd.backends.hash_object_cudf(obj_multi, index=index)
     assert isinstance(result, cudf.Series)
     dd.assert_eq(result, expected)
@@ -638,7 +639,7 @@ def test_make_meta_backends(index):
     df = df.set_index(index)
 
     # Check "empty" metadata types
-    chk_meta = make_meta(df)
+    chk_meta = make_meta_util(df)
     dd.assert_eq(chk_meta.dtypes, df.dtypes)
 
     # Check "non-empty" metadata types
@@ -777,3 +778,20 @@ def test_index_map_partitions():
     mins_gd = gddf.index.map_partitions(M.min, meta=gddf.index).compute()
 
     dd.assert_eq(mins_pd, mins_gd)
+
+
+def test_correct_meta():
+    # Need these local imports in this specific order.
+    # For context: https://github.com/rapidsai/cudf/issues/7946
+    import pandas as pd
+
+    from dask import dataframe as dd
+
+    import dask_cudf  # noqa: F401
+
+    df = pd.DataFrame({"a": [3, 4], "b": [1, 2]})
+    ddf = dd.from_pandas(df, npartitions=1)
+    emb = ddf["a"].apply(pd.Series, meta={"c0": "int64", "c1": "int64"})
+
+    assert isinstance(emb, dd.DataFrame)
+    assert isinstance(emb._meta, pd.DataFrame)